AUTHOR=Li Jinjin , Ye Qun , Jiao Hongxiao , Wang Wanyao , Zhang Kai , Chen Chen , Zhang Yuan , Feng Shuzhi , Wang Ximo , Chen Yubao , Gao Huailin , Wei Fengjiang , Li Wei-Dong 

TITLE=An early prediction model for type 2 diabetes mellitus based on genetic variants and nongenetic risk factors in a Han Chinese cohort

JOURNAL=Frontiers in Endocrinology

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2023.1279450

DOI=10.3389/fendo.2023.1279450

ISSN=1664-2392

ABSTRACT=<sec><title>Aims</title><p>We aimed to construct a prediction model of type 2 diabetes mellitus (T2DM) in a Han Chinese cohort using a genetic risk score (GRS) and a nongenetic risk score (NGRS).</p></sec><sec><title>Methods</title><p>A total of 297 Han Chinese subjects who were free from type 2 diabetes mellitus were selected from the Tianjin Medical University Chronic Disease Cohort for a prospective cohort study. Clinical characteristics were collected at baseline and subsequently tracked for a duration of 9 years. Genome-wide association studies (GWASs) were performed for T2DM-related phenotypes. The GRS was constructed using 13 T2DM-related quantitative trait single nucleotide polymorphisms (SNPs) loci derived from GWASs, and NGRS was calculated from 4 biochemical indicators of independent risk that screened by multifactorial Cox regressions.</p></sec><sec><title>Results</title><p>We found that HOMA-IR, uric acid, and low HDL were independent risk factors for T2DM (<italic>HR &gt;</italic>1; <italic>P&lt;</italic>0.05), and the NGRS model was created using these three nongenetic risk factors, with an area under the ROC curve (<italic>AUC</italic>) of 0.678; high fasting glucose (FPG &gt;5 mmol/L) was a key risk factor for T2DM (<italic>HR</italic> = 7.174, <italic>P</italic>&lt; 0.001), and its addition to the NGRS model caused a significant improvement in <italic>AUC</italic> (from 0.678 to 0.764). By adding 13 SNPs associated with T2DM to the GRS prediction model, the <italic>AUC</italic> increased to 0.892. The final combined prediction model was created by taking the arithmetic sum of the two models, which had an <italic>AUC</italic> of 0.908, a sensitivity of 0.845, and a specificity of 0.839.</p></sec><sec><title>Conclusions</title><p>We constructed a comprehensive prediction model for type 2 diabetes out of a Han Chinese cohort. Along with independent risk factors, GRS is a crucial element to predicting the risk of type 2 diabetes mellitus.</p></sec>