Please use this identifier to cite or link to this item: https://doi.org/10.4093/dmj.2019.0020
Title: Multiple biomarkers improved prediction for the risk of type 2 diabetes mellitus in Singapore Chinese men and women
Authors: Wang, Y.
Koh, W.-P. 
Sim, X. 
Yuan, J.-M.
Pan, A.
Keywords: Biomarkers
Case-control studies
Diabetes mellitus, type 2
Epidemiology
Prognosis
Issue Date: 2019
Publisher: Korean Diabetes Association
Citation: Wang, Y., Koh, W.-P., Sim, X., Yuan, J.-M., Pan, A. (2019). Multiple biomarkers improved prediction for the risk of type 2 diabetes mellitus in Singapore Chinese men and women. Diabetes and Metabolism Journal 43 : 20. ScholarBank@NUS Repository. https://doi.org/10.4093/dmj.2019.0020
Rights: Attribution-NonCommercial 4.0 International
Abstract: Background: Multiple biomarkers have performed well in predicting type 2 diabetes mellitus (T2DM) risk in Western populations. However, evidence is scarce among Asian populations. Methods: Plasma triglyceride-to-high density lipoprotein (TG-to-HDL) ratio, alanine transaminase (ALT), high-sensitivity C-reactive protein (hs-CRP), ferritin, adiponectin, fetuin-A, and retinol-binding protein 4 were measured in 485 T2DM cases and 485 age-and-sex matched controls nested within the prospective Singapore Chinese Health Study cohort. Participants were free of T2DM at blood collection (1999 to 2004), and T2DM cases were identified at the subsequent follow-up interviews (2006 to 2010). A weighted biomarker score was created based on the strengths of associations between these biomarkers and T2DM risks. The predictive utility of the biomarker score was assessed by the area under receiver operating characteristics curve (AUC). Results: The biomarker score that comprised of four biomarkers (TG-to-HDL ratio, ALT, ferritin, and adiponectin) was positively associated with T2DM risk (P trend <0.001). Compared to the lowest quartile of the score, the odds ratio was 12.0 (95% confidence interval [CI], 5.43 to 26.6) for those in the highest quartile. Adding the biomarker score to a base model that included smoking, history of hypertension, body mass index, and levels of random glucose and insulin improved AUC significantly from 0.81 (95% CI, 0.78 to 0.83) to 0.83 (95% CI, 0.81 to 0.86; P=0.002). When substituting the random glucose levels with glycosylated hemoglobin in the base model, adding the biomarker score improved AUC from 0.85 (95% CI, 0.83 to 0.88) to 0.86 (95% CI, 0.84 to 0.89; P=0.032). Conclusion: A composite score of blood biomarkers improved T2DM risk prediction among Chinese. Copyright © 2019 Korean Diabetes Association
Source Title: Diabetes and Metabolism Journal
URI: https://scholarbank.nus.edu.sg/handle/10635/209907
ISSN: 2233-6079
DOI: 10.4093/dmj.2019.0020
Rights: Attribution-NonCommercial 4.0 International
Appears in Collections:Elements
Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_4093_dmj_2019_0020.pdf392.41 kBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

3
checked on Dec 2, 2022

Page view(s)

67
checked on Dec 1, 2022

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons