Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

He, Yijie; Lakhani, Chirag M.; Rasooly, Danielle; Manrai, Arjun K.; Tzoulaki, Ioanna; Patel, Chirag

doi:10.2337/dc20-2049

Cited by 47 publications

(53 citation statements)

References 46 publications

(44 reference statements)

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“…As known exposures impacting PSD are still limited and no major advances in the field have been observed recently, agnostic exposure-wide analytical approaches that take into account of inter-correlation may provide further understanding of other, so far, unknown factors that also cover other domains of the exposome such as internal (e.g., inflammation) and external (e.g., chemical, lifestyle, psycho-social) correlates (13). In this regard, similar to previous studies in other phenotypes such as HIV (46), diabetes (47), depression (48), and childhood behavior (49), our research group is currently conducting a systematic exposomewide investigation of correlates of psychosis expression in the UK Biobank.…”

Section: Future Directionssupporting

confidence: 65%

Estimating Aggregate Environmental Risk Score in Psychiatry: The Exposome Score for Schizophrenia

et al. 2021

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To understand the role of environment in the pathoetiology of psychosis spectrum disorders, research has thus far mainly investigated the effects of single exposures in isolation, such as the association between cannabis use and schizophrenia. However, this approach fails to acknowledge the complexity of the exposome, which represents the totality of the environment involving many exposures over an individual's lifetime. Therefore, contemporary research adopting the exposome paradigm has aimed at capturing the combined effect of different environmental exposures by utilizing an aggregate environmental vulnerability score for schizophrenia: the exposome score for schizophrenia. Here, we attempt to provide a comprehensive overview of studies applying the exposome score for schizophrenia. First, we describe several approaches estimating exposomic vulnerability for schizophrenia, which falls into three categories: simple environmental sum scores (sum of dichotomized exposures), meta-analysis-based environmental risk score (sum scores weighted by estimates from meta-analyses), and the exposome score (sum score weighted by estimates from an analysis in an independent training dataset). Studies show that the exposome score for schizophrenia that assumes interdependency of exposures performs better than scores that assume independence of exposures, such as the environmental sum score and the meta-analysis-based environmental risk score. Second, we discuss findings on the pluripotency of the exposome score for schizophrenia and summarize findings from gene-environment studies using the exposome score for schizophrenia. Finally, we discuss possible scientific, clinical, and population-based applications of exposome score for schizophrenia, as well as limitations and future directions for exposome research to understand the etiology of psychosis spectrum disorders.

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Section: Future Directionssupporting

confidence: 65%

Estimating Aggregate Environmental Risk Score in Psychiatry: The Exposome Score for Schizophrenia

et al. 2021

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“…Our study adds to knowledge on the interplay between genetic and lifestyle factors by formally investigating whether polygenic scores for type 2 diabetes capturing overall genetic risk or distinct pathophysiological mechanisms could help prioritize individuals who would benefit the most from targeted dietary recommendations. Previous studies have shown no appreciable interactions between genetic and lifestyle factors on the development of type 2 diabetes [ 6 , 9 , 31 ], indicating that genetic risk does not modify the beneficial effect of healthy lifestyle interventions. However, previous studies considered a limited number of variants to generate type 2 diabetes polygenic scores, and the lack of significant interactions reported in these studies is often attributed to the mixture of variants affecting different pathways into a single score [ 6 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

Polygenic scores, diet quality, and type 2 diabetes risk: An observational study among 35,759 adults from 3 US cohorts

Merino

Guasch‐Ferré

et al. 2022

PLoS Med

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Background Both genetic and lifestyle factors contribute to the risk of type 2 diabetes, but the extent to which there is a synergistic effect of the 2 factors is unclear. The aim of this study was to examine the joint associations of genetic risk and diet quality with incident type 2 diabetes. Methods and findings We analyzed data from 35,759 men and women in the United States participating in the Nurses’ Health Study (NHS) I (1986 to 2016) and II (1991 to 2017) and the Health Professionals Follow-up Study (HPFS; 1986 to 2016) with available genetic data and who did not have diabetes, cardiovascular disease, or cancer at baseline. Genetic risk was characterized using both a global polygenic score capturing overall genetic risk and pathway-specific polygenic scores denoting distinct pathophysiological mechanisms. Diet quality was assessed using the Alternate Healthy Eating Index (AHEI). Cox models were used to calculate hazard ratios (HRs) for type 2 diabetes after adjusting for potential confounders. With over 902,386 person-years of follow-up, 4,433 participants were diagnosed with type 2 diabetes. The relative risk of type 2 diabetes was 1.29 (95% confidence interval [CI] 1.25, 1.32; P < 0.001) per standard deviation (SD) increase in global polygenic score and 1.13 (1.09, 1.17; P < 0.001) per 10-unit decrease in AHEI. Irrespective of genetic risk, low diet quality, as compared to high diet quality, was associated with approximately 30% increased risk of type 2 diabetes (Pinteraction = 0.69). The joint association of low diet quality and increased genetic risk was similar to the sum of the risk associated with each factor alone (Pinteraction = 0.30). Limitations of this study include the self-report of diet information and possible bias resulting from inclusion of highly educated participants with available genetic data. Conclusions These data provide evidence for the independent associations of genetic risk and diet quality with incident type 2 diabetes and suggest that a healthy diet is associated with lower diabetes risk across all levels of genetic risk.

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“…In previous studies investigating the incremental value of PRSs integrated to clinical risk scores, CHD PRSs have yielded varying performance improvements over routinely used clinical risk assessment tools, such as PCE or QRISK3 4 , 16 – 18 . For T2D, the improvements with PRS over clinical risk assessment tools have been moderate 4 , 19 . Here, when added on top of with age and sex, the PRS CHD had a higher AUC for incident CHD than any of the eight other individual risk factors of GRIT-CHD.…”

Section: Discussionmentioning

confidence: 99%

“…However, the primary care data allowed us to calculate the QRISK3 and QDiabetes clinical scores more accurately as they were derived using the same data sources 28 , 29 . Following the recommendations in recently published reporting guidelines for genetic prediction studies 30 , and in contrast to previous studies 5 , 16 – 19 , we used the external UK Biobank cohort for out-of-sample evaluation of our risk tools. As the predictive performance of risk models can greatly worsen when assessed outside of their derivation datasets 31 , our results indicate that GRIT-CHD and GRIT-T2D may generalize well also to other cohorts.…”

Section: Discussionmentioning

confidence: 99%

Integration of questionnaire-based risk factors improves polygenic risk scores for human coronary heart disease and type 2 diabetes

et al. 2022

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Large-scale biobank initiatives and commercial repositories store genomic data collected from millions of individuals, and tools to leverage the rapidly growing pool of health and genomic data in disease prevention are needed. Here, we describe the derivation and validation of genomics-enhanced risk tools for two common cardiometabolic diseases, coronary heart disease and type 2 diabetes. Data used for our analyses include the FinnGen study (N = 309,154) and the UK Biobank project (N = 343,672). The risk tools integrate contemporary genome-wide polygenic risk scores with simple questionnaire-based risk factors, including demographic, lifestyle, medication, and comorbidity data, enabling risk calculation across resources where genome data is available. Compared to routinely used clinical risk scores for coronary heart disease and type 2 diabetes prevention, the risk tools show at least equivalent risk discrimination, improved risk reclassification (overall net reclassification improvements ranging from 3.7 [95% CI 2.8–4.6] up to 6.2 [4.6–7.8]), and capacity to be improved even further with standard lipid and blood pressure measurements. Without the need for blood tests or evaluation by a health professional, the risk tools provide a powerful yet simple method for preliminary cardiometabolic risk assessment for individuals with genome data available.

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Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

Cited by 47 publications

References 46 publications

Estimating Aggregate Environmental Risk Score in Psychiatry: The Exposome Score for Schizophrenia

Estimating Aggregate Environmental Risk Score in Psychiatry: The Exposome Score for Schizophrenia

Polygenic scores, diet quality, and type 2 diabetes risk: An observational study among 35,759 adults from 3 US cohorts

Integration of questionnaire-based risk factors improves polygenic risk scores for human coronary heart disease and type 2 diabetes

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