A polygenic risk score for the prediction of Alzheimer's disease in rural‐dwelling older adults in China. (31st December 2021) - Hou, Tingting; Liu, Keke; Liu, Cuicui; Cong, Lin; Wang, Yongxiang; Du, Yifeng; Qiu, Chengxuan
Abstract: Background: Polygenic risk score (PRS), a weighted sum of the risk alleles of single nucleotide polymorphisms (SNPs), has been developed to predict Alzheimer's disease (AD) among different ethnic populations. However, such a PRS is not available among the Chinese elderly population. We sought to develop a PRS for AD and further evaluate its predictive ability among rural‐dwelling older adults. Method: This population‐based study included 4691 participants (age ≥65 years; 57.3% women) who were examined in 2018;of these, 2089 had been previously examined and diagnosed as free of dementia in 2014‐2015.We performed multiplex PCR assay to detect 58SNPs. AD was diagnosed following the NIA‐AA criteria. We applied logistic models to the 2018 data for the cross‐sectional SNPs‐AD associations, and identified candidate risk SNPs potentially associated with AD risk (p<0.30). The area under the ROC curve (AUC) was calculated to evaluate its discriminative ability. Cox models were employed to examine the longitudinal PRS‐AD association using the follow‐up data. We used C‐index to assess the predictive ability of the model. Finally, we conducted the internal validation of the model using bootstrap methods. Result: In the cross‐sectional analysis (n=4691, 181 AD cases), 17 risk SNPs were identified for the PRS. A high PRS (>75th vs. <25th percentile) was associated with an age‐ and sex‐adjusted odds ratio of 2.62 for AD (95% CI: 1.65‐4.25); The AUC of PRS alone to discriminate AD was 0.597 (0.554‐0.638); when adding age and sex, AUC increased to 0.80 (0.763‐0.837). Of the 2089 baseline dementia‐free participants, 85 developed AD during the 4‐year follow‐up period. A high PRS (>75th vs. <25th percentile) was associated with a2.07‐fold risk of AD (1.12‐3.81) after adjusting for age and sex; The C‐statistic for AD prediction was improved from 0.783 to 0.826 when adding the PRS to the base model of age, sex, and education. Calibration plots showed good performance of the model for predicting the 3‐year risk of AD. Conclusion: Polygenic risk score that incorporates common genetic variants is associated with an increased AD risk in Chinese elderly population. The PRS may help identify individuals at risk for AD.