Primary care physicians routinely calculate Body Mass Index (BMI) to assess health risks, but BMI is widely criticized for its inaccuracy and lack of inclusivity—particularly in accounting for racial and gender differences in body composition. These limitations contribute to health disparities by leading to systematic misclassification: some groups are more likely to be overdiagnosed or underdiagnosed based on flawed BMI assumptions, which can result in unequal treatment, delayed interventions, and poorer health outcomes. This study introduces the In Nova Index, a race-, gender-, and age-specific anthropometric tool developed using NHANES datasets (2015–2016; 2017–2018) and validated against DXA-measured body fat percentage. In Phase 1, InNova models showed strong correlations (r > 0.900) across most subgroups, with particularly high accuracy for Asian men (r = 0.975), Black men (r = 0.985), and Latinx women (r = 0.985). All models outperformed BMI, which showed especially weak correlations for Asian women (r = 0.117) and moderate performance for most other groups. In Phase 2, age was added to reflect physiological changes over time. Age-stratified models further improved predictive power, with updated InNova correlations, which ranged from r = 0.899 to r = 0.995 across all age, race, and gender combinations. BMI, by contrast, ranged from r = 0.117 to 0.721 across these groups. These findings suggest that the InNova Index offers a more accurate, inclusive, and equitable alternative to BMI. With applications in clinical care, public health screening, and population-level risk assessment, the InNova Index has the potential to reduce health disparities and improve outcomes by better capturing the complexity of human body composition.

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