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Conicity-index predicts all-cause mortality in Chinese older people: a 10-year community follow-up

BACKGROUND: Abdominal obesity (AO) has been regarded as the most dangerous type of obesity. The Conicity-index (C-index) had a high ability to discriminate underlying AO. The purpose of this study was to determine the ability of C-index to predict all-cause mortality among non-cancer Chinese older p...

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Autores principales: Zhang, Anhang, Li, Yingnan, Ma, Shouyuan, Bao, Qiligeer, Sun, Jin, Cai, Shuang, Li, Man, Su, Yongkang, Cheng, Bokai, Dong, Jing, Zhang, Yan, Wang, Shuxia, Zhu, Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9756661/
https://www.ncbi.nlm.nih.gov/pubmed/36522628
http://dx.doi.org/10.1186/s12877-022-03664-6
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author Zhang, Anhang
Li, Yingnan
Ma, Shouyuan
Bao, Qiligeer
Sun, Jin
Cai, Shuang
Li, Man
Su, Yongkang
Cheng, Bokai
Dong, Jing
Zhang, Yan
Wang, Shuxia
Zhu, Ping
author_facet Zhang, Anhang
Li, Yingnan
Ma, Shouyuan
Bao, Qiligeer
Sun, Jin
Cai, Shuang
Li, Man
Su, Yongkang
Cheng, Bokai
Dong, Jing
Zhang, Yan
Wang, Shuxia
Zhu, Ping
author_sort Zhang, Anhang
collection PubMed
description BACKGROUND: Abdominal obesity (AO) has been regarded as the most dangerous type of obesity. The Conicity-index (C-index) had a high ability to discriminate underlying AO. The purpose of this study was to determine the ability of C-index to predict all-cause mortality among non-cancer Chinese older people. METHODS: The participants were residents of the Wanshou Road community in Beijing, China. Receiver operating curve (ROC) curves were used to determine the sensitivity and specificity of the best cut-off values for different anthropometric measures for predicting all-cause mortality. The area under the curve (AUC) of the ROC curves were calculated to compare the relative ability of various anthropometric measures to correctly identify older people in the community where all-cause mortality occurs. Included subjects were grouped according to C-index tertiles. The association between C-index and all-cause mortality was verified using Kaplan–Meier survival analysis and different Cox regression models. RESULTS: During a mean follow-up period of 9.87 years, 1821 subjects completed follow-up. The average age was 71.21 years, of which 59.4% were female. The ROC curve results showed that the AUC of the C-index in predicting all-cause mortality was 0.633. Kaplan–Meier survival curves showed a clear dose–response relationship between C-index and all-cause mortality. With the increase of C-index, the survival rate of the study population showed a significant downward trend (P < 0.05). Adjusted for age, gender, hip circumference, systolic blood pressure, diastolic blood pressure, fasting blood glucose (FBG), 2-h postprandial blood glucose (2hPG), glycosylated hemoglobin, high-density lipids protein (LDL), triglyceride, serum creatinine, serum uric acid, urine albumin-creatinine ratio (UACR), Mini-Mental State Examination (MMSE), smoking history, and drinking history, COX regression analysis showed that in the model adjusted for all covariates, the risk of all-cause mortality in tertile 3 was 1.505 times that in tertile 1, and the difference was statistically significant. CONCLUSIONS: The C-index is an independent risk factor for all-cause mortality in the non-cancer Chinese older people.
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spelling pubmed-97566612022-12-17 Conicity-index predicts all-cause mortality in Chinese older people: a 10-year community follow-up Zhang, Anhang Li, Yingnan Ma, Shouyuan Bao, Qiligeer Sun, Jin Cai, Shuang Li, Man Su, Yongkang Cheng, Bokai Dong, Jing Zhang, Yan Wang, Shuxia Zhu, Ping BMC Geriatr Research BACKGROUND: Abdominal obesity (AO) has been regarded as the most dangerous type of obesity. The Conicity-index (C-index) had a high ability to discriminate underlying AO. The purpose of this study was to determine the ability of C-index to predict all-cause mortality among non-cancer Chinese older people. METHODS: The participants were residents of the Wanshou Road community in Beijing, China. Receiver operating curve (ROC) curves were used to determine the sensitivity and specificity of the best cut-off values for different anthropometric measures for predicting all-cause mortality. The area under the curve (AUC) of the ROC curves were calculated to compare the relative ability of various anthropometric measures to correctly identify older people in the community where all-cause mortality occurs. Included subjects were grouped according to C-index tertiles. The association between C-index and all-cause mortality was verified using Kaplan–Meier survival analysis and different Cox regression models. RESULTS: During a mean follow-up period of 9.87 years, 1821 subjects completed follow-up. The average age was 71.21 years, of which 59.4% were female. The ROC curve results showed that the AUC of the C-index in predicting all-cause mortality was 0.633. Kaplan–Meier survival curves showed a clear dose–response relationship between C-index and all-cause mortality. With the increase of C-index, the survival rate of the study population showed a significant downward trend (P < 0.05). Adjusted for age, gender, hip circumference, systolic blood pressure, diastolic blood pressure, fasting blood glucose (FBG), 2-h postprandial blood glucose (2hPG), glycosylated hemoglobin, high-density lipids protein (LDL), triglyceride, serum creatinine, serum uric acid, urine albumin-creatinine ratio (UACR), Mini-Mental State Examination (MMSE), smoking history, and drinking history, COX regression analysis showed that in the model adjusted for all covariates, the risk of all-cause mortality in tertile 3 was 1.505 times that in tertile 1, and the difference was statistically significant. CONCLUSIONS: The C-index is an independent risk factor for all-cause mortality in the non-cancer Chinese older people. BioMed Central 2022-12-16 /pmc/articles/PMC9756661/ /pubmed/36522628 http://dx.doi.org/10.1186/s12877-022-03664-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhang, Anhang
Li, Yingnan
Ma, Shouyuan
Bao, Qiligeer
Sun, Jin
Cai, Shuang
Li, Man
Su, Yongkang
Cheng, Bokai
Dong, Jing
Zhang, Yan
Wang, Shuxia
Zhu, Ping
Conicity-index predicts all-cause mortality in Chinese older people: a 10-year community follow-up
title Conicity-index predicts all-cause mortality in Chinese older people: a 10-year community follow-up
title_full Conicity-index predicts all-cause mortality in Chinese older people: a 10-year community follow-up
title_fullStr Conicity-index predicts all-cause mortality in Chinese older people: a 10-year community follow-up
title_full_unstemmed Conicity-index predicts all-cause mortality in Chinese older people: a 10-year community follow-up
title_short Conicity-index predicts all-cause mortality in Chinese older people: a 10-year community follow-up
title_sort conicity-index predicts all-cause mortality in chinese older people: a 10-year community follow-up
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9756661/
https://www.ncbi.nlm.nih.gov/pubmed/36522628
http://dx.doi.org/10.1186/s12877-022-03664-6
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