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Heterogeneity in phenotype, disease progression and drug response in type 2 diabetes

Nature Medicine volume 28, pages 982–988 (2022)Cite this article

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Abstract

Type 2 diabetes (T2D) is a complex chronic disease characterized by considerable phenotypic heterogeneity. In this study, we applied a reverse graph embedding method to routinely collected data from 23,137 Scottish patients with newly diagnosed diabetes to visualize this heterogeneity and used partitioned diabetes polygenic risk scores to gain insight into the underlying biological processes. Overlaying risk of progression to outcomes of insulin requirement, chronic kidney disease, referable diabetic retinopathy and major adverse cardiovascular events, we show how these risks differ by patient phenotype. For example, patients at risk of retinopathy are phenotypically different from those at risk of cardiovascular events. We replicated our findings in the UK Biobank and the ADOPT clinical trial, also showing that the pattern of diabetes drug monotherapy response differs for different drugs. Overall, our analysis highlights how, in a European population, underlying phenotypic variation drives T2D onset and affects subsequent diabetes outcomes and drug response, demonstrating the need to incorporate these factors into personalized treatment approaches for the management of T2D.

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Fig. 1: A visual representation of the phenotypic characteristics of 23,137 patients at diagnosis of T2D.
Fig. 2: Visualizing the heterogeneity in diabetes progression in Scottish patients with T2D.
Fig. 3: Visualizing the heterogeneity in diabetes progression in UKBB data.
Fig. 4: Visualizing the heterogeneity in anti-diabetic drug failure using ADOPT trial data.
Fig. 5: Distribution of T2D pPSs across the phenotypic tree.

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Data availability

The data that support the findings of this study are from anonymized real-world medical records available through the Scottish Care Information-Diabetes Collaboration, Tayside & Fife, Scotland unit (https://www.sci-diabetes.scot.nhs.uk/). UKBB primary care data are not publicly available but are accessible for research on approval from the UKBB (https://www.ukbiobank.ac.uk/enable-your-research). ADOPT trial data are not publicly available due to governance limitations but are available for research by approval from GlaxoSmithKline.

Code availability

All the R code that supports this analysis is specific to the Scottish Care Information-Diabetes Collaboration data and UKBB and ADOPT trial variables; thus, data fields are not made available. Codes used for implementing the DDRTree (version 0.1.5) algorithm are available publicly in the ‘monocle’ package in the Bioconductor repository (https://www.bioconductor.org/packages/release/bioc/html/monocle.html).

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Acknowledgements

We thank all personnel at the Health Informatics Centre for linking different datasets, maintaining all statistical packages and providing the data. The research was supported by the National Institute for Health Research using Official Development Assistance funding (INSPIRED 16/136/102) and Health Data Research UK, which receives its funding from HDR UK Ltd. (HDR-5012), funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the British Heart Foundation and the Wellcome Trust. The views expressed in this publication are those of the authors and not necessarily those of the National Institute for Health Research or the UK Department of Health and Social Care. J.M.D. is supported by an independent fellowship funded by Research England’s Expanding Excellence in England (E3) fund. E.R.P. holds a Wellcome Trust New Investigator Award. This research was funded in whole or in part by the Wellcome Trust (102820/Z/13/Z).

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Authors and Affiliations

  1. Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK

    Anand Thakarakkattil Narayanan Nair, Aravind Lathika Rajendrakumar, Simona Hapca, Sushrima Gan, Adem Y. Dawed, Louise A. Donnelly, Rory McCrimmon, Alex S. F. Doney, Colin N. A. Palmer & Ewan R. Pearson

  2. Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK

    Agata Wesolowska-Andersen

  3. Novo Nordisk Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

    Caroline Brorsson

  4. Madras Diabetes Research Foundation, Chennai, India

    Viswanathan Mohan & Ranjit M. Anjana

  5. Institute of Biomedical and Clinical Science, Royal Devon and Exeter Hospital, University of Exeter, Exeter, UK

    Andrew T. Hattersley & John M. Dennis

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  1. Anand Thakarakkattil Narayanan Nair
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Contributions

E.R.P., A.W.A., C.B. and A.T.N. conceived and designed the study. A.L.R., S.H., L.D., J.M.D., A.D. and A.T.N. were involved in data preparation and data analysis. E.R.P., L.D. and A.T.N. interpreted the results and wrote the manuscript. A.D., S.G., A.W.A., R.M.A., V.M., C.N.A.P., R.M.C., A.S.F.D., J.M.D., A.T.H. and C.B. provided critical inputs to the revision of the manuscript.

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Correspondence to Ewan R. Pearson.

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Nature Medicine thanks Jose Florez, Robert Sladek and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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.

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Extended data

Extended Data Fig. 1 Study sample selection flow chart.

Study sample selection flow chart from (a) Tayside and Fife (SCI-DC), (b) UKBB and (c) ADOPT data (data flow for merging all phenotypes).

Extended Data Fig. 2 A visual representation of the phenotypic characteristics of (n = 7332) patients at diagnosis of T2D from UKBB.

A mapping function was used to position individuals in UKBB (n = 7332) onto the Scottish (reference tree) tree. The phenotype values are overlaid on the tree structure to visualise the distribution of nine phenotypes (HbA1c, BMI, HDL-c, TC, TG, ALT, creatinine, and SBP and DBP) over the reduced tree structure. Each point in the figure represents one individual. The magenta colour of the point indicates a higher value of the phenotypic variable for that individual and the green colour indicates lower values.

Extended Data Fig. 3 A visual representation of the phenotypic characteristics of (n = 4150) patients at diagnosis of T2D from ADOPT data.

A mapping function was used to position individuals in ADOPT (n = 4150) to the Scottish (reference) tree. The phenotype values are overlaid on the tree structure to visualise the distribution of nine phenotypes (HbA1c, BMI, HDL-c, TC, TG, ALT, creatinine, and SBP and DBP) over the reduced tree structure. Each point in the figure represents one individual. The magenta colour of the point indicates a higher value of the phenotypic variable for that individual and the green colour indicates lower values. Grey areas represent Scottish patients not represented in the clinical trial.

Extended Data Fig. 4 Distribution of C-peptide, adiponectin, and leptin over the tree structure.

For a sub-population of Scottish patients, c-peptide (a), adiponectin (b) and leptin (c) measurements were available and are overlaid on the tree. The magenta color indicates higher values of the markers, and the green color indicates lower values (A-C). (d) shows the beta for the linear regression (with 95% CI) of C-peptide against the tree dimensions. (e) shows the beta for the linear regression (with 95% CI) of Adiponectin against the tree dimensions. (f) shows the beta for the linear regression (with 95% CI) of leptin against the tree dimensions. C-peptide and leptin showed a significant positive correlation with dimension 1 and negative correlation with dimension 2 whilst Adiponectin was significantly positively correlated with dimension 2 and negatively correlated with dimension 1.

Extended Data Fig. 5 Distribution of measured beta-cell function and insulin resistance across the tree.

Panel a & b: A visual representation of the phenotypic characteristics (log HOMA B and log HOMA IR) of n = 4150 patients at diagnosis of T2D from ADOPT trial. A mapping function was used to position individuals in ADOPT trial to the Scottish (reference) tree. Panel c shows the beta for the regression of each phenotype against the tree dimensions with 95% CI (n = 4150) – HOMA B showed a positive correlation with dimension 1 and negative correlation with dimension 2; while HOMA IR positively correlated with dimension 1 and negatively correlated with dimension 2.

Extended Data Fig. 6 Distribution of albuminuria at T2D diagnosis over the tree structure.

Albuminuria at T2D diagnosis over the tree structure (N = 15977; individuals with albumin measurement close to T2D diagnosis), B: Linear regression estimates (with 95% CI) (n = 15977) between the DDRTree dimensions and albuminuria showing the association between phenotypes and dimensions [The Global Moran’s I value: 0.003, p value<0.0001] (T2D: Type 2 Diabetes, DDRTree: Discriminative Dimensionality Reduction via Learning a Tree).

Extended Data Fig. 7 Pattern of distribution of local Moran’s I of phenotype and diabetes outcome event probability.

a) Local Moran’s I values for nine phenotypes at T2D diagnosis, magenta color indicates high values and green indicate lower values of Local Moran’s I. (b) Local Moran’s I values for diabetes progression event probabilities indicated by insulin initiation, CKD, MACE and DR, Magenta color indicate high values and green indicate lower values.

Extended Data Fig. 8 Visualizing the heterogeneity in diabetes progression in Scottish patients with T2DM.

All predictions are from models with DDRTree dimensions fitted with a spline function. a. Predicted probability of insulin initiation (use of insulin for more than 6 months or a clinical requirement for insulin, indicated as two or more HbA1c reading > =8.5% more than three months apart while taking two or more oral antihyperglycaemic agents) at 5 year from the diagnosis of T2D. b. Probability of severe or referrable (R3/R4) diabetic retinopathy at 5-year period. c. Probability of incident major adverse cardiac events (identified from SMR based on ICD 9 and ICD 10 codes) at 5-year period. d. Probability of incident chronic kidney disease (eGFR < = 60 ml/min/1.73m2 on at least 2 readings which were 90 days apart) at 5-year period. All outcomes (A-D) probabilities were generated from a competing risk model constructed with the spline function of DDRTree dimensions.

Extended Data Fig. 9 Pattern of distribution of event probabilities derived from competing risk models constructed with continuous variables as covariates.

a. Predicted probability of insulin initiation (use of insulin for more than 6 months or a clinical requirement for insulin, indicated as two or more HbA1c reading > =8.5% more than three months apart while taking two or more oral antidiabetic agents) at 5- year period from the diagnosis of T2D (n = 22595). b. Probability of incident diabetic retinopathy (R3/R4) at 5-year period (n = 22759). c. Probability of incident major adverse cardiac events (identified from SMR and GRO based on ICD 9 and ICD 10 codes) at 5-year period (n = 18239). d. Probability of incident chronic kidney disease (eGFR < = 60 ml/min/1.73m2 on at least 2 readings which were 90 days apart) at 5-year period (n = 19956). For all outcomes (A-D) probabilities were generated from a competing risk model constructed with continuous variables (age of diagnosis, sex, HbA1c, BMI, HDL-C, TG, TC, ALT, BP, and Creatinine) and competing risk of death. e. Linear regression estimates (with 95% CI) between the DDRTree dimensions and the four diabetes outcomes probability f. Spatial autocorrelation diabetes outcome probability; The Moran’s I statistic is shown on the X-axis, with higher values representing phenotypes that are more strongly autocorrelated; all values are p < 0.001.

Extended Data Fig. 10

Graphical representation of the complete analysis.

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Nair, A.T.N., Wesolowska-Andersen, A., Brorsson, C. et al. Heterogeneity in phenotype, disease progression and drug response in type 2 diabetes. Nat Med 28, 982–988 (2022). https://doi.org/10.1038/s41591-022-01790-7

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