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

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 predictio...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441664/
https://www.ncbi.nlm.nih.gov/pubmed/36072930
http://dx.doi.org/10.3389/fendo.2022.965890
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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
author_facet 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
author_sort Zhu, Xiaoyue
collection PubMed
description 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 according to different definition criteria of the American Diabetes Association (ADA) in the Chinese population. METHODS: A longitudinal study consisted of baseline survey and two follow-ups was conducted, and a pooled data were analyzed. Prediabetes was defined as either impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or elevated glycosylated hemoglobin (HbA1c) according to the ADA criteria. Robust generalized estimating equation models were used. RESULTS: A total of 5,713 (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 individuals were less likely to have IFG (aOR=0.70) and more likely to suffer from IGT than male individuals (aOR=1.41). Several inconsistency 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 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.
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spelling pubmed-94416642022-09-06 Factors correlated with targeted prevention for prediabetes classified by impaired fasting glucose, impaired glucose tolerance, and elevated HbA1c: A population-based longitudinal study 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 Front Endocrinol (Lausanne) Endocrinology 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 according to different definition criteria of the American Diabetes Association (ADA) in the Chinese population. METHODS: A longitudinal study consisted of baseline survey and two follow-ups was conducted, and a pooled data were analyzed. Prediabetes was defined as either impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or elevated glycosylated hemoglobin (HbA1c) according to the ADA criteria. Robust generalized estimating equation models were used. RESULTS: A total of 5,713 (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 individuals were less likely to have IFG (aOR=0.70) and more likely to suffer from IGT than male individuals (aOR=1.41). Several inconsistency 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 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. Frontiers Media S.A. 2022-08-22 /pmc/articles/PMC9441664/ /pubmed/36072930 http://dx.doi.org/10.3389/fendo.2022.965890 Text en Copyright © 2022 Zhu, Yang, He, Hu, Yin, Bai, Li, Cai, Guo, Li, Yan, Li, Shen, Sun, Liu, Sun and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
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
Factors correlated with targeted prevention for prediabetes classified by impaired fasting glucose, impaired glucose tolerance, and elevated HbA1c: A population-based longitudinal study
title Factors correlated with targeted prevention for prediabetes classified by impaired fasting glucose, impaired glucose tolerance, and elevated HbA1c: A population-based longitudinal study
title_full Factors correlated with targeted prevention for prediabetes classified by impaired fasting glucose, impaired glucose tolerance, and elevated HbA1c: A population-based longitudinal study
title_fullStr Factors correlated with targeted prevention for prediabetes classified by impaired fasting glucose, impaired glucose tolerance, and elevated HbA1c: A population-based longitudinal study
title_full_unstemmed Factors correlated with targeted prevention for prediabetes classified by impaired fasting glucose, impaired glucose tolerance, and elevated HbA1c: A population-based longitudinal study
title_short Factors correlated with targeted prevention for prediabetes classified by impaired fasting glucose, impaired glucose tolerance, and elevated HbA1c: A population-based longitudinal study
title_sort factors correlated with targeted prevention for prediabetes classified by impaired fasting glucose, impaired glucose tolerance, and elevated hba1c: a population-based longitudinal study
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441664/
https://www.ncbi.nlm.nih.gov/pubmed/36072930
http://dx.doi.org/10.3389/fendo.2022.965890
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