Sex Differences in the Prediction of Metabolic Abnormalities Via Body Mass Index in an Eastern Chinese Population

Overview

Journal Front Nutr

Specialty Nutritional Sciences

Date 2025 Mar 13

PMID 40078410

Authors

Yingjie Gao

Kaimeng Jin

Jin Chen

Ben Chen

Yan Guo

Jin Lu

Affiliations

Soon will be listed here.

Abstract

Objective: Body mass index (BMI) is important for predicting the occurrence of metabolic abnormality, but sex differences exist. We aimed to investigate potential sex differences in the predictive value of BMI for metabolic abnormality and to calculate the optimal BMI cut-offs for predicting metabolic abnormality for each sex.

Methods: Participants ( = 4,623) who attended a health check-up centre continuously in Eastern China between January 2022 and December 2023 were evaluated for metabolic abnormalities. We calculated the proportions of different metabolic abnormalities in different sexes. Receiver operating characteristic (ROC) curves were calculated to determine the optimal BMI cut-off values for predicting metabolic abnormality in males and females. The recognition rate of each metabolic abnormality using different BMI cut-off values for men and women were evaluated.

Results: Among 4,623 participants (2,234 men and 2,389 women), the age-adjusted prevalence of all metabolic abnormalities was significantly greater among males than among females ( < 0.001). The optimal cut-off values for predicting metabolic abnormalities were 23.5 kg/m (males) and 21.8 kg/m (females). When BMI ≥24 kg/m was used as the cut-off value the recognition rates of each abnormal metabolic factor in the male group were all above 50%, while they were mostly below 50% in the female group, except for the recognition of hyperglycaemia and hypertriglyceridemia. However, in females, when BMI ≥22 kg/m was used as the cut-off value, the recognition rates for each abnormal metabolic factor were all above 50%, greater than that when BMI ≥24 kg/m was used ( < 0.001).

Conclusion: There were sex differences in the BMI thresholds for predicting metabolic abnormalities in the health check-up population.

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