Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity

In the paper

“BMI cut-offs may not hold the same predictive value for individuals over the age of 65 as they do for younger adults, as modest overweight status may not carry the same health implications for the older population [31].”

What the sources say

• Supported

  The Discussion concludes that BMI ranges considered ideal for young and middle-aged people are not ideal for older patients. It suggests that a BMI of 25–35 kg/m² may be optimal for health in the older population, supporting the claim's qualified point about modest overweight status. The supplied passage has no page number. (LLM, conf=0.93)

  cites [31] Soysal, 2022, What is the Optimal Body Mass Index Range for Older Adults?, Ann. Geriatr. Med. Res. source

In the paper

“Healthcare providers and public health agencies alike recognize obesity to be a risk factor for multiple outcomes such as cardiovascular disease, diabetes, chronic orthopedic problems, accidents, collagen vascular diseases, lower quality of life, osteoarthritis, and autoimmune disorders [13,14].”

What the sources say

• Partly supported

  The Introduction identifies obesity as a CVD risk factor, and the Discussion links excess fat accumulation to features of diabetes. The supplied passages do not support the additional claims about orthopedic problems, accidents, collagen vascular diseases, lower quality of life, osteoarthritis, or autoimmune disorders. Page numbers are not provided in our copy. (LLM, conf=0.88)

  cites [13] Darbandi, 2020, Discriminatory Capacity of Anthropometric Indices for Cardiovascular Disease in Adults: A Systematic Review and Meta-Analysis, Prev. Chronic Dis. sourceExperimental — accuracy not yet measured
• Not checked yetcites [14] (2024, February 20). Health Effects of Overweight and Obesity, Available online: https://www.cdc.gov/healthy-weight-growth/about/index.html.

In the paper

“Higher BMI is influenced heavily by sedentary lifestyles, structural elements (e.g., parks, sidewalks, bicycle lanes) in the lived environment, affordability and availability of healthy foods (so-called “food deserts” are neighborhoods where fresh vegetables and fruits are not conveniently obtained), and marketing and promotion of unhealthy foods in society [22].”

What the sources say

• Partly supported

  The Introduction links physical activity to weight control and suggests that increased availability of processed fast food contributed to the obesity epidemic. It does not establish effects of parks, sidewalks, bicycle lanes, healthy-food affordability or “food deserts,” or marketing; it says the specific factors behind obesogenic environments remain poorly understood. (LLM, conf=0.88)

  cites [22] Silventoinen, 2020, Obesity and eating behavior from the perspective of twin and genetic research, Neurosci. Biobehav. Rev. sourceExperimental — accuracy not yet measured

In the paper

“BMI is relatively simple to measure in adults [4,5,6,7,8,9] and functionally sums a variety of complex body indicators.”

What the sources say

• Partly supported

  The Methods section (page unavailable) calculates BMI from weight and height, consistent with BMI being a relatively simple measure. But the paper studies young children and does not address ease of measurement in adults. Its Discussion (page unavailable) compares BMI metrics with adiposity measures; it does not establish that BMI sums a variety of body indicators. (LLM, conf=0.70)

  cites [4] Freedman, 2017, The Limitations of Transforming Very High Body Mass Indexes into z-Scores among 8.7 Million 2- to 4-Year-Old Children, J. Pediatr. sourceExperimental — accuracy not yet measured
• ?Could not verify

  The citation is attached to the statement that BMI is relatively simple to measure in adults. The Assessment of pediatric patients section describes using BMI percentiles for ages 2–20, but does not address how simple BMI is to measure in adults. Searches for adult BMI measurement and its simplicity found no supporting passage in our copy. (LLM, conf=0.73)

  cites [5] Cuda, 2022, Assessment, differential diagnosis, and initial clinical evaluation of the pediatric patient with obesity: An Obesity Medical Association (OMA) Clinical Practice Statement 2022, Obes. Pillars sourceExperimental — accuracy not yet measured
• Partly supported

  The Methods (BMI classification; page unavailable) describe routinely collecting BMI from measured height and weight, which supports the idea that it is relatively straightforward to obtain. The study concerns children aged 2–17, however, and does not address how simple BMI is to measure in adults. The citation is attached to that first clause, not the subsequent claim about complex body indicators. (LLM, conf=0.81)

  cites [6] Southcombe, 2023, More than just body mass index: Using the Edmonton obesity staging system for pediatrics to define obesity severity in a multi-ethnic Australian pediatric clinical cohort, Obes. Sci. Pract. sourceExperimental — accuracy not yet measured
• Partly supported

  The Background describes weight-and-height-based measures, including BMI, as practical and repeatable; the Methods say BMI is calculated from weight and height. This supports the claim that BMI is relatively simple to measure, but the paper studies children and does not specifically establish that point in adults. The citation is attached to the first clause, so the sentence’s separate claim about complex body indicators is not assessed here. (LLM, conf=0.82)

  cites [7] Licenziati, 2022, A height-weight formula to measure body fat in childhood obesity, Ital. J. Pediatr. sourceExperimental — accuracy not yet measured
• ?Could not verify

  The citation is attached to the claim that BMI is relatively simple to measure in adults. The paper’s main text (page unavailable) mentions adult use of BMI reduction thresholds, but does not address how simple BMI is to measure; searches for adult BMI measurement and simplicity found no supporting passage. The statement about complex body indicators follows the citation and is not attributed to this source here. (LLM, conf=0.72)

  cites [8] Ryder, 2022, Metrics matter: Toward consensus reporting of BMI and weight-related outcomes in pediatric obesity clinical trials, Obesity sourceExperimental — accuracy not yet measured
• Partly supported

  In Subjects and Methods > BMI Metrics (page unavailable), the paper describes calculating BMI from measured height and weight, supporting the point that it is relatively simple to obtain. The study sample comprises children aged 2–17, however, so it does not establish the claim specifically for adults. The citation is attached to the measurement clause, not the subsequent claim about body indicators. (LLM, conf=0.82)

  cites [9] Freedman, 2020, Distance and percentage distance from median BMI as alternatives to BMI z score, Br. J. Nutr. sourceExperimental — accuracy not yet measured

In the paper

“Hence, the “overweight” range included men with a low estimated body fat as well as men whose body fat percentage is compatible with obesity [33].”

What the sources say

• Partly supported

  The Results (Table 2; page unavailable) report men with BMI below the obesity cutoff whose body-fat percentage met the obesity criterion, and Table 4 shows that BMI cutoffs within the overweight range do not cleanly separate obese from non-obese body-fat levels. The copy does not establish that men specifically in the overweight BMI range had *low* estimated body fat; its relevant category is the broader “non-obese” body-fat category. (LLM, conf=0.82)

  cites [33] Shah, N.R., and Braverman, E.R. (2012). Measuring adiposity in patients: The utility of body mass index (BMI), percent body fat, and leptin. PLoS ONE, 7. sourceExperimental — accuracy not yet measured

In the paper

“Incorporating body composition analysis techniques such as dual-energy X-ray absorptiometry (DEXA) or bioelectrical impedance analysis (BIA) alongside BMI measurements can provide a more comprehensive assessment of body fat percentage and distribution.”

What the sources say

• Partly supported

  Section 3 explains that BMI cannot distinguish fat from nonfat mass, while Section 2.4 says DXA estimates whole-body and regional fat mass, supporting a more comprehensive assessment. The BIA sections describe estimates of body composition and fat mass, but do not establish that BIA assesses fat distribution. (LLM, conf=0.83)

  cites [34] Marra, 2019, Assessment of Body Composition in Health and Disease Using Bioelectrical Impedance Analysis (BIA) and Dual Energy X-ray absorptiometry (DXA): A Critical Overview, Contrast Media Mol. Imaging sourceExperimental — accuracy not yet measured

In the paper

“High BMI is also associated with an increased risk of colorectal cancer [15], primary liver cancer, and cancer mortality [16].”

What the sources say

• Supported

  The citation to this paper is attached to the colorectal-cancer portion of the sentence, not to primary liver cancer or cancer mortality. In Results > Obese vs. Normal Category of BMI, the pooled relative risk of colorectal cancer for obese versus normal BMI was 1.334 (95% CI, 1.253–1.420). (LLM, conf=0.95)

  cites [15] Ma, Y., Yang, Y., Wang, F., Zhang, P., Shi, C., Zou, Y., and Qin, H. (2013). Obesity and risk of colorectal cancer: A systematic review of prospective studies. PLoS ONE, 8. source
• Partly supported

  The Results report that higher BMI is associated with primary liver cancer occurrence and liver cancer-related mortality. Citation [16] supports the primary liver cancer claim, but the paper's mortality finding is specific to liver cancer, not cancer mortality generally. The colorectal cancer claim is attached to [15], not this source. (LLM, conf=0.86)

  cites [16] Sohn, 2021, Obesity and the risk of primary liver cancer: A systematic review and meta-analysis, Clin. Mol. Hepatol. sourceExperimental — accuracy not yet measured

In the paper

“BMI does not measure body fat distribution (e.g., it does not recognize more harmful abdominal fat), is a limited measure of body fat (inviting misclassification of persons who are muscular or have larger bone structures) [11], and does not inform practitioners of either the origin or the heterogeneity of obesity and its outcomes in specific individuals or populations.”

What the sources say

• Partly supported

  Section 4, “Anatomic Limitations” (page unavailable), says BMI has only a fair correlation with body fat and is limited for assessing an individual’s adiposity. The paper does not specifically say that BMI misclassifies muscular people or people with larger bone structures; its discussion of frame size in Section 2 concerns historical weight tables. (LLM, conf=0.83)

  cites [10] Bray, G.A. (2023). Beyond BMI. Nutrients, 15. sourceExperimental — accuracy not yet measured
• Not checked yetcites [11] (2024, May 27). Why Use BMI?. Available online: https://www.hsph.harvard.edu/obesity-prevention-source/obesity-definition/obesity-definition-full-story/.

In the paper

“Obesity is a risk factor for death from SARS-CoV-2 infection (COVID-19 disease), perhaps related to stress on lung capacity and complexities in ventilator management in intensive care settings [17].”

What the sources say

• Supported

  The Abstract’s Conclusions state that obesity is significantly associated with higher mortality among COVID-19 patients. The Discussion’s Public Health Implication section likewise reports increased mortality among infected patients with obesity. These passages support the attributed claim that obesity is a risk factor for death from COVID-19. (LLM, conf=0.92)

  cites [17] Singh, 2022, Association of Obesity With COVID-19 Severity and Mortality: An Updated Systemic Review, Meta-Analysis, and Meta-Regression, Front. Endocrinol. source

In the paper

“As mentioned previously, BMI estimates fat amount but is not precise in measuring fat distribution, as with abdominal fat or central fatness, which are better predictors of the risk of mortality [50].”

What the sources say

• Partly supported

  The Introduction (page unavailable) says BMI cannot determine body-fat distribution, and the Discussion (page unavailable) says waist–hip ratio provides additional information about distribution and is associated with mortality. The paper does not establish that abdominal or central fatness measures are better predictors of mortality than BMI. (LLM, conf=0.86)

  cites [50] Dong, B., Peng, Y., Wang, Z., Adegbija, O., Hu, J., Ma, J., and Ma, Y.H. (2018). Joint association between body fat and its distribution with all-cause mortality: A data linkage cohort study based on NHANES (1988–2011). PLoS ONE, 13. sourceExperimental — accuracy not yet measured

In the paper

“Skinfold thickness is considered a valuable tool but requires considerable technical skill and experience to be measured accurately.”

What the sources say

• Partly supported

  Section 3.3 (Skinfolds; page unavailable) describes skinfold measurement as an inexpensive way to estimate body fat, and Section 4 (Practical Applications; page unavailable) calls it an excellent option for assessing athletes’ body composition. The passages found do not say that accurate measurement requires considerable technical skill or experience. (LLM, conf=0.81)

  cites [61] Holmes, C.J., and Racette, S.B. (2021). The Utility of Body Composition Assessment in Nutrition and Clinical Practice: An Overview of Current Methodology. Nutrients, 13. sourceExperimental — accuracy not yet measured

In the paper

“Basing an obesity diagnosis solely on BMI can be inaccurate and may incur discriminatory and costly patient consequences, as when employers increase employees’ health insurance costs if the BMI is above a given threshold [52].”

What the sources say

• Supported

  The Introduction (p. 2) describes proposed rules allowing employers to penalize employees through health-insurance costs for failing a BMI criterion. The Discussion (p. 3) reports substantial misclassification when BMI is used as a proxy for cardiometabolic health and calls such policies discriminatory. The paper describes the employer penalty as a proposed policy, not a documented instance of its use. (LLM, conf=0.90)

  cites [52] Tomiyama, 2016, Misclassification of cardiometabolic health when using body mass index categories in NHANES 2005–2012, Int. J. Obes. source

In the paper

“Semaglutide injections may not be covered by health insurance for people falling below certain BMI cut-offs; a more comprehensive assessment could provide a more accurate assessment of need [60].”

What the sources say

• Partly supported

  The Insurance Coverage/Cost section identifies insurance coverage as a substantial barrier to semaglutide use. The Introduction gives BMI thresholds for medication candidacy and recommends personalized treatment decisions, but the paper does not say that insurers deny coverage below those thresholds or that a more comprehensive assessment would more accurately establish need. No page numbers were provided in our copy. (LLM, conf=0.84)

  cites [60] Chao, 2022, Clinical Insight on Semaglutide for Chronic Weight Management in Adults: Patient Selection and Special Considerations, Drug Des. Dev. Ther. sourceExperimental — accuracy not yet measured

In the paper

“Exploring alternative metrics such as waist-to-hip ratio (WHR) or waist-to-height ratio (WHtR) in addition to BMI could offer a more comprehensive assessment of body composition and associated health risks in diverse populations.”

What the sources say

• Supported

  The Introduction says BMI alone gives an incomplete assessment of body mass, identifies WHR and WHtR as additional measures, and supports using multiple measures to assess obesity-related risks. It also notes that this approach has been used across different populations, ages, and ethnicities. This supports the citing sentence’s tentative proposal, though the paper’s own study focuses on older adults. (LLM, conf=0.88)

  cites [41] Carvalho, V., Moreira, L.B., Luft, V.C., and Fuchs, S.C. (2023). Waist-to-Height Ratio: A Sensitive Tool for Assessing the Need for Nutritional Risk Management in Elderly Populations from Brazil. Healthcare, 11. source

In the paper

“Apart from genetic factors, the relationship between BMI and percent body fat is highly variable by race, ethnicity, sex, and age (acknowledging that these simple constructs may mask more complex social elements), and these factors may interact [21].”

What the sources say

• ?Could not verify

  The Online Methods describe adjusting BMI for age and standardizing it within sex groups, but do not examine percent body fat. Searches for the BMI–percent-body-fat relationship and for variation by race, ethnicity, sex, age, or their interactions found no passage addressing the attributed claim. Page numbers were not available in our copy. (LLM, conf=0.75)

  cites [21] Yang, 2015, Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index, Nat. Genet. sourceExperimental — accuracy not yet measured

In the paper

“Body mass index (BMI) is used to screen for macro-nutritional status, whether undernutrition, normal range, or overnutrition [1].”

What the sources say

• ?Not checked — no full text of the sourcecites [1] Eknoyan, 2008, Adolphe Quetelet (1796–1874)—The average man and indices of obesity, Nephrol. Dial. Transplant. source

In the paper

“An individual’s weight in kilograms divided by height in meters squared was devised by Adolphe Quetelet, a Belgian mathematician, in the 1830s to enable the comparison of White middle-aged European men of varying heights via a common parameter.”

What the sources say

• ?Not checked — no full text of the sourcecites [1] Eknoyan, 2008, Adolphe Quetelet (1796–1874)—The average man and indices of obesity, Nephrol. Dial. Transplant. source

In the paper

“The American Medical Association (AMA) issued a report and policy recommendation in June 2023 that urged medical professionals to do more comprehensive assessments of overweight and rely less on BMI [23,24].”

What the sources say

• Not checked yetcites [23] (2023, June 14). AMA Adopts New Policy Clarifying Role of BMI as a Measure in Medicine. Available online: https://www.ama-assn.org/press-center/press-releases/ama-adopts-new-policy-clarifying-role-bmi-measure-medicine#:~:text=Under%20the%20newly%20adopted%20policy,of%20non%2DHispanic%20white%20populations.
• ?Not checked — no full text of the sourcecites [24] Tanne, 2023, Obesity: Avoid using BMI alone when evaluating patients, say US doctors’ leaders, BMJ source

In the paper

“Such negative stereotypes because of body weight, better termed “weight stigma”, affect people with obesity; persons feeling ashamed or embarrassed may hesitate to seek timely professional help, may have trouble finding needed social support, and can impede weight loss maintenance [19].”

What the sources say

• ?Not checked — no full text of the sourcecites [19] Puhl, 2020, Weight stigma as a psychosocial contributor to obesity, Am. Psychol. source

In the paper

“As a simple, inexpensive clinical screening tool, BMI remains popular in patient medical encounters, though its limitations for individual patient forecasting are well documented [12].”

What the sources say

• Not checked yetcites [12] (2021, May 05). Why BMI Is a Flawed Health Standard, Especially for People of Color. Available online: https://www.washingtonpost.com/lifestyle/wellness/healthy-bmi-obesity-race-/2021/05/04/655390f0-ad0d-11eb-acd3-24b44a57093a_story.html.

In the paper

“BMI does not measure functional health and fitness but is merely an anthropometric measure.”

In the paper

“Four categories of BMI are in common use today to estimate patient conditions: (1) underweight or undernourished status (<18.5 kg/m2); (2) normal BMI (18.5–<25.0 kg/m2); (3) overweight status (25.0–<30.0 kg/m2); and (4) persons with obesity as ≥30.0 kg/m2 [2].”

What the sources say

• Not checked yetcites [2] (2010, May 06). A Healthy Lifestyle—WHO Recommendations. Available online: https://www.who.int/europe/news-room/fact-sheets/item/a-healthy-lifestyle---who-recommendations.

In the paper

“Overweight status is associated with an increased risk of depressive disorder among adults [18].”

What the sources say

• ?Not checked — no full text of the sourcecites [18] Luppino, 2010, Overweight, obesity, and depression: A systematic review and meta-analysis of longitudinal studies, Arch. Gen. Psychiatry source

In the paper

“Among adults, BMI has been used to assess overweight status independent of age or sex [28], though its cut-offs for the elderly are debated [30].”

What the sources say

• Not checked yetcites [28] (2024, March 01). Obesity and Overweight. Available online: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.
• ?Not checked — no full text of the sourcecites [30] Liu, 2023, Deciphering the “obesity paradox” in the elderly: A systematic review and meta-analysis of sarcopenic obesity, Obes. Rev. source

In the paper

“BMI estimates adiposity crudely, given that muscles and bones have a higher density than fat (adipose tissue), and some fat dispositions are worse than others, as with abdominal adiposity.”

In the paper

“Persons pursuing a weight loss plan through diet and exercise can use BMI as the anchor assessment and calculate weight goals with assistance from a primary care provider, enabling them to simply track weight over time since height in adults is stable over the short term [27].”

What the sources say

• Not checked yetcites [27] (2011, August 02). Body Mass Index: Considerations for Practitioners, Available online: https://stacks.cdc.gov/view/cdc/25368.

In the paper

“BMI has substantial utility in population-level statistics, summarizing current burdens of under- or over-nutrition and assessing trends in communities.”

In the paper

“In their 2023 report, The Lancet Diabetes and Endocrinology Commission highlighted similar caveats in the context of the definition and diagnosis of obesity [25].”

What the sources say

• ?Not checked — no full text of the sourcecites [25] Rubino, 2023, Lancet Diabetes & Endocrinology Commission on the Definition and Diagnosis of Clinical Obesity, Lancet Diabetes Endocrinol. source

In the paper

“Polynesian body habitus may also differ, such that different cut-offs are appropriate.”

In the paper

“BIS in HIV-infected breastfeeding South African mothers provided valid measures of fat-free mass, whereas BMI was helpful only in predicting fat mass [63].”

What the sources say

• ?Not checked — no full text of the sourcecites [63] Papathakis, 2005, Comparison of isotope dilution with bioimpedance spectroscopy and anthropometry for assessment of body composition in asymptomatic HIV-infected and HIV-uninfected breastfeeding mothers, Am. J. Clin. Nutr. source

In the paper

“However, even with different cut-offs, changes over time may be valid for tracking changes within individuals and summarizing population trends.”

In the paper

“For example, for the same BMI, females have a higher percent of fat than males, and for similar fat-free body mass, Black women had significantly more percent of fat and, hence, a higher body weight than White women [54].”

What the sources say

• ?Not checked — no full text of the sourcecites [54] Jackson, 2002, The effect of sex, age and race on estimating percentage body fat from body mass index: The Heritage Family Study, Int. J. Obes. Relat. Metab. Disord. source

In the paper

“Young Indians living in India were found to have more visceral and abdominal fat and reduced glucose disposal rates than Caucasians with matched age and BMI [40].”

What the sources say

• ?Not checked — no full text of the sourcecites [40] Raji, 2001, Body Fat Distribution and Insulin Resistance in Healthy Asian Indians and Caucasians, J. Clin. Endocrinol. Metab. source

In the paper

“Though cross-sectional imaging can illustrate longitudinal changes in a single individual’s body composition, it is harder to interpret imaging for comparative predictions for groups of people [66].”

What the sources say

• ?Not checked — no full text of the sourcecites [66] MacDonald, 2011, The advantages and limitations of cross-sectional body composition analysis, Curr. Opin. Support. Palliat. Care source

In the paper

“Implementing these recommendations can significantly improve the effectiveness of anthropometry in assessing population health and guiding clinical care.”

In the paper

“BMI will continue to be a simple and convenient tool for assessing macro-nutrition at the population level, recognizing, of course, that BMI does not measure micronutrients [55].”

What the sources say

• ?Not checked — no full text of the sourcecites [55] Vermund, S., Scott, M., and Humphries, D. (2021). Public Health and Clinical Implications of Nutrition-Infection Interactions. Nutrition and Infectious Diseases Shifting the Clinical Paradigm: Shifting the Clinical Paradigm, Humana Press/Springer Nature Switzerland AG. source

In the paper

“Healthcare inequities are evident when one considers the cost and availability of advanced diagnostics in lower-resource settings.”

In the paper

“The obesity crisis or progress in reducing undernutrition cannot be assessed by BMI alone; however, the global rise in obesity and decline in underweight are attributed to a shift in the population distribution of BMI, and this can mask social inequity both within and across populations [63].”

In the paper

“These measures are also relatively simple in the hands of trained practitioners and are low-cost; they can better evaluate adiposity instead of an exclusive focus on excess weight.”

In the paper

“More extreme BMI findings have a high predictive utility for obesity-related medical conditions, as well as for undernutrition and cachexia.”

In the paper

“Work-to-date suggests that semaglutide mimics the GLP-1 hormone released in the gastrointestinal tract with food challenges, stimulating more insulin production, reducing blood glucose, and promoting satiety sensations and appetite suppression [59].”

What the sources say

• ?Not checked — no full text of the sourcecites [59] Phillips, 2022, Clinical review of subcutaneous semaglutide for obesity, J. Clin. Pharm. Ther. source

In the paper

“In healthcare systems, bariatric surgery may be restricted to patients with a high BMI (e.g., >35 kg/m2); a more objective assessment (co-incident type 2 diabetes, for example) could address the limitations of using BMI eligibility criteria alone [12].”

What the sources say

• Not checked yetcites [12] (2021, May 05). Why BMI Is a Flawed Health Standard, Especially for People of Color. Available online: https://www.washingtonpost.com/lifestyle/wellness/healthy-bmi-obesity-race-/2021/05/04/655390f0-ad0d-11eb-acd3-24b44a57093a_story.html.

In the paper

“The BMI is helpful when it is used as a screening test and population-appropriate cut-offs are used.”

In the paper

“Genetic factors play a significant role in obesity predisposition and response to weight management. Variations in genes related to metabolism, appetite regulation, and fat storage influence obesity susceptibility [42].”

What the sources say

• Not checked yetcites [42] Tirthani, E., Said, M.S., and Rehman, A. (2024). Genetics and Obesity. StatPearls, StatPearls Publishing LLC.

In the paper

“Three-dimensional photonic scanning (3DPS), positron emission tomography (PET), and single-photon emission computed tomography (SPECT) are comparatively costly and complex imaging tools that assess adiposity and some physiological functions [67].”

What the sources say

• ?Not checked — no full text of the sourcecites [67] Santhanam, 2021, A systematic review of imaging studies of human brown adipose tissue, Ann. N. Y. Acad. Sci. source

In the paper

“Similarly, waist circumference, also measured with a tape measure, assesses abdominal fat, with elevated values associated with a higher risk of metabolic diseases [69].”

What the sources say

• ?Not checked — no full text of the sourcecites [69] Chan, 2003, Waist circumference, waist-to-hip ratio and body mass index as predictors of adipose tissue compartments in men, QJM source

In the paper

“While BMI remains a cornerstone of public health surveillance and screening efforts, its limitations necessitate the embracing of emerging technologies and adopting a multifaceted approach to body composition assessment to better address the diverse needs of populations worldwide.”

In the paper

“Medical evaluations with greater predictive value need accurate measures and a definitive diagnosis of harmful adiposity, as well as functional assessments; such assessments encounter health system constraints of accessibility and affordability [64].”

In the paper

“Advances in data sciences and artificial intelligence may help address guideline discrepancies by refining criteria and cut-offs by key relevant cofactors like race, ethnicity, sex, age, and fitness.”

In the paper

“Simple, point-of-care assessment tools to better assess macro-nutritional status could supplant BMI as a screening tool of choice, but these are not yet affordable and/or validated.”

In the paper

“Lean body mass, essential for evaluating muscle health and overall metabolic function, measures the body’s mass excluding fat and can be assessed through DEXA scans, hydrostatic weighing, and bioelectrical impedance analysis [70].”

What the sources say

• ?Not checked — no full text of the sourcecites [70] Roubenoff, 1991, The meaning and measurement of lean body mass, Nutr. Rev. source

In the paper

“Developing algorithms that integrate multiple data points can help healthcare providers tailor interventions and treatment plans more effectively to address obesity and its associated health risks [66].”

What the sources say

• ?Not checked — no full text of the sourcecites [66] MacDonald, 2011, The advantages and limitations of cross-sectional body composition analysis, Curr. Opin. Support. Palliat. Care source

In the paper

“Leveraging data science and artificial intelligence to analyze the complex interactions between body composition metrics and blood-derived variables can enhance the accuracy and predictive power of this innovative approach.”

In the paper

“Workforce issues must be considered in such optimization efforts, as the need for skilled practitioners, test risks, and costs are all factors that determine the extent to which more complex approaches are useful in specific settings.”

In the paper

“Such work may improve the specific combinations of screening, diagnostic, and predictive tools, with BMI as a likely component of efficient algorithms.”