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Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes


The state of intermediate hyperglycemia is indicative of elevated risk of developing type 2 diabetes1. 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 diabetes2,3 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 constructs4. 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.

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Fig. 1: Distribution of the cluster feature variables.
Fig. 2: Characteristics potentially contributing to cluster pathomechanism.
Fig. 3: Cluster-specific outcomes.

Data availability

For TUEF/TULIP data, all requests for data and materials will be promptly reviewed by the Data Access Steering Committee of the Institute of Diabetes and Metabolic Research, Tübingen, to verify whether the request is subject to any intellectual property or confidentiality obligations. Individual-level data may be subject to confidentiality. Any data and materials that can be shared will be released via a Material Transfer Agreement. Access to individual-level data of the Whitehall II study is subject to a separate data-sharing agreement according to the data-sharing policy of Whitehall II. This policy conforms to the MRC Policy on Research Data Sharing. More details can be found on the Whitehall II webpage:

Code availability

The R code used to generate all results of this manuscript is available upon request. Requests will be reviewed by the Data Access Steering Committee of the Institute of Diabetes and Metabolic Research, Tübingen.


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We thank all the research volunteers for their participation. We thank all participants in the Whitehall II Study, Whitehall II researchers and support staff who made the study possible. We gratefully acknowledge the excellent technical assistance of the Diabetes Research Unit Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Germany. We thank J. Kriebel and H. Grallert (Molecular Epidemiology, Helmholtz Center Munich) for generating the Global Screening Array data. This study was supported in part by a grant (01GI0925) from the Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD e.V.) and from the state of Baden-Württemberg to R.W. and A.F. (32-5400/58/2, Forum Gesundheitsstandort Baden-Württemberg). The UK Medical Research Council (MR/K013351/1; G0902037), British Heart Foundation (RG/13/2/30098) and the US National Institutes of Health (R01HL36310, R01AG013196) have supported collection of data in the Whitehall II Study.

Author information

Authors and Affiliations



R.W. analyzed the data and wrote the manuscript. M.H., A.G.T., J.M., F.S., E.R. and A.F. contributed to data acquisition and the interpretation of data, and edited the manuscript. M.H.d.A., A.P. A.L.B. and N.S. contributed to the interpretation of data and edited the manuscript. H.-U.H. and A.F. contributed to the concept of the work and edited the manuscript. All authors have reviewed the manuscript.

Corresponding author

Correspondence to Robert Wagner.

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The authors declare no competing interests.

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Peer review information Jennifer Sargent was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Assignment of proxy variables from the Whitehall II cohort to the original clustering variables.

Assignment of proxy variables from the Whitehall II cohort (variables that were both assessed in Whitehall-II and the original clustering cohort TUEF/TULIP) to the original clustering variables. Clusters were identified in Whitehall II using the Euclidean distances of the subjects computed from these variables to the cluster medians in TUEF/TULIP. The upper row shows the original clustering variables available in TUEF/TULIP, the lower row the variables in Whitehall-II. Arrows show the physiological connection between the variables.

Extended Data Fig. 2 Key features of the clusters.

NGT: normal glucose tolerance, IFG: impaired fasting glucose, IGT: impaired glucose tolerance, T2D: type 2 diabetes.

Extended Data Fig. 3 Transitions into Ahlqvist-diabetes-classes for subjects who were assigned to clusters in the Whitehall II study and developed diabetes during follow-up.

Transitions into Ahlqvist-diabetes-classes (right hand side) for subjects who were assigned to clusters in the Whitehall II study and developed diabetes during follow-up (left hand side, N = 201).

Extended Data Fig. 4 Kaplan-Meier curves to compare the risk discrimination between Hulman-classes and clusters.

Kaplan-Meier curves to compare the risk discrimination between Hulman-classes (A, n = 416 individuals with follow-up) and clusters (B, n = 421 individuals with follow-up) showing probabilities of remaining diabetes free in the TUEF/TULIP cohort. P-values indicate two-sided log-rank tests.

Extended Data Fig. 5 Kaplan-Meier curves to compare the risk discrimination between quintiles of baseline glucose AUC levels and clusters.

Kaplan-Meier curves to compare the risk discrimination between quintiles of baseline glucose AUC levels (A, n = 6643 individuals with follow-up) and clusters (B, n = 6643 individuals with follow-up) for diabetes development in the Whitehall II study. P-values indicate two-sided log-rank tests.

Extended Data Fig. 6 Cumulative incidence of chronic kidney disease stage 3 or worse.

Cumulative incidence of chronic kidney disease stage 3 or worse in the Whitehall-II study (N = 5182).

Extended Data Fig. 7 Cluster-stratified carotid intima media thickness.

Cluster-stratified carotid intima media thickness (IMT) in the subset with ultrasound measurements (N = 479) in the TUEF/TULIP study. IMT was measured in 60%, 37%, 46%, 72%, 45% and 55% of the participants of cluster 1 through 6, respectively. Boxes (hinges) denote the 25th and 75th percentiles with an additional horizontal line indicating the median. Whiskers show the highest and lowest data points excluding outliers (defined as at least 1.5×interquartile range below the lower or above the upper hinge).

Extended Data Fig. 8 Cluster stability plot.

Cluster stability plot showing all consecutive cluster transitions in the iterative re-clustering of repeated measurements in TUEF/TULIP (N = 429 individuals with repeated measurements).

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Wagner, R., Heni, M., Tabák, A.G. et al. Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes. Nat Med 27, 49–57 (2021).

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