期刊
NATURE MEDICINE
卷 27, 期 1, 页码 49-+出版社
NATURE PORTFOLIO
DOI: 10.1038/s41591-020-1116-9
关键词
-
资金
- Federal Ministry of Education and Research (BMBF) [01GI0925]
- state of Baden-Wurttemberg [325400/58/2]
- UK Medical Research Council [MR/K013351/1, G0902037]
- British Heart Foundation [RG/13/2/30098]
- US National Institutes of Health [R01HL36310, R01AG013196]
- MRC [MR/R024227/1] Funding Source: UKRI
Analysis of high-risk individuals revealed six distinct subphenotypes of type 2 diabetes, among which three had elevated glycemia levels, with clusters 3 and 5 having imminent diabetes risks.
The state of intermediate hyperglycemia is indicative of elevated risk of developing type 2 diabetes(1). However, the current definition of prediabetes neither reflects subphenotypes of pathophysiology of type 2 diabetes nor is predictive of future metabolic trajectories. We used partitioning on variables derived from oral glucose tolerance tests, MRI-measured body fat distribution, liver fat content and genetic risk in a cohort of extensively phenotyped individuals who are at increased risk for type 2 diabetes(2,3) to identify six distinct clusters of subphenotypes. Three of the identified subphenotypes have increased glycemia (clusters 3, 5 and 6), but only individuals in clusters 5 and 3 have imminent diabetes risks. By contrast, those in cluster 6 have moderate risk of type 2 diabetes, but an increased risk of kidney disease and all-cause mortality. Findings were replicated in an independent cohort using simple anthropomorphic and glycemic constructs(4). This proof-of-concept study demonstrates that pathophysiological heterogeneity exists before diagnosis of type 2 diabetes and highlights a group of individuals who have an increased risk of complications without rapid progression to overt type 2 diabetes. Clustering of patients with prediabetes using simple clinical features reveals six distinct groups with differing risk of developing type 2 diabetes and its associated complications.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据