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Coronary Heart Disease Research

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For almost 75 years, the NHLBI has been at the forefront of improving the nation’s health and reducing the burden of  heart and vascular diseases . Heart disease, including coronary heart disease, remains the leading cause of death in the United States. However, the rate of heart disease deaths has declined by 70% over the past 50 years, thanks in part to NHLBI-funded research. Many current studies funded by the NHLBI focus on discovering genetic associations and finding new ways to prevent and treat the onset of coronary heart disease and associated medical conditions.

NHLBI research that really made a difference

The NHLBI supports a wide range of long-term studies to understand the risk factors of coronary heart disease. These ongoing studies, among others, have led to many discoveries that have increased our understanding of the causes of cardiovascular disease among different populations, helping to shape evidence-based clinical practice guidelines.

• Risk factors that can be changed:  The NHLBI  Framingham Heart Study (FHS)  revealed that cardiovascular disease is caused by modifiable risk factors such as smoking,  high blood pressure ,  obesity , high  cholesterol  levels, and physical inactivity. It is why, in routine physicals, healthcare providers check for high blood pressure, high cholesterol, unhealthy eating patterns, smoking, physical inactivity, and unhealthy weight. The FHS found that cigarette smoking increases the risk of heart disease. Researchers also showed that cardiovascular disease can affect people differently depending on sex or race, underscoring the need to address health disparities. 
• Risk factors for Hispanic/Latino adults:  The  Hispanic Community Health Study/Study of Latinos (HCHS/SOL)  found that heart disease risk factors are widespread among Hispanic/Latino adults in the United States , with 80% of men and 71% of women having at least one risk factor. Researchers also used HCHS/SOL genetic data to explore genes linked with central adiposity (the tendency to have excess body fat around the waist) in Hispanic/Latino adults. Before this study, genes linked with central adiposity, a risk factor for coronary heart disease, had been identified in people of European ancestry. These results showed that those genes also predict central adiposity for Hispanic/Latino communities. Some of the genes identified were more common among people with Mexican or Central/South American ancestry, while others were more common among people of Caribbean ancestry.
• Risk factors for African Americans:  The  Jackson Heart Study (JHS) began in 1997 and includes more than 5,300 African American men and women in Jackson, Mississippi. It has studied genetic and environmental factors that raise the risk of heart problems, especially high blood pressure, coronary heart disease,  heart failure ,  stroke , and  peripheral artery disease (PAD) . Researchers discovered a gene variant in African American individuals that doubles the risk of heart disease. They also found that even small spikes in blood pressure can lead to a higher risk of death. A community engagement component of the JHS is putting 20 years of the study’s findings into action by turning traditional gathering places, such as barbershops and churches, into health information hubs.
• Risk factors for American Indians:  The NHLBI actively supports the  Strong Heart Study , a long-term study that began in 1988 to examine cardiovascular disease and its risk factors among American Indian men and women. The Strong Heart Study is one of the largest epidemiological studies of American Indian people ever undertaken. It involves a partnership with 12 Tribal Nations and has followed more than 8,000 participants, many of whom live in low-income rural areas of Arizona, Oklahoma, and the Dakotas. Cardiovascular disease remains the leading cause of death for American Indian people. Yet the prevalence and severity of cardiovascular disease among American Indian people has been challenging to study because of the small sizes of the communities, as well as the relatively young age, cultural diversity, and wide geographic distribution of the population. In 2019, the NHLBI renewed its commitment to the Strong Heart Study with a new study phase that includes more funding for community-driven pilot projects and a continued emphasis on training and development. Read more about the  goals and key findings  of the Strong Heart Study.

Current research funded by the NHLBI

Within our  Division of Cardiovascular Sciences , the Atherothrombosis and Coronary Artery Disease Branch of its  Adult and Pediatric Cardiac Research Program and the  Center for Translation Research and Implementation Science  oversee much of our funded research on coronary heart disease.

Research funding  

Find  funding opportunities  and  program contacts for research on coronary heart disease. 

Current research on preventing coronary heart disease

• Blood cholesterol and coronary heart disease: The NHLBI supports new research into lowering the risk of coronary heart disease by reducing levels of cholesterol in the blood. High levels of blood cholesterol, especially a type called low-density lipoprotein (LDL) cholesterol, raise the risk of coronary heart disease. However, even with medicine that lowers LDL cholesterol, there is still a risk of coronary heart disease due to other proteins, called triglyceride-rich ApoB-containing lipoproteins (ApoBCLs), that circulate in the blood. Researchers are working to find innovative ways to reduce the levels of ApoBCLs, which may help prevent coronary heart disease and other cardiovascular conditions.
• Pregnancy, preeclampsia, and coronary heart disease risk: NHLBI-supported researchers are investigating the link between developing preeclampsia during pregnancy and an increased risk for heart disease over the lifespan . This project uses “omics” data – such as genomics, proteomics, and other research areas – from three different cohorts of women to define and assess preeclampsia biomarkers associated with cardiovascular health outcomes. Researchers have determined that high blood pressure during pregnancy and low birth weight are predictors of atherosclerotic cardiovascular disease in women . Ultimately, these findings can inform new preventive strategies to lower the risk of coronary heart disease.
• Community-level efforts to lower heart disease risk among African American people: The NHLBI is funding initiatives to partner with churches in order to engage with African American communities and lower disparities in heart health . Studies have found that church-led interventions reduce risk factors for coronary heart disease and other cardiovascular conditions. NHLBI-supported researchers assessed data from more than 17,000 participants across multiple studies and determined that these community-based approaches are effective in lowering heart disease risk factors .

Find more NHLBI-funded studies on  preventing coronary heart disease  on the NIH RePORTER.

Learn about the impact of COVID-19 on your risk of coronary heart disease.

Current research on understanding the causes of coronary heart disease

• Pregnancy and long-term heart disease:  NHLBI researchers are continuing the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b)   study to understand the relationship between pregnancy-related problems, such as gestational hypertension, and heart problems. The study also looks at how problems during pregnancy may increase risk factors for heart disease later in life. NuMoM2b launched in 2010, and long-term studies are ongoing, with the goal of collecting high-quality data and understanding how heart disease develops in women after pregnancy.
• How coronary artery disease affects heart attack risk: NHLBI-funded researchers are investigating why some people with coronary artery disease are more at risk for heart attacks than others. Researchers have found that people with coronary artery disease who have high-risk coronary plaques are more likely to have serious cardiac events, including heart attacks. However, we do not know why some people develop high-risk coronary plaques and others do not. Researchers hope that this study will help providers better identify which people are most at risk of heart attacks before they occur.
• Genetics of coronary heart disease:  The NHLBI supports studies to identify genetic variants associated with coronary heart disease . Researchers are investigating how genes affect important molecular cascades involved in the development of coronary heart disease . This deeper understanding of the underlying causes for plaque buildup and damage to the blood vessels can inform prevention strategies and help healthcare providers develop personalized treatment for people with coronary heart disease caused by specific genetic mutations.

Find more NHLBI-funded studies on understanding the  causes of coronary heart disease  on the NIH RePORTER.

Recent findings suggest that cholesterol-lowering treatment can lower the risk of heart disease complications in people with HIV.

Current research on treatments for coronary heart disease

• Insight into new molecular targets for treatment: NHLBI-supported researchers are investigating the role of high-density lipoprotein (HDL) cholesterol in coronary heart disease and other medical conditions . Understanding how the molecular pathways of cholesterol affect the disease mechanism for atherosclerosis and plaque buildup in the blood vessels of the heart can lead to new therapeutic approaches for the treatment of coronary heart disease. Researchers have found evidence that treatments that boost HDL function can lower systemic inflammation and slow down plaque buildup . This mechanism could be targeted to develop a new treatment approach for coronary heart disease.
• Long-term studies of treatment effectiveness: The NHLBI is supporting the International Study of Comparative Health Effectiveness with Medical and Invasive Approaches (ISCHEMIA) trial EXTENDed Follow-up (EXTEND) , which compares the long-term outcomes of an initial invasive versus conservative strategy for more than 5,000 surviving participants of the original ISCHEMIA trial. Researchers have found no difference in mortality outcomes between invasive and conservative management strategies for patients with chronic coronary heart disease after more than 3 years. They will continue to follow up with participants for up to 10 years. Researchers are also assessing the impact of nonfatal events on long-term heart disease and mortality. A more accurate heart disease risk score will be constructed to help healthcare providers deliver more precise care for their patients.
• Evaluating a new therapy for protecting new mothers: The NHLBI is supporting the Randomized Evaluation of Bromocriptine In Myocardial Recovery Therapy for Peripartum Cardiomyopathy (REBIRTH) , for determining the role of bromocriptine as a treatment for peripartum cardiomyopathy (PPCM). Previous research suggests that prolactin, a hormone that stimulates the production of milk for breastfeeding, may contribute to the development of cardiomyopathy late in pregnancy or the first several months postpartum. Bromocriptine, once commonly used in the United States to stop milk production, has shown promising results in studies conducted in South Africa and Germany. Researchers will enroll approximately 200 women across North America who have been diagnosed with PPCM and assess their heart function after 6 months. 
• Impact of mental health on response to treatment:  NHLBI-supported researchers are investigating how mental health conditions can affect treatment effectiveness for people with coronary heart disease. Studies show that depression is linked to a higher risk for negative outcomes from coronary heart disease. Researchers found that having depression is associated with poor adherence to medical treatment for coronary heart disease . This means that people with depression are less likely to follow through with their heart disease treatment plans, possibly contributing to their chances of experiencing worse outcomes. Researchers are also studying new ways to treat depression in patients with coronary heart disease .

Find more NHLBI-funded studies on  treating coronary heart disease  on the NIH RePORTER.  

Researchers have found no clear difference in patient survival or heart attack risk between managing heart disease through medication and lifestyle changes compared with invasive procedures. 

Coronary heart disease research labs at the NHLBI

• Laboratory of Cardiac Physiology
• Laboratory of Cardiovascular Biology
• Minority Health and Health Disparities Population Laboratory
• Social Determinants of Obesity and Cardiovascular Risk Laboratory
• Laboratory for Cardiovascular Epidemiology and Genomics
• Laboratory for Hemostasis and Platelet Biology

Related coronary heart disease programs

• In 2002, the NHLBI launched  The Heart Truth® ,  the first federally sponsored national health education program designed to raise awareness about heart disease as the leading cause of death in women. The NHLBI and  The Heart Truth®  supported the creation of the Red Dress® as the national symbol for awareness about women and heart disease, and also coordinate  National Wear Red Day ® and  American Heart Month  each February. 
• The  Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC)  facilitates access to and maximizes the scientific value of NHLBI biospecimen and data collections. A main goal is to promote the use of these scientific resources by the broader research community. BioLINCC serves to coordinate searches across data and biospecimen collections and provide an electronic means for requesting additional information and submitting requests for collections. Researchers wanting to submit biospecimen collections to the NHLBI Biorepository to share with qualified investigators may also use the website to initiate the application process. 
• Our  Trans-Omics for Precision Medicine (TOPMed) Program  studies the ways genetic information, along with information about health status, lifestyle, and the environment, can be used to predict the best ways to prevent and treat heart, lung, blood, and sleep disorders. TOPMed specifically supports NHLBI’s  Precision Medicine Activities. 
• NHLBI  population and epidemiology studies  in different groups of people, including the  Atherosclerosis Risk in Communities (ARIC) Study , the  Multi-Ethnic Study of Atherosclerosis (MESA) , and the  Cardiovascular Health Study (CHS) , have made major contributions to understanding the causes and prevention of heart and vascular diseases, including coronary heart disease.
• The  Cardiothoracic Surgical Trials Network (CTSN)  is an international clinical research enterprise that studies  heart valve disease ,  arrhythmias , heart failure, coronary heart disease, and surgical complications. The trials span all phases of development, from early translation to completion, and have more than 14,000 participants. The trials include six completed randomized clinical trials, three large observational studies, and many other smaller studies.

Learn how heart disease may be different for women than for men.

Explore more NHLBI research on coronary heart disease

The sections above provide you with the highlights of NHLBI-supported research on coronary heart disease. You can explore the full list of NHLBI-funded studies on the NIH RePORTER .

To find more studies:

• Type your search words into the  Quick Search  box and press enter. 
• Check  Active Projects  if you want current research.
• Select the  Agencies  arrow, then the  NIH  arrow, then check  NHLBI .

If you want to sort the projects by budget size — from the biggest to the smallest — click on the  FY Total Cost by IC  column heading.

• Research article
• Open access
• Published: 21 June 2021

A novel approach for heart disease prediction using strength scores with significant predictors

• Armin Yazdani 1 ,
• Kasturi Dewi Varathan   ORCID: orcid.org/0000-0003-3421-4501 2 ,
• Yin Kia Chiam 1 ,
• Asad Waqar Malik 3 &
• Wan Azman Wan Ahmad 4  

BMC Medical Informatics and Decision Making volume  21 , Article number:  194 ( 2021 ) Cite this article

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Cardiovascular disease is the leading cause of death in many countries. Physicians often diagnose cardiovascular disease based on current clinical tests and previous experience of diagnosing patients with similar symptoms. Patients who suffer from heart disease require quick diagnosis, early treatment and constant observations. To address their needs, many data mining approaches have been used in the past in diagnosing and predicting heart diseases. Previous research was also focused on identifying the significant contributing features to heart disease prediction, however, less importance was given to identifying the strength of these features.

This paper is motivated by the gap in the literature, thus proposes an algorithm that measures the strength of the significant features that contribute to heart disease prediction. The study is aimed at predicting heart disease based on the scores of significant features using Weighted Associative Rule Mining.

A set of important feature scores and rules were identified in diagnosing heart disease and cardiologists were consulted to confirm the validity of these rules. The experiments performed on the UCI open dataset, widely used for heart disease research yielded the highest confidence score of 98% in predicting heart disease.

This study managed to provide a significant contribution in computing the strength scores with significant predictors in heart disease prediction. From the evaluation results, we obtained important rules and achieved highest confidence score by utilizing the computed strength scores of significant predictors on Weighted Associative Rule Mining in predicting heart disease.

Peer Review reports

Introduction

Cardiovascular disease (CVD) is one of the most life-threatening diseases in the world. The World Health Organization (WHO) as well as the Global Burden of Disease (GBD) study reported cardiovascular disease as the main cause of death around the globe annually [ 40 , 56 ].  WHO revealed that CVD is expected to affect almost 23.6 million people by the year 2030. In some industrialized countries such as the United States of America, the rate is about 1 in 4 deaths [ 34 ]. The Middle East and North Africa (MENA) region has an even higher percentage, which is 39.2% of the mortality rate [ 20 ]. Hence, early and accurate diagnosis and the provision of appropriate treatments are keys to reducing the amount of death causing cardiovascular diseases. Availability of such services is essential for those who have a high risk of developing heart disease [ 29 ].

There are many features that contribute to heart disease prediction. Researchers in the past were more focused on identifying significant features to be used in their heart disease prediction models [ 8 ]. Less importance was given to determining the relationships between these features and to identifying their level of priority [ 32 , 32 ] within the prediction model. To address the issues which hinder early and accurate diagnosis, many data mining related studies were previously conducted [ 9 , 16 , 28 ].

Weighted Association Rule Mining (WARM) is one of the data mining techniques used to discover the relationships between features and to determine mining rules that lead to certain predictions [ 22 ]. The weight that is used in this mining technique provides users with a convenient way to indicate the importance of the features that contributes to heart disease and helps obtain more accurate rules [ 4 ]. In many prediction models, different features have different importance. Hence, different weights are assigned to different features based on their predicting capabilities [ 48 ]. The failure in determining the weight indicates the failure in determining the importance of the features.

Past research had used Weighted Associative Rule Mining (WARM) in heart disease prediction [ 18 , 31 , 46 , 48 , 50 ]. However, the prediction model reported in these studies still demands further exploration in terms of the number of features used, the strength of these features and the evaluation of scores obtained. In this research, we proposed an algorithm to compute the weight of each feature that contributes to heart disease prediction. We have experimented on all features as well as selected significant features using WARM. The results obtained showed that the significant features outperformed all features with the highest confidence score of 98% in predicting heart disease. To the best of our knowledge, this study is the first that used strength scores of significant predictors in WARM.

The rest of the paper is organized as follows: Sect.  2 presents the background of the study followed by Sect.  3 on research objectives. Section  4 presents the methodology and Sect.  5 displays the results obtained by this research. Section 6 includes the discussions and Sect. 7 benchmarks this research against previous studies. Finally, Sect. 8 concludes the research with a summary of the findings and future work.

Related works

CVDs are disorders of the heart and blood vessels and include coronary heart disease, cerebrovascular disease and other conditions. Heart attacks and strokes are the main causes of mortality in cardiovascular disease in which the rate nears one out of three [ 6 ]. With the high rate of mortality, diagnosis and prevention measures need to be performed effectively and efficiently. Many data mining techniques have been used to help address these issues (Amin et al. [ 8 ]). Most of the past research looked into identifying features that contribute to better heart prediction accuracy [ 9 ]. However, very little researches looked into the relationships that exist between these features. The relationship between each feature that contributes to heart disease prediction can be obtained by using the Associative Rule Mining (ARM) technique [ 11 ]. The ARM technique is popular in transactional and relational datasets. The hidden knowledge in large datasets such as business transactions developed the interest of many business owners to understand the patterns that can help them to improve their business decisions (Agarwal and Mithal [ 1 ]). For instance, discovering the frequently bought items by customers in market basket analysis. This analysis looks at the various items found in customers’ shopping cart and identifies the associations between them. A good example would be if customers were looking to purchase milk, they were likely to purchase bread on the same trip to the supermarket. This approach is also widely used in the healthcare industry specifically in privacy preservation of healthcare data [ 15 ], predicting cancer associated protein interactions [ 12 ], predicting obstructive sleep apnea [ 43 ] and predicting co-diseases in Thyroid patients [ 23 ].

ARM is also used in heart disease prediction. Table 1 shows the studies that used ARM in heart disease prediction. Akbaş et al. [ 3 ], Shuriyaa and Rajendranb [ 42 ], Srinivas et al. [ 49 ], Khare and Gupta [ 24 ] and Lakshmi and Reddy [ 27 ] have used ARM on UCI dataset. Some of the studies listed in Table 1 used private datasets from hospitals and heart centres. Although the scores that were obtained from these datasets are high (99% by Sonet et al. [ 45 ]), 100% by Thanigaivel and Kumar [ 52 ], the studies have a limitation in terms of reproduction, as the datasets are not open for access. Akbaş et al. [ 3 ] on the other hand obtained a score of 97.8% in confidence using the UCI dataset. However, the confidence score obtained predicted people with no risk of heart disease.

Weighted Associative Rule Mining (WARM) is an extension of ARM, in which weights are assigned to differentiate the importance of the features mined. Let T be the training dataset in which contains T = {r 1 , r 2 , r 3 … r i } with a set of weight associated with each {attribute, attribute value} pair. Every ith record r i is a set of value and weight w i attached to each feature of r i tuple / record. In a weighted framework, each record is a set of triple {a i , v i , w i } where feature a i has a value of v i and weight of w i where 0 < w j  <  = 1.

Assigning a correct weight to each feature is a hard task. In various fields of studies, there are different ways of calculating the weights of features. For instance, according to Malarvizhi and Sathiyabhama [ 30 ] in web mining, visitor page dwelling time is a way of calculating weightage. WARM is widely used in research on shopping basket scenarios and in predicting customers’ behaviour. Chengis et al. [ 10 ] investigated on assigning weight before and after ARM. WARM was also used in predicting disease comorbidities using clinical as well as molecular data (Lakshmi and Vadivu 26 ). This technique is also used in predicting breast cancer [ 5 ]. Recent research by Park and Lim [ 39 ] used this technique to reduce design failures of pre-alarming systems in the shipbuilding industry.

However, not many researchers focused on applying WARM to cardiovascular disease. Table 2 shows studies on heart disease prediction using WARM. However, the weight of features was not precisely calculated (Jabbar et al. [ 21 ], Sundar et al. [ 50 ], Soni and Vyas [ 48 ]). Soni et al. [ 47 ] proposed a new framework, which was an associative classifier that used WARM. Different weights were assigned to different attributes based on their predicting capability. Their theoretical model yielded a confidence score of 79.5%. Soni and Vyas [ 48 ] also applied WARM and the confidence level they achieved was was 79.5%. Their research assigned weights based on age range, smoking habits, hypertension and BMI range. On the other hand, Soni et al. [ 46 ] assigned weights to each of the attributes based on the advice obtained from the medical experts. They presented an intelligent and effective heart attack prediction system using a weighted associative classifier by achieving a maximum score of 80% confidence. Meanwhile, Sundar et al. [ 50 ] developed a system using two data mining techniques, which are Naïve Bayes and WARM. Their experiments showed that WARM achieved a score of 84% on confidence score, outperforming Naïve Bayes, which obtained only 78%. Chauhan et al. [ 11 ] also used WARM in predicting heart disease. They obtained an accuracy score of 60.4%. Kharya et al. [ 25 ] used Weighted Bayesian Association Rule Mining Algorithm, which combines WARM with heart disease dataset. However, they failed to indicate the results obtained in their study. Ibrahim and Sivabalakrishnan [ 19 ] have used Random Walker Memetic algorithm-based WARM for predicting coronary disease. They obtained an accuracy of 95% using the UCI heart disease dataset.

Despite having research that is based on WARM in predicting heart disease, none of them was focused on identifying the important features to be used in heart disease prediction which would contribute to better prediction performance. The weight of each feature plays an equally important role in deciding which feature has the highest impact (strength) in predicting heart disease. The right weight of the significant features identified will yield an effective prediction model. Thus, this research is focused on identifying the weight of significant features and utilizing the generated score in predicting heart disease.

Research objectives

The main objectives of this research are as follows:

To compute the weight of significant features in heart disease prediction.

To predict heart disease using the computed weight of significant features (using WARM).To evaluate the performance of WARM in predicting heart disease.

Proposed methodology

This section describe in detail the methodology used as shown in Fig. 1 . It contains 5 main stages which are data pre-processing, feature selection, feature weight computation, apply WARM and model evaluation.

Methodology

This research uses the heart disease dataset that is obtained from UCI Machine Learning Repository [ 13 ]. UCI Machine Learning Repository is one of the largest available datasets, having over 417 various datasets. The Cleveland dataset from UCI Machine Learning Repository is one of the datasets on heart disease, which is widely used by researchers to date (Amin et al. [ 8 ]). This research will also use this dataset of which contains 303 rows. The dataset contains 76 features in which 14 attributes including class label are used. The 14 features together with their descriptions and data types are shown in Table 3 .

Experimental Setup

In this research, Weka 3.8 was used to conduct the experiments. The retrieved Cleveland dataset went through a pre-processing phase. The significant features were retrieved from a total of 14 factors from the Cleveland dataset (Amin [ 7 ]). Further, the weight of each significant feature was computed and assigned back to them accordingly. WARM was applied to the heart disease dataset to generate rules. Finally, evaluation was performed to obtain the confidence score of the best rules generated using WARM based on significant features. The detailed explanation of each process is explained in the following sections.

Data Pre-Processing

In the data pre-processing phase, all missing records were deleted from the dataset, which consists of 6 instances. Based on Table 3 , there are 13 normal attributes(age’, ‘sex’, ‘cp’, ‘trestbps’, ‘chol’, ‘fbs’, ‘restecg’, ‘thalach’, ‘exang’, ‘oldpeack’, ‘slope’, ‘ca’, ‘thal’) and 1 class label(‘goal’), which refers to the criticality level of heart disease in patients. It ranged from 0–4, in which 0 refers to’No Heart Disease’ and the other values indicates the presence of heart disease at different criticality levels. Since this research aims at predicting the presence of heart disease and not its criticality levels, the range from 1 to 4 is thus normalized to 1, which indicates the presence of heart disease, and 0 to represent the absence of heart disease. Data normalization is also performed as a part of the data transformation process that involved mounting data into nominal data. This is required, as WARM utilizes nominal data only. All the ranges formed for each features are indicated in Table 4 .

Feature Selection

Features were selected based on experiments conducted by Amin et al. [ 8 ] since they had used the same dataset (UCI). They performed a set of experiments that dealt with 8100 combinations of features with 7 different classification models (K-NN, Decision Tree, Naïve Bayes, Logistic Regression, Neural Network and Vote) to identify significant features. Table 5 shows the features obtained from the highest performance of each classification models. The highlighted columns indicate the features which appeared more than 10 times and thus were selected as significant features. The selected 8 features are sex, CP, Fbs, Exang, Oldpeak, Slope, CA, and Thal.

Feature weight computation

This section explains how the weight of the features was calculated. The fundamental of WARM states that different features in a dataset have different importance in predicting heart disease. The weight of each feature ranges from 0 to 1. Thus, a weight that is closer to 1 indicates a more significant feature. On the other hand, a weight that is closer to 0 is the least significant in heart disease prediction.

Calculate feature weight

The first step was to calculate the individual feature weights. Let R be the set of features R = {n 0 , n 1 , n 2 … n i } and (n > 0). In this experiment, the total number of features is 13 and after feature selection, it is reduced to 8 (Sex, CP, Fbs, Exang, Oldpeak, Slope, CA, and Thal). W (n) is the weight of each feature (W is the weight of each feature to be calculated and n represents a feature),

For example, the value of sex as displayed in Table 5 is’20’ and the sum of all the features will be’121’. The total value of significant features (Sex, CP, Fbs, Exang, Oldpeak, Slope, CA, andThal) is calculated as (20 + 18 + 12 + 12 + 14 + 12 + 19 + 14 = 121). Thus, to calculate the weight of ‘sex’ (weight of features, WOF):

Table 6 displays the calculated weights for each of the significant features. All weights were computed accordingly. From the distribution of the weights, CA has the greatest strength followed by Sex, CP, Oldpeak and Thal, Fbs, Exang and Slope has the similar weight of 0.09 each.

Calculate feature value weight

This section explains how feature values are computed. Feature values represent all the values that a feature contains. For instance, feature values for sex are male and female. Let A be the number of each feature value contained in the dataset and (A  ∪  B) be the total number of records.

Table 7 shows the total sub value of each feature based on the UCI dataset. Male value is represented by 203 records and female by 94 records which gives a total of 297 records from the UCI dataset. To calculate the value of each feature weight, let A be the selected value and B be the rest of the features value,

Figure  2 shows the comparison of the percentage of males and females in the Cleveland heart disease dataset.

Comparison on the percentage of male and female in Cleveland heart disease dataset

Calculate total weight for feature

This section explains how the total weight for features is computed. The feature weight (W (n)) and feature value weight (W (value)) gives the total weight (W (t)) for the feature. The computation is shown below.

Example of calculating the total weight of feature W (t):

This section detailed out the algorithm to obtain the weighted score of each feature in predicting heart disease. The algorithm is stated as follows:

Not all features in the heart disease dataset have the same level of significance in predicting the risk of heart disease. Thus, different weights based on their prediction capability are assigned. These values are then imported into Weka 3.8 to experiment with WARM using Apriori Algorithm.

Apriori algorithm

The Apriori algorithm is a well-known approach in WARM. Apriori was first proposed by Agrawal and Srikant [ 2 ]. The algorithm starts with a dataset including transactions that wants to construct frequent item sets, having at least a user-specified threshold. In the algorithmic process of Apriori, an item set X of length k is frequent if and only if every subset of X, having length k—1, is also frequent. This consideration results in a substantial reduction of search space and allows rule discovery in a computationally feasible time. Apriori generates a rule of the form: s =  > (f – s) if and only if the confidence of the rule is above the user-defined threshold. Confidence is essentially the accuracy of the rule and is used in Apriori to rank the rules (Agrawal & Srikant [ 2 ]; Mutter et al. [ 51 ]).

Weighted confidence

The confidence level is used in order to show how often the rule appears to be true. Let Y be the ‘goal’, then the weighted confidence of a rule X → Y can be calculated as the ratio of weighted support of \(\left( {X \cup Y} \right)\) over the weighted support of (X).

For instance, the rule {sex = Male, CA = 3} → {heart disease} has a confidence of 0.2/0.2 = 1.0. It means a patient who is a male and having 3 CA (major vessels coloured by fluoroscopy) has a 100% chance of having heart disease.

This phase generates rules based on the Apriori algorithm in Weighted Associative Rule Mining. Two sets of rules and confidence scores were generated for the followings:

All features—this includes all the 13 features.

Selected significant features (8 features).

The following section provides a detailed explanations of the results obtained which are the rules and confidence scores.

Results (rules and confidence level generated)

The rules and confidence level generated for all the (13) features and the selected significant features (8) are shown in this section.

All features

Table 8 shows the top 20 rules and confidence scores obtained for all the features using WARM. The rules were sorted by the highest confidence scores.

The highest confidence level achieved for predicting the risk of having heart disease is 96% and the number of features used to generate this rule is 3(CP, Slope and Thal). This can be clearly seen in Table 8 (Rule Number 7). The rule states that if the value of Chest Pain (CP) is asymptomatic, the slope is flat and the value of Thallium (Thal) is reversible, therefore, the patient has a very high tendency (confidence level = 96%) of having the risk of heart disease. All the highlighted rows in Table 8 show the rules that contributed to the prediction of the risk of having heart disease. Further, the Table 9 is the summary that shows the frequency of each features used in the rules, which were generated from Table 8 (which contains the rules that predicts heart disease). It shows the rule number and the features used in each of the top 20 rules. From the top 20 rules, only 6 rules predicts heart disease and others are non-sick rules which predicts no heart disease.

Although all 13 features have been used for rules and confidence score generation as shown in Table 8 , only 9 features have been used for heart disease prediction based on the top 20 rules. The most significant feature in predicting heart disease is CP. This feature exist in all the 6 rules generated that predicts heart disease. Thal and Oldpeak exist in 4 rules out of the 6 rules in predicting heart disease.

Selected significant features

This section emphasizes on the rules and confidence scores obtained by the selected significant features. Table 10 shows the top 20 rules generated from the significant features using WARM. The confidence score obtained in predicting the risk of having heart disease using 8 selected significant features shows a comparatively high confidence level at 98%. The rule obtained for the top confidence score states as.

CP = asymptomatic, Exang = Yes, Oldpeak = greaterThanZero, Thal = reversible =  =  > class_HD = Heart Disease.

which means if Chest Pain (CP) is asymptomatic, exercise-induce angina (Exang) is present, Oldpeak (ST depression induced by exercise relative to rest) is present and, Thallium heart scan (Thal) is reversible then the patient is diagnosed as having heart disease. From the top 20 rules generated, 11 rules are meant for predicting heart disease as highlighted in Table 10 . Table 11 shows the summary of the frequency of existence of each features contained in the rules that predicts heart disease. There are a total of 11 rules out of 20 rules generated using significant features to predict the presence of heart disease. The most significant feature that exists in all the positive rules that predicts the Heart Disease is Chest pain (CP). Thallium heart scan (Thal) is seen in 9 out of 11 rules and Oldpeak (ST depression induced by exercise relative to rest) is seen in 7 rules.

Discussions

The implementation of WARM on selected significant features managed to achieve the highest confidence score in predicting heart disease which is 98% compared to 96% obtained from all features. It can be concluded that WARM predicts the risk of having heart disease well. From the top 20 rules generated, only 6 rules were based all features. On the other hand, 11 rules from the top 20 generated were based on the selected 8 features.

Studying the top 20 rules generated revealed some significant information. These findings were validated by a cardiologist:-

Asymptomatic chest pain, positive exercise-induced angina, Oldpeak > 0 and reversible thallium heart scan implies the presence of heart disease.

CP = asymptomatic, Exang = Yes, Oldpeak = greaterThanZero, Thal = reversible =  =  > class_HD = Heart Disease

Asymptomatic chest pain is one of the most important features as it appears in all the rules generated in detecting heart disease.

Reversible thallium heart scan and Oldpeak greater than zero are positively correlated with heart disease.

Males are more prone to have heart disease compared to females as all the sick rules stated sex as male and the healthy rules stated sex as female.

There is a strong negative correlation between CA and Thal for heart disease prediction.

The most common features that exist in healthy rules are Sex = Female, Exang (Exercise induce angina) = No and CA (Number of major vessels coloured by fluoroscopy) = Zero. A patient will be predicted as not having heart disease if the patient is female, angina is not induced by exercise and has no major vessels coloured by fluoroscopy.

Slope is not featured in any of the healthy rules.

This study managed to determine the processes involved in obtaining significant features and to devise a scoring mechanism to obtain the strength of each feature. This will enable for the correct weight to be imposed on each of the significant features to be used in WARM for predicting heart disease. The confidence score obtained in this study is the highest obtained in heart disease prediction using WARM based on the UCI dataset. This study can be used as a guide for computing thestrength scores of significant features found in other heart disease datasets.

Comparative analysis with existing work

This section performs comparison between the proposed work and existing works using WARM. The results obtained in this research proved that the weighted scores imposed on WARM for 8 significant features have the highest confidence score of 98% compared with other existing studies. Figure  3 shows the confidence score of all the existing studies on WARM that used the UCI Cleveland heart disease dataset in comparison with the proposed work. The confidence score obtained by both the experiments which includes all features and significant features in predicting heart disease using WARM achieved a significant difference in terms of the confidence score achieved compared to previous studies. The use of the significant features score in WARM provides the highest confidence of 98% predicting heart disease.

Result comparison on WARM using UCI Cleveland heart disease dataset

Table 12 presents a comparative analysis of WARM using significant features versus existing results of ARM in heart disease prediction. Rules that gave the highest confidence scores were retrieved and compared in this table. Research by Said et al. [ 41 ] and Khare and Gupta [ 24 ] showed lower confidence scores compared to this research. Although Sonet et al., [ 45 ] managed to obtain a confidence score of 99%, the rule generated for this score is questionable. The rule stated that if a patient has diabetes, then the patient will have heart disease. Although the risk of having heart disease is proven to be higher in diabetic patient, this rule cannot be generalized for all diabetic patients. This is the result of bias that might have existed in their dataset. The dataset used in their study is collected from 4 different medical institutions with a total of 131 records and is not an open dataset. Besides that, the dataset contained different features from the dataset used in this study.

This study also benchmarked the rules generated using the UCI dataset by past researches with the rules generated in our study. The extracted healthy rules are shown in Table 13 and sick rules are shown in Table 14 . Table 13 shows that our experiment with 8 significant features obtained the optimum confidence score of 100% for predicting healthy rules. The rules retrieved for this stated that if the sex is female, chest paint is non-angina and thallium heart scan is normal, this person is then predicted not to have heart disease.

Table 14 shows the sick rules together with the highest confidence scores of this research in comparison with other resesarch on associative and WARM for heart disease prediction. This study achieved a confidence score of 98% which is better than all the other predicted sick rules. To the best of our knowledge, the significant features’ weighted scores in our study managed to beat the scores obtained by all other research using ARM and WARM to predict heart disease.

This research contributed to obtaining the highest confidence score using significant features in WARM for heart disease prediction. Assigning appropriate weight scores have proven to improve the performance of confidence level in the prediction. A set of significant features with different weights to represent the strength of each of the features was used in heart disease prediction. To the best of our knowledge, this is the first study that made use of significant features in executing WARM. This research has also contributed to listing the top rules in predicting heart disease based on the UCI dataset. This is the first research that benchmarked the healthy rules and sick rules with the highest confidence scores. Future researches may look into predicting the risk levels of heart disease, as this will help medical practitioners and patients to gauge their heart disease severity. The algorithm used in this study for measuring weight can be further explored for use with other datasets to cater to other prediction models using the weighted approach. The machine learning techniques used in feature selection phase of this research is limited to the most popular techniques used in heart disease prediction research. Future researchers should look into exploring other machine learning techniques in selecting the significant features.

Data availability of materials

The datasets analysed during the current study are available as Cleveland Dataset in UCI Machine Learning Repository, [ https://www.kaggle.com/ronitf/heart-disease-uci ]

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Acknowledgements

The authors would like to thank Fundamental Research Grant Scheme (FRGS/1/2017/ICT01/UM/02/4, FP057-2017A) and Faculty Research Grant Scheme of Universiti Malaya (Project Code: GF011D-2019) for funding this research.

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Yazdani, A., Varathan, K.D., Chiam, Y.K. et al. A novel approach for heart disease prediction using strength scores with significant predictors. BMC Med Inform Decis Mak 21 , 194 (2021). https://doi.org/10.1186/s12911-021-01527-5

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Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyse huge complex medical data, helping healthcare professionals to predict heart disease. This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naïve Bayes, decision tree, K-nearest neighbor, and random forest algorithm. It uses the existing dataset from the Cleveland database of UCI repository of heart disease patients. The dataset comprises 303 instances and 76 attributes. Of these 76 attributes, only 14 attributes are considered for testing, important to substantiate the performance of different algorithms. This research paper aims to envision the probability of developing heart disease in the patients. The results portray that the highest accuracy score is achieved with K-nearest neighbor.

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Shah, D., Patel, S. & Bharti, S.K. Heart Disease Prediction using Machine Learning Techniques. SN COMPUT. SCI. 1 , 345 (2020). https://doi.org/10.1007/s42979-020-00365-y

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News and Comment

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What to know

Learn facts about how race, ethnicity, age, and other risk factors can contribute to heart disease risk. It’s important for everyone to know the facts about heart disease.

Heart disease in the United States

In the United States:

• Heart disease is the leading cause of death for men, women, and people of most racial and ethnic groups. 1
• One person dies every 33 seconds from cardiovascular disease. 1
• About 695,000 people died from heart disease in 2021—that's 1 in every 5 deaths . 1 2
• Heart disease costs about $239.9 billion each year from 2018 to 2019. 3 This includes the cost of health care services, medicines, and lost productivity due to death.

Coronary artery disease (CAD)

• Coronary heart disease is the most common type of heart disease, killing 375,476 people in 2021. 2
• About 1 in 20 adults age 20 and older have CAD (about 5%). 2
• In 2021, about 2 in 10 deaths from CAD happened in adults less than 65 years old. 1

Heart attack

• In the United States, someone has a heart attack every 40 seconds. 2
• 605,000 are a first heart attack. 2
• 200,000 happen to people who have already had a heart attack. 2
• About 1 in 5 heart attacks are silent—the damage is done, but the person is not aware of it. 2

Did you know?‎

Who is affected

Heart disease deaths vary by sex, race, and ethnicity.

Heart disease is the leading cause of death for people of most racial and ethnic groups in the United States. These include African American, American Indian, Alaska Native, Hispanic, and White men. For women from the Pacific Islands and Asian American, American Indian, Alaska Native, and Hispanic women, heart disease is second only to cancer. 1

Below are the percentages of all deaths caused by heart disease in 2021, listed by ethnicity, race, and sex.

Race or Ethnic Group

% of Deaths

American Indian or Alaska Native

Black (Non-Hispanic)

Native Hawaiin or Other Pacific Islander

White (Non-Hispanic)

Americans at risk for heart disease

High blood pressure , high blood cholesterol , and smoking are key risk factors for heart disease.

Several other medical conditions and lifestyle choices can also put people at a higher risk for heart disease, including:

• Overweight and obesity
• Unhealthy diet
• Physical inactivity
• Excessive alcohol use

What CDC is doing

• Million Hearts ®
• CDC: Heart Disease Communications Kit
• National Heart, Lung, and Blood Institute
• National Center for Health Statistics. Multiple Cause of Death 2018–2021 on CDC WONDER Database. Accessed February 2, 2023. https://wonder.cdc.gov/mcd.html
• Tsao CW, Aday AW, Almarzooq ZI, et al. Heart Disease and Stroke Statistics—2023 Update: A Report From the American Heart Association. Circulation. 2023;147:e93–e621.
• National Center for Health Statistics. Percentage of coronary heart disease for adults aged 18 and over, United States, 2019—2021. National Health Interview Survey. Accessed February 17, 2023. https://wwwn.cdc.gov/NHISDataQueryTool/SHS_adult/index.html

Heart Disease

Heart disease is the leading cause of death in the United States.

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Everything counts!

Research finds step-count and time are equally valid in reducing health risks

Kira Sampson

BWH Communications

A new study suggests that both step-count and time-based exercise goals are equally effective in reducing risks of heart disease and early death.

Researchers from Harvard-affiliated Brigham and Women’s Hospital reviewed data on healthy women age 62+, who used wearable devices to record their physical activity, and then tracked their health outcomes. After a median follow-up of nine years, the researchers found higher levels of physical activity, whether in time of exercise or step counts, were associated with large risk reductions in mortality and cardiovascular disease. The most active quarter of women in the study had a 30 to 40 percent reduced risk compared to the least active quarter.

Results of the study are published in JAMA Internal Medicine . 

“We recognized that existing physical activity guidelines focus primarily on activity duration and intensity but lack step-based recommendations,” said lead author Rikuta Hamaya, a researcher in the  Division of Preventive Medicine  at BWH. “With more people using smartwatches to measure their steps and overall health, we saw the importance of ascertaining how step-based measurements compare to time-based targets in their association with health outcomes — is one better than the other?” 

“Movement looks different for everyone, and nearly all forms of movement are beneficial to our health.”  Rikuta Hamaya

The current  U.S. guidelines , last updated in 2018, recommend that adults engage in at least 150 minutes of moderate to vigorous physical activity (e.g., brisk walking) or 75 minutes of vigorous activity (e.g., jogging) per week. At that time, most of the existing evidence on health benefits came from studies where participants self-reported their physical activity. Few data points existed on the relationship between steps and health. Fast-forward to the present — with wearables being ubiquitous, step counts are now a popular metric among many fitness-tracking platforms. How do time-based goals stack up against step-based ones? Investigators sought to answer this question. 

For this study, investigators collected data from 14,399 women who participated in the Women’s Health Study, and were healthy (free from cardiovascular disease and cancer). Between 2011 and 2015, participants aged 62 years and older were asked to wear research-grade wearables for seven consecutive days to record their physical activity levels, only removing the devices for sleep or water-related activities. Throughout the study period, annual questionnaires were administered to ascertain health outcomes of interest, in particular, death from any cause and cardiovascular disease. Investigators followed up with participants through the end of 2022. 

At the time of device wear, researchers found that participants engaged in a median of 62 minutes of moderate-to-vigorous intensity physical activity per week and accumulated a median of 5,183 steps per day. During a median follow-up of nine years, approximately 9 percent of participants had passed and roughly 4 percent developed cardiovascular disease.

Higher levels of physical activity (whether assessed as step counts or time in moderate to vigorous activity) were associated with large risk reductions in death or cardiovascular disease — the most active quarter of women reduced their risk by 30-40 percent compared with the least-active quarter. Individuals in the top three quartiles of physical activity outlived those in the bottom quartile by an average of 2.22 and 2.36 months respectively, based on time and step-based measurements, at nine years of follow-up. This survival advantage persisted regardless of differences in body mass index (BMI). 

While both metrics are useful in portraying health status, Hamaya explained that each has its advantages and downsides. For one, step counts may not account for differences in fitness levels. For example, if a 20-year-old and 80-year-old both walk for 30 minutes at moderate intensity, their step counts may differ significantly. Conversely, steps are straightforward to measure and less subject to interpretation compared to exercise intensity. Additionally, steps capture even sporadic movements of everyday life, not just exercise, and these kinds of daily life activities likely are those carried out by older individuals. 

“For some, especially for younger individuals, exercise may involve activities like tennis, soccer, walking, or jogging, all of which can be easily tracked with steps. However, for others, it may consist of bike rides or swimming, where monitoring the duration of exercise is simpler,” said Hamaya. “That’s why it’s important for physical-activity guidelines to offer multiple ways to reach goals. Movement looks different for everyone, and nearly all forms of movement are beneficial to our health.” 

The authors note that this study incorporates only a single assessment of time and step-based physical activity metrics. Further, most women included in the study were white and of higher socioeconomic status. Finally, this study was observational, and thus causal relations cannot be proven. In the future, Hamaya aims to collect more data via a randomized controlled trial to better understand the relationship between time and step-based exercise metrics and health. 

“The next federal physical activity guidelines are planned for 2028,” said senior author I-Min Lee, an epidemiologist in the Division of Preventive Medicine at BWH. “Our findings further establish the importance of adding step-based targets, in order to accommodate flexibility of goals that work for individuals with differing preferences, abilities and lifestyles.”  

Disclosures : Hamaya reported receiving consulting fees from DeSC Healthcare, Inc., outside of the submitted work. Co-authors Christopher Moore, Julie Buring, Kelly Evenson, and Lee reported receiving institutional support from the National Institutes of Health during the conduct of the study.

This research was supported in part by the National Institutes of Health (CA154647, CA047988, CA182913, HL043851, HL080467, and HL09935), the National Cancer Institute (5R01CA227122), Office of the Director, Office of Disease Prevention, and Office of Behavioral and Social Sciences Research; and by the extramural research program at the National Heart, Lung, and Blood Institute. 

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New study finds step-count and time are equally valid in reducing health risks

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Heart disease researchers at UCL awarded £3 million

24 May 2024

Researchers at the UCL Institute of Cardiovascular Science will receive £3 million over the next five years from the British Heart Foundation (BHF) as part of the charity’s Research Excellence Awards funding scheme.

The new Centre of Research Excellence at UCL will enable cutting-edge research, innovative teaching and training, forward-looking policy development and advocacy with and for patients.

The centre will transform lives by trialling new treatments for rare diseases and cardiac arrythmia, delivering a simulation lab and robotics programme to develop devices for congenital and structural heart disorders, and enabling research to better understand ethnic differences in cardiovascular health.

Professor Perry Elliott, Head of the UCL Institute of Cardiovascular Science, said: “The award of a Centre of Research Excellence represents a landmark in our long-standing relationship with the BHF and is a substantial boost to our ambitions for cardiovascular science at UCL.

“The centre builds on an established track record of transformative science and benefits from a strategic network comprising three NIHR Biomedical Research Centres, the UCL Partners Academic Health Sciences Network, Barts Heart Centre, Royal Free Hospital, UCL Hospitals NHS Foundation Trust and Great Ormond Street Hospital.

“Over the next five years, we will use our Centre of Research Excellence to create a new diverse multidisciplinary workforce who will focus on transforming lives through innovation in therapeutics and targeted prevention.

The funding aims to support universities to cultivate world-class research environments that encourage collaboration, inclusion and innovation, and where visionary scientists can drive lifesaving breakthroughs.

The awards are part of a £35 million boost to UK cardiovascular disease research, supporting the Government’s ambition for the UK to become a science superpower. King’s College London and Imperial College London also received funding.

Professor Bryan Williams, Chief Scientific and Medical Officer at the British Heart Foundation, also a UCL professor of medicine and honorary consultant physician at UCLH, said: “We’re delighted to continue to support research at these three outstanding institutions to address the biggest challenges in cardiovascular disease. This funding recognises the incredible work happening in London and will help to cement its status as a leading destination for cardiovascular disease research.

“With generous donations from our supporters, this funding will power cutting-edge science and unlock lifesaving discoveries that can turn the tide on the devastation caused by heart and circulatory diseases.”

Research Excellence Awards offer researchers greater flexibility than traditional research funding, allowing scientists to quickly launch ambitious projects that can act as a springboard for larger, transformative funding applications.

The funding also aims to break down the silos that have traditionally existed in research, encouraging collaboration between experts from diverse fields. From clinicians to data scientists, biologists to engineers, the funding will support universities to attract the brightest minds, nurture new talent and foster collaboration to tackle the biggest questions in heart and circulatory disease research.

• UCL Institute of Cardiovascular Science
• Professor Perry Elliott’s academic profile
• UCL Population Health Sciences
• Credit: iStock /  magicmine

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Mark greaves.

T: +44 (0)7990 675947

E: m.greaves [at] ucl.ac.uk

Understanding a broken heart

Recurrent heart failure linked to accumulated stress in immunity-forming stem cells.

The stress of heart failure is remembered by the body and appears to lead to recurrent failure, along with other related health issues, according to new research. Researchers have found that heart failure leaves a "stress memory" in the form of changes to the DNA modification of hematopoietic stem cells, which are involved in the production of blood and immune cells called macrophages. These immune cells play an important role in protecting heart health. However, a key signaling pathway (a chain of molecules which relays signals inside a cell), called transforming growth factor beta (TGF-β), in the hematopoietic stem cells was suppressed during heart failure, negatively affecting macrophage production. Improving TGF-β levels could be a new avenue for treating recurrent heart failure, while detecting accumulating stress memory could provide an early warning system before it occurs.

Healthier lives and improved well-being are among the United Nations' global Sustainable Development Goals. Positively, a recent study shows that life expectancy worldwide is projected to increase by about 4.5 years by 2050. Much of this is thanks to public health efforts to prevent disease and improved survival from illnesses, such as cardiovascular disorders. However, heart disease is still the leading cause of death worldwide, with 26 million people estimated to be affected by heart failure.

Once heart failure has occurred, it has a tendency to reoccur along with other health issues, such as kidney and muscle problems. Researchers in Japan wanted to understand what causes this recurrence and the deterioration of other organs, and whether it can be prevented.

"Based on our earlier research, we hypothesized that recurrence may be caused by stress experienced during heart failure accumulating in the body, particularly in hematopoietic stem cells," explained Project Professor Katsuhito Fujiu from the Graduate School of Medicine at the University of Tokyo. Hematopoietic stem cells are found in bone marrow and are the source of blood cells and a type of immune cell called macrophages, which help to protect heart health.

By studying mice with heart failure, the researchers found evidence of stress imprinting on the epigenome, that is, chemical changes occurred to the mice's DNA. An important signaling pathway, called the transforming growth factor beta, which is involved in regulating many cellular processes, was suppressed in the hematopoietic stem cells of mice with heart failure, leading to the production of dysfunctional immune cells.

This change persisted over an extended period of time, so when the team transplanted bone marrow from mice with heart failure into healthy mice, they found that the stem cells continued to produce dysfunctional immune cells. The latter mice later developed heart failure and became prone to organ damage.

"We termed this phenomenon stress memory because the stress from heart failure is remembered for an extended period and continues to affect the entire body. Although various other types of stress might also imprint this stress memory, we believe that the stress induced by heart failure is particularly significant," said Fujiu.

The good news is that by identifying and understanding these changes to the TGF-β signaling pathway, new avenues are now open for potential future treatments. "Completely new therapies could be considered to prevent the accumulation of this stress memory during hospitalization for heart failure," said Fujiu. "In animals with heart failure, supplementing additional active TGF-β has been shown to be a potential treatment. Correcting the epigenome of hematopoietic stem cells could also be a way to deplete stress memory."

Now that it has been identified, the team hopes to develop a system that can detect and prevent the accumulation of stress memory in humans, with a long-term goal of being able to not only prevent the recurrence of heart failure, but also catch the condition before it can fully develop.

• Heart Disease
• Stroke Prevention
• Immune System
• Mental Health
• Heart failure
• White blood cell
• Adult stem cell
• Embryonic stem cell
• Natural killer cell

Story Source:

Materials provided by University of Tokyo . Note: Content may be edited for style and length.

Journal Reference :

• Yukiteru Nakayama, Katsuhito Fujiu, Tsukasa Oshima, Jun Matsuda, Junichi Sugita, Takumi James Matsubara, Yuxiang Liu, Kohsaku Goto, Kunihiro Kani, Ryoko Uchida, Norifumi Takeda, Hiroyuki Morita, Yingda Xiao, Michiko Hayashi, Yujin Maru, Eriko Hasumi, Toshiya Kojima, Soh Ishiguro, Yusuke Kijima, Nozomu Yachie, Satoshi Yamazaki, Ryo Yamamoto, Fujimi Kudo, Mio Nakanishi, Atsushi Iwama, Ryoji Fujiki, Atsushi Kaneda, Osamu Ohara, Ryozo Nagai, Ichiro Manabe, Issei Komuro. Heart failure promotes multimorbidity through innate immune memory . Science Immunology , 2024; 9 (95) DOI: 10.1126/sciimmunol.ade3814

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Epigenetic Biomarkers of Socioeconomic Status are Associated with Age-Related Chronic Diseases and Mortality in Older Adults

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Later-life health is patterned by socioeconomic influences across the lifecourse. However, the pathways underlying the biological embedding of socioeconomic status (SES) and its consequences on downstream morbidity and mortality are not fully understood. Epigenetic markers like DNA methylation (DNAm) may be promising surrogates of underlying biological processes that can enhance our understanding of how SES shapes population health. Studies have shown that SES is associated with epigenetic aging measures, but few have examined relationships between early and later-life SES and DNAm sites across the epigenome. In this study, we trained and tested DNAm-based surrogates, or “biomarkers,” of childhood and adult SES in two large, multi-racial/ethnic samples of older adults—the Health and Retirement Study (HRS) (N=3,527) and the Multi-Ethnic Study of Atherosclerosis (MESA) (N=1,182). Both biomarkers were associated with downstream morbidity and mortality, and these associations persisted after controlling for measured SES, and in some cases, epigenetic aging clocks. Both childhood and adult SES biomarker CpG sites were enriched for genomic features that regulate gene expression (e.g., DNAse hypersensitivity sites and enhancers) and were implicated in prior epigenome-wide studies of inflammation, aging, and chronic disease. Distinct patterns also emerged between childhood CpGs and immune system dysregulation and adult CpGs and metabolic functioning, health behaviors, and cancer. Results suggest DNAm-based surrogate biomarkers of SES may be useful proxies for unmeasured social exposures that can augment our understanding of the biological mechanisms between social disadvantage and downstream health.

Significance Statement Information on DNA methylation (DNAm)—an epigenetic modification that plays a central role in regulating gene expression—is increasingly available in large epidemiological studies. Since DNAm is relatively stable but responsive to environmental influences, genome-wide signatures are promising surrogates or biomarkers of exposure that may both shed light on biological mechanisms between adverse environments and downstream health and/or act as proxies for unmeasured exposures. To better understand the biological embedding of social disadvantage, this study trained and tested DNAm-based surrogates of childhood and adult socioeconomic status (SES) in two US-based cohorts of older adults. Findings reveal distinct DNAm signatures of SES that connect social adversity across the lifecourse with dysregulated immune system responses, inflammatory pathways, poorer metabolic functioning, chronic diseases, and cancer.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This analysis was supported by the National Institute on Aging (NIA, R00 AG056599 (Schmitz)), the National Heart, Lung, and Blood Institute (NHLBI, R01 HL141292 (Smith)), and the National Human Genome Research Institute (NHGRI, T32 HG000040 (Opsasnick)). The NIA supports the Health and Retirement Study (U01AG009740) and its genotyping (RC2 AG0336495, RC4 AG039029). Research in MESA is supported by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the NHLBI and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS). The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. Funding for SHARe genotyping was provided by NHLBI Contract N02-HL-64278. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org [mesa-nhlbi.org]. Funding for MESA SHARe genotyping was provided by NHLBI Contract N02-HL-64278. Genotyping was performed at Affymetrix (Santa Clara, California, USA) and the Broad Institute of Harvard and MIT (Boston, Massachusetts, USA) using the Affymetrix Genome-Wide Human SNP Array 6.0. The MESA Epigenomics & Transcriptomics Studies were funded by NIH grants R01HL101250, R01HL119962, R01DK101921, R01HL135009, and 1RF1AG054474. This paper has been reviewed and approved by the MESA Publications and Presentations Committee. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIA, NHLBI, NHGRI, or NCATS.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

IRBs of University of Wisconsin-Madison and University of Michigan gave ethical approval for this work.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Competing Interest Statement: The authors declare no conflicts of interest.

Data Availability

This study used restricted individual level information from the HRS, and our contractual agreement does not permit public dissemination of the data. Details on how to access restricted data for the HRS can be found at https://hrs.isr.umich.edu/data-products/restricted-data. Data used in this analysis from MESA can be obtained through the MESA Data Coordinating Center (https://www.mesanhlbi.org/) and through the database of Genotypes and Phenotypes (dbGaP; phs000209). Analysis code is posted on github: https://github.com/laurenschmitz/epigenetic-SES-biomarker.

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Cardiovascular disease.

Edgardo Olvera Lopez ; Brian D. Ballard ; Arif Jan .

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Last Update: August 22, 2023 .

• Continuing Education Activity

The cardiovascular system consists of the heart and its blood vessels. A wide array of problems can arise within the cardiovascular system, a few of which include endocarditis, rheumatic heart disease, and conduction system abnormalities. Cardiovascular disease, also known as heart disease, refers to the following 4 entities: coronary artery disease (CAD) which is also referred to as coronary heart disease (CHD), cerebrovascular disease, peripheral artery disease (PAD), and aortic atherosclerosis. CAD results from decreased myocardial perfusion that causes angina due to ischemia and can result in myocardial infarction (MI), and/or heart failure. It accounts for one-third to one-half of all cases of cardiovascular disease. Cerebrovascular disease is the entity associated with strokes, also termed cerebrovascular accidents, and transient ischemic attacks (TIAs). Peripheral arterial disease (PAD) is arterial disease predominantly involving the limbs that may result in claudication. Aortic atherosclerosis is the entity associated with thoracic and abdominal aneurysms. This activity reviews the evaluation and treatment of cardiovascular disease and the role of the medical team in evaluating and treating these conditions.

• Review the cause of coronary artery disease.
• Describe the pathophysiology of atherosclerosis.
• Summarize the treatment options for heart disease.
• Outline the evaluation and treatment of cardiovascular disease and the role of the medical team in evaluating and treating this condition.
• Introduction

The cardiovascular system consists of the heart and blood vessels. [1]  There is a wide array of problems that may arise within the cardiovascular system, for example, endocarditis, rheumatic heart disease, abnormalities in the conduction system, among others, cardiovascular disease (CVD) or heart disease refer to the following 4 entities that are the focus of this article [2] :

• Coronary artery disease (CAD): Sometimes referred to as Coronary Heart Disease (CHD), results from decreased myocardial perfusion that causes angina, myocardial infarction (MI), and/or heart failure. It accounts for one-third to one-half of the cases of CVD.
• Cerebrovascular disease (CVD): Including stroke and transient ischemic attack (TIA)
• Peripheral artery disease (PAD): Particularly arterial disease involving the limbs that may result in claudication
• Aortic atherosclerosis:  Including thoracic and abdominal aneurysms

Although CVD may directly arise from different etiologies such as emboli in a patient with atrial fibrillation resulting in ischemic stroke, rheumatic fever causing valvular heart disease, among others, addressing risks factors associated to the development of atherosclerosis is most important because it is a common denominator in the pathophysiology of CVD.

The industrialization of the economy with a resultant shift from physically demanding to sedentary jobs, along with the current consumerism and technology-driven culture that is related to longer work hours, longer commutes, and less leisure time for recreational activities, may explain the significant and steady increase in the rates of CVD during the last few decades. Specifically, physical inactivity, intake of a high-calorie diet, saturated fats, and sugars are associated with the development of atherosclerosis and other metabolic disturbances like metabolic syndrome, diabetes mellitus, and hypertension that are highly prevalent in people with CVD. [3] [2] [4] [5]

According to the INTERHEART study that included subjects from 52 countries, including high, middle, and low-income countries, 9 modifiable risks factors accounted for 90% of the risk of having a first MI: smoking, dyslipidemia, hypertension, diabetes, abdominal obesity, psychosocial factors, consumption of fruits and vegetables, regular alcohol consumption, and physical inactivity. It is important to mention that in this study 36% of the population-attributable risk of MI was accounted to smoking. [6]

Other large cohort studies like the Framingham Heart Study [7] and the Third National Health and Nutrition Examination Survey (NHANES III) [5] have also found a strong association and predictive value of dyslipidemia, high blood pressure, smoking, and glucose intolerance. Sixty percent to 90% of CHD events occurred in subjects with at least one risk factor.

These findings have been translated into health promotion programs by the American Heart Association with emphasis on seven recommendations to decrease the risk of CVD: avoiding smoking, being physically active, eating healthy, and keeping normal blood pressure, body weight, glucose, and cholesterol levels. [8] [9]

On the other hand, non-modifiable factors as family history, age, and gender have different implications. [4] [7] Family history, particularly premature atherosclerotic disease defined as CVD or death from CVD in a first-degree relative before 55 years (in males) or 65 years (in females) is considered an independent risk factor. [10] There is also suggestive evidence that the presence of CVD risk factors may differently influence gender. [4] [7]  For instance, diabetes and smoking more than 20 cigarettes per day had increased CVD risk in women compared to men. [11] Prevalence of CVD increases significantly with each decade of life. [12]  

The presence of HIV (human immunodeficiency virus), [13]  history of mediastinal or chest wall radiation, [14]  microalbuminuria, [15] , increased inflammatory markers [16] [17]  have also been associated with an increased rate and incidence of CVD. 

Pointing out specific diet factors like meat consumption, fiber, and coffee and their relation to CVD remains controversial due to significant bias and residual confounding encountered in epidemiological studies. [18] [19]

• Epidemiology

Cardiovascular diseases (CVD) remain among the 2 leading causes of death in the United States since 1975 with 633,842 deaths or 1 in every 4 deaths, heart disease occupied the leading cause of death in 2015 followed by 595,930 deaths related to cancer. [2]  CVD is also the number 1 cause of death globally with an estimated 17.7 million deaths in 2015, according to the World Health Organization (WHO). The burden of CVD further extends as it is considered the most costly disease even ahead of Alzheimer disease and diabetes with calculated indirect costs of $237 billion dollars per year and a projected increased to $368 billion by 2035. [20]

Although the age-adjusted rate and acute mortality from MI have been declining over time, reflecting the progress in diagnosis and treatment during the last couple of decades, the risk of heart disease remains high with a calculated 50% risk by age 45 in the general population. [7] [21]  The incidence significantly increases with age with some variations between genders as the incidence is higher in men at younger ages. [2]  The difference in incidence narrows progressively in the post-menopausal state. [2]

• Pathophysiology

Atherosclerosis is the pathogenic process in the arteries and the aorta that can potentially cause disease as a consequence of decreased or absent blood flow from stenosis of the blood vessels. [22]

It involves multiple factors dyslipidemia, immunologic phenomena, inflammation, and endothelial dysfunction. These factors are believed to trigger the formation of fatty streak, which is the hallmark in the development of the atherosclerotic plaque [23] ; a progressive process that may occur as early as in the childhood. [24]  This process comprises intimal thickening with subsequent accumulation of lipid-laden macrophages (foam cells) and extracellular matrix, followed by aggregation and proliferation of smooth muscle cells constituting the formation of the atheroma plaque. [25]  As this lesions continue to expand, apoptosis of the deep layers can occur, precipitating further macrophage recruitment that can become calcified and transition to atherosclerotic plaques. [26]

Other mechanisms like arterial remodeling and intra-plaque hemorrhage play an important role in the delay and accelerated the progression of atherosclerotic CVD but are beyond the purpose of this article. [27]

• History and Physical

The clinical presentation of cardiovascular diseases can range from asymptomatic (e.g., silent ischemia, angiographic evidence of coronary artery disease without symptoms, among others) to classic presentations as when patients present with typical anginal chest pain consistent of myocardial infarction and/or those suffering from acute CVA presenting with focal neurological deficits of sudden onset. [28] [29] [28]

Historically, coronary artery disease typically presents with angina that is a pain of substernal location, described as a crushing or pressure in nature, that may radiate to the medial aspect of the left upper extremity, to the neck or the jaw and that can be associated with nausea, vomiting, palpitations, diaphoresis, syncope or even sudden death. [30]  Physicians and other health care providers should be aware of possible variations in symptom presentation for these patients and maintain a high index of suspicion despite an atypical presentation, for example, dizziness and nausea as the only presenting symptoms in patients having an acute MI [31] ), particularly in people with a known history of CAD/MI and for those with the presence of CVD risk factors. [32] [33] [34] [33] [32]  Additional chest pain features suggestive of ischemic etiology are the exacerbation with exercise and or activity and resolution with rest or nitroglycerin. [35]

Neurologic deficits are the hallmark of cerebrovascular disease including TIA and stroke where the key differentiating factor is the resolution of symptoms within 24 hours for patients with TIA. [36]  Although the specific symptoms depend on the affected area of the brain, the sudden onset of extremity weakness, dysarthria, and facial droop are among the most commonly reported symptoms that raise concern for a diagnosis of a stroke. [37] [38]  Ataxia, nystagmus and other subtle symptoms as dizziness, headache, syncope, nausea or vomiting are among the most reported symptoms with people with posterior circulation strokes challenging to correlate and that require highly suspicion in patients with risks factors. [39]

Patients with PAD may present with claudication of the limbs, described as a cramp-like muscle pain precipitated by increased blood flow demand during exercise that typically subsides with rest. [40] Severe PAD might present with color changes of the skin and changes in temperature. [41]  

Most patients with thoracic aortic aneurysm will be asymptomatic, but symptoms can develop as it progresses from subtle symptoms from compression to surrounding tissues causing cough, shortness of breath or dysphonia, to the acute presentation of sudden crushing chest or back pain due to acute rupture. [42]  The same is true for abdominal aortic aneurysms (AAA) that cause no symptoms in early stages to the acute presentation of sudden onset of abdominal pain or syncope from acute rupture. [43]

A thorough physical examination is paramount for the diagnosis of CVD. Starting with a general inspection to look for signs of distress as in patients with angina or with decompensated heart failure, or chronic skin changes from PAD. Carotid examination with the patient on supine position and the back at 30 degrees for the palpation and auscultation of carotid pulses, bruits and to evaluate for jugular venous pulsations on the neck is essential. Precordial examination starting with inspection, followed by palpation looking for chest wall tenderness, thrills, and identification of the point of maximal impulse should then be performed before auscultating the precordium. Heart sounds auscultation starts in the aortic area with the identification of the S1 and S2 sounds followed by characterization of murmurs if present. Paying attention to changes with inspirations and maneuvers to correctly characterize heart murmurs is encouraged. Palpating peripheral pulses with bilateral examination and comparison when applicable is an integral part of the CVD examination. [44]

Thorough clinical history and physical exam directed but not limited to the cardiovascular system are the hallmarks for the diagnosis of CVD. Specifically, a history compatible with obesity, angina, decreased exercise tolerance, orthopnea, paroxysmal nocturnal dyspnea, syncope or presyncope, and claudication should prompt the clinician to obtain a more detailed history and physical exam and, if pertinent, obtain ancillary diagnostic test according to the clinical scenario (e.g., electrocardiogram and cardiac enzymes for patients presenting with chest pain). 

Besides a diagnosis prompted by clinical suspicion, most of the efforts should be oriented for primary prevention by targeting people with the presence of risk factors and treat modifiable risk factors by all available means. All patient starting at age 20 should be engaged in the discussion of CVD risk factors and lipid measurement. [9]  Several calculators that use LDL-cholesterol and HDL-cholesterol levels and the presence of other risk factors calculate a 10-year or 30-year CVD score to determine if additional therapies like the use of statins and aspirin are indicated for primary prevention, generally indicated if such risk is more than ten percent. [10]  Like other risk assessment tools, the use of this calculators have some limitations, and it is recommended to exert precaution when assessing patients with diabetes and familial hypercholesterolemia as their risk can be underestimated. Another limitation to their use is that people older than 79 were usually excluded from the cohorts where these calculators were formulated, and individualized approach for these populations is recommended by discussing risk and benefits of adjunctive therapies and particular consideration of life expectancy. Some experts recommend a reassessment of CVD risk every 4 to 6 years. [9]

Preventative measures like following healthy food habits, avoiding overweight and following an active lifestyle are pertinent in all patients, particularly for people with non-modifiable risk factors such as family history of premature CHD or post-menopause. [9] [8]

The use of inflammatory markers and other risk assessment methods as coronary artery calcification score (CAC) are under research and have limited applications that their use should not replace the identification of people with known risk factors, nonetheless these resources remain as promising tools in the future of primary prevention by detecting people with subclinical atherosclerosis at risk for CVD. [45]

• Treatment / Management

Management of CVD is very extensive depending on the clinical situation (catheter-directed thrombolysis for acute ischemic stroke, angioplasty for peripheral vascular disease, coronary stenting for CHD); however, patients with known CVD should be strongly educated on the need for secondary prevention by risk factor and lifestyle modification. [9] [46]

• Differential Diagnosis
• Acute pericarditis
• Angina pectoris
• Artherosclerosis
• Coronary artery vasospasm
• Dilated cardiomyopathy
• Giant cell arteritis
• Hypertension
• Hypertensive heart disease
• Kawasaki disease
• Myocarditis

The prognosis and burden of CVD have been discussed in other sections.

• Complications

The most feared complication from CVD is death and, as explained above, despite multiple discoveries in the last decades CVD remains in the top leading causes of death all over the world owing to the alarming prevalence of CVD in the population. [2]  Other complications as the need for longer hospitalizations, physical disability and increased costs of care are significant and are the focus for health-care policymakers as it is believed they will continue to increase in the coming decades. [20]

For people with heart failure with reduced ejection fraction (HFreEF) of less than 35%, as the risk of life-threatening arrhythmias is exceedingly high in these patients, current guidelines recommend the implantation of an implantable-cardioverter defibrillator (ICD) for those with symptoms equivalent to a New York Heart Association (NYHA) Class II-IV despite maximal tolerated medical therapy. [47]

Strokes can leave people with severe disabling sequelae like dysarthria or aphasia, dysphagia, focal or generalized muscle weakness or paresis that can be temporal or cause permanent physical disability that may lead to a complete bedbound state due to hemiplegia with added complications secondary to immobility as is the higher risk of developing urinary tract infections and/or risk for thromboembolic events. [48] [49]

There is an increased risk of all-cause death for people with PAD compared to those without evidence of peripheral disease. [50]  Chronic wounds, physical limitation, and limb ischemia are among other complications from PAD. [51]

• Consultations

An interprofessional approach that involves primary care doctors, nurses, dietitians, cardiologists, neurologists, and other specialists is likely to improve outcomes. This has been shown to be beneficial in patients with heart failure, [52]  coronary disease, [53]  and current investigations to assess the impact on other forms of CVD are under planning and promise encouraging results.

• Deterrence and Patient Education

Efforts should be directed toward primary prevention by leading a healthy lifestyle, and an appropriate diet starting as early as possible with the goal of delay or avoid the initiation of atherosclerosis as it relates to the future risk of CVD. The AHA developed the concept of "ideal cardiovascular health" defined by the presence of [8] :

• Ideal health behaviors: Nonsmoking, body mass index less than 25 kg/m2, physical activity at goal levels, and the pursuit of a diet consistent with current guideline recommendations
• Ideal health factors: Untreated total cholesterol less than 200 mg/dL, untreated blood pressure less than 120/80 mm Hg, and fasting blood glucose less than 100 mg/dL) with the goal to improve the health of all Americans with an expected decrease in deaths from CVD by 20%

Specific attention should be made to people at higher risk for CVD as are people with diabetes, hypertension, hyperlipidemia, smokers, and obese patients. Risk factors modification by controlling their medical conditions, avoiding smoking, taking appropriate measures to lose weight and maintaining an active lifestyle is of extreme importance. [8] [9] [10] The recommendations on the use of statins and low-dose aspirin for primary and secondary prevention has been discussed in other sections.

• Pearls and Other Issues

Cardiovascular disease generally refers to 4 general entities: CAD, CVD, PVD, and aortic atherosclerosis. 

CVD is the main cause of death globally.

Measures aimed to prevent the progression of atherosclerosis are the hallmark for primary prevention of CVD.

Risk factor and lifestyle modification are paramount in the prevention of CVD.

• Enhancing Healthcare Team Outcomes

An interprofessional and patient-oriented approach can help to improve outcomes for people with cardiovascular disease as shown in patients with heart failure (HF) who had better outcomes when the interprofessional involvement of nurses, dietitians, pharmacists, and other health professionals was used (Class 1A). [52]

Similarly, positive results were obtained in people in an intervention group who were followed by an interprofessional team comprised of pharmacists, nurses and a team of different physicians. This group had a reduction in all-cause mortality associated with CAD by 76% compared to the control group. [53]  Healthcare workers should educate the public on lifestyle changes and reduce the modifiable risk factors for heart disease to a minimum.

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Atherosclerosis as a result of coronary heart disease. Contributed by National Heart, Lung and Blood Institute (NIH)

Coronary Artery Disease Pathophysiology. Coronary artery disease is usually caused by an atherosclerotic plaque that blocks the lumen of a coronary artery, typically the left anterior descending artery. Contributed by S Bhimji, MD

Disclosure: Edgardo Olvera Lopez declares no relevant financial relationships with ineligible companies.

Disclosure: Brian Ballard declares no relevant financial relationships with ineligible companies.

Disclosure: Arif Jan declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Olvera Lopez E, Ballard BD, Jan A. Cardiovascular Disease. [Updated 2023 Aug 22]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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