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Combining Polygenic Risk Scores With Clinical and Behavioral Factors for Predicting Type 2 Diabetes
Session:
Sessão de Posters 32 - Comportamento, mente e metabolismo
Speaker:
Ana Filipa Escórcio Silva
Congress:
CPC 2026
Topic:
J. Preventive Cardiology
Theme:
28. Risk Factors and Prevention
Subtheme:
28.7 Diabetes and the Heart
Session Type:
Posters Eletrónicos
FP Number:
---
Authors:
Filipa Escórcio Silva; Maria Isabel Mendonça; Gonçalo Abreu; Francisco Sousa; Matilde Ferreira; Sónia Freitas; Mariana Rodrigues; Eva Henriques; Sofia Borges; António Drumond; Ana Célia Sousa; Roberto Palma Dos Reis
Abstract
<p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><strong>Introduction:</strong> Type 2 diabetes mellitus (T2DM) is one of the fastest-growing noncommunicable multifactorial and polygenic diseases, leading to many health complications and significant morbidity and mortality.</span></span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><strong>Objective:</strong> Investigate the predictive capacity of a polygenic risk score (PRS-D) with genetic variants associated with T2DM, and evaluate the increase in predictive value when other behavioural/clinical risk factors are added.</span></span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><strong>Methods:</strong> A case–control study was conducted with T2DM patients from our dataset Centre and controls without diabetes or pre-diabetes. DNA samples were genotyped for 15 genetic polymorphisms associated with T2DM and oxidation (IGF2BP2 rs4402960, PPARG rs1801282, TCF7L2 rs7903146, SLC30A8 rs1326634, MC4R rs17782313, ADIPOQ rs266729, FTO rs8050136, TAS2R50 rs1376251, HNF4A rs1884613, MTHFD1L rs6922269, MTHFR677 rs1801133, MTHFR rs1298, PONN108 rs705379, PON192 rs662, PON55 rs854560). A weighted PRS-D was calculated by summing all risk alleles multiplied by their respective odds ratios (ORs). T2DM risk was assessed using multivariable logistic regression, and the area under the receiver operating characteristic curve (AUC) was calculated to evaluate predictive power of PRS-D alone and combined with clinical and behavioural factors (hypertension, obesity, and sedentary lifestyle).</span></span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><strong>Results:</strong> Our population included 2,097 participants: 846 T2DM patients (mean age 55.3±6.9; 78.7% male) and 1,251 controls (mean age 51.4±8.5; 71.9% male). PRS-D was significantly higher in patients than controls (12.5±2.6 vs. 12.0±2.5; p<0.0001). Bivariate analysis of risk factors showed that patients had 79.9% hypertension, 44.3% obesity, and 63.6% sedentary lifestyle. Adding the PRS-D improved model discrimination, increasing the AUC for physical inactivity (0.614→0.691), hypertension (0.665→0.710), and obesity (0.598→0.689). DeLong test confirmed significance (p<0.0001). After adjusting for age and sex, PRS-D, physical inactivity, obesity, and hypertension remained significantly associated with T2DM. PRS-D increased risk by 9.1% per unit (OR=1.091; 95% CI: 1.050–1.133), while physical inactivity (OR=2.121), obesity (OR=1.588), and hypertension (OR=3.042) showed robust independent associations.</span></span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><strong>Conclusions</strong>: PRS-D is associated with T2DM susceptibility and improves disease prediction even with a limited number of loci. Further study may optimize its use for clinical and public health applications, though classical risk factors remain the strongest predictors.</span></span></span></p>
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