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Pathway-Specific Polygenic Risk Score and Risk of Hyperglycaemic Progression in Patients With Type 2 Diabetes
Sylvia Liu1, Huili Zheng1, Jia Le Ivan Tan1
1Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Singapore.
Aim:
Pathway-specific polygenic risk scores (psPRS) provide insights into the heterogeneity of type 2 diabetes. We aim to study whether psPRS for beta cell dysfunction, obesity and metabolic syndrome associate with risk of hyperglycaemic progression in Southeast Asian patients with type 2 diabetes.
Participants And Methods:
One thousand and fifty-nine insulin-naïve outpatients with type 2 diabetes were enrolled from a secondary hospital and a primary care facility. The psPRS were constructed as a weighted sum of risk alleles associated with specific metabolic pathways. Hyperglycaemic progression was defined as sustained insulin therapy lasting longer than 6 months.
Results:
During a median of 11.2 (8.9-11.8) years of follow-up, 188 participants initiated sustained insulin therapy. One SD increment in psPRS for metabolic syndrome was associated with a 17% increased risk for hyperglycaemic progression after adjustment for clinical risk factors (adjusted HR 1.17 [1.02-1.36]). Consistent results were obtained when hyperglycaemic progression was defined as a composite of sustained insulin therapy and requirement for insulin treatments (HbA1c > 8.5% with two or more non-insulin medications). Adding metabolic syndrome psPRS onto clinical risk predictors (diabetes onset age, HbA1c, HDL cholesterol, eGFR, urinary albumin-to-creatinine ratio) did not improve risk discrimination (delta AUC -0.01 [95% CI -0.02 to 0.01], p = 0.50). Neither psPRS for beta cell dysfunction nor obesity was associated with the risk of hyperglycaemic progression.
Conclusion:
psPRS for metabolic syndrome is nominally associated with risk of hyperglycaemic progression in multi-ancestry Southeast Asian people with type 2 diabetes. The psPRS does not provide incremental value for prediction of hyperglycaemic progression beyond clinical risk predictors.
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