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From normal population to prediabetes and diabetes: study of influencing factors and prediction models

OBJECTIVE: The purpose of this study is to investigate the independent influencing factors of the transition from normal population to prediabetes, and from prediabetes to diabetes, and to further construct clinical prediction models to provide a basis for the prevention and management of prediabete...

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Autores principales: Gong, Di, Chen, Xiaohong, Yang, Lin, Zhang, Yongjian, Zhong, Qianqian, Liu, Jing, Yan, Chen, Cai, Yongjiang, Yang, Weihua, Wang, Jiantao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10640999/
https://www.ncbi.nlm.nih.gov/pubmed/37964953
http://dx.doi.org/10.3389/fendo.2023.1225696
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author Gong, Di
Chen, Xiaohong
Yang, Lin
Zhang, Yongjian
Zhong, Qianqian
Liu, Jing
Yan, Chen
Cai, Yongjiang
Yang, Weihua
Wang, Jiantao
author_facet Gong, Di
Chen, Xiaohong
Yang, Lin
Zhang, Yongjian
Zhong, Qianqian
Liu, Jing
Yan, Chen
Cai, Yongjiang
Yang, Weihua
Wang, Jiantao
author_sort Gong, Di
collection PubMed
description OBJECTIVE: The purpose of this study is to investigate the independent influencing factors of the transition from normal population to prediabetes, and from prediabetes to diabetes, and to further construct clinical prediction models to provide a basis for the prevention and management of prediabetes and diabetes. MATERIALS AND METHODS: The data for this study were based on clinical information of participants from the Health Management Center of Peking University Shenzhen Hospital. Participants were classified into normal group, prediabetes group, and diabetes group according to their functional status of glucose metabolism. Spearman’s correlation coefficients were calculated for the variables, and a matrix diagram was plotted. Further, univariate and multivariate logistic regression analysis were conducted to explore the independent influencing factors. The independent influencing factors were used as predictors to construct the full-variable prediction model (Full.model) and simplified prediction model (Simplified.model). RESULTS: This study included a total of 5310 subjects and 22 variables, among which there were 1593(30%) in the normal group, 3150(59.3%) in the prediabetes group, and 567(10.7%) in the diabetes group. The results of the multivariable logistic regression analysis showed that there were significant differences in 9 variables between the normal group and the prediabetes group, including age(Age), body mass index(BMI), systolic blood pressure(SBP), urinary glucose(U.GLU), urinary protein(PRO), total protein(TP), globulin(GLB), alanine aminotransferase(ALT), and high-density lipoprotein cholesterol(HDL-C). There were significant differences in 7 variables between the prediabetes group and the diabetes group, including Age, BMI, SBP, U.GLU, PRO, triglycerides(TG), and HDL.C. The Full.model and Simplified.model constructed based on the above influencing factors had moderate discriminative power in both the training set and the test set. CONCLUSION: Age, BMI, SBP, U.GLU, PRO, TP, and ALT are independent risk factors, while GLB and HDL.C are independent protective factors for the development of prediabetes in the normal population. Age, BMI, SBP, U.GLU, PRO, and TG are independent risk factors, while HDL.C is an independent protective factor for the progression from prediabetes to diabetes. The Full.model and Simplified.model developed based on these influencing factors have moderate discriminative power.
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spelling pubmed-106409992023-11-14 From normal population to prediabetes and diabetes: study of influencing factors and prediction models Gong, Di Chen, Xiaohong Yang, Lin Zhang, Yongjian Zhong, Qianqian Liu, Jing Yan, Chen Cai, Yongjiang Yang, Weihua Wang, Jiantao Front Endocrinol (Lausanne) Endocrinology OBJECTIVE: The purpose of this study is to investigate the independent influencing factors of the transition from normal population to prediabetes, and from prediabetes to diabetes, and to further construct clinical prediction models to provide a basis for the prevention and management of prediabetes and diabetes. MATERIALS AND METHODS: The data for this study were based on clinical information of participants from the Health Management Center of Peking University Shenzhen Hospital. Participants were classified into normal group, prediabetes group, and diabetes group according to their functional status of glucose metabolism. Spearman’s correlation coefficients were calculated for the variables, and a matrix diagram was plotted. Further, univariate and multivariate logistic regression analysis were conducted to explore the independent influencing factors. The independent influencing factors were used as predictors to construct the full-variable prediction model (Full.model) and simplified prediction model (Simplified.model). RESULTS: This study included a total of 5310 subjects and 22 variables, among which there were 1593(30%) in the normal group, 3150(59.3%) in the prediabetes group, and 567(10.7%) in the diabetes group. The results of the multivariable logistic regression analysis showed that there were significant differences in 9 variables between the normal group and the prediabetes group, including age(Age), body mass index(BMI), systolic blood pressure(SBP), urinary glucose(U.GLU), urinary protein(PRO), total protein(TP), globulin(GLB), alanine aminotransferase(ALT), and high-density lipoprotein cholesterol(HDL-C). There were significant differences in 7 variables between the prediabetes group and the diabetes group, including Age, BMI, SBP, U.GLU, PRO, triglycerides(TG), and HDL.C. The Full.model and Simplified.model constructed based on the above influencing factors had moderate discriminative power in both the training set and the test set. CONCLUSION: Age, BMI, SBP, U.GLU, PRO, TP, and ALT are independent risk factors, while GLB and HDL.C are independent protective factors for the development of prediabetes in the normal population. Age, BMI, SBP, U.GLU, PRO, and TG are independent risk factors, while HDL.C is an independent protective factor for the progression from prediabetes to diabetes. The Full.model and Simplified.model developed based on these influencing factors have moderate discriminative power. Frontiers Media S.A. 2023-10-26 /pmc/articles/PMC10640999/ /pubmed/37964953 http://dx.doi.org/10.3389/fendo.2023.1225696 Text en Copyright © 2023 Gong, Chen, Yang, Zhang, Zhong, Liu, Yan, Cai, Yang 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
Gong, Di
Chen, Xiaohong
Yang, Lin
Zhang, Yongjian
Zhong, Qianqian
Liu, Jing
Yan, Chen
Cai, Yongjiang
Yang, Weihua
Wang, Jiantao
From normal population to prediabetes and diabetes: study of influencing factors and prediction models
title From normal population to prediabetes and diabetes: study of influencing factors and prediction models
title_full From normal population to prediabetes and diabetes: study of influencing factors and prediction models
title_fullStr From normal population to prediabetes and diabetes: study of influencing factors and prediction models
title_full_unstemmed From normal population to prediabetes and diabetes: study of influencing factors and prediction models
title_short From normal population to prediabetes and diabetes: study of influencing factors and prediction models
title_sort from normal population to prediabetes and diabetes: study of influencing factors and prediction models
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10640999/
https://www.ncbi.nlm.nih.gov/pubmed/37964953
http://dx.doi.org/10.3389/fendo.2023.1225696
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