The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index; 48 of the 92 sources its claims cite were checked for support.
Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity
Open accessAbstract
Obesity reflects excessive fat deposits. At-risk individuals are guided by healthcare professionals to eat fewer calories and exercise more, often using body mass index (BMI; weight/height2) thresholds for screening and to guide progress and prognosis. By conducting a mini-narrative review of original articles, websites, editorials, commentaries, and guidelines, we sought to place BMI in the context of its appropriate use in population health, clinical screening, and monitoring in clinical care. The review covers studies and publications through 2023, encompassing consensus reviews and relevant literature. Recent consensus reviews suggest that BMI is a valuable tool for population surveys and primary healthcare screening but has limitations in predicting the risk of chronic diseases and assessing excess fat. BMI can guide nutritional and exercise counseling, even if it is inadequate for reliable individual risk prediction. BMI cut-offs must be reconsidered in populations of varying body build, age, and/or ethnicity. Since BMI-diagnosed overweight persons are sometimes physically and physiologically fit by other indicators, persons who are overweight on BMI should be more fully evaluated, diagnosed, and monitored with combined anthropometric and performance metrics to better clarify risks. The use of combined anthropometric and performance metrics involves integrating measurements of body composition with assessments of physical function and fitness to provide a more comprehensive evaluation of an individual’s health and fitness status. Eligibility for bariatric surgery or semaglutide satiety/appetite-reduction medications should not be determined by BMI alone. Awareness of the advantages and limitations of using BMI as a tool to assess adult obesity can maximize its appropriate use in the context of population health and in rapid clinical screening and evaluation.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This mini-narrative review argues that BMI remains useful for population surveillance and rapid primary-care screening but is inadequate for individual risk prediction, body-fat assessment, and universal cut-offs. It recommends combining BMI with anthropometric, body-composition, and performance/fitness metrics and avoiding BMI alone for treatment eligibility.
Key findings
- BMI is simple, inexpensive, and useful for population trends and screening, but it does not measure body fat distribution, functional health/fitness, or the heterogeneity of obesity and its outcomes.
- BMI cut-offs vary by institution and population: NIH 1993 used overweight ≥27.8 kg/m² for men and ≥27.3 kg/m² for women; WHO 1993 used normal 20–24.9, overweight 25–29.9, and obesity ≥30; Chinese adults 2002 used overweight 24–27.9 and obesity ≥28; Indians 2015 used overweight 23–24.9 and obesity ≥25; South Asians 2023 used overweight ≥23.
- Misclassification and risk-prediction limits are reported: in 40,420 US adults aged >18 years, modeled results suggested >70 million US adults would be misclassified as cardiometabolically healthy or unhealthy by BMI; in 899 women, lean but unfit women had higher all-cause mortality risk than overweight but more fit women; in 15,828 patients with stable coronary heart disease, risk factors rose above BMI 25 kg/m², BMI <20 and ≥35 kg/m² indicated poor prognosis, and lowest all-cause/cardiovascular mortality was at BMI 25–35 kg/m².
- Body fat varies widely at the same BMI: men with BMI 27 kg/m² had a 95% CI for body fat percentage from 10% to 32%, and the BMI cutoff for type 2 diabetes risk equivalent to 25 kg/m² in White populations was as low as 19.2 kg/m² in South Asians.
Methods
- Mini-narrative review, not a formal scoping review; the authors searched Google Scholar for popular scientific articles and news critiquing BMI, searched PubMed for principal scientific peer views, and obtained alternative or complementary measures from the CDC, NIH, The Lancet Diabetes and Endocrinology Commission, AMA, and WHO, and assessed the AMA Council on Science and Public Health critique of BMI.
- The review covers studies and publications through 2023, encompassing consensus reviews and relevant literature.
- No systematic search strategy, inclusion/exclusion criteria, quality appraisal, or quantitative synthesis is reported.
Limitations
- Stated: the authors describe the work as a mini-narrative review rather than a formal scoping review.
- Evident: no systematic search dates, search strings, inclusion/exclusion criteria, risk-of-bias assessment, or meta-analysis are provided, so conclusions rely on selected literature and consensus reports.
- Evident: the cited numerical findings come from heterogeneous studies with different populations and designs, and the paper does not pool them or formally compare their predictive performance.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
28 Own interpretation · 25 No full text of the sources · 23 Partly supported · 13 Supported · 9 Not checked yet · 4 Couldn’t confirm · 2 Supported so far · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
What the sources say
- Supportedcites [10] Bray, G.A. (2023). Beyond BMI. Nutrients, 15. source
Section 4, “Anatomic Limitations” (page not provided), says BMI gives no indication of body-fat distribution. It also reports greater health risks associated with central adiposity, supporting the abdominal-fat example. (LLM, conf=0.95)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Nevertheless, the lowest rates of all-cause and cardiovascular mortality among these patients with coronary heart disease were observed in the BMI range of 25 to 35 kg/m2, suggesting that BMI alone is too simplistic to give a comprehensive risk assessment in a clinical setting without consideration of many other factors.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
What the sources say
- Supportedcites [29] Held, 2022, Body Mass Index and Association With Cardiovascular Outcomes in Patients With Stable Coronary Heart Disease—A STABILITY Substudy, J. Am. Heart Assoc. source
The Conclusions state that all-cause and cardiovascular mortality were lowest at a BMI of 25 to 35 kg/m2 in patients with stable coronary heart disease. The Results give a more precise finding: event rates were lowest in the 25 to <30 kg/m2 category, with the spline-plot minimum around 27 kg/m2. (LLM, conf=0.94)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
What the sources say
- Partly supportedcites [10] Bray, G.A. (2023). Beyond BMI. Nutrients, 15. sourceExperimental — accuracy not yet measured
The Abstract and Section 5 state that BMI does not reveal obesity’s underlying causes or heterogeneity. Section 8 says evaluating its impacts on an individual requires additional information. Our copy does not establish the further claim that BMI cannot inform practitioners about variation in outcomes across specific populations; it describes BMI as useful for tracking population trends. (LLM, conf=0.78)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“However, the genetics of obesity are not sufficient to account for the variability of BMI [20,21].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
What the sources say
- Not checked — no full text of the sourcecites [20] Rohde, 2019, Genetics and epigenetics in obesity, Metabolism source
- Supportedcites [21] Yang, 2015, Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index, Nat. Genet. source
The Discussion estimates BMI heritability at 0.3–0.4 and reports that imputed genetic variants explain 27% of BMI phenotypic variance. Both findings support the claim that genetics alone do not account for all variability in BMI. (LLM, conf=0.90)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“BMI was developed originally from a single sample of White, middle-aged European men [3].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
What the sources say
- Partly supportedcites [3] Nuttall, 2015, Body Mass Index: Obesity, BMI, and Health: A Critical Review, Nutr. Today sourceExperimental — accuracy not yet measured
The “Lambert Adolph Jacque Quetelet” section says Quetelet identified the weight-to-height-squared relationship in normal young adults, and “Mathematical Adjustment of Body Build” says he developed the Quetelet Index in the 1800s. Our copy does not say it came from a single sample of White European men; its description of young adults also conflicts with “middle-aged.” No page numbers were supplied for these passages. (LLM, conf=0.84)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Moreover, BMI serves as a useful tool to predict the risk and prognosis of certain chronic diseases.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
What the sources say
- Partly supportedcites [29] Held, 2022, Body Mass Index and Association With Cardiovascular Outcomes in Patients With Stable Coronary Heart Disease—A STABILITY Substudy, J. Am. Heart Assoc. sourceExperimental — accuracy not yet measured
The Discussion (page not provided) reports that BMI was associated with cardiovascular death, total death, and heart-failure hospitalization in patients with stable coronary heart disease. This supports BMI as a prognostic marker in that population, but the paper does not establish its usefulness for predicting the risk and prognosis of chronic diseases more generally. (LLM, conf=0.84)
plaincorrelationaln = 15,828- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“A BMI below 20 kg/m2 and a BMI of 35 kg/m2 or higher were significant indicators of a poor prognosis [29].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
What the sources say
- Partly supportedcites [29] Held, 2022, Body Mass Index and Association With Cardiovascular Outcomes in Patients With Stable Coronary Heart Disease—A STABILITY Substudy, J. Am. Heart Assoc. sourceExperimental — accuracy not yet measured
The Abstract Conclusions describe both BMI <20 and ≥35 kg/m2 as strong markers of poor prognosis in patients with stable coronary heart disease. However, the Results (BMI and Association With Clinical Outcomes; page unavailable) report that, after multivariable adjustment, the elevated risks of cardiovascular death and heart-failure hospitalization at ≥35 kg/m2 were not statistically significant; increased total mortality was confined to the very-low-BMI group. Thus, calling both thresholds significant indicators overstates the findings. (LLM, conf=0.88)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The importance of this diversity is illustrated by the South Asian body habitus that is different enough from others to suggest a need for completely different cut-offs for obesity in BMI screening [26].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
What the sources say
- Supportedcites [26] Gray, L.J., Yates, T., Davies, M.J., Brady, E., Webb, D.R., Sattar, N., and Khunti, K. (2011). Defining obesity cut-off points for migrant South Asians. PLoS ONE, 6. source
The Discussion states that South Asians face dysglycemia and dyslipidemia at substantially lower levels of adiposity than White Europeans and that their BMI cut-off points need to be substantially lower. This supports the citing sentence’s suggestion of different BMI thresholds for obesity screening. (LLM, conf=0.92)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“Cutoff values for categories have fluctuated over the past century, depending upon the given year and guideline document (Table 1).”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
What the sources say
- Supportedcites [3] Nuttall, 2015, Body Mass Index: Obesity, BMI, and Health: A Critical Review, Nutr. Today source
The section “WHO and the Categorization of BMIs Into Quartiles” describes different category cutoffs in Garrow’s 1981 classification, the WHO guidelines, and the NIH guidelines. It also says the NIH lowered its normal-to-overweight cutoff in 1998 to align with the WHO guidelines, supporting the claim that cutoffs have varied by year and guideline. (LLM, conf=0.90)
- Not checked yetcites [35] Becker, 1998, Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults—The Evidence Report, Obes. Res.
- Not checked — no full text of the sourcecites [36] Zhou, 2002, Effect of body mass index on all-cause mortality and incidence of cardiovascular diseases-report for meta-analysis of prospective studies open optimal cut-off points of body mass index in Chinese adults, Biomed. Environ. Sci.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In a study of 40,420 persons aged >18 years in the US, researchers used nutritional status measures, including cholesterol and glucose, to determine whether subjects were at risk of cardiometabolic health disease by comparing their predictive values with BMI; modeled results suggested that >70 million US adults would be misclassified as cardiometabolically healthy or unhealthy by BMI [52].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
What the sources say
- Supportedcites [52] Tomiyama, 2016, Misclassification of cardiometabolic health when using body mass index categories in NHANES 2005–2012, Int. J. Obes. source
The paper’s opening passage (p. 2) reports 40,420 NHANES participants aged 18 or older and estimates that 74,936,678 US adults would be misclassified as cardiometabolically healthy or unhealthy if BMI categories were used as the main indicator of health. The Discussion (p. 3) repeats the estimate, supporting the claim’s rounded figure of more than 70 million. (LLM, conf=0.94)
hedgeddirectn = 40,420- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
- It may state two separate claims as one. 36
- Probably not a substantial claim. 28
Every source the paper cites for this claim was checked and supports it.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
What the sources say
- Supportedcites [31] Soysal, 2022, What is the Optimal Body Mass Index Range for Older Adults?, Ann. Geriatr. Med. Res. source
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)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
What the sources say
- Partly supportedcites [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
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)
- 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.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
What the sources say
- Partly supportedcites [22] Silventoinen, 2020, Obesity and eating behavior from the perspective of twin and genetic research, Neurosci. Biobehav. Rev. sourceExperimental — accuracy not yet measured
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)
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
What the sources say
- Partly supportedcites [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
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)
- Could not verifycites [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
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)
- Partly supportedcites [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
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)
- Partly supportedcites [7] Licenziati, 2022, A height-weight formula to measure body fat in childhood obesity, Ital. J. Pediatr. sourceExperimental — accuracy not yet measured
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)
- Could not verifycites [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
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)
- Partly supportedcites [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 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)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
What the sources say
- Partly supportedcites [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
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)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
What the sources say
- Partly supportedcites [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
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)
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“High BMI is also associated with an increased risk of colorectal cancer [15], primary liver cancer, and cancer mortality [16].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
What the sources say
- Supportedcites [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
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)
- Partly supportedcites [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
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)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
What the sources say
- Partly supportedcites [10] Bray, G.A. (2023). Beyond BMI. Nutrients, 15. sourceExperimental — accuracy not yet measured
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)
- 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/.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
What the sources say
- Supportedcites [17] Singh, 2022, Association of Obesity With COVID-19 Severity and Mortality: An Updated Systemic Review, Meta-Analysis, and Meta-Regression, Front. Endocrinol. source
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)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
What the sources say
- Partly supportedcites [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
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)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Skinfold thickness is considered a valuable tool but requires considerable technical skill and experience to be measured accurately.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.2. Comprehensive Macro-Nutritional Assessments
What the sources say
- Partly supportedcites [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
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)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.2. Comprehensive Macro-Nutritional Assessments
What the sources say
- Supportedcites [52] Tomiyama, 2016, Misclassification of cardiometabolic health when using body mass index categories in NHANES 2005–2012, Int. J. Obes. source
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)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.2. Comprehensive Macro-Nutritional Assessments
What the sources say
- Partly supportedcites [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
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)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
What the sources say
- Supportedcites [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
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)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
What the sources say
- Could not verifycites [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
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)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Could not verify
In the paper
“Body mass index (BMI) is used to screen for macro-nutritional status, whether undernutrition, normal range, or overnutrition [1].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
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.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“BMI does not measure functional health and fitness but is merely an anthropometric measure.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
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.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Overweight status is associated with an increased risk of depressive disorder among adults [18].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
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.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“BMI has substantial utility in population-level statistics, summarizing current burdens of under- or over-nutrition and assessing trends in communities.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Polynesian body habitus may also differ, such that different cut-offs are appropriate.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 1. Introduction
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.2. Comprehensive Macro-Nutritional Assessments
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, even with different cut-offs, changes over time may be valid for tracking changes within individuals and summarizing population trends.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.2. Comprehensive Macro-Nutritional Assessments
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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Implementing these recommendations can significantly improve the effectiveness of anthropometry in assessing population health and guiding clinical care.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.2. Comprehensive Macro-Nutritional Assessments
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.2. Comprehensive Macro-Nutritional Assessments
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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Healthcare inequities are evident when one considers the cost and availability of advanced diagnostics in lower-resource settings.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.2. Comprehensive Macro-Nutritional Assessments
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 4. Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.2. Comprehensive Macro-Nutritional Assessments
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“More extreme BMI findings have a high predictive utility for obesity-related medical conditions, as well as for undernutrition and cachexia.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 4. Discussion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.2. Comprehensive Macro-Nutritional Assessments
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
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.2. Comprehensive Macro-Nutritional Assessments
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.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The BMI is helpful when it is used as a screening test and population-appropriate cut-offs are used.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 4. Discussion
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
What the sources say
- Not checked yetcites [42] Tirthani, E., Said, M.S., and Rehman, A. (2024). Genetics and Obesity. StatPearls, StatPearls Publishing LLC.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.2. Comprehensive Macro-Nutritional Assessments
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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 4. Discussion
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 5. Conclusions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
- Rests on an assumption: Radiation exposure in both DXA and CT is a disadvantage.
- Rests 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.
- Rests on an assumption: Waist-to-hip ratio indicates fat distribution and serves as a reliable predictor of cardiovascular risk.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 5. Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 4. Discussion
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 5. Conclusions
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 4. Discussion
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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 5. Conclusions
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
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 5. Conclusions
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 5. Conclusions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 5. Conclusions
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper relies on this without support.
Radiation exposure in both DXA and CT is a disadvantage.
“Radiation exposure in both DXA and CT is a disadvantage.”
The paper relies on this without support.
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.
“Although MRI does not involve ionizing radiation, it 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.”
The paper relies on this without support.
Waist-to-hip ratio indicates fat distribution and serves as a reliable predictor of cardiovascular risk.
“The waist-to-hip ratio (WHR), determined using a simple tape measure, indicates fat distribution and serves as a reliable predictor of cardiovascular risk.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 4. Discussion
“The 2023 AMA report states: “There are issues with BMI in diagnosing obesity. Further measures are recommended for clinical use. BMI loses its predictability in assessing the fat mass on the individual level because of the body variation among different age, sex, and race groups. Therefore, BMI should not be used as the only criterion in decision- or policy-making” [18].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > References
“18. Luppino F.S. de Wit L.M. Bouvy P.F. Stijnen T. Cuijpers P. Penninx B.W. Zitman F.G. Overweight, obesity, and depression: A systematic review and meta-analysis of longitudinal studies Arch. Gen. Psychiatry 2010 67 220 229”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > Abstract
“The review covers studies and publications through 2023, encompassing consensus reviews and relevant literature.”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > References
“57. Yang X. Hao X. Liu M. Hu Y. Wang X. Liu Y. The prevalence of obesity and relationship between obesity indicators and chronic diseases in Northern Shaanxi, China Endokrynol. Pol. 2024 75 71 82”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
“those with a BMI greater than 30 kg/m2 as obese [3].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
“| Universal (1993 by WHO) [3] | * | 20–24.9 | 25–29.9 | ≥30 |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
“In 2015, in India, a BMI > 23 kg/m2 and <25 kg/m2 were regarded as overweight and >25 kg/m2 as obese for Indians [37].”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
“| Indians in India (2015) [37] | * | * | 23–24.9 | ≥25 |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
“In 2002, guidelines for Chinese adults used yet another threshold for being overweight (BMI > 24 kg/m2)”
§ Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity > 3. Results > 3.1. Critical Assessment
“| Chinese adults in China (2002) [36] | ≤18.5 | 18.6–23.9 | 24–27.9 | ≥28 |”
Citations
Cites
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Overweight, Obesity, and Depression: A Systematic Review and Meta-analysis of Longitudinal Studies
Luppino, F. S., de Wit, L. M., Bouvy, P. F., Stijnen, T., Cuijpers, P., Penninx, B. W. J. H., & Zitman, F. G. (2010) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2010.2 · Cited by 4,322 · cited 2×
“Therefore, BMI should not be used as the only criterion in decision-or policy-making” [18].”
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Body Mass Index: Obesity, BMI, and Health A Critical Review
Nuttall, F. Q. (2015) · Nutrition Today · doi:10.1097/nt.0000000000000092 · Cited by 2,284 · cited 5×
“In contrast, the WHO classified people with a BMI be-tween 20 and 24.9 kg/m 2 as having normal weight, those with a BMI between 25 and 29.9 kg/m 2 as overweight, and those with a BMI greater than 30 kg/m 2 as obese [3].”
BackgroundMethodInfluentialAdolphe Quetelet (1796 1874) the average man and indices of obesity
Eknoyan, G. (2007) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfm517 · Cited by 746 · cited 2×
“Body mass index (BMI) is used to screen for macro-nutritional status, whether under-nutrition, normal range, or overnutrition [1].”
MethodStudy, T. L. C., Yang, J., Bakshi, A., Zhu, Z., Hemani, G., Vinkhuyzen, A. A. E., Lee, S. H., Robinson, M. R., Perry, J. R. B., Nolte, I. M., et al. (2015) · Nature Genetics · doi:10.1038/ng.3390 · Cited by 745
Jackson, A., Stanforth, P., Gagnon, J., Rankinen, T., Leon, A., Rao, D., Skinner, J., Bouchard, C., & Wilmore, J. (2002) · International Journal of Obesity · doi:10.1038/sj.ijo.0802006 · Cited by 694 · cited 1×
“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].”
BackgroundAssessment methods in human body composition
Lee, S. Y., & Gallagher, D. (2008) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/mco.0b013e32830b5f23 · Cited by 694 · cited 2×
“Multifrequency BIA and bioimpedance spectroscopy (BIS) both distinguish between intracellular and extracellular water to detect fluid shifts and evaluate hydration levels [62].”
BackgroundCarnethon, M. R. (2003) · JAMA · doi:10.1001/jama.290.23.3092 · Cited by 691
Obesity and Risk of Colorectal Cancer: A Systematic Review of Prospective Studies
Ma, Y., Yang, Y., Wang, F., Zhang, P., Shi, C., Zou, Y., & Qin, H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0053916 · Cited by 622 · cited 1×
“High BMI is also associated with an increased risk of colorectal cancer [15], primary liver cancer, and cancer mortality [16].”
Background
Research works printed but not linked: 1
- (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.
Cited by
Sorted by citations
Ahmed, S. K., & Mohammed, R. A. (2025) · Metabolism Open · doi:10.1016/j.metop.2025.100375 · Cited by 432
“Because of this, researchers are calling for more sophisticated classification systems that take into consideration distribution of fat-visceral versus subcutaneous fat-metabolic health, and comorbid conditions [126,127].”
Ayesh, H., Nasser, S. A., Ferdinand, K. C., & Leon, B. G. C. (2025) · Circulation Research · doi:10.1161/circresaha.124.325535 · Cited by 42
Ketogenic Diets for Body Weight Loss: A Comparison with Other Diets
Dyńka, D., Rodzeń, Ł., Rodzeń, M., Pacholak-Klimas, A., Ede, G., Sethi, S., Łojko, D., Bartoń, K., Berry, K., Deptuła, A., et al. (2025) · Nutrients · doi:10.3390/nu17060965 · Cited by 41
“Importantly, however, BMI is an imperfect measure of body fat content (and therefore cannot replace clinical assessment), as it is affected by a number of factors, such as age, gender, and physical activity (and muscle content), although it works fairly well as a screening test [27,28].”
Beyond BMI: Rethinking Obesity Metrics and Cardiovascular Risk in the Era of Precision Medicine
Tănăsescu, M., Rosu, A.-M., Mincă, A., Rosu, A.-L., Grigorie, M., Timofte, D., & Ionescu, D. (2025) · Diagnostics · doi:10.3390/diagnostics15233025 · Cited by 33
“This is critical in cardiovascular risk prediction, where visceral fat, rather than total body weight, is a stronger determinant of adverse outcomes [18].”
Abdulla, A., Sadida, H. Q., Jerobin, J., Elfaki, I., Mir, R., Mirza, S., Singh, M. K., Macha, M. A., Shahab Uddin, M. S., Fakhro, K. A., et al. (2024) · Journal of the National Cancer Center · doi:10.1016/j.jncc.2024.11.001 · Cited by 26
“8 , 9 The normal ranges for BMI can vary significantly across different racial and ethnic groups due to genetic, environmental, and cultural factors.”
Itani, L., & Ghoch, M. E. (2024) · Nutrients · doi:10.3390/nu16223838 · Cited by 21
Zhang, N., Xie, G., Jian, G., Jiang, K., Lu, Z., Wang, Z., Wang, J., Lan, H., Wang, W., He, S., et al. (2025) · Cardiovascular Diabetology · doi:10.1186/s12933-025-02990-4 · Cited by 19
Liver and obesity: a narrative review
Lonardo, A., & Weiskirchen, R. (2025) · Exploration of Medicine · doi:10.37349/emed.2025.1001334 · Cited by 21
Sakizlian, D. D. M., Boldeanu, L., Mitrea, A., Clenciu, D., Vladu, I. M., Plasiciuc, A. E. C., Șarla, A. V., Siloşi, I., Boldeanu, M. V., Assani, M.-Z., et al. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26136227 · Cited by 16
“This underscores the need for more nuanced approaches to risk assessment beyond traditional BMI categorizations [26,27].”
Maleki, F., Hosseinpour, M., Delpisheh, A., Bahardoust, M., Hajizadeh‐Sharafabad, F., & Pashaei, M. R. (2024) · BMC Gastroenterology · doi:10.1186/s12876-024-03446-x · Cited by 16
Functional Burden and Quality of Life in Hip and Knee Osteoarthritis: A Cross-Sectional Study
Pavel, R. M. S., Purza, A. L., Ţiţ, D. M., et al. (2025) · Medicina · doi:10.3390/medicina61071155 · Cited by 15
“It also underscores the limitations of using BMI alone as a functional predictor, especially in clinical samples where body composition is not homogenous [46].”
Adekunle, O. A., Le, P., Gupta, D. Y., Rothberg, M. B., Tran, H. T., Yue, Y., & Gasoyan, H. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.16544 · Cited by 12
Sarno, G., Reytor-González, C., Frías-Toral, E., Campuzano-Donoso, M., Katsanos, C. S., & Simancas‐Racines, D. (2025) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2025.06.010 · Cited by 13
Intermittent fasting for adults with overweight or obesity
Garegnani, L., Oltra, G. V., Ivaldi, D., Burgos, M. A., Andrenacci, P. J., Rico, S., Boyd, M. J., Radler, D. R., Liquitay, C. M. E., & Eva Madrid, A. (2026) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd015610.pub2 · Cited by 13
Zhang, Z., Ruan, X.-Y., & Ma, W. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2024.1510218 · Cited by 11
“First, BMI cannot distinguish between muscle mass and fat mass, leading to potential misclassification of obesity in individuals with high muscle mass but low body fat (14).”
Hajihashemi, P., Mohammadifard, N., Bateni, M., Haghighatdoost, F., Boshtam, M., Najafian, J., Sadeghi, M., Shabani, N., & Sarrafzadegan, N. (2025) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2025.100936 · Cited by 12
Association between body roundness index and sleep disorder: the mediating role of depression
Gong, H., & Zhao, Y. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-06664-z · Cited by 12
“However, BMI fails to accurately capture fat distribution, particularly visceral fat [7, 8], which may be more closely associated with the onset of sleep disorders [9, 10].”
LORENZO, A. D., Itani, L., Ghoch, M. E., Gualtieri, P., Frank, G., Raffaelli, G., Pellegrini, M., & Renzo, L. D. (2024) · Nutrients · doi:10.3390/nu16152415 · Cited by 10
“However, this traditional classification system has always been subject to criticism [13] due to several limitations [14,15], such as not fitting for all ethnicities (e.g., Asians) [16], and its inability to discriminate between the body compartments (e.g., bone, fat and muscle) [17].”
BackgroundLean MASLD and Cardiovascular Disease: A Review
Lebovics, N., Heering, G., Frishman, W. H., & Lebovics, E. (2025) · Cardiology in Review · doi:10.1097/crd.0000000000000893 · Cited by 11
Przybyłowski, A., Górski, M. K., Gwioździk, W., & Polaniak, R. (2025) · Healthcare · doi:10.3390/healthcare13161967 · Cited by 11
“Waist circumference (WC) and waist-to-hip ratio (WHR) are key indicators, with WC being particularly useful in identifying central or visceral obesity, a major risk factor for metabolic diseases [23,26].”
Lui, D. T. W., Fong, C. H. Y., Zou, X., Xu, A., Tse, H., Woo, J., Lam, T. H., WOO, Y. C., Cheung, B. M. Y., Janus, E. D., et al. (2026) · PLoS Medicine · doi:10.1371/journal.pmed.1004749 · Cited by 10
“It also does not inform practitioners of the heterogeneity of obesity and its outcomes in specific individuals or populations [3].”
Lima, V. P., Cavalcante, E. D. O., Leão, M. A., LaFata, E. M., Sampaio, A. M., & Neto, P. R. N. (2024) · Journal of Eating Disorders · doi:10.1186/s40337-024-01108-7 · Cited by 9
“It correlates highly with direct measures of adiposity (e.g., dual-energy x-ray absorptiometry) despite its limitations in assessing specific phenotypes of body composition (e.g., bodybuilders) and not to be able in distinguishing lean body mass from fat mass [33–35].”
Oh, J., Mun, W.-L., Lee, Y.-E., Roh, S.-Y., & Kim, G. (2025) · Nutrients · doi:10.3390/nu17020253 · Cited by 8
“The obesity rate among Korean adolescents is already significantly higher than the East Asian regional average as defined by the WHO [31].”
Wagner, J. G., Chen, L., Jiang, F., et al. (2025) · Journal of Orthopaedic Research® · doi:10.1002/jor.26048 · Cited by 8
The impact of obesity and lipids on cancer: Insights into mechanisms and potential interventions
Romero, M., Lian, Y., Piquer, A., Borràs-Ferré, N., Antonio Zorzano, A., & Ivanova, S. (2025) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2025.07.006 · Cited by 8
Sepulveda, R. A. Y., Vasquez-Bonilla, A. A., Olivares, R., Olivares, P., Zavala-Crichton, J. P., Hinojosa-Torres, C., Muñoz-Strale, C., Giakoni-Ramírez, F., de Souza-Lima, J., Páez‐Herrera, J., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-15264-6 · Cited by 12
Epidemiology and Natural History of Preclinical and Clinical Obesity: Insights From a UK Cohort
Zahid, S., Yao, Z., Grimes, S. N., Kim, A. H., Peng, A. W., Blumenthal, R., Arvanitis, M., Battle, A. J., & Blaha, M. J. (2026) · Obesity · doi:10.1002/oby.70126 · Cited by 11
Malik, Z., Sohn, W., & Williams, K. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13133856 · Cited by 7
Milanese, C., Itani, L., Cavedon, V., & Ghoch, M. E. (2025) · Nutrients · doi:10.3390/nu17132162 · Cited by 9
“This last fact relates to the classification categories of the BMI relying on broad ranges that are not widely accepted due to several limitations [11,12].”
Center Stage: Putting Obesity Staging Systems Into the Spotlight
Zahid, S., Peng, A. W., Razavi, A. C., Yao, Z., Blumenthal, R. S., & Blaha, M. J. (2025) · Preventing Chronic Disease · doi:10.5888/pcd22.250222 · Cited by 8
Su, B., Cao, Y., Ma, L., & Huang, J. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1734041 · Cited by 11
“…recognized assessment index and the vast majority of existing epidemiological and clinical mechanistic studies employ this metric for grouping, it remains a relatively feasible perspective for integrating evidence from the literature and conducting risk strati fi cation at the present stage (4).”
Podeanu, M.-A., Vintilescu, Ş. B., Ionele, C. M., Sandu, R. E., Niculescu, C., Florescu, M. M., & Stepan, M. D. (2024) · Life · doi:10.3390/life14091206 · Cited by 7
“Over the years, various methods were used to assess body weight, but, until today, at populational level, the one that proved to be the most efficient was the BMI [51,52].”
MethodPiecuch, M., Chylak, M., Górski, M. K., Garbicz, J., Szczyrba, A., Buczkowska, M., Malinowska-Borowska, J., Nowak, J. U., Niedziela, J. T., Gąsior, M., et al. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13216625 · Cited by 6
“One of the reasons for this criticism is its inability to distinguish the variable proportion of body fat in body composition [4].”
BackgroundInfluentialGu, C., Kang, X., Chen, X., Long, Z., Yang, F., & Luo, W. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1561990 · Cited by 6
Miquel, J. J. G., López, P. J. T., Jansana, M. D. M., et al. (2025) · Metabolites · doi:10.3390/metabo15080495 · Cited by 6
“However, BMI lacks the capacity to reflect fat distribution, particularly visceral adiposity, which plays a central role in the pathogenesis of MS [5].”
Mateos, I. C., Morales, C., & Aguilera, B. G. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14155434 · Cited by 9
“However, body weight reduction alone does not discriminate accurately between body fat mass and lean mass or between visceral and subcutaneous fat distribution, which are clinical parameters that have been shown to be associated with changes in cardiometabolic risk [23–25].”
Wasiluk, A., Saczuk, J., & Asienkiewicz, R. (2025) · Life · doi:10.3390/life15121808 · Cited by 6
“Regression analyses between BMI and %BF revealed low coefficients of determination (R 2 ), particularly in the MUO and MHNW groups, indicating that BMI does not fully capture individual differences in fat content and distribution [21,22].”
Management of undernutrition during preconception and pregnancy in an urban setting in North India
Dhabhai, N., Chowdhury, R., Taneja, S., et al. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1405247 · Cited by 6
Body Composition and Its Outcomes and Management in Multiple Sclerosis: Narrative Review
Kidwell-Chandler, A. L., Jackson, J., Jeng, B., Silveira, S. L., Pilutti, L. A., Hibbing, P. R., & Motl, R. W. (2025) · Nutrients · doi:10.3390/nu17061021 · Cited by 7
Alexandre, M., Poínhos, R., CRI-O Group, Oliveira, B. M. P. M., & Correia, F. (2025) · Nutrients · doi:10.3390/nu17111858 · Cited by 6
“Despite reliance on body mass index (BMI) for obesity diagnosis, BMI alone falls short in distinguishing between lean mass and fat mass or capturing fat distribution [12].”
Tu, J., Wu, B., Chen, H., & Guo, Y. (2025) · BMC Endocrine Disorders · doi:10.1186/s12902-025-01989-y · Cited by 5
“Unlike BMI, which conflates lean mass with fat mass and fails to distinguish metabolically healthy obesity [30, 31], WWI employs mathematical modeling to eliminate confounding effects of height and total body weight [19, 29], thereby providing a more precise assessment of abdominal fat distribution…”
Moving away from BMI: a new era of diagnostic criteria in obesity
Sibal, R., Balamurugan, G., Graham, Y. N. H., & Mahawar, K. K. (2025) · Expert Review of Endocrinology & Metabolism · doi:10.1080/17446651.2025.2537160 · Cited by 5
“Exploring alternative anthropometric metrics such as waist circumference, waist-to-height ratio (WHtR) or Body Shape Index, in addition to BMI could offer a more comprehensive analysis of body composition and associated obesity-related health risks in diverse populations [42].”
Miguel, J. J. G., López, P. J. T., Jansana, M. D. M., López‐González, Á. A., Sbert, P. R., Busquets-Cortés, C., & Ramírez‐Manent, J. I. (2025) · Medical Sciences · doi:10.3390/medsci13030160 · Cited by 5
“Its inability to reflect fat distribution or differentiate between lean and adipose tissue limits its utility for detecting MASLD, particularly in cases of metabolically unhealthy normal weight or sarcopenic obesity [43,44].”
Predictive performance of traditional and novel anthropometric indices for diabetes and hypertension
Pezeshki, M., Esmaeili, S., Mahdavi‐Gorabi, A., Sadegi, T., Kiaee, Z. F., Assar, O., Semnani, K., & Qorbani, M. (2025) · Journal of Diabetes Investigation · doi:10.1111/jdi.70172 · Cited by 6
“Several additional anthropometric indices derived from these simple measurements (weight, height, and WC) have also been proposed to account for shortcomings associated with the sole use of these measures 7,8 .”
Tomasiewicz, A., Targowski, T., Makuch, S., et al. (2025) · Nutrients · doi:10.3390/nu17243843 · Cited by 5
Uti, D. E., Omang, W. A., Alum, E. U., Ugwu, O. P.-C., Wokoma, M. A., Oplekwu, I. R., Atangwho, I. J., & Egbung, G. E. (2026) · Nutrition and Metabolic Insights · doi:10.1177/11786388251408961 · Cited by 7
“28 Moreover, many healthcare systems lack standardized protocols for routine screening and early detection of obesity-related complications, particularly in pediatric and geriatric populations.”
Weber, C., Wind, D., Petzsch, P., Supprian, T., Dilthey, A., Christl, J., & Finzer, P. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01941-1 · Cited by 5
“This reflects some of the general limitations of the BMI in certain populations [78].”
InfluentialLim, Y., & Lee, S. K. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1844870 · Cited by 5
“These fi ndings suggest that clinical obesity better captures the pathophysiological burden relevant to cardio-vascular events than BMI alone, which is an imperfect surrogate because it cannot differentiate fat from lean mass or capture fat distribution (29).”
Morvaridi, M., Behbahani, H. B., Alipour, M., Javid, A. Z., Keramatzadeh, S., Shokri, S., Tofighzadeh, P., Fayazfar, F., Dehkordi, H. S., Ghadimi, E., et al. (2025) · BMC Nephrology · doi:10.1186/s12882-025-04089-0 · Cited by 5
“Finally, the influence of BMI on MIS scores highlights the potential limitations of using BMI alone as a nutritional marker, as it does not account for body composition changes, such as muscle wasting, which are common in this population [48, 49].”
Wang, C., Huang, X., He, S., Kuang, M., Xie, G., Sheng, G., & Zou, Y. (2024) · Lipids in Health and Disease · doi:10.1186/s12944-024-02299-3 · Cited by 5
“While BMI has long been used as the most common obesity indicator [14], it should be noted that BMI has limited ability to distinguish between lean body mass and fat, and does not accurately reflect actual BF across different genders and age groups [14–16].”
BackgroundRelationship Between Body Mass Index and Fat Mass Percentage with Proprioception in Children
Guzmán-Muñoz, E., Concha‐Cisternas, Y., Méndez-Rebolledo, G., Vásquez-Muñoz, M., Valdés-Badilla, P. A., Herrera-Valenzuela, T., Núñez-Espinosa, C. A., & Hernández-Martínez, J. (2025) · Journal of Functional Morphology and Kinesiology · doi:10.3390/jfmk10010059 · Cited by 6
Soler, M. G., López, P. J. T., López‐González, Á. A., Paublini, H., Rifá, E. M.-A., Herrero, M. T. V., & Ramírez‐Manent, J. I. (2025) · Diabetology · doi:10.3390/diabetology6040032 · Cited by 4
“However, BMI has significant limitations [17]: it does not distinguish between lean mass and fat, nor does it identify the distribution of adipose tissue, critical factors in determining IR.”
Alarfaj, S. J. (2025) · Medicina · doi:10.3390/medicina61081393 · Cited by 5
“However, BIA is not commonly used in clinical practice, making BMI the most practical choice to measure the effect on weight [42].”
Zahid, S., Peng, A. W., Razavi, A. C., Yao, Z., Blumenthal, R. S., & Blaha, M. J. (2025) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2025.101281 · Cited by 6
Prospective associations of obesity heterogeneity, serum proteins, and carotid atherosclerosis risk
Zhong, H., Chen, J., Jia, S., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105935 · Cited by 4
“26 In addition, previous studies indicated that the prevalence of MHO was underestimated when defined based on BMI compared to DXA-derived BF%.”
Chen, Z., Li, J., Rao, H., Zhang, J., Xiao, Z., Sun, W., & Xiao, M. (2025) · BMC Cardiovascular Disorders · doi:10.1186/s12872-025-05135-3 · Cited by 4
“Traditional metrics, such as body mass index (BMI), are widely utilized but fail to distinguish between fat and lean mass, and cannot capture the heterogeneous health impacts of regional fat deposition [7, 8].”
Central Obesity: An Emerging Player in Cardiac Remodelling and Dysfunction
Suiunov, K., Mamazhakypov, A., Polupanov, A. G., Сакибаев, К. Ш., Kyzy, M. S., Asanbaev, C., Sydykov, A. S., & Калматович, К. Р. (2025) · Heart Lung and Circulation · doi:10.1016/j.hlc.2025.05.096 · Cited by 5
Translating New Obesity Definitions Into Clinical Practice
Zahid, S., Yao, Z., Blumenthal, R. S., & Blaha, M. J. (2025) · JAMA · doi:10.1001/jama.2025.24909 · Cited by 5
Nechita, L., Gurau, T. V., Mușat, C. L., et al. (2025) · Diagnostics · doi:10.3390/diagnostics15243238 · Cited by 4
“Although BMI has limitations in athletic populations due to higher lean mass [19], it remains a useful screening tool when interpreted alongside performance-specific parameters (Table 4).”
Multimodal (Bio)Markers and Risk of Obesity – A Comprehensive Scoping Review
Vahid, F., Loyola-Leyva, A., Tur, J. A., Bouzas, C., Devaux, Y., Malisoux, L., García-Hernández, S., Carvalho, M. D., Ródenas-Munar, M., Turner, J., et al. (2025) · Advances in Nutrition · doi:10.1016/j.advnut.2025.100579 · Cited by 8
Weighting health-related estimates in the GCAT cohort and the general population of Catalonia
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“This reinforces the notion that BMI measures may have limitations in accurately determining body composition and obesity 40 .”
Wu, R., Li, M., Liao, Y., Zhang, J., Xu, C., & Yan, X. J. (2025) · BMC Public Health · doi:10.1186/s12889-025-24086-0 · Cited by 6
“Numerous studies have highlighted that reliance solely on BMI significantly underestimates the prevalence of obesity, as it fails to detect a substantial proportion of individuals with excessive body fat despite having a normal body weight [6].”
Hussain, M., Keyte, R., Kalika, E., & Mantzios, M. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1545056 · Cited by 4
“…the current studies used BMI as a biometric variable; however, given the ongoing discussions on the limitations of BMI as a health indicator (e.g., Wu et al., 2024), such as assessing excess fat, and differences in body build and ethnicity, future research should consider using alternative…”
Norton, H., Śliwińska-Stańczyk, P., Hala, T., et al. (2024) · Rheumatology and Therapy · doi:10.1007/s40744-024-00726-6 · Cited by 4
Soheilifard, S., Faramarzi, E., & Mahdavi, R. (2025) · Journal of Health Population and Nutrition · doi:10.1186/s41043-025-00761-1 · Cited by 4
“BMI does not consider variations in body fat distribution, which impacts metabolic health, leading to the identification of distinct cardiometabolic risk markers [4, 5].”
Hu, D., Li, G., Ge, D., Ke, L., Shu, H., Chen, Y., & Hao, Z. (2025) · Lipids in Health and Disease · doi:10.1186/s12944-025-02497-7 · Cited by 4
“Additionally, the implications of obesity may vary across different ethnic groups for a given BMI [20, 21], such as in some Asian populations who exhibit a lower relative risk.”
Abernethy, D., Bennie, J. A., & Pavey, T. (2025) · Journal of Diabetes Research · doi:10.1155/jdr/7466757 · Cited by 5
“As BMI does not distinguish between fat and lean mass, nor does it account for fat distribution, it may lead to less accurate interpretations of the combined e ff ects of PA and adiposity on the risk of diabetes [64].”
Cap, D. M., Dong, K. T., Nguyen, D. T. H., Nguyen, H. T., Tang, K. X., Cao, A. T., Nguyen, L. T., Nguyen, T. T. H., & Pham, D. M. (2025) · Obesity Medicine · doi:10.1016/j.obmed.2025.100600 · Cited by 4
Park, S., Ning, H., Carnethon, M. R., & VanWagner, L. B. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.124.037948 · Cited by 5
“74 Our study used the standard BMI cutoffs, which are higher than proposed cutoffs for men and White women, but are deemed sufficient, because they are viewed as a global measure of health along with other metrics in LS7 definitions.”
Alfaro, E., Aparicio, J. E. V., Escolano, R. V., et al. (2025) · Wellbeing Space and Society · doi:10.1016/j.wss.2025.100271 · Cited by 4
Alonazi, A., Alsunaid, F., Alofaisan, L., Alqarni, M. G., Alhumoud, J., & Kashoo, F. Z. (2025) · Children · doi:10.3390/children12070899 · Cited by 4
Golmohamadi, S., Murray, K., & Farrer, L. M. (2025) · Body Image · doi:10.1016/j.bodyim.2025.101936 · Cited by 3
Visceral Adiposity Index in Relation to Rotterdam Phenotypes of Polycystic Ovary Syndrome
Pluta, D., Staśczak, A., Stokowy, T., Migacz, M., Kochman, K., & Holecki, M. T. (2025) · Biomedicines · doi:10.3390/biomedicines13081997 · Cited by 4
“BMI does not take into account the percentage of muscle and adipose tissue and is not specific to gender and ethnicity [27].”
Soliman, A. R., Ahmed, R. M., Meligi, A. A. H. E., Abo-Zeid, A. G., Amer, M. A., Atef, A., Yousry, A., Saleh, A. A., Fahmy, E. M., Makar, W., et al. (2025) · The Egyptian Journal of Internal Medicine · doi:10.1186/s43162-025-00513-x · Cited by 3
“However, the clinical significance of elevated BMI lies not merely in weight classification but in its strong association with numerous serious health complications that significantly impact morbidity, mortality, and quality of life [4].”
Lee, M. J., Seo, B.-J., & Cho, J. (2025) · Life · doi:10.3390/life15101543 · Cited by 4
“Body mass index (BMI), calculated by dividing an individual’s weight in kilograms by the square of their height in meters (kg/m 2 ), is a widely used indicator of obesity as it reflects body fat composition with relatively high accuracy [25].”
Choa, R., Miller, K., Joshi, A., et al. (2025) · Blood Immunology & Cellular Therapy · doi:10.1016/j.bict.2025.100015 · Cited by 3
Yin, S., Su, J., Bao, J., Yu, H., Liu, M., & Ji, X. (2025) · Aging Clinical and Experimental Research · doi:10.1007/s40520-025-03215-0 · Cited by 3
Yaşar, O. M., Gürses, V. V., Ciğerci, A. E., Bal, E., Pehlivan, Y., Baş, M., Malkoç, N., Bektaş, M., Başkaya, G., Dündar, S., et al. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1700684 · Cited by 5
“Body mass index (BMI), calculated as body mass in kilograms divided by height in meters squared (kg/ m 2 ), is one of the most widely used indices for estimating adiposity in both clinical and young active adults settings (12–16).”
Sharing approaches in predictive genomics across animals, plants and humans
Arirangan, S., de Oliveira, L. F., Hasan, M. N., Sherman, A. B., Tuinstra, M. R., Brito, L. F., Wedow, R., & Tegtmeyer, M. (2026) · Nature Genetics · doi:10.1038/s41588-025-02491-w · Cited by 4
Ahmed, I. S., Tapponi, S. L., Manaa, H. M., Soltani, A., Hussein, E. S., Parvaresh, F. A., & Hussain, Z. (2026) · Journal of Obesity · doi:10.1155/jobe/6556057 · Cited by 5
Chaudhary, P., Yang, S., Waldrop, S., Katzmarzyk, P. T., & Staiano, A. E. (2026) · Obesity · doi:10.1002/oby.70198 · Cited by 4
“Body mass index (BMI) percentiles cannot distinguish fat from lean mass or capture visceral adiposity, metabolic dysfunction, or functional limitations [3].”
Gunnarsson, S., Karlsson, C., Prasad, R. B., & Hansson, S. F. (2026) · EBioMedicine · doi:10.1016/j.ebiom.2026.106272 · Cited by 3
Davey, C. H., Duwadi, D., Phillips, J. C., Dawson-Rose, C. S., Nokes, K. M., Perazzo, J. D., Schnall, R., Orton, P. M., Hamilton, M. J., Musanti, R., et al. (2024) · AIDS and Behavior · doi:10.1007/s10461-024-04509-6 · Cited by 3
“Current consensus reviews indicate that BMI serves as an important tool for healthcare screenings and population surveys, but it is limited in its ability to predict accurately chronic disease risk and assessing excess body fat [60].”
InfluentialBarros, A. Q. S., Aguiar, Í. W. O., Adriano, L. S., et al. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1467339 · Cited by 3
Abdullah, N., Goh, Y., Khuzaimi, A. N., Arifin, A. S. K., Yusuf, N. A. M., Jalal, N. A., Ismail, N., Husin, N. F., Kamaruddin, M. A., & Jamal, R. A. (2025) · Journal of Diabetes · doi:10.1111/1753-0407.70039 · Cited by 3
“While body mass index (BMI), waist circumference (WC), and waist- to-height ratio provide useful information on obesity and fat accumulation, they do not offer insights into muscle mass, which plays a crucial role in glucose metabolism [13].”
Gu, P., Shi, B., Zhang, Z., et al. (2025) · Journal of Health Population and Nutrition · doi:10.1186/s41043-025-00914-2 · Cited by 3
“For instance, individuals with well-developed muscles may be misclassified as over-weight, while those with normal body weight but excessive visceral fat might be overlooked [28].”
Self-citationLee, G. Y., Lee, J., Kim, J., Chung, K., & Han, S. N. (2025) · European Journal of Nutrition · doi:10.1007/s00394-025-03713-x · Cited by 3
Suratkal, J. P., D'Silva, T., & AlHilli, M. M. (2025) · Gynecologic Oncology Reports · doi:10.1016/j.gore.2025.101718 · Cited by 2
Kapellou, A., Fotis, T., Vrachnos, D. M., Salata, E., Ntoumou, E., Papailia, S. I., & Vittas, S. (2025) · Biomedicines · doi:10.3390/biomedicines13081791 · Cited by 2
“Body mass index (BMI) is a widely used metric for classifying weight status, with a BMI of 25–29.9 kg/m 2 indicating overweight and a BMI ≥ 30 kg/m 2 defining obesity; both are associated with increased health risks [11,12].”
InfluentialThe Whole Day Movement Matters to Executive Function Among College Students
Zhang, X., Moss, S., Wu, C., Keller, J., & Gu, X. (2025) · Behavioral Sciences · doi:10.3390/bs15081129 · Cited by 2
“Although a useful tool, using BMI as a sole measurement of weight status without other anthropometric measures may not adequately capture known health risks (Wu et al., 2024).”
Pertegaz, P. G., López, P. J. T., Campayo, I. C., Busquets-Cortés, C., López‐González, Á. A., & Ramírez‐Manent, J. I. (2025) · Medical Sciences · doi:10.3390/medsci13030153 · Cited by 3
“BMI does not distinguish between fat and lean mass, nor does it provide information on fat distribution, often leading to misclassification—particularly among individuals with sarcopenic obesity or normal-weight central adiposity [16,17].”
Solé, B., Montejo, L., Budde, M., Valentí, M., Borràs, R., Martin‐Parra, S., Ruiz, A. L., Martínez‐Arán, A., Adorjan, K., Heilbronner, M., et al. (2025) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.70028 · Cited by 2
“Its simplicity and ease of calculation also make it practical for clinical and public health applications [63].”
Mąkosza, K. M., Wierzgoń, J., Muc‐Wierzgoń, M., et al. (2025) · Cancers · doi:10.3390/cancers17162654 · Cited by 2
“Incorporating body composition analysis and routine nutritional screening may improve risk stratification and support personalized dietary counseling and therapeutic planning [53].”
Cabrera, C. I. T., Capote, D. G., López, P. J. T., et al. (2025) · Clínica e Investigación en Arteriosclerosis (English Edition) · doi:10.1016/j.artere.2026.500871 · Cited by 2
Nikalansooriya, A., Waidyarathna, G. R. N. N., Kaththiriarachchi, L., & Chandrasekara, A. (2025) · Cureus · doi:10.7759/cureus.96189 · Cited by 8
“Furthermore, the universal BMI cut-off may not be suitable for all populations, restricting its effectiveness as a precise screening tool for assessing undernutrition in adults [43].”
Geeitha, S., Muthugowtham, S., Rubankumar, M., et al. (2025) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca66325.2025.11280549 · Cited by 2
“BMI is insufficient for accurate individual risk identification; it can direct dietary and exercise guidance [7].”
Liu, M., Pei, J., Zeng, C., Xin, Y., Tang, P., & Hu, X. (2025) · Nutrition Journal · doi:10.1186/s12937-025-01251-0 · Cited by 2
“Although BMI is commonly utilized to gauge obesity, it fails to differentiate between fat distribution and muscle mass [14], which is problematic since abdominal fat is closely linked to cardiovascular risk, making fat distribution a more critical factor than overall weight [15, 16].”
Abbaspour, F., Shafiee, G., Golestani, A., Birjandi, S. M., Gohari, K., Sheidaei, A., & Heshmat, R. (2025) · Journal of Diabetes and Metabolic Disorders · doi:10.1007/s40200-025-01805-3 · Cited by 2
“However, BMI cannot accurately assess body fat, health status, or associated risks, as it does not distinguish between fat and lean tissue [ 4 ].”
Tu, J., Yi, T.-P., & Tu, M.-Q. (2025) · The International Journal of Psychiatry in Medicine · doi:10.1177/00912174251410075 · Cited by 2
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“Because BMI reflects both nutritional intake and body composition, it remains a simple yet valuable tool for identifying individuals at higher risk of musculoskeletal impairment [58].”
Metadata
- Authors
- Wu, Y., Li, D., & Vermund, S. H.
- Affiliations
- Yale University (US)
- Corresponding author
- Vermund, Sten H.
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2024-06-10
- Volume
- 21
- Issue
- 6
- Pages
- 757
- ISSN
- 1660-4601 (ISSN-L)
- PMC
- PMC11204233
- OpenAlex
- W4399491352
- Cited by
- 332
- References
- 66
- Citations by year
- 2026: 168 · 2025: 148 · 2024: 16
- FWCI
- 77.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Health Sciences › Medicine › Physiology (medical) › Body Composition Measurement Techniques
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- body mass index, adult obesity, obesity screening, anthropometry, body composition, physical fitness, chronic disease risk prediction, semaglutide, obesity diagnosis
- MeSH terms
- Adult, Humans, Obesity, Body Mass Index
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