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

Yixuan He, Chirag M Lakhani, Danielle Rasooly, Arjun K Manrai, Ioanna Tzoulaki, Chirag J Patel, Yixuan He, Chirag M Lakhani, Danielle Rasooly, Arjun K Manrai, Ioanna Tzoulaki, Chirag J Patel

Abstract

Objective: To establish a polyexposure score (PXS) for type 2 diabetes (T2D) incorporating 12 nongenetic exposures and examine whether a PXS and/or a polygenic risk score (PGS) improves diabetes prediction beyond traditional clinical risk factors.

Research design and methods: We identified 356,621 unrelated individuals from the UK Biobank of White British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 nongenetically ascertained exposure and lifestyle variables for the PXS in prospective T2D. We computed the clinical risk score (CRS) and PGS by taking a weighted sum of eight established clinical risk factors and >6 million single nucleotide polymorphisms, respectively.

Results: In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Individuals in the top 10% of PGS, PXS, and CRS had 2.00-, 5.90-, and 9.97-fold greater risk, respectively, compared to the remaining population. Addition of PGS and PXS to CRS improved T2D classification accuracy, with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively.

Conclusions: For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. However, the concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.

© 2021 by the American Diabetes Association.

Figures

Figure 1
Figure 1
Study design. PXS, CRS, and PGS were calculated and compared for predictive accuracy. PGS was calculated using previously published weights. CRS factors included sex, age, family history, BMI, systolic blood pressure, serum glucose levels, serum HDL-C, and serum triglycerides. PXS factors were selected using a lasso-based method that relied on summary statistics from XWAS. CRF, clinical risk factor.
Figure 2
Figure 2
Reclassification of predicted T2D risk. The reclassified predicted risk with addition of PGS (A), PXS (B), or PGS + PXS (C) to the CRS model in the continuous case or the categorical case with a threshold of 12.5% risk. The overall NRI is the sum of the net reclassifications for cases (P[up|case] − P[down|case]) and noncases (P[down|noncase] − P[up|noncase]). A positive NRI indicates improved reclassification.

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Source: PubMed

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