{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T14:34:52Z","timestamp":1782311692605,"version":"3.54.5"},"reference-count":29,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2016,8,9]],"date-time":"2016-08-09T00:00:00Z","timestamp":1470700800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003454","name":"Guangxi Zhuang Region Health Department","doi-asserted-by":"publisher","award":["Z2012398"],"award-info":[{"award-number":["Z2012398"]}],"id":[{"id":"10.13039\/501100003454","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J of Diabetes Invest"],"published-print":{"date-parts":[[2017,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Aims\/Introduction<\/jats:title><jats:p>Type 2 diabetes is a metabolic disease characterized by insulin resistance, and is associated with the effects of genetic and environmental factors. The present study aimed to not only analyze the influence of a single factor for type 2 diabetes, but also to investigate the interaction effects between risk factors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>A total of 6,660 individuals selected by the method of cluster random sampling accepted a cross\u2010sectional survey (questionnaire investigation, physical measurement, laboratory examination and liver ultrasound examination). The classification tree was used to analyze the risk factors and their interactions in type 2 diabetes. The clinical and metabolic characteristics were compared between type 2 diabetes patients and controls, and the non\u2010conditional logistic regression model was used to quantitatively analyze the interactions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 338 participants were classified as type 2 diabetes (217 men and 121 women), the classification tree model showed three variables with close associations with type 2 diabetes: age, triglycerides (<jats:styled-content style=\"fixed-case\">TG<\/jats:styled-content>) and non\u2010alcoholic fatty liver disease (<jats:styled-content style=\"fixed-case\">NAFLD<\/jats:styled-content>). Type 2 diabetes patients had higher age and incidences of high <jats:styled-content style=\"fixed-case\">TG<\/jats:styled-content>,<jats:styled-content style=\"fixed-case\"> NAFLD<\/jats:styled-content>, hypertension, high body mass index, high uric acid, high total cholesterol, high low\u2010density lipoprotein cholesterol and low high\u2010density lipoprotein cholesterol. The multivariate logistic regression analysis showed that the following factors had interactions in type2 diabetes: high <jats:styled-content style=\"fixed-case\">TG<\/jats:styled-content> \u00d7 advanced age (odds ratio 2.499, 95% confidence interval 1.868\u20133.344, <jats:italic>P<\/jats:italic> = 0.000), <jats:styled-content style=\"fixed-case\">NAFLD<\/jats:styled-content> \u00d7 advanced age (odds ratio 1.250, 95% confidence interval 1.048\u20131.491, <jats:italic>P<\/jats:italic> = 0.013) and <jats:styled-content style=\"fixed-case\">NAFLD<\/jats:styled-content> \u00d7 high <jats:styled-content style=\"fixed-case\">TG<\/jats:styled-content> (odds ratio 1.349, 95% confidence interval 1.144\u20131.590, <jats:italic>P<\/jats:italic> = 0.000).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The present study showed that type 2 diabetes resulted from the interactions of many factors; the interactions among age, <jats:styled-content style=\"fixed-case\">TG<\/jats:styled-content> and <jats:styled-content style=\"fixed-case\">NAFLD<\/jats:styled-content> are important risk factors for type 2 diabetes.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jdi.12549","type":"journal-article","created":{"date-parts":[[2016,7,6]],"date-time":"2016-07-06T16:25:47Z","timestamp":1467822347000},"page":"188-194","source":"Crossref","is-referenced-by-count":21,"title":["Analysis of risk factors and their interactions in type 2 diabetes mellitus: A cross\u2010sectional survey in Guilin, China"],"prefix":"10.1111","volume":"8","author":[{"given":"Disha","family":"Zou","sequence":"first","affiliation":[{"name":"Department of Endocrinology The Affiliated Hospital of Guilin Medical University Guilin, Guangxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Endocrinology The Affiliated Hospital of Guilin Medical University Guilin, Guangxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nina","family":"Zou","sequence":"additional","affiliation":[{"name":"The Nursing Group of Loudi Health School Loudi Hunan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Endocrinology The Affiliated Hospital of Guilin Medical University Guilin, Guangxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2016,8,9]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(05)61032-X"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2015.10029"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph121114764"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2009.10.007"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2013.11.002"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.2337\/dc12-0958"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdiacomp.2014.10.011"},{"key":"e_1_2_7_9_1","volume-title":"Global Status Report on Noncommunicable Diseases: 2010","author":"World Health Organization","year":"2011"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1136\/jech.2009.087544"},{"key":"e_1_2_7_11_1","first-page":"173","article-title":"Comparing Performances of logistic regression, classification & regression trees and artificial neural networks for predicting albuminuria in Type 2 diabetes mellitus","volume":"16","author":"Omurlu IK","year":"2014","journal-title":"Int J Sci Basic Appl Res"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S"},{"key":"e_1_2_7_13_1","first-page":"83","article-title":"Cooperative Meta\u2010Analysis Group of the Working Group on Obesity in China. 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