is a speech impediment genetic

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Communication Matters

Are speech disorders inherited.

There are several types of speech and language disorders that appear to be closely tied with genetics.

As many as 50%-70% of children who have SLI also have at least one family member who struggles with the disorder.
The incidence in families with a history of SLI is estimated at approximately 20%–40%, whereas in the general population the estimated incidence is about 4%.
Identical twins show a higher concordance rate for language-based learning disorders compared to fraternal twins.
There are higher rates of SLI in males compared to females.
There are signs that genetics plays a part in at least some of these instances.
There are higher concordance rates for identical male twins (70%) on articulation and language disorders compared to fraternal twins (46%).
Mutations on the FOXP2 gene has been linked to family members with CAS.

Only a small fraction of all cases of speech and language disorders can be explained by genetic findings. There are many potential causes of speech and language disorders, some of which are still unknown.

Tips for Parents:

Don’t play the blame game

Advancements in medical and scientific research reveal that you can inherit susceptibility to speech and language disorders, just like you might inherit increased risks for diabetes or other medical conditions.

Ask your child’s pediatrician about precautions if you received speech-language services as a child

Understanding family medical history can help you make better decisions about preventative care and speech therapy. When completing health forms, keep in mind that even though “speech disorder” might not be listed, there may be genetic tendencies in your family. Talk with the doctor about other related health issues that may be genetic. Some early genetic findings related to communication include ties to disorders such as Autism or Fragile X Syndrome.

Find a qualified SLP

All speech-language pathologists are trained in general knowledge regarding genetics, syndromes, and disorders. Ask the speech language pathologist if they are familiar with the most recent research if you have questions about a particular diagnosis. It can be challenging for clinical SLPs to find, access, and keep abreast of this literature, but they can work with you to ensure therapy is effective and evidence-based.

Begin speech and language services as early as possible

Don’t write off difficulties if speech and language disorders don’t run in your family. The importance of early intervention , or getting help for young children, cannot be emphasized enough when there may be a family history of speech and language difficulties.

https://www.ncbi.nlm.nih.gov/pubmed/21663442

https://www.ncbi.nlm.nih.gov/pubmed/23586582

https://www.asha.org/Articles/The-Role-of-Genetics-in-Assessments/

https://jslhr.pubs.asha.org/article.aspx?articleid=1781178

Tags: Speech , Language , Communication , Stuttering , talking

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Types of Speech Impediments

Phynart Studio / Getty Images

Articulation Errors

Ankyloglossia, treating speech disorders.

A speech impediment, also known as a speech disorder , is a condition that can affect a person’s ability to form sounds and words, making their speech difficult to understand.

Speech disorders generally become evident in early childhood, as children start speaking and learning language. While many children initially have trouble with certain sounds and words, most are able to speak easily by the time they are five years old. However, some speech disorders persist. Approximately 5% of children aged three to 17 in the United States experience speech disorders.

There are many different types of speech impediments, including:

Articulation errors

This article explores the causes, symptoms, and treatment of the different types of speech disorders.

Speech impediments that break the flow of speech are known as disfluencies. Stuttering is the most common form of disfluency, however there are other types as well.

Symptoms and Characteristics of Disfluencies

These are some of the characteristics of disfluencies:

Repeating certain phrases, words, or sounds after the age of 4 (For example: “O…orange,” “I like…like orange juice,” “I want…I want orange juice”)
Adding in extra sounds or words into sentences (For example: “We…uh…went to buy…um…orange juice”)
Elongating words (For example: Saying “orange joooose” instead of "orange juice")
Replacing words (For example: “What…Where is the orange juice?”)
Hesitating while speaking (For example: A long pause while thinking)
Pausing mid-speech (For example: Stopping abruptly mid-speech, due to lack of airflow, causing no sounds to come out, leading to a tense pause)

In addition, someone with disfluencies may also experience the following symptoms while speaking:

Vocal tension and strain
Head jerking
Eye blinking
Lip trembling

Causes of Disfluencies

People with disfluencies tend to have neurological differences in areas of the brain that control language processing and coordinate speech, which may be caused by:

Genetic factors
Trauma or infection to the brain
Environmental stressors that cause anxiety or emotional distress
Neurodevelopmental conditions like attention-deficit hyperactivity disorder (ADHD)

Articulation disorders occur when a person has trouble placing their tongue in the correct position to form certain speech sounds. Lisping is the most common type of articulation disorder.

Symptoms and Characteristics of Articulation Errors

These are some of the characteristics of articulation disorders:

Substituting one sound for another . People typically have trouble with ‘r’ and ‘l’ sounds. (For example: Being unable to say “rabbit” and saying “wabbit” instead)
Lisping , which refers specifically to difficulty with ‘s’ and ‘z’ sounds. (For example: Saying “thugar” instead of “sugar” or producing a whistling sound while trying to pronounce these letters)
Omitting sounds (For example: Saying “coo” instead of “school”)
Adding sounds (For example: Saying “pinanio” instead of “piano”)
Making other speech errors that can make it difficult to decipher what the person is saying. For instance, only family members may be able to understand what they’re trying to say.

Causes of Articulation Errors

Articulation errors may be caused by:

Genetic factors, as it can run in families
Hearing loss , as mishearing sounds can affect the person’s ability to reproduce the sound
Changes in the bones or muscles that are needed for speech, including a cleft palate (a hole in the roof of the mouth) and tooth problems
Damage to the nerves or parts of the brain that coordinate speech, caused by conditions such as cerebral palsy , for instance

Ankyloglossia, also known as tongue-tie, is a condition where the person’s tongue is attached to the bottom of their mouth. This can restrict the tongue’s movement and make it hard for the person to move their tongue.

Symptoms and Characteristics of Ankyloglossia

Ankyloglossia is characterized by difficulty pronouncing ‘d,’ ‘n,’ ‘s,’ ‘t,’ ‘th,’ and ‘z’ sounds that require the person’s tongue to touch the roof of their mouth or their upper teeth, as their tongue may not be able to reach there.

Apart from speech impediments, people with ankyloglossia may also experience other symptoms as a result of their tongue-tie. These symptoms include:

Difficulty breastfeeding in newborns
Trouble swallowing
Limited ability to move the tongue from side to side or stick it out
Difficulty with activities like playing wind instruments, licking ice cream, or kissing
Mouth breathing

Causes of Ankyloglossia

Ankyloglossia is a congenital condition, which means it is present from birth. A tissue known as the lingual frenulum attaches the tongue to the base of the mouth. People with ankyloglossia have a shorter lingual frenulum, or it is attached further along their tongue than most people’s.

Dysarthria is a condition where people slur their words because they cannot control the muscles that are required for speech, due to brain, nerve, or organ damage.

Symptoms and Characteristics of Dysarthria

Dysarthria is characterized by:

Slurred, choppy, or robotic speech
Rapid, slow, or soft speech
Breathy, hoarse, or nasal voice

Additionally, someone with dysarthria may also have other symptoms such as difficulty swallowing and inability to move their tongue, lips, or jaw easily.

Causes of Dysarthria

Dysarthria is caused by paralysis or weakness of the speech muscles. The causes of the weakness can vary depending on the type of dysarthria the person has:

Central dysarthria is caused by brain damage. It may be the result of neuromuscular diseases, such as cerebral palsy, Huntington’s disease, multiple sclerosis, muscular dystrophy, Huntington’s disease, Parkinson’s disease, or Lou Gehrig’s disease. Central dysarthria may also be caused by injuries or illnesses that damage the brain, such as dementia, stroke, brain tumor, or traumatic brain injury .
Peripheral dysarthria is caused by damage to the organs involved in speech. It may be caused by congenital structural problems, trauma to the mouth or face, or surgery to the tongue, mouth, head, neck, or voice box.

Apraxia, also known as dyspraxia, verbal apraxia, or apraxia of speech, is a neurological condition that can cause a person to have trouble moving the muscles they need to create sounds or words. The person’s brain knows what they want to say, but is unable to plan and sequence the words accordingly.

Symptoms and Characteristics of Apraxia

These are some of the characteristics of apraxia:

Distorting sounds: The person may have trouble pronouncing certain sounds, particularly vowels, because they may be unable to move their tongue or jaw in the manner required to produce the right sound. Longer or more complex words may be especially harder to manage.
Being inconsistent in their speech: For instance, the person may be able to pronounce a word correctly once, but may not be able to repeat it. Or, they may pronounce it correctly today and differently on another day.
Grasping for words: The person may appear to be searching for the right word or sound, or attempt the pronunciation several times before getting it right.
Making errors with the rhythm or tone of speech: The person may struggle with using tone and inflection to communicate meaning. For instance, they may not stress any of the words in a sentence, have trouble going from one syllable in a word to another, or pause at an inappropriate part of a sentence.

Causes of Apraxia

Apraxia occurs when nerve pathways in the brain are interrupted, which can make it difficult for the brain to send messages to the organs involved in speaking. The causes of these neurological disturbances can vary depending on the type of apraxia the person has:

Childhood apraxia of speech (CAS): This condition is present from birth and is often hereditary. A person may be more likely to have it if a biological relative has a learning disability or communication disorder.
Acquired apraxia of speech (AOS): This condition can occur in adults, due to brain damage as a result of a tumor, head injury , stroke, or other illness that affects the parts of the brain involved in speech.

If you have a speech impediment, or suspect your child might have one, it can be helpful to visit your healthcare provider. Your primary care physician can refer you to a speech-language pathologist, who can evaluate speech, diagnose speech disorders, and recommend treatment options.

The diagnostic process may involve a physical examination as well as psychological, neurological, or hearing tests, in order to confirm the diagnosis and rule out other causes.

Treatment for speech disorders often involves speech therapy, which can help you learn how to move your muscles and position your tongue correctly in order to create specific sounds. It can be quite effective in improving your speech.

Children often grow out of milder speech disorders; however, special education and speech therapy can help with more serious ones.

For ankyloglossia, or tongue-tie, a minor surgery known as a frenectomy can help detach the tongue from the bottom of the mouth.

A Word From Verywell

A speech impediment can make it difficult to pronounce certain sounds, speak clearly, or communicate fluently.

Living with a speech disorder can be frustrating because people may cut you off while you’re speaking, try to finish your sentences, or treat you differently. It can be helpful to talk to your healthcare providers about how to cope with these situations.

You may also benefit from joining a support group, where you can connect with others living with speech disorders.

National Library of Medicine. Speech disorders . Medline Plus.

Centers for Disease Control and Prevention. Language and speech disorders .

Cincinnati Children's Hospital. Stuttering .

National Institute on Deafness and Other Communication Disorders. Quick statistics about voice, speech, and language .

Cleveland Clinic. Speech impediment .

Lee H, Sim H, Lee E, Choi D. Disfluency characteristics of children with attention-deficit/hyperactivity disorder symptoms . J Commun Disord . 2017;65:54-64. doi:10.1016/j.jcomdis.2016.12.001

Nemours Foundation. Speech problems .

Penn Medicine. Speech and language disorders .

Cleveland Clinic. Tongue-tie .

University of Rochester Medical Center. Ankyloglossia .

Cleveland Clinic. Dysarthria .

National Institute on Deafness and Other Communication Disorders. Apraxia of speech .

Cleveland Clinic. Childhood apraxia of speech .

Stanford Children’s Hospital. Speech sound disorders in children .

Abbastabar H, Alizadeh A, Darparesh M, Mohseni S, Roozbeh N. Spatial distribution and the prevalence of speech disorders in the provinces of Iran . J Med Life . 2015;8(Spec Iss 2):99-104.

By Sanjana Gupta Sanjana is a health writer and editor. Her work spans various health-related topics, including mental health, fitness, nutrition, and wellness.

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Overcoming Speech Impediment: Symptoms to Treatment

There are many causes and solutions for impaired speech

Types and Symptoms
Speech Therapy
Building Confidence

Speech impediments are conditions that can cause a variety of symptoms, such as an inability to understand language or speak with a stable sense of tone, speed, or fluidity. There are many different types of speech impediments, and they can begin during childhood or develop during adulthood.

Common causes include physical trauma, neurological disorders, or anxiety. If you or your child is experiencing signs of a speech impediment, you need to know that these conditions can be diagnosed and treated with professional speech therapy.

This article will discuss what you can do if you are concerned about a speech impediment and what you can expect during your diagnostic process and therapy.

FG Trade / Getty Images

Types and Symptoms of Speech Impediment

People can have speech problems due to developmental conditions that begin to show symptoms during early childhood or as a result of conditions that may occur during adulthood.

The main classifications of speech impairment are aphasia (difficulty understanding or producing the correct words or phrases) or dysarthria (difficulty enunciating words).

Often, speech problems can be part of neurological or neurodevelopmental disorders that also cause other symptoms, such as multiple sclerosis (MS) or autism spectrum disorder .

There are several different symptoms of speech impediments, and you may experience one or more.

Can Symptoms Worsen?

Most speech disorders cause persistent symptoms and can temporarily get worse when you are tired, anxious, or sick.

Symptoms of dysarthria can include:

Slurred speech
Slow speech
Choppy speech
Hesitant speech
Inability to control the volume of your speech
Shaking or tremulous speech pattern
Inability to pronounce certain sounds

Symptoms of aphasia may involve:

Speech apraxia (difficulty coordinating speech)
Difficulty understanding the meaning of what other people are saying
Inability to use the correct words
Inability to repeat words or phases
Speech that has an irregular rhythm

You can have one or more of these speech patterns as part of your speech impediment, and their combination and frequency will help determine the type and cause of your speech problem.

Causes of Speech Impediment

The conditions that cause speech impediments can include developmental problems that are present from birth, neurological diseases such as Parkinson’s disease , or sudden neurological events, such as a stroke .

Some people can also experience temporary speech impairment due to anxiety, intoxication, medication side effects, postictal state (the time immediately after a seizure), or a change of consciousness.

Speech Impairment in Children

Children can have speech disorders associated with neurodevelopmental problems, which can interfere with speech development. Some childhood neurological or neurodevelopmental disorders may cause a regression (backsliding) of speech skills.

Common causes of childhood speech impediments include:

Autism spectrum disorder : A neurodevelopmental disorder that affects social and interactive development
Cerebral palsy : A congenital (from birth) disorder that affects learning and control of physical movement
Hearing loss : Can affect the way children hear and imitate speech
Rett syndrome : A genetic neurodevelopmental condition that causes regression of physical and social skills beginning during the early school-age years.
Adrenoleukodystrophy : A genetic disorder that causes a decline in motor and cognitive skills beginning during early childhood
Childhood metabolic disorders : A group of conditions that affects the way children break down nutrients, often resulting in toxic damage to organs
Brain tumor : A growth that may damage areas of the brain, including those that control speech or language
Encephalitis : Brain inflammation or infection that may affect the way regions in the brain function
Hydrocephalus : Excess fluid within the skull, which may develop after brain surgery and can cause brain damage

Do Childhood Speech Disorders Persist?

Speech disorders during childhood can have persistent effects throughout life. Therapy can often help improve speech skills.

Speech Impairment in Adulthood

Adult speech disorders develop due to conditions that damage the speech areas of the brain.

Common causes of adult speech impairment include:

Head trauma
Nerve injury
Throat tumor
Stroke
Parkinson’s disease
Essential tremor
Brain tumor
Brain infection

Additionally, people may develop changes in speech with advancing age, even without a specific neurological cause. This can happen due to presbyphonia , which is a change in the volume and control of speech due to declining hormone levels and reduced elasticity and movement of the vocal cords.

Do Speech Disorders Resolve on Their Own?

Children and adults who have persistent speech disorders are unlikely to experience spontaneous improvement without therapy and should seek professional attention.

Steps to Treating Speech Impediment

If you or your child has a speech impediment, your healthcare providers will work to diagnose the type of speech impediment as well as the underlying condition that caused it. Defining the cause and type of speech impediment will help determine your prognosis and treatment plan.

Sometimes the cause is known before symptoms begin, as is the case with trauma or MS. Impaired speech may first be a symptom of a condition, such as a stroke that causes aphasia as the primary symptom.

The diagnosis will include a comprehensive medical history, physical examination, and a thorough evaluation of speech and language. Diagnostic testing is directed by the medical history and clinical evaluation.

Diagnostic testing may include:

Brain imaging , such as brain computerized tomography (CT) or magnetic residence imaging (MRI), if there’s concern about a disease process in the brain
Swallowing evaluation if there’s concern about dysfunction of the muscles in the throat
Electromyography (EMG) and nerve conduction studies (aka nerve conduction velocity, or NCV) if there’s concern about nerve and muscle damage
Blood tests, which can help in diagnosing inflammatory disorders or infections

Your diagnostic tests will help pinpoint the cause of your speech problem. Your treatment will include specific therapy to help improve your speech, as well as medication or other interventions to treat the underlying disorder.

For example, if you are diagnosed with MS, you would likely receive disease-modifying therapy to help prevent MS progression. And if you are diagnosed with a brain tumor, you may need surgery, chemotherapy, or radiation to treat the tumor.

Therapy to Address Speech Impediment

Therapy for speech impairment is interactive and directed by a specialist who is experienced in treating speech problems . Sometimes, children receive speech therapy as part of a specialized learning program at school.

The duration and frequency of your speech therapy program depend on the underlying cause of your impediment, your improvement, and approval from your health insurance.

If you or your child has a serious speech problem, you may qualify for speech therapy. Working with your therapist can help you build confidence, particularly as you begin to see improvement.

Exercises during speech therapy may include:

Pronouncing individual sounds, such as la la la or da da da
Practicing pronunciation of words that you have trouble pronouncing
Adjusting the rate or volume of your speech
Mouth exercises
Practicing language skills by naming objects or repeating what the therapist is saying

These therapies are meant to help achieve more fluent and understandable speech as well as an increased comfort level with speech and language.

Building Confidence With Speech Problems

Some types of speech impairment might not qualify for therapy. If you have speech difficulties due to anxiety or a social phobia or if you don’t have access to therapy, you might benefit from activities that can help you practice your speech.

You might consider one or more of the following for you or your child:

Joining a local theater group
Volunteering in a school or community activity that involves interaction with the public
Signing up for a class that requires a significant amount of class participation
Joining a support group for people who have problems with speech

Activities that you do on your own to improve your confidence with speaking can be most beneficial when you are in a non-judgmental and safe space.

Many different types of speech problems can affect children and adults. Some of these are congenital (present from birth), while others are acquired due to health conditions, medication side effects, substances, or mood and anxiety disorders. Because there are so many different types of speech problems, seeking a medical diagnosis so you can get the right therapy for your specific disorder is crucial.

Centers for Disease Control and Prevention. Language and speech disorders in children .

Han C, Tang J, Tang B, et al. The effectiveness and safety of noninvasive brain stimulation technology combined with speech training on aphasia after stroke: a systematic review and meta-analysis . Medicine (Baltimore). 2024;103(2):e36880. doi:10.1097/MD.0000000000036880

National Institute on Deafness and Other Communication Disorders. Quick statistics about voice, speech, language .

Mackey J, McCulloch H, Scheiner G, et al. Speech pathologists' perspectives on the use of augmentative and alternative communication devices with people with acquired brain injury and reflections from lived experience . Brain Impair. 2023;24(2):168-184. doi:10.1017/BrImp.2023.9

Allison KM, Doherty KM. Relation of speech-language profile and communication modality to participation of children with cerebral palsy . Am J Speech Lang Pathol . 2024:1-11. doi:10.1044/2023_AJSLP-23-00267

Saccente-Kennedy B, Gillies F, Desjardins M, et al. A systematic review of speech-language pathology interventions for presbyphonia using the rehabilitation treatment specification system . J Voice. 2024:S0892-1997(23)00396-X. doi:10.1016/j.jvoice.2023.12.010

By Heidi Moawad, MD Dr. Moawad is a neurologist and expert in brain health. She regularly writes and edits health content for medical books and publications.

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FOXP2-related speech and language disorder

Description.

In addition to having problems with producing speech (expressive language), people with FOXP2 -related speech and language disorder may have difficulty with understanding speech (receptive language). Some also have trouble with other language-related skills, such as reading, writing, spelling, and grammar. In some affected individuals, problems with speech and language are the only features of the condition. Others also have delayed development in other areas, including motor skills such as walking and tying shoelaces, and autism spectrum disorders, which are conditions characterized by impaired communication and social interaction.

FOXP2 -related speech and language disorder appears to be a relatively uncommon cause of problems with speech and language development. The total prevalence of apraxia is estimated to be 1 to 2 in 1,000 people, and it is likely that FOXP2 -related speech and language disorder accounts for only a small portion of cases.

The genetic changes that underlie FOXP2 -related speech and language disorder disrupt the activity of the FOXP2 gene. Because forkhead box P2 is a transcription factor, these changes affect the activity of other genes in the developing brain. Researchers are working to determine which of these genes are involved and how changes in their activity lead to abnormal speech and language development.

Additional features that are sometimes associated with FOXP2 -related speech and language disorder, including delayed motor development and autism spectrum disorders, likely result from changes to other genes on chromosome 7. For example, in affected individuals with a deletion involving chromosome 7, a loss of FOXP2 is thought to disrupt speech and language development, while the loss of nearby genes accounts for other signs and symptoms. People with maternal UPD for chromosome 7 have FOXP2 -related speech and language disorder as part of a larger condition called Russell-Silver syndrome . In addition to speech and language problems, these individuals have slow growth, distinctive facial features, delayed development, and learning disabilities.

Learn more about the gene and chromosome associated with FOXP2-related speech and language disorder

chromosome 7

Inheritance

When the condition is caused by rearrangements of the structure of chromosome 7, its pattern of inheritance can be complex and depends on the specific genetic change.

Other Names for This Condition

Speech and language disorder with orofacial dyspraxia
Speech-language disorder 1

Additional Information & Resources

Genetic testing information.

Genetic and Rare Diseases Information Center

Patient support and advocacy resources.

National Organization for Rare Disorders (NORD)

Catalog of Genes and Diseases from OMIM

SPEECH-LANGUAGE DISORDER 1; SPCH1

Scientific Articles on PubMed

Feuk L, Kalervo A, Lipsanen-Nyman M, Skaug J, Nakabayashi K, Finucane B, Hartung D, Innes M, Kerem B, Nowaczyk MJ, Rivlin J, Roberts W, Senman L, Summers A, Szatmari P, Wong V, Vincent JB, Zeesman S, Osborne LR, Cardy JO, Kere J, Scherer SW, Hannula-Jouppi K. Absence of a paternally inherited FOXP2 gene in developmental verbal dyspraxia. Am J Hum Genet. 2006 Nov;79(5):965-72. doi: 10.1086/508902. Epub 2006 Sep 27. Citation on PubMed or Free article on PubMed Central
Fisher SE, Vargha-Khadem F, Watkins KE, Monaco AP, Pembrey ME. Localisation of a gene implicated in a severe speech and language disorder. Nat Genet. 1998 Feb;18(2):168-70. doi: 10.1038/ng0298-168. Erratum In: Nat Genet 1998 Mar;18(3):298. Citation on PubMed
Lai CS, Fisher SE, Hurst JA, Vargha-Khadem F, Monaco AP. A forkhead-domain gene is mutated in a severe speech and language disorder. Nature. 2001 Oct 4;413(6855):519-23. doi: 10.1038/35097076. Citation on PubMed
MacDermot KD, Bonora E, Sykes N, Coupe AM, Lai CS, Vernes SC, Vargha-Khadem F, McKenzie F, Smith RL, Monaco AP, Fisher SE. Identification of FOXP2 truncation as a novel cause of developmental speech and language deficits. Am J Hum Genet. 2005 Jun;76(6):1074-80. doi: 10.1086/430841. Epub 2005 Apr 22. Citation on PubMed or Free article on PubMed Central
Morgan A, Fisher SE, Scheffer I, Hildebrand M. FOXP2-Related Speech and Language Disorder. 2016 Jun 23 [updated 2023 Jan 26]. In: Adam MP, Feldman J, Mirzaa GM, Pagon RA, Wallace SE, Bean LJH, Gripp KW, Amemiya A, editors. GeneReviews(R) [Internet]. Seattle (WA): University of Washington, Seattle; 1993-2024. Available from http://www.ncbi.nlm.nih.gov/books/NBK368474/ Citation on PubMed
Tomblin JB, O'Brien M, Shriberg LD, Williams C, Murray J, Patil S, Bjork J, Anderson S, Ballard K. Language features in a mother and daughter of a chromosome 7;13 translocation involving FOXP2. J Speech Lang Hear Res. 2009 Oct;52(5):1157-74. doi: 10.1044/1092-4388(2009/07-0162). Citation on PubMed or Free article on PubMed Central
Zeesman S, Nowaczyk MJ, Teshima I, Roberts W, Cardy JO, Brian J, Senman L, Feuk L, Osborne LR, Scherer SW. Speech and language impairment and oromotor dyspraxia due to deletion of 7q31 that involves FOXP2. Am J Med Genet A. 2006 Mar 1;140(5):509-14. doi: 10.1002/ajmg.a.31110. Citation on PubMed

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Genetics of speech and language disorders

Affiliation.

1 National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland 20892, USA.
PMID: 21663442
DOI: 10.1146/annurev-genom-090810-183119

Vocal communication mediated by speech and language is a uniquely human trait, and has served an important evolutionary role in the development of our species. Deficits in speech and language functions can be of numerous types, including aphasia, stuttering, articulation disorders, verbal dyspraxia, and specific language impairment; language deficits are also related to dyslexia. Most communication disorders are prominent in children, where they are common. A number of these disorders have been shown to cluster in families, suggesting that genetic factors are involved, but their etiology at the molecular level is not well understood. In the past decade, genetic methods have proven to be powerful for understanding these etiologies. Linkage studies and molecular genetic analyses in a large family containing multiple individuals affected with verbal dyspraxia led to the discovery of mutations in the FOXP2 gene. This gene encodes a forkhead domain transcription factor, a finding that has led researchers to a new avenue of investigation into the substrates and mechanisms that underlie human speech development. In studies of stuttering, linkage and candidate gene approaches in consanguineous families identified mutations in the lysosomal enzyme-targeting pathway genes GNPTAB, GNPTG, and NAGPA, revealing a role for inherited defects in cell metabolism in this disorder. In specific language impairment, linkage studies have identified several loci, and candidate gene association studies are making progress in identifying causal variants at these loci. Although only a small fraction of all cases of speech and language disorders can be explained by genetic findings to date, the significant progress made thus far suggests that genetic approaches will continue to provide important avenues for research on this group of disorders.

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Genetics and Speech Disorders

Image Courtesy of futurity.org

Maybe you get your brown hair from your dad and your blue eyes from your mom. But did you ever consider that perhaps your speech and language patterns are also inherited? Advancements in medical and scientific research have increasingly been revealing that you can also inherit susceptibility to speech disorders , just like you might inherit increased risks for diabetes or other medical conditions. Genetics and speech disorders involve a complicated twisting and turning of details that all come together to form clues about your genetic makeup. Just like with other medical and health issues, the more we know about the likelihood of our genes playing a role in our overall well-being, we can make preventative and proactive decisions.

Specific Speech Disorders and Genetics

There are several specific types of speech and language disorders that appear to be closely tied with genetics. Scientists have begun identifying specific genes that are responsible for the ways we speak and communicate. Probably the most important discovery has been the genes FOXP 2 , KIAA0319, CNTNAP 2 , ATP 2 C 2 , and CMIP.

In 2001 scientists from the U.K. found that rare mutations of FOXP 2 can be responsible for many members of the same family struggling with specific language impairment (SLI). Researchers have also found genetic links with other genes to stuttering, speech-sound disorder, and developmental verbal dyspraxia (DVD).

Image Courtesy of pubpages.unh.edu

What is SLI? – Specific language impairment is one of the most common learning disabilities that appears in childhood, affecting as many as 8% of kids. It is sometimes also referred to as developmental language disorder or developmental dysphasia. Kids with this disorder find it challenging to build strong vocabularies, even though their hearing is unaffected. Scientists now believe that as many as 50%-70% of children who have SLI also have at least one family member who struggles with the disorder.
What is stuttering? – Stuttering takes on many forms of repetitions – beginning sound, ending sounds , and partial word repeating. There are signs that genetics plays a part in at least some of these instances.
What is speech-sound disorder? – Speech-sound disorders include a wide range of articulation and phonological problems, including leaving off or adding extra sounds to words.
What is developmental verbal dyspraxia (DVD)? – Also known as childhood apraxia of speech, this disorder makes it difficult for children to coordinate their muscles needed for clear speaking.

Why Do I Need to Know About Genetics and Speech Disorders?

Just like so many aspects of life, the more you know, the more you can do. Perhaps genetics are the reason why several of your cousins have speech disorders. Understanding family medical history can help you make better decisions about preventative care and speech therapy. When you complete your child’s health forms and go through all of the options – family histories of cancer, diabetes, medical illness, and so on – keep in mind that even though “speech disorder” might not be listed as a family medical condition doesn’t mean that there aren’t genetic tendencies in your family. It often isn’t until you meet with a speech therapist that family histories of speech disorders will become relevant .

If you have a family history of any kind of speech disorder, early prevention can help to give your child the best opportunity for healthy communication skills.
Talk with your child’s pediatrician about other related health issues that may be genetic. Some of the early genetic findings related to communication include ties to disorders such as Autism as well, with what is known as Fragile X Syndrome .

Image Courtesy of fragilex.org

There are many causes of speech and language disorders , and scientists are only beginning to scratch the surface when it comes to genetics and speech disorders. If you or your child struggle with communication, talk with your healthcare provider, a speech therapist, and other qualified individuals. You don’t have to have a family history in order to find a cause and work toward improved communication skills.

Parent's Guide to Speech & Communication Challenges

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Home / Blog

Speech Impediment Guide: Definition, Causes, and Resources

December 8, 2020

Tables of Contents

What Is a Speech Impediment?

Types of speech disorders, speech impediment causes, how to fix a speech impediment, making a difference in speech disorders.

Communication is a cornerstone of human relationships. When an individual struggles to verbalize information, thoughts, and feelings, it can cause major barriers in personal, learning, and business interactions.

Speech impediments, or speech disorders, can lead to feelings of insecurity and frustration. They can also cause worry for family members and friends who don’t know how to help their loved ones express themselves.

Fortunately, there are a number of ways that speech disorders can be treated, and in many cases, cured. Health professionals in fields including speech-language pathology and audiology can work with patients to overcome communication disorders, and individuals and families can learn techniques to help.

A woman struggles to communicate due to a speech disorder.

Commonly referred to as a speech disorder, a speech impediment is a condition that impacts an individual’s ability to speak fluently, correctly, or with clear resonance or tone. Individuals with speech disorders have problems creating understandable sounds or forming words, leading to communication difficulties.

Some 7.7% of U.S. children — or 1 in 12 youths between the ages of 3 and 17 — have speech, voice, language, or swallowing disorders, according to the National Institute on Deafness and Other Communication Disorders (NIDCD). About 70 million people worldwide, including some 3 million Americans, experience stuttering difficulties, according to the Stuttering Foundation.

Common signs of a speech disorder

There are several symptoms and indicators that can point to a speech disorder.

Unintelligible speech — A speech disorder may be present when others have difficulty understanding a person’s verbalizations.
Omitted sounds — This symptom can include the omission of part of a word, such as saying “bo” instead of “boat,” and may include omission of consonants or syllables.
Added sounds — This can involve adding extra sounds in a word, such as “buhlack” instead of “black,” or repeating sounds like “b-b-b-ball.”
Substituted sounds — When sounds are substituted or distorted, such as saying “wabbit” instead of “rabbit,” it may indicate a speech disorder.
Use of gestures — When individuals use gestures to communicate instead of words, a speech impediment may be the cause.
Inappropriate pitch — This symptom is characterized by speaking with a strange pitch or volume.

In children, signs might also include a lack of babbling or making limited sounds. Symptoms may also include the incorrect use of specific sounds in words, according to the American Speech-Language-Hearing Association (ASHA). This may include the sounds p, m, b, w, and h among children aged 1-2, and k, f, g, d, n, and t for children aged 2-3.

Signs of speech disorders include unintelligible speech and sound omissions, substitutions, and additions.

Categories of Speech Impediments

Speech impediments can range from speech sound disorders (articulation and phonological disorders) to voice disorders. Speech sound disorders may be organic — resulting from a motor or sensory cause — or may be functional with no known cause. Voice disorders deal with physical problems that limit speech. The main categories of speech impediments include the following:

Fluency disorders occur when a patient has trouble with speech timing or rhythms. This can lead to hesitations, repetitions, or prolonged sounds. Fluency disorders include stuttering (repetition of sounds) or (rapid or irregular rate of speech).

Resonance disorders are related to voice quality that is impacted by the shape of the nose, throat, and/or mouth. Examples of resonance disorders include hyponasality and cul-de-sac resonance.

Articulation disorders occur when a patient has difficulty producing speech sounds. These disorders may stem from physical or anatomical limitations such as muscular, neuromuscular, or skeletal support. Examples of articulation speech impairments include sound omissions, substitutions, and distortions.

Phonological disorders result in the misuse of certain speech sounds to form words. Conditions include fronting, stopping, and the omission of final consonants.

Voice disorders are the result of problems in the larynx that harm the quality or use of an individual’s voice. This can impact pitch, resonance, and loudness.

Impact of Speech Disorders

Some speech disorders have little impact on socialization and daily activities, but other conditions can make some tasks difficult for individuals. Following are a few of the impacts of speech impediments.

Poor communication — Children may be unable to participate in certain learning activities, such as answering questions or reading out loud, due to communication difficulties. Adults may avoid work or social activities such as giving speeches or attending parties.
Mental health and confidence — Speech disorders may cause children or adults to feel different from peers, leading to a lack of self-confidence and, potentially, self-isolation.

Resources on Speech Disorders

The following resources may help those who are seeking more information about speech impediments.

Health Information : Information and statistics on common voice and speech disorders from the NIDCD

Speech Disorders : Information on childhood speech disorders from Cincinnati Children’s Hospital Medical Center

Speech, Language, and Swallowing : Resources about speech and language development from the ASHA

Children and adults can suffer from a variety of speech impairments that may have mild to severe impacts on their ability to communicate. The following 10 conditions are examples of specific types of speech disorders and voice disorders.

1. Stuttering

This condition is one of the most common speech disorders. Stuttering is the repetition of syllables or words, interruptions in speech, or prolonged use of a sound.

This organic speech disorder is a result of damage to the neural pathways that connect the brain to speech-producing muscles. This results in a person knowing what they want to say, but being unable to speak the words.

This consists of the lost ability to speak, understand, or write languages. It is common in stroke, brain tumor, or traumatic brain injury patients.

4. Dysarthria

This condition is an organic speech sound disorder that involves difficulty expressing certain noises. This may involve slurring, or poor pronunciation, and rhythm differences related to nerve or brain disorders.

The condition of lisping is the replacing of sounds in words, including “th” for “s.” Lisping is a functional speech impediment.

6. Hyponasality

This condition is a resonance disorder related to limited sound coming through the nose, causing a “stopped up” quality to speech.

7. Cul-de-sac resonance

This speech disorder is the result of blockage in the mouth, throat, or nose that results in quiet or muffled speech.

8. Orofacial myofunctional disorders

These conditions involve abnormal patterns of mouth and face movement. Conditions include tongue thrusting (fronting), where individuals push out their tongue while eating or talking.

9. Spasmodic Dysphonia

This condition is a voice disorder in which spasms in the vocal cords produce speech that is hoarse, strained, or jittery.

10. Other voice disorders

These conditions can include having a voice that sounds breathy, hoarse, or scratchy. Some disorders deal with vocal folds closing when they should open (paradoxical vocal fold movement) or the presence of polyps or nodules in the vocal folds.

Speech Disorders vs. Language Disorders

Speech disorders deal with difficulty in creating sounds due to articulation, fluency, phonology, and voice problems. These problems are typically related to physical, motor, sensory, neurological, or mental health issues.

Language disorders, on the other hand, occur when individuals have difficulty communicating the meaning of what they want to express. Common in children, these disorders may result in low vocabulary and difficulty saying complex sentences. Such a disorder may reflect difficulty in comprehending school lessons or adopting new words, or it may be related to a learning disability such as dyslexia. Language disorders can also involve receptive language difficulties, where individuals have trouble understanding the messages that others are trying to convey.

About 5% of children in the U.S. have a speech disorder such as stuttering, apraxia, dysarthria, and lisping.

Resources on Types of Speech Disorders

The following resources may provide additional information on the types of speech impediments.

Common Speech Disorders: A guide to the most common speech impediments from GreatSpeech

Speech impairment in adults: Descriptions of common adult speech issues from MedlinePlus

Stuttering Facts: Information on stuttering indications and causes from the Stuttering Foundation

Speech disorders may be caused by a variety of factors related to physical features, neurological ailments, or mental health conditions. In children, they may be related to developmental issues or unknown causes and may go away naturally over time.

Physical and neurological issues. Speech impediment causes related to physical characteristics may include:

Brain damage
Nervous system damage
Respiratory system damage
Hearing difficulties
Cancerous or noncancerous growths
Muscle and bone problems such as dental issues or cleft palate

Mental health issues. Some speech disorders are related to clinical conditions such as:

Autism spectrum disorder
Down syndrome or other genetic syndromes
Cerebral palsy or other neurological disorders
Multiple sclerosis

Some speech impairments may also have to do with family history, such as when parents or siblings have experienced language or speech difficulties. Other causes may include premature birth, pregnancy complications, or delivery difficulties. Voice overuse and chronic coughs can also cause speech issues.

The most common way that speech disorders are treated involves seeking professional help. If patients and families feel that symptoms warrant therapy, health professionals can help determine how to fix a speech impediment. Early treatment is best to curb speech disorders, but impairments can also be treated later in life.

Professionals in the speech therapy field include speech-language pathologists (SLPs) . These practitioners assess, diagnose, and treat communication disorders including speech, language, social, cognitive, and swallowing disorders in both adults and children. They may have an SLP assistant to help with diagnostic and therapy activities.

Speech-language pathologists may also share a practice with audiologists and audiology assistants. Audiologists help identify and treat hearing, balance, and other auditory disorders.

How Are Speech Disorders Diagnosed?

Typically, a pediatrician, social worker, teacher, or other concerned party will recognize the symptoms of a speech disorder in children. These individuals, who frequently deal with speech and language conditions and are more familiar with symptoms, will recommend that parents have their child evaluated. Adults who struggle with speech problems may seek direct guidance from a physician or speech evaluation specialist.

When evaluating a patient for a potential speech impediment, a physician will:

Conduct hearing and vision tests
Evaluate patient records
Observe patient symptoms

A speech-language pathologist will conduct an initial screening that might include:

An evaluation of speech sounds in words and sentences
An evaluation of oral motor function
An orofacial examination
An assessment of language comprehension

The initial screening might result in no action if speech symptoms are determined to be developmentally appropriate. If a disorder is suspected, the initial screening might result in a referral for a comprehensive speech sound assessment, comprehensive language assessment, audiology evaluation, or other medical services.

Initial assessments and more in-depth screenings might occur in a private speech therapy practice, rehabilitation center, school, childcare program, or early intervention center. For older adults, skilled nursing centers and nursing homes may assess patients for speech, hearing, and language disorders.

How Are Speech Impediments Treated?

Once an evaluation determines precisely what type of speech sound disorder is present, patients can begin treatment. Speech-language pathologists use a combination of therapy, exercise, and assistive devices to treat speech disorders.

Speech therapy might focus on motor production (articulation) or linguistic (phonological or language-based) elements of speech, according to ASHA. There are various types of speech therapy available to patients.

Contextual Utilization — This therapeutic approach teaches methods for producing sounds consistently in different syllable-based contexts, such as phonemic or phonetic contexts. These methods are helpful for patients who produce sounds inconsistently.

Phonological Contrast — This approach focuses on improving speech through emphasis of phonemic contrasts that serve to differentiate words. Examples might include minimal opposition words (pot vs. spot) or maximal oppositions (mall vs. call). These therapy methods can help patients who use phonological error patterns.

Distinctive Feature — In this category of therapy, SLPs focus on elements that are missing in speech, such as articulation or nasality. This helps patients who substitute sounds by teaching them to distinguish target sounds from substituted sounds.

Core Vocabulary — This therapeutic approach involves practicing whole words that are commonly used in a specific patient’s communications. It is effective for patients with inconsistent sound production.

Metaphon — In this type of therapy, patients are taught to identify phonological language structures. The technique focuses on contrasting sound elements, such as loud vs. quiet, and helps patients with unintelligible speech issues.

Oral-Motor — This approach uses non-speech exercises to supplement sound therapies. This helps patients gain oral-motor strength and control to improve articulation.

Other methods professionals may use to help fix speech impediments include relaxation, breathing, muscle strengthening, and voice exercises. They may also recommend assistive devices, which may include:

Radio transmission systems
Personal amplifiers
Picture boards
Touch screens
Text displays
Speech-generating devices
Hearing aids
Cochlear implants

Resources for Professionals on How to Fix a Speech Impediment

The following resources provide information for speech therapists and other health professionals.

Assistive Devices: Information on hearing and speech aids from the NIDCD

Information for Audiologists: Publications, news, and practice aids for audiologists from ASHA

Information for Speech-Language Pathologists: Publications, news, and practice aids for SLPs from ASHA

Speech Disorder Tips for Families

For parents who are concerned that their child might have a speech disorder — or who want to prevent the development of a disorder — there are a number of activities that can help. The following are tasks that parents can engage in on a regular basis to develop literacy and speech skills.

Introducing new vocabulary words
Reading picture and story books with various sounds and patterns
Talking to children about objects and events
Answering children’s questions during routine activities
Encouraging drawing and scribbling
Pointing to words while reading books
Pointing out words and sentences in objects and signs

Parents can take the following steps to make sure that potential speech impediments are identified early on.

Discussing concerns with physicians
Asking for hearing, vision, and speech screenings from doctors
Requesting special education assessments from school officials
Requesting a referral to a speech-language pathologist, audiologist, or other specialist

When a child is engaged in speech therapy, speech-language pathologists will typically establish collaborative relationships with families, sharing information and encouraging parents to participate in therapy decisions and practices.

SLPs will work with patients and their families to set goals for therapy outcomes. In addition to therapy sessions, they may develop activities and exercises for families to work on at home. It is important that caregivers are encouraging and patient with children during therapy.

Resources for Parents on How to Fix a Speech Impediment

The following resources provide additional information on treatment options for speech disorders.

Speech, Language, and Swallowing Disorders Groups: Listing of self-help groups from ASHA

ProFind: Search tool for finding certified SLPs and audiologists from ASHA

Baby’s Hearing and Communication Development Checklist: Listing of milestones that children should meet by certain ages from the NIDCD

If identified during childhood, speech disorders can be corrected efficiently, giving children greater communication opportunities. If left untreated, speech impediments can cause a variety of problems in adulthood, and may be more difficult to diagnose and treat.

Parents, teachers, doctors, speech and language professionals, and other concerned parties all have unique responsibilities in recognizing and treating speech disorders. Through professional therapy, family engagement, positive encouragement and a strong support network, individuals with speech impediments can overcome their challenges and develop essential communication skills.

Additional Sources

American Speech-Language-Hearing Association, Speech Sound Disorders

Identify the Signs, Signs of Speech and Language Disorders

Intermountain Healthcare, Phonological Disorders

MedlinePlus, Speech disorders – children

National Institutes of Health, National Institutes on Deafness and Other Communication Disorders, “Quick Statistics About Voice, Speech, Language”

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Are Speech Issues Hereditary?

Perhaps you know that you got your brown eyes from your mother and your blonde hair from your father. But not many people realize that speech and language patterns and abilities are also inherited from one’s parents. Developments in scientific and medical research have revealed that it is also possible to inherit a predisposition or susceptibility to speech and language disorders. This works in the same way that other risks of developing conditions can be inherited, such as diabetes or heart disease.

The link between genetics and speech and language disorders is complex and can reveal clues about an individual’s genetic makeup. Just as in the case of other medical conditions and health problems, the more that is known about an individual’s genetics, the more opportunities there are to practice prevention and be proactive.

Speech therapy is the best resource when it comes to supporting the development of speech and language skills, as well as strengthening and improving these skills over time. If you think you or a loved one would benefit from spending time with a speech therapist, get started by scheduling your free introductory call today.

Do Speech Issues Run in Families? Can Speech Impediments be Passed Down?

Significant evidence links genetic factors to a wide variety of speech and language difficulties. Studies are cross-disciplinary, meaning that physicians, scientists, and speech-language pathologists are working together through research to identify specific genetic factors that are linked to the development of communication disorders. Researchers have already identified more than 400 genes linked to hearing impairment, and research is ongoing to establish specific genetic links to such communication disorders as stuttering, voice disorders, and expressive and receptive language disorders.

What Kind of Speech Disorders are Hereditary?

Several types of speech and language disorders appear to be closely connected with genetics:

Specific Language Impairment

Specific language impairment (also referred to as SLI) is a type of communication disorder that disrupts the development of a child’s language skills who does not have hearing loss. SLI can affect a child’s ability to speak, listen, read, and write. Specific language impairment is also often referred to as language delay, developmental language disorder, or developmental dysphasia.

Studies have shown that as many as 50-70% of children with SLI have at least one member of their family who is affected by the same disorder.

Stuttering is a speech fluency disorder that disrupts the regular fluency and flow of speech. Individuals who stutter know what they want to say, but have difficulty getting the words out.

Recent research has shown that genetics may play a part in at least some cases of stuttering.

Speech-Sound Disorders

Speech sound disorders are among the most commonly seen communication disorders in children. Speech sound disorder is an umbrella term that refers to a single difficulty or a combination of difficulties related to perception, motor skills, or phonology. There is a growing body of evidence that suggests there is an underlying genetic basis for the development of speech sound disorders.

Childhood Apraxia of Speech (CAS)

Childhood apraxia of speech is typically present from birth and affects the child’s ability to plan and produce specific letter sounds through the movement of the articulators, often making speech unclear or difficult to understand. There has been a specific gene mutation (FOXP2) identified that links family members with CAS.

Why is it Important to Understand the Role of Genetics in Speech Problems?

Just as is true in so many parts of life, the more information we have, the more proactive we can be. Understanding your family’s medical history can help inform decisions about preventative practices and care. When looking at a family’s medical history, speech and language disorders aren’t always included. In fact, a family history of communication disorders may not seem relevant until your child meets with a speech therapist.

If you are aware of a communication disorder among family members, early prevention and intervention can give your child the optimal chance to develop strong and healthy communication skills. It is highly important to discuss any family health issues that may be genetic with your healthcare provider for the sake of awareness and so that preventative measures can be taken.

If you or your child is struggling with communication, connecting with a speech and language pathologist is a good first step. Get started by scheduling your free introductory call today!

Tips for Parents

Avoid the Blame Game

Don’t spend time feeling guilty or badly if you struggle (or have struggled) with a communication disorder and may have passed a genetic predisposition onto your child. Focus on supporting your child as they learn and grow and encouraging them wherever they may be with their communication skills.

Find a Qualified Speech and Language Pathologist

Speech and language pathologists are expertly trained in general knowledge surrounding the development of communication disorders, the role of genetics, and how to effectively treat these disorders. At Great Speech, connecting with an optimally suited speech therapist is simple and easy. With a network of more than 50 qualified and experienced SLPs, connecting each individual with the best therapist to fit their needs is always possible.

Early Intervention

If you are concerned about the development of your child’s communication skills, early intervention is essential, whether there is a genetic link or not. The importance of beginning speech and language therapy as early as possible cannot be overstated, especially when there is a family history of communication disorders.

Speech therapy is a great option for all children, even those who are developing as they should. Don’t wait for your child to fall behind, get started with speech therapy by scheduling your free introductory call today!

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Committee on the Evaluation of the Supplemental Security Income (SSI) Disability Program for Children with Speech Disorders and Language Disorders; Board on the Health of Select Populations; Board on Children, Youth, and Families; Institute of Medicine; Division of Behavioral and Social Sciences and Education; National Academies of Sciences, Engineering, and Medicine; Rosenbaum S, Simon P, editors. Speech and Language Disorders in Children: Implications for the Social Security Administration's Supplemental Security Income Program. Washington (DC): National Academies Press (US); 2016 Apr 6.

Cover of Speech and Language Disorders in Children

Speech and Language Disorders in Children: Implications for the Social Security Administration's Supplemental Security Income Program.

Hardcopy Version at National Academies Press

2 Childhood Speech and Language Disorders in the General U.S. Population

Speech and language disorders in children include a variety of conditions that disrupt children's ability to communicate. Severe speech and language disorders are particularly serious, preventing or impeding children's participation in family and community, school achievement, and eventual employment. This chapter begins by providing an overview of speech and language development and disorders. It then addresses the following topics within the committee's charge: (1) current standards of care for assessing and diagnosing speech and language disorders; (2) causes of and risk factors for these disorders; (3) their prevalence and its relationship to age, development, and gender; and (4) common comorbidities (i.e., other co-occurring conditions).

OVERVIEW OF CHILDHOOD SPEECH AND LANGUAGE DISORDERS

Differentiating Language from Speech

The words “language” and “speech” are often used interchangeably in casual conversation, but in the context of communication disorders, it is important to understand the differences between them. Language refers to the code, or symbol system, for transforming unobservable mental events, such as thoughts and memories, into events that can be perceived by other people. Being a competent language user requires two essential capabilities. One, known as expressive language or language production , is the ability to encode one's ideas into language forms and symbols. The other, known as receptive language or language comprehension , is the ability to understand the meanings that others have expressed using language. People commonly express themselves by speaking and understand others' meanings by listening. However, language also can be expressed and understood in other ways—for example, by reading, writing, and signing ( Crystal, 2009 ).

Speech has a narrower meaning than language because it refers specifically to sounds produced by the oral mechanism, including the lips, tongue, vocal cords, and related structures ( Caruso and Strand, 1999 ). Speech is the most common way to transmit language and, unlike language, can be observed directly. Speech disorders are sometimes mistakenly equated with language disorders, and conversely, normal speech is sometimes assumed to reflect normal language. In fact, speech disorders and language disorders can occur separately or together. For example, a child might have a speech disorder, such as extremely poor articulation, yet have intact language skills. Another child might have a language disorder, such as extremely poor comprehension, yet be able to produce speech sounds normally. Finally, some children have both language disorders and speech disorders. In young children who are producing little if any speech, it can be difficult to determine whether a speech disorder, a language disorder, or both are present. As noted in Chapter 3 on treatment, early intervention for such children generally is designed to facilitate both language and speech skills. When children reach an age that allows each area to be assessed separately, it becomes possible to narrow the focus of treatment according to whether deficits are found only in speech, only in language, or in both.

In this report, the terms “speech disorders,” “language disorders,” and “speech and language disorders” are used (see Box 1-2 ). The terms “speech disorders” and “language disorders” are used only to refer to these disorders as defined in this chapter, while the term “speech and language disorders” denotes all of the disorders encompassed by these two categories.

Overview of Speech and Language Development and Disorders

The foundations for the development of speech and language begin in utero, with the growth of the anatomical structures and physiological processes that will eventually support sensory, motor, attention, memory, and learning skills. As discussed in the later section of this chapter on causes and risk factors, virtually every factor that threatens prenatal development of the fetus—from genetic abnormalities, to nutritional deficiencies, to exposure to environmental toxins—is associated with an increased risk of developing speech and/or language disorders. Before the end of the prenatal period, fetuses are able to hear, albeit imperfectly, speech and other environmental sounds, and within a few minutes after birth they show special attention to human faces and voices. This early interest in other people appears to set the stage for forming relationships with caregivers, who scaffold the child's growing ability to anticipate, initiate, and participate in social routines (e.g., Locke, 2011 ). The social experiences and skills that occur during the infant's first months of life are important precursors to pragmatic language skills: the infant first learns to engage in reciprocal interactions and to convey communicative intentions through nonlinguistic means such as gestures, and begins to accomplish these same goals through language forms such as early words. In the first few months of life, infants show improvement in their ability to recognize increasingly detailed patterns of speech, a precursor to linking spoken words with their meanings. Also in the first months of life, infants begin to use their oral mechanisms to produce nonspeech sounds, such as cooing and squealing, as they develop control of their muscles and movements. Thus, they are able to produce increasingly consistent combinations of speech-like sounds and syllables (babbling), a precursor to articulating recognizable words (e.g., Kent, 1999 ).

Evidence from neurophysiological habituation, neuroimaging, and preferential looking studies shows that children begin to recognize speech patterns that recur in their environments early in the first year of life ( Friedrich et al, 2015 ; Pelucchi et al., 2009 ; Werker et al., 2012 ). When tested using behavioral measures, most 12- to 18-month-old children show that they can understand at least a few words in the absence of gestural or other cues to their meaning (e.g., Miller and Paul, 1995 ). They also can produce at least a few intelligible words during this period (e.g., Squires et al., 2009 ), showing that they are acquiring both expressive language and speech skills. Their speech skills progress in a systematic fashion over the next few years, as they learn first to say relatively simpler consonants (e.g., “m,” “d,” “n”) and later to say more challenging consonants (e.g., “s,” “th,” “sh”) and consonant clusters (e.g., “bl,” “tr,” “st”) ( Shriberg, 1993 ). Receptive language, expressive language, and speech all develop at a rapid pace through the preschool period as children learn to understand and say thousands of individual words, as well as learn the grammatical (or morpho-syntactic) rules that enable them to understand and produce increasingly lengthy, sophisticated, intelligible, and socially acceptable combinations of words in phrases and sentences (e.g., Fenson et al., 2007 ). These speech and language skills enable children to achieve communication goals as diverse as understanding a simple story, taking a turn in a game, expressing an emotion, sharing a personal experience, and asking for help (e.g., Boudreau, 2008 ). By the end of the preschool period, children's ability to understand the language spoken by others and to speak well enough for others to understand them provides the scaffolding for their growing independence.

The end of the preschool period is also when most children show signs that they can think consciously about sounds and words, an ability known as metalinguistic awareness ( Kim et al., 2013 ). Awareness of the phonological (sound) characteristics of words, for example, enables children to identify words that rhyme or words that begin or end with the same speech sound. Such phonological awareness skills have been linked to children's ability to learn that speech sounds can be represented by printed letters—one of the skills necessary for learning to read words ( Troia, 2013 ). Reading requires more than recognizing individual words, however. Competent readers also must understand how words combine to express meanings in connected text, such as phrases, sentences, and paragraphs. Strong evidence shows that children's receptive language skills—such as their knowledge of vocabulary and grammar—are important contributors as well to this aspect of reading comprehension ( Catts and Kamhi, 2012 ; Duke et al., 2013 ).

In short, by the time children enter elementary school, the speech and language skills they have acquired through listening and speaking provide the foundation for reading and writing. These new literacy skills are critical for learning and social development through the school years and beyond. At the same time, ongoing growth in spoken language skills contributes to building personal and professional relationships and participating independently in society.

It is worth noting that children's speech and language experiences may vary substantially depending on the values and expectations of their culture, community, and family. This point is most obvious for children being raised in multilingual environments, who acquire more than one language. Although the majority of people in the world speak two languages, bilingualism currently is not the norm in the United States, and bilingualism has sometimes been assumed to increase the risk of speech and language disorders. However, there is no evidence that speech or language disorders are more prevalent in bilingual than in monolingual children with similar biological and sociodemographic profiles ( Gillam et al., 2013 ; Goldstein and Gildersleeve-Neumann, 2012 ; Kohnert and Derr, 2012 ).

Similarly, some investigators have reported differences in the amounts and kinds of language experienced by children according to their socioeconomic circumstances, and some of these differences have been associated with scores on later tests that emphasize language skills, including tests of vocabulary and verbal intelligence ( Hart and Risley, 1995 ; Hurtado et al., 2014 ). The language spoken to children certainly influences their language skills, and some aspects of language have been linked to parents' socioeconomic and educational backgrounds (e.g., Hoff, 2013 ). However, the range of language variations observed to date has not been found to increase the risk of speech or language disorders independent of other factors associated with low socioeconomic status, including inadequate or poor-quality health care, hunger, reduced educational and social resources, and increased exposure to environmental hazards ( Harrison and McLeod, 2010 ; Parish et al., 2010 ; Pentimonti et al., 2014 ).

Speech Disorders

As described above, speech refers to the production of meaningful sounds (words and phrases) from the complex coordinated movements of the oral mechanism. Speech requires coordinating breathing (respiration) with movements that produce voice (phonation) and sounds (articulation). Respiration yields a stream of breath, which is set into vibration by laryngeal mechanisms (voice box, vocal cords) to yield audible phonation or voicing. Exquisitely timed and coordinated movements by the articulatory mechanisms, including the jaw, lips, tongue, soft palate, teeth, and upper airway (pharynx), then modify this voiced stream to yield the speech sounds, or phonemes, of the speaker's native language ( Caruso and Strand, 1999 ). Speech disorders are deficits that may prevent speech from being produced at all, or result in speech that cannot be understood or is abnormal in some other way. This broad category includes three main subtypes: speech sound disorders, voice disorders, and stuttering. Speech sound disorders can be further classified into articulation disorders, dysarthria, and childhood apraxia of speech. The speech variations produced by speakers of different dialects and non-native speakers of English are not defined as speech disorders unless they significantly impede communication or educational achievement.

Speech sound disorders , often termed articulation or phonological disorders, are deficits in the production of individual speech sounds, or sequences of speech sounds, caused by inadequate planning, control, or coordination of the structures of the oral mechanism. Dysarthria is a speech sound disorder caused by medical conditions that impair the muscles or nerves that activate the oral mechanism ( Caruso and Strand, 1999 ). Dysarthric speech may be difficult to understand as a result of speech movements that are weak, imprecise, or produced at abnormally slow or rapid rates ( Morgan and Vogel, 2008 ; Pennington et al., 2009 ). Neuromuscular conditions, including stroke, infections (e.g., polio, meningitis), cerebral palsy, and trauma, can cause dysarthria. Another rare speech sound disorder, childhood apraxia of speech , is caused by difficulty with planning and programming speech movements ( ASHA, 2007 ). Children with this disorder may be delayed in learning the speech sounds expected for their age, or they may be physically capable of producing speech sounds but fail to produce the same sounds correctly when attempting to use them in words, phrases, or sentences.

Voice disorders (also known as dysphonias ) occur when the laryngeal structures, including the vocal cords, do not function correctly ( Carding et al., 2006 ). For example, a voice that sounds hoarse or breathy may be due to growths on the vocal cords, allergies, paralysis, infection, or excessive vocal abuse when speaking. A complete inability to produce any sound, called aphonia , may be caused by inflammation, infection, or injury to the vocal cords.

Stuttering (also known as fluency disorder or dysfluency ) is a speech disorder that disrupts the ability to speak as smoothly as desired. Dysfluent speech contains an excessive amount of repetitions of sounds, words, and phrases, and involuntary breaks, or “blocks.” Severe stuttering can effectively prevent a speaker from speaking at all; it may also lead to other abnormal physical and emotional behaviors as the speaker struggles to end a particular block or avoid blocks in the future ( Conture, 2001 ).

Language Disorders

As described above, language refers to the code, or system of symbols, for representing ideas in various modalities, including hearing and speaking, reading, and writing. Language may also refer to the ability to interpret and produce manual communication, such as American Sign Language. Language disorders interfere with a child's ability to understand the code, to produce the code, or both ( American Psychiatric Association, 2013 ; WHO, 1992 ). Children with expressive language disorders have difficulty in formulating their ideas and messages using language. Children with receptive language disorders have difficulty understanding messages encoded in language. Children with expressive-receptive language disorders have difficulty both understanding and producing messages coded in language.

Language disorders may also be classified according to whether they affect pragmatics, semantics, or grammar. Pragmatic language disorders may be seen in children who generally lack social reciprocity, a contributor to the dynamic turn-taking exchanges that typify the earliest communicative interactions (e.g., Sameroff, 2009 ). A child with a receptive pragmatic language disorder may have difficulty understanding messages that involve abstract ideas, such as idioms, metaphors, and irony. A child with an expressive pragmatic disorder may have difficulty producing messages that are socially appropriate for a given listener or context. A child with a receptive semantic disorder may not understand as many vocabulary words as expected for his or her age, while a child with an expressive semantic disorder may find it difficult to produce the right word to convey the intended meaning accurately. A child with a receptive grammatical deficit may not understand the differences between word endings that indicate concepts such as past ( walked ) or present ( walking ), or may not understand complex sentences (e.g., The man that the boy saw was running away ). Similarly, a child with an expressive grammatical disorder may produce short, incomplete sentences that lack the grammatical endings or structures necessary to express ideas clearly or completely.

Language disorders can interfere with any of these subsystems, singly or in combination. For example, children with severe pragmatic deficits may appear uninterested in communicating with others. Other children may try to communicate, but suffer from semantic disorders that prevent them from acquiring the words they need to express their messages. Still other children have normal pragmatic skills and vocabularies, but produce grammatical errors when they attempt to combine words into phrases and sentences. Finally, children with phonological disorders may be delayed in learning which sounds belong in words.

As mentioned earlier, language disorders first identified in the preschool period have been linked to learning disabilities when children enter school ( Sun and Wallach, 2014 ). In fact, the Individuals with Disabilities Education Act (IDEA) (Section 300.8) defines a specific learning disability as “a disorder in one or more of the basic psychological processes involved in understanding or in using language, spoken or written, that may manifest itself in the imperfect ability to listen, think, speak, read, write, spell, or to do mathematical calculations.” Strong evidence suggests that early language disorders increase the risk of poor literacy, mental health, and employment outcomes well into adulthood (e.g., Atkinson et al., 2015 ; Clegg et al., 2015 ; Law et al., 2009 ). For this reason, children with a history of language disorders as preschoolers are monitored closely when they enter elementary school, so that services can be provided to those whose language disorders adversely affect literacy, learning, and academic achievement.

Box 2-1 summarizes the major types of speech and language disorders in children.

Types of Speech and Language Disorders in Children.

Co-occurring Speech and Language Disorders

Speech and language disorders may co-occur in children, and in children with severe disorders it is plausible that less obvious deficits in other aspects of development, such as cognitive and sensorimotor processing, may also be implicated. In the first few years of life it may be particularly difficult to determine whether a child's failure to speak is the result of a speech disorder, of a language disorder, or of both. For one thing, many speech and language abilities emerge during the early years of development, and disorders cannot be identified until children have reached the ages at which various speech and language abilities are expected. This difficulty is compounded by the fact that children under the age of approximately 30 months are often difficult to evaluate because they may be reluctant or unable to engage in formal standardized tests of their speech and language skills.

Fortunately, effective treatments for very young nonspeaking children exist that do not depend on differentiating speech from language disorders, and a child's rate of progress in treatment may provide important evidence on the nature and severity of the disorders.

DIAGNOSING SPEECH AND LANGUAGE DISORDERS

Speech and language disorders can accompany or result from any of the conditions that interfere with the development of perceptual, motor, cognitive, or socioemotional function. Accordingly, conditions as varied as Down syndrome, fragile X syndrome, autism spectrum disorder, traumatic brain injury, and being deaf or hard of hearing are known to increase the potential for childhood speech and/or language disorders, and many children with such conditions will also have speech and language disorders. In addition, studies of children with primary speech and language disorders often reveal that they have abnormalities in other areas of development. For example, studies by Brumbach and Goffman (2014) suggest that children with primary language impairment show general deficits in gross and fine motor performance, and such children also show deficits in working memory and procedural learning ( Lum et al., 2014 ). Conversely, some children who have primary speech sound disorders as preschoolers have deficits in reading and spelling during their elementary school years ( Lewis et al., 2011 ). In short, considerable evidence suggests that spoken language skills, including speech sound production, constitute an integrated system and that clear deficits in one area may coexist with deficits in other areas that can compromise future development in language-related domains such as literacy. Intensive monitoring of speech and language development in such children is important for early detection and intervention to lessen the effects of speech and language disorders.

In many children, however, speech and language disorders occur for unknown reasons. In such children, diagnosing speech and language disorders is a complex process that requires assessing not only speech and language skills but also cognitive, perceptual, motor, and socioemotional development; biological, medical, and socioeconomic circumstances; and cultural and linguistic environments. Best-practice guidelines recommend evaluating across multiple domains and obtaining information from multiple sources, including a combination of formal, standardized, or norm-referenced tests; criterion-referenced observations by speech-language pathologists and other professionals; and judgments of familiar caregivers about the child's speech and language competence relative to community expectations for children of the same age ( ASHA, 2004 ; Nelson et al., 2006 , 2008 ; Royal College of Speech & Language Therapists, 2005 ; Shevell et al., 2003 ; Wilkinson et al., 2013 ).

On norm-referenced tests, children's scores are compared with average scores from large, representative samples of children of the same age. Children scoring below a cutoff value are defined as having a deficit, and severity is defined according to how far below average their scores fall. Deficits can range from mild to severe. In clinical practice, scores that fall more than two but less than three standard deviations below the mean are described as severely or extremely low; only 2.14 percent of children would be expected to score this poorly. Scores that fall three or more standard deviations below the mean are extraordinarily low; only 0.13 percent of children would be expected to score this poorly ( Urbina, 2014 ). Figure 2-1 represents these numbers in graphic terms. It shows that only 1 child in 1,000 would be expected to score three or more standard deviations below the mean, and only about 22 children in 1,000 would score more than two but less than three standard deviations below the mean.

In a normative sample of 1,000 children, only 1 child (shown in orange) is expected to score three or more standard deviations below the mean. Another 22 children (shown in light green) are expected to score more than two but less than three standard (more...)

In practice, few norm-referenced speech and language tests include a separate severity category for scores that are three or more standard deviations below the mean; all scores two or more standard deviations below the mean are classified together as “severe” or “very low” ( Spaulding et al., 2012 ). As noted in Chapter 1 , these clinical criteria for defining severity are not identical to the legal standards for severity specified in the regulations for the Supplemental Security Income (SSI) program, which also considers functional limitations (that are the result of the interactive and cumulative effects of all impairments) to determine the severity. Chapter 4 includes an in-depth review of how children are evaluated for disability in the SSI eligibility determination process.

Norm-referenced testing is not always possible because children may be too young or too disabled to participate in formal standardized testing procedures. In children younger than 3 years and others incapable of formal testing, behaviors and skills are compared with those of typically developing children using criterion-referenced measures or observational checklists ( Salvia et al., 2012 ). Some criterion-referenced measures involve detailed observations of specific skills, such as parent checklists of the number of words that children say. For example, 3-year-old children are expected to say 50 or more different words; those who fail to reach this criterion may be identified as having a significant vocabulary delay. Similarly, by 9-10 months of age, children are expected to communicate with their caregivers using nonlinguistic signals such as pointing and clapping; a 12-month-old who appears uninterested in others and fails to produce such basic communicative precursors to language may be identified as having a significant delay in the pragmatic domain of language. Still other criterion-referenced measures involve more global judgments of whether the child's language abilities are generally commensurate with those of peers, such as asking parents whether they are concerned about their child's ability to talk or understand as well as other children of the same age. In many cases, children are diagnosed as having language delays when their level of performance on some criterion-referenced skill is inconsistent with age to a significant degree, usually defined as a “percentage of delay” relative to chronological age. For example, a 24-month-old with the skills of children half her age (i.e., 12-month-old children) can be described as having a 50 percent delay; if her skills are comparable to those of 18-month-olds, she is described as having a 25 percent delay. In many states, delays of more than 20-25 percent are used to identify children under age 3 years for early intervention under Part C of the 2004 IDEA ( Ringwalt, 2015 ).

Validated norm-referenced tests may not be available for children who are members of cultural and linguistic communities that are not represented adequately in normative samples (e.g., AERA et al., 2014 ; Roseberry-McKibbin, 2014 ). In addition, norm-referenced test scores may be influenced by such extraneous factors as additional or confounding deficits (e.g., poor vision, inability to respond actively to test items), fatigue, and emotional state on a given day ( Urbina, 2014 ). Finally, norm-referenced testing may not adequately reflect the functional limitations that speech and language deficits impose on the child's ability to participate in some demanding, real-world contexts. For example, a child with a speech sound disorder may be able to articulate a single word reasonably clearly on a norm-referenced speech test, but be incapable of coordinating the many events necessary to produce an intelligible sentence in fast-paced, dynamic conversation. Similarly, a child with an expressive language disorder may be able to produce single words and short phrases successfully elicited by a norm-referenced test, but be incapable of producing grammatical sentences, much less stories that include them. And a child with a receptive language disorder may understand words presented individually and point to a picture on a norm-referenced test, but be unable to comprehend sentences, especially if the sentences are lengthy, complex, spoken at the normal rate of two to four words per second, or spoken in noisy or distracting environments. For all of these reasons, best diagnostic practices require that evidence from norm- and criterion-referenced testing by professionals be considered in conjunction with judgments made by people who are familiar with the child's usual functioning in his or her daily environment (e.g., Paul and Norbury, 2012 ).

CAUSES AND RISK FACTORS

This chapter now turns to an overview of known underlying causes of speech and language disorders, followed by a summary of factors that have been associated with an increased risk of speech and language disorders having no known cause. Although prevalence estimates are available for some of the causes described below, and speech and language disorders are frequently mentioned among their sequelae, evidence on the percentage of speech and language disorders attributable solely to the underlying condition is not available. For example, Down syndrome, a chromosomal disorder with a prevalence of 1:700 live births, causes deficits spanning multiple areas of development, including not only speech and language but also cognition and sensorimotor skills, making it difficult to quantify the syndrome's causal role specifically in speech and language disorders.

Speech and Language Disorders with Known Causes

Determining the underlying etiology of a speech or language disorder is essential to providing the child with an appropriate set of interventions and the parents with an understanding of the cause and natural history of their child's disability. A variety of congenital and acquired conditions may result in abnormal speech and/or language development. These conditions include primary disorders of hearing, as well as specific genetic diseases, brain malformation syndromes, inborn errors of metabolism, toxic exposures, nutritional deficiencies, injuries, and epilepsy.

Children who are deaf or hard of hearing provide an especially clear example of the interrelationships among the many causes and consequences of speech and language disorders in childhood ( Fitzpatrick, 2015 ). Because adequate hearing is critically important for developing and using receptive language, expressive language, and speech, being deaf or hard of hearing can lead to speech and language disorders, which in turn contribute to socioemotional and academic disabilities. This is particularly the case when the onset of hearing problems is either congenital or acquired during the first several years of life. Therefore, it is essential that hearing be assessed in children being evaluated for speech and language disorders.

Childhood hearing loss may result from or be associated with a wide variety of causes, which are categorized in Box 2-2 . Hearing may be affected by disorders of either the sensory component of the auditory system (i.e., peripheral) or the processing of auditory information within the brain (i.e., central). Peripheral causes may be either unilateral or bilateral and are subdivided into conductive types, which are due to developmental or acquired abnormalities of the structures of the outer or middle ear, and sensorineural types, which are due to a variety of disorders affecting the sound-sensing organ—the cochlea—and its nerve that goes to the brain—the cochlear nerve.

Examples of Conditions Affecting Hearing Early in Life That May Affect the Development of Speech and Language.

Conductive-related causes of reduced hearing levels include congenital structural malformations of the outer and inner ear, consequences of acute or recurrent middle-ear infections, eustachian tube dysfunction, tumors, and trauma. Sensorineural types are even more diverse. A variety of genetic disorders have been identified that affect the function of the cochlea or cochlear nerve, and the disorder may be sporadic or inherited in an autosomal dominant, autosomal recessive, or X-linked manner, depending on the specific gene. Sensorineural types may be secondary to medical illness or even treatments for babies who must be placed in neonatal intensive care units because of either prematurity or a variety of perinatal disorders, such as hypoxia (oxygen deficiency), disturbances of blood flow, infections, or hyperbilirubinemia (excessive bilirubin levels that lead to jaundice and brain dysfunction known as kernicterus). Prenatal infections due to maternal cytomegalovirus, toxoplasmosis, or rubella (TORCH infections) can have a significant congenital impact on the sensorineural hearing mechanism, as can postnatal infectious illnesses such as meningitis (inflammation of membranes around the brain and spinal cord). Ironically, the treatment of meningitis or other bacterial infections with certain antibiotics can result in decreased hearing levels, as some of these life-saving drugs are ototoxic (i.e., harmful to structures of the ear). The impact of antibiotics on central hearing function is much less common in childhood and generally does not lead to total deafness.

The best-recognized cause affecting central hearing is Landau-Kleffner syndrome, or acquired epileptic aphasia, a rare condition that typically presents in early childhood with either minimal speech and language development or loss of previously acquired speech and language due to cortical deafness secondary to persistent epileptiform activity in the electroencephalogram, even in the absence of clinical seizures. Lastly, neonatal hyperbilirubinemia (kernicterus) can impact both sensorineural and central hearing, the latter as a result of dysfunction at the level of the brainstem. Importantly, in addition to the causes described above, many factors that impact hearing are themselves caused by, or co-occur with, underlying conditions that affect other aspects of children's development.

Apart from being deaf or hard of hearing, there are a diverse set of conditions that should be considered as other potential causes of speech and language disorders, as summarized in Box 2-2 . As is the case with hearing, abnormal development of anatomic structures critical to the proper generation of speech may lead to speech sound disorders or voice disorders. For example, articulation and phonological disorders may result from cleft palate. A wide variety of genetic syndromes are known to be associated with disordered speech and language development. These include well-characterized conditions that are due to an abnormal number of a specific chromosome, such as Down syndrome (associated with three rather than two copies of chromosome 21) ( Tedeschi et al., 2015 ) or Klinefelter syndrome (which occurs in boys who have a normal Y chromosome together with two or more X chromosomes, rather than one X chromosome).

Well-recognized genetic syndromes due to a mutation in a single gene (such as fragile X syndrome, neurofibromatosis type I, Williams syndrome, and tuberous sclerosis) are associated with speech or language disorders, and current research has demonstrated that alterations in small groups of genes (copy number variations such as 16p11.2 deletion) may increase the risk of a speech or language disability. In general, when indicated by history and clinical examination, these genetic conditions can be detected with clinically available blood-based laboratory tests. Primary malformations of the central nervous system—such as hydrocephalus (an expansion of the fluid-filled cavities within the brain), agenesis of the corpus callosum (the absence of the main structure that connects the right and left hemispheres of the brain), and both gross and microscopic abnormalities of cortical development (cortical dysplasia, an abnormal layering or location of neurons)—also may be associated with speech and language disorders. In general, these primary disruptions in brain anatomy may be diagnosed by magnetic resonance imaging (MRI) and in some cases discovered via an in utero maternal-fetal ultrasound examination.

A variety of prenatal and postnatal toxic exposures may result in abnormal brain development with resultant neurodevelopmental consequences. Maternal alcohol and other substance use are well recognized in this regard, as is postnatal exposure to lead. Similarly, abnormal prenatal growth, postnatal nutritional deprivation, and hypothyroidism (underactive thyroid) have developmental consequences. Injuries to the developing brain, such as perinatal stroke from brain hemorrhages or ischemia (inadequate blood supply), accidental trauma, and nonaccidental trauma (child abuse), must also be considered, as must such neoplastic conditions as primary brain tumors, metastatic disease, and the consequences of oncological therapies (e.g., chemotherapy and radiation). Some children with cerebral palsy (a condition that results in abnormal motor development and that has numerous causes) may also have an associated speech or language disorder. In addition, speech and language disorders may be secondary to poorly controlled epilepsy associated with a variety of causes, including structural abnormalities in cortical development, genetic disorders (e.g., mutations in ion channel genes), and complex epileptic encephalopathies (e.g., West, Lennox-Gastault, or Landau-Kleffner syndromes) ( Campbell et al., 2003 ; Feldman and Messick, 2009 ).

Box 2-3 presents a listing of examples of speech and language disorders with known causes.

Examples of Speech and Language Disorders with Known Causes.

Risk Factors Associated with Speech and Language Disorders with No Known Cause

In addition to the etiologies described above, a number of variables have been associated with an increased risk of childhood speech and/or language disorders with no known cause. Findings in this literature are somewhat inconsistent ( Harrison and McLeod, 2010 ; Nelson et al., 2006 ), varying with characteristics of the children examined (e.g., age, phenotype, severity, comorbidity) and with research design features (e.g., sample size, control for confounding, statistical analyses).

Studies of speech and language disorders in children, such as speech sound disorders ( Lewis et al., 2006 , 2007 ) and specific language impairment ( Barry et al., 2007 ; Bishop, 2006 ; Bishop and Hayiou-Thomas, 2008 ; Rice, 2012 ; Tomblin and Buckwalter, 1998 ), show that these conditions are familial (i.e., risk for these disorders is elevated for family members of affected individuals) and that this familiality is partially heritable (i.e., genetic factors shared among biological family members contribute to family aggregation). However, heritability estimates (i.e., the proportion of phenotypic variance that can be attributed to genetic variance) for some speech and language disorders, such as specific language impairment, have been inconsistent ( Bishop and Hayiou-Thomas, 2008 ). For example, twin studies on heritability of language disorders have shown a range of estimates of heritability, from 45 percent for deficient language achievement ( Tomblin and Buckwalter, 1998 ) to 25 percent for specific language impairment ( DeThorne et al., 2005 ). One study of 579 4-year-old twins with low language performance and their co-twins found heritability was greater for more severe language impairment, suggesting a stronger influence of genes at the lower end of language ability ( Viding et al., 2004 ). Finally, a review of twin data found that the environment shared by the twins was “relatively unimportant” in causing specific language impairment compared with genetic factors ( Bishop, 2006 ). Overall, the evidence suggests that susceptibility to speech and language disorders results from interactions between genetic and environmental factors ( Newbury and Monaco, 2010 ).

To date, the evidence best supports a cumulative risk model in which increases in risk are larger for combinations of risk factors than for individual factors ( Harrison and McLeod, 2010 ; Lewis et al., 2015 ; Pennington and Bishop, 2009 ; Reilly et al., 2010 ; Whitehouse et al., 2014 ). In a study of speech sound disorders, for example, Campbell and colleagues (2003) found that three variables—male sex, low maternal education, and positive family history of developmental communication disorders—were individually associated with increased odds of speech sound disorder, but the odds of such a disorder were nearly eight times larger in a child with all three risk factors than in a child with none of them. Based on a national database in the United Kingdom, Dockrell and colleagues (2014) report higher odds (2.5) of speech, language, and communication needs in boys than in girls, and they document a strong social gradient for childhood speech, language, and communication disorders in which the odds were 2.3 times greater for children entitled to free school lunches and living in more deprived neighborhoods than for children without these factors. It is important to note that risk indices such as odds ratios cannot provide evidence on the proportion of cases of the disorder that are caused by the factor in question, both because they could reflect the influence of some other, unknown causal factor and because they are influenced by the composition of the samples (e.g., base rate, severity) in which they are calculated.

Research has shown a strong association between poverty and developmental delays, such as language delays. For example, in a study of 513 3-year-olds who had been exposed to risk factors that included inadequate income, lack of social supports, poor maternal prenatal care, and high family stress, King and colleagues (2005) found that 10 percent of children—four times the expected 2.5 percent—had severe delays, scoring two or more standard deviations below the mean on a norm-referenced language test. Walker and colleagues (2011) showed that experiences in early life affect the structure and functioning of the brain. For example, a malnourished expectant mother who faces barriers in accessing prenatal care is at risk of having a child who is premature, is small for his or her gestational age, or experiences perinatal complications ( Adams et al., 1994 ; Walker et al., 2011 ). Children exposed to such factors in the womb are at increased risk for developing a disability such as specific language impairment ( Spitz et al., 1997 ; Stanton-Chapman et al., 2004 ). Lastly, a variety of other psychosocial factors—including deprivation of appropriate stimuli from parents and caretakers ( Akca et al., 2012 ; Fernald et al., 2013 ; Hart and Risley, 1995 ), excess media (television and screen time) exposure ( Christakis et al., 2009 ; Zimmerman et al., 2007 ), and poor sleep hygiene ( Earle and Myers, 2014 )—need to be considered as potential risk factors for speech and language disorders.

Law and colleagues (2000) found that there existed no systematic synthesis of the evidence concerning the prevalence of pediatric speech and language disorders with primary causes; their observation remains true in 2015 ( Wallace et al., 2015 ). Estimating the prevalence of these disorders with confidence is difficult for several reasons. First, because the characteristics of these disorders differ with age, the diagnostic tools by which they are identified necessarily vary in format, ranging from simple parental reports at the earliest ages to formal standardized testing at later ages. Second, because these disorders can vary in scope—from problems with relatively discrete skills (e.g., producing individual speech sounds) to problems with broader and less observable sets of abilities (e.g., drawing inferences from or comprehending language that is ambiguous, indirect, or nonliteral)—there exists no single diagnostic tool capable of addressing the full range of pediatric speech and language skills. Third, as with many pediatric psychological and behavioral disorders, diagnostic criteria involve integrating observations from multiple sources and time points.

As a result, there currently is no single reference standard for identifying pediatric speech and language disorders of primary origin in children of all ages. Instead, prevalence estimates come from studies that focused on different ages and used different diagnostic tools and criteria. Law and colleagues (2000) found a median prevalence of 5.95 percent in the four studies they reviewed; they observe that this value is consistent with several other estimates, but emphasize the need for caution pending additional evidence from well-designed population studies.

The following subsections describe prevalence estimates from studies that have attempted to distinguish speech disorders from language disorders. However, these estimates also must be viewed with caution, given differences among studies in sample composition and diagnostic criteria.

Consistent with the varying expectations for speech skills in children of different ages, estimates of the incidence (i.e., the risk of acquiring a disorder for an individual in a specified population) and prevalence (i.e., the percentage of individuals affected by a disorder in a specified population at a specific point in time) of speech disorders vary according to age, the presence of other neurodevelopmental disorders, and the diagnostic criteria employed.

Most of the literature on the prevalence of speech disorders has focused on children with articulation or phonological disorders due to unknown causes. Shriberg and colleagues (1999 , cited in Pennington and Bishop, 2009 ) report a mean prevalence of 8.2 percent for such disorders; Bishop (2010) estimates prevalence at 10 percent. The prevalence of these disorders varies with age, however, decreasing from 15-16 percent at age 3 ( Campbell et al., 2003 ) to approximately 4 percent at age 6 ( Shriberg et al., 1999 ). Evidence suggests that speech sound disorders affect more boys than girls ( Eadie et al., 2015 ), particularly in early life. In preschoolers, the ratio of affected boys to girls is 2 or 3:1, declining by age 6 to 1.2:1 ( Pennington and Bishop, 2009 ; Shriberg et al., 1999 ). Although many children with speech sound disorders as preschoolers will progress into the normal range by the time of school entry, the close ties between spoken and written language have motivated many studies of the extent to which speech sound disorders are associated with an increased risk of reading, writing, or spelling disorders. To date, evidence from several studies (e.g., Lewis et al., 2015 ; Pennington and Bishop, 2009 ; Skebo et al., 2013 ) suggests that in comparison with their unaffected peers, children with speech sound disorders but normal-range language skills may have somewhat lower reading scores than their peers, but they rarely meet eligibility criteria for a reading disability ( Skebo et al., 2013 ). However, severity has not been considered to date in studies of the relationship between speech sound disorders and reading skills ( Skebo et al., 2013 ).

Little evidence is available concerning the epidemiology of voice disorders in children (dysphonias) not attributable to other developmental disorders. In a prospective population-based cohort of 7,389 8-year-old British children, 6-11 percent were identified as dysphonic; male sex, number of siblings, asthma, and frequent upper respiratory infections were among the factors associated with an increased risk of voice disorders ( Carding et al., 2006 ).

Stuttering is estimated to have a lifetime incidence of 5 percent but a population prevalence of just under 1 percent ( Bloodstein and Ratner, 2008 ). The prevalence of stuttering before the age of 6 years is much higher than that at later ages; evidence from several sources suggests that rates of natural recovery from stuttering in children before age 6 may be as high as 85 percent ( Yairi and Ambrose, 2013 ). Evidence indicates that stuttering affects only slightly more boys than girls during the preschool period, although higher ratios of affected males to females have been observed at later ages. Finally, approximately 60 percent of cases of developmental stuttering co-occur with other speech and language disorders ( Kent and Vorperian, 2013 ).

As with speech disorders, estimates of the prevalence of language disorders vary across studies by age, the presence of other neurodevelopmental disorders, and the diagnostic criteria employed. Language disorders with no known cause, sometimes referred to as “specific” (or “primary”) language impairments (e.g., Reilly et al., 2014 ), are highly prevalent, affecting 6-15 percent of children when identified through formal norm-referenced testing in population-based samples ( Law et al., 2000 ). This is consistent with the cutoff values of 1.0-1.5 standard deviations below the mean employed in several investigations (e.g., Tomblin et al., 1997b ). By contrast, prevalence estimates are generally higher when based on parent or teacher reports. For example, in a survey of parents and teachers conducted in a nationally representative sample of 4,983 4- to 5-year-old children in Australia, McLeod and Harrison (2009) found that prevalence estimates based on parent and teacher reports were somewhat higher than those based on norm-referenced testing, with 22-25 percent of children perceived as having deficits in talking (expressive language) and 10-17 percent as having deficits in understanding (receptive language). As noted by Law and colleagues (2000) , the discrepancy between prevalence rates defined according to norm- and criterion-referenced methods could be due to a number of factors, including the inability of norm-referenced tests to capture or reflect the child's language functioning in relatively more challenging situations, such as classrooms and conversations.

Language disorders that have no known cause have been reported to affect more boys than girls, but it appears that the gender imbalance is greater in clinical than in population-based samples (e.g., Pennington and Bishop, 2009 ). For example, the ratio of affected males to females has ranged from 2:1 to 6:1 across several clinical samples, but boys were only slightly more likely to be affected than girls (1.3:1) in a large population-based sample of U.S. kindergarten children ( Tomblin et al., 1997b ).

As noted earlier, many aspects of literacy depend heavily on the language knowledge and skills that children acquire before they enter school ( Catts and Kamhi, 2012 ), and children with severe language disorders have a substantially increased risk of deficits in reading and academic achievement. Estimates vary, but children diagnosed with language disorders with no known cause as preschoolers are at least four times more likely to have reading disabilities than their unaffected peers ( Pennington and Bishop, 2009 ). Similarly, evidence from a large-scale, prospective methodologically sound cohort study of kindergarteners followed longitudinally showed that the majority of those with language disorders with no known cause continued to exhibit language and/or academic difficulties through adolescence ( Tomblin and Nippold, 2014 ).

One study that helped frame the committee's understanding of prevalence estimates of speech and language disorders was a study of specific language impairment conducted by Tomblin and colleagues (1997b) . This study selected a geographic region in the upper Midwest of the United States and sampled rural, suburban, and urban schools within that region. All eligible 5- to 6-year-old children were systematically screened and followed up with diagnostic testing for specific language impairment. Children were not included if they spoke a language other than English, failed a hearing test, or demonstrated low functioning in nonverbal intelligence (suggesting overall lower intellectual functioning). When a cutoff 1.25 standard deviations below the mean (i.e., approximately the 10th percentile, or the lowest 10 percent of the normative sample) on at least two language scores was used, the prevalence rate of specific language impairment was estimated at 7.4 percent of kindergarten children. The prevalence of specific language impairment for boys was 8 percent and for girls was 6 percent.

When the cutoff was set at two standard deviations below the mean (i.e., approximately the 2nd percentile), the prevalence estimate dropped to 1.12 percent. Using 1.25 standard deviations below the mean as the criterion, there were slightly higher rates of specific language impairment among African American and Native American children relative to white and Hispanic children. Only 29 percent of the parents of the kindergarteners diagnosed with specific language impairment reported having been informed that their children had speech or language problems. It is important to note that large-scale epidemiological studies on autism spectrum disorder, learning disorders, and attention deficit hyperactivity disorder have clearly demonstrated that active case-finding strategies lead to higher and more accurate rates of identification of children with neurodevelopmental disorders ( Barbaresi et al., 2002 , 2005 , 2009 ; CDC, 2014 ; Katusic et al., 2001 ) relative to studies depending only on parent reports. Studies that followed this sample of children with specific language impairment into their school years demonstrated that as a group, they also experienced lower academic achievement.

The Tomblin et al. (1997a) study underscores several methodological issues relevant for the current report: differences in severity level for case identification, comorbidity with other disorders considered primary disabilities, and differences in prevalence related to gender and racial or ethnic identity. Subsequent studies with the children included in this study identified low maternal and paternal education and paternal history of speech, learning, or intellectual difficulties as risk factors for specific language impairment ( Tomblin et al., 1997a ).

Table 2-1 provides a summary of prevalence estimates from the studies of U.S. children that the committee also reviewed. This list is not the result of a meta-analysis, nor is it exhaustive; rather, the table includes a number of well-designed studies that employed clear and consistent definitions. The committee reviewed numerous well-designed studies and meta-analyses from other countries (e.g., Beitchman et al., 1996a , b , c [Canada]; Law et al., 2000 [United Kingdom, others]; McLeod and Harrison, 2009 [Australia]). For the purposes of this study, however, the committee limited the summary of prevalence estimates to U.S. children. Table 2-1 includes the populations and conditions studied, the diagnostic criteria used to identify the conditions, and the prevalence of the conditions (or percent positive). Confidence intervals are included when available. As noted earlier, and as is evident from the table, the studies reviewed vary greatly in terms of ages, diagnostic tools or criteria, and methods used. The estimates presented in the table (in addition to estimates based on national survey data presented in Chapter 5 ) indicate that speech and language disorders affect between 3 and 16 percent of U.S. children.

Estimates of the Prevalence of Speech and Language Disorders from Studies of U.S. Children.

COMMON COMORBIDITIES

An examination of comorbidities (i.e., other co-occurring conditions) of speech and language disorders is complicated by the central role of language and communication in the development and behavior of children and adolescents. Speech and language disorders are a definitional component of certain conditions, most prominently autism spectrum disorder ( American Psychiatric Association, 2013 ). Other neurodevelopmental disorders, including cognitive impairment, are universally associated with varying degrees of delays and deficits in language and communication skills ( American Psychiatric Association, 2013 ). In addition to their co-occurrence with a wide range of neurodevelopmental disorders, speech and language delays in toddlers and preschool-age children are associated with a significantly increased risk for long-term developmental challenges, such as language-based learning disorders ( Beitchman et al., 1996a , b , c , 1999 , 2001 , 2014 ; Brownlie et al., 2004 ; Stoeckel et al., 2013 ; Voci et al., 2006 ; Young et al., 2002 ). While specific language impairments (i.e., those not associated with other diagnosable neurodevelopmental disorders) are relatively common, it is likely that substantially greater numbers of children and adolescents experience significant speech and/or language impairment associated with other diagnosable disorders. Finally, speech and language delays and deficits may lead to impairments in other aspects of a child's functional skills (e.g., social interaction, behavior, academic achievement) even when not associated with other diagnosable disorders ( Beitchman et al., 1996c , 2001 , 2014 ; Brownlie et al., 2004 ; Voci et al., 2006 ; Young et al., 2002 ). This section, therefore, examines the association of speech and language disorders from the following perspectives: (1) speech and language disorders that are comorbid with other diagnosable disorders, and (2) speech and language disorders in early childhood that confer a quantifiable risk for the later development of comorbid conditions. Together, these two perspectives create a comprehensive picture of the association of speech and language disorders with other neurodevelopmental disorders.

Autism spectrum disorder is a highly prevalent neurodevelopmental disorder, affecting an estimated 1 in 68 8-year-old children in the United States ( CDC, 2014 ). By definition, all children with autism spectrum disorder have deficits in communication, ranging from a complete absence of verbal and nonverbal communication skills, to atypical language (e.g., echolalia or “scripted” language), to more subtle deficits in pragmatic (i.e., social) communication ( American Psychiatric Association, 2013 ). The formal diagnostic criteria for autism spectrum disorder require documentation of deficits in the social-communication domain ( American Psychiatric Association, 2013 ). In clinical practice, when children present with significant delays in the development of communication skills, autism spectrum disorder is one of the primary diagnostic considerations ( Myers and Johnson, 2007 ).

All children and adolescents with intellectual disability have varying degrees of impairment in communication skills ( American Psychiatric Association, 2013 ). Among those with mild intellectual disability, deficits in communication may be relatively subtle, including inability to understand or employ highly abstract language or impairment in social communication. In contrast, children and adolescents with severe or profound levels of intellectual disability may be able only to communicate basic requests, understand concrete instructions, and communicate with simple phrases or single words; others may be unable to employ or understand spoken language. A number of specific genetic disorders are directly associated with varying degrees of intellectual disability together with abnormalities of speech and language (see Box 2-3 ). Some of these genetic conditions often are also associated with specific profiles of speech and language impairment ( Feldman and Messick, 2009 ). Examples include dysfluent speech in children with Down syndrome, echolalia in boys with fragile X syndrome, and fluent but superficial social language in children with Williams syndrome ( Feldman and Messick, 2009 ).

Language-based learning disorders, including reading and written language disorders, are often associated with speech and language disorders. The association between language impairment and reading disorders has been demonstrated in studies examining the likelihood that family members of subjects with language impairment are at increased risk for reading disorder ( Flax et al., 2003 ). Both epidemiologic and clinic-based studies have demonstrated that children with speech sound disorders and language disorders are at increased risk for reading disorder ( Pennington and Bishop, 2009 ). Similarly, multiple studies have demonstrated a strong association between attention deficit hyperactivity disorder and speech and language disorders ( Pennington and Bishop, 2009 ; Tomblin, 2014 ).

The comorbidity of speech and language disorders and other neurodevelopmental disorders may not be apparent in pre-school-age children, since these very young children may not yet manifest the developmental lags or symptoms required to make comorbid diagnoses of such conditions as learning disorders and attention deficit hyperactivity disorder. In their prospective community-based study, for example, Beitchman and colleagues (1989) found significant differences in measures of “reading readiness” among 5-year-old children with poor language comprehension compared with children with either high overall speech and language ability or isolated articulation difficulties ( Beitchman et al., 1989 ). Similarly, there was a tendency for 5-year-olds with a combination of low articulation and poor language comprehension to have higher teacher ratings of hyperactivity and inattention and lower maternal ratings of social competence ( Beitchman et al., 1989 ). By age 12, the children who earlier had shown combined deficits in speech and language had significantly lower levels of reading achievement and higher rates of diagnosed psychiatric disorders (57.1 percent versus 23.7 percent for children with normal speech and language at age 5) ( Beitchman et al., 1994 ). By age 19, children with documented language impairment at age 5 had significantly higher rates of reading disorder (36.8 percent versus 6.4 percent), math disorder (53.9 percent versus 12.2 percent), and psychiatric disorders (40 percent versus 21 percent) compared with their peers with normal language ability at age 5 ( Young et al., 2002 ).

In summary, speech and language disorders are frequently identified in association with (i.e., comorbid with) a wide range of other neurodevelopmental disorders. Children with comorbid conditions can be expected to be more severely impaired and to experience greater functional limitations (due to the interactive and cumulative effects of multiple conditions) than children who do not have comorbid conditions. Furthermore, young children with language impairments are at high risk for later manifestation of learning and psychiatric disorders. It is therefore important both to carefully examine the speech and language skills of children with other developmental disorders and to identify other neurodevelopmental disorders among children presenting with speech and language impairment. Among populations of children with conditions as diverse as autism spectrum disorder, attention deficit hyperactivity disorder, traumatic brain injury, and genetic disorders, speech and language disorders may be the most easily identified impairments because of the central role of language and communication in the functional capacity of children and adolescents.

FINDINGS AND CONCLUSIONS

2-1. Speech and language disorders are prevalent, affecting between 3 and 16 percent of U.S. children. Prevalence estimates vary according to age and the diagnostic criteria employed, but best evidence suggests that approximately 2 percent of children have speech and/or language disorders that are severe according to clinical standards. 2-2. Some speech and language disorders result from known biological causes. 2-3. In many cases, these disorders have no identifiable cause, but factors including male sex and reduced socioeconomic and educational resources have been associated with an increased risk of the disorders. 2-4. Diagnosing speech and language disorders in children is a complex process that requires integrating information on speech and language with information on biological and medical factors, environmental circumstances, and other areas of development. 2-5. Speech and language disorders frequently co-occur with other neurodevelopmental disorders and may be among the earliest symptoms of serious neurodevelopmental conditions. 2-6. Children with severe speech and language disorders have an increased risk of a variety of adverse outcomes, including mental health and behavior disorders, learning disabilities, poor academic achievement, and limited employment and social participation.

Conclusions

2-1. Severe speech and language disorders represent serious threats to children's social, emotional, educational, and employment outcomes. 2-2. Severe speech and language disorders are debilitating at any age, but their impacts on children are particularly serious because of their widespread adverse effects on development and the fact that these negative consequences cascade and build on one another over time. 2-3. Severe speech and language disorders may be one of the earliest detectable symptoms of other serious neurodevelopmental conditions; for this reason, they represent an important point of entry to early intervention and other services. 2-4. It is critically important to identify such disorders for two reasons: first, because they may be an early symptom of other serious neurodevelopmental disorders, and second, so that interventions aimed at forestalling or minimizing their adverse consequences can be undertaken.

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Cite this Page Committee on the Evaluation of the Supplemental Security Income (SSI) Disability Program for Children with Speech Disorders and Language Disorders; Board on the Health of Select Populations; Board on Children, Youth, and Families; Institute of Medicine; Division of Behavioral and Social Sciences and Education; National Academies of Sciences, Engineering, and Medicine; Rosenbaum S, Simon P, editors. Speech and Language Disorders in Children: Implications for the Social Security Administration's Supplemental Security Income Program. Washington (DC): National Academies Press (US); 2016 Apr 6. 2, Childhood Speech and Language Disorders in the General U.S. Population.
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Health Library Speech Disorders

What is a speech disorder.

Many children will experience a temporary delay in speech and language development. Most will eventually catch up. Others will continue to have difficulty with communication development. Communication disorders include speech disorders and language disorders. Speech disorders are discussed in this article and some general guidelines are also given.  This will help you decide if your child needs to be tested by a speech-language pathologist.

A child with a speech disorder may have difficulty with speech sound production, voice, resonance or fluency (the flow of speech).

Speech Sound Disorders

A child with a speech sound disorder is unable to say all of the speech sounds in words. This can make the child’s speech hard to understand. People may not understand the child in everyday situations. For most children, the cause of the speech sound disorder is unknown. Other speech sound disorders can be linked to things such as a cleft palate, problems with the teeth, hearing loss, or difficulty controlling the movements of the mouth.

Reasons for Concern

The child doesn't babble using consonant sounds (particularly b, d, m, and n) by age 8 or 9 months.
The child uses mostly vowel sounds or gestures to communicate after 18 months.
The child’s speech cannot be understood by many people at age 3.
The child’s speech is difficult to understand at age 4 or older.

Voice Disorders

The voice is produced as air from the lungs moves up through and vibrates the vocal folds. This is called phonation. With voice disorders, the voice may be harsh, hoarse, raspy, cut in and out, or show sudden changes in pitch. Voice disorders can be due to vocal nodules, cysts, papillomas, paralysis or weakness of the vocal folds.

The voice is hoarse, harsh or breathy.
The voice is always too loud or too soft.
The pitch is inappropriate for the child's age or gender.
The voice often "breaks" or suddenly changes pitch.
Frequent loss of voice

Resonance Disorders

Resonance is the overall quality of the voice. A resonance disorder is when the quality of the voice changes as it travels through the different-shaped spaces of the throat, nose and mouth. Resonance disorders include the following:

Hyponasality (Denasality): This is when not enough sound comes through the nose, making the child sound “stopped up.” This might be caused by a blockage in the nose or by allergies.

Hypernasality : This happens when the movable, soft part of the palate (the velum) does not completely close off the nose from the back of the throat during speech. Because of this, too much sound escapes through the nose. This can be due to a history of cleft palate, a submucous cleft, a short palate, a wide nasopharynx, the removal of too much tissue during an adenoidectomy, or poor movement of the soft palate.

Cul-de-Sac Resonance: This is when there is a blockage of sound in the nose, mouth or throat. The voice sounds muffled or quiet as a result.

Reasons for Concern:

Speech sounds hyponasal or hypernasal
Air is heard coming out of the nose during speech

Fluency Disorders (Stuttering)

Fluency is the natural “flow” or forward movement of speech. Stuttering is the most common type of fluency disorder. Stuttering happens when there are an abnormal number of repetitions , hesitations, prolongations, or blocks in this rhythm or flow of speech. Tension may also be seen in the face, neck, shoulders or fists. There are many theories about why children stutter. At present, the cause is most likely linked to underlying neurological differences in speech and language processing. Internal reactions from the person talking, and external reactions from other listeners, may impact stuttering, but they do not cause stuttering.

The parents are concerned about stuttering.
The child has an abnormal number of repetitions, hesitations, prolongations or blocks in the natural flow of speech.
The child exhibits tension during speech.
The child avoids speaking due to a fear of stuttering.
The child considers themselves to be someone who stutters.

Treatment for Speech Disorders

Early intervention is very important for children with communication disorders. Treatment is best started during the toddler or preschool years. These years are a critical period of normal language learning. The early skills needed for normal speech and language development can be tested even in infants. At that age, the speech-language pathologist works with the parents on stimulating speech and language development in the home. Active treatment in the form of individual therapy usually starts between the ages of 2 and 4 years.

If you have concerns about your child’s communication skills, discuss them with your child’s doctor. The doctor will likely refer the child to a speech-language pathologist for evaluation and treatment.

All children with speech and language disorders should also have their hearing tested.

Helping Your Child

Children learn speech and language skills by listening to the speech of others, and practicing as they talk to others. Parents are the most important teachers for their child in their early years.

They can help the child by giving lots of opportunities to listen to speech and to talk. This can be done by frequently pointing out and naming important people, places, and things. They can also read and talk to the child throughout the day, especially during daily routines, interactive plays, and favorite activities. Parents can give the child models of words and sentences to repeat.

Parents can also set up opportunities for the child to answer questions and talk. Listening to music, singing songs and sharing nursery rhymes are also great ways to build speech and language skills while having fun with your child.

Last Updated 12/2023

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Diseases & Conditions

Stuttering is a speech condition that disrupts the normal flow of speech. Fluency means having an easy and smooth flow and rhythm when speaking. With stuttering, the interruptions in flow happen often and cause problems for the speaker. Other names for stuttering are stammering and childhood-onset fluency disorder.

People who stutter know what they want to say, but they have a hard time saying it. For example, they may repeat or stretch out a word, a syllable, or a consonant or vowel sound. Or they may pause during speech because they've reached a word or sound that's hard to get out.

Stuttering is common among young children as a usual part of learning to speak. Some young children may stutter when their speech and language abilities aren't developed enough to keep up with what they want to say. Most children outgrow this type of stuttering, called developmental stuttering.

But sometimes stuttering is a long-term condition that remains into adulthood. This type of stuttering can affect self-esteem and communicating with other people.

Children and adults who stutter may be helped by treatments such as speech therapy, electronic devices to improve speech fluency or a form of mental health therapy called cognitive behavioral therapy.

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Stuttering symptoms may include:

Having a hard time starting a word, phrase or sentence.
Stretching out a word or sounds within a word.
Repeating a sound, syllable or word.
Brief silence for certain syllables or words, or pausing before or within a word.
Adding extra words such as "um" if expecting to have problems moving to the next word.
A lot of tension, tightness or movement of the face or upper body when saying a word.
Anxiety about talking.
Not being able to communicate well with others.

These actions may happen when stuttering:

Rapid eye blinks.
Trembling of the lips or jaw.
Unusual face movements, sometimes called facial tics.
Head nodding.
Tightening of fists.

Stuttering may be worse when the person is excited, tired or under stress, or when feeling self-conscious, hurried or pressured. Situations such as speaking in front of a group or talking on the phone can be especially hard for people who stutter.

But most people who stutter can speak without stuttering when they talk to themselves and when they sing or speak along with someone else.

When to see a doctor or speech-language pathologist

It's common for children between the ages of 2 and 5 years to go through periods when they may stutter. For most children, this is part of learning to speak, and it gets better on its own. But stuttering that continues may need treatment to improve speech fluency.

Call your healthcare professional for a referral to a specialist in speech and language called a speech-language pathologist. Or you can contact the speech-language pathologist directly for an appointment. Ask for help if stuttering:

Lasts more than six months.
Happens along with other speech or language problems.
Happens more often or continues as the child grows older.
Includes muscle tightening or physically struggling when trying to speak.
Affects the ability to effectively communicate at school or work or in social situations.
Causes anxiety or emotional problems, such as fear of or not taking part in situations that require speaking.
Begins as an adult.

Researchers continue to study the underlying causes of developmental stuttering. A combination of factors may be involved.

Developmental stuttering

Stuttering that happens in children while they're learning to speak is called developmental stuttering. Possible causes of developmental stuttering include:

Problems with speech motor control. Some evidence shows that problems in speech motor control, such as timing, sensory and motor coordination, may be involved.
Genetics. Stuttering tends to run in families. It appears that stuttering can happen from changes in genes passed down from parents to children.

Stuttering that happens from other causes

Speech fluency can be disrupted from causes other than developmental stuttering.

Neurogenic stuttering. A stroke, traumatic brain injury or other brain disorders can cause speech that is slow or has pauses or repeated sounds.
Emotional distress. Speech fluency can be disrupted during times of emotional distress. Speakers who usually do not stutter may experience problems with fluency when they are nervous or feel pressured. These situations also may cause speakers who stutter to have greater problems with fluency.
Psychogenic stuttering. Speech difficulties that appear after an emotional trauma are uncommon and not the same as developmental stuttering.

Risk factors

Males are much more likely to stutter than females are. Things that raise the risk of stuttering include:

Having a childhood developmental condition. Children who have developmental conditions, such as attention-deficit/hyperactivity disorder, autism or developmental delays, may be more likely to stutter. This is true for children with other speech problems too.
Having relatives who stutter. Stuttering tends to run in families.
Stress. Stress in the family and other types of stress or pressure can worsen existing stuttering.

Complications

Stuttering can lead to:

Problems communicating with others.
Not speaking or staying away from situations that require speaking.
Not taking part in social, school or work activities and opportunities for success.
Being bullied or teased.
Low self-esteem.
Stuttering. American Speech-Language-Hearing Association. https://www.asha.org/public/speech/disorders/stuttering/. Accessed Feb. 2, 2024.
Fluency disorders. American Speech-Language-Hearing Association. https://www.asha.org/practice-portal/clinical-topics/fluency-disorders/. Accessed Feb. 2, 2024.
Childhood-onset fluency disorder (stuttering). In: Diagnostic and Statistical Manual of Mental Disorders DSM-5-TR. 5th ed. American Psychiatric Association; 2022. https://dsm.psychiatryonline.org. Accessed Feb. 2, 2024.
Stuttering. National Institute on Deafness and Other Communication Disorders. https://www.nidcd.nih.gov/health/stuttering. Accessed Feb. 2, 2024.
Sander RW, et al. Stuttering: Understanding and treating a common disability. American Family Physician. 2019;100:556.
Laiho A, et al. Stuttering interventions for children, adolescents and adults: A systematic review as part of the clinical guidelines. Journal of Communication Disorders. 2022; doi:10.1016/j.jcomdis.2022.106242.
6 tips for speaking with someone who stutters. The Stuttering Foundation. https://www.stutteringhelp.org/6-tips-speaking-someone-who-stutters-0. Accessed Feb. 2, 2024.
7 tips for talking with your child. The Stuttering Foundation. https://www.stutteringhelp.org/7-tips-talking-your-child-0. Accessed Feb. 2, 2024.
Clark HM (expert opinion). Mayo Clinic. Feb. 11, 2024.

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Open access
Published: 15 August 2024

Behavioural and neurodevelopmental characteristics of SYNGAP1

Nadja Bednarczuk 1 ,
Harriet Housby 1 ,
Irene O. Lee 1 ,
IMAGINE Consortium 1 ,
David Skuse 1 &
Jeanne Wolstencroft ORCID: orcid.org/0000-0001-6160-9731 1

Journal of Neurodevelopmental Disorders volume 16 , Article number: 46 ( 2024 ) Cite this article

120 Accesses

Metrics details

SYNGAP1 variants are associated with varying degrees of intellectual disability (ID), developmental delay (DD), epilepsy, autism, and behavioural difficulties. These features may also be observed in other monogenic conditions. There is a need to systematically compare the characteristics of SYNGAP1 with other monogenic causes of ID and DD to identify features unique to the SYNAGP1 phenotype. We aimed to contrast the neurodevelopmental and behavioural phenotype of children with SYNGAP1-related ID (SYNGAP1-ID) to children with other monogenic conditions and a matched degree of ID.

Participants were identified from the IMAGINE-ID study, a UK-based, national cohort study of neuropsychiatric risk in children with ID of known genetic origin. Thirteen children with SYNGAP1 variants (age 4–16 years; 85% female) were matched (2:1) with 26 controls with other monogenic causes of ID for chronological and mental age, sex, socio-economic deprivation, adaptive behaviour, and physical health difficulties. Caregivers completed the Development and Wellbeing Assessment (DAWBA) and physical health questionnaires.

Our results demonstrate that seizures affected children with SYNGAP1-ID (84.6%) more frequently than the ID-comparison group (7.6%; p = < 0.001). Fine-motor development was disproportionally impaired in SYNGAP1-ID, with 92.3% of children experiencing difficulties compared to 50% of ID-comparisons( p = 0.03). Gross motor and social development did not differ between the two groups. Children with SYNGAP1-ID were more likely to be non-verbal (61.5%) than ID-comparisons (23.1%; p = 0.01). Those children able to speak, spoke their first words at the same age as the ID-comparison group (mean = 3.25 years), yet achieved lower language competency ( p = 0.04). Children with SYNGAP1-ID compared to the ID-comparison group were not more likely to meet criteria for autism (SYNGAP1-ID = 46.2%; ID-comparison = 30.7%; p = .35), attention-deficit hyperactivity disorder (15.4%;15.4%; p = 1), generalised anxiety (7.7%;15.4%; p = .49) or oppositional defiant disorder (7.7%;0%; p = .15).

For the first time, we demonstrate that SYNGAP1-ID is associated with fine motor and language difficulties beyond those experienced by children with other genetic causes of DD and ID. Targeted occupational and speech and language therapies should be incorporated early into SYNGAP1-ID management.

Introduction

The SYNGAP1 gene is one of the more common genetic causes of intellectual disability (ID), with an estimated prevalence of 0.5–1% of children with ID [ 1 ]. SYNGAP1 encodes a Ras-specific GTPase-activating protein, SynGAP, which is localised to the post-synaptic density of cortical neurons and influences important cellular signalling pathways in growth and survival [ 2 , 3 ]. It plays a complex role in neurodevelopment and ongoing neurological function [ 3 ]. For instance, SynGAP regulates synaptic formation, maturation and plasticity in critical periods of cortical development [ 3 , 4 , 5 ]. Non-synaptic functions have also been linked to SynGAP, including axonal outgrowth and neuronal migration [ 5 , 6 ]. Any disruption in typical SynGAP function, can result in abnormal cortical connectivity and disrupted neuronal signalling, which can in turn impair cognitive function [ 3 , 5 ]. Indeed, SynGAP has been shown to be particularly important for learning and memory [ 3 , 7 ]. Consequently, rare coding variants in SYNGAP1, which encode SynGAP, are strongly associated with intellectual disability (ID) and developmental delay[ 1 ]. Children with SYNGAP1-related ID (SYNGAP1-ID) may also experience seizures, hypotonia, digestive and sleeping difficulties [ 8 ].

The developmental phenotype associated with SYNGAP1-ID has only been described in case series. Most children have global developmental delay, but the severity of the impairment varies [ 8 , 9 ]. A study of the neurodevelopmental profile of 17 children with SYNGAP1 reported a mean age of walking of over 2 years and most children speaking their first words at approximately 2.5 years [ 10 ]. A significant proportion of children also remain non-verbal [ 11 ]. It is unclear which features, if any, of the developmental phenotype differ from that observed in other genetic disorders causing developmental delay.

Various neurodevelopmental conditions have been described in SYNGAP1-ID, in particular elevated rates of autism [ 9 , 12 , 13 , 14 ]. In the largest cohort of 57 individuals, an autism rate of 53% was reported [ 9 ]. Some case series have mirrored these autism rates, whilst others report rates of up to 73% [ 15 , 16 ]. The SYNGAP1 gene has been considered to have high degree of autism specificity [ 17 , 18 ]. Aggressive behaviour affects up to 60% of children [ 16 ]. Caregivers have reported varying degrees of impulsivity and self-injurious behaviour [ 11 , 16 ]. Sensory processing impairments are common among children with SYNGAP1-ID [ 11 ]. Rates of ADHD, conversely, appear to be low, affecting only 7% of individuals with SYNGAP1-ID [ 15 ].

Few studies have compared the behavioural phenotype of SYNGAP1-ID to that of other monogenic causes of ID. Two recent research studies have attempted to delineate the SYNGAP1-ID phenotype by comparing the behavioural profile of those with SYNGAP1-ID to those with other disorders affecting synaptic dysfunction, specifically Phelan-McDermid Syndrome. Naveed et al . compared Social Responsiveness Scale scores [ 19 ] in the two conditions, finding similar levels of difficulty in social interaction [ 20 ]. Lyons-Warren et al . utilised the Short Sensory-Profile 2 [ 21 ] to assess sensory processing. Atypical sensory processing was observed in both SYNGAP1-ID and Phelan-McDermid Syndrome [ 22 ]. Beyond these studies, no other study has employed standardised developmental and behavioural measures to compare SYNGAP1-ID to other genetic causes of ID.

We aimed to systematically assess the behavioural and neurodevelopmental phenotype of SYNGAP1- ID and compare this to a matched comparison group with an equivalent level of intellectual disability caused by other, heterogenous monogenic disorders. We sought to identify behavioural and neurodevelopmental patterns that are unique to children with SYNGAP-ID.

Participants

Participants with SYNGAP1 variants were identified from the IMAGINE-ID (the Intellectual Disability and Mental Health: Assessing the Genomic Impact on Neurodevelopment) study. IMAGINE-ID is a large, national cohort study of children with intellectual disability or developmental delay of known genetic origin. Children were recruited to IMAGINE via regional genetic services, charities, support groups and by self-referral [ 23 ]. Molecular genetic diagnoses had to be established by an National Health Service (NHS) accredited diagnostic laboratory and all pathogenic variants were categorised according to the American College of Medical Genetics and genomics guidelines [ 24 ]. Participants with pathogenic or likely pathogenic genetic variants were included. Parents or guardians provided consent on behalf of children younger than 16 years and for those older than 16 years lacking capacity, consultees acted on their behalf. IMAGINE-ID was approved by the London Queen Square Research Ethics committee (14/LO/1069).

A comparison group with intellectual disability due to other genetic variants were also identified from the IMAGINE-ID cohort. Participants in the comparison group were selected using a matched block design. Blocks were matched for age, sex, level of socio-economic deprivation, physical health difficulty, and degree of developmental delay. The degree of developmental delay was assessed using caregiver-reported mental age[ 25 ] and the Adaptive Behaviour Assessment 3rd Edition (ABAS-3) generalised adaptive composite score [ 26 ]. Within blocks, participants were selected at random at a 2:1 ratio. Genetic variant did not affect ID-comparison group participant inclusion. The genetic variants present within the ID-comparison group are displayed in Table 1 . Full details of genetic variants for the SYNGAP1-ID and ID-comparison group are listed in Supplementary Table 1 and 2 respectively.

Behavioural and developmental phenotype

Behavioural and developmental data were collected via online questionnaires completed by caregivers on behalf of their child. This included the Developmental and Wellbeing assessment (DAWBA) and the Strengths and Difficulties Questionnaire (SDQ). The DAWBA is a structured psychiatric interview which assesses developmental and behavioural difficulties alongside neuropsychiatric diagnoses [ 27 ]. Provided information was reviewed and rated according to DSM-5 diagnostic criteria by two experienced clinicians. The presence of autism, attention-deficit hyperactivity disorder (ADHD), generalised anxiety disorder, oppositional defiant disorder and conduct disorder were assessed. Inter-rater reliability has been reported in previous IMAGINE-ID publications [ 23 , 28 ]. The SDQ is a parent-rated questionnaire that measures emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship problems and prosocial behaviour. A total difficulty score is calculated from the first four sub-scales. Higher scores reflect greater emotional and behavioural difficulties [ 29 ].

Adaptive functioning was assessed with the Adaptive Behaviour Assessment (ABAS-3) and caregiver-reported mental age [ 26 ]. Language competency was assessed by dividing caregiver estimated language age by the child’s chronological age, with values of one indicating that the child’s language age equalled their chronological age. Caregivers also completed a physical health questionnaire to assess any relevant past medical history.

Statistical analysis

Statistical analysis was performed in R (Version 4.3.0). All data were tested for normality using Shapiro-Wilks tests. Normally distributed data were compared using t-tests. Non-normally distributed data were assessed using non-parametric tests, including Mann–Whitney-U testing. Bonferroni corrections were applied throughout to account for multiple testing. Wilcox rank effect sizes (r) were calculated for significant results. Categorical data were tested using chi-squared tests with Yates’s correction to account for sample size.

Participant demographics

Thirteen children with SYNGAP1 variants were identified from the IMAGINE-ID cohort alongside 26 children in the ID-Comparison group (Table 2 ). Adaptive functioning was extremely low, that is 3 standard deviations below the population mean in the SYNGAP1 group (median 57.7; range 46–75), which was matched for in the ID-comparison group (median 51.1; range 46–71; p = 0.19).

Children with SYNGAP1-ID were born at a mean of 38 weeks (range 34–42 weeks) with a mean birthweight of 3.0 kg (2.3kg -3.6kg). Children in the ID-comparison group were born at a mean of 38.8 weeks (range: 33–42 weeks; p = 0.51) with a birth weight of 3.2 kg (1.9 kg – 4.5 kg; p = 0.769). Physical health co-morbidities are shown in Table 2 . Both SYNGAP1-ID and the ID-comparison group experienced gastrointestinal difficulties (SYNGAP1-ID n = 6; ID-comparator n = 12; p = 0.79, of which constipation and gastro-oesophageal reflux were most common. Respiratory co-morbidities were more common, although not significantly, in the ID-comparison group ( n = 12; 8 = recurrent chest infections, 4 = asthma) compared to SYNGAP1-ID ( n = 1; recurrent chest infection, p = 0.06). Rates of cardiac and musculoskeletal difficulties were similar in both groups [Table 2 ].

Neurological Symptoms & Epilepsy

The most common neurological symptoms experienced by children with SYNGAP1-ID were seizures. Seizures affected children with SYNGAP1-ID ( n = 11; 84.6%) more frequently than the ID-comparison group ( n = 2; 7.6%, p = < 0.001), most commonly absence seizures ( n = 8; 72.7%) and atonic seizures ( n = 3; 23.1%). No neonatal seizures were reported for children with SYNGAP1 ID or the comparison group. Of those children with SYNGAP-ID experiencing seizures, ten (90.9%) were receiving anti-epileptic medication.

Muscle and movement difficulties were also frequently reported in children with SYNGAP1-ID ( n = 10; 76.9%). Nine children (90%) experienced hypotonia, three (30%) experienced ataxia, two (20%) experienced hypertonia. The ID comparison group experienced similar rates of muscle and movement difficulties ( n = 17; 65.4%- p = 0.483). Ataxia (ID-comparison n = 1; 3.8%; SYNGAP1-ID = 3; 23.1%; p = 0.09) displayed a trend towards being more common in children with SYNGAP1-ID. Rates of hypotonia in the ID-comparison group ( n = 14; 82.4%- p = 0.58) were not significantly different from SYNGAP1-ID. No cases of visual or hearing impairment were reported in children with SYNGAP1-ID.

Developmental milestones

Three children with SYNGAP1-ID (23%) experienced developmental regression compared to only one child (3.8%) in the ID comparison group ( p = 0.14).

Gross & fine motor

Most children with SYNGAP1-ID ( n = 12; 92.3%) and the ID-comparison group ( n = 24; 92.3%) were able to sit and walk independently. There was no difference in the age of walking observed between SYNGAP1-ID (mean 2.3 years) and the comparison group (mean 2.23 years; p = 0.30; Wilcox effect size [r] = 0.17).

Fine motor development, however, was significantly affected in children with SYNGAP1-ID. Only one child with SYNGAP1-ID was able to independently do up buttons and achieved this milestone at 8 years of age. Contrastingly, 50% ( n = 13) of the ID-comparison group were able to do-up buttons ( p = 0.03) and achieved this milestone at a mean of 7.6 years.

Social development was assessed using age of smiling. There was no significant difference in the number of children able to smile by two months (upper limit of normal range) between SYNGAP1-ID ( n = 7; 53.8%) and ID-comparisons ( n = 7; 26.9%; p = 0.13). Among the other children, the mean age of smiling in SYNGAP1-ID was 7 months (range 3–15 months) and 4.5 months (range 3–7 months) for the ID-comparison group, with no significant difference in achieving this milestone across groups ( p = 0.61; r = 0.15).

Speech & language

Children with SYNGAP1-ID were more likely to be non-verbal ( n = 8; 61.5%) than the ID-comparison group ( n = 6;23.1%; p = 0.01). SYNGAP1-ID children that were able to speak ( n = 5, mean = 3.3 years) achieved this milestone at the same age as the comparison group (mean = 3.3 years; p = 0.84)[Fig. 1 a]. Among children able to speak, caregiver-estimated language competence (i.e., the child’s language age divided by their chronological age) was moderately lower in SYNGAP1-ID (mean = 0.4) compared to the ID-comparison group (mean = 0.6; p = 0.040, Wilcox effect size r = 0.44) [Fig. 1 b], with language competency values of 1 indicating that the child’s language age is equal to their chronological age.

a Box plot demonstrating the age of speaking their first words for children with SYNGAP1-ID and the ID-comparison group. b Box plots highlighting spectrum of language competency within children able to speak in the SYNGAP1-ID and ID-comparison group. Even when matched for developmental level, language competency in SYNGAP1-ID is lower than in the ID-comparison group

Neuropsychiatric diagnoses & behavioural difficulty

Emotional and behavioural adjustment.

There was no significant difference in the total SDQ score between SYNGAP1-ID (median 20; range 8–27) and the ID-comparison group (median 18.5; 11–31; p = 0.98). Most children with SYNGAP1-ID (53.8%) scored in the ‘very high’ severity band of the DAWBA, indicative of difficulties experienced by the extreme 10% of the population [ 29 ]. This was higher, although not significantly, than the ID-comparison group ( n = 10, 38.5%; p = 0.67; r = 0.005). SYNGAP1-ID Total SDQ scores did not significantly differ between non-verbal children ( n = 17; 8–31) and those able to speak ( n = 19; 10–30; p = 0.35). There were no significant differences in SDQ sub-scales between groups [Table 3 ].

Psychiatric Diagnoses

The incidence of DSM-5 autism diagnosis among children with SYNGAP1-ID ( n = 6; 46.2%) was greater, although not significantly, compared to the ID-comparison group ( n = 8; 30.7%; p = 0.55). 7.7% ( n = 1) of children with SYNGAP1-ID met the diagnostic criteria for generalised anxiety compared to 15.3% ( n = 4) of the ID-comparison group ( p = 0.49). Rates of ADHD (SYNGAP1-ID n = 2; 15.3%; ID-comparison n = 4; 15.3%; p = 1) and oppositional defiant disorder (SYNGAP1-ID n = 1; 7.7%; ID-comparison n = 0; p = 0.15) were not different across groups.

We also interrogated other behavioural features previously reported in the literature. Temper outbursts occurred in 12 (92.3%) of SYNGAP1-ID children and 25 (96.2%) of the ID-comparison group ( p = 0.46). In six SYNGAP1-ID children (50%), temper outbursts involved aggressive behaviour, which did not differ from the trend observed in the comparison group ( n = 14; p = 0.26). Self-injurious behaviour, such as head-banging and skin-picking, was present in 2 children (15.4%) with SYNGAP1-ID and 8 children (30.7%) from the ID-comparison group ( p = 0.16). Children with SYNGAP1-ID more frequently displayed fascination with particular sensations ( n = 9; 69.3%) compared to the ID-comparison group ( n = 10; 38.5%- p = 0.17). Hyposensitivity to pain was observed in both children with SYNGAP1-ID ( n = 11; 84.6%) and ID-comparisons ( n = 20; 76.9%; p = 0.92).

We report the first systematic comparison of the neurodevelopmental and behavioural phenotype of SYNGAP1-ID to children with the same level of intellectual disability due to other heterogeneous genomic conditions. Our results highlight a specific pattern of neuro-behavioural characteristics that should be a focus for clinical care. These characteristics include significant global developmental delay, particularly impacting fine motor and speech and language development, gait abnormalities, autism, and in particular epilepsy. There was a striking propensity for children with SYNGAP1-ID to experience seizures, of which absence and atonic seizures were most commonly observed. Our findings on seizures support the existing literature on the seizure phenotype in SYNGAP1 [ 9 ].

Another unique feature of the SYNGAP1 developmental profile is the pronounced motor control difficulties. Although most children were able to walk independently, ataxia occurred more frequently in SYNGAP1-ID. Hypotonia, however, was a common feature among both groups. This mirrors previous reports of gait abnormalities, particularly ataxia, in this cohort [ 9 , 15 ]. Locomotor abnormalities have been replicated in SYNGAP1 animal models, which may mirror observed gait abnormalities [ 30 ]. Fine motor skills were especially affected in SYNGAP1-ID, with only one child able to do up buttons compared to 50% of the ID-comparison group. Previous case series have reported some delays in fine motor ability, but have not considered functional outcomes [ 15 ].

Speech development is also disproportionally affected in SYNGAP1-ID, beyond delays observed in children matched for degree of developmental delay. SYNGAP1-ID children are not only more likely to be non-verbal, but also achieve lower levels of language competency. Similar findings were described in a recent case series, which also highlighted limited language attainment for those children able to speak [ 15 ]. Interestingly, we did not observe a difference in emotional and behavioural difficulties, as assessed by SDQ scores, between non-verbal children and those able to speak. Difficulties in language development may be explained by sensory processing difficulties [ 22 ]. SYNGAP1 mutations have been shown to lead to dysregulated cortical sensory system development, including atypical sensory map organisation [ 3 , 31 ]. This results in distorted sensory processing, including the processing of incoming auditory signals. Altered electrophysiological responses to auditory stimuli have been demonstrated in individuals with SYNGAP1 variants when compared to individuals with Trisomy 21 and neurotypical controls [ 32 ]. Impaired perception and processing of auditory signals may adversely affect speech and language development, which may contribute to the language delays we observed in our cohort. As such, speech and language therapy should be a therapeutic priority.

The above difficulties, including locomotor, spatial learning, and sensory processing abnormalities, may be further explained by multiple down-stream effects of atypical synaptic formation and function caused by SYNGAP1 mutations [ 3 ]. The mutations result in premature functional maturation of excitatory neurones [ 33 ]. This in turn results in abnormal cortical circuits and connectivity, which may explain SYNGAP1’s impact on cognition. It may also disrupt more specific processes required to develop fine motor control and language skills [ 3 , 33 ]. Similarly, atypical cortical connectivity may contribute to the previously described abnormal development of the brain’s sensory systems [ 3 , 33 ]. Aberrant synaptic plasticity during critical periods of development can also inhibit activity-dependent synapse formation and strengthening, which in turn hinders learning [ 5 , 7 , 34 ]. Lastly, abnormal maturation of excitatory neurones and synapses can result in an imbalance between excitatory and inhibitory neuronal connections (E/I imbalance; Clement et al., [ 35 ]). E/I imbalance has been proposed as a hypothesis underlying the development of autism [ 36 ] and may explain the high rate (46% of SYNGAP1-ID children) of autism observed in our cohort, which is consistent with that of previous reports [ 9 , 15 ].

The strength of this study is that it is the first systematic exploration of the neurodevelopmental and behavioural differences between SYNGAP1-ID and children with ID of genetic origin and an equivalent level of developmental delay and ID. Hereby, we identify behavioural and developmental features unique to SYNGAP1-ID. Assessing children presenting with neurodevelopmental delay or ID due to a suspected genetic diagnosis is often challenging due to significant overlap in phenotypic features between conditions. By comparing SYNGAP1-ID to a heterogenous group of monogenic conditions all associated with ID we aimed to mimic this clinical challenge and further aid clinicians in identifying SYNGAP1-associated features and providing appropriate counselling of children and their families. However, the heterogeneity within our comparison group limits our ability to draw conclusions about the impact of genetic function and mutation type on the observed phenotypic variation. We must also acknowledge that our sample size may limit the power of our study to detect significant differences in certain characteristics, such as autism, sensory sensitivities, and motor difficulties. Future work should focus on large, pooled cohorts to confirm our findings and further bridge the gap between genotypic diagnosis and phenotypic presentations in SYNGAP1-ID. There is also a need to assess the longitudinal development of these children and consider the clinical utility of therapy implementation.

In conclusion, our results demonstrate that children with SYNGAP1-ID are more prone to seizures and motor difficulties, such as ataxia, than children with other monogenic conditions leading to ID. They also experience greater difficulties in fine motor and speech and language development as well as higher rates of autism when compared to children with equal levels of intellectual disability due to other, heterogenous genomic conditions. Identification of features specific to the SYNGAP1 phenotype can not only help guide diagnosis and clinical counselling, but also provide clinically relevant endpoints for future therapeutic trials. This will be particularly relevant in the promising advancement of animal-models of gene re-activation therapeutic approaches, which can lead to improvements in seizure threshold, learning, memory, and cognitive function [ 37 ]. Our findings also provide strong evidence for the implementation of more targeted therapeutic interventions in children with SYNGAP1, such as early speech and language and occupational therapy support.

Availability of data and materials

The full phenotypic IMAGINE dataset is available from the UK Data Archive under special licence access (SN 8621). Requests for genotype or linked genotypic-phenotypic data can be made through the IMAGINE data access committee.

Abbreviations

Intellectual Disability

Developmental delay

SYNGAP1-related intellectual disability

Attention deficit hyperactivity disorder

Developmental and Well-being Assessment

Adaptive Behaviour Assessment System

Strengths and Difficulties Questionnaire

Excitatory and inhibitory neuronal imbalance

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Acknowledgements

We wish to thank all the families and children who took part in the study.

This research was funded by the MRC [MR/L011166/1] and the MRF [MR/N022572/1].

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NB, DS, and JW designed and conceptualised this study. Data collection and analysis was performed by HH, IL and NB. The first draft of the manuscript was written by NB. All authors commented on previous versions of the manuscript and approved the final manuscript.

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Bednarczuk, N., Housby, H., Lee, I.O. et al. Behavioural and neurodevelopmental characteristics of SYNGAP1. J Neurodevelop Disord 16 , 46 (2024). https://doi.org/10.1186/s11689-024-09563-8

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Published: 21 August 2024

Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories

Andras Gezsi ORCID: orcid.org/0000-0003-1022-6356 1 na1 ,
Sandra Van der Auwera ORCID: orcid.org/0000-0002-1757-7768 2 , 3 na1 ,
Hannu Mäkinen 4 ,
Nora Eszlari 5 , 6 ,
Gabor Hullam 1 , 5 ,
Tamas Nagy 1 , 5 , 6 ,
Sarah Bonk 2 ,
Rubèn González-Colom ORCID: orcid.org/0000-0001-7330-9621 7 ,
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Kevin Kirchner 2 ,
Andras Millinghoffer 9 ,
Carsten O. Schmidt ORCID: orcid.org/0000-0001-5266-9396 10 ,
Bence Bolgar 1 ,
Josep Roca 7 ,
Isaac Cano ORCID: orcid.org/0000-0003-2938-7459 7 ,
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Peter Antal ORCID: orcid.org/0000-0002-4370-2198 1 na2 &
Gabriella Juhasz ORCID: orcid.org/0000-0002-5975-4267 5 , 6 na2

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Epidemiology
Genetics research
Genome-wide association studies

The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients.

Introduction

Major depressive disorder (MDD), characterized by depressed mood and loss of interest, is one of the most common mental disorders and typically has a recurrent or chronic course that leads to suffering, disability, increased suicide risk, and increased all-cause mortality 1 . Additionally, approximately one-third of patients are resistant to current treatments 2 , partly due to the lack of a comprehensive biological model, as MDD shows remarkable heterogeneity in both its clinical manifestation and underlying neurobiology 3 .

Genome-wide association studies (GWASs) have suggested divergent pathways and mostly nonspecific cellular processes involved in MDD. Furthermore, genetic correlation studies have indicated shared genetic contributions to several somatic and mental disorders, reflecting the pleiotropy of genetic variants and common biological processes involved 4 , 5 , 6 . As twin studies have estimated that only 40% of phenotype heritability can be attributed to additive genetic effects 6 , this suggests the involvement of comorbid conditions and non-genetic risk factors.

The inclusion of additional phenotypic information when subtyping depressive disorders can indeed reduce genetic heterogeneity and inform the development of aetiological models, even if such subtyping was based on restricted sets of predefined criteria and associated symptoms 7 . Furthermore, a recent study demonstrated strong multimorbidity patterns among 439 common diseases on both phenotypic and genetic levels 8 , revealing strong correlation patterns between psychiatric and cardiovascular and respiratory disorders. The multimorbidity paradigm shift also highlights the importance of considering multimorbidity patterns when identifying depression subtypes and determining distinct biological pathways in their background 9 , 10 .

Thus, in the Temporal Disease Map-Based Stratification of Depression-Related Multimorbidities (TRAJECTOME) project, our aim is to filter and include all relevant information on trajectories of MDD-related multimorbidities from large population cohorts, including individuals with and without MDD, to facilitate the identification of biologically and clinically informative depression subtypes as well as their distinct neurobiological and genetic backgrounds, and thereby suggest potential biomarkers for precision screening and treatment. Our main hypothesis was that the use of age-dependent strongly relevant MDD-related multimorbidities enriches the genetic basis of MDD, such that specific participant clusters are associated with distinct genetic profiles contributing to the pathology of major depressive disorder.

To date, studies attempting to identify subgroups within chronic somatic diseases have mainly used cross-sectional data and have not considered associations with psychiatric disorders 11 , 12 , 13 . Recently, this approach was expanded from phenotypic data to genetic data 8 , explaining 46% of identified multimorbidities according to shared genetic components and identifying central hub diseases that are highly relevant to the majority of multimorbidity relationships. Furthermore, a Danish group identified five time-dependent psychiatric-multimorbidity clusters in schizophrenia patients associated with heterogeneity in aetiological factors 14 .

Moving beyond these attempts, our approach is based on dynamic Bayesian networks to identify a minimal, nonredundant set of MDD-related multimorbidity trajectories that convey all relevant information about MDD in an individual’s entire medical history. This filtered set includes all multimorbidities with nonmediated causal relationships to MDD and those with potential shared genetic and non-genetic factors. In a previous cross-sectional analysis, we demonstrated that such Bayesian filtering of pairwise comorbidity associations significantly boosted the shared molecular background 15 . In this project, we leveraged this enrichment effect and extended our previous approach into the temporal dimension. The method involved three steps. First, we utilized the statistical concept of the Markov boundary to identify the minimal set of multimorbidity trajectories relevant to a target variable (MDD). Second, we developed a unique, data-driven method of measuring patient similarity in this filtered set of MDD-related trajectories. Finally, we used privacy-preserving Bayesian federated clustering of individuals in a transcohort setting: we used five general-population cohorts ( N = 1,189,509) for discovery and two additional cohorts ( N = 387,089) to validate the clusters’ associated genetic profiles and non-genetic risk factors.

This approach provides a temporal, systems-based perspective on the complex pattern of time- and comorbidity-dependent courses of MDD and their associated biological risk factors, yielding a distilled molecular understanding of the disease network and possibly indicating novel long-term treatment approaches. Moreover, our approach enables the dissection of shared genetic factors between MDD and related conditions and may pave the way for personalized treatment plans targeting not diseases but specific shared pathways in multimorbid conditions, particularly in the realm of psychiatric disorders.

The TRAJECTOME project involves data from 1,576,598 participants from seven European general-population cohorts (Table 1 , Supplementary Data 1 A, B). To identify distinct MDD-related multimorbidity-based clusters and assess their biological profiles, we used individual disease onset information from large cohorts divided into discovery and validation cohorts. The observed differences between cohorts included age range, birth year, and socioeconomic factors, which may have influenced the availability of medical care and disease diagnosis and affected the prevalence rates of lifetime MDD diagnosis (7–19%) in the cohorts (Supplementary Figs. S1 and S2 ).

Dynamic Bayesian network analysis reveals seven MDD-related multimorbidity clusters

To identify MDD-related multimorbidity clusters from the discovery cohorts’ temporal trajectories, we selected 86 predetermined cross-cohort diseases strongly related to MDD (Methods, Fig. 1 , Supplementary Data 2 and 3 ). Based on these diseases, we computed weighted direct MDD-related multimorbidity scores for each participant in each cohort over different time intervals and used these scores as input in the cluster analysis. The seven identified clusters reflected different temporal trajectories of the MDD-related multimorbidity burden throughout the lifespan (Supplementary Fig. S3.1 ) and corresponded to specific clinical subtypes, which we characterized according to genetic profiles and non-genetic risk factors. The fundamental hypothesis of our method was that defining the clusters based only on diseases strongly relevant to MDD would focus the clusters’ profiles on pleiotropic genetic and non-genetic factors of these diseases (Fig. 1 ). In contrast, the influence of factors affecting MDD only through other diseases was diminished (such indirectly related diseases are associated with but not strongly relevant to MDD 15 ). The resulting clusters represent special multitraits and, as such, combine evidence from the multimorbidities strongly relevant to MDD and enrich their common influencing factors. Therefore, our framework represents a more powerful method of identifying shared mechanisms influencing MDD and each identified clinical subtype.

A Rationale and hypothesis of the study. Accumulating evidence suggested that MDD is frequently comorbid not only with other psychiatric disorders but also with several somatic diseases contributing to worse health-related outcomes and decreasing quality of life 68 , 69 , 70 , 71 . Thanks to network medicine and system biology approaches, it has been demonstrated that comorbid conditions partially represent common biological mechanisms 72 , 73 , 74 , 75 . Furthermore, directly related comorbidities of depression, where the relationships are not mediated by other disorders, represent stronger molecular-level relationships 15 and are time-dependent (i.e., vary with onset age 76 ). Finally, a recent comorbidity mapping study of asthma supported that comorbidities are indeed suitable to delineate distinct subgroups of complex multifactorial disorders 77 . B The cohort-specific datasets contain the onset ages of diseases in three-character ICD-10 categories. Data were collected from the participants over various periods, depicted by the length of the grey lines, with disease onsets marked by an ‘x’. Participant trajectories were discretized into cumulative time intervals, as shown at the bottom of the figure. C The structure of the inhomogeneous dynamic Bayesian network used. The boxes correspond to intervals, the nodes in the boxes correspond to diseases, and the solid and dashed edges indicate direct relations between the diseases. This method determined the strongly relevant MDD-related multimorbidities; these nodes are in the Markov boundary of the target variable, indicated by the grey-shaded region and a thick black node border. Genetic and other non-genetic variables also influenced the onset of the diseases (dotted edges). One aim of the study was to identify pleiotropic genetic variants (edges with α) that influence the onset of MDD and its related multimorbidities. These variants confound the direct relationship (edge β) between MDD and its strongly relevant comorbid conditions. D Overview of the study pipeline. We determined MDD-related cross-cohort clusters of all participants in the UKB, CHSS, and THL cohorts by utilizing the temporal trajectories of the participants’ MDD-related multimorbidity burden. The seven identified clusters were then characterized based on disease and non-genetic risk-factor profiles and genetic contributions, and the findings were validated in the two independent cohorts (the FinnGen and SHIP cohorts).

To determine the clinical characteristics of the identified clusters, we investigated their temporal disease patterns in terms of the mean and distribution of onset age of each cross-cohort disease (Fig. 2 ). We also evaluated the disease risk based on Cox regression for each cross-cohort disease and the MDD-free survival using Kaplan‒Meier estimates (Fig. 3 ).

A The average onset ages of the most prevalent (>5%) cross-cohort diseases (per line) according to the seven clusters in the UKB cohort. The node colour indicates the clusters, and the node size is proportional to the observed prevalence of the disease (in %) in the cluster. MDD is highlighted with red colour. B The onset distribution of all cross-cohort diseases in the clusters of the UKB cohort is shown as violin plots, where the colour indicates the cluster. Each of the seven violin plots represents one cluster, computed using data from the entire cohort ( N = 502,504), with individuals weighted by their posterior probability of belonging to the respective cluster. The onset distribution and the mean age of MDD onset are indicated with inline red violin plots and red vertical bars, respectively.

A Coefficient of cluster membership (hazard ratio [HR] in the weighted Cox proportional hazards regression model) with respect to the onset of each cross-cohort disease in the UKB cohort ( N = 502,504). The top five diseases with the strongest increase/decrease in risk in each cluster are indicated in the plot and listed on the right. The colour of the markers corresponds to the main ICD-10 disease category. D: Diseases of the blood and blood-forming organs, E: Endocrine, nutritional and metabolic diseases, F: Mental, behavioural and neurodevelopmental disorders, G: Diseases of the nervous system, H: Diseases of the eye, ear and mastoid process, I: Diseases of the circulatory system, J: Diseases of the respiratory system, K: Diseases of the digestive system, L: Diseases of the skin and subcutaneous tissue, M: Diseases of the musculoskeletal system and connective tissue, N: Diseases of the genitourinary system. B Values and 95% confidence intervals of cluster membership coefficients (hazard ratios) from the weighted Cox proportional hazards regression models for the onset of MDD across various cohorts. Points indicate coefficient values and error bars represent the 95% confidence intervals. Colours represent the different cohorts. C Weighted Kaplan‒Meier estimates of MDD-free survival in the various cohorts throughout participants’ lifespans. Survival curves are labelled by cluster numbers, and the colours of the curves indicate the distinct clusters. The dotted grey curves indicate the mean MDD-free survival in the whole cohort, regardless of cluster membership. (In A and C : UKB N = 502,504; THL N = 41,092; CHSS N = 645,913; FinnGen N = 385,640; SHIP N = 1449).

Regarding the mean onset age of the cross-cohort diseases in the UK Biobank (UKB) cohort (Fig. 2 ), Clusters 1–4 had a later onset age and a longer period of low disease burden. Cluster 6 was similar but had higher disease prevalence in older age. In Cluster 5, the mean onset ages of diseases, especially musculoskeletal, respiratory, and genitourinary diseases, were earlier, whereas in Cluster 7, the mean onset ages of allergic and respiratory inflammatory diseases, migraine, and dermatitis were earlier. Thus, the onset ages of comorbid diseases in Cluster 7 exhibited a bimodal distribution (Fig. 2B ), with the first peak occurring before the age of 20 years and the second peak, which reflected age-related disorders, occurring later, comparable to the peak of Cluster 6 (complete trajectories of all cross-cohort diseases as well as comparisons among the cohorts are provided in Supplementary Figs. S3.2 and S4.1 – S4.4 ). The distribution of the onset ages (Fig. 2B ) among the clusters is also reflected in their age distributions. Clusters 5 and 7 mainly included younger individuals assigned at early ages of disease onset (Supplementary Fig. S5 ), whereas the remaining clusters mainly included older individuals who were only assigned with high certainty after the first diseases had emerged at an older age.

Concerning the prevalence of comorbid diseases, we found a clear distinction in the burden of MDD-related disorders among the clusters (Fig. 3A ). Clusters 1–4, and especially Clusters 1–2, exhibited a low prevalence of almost all cross-cohort diseases. A substantial decrease in the prevalence rates of psychiatric and respiratory diseases was observed along with slightly increased prevalence rates of cerebrovascular and kidney diseases and hypertension (in Cluster 3) or of lipid metabolic disorders and hypothyroidism (in Cluster 4). Clusters 5–6 exhibited a higher MDD-related disease burden with increased prevalence rates of almost all cross-cohort diseases (Fig. 3A ). In Cluster 5, the prevalence rates of schizophrenia and musculoskeletal diseases leading to pain disorders were increased, while in Cluster 6, the prevalence rates of severe reactions to stress, somatoform disorders, and respiratory tract infections were increased. Cluster 7 had a divergent disease profile, with slight decreases in the prevalence rates of most disorders, except for allergic and respiratory inflammatory diseases, migraine, and dermatitis, which each had a strongly increased prevalence (Fig. 3A ).

Regarding the evaluation of MDD, the same disease-burden pattern in terms of onset age and prevalence was observed in all five cohorts (Fig. 3B, C ), with Clusters 1–4 having a low MDD burden in contrast to Clusters 5–7 having a high MDD burden with greater variations among the cohorts. Focussing on the complete set of psychiatric diseases (Chapter V. of ICD-10: F00-F99) to assess their temporal disease patterns across the identified clusters, the analysis revealed significant differences among the clusters in terms of the onset and prevalence of various psychiatric disorders (Supplementary Fig. S6 ).

This pattern was also reflected in the correlations of cluster membership probabilities throughout all cohorts (Supplementary Fig. S7 ), with strong positive correlations of cluster membership among Clusters 1–4 and between Clusters 5 and 6 and mainly negative correlations of cluster membership with Cluster 7, reflecting the three divergent risk profiles. Distributions of the cluster membership probabilities in the UKB cohort (Supplementary Figs. S8.1 – S8.7 ) and of the final cluster assignment in all cohorts (Supplementary Fig. S9 ) showed that most participants were assigned to one of the low-risk Clusters 1–4. Within the remaining risk-conferring clusters, the majority of individuals were assigned to the early-onset clusters (Clusters 5&7), and the fewest individuals were assigned to the late-onset Cluster 6. These findings suggest that the identified clusters had distinct clinical characteristics, which could have implications for personalized healthcare approaches, early intervention strategies 16 , and targeted treatment plans for individuals within each cluster.

GWAS analysis of MDD-related multimorbidity clusters in the UKB cohort identifies immune system-related genetic profiles

To explore the genetic contribution of the clusters, we conducted GWAS analyses in the UKB cohort ( N = 249,167), where the posterior log-odds of the cluster memberships were used as the target variables. Analyses of all clusters revealed 6141 distinct genome-wide significant single-nucleotide polymorphisms (SNPs) spanning 42 risk loci on 20 different chromosomes (Table 2 ). Individual Manhattan and QQ plots (Fig. 4A , Supplementary Figs. S10.1 – S10.7 ) and genomic risk loci (Supplementary Data 4 – 10 ), gene-based (Supplementary Data 11 – 18 ), and functional enrichment analyses results (Supplementary Data 19 ) are provided in the Supplement.

A Gene-based genome-wide Manhattan plots for the seven clusters in the UKB cohort. Association analyses were first performed for each cluster using linear regression to test the association between each SNP and the posterior log odds of cluster membership, controlling for age, sex, the first ten genetic principal components, and the genotyping array. Next, MAGMA gene-level analysis was performed to identify putative significant genes using a SNPwise-multi model, defining the SNP set of each gene with a ± 10 kb window. In the plot, nominal p -values are displayed. The genome-wide significant genes are indicated with red dots, and the significance threshold (2.7 × 10 -6 ) is depicted with a dashed dark red line. B Genetic correlation ( r g ) plot from GWAS summary statistics on the posterior log-odds of cluster membership among Clusters 1–7 in the UKB cohort. The colour of the dots indicates the value of the genetic correlation. C Genetic correlation ( r g ) plot from GWAS summary statistics on the posterior log-odds of cluster membership among Clusters 1–7 in the FinnGen cohort. The area and the colour of the circles represent the magnitude and direction (blue = positive, red = negative) of the genetic correlation between two clusters. D Overlap between genome-wide significant genes from MAGMA analyses of Clusters 1–7 in the UKB cohort. Black dots indicate clusters within the comparison. The intersection size corresponds to the number of genes uniquely shared by these clusters.

The overall pattern of risk-conferring and protective clusters was also apparent at the genetic level (Fig. 4B ). In Clusters 1–4, which had a low disease burden, there were many significant loci, genes, and gene sets (Table 2 ) that were mostly linked to the immune system, including major histocompatibility complex genes ( HLA genes), receptors (interleukin- and Toll-like receptors), and cytokines. This was also reflected in functional enrichment analyses that identified significant enrichment in several gene sets (Supplementary Fig. S11 ), including positive regulation of immune system process , regulation of cytokine production, MHC class II protein complex assembly, Toll-like receptor binding , immune receptor activity, cytokine-cytokine receptor interaction , and Th1 and Th2 cell differentiation , involved in the immune response. Genome-wide significant SNPs in Clusters 1–4 exhibited substantial overlap with allergic diseases (asthma, allergic rhinitis, eczema, and hay fever), cardiometabolic traits (BMI, C-reactive protein [CRP], and high-density lipoprotein [HDL]), chronic diseases (rheumatoid arthritis, multiple sclerosis, and diabetes), inflammatory conditions of the colon (inflammatory bowel disease), and blood measures (white blood cell count and vitamin D level), consistent with results from gene-based and gene set-based analyses, which showed a strong link to immune-related biological processes (Supplementary Data 18 and 19 ). In addition, the genetic correlations among these clusters were strong (Fig. 4B ).

The three clusters with a high disease burden, Clusters 5–7, exhibited distinct individual patterns. GWAS signals for Clusters 5–6 were weaker, with only a few GWAS loci showing overlapping results with psoriasis (Cluster 5) and cardiovascular conditions, asthma, rheumatoid arthritis and different blood measures (Cluster 6) (Table 2 ). In contrast, Cluster 7 had a negative genetic correlation with all previous clusters and the strongest genetic contribution. Thus, the effect alleles of significant SNPs completely differed between low-disease-burden clusters and Cluster 7. Due to the high number of identified loci, overlap with other diseases was high and included apolipoprotein A1, coeliac disease, coronary artery disease, vasculitis, and cholangitis (in addition to the diseases associated with the other clusters). A comparison of significant genes and gene sets among clusters is shown in Fig. 4D and Supplementary Fig. S11 . Most genes were shared with Cluster 7, as it contained by far the highest number of significant genes.

To evaluate genetic similarity with other major psychiatric disorders, we assessed genetic correlations with Psychiatric Genomics Consortium (PGC) GWAS results for MDD, bipolar disorder (BD) and schizophrenia 6 , 17 , 18 . Only Clusters 5–6 showed a significant positive genetic correlation with MDD, whereas Clusters 1–2 exhibited negative genetic correlations with MDD and BD and Cluster 7 a positive correlation with BD. Regarding schizophrenia, no cluster exhibited a significant genetic correlation. Regarding asthma (GWAS on UKB data), genetic correlations revealed a similar pattern as the genetic correlations among clusters, with negative correlations in Clusters 1–4, positive correlations in Clusters 5&7, and no significant correlation in Cluster 6. Moreover, we performed a UKB-specific case-only analysis focusing on individuals diagnosed with MDD, and found substantial genetic correlations (0.78–1) between the original population-based clusters and the MDD-specific clusters, underscoring a significant genetic similarity across these groups (Supplementary Fig. S12 ). However, the reduced sample size ( N = 28,853) in the MDD case-only scenario led to lower heritability estimates compared to the original clusters. Extending the analysis to various depression phenotypes (Supplementary Fig. S12 ) showed high genetic correlation among these and highly similar genetic correlation patterns observed between them and the clusters.

To compare clinical observations with genetic predispositions, we calculated genetic correlations between the seven MDD-related clusters and all 86 cross-cohort diseases in the UKB cohort. The clinical and genetic correlations were comparable (Supplementary Figs. S13 and S14 ), and Clusters 1–4 and 7 were mainly associated with lower genetic risk for the diseases ( r g < 0), whereas Clusters 5–6 were associated with a higher disease burden.

Finally, these results allowed us to assess the extent of pleiotropy among the clusters and MDD at the level of genes and functional modules of the human interactome, as pleiotropy may point to shared underlying genetic mechanisms between MDD and the clusters. At the gene level, we defined pleiotropy as the intersection of statistically significant genes in each cluster and MDD-associated genes according to the latest GWAS meta-analysis 5 ; in brief, we found 17 pleiotropic genes (Table 2 ). Our results demonstrate significant enrichment of MDD-associated genes within the clusters, validating our hypothesis that strongly relevant MDD-related multimorbidities enhance the genetic background of MDD. According to the hypergeometric test, significant overlap was observed between MDD genes and cluster-specific genes in three clusters. Additionally, gene set enrichment analysis revealed that MDD genes were consistently and significantly enriched across the ranked list of cluster-specific genes in all seven clusters. Moreover, we identified cluster-specific functional modules significantly influenced by MDD-associated genes; thus, they can be considered pleiotropic. We identified 31 relevant modules, at least one in each cluster (Supplementary Fig. S15.1 – S15.7 ), which indicates that network-based enrichment captured greater pleiotropy between MDD and the clusters at the level of functional modules than at the level of individual genes. In these modules, several other MDD-associated genes had a significant pleiotropic influence, such as ETFDH , PAX5 , ZDHHC5 , DENND1B , PLCG1 , MICB , STK19 , CDK14 , EP300 , ERBB4 , RERE , BAG5 , CNTNAP5 , LRP1B , NRG1 , POGZ , and XRCC3 . These findings could provide insights into the complex time- and comorbidity-dependent courses of MDD, which may guide the development of novel long-term therapeutic and pharmaceutical approaches.

Non-genetic risk-factor profiles of MDD-related multimorbidity clusters in the UKB cohort

To assess the non-genetic risk factors collected cross-sectionally within the clusters, we examined associations of behavioural and physiological factors with the MDD-related clusters. This analysis determined the specific risk-factor profiles for each identified MDD-related cluster, offering a snapshot of all participants at the time when these factors were evaluated. Clusters 1–4 and Clusters 5–6 were similar as a group but considerably different from each other. Cluster 7 appeared unique in terms of several factors (Fig. 5A , Supplementary Fig. S16 ). The structure of the non-genetic risk-factor profiles of the clusters was consistent with the observations made at the clinical and genetic levels in the UKB cohort. In sum, Clusters 1–2 were associated with a higher age but a lower burden of several behavioural risk factors. In contrast, Clusters 3–4 were associated with increases in several behavioural and physiological risk factors, including higher BMI, lower education level and smoking, as well as lower income and insomnia (in Cluster 4). In contrast, the three remaining clusters were associated with lower age; Clusters 5–6 were additionally associated with overall increased risk, including stress and psychological traits, while Cluster 7 had a more favourable risk-factor profile in general.

Simple linear regression models, including one factor at a time with age and sex as covariates, were calculated for each cluster in the A UKB cohort and B THL cohorts. The posterior log-odds of being in a given cluster were the dependent variable. The direction of the triangles reflects the sign of the coefficient (upwards = positive; downwards = negative), and the colour reflects the magnitude. Statistical analyses were performed using two-sided t -tests to assess the significance of each factor’s effect on cluster membership. Adjustments for multiple comparisons were made using the Bonferroni correction. The size of the triangles is proportional to the −log 10 p- value, and only significant values are shown (−log 10 (p) > 4). Sex was coded as follows: 1 = male, 2 = female. The risk factors of stress and neuroticism score were not available in the THL cohorts. *Alcohol intake and depression score were not available in the FinHealth17 and Finrisk cohorts, respectively, but were available in the other THL cohort.

Validation of MDD-related multimorbidity profiles at the genetic and non-genetic risk-factor levels

In the additional cohorts, we validated the characteristics of MDD-related multimorbidity profiles identified in the UKB cohort on all levels. Validation of genetic findings was performed in the Finnish cohorts (FinnGen and THL cohorts). These isolated populations are of special interest as they are more likely to exhibit deleterious variants and yield previously unknown genetic associations 19 . Although the THL cohorts were considerably smaller, the overall pattern of correlations among the clusters was replicated in GWAS analyses (Supplementary Fig. S17 ) and comparable to those of the UKB cohort. A GWAS of the 23,786 participants, including 8,711,904 SNPs, revealed very small or negative heritability estimates, indicating limited power to detect significant loci in this cohort 20 . As replication at the individual genetic level was not feasible, we conducted validation at the level of aggregated genetic signals using polygenic risk scores (PRSs) derived from the UKB summary statistics. Thus, all PRSs showed a significant positive association with the cluster probability in the THL cohorts (Benjamini–Hochberg adjusted p -values range from 1.0 × 10 -15 to 1.7 × 10 -2 , Supplementary Fig. S18 , see Supplementary Data 20 for details of explained variance by the PRS).

Using the data from the FinnGen cohort ( N = 277,252; 9,706,223 SNPs), which had a sample size comparable to that of the UKB cohort, a large proportion of genetic findings were replicated at the levels of SNPs, genes, and functional enrichment (Table 3 ; Supplementary Figs. S19.1 – S20 , S11 ; Supplementary Data 21 – 30 ). The replicated genes included HLA genes, especially in Clusters 1–2, and several additional genes throughout the genome related to Clusters 1–4 and 7 (Supplementary Fig. S20 ; Supplementary Data 21B ). Genetic correlation patterns among the clusters largely overlapped with the patterns observed in the UKB cohort (Fig. 4C ), and the strong genetic correlations among clusters in the FinnGen and UKB cohorts pointed to similar genetic factors driving the associations (Supplementary Data S31 ). Significant loci in the FinnGen clusters also revealed a strong link with immunological phenotypes (rheumatoid arthritis, asthma, and IgG levels). The functional enrichment analysis showed several Gene Ontology (GO) terms that overlapped with the UKB cohort, such as MHC protein complex assembly and MHC class II receptor activity, as well as overlaps with numerous Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to various diseases, such as type I diabetes mellitus, rheumatoid arthritis, asthma and IBD in Clusters 1–2 (Supplementary Fig. S11 ).

The reliability of the non-genetic mostly behavioural risk factors was validated in the THL cohorts (Supplementary Data 1 ). Although overall effects were weaker due to the smaller sample size, the pattern was similar to that in the UKB cohort (Fig. 5B , Supplementary Fig. S21 ). In general, Clusters 1–4 had more protective factors, whereas a greater accumulation of risk factors was observed in Clusters 5–6, and Cluster 7 contained a mixture of protective factors and risk factors. Collectively, these findings show that the characteristics of the MDD-related multimorbidity clusters, including genetic contributions and non-genetic risk factors, were validated in additional cohorts, supporting the robustness and generalizability of the results.

As only a few cohorts had a lifetime assessment of disease onset ages, the applicability of the clusters was tested in a setting with limited availability of disease information. The SHIP study only provided information for 13 cross-cohort diseases (Supplementary Data 2 ) to generate the seven MDD-related comorbidity clusters, as described above. The correlations among the derived cluster probabilities exhibited a pattern similar to that in the larger UKB and FinnGen cohorts (Supplementary Fig. S7 ). Simulations also showed that the SHIP dataset had an accuracy of 67.5%, which was far better than that expected with a totally random null model (14%) and halfway between the randomly chosen variable set of the same size (52%) and the optimal set of the same size (81%) (Supplementary Fig. S22 ).

To evaluate the biological meaningfulness of the profiles, we calculated PRSs, similar to the THL cohorts, and assessed the profiles of non-genetic risk factors in comparison with those of the UKB cohort. Five MDD-related cluster PRSs in the SHIP cohort ( N = 1108) were positively correlated with their cluster membership probability; for Clusters 1 and 7 these correlations reached (suggestive) significance ( p cl1 = 0.025; p cl7 = 0.067, see Supplementary Data 20 for details of explained variance by the PRS). The correlation pattern among these seven PRSs was similar to the correlation patterns observed at the phenotypic and genetic levels in the UKB and FinnGen cohorts (Supplementary Fig. S23 ). At the level of non-genetic factors, the association patterns of clusters with age, BMI, blood pressure, insomnia, neuroticism score, and current depression were replicated in the SHIP cohort (Supplementary Fig. S24 ). These findings suggest that the MDD-related multimorbidity clusters can be applied to settings with limited disease information, which further supports the generalizability of our approach.

In the TRAJECTOME project, we utilized information on lifetime disease trajectories to define clusters based on temporal patterns of MDD-related multimorbidity burden. Based on data from 1.2 million individuals in the general population of three European countries (i.e., discovery cohorts), we identified seven MDD-related clusters with distinct clinical, genetic, and non-genetic risk-factor profiles. We further validated their applicability in independent cohorts, including those with a limited set of disease information. Based on these profiles, we extracted biologically meaningful information that can be used to interpret the clusters with respect to their aetiology, clinical relevance, and possible disease prevention strategies.

We identified four clusters with a low-risk profile accompanied by a low burden of MDD and MDD-related disorders as well as three clusters associated with risk-conferring profiles. Individuals in Clusters 1–4 were healthy until older age with a low risk of developing MDD. This favourable clinical pattern regarding MDD was also observed at the levels of genetic and non-genetic risk-factor profiles.

With the strong genetic correlations among these clusters, GWAS results suggested a substantial and consistent contribution of the immune system and protection against allergic, autoimmune, and inflammatory diseases. However, clear differences in MDD-related pleiotropic functional genetic modules supported more specificity at the genetic network level. For example, individuals in Clusters 3–4 developed age-related cerebrovascular or metabolic disorders, possibly due to a slight increase in genetic vulnerability as well as less-favourable lifestyle habits. Based on large-scale meta-analyses, depression risk is also dependent on non-genetic behavioural factors, including sleep, stress, diet, physical activity and social interactions 21 , 22 . Our approach extends these findings by using genetic and clinical data to identify biological subgroups resilient to depression on multiple levels.

In contrast, Clusters 5–6 exhibited a high-risk profile in terms of the prevalence of MDD and most MDD-related disorders. The non-genetic risk-factor profile was largely disadvantageous, and the genetic profile revealed a strong correlation between Clusters 5 and 6, albeit distinct MDD-related pleiotropic genetic functional networks. Differences between these clusters were mainly due to clinical manifestations, with Cluster 5 showing earlier MDD onset and the highest risk of schizophrenia; Cluster 6 showed later MDD onset accompanied by an increased risk of stress-related behavioural problems and somatoform disorders. This pattern of high MDD risk combined with multimorbidities and poor lifestyle habits might contribute to the worst outcomes, as a high multimorbidity burden has a deleterious effect on the clinical course of MDD 23 , and the quality of life is dramatically lower in depression patients with chronic physical conditions 24 . A recent analysis confirmed this pattern of known behavioural risk factors for depression using UKB data, proposing that inflammatory processes are a common neurobiological pathway 25 . Thus, our study provides clear evidence of disease and risk-factor patterns related to MDD that might benefit from behavioural interventions.

In contrast to the high disease burden in Clusters 5–6, which were strongly associated with non-genetic risk factors, Cluster 7 showed a strong contribution of inflammation-related genetic predispositions regardless of non-genetic factors. The early manifestation of cluster assignment also suggests a substantial contribution of genetics rather than long-term non-genetic risk factors. Although most MDD-related diseases showed decreased prevalence in this cluster, MDD and a group of respiratory disorders (asthma and allergic rhinitis) exhibited sharply increased prevalence, driving the risk association. A strong link between MDD and highly heritable, usually early-onset immune-related diseases has been identified previously at both phenotypic and genetic levels 26 , 27 , 28 , 29 , 30 , 31 . The strong genetic correlations between these diseases and Clusters 5&7 point to genetic subgroups within depression that have a shared aetiology.

The findings obtained by using our approach confirmed that inflammatory signalling is part of the underlying aetiology of depression 32 in Cluster 7. Despite the low genetic risk of MDD in Clusters 1–4 and Cluster 7 as well as the advantageous behavioural profiles, Cluster 7 had an increased risk for early-onset depression, which might be due to contrasting profiles of genes involved in inflammatory signalling pathways. However, in Cluster 5, besides genetic risk, external stressors (such as psychosocial stress or disadvantageous behavioural factors 33 ) may also contribute to inflammation. Thus, the temporal trajectories of MDD-related multimorbidity clusters in our study revealed highly pleiotropic inflammation-related genetic loci that exert protective or risk effects in a cluster-specific manner by engaging distinct molecular networks and non-genetic risk factors. The observed heterogeneity within MDD risk could explain previous contradictory findings regarding the relationship between MDD and inflammatory genes 5 , 6 , 34 , 35 , 36 . Our cluster-related findings also corroborate previous inflammatory, metabolic and related hypotheses regarding pathomechanisms of depression 37 , 38 , 39 , 40 . At the clinical level, further detailed phenotyping 4 of MDD patients within these clusters could enable phenotypic and biological subtyping of depression to develop targeted prevention and intervention strategies.

Although our method was supported by temporal disease information from public health data, this approach had several limitations and involved simplification. Differences in healthcare systems lead to differences in disease rates, possibly related to differences in year of birth and age. Additionally, our method is currently unable to distinguish between chronic and acute diseases, which may have different long-term impacts on the development of depression and other conditions. However, the cross-cohort acute diseases were mainly acute inflammatory diseases that may share pathophysiology with other immune-related diseases and depression. Finally, our Bayesian network methodology is sensitive to unknown confounders and selection bias, potentially causing spurious correlations, but the fairly complete nature of our cohorts and the cross-cohort design mitigate this danger.

In conclusion, we identified seven MDD-related multimorbidity clusters with unique genetic and non-genetic risk-factor profiles that highlight the involvement of neuroinflammatory processes in depression and provide a strategy for subtyping depression patients. This bridges the gap between complex multimorbidity patterns associated with MDD over the course of an individual’s life and recommendations for prevention, early intervention and personalized psychological and pharmacological therapy. This approach could also be expanded to other complex diseases with a high load of comorbidities and shared genetic and non-genetic risk factors.

Description of the training cohorts

Under application number 1602 , we extracted data from the UK Biobank (UKB) database, which includes medical and phenotypic data of participants recruited from NHS patient registers of people aged 40–69 years 41 . Ethical approval was given by the National Research Ethics Service Committee North West–Haydock 42 , and all participants provided written informed consent. All procedures were conducted in accordance with the Declaration of Helsinki.

To identify MDD-related clusters based on disease trajectories, 502,504 participants who had available disease onset information for 1,127 ICD-10 categories were included (for details, see the “Cross-cohort disease categories and relevance scores” section).

Quality control of GWAS data in UKB data

Our genomic quality control (QC) methods were detailed previously 43 , but in the present analyses, we did not restrict participants according to the availability of complete dietary data.

Specifically, we selected participants with White British ancestry (UKB data field 22006, defined both by self-report and genetic ancestry) and without putative sex chromosome aneuploidy (data field 22019). We used v3 genetic data of UKB with genotyped variants and, when genotyped variants were not present, we used imputed variants as well and positioned them according to the GRCh37/hg19 genome assembly. Variant QC consisted of several steps. First, multiallelic variants as well as variants with a minor allele frequency (MAF) < 0.01 were excluded, retaining only single-nucleotide polymorphisms (SNPs). For imputed SNPs, both info and certainty parameters had to be at least 0.9. Furthermore, SNPs and participants were excluded according to the missingness rate (in an iterative manner, with cut-off points at 0.1, 0.05 and 0.01), and SNPs violating Hardy-Weinberg equilibrium ( p < 1 × 10 -5 ) were excluded. Before further calculations for participant filtering, linkage disequilibrium (LD) pruning, with an R 2 of 0.2, was applied to SNPs. The maximal set of unrelated individuals was selected 44 (data field 22020), and a kin-cut-off of 0.044 was applied. Finally, a sex check and heterozygosity outlier detection were performed, as described previously 45 .

For the GWAS analyses, we selected participants who did not withdraw their consent before February 2022 and did not have missing data on sex, age, or genotyping array. To control for population stratification, principal component analysis was performed with the final set of participants and with the SNP subset after the LD pruning (described above).

Catalan Health Surveillance System

Since 2011, the Catalan Health Surveillance System (CHSS) has collected detailed information on healthcare utilization from the entire population of Catalonia (northeastern Spain; 7.5 million inhabitants). The CHSS integrates the information contained in the Minimum Basic Dataset for Healthcare Units registry provided by 63 hospitals, 49 mental health centres, 370 primary care teams and 72 long-term care centres every 6 months. The CHSS assembles information on the use of public healthcare resources, pharmacological treatments, socioeconomic and educational status, psychological health and other billable healthcare costs, such as nonurgent medical transportation, ambulatory rehabilitation, domiciliary oxygen therapy, and dialysis 46 .

From the 7.5 million individuals documented in the CHSS, we considered only registry data from all citizens living in the integrated health district of Barcelona-Esquerra (“AISBE”) between 2011 and 2019 ( N = 645,913), with a mainly White European ancestry, as input to identify MDD-related clusters, extracting over 42 million diagnostic codes recorded between 1913 and 2019. Notably, approximately 50% of the records were from the period after 2012, when the Catalan health system underwent digitization and implemented electronic medical records. Conversely, only 2 million records were available from before 2000, with approximately 200,000 records available from before 1950.

Finnish Institute for Health and Welfare (THL)

For cluster analysis, data from 41,092 participants in Finnish population surveys were included 47 ; these surveys included Finrisk 1992 ( N = 5019), Finrisk 1997 ( N = 7130), Finrisk 2002 ( N = 7207), Finrisk 2007 ( N = 4635), Finrisk 2012 ( N = 5396) 48 , Health 2000/2011 ( N = 6004) and FinHealth 49 2017 ( N = 5074) ( https://urn.fi/URN:ISBN:978-952-343-449-3 ). After excluding related individuals (IBD > 0.2), 30,961 participants were retained from the Finnish population surveys 47 as follows: Finrisk 1997 ( N = 6723), Finrisk 2002 ( N = 5698), Finrisk 2007 ( N = 4635), Finrisk 2012 ( N = 3078) 48 , Health 2000/2011 ( N = 5944) and FinHealth 2017 ( N = 4883). These participants, aged 20–100 years, were chosen at random from the Finnish population and represented different parts of Finland. For GWAS data used from THL cohorts, see the “Quality control of GWAS data from the FinnGen and THL cohorts” section.

Description of validation cohorts

Finngen project.

We used data from the FinnGen project 19 ( https://www.finngen.fi/en ) from data freeze 10 (DF10; excluding THL cohorts) to generate the MDD-related clusters in an independent cohort from the Finnish population. In brief, FinnGen is a public‒private project aiming to collect genotype data from half a million Finnish people and combine these data with data from various health registries. The participants consist of legacy individuals recruited before the start of the FinnGen project and prospective individuals; these latter participants were recruited on a voluntary basis during hospital visits if the patient provided consent for their data to be entered in the biobank.

The THL cohorts and FinnGen cohort contain disease information collected in the following registries: Causes of death (STAT, 1969), Register of Primary Health Care Visits, HILMO (2011), Care Register for Health Care inpatient visits, HILMO (THL, 1969), Care Register for Health Care, specialist outpatient visits, HILMO (THL, 1998), Finnish Cancer Registry (CANC, 1953), Cervical cancer screening (THL, 1991), Breast cancer screening (THL, 1992), and the Finnish Registry for Kidney Diseases (1964).

In total, FinnGen DF10 consists of 430,897 participants with genotype data; after excluding the THL cohorts, 385,640 participants remained for cluster analysis.

Quality control of GWAS data from the FinnGen and THL cohorts

The genotyping of FinnGen participants was performed on a Thermo Fisher axiom custom array consisting of 736,145 probes for 655,793 genetic markers. Processing of samples included removing samples where the genetic sex did not match the participant-reported sex in the registries, samples with missing variant information >0.02, samples with excess heterozygosity in common variants (allele frequency >0.05) and samples with excess relatedness to other samples (IBD > 0.1). The processing of variants depended on whether the variant was used in imputation. Quality control included removing variants if allele frequency in the panel was <0.001 (for imputation QC only), removing variants where the allele frequency differed significantly among panels, removing a variant from all batches (FinnGen chip data and legacy data, processed separately) if HWE p < 10 -10 across all batches; removing any batch missing >0.03 of data (for legacy samples, the missingness threshold was 0.05 due to the exclusion of too many variants for imputation purposes), and removing batches where more than 15% of the batches had missingness >0.04. Finally, variants within a batch were removed if the p-value for HWE was >10 -6 or if the missingness rate was >0.02. The legacy participants were genotyped on various generations of Illumina GWAS arrays. The Sisuv4 reference panel was used to impute an additional 20,175,454 genetic markers. Information on the generation of the imputation panel and the QC steps used to produce the imputed genotypes is available elsewhere ( https://finngen.gitbook.io/finngen-analyst-handbook/finngen-data-specifics/genotype-data/imputation-panel/sisu-v4-reference-panel ).

Finrisk cohorts were genotyped at the Sanger Institute, Hinxton, UK; FIMM, Helsinki, Finland and Broad Institute, Cambridge, MA, USA using Illumina HumanCoreExome-12v1, Illumina HumanCoreExome-24v1, Illumina HumanOmniExpress-12v1, Illumina Human610-Quadv1, and Illumina GSAMD-24v1-0_20011747_A1 arrays. The Health 2000/2011 cohorts were genotyped at the Sanger Institute, Hinxton, UK; FIMM, Helsinki, Finland and Broad Institute, Cambridge, MA, USA using IlluminaHuman610K and Human610-Quadv1; Illumina HumanCoreExome-24-v1; and Broad_GWAS_supplemental_15061359_A1 genotyping arrays, respectively. The FinHealth 2017 cohort was genotyped at Thermo Fisher Scientific, San Diego, CA, USA, using the Affymetrix Axiom FinnGen1 array. Before imputation, variants with call rate <0.98, HWE p < 10 -6 , and minor allele count (MAC) < 3 were removed. For THL cohorts, the Sisuv3 reference panel was used to impute an additional 20,175,454 genetic markers. Information on the generation of the imputation panel and the QC steps to produce the imputed genotypes is available elsewhere ( https://finngen.gitbook.io/finngen-analyst-handbook/finngen-data-specifics/genotype-data/imputation-panel/sisu-v3-reference-panel ).

Study of Health in Pomerania

The Study of Health in Pomerania (SHIP) is a general-population-based research project on adult residents in northeastern Germany 50 . In this study, we analysed data from the SHIP-START cohort; in this cohort, at baseline, 4308 White European participants were recruited between 1997 and 2001. To date, three regular follow‐ups have been carried out (SHIP-START-1/2/3) as well as a detailed assessment of life events and mental disorders (SHIP-LEGEND) from 2007 to 2010, including 2400 participants from the baseline SHIP-START-0 cohort 51 .

For cluster analysis, 1449 participants who took part in SHIP-START-3 and SHIP-LEGEND and had available baseline information from SHIP-START-0 were included. Regarding the age of onset of chronic diseases (Supplementary Data 2 ), self-reported results were used from baseline and follow-up data. The age of onset for diseases in the F section of ICD-10 codes (F32, F33, F17, F41, F43, F40, and F45) was determined from a combination of self-reported diagnoses from SHIP-LEGEND data and data from the health insurance system that have been collected since the end of 2003. Finally, information for 37 diseases was available. Hereafter, data from the SHIP-START cohort are referred to as SHIP data.

Quality control of GWAS data in the SHIP cohort

The SHIP-START-0 participants were genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0. Hybridization of genomic DNA was performed in accordance with the manufacturer’s standard recommendations. Genetic data were stored using the database Caché (InterSystems). Genotypes were determined using the Birdseed2 clustering algorithm. For QC purposes, several control samples were added. At the chip level, only participants with a genotyping rate on QC probe sets (QC call rate) of at least 86% were included. Finally, all arrays had a sample call rate >92%. The overall genotyping efficiency was 98.55%. Imputation of genotypes was performed using the HRCv1.1 reference panel and the Eagle and minimac3 software implemented in the Michigan Imputation Server for prephasing and imputation, respectively. SNPs with an HWE p < 0.0001 or a call rate <0.95 as well as monomorphic SNPs were removed before imputation.

Ethics statements

This study, including both the data collection and the current analyses, has received ethical approval from appropriate institutional review boards for all involved cohorts. Specifically, the analysis involved data from the following cohorts: UK Biobank (UKB), Catalan Health Surveillance System (CHSS), Finnish Institute for Health and Welfare (THL), FinnGen, and Study of Health in Pomerania (SHIP). Comprehensive ethical approvals were obtained for each of these cohorts, ensuring that all procedures followed were in accordance with the ethical standards of the responsible committee and with the Helsinki Declaration.

Furthermore, all participants in the study provided written informed consent. Detailed information regarding the ethical approvals, including the specific committees and approval numbers, is available in the Supplementary Information.

Identification of MDD-related clusters based on disease trajectories

Assessing diseases strongly relevant to mdd.

We used a Bayesian network-based Markov Chain Monte Carlo (BN-MCMC) method to assess the strongly relevant variables with respect to our target variable (MDD). Bayesian networks (BNs) use directed acyclic graphs (DAGs) to represent multivariate dependencies and conditional independencies among the variables. The nodes in these graphs represent variables, and the edges represent direct relationships between the corresponding nodes. Assessments of the complex structure of the variables are called learning the structure of the BN based on the observed data. However, in most cases, there are many DAGs with nonnegligible a posteriori probabilities (i.e., the best network has many alternatives that are almost as probable as the best network). Even in these cases, there are usually certain structural features, such as the strong relevance of two variables, which can be extracted reliably.

Strongly relevant variables statistically isolate the target variable from all other variables. Therefore, strong relevance is a different concept than a standard pairwise association. First, if the dependency of disease A on the target disease B is indirect (e.g., due to mediation through a third disease C ), then A and B are associated but not strongly relevant to each other. Second, if A has no direct effect on B , but A and C interact with each other to affect B (e.g., disease A does not cause disease B , and vice versa, but the presence of diseases A and B together causes C ), then A is not associated with B but is strongly relevant due to the interactional effect. Therefore, strong relevance indicates either a direct/nonmediated association or an interactional relevance. Below, we refer to a variable’s probability of strong relevance with respect to MDD as the variable’s relevance score .

In the Bayesian learning framework, we can estimate the posterior probability that two variables are strongly relevant to each other (i.e., they have a direct influence on each other) as follows:

where G represents a BN structure (a graph), D is the dataset; I(.) denotes the indicator function, which is 1 if the property holds and 0 otherwise; and \({{{{\rm{Edge}}}}}_{G}\left(X\to Y\right)\) means that an edge points from node X to Y in the G graph. Specifically, the indicator function yields 1 if there is a direct edge from X to Y or from Y to X or if there is a common child node Z of nodes X and Y .

Note, that in our methodology, the directed arrows in the Bayesian network represent direct probabilistic relationships between diseases. This means that the presence of one disease (e.g., MDD) directly influences the probability distribution of another. To assess the strong relevance of each disease to MDD, we focused on the concept of the Markov Boundary, which is the smallest set containing all variables carrying information about a target variable that cannot be obtained from any other variable. In other words, we cannot drop any variable from this set without losing information. By examining the diseases that are in the Markov Boundary of the target variable, we calculate the strength of their probabilistic relationship with the target variable. More specifically, within the Bayesian statistical framework employed in our study, we compute the posterior probability of each variable being within the Markov Boundary of MDD (i.e., the probability of their strong relevance with respect to MDD).

We also note that in our Bayesian network framework, we focus on capturing structural probabilistic relationships between variables rather than quantifying interaction terms that occur in regression models. Although these interactions are quantified at the parametric level in Bayesian networks—through the conditional probability distributions of variables given others—our analyses primarily aimed to elucidate the structural relationships by performing exact Bayesian averaging over the parametric level rather than quantifying these interaction effects directly.

It should be also noted that while the relationships in our Bayesian network are direct and unmediated by other diseases, they do not necessarily imply causation. This directness refers to the absence of intermediate variables within the network’s model structure, distinguishing these relationships from mere correlations at the abstraction level defined by the entire set of variables in the analysis. However, direct probabilistic relationships in the Bayesian network are derived from observational data, not from interventional studies that manipulate one variable to directly observe its effect on another. Without the ability to control or manipulate the conditions, the relationships might still be influenced by unobserved confounding factors. The direct relationships in the network are based on the strongest statistical dependencies observed in the data, but these dependencies alone do not fulfil all criteria required to establish causality, such as eliminating all potential confounders and demonstrating that the relationship is not reversible.

The posterior probabilities \(P\left({G|D}\right)\) are estimated using a DAG-based MCMC simulation. We applied the Metropolis-coupled Markov Chain sampler with a burn-in period of 2 × 10 6 steps and then collected 10 7 samples (i.e., network structures). We restricted the space of the possible structures by limiting the number of parents per node to 8. Convergence diagnostic testing using Geweke 52 scores indicated that the MCMC chains had converged for 618 out of 621 (99.5%) of the posterior probabilities of the variables’ strong relevance, with their z-scores within the acceptable range of −2 to 2, suggesting overall convergence of the chains.

We modelled the participant trajectories using an inhomogeneous dynamic BN to utilize the disease onset information. More specifically, we discretized the first onset time of the diseases to cumulative time intervals ([0–20], [0–40], [0–60], and [0–70]) and transformed the disease onsets into binary variables that had a value of 1 if the disease was diagnosed in the given time interval and 0 otherwise (Fig. 1B ). The dynamic BN accounts for censoring of the participants by including only those participants in each cumulative time interval who have complete disease onset information up to the end of that interval. Then, we separately estimated the strong relevance of all variables with respect to either F32 or F33 (ICD-10 disease categories jointly defining MDD) for each time interval t . The variables from time interval t-1 were also included in the model, but variables in t − 1 could only act as parent nodes, i.e., no edge could point to a variable in t − 1 . See Fig. 1C for a graphical illustration of this method.

Cross-cohort disease categories and relevance scores

As a preliminary step, we determined the set of cross-cohort disease variables as follows. (1) First, for each cohort (UKB, CHSS, and THL), we filtered diseases (according to three-character ICD-10 disease categories) with a prevalence >1% either in the whole cohort or in the subset of depressed participants (i.e., patients diagnosed with either F32 or F33). The primary objective of this pre-filtering step was to exclude rare disorders, as our goal was to identify general multimorbidity trajectory clusters that are broadly applicable. Consequently, this initial filtering led to differing numbers of diseases being considered across each cohort, namely 266 disorders for UKB, 356 for CHSS, and 339 for THL. (2) Next, we estimated the strong relevance of all such diseases with respect to MDD by learning the structural features of the formerly described inhomogeneous dynamic BN. We performed this analysis separately for each cohort. (3) Finally, we selected diseases that had a posterior probability of strong relevance with respect to MDD higher than 0.5 in at least one time interval for at least one cohort, selecting only those disease variables that were consistently available across all cohorts, allowing for uniform analysis across all datasets. This preliminary filtering procedure aimed to gather the broadest possible set of potentially relevant diseases, resulting in 86 cross-cohort disease categories . The prevalence rates and summary statistics of the first onsets of these cross-cohort disease categories are shown in Supplementary Data 2 for each cohort.

Finally, we performed the same analyses using only the cross-cohort disease variables together with the sex and household income status variables of the samples. This resulted in cohort-specific relevance scores for each variable, from which we defined cross-cohort relevance scores by computing a linear combination of the cohort-specific relevance scores for each time interval by applying uniform weights on the cohorts. The cross-cohort relevance scores are shown in Supplementary Data 3 . Computed in this way, the cross-cohort relevance score of a variable corresponds to the expected probability that the variable is strongly relevant with respect to MDD in a given time interval.

Clustering of participants

Based on the cross-cohort relevance scores, we computed the weighted direct MDD-related multimorbidity scores for each participant in each cohort and for each time interval. The score for the i th participant in the t th time interval is computed as follows:

where d represents the cross-cohort diseases, I(.) denotes an indicator function that yields 1 if the first onset of disease d for the i –th sample occurs in the t –th time interval and 0 otherwise; and relevance‑score (t) (d) denotes the cross-cohort relevance score of disease d in the t th time interval. These weighted direct MDD-related multimorbidity scores defined the 4-dimensional space of the samples that we used to cluster the participants.

Finally, we clustered all participants &&using the k-means clustering algorithm in the 4-dimensional space defined by the weighted direct MDD-related multimorbidity scores. More specifically, the clusters were determined based on the participants with complete observed multimorbidity scores, i.e., participants older than 70 years. In younger participants, one or more multimorbidity scores were not available because there were no observations of their future disease onset. However, based on their partial scores, they were assigned to clusters by allocating them to the cluster with the nearest cluster centre. The number of clusters was determined by manual investigations based on expert knowledge with the help of various cluster metrics (such as the silhouette score of the resulting clusters). See Fig. 1 for a graphical overview of the method and Supplementary Methods for further details on the investigated cluster configurations.

The likelihood of cluster membership for the i –th sample and for the j –th cluster is defined as:

where \({p}_{i}\) and \({c}_{j}\) represent the point that correspond to the i th sample and the j –th cluster’s cluster centre, respectively, in the space defined by the multimorbidity scores, and \({{||p}}_{i}-{c}_{j}{||}\) is their Euclidean distance.

The posterior probability of cluster membership for the i th sample and the j th cluster was the normalized likelihood shown below:

To control for uncertain participant trajectories in the following analyses, we excluded participants for whom the clustering algorithm demonstrated low confidence across all clusters. Specifically, we excluded participants who were both under 60 years of age and whose maximum posterior membership probability did not exceed 0.25 for any cluster. This threshold was chosen to remove individuals for whom the algorithm could not confidently assign a predominant cluster, thereby focusing our analysis on participants with more definitive cluster memberships. This exclusion criterion resulted in a subset of N = 364,008 participants. This subset was used for comparing clusters and deriving age-specific differences and was also the base set for genetic analysis.

In the case of GWAS and non-genetic risk-factor profiling analysis, the posterior log-odds of the cluster memberships were used as target variables as follows:

The posterior probability of cluster membership was used to calculate the disease profile of the clusters.

Our methodology employs a privacy-preserving federated approach to derive the MDD-related clusters across multiple cohorts without sharing individual-level data, making it suitable for collaborative studies where data sharing is restricted. Each participating site independently computes relevance scores for diseases, which are then aggregated to create cross-cohort relevance scores, ensuring that only non-identifiable, summarized information is exchanged between sites. Multimorbidity scores for each participant are calculated by aggregating cross-cohort relevance scores for the diseases they have experienced (see Eq. ( 2 )). These scores are then compiled into counts of occurrences at each site. The final clustering is performed using these aggregated counts, thereby ensuring the confidentiality of individual data throughout the process.

Inference over Bayesian network structures was performed with an in-house developed software called BN-BMLA 53 . All other computations were performed in R statistical software (version 4.1.1) 54 or Python (version 3.8). Clusters can be computed with a command line R script that is available online: https://github.com/gezsi/mdd-clustering .

Disease profile of MDD-related clusters

We used weighted Cox regression to determine the disease outcomes in the various clusters (i.e., the hazard ratio of cluster membership regarding disease occurrence) independently for each cohort. Specifically, for each cluster, we constructed Cox proportional hazard models, where the independent variable for a specific individual was a dummy variable created in the following way. We counted each participant twice, summing to a weight of 1. First, we set the value of the dummy variable to 1 and weighted this sample by the posterior probability of cluster membership. Next, we set the value of the dummy variable to 0 and used weight for this sample equal to 1 minus the probability of cluster membership. The covariates were sex, household income (if available), and the normalized birth year (in the case of the UKB cohort). The dependent variable was disease onset. Participants were right censored for a given target disease at their age if the disease was not diagnosed. We calculated separate models for each cross-cohort disease. P- values of the cluster membership variables were adjusted separately for each cohort using the Benjamini‒Hochberg method.

In addition, we calculated weighted Kaplan‒Meier estimates of MDD-free survival in the various clusters in each cohort. We weighted each participant in each cluster with the corresponding posterior probability of cluster membership.

Genetic analyses

Genome-wide association study.

Following site-specific QC measures (see cohort descriptions), Plink 2.0 55 ( https://www.cog-genomics.org/plink/2.0/ ) was used to perform linear regression models to assess the direct effect of each remaining genetic variant on the seven MDD-related clusters that reflected the posterior log-odds of cluster membership. All analyses were adjusted for age, sex, the first ten genetic principal components and site-specific variables (genotyping array in the UKB cohort, geographical region in the THL cohorts). Age was included in the model as a nonlinear variable using cubic splines with knots at ages of 40 and 60 years (R package splines v4.1.1, function bs ). In particular, because of the age span of participants, only the knot at 60 years could be applied in the UKB cohort. Continuous predictor and outcome variables were standardized in the analyses. We employed additive genetic models to assess the contribution of individual genotypes to the dependent variable. We excluded individuals for whom the clustering algorithm lacked sufficient confidence in assigning a predominant cluster, thus concentrating our analysis on those with more clearly defined cluster memberships. More specifically, participants under 60 years of age with a maximum posterior membership probability of no more than 0.25 for any cluster were excluded. Genetic data were available in the UKB ( N = 249,167) and THL ( N = 23,786) cohorts, which were both used to generate the MDD-related clusters. Additionally, genetic data were available in two completely independent cohorts (FinnGen, N = 277,252 and SHIP, N = 1126). We therefore treated the UKB sample as the discovery sample and the latter samples as replication samples. For replication of GWAS loci, a nominal significance level ( p < 0.05) was assumed.

The FinnGen GWAS analyses were performed with Regenie 56 (version 2.2.4) instead of PLINK 2.0 due to computational constraints in the FinnGen Sandbox pipeline. The parameters for the Regenie analyses were as follows: step 1, bsize 1000; step 2, --bsize 200, --bt false, --apply-rint false, --firth, --approx, --pThresh 0.01, --test additive and --firth-se.

Additional GWAS analysis of the UKB cohort using logistic regression analysis of the binarized presence/absence of disease onset was performed to compare the results of MDD-related multimorbidity clusters to the genetic results of all 86 cross-cohort disease categories used to inform the clusters. All filters and settings were the same as for the cluster membership analysis in the UKB cohort detailed above.

Post-GWAS analysis

To assess the impact of SNP results on biological processes, several post-GWAS tools were applied that extract information regarding significant loci, genes, and pathways and report genetic correlations with other phenotypes of interest based on their GWAS summary statistics.

The GWAS summary statistics for all seven MDD-related clusters for each cohort were first processed with FUMA 57 to identify lead SNPs and significant loci. The maximum p- value of lead SNPs was set to 5 × 10 -8 , r 2 ≥ 0.6 was set as the threshold for independent significant SNPs, and the maximum distance between LD blocks of independent significant SNPs was set to 250 kb. Furthermore, MAGMA (v1.10) 58 gene-level analysis was performed to identify putative significant genes using a SNPwise-multi model. We defined the SNP set of each gene including ±10 kb downstream or upstream of the gene, respectively. We used the 1000 Genomes European panel data to evaluate the LD between SNPs. We employed Holm’s correction method to adjust the p- values of the genes. We assessed the significance of the over-enrichment of MDD-associated genes (identified by Howard et al. 5 ) within the genes associated with each cluster by conducting one-sided hypergeometric tests to evaluate whether the association between the cluster genes and MDD genes was stronger than expected by chance. Additionally, we employed Gene Set Enrichment Analysis (GSEA) using the fgsea R package (v1.18) 59 to assess the significance of enrichment of these MDD-associated genes across the clusters. To account for multiple comparisons, we applied Holm’s correction method to adjust the p -values derived from these tests. In addition, we used the g:Profiler R package (v0.2.3, database version: e110_eg57_p18_4b54a898) 60 for functional enrichment analysis of each cluster’s sets of significant genes. We used Gene Ontology (excluding IEA evidence codes) and KEGG biological pathway data sources. We applied the g:SCS method for p- value adjustment, and the p < 0.01 threshold was used to indicate statistical significance. Using the variety of analysis tools included in the Complex-Traits Genetics Virtual Lab 61 (CTG-VL; https://genoma.io ), we additionally assessed the genetic heritability of the clusters, genetic correlations among clusters and genetic correlations with other phenotypes using the LD score regression method (LDSC v1.01) 62 . Genetic correlation is a quantitative statistical parameter reflecting the genetic relationship between two traits. This measure can reflect the pleiotropic action of genes or the correlation between causal loci in two traits, which is especially important for polygenic traits.

Polygenic risk scores

A polygenic risk score (PRS) is a genetic measurement that sums an individual’s risk-conferring alleles weighted by their estimated effect size for a specific phenotype or disease. The PRS employed in this study was calculated using PRS-CS (v1.0.0), a method that utilizes a high-dimensional Bayesian regression framework and places a continuous shrinkage (CS) prior on SNP effect sizes using GWAS summary statistics and an external linkage disequilibrium (LD) reference panel 63 . Here, the original effect sizes were taken from the UKB GWAS on cluster membership for all seven clusters. The LD reference panel was constructed using a European subsample of the UK Biobank 44 . For the remaining parameters, the default options implemented in PRS-CS were adopted. The PRSs for membership in Clusters 1–7 were calculated in the GWAS samples of the THL and SHIP cohorts. PRSs in the SHIP cohort were correlated with the cluster probabilities, whereas in the THL cohorts, due to the larger sample size, regression analyses between two factors could be performed adjusted for age, sex, batch, region and cohort (Supplementary Data 1 ).

Network-based analysis of pleiotropy

We assessed pleiotropy among the clusters and MDD at the level of functional modules by the following procedure. First, we defined an initial evidence score for the top genes in each cluster as their negative log-transformed adjusted p -value. Next, we applied the Personalized PageRank (also known as Random Walk with Restart, RWR) network propagation algorithm using the interactome network based on STRING ( https://string-db.org/ ; v11.5, filtered to high confidence edges with a combined score cut-off >0.7) to score all protein-coding genes initialized from the top genes of each cluster. Then, we selected the highest-scoring genes in the resulting score rankings by the Kneedle algorithm 64 and identified their nonoverlapping modules using spectral clustering. Next, we similarly initiated network propagation from seed genes significantly associated with MDD based on the gene-level MAGMA analysis results of Howard et al. 5 . Finally, we determined those cluster-specific modules where the propagated MDD scores’ sum was statistically significantly higher than according to a null model based on degree-aware permutations of the seed genes in the network. In all RWR experiments, we used a random restart probability of 0.5; however, in accordance with other studies 65 , the results were not sensitive to the parameter change.

Non-genetic risk-factor profiles of MDD-related clusters

Lifestyle and physiological risk factors play a fundamental role in the probability of lifetime MDD. Therefore, it was essential to determine whether one or more of the clusters had a defining risk-factor profile that allowed it to be identified. Apart from basic descriptors such as age, sex, income, and qualifications (Table 1 ), lifestyle-related factors such as present and past smoking habits and alcohol intake and physiological descriptors such as body mass index, systolic and diastolic blood pressure, pulse rate, C-reactive protein level, and presence of insomnia were investigated. Additionally, neuroticism (as a common personality trait with depression), life stress (as a representation of major negative life events), and current depression score were used as psychological factors to characterize clusters (for availability and descriptive statistics of these descriptors in the individual cohorts, see Supplementary Data 1A ).

Linear regression models were constructed for each cluster with Python 3.8 using the statsmodels package (v0.13.1). The corresponding posterior probability log-odds for a given cluster was used as the dependent variable. Two types of regression models were generated: (1) “simple regression models” including one risk factor at a time with age and sex as covariates and (2) “complex regression models” including all available risk factors simultaneously in a single model. Simple regression models allow us to explore the individual effect of each factor, whereas the complex model enables the analysis of joint effects and other multivariate aspects. In the case of complex regression models, a k-nearest neighbour-based imputation method 66 was applied to compute missing values, whereas in the case of simple regression models, only complete samples were used.

Clustering with a subset of diseases

A natural question that arises is how accurate the clustering will be if not all cross-cohort diseases are available. To assess cluster analysis performance in various limited disease subsets, we recalculated the cluster membership based on various disease subsets of a given size, increasing from only one disease to all cross-cohort diseases: (1) randomly selected diseases; (2) greedily selected, increasingly expanded sets of diseases; (3) a null model based on random cluster membership probabilities; and (4) another null model defined by uniform cluster membership probabilities. We calculated four performance measures, namely, the accuracy and balanced accuracy of the hard clustering (i.e., assigning each individual to the cluster in which its membership probability is highest) and the mean absolute error and mean squared error of the posterior probabilities of cluster membership, averaged over 10,000 random individuals from the UKB cohort selected a hundred times. The greedy variable selection method was performed as follows. First, we selected a single disease for which the accuracy of cluster analysis (compared to the original clustering of the samples using all cross-cohort diseases) was the highest. Next, we selected the disease from the remaining set of diseases that had the highest accuracy along with the first disease. We began to expand this set, always adding the disease for which the increase in accuracy was the highest. Note that this procedure may result in a suboptimal choice of the best-performing disease subset of a given size. The results for this clustering procedure are provided in Supplementary Fig. S22 .

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The following cohorts and biobank data were used for analysis which are available for further research upon application to the data owners: UK Biobank ( https://www.ukbiobank.ac.uk/ , application number:1602), Catalan Health Surveillance System (CHSS) registry data from all citizens living in the integrated health district of Barcelona-Esquerra (“AISBE”) ( https://doi.org/10.1186/s12913-019-4174-2 ), Finnish population surveys (THL, https://thl.fi/en/web/thlfi-en/research-and-development/research-and-projects/previous-research-and-projects ), FinnGen project ( https://www.finngen.fi/en ), and Study of Health in Pomerania (SHIP, https://doi.org/10.1093/ije/dyac034 ).

Code availability

The software tools and scripts used in this study are publically available at the following GitHub repository 67 : https://github.com/gezsi/mdd-clustering . While the original data used in our analyses cannot be shared publicly, users can apply these tools to their own datasets to perform MDD-related clustering or execute the full pipeline for clustering individuals with respect to any target disease. For those who have access to the original data, replication of our analyses is possible following the provided instructions in the repository.

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Acknowledgements

This research has been conducted using the UK Biobank Resource under Application Number 1602. Linked health data Copyright © 2019, NHS England. Re-used with the permission of the UK Biobank. All rights reserved. This study was supported by the Hungarian National Research, Development, and Innovation Office 2019-2.1.7-ERA-NET-2020-00005 under the frame of ERA PerMed (ERAPERMED2019-108); the Hungarian National Research, Development, and Innovation Office (K 143391, K 139330, PD 146014, and PD 134449 grants); the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022); and the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, under the TKP2021-EGA funding scheme (TKP2021-EGA-25 and TKP2021-EGA-02). N.E. is supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. Supported by the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory. We would like to thank Peter Petschner for his work on identifying a specific issue related to a processing step executed by Plink. SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grant nos. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs and the Social Ministry of the Federal State of Mecklenburg-West Pomerania, and the ‘Greifswald Approach to Individualized Medicine (GANI_MED)’ network funded by the Federal Ministry of Education and Research (grant no. 03IS2061A). Generation of ExomeChip data was supported by the Federal Ministry of Education and Research (grant no. 03Z1CN22). Data collection in SHIP-LEGEND was supported by the German Research Foundation. L.G. was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – grant no. 403694598. This study was supported by the Federal Ministry of Education and Research (BMBF, grant no. 01KU2004) under the frame of ERA PerMed (ERAPERMED2019-108). We thank the participants and investigators of the SHIP study. This study was funded by the Academy of Finland under the frame of ERA PerMed (TRAJECTOME project, ERAPERMED2019-108). We want to acknowledge the participants and investigators of the Finrisk, Health 2000/2011, FinHealth 2017 and FinnGen study. The authors wish to acknowledge CSC – IT Center for Science, Finland, for computational resources. The Catalan cohort was extracted from the Catalan Health Surveillance System database, owned and managed by the Catalan Health Service, with the earnest collaboration of the Digitalization for the Sustainability of the Healthcare (DS3) - IDIBELL group. This study was supported by the Catalan Department of Health (SLD002/19/000002) under the frame of ERA PerMed (ERAPERMED2019-108).

Open access funding provided by Semmelweis University.

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These authors contributed equally: Andras Gezsi, Sandra Van der Auwera.

These authors jointly supervised this work: Peter Antal, Gabriella Juhasz.

Authors and Affiliations

Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics, Budapest, Hungary

Andras Gezsi, Gabor Hullam, Tamas Nagy, Bence Bolgar & Peter Antal

Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany

Sandra Van der Auwera, Sarah Bonk, Linda Garvert & Kevin Kirchner

German Centre for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany

Sandra Van der Auwera

Department of Public Health and Welfare, Population Health Unit, Public Health Research Team, Finnish Institute for Health and Welfare, Helsinki, Finland

Hannu Mäkinen, Teemu Paajanen & Mikko Kuokkanen

Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary

Nora Eszlari, Gabor Hullam, Tamas Nagy, Xenia Gonda, Zsofia Gal & Gabriella Juhasz

NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary

Nora Eszlari, Tamas Nagy, Xenia Gonda, Zsofia Gal & Gabriella Juhasz

Clínic Barcelona, Fundació de Recerca Clinic Barcelona - Institut d’Investigacions Biomèdiques August Pi i Sunyer (FRCB-IDIBAPS), Universitat de Barcelona, Barcelona, Spain

Rubèn González-Colom, Josep Roca & Isaac Cano

Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary

Xenia Gonda

Abiomics Europe Ltd., Budapest, Hungary

Andras Millinghoffer

Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany

Carsten O. Schmidt

Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine at University of Texas Rio Grande Valley, Brownsville, TX, USA

Mikko Kuokkanen

Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland

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G.J. and P.A. developed the concept and designed experiments with advice from J.R., I.C., S.V.A., and M.K. G.J., M.K., J.R., I.C., and C.O.S., provided data. A.G., T.N., A.M., and B.B. developed analytical tools. A.G., G.H., N.E., T.N., Z.G., S.V.A., S.B., L.G., K.K., R.G.-C. H.M., T.P., and M.K. collated, cleaned and analysed data. All authors discussed the results and provided critical feedback. A.G., S.V.A., C.O.S., P.A., and G.J. interpreted the results. A.G., S.V.A., X.G., P.A., and G.J. wrote the main paper for which Z.G. gave support. A.G., S.V.A., N.E., and T.N. wrote the Supplementary Information. All authors read and approved the final manuscript.

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Gezsi, A., Van der Auwera, S., Mäkinen, H. et al. Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories. Nat Commun 15 , 7190 (2024). https://doi.org/10.1038/s41467-024-51467-7

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IMAGES

6 Types of Speech Impediments
Speech Impediment Guide: Definition, Causes & Resources
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Speech Impediment Guide: Definition, Causes & Resources
speech impediment causes lisps symptoms
Speech Impediment: What are Speech Impediments in Children

COMMENTS

Are Speech Disorders Inherited?
Evidence exists linking genetic factors to a variety of speech and language difficulties. Recent studies of molecular genetics and neuroimaging are cross-disciplinary, combining forces between speech-language pathologists, physicians, and scientists.Researchers have already identified over 400 genes linked to hearing loss, and ongoing studies investigate genetic links to stuttering, voice ...
Types of Speech Impediments
A speech impediment, also known as a speech disorder, is a condition that can affect a person's ability to form sounds and words, ... Genetic factors, as it can run in families; Hearing loss, as mishearing sounds can affect the person's ability to reproduce the sound;
Speech Impediment: Types in Children and Adults
Autism spectrum disorder: A neurodevelopmental disorder that affects social and interactive development. Cerebral palsy: A congenital (from birth) disorder that affects learning and control of physical movement. Hearing loss: Can affect the way children hear and imitate speech. Rett syndrome: A genetic neurodevelopmental condition that causes ...
Genetic Advances in the Study of Speech and Language Disorders
In this review, we summarize advances in the genetic investigation of stuttering, speech-sound disorder (SSD), specific language impairment (SLI), and developmental verbal dyspraxia (DVD). We discuss how the identification and study of specific genes and pathways, including FOXP2, CNTNAP2, ATP2C2, CMIP, and lysosomal enzymes, may advance our ...
Genetic architecture of childhood speech disorder: a review
In 2001, investigation of a large three generational family with severe speech disorder, known as childhood apraxia of speech (CAS), revealed the first causative gene; FOXP2. A long hiatus then ...
The Genetic and Molecular Basis of Developmental Language Disorder: A
At this time, further genetic sequencing studies of SLI, or rather DLD, remain elusive, although further progress has been made in the field of severe speech disorder and namely CAS, which typically co-occurs with language impairment. Eising et al. (2019) applied a de novo paradigm to 19 individuals with CAS.
PDF The Role of Genetics in Speech, Language, and Reading Disorders among
A growing body of evidence suggests an underlying genetic basis for speech sound disorders, the most common speech and language disorder in children. Despite this, researchers have not identi-fied a particular gene which predisposes children to this condition; however, several candidate genes are presently being studied. Dr.
Genetic pathways involved in human speech disorders
Introduction. Following decades of speculation over genetic contributions to distinctive human communication skills, advances in molecular methods enabled scientists to begin identifying critical genomic factors [1].Much research so far focused on linkage mapping and association screening of developmental speech and language impairments, revealing that while such disorders have a complex ...
Speech and Language Disorders
Disorders of speech and language are common in preschool age children. Disfluencies are disorders in which a person repeats a sound, word, or phrase. Stuttering may be the most serious disfluency. It may be caused by: Genetic abnormalities. Emotional stress. Any trauma to brain or infection.
The Genetics of Speech and Language Impairments
Genetic factors have an important role in many such cases. 1, 2 Children with specific language impairment are four times as likely to have a family history of the disorder as are children who do ...
FOXP2-related speech and language disorder
The inheritance pattern of FOXP2-related speech and language disorder depends on its genetic cause.Mutations within the FOXP2 gene and deletions of genetic material from chromosome 7 that include FOXP2 have an autosomal dominant pattern of inheritance, which means one copy of the altered gene or chromosome in each cell is sufficient to cause the disorder.
Genetic aetiologies for childhood speech disorder: novel ...
Childhood apraxia of speech (CAS), the prototypic severe childhood speech disorder, is characterized by motor programming and planning deficits. Genetic factors make substantive contributions to ...
Genetics of speech and language disorders
Vocal communication mediated by speech and language is a uniquely human trait, and has served an important evolutionary role in the development of our species. Deficits in speech and language functions can be of numerous types, including aphasia, stuttering, articulation disorders, verbal dyspraxia, and specific language impairment; language ...
Genetics and Speech Disorders
There are several specific types of speech and language disorders that appear to be closely tied with genetics. Scientists have begun identifying specific genes that are responsible for the ways we speak and communicate. Probably the most important discovery has been the genes FOXP 2, KIAA0319, CNTNAP 2, ATP 2 C 2, and CMIP.
Speech Impediment Guide: Definition, Causes, and Resources
Commonly referred to as a speech disorder, a speech impediment is a condition that impacts an individual's ability to speak fluently, correctly, or with clear resonance or tone. Individuals with speech disorders have problems creating understandable sounds or forming words, leading to communication difficulties.
Are Speech Issues Hereditary? What Kind? Passed Down? Family
Speech sound disorder is an umbrella term that refers to a single difficulty or a combination of difficulties related to perception, motor skills, or phonology. There is a growing body of evidence that suggests there is an underlying genetic basis for the development of speech sound disorders. Childhood Apraxia of Speech (CAS)
Speech Sound Disorders
Childhood apraxia of speech is not common but will cause speech problems. Some children have speech problems because the muscles needed to make speech sounds are weak. This is called dysarthria. Your child may have speech problems if he has. a developmental disorder, like autism; a genetic syndrome, like Down syndrome;
Speech disorder
Speech disorders, impairments, or impediments, are a type of communication disorder in which normal speech is disrupted. [1] This can mean fluency disorders like stuttering, cluttering or lisps.Someone who is unable to speak due to a speech disorder is considered mute. [2] Speech skills are vital to social relationships and learning, and delays or disorders that relate to developing these ...
Childhood Speech and Language Disorders in the General U.S. Population
Speech and language disorders in children include a variety of conditions that disrupt children's ability to communicate. Severe speech and language disorders are particularly serious, preventing or impeding children's participation in family and community, school achievement, and eventual employment. This chapter begins by providing an overview of speech and language development and disorders ...
Speech & Language Disorders in Children
A child with a speech disorder may have difficulty with speech sound production, voice, resonance or fluency (the flow of speech). Speech Sound Disorders. A child with a speech sound disorder is unable to say all of the speech sounds in words. This can make the child's speech hard to understand. People may not understand the child in everyday ...
Stuttering
Problems with speech motor control. Some evidence shows that problems in speech motor control, such as timing, sensory and motor coordination, may be involved. Genetics. Stuttering tends to run in families. It appears that stuttering can happen from changes in genes passed down from parents to children. Stuttering that happens from other causes
Behavioural and neurodevelopmental characteristics of SYNGAP1
The SYNGAP1 gene is one of the more common genetic causes of intellectual disability (ID), with an estimated prevalence of 0.5-1% of children with ID [].SYNGAP1 encodes a Ras-specific GTPase-activating protein, SynGAP, which is localised to the post-synaptic density of cortical neurons and influences important cellular signalling pathways in growth and survival [2, 3].
Unique genetic and risk-factor profiles in clusters of major ...
Major depressive disorder is a heterogeneous condition with varied presentation of symptoms, comorbidities, and related genetic factors. This study aimed to identify clusters of major depressive ...
'Keyboard Warriors' Who Stoked UK Riots Test the Limits of Free Speech
A self-described "free speech absolutist," Mr. Musk has ample commercial and legal motives to pick a fight with the British government. But his critique has captured genuine differences in how ...

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Types of Speech Impediments

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Symptoms and Characteristics of Disfluencies

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Symptoms and Characteristics of Articulation Errors

Causes of Articulation Errors

Symptoms and Characteristics of Ankyloglossia

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