A New Mathematical Model to Index Body Weight in Healthy Chinese Han Adults

Area of Science:

• Anthropometry
• Biostatistics
• Public Health

Background:

• Body Mass Index (BMI) is a standard metric for diagnosing overweight and obesity.
• However, BMI is influenced by physiological variables like age and height, potentially leading to diagnostic inaccuracies.
• There is a need for improved methods to accurately assess overweight and obesity.

Purpose of the Study:

• To test the hypothesis that body weight is nonlinearly related to age and height.
• To develop an Optimized Multivariate Allometric Model (OMAM) to correct for these nonlinear effects.
• To define a new criterion for overweight diagnosis using OMAM and compare its efficacy against BMI.

Main Methods:

• An Optimized Multivariate Allometric Model (OMAM) was developed using data from 1498 Chinese Han adults.
• The model incorporated age, height, and sex to correct for nonlinear influences on body weight.
• A new threshold ($W_C$ >1.1440) was established to define overweight status.

Main Results:

• OMAM successfully corrected for the nonlinear effects of age, height, and sex on body weight.
• The new OMAM criterion reclassified 21.9% of individuals with high BMI as normal weight, reducing false positives.
• The OMAM criterion demonstrated higher specificity and accuracy in identifying associated health conditions like diabetes and hypertension compared to BMI.

Conclusions:

• The Optimized Multivariate Allometric Model (OMAM) provides a more accurate method for overweight screening than traditional BMI.
• OMAM's criterion reduces misclassifications and unnecessary interventions, particularly in men.
• Further validation of OMAM in diverse populations is recommended to support its clinical utility.