Back

Genomic Risk Scores and Oral Contraceptive-Associated Ischemic Stroke Risk

Lin, F.; Tomppo, L.; Gaynor, B.; Ryan, K.; Cole, J. W.; Mitchell, B. D.; Putaala, J.; Kittner, S. J.

2022-09-29 genetic and genomic medicine
10.1101/2022.09.29.22280517 medRxiv
Show abstract

BackgroundOral contraceptives (OCs) are generally safe but vascular risk factors increase OC-associated ischemic stroke risk. We performed a case-control study to evaluate whether a genomic risk score for ischemic stroke modifies OC-associated ischemic stroke risk. MethodsThe Genetics of Early-Onset Stroke (GEOS) study includes 340 premenopausal women (143 ischemic stroke cases and 197 controls) with data on OC use within 30 days before the index event (for cases) or interview (for controls). Using a previously validated genetic risk score (metaGRS) for ischemic stroke based on 19 polygenic risk scores of vascular events and risk factors, we stratified our sample into tertiles of genomic risk. We evaluated the association between OC use and ischemic stroke within each tertile. We tested if the association between OC use and ischemic stroke depended on the genomic risk of stroke using logistic regression with an OC use x metaGRS interaction term. ResultsAmong all women, OC use was significantly associated with ischemic stroke (odds ratio = 2.4, p = 0.002). The odds ratio for ischemic stroke associated with OC use increased from 1.5 in the tertile with the lowest genomic risk to 3.9 in the tertile with the highest genomic risk of ischemic stroke. The formal test of interaction was consistent with our hypothesis (p = 0.07) that the genomic risk score modifies the association of OC use with ischemic stroke. ConclusionsOur results suggest that genomic profile modifies the OC-associated ischemic stroke risk. Larger studies are warranted to determine whether a genomic risk score could be clinically useful in reducing OC-associated ischemic stroke.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.