Polygenic risk scores predict cognitive impairment as measured by the Preclinical Alzheimer Cognitive Composite (PACC) score in the ADNI cohort

Abstract

AbstractBackgroundThe development of diagnostic tools to identify disease risk is critical to enable selection of suitable individuals for inclusion in clinical trials and cohort studies. The utility of Polygenic Risk Scores (PRS) is gaining increasing attention for generating an individual genetic risk profile and subsequent estimation of future disease risk in Alzheimer’s Disease. We have previously reported on the performance of our PRS to differentiate Alzheimer Disease cases from healthy controls in the ADNI cohort and also cognitive decline using ADAS‐Cog13 and CDR‐SB scales. In this work we have analysed whether the PRS is able to predict more subtle changes in cognition by using PACC scores of ADNI cohort participants. The aim being to demonstrate the utility of PRS approaches in longitudinal studies and future clinical practice.MethodThis study explored the performance of the Cytox genoSCORE™ PRS algorithm as measured using a receiver operating characteristic curve (ROC) curve to predict a PACC score of <‐1, as measured upon entry into the study. 724 subjects who entered the study as either cognitively normal, or with a diagnosis of mild cognitive impairment are included in the analysis based on availability of PACC scores and of genetic data in the ADNI database. Further analyses to predict longitudinal changes in PACC have also been performed.ResultIn a population of 724 individuals genoSCORE™ has an accuracy as measured by area under the curve (AUC) of 65.6% in predicting those individuals with cognitive impairment as measured by the PACC scale (threshold <‐1). A similar analysis performed using age as the predictor in the same group showed no discernible correlation with lower PACC scores.ConclusionIdentifying individuals of higher risk for cognitive decline due to Alzheimer’s Disease irrespective of their baseline cognitive performance will allow clinical trialists to enrich their studies with more appropriate patients. In addition, use of such risk evaluation techniques in clinical practice will enable clinicians to make more informed decisions on how to manage their patients. Cytox is able to provide a robust end‐to‐end process to provide this comprehensive genetic risk assessment.