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Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study

BACKGROUND: Emerging studies have investigated the association between different anthropometric indices with diabetes risk but the results were inconsistent. The aims of the study were to examine the associations of different anthropometric indices with incident diabetes risk and whether novel anthr...

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Autores principales: Yang, Jing, Wang, Fei, Wang, Jing, Han, Xu, Hu, Hua, Yu, Caizheng, Yuan, Jing, Yao, Ping, Miao, Xiaoping, Wei, Sheng, Wang, Youjie, Chen, Weihong, Liang, Yuan, Guo, Huan, Zhang, Xiaomin, Zheng, Dan, Tang, Yuhan, Yang, Handong, He, Meian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6142386/
https://www.ncbi.nlm.nih.gov/pubmed/30223783
http://dx.doi.org/10.1186/s12877-018-0912-2
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author Yang, Jing
Wang, Fei
Wang, Jing
Han, Xu
Hu, Hua
Yu, Caizheng
Yuan, Jing
Yao, Ping
Miao, Xiaoping
Wei, Sheng
Wang, Youjie
Chen, Weihong
Liang, Yuan
Guo, Huan
Zhang, Xiaomin
Zheng, Dan
Tang, Yuhan
Yang, Handong
He, Meian
author_facet Yang, Jing
Wang, Fei
Wang, Jing
Han, Xu
Hu, Hua
Yu, Caizheng
Yuan, Jing
Yao, Ping
Miao, Xiaoping
Wei, Sheng
Wang, Youjie
Chen, Weihong
Liang, Yuan
Guo, Huan
Zhang, Xiaomin
Zheng, Dan
Tang, Yuhan
Yang, Handong
He, Meian
author_sort Yang, Jing
collection PubMed
description BACKGROUND: Emerging studies have investigated the association between different anthropometric indices with diabetes risk but the results were inconsistent. The aims of the study were to examine the associations of different anthropometric indices with incident diabetes risk and whether novel anthropometric indices improve diabetes prediction beyond traditional indices among elderly Chinese. METHODS: Nine thousand nine hundred sixty-two elderly individuals (age ≥ 60 years old) derived from the prospective Dongfeng-Tongji cohort were included. Hazard ratio (HR) and corresponding 95% confidence interval (CI) were evaluated by Cox proportional hazard model to examine the associations between traditional anthropometric indices (body mass index [BMI], waist circumference [WC], waist-to-height ratio [WHtR]), novel anthropometric indices (visceral adiposity index [VAI], a body shape index [ABSI], body roundness index [BRI]) and diabetes risk. Receiver operating characteristic (ROC) curve and area under curve (AUC) were applied to compare the novel anthropometric indices with the traditional indices in diabetes prediction. RESULTS: During mean 4.6 years of follow-up, 614 incident cases of type 2 diabetes (T2D) were identified. Significant positive associations were detected between BMI, WC, WHtR, VAI and BRI and incident T2D risk. For ABSI, no significant association was observed in either men or women. BMI was the strongest predictor in diabetes in men (AUC = 0.655) comparable with the other anthropometric indices (P < 0.05). Similar as men, BMI was the strongest predictor (AUC = 0.635) in women. Except for WC, the AUC of BMI was larger than WHtR, VAI, and BRI. In contrast, ABSI was not a good predictor in either men (AUC = 0.507) or women (AUC = 0.503). CONCLUSIONS: In elderly Chinese, BMI, WC, WHtR, VAI and BRI were positively associated with incident T2D risk. Among them, BMI was the strongest predictor in both men and women. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12877-018-0912-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-61423862018-09-20 Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study Yang, Jing Wang, Fei Wang, Jing Han, Xu Hu, Hua Yu, Caizheng Yuan, Jing Yao, Ping Miao, Xiaoping Wei, Sheng Wang, Youjie Chen, Weihong Liang, Yuan Guo, Huan Zhang, Xiaomin Zheng, Dan Tang, Yuhan Yang, Handong He, Meian BMC Geriatr Research Article BACKGROUND: Emerging studies have investigated the association between different anthropometric indices with diabetes risk but the results were inconsistent. The aims of the study were to examine the associations of different anthropometric indices with incident diabetes risk and whether novel anthropometric indices improve diabetes prediction beyond traditional indices among elderly Chinese. METHODS: Nine thousand nine hundred sixty-two elderly individuals (age ≥ 60 years old) derived from the prospective Dongfeng-Tongji cohort were included. Hazard ratio (HR) and corresponding 95% confidence interval (CI) were evaluated by Cox proportional hazard model to examine the associations between traditional anthropometric indices (body mass index [BMI], waist circumference [WC], waist-to-height ratio [WHtR]), novel anthropometric indices (visceral adiposity index [VAI], a body shape index [ABSI], body roundness index [BRI]) and diabetes risk. Receiver operating characteristic (ROC) curve and area under curve (AUC) were applied to compare the novel anthropometric indices with the traditional indices in diabetes prediction. RESULTS: During mean 4.6 years of follow-up, 614 incident cases of type 2 diabetes (T2D) were identified. Significant positive associations were detected between BMI, WC, WHtR, VAI and BRI and incident T2D risk. For ABSI, no significant association was observed in either men or women. BMI was the strongest predictor in diabetes in men (AUC = 0.655) comparable with the other anthropometric indices (P < 0.05). Similar as men, BMI was the strongest predictor (AUC = 0.635) in women. Except for WC, the AUC of BMI was larger than WHtR, VAI, and BRI. In contrast, ABSI was not a good predictor in either men (AUC = 0.507) or women (AUC = 0.503). CONCLUSIONS: In elderly Chinese, BMI, WC, WHtR, VAI and BRI were positively associated with incident T2D risk. Among them, BMI was the strongest predictor in both men and women. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12877-018-0912-2) contains supplementary material, which is available to authorized users. BioMed Central 2018-09-17 /pmc/articles/PMC6142386/ /pubmed/30223783 http://dx.doi.org/10.1186/s12877-018-0912-2 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Yang, Jing
Wang, Fei
Wang, Jing
Han, Xu
Hu, Hua
Yu, Caizheng
Yuan, Jing
Yao, Ping
Miao, Xiaoping
Wei, Sheng
Wang, Youjie
Chen, Weihong
Liang, Yuan
Guo, Huan
Zhang, Xiaomin
Zheng, Dan
Tang, Yuhan
Yang, Handong
He, Meian
Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study
title Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study
title_full Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study
title_fullStr Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study
title_full_unstemmed Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study
title_short Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study
title_sort using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6142386/
https://www.ncbi.nlm.nih.gov/pubmed/30223783
http://dx.doi.org/10.1186/s12877-018-0912-2
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