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Background: Genome-wide polygenic risk scores (PRS) have shown high specificity and sensitivity in predicting type 2 diabetes (T2D) risk in Europeans. However, PRS-driven information on other ethnic groups is sparse, including Asian Indians (AI), who have a heightened risk for T2D. We examined the predictive efficacy of ancestry-derived (PRSAI) and European-derived (PRSEU) with a clinical risk score (CRS) of T2D. Methods: Weighted PRSs were computed using 2921 variants (common and rare) from (Punjabi/Sikh) genome-wide association studies and 432 variants from European meta-analyses studies in 4,602 individuals (2,574 cases and 2,028 controls) of the Asian Indian Diabetes Heart Study/Sikh Diabetes Study. Both Ancestry-specific PRSAI and European PRSEU were validated on 9372 UK Biobank South Asian individuals (1943 cases/7429 controls). Results: Ancestry-specific PRSAI showed 36.2% improved efficacy than PRSEU in Punjabi Asian Indians. Similarly, Ancestry-specific PRSAI was 18.6% superior to PRSEU in Asian Indians from UKBB based on Nagelkerke’s R2. Sensitivity analysis explained enhanced sensitivity (area under the curve-AUC) of ancestry-specific PRS over PRSEU in predicting T2D risk. Conclusions: Our data suggest expanding genetic studies in diverse ethnic groups to exploit the full potential of PRS to improve health outcomes. More genetic evaluations, specifically in understudied/underrepresented populations, would help increase the transferability of genome-wide polygenic scores across racial and ethnic groups to make their integration into clinical practice easier. Disclosure G.K.Tung: None. M.Rout: None. D.K.Sanghera: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK082766, R01DK118427); Presbyterian Health Foundation

Introduction: Polygenic risk score (PRS) has been shown to be highly effective in predicting coronary artery disease (CAD) risk in Western European populations. However, such studies on South Asians are scarce despite the fact that they account for 50% of the global burden of CAD. Hypothesis: In this study, we evaluated the predictive efficacy of PRS derived from the Asian Indian (AI) ancestry-specific score (PRS AI ) and European-derived PRS (PRS EU ). Also, we compared these with the clinical risk score (CRS). Methods: The study used 4602 participants (791 CAD cases and 3790 controls) from the Asian Indian Diabetic Heart Study/Sikh Diabetes Study. Weighted PRS was constructed using 100 significant SNPs from our Punjabi/Sikh CAD GWAS and 75 SNPs identified from the European GWAS catalog. The CRS was derived using the clinical risk factors described for the Framingham risk score. Results: Ancestry-specific PRS AI showed an enhanced efficacy of over 30% in estimating the relative risk for CAD over PRS EU . In sensitivity analysis, the area under the curve (AUC) for PRS AI and PRS EU were 0.84 and 0.72, respectively, while the AUC remained unchanged on combining the PRS (AI+EU) , i.e., 0.84. PRS AI also predicted the risk for increased waist to hip ratio (β=0.11, p=4.1x10 -13 ) in both gender, while fasting glucose levels (β=0.08, 3.0x10 -3 ) were confined to females. Conclusions: The results highlight evidence for the utility of PRS for identifying genetically predisposed high-risk individuals and attest to its broader clinical value. Also, sex differences may play a role in determining the risk for CAD, and increased fasting glucose and type 2 diabetes (T2D) are known to increase the risk of heart disease in women more than men, especially after menopause.

Background Acute Ischemic Stroke (AIS), a major cause of disability, was previously associated with multiple metabolomic changes, but many findings were contradictory. Case-control and longitudinal study designs could have played a role in that. To clarify metabolomic changes, we performed a simultaneous comparison of ischemic stroke metabolome in acute, chronic stages of stroke and controls. Methods Through the nuclear magnetic resonance (NMR) platform, we evaluated 271 serum metabolites from a cohort of 297 AIS patients in acute and chronic stages and 159 controls. We used Sparse Partial Least Squares-Discriminant analysis (sPLS-DA) to evaluate group disparity; multivariate regression to compare metabolome in acute, chronic stages of stroke and controls; and mixed regression to compare metabolome acute and chronic stages of stroke. We applied false discovery rate (FDR) to our calculations. Results The sPLS-DA revealed separation of the metabolome in acute, chronic stages of stroke and controls. Regression analysis identified 38 altered metabolites. Ketone bodies, branched-chain amino acids (BCAAs), energy, and inflammatory compounds were elevated in the acute stage, but declined in the chronic stage, often to the same levels as in controls. Levels of other amino acids, phosphatidylcholines, phosphoglycerides, and sphingomyelins mainly did not change between acute and chronic stages, but was different comparing to controls. Conclusion Our pilot study identified metabolites associated with acute stage of ischemic stroke and those that are altered in stroke patients comparing to controls regardless of stroke acuity. Future investigation in a larger independent cohort is needed to validate these findings.

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