obesity related thesis

A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity

Affiliations.

• 1 Centre for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia.
• 2 Centre for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia. Electronic address: [email protected].
• 3 RIADI Laboratory, University of Manouba, Manouba, Tunisia; College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia.
• 4 Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia.
• PMID: 34426171
• DOI: 10.1016/j.compbiomed.2021.104754

Obesity is considered a principal public health concern and ranked as the fifth foremost reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular diseases. The World Health Organization also predicted that 30% of death in the world will be initiated with lifestyle diseases in 2030 and can be stopped through the suitable identification and addressing of associated risk factors and behavioral involvement policies. Thus, detecting and diagnosing obesity as early as possible is crucial. Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors and condition likelihoods. The present study conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020. Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity. Next, the main diseases and health consequences of obesity and overweight are investigated. Ultimately, this study recognized the machine learning methods that can be used for the prediction of obesity. Finally, this study seeks to support decision-makers looking to understand the impact of obesity on health in the general population and identify outcomes that can be used to guide health authorities and public health to further mitigate threats and effectively guide obese people globally.

Keywords: Diseases; Machine learning; Obesity; Overweight; Risk factors.

Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Publication types

• Research Support, Non-U.S. Gov't
• Systematic Review
• Machine Learning
• Metabolic Syndrome*
• Obesity* / epidemiology
• Risk Factors

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• Review Article
• Published: 07 May 2024

Epidemiology and Population Health

Obesity: a 100 year perspective

• George A. Bray   ORCID: orcid.org/0000-0001-9945-8772 1  

International Journal of Obesity ( 2024 ) Cite this article

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• Biological techniques
• Health care
• Weight management

This review has examined the scientific basis for our current understanding of obesity that has developed over the past 100 plus years. Obesity was defined as an excess of body fat. Methods of establishing population and individual changes in levels of excess fat are discussed. Fat cells are important storage site for excess nutrients and their size and number affect the response to insulin and other hormones. Obesity as a reflection of a positive fat balance is influenced by a number of genetic and environmental factors and phenotypes of obesity can be developed from several perspectives, some of which have been elaborated here. Food intake is essential for maintenance of human health and for the storage of fat, both in normal amounts and in obesity in excess amounts. Treatment approaches have taken several forms. There have been numerous diets, behavioral approaches, along with the development of medications.. Bariatric/metabolic surgery provides the standard for successful weight loss and has been shown to have important effects on future health. Because so many people are classified with obesity, the problem has taken on important public health dimensions. In addition to the scientific background, obesity through publications and organizations has developed its own identity. While studying the problem of obesity this reviewer developed several aphorisms about the problem that are elaborated in the final section of this paper.

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Bray, G.A. Obesity: a 100 year perspective. Int J Obes (2024). https://doi.org/10.1038/s41366-024-01530-6

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The association between obesity and quality of life: a retrospective analysis of a large-scale population-based cohort study

• J. Stephenson 1 ,
• C. M. Smith 1 ,
• B. Kearns 2 ,
• A. Haywood 2 &
• P. Bissell 1  

BMC Public Health volume  21 , Article number:  1990 ( 2021 ) Cite this article

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The relationship between obesity and health-related quality of life (HRQoL) may be confounded by factors such as multimorbidity. The aim of the study was to explore this relationship, controlling for long-term conditions and other health, lifestyle and demographic factors in a general adult population. There was specific interest in the impact of high weight status, measured by body mass index (BMI) levels (obesity, morbid obesity) compared with individuals of normal weight.

Health, lifestyle and demographic data were collected from 64,631 individuals aged 16 years and over registered in the Yorkshire Health Study; a long-term cohort study. Data were collected in 2 waves: from patients attending GP surgeries in the South Yorkshire region; and using online recruitment across the entire Yorkshire and Humber area. Univariable and multivariable regression methods were utilised to identify factors associated with HRQoL as measured by the EQ-5D summary score. Long-term conditions were tested as both covariates and mediating factors on the causal pathway between obesity and HRQoL.

Increasing levels of obesity are associated with reduced HRQoL, although this difference is negligible between those of normal weight and those who are overweight. Individuals with obesity and morbid obesity score 4.9 and 11.3 percentage points less on the EQ-5D summary scale respectively than those of normal weight. Concurrent physical, and particularly mental health-related long-term conditions are substantively related to HRQoL: those with 3 or more reported mental or physical health conditions score 29.8 and 14.6 percentage points less on the EQ-5D summary scale respectively than those with fewer conditions. Long-term conditions can be conceptualised as lying on the causal path between obesity and HRQoL, but there is weak evidence for a partial mediating relationship only.

Conclusions

To conclude, in agreement with the established literature we have found a clear inverse relationship between increasing weight status and decreasing HRQoL and confirmed the mediating role of long-term conditions in the reduction of HRQoL in people with obesity. Nevertheless, a high BMI remains independently related to HRQoL, suggesting that ‘healthy people with obesity’ may be in transition to an unhealthy future.

Peer Review reports

Health-related quality of life (HRQoL) is a broad subjective concept that encompasses both physical and mental health, which are themselves in complex relationships with other external factors such as health, socio-economic status, the environment and other factors [ 1 ]. Obesity is a condition of ‘abnormal or excessive fat accumulation that may impair health’, defined by the WHO [ 2 ] as a body mass index (BMI) greater than 30 kg/m 2 , with a BMI of more than 40 kg/m 2 defined as morbid obesity. The aetiology of obesity is complex and multifaceted, stemming from biological, behavioural and environmental causes [ 3 ].

Worldwide obesity has tripled since 1975, and in 2016, 1.9 billion adults (39% of the worldwide adult population) were considered to be overweight: i.e. have a BMI in the range 25 kg/m 2  ≤ BMI < 30 kg/m 2 ; and 650 million (13% of the worldwide population) were considered to have obesity: i.e. have a BMI in the range BMI ≤ 30 kg/m 2 [ 2 ]. In England in 2018, 63% of adults were classified as being overweight or having obesity, with 2 and 4% of men and women respectively being defined as having morbid obesity: i.e. have a BMI in the range BMI ≤ 40 kg/m 2 [ 4 ]. It has been predicted that by 2050 Britain could be a mainly obese society [ 3 ]. Connelly reported a noticeable increase in the proportion of the United Kingdom population at very high risk of chronic disease due to their weight [ 4 ]. Physical associations include long-term health conditions such as Type 2 diabetes, hypertension, dyslipidaemia, coronary artery disease, stroke, various cancers, reduced reproductive function, osteoarthritis, liver and gall bladder disease, chronic pain and adverse respiratory effects [ 3 , 5 , 6 ]. The proportion of individuals reporting long-term conditions (LTCs) has been shown to increase linearly with increasing BMI, and to be independently related to BMI, after adjusting for age and gender [ 7 ]. Similarly, the number of reported LTCs increases with BMI, with 25 and 42% of individuals with moderate and morbid obesity respectively reporting 3 or more LTCs, compared with 12% of normal weight individuals. In addition to physical disease, obesity is also associated with mental health conditions: sleep disorders, anxiety, depression low self-esteem, motivational disorders, eating disorders, impaired body image [ 1 , 8 , 9 , 10 ] and serious psychiatric disorders [ 10 , 11 ].

Obesity is associated with physical, mental and economic consequences. The economic consequences of obesity are substantial and increasing [ 12 ]. In the UK alone it is estimated that by 2050 the societal and business costs of obesity will reach £49.9billion per year [ 3 ]. These costs have been categorised by Seidall [ 13 ] as direct costs from treating obesity and its related diseases; societal costs arising from loss of work due to increased absence, physical limitations, lower life expectancy and unemployment benefits; and personal costs stemming e.g. from stigmatisation and discrimination leading to lower incomes and higher healthcare costs. Physical and mental long-term conditions can impact both on each other and Health Related Quality of Life [ 6 , 14 , 15 , 16 ], and the relationship between obesity and HRQoL can be both mediated and confounded by the presence of comorbidities [ 17 , 18 ] and other effects such as medication [ 11 ] and polypharmacy [ 19 ].

The Yorkshire Health Study (YHS) is an observational cohort study of health and lifestyle in Yorkshire and the Humber [ 20 , 21 ] supported by NIHR CLAHRC (Collaboration for Leadership in Applied Health Research and Care). Adults (aged 16 and over) residing in the in the Yorkshire and Humber region of England are eligible to enter.

The data, from 70,836 adults, was collected in two waves: the first 27,813 were recruited via GP surgeries in South Yorkshire between 2010 and 2012; the second wave of data collection, from 2013 to 2015 utilised online recruitment and the National Clinical Research Network to recruit 43,023 participants. The majority of participants, whether recruited in Waves 1 or 2, completed one survey only. It is well established that there is an inverse relationship between QoL and obesity [ 12 , 17 , 22 , 23 , 24 ]. There are many research studies that demonstrate improved quality of life following both dietary and surgical weight loss [ 25 , 26 , 27 ].

The aim of this study was to utilise a large, contemporary cohort from the UK to explore the relationships between obesity and HRQoL, controlling for LTCs and other health, lifestyle and demographic factors in a general adult population; considering specifically the impact of high levels of BMI (obesity and morbid obesity) in comparison to BMI levels corresponding to individuals of normal weight.

Personal (age, gender, academic history, employment status, socio-economic status, quality of life), health (history of diabetes, physical and mental long-term conditions, frequency of visits to health care professionals, frequency of visits to hospital, days off work due to sickness) and lifestyle (smoking status, weekly levels of walking and exercise) data were collected from participants who responded to either Wave 1 and/or the full version of the questionnaire administered in Wave 2 of the YHS.

HRQoL, as measured by the EQ-5D summary index (measured on a scale from 0 to 1, with higher values representing higher QoL, and derived from scores on individual EQ-5D domains of mobility, self-care, activities, pain and anxiety), was considered to be the outcome measure in the current investigation. The key predictor variable was weight status, measured using BMI, categorised for the purposes of the current investigation as Normal weight (18 kg/m 2  ≤ BMI < 25 kg/m 2 ); Overweight (25 kg/m 2  ≤ BMI < 30 kg/m 2 ), Obese (30 kg/m 2  ≤ BMI < 40 kg/m 2 ), and Morbidly obese (BMI ≥ 40 kg/m 2 ). This variable was collected in both waves of the survey. Individuals with BMI less than 18 kg/m 2 were not included in the analysis, as BMIs in this range may be indicative of illness or eating disorder. An investigation into the relationship between QoL and BMI using the first wave only of the YHS [ 17 ] revealed the relationship to be monotonic and approximately linear in individuals with BMI values of 18 kg/m 2 or more: inclusion of underweight individuals’ results in a curvilinear effect.

Additionally, a number of variables, also collected in one or both waves of the survey, were collected and examined for potential inclusion as covariates in the analysis (Table  1 ). The first mentioned category of the categorical variables above was considered to be the reference category in all cases.

In addition to modelling the LTC variables as covariates in a multiple regression model, these variables were assessed for their effect as mediating variables on the causal pathway between BMI and QoL; in the light of findings by Doll et al. [ 7 ] that the proportion of individuals reporting LTCs, and the number of reported LTCs are significantly predicted by BMI in controlled models.

Physical exercise (including activities such as swimming, playing football, cycling and aerobics) and walking time (including walking to work, to shops and leisure walking) in the week preceding data collection were estimated using the mid-point of options presented as ranges of times (none; 0–1 h per week; 2–3 h per week etc.) offered to respondents as response categories.

The data set was checked before analysis for errors. Any values outside of theoretical or plausible ranges were deleted or replaced with a limiting value as appropriate, with limits for inclusion of BMI values obtained using guidelines. The extent and nature of data missingness was investigated. Missing values were assessed for nature of missingness using Little’s test for data missing completely at random (MCAR) and separate variance t -tests and cross-tabulations. Data missing at random (MAR) was inferred if the MCAR test was statistically significant but missingness could be predicted from variables other than the outcome variable from separate variance t -tests and cross-tabulations. Following verification of missing data on key variables to be MCAR or MAR, complete case analysis was used with respect to both the key predictor variable (weight status as measured by BMI category) and the outcome measure (EQ-5D score) with no imputation conducted on these variables. Controlling variables with more than 5% missing values on remaining cases were dropped from further analysis. Controlling variables with less than 5% missing values that could be shown or inferred to be MCAR or MAR were imputed using expectation maximisation.

The data were summarised descriptively, by weight status (BMI category) and as a full cohort. A series of simple (univariable) regression models were conducted on valid cases, with imputation where necessary and appropriate, considering both the key variable of weight status and each controlling variable in turn as predictors. Controlling variables showing some substantive relationship with the outcome measure were carried forward for inclusion in a subsequent main effects multiple linear regression analysis alongside weight status. Included variables were assessed for collinearity and regression assumptions for the final multiple model were checked post-estimation using residual plots.

Model transferability was assessed by cross-validation. A regression equation was constructed based on a random 80% of cases with model coefficients used to obtain predicted values on the remaining validation sample. The correlation between predicted and actual values in the validation sample was then compared with the corresponding statistic for the main sample; with low or no reductions representing good model transferability.

Ethical approval for the YHS was granted by the NHS Research Ethics Committee (09/H1306/97).

Valid data were collected on 64,631 individuals. Data checking revealed a small proportion of certain variables with implausible or impossible data values. These were investigated on an individual basis and deleted or amended where necessary.

Calculated BMI values of the cleaned data set ranged from 8.32 to 85.9 kg/m 2 ; with a mean value of 26.7 kg/m 2 (SD 5.50 kg/m 2 ). The BMI ranges and corresponding frequencies associated with each original and merged category are summarised in Table  2 .

A summary of participant characteristics (by weight status) before imputation and variable deletion is summarised in Table  3 ; with data based on respondents from whom a valid weight status could be deduced.

While most differences across groups were statistically significant at the 5% significance level, reflecting the large sample size, few substantive differences across groups were observed. Uni-variable tests of significance revealed low effect sizes (measured by the ϕ and partial-η 2 statistics) of less than 5% for most reported variables in the table above. However, some cross-group differences of non-negligible magnitude were observed with respect to gender, diabetes status and academic qualifications. A higher proportion of women than men were in the group with morbid obesity; however, overall mean male BMI (26.9 kg/m 2 ; SD 4.83 kg/m 2 ) was higher than the mean female BMI (26.6 kg/m 2 ; SD 5.84 kg/m 2 ). The proportion of those in the Normal weight group who were qualified to degree level or above was, at 12.1%, more than double that of those in the group with obesity (5.5%) and over 3 times that of those in the group with morbid obesity (3.5%). The proportion of those in the Normal weight group who suffered from 3 or more long-term mental health-related conditions was, at 6.7%, less than half that of those in the group with obesity (15.7%) and less than a third that of those in the group with morbid obesity (24.5%).

Little’s test for MCAR using all quantitative variables with complete or near-complete cases revealed no evidence that missing EQ-5D scores were not MCAR ( p  = 0.408). Separate variance t-tests revealed no evidence that missing weight statuses were not MAR. The variables corresponding to diabetes status, employment status, IMD, exercise levels, alcohol consumption and days off work due to sickness were not carried forward for consideration due to excessive proportions of missing values on these variables.

P -values, parameter estimates, associated confidence intervals, and effect sizes (using the partial-η 2 statistic) from a series of univariable regression analyses conducted the outcome measure of EQ-5D score on an imputed data set including the key predictor variable and all controlling variables with complete or near-complete set of cases as identified in Table 3 above, are summarised in Table  4 .

A mediation analysis revealed that both of the variables modelling mental or physical health-related LTCs exhibited some mediating effect on the relationship between weight status and HRQoL. All paths in the mediation models considering weight status as a predictor, and the mental or physical health-related LTCs in turn as mediators were significant. Path coefficients for weight status were revealed to be − 0.010 in a univariable regression of QoL on weight status; − 0.007 in a model including the variable modelling mental health LTCs and − 0.007 in a model including the variable modelling physical health LTCs. Hence while conditions for partial mediation were met, the conditions were full mediation were not met. The substantive mediating effect was low and weight status continued to significantly predict the outcome in the presence of the mediating variable. Hence analysis proceeded with LTCs being modelled as a controlling covariate.

The simple regression models suggested that age, presence/absence of long-term conditions, level of contact with health professions in last 3 months, number of hours per week spent walking, and number of hospital outpatient visits in previous 3 months should be included alongside a weight status category in a multiple model. As strong evidence for statistical significance was expected in most cases due to the size of the data set, assessments for inclusion were made primarily on the basis of effect sizes, with an associated partial-η 2 statistic of about 0.025 or more considered to indicate grounds for inclusion of a particular variable. As the predictor variable of key contextual interest, this did not apply to any of the weight status categories. Model parameters from this multiple model are summarised in Table  5 .

The R 2 and adjusted-R 2 statistics for this model were both 0.390; representing a moderately good fit to the data. No evidence for collinearity was revealed, with variance inflation factors all within tolerable limits. Analysis of residuals revealed no clear evidence for violations of regression assumptions, with normally distributed standardised residuals which exhibited no clear pattern when plotted against standardised predicted values. The model showed very good cross-validation properties, with negligible loss in correlation computed from the validation sample fitted values against predictions from the training sample model coefficients.

Hence controlling for other categorical factors and covariates, compared to individuals in the Normal weight category; HRQoL was essentially the same in individuals in the Overweight category; slightly lower (4.9 percentage points less on the EQ-5D summary index) in individuals in the Obese category and lower (11.3 percentage points less on the EQ-5D summary index) in individuals in the Morbidly obese category. Hence the effect of morbid obesity, compared to normal weight, has approximately the same impact as 3 or more physical long-term conditions or an increase in age of about 55 years. Amongst the controlling variables, those with the greatest substantive effect on QoL were mental and physical health-related LTCs: those with 3 or more mental health conditions scored 29.8 percentage points less on the EQ-5D summary index than those with 2 or fewer conditions; and those with 3 or more physical health conditions scored 14.6 percentage points less on the EQ-5D summary index than those with 2 or fewer conditions. Higher quality of life was also reported by younger people, by those who saw health professionals more infrequently and spent less time visiting hospital as an outpatient, and by those who spent more time walking.

Key findings

The analysis has revealed a clear relationship indicating lower levels of QoL with weight status defined by categories of increasing BMI in individuals with BMIs in the range of 18 kg/m 2 and above. This monotonic decrease in QoL, recorded in groups categorised by increasing BMI, is consistent with both the findings relating to the individual EQ-5D items in the analysis by Kearns et al. [ 17 ] of the first wave of the YHS data, and the wider literature [ 12 , 23 ]. The effect on QoL of weight status category is substantial, particularly for those in the highest BMI category. This reduction in QoL as a result of increasing BMI is greater than that found linked to cancer, myocardial infarction and diabetes, and similar to having schizophrenia, heart failure or kidney failure (Sullivan 2001). However, the EQ-5D summary index is a highly negatively skewed measure, with about one third of our respondents scoring the maximum value of 1.00 and over half of respondents scoring 0.84 or more.

Comparing the estimates and magnitudes thereof of the weight status variables in the simple and multiple models reveals that the effect of weight status is smaller in the multiple (controlled) model. The variables corresponding to mental and physical health-related LTCs in the multiple model appear to be of greater effect on QoL than weight status itself. This may be due to a proportion of the residual variance ascribed to weight status in the simple model being ascribed to other variables in the multiple model; specifically, LTCs, which are already known to be related to weight status from the descriptive analysis and is reflected in the 2007 Sach analysis of BMI and quality of life. It may also reflect the status of obesity as a risk factor for many LTCs [ 3 , 5 , 6 , 7 , 8 ]. However, there are no changes of direction of association of parameter coefficients or substantial changes in parameter estimates or inferences of significance between the models. Further work considering the impact of specific individual conditions may be beneficial.

The mediation analysis reveals that the presence of mental or physical health-related LTCs has a limited partial mediating effect on the underlying relationship between weight status and QoL. In the current analysis, LTCs are analysed as controlling factors. Nonetheless, LTCs can alternatively be conceptualised as lying on the causal path between BMI and QoL [ 1 , 10 , 17 ]; although the direct link between BMI and QoL is stronger and more intuitive. Further model-testing work is needed to establish the existence of, and direction of associations between other constructs represented in the YHS.

The unique contribution of BMI to QoL is consistent with Scottish data [ 18 ] which found an independent relationship between obesity and Quality of Life. This is in contrast to the ‘Healthy Obesity’ hypothesis and may represent a subset of the population ‘in transition’ to unhealthy obesity [ 28 ] via metabolic syndrome, not measured in our study.

The largest unique effect in the multiple model was the presence of 3 or more mental health LTCs. This may be an artefact of the data, explained by a presumed higher likelihood of MH LTCs being related in our sample, compared to the ‘independence’ of the physical domains of LTC. The second biggest effect is degree of contact with a health professional, which we presume is acting as a proxy measure for general health.

Strengths and limitations

The strengths of the YHS are its large sample size which allows for an exploration of detailed obesity categories, comprehensive examination of a wide range of variables, and the use of EQ-5D which measures HRQoL using public preferences.

Most measures captured by the YHS are self-reported and may not be completely reliable; particularly those requiring accurate recall, such as activity levels or levels of contact with healthcare professionals over an extended period of time; or the ability of respondents to distinguish between, for example, hospital visits as an out-patient or day case. The key predictor of BMI requires accurate self-reporting of both height and body weight in appropriate units. In addition, self-reported height and weight are respectively over and underestimated in both men and women (Niedhammer 2000, Spencer 2002, Taylor 2006). In the current study, analysis was restricted to variables which were derived from items elicited in both waves of the questionnaire.

The fit of the multiple regression model to the data, though of moderately high magnitude, may have been constrained in magnitude by uncertainties in the integrity of certain measures and the limited availability of variables for which an acceptable proportion of valid cases were available. Nonetheless, a moderately good fit was obtained and cross-validation procedures revealed that model portability is good; it should be expected that the model will perform equally well on samples other than that from which parameter coefficients were derived.

Implications for future work

This study has demonstrated that further work is needed to establish the existence of, and direction of associations; for example, it seems plausible that not only can factors such as BMI and exercise impact on quality of life (as was assumed in this analysis), but also that variables such as exercise level and BMI are correlated with a plausible association in either direction. A number of models are required to be tested for model fit using, for example, a confirmatory factor analysis approach in order to ensure that an optimal series of relationships are tested.

To conclude, in agreement with the established literature we have found a clear inverse relationship between increasing weight status and decreasing QoL, using a large regional cohort study. We have investigated the influence of other demographic, lifestyle and health related domains on this relationship and confirmed the mediating role of LTCs in the reduction of QoL in people with obesity. Nevertheless, a high weight status remains independently related to QoL, suggesting that the ‘healthy obese’ may be in transition to an unhealthy future.

Availability of data and materials

Anonymised data and details regarding using the resource for recruiting participants to studies can be gathered by contacting Professor Elizabeth Goyder ( [email protected] ). Multi-disciplinary collaboration is strongly encouraged.

Abbreviations

Body mass index

Health-related quality of life

• Quality of life
• Long-term conditions

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Acknowledgements

This report is independent research funded by the National Institute for Health Research (NIHR) Collaborations for Leadership in Applied Health Research and Care (CLAHRC) Yorkshire and Humber. The views expressed in this publication are those of the authors and not necessarily those of the Yorkshire Health Study Management Team or Steering Committee, National Institute for Health Research or the Department of Health and Social Care.

This work was supported by the National Institute for Health Research (NIHR) Collaborations for Leadership in Applied Health Research and Care (CLAHRC) Yorkshire and Humber (NIHR200166). Analysis was supported by the Universities of Huddersfield and Sheffield.

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PB, AH, CS and BK had the original concept. JS and CS designed the work. All authors agreed the methodology. JS performed the statistical analyses. All authors interpreted the results. CS and JS drafted the manuscript. All authors fed back comments. All authors read and approved the final manuscript.

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The protocol for the South Yorkshire Cohort (the early name for the Yorkshire Health Study) was approved by the NHS Research Ethics Committee on 27 April 2010 (09/H1306/97). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants based on the principle of ‘patient-centred informed consent’ i.e. where patient information and consent aim to replicate that of real world routine healthcare rather than conform to the needs of standard trial designs. Therefore all cohort patients consented to provide observational data at the outset, be contacted again, and for their information to be used to look at the benefit of healthcare treatments; however, consent to “try” a particular intervention in the future was sought only from those offered that intervention. This method of obtaining consent replicates the ‘patient-centred’ information and consent procedures that exist in routine health care, where clinicians provide patients with the information they need, at the time they need it. The consent procedure is described fully in the South Yorkshire Cohort Protocol [ 29 ]. Research on human data was performed in accordance with the Declaration of Helsinki.

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Stephenson, J., Smith, C.M., Kearns, B. et al. The association between obesity and quality of life: a retrospective analysis of a large-scale population-based cohort study. BMC Public Health 21 , 1990 (2021). https://doi.org/10.1186/s12889-021-12009-8

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Published : 03 November 2021

DOI : https://doi.org/10.1186/s12889-021-12009-8

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• Preventing Childhood Obesity
• Health Care Strategies
• About Obesity
• What Can Be Done
• About Healthy Weight and Growth
• Body Mass Index (BMI)
• About Nutrition
• About Physical Activity

Obesity Strategies: What Can Be Done

At a glance.

Obesity is a complex and costly chronic disease with many contributing factors. Access to healthy, affordable foods and safe, convenient places for physical activity can impact obesity. Addressing obesity requires organizations and people to work together to create communities, environments, and systems that support healthy, active lifestyles for all.

The federal government is:

• Studying what works in communities to make it easier for people to be more physically active and have a healthier diet.
• Monitoring trends in obesity and related risk factors.
• Developing and promoting  guidelines on dietary patterns and amounts of physical activity needed for good health .
• Helping families with lower incomes get affordable, nutritious foods through programs such as the Supplemental Nutrition Program for Women, Infants, and Children (WIC) and farm-to-education programs.
• Supporting children and families who are at higher risk for obesity through services at Federally Qualified Health Centers, Head Start, WIC, and other service agencies.
• Funding programs and providing training and resources for initiatives that promote healthy eating, food and nutrition security, and physical activity .
• Working with state, tribal, local, and territory governments, academia, the private sector, and nonprofit and community groups to implement the White House National Strategy on Hunger, Nutrition, and Health —to end hunger and reduce diet-related diseases and disparities.

Some states and communities are:

Two priority obesity-prevention strategies for state and local programs are:

• Improving nutrition, physical activity, and breastfeeding in early care and education programs.
• Establishing policies and activities that implement, spread, and sustain Family Healthy Weight Programs .

In addition, state and local programs are:

• Designing communities that connect sidewalks, bicycle routes, and public transportation with homes, schools, parks, and workplaces to increase physical activity.
• Expanding voucher incentive and produce prescription programs to make healthy foods more available.
• Promoting food service and nutrition guidelines in worksites, food pantries, and faith-based organizations.
• Implementing policies and activities that achieve continuity of care for breastfeeding .
• Partnering with business and civic leaders to plan and carry out local, culturally tailored interventions to address poor nutrition, physical inactivity, and tobacco use.

Health Care providers can:

• Measure patients' weight and height, calculate body mass index , and counsel them on its role in disease prevention.
• Refer patients with obesity to intensive programs, including Family Healthy Weight and Diabetes Prevention programs.
• Counsel patients about nutrition, physical activity, and optimal sleep.
• Use respectful and non-stigmatizing, person-first language in all weight-related discussions.
• Connect patients and families with community services to help them access healthy foods and ways to be active.
• Discuss the use of medications and other treatments for excess weight.
• Seek continuing medical education  about obesity.

Everyone can take steps to:

• MyPlate resources.
• Tips for healthy eating for a healthy weight .
• Get the recommended amount of physical activity.
• Get enough sleep .
• Manage stress .
• Talk to your health care provider about whether weight is a health concern. If so, discuss available obesity treatment options to help reduce potential health risks.
• Get involved in local efforts, such as local committees and councils , to improve options for healthier foods and physical activity.

CDC's obesity prevention efforts focus on policy and environmental strategies to make healthy eating and active living accessible for everyone.

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May 14, 2024

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Severe obesity in childhood can halve life expectancy, global modeling study finds

by European Association for the Study of Obesity

New research being presented at the European Congress on Obesity (ECO) in Venice, Italy (12-15 May) has for the first time quantified the impact of different aspects of childhood obesity on long-term health and life expectancy.

The modeling by stradoo GmbH, a life sciences consultancy in Munich, Germany, presented by Dr. Urs Wiedemann, of stradoo, and colleagues at universities and hospitals in the UK, Netherlands, France, Sweden, Spain, U.S. and Germany, found that age of onset, severity and duration of obesity all take their toll on life expectancy .

The development of obesity at a very young age was found to have a particularly profound effect.

For example, a child living with severe obesity (BMI Z-score of 3.5) at the age of four, who doesn't subsequently lose weight, has a life expectancy of 39 years—about half of the average life expectancy.

Dr. Wiedemann says, "While it's widely accepted that childhood obesity increases the risk of cardiovascular disease and related conditions such as type 2 diabetes (T2D), and that it can reduce life expectancy, evidence on the size of the impact is patchy. A better understanding of the precise magnitude of the long-term consequences and the factors that drive them could help inform prevention policies and approaches to treatment, as well as improve health and lengthen life."

To learn more, the researchers created an early-onset obesity model that allowed them to estimate the effect of childhood obesity on cardiovascular disease and related conditions such as type 2 diabetes (TD2), as well as life expectancy.

Four key variables were included: age of obesity onset, obesity duration, irreversible risk accumulation (a measure of irreversible risks of obesity—health effects that remain even after weight loss) and severity of obesity.

Severity of obesity was based on BMI Z-scores. A widely used measure of weight in childhood and adolescence, BMI Z-scores indicate how strongly an individual's BMI deviates from the normal BMI for their age and sex, with higher values representing higher weight.

For example, a 4-year-old boy with an average height of 103 cm and a "normal" weight of about 16.5 kg will have a BMI Z-score of 0. A boy of the same age and height who weighs 19.5 kg will have a BMI Z-score of 2, which is just in the obese range, and one who weighs 22.7 kg will have a BMI Z-score of 3.5, which indicates severe obesity.

Data came from 50 existing clinical studies on obesity and obesity-related comorbidities, such as type 2 diabetes, cardiovascular events and fatty liver. The studies included more than 10 million participants from countries around the world, approximately 2.7 million of whom were aged between 2 and 29 years.

The model shows that earlier onset and more severe obesity increase the likelihood of developing related comorbidities.

For example, an individual with a BMI Z-score of 3.5 (which indicates severe obesity ) at age 4 and who doesn't go on to lose weight has a 27% likelihood of developing T2D by the age of 25 and a 45% chance of developing T2D by the age of 35.

In contrast, an individual with a BMI Z-score of 2 at age 4 will have a 6.5% chance of T2D by the age of 25 and 22% chance by the age of 35.

The early-onset obesity model also shows that a higher BMI Z-score at an early age leads to a lower life expectancy.

For example, a BMI Z-score of 2 at age 4 without subsequent weight reduction decreases average life expectancy from approx. 80 to 65 years. Life expectancy is further reduced to 50 years for a BMI Z-score of 2.5 and 39 years for a BMI Z-score of 3.5.

In contrast, a BMI Z-score of 3.5 at age 12 without subsequent weight reduction yields an average life expectancy of 42 years.

Comparisons with data from studies not included as input for the model and the opinions of leading experts confirmed the model's accuracy.

It was also possible to model the effect of weight loss on life expectancy and long-term health. For example, an individual living with severe early-onset obesity (BMI Z-score of 4 at age 4) who doesn't subsequently lose weight has a life expectancy of 37 years and a 55% risk of developing type 2 diabetes at age of 35. Weight loss that results in a BMI Z-score of 2 (just in the obese range) at age of 6, will increase the life expectancy to 64 and reduce the risk of type 2 diabetes to 29%.

The modeling also shows that earlier weight loss returns more years of life than later weight loss .

Dr. Wiedemann says, "The early-onset obesity model shows that weight reduction has a striking effect on life expectancy and comorbidity risk, especially when weight is lost early in life."

The model's limitations include not taking into account the cause of obesity, genetic risk factors, ethnic or sex differences, as well as not factoring in how different co-morbidities interact with each other.

Dr. Wiedemann concludes, "The impact of childhood obesity on life expectancy is profound. It is clear that childhood obesity should be considered a life-threatening disease. It is vital that treatment isn't put off until the development of type 2 diabetes, high blood pressure or other 'warning signs' but starts early. Early diagnosis should and can improve quality and length of life."

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Feingold KR, Anawalt B, Blackman MR, et al., editors. Endotext [Internet]. South Dartmouth (MA): MDText.com, Inc.; 2000-.

Endotext [Internet].

• www.endotext.org

Definitions, Classification, and Epidemiology of Obesity

Jonathan Q. Purnell , MD.

Last Update: May 4, 2023 .

Recent research has established the physiology of weight regulation, the pathophysiology that leads to unwanted weight gain with establishment of a higher body-weight set point, and the defense of the overweight and obese state even when reasonable attempts in lifestyle improvement are made. This knowledge has informed our approach to obesity as a chronic disease. The assessment of adiposity risk for the foreseeable future will continue to rely on cost-effective and easily available measures of height, weight, and waist circumference. This risk assessment then informs implementation of appropriate treatment plans and weight management goals. Within the United States, prevalence rates for generalized obesity (BMI > 30 kg/m 2 ), extreme obesity (BMI > 40 kg/m 2 ), and central obesity continue to rise in children and adults with peak obesity rates occurring in the 5 th -6 th decades. Women may have equal or greater obesity rates than men depending on race, but less central obesity than men. Obesity disproportionately affects people by race and ethnicity, with the highest prevalence rates reported in Black women and Hispanic men and women. Increasing obesity rates in youth (ages 2-19 years) are especially concerning. This trend will likely continue to fuel the global obesity epidemic for decades to come, worsening population health, creating infrastructural challenges as countries attempt to meet the additional health-care demands, and greatly increasing health-care expenditures world-wide. To meet this challenge, societal and economic innovations will be necessary that focus on strategies to prevent further increases in overweight and obesity rates. For complete coverage of all related areas of Endocrinology, please visit our on-line FREE web-text, WWW.ENDOTEXT.ORG .

• INTRODUCTION

Unwanted weight gain leading to overweight and obesity has become a significant driver of the global rise in chronic, non-communicable diseases and is itself now considered a chronic disease. Because of the psychological and social stigmata that accompany developing overweight and obesity, those affected by these conditions are also vulnerable to discrimination in their personal and work lives, low self-esteem, and depression ( 1 ). These medical and psychological sequelae of obesity contribute to a major share of health-care expenditures and generate additional economic costs through loss of worker productivity, increased disability, and premature loss of life ( 2 - 4 ).

The recognition that being overweight or having obesity is a chronic disease and not simply due to poor self-control or a lack of will power comes from the past 70 years of research that has been steadily gaining insight into the physiology that governs body weight (homeostatic mechanisms involved in sensing and adapting to changes in the body’s internal metabolism, food availability, and activity levels so as to maintain fat content and body weight stability), the pathophysiology that leads to unwanted weight gain maintenance, and the roles that excess weight and fat maldistribution (adiposity) play in contributing to diabetes, dyslipidemia, heart disease, non-alcoholic fatty liver disease, obstructive sleep apnea, and many other chronic diseases ( 5 , 6 ).

Expression of overweight and obesity results from an interaction between an individual’s genetic predisposition to weight gain and environmental influences. Gene discovery in the field of weight regulation and obesity has identified several major monogenic defects resulting in hyperphagia accompanied by severe and early-onset obesity ( 7 ) as well as many more minor genes with more variable impact on weight and fat distribution, including age-of-onset and severity. Several of these major obesity genes now have a specific medication approved to treat affected individuals ( 8 ). However, currently known major and minor genes explain only a small portion of body weight variations in the population ( 7 ). Environmental contributors to obesity have also been identified ( 9 ) but countering these will likely require initiatives that fall far outside of the discussions taking place in the office setting between patient and provider since they involve making major societal changes regarding food quality and availability, work-related and leisure-time activities, and social and health determinants including disparities in socio-economic status, race, and gender.

Novel discoveries in the fields of neuroendocrine ( 6 ) and gastrointestinal control ( 10 ) of appetite and energy expenditure have led to an emerging portfolio of medications that, when added to behavioral and lifestyle improvements, can help restore appetite control and allow modest weight loss maintenance ( 8 ). They have also led to novel mechanisms that help to explain the superior outcomes, both in terms of meaningful and sustained weight loss as well as improvements or resolution of co-morbid conditions, following metabolic-bariatric procedures such as laparoscopic sleeve gastrectomy and gastric bypass ( 11 , 12 ).

Subsequent chapters in this section of Endotext will delve more deeply into these determinants and scientific advances, providing a greater breadth of information regarding mechanisms, clinical manifestations, treatment options, and prevention strategies for those with overweight or obesity.

• DEFINITION OF OVERWEIGHT AND OBESITY

Overweight and obesity occur when excess fat accumulation (globally, regionally, and in organs as ectopic lipids) increases risk for adverse health outcomes . Like other chronic diseases, this definition does not require manifistation of an obesity-related complication, simply that the risk for one is increased. This allows for implementation of weight management strategies targeting treatment and prevention of these related conditions. It is important to point out that thresholds of excess adiposity can occur at different body weights and fat distributions depending on the person or population being referenced.

Ideally, an obesity classification system would be based on a practical measurement widely available to providers regardless of their setting, would accurately predict health risk (prognosis), and could be used to assign treatment stategies and goals. The most accurate measures of body fat adiposity such as underwater weighing, dual-energy x-ray absorptiometry (DEXA) scanning, computed tomograpy (CT), and magnetic resonance imaging (MRI) are impractical for use in everyday clinical encounters. Estimates of body fat, including body mass index (BMI, calculated by dividing the body weight in kilograms by height in meters squared) and waist circumference, have limitations compared to these imaging methods, but still provide relevant information and are easily obtained in a variety of practice settings.

It is worth pointing out two important caveats regarding cuurent thresholds used to diagnose overweight and obesity. The first is that although we favor the assignement of specific BMI cut-offs and increasing risk ( Table 1 ), relationships between body weight or fat distribution and conditions that impair health actually represent a continum. For example, increased risk for type 2 diabetes and premature mortality occur well below a BMI of 30 kg/m 2 (the threshold to define obesity in populations of European extraction) ( 13 ). It is in these earlier stages that preventative strategies to limit further weight gain and/or allow weight loss will have their greatest health benefits. The second is that historic relationships between increasing BMI thresholds and the precense and severity of co-morbidities have been disrupted as better treatments for obesity-complications become available. For example, in the past several decades, atherosclerotic cardiovascular (ASCVD) mortality has steadily declined in the US population ( 14 ) even as obesity rates have risen (see below). Although it is generally accepted that this decline in ASCVD deaths is due to better care outside the hospital during a coronary event (e.g., better coordination of “first responders” services such as ambulances and more widespread use by the public of cardiopulmonary resusitation and defibrillator units), advances in intensive care, smoking cessation, and in the office (increased use of aspirin, statins, PCSK9 inhibitors, and blood pressure medications) ( 15 ), these data have also been cited to support the claim that being overweight might actually protect against heart disease ( 16 ). In this regard, updated epidemiological data on the health outcomes related to being overweight or having obesity should include not just data on morbidity and mortality, but also health care metrics such as utilization and costs, medications used, and the number of treatment-related procedures performed.

• CLASSIFICATION OF OVERWEIGHT, OBESITY, AND CENTRAL OBESITY

Fat Mass and Percent Body Fat

Fat mass can be directly measured by one of several imaging modalities, including DEXA, CT, and MRI, but these systems are impractical and cost prohibitive for general clinical use. Instead, they are mostly used for research. Fat mass can be measured indirectly using water (underwater weighing) or air displacement (BODPOD), or bioimpedance analysis (BIA). Each of these methods estimates the proportion of fat or non-fat mass and allows calcutation of percent body fat. Of these, BODPOD and BIA are often offered through fitness centers and clinics run by obesity medicine specialists. However, their general use in the care of patients who are overweight and with obesity is still limited. Interpretation of results from these procedures may be confounded by common conditions that accompany obesity, especially when fluid status is altered such as in congenstive heart failure, liver disease, or chronic kidney disease. Also, ranges for normal and abnormal are not well established for these methods and, in practical terms, knowing them will not change current recommendations to help patients achieve sustained weight loss.

Body Mass Index

Body mass index allows comparison of weights independently of stature across populations. Except in persons who have increased lean weight as a result of intense exercise or resistance training (e.g., bodybuilders), BMI correlates well with percentage of body fat, although this relationship is independently influenced by sex, age, and race ( 17 ). This is especially true for South Asians in whom evidence suggests that BMI-adjusted percent body fat is greater than other populations ( 18 ). In the United States, data from the second National Health and Nutrition Examination Survey (NHANES II) were used to define obesity in adults as a BMI of 27.3 kg/m 2 or more for women and a BMI of 27.8 kg/m 2 or more for men ( 19 ). These definitions were based on the gender-specific 85 th percentile values of BMI for persons 20 to 29 years of age. In 1998, however, the National Institutes of Health (NIH) Expert Panel on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults adopted the World Health Organization (WHO) classification for overweight and obesity ( Table 1 ) ( 20 ). The WHO classification, which predominantly applied to people of European ancestry, assigns increasing risk for comorbid conditions—including hypertension, type 2 diabetes mellitus, and cardiovascular disease—to persons with higher a BMI relative to persons of normal weight (BMI of 18.5 - 25 kg/m 2 ) ( Table 1 ). However, Asian populations are known to be at increased risk for diabetes and hypertension at lower BMI ranges than those for non-Asian groups due largely to predominance of central fat distribution and higer percentage fat mass (see below). Consequently, the WHO has suggested lower cutoff points for consideration of therapeutic intervention in Asians: a BMI of 18.5 to 23 kg/m 2 represents acceptable risk, 23 to 27.5 kg/m 2 confers increased risk, and 27.5 kg/m 2 or higher represents high risk ( 21 , 22 ).

Classification of Overweight and Obesity by BMI, Waist Circumference, and Associated Disease Risk. Adapted from reference ( 20 ).

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Disease risk for type 2 diabetes, hypertension, and cardiovascular disease.

Increased waist circumference can also be a marker for increased risk even in persons of normal weight.

Fat Distribution (Central Obesity)

In addition to an increase in total body weight, a proportionally greater amount of fat in the abdomen or trunk compared with the hips and lower extremities has been associated with increased risk for metabolic syndrome, type 2 diabetes mellitus, hypertension, and heart disease in both men and women ( 23 , 24 ). Abdominal obesity is commonly reported as a waist-to-hip ratio, but it is most easily quantified by a single circumferential measurement obtained at the level of the superior iliac crest ( 20 ). For the practioner, waist circumference should be measured in a standardized way ( 20 ) at each patient’s visit along with body weight. The original US national guidelines on overweight and obesity categorized men at increased relative risk for co-morbidities such as diabetes and cardiovascular disease if they have a waist circumference greater than 102 cm (40 inches) and women if their waist circumference exceeds 88 cm (35 inches) ( Table 1 ) ( 20 ). These waist circumference thresholds are also used to define the “metabolic syndrome” by the most recent guidelines from the American Heart Association and the National Lipid Association (e.g., triglyceride levels > 150 mg/dL, hypertension, elevated fasting glucose (100 – 125 mg/dL)) or prediabetes (hemoglobin A1c between 5.7 and 6.4%) ( 25 , 26 ). Thus, an overweight person with predominantly abdominal fat accumulation would be considered “high” risk for these diseases even if that person does not meet BMI criteria for obesity. Such persons would have “central obesity.” It is commonly accepted that the predictive value for increased health risk by waist circumference is in patients at lower BMI’s (< 35 kg/m 2 ) since those with class 2 obesity or higher will nearly universally have waist circumferences that exceed disease risk cut-offs.

However, the relationships between central adiposity with co-morbidities are also a continuum and vary by race and ethnicity. For example, in those of Asian descent, abdominal (central) obesity has long been recognized to be a better disease risk predictor than BMI, especially for type 2 diabetes ( 27 ). As endorsed by the International Diabetes Federation ( 28 ) and summarized in a WHO report in 2008 ( 29 ), different countries and health organizations have adopted differing sex- and population-specific cut offs for waist circumference thresholds predictive of increased comorbidity risk. In addition to the US criteria, alternative thresholds for central obesity as measured by waist circumference include > 94 cm (37 inches) and > 80 cm (31.5 inches) for men and women of European anscestry and > 90 cm (35.5 inches) and > 80 cm (31.5 inches) for men and women of South Asian, Japanese, and Chinese origin ( 28 , 29 ), respectively.

• EPIDEMIOLOGY OF OVERWEIGHT AND OBESITY IN THE UNITED STATES

In the United States (US), data from the National Health and Nutrition Examination Survey using measured heights and weights shows that the steady increase in obesity prevalence in both children and adults over the past several decades has not waned, although there are exceptions among subpopulations as described in greater detail below. In the most recently published US report (2017-2020), 42.4% of adults (BMI ≥ 30 kg/m 2 ) ( 30 ) and 20.9% of youth (BMI ≥ 95 th percentile of age- and sex-specific growth charts) ( 31 ) have obesity, and the age-adjusted prevalence of severe obesity (BMI ≥ 40 kg/m 2 ) was 9.2% ( 30 ) ( Figure 1 ).

Trends in age-adjusted obesity (BMI ≥ 30 kg/m 2 ) and severe obesity (BMI ≥ 40 kg/m 2 ) prevalence among adults aged 20 and over: United States, 1999–2000 through 2017–2018. Taken from reference ( 30 ).

Obesity and Severe Obesity in Adults: Relationships with Age, Sex, and Demographics

Age-Adjusted Prevalence of Obesity and Severe Obesity in US Adults. National Health and Nutrition Examination Survey data, prevalence estimates are weighted and age-adjusted to the projected 2000 Census population using age groups 20-39, 40-59, and 60 or older. Significant linear trends (P < .001) for all groups except for obesity among non-Hispanic Black men, which increased from 1999-2000 to 2005-2006 and then leveled after 2005-2006. Data taken from reference ( 31 ).

On average, the obesity rate in US adults has nearly tripled since the 1960’s (Reference ( 32 ) and Figure 2 ). These large increases in the number of people with obesity and severe obesity, while at the same time the level of overweight has remained steady ( 32 , 33 ), suggests that the “obesogenic” environment is disproportionately affecting those portions of the population with the greatest genetic potential for weight gain ( 34 ). This currently leaves slightly less than 30% of the US adult population as having a healthy weight (BMI between 18.5 and 25 kg/m 2 ).

Men and women now have similar rates of obesity and the peak rates of obesity for both men and women in the US occur between the ages of 40 and 60 years ( Figures 2 and 3 ). In studies that have measured body composition, fat mass also peaks just past middle age in both men and women, but percent body fat continues to increase past this age, particularly in men because of a proportionally greater loss in lean mass ( 35 - 37 ). The menopausal period has also been associated with an increase in percent body fat and propensity for central (visceral) fat distribution, even though total body weight may change very little during this time ( 38 - 41 ).

The rise in obesity prevalence rates has disproportionately affected US minority populations ( Figure 2 ). The highest prevelance rates of obesity by race and ethnicity are currently reported in Black women, native americans, and Hispanics ( Figure 2 and reference ( 42 )). In general, women and men who did not go to college were more likely to have obesity than those who did, but for both groups these relationships varied depending on race and ethnicity (see below). Amongst women, obesity prevelance rates decreased with increasing income in women (from 45.2% to 29.7%), but there was no difference in obesity prevalence between the lowest (31.5%) and highest (32.6%) income groups among men ( 43 ).

Prevalence of obesity among adults aged 20 and over, by sex and age: United States, 2017–2018. Taken from reference ( 30 ).

The interactions of socieconomic status and obesity rates varied based on race and ethnicity ( 43 ). For example, the expected inverse relationship between obesity and income group did not hold for non-Hispanic Black men and women in whom obesity prevelance was actually higher in the highest compared to lowest income group (men) or showed no relationship to income by racial group at all (women) ( 43 ). Obesity prevalence was lower among college graduates than among persons with less education for non-Hispanic White women and men, Black women, and Hispanic women, but not for Black and Hispanic men. Asian men and women have the lowest obesity prevelance rates, which did not vary by eduction or income level ( 43 ).

Central Obesity

As discussed above, central weight distribution occurs more commonly in men than women and increases in both men and women with age. In one of the few datasets that have published time-trends in waist circumference, it has been shown that over the past 20 years, age-adjusted waist circumferences have tracked upward in both US men and women ( Figure 4 ). Much of this likely reflects the population increases in obesity prevelance since increasing fat mass and visceral fat track together ( 52 ).

Age-adjusted mean waist circumference among adults in the National Health and Nutrition Examination Survey 1999-2012. Adapted from ( 51 ).

Childhood obesity is a risk factor for adulthood obesity ( 44 - 46 ). In this regard, the similar tripling of obesity rates in US youth (ages 2-19 years old) ( Figure 5 ) to 20.9% in 2018 ( 31 ) is worrisome and will contribute to the already dismal projections of the US adult population approaching 50% obesity prevelance by the year 2030 ( 47 ). Obesity prevalence was 26.2% among Hispanic children, 24.8% among non-Hispanic Black children, 16.6% among non-Hispanic White children, and 9.0% among non-Hispanic Asian children ( 48 ). Like adults, obesity rates in children are greater when they are live in households with lower incomes and less education of the head of the household ( 49 ). In this regard, these obesity gaps have been steadily widening in girls, whereas the differences between boys has been relatively stable ( 49 ).

Trends in obesity among children and adolescents aged 2–19 years, by age: United States, 1963–1965 through 2017–2018. Obesity is defined as body mass index (BMI) greater than or equal to the 95th percentile from the sex-specific BMI-for-age 2000 CDC Growth Charts. Taken from reference ( 50 ).

With regard to socieconomic status, the inverse trends for lower obesity rates and higher income and education (of households) held in all race and ethnic origin groups with the following exceptions: obesity prevalence was lower in the highest income group only in Hispanic and Asian boys and did not differ by income among non-Hispanic Black girls ( 49 ).

• INTERNATIONAL TRENDS IN OBESITY

Historically, international obesity rates have been lower than in the US, and most developing countries considered undernutrition to be their topmost health priority ( 53 ). However, international rates of overweight and obesity have been rising steadily for the past several decades and, in many countries, are now meeting or exceeding those of the US ( Figure 6 ) ( 54 , 55 ). In 2016, 1.3 billion adults were overweight worldwide and, between 1975 to 2016, the number of adults with obesity increased over six-fold, from 100 million to 671 million (69 to 390 million women, 31 to 281 million men) ( 54 ). Especially worrisome have been similar trends in the youth around the world ( Figure 6 ), from 5 million girls and 6 million boys with obesity in 1975 to 50 million girls and 74 million boys in 2016 ( 54 ), as this means the rise in obesity rates will continue for decades as they mature into adults.

The growth in the wordwide prelance of overweight and obesity is thought to be primarily driven by economic and technological advancements in all developing societies ( 56 , 57 ). These forces have been ongoing in the US and other Western countries for many decards but are being experienced by many developing countries on a compressed timescale. Greater worker productivity in advancing economies means more time spent in sedentary work (less in manual labor) and less time spent in leisure activity. Greater wealth allows the purchase of televisions, cars, processed foods, and more meals eaten out of the house, all of which have been associated with greater rates of obesity in children and adults. More details and greater discussion of these issues can be found in Endotext Chapters on Non-excercise Activity Thermogenesis ( 58 ) and Obesity and the Environment ( 9 ).

Regardless of the causes, these trends in global weight gain and obesity are quickly creating a tremendous burden on health-care systems and cost to countries attempting to respond to the increased treatment demands ( 59 ). They are also feuling a rise in global morbity and mortality for chronic (non-communicable) diseases, especially for cardiovascular disease and type 2 diabetes mellitus, and especially in Asian and South Asian populations where rates of type 2 diabetes are currently exploding ( 15 , 60 - 63 ). Efforts need to be made to deliver adequate health care to those currently with obesity and, at the same time, find innovative and alternative solutions that allow economies to prosper and to incorporate technologies that will reverse current trends in obesity and obesity-related complications.

Trends in the number of adults, children, and adolescents with obesity and with moderate and severe underweight by region. Children and adolescents were aged 5–19 years. (Taken from ( 54 )).

Obesity is both a chronic disease in its own right and a primary contributor to other leading chronic diseases such as type 2 diabetes, dyslipidemia, hypertension, and cardiovascular diseases. In the clinic, obesity is still best defined using commonly available tools, including BMI and waist circumference; although it is hoped that newer imaging modalities allowing more precise quantification of amount and distribution of excess lipid depots will improve obesity risk assessment. The general rise in obesity taking place in the US over the past 50 years is now occurring globally. In the US, the prevalence rates of obesity in adult men and women are now similar at 40%, and minorities are disproportionately affected, including Blacks, Native Americans, and Hispanics, with obesity rates of 50% or higher. Particularly worrisome is the global increase in obesity prevalence in children and adolescents as these groups will continue to contribute to a rising adult obesity rates for several decades to come. As important as finding solutions that address the global logistical and financial challenges facing health-care systems attempting to meet current demands of obesity and weight-related co-morbidities will be finding innovative solutions that prevent and reverse current population weight gain trends.

This electronic version has been made freely available under a Creative Commons (CC-BY-NC-ND) license. A copy of the license can be viewed at http://creativecommons.org/licenses/by-nc-nd/2.0/ .

• Cite this Page Purnell JQ. Definitions, Classification, and Epidemiology of Obesity. [Updated 2023 May 4]. In: Feingold KR, Anawalt B, Blackman MR, et al., editors. Endotext [Internet]. South Dartmouth (MA): MDText.com, Inc.; 2000-.

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Guest Essay

A Year on Ozempic Taught Me We’re Thinking About Obesity All Wrong

By Johann Hari

Mr. Hari is a British journalist and the author of “Magic Pill: The Extraordinary Benefits — and Disturbing Risks — of the New Weight Loss Drugs.”

Ever since I was a teenager, I have dreamed of shedding a lot of weight. So when I shrank from 203 pounds to 161 in a year, I was baffled by my feelings. I was taking Ozempic, and I was haunted by the sense that I was cheating and doing something immoral.

I’m not the only one. In the United States (where I now split my time), over 70 percent of people are overweight or obese, and according to one poll, 47 percent of respondents said they were willing to pay to take the new weight-loss drugs. It’s not hard to see why. They cause users to lose an average of 10 to 20 percent of their body weight, and clinical trials suggest that the next generation of drugs (probably available soon) leads to a 24 percent loss, on average. Yet as more and more people take drugs like Ozempic, Wegovy and Mounjaro, we get more confused as a culture, bombarding anyone in the public eye who takes them with brutal shaming.

This is happening because we are trapped in a set of old stories about what obesity is and the morally acceptable ways to overcome it. But the fact that so many of us are turning to the new weight-loss drugs can be an opportunity to find a way out of that trap of shame and stigma — and to a more truthful story.

In my lifetime, obesity has exploded, from being rare to almost being the norm. I was born in 1979, and by the time I was 21, obesity rates in the United States had more than doubled . They have skyrocketed since. The obvious question is, why? And how do these new weight-loss drugs work? The answer to both lies in one word: satiety. It’s a concept that we don’t use much in everyday life but that we’ve all experienced at some point. It describes the sensation of having had enough and not wanting any more.

The primary reason we have gained weight at a pace unprecedented in human history is that our diets have radically changed in ways that have deeply undermined our ability to feel sated. My father grew up in a village in the Swiss mountains, where he ate fresh, whole foods that had been cooked from scratch and prepared on the day they were eaten. But in the 30 years between his childhood and mine, in the suburbs of London, the nature of food transformed across the Western world. He was horrified to see that almost everything I ate was reheated and heavily processed. The evidence is clear that the kind of food my father grew up eating quickly makes you feel full. But the kind of food I grew up eating, much of which is made in factories, often with artificial chemicals, left me feeling empty and as if I had a hole in my stomach. In a recent study of what American children eat, ultraprocessed food was found to make up 67 percent of their daily diet. This kind of food makes you want to eat more and more. Satiety comes late, if at all.

One scientific experiment — which I have nicknamed Cheesecake Park — seemed to me to crystallize this effect. Paul Kenny, a neuroscientist at Mount Sinai Hospital in New York, grew up in Ireland. After he moved in 2000 to the United States, when he was in his 20s, he gained 30 pounds in two years. He began to wonder if the American diet has some kind of strange effect on our brains and our cravings, so he designed an experiment to test it. He and his colleague Paul Johnson raised a group of rats in a cage and gave them an abundant supply of healthy, balanced rat chow made out of the kind of food rats had been eating for a very long time. The rats would eat it when they were hungry, and then they seemed to feel sated and stopped. They did not become fat.

But then Dr. Kenny and his colleague exposed the rats to an American diet: fried bacon, Snickers bars, cheesecake and other treats. They went crazy for it. The rats would hurl themselves into the cheesecake, gorge themselves and emerge with their faces and whiskers totally slicked with it. They quickly lost almost all interest in the healthy food, and the restraint they used to show around healthy food disappeared. Within six weeks, their obesity rates soared.

After this change, Dr. Kenny and his colleague tweaked the experiment again (in a way that seems cruel to me, a former KFC addict). They took all the processed food away and gave the rats their old healthy diet. Dr. Kenny was confident that they would eat more of it, proving that processed food had expanded their appetites. But something stranger happened. It was as though the rats no longer recognized healthy food as food at all, and they barely ate it. Only when they were starving did they reluctantly start to consume it again.

Though Dr. Kenny’s study was in rats, we can see forms of this behavior everywhere. We are all living in Cheesecake Park — and the satiety-stealing effect of industrially assembled food is evidently what has created the need for these medications. Drugs like Ozempic work precisely by making us feel full. Carel le Roux, a scientist whose research was important to the development of these drugs, says they boost what he and others once called “satiety hormones.”

Once you understand this context, it becomes clear that processed and ultraprocessed food create a raging hole of hunger, and these treatments can repair that hole. Michael Lowe, a professor of psychology at Drexel University who has studied hunger for 40 years, told me the drugs are “an artificial solution to an artificial problem.”

Yet we have reacted to this crisis largely caused by the food industry as if it were caused only by individual moral dereliction. I felt like a failure for being fat and was furious with myself for it. Why do we turn our anger inward and not outward at the main cause of the crisis? And by extension, why do we seek to shame people taking Ozempic but not those who, say, take drugs to lower their blood pressure?

The answer, I think, lies in two very old notions. The first is the belief that obesity is a sin. When Pope Gregory I laid out the seven deadly sins in the sixth century, one of them was gluttony, usually illustrated with grotesque-seeming images of overweight people. Sin requires punishment before you can get to redemption. Think about the competition show “The Biggest Loser,” on which obese people starve and perform extreme forms of exercise in visible agony in order to demonstrate their repentance.

The second idea is that we are all in a competition when it comes to weight. Ours is a society full of people fighting against the forces in our food that are making us fatter. It is often painful to do this: You have to tolerate hunger or engage in extreme forms of exercise. It feels like a contest in which each thin person creates additional pressure on others to do the same. Looked at in this way, people on Ozempic can resemble athletes like the cyclist Lance Armstrong who used performance-enhancing drugs. Those who manage their weight without drugs might think, “I worked hard for this, and you get it for as little as a weekly jab?”

We can’t find our way to a sane, nontoxic conversation about obesity or Ozempic until we bring these rarely spoken thoughts into the open and reckon with them. You’re not a sinner for gaining weight. You’re a typical product of a dysfunctional environment that makes it very hard to feel full. If you are angry about these drugs, remember the competition isn’t between you and your neighbor who’s on weight-loss drugs. It’s between you and a food industry constantly designing new ways to undermine your satiety. If anyone is the cheat here, it’s that industry. We should be united in a struggle against it and its products, not against desperate people trying to find a way out of this trap.

There are extraordinary benefits as well as disturbing risks associated with weight-loss drugs. Reducing or reversing obesity hugely boosts health, on average: We know from years of studying bariatric surgery that it slashes the risks of cancer, heart disease and diabetes-related death. Early indications are that the new anti-obesity drugs are moving people in a similar radically healthier direction, massively reducing the risk of heart attack or stroke. But these drugs may increase the risk for thyroid cancer. I am worried they diminish muscle mass and fear they may supercharge eating disorders. This is a complex picture in which the evidence has to be weighed very carefully.

But we can’t do that if we remain lost in stories inherited from premodern popes or in a senseless competition that leaves us all, in the end, losers. Do we want these weight loss drugs to be another opportunity to tear one another down? Or do we want to realize that the food industry has profoundly altered the appetites of us all — leaving us trapped in the same cage, scrambling to find a way out?

Johann Hari is a British journalist and the author of “Magic Pill: The Extraordinary Benefits — and Disturbing Risks — of the New Weight Loss Drugs,” among other books.

Source photographs by seamartini, The Washington Post, and Zana Munteanu via Getty Images.

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Fatphobia Hurts Thin People Too

You can look healthy and still be at risk for metabolic diseases..

In 2020 more than 40 percent of Americans reported experiencing weight stigma , not only from friends and family members but also from their nurses and doctors . Whether the issue is scoliosis or cancer , patients often find that their health care professionals can’t see beyond their obesity, and their concerns are drowned out by tired demands of weight loss. The doctor’s office, in turn, becomes yet another untrustworthy place to seek help.

Fatphobia is undoubtedly pernicious, and we believe that its perils are broader than generally recognized. Just as many presume that people with obesity are unhealthy, the same bias reinforces that people of normal weight must be healthy, contributing to the neglect of hypertension, diabetes, and other metabolic diseases in which the body’s ability to process energy and nutrients malfunctions. These diseases, due to fatphobia, are too often assumed to affect only people with visible fat, so today’s Health at Every Size movement tries to address this issue by suggesting that all body types can be healthy. But the inverse is also true: All body types can be unhealthy. Thin people can harbor fat that threatens their well-being. And for those people, weight loss isn’t the answer.

Body mass index, a metric that tries to assess body fat using height and weight, is well known for its inaccuracies and biased methodology. For instance, Hispanic and Asian people with seemingly normal BMIs are more likely to have obesity—taking the form of fat deeper in their bodies—than white people are. South Asians in particular are prone to high blood pressure and Type 2 diabetes regardless of weight, and on average, heart disease strikes them a decade earlier, with 40 percent increased mortality. One factor driving these health disparities might be diagnostic overshadowing, in which one visible condition obscures other clinically relevant signals. In other words, doctors might overlook that their thin patients can still get sick with metabolic diseases.

Having a normal BMI with high body fat levels is called “ normal weight obesity .” It affects 30 million Americans—nearly 10 percent of the population—although researchers believe that this number may be on the rise. Several studies emphasize this condition’s ability to go unrecognized and untreated, due at least partially to its murky diagnostic criteria.

Experts point to the difference between subcutaneous and visceral fat as a reason BMI underestimates obesity in some groups. Subcutaneous fat is under our skin, the kind we can all see; however, visceral fat covers our internal organs, like the liver, pancreas, and intestines. Although too much of either kind of fat can be problematic, visceral fat—the type we can’t see and a small fraction of total body fat, adding negligible amounts to our weight—especially increases one’s risk of metabolic disease.

Normal weight obesity might be tricky to detect, but there are ways. One indicator, for example, is an abnormal adipokine ratio , which refers to the molecules secreted by visceral fat cells to help regulate metabolism; a simple blood test can check for this. Another emerging lab test is the cardiometabolic index , which assesses a patient’s waist size–to–height ratio, “good cholesterol” levels, and ratio of triglycerides—a type of fat found in the blood. With these different diagnostic signals, doctors can better detect normal weight obesity early.

The problem is that doctors might sign a clean bill of health without knocking around or conducting these tests because patients with normal weight obesity can appear to be physically fit. Preventive measures and lifestyle counseling, in turn, may be neglected even though they promote health and well-being for everyone, whatever their body shape.

We are both of South Asian descent and, looking at our own families, you can find every kind of metabolic disease within a few generations, including one skinny uncle who nearly died of a heart attack at age 44. He can’t remember a time a doctor talked to him about improving his diet and exercise, let alone broached the topic of heart disease—themes that, for fat people, can dominate medical visits to the exclusion of other issues. The double standards regarding obesity benefit nobody.

It’s true that BMI can serve as a loose signal for metabolic disease risk and that excess weight is associated with worse health outcomes . But we have allowed this rule of thumb to shape individual treatment plans. Health care practitioners do their best work when they look at patients as a whole, considering all the numbers, from insulin to blood pressure to cholesterol levels to liver function tests. This deeper understanding is generally reassuring for patients—that their doctors see them as a person rather than just a number on the scale.

Such holistic attention is crucial to identify normal weight obesity. Doctors should assess thin patients’ risk by specifically looking for a family history of metabolic diseases. An additional step could include estimating visceral fat through a bioelectrical impedance weighing scale, which passes a small electrical current through the body to estimate fat quantities; it’s cheaper but admittedly less precise than full-body imaging scans.

Treating normal weight obesity could also be simpler than addressing typical obesity. Although diet and exercise are important for health, they seldom achieve sustained weight loss, with an estimated 80 percent of efforts failing after a year . But for patients with normal weight obesity, that’s actually OK: They don’t need to lose weight. Just fat.

So, rather than Ozempic , Zepbound , and other GLP-1 drugs that cause 15–21 percent weight loss, a Mediterranean diet and intense aerobic exercise might be treatment enough, with research showing that these interventions lower visceral fat with little effect on subcutaneous fat. Doctors just need to more consistently recommend these lifestyle changes for normal weight obesity, instead of so quickly stamping thin patients with a clean bill of health. Down the road, perhaps someone will invent an Ozempic that doesn’t cause patients to shed so many pounds, maintaining the drug’s benefits to metabolic health independent of weight loss .

All of us, not just physicians and pharmaceutical companies, can play a part. Our culture is obsessed with thinness, enamored with the aesthetics of smaller bodies, so changing cultural perceptions inevitably requires broad social advocacy and political will. The standard-bearers of weight-inclusive efforts have long been people with excess weight—and understandably so. But in a world where weight has become a cheap heuristic for health, fatphobia threatens us all.

Tech can’t replace human coaches in obesity treatment

• Feinberg School of Medicine

A new Northwestern Medicine study shows that technology alone can’t replace the human touch to produce meaningful weight loss in obesity treatment.

“Giving people technology alone for the initial phase of obesity treatment produces unacceptably worse weight loss than giving them treatment that combines technology with a human coach,” said corresponding study author Bonnie Spring, director of the Center for Behavior and Health and professor of preventive medicine at Northwestern University Feinberg School of Medicine.

The need for low cost but effective obesity treatments delivered by technology has become urgent as the ongoing obesity epidemic exacerbates burgeoning health care costs.

But current technology is not advanced enough to replace human coaches, Spring said.

In the new SMART study, people who initially only received technology without coach support were less likely to have a meaningful weight loss, considered to be at least 5% of body weight, compared to those who had a human coach at the start.

Investigators intensified treatment quickly (by adding resources after just two weeks) if a person showed less than optimal weight loss, but the weight loss disadvantage for those who began their weight loss effort without coach support persisted for six months, the study showed. 

Eventually more advanced technology may be able to supplant human coaches, Spring said.  

“At this stage, the average person still needs a human coach to achieve clinically meaningful weight loss goals because the tech isn’t sufficiently developed yet,” Spring said. “We may not be so far away from having an AI chat bot that can sub for a human, but we are not quite there yet. It’s within reach. The tech is developing really fast.”  

Previous research showed that mobile health tools for tracking diet, exercise and weight increase engagement in behavioral obesity treatment. Before this new study, it wasn’t clear whether they produced clinically acceptable weight loss in the absence of support from a human coach.  

Scientists are now trying to parse what human coaches do that makes them successful, and how AI can better imitate a human, not just in terms of content but in emotional tone and context awareness, Spring said.

“We predicted that starting treatment with technology alone would save money and reduce burden without undermining clinically beneficial weight loss, because treatment augmentation occurred so quickly once poor weight loss was detected,” Spring said. “That hypothesis was disproven, however.”

Drug and surgical interventions also are available for obesity but have some drawbacks.   “They’re very expensive, convey medical risks and side effects and aren’t equitably accessible,” Spring said. Most people who begin taking a GLP-1 agonist stop taking the drug within a year against medical advice, she noted.

Many people can achieve clinically meaningful weight loss without antiobesity medications, bariatric surgery or even behavioral treatment, Spring said.   In the SMART study, 25% of people who began treatment with technology alone achieved 5% weight loss after six months without any treatment augmentation. (In fact, the team had to take back the study technologies after three months to recycle to new participants.)

An unsolved problem in obesity treatment is matching treatment type and intensity to individuals’ needs and preferences. “If we could just tell ahead of time who needs which treatment at what intensity, we might start to manage the obesity epidemic,” Spring said.

The SMART Weight Loss Management study was a randomized controlled trial that compared two different stepped care treatment approaches for adult obesity.

Stepped care offers a way to spread treatment resources across more of the population in need. The treatment that uses the least resources but that will benefit some people is delivered first; then treatment is intensified only for those who show insufficient response. Half of participants in the SMART study began their weight loss treatment with technology alone.   The other half began with gold standard treatment involving both technology and a human coach.

The technology used in the SMART trial was a Wireless Feedback System (an integrated app, Wi-Fi scale and Fitbit) that participants used to track and receive feedback about their diet, activity and weight.

The study was published May 14 in JAMA. Other Northwestern authors are Dr. Juned Siddique, Jean Reading, Samuel Battalio, Elyse Daly, Laura Scanlan and H. Gene McFadden.

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