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Body mass index

Body mass index (BMI) is a value derived from the weight and height of an adult, serving as an indirect screening tool for body fatness and potential weight-related health conditions.^[1] It is calculated using the formula \mathrm{BMI} = \frac{\mathrm{weight\ (kg)}}{\mathrm{height\ (m)}^2}, or in imperial units as \mathrm{BMI} = \frac{\mathrm{weight\ (lb)}}{\mathrm{height\ (in)}^2} \times 703.^[2] This metric, originally termed the Quetelet Index, was developed in the 1830s by Belgian mathematician, astronomer, and statistician Adolphe Quetelet to describe the "average man" in population studies rather than for individual medical diagnosis.^[3] BMI categorizes adults as underweight (less than 18.5), normal weight (18.5–24.9), overweight (25.0–29.9), or obese (30.0 or higher), with higher values generally indicating greater risks of conditions such as cardiovascular disease, type 2 diabetes, and certain cancers.^[4] Large-scale meta-analyses of individual participant data have demonstrated a J-shaped relationship between BMI and all-cause mortality, with the lowest risks typically in the normal weight range (around 22.5–25 kg/m² among never-smokers), increasing risks for both underweight and overweight/obese categories, though the association for overweight is less pronounced than for obesity.30175-1/fulltext)^[5] Despite its widespread adoption in clinical practice and public health surveillance for its simplicity and cost-effectiveness, BMI has significant limitations as a measure of adiposity or health.^[6] It fails to differentiate between lean mass and fat mass, often misclassifying muscular individuals like athletes as overweight or obese, and does not account for fat distribution (e.g., visceral vs. subcutaneous), age, sex, ethnicity, or frame size, which can lead to inaccuracies in assessing individual health risks.^[7]^[8] Systematic reviews highlight that while BMI correlates moderately with body fat at the population level, its utility diminishes for personalized assessments, prompting calls for complementary measures like waist circumference or body composition analysis.^[9] Nonetheless, empirical evidence from prospective cohorts underscores BMI's value in predicting population-level mortality and morbidity trends, even amid these flaws.^[10]

Definition and Calculation

Mathematical Formula

The body mass index (BMI) is computed as body mass in kilograms divided by the square of stature in meters, yielding units of kg/m².^[3] This formula, originally termed the Quetelet Index, was formulated by Belgian astronomer and statistician Adolphe Quetelet in 1832 to quantify characteristics of l'homme moyen ("the average man") in adult populations.^[3]^[11] Quetelet derived the height-squared denominator from empirical observations that, among adults of varying statures, body weight tends to scale proportionally to the square of height rather than the cube anticipated under strict geometric similarity assumptions for scaled volumes.^[11] This scaling reflects average body proportions across populations, where taller individuals exhibit relatively slimmer builds, stabilizing the index for comparative purposes in statistical analysis.^[12] The index thus serves as a height-normalized descriptor of mass distribution, independent of individual growth trajectories.^[3] In imperial units prevalent in the United States, BMI is equivalently calculated as 703 multiplied by body weight in pounds divided by the square of height in inches.^[13] This conversion factor arises from unit equivalences: 1 kg ≈ 2.20462 lb and 1 m ≈ 39.3701 in, ensuring numerical consistency with the metric formulation.^[14] Quetelet's approach prioritized population-level averages over individual physiology, positioning BMI as a tool for aggregate human variation rather than personalized assessment.^[15]

Categorical Interpretation

The World Health Organization (WHO) established standard BMI categories in the 1990s to classify body weight relative to height based on population-level data associating BMI ranges with health risks. These include underweight (BMI < 18.5), normal weight (BMI 18.5–24.9), overweight (BMI 25.0–29.9), and obesity (BMI ≥ 30.0).^[16] The thresholds were derived from actuarial data and epidemiological studies linking BMI distributions to morbidity and mortality patterns in Western populations, serving as statistical cutoffs rather than precise indicators of individual health status.^[4] These categories function as proxies for excess adiposity and associated risks, but BMI does not directly measure body fat percentage or composition, limiting its diagnostic utility.^[17] Large cohort studies have shown that BMI values above 25 are correlated with increased all-cause mortality, with risks escalating progressively in the obese range, though interpretations must account for confounders like smoking, preexisting conditions, and muscle mass.^[18] Thus, categorical interpretations emphasize screening for further assessment rather than standalone diagnoses, as BMI misclassifies adiposity in athletes, elderly individuals, or those with atypical fat distribution.^[19] In 2025, expert proposals advanced a revised framework for obesity diagnosis, incorporating waist circumference alongside BMI, effectively reclassifying many in the overweight range (25–<30) as obese if central adiposity is elevated.^[20] This shift would increase the proportion of U.S. adults categorized as obese by approximately 18.8%, highlighting ongoing debates over BMI's sufficiency as a sole metric and pushing toward multidimensional risk evaluation grounded in causal adiposity measures.^[21] Such adjustments reflect empirical refinements to better align categories with observed metabolic and cardiovascular outcomes in diverse cohorts.^[20]

History

Origins in Statistics

The body mass index originated as the Quetelet index, formulated by Belgian astronomer and statistician Lambert Adolphe Jacques Quetelet in the early 1830s as part of his efforts to quantify the "average man" in the field of social physics.^[3] Quetelet introduced the index in correspondence in 1832 and elaborated on it in his 1835 treatise Sur l'homme et le développement de ses facultés, ou Essai de physique sociale, where he analyzed body proportions to describe typical human development across populations.^[22] His work drew on anthropometric data from European civilians and soldiers, primarily French and Scottish cohorts, to establish empirical norms for height and weight variation without any intent to assess individual health or pathology.^[23] Quetelet derived the formula—mass in kilograms divided by the square of height in meters—through algebraic examination of how weight scales with height in adults of similar build, assuming geometric similarity where volume (and thus mass) increases with the square of linear dimensions.^[24] This normalization addressed the non-linear relationship observed in his datasets, yielding a dimensionless ratio that minimized variation attributable to stature alone and highlighted deviations from population means.^[3] The approach was rooted in descriptive statistics rather than causal inference, aiming to identify l'homme moyen (the average man) as a benchmark for social and demographic analysis.^[25] Prior to the 20th century, the Quetelet index served exclusively as a tool in anthropometry and demography for population-level descriptions, such as charting average physique across ages and regions in European samples.^[22] It found application in statistical surveys of physical development but made no claims about health outcomes, disease risk, or medical intervention, reflecting Quetelet's focus on probabilistic laws governing human aggregates over individual diagnostics.^[26] Medical professionals largely overlooked it during this period, as clinical practice emphasized direct vital signs and symptoms rather than abstracted indices derived from non-clinical data.^[27]

Adoption as Health Indicator

In 1972, physiologist Ancel Keys revived interest in BMI through the paper "Indices of Relative Weight and Obesity," which evaluated multiple weight-for-height indices across diverse populations and promoted BMI for its simplicity, correlation with body fat percentage, and applicability to epidemiological assessments of health risks, including cardiovascular disease.^[28]^[29] Keys' analysis, informed by longitudinal studies like the Framingham Heart Study that established excess weight as an independent predictor of coronary events, positioned BMI as a practical proxy for relative adiposity at the population level rather than for individual diagnostics.^[30] The World Health Organization formalized BMI's role in health monitoring during the 1990s, with a 1997 expert consultation establishing standardized cutoffs—BMI of 25–29.9 kg/m² for overweight and ≥30 kg/m² for obesity—to enable consistent global tracking of trends linked to elevated morbidity and mortality.^[31] This endorsement drew on meta-analytic evidence associating BMI ≥30 with approximately 1.5- to 2-fold higher all-cause mortality risk relative to normal weight (BMI 18.5–24.9 kg/m²), primarily through comorbidities like hypertension and type 2 diabetes, though effect sizes varied by age and smoking status.^[32]30288-2/fulltext) Population data underscored BMI's utility for surveillance, as rising averages correlated with epidemic-level increases in obesity-related burdens across industrialized nations. Following 2000, BMI integrated into major guidelines, such as the U.S. Centers for Disease Control and Prevention's adult obesity definitions and the UK's National Institute for Health and Care Excellence recommendations for risk stratification in primary care. Despite 2020s critiques emphasizing BMI's insensitivity to muscle mass, fat distribution, or fitness—evident in studies showing misclassification for athletes or the elderly—empirical validations reaffirm its effectiveness for aggregate monitoring of obesity epidemics, where U.S. adult prevalence exceeded 40% (BMI ≥30 kg/m²) by 2023 amid stable or rising trends.^[33]^[34]^[35] This persistence reflects causal links from excess adiposity to systemic inflammation and metabolic dysregulation, observable in large cohorts despite individual variances.^[36]

Classification Systems

Adult Thresholds

The World Health Organization (WHO) defines adult BMI categories as underweight (<18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25–29.9 kg/m²), and obesity subdivided into class I (30–34.9 kg/m²), class II (35–39.9 kg/m²), and class III (≥40 kg/m²), with health risks escalating from normal weight as the reference for lowest population-level morbidity and mortality.^[37]^[38] These thresholds stem from actuarial analyses of large cohorts revealing a J-shaped mortality curve, where all-cause death rates rise below BMI 18.5 kg/m² due to frailty and malnutrition, remain minimal in the normal range, and increase progressively in overweight and obese categories from heightened cardiovascular, metabolic, and cancer risks.^[39]^[40] Longitudinal studies of never-smokers confirm the nadir of mortality risk at BMI 23–24 kg/m², aligning the upper normal weight segment with optimal longevity in populations free of smoking-related confounders.^[5]^[40] WHO standards serve as the global benchmark for adult screening, applied across diverse healthcare systems despite critiques that BMI overlooks body composition nuances.^[41] In 2025, emerging evidence prompted calls to supplement or replace BMI with body fat percentage metrics, which demonstrate superior prediction of cardiometabolic outcomes in validation cohorts, potentially reclassifying substantial adult populations.^[42]^[43]^[44]

Pediatric and Youth Adjustments

Unlike in adults, where fixed BMI thresholds apply, pediatric assessments require age- and sex-specific adjustments to account for rapid growth phases, including height velocity peaks during puberty that temporarily elevate BMI before stabilization.^[45] The Centers for Disease Control and Prevention (CDC) provides BMI-for-age growth charts for U.S. children and adolescents aged 2 to 20 years, derived from National Health and Nutrition Examination Survey (NHANES) data collected between 1963 and 1994, with updates incorporating later measurements.^[45] ^[46] These charts plot BMI against age, using percentile curves to classify weight status: below the 5th percentile indicates underweight, the 5th to less than the 85th percentile healthy weight, the 85th to less than the 95th percentile overweight, and at or above the 95th percentile obesity, with extended charts for severe obesity beyond the 97th percentile employing modified z-score calculations.^[47] ^[48] The World Health Organization (WHO) offers complementary BMI-for-age standards for children aged 5 to 19 years, based on longitudinal data from diverse populations emphasizing breastfed infants and healthy growth patterns, utilizing z-scores where values between -2 and +1 standard deviations (SD) denote normal range, above +1 SD overweight, and above +2 SD obesity.^[49] ^[50] Both systems employ the LMS (lambda-mu-sigma) method to transform raw BMI values into percentiles or z-scores, enabling precise tracking of deviations from population norms while adjusting for skewed distributions in higher ranges.^[51] Applying adult cutoffs, such as 25 kg/m² for overweight, would misclassify many healthy growing children, particularly during adolescent growth spurts when BMI naturally fluctuates upward before declining.^[52] Empirical data underscore the value of longitudinal percentile tracking, as childhood overweight or obesity exhibits persistence into adulthood, with studies reporting tracking coefficients indicating stability; for instance, obese children aged 3 to 12 years face a 50% to 80% likelihood of adult obesity, rising with parental obesity and decreasing slightly with younger onset age.^[53] ^[54] Recent disruptions, such as COVID-19 lockdowns, accelerated BMI trajectories, with cross-sectional analyses showing average z-score increases of 0.19 to 0.22 in youth, translating to BMI rises of approximately 1 to 2 kg/m², particularly in ages 2 to 11 years, due to reduced physical activity and altered eating patterns.^[55] ^[56] ^[57] These shifts highlight the utility of percentile monitoring for early detection of adverse trajectories, supporting targeted interventions grounded in observed growth patterns rather than static thresholds.^[58]

Ethnic and Regional Variations

The World Health Organization's 2004 expert consultation recommended lower BMI thresholds for Asian populations, classifying overweight as 23–27.5 kg/m² and obesity as ≥27.5 kg/m², due to evidence of elevated risks of type 2 diabetes, cardiovascular disease, and hypertension at BMIs below the standard 25 kg/m² threshold, attributed to higher visceral fat accumulation relative to subcutaneous fat despite lower overall body weight.^[59]^[60] This adjustment reflects data from multiple Asian cohorts showing risk equivalence to Western populations at standard cutoffs, with observed morbidity rising from BMIs as low as 22 kg/m² in some groups.^[61] In Japan, the Japan Society for the Study of Obesity defines obesity as BMI ≥25 kg/m², based on national surveys linking this level to increased metabolic syndrome prevalence, diverging from global norms to account for population-specific fat distribution patterns.^[62] Similarly, Singapore's Ministry of Health guidelines adopt overweight as 23.0–27.4 kg/m² and obesity as ≥27.5 kg/m², supported by local studies correlating these levels with higher body fat percentages and diabetes incidence compared to Caucasians at equivalent BMIs.^[63] For South Asians, particularly in the UK, ethnicity-specific adjustments propose obesity thresholds around 27.5 kg/m², as standard BMI underestimates diabetes risk; a 2021 analysis of UK Biobank data estimated equivalent risk cutoffs at 23–27.5 kg/m² for overweight and ≥27.5 kg/m² for obesity, potentially misclassifying up to 1 million individuals without ethnic tailoring.^[64]^[65] In contrast, African Americans exhibit higher lean muscle mass and lower percentage body fat at the same BMI as Caucasians, leading to potential overestimation of obesity risk; NHANES data indicate ~5% lower predicted fat mass in Black adults at BMI 25 kg/m², though overall adiposity-related comorbidities remain elevated due to other factors like insulin resistance.^[66]^[67] Meta-analyses confirm ethnic variations in BMI-health outcome associations, with Asians facing 10–20% higher relative risks of type 2 diabetes per BMI unit increase compared to Europeans, underscoring the need for adjusted thresholds to avoid under-detection in high-risk groups.^[68] A 2025 study further highlighted racial/ethnic-specific BMI cutoffs for metabolic outcomes, revealing substantial differences (e.g., lower values for Asians and Hispanics predicting equivalent risks to Caucasians), cautioning against universal Western-derived norms that may propagate assessment biases.^[69]

Empirical Health Associations

Links to Mortality and Morbidity

The association between body mass index (BMI) and all-cause mortality follows a J- or U-shaped pattern in large prospective cohort studies and meta-analyses involving millions of participants, with elevated risks at both underweight (BMI <18.5 kg/m²) and obese (BMI ≥30 kg/m²) extremes after adjustment for confounders such as age, smoking, and preexisting conditions. The nadir of mortality risk is generally observed in the normal weight range of 22.5–25 kg/m² among never-smokers and healthy populations, while obesity confers a 20–50% higher risk relative to this reference, as evidenced by dose-response analyses pooling over 230 observational studies. For instance, a 2016 systematic review and meta-analysis reported hazard ratios of 1.18 for BMI 30–35 kg/m² and up to 1.34 for BMI ≥35 kg/m², reflecting progressive increases driven by adiposity-related complications.^[5] ^[39] Some meta-analyses, including a 2024 review of general adult populations, indicate the lowest mortality at BMI 25–30 kg/m² (overweight range), potentially attributable to methodologic factors like reverse causation in sicker individuals or underascertainment of early-life obesity effects, though this finding contrasts with never-smoker subgroups where risks rise modestly above 25 kg/m². Confounder-adjusted models affirm causal contributions from excess adiposity, including visceral fat-mediated inflammation and metabolic dysregulation, which exacerbate cardiovascular and respiratory strain independent of behavioral covariates.^[70] Regarding morbidity, obesity (BMI ≥30 kg/m²) approximately doubles the relative risk for major conditions such as cardiovascular disease (CVD), type 2 diabetes, and site-specific cancers compared to normal BMI, based on pooled data from prospective studies tracking incident events. A meta-analysis of over 300,000 adults linked obese BMI categories to elevated coronary artery disease incidence, while similar analyses show risk onset for diabetes accelerating above BMI 25 kg/m² and persisting after multivariable adjustment for lifestyle factors. Cancer risks for obesity-associated types (e.g., endometrial, colorectal) exhibit relative risks of 1.5–2.0, mediated by hyperinsulinemia and adipokine imbalances. In Canada, obesity prevalence rose from 25% in 2009 to 32.7% in 2023, correlating temporally with heightened comorbidity burdens like CVD and diabetes, underscoring population-level impacts.^[71] ^[72] ^[73] ^[74]

Evidence from Meta-Analyses

A 2024 meta-analysis of prospective cohort studies involving over 3.9 million adults found a U-shaped association between BMI and all-cause mortality, with the nadir of risk occurring in the overweight range of 25–30 kg/m², and elevated risks both below 18.5 kg/m² and above 30 kg/m², indicating a nonlinear dose-response where deviations from this range independently predict higher mortality after adjusting for confounders like smoking and preexisting conditions.^[70] This pattern held across diverse populations, though low BMI risks were partly attributable to reverse causation from smoking and chronic illness, while high BMI risks persisted even after excluding early deaths.^[70] Meta-analyses on BMI and COVID-19 outcomes, including an updated 2021 systematic review, consistently link obesity (BMI ≥30 kg/m²) to increased severity, with odds ratios for severe disease or mortality ranging from 1.5 to 2.3 times higher than in normal-weight individuals, driven by mechanisms such as impaired immune response and respiratory mechanics.^[75] These findings were robust across hospitalized cohorts globally, with dose-dependent effects showing progressively worse prognosis at BMI levels above 35 kg/m².^[76] Subgroup analyses in joint fitness-BMI meta-analyses reveal that cardiorespiratory fitness modifies mortality risks more strongly than BMI alone; a 2025 pooled analysis of over 1 million participants demonstrated that unfit normal-weight individuals (BMI 18.5–24.9 kg/m²) face approximately 1.9-fold higher all-cause mortality compared to fit counterparts, while fit obese individuals (BMI ≥30 kg/m²) exhibit risks comparable to fit normal-weight, underscoring fitness as a dominant predictor independent of adiposity.^[77] These associations were consistent across sexes, ages, and follow-up durations exceeding 10 years, with unfit status conferring double the mortality hazard regardless of BMI category.^[78] Regarding metabolically healthy obesity (MHO), defined by absence of metabolic syndrome components despite BMI ≥30 kg/m², long-term meta-analyses indicate limited persistence of this phenotype, with over 95% transitioning to metabolically unhealthy states within 10–15 years, accompanied by elevated risks of cardiovascular events (hazard ratio 1.4–1.5) and all-cause mortality compared to metabolically healthy normal-weight individuals.^[79] This counters claims of sustained "healthy obesity," as MHO cohorts show dose-response increases in adverse outcomes over extended follow-up, independent of initial metabolic status.^[80]

Modifying Influences

Fitness and Activity Levels

Cardiorespiratory fitness (CRF), typically assessed via maximal oxygen uptake (VO₂ max), substantially modifies the predictive value of BMI for mortality outcomes, often overriding BMI category in prognostic strength. A 2025 systematic review and meta-analysis published in the British Journal of Sports Medicine examined the joint effects of CRF and BMI on CVD and all-cause mortality across multiple cohorts, finding that higher CRF levels attenuate the elevated risks tied to overweight (BMI 25–29.9 kg/m²) and obesity (BMI ≥30 kg/m²).^[81] Specifically, fit overweight and obese individuals showed no statistically significant increase in CVD mortality hazard ratios compared to fit normal-weight (BMI 18.5–24.9 kg/m²) counterparts, while unfit normal-weight individuals exhibited markedly higher risks.^[81] This "fitness versus fatness" paradigm is supported by earlier meta-analyses, which report that unfit status doubles all-cause mortality risk irrespective of BMI, whereas fit overweight and obese individuals experience mortality rates akin to fit normal-weight peers—a relative risk reduction of roughly 50% for fit obese versus unfit normal-weight cases in some datasets.^[78] ^[82] For instance, in a cohort of women with suspected ischemic heart disease, physically fit overweight or obese participants faced 40% lower long-term mortality than unfit normal-weight women.^[82] These patterns hold across studies like the Aerobics Center Longitudinal Study, where unfit lean men had higher CVD mortality than fit obese men.^[83] Mechanistically, elevated VO₂ max enhances endothelial function, promoting vasodilation, reducing oxidative stress, and mitigating atherogenesis, thereby conferring cardioprotection independent of adiposity.^[84] ^[85] Population surveys, including NHANES, reinforce this through objectively measured activity data, showing that higher physical activity volumes—particularly vigorous-intensity—diminish the BMI-mortality risk gradient by independently lowering all-cause and CVD event rates, often by 20–50% in adjusted models.^[86] ^[87] Such evidence prioritizes aerobic capacity enhancement over BMI-centric interventions, as sustained activity drives superior outcomes even among those exceeding obesity thresholds.^[88]

Body Composition Factors

Visceral adipose tissue, concentrated in android (central) distributions, elevates health risks more than equivalent subcutaneous (gynoid) fat due to direct drainage into the portal vein, delivering free fatty acids and pro-inflammatory cytokines like interleukin-6 to the liver, which induces hepatic insulin resistance and systemic metabolic dysfunction.^[89] ^[90] Waist-to-hip ratio, reflecting this central adiposity, predicts cardiovascular disease mortality and myocardial infarction independently of BMI, with meta-analyses showing elevated ratios associated with odds ratios up to 1.98 for infarction risk.^[91] ^[92] Empirical imaging studies confirm visceral fat's causal role in inflammation-driven complications, as opposed to BMI's aggregate mass metric.^[93] Lean body mass confounds BMI interpretations, as high muscle volume can inflate BMI without corresponding fat excess, while sarcopenic obesity—high adiposity paired with low muscle—amplifies mortality risks beyond simple obesity.^[94] ^[95] A June 2025 University of Florida analysis of longitudinal data demonstrated BMI's frequent misclassification of muscular individuals as obese or overweight, emphasizing its failure to parse composition quality over total mass.^[96] Dual-energy X-ray absorptiometry (DEXA) and MRI validations reveal BMI correlates with body fat percentage at approximately 70% accuracy in population cohorts but overlooks muscle-fat partitioning and ectopic fat deposition.^[97] ^[98] Excess adiposity impairs mitochondrial function through lipid overload, elevating reactive oxygen species and disrupting oxidative phosphorylation, which causally propagates insulin resistance and energy inefficiency independent of BMI thresholds.^[99] This mechanism underscores why composition-specific metrics outperform BMI in risk stratification, as validated by tissue-level assays showing reduced respiratory capacity in obese adipose depots.^[100]

Applications

Clinical Screening

BMI is employed in clinical settings as an initial screening tool to identify individuals at elevated risk for obesity-associated conditions, facilitating triage and guiding subsequent diagnostic evaluations. Major guidelines, including those from the American Heart Association (AHA) and American Diabetes Association (ADA), endorse annual BMI assessment for adults to classify overweight (BMI 25.0–29.9 kg/m²) and obesity (BMI ≥30 kg/m²), with elevated values prompting targeted tests such as HbA1c for glycemic control or lipid panels for cardiovascular risk.^[101]^[102] This approach leverages BMI's simplicity, requiring only height and weight measurements without specialized equipment, enabling routine integration into primary care encounters where it is documented for the majority of eligible patients.^[103] Empirical correlations substantiate BMI's utility in predicting metabolic derangements; for instance, higher BMI values are positively associated with elevated total cholesterol, low-density lipoprotein cholesterol, and triglycerides, while inversely linked to high-density lipoprotein cholesterol.^[104]^[105] Per-unit increases in BMI correspond to measurable shifts in lipid profiles, such as heightened odds of low HDL (approximately 9% increased probability per unit) and dose-dependent rises in LDL cholesterol, particularly in non-obese ranges.^[106]^[107] These associations support BMI's role in flagging patients for confirmatory labs, as dyslipidemia prevalence escalates with BMI categories.^[108] Randomized controlled trials demonstrate that BMI-informed screening enables interventions curbing disease progression, as evidenced by the Diabetes Prevention Program (DPP), which targeted overweight adults (BMI ≥24 kg/m² in certain groups) with prediabetes and achieved a 58% reduction in type 2 diabetes incidence through lifestyle modifications yielding ∼4 kg weight loss.^[109]^[110] In this trial, BMI served as a verifiable entry criterion, correlating with improved insulin sensitivity and delayed onset of diabetes via sustained behavioral changes.^[109] Such outcomes underscore BMI's practical value in clinical decision-making, despite its limitations in distinguishing fat from lean mass, by prioritizing actionable risk stratification over precision adiposity metrics.^[109]

Public Health Monitoring

Organizations such as the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) utilize BMI as a standardized metric for global and national surveillance of overweight and obesity trends, defining obesity as BMI ≥ 30 kg/m².^[37]^[41] The WHO's Global Health Observatory compiles age-standardized prevalence estimates from member states, revealing that adult obesity more than doubled worldwide from 1990 to 2022, enabling identification of regional epidemics driven by shifts in caloric intake and physical inactivity.^[111]^[41] In the United States, the CDC's National Health and Nutrition Examination Survey (NHANES) provides direct anthropometric measurements, tracking adult obesity prevalence from approximately 34% in 2009–2010 to 40.3% in 2021–2023, a rise of over 6 percentage points that correlates with increased sedentary behavior and processed food consumption.^[112]^[113] At the population level, BMI's limitations in distinguishing fat from lean mass are mitigated by aggregation effects, where systematic errors in individual classification cancel out, allowing reliable tracking of mean adiposity changes over time.^[114] Meta-analyses of cohort studies confirm that population BMI trends align closely with direct measures of body fatness, such as from dual-energy X-ray absorptiometry, supporting causal inferences about environmental drivers like urbanization and dietary shifts.^[115] This surveillance data underpins policy responses; for instance, the documented U.S. obesity increase has been linked to annual healthcare costs exceeding $173 billion, prompting targeted interventions in nutrition labeling and urban planning to address root causes.^[116] Recent analyses of post-COVID-19 lockdown effects, drawing from longitudinal population data, attribute modest BMI elevations—typically 0.1–0.5 kg/m² on average—to disrupted routines, reduced activity, and altered eating patterns, with prevalence increases of 1–3% in affected cohorts.^[117]^[57] These findings, observed in studies up to 2025, informed public health reopenings by highlighting the need for sustained monitoring to reverse transient gains and prevent entrenched rises, demonstrating BMI's role in enabling evidence-based attribution of acute disruptions to long-term trends.^[118]^[119]

Policy and Economic Contexts

In the United States, military enlistment standards incorporate BMI-derived weight limits, with the Army setting maximum allowable weights by height and age that generally cap at BMI values around 30 for younger recruits, such as 212 pounds for a 70-inch-tall individual aged 17-20, to ensure physical readiness.^[120] The Air Force applies a stricter initial BMI range of 17.5 to 27.5 for applicants, using body composition assessments like tape measurements for those exceeding thresholds to differentiate fat from muscle.^[121] These criteria reflect actuarial assessments of obesity-related risks to performance and injury, with waivers possible but requiring remediation. Airlines employ average passenger weight assumptions—typically 190-200 pounds including carry-ons—for fuel calculations, derived from periodic surveys correlating with BMI trends, rather than individual BMI mandates. Policies for larger passengers, such as requiring extra seats if encroaching on adjacent space, address operational safety and comfort without direct BMI cutoffs, though debates in the 2020s have explored weight-based pricing to offset fuel costs amid rising average BMIs.^[122]^[123] Health insurance premiums indirectly scale with BMI through risk pooling, as obesity elevates per-person medical costs by 36-42% compared to normal weight, contributing to higher group rates under frameworks like the Affordable Care Act, which prohibits explicit surcharges for BMI but allows cost reflections via overall claims data.^[124] In some international markets, insurers apply direct loadings of 10-50% for BMI over 30, justified by longitudinal data showing annual cost increases of $69-93 per BMI unit.^[125]^[126] Obesity, often tracked via BMI, imposes economic burdens equivalent to 0.8-2.4% of GDP in studied Western nations as of 2019, encompassing direct medical expenditures and indirect productivity losses, with projections reaching 3% globally by 2035 absent interventions.^[127]^[128] Policies like sugar-sweetened beverage taxes, implemented in cities such as Philadelphia since 2017, leverage BMI metrics from epidemiological studies to target caloric intake drivers, yielding modest reductions in youth BMI percentiles (e.g., 0.06-0.12 points prevented increases) and slower adult BMI rises.^[129]^[130] Workplace wellness initiatives addressing high BMI demonstrate return on investment ratios of approximately 3:1, driven by reduced absenteeism and claims from behavior modifications like activity tracking, as evidenced in employer analyses prioritizing cost-benefit over equity.^[131]^[132] Amid 2020s debates questioning BMI's standalone policy role—such as American Medical Association guidance to supplement it with adiposity measures—empirical savings from BMI-informed mandates persist, countering pushback with data on net fiscal gains.^[133]^[134]

Limitations

Fat-Muscle Differentiation Issues

Body mass index (BMI) fails to distinguish between adipose tissue and lean mass, leading to systematic misclassification of muscular individuals as overweight or obese. This issue is pronounced in athletes, where high muscle density elevates BMI without corresponding fat accumulation; for example, prospective National Football League (NFL) players at the scouting combine had an obesity rate of 53.4% by BMI criteria but only 8.9% when assessed via body fat percentage, demonstrating BMI's propensity to overestimate adiposity in those with elevated lean mass. Similar patterns occur in other athletic cohorts, such as college football players, where BMI classifies over half as obese despite healthy body composition profiles confirmed by skinfold and densitometry measures.^[135] Comparisons with gold-standard techniques like dual-energy X-ray absorptiometry (DEXA) reveal BMI's moderate correlation with body fat percentage (typically r = 0.7–0.8 in general populations), accounting for roughly 50–64% of variance in fat mass, but with substantial inaccuracies at compositional extremes.^[98]^[115] In muscular subgroups, BMI overestimates fat content due to its reliance on total mass relative to height squared, ignoring density differences—muscle being denser than fat—resulting in errors of 10–20% or more in adiposity estimates for athletes.^[6] This empirical shortfall highlights BMI's causal irrelevance for health outcomes, as lean mass confers metabolic benefits and reduced mortality risk independent of total weight, whereas fat mass drives inflammation and disease pathology.^[136] Direct body fat measures superiorly predict morbidity and mortality compared to BMI, with a 2025 cohort analysis of over 4,200 U.S. adults showing body fat percentage as a stronger indicator of 15-year all-cause mortality, particularly by identifying risks obscured in normal-weight high-fat individuals.^[137]^[44] BMI's diagnostic sensitivity for obesity against DEXA-validated fat thresholds approximates 70% in average populations but declines markedly in high-muscle groups, underscoring its inadequacy for precise fat-muscle partitioning.^[98]

Demographic and Physiological Variations

In older adults, the BMI associated with lowest all-cause mortality shifts upward compared to younger populations, often falling in the 23.0–29.9 kg/m² range, reflecting adaptations to sarcopenia—age-related muscle loss that increases frailty risks—and the protective role of fat reserves against catabolic states.^[138] Studies indicate that classifying elderly individuals as obese using standard BMI thresholds (≥30 kg/m²) may overlook survival benefits from moderate overweight status, as underweight BMI (<22 kg/m²) correlates with higher mortality in this group.^[95] Sex differences further complicate BMI interpretation, as women typically carry 10% more total adipose tissue than men at the same BMI due to lower muscle mass and distinct fat distribution patterns favoring gluteofemoral depots.^[139] This leads to systematic underestimation of fatness in men and overestimation in women relative to adiposity levels, with analyses showing BMI's correlation with body fat percentage weakening across sexes when muscle variability is unadjusted.^[140] Such discrepancies contribute to misclassification rates in health risk assessments, particularly when BMI proxies for metabolic outcomes without sex-specific calibrations.^[8] Ethnic variations highlight BMI's non-universal applicability: for Asian populations, standard overweight thresholds (≥25 kg/m²) underestimate cardiometabolic risks, prompting WHO-endorsed lower cutoffs of ≥23 kg/m² for overweight and ≥27.5 kg/m² for obesity to better align with elevated diabetes and cardiovascular disease incidence at moderate BMIs.^[141] Conversely, in African American cohorts, higher BMI often links to paradoxically lower mortality—the "obesity paradox"—observed in studies like the Jackson Heart Study, where overweight and class I obesity (BMI 25–34.9 kg/m²) associated with reduced all-cause death rates compared to normal weight, potentially attributable to genetic protections against cachexia, survivor bias, or unmeasured fitness factors.^[142]^[143] Physiological states like pregnancy and edema transiently inflate BMI without proportional fat accrual: gestational weight gain averages 11–16 kg, incorporating fetal mass (∼3.5 kg), placenta, amniotic fluid, and expanded maternal blood volume, rendering pre- and post-partum BMI comparisons misleading for adiposity tracking.^[144] Edema, prevalent in late pregnancy affecting up to 80% of women via venous compression and hormonal fluid retention, adds extracellular water that elevates BMI independently of caloric surplus, necessitating clinical adjustments to avoid conflating hydration status with obesity.^[145] These factors underscore the need for contextual modifiers in BMI application to enhance predictive precision across demographics.^[146]

Criticisms and Debates

Overreliance and Misclassification Claims

Critics argue that BMI's categorical thresholds—such as overweight at 25.0–29.9 kg/m² and obesity at ≥30 kg/m²—exacerbate overreliance by imposing arbitrary cutoffs that fail to capture individual variability in body composition, leading to widespread misclassification when compared to direct adiposity measures like dual-energy X-ray absorptiometry (DXA).^[147] A 2025 debate in the International Journal of Behavioral Nutrition and Physical Activity highlighted BMI's feasibility for broad health assessments but questioned the validity of these categories for precise health interpretations, noting their origins in population averages rather than causal mechanisms of disease risk.^[147] Empirical evidence supports claims of significant misclassification, particularly in under-detecting excess adiposity among those with normal or overweight BMI. A study of U.S. adults using DXA found that BMI misclassifies at least 50% of individuals with excess body fat as normal weight or merely overweight, potentially overlooking metabolic risks in "normal weight obesity."^[148] Similarly, a 2025 University of Florida analysis of young adults demonstrated BMI's inferiority to body fat percentage in predicting 15-year mortality risk, attributing this to BMI's inability to differentiate fat from lean mass, resulting in error rates exceeding 30% for health outcome forecasts.^[96] These findings underscore how individual-level application amplifies errors, as muscular individuals are often falsely flagged as obese while sedentary persons with high visceral fat evade detection.^[149] Defenders of BMI counter that such misclassifications reflect misuse rather than invalidity, emphasizing its validated population-level utility where correlations with body fat percentage often exceed 0.7, enabling reliable epidemiological tracking without confounding individual outliers.^[150] Overreliance arises primarily from neglecting contextual factors like age, ethnicity, and fitness, which BMI was never designed to isolate, rather than from the metric's core formula.^[147] Proponents highlight BMI's advantages as a cost-effective, non-invasive screening tool for public health surveillance, where its simplicity facilitates large-scale data collection and risk stratification at minimal expense compared to advanced imaging.^[6] This balance suggests BMI remains empirically defensible for aggregate analyses but warrants supplementary measures for clinical decisions to mitigate category-driven errors.^[33]

Cultural Narratives on Obesity

The body positivity movement, which emerged prominently in the 2010s and advocates unconditional self-acceptance of larger body sizes, has been critiqued for minimizing the causal health detriments of obesity in favor of psychological affirmation. Proponents frame obesity as a neutral variation akin to height, but detractors contend this narrative sidesteps mechanistic evidence of adipose tissue dysfunction driving inflammation, insulin resistance, and organ strain, thereby eroding incentives for preventive action. Such cultural shifts, amplified by social media and select academic discourse, reflect institutional tendencies toward equity-focused interpretations over rigorous causal analysis.^[151]^[152] The "fat but fit" concept posits that physical activity can offset obesity's risks, yet cohort studies demonstrate its rarity and impermanence, with metabolically healthy obesity persisting in under 10% of cases beyond a decade due to progressive cardiometabolic deterioration. Media portrayals normalizing obesity often disregard such data, including epidemiology showing a relative risk of approximately 1.5 for obesity-linked cancers like colorectal and endometrial, independent of fitness levels. These omissions prioritize inclusivity narratives, potentially confounding public understanding of modifiable caloric imbalance as the primary driver.^[153]^[154]^[155] Assertions that BMI-related stigma exacerbates obesity by eroding motivation are challenged by observations that frank acknowledgment of risks correlates with higher engagement in lifestyle interventions, fostering agency rather than resignation. Right-leaning commentaries stress personal accountability, attributing obesity predominantly to behavioral choices over systemic victimhood, aligning with causal models where energy surplus directly precipitates fat accumulation. In contrast, stigma-reduction efforts within body positivity frameworks have shown limited efficacy in prompting sustained weight management, sometimes reinforcing avoidance of accountability.^[156] Amid 2020s advocacy to diminish BMI's role in guidelines—citing overreliance concerns—subsequent meta-analyses, including those through 2025, have reinforced obesity's independent contribution to cardiovascular mortality and all-cause death, with hazard ratios escalating linearly beyond BMI thresholds of 25. Body positivity's empirical critiques highlight its failure to attenuate these outcomes, as acceptance rhetoric correlates with stalled public health progress against rising adiposity prevalence.^[81]^[157]

Alternatives

Direct Adiposity Measures

Direct measures of adiposity quantify body fat percentage (BF%) through techniques that differentiate fat mass from lean mass and bone, offering a more precise assessment than BMI by avoiding conflation with muscle or bone density variations. Common methods include dual-energy X-ray absorptiometry (DEXA), which uses low-dose X-rays to scan and segment body composition with high precision (correlation coefficients of 0.77–0.95 against computed tomography gold standards for fat mass); hydrostatic weighing, which calculates body density via underwater weighing and assumes constant lean tissue density; and bioelectrical impedance analysis (BIA), which estimates fat via electrical conductivity differences between tissues but is susceptible to hydration status errors.^[158]^[159]^[160] These methods demonstrate empirical advantages over BMI in predicting health outcomes, particularly cardiovascular disease (CVD) and mortality. A 2025 study of young adults found BF% superior to BMI for forecasting 15-year all-cause mortality risk, with BF% independently associating after adjusting for confounders like age and smoking. Similarly, BF% correlates more strongly with CVD risk factors such as hypertension and dyslipidemia than BMI, as higher BF% signals metabolic dysfunction even at normal BMI levels. For ectopic fat—lipid accumulation in non-adipose tissues like liver and viscera, which causally drives insulin resistance and CVD via lipotoxicity—these measures highlight total adiposity's role, though visceral-specific quantification (approximable via DEXA regional scans) underscores causality beyond mere excess weight.^[44]^[161]^[162] Obesity thresholds based on BF% are approximately 25% for men and 32% for women, reflecting levels where metabolic risks escalate, independent of height-weight ratios. These cutoffs outperform BMI classifications for individuals with high muscle mass, such as athletes, where BMI often overestimates adiposity risk—e.g., a muscular individual with BMI >30 may have BF% <20%, evading false-positive obesity labeling.^[163]^[164] Despite superior accuracy (DEXA error margins 1–2% vs. BIA's 3–5%), practical drawbacks include high costs (DEXA scans $100–300), limited accessibility, time requirements (hydrostatic trials demand multiple submersions), and contraindications like pregnancy for DEXA due to radiation. BIA offers portability and low cost ($20–50 devices) but lower reliability in dehydrated or elderly populations. Overall, while direct BF% assessments prioritize causal adiposity burdens like ectopic deposition, their clinical adoption lags BMI due to scalability trade-offs, favoring targeted use in research or high-risk screening.^[165]^[166]^[167]

Geometric and Ratio-Based Indices

Geometric and ratio-based indices seek to refine BMI's mass-to-height-squared ratio by incorporating allometric scaling principles or linear body measurements to better approximate adiposity distribution and health risks. These alternatives address BMI's assumption of uniform scaling, which empirical data indicate underperforms for heterogeneous body compositions, as human dimensions do not expand isometrically with growth or across populations.^[168] For instance, BMI Prime normalizes an individual's BMI by dividing it by 25 kg/m², the upper threshold of the healthy BMI range, yielding a value of 1.0 at that boundary to quantify deviation from optimal weight-for-height.^[169] The waist-to-height ratio (WHtR), calculated as waist circumference divided by height, exemplifies a ratio-based approach prioritizing central adiposity. A WHtR below 0.5 is associated with lower cardiometabolic risk across adults. Meta-analyses demonstrate WHtR's superior predictive power over BMI for incident type 2 diabetes, with summary relative risks per standard deviation increase of 2.81 (95% CI: 2.60–3.04) for WHtR versus 2.23 (2.12–2.35) for BMI.^[170]^[171] This edge stems from WHtR's sensitivity to visceral fat accumulation, which correlates more strongly with insulin resistance and cardiovascular endpoints than BMI's aggregate mass metric.^[172] A Body Shape Index (ABSI), defined as waist circumference divided by (BMI^{2/3} × height^{1/2}), integrates geometric normalization to isolate abdominal shape independent of overall size. Validation in large cohorts shows ABSI predicts all-cause mortality hazard ratios of 1.13–1.50 per standard deviation increase, persisting after adjusting for BMI and outperforming it in risk stratification.^[173]^[174] Such indices leverage tape-measurable inputs for clinical feasibility while aligning with causal pathways linking ectopic fat to morbidity, offering verifiable improvements over BMI's cubic scaling limitations where mass scales closer to height^{2.5–3} in empirical models.^[175]

Technology-Driven Assessments

Technological advancements in body composition assessment have introduced methods that surpass BMI's inability to differentiate fat from lean mass, incorporating bioelectrical impedance analysis (BIA) in consumer wearables and AI-enhanced imaging for precise fat distribution mapping. BIA devices, integrated into smart scales and watches, estimate body fat percentage, muscle mass, and visceral fat by measuring electrical conductivity through tissues, with multi-frequency models showing correlations of 0.71 to 0.90 against dual-energy X-ray absorptiometry (DEXA) and MRI gold standards for fat mass and skeletal muscle.^[176]^[177] By 2025, home-based BIA systems via smartphone apps and scales have gained traction for accessibility, correlating over 85% with clinical references in population studies while enabling longitudinal tracking of composition changes.^[178]^[179] AI-driven tools applied to MRI and CT scans provide causal insights into obesity subtypes, such as visceral adipose tissue accumulation linked to metabolic risks independent of total BMI, by automating segmentation of fat depots with precision exceeding manual methods. These systems detect sarcopenic obesity—concurrent muscle loss and fat gain misclassified as healthy by BMI—through volumetric analysis, revealing prevalence rates up to 4.5% in older adults overlooked by BMI thresholds.^[180]^[181] Wearable BIA further mitigates BMI's fat-muscle conflation errors, offering subtype differentiation like ectopic fat infiltration, with deep learning models from abdominal MRI achieving high accuracy (r > 0.85) for muscle quality and fat tracking over time.^[182]^[183] Despite these strengths, technology-driven assessments face limitations including cost barriers—MRI/AI scans averaging $500–$1,500 per session versus BMI's negligible expense—and accuracy variability from factors like hydration status affecting BIA by up to 5% in body fat estimates. Consumer devices exhibit moderate agreement with DEXA in individuals (r ≈ 0.80–0.90), but systematic underestimation occurs in athletes or dehydrated states, rendering them supplementary rather than primary for large-scale epidemiological use where BMI's speed persists.^[184]^[185] Ongoing validation emphasizes their role in clinical precision over population screening, with BIA proposed as a BMI adjunct but not full replacement due to electrode configuration inconsistencies across devices.^[186]^[187]

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Sarcopenic obese men had the highest risk of all-cause mortality but not CVD mortality. Efforts to promote healthy aging should focus on preventing obesity and ...
[95]
Sarcopenia and Sarcopenic Obesity and Mortality Among Older ...
Mar 25, 2024 · These findings suggest that sarcopenic obesity may be associated with worse survival, and conducting screening for muscle function may help prevent premature ...
[96]
Study shows BMI's weakness as a predictor of future health - UF News
Jun 24, 2025 · Medical researchers say BMI fails in predicting the risk of future death, suggesting the calculation is deeply flawed.
[97]
The Relationship Between Body Fat Percentage and Body Mass ...
This study determined the relationship between BMI and body fat (BF)% among adult Nigerians of different ethnic groups residing in an urban setting.
[98]
Accuracy of body mass index compared to whole-body dual energy ...
Oct 13, 2023 · Body fat percent was obtained from the whole-body DEXA scan reports and were compared to BMI to evaluate prevalence of obesity. Data was ...Figure 1 · Figure 2 · Table 3
[99]
Mitochondrial Dysfunction in Obesity - PMC - PubMed Central - NIH
Nutrient excess leads to mitochondrial dysfunction, which in turn leads to obesity-related pathologies, in part due to the harmful effects of ROS. The recent ...
[100]
High fat diet consumption results in mitochondrial dysfunction ...
Excess fat promotes mitochondrial dysfunction in purified oligodendrocytes. •. Chronic high fat diet causes oxidative stress in the brain and spinal cord. •.
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8. Obesity and Weight Management for the Prevention and ...
Dec 9, 2024 · This section aims to provide evidence-based recommendations for obesity management, including behavioral, pharmacologic, and surgical interventions,
[102]
Clinical Assessment and Management of Adult Obesity | Circulation
Dec 11, 2012 · A desirable or healthy BMI is 18.5 to 24.9 kg/m2; overweight is 25 to 29.9 kg/m2; and obesity is ≥30 kg/m2. Obesity is further subdefined into ...
[103]
The prevalence of obesity documentation in Primary Care Electronic ...
The objective of this study is to examine the prevalence of obesity documentation in the patient's problem list for patients with eligible body mass indexes ( ...
[104]
Correlation between Body Mass Index and Lipid Profile in patients ...
Studies have shown a direct relationship between increasing BMI and raised TC, LDL-C, and TG and an inverse correlation with HDL-C. This correlation between ...
[105]
Association of body mass index and lipid profiles - PubMed
Prior epidemiologic studies have shown that increasing body mass index (BMI) is associated with higher total cholesterol and low-density lipoprotein cholesterol ...
[106]
[PDF] Associations Between Body Mass Index (BMI) and Dyslipidemia
Jan 26, 2025 · The relationship between HDL and BMI showed that by one‐unit elevation of. BMI, the probability of having low HDL increased by 9%. A decrease ...
[107]
Sex differences in the non-linear association between BMI and LDL ...
Nov 14, 2021 · Quantitatively, in a previous study, LDL-C levels among females with BMI between 27.1 kg/m2 and 30.0 kg/m2 increased by 17 mg/dl compared to ...Study Participants · Statistical Analysis · Subgroup Analysis
[108]
Impact of Body Mass Index on Coronary Heart Disease Risk Factors ...
The effect of BMI on high-risk total cholesterol and LDL cholesterol levels was not as strong as that on hypertension, diabetes, elevated triglycerides, and ...Missing: per | Show results with:per
[109]
The Diabetes Prevention Program and Its Outcomes Study: NIDDK's ...
Apr 24, 2025 · The lifestyle intervention led to reduced weight, increased self-reported physical activity, and improved estimated insulin sensitivity and ...
[110]
The Diabetes Prevention Program and Its Outcomes Study: NIDDK's ...
Compared with placebo, the lifestyle intervention had reduced body weight by ∼4 kg and diabetes development by 58%. Adherence to metformin was ∼70%, and it ...
[111]
Obesity among adults, BMI >= 30, prevalence (age-standardized ...
Obesity among adults, BMI >= 30, prevalence (age-standardized estimate) (%). Appears in: Body mass index among adults|; NCD risk factors: BMI ...
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Obesity and Severe Obesity Prevalence in Adults - CDC
Sep 24, 2024 · The prevalence of obesity among adults was 40.3% during August 2021–August 2023 (Figure 1, Table 1). The prevalence was 39.2% in men and 41.3% in women.Missing: variations assessment<|control11|><|separator|>
[113]
Overweight & Obesity Statistics - NIDDK
More than 2 in 5 adults (42.4%) have obesity.2; About 1 in 11 adults (9.2%) have severe obesity.2. According to 2017–2018 NHANES data.
[114]
a harmonised meta-analysis of eight prospective cohort studies - PMC
Dec 7, 2021 · BMI is not a direct measure of body fat and may misclassify ... Worldwide trends in body-mass index, underweight, overweight, and obesity ...
[115]
Different correlation of body mass index with body fatness and ...
Mar 1, 2023 · ... BMI and percentage body fat is also different according to race/ethnic groups. ... obesity and overweight: A systematic review and meta-analysis.
[116]
About Obesity - CDC
Jan 23, 2024 · Obesity costs the US healthcare system almost $173 billion a year. Obesity also affects the nation's military readiness.
[117]
Body Mass Index Changes from Before to 3 Years After the COVID ...
Aug 30, 2025 · Results: In the entire student body, the BMI Z-score in 2021, 1 year after the COVID-19 lockdown, had increased significantly compared with ...
[118]
Trends in obesity defined by body mass index among adults before ...
Jul 14, 2025 · The prevalence of BMI-defined obesity increased from 24.95% in 2009 to 32.69% in 2023 (absolute increase 7.74%). The COVID-19 pandemic period ...
[119]
Impact of the COVID-19 Pandemic on BMI and Obesity among ...
Oct 17, 2025 · Results: Mean BMI increased from 30.05 pre-COVID to 30.14 during COVID, then declined to 29.96 post-COVID; obesity prevalence followed a similar ...
[120]
US Army Enlistment Weight Requirements - Military.com
Jun 24, 2025 · Below is the Army's weight minimum and maximum allowable for recruits to enlist. It is broken down by height and then further into age ...
[121]
Physical Requirements FAQs - U.S. Air Force
Waivers are not required for applicant/recruits with permanent ... We utilize a Body Mass Index (BMI) range of 17.5 to 27.5 to determine ...
[122]
Guide to Passenger Weight Policies on Airlines - iFLY
Jul 22, 2024 · Every airline has an overweight passenger policy, and passengers are required to accept the rule. Since each airline has its own policy, airline ...
[123]
'Fat tax': 50% of heavier flyers would pay by their weight - New Atlas
Dec 18, 2024 · Unsurprisingly, nearly half of respondents (42.1%) under 160 lb were in favor of the unit body weight policy, compared to those weighing over ...<|separator|>
[124]
Assessing the Impact of Body Mass Index Information on the ... - NIH
A recent systematic review found that total annual healthcare costs were 36% higher for individuals with obesity as compared to normal weight, and costs were ...Creation Of Bmi Risk Markers · Table 1 · Role Of Bmi In Dx-Based...
[125]
How Being Overweight Affects Your Health Insurance Policy
Jun 12, 2025 · If your BMI is over 30, the insurer might add a loading fee, which is an additional charge on your premium. This loading could range from 10% to ...
[126]
The healthcare costs of increased body mass index–evidence from ...
Jun 1, 2024 · For females, a unit increase in BMI increased average annual healthcare costs by $68.7 – $69.1 in the multivariable models, and by $92.9 – ...
[127]
Economic impacts of overweight and obesity: current and future ...
Nov 4, 2021 · We estimate obesity costs between 0.8% and 2.4% of gross domestic product (GDP) in 2019 in the eight countries in the study. Our projections ...
[128]
[PDF] Economic impact of overweight and obesity to surpass $4 trillion by ...
Mar 2, 2023 · Continued failure to improve prevention and treatment could contribute to a total economic impact of US$4.32 trillion by 2035 – nearly 3% of ...<|separator|>
[129]
City-Level Sugar-Sweetened Beverage Taxes and Youth Body Mass ...
Jul 31, 2024 · ... BMI percentile and overweight and obesity status. ... tax implementation, the policy may have prevented increases in BMI percentile among youth.
[130]
Did Philadelphia's soda tax have an impact on people's health?
Nov 4, 2024 · They found that BMI increased over time in both areas, but the rate at which BMI went up was slower for people in Philadelphia. The new article, ...
[131]
[PDF] Workplace Wellness Programs Study - U.S. Department of Labor
It found the ROI to be 3:1 for direct ... When BMI is between 25 and 30, a person is considered overweight; a BMI of 30 or greater indicates obesity.
[132]
[PDF] Expect Wellness Programs - Partnership for Public Health
In their analysis, they concluded that there is an average ROI of about 3:1 from worksite health promotion ... wellness programs to reduce health risks within the ...
[133]
Obesity: Avoid using BMI alone when evaluating patients, say US ...
Jun 19, 2023 · Doctors should no longer use only body mass index (BMI) when assessing patients for overweight or obesity, the American Medical Association (AMA) decided.Missing: debates mandates 2020s<|control11|><|separator|>
[134]
New Obesity Definition Challenges Current Use of B.M.I.
Jan 14, 2025 · Obesity should be assessed in a way that goes beyond the standard measure of body mass index, or BMI, according to a new definition of the condition.
[135]
Body Composition and Bone Mineral Density of Division 1 ... - NIH
All positions were classified as overweight or obese based on BMI (>25 kg/m2), yet other than offensive and defensive linemen, all positions had healthy percent ...
[136]
Body Mass Index (BMI): Still be used? - ScienceDirect.com
This means that individuals with high muscle mass, such as athletes, may be classified as subjects with overweight or obesity, even if their body fat ...
[137]
Body Mass Index vs Body Fat Percentage as a Predictor of Mortality ...
Jul 28, 2025 · Purpose: Body mass index (BMI) is the current standard body composition measurement. We examined BMI vs body fat percentage (BF%) for 15-year ...
[138]
What is the Optimal Body Mass Index Range for Older Adults? - PMC
Mar 25, 2022 · In the United States during the 90s, there were approximately 32 million older adults and 26.1% of them had a body mass index (BMI) >30 kg/m2, ...
[139]
Sex- and Gender-Related Differences in Obesity - PubMed Central
Jul 4, 2024 · For the same BMI, the percentage of total adipose tissue in females is approximately 10% greater compared to males [11,12]. Although females ...
[140]
BMI, aka body mass index: What the science says - Stanford Medicine
Nov 15, 2024 · BMI is an imperfect way to measure body fat in multiple groups given that it does not account for differences across race/ethnic groups, sexes, genders, and ...Bmi, Aka Body Mass Index... · The Debate · What The Science SaysMissing: misclassification | Show results with:misclassification
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BMI Cut Points to Identify At-Risk Asian Americans for Type 2 ...
Dec 13, 2014 · BMI cutoffs have been established to identify overweight (BMI ≥25 kg/m2) or obese (BMI ≥30 kg/m2) individuals (12).Overweight/Obesity and Type... · Asian American Studies of... · Conclusions
[142]
Obesity and overall mortality: findings from the Jackson Heart Study
Jan 6, 2021 · Overall mortality has been reported to be lower among individuals classified as overweight/obese based on body mass index (BMI, kg/m2) when ...
[143]
BMI–Mortality Paradox and Fitness in African American and ...
Others have reported reductions in mortality risk among overweight or obese subjects, a puzzling observation that has been termed the obesity paradox (4–6).
[144]
Overweight, obesity and excessive weight gain in pregnancy as risk ...
Women who have a pre‐pregnancy body mass index greater than 25 kg m–2 are more likely than those with a body mass index in the ideal range (20–24.99 kg m–2) to ...
[145]
Lower-Extremity Edema During Late Pregnancy - Merck Manuals
Edema is common during late pregnancy. It typically involves the lower extremities but occasionally appears as swelling or puffiness in the face or hands.
[146]
Lower-Extremity Edema During Late Pregnancy - MSD Manuals
Edema is common during late pregnancy. It typically involves the lower extremities but occasionally appears as swelling or puffiness in the face or hands.
[147]
BMI or not to BMI? debating the value of body mass index as a ...
Feb 25, 2025 · ... BMI or misunderstand the limitations of its application [23, 24]. ... overweight and obesity: a systematic review. Obes Rev. 2018;19(12): ...
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Accuracy of Body Mass Index to Diagnose Obesity In the US Adult ...
By using BMI as a marker of obesity, we misclassify ≥ 50% of patients with excess body fat as being normal or just overweight and we miss the opportunity to ...
[149]
BMI a poor predictor of future health - UF Health
Jun 24, 2025 · Today, BMI categorizes an individual as either underweight (BMI under 18.5), normal weight (18.5 to 24.9), overweight (25 to 29.9) or obese (30 ...
[150]
Relationship between Body mass index (BMI) and body fat ...
Sep 3, 2013 · BMI strongly correlate with BF % estimated by bioelectrical impedance, in this sub population of South Asian adults.
[151]
Why the body positivity movement risks turning toxic
Sep 14, 2022 · Critics of body positivity say the movement is not as accepting of all bodies as it claims.
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The Problem with Body Positivity | Psychology Today
Jun 13, 2022 · Body positivity messages can be controlling, diminish autonomy, and may cause depression. Forcing positivity can be stressful, and you shouldn' ...
[153]
Fat and healthy is a myth, new study says - Los Angeles Times
Dec 2, 2013 · New research finds that even when a person is “metabolically healthy ... obesity from those who argued they were “fat but fit.” The latest ...Missing: debunked | Show results with:debunked
[154]
'Fat but fit' is not a real thing, obesity study explains - StudyFinds
The myth of being “fat but fit” is, again, being debunked by a new study. Researchers found that obese individuals, even ...
[155]
Obesity and Cancer Fact Sheet - NCI
Jan 28, 2025 · A fact sheet that summarizes the evidence linking overweight and obesity to the risk of various cancers and discusses how obesity affects ...What is obesity? · How might obesity increase... · Does losing weight lower the...
[156]
How and why weight stigma drives the obesity 'epidemic' and harms ...
Aug 15, 2018 · Weight stigmatization and bias reduction: perspectives of overweight and obese adults. ... motivation. Stigma Heal. 2018;3(2):85–92 ...
[157]
Global Trends in Cardiovascular Mortality Attributable to High Body ...
Oct 7, 2025 · Research has shown that obesity contributes to CVD risk through mechanisms such as hypertension, metabolic dysregulation, and reduced ...
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Comparison of DXA and CT in the Assessment of Body Composition ...
Our results demonstrate strong correlations between DXA- and CT-derived body composition measurements in AN, obese, and lean controls (r = 0.77–0.95, P < 0.0001) ...
[159]
Comparing Methods of Body Composition Analysis
Apr 8, 2024 · Hydrostatic weighing is a more precise method of measuring overall body fat. Before advanced imaging techniques were used for BCA, hydrostatic ...
[160]
Body Composition Methods: Comparisons and Interpretation - PMC
We provide here the most common methods for assessing body composition, including anthropometry, body density, and dual-energy X-ray absorptiometry (DXA).
[161]
Percent body fat is a better predictor of cardiovascular risk factors ...
A higher PBF and/or BMI often indicates a higher level of cardiovascular risk (14). However, the relationship between PBF and BMI is not linear (15,16). A high ...<|separator|>
[162]
Links between ectopic fat and vascular disease in humans - PMC
In addition to overall obesity, ectopic fat has been found to contribute to cardiovascular disease (CVD). Ectopic fat is fat accumulation in or around specific ...
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Ideal Body Fat Percentage for Men, Women, How to Calculate It
Ideal body fat percentage for men ; Athletes, 6% to 13% ; Fitness, 14% to 17% ; Acceptable, 18% to 24% ; Obesity, ≥25% ...How to calculate · For women · For men · BMI calculator
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DEXA vs. Other Body Fat Testing Methods
Jun 11, 2025 · DEXA: 19.2-20.8% range. That precision matters when you're making decisions about nutrition, training, and health goals. When Accuracy Becomes ...
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DEXA vs BIA | Pros and Cons - Nanaimo Body Composition
In conclusion, while BIA scans can offer a basic assessment of body composition, DEXA scans stand out as the superior choice for those seeking accurate, ...
[166]
DEXA vs BIA: Which Is the Best for Body Composition Analysis?
DEXA is the superior choice for the highest accuracy and precision. For a cheaper and more general outlook of your body composition, you could consider a BIA ...
[167]
DEXA Scan vs. Hydrostatic Weighing for Body Fat Analysis
Apr 25, 2019 · Hydrostatic weighing is an early technique in body composition analysis that, at the time, used the most sensible concepts to measure body fat and lean mass.
[168]
Does my BMI look big in this? | University of Oxford
Jan 16, 2013 · ... BMI relies on is flawed: 'If all three dimensions of a human being scaled equally as they grew, then a formula of the form weight/height3 ...
[169]
Quetelet's equation, upper weight limits, and BMI prime - PubMed
Healthy BMI range is 18.5 to 24.9. New equations calculate weight limits above which adults are considered unhealthy. A BMI Prime system relates weight to ...
[170]
Comparisons of the strength of associations with future type 2 ...
Dec 1, 2012 · The present meta-analysis showed that WHtR has a modestly but statistically greater importance than BMI and WHR in prediction of diabetes.
[171]
Comparisons of the Strength of Associations With Future Type 2 ...
Nov 9, 2012 · The aim of this meta-analysis was to compare the association of waist-to-height ratio (WHtR) with risk of incident diabetes with the ...
[172]
Diagnostic Accuracy of Waist-to-Height Ratio, Waist Circumference ...
Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis.
[173]
A New Body Shape Index Predicts Mortality Hazard Independently ...
Body shape, as measured by ABSI, appears to be a substantial risk factor for premature mortality in the general population derivable from basic clinical ...
[174]
A Body Shape Index (ABSI) achieves better mortality risk ... - Nature
Sep 3, 2020 · We compared the ability of alternative waist indices to complement body mass index (BMI) when assessing all-cause mortality.
[175]
Now There Are Better Ways Than BMI Charts to Assess Health Risks
Jan 1, 2024 · The body mass index is flawed, and medicine now has better options to measure obesity. ... A further problem is that BMI is based on height ...
[176]
Comparison of two bioelectrical impedance analysis devices with ...
The correlation coefficients between BIA and DXA or MRI ranged from 0.71 to 0.89 for BF, SMM, and VF by HBF 359 and from 0.77 to 0.90 for BF, FFM, and VF by BC ...
[177]
Application-Based Bioelectrical Impedance Analysis Provides ...
A recent innovation in body composition assessment is the development of application-based BIA systems, which enable body composition analysis via mobile ...
[178]
Assessing Body Composition via a Smartphone Computer Vision ...
Aug 5, 2025 · Smartphone applications utilizing computer vision (CV) offer a promising alternative. This study evaluated the agreement and test-retest ...
[179]
BMI is BAD, a new study suggests. Here's a better way to measure ...
Jun 24, 2025 · In BMI world, a body mass between 18.5 and 24.9 is a healthy weight, between 25 and 29.9 is overweight, between 30 and 34.9 is obese, between 35 ...
[180]
AI in Adipose Imaging: Revolutionizing Visceral Adipose Tissue ...
Oct 9, 2025 · This review explores the role of artificial intelligence (AI) in visceral adipose tissue (VAT) and ectopic fat imaging.
[181]
Detecting sarcopenia in obesity: emerging new approaches - NIH
Jul 18, 2024 · ... BMI-based, ethnic cut-point-specific overweight or obesity. Another community-based study of older adults reported a lower 4.5% prevalence ...
[182]
Deep learning models for deriving optimised measures of fat and ...
Jul 17, 2025 · Here we evaluate the accuracy, precision and ability to track change for multiple deep learning (DL) models that quantify fat and muscle mass from abdominal ...
[183]
“Bioelectrical impedance analysis in managing sarcopenic obesity ...
The limitation with the use of BMI as a measure of obesity is BMI does ... sarcopenic or nonsarcopenic obese adults. A cross‐sectional study showed ...
[184]
Accuracy of Bioelectrical Impedance Consumer Devices for ... - NIH
Because of the lower agreement between foot-to-foot BIA and DXA or MRI for the assessment of body composition in individuals, tetrapolar electrode arrangement ...
[185]
Reliability and Validity of Contemporary Bioelectrical Impedance ...
Nov 10, 2022 · Conclusions: Caution should be taken when using BIA devices to assess BF% as devices demonstrated unacceptable agreement compared to DEXA.
[186]
BMI is a limited measurement for body composition. Could BIA be ...
Aug 3, 2025 · BMI is still commonly used to assess obesity even though research has shown it's not necessarily a reliable metric. A new study proposes doctors ...
[187]
Real-world assessment of Multi-Frequency Bioelectrical Impedance ...
Sep 16, 2025 · Now there is adequate scientific support to replace BMI with BIA as a better assessment of actual body composition.Missing: driven alternatives