Multi-omic prediction of incident type 2 diabetes

Abstract

Aims/hypothesis: The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes.

Methods: We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA_1c.

Results: Among single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA_1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period.

Conclusions/interpretation: Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA_1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions.

Keywords: Biomarkers; Genomics; Metabolomics; Prediction models; Proteomics; Type 2 diabetes.

Fig. 1

Study design. We designed a case-cohort (N=1105) for incident type 2 diabetes within the EPIC-Norfolk study. Genotyping, proteomics (SomaScan v4), metabolomics (Metabolon Discovery HD4) and biomarker profiling were done in samples from the baseline assessment. T2D, type 2 diabetes. Created with BioRender.com

Fig. 2

Multi-omic prediction of type 2 diabetes incidence. (a–d) C index of the prediction models in all individuals from the internal validation set or by stratifying into individuals with prediabetes (HbA_1c ≥42 mmol/mol [6.5%], n=45) and individuals with normoglycaemia (HbA_1c <42 mmol/mol [6.5%], n=171). The 95% CI from bootstrapping is shown. (e–h) Top ten features selected from each of the omic layers. Selection scores are shown normalised to the feature with the highest score for interpretability. 2-linoleoyl-GPC, 2-linoleoyl-glyceroposphocholin; MXRA8, matrix remodelling associated protein 8; 1-oleoyl-2-linoleoyl-GPC, 1-oleoyl-2-linoleoyl-glyceroposphocholin; SLIK3, SLIT and NTRK like family member 3; T2D, type 2 diabetes

Fig. 3

Cumulative incidence of type 2 diabetes over 20 years in individuals with prediabetes compared with quartiles of clinical + polygenic risk. The clinical + PGS model was used to divide individuals with normoglycaemia into quartiles according to predicted risk and to estimate the cumulative incidence among these groups compared with the cumulative incidence in individuals with prediabetes