Sex differences in the prediction of metabolic abnormalities via body mass index in an Eastern Chinese population

ObjectiveBody 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...

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Main Authors: Yingjie Gao, Kaimeng Jin, Jin Chen, Ben Chen, Yan Guo, Jin Lu
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-02-01
Series:Frontiers in Nutrition
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fnut.2025.1491818/full
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author Yingjie Gao
Kaimeng Jin
Jin Chen
Ben Chen
Yan Guo
Jin Lu
author_facet Yingjie Gao
Kaimeng Jin
Jin Chen
Ben Chen
Yan Guo
Jin Lu
author_sort Yingjie Gao
collection DOAJ
description ObjectiveBody 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.MethodsParticipants (n = 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.ResultsAmong 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 (p < 0.001). The optimal cut-off values for predicting metabolic abnormalities were 23.5 kg/m2 (males) and 21.8 kg/m2 (females). When BMI ≥24 kg/m2 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/m2 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/m2 was used (p < 0.001).ConclusionThere were sex differences in the BMI thresholds for predicting metabolic abnormalities in the health check-up population.
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spelling doaj-art-16f18714f4e54cb189f5fdc214f2bb8e2025-08-20T02:45:37ZengFrontiers Media S.A.Frontiers in Nutrition2296-861X2025-02-011210.3389/fnut.2025.14918181491818Sex differences in the prediction of metabolic abnormalities via body mass index in an Eastern Chinese populationYingjie GaoKaimeng JinJin ChenBen ChenYan GuoJin LuObjectiveBody 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.MethodsParticipants (n = 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.ResultsAmong 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 (p < 0.001). The optimal cut-off values for predicting metabolic abnormalities were 23.5 kg/m2 (males) and 21.8 kg/m2 (females). When BMI ≥24 kg/m2 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/m2 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/m2 was used (p < 0.001).ConclusionThere were sex differences in the BMI thresholds for predicting metabolic abnormalities in the health check-up population.https://www.frontiersin.org/articles/10.3389/fnut.2025.1491818/fullbody mass indexmetabolic abnormalitiessex differencesChinese populationhealth check-up
spellingShingle Yingjie Gao
Kaimeng Jin
Jin Chen
Ben Chen
Yan Guo
Jin Lu
Sex differences in the prediction of metabolic abnormalities via body mass index in an Eastern Chinese population
Frontiers in Nutrition
body mass index
metabolic abnormalities
sex differences
Chinese population
health check-up
title Sex differences in the prediction of metabolic abnormalities via body mass index in an Eastern Chinese population
title_full Sex differences in the prediction of metabolic abnormalities via body mass index in an Eastern Chinese population
title_fullStr Sex differences in the prediction of metabolic abnormalities via body mass index in an Eastern Chinese population
title_full_unstemmed Sex differences in the prediction of metabolic abnormalities via body mass index in an Eastern Chinese population
title_short Sex differences in the prediction of metabolic abnormalities via body mass index in an Eastern Chinese population
title_sort sex differences in the prediction of metabolic abnormalities via body mass index in an eastern chinese population
topic body mass index
metabolic abnormalities
sex differences
Chinese population
health check-up
url https://www.frontiersin.org/articles/10.3389/fnut.2025.1491818/full
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