PMID- 37748711
OWN - NLM
STAT- MEDLINE
DCOM- 20231030
LR  - 20231030
IS  - 1872-8227 (Electronic)
IS  - 0168-8227 (Linking)
VI  - 204
DP  - 2023 Oct
TI  - An environment-wide association study for the identification of non-invasive 
      factors for type 2 diabetes mellitus: Analysis based on the Henan Rural Cohort 
      study.
PG  - 110917
LID - S0168-8227(23)00680-0 [pii]
LID - 10.1016/j.diabres.2023.110917 [doi]
AB  - AIM: To explore the influencing factors of Type 2 diabetes mellitus (T2DM) in the 
      rural population of Henan Province and evaluate the predictive ability of 
      non-invasive factors to T2DM. METHODS: A total of 30,020 participants from the 
      Henan Rural Cohort Study in China were included in this study. The dataset was 
      randomly divided into a training set and a testing set with a 50:50 split for 
      validation purposes. We used logistic regression analysis to investigate the 
      association between 56 factors and T2DM in the training set (false discovery 
      rate < 5 %) and significant factors were further validated in the testing set 
      (P < 0.05). Gradient Boosting Machine (GBM) model was used to determine the 
      ability of the non-invasive variables to classify T2DM individuals accurately and 
      the importance ranking of these variables. RESULTS: The overall population 
      prevalence of T2DM was 9.10 %. After adjusting for age, sex, educational level, 
      marital status, and body measure index (BMI), we identified 13 non-invasive 
      variables and 6 blood biochemical indexes associated with T2DM in the training 
      and testing dataset. The top three factors according to the GBM importance 
      ranking were pulse pressure (PP), urine glucose (UGLU), and waist-to-hip ratio 
      (WHR). The GBM model achieved a receiver operating characteristic (AUC) curve of 
      0.837 with non-invasive variables and 0.847 for the full model. CONCLUSIONS: Our 
      findings demonstrate that non-invasive variables that can be easily measured and 
      quickly obtained may be used to predict T2DM risk in rural populations in Henan 
      Province.
CI  - Copyright (c) 2023 Elsevier B.V. All rights reserved.
FAU - Li, Shuoyi
AU  - Li S
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, Henan 450001, PR China.
FAU - Chen, Ying
AU  - Chen Y
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, Henan 450001, PR China.
FAU - Zhang, Liying
AU  - Zhang L
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, Henan 450001, PR China.
FAU - Li, Ruiying
AU  - Li R
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, Henan 450001, PR China.
FAU - Kang, Ning
AU  - Kang N
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, Henan 450001, PR China.
FAU - Hou, Jian
AU  - Hou J
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, Henan 450001, PR China.
FAU - Wang, Jing
AU  - Wang J
AD  - China-Australia Joint Research Center for Infectious Diseases, School of Public 
      Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, 
      PR China.
FAU - Bao, Yining
AU  - Bao Y
AD  - China-Australia Joint Research Center for Infectious Diseases, School of Public 
      Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, 
      PR China.
FAU - Jiang, Feng
AU  - Jiang F
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, Henan 450001, PR China.
FAU - Zhu, Ruifang
AU  - Zhu R
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, Henan 450001, PR China.
FAU - Wang, Chongjian
AU  - Wang C
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, Henan 450001, PR China. Electronic address: 
      tjwcj2008@zzu.edu.cn.
FAU - Zhang, Lei
AU  - Zhang L
AD  - China-Australia Joint Research Center for Infectious Diseases, School of Public 
      Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, 
      PR China; Artificial Intelligence and Modelling in Epidemiology Program, 
      Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia; Central 
      Clinical School, Faculty of Medicine, Monash University, Melbourne, Australia. 
      Electronic address: lei.zhangl@monash.edu.
LA  - eng
PT  - Journal Article
DEP - 20230923
PL  - Ireland
TA  - Diabetes Res Clin Pract
JT  - Diabetes research and clinical practice
JID - 8508335
SB  - IM
MH  - Humans
MH  - *Diabetes Mellitus, Type 2/diagnosis/epidemiology
MH  - Cohort Studies
MH  - Risk Factors
MH  - Rural Population
MH  - Body Mass Index
MH  - Waist Circumference
MH  - China/epidemiology
OTO - NOTNLM
OT  - Gradient Boosting Machine
OT  - Non-invasive factors
OT  - Rural population
OT  - Type 2 Diabetes Mellitus
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/09/26 00:42
MHDA- 2023/10/30 06:47
CRDT- 2023/09/25 19:16
PHST- 2023/06/12 00:00 [received]
PHST- 2023/09/16 00:00 [revised]
PHST- 2023/09/21 00:00 [accepted]
PHST- 2023/10/30 06:47 [medline]
PHST- 2023/09/26 00:42 [pubmed]
PHST- 2023/09/25 19:16 [entrez]
AID - S0168-8227(23)00680-0 [pii]
AID - 10.1016/j.diabres.2023.110917 [doi]
PST - ppublish
SO  - Diabetes Res Clin Pract. 2023 Oct;204:110917. doi: 10.1016/j.diabres.2023.110917. 
      Epub 2023 Sep 23.