Key result
Optimized allometric weight model reclassifies ~22% of BMI-defined overweight adults with better cardiometabolic accuracy.
Why the study?
Does an optimized multivariate allometric model (OMAM) improve the accuracy of overweight diagnosis and cardiometabolic risk identification compared to traditional BMI in Chinese Han adults?
Observational (n=1,498)
Does an optimized multivariate allometric model (OMAM) improve the accuracy of overweight diagnosis and cardiometabolic risk identification compared to traditional BMI in Chinese Han adults?
Answers are grounded in this paper and cite their sources.
A new mathematical model (OMAM) correcting for age, height, and sex may provide a more accurate threshold for overweight diagnosis and cardiometabolic risk assessment than traditional BMI in Chinese Han adults.
Body mass index (BMI) is traditionally used to diagnose overweight and obesity, but it is influenced by physiological variables. This study tested the hypothesis that body weight is nonlinearly related to age and height, and that an optimized multivariate allometric model (OMAM) could correct for these effects and define a new criterion for overweight diagnosis. A total of 1498 Chinese Han adults were enrolled. The normal weight group (BMI < 25.0 kg/m², n = 1224) was divided into subgroup A (n = 857) to develop OMAM equations and determine the threshold, and subgroup B (n = 367) to validate them. The overweight group (BMI ≥ 25.0 kg/m², n = 274) was used to test the new criterion. OMAM corrected the nonlinear influence of age, height, and sex on weight. A corrected weight value W C >1.1440 was defined as the new threshold. This criterion reclassified 21.9% of overweight individuals as normal weight and reduced false positives, notably lowering the overweight rate to 61.3% in men, while minimizing unnecessary interventions. Compared with BMI, the new criterion showed higher specificity and accuracy in identifying diabetes, hypertension, coronary heart disease, and metabolic syndrome in the external CAPITAL cohort. These findings support the clinical utility of OMAM in overweight screening. Further validation in non-Chinese Han populations is warranted.
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Zhang et al. (2026) conducted an observational in Overweight and obesity (n=1,498). Optimized multivariate allometric model (OMAM) vs. Body mass index (BMI) was evaluated on Reclassification of overweight individuals and identification of cardiometabolic diseases. An optimized multivariate allometric model reclassified 21.9% of BMI-defined overweight individuals as normal weight and showed higher specificity and accuracy than BMI for cardiometabolic diseases.
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