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

May combine two claims.

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

“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].”

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

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Obesity is 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.

“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].”

• cites [13] Darbandi, 2020, Discriminatory Capacity of Anthropometric Indices for Cardiovascular Disease in Adults: A Systematic Review and Meta-Analysis, Prev. Chronic Dis. sourcePartly 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)

  Experimental — accuracy not yet measured
• cites [14] (2024, February 20). Health Effects of Overweight and Obesity, Available online: https://www.cdc.gov/healthy-weight-growth/about/index.html.Not checked yet

  Not checked in the free run.

May combine two claims.

Higher BMI is influenced heavily by sedentary lifestyles, structural elements in the lived environment, affordability and availability of healthy foods, and marketing and promotion of unhealthy foods.

“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].”

• cites [22] Silventoinen, 2020, Obesity and eating behavior from the perspective of twin and genetic research, Neurosci. Biobehav. Rev. sourcePartly 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)

  Experimental — accuracy not yet measured

May combine two claims.

BMI is relatively simple to measure in adults and functionally sums a variety of complex body indicators.

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

• 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. sourcePartly 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)

  Experimental — accuracy not yet measured
• 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 sourceCould 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)

  Experimental — accuracy not yet measured
• 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. sourcePartly 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)

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

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

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

  Experimental — accuracy not yet measured

May combine two claims.

The overweight BMI range included men with a low estimated body fat as well as men whose body fat percentage is compatible with obesity.

“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].”

• 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. sourcePartly 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)

  Experimental — accuracy not yet measured

Classified as probably not a substantial claim.

Incorporating DEXA or BIA alongside BMI measurements can provide a more comprehensive assessment of body fat percentage and distribution.

“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.”

• 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 sourcePartly 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)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

High BMI is associated with an increased risk of colorectal cancer, primary liver cancer, and cancer mortality.

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

• 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. sourceSupported
• cites [16] Sohn, 2021, Obesity and the risk of primary liver cancer: A systematic review and meta-analysis, Clin. Mol. Hepatol. sourcePartly 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)

  Experimental — accuracy not yet measured

May combine two claims.

BMI is a limited measure of body fat and invites misclassification of persons who are muscular or have larger bone structures.

“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.”

• cites [10] Bray, G.A. (2023). Beyond BMI. Nutrients, 15. sourcePartly 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)

  Experimental — accuracy not yet measured
• cites [11] (2024, May 27). Why Use BMI?. Available online: https://www.hsph.harvard.edu/obesity-prevention-source/obesity-definition/obesity-definition-full-story/.Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Obesity is a risk factor for death from SARS-CoV-2 infection.

“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].”

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

May combine two claims.

BMI estimates fat amount but is not precise in measuring fat distribution, such as abdominal fat or central fatness, which are better predictors of the risk of mortality.

“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].”

• 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. sourcePartly 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)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

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

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

• 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. sourcePartly 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)

  Experimental — accuracy not yet measured

May combine two claims.

Basing an obesity diagnosis solely on BMI can be inaccurate and may incur discriminatory and costly patient consequences.

“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].”

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

May combine two claims.

Semaglutide injections may not be covered by health insurance for people falling below certain BMI cut-offs, and a more comprehensive assessment could provide a more accurate assessment of need.

“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].”

• cites [60] Chao, 2022, Clinical Insight on Semaglutide for Chronic Weight Management in Adults: Patient Selection and Special Considerations, Drug Des. Dev. Ther. sourcePartly 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)

  Experimental — accuracy not yet measured

Classified as probably not a substantial claim.

Alternative metrics such as waist-to-hip ratio or waist-to-height ratio in addition to BMI could offer a more comprehensive assessment of body composition and associated health risks in diverse populations.

“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.”

• 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. sourceSupported

May combine two claims.

The relationship between BMI and percent body fat is highly variable by race, ethnicity, sex, and age, and these factors may interact.

“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].”

• cites [21] Yang, 2015, Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index, Nat. Genet. sourceCould 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)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

BMI is used to screen for macro-nutritional status, whether undernutrition, normal range, or overnutrition.

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

• cites [1] Eknoyan, 2008, Adolphe Quetelet (1796–1874)—The average man and indices of obesity, Nephrol. Dial. Transplant. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

BMI was devised by Adolphe Quetelet in the 1830s to enable comparison of White middle-aged European men of varying heights via a common parameter.

“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.”

• cites [1] Eknoyan, 2008, Adolphe Quetelet (1796–1874)—The average man and indices of obesity, Nephrol. Dial. Transplant. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

The American Medical Association issued a report and policy recommendation in June 2023 urging medical professionals to do more comprehensive assessments of overweight and rely less on BMI.

“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].”

• cites [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 yet

  Not checked in the free run.

• cites [24] Tanne, 2023, Obesity: Avoid using BMI alone when evaluating patients, say US doctors’ leaders, BMJ sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Weight stigma affects people with obesity and can lead to delayed help-seeking, trouble finding social support, and impeded weight loss maintenance.

“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].”

• cites [19] Puhl, 2020, Weight stigma as a psychosocial contributor to obesity, Am. Psychol. sourceNot checked — no full text of the source

May combine two claims.

BMI remains popular as a simple, inexpensive clinical screening tool in patient medical encounters, though its limitations for individual patient forecasting are well documented.

“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].”

• cites [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.Not checked yet

  Not checked in the free run.

May combine two claims.

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

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

Probably not a substantial claim: may not pass S3 (load-bearing).

Four BMI categories are in common use today: underweight (<18.5 kg/m2), normal (18.5-<25.0 kg/m2), overweight (25.0-<30.0 kg/m2), and obesity (≥30.0 kg/m2).

“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].”

• cites [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.Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Overweight status is associated with an increased risk of depressive disorder among adults.

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

• cites [18] Luppino, 2010, Overweight, obesity, and depression: A systematic review and meta-analysis of longitudinal studies, Arch. Gen. Psychiatry sourceNot checked — no full text of the source

May combine two claims.

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

“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].”

• cites [28] (2024, March 01). Obesity and Overweight. Available online: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.Not checked yet

  Not checked in the free run.

• cites [30] Liu, 2023, Deciphering the “obesity paradox” in the elderly: A systematic review and meta-analysis of sarcopenic obesity, Obes. Rev. sourceNot checked — no full text of the source

May combine two claims.

BMI estimates adiposity crudely because muscles and bones have a higher density than fat, and some fat dispositions are worse than others.

“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.”

May combine two claims.

Persons pursuing weight loss through diet and exercise can use BMI as an anchor assessment and track weight over time because height in adults is stable over the short term.

“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].”

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

  Not checked in the free run.

May combine two claims.

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

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

Probably not a substantial claim: may not pass S3 (load-bearing).

The Lancet Diabetes and Endocrinology Commission in 2023 highlighted caveats in the definition and diagnosis of obesity.

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

• cites [25] Rubino, 2023, Lancet Diabetes & Endocrinology Commission on the Definition and Diagnosis of Clinical Obesity, Lancet Diabetes Endocrinol. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

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

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

May combine two claims.

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

“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].”

• cites [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. sourceNot checked — no full text of the source

May combine two claims.

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

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

May combine two claims.

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 a higher body weight than White women.

“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].”

• cites [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. sourceNot checked — no full text of the source

May combine two claims.

Young Indians living in India have more visceral and abdominal fat and reduced glucose disposal rates than Caucasians with matched age and BMI.

“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].”

• cites [40] Raji, 2001, Body Fat Distribution and Insulin Resistance in Healthy Asian Indians and Caucasians, J. Clin. Endocrinol. Metab. sourceNot checked — no full text of the source

May combine two claims.

Cross-sectional imaging can illustrate longitudinal changes in a single individual's body composition, but it is harder to interpret imaging for comparative predictions for groups of people.

“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].”

• cites [66] MacDonald, 2011, The advantages and limitations of cross-sectional body composition analysis, Curr. Opin. Support. Palliat. Care sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

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

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

May combine two claims.

BMI will continue to be a simple and convenient tool for assessing macro-nutrition at the population level, though it does not measure micronutrients.

“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].”

• cites [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. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

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

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

May combine two claims.

The obesity crisis or progress in reducing undernutrition cannot be assessed by BMI alone.

“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].”

May combine two claims.

Waist circumference, waist-to-hip ratio, and waist-to-height ratio are relatively simple, low-cost measures that can better evaluate adiposity instead of an exclusive focus on excess weight.

“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.”

May combine two claims.

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

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

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

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.

“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].”

• cites [59] Phillips, 2022, Clinical review of subcutaneous semaglutide for obesity, J. Clin. Pharm. Ther. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Bariatric surgery may be restricted to patients with a high BMI, and a more objective assessment such as co-incident type 2 diabetes could address the limitations of using BMI eligibility criteria alone.

“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].”

• cites [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.Not checked yet

  Not checked in the free run.

Classified as probably not a substantial claim.

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

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

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Genetic factors play a significant role in obesity predisposition and response to weight management, and variations in genes related to metabolism, appetite regulation, and fat storage influence obesity susceptibility.

“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].”

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

  Not checked in the free run.

May combine two claims.

3DPS, PET, and SPECT are comparatively costly and complex imaging tools that assess adiposity and some physiological functions.

“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].”

• cites [67] Santhanam, 2021, A systematic review of imaging studies of human brown adipose tissue, Ann. N. Y. Acad. Sci. sourceNot checked — no full text of the source

May combine two claims.

Waist circumference assesses abdominal fat, with elevated values associated with a higher risk of metabolic diseases.

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

• cites [69] Chan, 2003, Waist circumference, waist-to-hip ratio and body mass index as predictors of adipose tissue compartments in men, QJM sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

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

“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.”

• AssumptionRests on an assumption: Radiation exposure in both DXA and CT is a disadvantage.
• AssumptionRests on an assumption: MRI requires one to be stable in a loud and noisy environment for a long time, which can be costly, tiring, and disruptive for patients, particularly those with claustrophobia.
• AssumptionRests on an assumption: Waist-to-hip ratio indicates fat distribution and serves as a reliable predictor of cardiovascular risk.

Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

Medical evaluations with greater predictive value need accurate measures and a definitive diagnosis of harmful adiposity, as well as functional assessments.

“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].”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

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.

“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.”

May combine two claims.

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.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Lean body mass is essential for evaluating muscle health and overall metabolic function.

“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].”

• cites [70] Roubenoff, 1991, The meaning and measurement of lean body mass, Nutr. Rev. sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S2 (checkable).

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.

“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].”

• cites [66] MacDonald, 2011, The advantages and limitations of cross-sectional body composition analysis, Curr. Opin. Support. Palliat. Care sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S4 (based).

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

“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.”

Classified as probably not a substantial claim.

Workforce issues, including the need for skilled practitioners, test risks, and costs, determine the extent to which more complex approaches are useful in specific settings.

“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.”

Probably not a substantial claim: may not pass S2 (checkable).

Comparing BMI screening test efficiencies and population-specific predictive values may improve combinations of screening, diagnostic, and predictive tools, with BMI as a likely component of efficient algorithms.

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