BMI or BRI? How to Assess Your Body Weight – Yale Medicine

Assessment of Metabolic Health Through Body Weight Metrics and Their Alignment with Sustainable Development Goals

Introduction

Body weight serves as a crucial indicator of overall metabolic health, reflecting the body’s efficiency in converting food into energy. Two primary indices, Body Mass Index (BMI) and Body Roundness Index (BRI), are utilized to evaluate whether an individual maintains a healthy weight. These tools assist healthcare professionals in assessing risks related to heart disease, stroke, diabetes, and other chronic illnesses, thereby contributing to the achievement of Sustainable Development Goal (SDG) 3: Good Health and Well-being.

Comparison of BMI and BRI

Although both BMI and BRI measure healthy weight ranges, their methodologies differ significantly:

1. BMI: Calculated using height and weight, BMI does not account for the distribution of weight across the body.
2. BRI: Derived from height and waist circumference, BRI potentially offers a more accurate assessment of whether excess weight is primarily fat or muscle.

Research Findings Supporting BRI

A 2024 study published in JAMA Network Open, involving nearly 33,000 adults over a 19-year period, highlighted the predictive value of BRI. The research demonstrated that individuals with either very low or very high BRI values exhibited the highest mortality risk from all causes. These findings suggest that BRI could be a reliable indicator of unhealthy weight extremes, aligning with SDG 3 by promoting early detection and prevention of health risks.

Limitations of BMI

• Overestimation: BMI may inaccurately classify muscular individuals, such as bodybuilders and professional athletes, as overweight or obese.
• Underestimation: Conversely, individuals with low muscle mass but high fat accumulation, particularly around the waist, may have a healthy BMI but an unhealthy BRI.

For example, an older adult with reduced muscle mass but increased abdominal fat may present a normal BMI yet possess elevated health risks identifiable through BRI. This discrepancy underscores the importance of incorporating multiple assessment tools to enhance diagnostic accuracy.

Consideration of Demographic Variability

BMI does not adjust for factors such as race, age, and gender, which influence fat distribution. Notably:

• Asian populations, older adults, and women tend to have higher body fat percentages at lower BMIs compared to white, younger, or male counterparts.
• The original BMI classification thresholds were established based on data from predominantly non-Hispanic white cohorts, limiting their universal applicability.

These limitations highlight the need for more inclusive and precise measurement approaches, supporting SDG 10: Reduced Inequalities by promoting equitable health assessments across diverse populations.

Recommendations from Medical Authorities

In response to these challenges, the American Medical Association advocates for the use of supplementary body fat measures alongside BMI. Recommended alternatives include:

• Body Adiposity Index
• Relative Fat Mass
• Waist Circumference

This multi-faceted approach aims to improve the accuracy of health risk evaluations, contributing to SDG 3 by enhancing disease prevention strategies.

Validation of BMI Effectiveness

Despite its limitations, BMI remains a valid tool for most individuals. A 2025 study published in JAMA analyzed data from 2,225 adults under 60 years of diverse racial backgrounds. Findings include:

1. 39.7% were classified as obese based on BMI alone.
2. 98% of those classified as obese by BMI also met obesity criteria using secondary measures such as BRI or direct body fat scans.
3. BMI misclassified only 2% of individuals as obese, demonstrating high reliability in most cases.

This evidence supports the continued use of BMI as a primary screening tool while encouraging complementary assessments to address its limitations.

Conclusion

Integrating multiple body composition indices enhances the precision of metabolic health evaluations, directly supporting Sustainable Development Goal 3: Good Health and Well-being. Additionally, recognizing demographic differences in fat distribution aligns with SDG 10: Reduced Inequalities, ensuring equitable healthcare. Ongoing research and adoption of comprehensive assessment tools are essential for advancing global health outcomes and fostering sustainable development.

1. Sustainable Development Goals (SDGs) Addressed or Connected to the Issues Highlighted in the Article

1. SDG 3: Good Health and Well-being
   • The article discusses metabolic health, obesity, and risks for chronic diseases such as heart disease, stroke, and diabetes.
   • It focuses on health metrics (BMI, BRI) that help identify unhealthy weight and associated health risks.
2. SDG 10: Reduced Inequalities
   • The article mentions differences in body fat distribution and BMI accuracy across races, ages, and genders, highlighting health disparities.

2. Specific Targets Under Those SDGs Identified Based on the Article’s Content

1. SDG 3 Targets:
   • Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.
   • Target 3.8: Achieve universal health coverage, including access to quality essential health-care services and access to safe, effective, quality, and affordable essential medicines and vaccines for all.
2. SDG 10 Targets:
   • Target 10.2: Empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status.

3. Indicators Mentioned or Implied in the Article to Measure Progress Towards the Identified Targets

1. Indicators related to SDG 3:
   • Prevalence of obesity and overweight measured by Body Mass Index (BMI) and Body Roundness Index (BRI).
   • Mortality rates from cardiovascular diseases, stroke, and diabetes as linked to unhealthy weight.
   • Use of multiple body fat measurement tools (BMI, BRI, body adiposity index, relative fat mass, waist circumference) to improve diagnosis and treatment.
2. Indicators related to SDG 10:
   • Disaggregation of health data by race, age, and gender to identify disparities in obesity and related health risks.
   • Proportion of population receiving appropriate health assessments that consider demographic differences.

4. Table of SDGs, Targets, and Indicators

Source: yalemedicine.org