Assessing the prediction of type 2 diabetes risk using polygenic and clinical risk scores in South Asian study populations

Madhusmita Rout; Gurpreet S Wander; Sarju Ralhan; Jai Rup Singh; Christopher E Aston; Piers R Blackett; Steven Chernausek; Dharambir K Sanghera

doi:10.1177/20420188231220120

Assessing the prediction of type 2 diabetes risk using polygenic and clinical risk scores in South Asian study populations

Ther Adv Endocrinol Metab. 2023 Dec 25:14:20420188231220120. doi: 10.1177/20420188231220120. eCollection 2023.

Authors

Madhusmita Rout¹, Gurpreet S Wander², Sarju Ralhan², Jai Rup Singh³, Christopher E Aston⁴, Piers R Blackett^{5

6}, Steven Chernausek^{5

6}, Dharambir K Sanghera^{7

6

8

9

10}

Affiliations

¹ Department of Pediatrics, Section of Genetics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
² Hero DMC Heart Institute, Ludhiana, Punjab, India.
³ Central University of Punjab, Bathinda, Punjab, India.
⁴ Section of Developmental and Behavioral Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
⁵ Department of Pediatrics, Section of Endocrinology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
⁶ Harold Hamm Diabetes Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
⁷ Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd., Rm 317 BMSB, Oklahoma City, OK 73104, USA.
⁸ Department of Pharmaceutical Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
⁹ Department of Physiology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
¹⁰ Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.

Abstract

Background: Genome-wide polygenic risk scores (PRS) have shown high specificity and sensitivity in predicting type 2 diabetes (T2D) risk in Europeans. However, the PRS-driven information and its clinical significance in non-Europeans are underrepresented. We examined the predictive efficacy and transferability of PRS models using variant information derived from genome-wide studies of Asian Indians (AIs) (PRS_AI) and Europeans (PRS_EU) using 13,974 AI individuals.

Methods: Weighted PRS models were constructed and analyzed on 4602 individuals from the Asian Indian Diabetes Heart Study/Sikh Diabetes Study (AIDHS/SDS) as discovery/training and test/validation datasets. The results were further replicated in 9372 South Asian individuals from UK Biobank (UKBB). We also assessed the performance of each PRS model by combining data of the clinical risk score (CRS).

Results: Both genetic models (PRS_AI and PRS_EU) successfully predicted the T2D risk. However, the PRS_AI revealed 13.2% odds ratio (OR) 1.80 [95% confidence interval (CI) 1.63-1.97; p = 1.6 × 10^-152] and 12.2% OR 1.38 (95% CI 1.30-1.46; p = 7.1 × 10^-237) superior performance in AIDHS/SDS and UKBB validation sets, respectively. Comparing individuals of extreme PRS (ninth decile) with the average PRS (fifth decile), PRS_AI showed about two-fold OR 20.73 (95% CI 10.27-41.83; p = 2.7 × 10^-17) and 1.4-fold OR 3.19 (95% CI 2.51-4.06; p = 4.8 × 10^-21) higher predictability to identify subgroups with higher genetic risk than the PRS_EU. Combining PRS and CRS improved the area under the curve from 0.74 to 0.79 in PRS_AI and 0.72 to 0.75 in PRS_EU.

Conclusion: Our data suggest the need for extending genetic and clinical studies in varied ethnic groups to exploit the full clinical potential of PRS as a risk prediction tool in diverse study populations.

Keywords: Asian Indians; UK Biobank; clinical risk score; diabetes; polygenic risk score.

Abstract

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