Body mass index (BMI) is a widely used anthropometric measure that estimates body fat based on an individual's weight and height, calculated as weight in kilograms divided by the square of height in meters (kg/m²) [1]. Developed in the 19th century by Adolphe Quetelet as a statistical tool, BMI was later adopted by the World Health Organization (WHO) and the U.S. Centers for Disease Control and Prevention (CDC) as a standard for classifying weight status into categories such as underweight, normal weight, overweight, and obesity [2]. While BMI serves as a practical and cost-effective screening tool in public health and clinical settings, it does not directly measure body fat or distinguish between fat mass and muscle mass, leading to potential misclassification, particularly among athletes, older adults, and certain ethnic groups [3]. Its limitations include failure to account for fat distribution—especially visceral adiposity—which is more strongly linked to metabolic syndrome, type 2 diabetes, and cardiovascular disease than overall body weight [4]. As a result, experts recommend supplementing BMI with additional assessments such as waist circumference, waist-to-height ratio, and body composition analysis using techniques like DEXA scans or bioelectrical impedance [5]. Epidemiological studies show a U-shaped relationship between BMI and all-cause mortality, with increased risks at both low and high extremes, though this association varies by age, sex, and ethnicity [6]. Due to these complexities, public health policies increasingly emphasize a holistic approach that considers social determinants of health, cultural norms, and metabolic health beyond BMI alone, with emerging alternatives like the Body Roundness Index (BRI) being explored for improved risk prediction [7].
Definition and Calculation of BMI
Body mass index (BMI) is a widely used anthropometric measure that estimates body fat based on an individual's weight and height [1]. It serves as a practical screening tool in both public health and clinical settings to categorize individuals into weight status groups such as underweight, normal weight, overweight, and obesity. Although BMI does not directly measure body fat, it correlates with more direct assessments of adiposity, including skinfold thickness, bioelectrical impedance, and dual-energy X-ray absorptiometry (DXA) [3]. Despite its utility for population-level analysis, BMI has notable limitations, including its inability to differentiate between fat mass and muscle mass, which can lead to misclassification—particularly among athletes and older adults [10].
How BMI Is Calculated
BMI is calculated using a straightforward mathematical formula that divides a person’s weight by the square of their height. The specific formula varies depending on the unit system used.
Metric Units Formula
For weight measured in kilograms (kg) and height in meters (m), the formula is:
BMI = weight (kg) ÷ [height (m)]²
[3]
For example, an adult weighing 70 kg and standing 1.75 meters tall would have a BMI of:
70 ÷ (1.75)² = 70 ÷ 3.0625 = 22.86 kg/m².
Imperial Units Formula
For weight in pounds (lb) and height in inches (in), the formula adjusts for unit conversion:
BMI = [weight (lb) ÷ (height (in))²] × 703
[3]
For instance, a person who weighs 154 pounds and is 68 inches tall would calculate their BMI as:
(154 ÷ 68²) × 703 = (154 ÷ 4624) × 703 ≈ 23.3 kg/m².
Units of BMI
BMI is expressed in units of kilograms per square meter (kg/m²), reflecting the metric derivation of the index [1]. This standardized unit allows for consistent interpretation across different populations and facilitates international comparisons in health research and surveillance.
Mathematical Rationale Behind the Formula
The use of height squared in the BMI formula stems from empirical observations made by Belgian statistician Adolphe Quetelet in the 19th century, who noted that adult body weight tends to scale approximately with the square of stature rather than the cube, as would be expected if humans grew isometrically [14]. This deviation from cubic scaling reflects the fact that taller individuals are relatively leaner and do not gain mass at the same rate as volume would predict. The quadratic relationship thus serves as a statistical approximation to normalize body mass across varying heights, making BMI a scale-invariant measure useful for population comparisons [15]. However, allometric studies suggest that the optimal exponent may vary by sex, age, and ethnicity—ranging between 1.6 and 2.5—indicating that the fixed exponent of 2 is a simplification [16].
Online Tools and Practical Use
To simplify computation, numerous online tools are available, such as the CDC’s Adult BMI Calculator, which allows individuals and healthcare providers to quickly determine BMI without manual calculation [17]. These tools often include immediate categorization based on standard thresholds, enhancing accessibility and usability in both clinical and personal health contexts.
Standard BMI Categories and Interpretation
Body mass index (BMI) is used globally as a standardized method to classify weight status in adults, providing a framework for identifying individuals at risk for health complications related to underweight, overweight, and obesity. The classification system is based on BMI values derived from weight in kilograms divided by the square of height in meters (kg/m²) [1]. These categories are widely adopted by major health organizations, including the World Health Organization (WHO) and the U.S. Centers for Disease Control and Prevention (CDC), and serve as a foundational tool in public health surveillance and clinical screening [2].
Adult BMI Categories
For adults aged 18 and over, BMI categories are defined using fixed numerical thresholds that do not vary by age or sex. These categories are:
- Underweight: BMI less than 18.5
- Normal weight (healthy weight): BMI 18.5 to 24.9
- Overweight: BMI 25.0 to 29.9
- Obesity: BMI 30.0 or higher [20]
These classifications apply to both men and women and are used consistently across most clinical and epidemiological contexts. However, it is important to note that BMI is a screening tool rather than a diagnostic measure; a high or low BMI may indicate potential health risks but does not directly assess body fatness or metabolic health [21].
Obesity Subclassification
Obesity is further divided into three classes to reflect increasing levels of health risk:
- Obesity Class I: BMI 30.0 to 34.9
- Obesity Class II: BMI 35.0 to 39.9
- Obesity Class III (also known as severe, extreme, or morbid obesity): BMI 40.0 or higher [22]
This subclassification is clinically significant because higher BMI categories are associated with greater risks of comorbidities such as type 2 diabetes, cardiovascular disease, obstructive sleep apnea, and certain cancers [23]. The U.S. Preventive Services Task Force recommends that adults with a BMI ≥30 kg/m² be considered for intensive behavioral interventions to reduce weight and improve health outcomes [24].
Ethnic and Population-Specific Adjustments
While standard BMI categories are applied globally, research has shown that health risks associated with adiposity vary across racial and ethnic groups. As a result, some populations require adjusted cutoffs to accurately reflect metabolic risk. For example:
- Individuals of South Asian, East Asian, Middle Eastern, and certain Hispanic backgrounds tend to have higher body fat percentages and increased risks of type 2 diabetes and cardiovascular disease at lower BMI levels compared to White populations [25].
- In response, health authorities such as the UK’s National Institute for Health and Care Excellence (NICE) and the World Health Organization recommend lower BMI thresholds for initiating preventive actions in these groups—such as defining overweight as ≥23 kg/m² and obesity as ≥27.5 kg/m² for South Asians [26].
These adjustments acknowledge that universal BMI cutoffs, largely derived from studies of White, middle-aged populations, may lead to underdiagnosis in some groups and overdiagnosis in others, contributing to health inequities [27].
Interpretive Limitations and Clinical Context
Despite its widespread use, BMI should not be interpreted in isolation. It does not distinguish between fat mass and muscle mass, meaning that highly muscular individuals—such as athletes—may be classified as overweight or obese despite having low body fat and excellent metabolic health [28]. Conversely, individuals with a "normal" BMI may have high visceral fat and metabolic dysfunction, a condition known as "normal-weight obesity" or "metabolically obese normal weight" (MONW) [29].
Therefore, clinicians are encouraged to supplement BMI with additional assessments such as waist circumference, waist-to-height ratio, and body composition analysis using techniques like DEXA scans or bioelectrical impedance [5]. These measures provide more accurate insights into visceral adiposity and metabolic risk, which are stronger predictors of cardiometabolic disease than BMI alone [31].
Visual Representation of BMI Categories
This visual aid helps illustrate how BMI categories correspond to increasing health risks and can support patient education in clinical settings. However, it should be used with caution, as body shape and fat distribution vary widely within each category and are not fully captured by BMI.
Use in Public Health and Clinical Practice
Body mass index (BMI) serves as a foundational tool in both public health surveillance and clinical care, functioning primarily as a cost-effective and standardized screening method for identifying individuals and populations at risk for weight-related health conditions. Despite its well-documented limitations, BMI remains widely implemented due to its simplicity and strong correlation with adiposity at the population level. Its application spans from national health monitoring systems to individual patient assessments, where it informs risk stratification, intervention planning, and policy development.
Public Health Surveillance and Population-Level Monitoring
In public health, BMI is a cornerstone metric for tracking the prevalence of overweight and obesity across populations. National and global health organizations rely on BMI data to monitor long-term trends, evaluate the effectiveness of interventions, and allocate resources. For example, the U.S. Centers for Disease Control and Prevention (CDC) uses data from the National Health and Nutrition Examination Survey (NHANES) and electronic health records (EHRs) to report that approximately 40.3% of U.S. adults had obesity during August 2021–August 2023 [32]. Similarly, the World Health Organization (WHO) incorporates BMI into its STEPwise approach to surveillance (STEPS), a standardized method for collecting data on noncommunicable disease risk factors globally [33]. These surveillance systems enable geospatial analysis of obesity prevalence, supporting targeted public health initiatives in high-risk communities.
BMI’s utility in population health is further demonstrated by its role in forecasting future trends. Statistical models, including Gaussian process regression and Bayesian hierarchical models, use historical BMI data to project obesity prevalence and associated health burdens. The Global Burden of Disease (GBD) Study 2021, for instance, forecasts that severe obesity will affect over 25% of adults in many U.S. states by 2050 [34]. Such projections guide policy decisions on nutrition, urban planning, and healthcare capacity. Evidence-based interventions informed by BMI data include sugar-sweetened beverage taxes, restrictions on junk food marketing to children, and community-based programs like those in King County, Washington, which successfully reduced youth obesity through coordinated school and policy efforts [35].
Clinical Screening and Risk Assessment
In clinical settings, BMI is used as an initial screening tool to identify patients at increased risk for conditions such as type 2 diabetes, cardiovascular disease, and obstructive sleep apnea. A BMI of 30 kg/m² or higher is generally classified as obesity, with further subclassifications into Class I (30.0–34.9), Class II (35.0–39.9), and Class III (≥40.0, also known as severe or morbid obesity) [20]. These categories help clinicians determine eligibility for intensive behavioral interventions, pharmacotherapy, or bariatric surgery. The U.S. Preventive Services Task Force (USPSTF) recommends routine BMI screening in adults and children, with children at or above the 95th percentile for BMI-for-age referred for comprehensive weight management programs [24].
For children and adolescents, BMI is assessed using age- and sex-specific growth charts, such as the CDC’s 2000 and 2022 Extended BMI-for-Age Growth Charts, which account for developmental changes in body composition [38]. This approach allows for early identification of weight issues and timely intervention. However, BMI is not a diagnostic tool; it serves as a prompt for further evaluation, including assessment of diet, physical activity, family history, and metabolic health markers.
Limitations and the Need for Complementary Measures
Despite its widespread use, BMI has significant limitations that affect its accuracy in individual risk assessment. It does not distinguish between fat mass and muscle mass, leading to potential misclassification. For example, athletes with high lean mass may be categorized as overweight or obese despite low body fat, while older adults with "normal" BMI may have high visceral adiposity—a condition known as "normal-weight obesity" [5]. Furthermore, BMI fails to capture fat distribution, which is a critical determinant of metabolic risk. Visceral fat, in particular, is strongly linked to insulin resistance and cardiovascular disease, yet BMI provides no information about its presence.
To address these shortcomings, clinical guidelines increasingly recommend supplementing BMI with additional assessments. is recognized as a vital sign for central adiposity, with thresholds of >88 cm in women and >102 cm in men indicating increased cardiometabolic risk [40]. The (WHtR), particularly a ratio ≥0.5, has been shown to be a superior predictor of metabolic syndrome and cardiovascular mortality compared to BMI [41]. Advanced body composition techniques such as (DXA) and (BIA) provide more accurate quantification of fat and lean mass, enabling better risk stratification in clinical and research settings [42].
Evolving Guidelines and Equity Considerations
Recent clinical guidelines reflect a shift toward more nuanced and equitable approaches to obesity assessment. The UK’s National Institute for Health and Care Excellence (NICE) updated its recommendations in 2026 to emphasize annual BMI checks for individuals with long-term conditions and to advocate for a multidisciplinary approach to obesity management [43]. Similarly, the American Medical Association (AMA) has acknowledged that BMI alone is an imperfect measure and should not be used in isolation to diagnose obesity or determine health status [44]. Instead, the AMA recommends considering social determinants of health, structural racism, and metabolic health indicators such as blood pressure, lipid profile, and HbA1c.
Ethnic-specific BMI thresholds are also being adopted to improve risk prediction in diverse populations. For instance, lower cutoffs (e.g., ≥27.5 kg/m² for obesity) are recommended for South Asian and East Asian individuals due to higher cardiometabolic risk at lower BMI levels [26]. These adjustments reflect growing recognition that universal BMI categories may perpetuate health inequities by underdiagnosing risk in some groups and overpathologizing others.
Conclusion
BMI remains a valuable tool in public health and clinical practice due to its simplicity, scalability, and strong association with population-level health risks. It plays a critical role in surveillance, policy development, and initial clinical screening. However, its limitations—particularly its inability to assess body composition, fat distribution, and metabolic health—necessitate a more comprehensive approach. Integrating BMI with measures such as , , and metabolic biomarkers enables more accurate and equitable health assessments. As public health and clinical practice evolve, the future lies in multimodal, personalized strategies that move beyond BMI to capture the full complexity of adiposity and metabolic risk.
Limitations of BMI as a Health Indicator
Body mass index (BMI) is a widely used anthropometric screening tool that estimates weight status based on height and weight. While it offers simplicity and scalability for population-level assessments, BMI has significant limitations when applied to individual health evaluation. These shortcomings stem from its inability to account for body composition, fat distribution, and demographic variability, leading to potential misclassification of health risk and contributing to health inequities. As a result, experts emphasize that BMI should not be used in isolation but rather as part of a broader clinical and public health assessment.
Does Not Measure Body Composition
One of the most critical limitations of BMI is its failure to differentiate between fat mass, muscle mass, bone density, and water weight. Because BMI is calculated solely from total body weight and height, it cannot distinguish lean mass from adipose tissue. This leads to frequent misclassification, particularly among individuals with high muscle mass—such as athletes, bodybuilders, or physically active individuals—who may be categorized as overweight or obese despite having low body fat and excellent metabolic health [28]. Conversely, individuals with a "normal" BMI may have high levels of body fat, especially visceral fat, a condition known as "normal-weight obesity" or "metabolically obese normal weight" (MONW), which increases the risk of type 2 diabetes and cardiovascular disease [47]. This discrepancy underscores the need for more precise body composition analysis, such as DEXA scans, which can accurately quantify fat and lean mass [48].
Fails to Account for Fat Distribution
BMI provides no information about where fat is stored in the body, a crucial factor in determining health risk. Abdominal fat, particularly visceral adipose tissue surrounding internal organs, is metabolically active and strongly linked to insulin resistance, inflammation, and cardiometabolic diseases such as type 2 diabetes and cardiovascular disease [49]. In contrast, subcutaneous fat is less harmful. Waist circumference is a far better predictor of visceral adiposity and associated risks than BMI alone [4]. Studies show that waist circumference explains obesity-related health risks more effectively than BMI and should be treated as a "vital sign" in clinical practice [51]. Alternative indices such as the waist-to-height ratio (WHtR), particularly a ratio ≥0.5, have demonstrated superior accuracy in detecting metabolic syndrome and cardiovascular risk across diverse populations [52].
Variability Across Ethnic and Racial Groups
BMI cutoffs were largely developed using data from white, middle-aged populations and may not accurately reflect health risks in other racial or ethnic groups. Research shows that South Asian, East Asian, Black African, and Arab populations often face higher risks of type 2 diabetes and cardiovascular disease at lower BMI levels compared to White populations [53]. For example, South Asians exhibit greater insulin resistance and visceral adiposity at lower BMI values, prompting public health bodies like the UK’s NICE to recommend lower BMI thresholds (e.g., ≥27.5 kg/m²) for initiating diabetes prevention programs [26]. Similarly, studies support lower BMI cutoffs for overweight (≥23 kg/m²) and obesity (≥27.5 kg/m²) in Asian populations to better capture metabolic risk [55]. Conversely, some Black individuals may have higher lean mass and bone density, leading to potential overestimation of health risk when using standard BMI categories [56]. These disparities highlight the need for ethnicity-specific guidelines and caution against the universal application of BMI thresholds.
Inadequate for Certain Age and Gender Groups
Body composition changes significantly with age, sex, and hormonal status, which BMI does not account for. Older adults often experience sarcopenia—the loss of muscle mass—alongside an increase in fat mass, even without weight gain, resulting in "normal weight obesity" [57]. This condition increases cardiometabolic risk despite a "healthy" BMI. Women also tend to have higher body fat percentages than men at the same BMI due to biological differences in fat distribution and hormonal influences [58]. After menopause, fat distribution shifts toward the abdomen, increasing visceral adiposity and metabolic risk. These sex- and age-related differences limit the accuracy of BMI across demographic groups and necessitate more nuanced assessment tools in clinical settings.
Overreliance Can Lead to Health Inequities and Stigmatization
The use of BMI as the sole clinical measure can contribute to health disparities and reinforce weight stigma, particularly among people of color and other marginalized groups. The AMA has acknowledged that relying solely on BMI can result in misdiagnosis, delayed treatment, and stigmatization, especially when applied without considering social determinants of health or structural racism [5]. Weight stigma itself has been shown to negatively impact health outcomes, leading to avoidance of medical care, reduced quality of life, and increased psychological distress [60]. In healthcare settings, implicit bias among providers can result in dismissive attitudes, inadequate pain management, and less empathetic communication for higher-weight patients [61]. This is compounded for racial and ethnic minorities, who often report negative weight-related care experiences and mistrust in the healthcare system [62].
Limitations in Predictive Validity for Cardiometabolic Risk
While BMI correlates with health risks at the population level, its predictive power for individual outcomes such as cardiovascular disease and type 2 diabetes is inferior to more direct measures. Studies show that waist-to-hip ratio (WHR) and waist circumference are stronger discriminators of cardiovascular mortality than BMI [63]. A 2023 study in JAMA found that WHR predicted all-cause and cardiovascular mortality more accurately than BMI across diverse populations [64]. Similarly, imaging-based quantification of visceral fat via MRI or CT scans provides more reliable risk prediction than BMI [65]. Direct metabolic markers such as hemoglobin A1c (HbA1c), triglyceride glucose-body mass index (TyG-BMI), and the Metabolic Score for Insulin Resistance (METS-IR) also outperform BMI in identifying early dysregulation and predicting disease onset [66].
Conclusion: Toward a More Comprehensive Approach
Given these limitations, BMI should be used as an initial screening tool rather than a diagnostic measure. Experts recommend combining BMI with other assessments such as waist circumference, body composition analysis (e.g., bioelectrical impedance or DEXA), and metabolic biomarkers to provide a more accurate and equitable picture of health [5]. Emerging alternatives like the Body Roundness Index (BRI) and "biological BMI"—a composite biomarker score derived from metabolomic data—are being explored to better capture metabolic risk and respond more sensitively to lifestyle interventions [68]. A shift toward health-promoting behaviors—such as physical activity, balanced nutrition, and mental well-being—regardless of body size, is gaining traction in public health discourse as a more inclusive and effective strategy for improving population health [69].
Variations Across Age, Sex, and Ethnicity
Body mass index (BMI) is a widely used anthropometric measure, but its interpretation and health implications vary significantly across age groups, sexes, and ethnicities. These variations stem from differences in body composition, fat distribution, and metabolic risk, which BMI does not directly capture. As a result, reliance on universal BMI thresholds can lead to misclassification of health risk, particularly in older adults, women, and certain racial or ethnic populations. Understanding these demographic differences is essential for accurate clinical assessment and equitable public health policy.
Age-Related Differences in BMI and Body Composition
Body composition changes substantially with age, affecting the validity of BMI as a proxy for adiposity. In older adults, the relationship between BMI and body fat percentage weakens due to age-related physiological shifts such as sarcopenia—the loss of lean muscle mass—and increased visceral fat accumulation [70]. Consequently, an older adult with a "normal" BMI may have body fat levels comparable to a younger person classified as overweight or obese, a condition sometimes referred to as "normal-weight obesity" [71].
This phenomenon reduces the sensitivity of BMI in detecting excess adiposity in aging populations. A meta-analysis found that BMI has low sensitivity for identifying obesity defined by body fat thresholds in older adults, often missing over half of those with high adiposity despite high specificity [72]. As a result, individuals with normal BMI but high visceral fat—known as the metabolically obese normal-weight (MONW) phenotype—may be overlooked in clinical screening, increasing their risk for metabolic syndrome, type 2 diabetes, and cardiovascular disease [29]. To improve risk assessment, clinicians are advised to supplement BMI with measures such as waist circumference or DEXA scans, which can detect visceral adiposity and sarcopenic obesity more accurately [74].
Sex Differences in BMI and Metabolic Risk
Sex differences in body composition and fat distribution significantly influence the relationship between BMI and health outcomes. Women generally have higher body fat percentages than men at the same BMI due to biological and hormonal factors, particularly the tendency to store fat in subcutaneous depots such as the gluteofemoral region [75]. While this fat distribution is less metabolically harmful than visceral fat, postmenopausal women experience a shift toward central adiposity, increasing their risk of insulin resistance and cardiovascular disease [76].
Men, by contrast, are more prone to visceral fat accumulation even at lower BMI values, which correlates more strongly with metabolic dysfunction [77]. As a result, the same BMI may confer different levels of cardiometabolic risk between sexes. For example, men often exhibit higher risk at lower BMI values than women, particularly in middle age (40–59 years), when obesity prevalence is highest [78]. These differences highlight the limitations of applying uniform BMI thresholds without considering biological sex and hormonal status. The use of sex-specific risk stratification tools, such as waist-to-hip ratio, can improve the accuracy of metabolic risk prediction in both men and women [79].
Ethnic and Racial Variations in BMI and Health Risk
Ethnic and racial differences in body composition and metabolic risk further challenge the universality of BMI thresholds. Standard BMI cutoffs—largely derived from studies of white, middle-class populations—may not accurately reflect health risks in other ethnic groups, leading to underdiagnosis or overdiagnosis of obesity-related conditions [27].
For example, populations of Asian descent, including South Asian, East Asian, and Arab groups, tend to have higher body fat percentages and greater visceral adiposity at lower BMI values compared to white populations [53]. This results in elevated risks of type 2 diabetes and cardiovascular disease at BMIs considered "normal" (18.5–24.9 kg/m²) by conventional standards. In response, the World Health Organization (WHO) and health authorities in the UK and Singapore have recommended lower BMI thresholds for public health action in Asian populations, such as defining overweight as ≥23 kg/m² and obesity as ≥27.5 kg/m² [82]. Similarly, studies in England have proposed ethnicity-specific BMI cutoffs based on type 2 diabetes risk, with Chinese and Black African populations showing elevated risk at BMIs as low as 24–28 kg/m² [83].
Conversely, some research suggests that Black individuals may have lower body fat percentages at the same BMI compared to white individuals, potentially due to greater bone mineral density and lean mass [84]. This can lead to overestimation of health risk when using standard BMI categories. However, this does not imply that Black individuals are protected from obesity-related disease; rather, it underscores the need for more precise, population-specific assessments. The use of alternative metrics such as waist-to-height ratio (WHtR), with a threshold of ≥0.5, has been shown to perform consistently across Hispanic, Chinese, Japanese, European-American, and African-American populations, offering a more equitable approach to risk screening [85].
Cultural and Historical Contexts of BMI Application
The global application of BMI reflects broader historical and cultural dynamics rooted in Western scientific paradigms. Originally developed by Adolphe Quetelet in the 19th century as a statistical tool, BMI was later popularized by Ancel Keys in the 1970s and formalized by the World Health Organization in 1997 [86]. Its standardization was based predominantly on data from white male populations, leading to the pathologization of body sizes common among non-Western and marginalized groups [87].
In many African, Caribbean, and Pacific Islander societies, larger body sizes have traditionally been associated with health, fertility, prosperity, and social status, contrasting sharply with Western thinness ideals [88]. For instance, among the Yorùbá of Nigeria, body size holds a neutral or positive meaning, with personal qualities such as moral character emphasized over physical appearance [89]. The imposition of Western BMI standards in these contexts can erode indigenous body ideals and contribute to body dissatisfaction, particularly in urbanizing and globalizing settings [90].
Moreover, the medicalization of fatness through BMI has contributed to the stigmatization of higher-weight bodies, particularly among women of color, reinforcing racialized fat phobia and structural inequities in healthcare [91]. Addressing these disparities requires a shift toward culturally responsive health assessment models that incorporate local values, holistic well-being, and population-specific biometrics [92].
Relationship with Cardiometabolic Risk and Mortality
Body mass index (BMI) is widely recognized as a significant predictor of cardiometabolic risk and all-cause mortality, though the strength and nature of this association vary across demographic and physiological contexts. Epidemiological evidence consistently demonstrates a non-linear, often J- or U-shaped relationship between BMI and mortality, with the lowest risk observed within the normal weight range (approximately 22–25 kg/m²) and increased risks at both low and high extremes [6]. This pattern underscores the complex interplay between body weight, body composition, and metabolic health, where both underweight and obesity are linked to elevated morbidity and mortality from conditions such as cardiovascular disease (CVD), type 2 diabetes (T2D), and certain cancers [94].
Non-Linear Association with All-Cause Mortality
Large-scale meta-analyses involving millions of participants confirm that the risk of all-cause mortality is minimized in the normal BMI range and rises progressively at both ends of the spectrum. A systematic review and dose-response analysis of 230 cohort studies found that the nadir of mortality risk occurs at a BMI of approximately 22–25 kg/m², with underweight (BMI <18.5 kg/m²) and obesity (BMI ≥30 kg/m²) associated with significantly higher mortality [6]. The Global Burden of Disease Study identifies high BMI as a leading risk factor for disability-adjusted life years (DALYs) and premature death, particularly due to its contribution to non-communicable diseases [96]. However, this association is not uniform across populations, and its interpretation must account for age, sex, ethnicity, and body composition.
Variation by Age: The Obesity Paradox in Older Adults
The relationship between BMI and mortality is notably modified by age. In older adults (typically aged 65 and above), the mortality risk associated with higher BMI is attenuated, and some studies report lower mortality among overweight individuals compared to those in the normal weight range—a phenomenon known as the "obesity paradox" [97]. This may reflect survival bias, where individuals with higher adipose reserves are better able to withstand illness or cachexia in later life. However, this does not imply that high BMI is protective; longitudinal data suggest that weight gain in midlife increases later mortality, while late-life weight loss may signal underlying disease rather than improved health [98]. Furthermore, older adults often experience sarcopenia—loss of muscle mass—alongside increasing fat mass, leading to "normal-weight obesity," where individuals with a healthy BMI have high body fat and elevated metabolic risk [28].
Sex Differences in Cardiometabolic Risk
Sex differences also influence the BMI-mortality relationship. Some analyses suggest that the optimal BMI for lowest mortality is slightly higher in women (23–26 kg/m²) than in men (22.5–25 kg/m²) [100]. Women tend to store more subcutaneous fat, particularly in the gluteofemoral region, which is less metabolically harmful than visceral fat. Men, by contrast, are more prone to central adiposity and visceral fat accumulation, which is strongly linked to insulin resistance and cardiovascular disease [77]. After menopause, fat distribution in women shifts toward the abdomen, increasing visceral adiposity and aligning their cardiometabolic risk profile more closely with men [102].
Ethnic and Racial Disparities in Risk Stratification
Ethnicity significantly modifies the relationship between BMI and health outcomes. Standard BMI cutoffs, largely derived from studies in White populations, may not accurately reflect risk in other ethnic groups. For example, South Asian, East Asian, Black Caribbean, and Arab populations tend to develop type 2 diabetes and cardiovascular disease at lower BMI levels compared to White populations [103]. South Asians exhibit higher insulin resistance and visceral adiposity at lower BMI, prompting public health bodies like the UK’s National Institute for Health and Care Excellence (NICE) to recommend lower BMI thresholds (≥27.5 kg/m²) for initiating diabetes prevention [26]. Similarly, studies in England propose ethnicity-specific BMI cutoffs for obesity based on type 2 diabetes risk, with Chinese and Black African populations showing elevated risk at BMIs as low as 24–28 kg/m² [53]. These findings highlight the need for tailored public health guidelines that account for ethnic differences in body composition and metabolic risk.
Socioeconomic and Structural Influences on BMI-Related Mortality
Socioeconomic status (SES) is a key determinant of BMI and its associated health outcomes. In high-income countries, lower SES—measured by education, income, or neighborhood deprivation—is consistently associated with higher BMI and increased obesity prevalence [106]. This inverse social gradient is particularly evident among women and tends to widen with age [107]. The mechanisms include differential access to healthy foods, safe spaces for physical activity, healthcare, and exposure to obesogenic environments. Recent research indicates that the strength of the SES-BMI association has increased over time, emphasizing the role of structural and environmental factors in shaping obesity risk [108].
Limitations of BMI in Predicting Cardiometabolic Risk
Despite its population-level utility, BMI is an imperfect predictor of individual cardiometabolic risk due to its failure to account for fat distribution, particularly visceral adiposity. Visceral fat, which accumulates around internal organs, is metabolically active and strongly associated with insulin resistance, inflammation, and cardiovascular disease [109]. A systematic review confirmed only moderate correlation between BMI and imaging-derived visceral fat measures, underscoring the need for more precise assessment tools [110]. Individuals with a "normal" BMI may have high visceral fat—a condition known as "normal-weight visceral obesity"—and exhibit metabolic syndrome, while some with high BMI may have predominantly subcutaneous fat and relatively low metabolic risk [111].
Clinical and Public Health Implications
Given these limitations, clinical guidelines increasingly recommend supplementing BMI with additional assessments. Waist circumference is recognized as a vital sign for visceral adiposity, with thresholds of >80 cm in women and >94 cm in men indicating increased metabolic risk [40]. The waist-to-height ratio (WHtR), particularly a ratio ≥0.5, has been shown to be a superior predictor of metabolic syndrome compared to BMI [52]. Advanced body composition tools such as DEXA scans and bioelectrical impedance can identify individuals with normal BMI but high adiposity-related risk, reinforcing the need for more precise adiposity assessment [114].
Emerging approaches such as "biological BMI"—a blood-based biomarker score derived from metabolomic profiling—offer promising avenues for more accurate, personalized health evaluation beyond traditional anthropometrics [68]. These tools reflect underlying metabolic dysregulation and are more responsive to lifestyle interventions, making them valuable for risk stratification in precision medicine [116].
In conclusion, while BMI remains a useful population-level screening tool for identifying weight-related health risks, its association with cardiometabolic mortality is modified by age, sex, ethnicity, and socioeconomic status. Its inability to capture visceral adiposity and metabolic health necessitates a multifactorial approach that integrates anthropometric, imaging, and metabolic data to improve risk prediction and support equitable, individualized care.
Alternative and Complementary Measures of Adiposity
While body mass index (BMI) remains a widely used screening tool in public health and clinical settings, its inability to differentiate between fat mass and lean muscle mass, or to account for fat distribution, has led to the development and adoption of alternative and complementary measures of adiposity. These metrics aim to improve the accuracy of health risk assessment by focusing on central adiposity, body composition, and metabolic function—factors more directly linked to cardiometabolic disease than BMI alone. A growing body of evidence supports the use of these tools to overcome the limitations of BMI, particularly in diverse populations where BMI may misclassify risk.
Anthropometric Alternatives to BMI
Several anthropometric indices have been proposed as superior predictors of metabolic and cardiovascular risk compared to BMI. Among the most widely supported is the waist-to-height ratio (WHtR), calculated as waist circumference divided by height. A WHtR of ≥0.5 is consistently associated with increased risk of type 2 diabetes, hypertension, and metabolic syndrome across diverse ethnic groups [117]. Unlike BMI, WHtR accounts for abdominal fat distribution, making it a more sensitive indicator of visceral adiposity. Meta-analyses have shown that WHtR outperforms both BMI and waist circumference in predicting cardiometabolic outcomes, with higher area under the curve (AUC) values in receiver operating characteristic analyses [118].
Another alternative is the Body Roundness Index (BRI), a geometric model that estimates body shape and visceral fat using waist circumference and height. BRI has demonstrated improved prediction of metabolic risk compared to BMI, particularly in identifying individuals with normal BMI but high visceral adiposity [119]. BRI’s ability to capture body roundness—a proxy for central obesity—makes it a promising tool for refining obesity classification.
In contrast, the body adiposity index (BAI), defined as (hip circumference / height^1.5) − 18, has shown limited validity despite its theoretical appeal. Multiple validation studies have found weak correlation between BAI and actual body fat percentage measured by DEXA, with poor performance across ethnic groups, particularly in men [120]. As a result, BAI is not recommended for routine clinical or epidemiological use.
Advanced Body Composition Assessment
To overcome the limitations of simple anthropometrics, advanced methods provide more precise quantification of fat and lean mass. DEXA scans are considered a gold standard for body composition analysis, offering high reproducibility in measuring regional fat distribution, including visceral adipose tissue (VAT), when advanced software is used [48]. DEXA can identify individuals with normal BMI but high body fat—a condition known as "normal-weight obesity"—and has been shown to reveal higher obesity prevalence than BMI alone [114].
Another method, bioelectrical impedance analysis (BIA), estimates body fat percentage and visceral fat by measuring the resistance of electrical flow through tissues. While less accurate than DEXA due to variability in hydration status, modern BIA devices offer reasonable approximations and are increasingly used in clinical and research settings [123]. BIA is particularly useful for tracking changes in body composition over time during weight loss interventions.
Imaging techniques such as computed tomography (CT) and MRI are the most accurate methods for quantifying visceral fat. These modalities provide detailed cross-sectional views of adipose tissue and are considered the reference standard in research [124]. However, their high cost, limited accessibility, and, in the case of CT, radiation exposure restrict their use to specialized settings.
Metabolic and Biomarker-Based Approaches
Beyond physical measurements, metabolic health status is increasingly recognized as a critical determinant of risk. Blood-based biomarkers offer a more direct assessment of adipose tissue function and disease risk. The visceral adiposity index (VAI), a sex-specific score incorporating waist circumference, BMI, triglycerides, and HDL cholesterol, has shown strong predictive power for metabolic syndrome and non-alcoholic fatty liver disease [125]. More recently, the visceral adiposity inflammatory index (VAII), which integrates VAI with C-reactive protein (CRP), has demonstrated enhanced prediction of stroke risk [126].
A novel approach, termed biological BMI (bBMI), uses metabolomic profiling to derive a blood-based biomarker score that reflects underlying metabolic dysregulation. bBMI has been shown to uncover hidden health risks not captured by traditional BMI and is more responsive to lifestyle interventions, making it a promising tool for personalized risk assessment [68]. Similarly, indices such as the triglyceride glucose-body mass index (TyG-BMI) and the Metabolic Score for Insulin Resistance (METS-IR) integrate lipid metabolism and adiposity into single risk scores that outperform BMI in predicting cardiovascular mortality [128].
Clinical and Public Health Recommendations
Given the limitations of BMI, major health organizations increasingly recommend supplementing it with additional assessments. The International Atherosclerosis Society and International Chair on Cardiometabolic Risk advocate for routine measurement of waist circumference as a "vital sign" in clinical practice to better assess visceral obesity and metabolic risk [41]. The UK’s National Institute for Health and Care Excellence (NICE) guidelines recommend combining BMI with waist-to-height ratio in adults to improve obesity screening [130].
The American Medical Association (AMA) has acknowledged that BMI alone is an imperfect clinical measure and should not be used in isolation to diagnose obesity or determine health status [5]. Instead, a holistic approach that includes body composition analysis, metabolic biomarkers, and social determinants of health is recommended for more accurate and equitable risk stratification.
In conclusion, while BMI remains a practical tool for population-level surveillance, its limitations necessitate the integration of alternative and complementary measures of adiposity. Anthropometric tools like WHtR and BRI, advanced body composition methods such as DEXA and MRI, and metabolic biomarkers including bBMI and VAI offer more precise, individualized assessments of health risk. The future of obesity assessment lies in multimodal approaches that move beyond BMI to capture the complexity of metabolic health across diverse populations.
Cultural and Historical Critiques of BMI
The adoption of Body Mass Index (BMI) as a global health standard is deeply rooted in Western scientific traditions and cultural ideologies, raising significant critiques from anthropological, historical, and social justice perspectives. While BMI is widely used as a public health tool, its origins, universal application, and role in shaping perceptions of health and morality reflect broader patterns of racialization, colonialism, and cultural bias. These critiques challenge the notion that BMI is a neutral or objective measure, instead positioning it as a socially constructed metric that reinforces Western ideals of body size and health.
Historical Development and Western-Centric Foundations
BMI originated in the 19th century as the "Quetelet Index," developed by Belgian mathematician Adolphe Quetelet as part of his work in "social physics," a field aimed at identifying statistical norms in human populations [132]. Quetelet sought to define the "average man" (l'homme moyen), a statistical ideal representing societal normality [133]. His work was not intended for clinical use but rather to quantify human variation and identify deviations from the norm, reflecting Enlightenment-era efforts to apply mathematical order to social life [134]. The index remained a demographic and actuarial tool until the mid-20th century, when it was reinterpreted as a health indicator.
The modern use of BMI was popularized by American physiologist Ancel Keys in the 1970s, who argued it was a simple, reliable proxy for body fat [135]. The standardization of BMI categories—overweight (25–29.9) and obesity (≥30)—was formalized by the World Health Organization (WHO) in 1997, when it declared obesity a global epidemic [136]. This classification system was largely based on data from white, middle-class populations in the United States and Europe, and its global dissemination reflects the export of Western biomedical norms [137].
Embedded Cultural Assumptions and Racial Hierarchies
The widespread use of BMI reflects deeper cultural assumptions about body size, morality, and racial superiority. Scholars have documented how ideals of thinness in Western societies became intertwined with racial and class hierarchies, particularly during the 19th and early 20th centuries [87]. Fatness was increasingly pathologized and associated with laziness, lack of self-control, and moral failing—traits that were racially coded and disproportionately attributed to non-white and lower-class populations [139].
Sabrina Strings has argued that BMI functions as a tool that "fetishizes white embodiment" by reinforcing a narrow, racially specific ideal of health and attractiveness [87]. The index's origins and validation relied predominantly on data from white males, leading to its misapplication across diverse populations [137]. For example, research shows that South Asian and Black African populations often have higher body fat percentages at lower BMIs than white individuals, increasing their risk of metabolic disease at BMI levels classified as "normal" [53]. Conversely, some Polynesian populations exhibit lower disease risk at higher BMIs, challenging the universality of BMI thresholds [143].
Global Imposition and Colonial Legacies
The global dissemination of BMI exemplifies broader patterns of medical and scientific colonialism. Western health metrics, developed in high-income countries, are often exported to low- and middle-income nations without adaptation to local contexts, reinforcing neocolonial power dynamics in global health [144]. This imposition marginalizes indigenous body size norms and health practices, such as the cultural valorization of larger bodies in many African, Pacific Islander, and Caribbean societies, where body fat has historically been associated with prosperity, fertility, and social status [88].
The uncritical use of BMI in international health programs risks misclassifying millions of people, leading to inappropriate interventions and reinforcing stigma. For instance, WHO and national guidelines now recommend lower BMI cutoffs for overweight and obesity in Asian populations due to higher metabolic risks at lower weights, acknowledging the inadequacy of a one-size-fits-all approach [53]. However, such adjustments remain exceptions rather than the rule.
Cultural Perceptions of Body Size and Health
Cultural norms around body size vary widely and often contradict Western thinness ideals. Among the Yorùbá of Nigeria, body size is not inherently linked to moral or health judgments, reflecting a neutral or even positive valuation of weight [89]. In Ghana, women frequently describe the ideal body as “not too skinny, but not too fat”—a preference for a fuller, curvier figure that signifies health, fertility, and prosperity [148]. Similarly, in the Eastern Mediterranean region, cultural ideals often favor fuller figures, and obesity may be seen as a sign of affluence and well-being [149].
In contrast, Western biomedical discourse often equates fatness with disease, leading to the medicalization of diverse body types and the imposition of Western aesthetic norms globally through public health campaigns and media [88]. This cultural imperialism in health metrics can erode local body ideals and contribute to body dissatisfaction, particularly in urbanizing and globalizing contexts [90].
Toward Culturally Responsive and Anti-Stigma Approaches
In response to these critiques, there is growing support for ethnicity-specific BMI thresholds and more holistic assessments of health. The American Diabetes Association and health authorities in the UK and Singapore have already adopted lower BMI cutoffs for defining obesity in South Asian and East Asian populations [152][53]. Some researchers advocate for replacing BMI with direct measures of body composition, such as waist-to-hip ratio, DXA, or metabolic markers, particularly in diverse clinical settings [154].
Anthropologists further call for a shift from individualized, biomedical models to structural and cultural understandings of health that consider the social determinants of body size, including food insecurity, environmental stress, and historical trauma [155]. Training healthcare providers in cultural humility and implicit bias reduction is essential to creating environments where all patients, regardless of body size or background, receive equitable and compassionate care [69].
The historical trajectory of BMI—from a 19th-century statistical construct to a cornerstone of global health—reveals how scientific tools are embedded in cultural, racial, and political contexts. Its adoption reflects not only the practical appeal of a simple, scalable metric but also the enduring influence of Western ideals that equate thinness with health, morality, and racial superiority. As critiques mount over its limitations and biases, there is growing recognition of the need to decolonize health metrics and develop culturally responsive, biologically accurate alternatives that reflect human diversity [157].