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Proteomic signatures for identification of impaired glucose tolerance

Abstract

The implementation of recommendations for type 2 diabetes (T2D) screening and diagnosis focuses on the measurement of glycated hemoglobin (HbA1c) and fasting glucose. This approach leaves a large number of individuals with isolated impaired glucose tolerance (iIGT), who are only detectable through oral glucose tolerance tests (OGTTs), at risk of diabetes and its severe complications. We applied machine learning to the proteomic profiles of a single fasted sample from 11,546 participants of the Fenland study to test discrimination of iIGT defined using the gold-standard OGTTs. We observed significantly improved discriminative performance by adding only three proteins (RTN4R, CBPM and GHR) to the best clinical model (AUROC = 0.80 (95% confidence interval: 0.79–0.86), P = 0.004), which we validated in an external cohort. Increased plasma levels of these candidate proteins were associated with an increased risk for future T2D in an independent cohort and were also increased in individuals genetically susceptible to impaired glucose homeostasis and T2D. Assessment of a limited number of proteins can identify individuals likely to be missed by current diagnostic strategies and at high risk of T2D and its complications.

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Fig. 1: Study design.
Fig. 2: Performance of LASSO-trained models for IGT and iIGT discrimination in the internal validation test set.
Fig. 3: Proposed three-stage screening strategy.
Fig. 4: Characterization of the association between top IGT and iIGT discriminatory proteins and glycemic traits, future T2D risk and genetic predisposition to metabolic phenotypes.
Fig. 5: Association of iIGT protein scores with incident cardiometabolic diseases.

Data availability

Data access for the Fenland and EPIC studies can be requested by bona fide researchers for specified scientific purposes through a simple application process via the study websites below. Data will either be shared through an institutional data sharing agreement, or arrangements will be made for analyses to be conducted remotely without the necessity for data transfer. Fenland: https://www.mrc-epid.cam.ac.uk/research/studies/fenland/information-for-researchers. EPIC-Norfolk: https://www.mrc-epid.cam.ac.uk/research/studies/epic-norfolk.Source data are provided with this paper.

Code availability

The code used for the machine learning developed framework has been deposited in the following repository: https://github.com/MRC-Epid/iigt_prediction_proteomics.

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Acknowledgements

The Fenland study (10.22025/2017.10.101.00001) is funded by the Medical Research Council (MC_UU_12015/1). We are grateful to all the volunteers and to the general practitioners and practice staff for assistance with recruitment. We thank the Fenland study investigators, Fenland study coordination team and epidemiology field, data and laboratory teams. We further acknowledge support for genomics from the Medical Research Council (MC_PC_13046). Proteomic measurements were supported and governed by a collaboration agreement between the University of Cambridge and SomaLogic. We thank I. von Carlowitz and K. Soucie for their contributions to the fasting proteome analysis.The EPIC-Norfolk study (10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 MC-UU_12015/1 and MC_UU_00006/1) and Cancer Research UK (C864/A14136). We are grateful to all the participants who have been part of the project and to the many members of the study teams at the University of Cambridge who have enabled this research. We thank all participants in the Whitehall II Study, Whitehall II researchers and support staff who make the study possible. 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. J.C.Z.S. is supported by a 4-year Wellcome Trust PhD Studentship and the Cambridge Trust, and C.L., E.W. and N.J.W. are funded by the Medical Research Council (MC_UU_12015/1). N.J.W. is an NIHR Senior Investigator. The WHII study and M.K. are supported by grants from the Wellcome Trust (221854/Z/20/Z), UK Medical Research Council (R024227) and NIA, NIH (R01AG056477). J.V.L. was supported by the Academy of Finland (311492 and 339568) and Helsinki Institute of Life Science (H970) grants paid to employer and by the Päivikki and Sakari Sohlberg foundation. The funders had no role in the study design, data collection and analysis and the decision to publish or in the preparation of the manuscript.

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Authors and Affiliations

Authors

Contributions

J.C.Z.S., M.P., N.J.W. and C.L. designed the analysis and drafted the manuscript. J.C.Z.S. analyzed the data. J.V.L. performed the replication analyses in the WHII study. M.S. and M.W. performed the analysis for assessing the effect of fasting status on protein levels. N.J.W. is the principal investigator of the Fenland study cohort, and M.K. is the principal investigator of the WHII study. All authors contributed to the interpretation of the results and critically reviewed the manuscript.

Corresponding author

Correspondence to Claudia Langenberg.

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Competing interests

M.S., M.W., D.D., R.O. and S.A.W. are employees of SomaLogic. E.W. and E.O. are now employees at AstraZeneca. The remaining authors declare no competing interests.

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Nature Medicine thanks Peter Rossing, Jesse Meyer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jennifer Sargent, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Feature selection ranking of proteins for postprandial glycaemia prediction.

Protein ranking based on the number of times selected over bootstrap resampling during feature selection for impaired glucose tolerance (IGT) (a) and isolated impaired glucose tolerance (iIGT) (b). Dashed lines represent thresholds for proteins selected in more than 80%, 90% or 95% of bootstrap samples to be taken forward to parameter optimization step.

Extended Data Fig. 2 Performance of LASSO trained protein-only models for IGT (a) and iIGT (b) discrimination in the internal validation test set.

a, Impaired glucose tolerance (IGT) discrimination was evaluated in the independent internal validation test set (N = 2881, 192 IGT individuals) for models based on proteins selected in more than 80% (65 proteins), 90% (18 proteins) or 95% (8 proteins) and kept after model optimization step or based on all proteins (4979 proteins). b, isolated impaired glucose tolerance (iIGT) discrimination was evaluated in the independent internal validation test set (N = 2819, 135 iIGT individuals) for models based on proteins selected in more than 80% (73 proteins), 90% (17 proteins) or 95% (3 proteins) and kept after model optimization step or based on all proteins (4979 proteins).

Extended Data Fig. 3

Performance of the T2D genetic risk score (T2D-GRS) for IGT (a) and iIGT (b) discrimination in the internal validation test set.

Extended Data Fig. 4 Validation of the clinical and clinical + protein models for IGT (a) and iIGT (b) in the independent WHII study.

The clinical + protein model significantly outperformed the clinical model (p-valueIGT = 5.26 × 10−5; p-valueiIGT = 1.5 × 10−17). The improvement was of similar magnitude than that observed in the Fenland study, although with overall lower AUROCs (clinical models: AUROCIGT = 0.66 (0.64–0.69), and AUROCiIGT = 0.60 (0.57–0.62); clinical + protein models: AUROCIGT = 0.70 (0.68–0.72) and AUROCiIGT = 0.69 (0.67–0.71)). Significant differences between the AUROCs were asses by the Delong method. This might be best explained by differences in the characteristics of the study population, the design and the lack of HbA1c to define iIGT (see Methods).

Extended Data Fig. 5 Performance of LASSO trained models for isolated impaired glucose tolerance discrimination in the internal validation test set having excluded the top 3 selected proteins.

Isolated impaired glucose tolerance (iIGT) discrimination performance in the independent internal validation test set (N = 2795, 111 iIGT individuals) for the standard clinical model, a 68-protein model (selected in >80% of bootstrap samples and kept during optimization), and a clinical + 7 protein model (selected in >95% of bootstrap samples).

Extended Data Fig. 6 Internal validation of proposed 3-stage screening strategy in the test set only.

In the first stage, individuals in the Fenland test set were divided into low and high risk according to the Cambridge T2D risk score. The high risk group would undergo a second stage involving measurement of HbA1c and of the 3 iIGT proteins. Individuals with HbA1c levels within the T2D or prediabetic range would be referred for intervention and lifestyle modifications. Individuals with HbA1c below the prediabetic range, would further stratified using the final clinical + 3 iIGT protein model to identify a high risk group, which on a third stage would be taken forward for OGTT testing to identify iIGT cases that would have been otherwise by current screening guidelines. The NNS in the strata of individuals at high predicted risk based on the patient-derived information model, but HbA1c levels below cut-offs for prediabetes (N = 1043) was 14, while by additionally applying the clinical + 3-protein iIGT model the NNS was of only 5 (N = 88 at high-risk). Figure was designed with biorender.com.

Extended Data Fig. 7 Comparison of protein ranking during feature selection over bootstrap resampling for isolated impaired glucose tolerance (iIGT) and impaired glucose tolerance (IGT).

Comparison is shown for proteins that were selected in more 80% of bootstrap samples (shown by the red line) for either IGT (N = 2881, 192 IGT individuals) or iIGT (N = 2795, 111 iIGT individuals).

Extended Data Fig. 8 Percentage of variance explained in impaired glucose tolerance and isolated impaired glucose tolerance top discriminatory protein levels by clinical, biochemical, anthropometric and lifestyle risk factors.

Linear mixed models were fitted for each of the 24 clinical, biochemical, anthropometric, genetic and lifestyle risk factor variables adjusting by age and sex to estimate the percentage of explained variance in plasma abundances of discriminatory proteins as well as for the principal component of the 65-IGT and 68-iIGT protein signatures. Cis and trans scores with missing values represent proteins for which no protein quantitative trait loci could be identified.

Extended Data Fig. 9 Association of iIGT protein scores using Olink explore proteomics measures with incident cardiometabolic diseases.

Association of iIGT prediction scores (left panel; red: Cambridge T2D risk score, orange: Cambridge T2D risk score variable + fasting glucose + 3 protein iIGT prediction model, darkblue: 3-protein iIGT prediction model) and individual top iIGT proteins (right panel) with 7 cardiometabolic disease outcomes in a sub-cohort the EPIC-Norfolk study (N = 602 individuals). 95% confidence intervals of hazard ratios (HR) are shown.

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Carrasco-Zanini, J., Pietzner, M., Lindbohm, J.V. et al. Proteomic signatures for identification of impaired glucose tolerance. Nat Med 28, 2293–2300 (2022). https://doi.org/10.1038/s41591-022-02055-z

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