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Effect of Metabolic Health and Obesity Phenotype on Risk of Diabetes Mellitus: A Population-Based Longitudinal Study
Xiaoyue Zhu1, Jingyao Hu1, Haijian Guo2
1Key Laboratory of Environment Medicine Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, Jiangsu Province, People's Republic of China.
Background:
Epidemiologic evidence on body mass index (BMI)-metabolic status phenotypes and diabetes risk remains controversial, especially for metabolically healthy obesity (MHO). We aimed to examine the effect of metabolic health and obesity phenotype on diabetes risk in the Chinese population.
Methods:
A population-based cohort study was carried out. The baseline survey was conducted in 2017, with two follow-up visits in 2018 and 2020. Diabetes was defined based on the criteria of the World Health Organization. Robust generalized estimating equation models with a binary distribution using a log link and exchange structure were applied for the pooled analysis sample.
Results:
A total sample of 9623 observations was pooled for the longitudinal data analysis. The average follow-up time was 1.64 years per person and the overall incidence density of diabetes was 6.94% person-years. Decreased diabetes risk was found in metabolically healthy overweight phenotype (RR = 0.65; 95% CI = 0.47-0.90) and no significant associations were detected for the MHO individuals (RR = 0.99; 95% CI = 0.63-1.53) compared with those of metabolically healthy normal weight, in contrast to metabolically unhealthy normal weight (MU-NW) (RR = 1.81; 95% CI = 1.28-2.55), metabolically unhealthy overweight (MU-OW) (RR = 2.02; 95% CI = 1.57-2.61) and metabolically unhealthy obesity (MUO) (RR = 2.48; 95% CI = 1.89-3.26) phenotypes. Significant associations between BMI-metabolic status phenotypes and diabetes were found in both males and females.
Conclusion:
The MUO phenotype needs to be accorded much more importance. MU-NW and MU-OW are also important component for targeted prevention. Our findings can be targeted for optimizing preventive strategies to mitigate the obviously increased prevalence of diabetes.
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