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Predictive performance of lipid parameters in identifying undiagnosed diabetes and prediabetes: a cross-sectional study in eastern China

BACKGROUND: Dyslipidaemia is a risk factor for abnormal blood glucose. However, studies on the predictive values of lipid markers in prediabetes and diabetes simultaneously are limited. This study aimed to assess the associations and predictive abilities of lipid indices and abnormal blood glucose....

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Autores principales: Zhou, Yimin, Yang, Guoping, Qu, Chen, Chen, Jiaping, Qian, Yinan, Yuan, Lei, Mao, Tao, Xu, Yan, Li, Xiaoning, Zhen, Shiqi, Liu, Sijun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8952267/
https://www.ncbi.nlm.nih.gov/pubmed/35331213
http://dx.doi.org/10.1186/s12902-022-00984-x
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author Zhou, Yimin
Yang, Guoping
Qu, Chen
Chen, Jiaping
Qian, Yinan
Yuan, Lei
Mao, Tao
Xu, Yan
Li, Xiaoning
Zhen, Shiqi
Liu, Sijun
author_facet Zhou, Yimin
Yang, Guoping
Qu, Chen
Chen, Jiaping
Qian, Yinan
Yuan, Lei
Mao, Tao
Xu, Yan
Li, Xiaoning
Zhen, Shiqi
Liu, Sijun
author_sort Zhou, Yimin
collection PubMed
description BACKGROUND: Dyslipidaemia is a risk factor for abnormal blood glucose. However, studies on the predictive values of lipid markers in prediabetes and diabetes simultaneously are limited. This study aimed to assess the associations and predictive abilities of lipid indices and abnormal blood glucose. METHODS: A sample of 7667 participants without diabetes were enrolled in this cross-sectional study conducted in 2016, and all of them were classified as having normal glucose tolerance (NGT), prediabetes or diabetes. Blood glucose, blood pressure and lipid parameters (triglycerides, TG; total cholesterol, TC; high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C; non-high-density lipoprotein cholesterol, non-HDL-C; and triglyceride glucose index, TyG) were evaluated or calculated. Logistic regression models were used to analyse the association between lipids and abnormal blood glucose. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to assess the discriminatory power of lipid parameters for detecting prediabetes or diabetes. RESULTS: After adjustment for potential confounding factors, the TyG was the strongest marker related to abnormal blood glucose compared to other lipid indices, with odds ratios of 2.111 for prediabetes and 5.423 for diabetes. For prediabetes, the AUCs of the TG, TC, HDL-C, LDL-C, TC/HDL-C, TG/HDL-C, non-HDL-C and TyG indices were 0.605, 0.617, 0.481, 0.615, 0.603, 0.590, 0.626 and 0.660, respectively, and the cut-off points were 1.34, 4.59, 1.42, 2.69, 3.39, 1.00, 3.19 and 8.52, respectively. For diabetes, the AUCs of the TG, TC, HDL-C, LDL-C, TC/HDL-C, TG/HDL-C, non-HDL-C and TyG indices were 0.712, 0.679, 0.440, 0.652, 0.686, 0.692, 0.705, and 0.827, respectively, and the cut-off points were 1.35, 4.68, 1.42, 2.61, 3.44, 0.98, 3.13 and 8.80, respectively. CONCLUSIONS: The TyG, TG and non-HDL-C, especially TyG, are accessible biomarkers for screening individuals with undiagnosed diabetes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12902-022-00984-x.
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spelling pubmed-89522672022-03-26 Predictive performance of lipid parameters in identifying undiagnosed diabetes and prediabetes: a cross-sectional study in eastern China Zhou, Yimin Yang, Guoping Qu, Chen Chen, Jiaping Qian, Yinan Yuan, Lei Mao, Tao Xu, Yan Li, Xiaoning Zhen, Shiqi Liu, Sijun BMC Endocr Disord Research BACKGROUND: Dyslipidaemia is a risk factor for abnormal blood glucose. However, studies on the predictive values of lipid markers in prediabetes and diabetes simultaneously are limited. This study aimed to assess the associations and predictive abilities of lipid indices and abnormal blood glucose. METHODS: A sample of 7667 participants without diabetes were enrolled in this cross-sectional study conducted in 2016, and all of them were classified as having normal glucose tolerance (NGT), prediabetes or diabetes. Blood glucose, blood pressure and lipid parameters (triglycerides, TG; total cholesterol, TC; high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C; non-high-density lipoprotein cholesterol, non-HDL-C; and triglyceride glucose index, TyG) were evaluated or calculated. Logistic regression models were used to analyse the association between lipids and abnormal blood glucose. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to assess the discriminatory power of lipid parameters for detecting prediabetes or diabetes. RESULTS: After adjustment for potential confounding factors, the TyG was the strongest marker related to abnormal blood glucose compared to other lipid indices, with odds ratios of 2.111 for prediabetes and 5.423 for diabetes. For prediabetes, the AUCs of the TG, TC, HDL-C, LDL-C, TC/HDL-C, TG/HDL-C, non-HDL-C and TyG indices were 0.605, 0.617, 0.481, 0.615, 0.603, 0.590, 0.626 and 0.660, respectively, and the cut-off points were 1.34, 4.59, 1.42, 2.69, 3.39, 1.00, 3.19 and 8.52, respectively. For diabetes, the AUCs of the TG, TC, HDL-C, LDL-C, TC/HDL-C, TG/HDL-C, non-HDL-C and TyG indices were 0.712, 0.679, 0.440, 0.652, 0.686, 0.692, 0.705, and 0.827, respectively, and the cut-off points were 1.35, 4.68, 1.42, 2.61, 3.44, 0.98, 3.13 and 8.80, respectively. CONCLUSIONS: The TyG, TG and non-HDL-C, especially TyG, are accessible biomarkers for screening individuals with undiagnosed diabetes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12902-022-00984-x. BioMed Central 2022-03-24 /pmc/articles/PMC8952267/ /pubmed/35331213 http://dx.doi.org/10.1186/s12902-022-00984-x 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
Zhou, Yimin
Yang, Guoping
Qu, Chen
Chen, Jiaping
Qian, Yinan
Yuan, Lei
Mao, Tao
Xu, Yan
Li, Xiaoning
Zhen, Shiqi
Liu, Sijun
Predictive performance of lipid parameters in identifying undiagnosed diabetes and prediabetes: a cross-sectional study in eastern China
title Predictive performance of lipid parameters in identifying undiagnosed diabetes and prediabetes: a cross-sectional study in eastern China
title_full Predictive performance of lipid parameters in identifying undiagnosed diabetes and prediabetes: a cross-sectional study in eastern China
title_fullStr Predictive performance of lipid parameters in identifying undiagnosed diabetes and prediabetes: a cross-sectional study in eastern China
title_full_unstemmed Predictive performance of lipid parameters in identifying undiagnosed diabetes and prediabetes: a cross-sectional study in eastern China
title_short Predictive performance of lipid parameters in identifying undiagnosed diabetes and prediabetes: a cross-sectional study in eastern China
title_sort predictive performance of lipid parameters in identifying undiagnosed diabetes and prediabetes: a cross-sectional study in eastern china
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8952267/
https://www.ncbi.nlm.nih.gov/pubmed/35331213
http://dx.doi.org/10.1186/s12902-022-00984-x
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