Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes

Self Cite

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 HbA1c. 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 HbA1c <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 HbA1c 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. Graphical Abstract

Section: Discussionmentioning

confidence: 99%

Multi-omic prediction of incident type 2 diabetes

Carrasco-Zanini,

Pietzner,

Wheeler

et al. 2023

Self Cite

“…Few studies have reported on the utility of high-throughput proteomics of plasma to more reliably estimate IR [ 13 ]. Several studies have focused on the identification of prevalent type 2 diabetes or the prediction of incident type 2 diabetes while others have examined surrogate measures of IR [ 13 ]. To the best of our knowledge, this is the first study to combine high-throughput methodology with a direct measure of insulin sensitivity.…”

Section: Discussionmentioning

confidence: 99%

“…Surrogate measures of IR possess suboptimal diagnostic sensitivity, especially among people without obesity, and are hampered by the lack of standardisation of the insulin assay [ 11 , 12 ]. Diagnostic approaches leveraging blood-based signatures derived from the measurement of multiple biomarkers have shown promise and may allow for the more reliable identification of individuals at high cardiometabolic risk [ 13 ]. Here, we assess the utility of this approach in explaining the variability in insulin sensitivity as estimated by the M value using high-throughput plasma proteomics in two of the largest studies to date that have implemented the EIC: the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) [ 14 ] and the Uppsala Longitudinal Study of Adult Men (ULSAM) [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts

Zanetti¹,

Stell²,

Gustafsson³

et al. 2023

Aims/hypothesis The euglycaemic–hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC. Methods We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M value variance explained (R2). Results A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R2 from 0.237 (95% CI 0.178, 0.303) to 0.456 (0.372, 0.536) in RISC. A similar pattern was observed in ULSAM, in which the M value R2 increased from 0.443 (0.360, 0.530) to 0.632 (0.569, 0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R2 despite differences in baseline cohort characteristics and clamp methodology (RISC to ULSAM: 0.491 [0.433, 0.539] for 51 proteins; ULSAM to RISC: 0.369 [0.331, 0.416] for 67 proteins). A randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R2 but to a lesser degree than in standard LASSO models: 0.352 (0.266, 0.439) in RISC and 0.495 (0.404, 0.585) in ULSAM. Reductions in improvements of R2 with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R2 0.444 [0.391, 0.497]; ULSAM to RISC R2 0.348 [0.300, 0.396]). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomised LASSO. The single most consistently selected protein across all analyses and models was IGF-binding protein 2. Conclusions/interpretation A plasma proteomic signature identified using a standard LASSO approach improves the cross-sectional estimation of the M value over routine clinical variables. However, a small subset of these proteins identified using a stability selection algorithm affords much of this improvement, especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin-resistant individuals at risk of insulin resistance-related adverse health consequences. Graphical Abstract

“…Technological advances in measurement of epigenetic markers, metabolites and proteins have made it possible to add other 'omic' measures to risk tools for prediction of type 2 diabetes. As with the genetic markers, the available evidence suggests that each of these types of additional 'omic' data provide novel insights into disease aetiology and pathogenesis [30][31][32] and although they are predictive of disease, especially when considered in isolation, they do not make a material difference to the predictive utility of risk tools when considered as an addition to existing information [33]. The next step forward is not to keep repeating the same mistake of hoping that the addition of yet further information will somehow improve the prediction of type 2 diabetes but instead to reflect on the principle that diagnosis, screening and high-risk prevention are part of the same process, and to consider whether personalisation of prevention may play a role in specific diagnostic subgroups that are hidden within the diffuse disorder that we label as type 2 diabetes.…”

Section: Development Of More Personalised Approaches To Type 2 Diabet...mentioning

confidence: 99%

Personalised prevention of type 2 diabetes

Wareham

2022

It is well established from clinical trials that behavioural interventions can halve the risk of progression from prediabetes to type 2 diabetes but translating this evidence of efficacy into effective real-world interventions at scale is an ongoing challenge. A common suggestion is that future preventive interventions need to be more personalised in order to enhance effectiveness. This review evaluates the degree to which existing interventions are already personalised and outlines how greater personalisation could be achieved through better identification of those at high risk, division of type 2 diabetes into specific subgroups and, above all, more individualisation of the behavioural targets for preventive action. Approaches using more dynamic real-time data are in their scientific infancy. Although these approaches are promising they need longer-term evaluation against clinical outcomes. Whatever personalised preventive approaches for type 2 diabetes are developed in the future, they will need to be complementary to existing individual-level interventions that are being rolled out and that are demonstrably effective. They will also need to ideally synergise with, and at the very least not detract attention from, efforts to develop and implement strategies that impact on type 2 diabetes risk at the societal level. Graphical abstract