BMI, Body Composition, and Longevity: Rethinking the Metrics of Health

Background

Quetelet’s index, the original name for BMI, was developed by Lambert Adolphe Jacques Quetelet. There had previously been ongoing discussions surrounding the classification of ideal body weight in humans. The Metropolitan Life Insurance tables for ideal body weight for men and women relied upon separate reference tables and provided body weight targets in one-inch height increments across three different frame sizes [1], or somatotypes [2]. 

Although Quetelet developed and published his index in 1835, it was not widely used until the 1970s, when Reubin Andres, Clinical Director of the National Institute on Aging, investigated actuarial data on roughly 4,200,000 life insurance policies along with mortality ratios. Andres concluded that Quetelet’s index was superior and less complicated than tables containing reference weights [1]. Of note, major advantages of Quetelet’s index, hereafter referred to as BMI, were that there was a U- or J-shaped curve associated with BMI values and, more importantly, the BMI range associated with minimal mortality was the same for men and women [3]. 

BMI, calculated as weight in kilograms divided by height in meters squared, has been used for decades in modern medicine and research. BMI is the international standard for the classification of health based on an individual’s height and weight [4]. Large-scale studies and meta-analyses have found strong associations between BMI and all-cause mortality; most have described a U-shaped association with minimum mortality in the healthy (20–25 kg/m^2) BMI range [1,5,6,7,8]. However, the nuances of this relationship become more complex upon closer examination. 

How Does BMI Relate to Human Longevity?

BMI Association with All-cause and Cause-specific Mortality 

In 2018, the largest study ever conducted on the association between BMI and mortality—evaluating over 3.6 million people in the United Kingdom—was published in The Lancet [5]. The research team concluded that BMI has a J-shaped relationship with all-cause mortality (Figure 1) and many specific causes of death [5]. 

According to study data, compared with individuals classified as healthy weight (BMI 18.5–24.9 kg/m²), life expectancy from age 40 was 3.5 years shorter in obese women (BMI ≥30.0 kg/m²) and 4.2 years shorter in obese men. Similarly, life expectancy was 4.5 years shorter in underweight women (BMI <18.5 kg/m²) and 4.3 years shorter in underweight men. The largest discrepancy in life expectancy, based on all-cause mortality data, was observed between those classified as having a healthy BMI and those with class III obesity (BMI >40 kg/m²), where life expectancy was 7.7 years shorter in women and 9.1 years shorter in men [5].

Associations between BMI and mortality were more robust at younger ages than at older ages (>70 years), where the variance in the data was greater. In accordance with previous research, an overweight BMI was associated with the lowest mortality risk in older individuals, but not in younger individuals  [1, 5, 9, 10].

BMI and all-cause mortality.

Figure 1. BMI and all-cause mortality. Reprinted from “Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK,” by K. Bhaskaran, 2018, The Lancet Diabetes & Endocrinology, Volume 6 (Issue 12), 944 - 953.

Cause-specific mortality (Figure 2) is more nuanced, where certain conditions, diseases, or external causes—such as accidents—present unique risks in relation to BMI. Surprisingly, mortality from injuries and external causes shows an inverse relationship, where lower BMI was associated with a 28% higher risk of mortality (HR, 1.28) [5]. There was minimal change, or a slight 11% decline in mortality (HR, 0.89), at higher BMI (>30 kg/m²), indicating there may be a protective effect of higher BMI in reducing mortality from events such as accidents, falls, or other physical injuries [5]. 

A J-shaped association exists between BMI and mortality attributed to communicable as well as non-communicable diseases where there was a 20-30% increase for every 5 kg/m² above a BMI of 25 kg/m² [11]. There were consistencies among several research datasets indicating that in select cancers [11,12,13], cardiovascular diseases [13,14], and heart failure [15], mortality was disproportionately higher for individuals with BMI >30 kg/m². This is the majority trend; however, there are cause-specific examples where higher BMI may not be associated with greater mortality. For example, neurological disease, aortic aneurysm, prostate cancer, and brain or lung cancer are unaffected—or even positively impacted—by BMI >30 kg/m²  [5].

Select BMI and Cause-specific Mortality Hazard Ratio Graphs

Figure 2. Select BMI and Cause-specific Mortality Hazard Ratio Graphs. Adapted from “Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK,” by K. Bhaskaran, 2018, The Lancet Diabetes & Endocrinology, Volume 6 (Issue 12), 944 - 953.

Hazard ratio comparison for adults who never smoked, BMI > 30 kg/m² versus healthy BMI [5]:

• Uterus Cancer:  272% increase in mortality (HR, 2.72)

• Liver Cancer: 85% Increase in mortality (HR, 1.85)

• Kidney Cancer: 67% Increase in mortality (HR, 1.67)

• Hypertensive Heart Disease: 276% Increase in mortality (HR, 2.76)

• Heart Failure: 77% Increase in mortality (HR, 1.77)

• Diabetes: 79% Increase in mortality (HR, 1.79)

Challenges in Applying BMI Universally

BMI is an effective tool for assessing risk among large groups; however, controversy remains, as its efficacy deteriorates in specific situations or when used to evaluate individuals. Physically active people who are heavily muscled are often misclassified as overweight or even obese based on BMI  [16]. Similarly, certain ethnic groups or populations may require adapted BMI classification [16,17]. There is also a growing body of evidence regarding what has been described as the “BMI paradox” [18].

Consistent healthy habits, including exercise and performance training, produce innumerable adaptations throughout the body at the DNA and protein levels. BMI does not account for the diversity of physiologic, metabolic, and structural changes that result after months or years of consistent training—especially for younger exercising individuals and athletes [19, 20]. Research indicates that BMI does not correlate well with lean mass and cannot be used as a surrogate marker of body composition in athletes [21]. 

In a head-to-head comparison with body fat percentage measures in college-aged individuals, BMI miscategorized athletes as overweight 70–80% of the time; interestingly, even non-athletes were misclassified 40% of the time [22]. Indeed, BMI has been described as masking body composition differences between exercisers and more sedentary individuals [22]. BMI is more suited for classifying health, disease, and mortality risk in large datasets, which represent the general population—primarily non-exercisers.

BMI also does not serve diverse populations equally, as it was primarily developed based on European body types and does not account for variations in stature, muscle mass, bone density, or fat distribution across ethnic groups [17]. For example, Asian populations tend to have higher body fat percentages at lower BMIs [23, 24], leading to underestimated health risks, while Black populations often have higher lean mass at the same BMI [25], potentially overestimating obesity-related risks. 

A World Health Organization (WHO) expert consultation panel recommended a lower BMI cutoff for obesity in Asian people of ≥27.5 kg/m² instead of ≥30.0 kg/m², whereas recommended cutoffs as low as ≥25.0 kg/m² can be found in the literature [26,27]. Despite BMI’s origin as a simple universal index for classifying health based upon height and weight in males and females, current evidence reveals BMI may not be an ideal single metric for determining longevity, as it fails to capture the complex interplay between genetics, body composition, and metabolic health across diverse populations. 

Recent meta-analyses have raised questions about the so-called “obesity paradox,” wherein higher BMI appears to confer a survival advantage in older adults [28] and in populations with certain chronic diseases [29]. The obesity paradox may partially reflect BMI’s inability to account for metabolic health, body composition, and physical fitness, which can lead to inaccurate predictions about health risk and mortality. 

In fact, some researchers argue that the term "obesity paradox" is really a product of misclassification and should instead be reframed as the “BMI paradox” [18,29]. Nevertheless, BMI is still heavily relied upon to classify health status, guide medication management, and inform healthcare decision-making.

For example, with a BMI of >30 kg/m², and without known blood glucose or blood pressure measurements, a clinician or exercise physiologist may consider those metrics as risk factors outside the healthy range—but not when BMI is <30 kg/m² [30]. 

In other situations, if your BMI is under 30 kg/m², you may not be offered medications or treatments, or your coverage could be limited—even withheld [31]—which potentially creates a disparity for those groups or individuals who have lower BMI but higher body fat percentage, and therefore might benefit from treatment even when their BMI is below the traditional obesity threshold.

BMI alone fails to differentiate between fat mass and lean mass, which play distinct roles in longevity [32,33]. For example, older adults with higher lean body mass but an elevated BMI due to muscle rather than fat often have better health and longevity outcomes  [32,33]. Conversely, individuals with normal BMI but high visceral fat—a condition sometimes referred to as “normal-weight obesity” (NWO)—may have a significantly elevated risk of metabolic disorders, morbidity, and early mortality [31]. This discrepancy further underscores the limitations of BMI as a standalone marker for predicting longevity.

Recent research appears to be reaching a consensus, highlighting the need for a shift toward more representative models of health and longevity. The American Medical Association adopted a new policy in 2023 supporting the removal of BMI as a standard measure in medicine, in large part because of heterogeneity across racial/ethnic groups, sexes, genders, and age groups [34]. The Opportunity for a More Representative Model Amidst a New Era of Weight Loss Medications

The time for a reliable and accessible metric that can act as a more suitable proxy for body composition assessments is now. The landscape of obesity management is undergoing a seismic shift with the arrival and success of GLP-1 receptor agonists (e.g., semaglutide and tirzepatide). These medications have demonstrated significant efficacy in helping people reduce body weight by 10–25% in one year, in addition to improvements in metabolic health markers [35,36]. 

These drugs also highlight limitations of BMI as a measure of health, in that it does not provide detailed evidence pertaining to changes in body composition, including lean mass—both of which have become recent points of emphasis in the context of GLP-1–induced weight loss [37,38,39], particularly sustainable weight loss [40].

These data heighten concerns about muscle preservation, long-term metabolic health, and potential for weight regain, since lean mass is tightly linked with resting metabolic rate (RMR) [43]. A decrease in body mass contributes to a lower metabolic rate because muscle is more metabolically active than fat. Each pound of muscle mass lost reduces RMR by approximately 6 kcal/day, whereas fat mass loss reduces RMR by just under 2 kcal/day [44]. Greater decreases in RMR associated with loss of lean mass make weight loss maintenance significantly more challenging [45].

Unfortunately, unlike fat mass—which is often replenished after meaningful weight loss, within 12 months—lean mass lost when total body weight is reduced is rarely rebuilt. Muscle building, or rebuilding, is a slow, laborious process requiring proper nutrition and exercise stimulus, especially in adults or older individuals. Research indicates that in ideal situations, where nutrition and exercise are tightly controlled, muscle mass gain occurs at a rate of 1–2 pounds per month [46,47]. 

According to previous research in which muscle cross-sectional area was measured, muscle mass increases are indeed slow, reported as 0.31 lbs over an 11-week intervention [48]. Muscle is considered one of the most important—if not the most important—organs of longevity [49], yet BMI does not adequately differentiate lean mass, limiting its utility for tracking the maintenance and growth of healthy muscle tissue as a critical component of human longevity.

Among pharmaceutical interventions, SGLT-2 inhibitors have emerged as potentially powerful tools for enhancing metabolic health without compromising muscle mass. These medications work by inhibiting glucose reabsorption in the kidneys, promoting glycosuria (glucose excretion in urine) and thereby lowering blood sugar levels. SGLT2 inhibitors work by blocking the SGLT2 protein in the kidneys responsible for reabsorbing glucose from the urine back into the bloodstream. 

By inhibiting SGLT2, these medications increase the excretion of glucose through the urine, effectively lowering blood glucose levels – Notably, it has been estimated that SGLT2 inhibitors can result in up to 70-90 grams of glucose being excreted in your urine per day [96], which is equivalent to the sugar content of 24 oz of Coca Cola. However, their benefits extend far beyond glucose control and weight reduction.

Unlike some weight loss interventions, SGLT-2 inhibitors do not disproportionately affect lean body mass. This preservation is in contrast to GLP-1 receptor agonists, which can lead to increased caloric intake reductions that reduce total protein intake, potentially resulting in muscle wasting. SGLT-2 inhibitors do not suppress appetite and therefore lead to a reduction in protein intake. By not reducing amino acid intake, SGLT-2 inhibitor therapies preserve lean muscle mass—a critical component of metabolic and physical health. This preservation helps sustain long-term energy balance and systemic resilience by maintaining muscle synthesis and repair.

The SUSTAIN 8 trial, published in Diabetologia, offers valuable insights into the comparative effects of the SGLT-2 inhibitor Canagliflozin (300 mg) and the GLP-1 receptor agonist Semaglutide (1.0 mg) over 52 weeks in patients with uncontrolled type 2 diabetes. Both therapies produced significant improvements in HbA1c and body weight, with Semaglutide achieving a slightly greater reduction in HbA1c (-1.5%) compared to Canagliflozin (-1.0%) and superior weight loss (-5.3 kg vs. -4.2 kg, respectively). [96] HbA1c reflects long-term blood glucose control, serving as a key indicator of diabetes management and risk reduction for complications. Similarly, weight loss is a pivotal factor in improving insulin sensitivity and reducing the strain on cardiovascular and metabolic systems. 

The trial’s DXA substudy (dual-energy X-ray absorptiometry) provided detailed insights into body composition changes, revealing notable differences in the quality of weight loss achieved by the two therapies. While both treatments effectively reduced fat mass—with Semaglutide achieving a slightly greater reduction (-3.4 kg vs. -2.6 kg for Canagliflozin)—Canagliflozin emerged as the more favorable option due to its superior ability to preserve lean mass. This preservation is particularly significant because lean mass, especially skeletal muscle, serves as a critical metabolic reserve, supporting glucose buffering and basal metabolic rate. By safeguarding this essential tissue, Canagliflozin may mitigate risks associated with metabolic slowdown and long-term physical decline.

While both groups experienced some absolute loss of lean mass, Canagliflozin preserved a higher proportion of lean mass relative to total weight loss. The greater lean mass loss observed with Semaglutide, although offset by its fat reduction, raises concerns about potential long-term metabolic consequences. As we've stressed, the loss of lean mass can contribute to a decline in basal metabolic rate, a phenomenon often referred to as "metabolic slowdown," which may predispose individuals to weight regain. [96]

While SGLT2 inhibitors represent a potential muscle-sparing alternative to GLP-1 receptor agonists, the meteoric rise of these medications presents an opportunity to refine how we assess individual health beyond BMI. Accessible alternatives may exist that provide better markers for lean mass and fat mass—metrics that correlate more closely with metabolic health improvements and more nuanced longevity benefits. An alternative index, or a complementary combination of metrics, may enable individuals and healthcare providers to ensure that weight loss efforts prioritize functional health gains and longevity rather than nonspecific weight loss.

What Alternatives Might Outperform BMI?

Given the growing recognition of BMI's limitations, researchers and clinicians have explored many alternative indices that may provide more information about overall health status and longevity potential. Plenty of alternative measurements and novel indices may be used in place of, or in addition to, BMI to improve predictive accuracy for determining obesity, disease risk, and healthspan at the individual level. 

Obesity, a state of excessive accumulation of body fat, is an important risk factor for multiple chronic pathologies including diabetes, coronary artery disease, hypertension and certain types of cancer [50]. The definition of obesity has changed over the last century. Currently, BMI ≥30 kg/m² is the criterion used to define obesity [1]. However, many options exist that have the potential to work synergistically or in place of BMI.

One large reason BMI remains the international standard is the ease with which it can be calculated. BMI requires only weight and height. Ten viable alternatives to BMI are presented in rank order based upon strength of research evidence and accessibility factors pertaining to equipment, measurement, and calculations (Table 1). In addition to the 10 alternatives presented below, there are other validated metrics described throughout the research literature. In an attempt to focus our analysis on metrics that share desirable simplicity and accessibility traits, like BMI, we have excluded indices that rely upon invasive procedures or blood biomarkers in this review.

Alternative Metrics:

1. Waist Circumference (WC)

WC = Direct measurement of waist circumference (inches or centimeters)

Threshold: Women = 35 inches (≥88 cm) | Men 40 inches (≥102 cm) in men

Waist circumference is a superior metric to BMI because it serves as a validated proxy for measuring central adiposity [52]. Unlike BMI, which fails to distinguish between fat mass and muscle mass, waist circumference is a surrogate for visceral fat, a key driver of conditions like type 2 diabetes, cardiovascular disease, and metabolic syndrome [53]. Studies have shown that individuals with excess abdominal fat (and subsequent higher waist circumference) are at a higher risk for early mortality, regardless of their BMI category [52,53,54,55,56]. Waist circumference is a simple, cost-effective measure that provides better predictive power for health outcomes than BMI alone, in addition to being more appropriate in diverse ethnic groups [55].

2. Waist to Height Ratio (WHtR) 

WHtR = Waist circumference divided by height (inches or centimeters)

Threshold: 0.5 can be used as a universal cut-off point

Waist-to-height ratio is a superior predictor of cardiometabolic and mortality risk in head-to-head comparison with BMI [56,57,58]. WHtR >0.5 reflects an increased accumulation of visceral fat, which is strongly linked to insulin resistance, cardiovascular disease, and reduced lifespan [57,58]. WHtR, in particular, has gained recognition as a simple yet effective tool for assessing metabolic health [57,58] and longevity outcomes [59] above and beyond BMI or other waist circumference indices. In addition, the consensus WHtR cutoff of 0.5 can be used in different sex and ethnic groups and is generally accepted as a universal cutoff for central obesity in children (aged ≥6 years) through adulthood [58].

3. Waist-to-hip ratio (WHR) 

WHR = Waist circumference divided by hip circumference

Threshold: Women = 0.85 | Men = 0.90,  WHR >1.0 for either sex signals higher risk

Unlike BMI, which only considers total body weight relative to height, WHR distinguishes between visceral fat (around the abdomen) and subcutaneous fat (around the hips and thighs) [60]. Visceral fat is more closely linked to insulin resistance, cardiovascular disease, and other metabolic disorders, making waist circumference indices—including WHR—a better indicator of overall health risk [57]. 

Data in Australian adults aged 20 to 69 years suggested that WHR [60] is a superior metric to BMI because it was a stronger predictor of all-cause mortality [61]. WHR also provides a clearer picture of body fat distribution, which is a better predictor of metabolic health and disease risk than nonspecific body mass  [57,60]. WHR maintains utility across different body types and muscle compositions, whereas BMI often misclassifies heavily muscled individuals as overweight or obese [22]. Interestingly, one study reported that body fat percentage measurements did not add to the prediction of mortality or CVD risk in middle-aged and older adults compared with the synergistic combination of BMI and WHR  [62]. 

4. Body Fat Percentage (BF%) 

BF% = Measure (or estimate) of fat mass divided by total body mass

Threshold: Women = 42% | Men = 30%

Unlike BMI, which assumes a correlation between total body weight relative to height and fatness, body fat percentage provides a direct measure of adiposity. Methods such as dual-energy X-ray absorptiometry (DEXA), bioelectrical impedance analysis (BIA), and skinfold calipers can estimate BF% with varying accuracy [63].

Dual-energy X-ray absorptiometry (DEXA) is widely regarded as the gold standard for body composition assessment, while BIA is more accessible, as it is built into many affordable, commercially available body weight scales [64]. Studies suggest that excessive body fat—particularly visceral fat—is more strongly associated with metabolic disease and mortality risk than BMI or total BF% [65].

A range of body fat percentage (BF%) thresholds has been used to classify obesity. Published thresholds vary between 20–25% for men and 30–37% for women [66]. In 2024, researchers at the U.S. Army Research Institute of Environmental Medicine defined clinically relevant "overweight" as 25% BF for men and 36% BF for women, and "obesity" as 30% and 42%, respectively [67].

Body fat percentage is a direct measure; however, strong consensus on predicting health risk and mortality based on threshold values will require further research [66,67]. Accessibility remains a challenge, as cost, specialized equipment, and training required to accurately measure it (as in the case of skinfold measurements) impacts its overall utility as a longevity metric.

5. Relative Fat Mass (RFM) 

RFM = 64 −(20 × height/waist circumference) + (12 × sex) | Female = 1 , Male = 0

Threshold: Women = 40% | Men = 30% (Meant to fit with BF% classification)

The relative fat mass (RFM) formula was first published in 2018 by researchers Woolcott and Bergman, using data from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2004 [68]. RFM is positioned as an alternative to BMI that acts as a proxy for body fat percentage [68,69]. 

Compared with BMI, RFM better predicted whole-body fat percentage, measured by dual-energy X-ray absorptiometry (DXA), among women and men [68]. RFM showed better accuracy than BMI and had fewer false-negative cases of body fat–defined obesity among women and men [70]. When coupled with additional metrics such as predicted body composition, RFM outperforms BMI and waist circumference (WC) measurement [71]. 

RFM maintains accuracy among diverse population samples, demonstrating reduced total obesity misclassification among women and men of different ethnicities, including Mexican-Americans, European-Americans, and African-Americans [68]. RFM is an accessible alternative to BMI and has better performance across diverse population groups. Continued research will be important to strengthen the evidence base for this promising BMI alternative.

6. Body Roundness Index (BRI) 

BRI = 364.2 − 365.5 × {1 − [(WC/2π)/(0.5 × height)]²}^0.5 

Threshold: Target BRI range = 3.4 - 5.45

The Body Roundness Index (BRI) was developed in 2013 by researcher Diana Thomas and colleagues, who introduced it as a new anthropometric measure that combines height and waist circumference to better predict body fat distribution and overall body shape  [72]. Compared with BMI, BRI essentially quantifies the human body as a two-dimensional elliptical shape [72]. BRI also includes a waist circumference measurement and therefore incorporates a measure of visceral adiposity. 

BRI data indicates it may be a good predictor of hypertension [73], non-alcoholic fatty liver disease [74], and metabolic syndrome in both men and women of diverse nationalities and ethnic groups [75]. In 2024, BRI was evaluated in relation to all-cause mortality; like BMI, there was a U-shaped relationship between BRI and mortality among adults  [76,77]. BRI is another popular index that presents a viable alternative or adjunct to BMI for assessing health, disease risk, and longevity.

7. Body Shape Index (ABSI)

ABSI = WC/(BMI^2/3 × Height^1/2) 

Threshold: ABSI_z-score < -0.868 signals very low risk

The Body Shape Index (ABSI) was developed using data from the United States National Health and Nutrition Examination Survey (NHANES) conducted between 1999 and 2004. Introduced in 2012, ABSI was designed to quantify abdominal obesity and better predict disease risk [78]. 

ABSI is a metric used to assess body fat distribution and the risk of chronic diseases. It has been associated with all-cause mortality, metabolic syndrome, diabetes, and hypertension [73, 79, 80, 81]. A 2020 comparison of twelve waist circumference indices concluded that ABSI was the best complementary pairing with BMI for mortality risk stratification [82]. 

Although ABSI has demonstrated positive associations with morbidity and mortality, the equation and interpretation of both the raw score and z-score lacks simplicity relative to other alternative indices.

8. Fat Mass Index (FMI) 

FMI = fat mass divided by height²

Threshold: FMI < 7.3 kg/m²

Fat mass index was first introduced in 1990 [83]. FMI adjusts fat mass relative to height and provides a more individualized assessment of adiposity compared to BMI [84,85]. It is considered superior to using body fat percentage alone because it accounts for height, offering a more accurate representation of fat distribution relative to body size [83,86]. 

The overall utility and accessibility of FMI are limited by the need for a body composition or fat mass measurement, followed by an additional calculation to produce the index value. FMI has a J-shaped association with mortality based on prospective study data for individuals under 65 years old, while a positive linear relationship is observed for those aged 65 and older [87]. Prospective cohort data showed a 50% increase in mortality risk, based on hazard ratios, as FMI increased from low to high [87].

9. Lean Mass Index (LMI) 

LMI = lean mass divided by height²

Threshold: LMI > 16.0 kg/m²

Lean Mass Index (LMI), also known as Fat-Free Mass Index (FFMI), was introduced alongside FMI in 1990 [83]. LMI has a reverse J-shaped curve associated with mortality based on prospective study data for individuals under 65 years old, while a negative linear relationship is observed for individuals aged 65 and older [87]. According to results from seven prospective cohort studies, high LMI was associated with a 30% reduction in mortality risk [87]. A 2010 research study demonstrated higher lean mass index was associated with better survival than low BMI suggesting LMI may have additional value as an index that correlates with longevity in select population subgroups [88].

10. Body Adiposity Index (BAI) 

BAI = ((hip circumference)/((height)(1.5))-18)) 

Threshold: Women = 40% | Men = 30% (Meant to fit with BF% classification)

The Body Adiposity Index (BAI) was developed and first published in 2011 [89]. BAI may be used to reflect percent body fat for adult women and men [89,90]. A 2018 systematic review concluded that BAI is not recommended as a marker for body fatness in adults [91] and further studies are needed to evaluate BAI’s association with adverse health effects [92]. BAI may function as a measure of overall adiposity, but it is unlikely to outperform BMI, as some data reveal it does not serve different ethnicities effectively [93, 94].

Ranked Alternatives to BMI

Table 1. Ranked Alternatives to BMI. Each category was rated as 0, 1, or 2. Overall score is the sum total of all four categories. WC = Waist Circumference, WHtR = Waist to Height Ratio, WHR = Waist to Hip Ratio, BF% = Body Fat Percentage, RFM = Relative Fat Mass, BRI = Body Roundness Index, ABSI = Body Shape Index, BAI = Body Adiposity Index, FMI = Fat Mass Index, LMI = Lean Mass Index

Our analysis of BMI alternative indices produced several key findings. First, six out of ten of the alternative indices evaluated rely on a waist circumference measurement. Indices that incorporate waist circumference clearly meet the criteria for simplicity and accessibility. They also offer added value, as waist circumference is considered a reliable proxy for visceral adiposity—a known risk factor for chronic age-related diseases, which are leading causes of death in humans.

Another key finding, based on our review of the literature, is that the majority of these indices can be calculated using simple inputs and just one or two basic tools. The first is a flexible, non-elastic tape measure—or even a piece of string or rope—to measure circumference and height. The second is a body weight scale. Like BMI, which is positioned as the international standard in part due to its simplicity, accessibility, and robust epidemiological data, most of the top-performing indices in the literature also meet these criteria. Many continue to amass supporting data, often demonstrating superior performance in head-to-head comparisons, or functioning as complementary additions to BMI for predicting health and longevity outcomes.

Overall, considering the ease with which indices such as WC, WHtR, and RFM can be calculated from waist circumference and height measurements, leveraging several indices in addition to BMI might provide a more comprehensive picture of health.

In January of this year, 2025, the International Journal of Surgery published a cross-sectional study in which clinician-researchers utilized nine different obesity-related indices to predict gallstone risk in over 3,654 participants from the National Health and Nutrition Examination Survey (NHANES) dataset for the 2017–2020 cycle [95]. Dr. Wu and colleagues used weighted logistic regression analyses to determine which indices were most predictive of gallstones [95]. 

With a multitude of reliable alternative indices available, it is time to consider complementing—or even replacing—isolated BMI with a strategic blend or constellation of accessible, powerful alternative indices for classification of individual health and longevity.

Conclusion

Persistent reliance on BMI as a primary health classification metric has led to oversimplifications that do not adequately serve individuals. While BMI remains useful for large-scale epidemiological studies, its limitations make it inadequate for personalizing health recommendations, monitoring, and predicting longevity with precision.

A shift toward models that measure or estimate body composition, abdominal adiposity, and/or muscle mass provides enhanced information for assessing healthspan and longevity. As new weight loss medications reshape how we approach obesity treatment, this transition offers a timely opportunity to clearly define what successful and sustainable weight management truly looks like. In the pursuit of longevity, precision matters, and it's time standard health metrics reflect that reality.

BMI is one of many important metrics that can be used to determine health and longevity. As such it should be paired with alternative indices as part of a more comprehensive quantification of longevity markers that are accessible, predictive, and appropriate for diverse populations as the global focus on improving longevity continue to grow.