AUTHOR=Zhu Xiaoyue , Yang Zhipeng , He Zhiliang , Hu Jingyao , Yin Tianxiu , Bai Hexiang , Li Ruoyu , Cai Le , Guo Haijian , Li Mingma , Yan Tao , Li You , Shen Chenye , Sun Kaicheng , Liu Yu , Sun Zilin , Wang Bei 

TITLE=Factors correlated with targeted prevention for prediabetes classified by impaired fasting glucose, impaired glucose tolerance, and elevated HbA1c: A population-based longitudinal study

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2022.965890

DOI=10.3389/fendo.2022.965890

ISSN=1664-2392

ABSTRACT=Background: There is still controversy surrounding the precise characterization of prediabetic population. We aim to identify and examine factors of demographic, behavioral, clinical and biochemical characteristics, and obesity indicators (anthropometric characteristics and anthropometric prediction equation) for prediabetes defined by different prediabetes criteria in the Chinese population. 
Methods: A longitudinal study consisted of baseline survey and two follow-ups was conducted and a pooled data was analyzed. Prediabetes was defined as either impaired fasting glucose (IFG), impaired glucose tolerance (IGT) or elevated glycosylated hemoglobin (HbA1c) according to the American Diabetes Association (ADA) criteria. Robust generalized estimating equation models were used. 
Results: 58.42% observations were prediabetes (IGT: 38.07%; IGT: 26.51%; elevated HbA1c: 23.45%). 9.66% prediabetes fulfilled all the three ADA criteria. Among demographic characteristics, higher age was more evident in elevated HbA1c (adjusted OR (aOR)=2.85), female was less likely to have IFG (aOR=0.70) than male and contrary association with IGT (aOR=1.41). Several inconsistencies correlations of biochemical characteristics and obesity indicators were detected by prediabetes criteria. Body adiposity estimator exhibited strong association with prediabetes (D10: aOR=4.05,). For IFG and elevated HbA1c, the odds of predicted lean body mass exceed other indicators (D10: aOR=3.34; aOR=3.64). For IGT, predicted percent fat presented the highest odds (D10: aOR=6.58). 
Conclusion: Some correlated factors of prediabetes under different criteria were differed and obesity indicators were easily measured for target identification. Our findings could be used for targeted intervention to optimize preventions to mitigate the obviously increased prevalence of diabetes.