A multi-omics risk score enables early and personalized screening for type 2 diabetes: A population-based cohort study

IF 3.3 3区医学 Q2 ENDOCRINOLOGY & METABOLISM

Diabetic Medicine Pub Date : 2026-10-07 Epub Date: 2026-04-11 DOI:10.1111/dme.70323

Fan Bu, Chen Fang, Xiao Zheng, Xiu-Juan Yu, Shan-Juan Yuan, Chao-Jie Liu, Zi-Tong Song, Yue-Ping Shen, Lei Zhang, Yu-Fang Pei

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引用次数: 0

Abstract

Objective

This study aims to develop a multi-omics model to predict type 2 diabetes onset using multi-omics data, beyond established clinical and genetic factors.

Methods

In the UK Biobank cohort (N = 21,312,893 incident type 2 diabetes cases), Ridge Cox regression models were constructed based on clinical factors defined by the Finnish Diabetes Risk Score (CliS), plasma metabolomics (MetS), proteomics (ProS) and polygenic risk score (PRS). Model performance was evaluated by C-index and net reclassification improvement (NRI). Clinical utility was assessed through net benefit, risk stratification and screening initiation ages. Independent validation of protein markers was performed in the Liyang Cohort (N = 10,056).

Results

The ProS was the top-performing single-omics predictor (C-index: 0.80). ComS (Combined risk score) achieved the highest discriminative ability (C-index: 0.84), significantly outperforming the CliS (C-index: 0.76, NRI: 0.328, p < 0.001). ComS reclassified 73 additional type 2 diabetes cases per 1000 participants not receiving intervention, without increasing false positives. Kaplan–Meier analysis confirmed superior risk stratification of ComS (Log-rank p < 0.0001). Critically, both MetS and ProS identified high-risk individuals missed by the conventional CliS. Screening initiation before age 40 was warranted for individuals in the top risk quintile of MetS, ProS, or ComS. In the Liyang Cohort, plasma FGF23 levels were significantly elevated in type 2 diabetes cases (p < 0.05), corroborating its role as a key proteomic contributor to risk prediction.

Conclusion

The combined multi-omics model enables more precise, earlier type 2 diabetes risk stratification, supporting personalized screening strategies years before clinical onset.

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多组学风险评分使2型糖尿病的早期和个性化筛查成为可能：一项基于人群的队列研究

目的：本研究旨在建立一个多组学模型，利用多组学数据预测2型糖尿病的发病，超越既定的临床和遗传因素。方法：在英国生物银行队列（N = 21,312,893例2型糖尿病病例）中，基于芬兰糖尿病风险评分（CliS）、血浆代谢组学（MetS）、蛋白质组学（ProS）和多基因风险评分（PRS）定义的临床因素构建Ridge Cox回归模型。采用c指数和净重分类改进（NRI）评价模型性能。临床效用通过净收益、风险分层和筛查起始年龄进行评估。在溧阳队列（N = 10,056）中进行了蛋白质标记物的独立验证。结果：ProS是最佳的单组学预测因子（C-index: 0.80）。ComS（联合风险评分）的判别能力最高（C-index: 0.84），显著优于CliS （C-index: 0.76, NRI: 0.328, p）。结论：联合多组学模型能够更精确、更早地进行2型糖尿病风险分层，支持在临床发病前数年的个性化筛查策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Diabetic Medicine 医学-内分泌学与代谢

CiteScore

7.20

自引率

5.70%

发文量

229

审稿时长

3-6 weeks

期刊介绍： Diabetic Medicine, the official journal of Diabetes UK, is published monthly simultaneously, in print and online editions. The journal publishes a range of key information on all clinical aspects of diabetes mellitus, ranging from human genetic studies through clinical physiology and trials to diabetes epidemiology. We do not publish original animal or cell culture studies unless they are part of a study of clinical diabetes involving humans. Categories of publication include research articles, reviews, editorials, commentaries, and correspondence. All material is peer-reviewed. We aim to disseminate knowledge about diabetes research with the goal of improving the management of people with diabetes. The journal therefore seeks to provide a forum for the exchange of ideas between clinicians and researchers worldwide. Topics covered are of importance to all healthcare professionals working with people with diabetes, whether in primary care or specialist services. Surplus generated from the sale of Diabetic Medicine is used by Diabetes UK to know diabetes better and fight diabetes more effectively on behalf of all people affected by and at risk of diabetes as well as their families and carers.”