Impact of Conventional Stroke Risk Factors on Early- and Late-Onset Ischemic Stroke: A Mendelian Randomization Study.

Background: Stroke incidence is decreasing in older ages but increasing in young adults. These divergent trends are at least partially attributable not only to diverging trends in stroke risk factors but may also be due to differences in the impact of stroke risk factors at different ages. To address this latter possibility, we used Mendelian randomization to assess differences in the association of stroke risk factors between early-onset ischemic stroke ([EOS]; onset 18-59 years) and late-onset ischemic stroke ([LOS]; onset ≥60 years).

The Impact of Conventional Stroke Risk Factors on Early and Late Onset Ischemic Stroke: a Mendelian Randomization Study.

Objective: Although stroke incidence is decreasing in older ages, it is increasing in young adults. While these divergent trends in stroke incidence are at least partially attributable to diverging prevalence trends in stoke risk factors, age-dependent differences in the impact of stroke risk factors on stroke may also contribute. To address this issue, we utilized Mendelian Randomization (MR) to assess differences in the association of stroke risk factors between early onset ischemic stroke (EOS) and late onset ischemic stroke (LOS).

Genomic risk scores and oral contraceptive-associated ischemic stroke risk: a call for collaboration.

Background: Oral 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.

Methods: The Genetics of Early-Onset Stroke study includes 332 premenopausal women (136 arterial ischemic stroke cases and 196 controls) with data on estrogen-containing OC use within 30 days before the index event (for cases) or interview (for controls).

A novel scatterplot-based method to detect copy number variation (CNV).

Most methods to detect copy number variation (CNV) have high false positive rates, especially for small CNVs and in real-life samples from clinical studies. In this study, we explored a novel scatterplot-based method to detect CNVs in microarray samples. Illumina SNP microarray data from 13,254 individuals were analyzed with scatterplots and by PennCNV.