Breschi Alessandra, Wang Yuliang, Short Sarah, Luk Wilman, Erani David, Kheradpour Pouya, Cimermancic Peter, Tong Gary J, Martin Jean Philippe, Liu Manway, Cao Lulu, Liu Daniel, Chatterjee Ranee, Kwee Lydia Coulter, Snyder Thomas M, Han Andrew, Drake Katherine, Kim Charles C
Verily Life Sciences, South San Francisco, CA, USA.
Onduo, P.C., Newton, MA, USA.
Commun Med (Lond). 2025 Jul 3;5(1):272. doi: 10.1038/s43856-025-00964-x.
Understanding diabetes at the molecular level can help refine diagnostic approaches and personalized treatment efforts.
We generated proteomic data from plasma collected from participants enrolled in the longitudinal observational cohort study Project Baseline Health Study (PBHS) (evaluated cohort, n = 738, 27.9% of the total PBHS cohort), and integrated those data with information from their medical history and laboratory tests to determine diabetes status. We then identified biomarker proteins associated with diabetes status.
Here we identify 87 differentially expressed proteins in people with diabetes compared to those without diabetes, 71 of which show higher expression. This proteomic profile, integrated with clinical data into a logistic regression model, can discriminate diabetes status with over 85% balanced accuracy.
Our approach indicates that proteomic data can enhance diabetes phenotyping, showing potential for marker-based stratification of diabetes diagnosis. These results suggest that a holistic molecular-clinical approach to diagnosis might help personalize treatments or interventions for people with diabetes.
在分子水平上了解糖尿病有助于优化诊断方法和个性化治疗方案。
我们从参与纵向观察队列研究“基线健康研究项目”（PBHS）的参与者所采集的血浆中生成蛋白质组学数据（评估队列，n = 738，占PBHS总队列的27.9%），并将这些数据与他们的病史和实验室检查信息相结合，以确定糖尿病状态。然后，我们鉴定了与糖尿病状态相关的生物标志物蛋白。
在此，我们鉴定出87种在糖尿病患者与非糖尿病患者中差异表达的蛋白质，其中71种表达上调。这种蛋白质组学特征与临床数据整合到逻辑回归模型中，能够以超过85%的平衡准确率区分糖尿病状态。
我们的方法表明蛋白质组学数据可以增强糖尿病表型分析，显示出基于标志物的糖尿病诊断分层的潜力。这些结果表明，一种整体的分子临床诊断方法可能有助于为糖尿病患者个性化治疗或干预。
