The body mass index (BMI) is a widely used metric for categorizing individuals based on their weight relative to their height. BMI is calculated by dividing a person's weight in kilograms by the square of their height in meters. Despite its widespread use, BMI has several limitations affecting its accuracy and utility in clinical and research settings. Rubino and colleagues (2025) have proposed a new classification system based on how excess body fat impacts an individual's health. We review BMI alternatives that may better operationalize adiposity, including advanced imaging technology and laboratory markers.
What is the Body Mass Index (BMI)?
Body mass index (BMI) is calculated as weight in kilograms divided by height in meters squared (kg/m²). The World Health Organization (WHO) classifies BMI into several categories: underweight (BMI < 18.5), normal weight (18.5 ≤ BMI < 24.9), overweight (25 ≤ BMI < 29.9), and obesity (BMI ≥ 30) (World Health Organization, 2020). The CDC provides an adult BMI calculator. BMI graphic © Elnur/Shutterstock.com.
Strengths of BMI
BMI is popular due to its simplicity and cost-effectiveness. It is a straightforward measure that can be easily calculated without requiring specialized equipment, making it accessible for large-scale epidemiological studies and routine clinical assessments (Daniels, 2009). It provides a quick, non-invasive method to estimate potential health risks, such as cardiovascular diseases and type 2 diabetes, based on body weight. Additionally, BMI has been instrumental in tracking the global obesity epidemic, providing a standardized measure that can be used internationally (Bray, 2023).
Limitations of BMI
However, BMI has significant limitations that undermine its effectiveness as a measure of body fat and health risk. One of the primary criticisms is that BMI does not account for body fat distribution. This is crucial because fat distribution, particularly visceral fat, predicts metabolic and cardiovascular risks more accurately than overall body fat (Bray, 2023; Gonzalez et al., 2017). For instance, individuals with the same BMI can have vastly different health outcomes depending on whether their fat is distributed around the abdomen or the hips (Després, 2012).
Another limitation is that BMI does not differentiate between muscle and fat mass. This can lead to misclassification, particularly in individuals with high muscle mass, such as athletes, who may be categorized as overweight or obese despite having low body fat (Després, 2012; et al., 1986; Romero-Corral et al., 2008). Conversely, older adults may have a normal BMI but high body fat due to muscle loss, a condition known as sarcopenic obesity, which BMI fails to identify (Gonzalez et al., 2017). BMI does not adjust for body composition differences associated with gender (Prentice & Jebb, 2001).
BMI also fails to provide insights into obesity's heterogeneity. It does not consider genetic, metabolic, physiological, or psychological factors that contribute to obesity, limiting its utility in understanding the condition's complex nature (Bray, 2023). This lack of specificity can lead to inadequate or inappropriate treatment plans for individuals with different obesity phenotypes (Müller et al., 2016).
Measurement Errors and Misclassification
The accuracy of BMI is further compromised when based on self-reported height and weight, which are often inaccurate. Studies have shown that self-reported BMI tends to underestimate actual BMI, leading to misclassifying individuals into incorrect weight categories (Gosse, 2014). This misclassification can skew research findings and public health policies, as the true prevalence of obesity may be underreported.
A New Approach to Defining Obesity
The traditional reliance on Body Mass Index (BMI) to diagnose obesity is undergoing a transformative shift as researchers advocate for a more nuanced understanding of the condition. This evolution reflects an acknowledgment of obesity’s complexity, extending far beyond weight and height ratios to encompass how excess body fat, or adiposity, impacts an individual's health. The newly proposed approach introduces a classification system that distinguishes between two stages of obesity—preclinical and clinical—each with unique diagnostic and therapeutic implications.
At its core, the proposed definition shifts the focus from simplistic metrics to the physiological and functional consequences of excess fat (Rubino et al., 2025). In this model, preclinical obesity is characterized by the presence of excessive fat that does not yet impair organ function or cause significant health issues. Individuals in this category may appear asymptomatic but face elevated risks of developing conditions such as diabetes, cardiovascular disease, or metabolic syndrome. Early identification of preclinical obesity provides an opportunity for preventive measures, such as lifestyle modifications, to halt or reverse disease progression.
In contrast, clinical obesity is defined by the harmful effects of excessive fat on organs and tissues, leading to noticeable health problems or limitations in daily activities. For example, individuals in this category may experience conditions like fatty liver disease, sleep apnea, or reduced mobility due to joint stress. The distinction between these two stages is crucial for tailoring interventions, as clinical obesity often requires more intensive treatments, including pharmacological or surgical options, alongside lifestyle changes.
One of the most innovative aspects of this approach is its integration of multiple diagnostic tools and biomarkers to assess adiposity comprehensively. Traditional BMI-based classifications fail to account for variables such as fat distribution, muscle mass, and demographic differences. The new framework proposes combining BMI with other indicators, including waist circumference, waist-to-hip ratio, and waist-to-height ratio, which are more closely correlated with health risks. For instance, central adiposity—fat stored around the abdomen—has been consistently linked to higher risks of cardiovascular and metabolic diseases, making it a critical factor in the revised diagnostic criteria.
Another cornerstone of this approach is its recognition of the multifactorial nature of obesity. By incorporating data from laboratory tests, medical history, and daily activity assessments, clinicians can better understand how excess fat affects an individual's health. Biomarkers, such as elevated levels of leptin or inflammatory markers like C-reactive protein (CRP), can provide insights into the metabolic and systemic effects of adiposity. These indicators enable a more precise diagnosis, guiding targeted interventions that address not only weight but also its underlying causes and consequences.
Personalization is a key principle underpinning this new framework. Age, gender, and ethnicity significantly influence how adiposity impacts health, and the revised criteria aim to account for these differences. For instance, research shows that individuals of Asian descent often develop obesity-related health risks at lower BMI thresholds than those of European descent, necessitating tailored diagnostic benchmarks. Similarly, older adults may experience a loss of muscle mass (sarcopenia) alongside increased fat accumulation, complicating BMI-based assessments. By integrating personalized factors, the new approach ensures more equitable and accurate diagnoses across diverse populations.
BMI Alternatives
In light of BMI’s limitations, researchers and clinicians advocate for alternative methods to assess adiposity—defined as the amount and distribution of body fat. These methods aim to provide a more accurate picture of an individual’s health by accounting for fat quantity, location, and its impact on physiological functions.
One commonly used method is waist circumference (WC) measurement, which serves as a proxy for central adiposity. Studies have shown that fat accumulation around the abdomen is a significant predictor of metabolic syndrome and cardiovascular diseases. Measuring waist circumference is simple and cost-effective, making it a practical tool for clinical and public health settings. However, it provides limited information about overall body fat distribution and cannot differentiate between subcutaneous and visceral fat.
Waist-to-hip ratio and waist-to-height ratio offer further refinements by considering the proportionality of abdominal fat to other body dimensions.
Waist-to-hip ratio (WHR), calculated as the ratio of waist circumference to hip circumference, helps assess the relative distribution of fat between the abdomen and lower body.
Waist-to-height ratio (WHtR), on the other hand, compares waist circumference to an individual's height. Evidence suggests it may be a stronger predictor of health risks than BMI alone.
Body Roundness Index
The Body Roundness Index (BRI) is a relatively new anthropometric measure to predict body fat and visceral adipose tissue. It has been proposed as a potentially superior alternative to traditional indices like the BMI, WC, WHR, and WHtR for predicting various health conditions, including metabolic syndrome (MetS) and hypertension. WebFCE provides a free adult BRI calculator.
The BRI runs from 1-20 (1 = narrow, 20 = more rounded). Zhang and colleagues (2024) found individuals with BRI scores of 6.9 or higher — representing the roundest body shapes — had the greatest risk of death from cancer, heart disease, and other illnesses. Their overall risk of mortality was nearly 50 percent higher than those with BRI scores between 4.5 and 5.5, which fell within the sample's midrange. Meanwhile, those with BRI scores between 5.46 and 6.9 had a 25 percent higher risk than the midrange group.
Those with the least round body shapes were also at increased risk. People with BRI scores below 3.41 faced a 25 percent higher mortality risk compared to those in the midrange, according to the study.
Strengths
Unlike the BMI, the BRI includes waist circumference, which is a key indicator of central adiposity. This allows for a better assessment of the health risks associated with fat distribution (Nahas, 2019).
One of the primary strengths of the BRI is its ability to predict metabolic syndrome (MetS) and hypertension with a high degree of accuracy (Rico-Martin et al., 2020). According to a systematic review and meta-analysis, the BRI demonstrated a higher area under the curve (AUC) for predicting MetS compared to BMI, Waist-to-Hip Ratio (WHR), A Body Shape Index (ABSI), and Body Adiposity Index (BAI).
This suggests that BRI is a better indicator of MetS than these traditional measures. Additionally, the BRI showed good discriminatory power for MetS in diverse populations, with AUC values greater than 0.7 (Rico-Martin et al., 2020).
Similarly, another study found that the BRI had a higher AUC for predicting hypertension compared to ABSI and was comparable to BMI, WC, and WHtR (Calderón‐García et al., 2021).
This indicates that BRI is a robust predictor of hypertension, making it a valuable tool for early diagnosis and intervention.
Moreover, the BRI has been shown to correlate significantly with various cardiovascular risk factors. For instance, in a study involving South African rural young adults, BRI was significantly correlated with insulin levels, homeostatic model assessment (HOMA)-β, and triglycerides (TG; Nkwana et al., 2021). This further underscores its utility in assessing cardiovascular health.
Limitations
Despite its strengths, the BRI is not without limitations. One of the main drawbacks is that its predictive power for MetS and hypertension is not significantly different from that of WC and WHtR. The differences in AUC values between BRI and these traditional measures were statistically non-significant (Calderón‐García et al., 2021; Rico-Martin et al., 2020). This raises questions about the added value of BRI over more established indices.
Another limitation is the variability in its predictive accuracy across different populations. For example, the pooled AUCs for BRI were higher in non-Chinese populations compared to Chinese populations for all indices (Rico-Martin et al., 2020). This suggests that the BRI may not be universally applicable and require population-specific adjustments.
Additionally, while the BRI is a good predictor of certain health conditions, it is not the best. For instance, WC and WHtR performed best when screening for MetS and hypertension. While BRI is useful, it may not always be the most effective measure (Calderón‐García et al., 2021; Rico-Martin et al., 2020).
Advanced Technologies
Advanced imaging technologies provide the most accurate measurements of adiposity. Dual-energy X-ray absorptiometry (DXA) is a widely used technique that measures fat mass, lean body mass, and bone density. By offering precise data on fat distribution, DXA can distinguish between visceral and subcutaneous fat. Similarly, magnetic resonance imaging (MRI) and computed tomography (CT) scans provide detailed images of fat deposition, making them valuable tools for research and specialized clinical assessments. However, the high cost and accessibility barriers associated with these methods limit their widespread use.
Laboratory markers are also gaining attention as indirect measures of adiposity. Elevated levels of certain hormones, such as leptin and adiponectin, as well as inflammatory markers like C-reactive protein (CRP), can indicate the presence of excess body fat and its metabolic consequences (Eckel, Rubino, & Baur, 2025). Combining these markers with imaging and anthropometric measures can provide a comprehensive view of adiposity and its health implications.
Conclusion
One of the most innovative aspects of Rubin and colleagues' (2025) approach is its integration of multiple diagnostic tools and biomarkers to assess adiposity comprehensively. The revised framework addresses the growing use of pharmacological treatments, such as GLP-1 receptor agonists, which are reshaping obesity management. These medications, including drugs like Ozempic, require precise diagnostic criteria to ensure their effective and cost-efficient use. For example, individuals with preclinical obesity may benefit more from lifestyle interventions, while those with clinical obesity might require medication or surgical options to address severe health risks. This differentiation minimizes unnecessary treatments and optimizes healthcare resources, aligning clinical practice with patients' needs.
Importantly, the proposed approach challenges societal attitudes and stigma surrounding obesity. For decades, obesity has been mischaracterized as a simple issue of willpower, overshadowing its biological, hormonal, and genetic underpinnings. By framing obesity as a condition defined by health impacts rather than appearance, the new definition promotes a more compassionate and science-based perspective. This shift is particularly relevant in healthcare settings, where stigma often deters individuals from seeking care or adhering to treatment plans. Educating healthcare providers and the public about the revised framework could foster more supportive environments, ultimately improving patient outcomes.
Despite its promise, the implementation of this new approach faces challenges. Questions remain about how it will influence diagnosis rates and healthcare policies, as well as its feasibility in resource-limited settings. Critics may also argue that the additional complexity could hinder adoption in clinical practice. However, proponents emphasize that the long-term benefits of a more accurate and equitable system outweigh these initial hurdles. By aligning diagnostic criteria with the intricate realities of obesity, the new framework has the potential to revolutionize care, enhance public health strategies, and reduce the global burden of obesity-related diseases.
Google Illuminate Discussion
Glossary
a body shape index (ABSI): an anthropometric measure that incorporates waist circumference, height, and weight to assess body shape and predict health risks. Unlike BMI, ABSI accounts for waist circumference relative to height and weight, offering insights into the risk of obesity-related conditions, such as cardiovascular disease, independent of body mass.
adiponectin: a protein hormone produced by fat cells that helps regulate glucose levels and fatty acid breakdown.
adiposity: the condition of having excessive body fat.
body adiposity index (BAI): a measure used to estimate an individual’s body fat percentage. BAI is calculated using hip circumference and height, rather than weight, providing an alternative to BMI for assessing body fat without requiring body weight measurements. It is primarily used to evaluate obesity and related health risks.
body fat: the total mass of fat tissue in the body, including both essential fat (necessary for normal bodily functions) and storage fat (fat stored in adipose tissue). Body fat percentage can be used to assess overall health, with too little or too much body fat associated with health risks.
body mass index (BMI): a metric calculated by dividing a person's weight in kilograms by the square of their height in meters (kg/m²). It is used to classify individuals into categories based on their weight relative to height.
body roundness index (BRI): an anthropometric measure designed to predict body fat and visceral adiposity, based on waist circumference and body shape.
C-reactive protein (CRP): a substance produced by the liver in response to inflammation, often used as a biomarker for assessing health risks.
central adiposity: the accumulation of excess fat in the abdominal area. It is often associated with an increased risk of metabolic conditions such as insulin resistance, cardiovascular diseases, and type 2 diabetes. Central adiposity is commonly measured using waist circumference or waist-to-hip ratio.
dual-energy X-ray absorptiometry (DXA): an imaging technique used to measure bone density and body composition, including fat mass and lean mass.
GLP-1 receptor agonists: a class of medications that mimic the hormone glucagon-like peptide-1 to regulate blood sugar and promote weight loss.
homeostatic model assessment (HOMA)-β: a method used to assess pancreatic β-cell function and insulin resistance.
leptin: a hormone secreted by fat cells that helps regulate appetite and energy balance.
metabolic syndrome (MetS): a cluster of conditions, including high blood pressure, high blood sugar, excess body fat around the waist, and abnormal cholesterol levels, increasing the risk of heart disease and diabetes.
obesity phenotypes: classifying individuals based on body fat distribution and associated metabolic conditions. Some common phenotypes include:
metabolically healthy obesity (MHO): individuals who have a high body BMI but do not exhibit typical metabolic disturbances like insulin resistance or inflammation.
metabolically unhealthy obesity (MUO): individuals who have obesity along with metabolic disorders such as insulin resistance, dyslipidemia, or hypertension.
abdominal or central obesity: excess fat in the abdominal region, often assessed using waist circumference or waist-to-hip ratio.
peripheral obesity: the accumulation of fat in areas such as the hips, thighs, and buttocks, which may have different metabolic implications compared to central obesity.
triglycerides (TG): a type of fat (lipid) found in the blood, elevated levels of which can increase the risk of cardiovascular disease.
visceral fat: a specific type of body fat stored within the abdominal cavity, surrounding internal organs such as the liver, pancreas, and intestines. Visceral fat is more metabolically active than subcutaneous fat and is closely linked to conditions like heart disease, type 2 diabetes, and inflammation.
waist circumference (WC): A measurement around the narrowest part of the waist, used to
assess central fat distribution and associated health risks.
waist-to-height ratio (WHtR): the ratio of an individual’s waist circumference to their height, often used to assess health risks related to fat distribution.
waist-to-hip ratio (WHR): a measurement comparing the circumference of the waist to that of the hips, used to assess fat distribution and associated health risks.
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