# Cross-validation of prediction equations for estimating the body mass index in adults without the use of body weight

## TL;DR

This study developed and validated equations to estimate BMI without using body weight, offering a practical solution for health assessments in resource-limited settings.

## Contribution

The study introduces new BMI prediction equations that do not require body weight measurements.

## Key findings

- Four equations (EM2, EM3, EF2, EF3) showed strong correlations (r > 0.90) for estimating BMI in males and females.
- The equations demonstrated high agreement (Intraclass Correlation Coefficient > 0.96) and substantial Weighted Kappa values (0.658–0.711).
- The proposed equations are safe and effective for BMI estimation when traditional weight measurements are not feasible.

## Abstract

Body Mass Index (BMI) is a widely accepted measure by the World Health Organization for assessing body composition, as it provides critical insights into health risks, life expectancy, and quality of life. However, in resource-limited settings, access to weighing scales is often inadequate, and environmental conditions, such as unstable terrain, may hinder accurate weight measurements. In these contexts, alternative methods for estimating BMI become essential for effective health assessment. This study aimed to develop and validate equations to estimate BMI without relying on body weight, providing a practical tool for nutritional assessment where traditional methods are not feasible.

Adults aged 18 to 59 of both sexes were included. Variables like waist circumference, height, hip circumference, age, and weight were used for equation development and validation. Participants were divided by sex, with regression and validation subgroups for each. Statistical tests included Student’s t-tests, Pearson correlation, Stepwise Regression, Intraclass Correlation Coefficient, Weighted Kappa Coefficient, and Bland-Altman statistics.

The study included 810 adults, with 63% (576) women. No significant differences were found in paired comparisons between regression and validation subgroups for both sexes (p > 0.05). Four equations were proposed for BMI estimation: EM2 and EM3 for males, and EF2 and EF3 for females. All equations showed strong positive correlations (r > 0.90), significant at p < 0.05. Regression analysis revealed R2 values between 0.861 and 0.901 (p < 0.000). Intraclass Correlation Coefficient values indicated agreement of 0.961 and 0.972 (p < 0.05), with Weighted Kappa values showing substantial agreement of 0.658 and 0.711 for both sexes (p < 0.05).

Adopting the proposed equations for estimating BMI in adults without using body weight is safe and effective for measuring this body measure in this population, particularly when weighing these individuals is not feasible.