Search

Background and ObjectiveMigraine is a highly prevalent neurovascular disorder among reproductive-aged women. Whether migraine history and migraine phenotype might serve as clinically useful markers of obstetric risk is not clear. The primary objective of this study was to examine associations of prepregnancy migraine and migraine phenotype with risks of adverse pregnancy outcomes.MethodsWe estimated associations of self-reported physician-diagnosed migraine and migraine phenotype with adverse pregnancy outcomes in the prospective Nurses' Health Study II (1989–2009). Log-binomial and log-Poisson models with generalized estimating equations were used to estimate relative risks (RRs) and 95% CIs for gestational diabetes mellitus (GDM), preeclampsia, gestational hypertension, preterm delivery, and low birthweight.ResultsThe analysis included 30,555 incident pregnancies after cohort enrollment among 19,694 participants without a history of cardiovascular disease, diabetes, or cancer. After adjusting for age, adiposity, and other health and behavioral factors, prepregnancy migraine (11%) was associated with higher risks of preterm delivery (RR = 1.17; 95% CI = 1.05–1.30), gestational hypertension (RR = 1.28; 95% CI = 1.11–1.48), and preeclampsia (RR = 1.40; 95% CI = 1.19–1.65) compared with no migraine. Migraine was not associated with low birthweight (RR = 0.99; 95% CI = 0.85–1.16) or GDM (RR = 1.05; 95% CI = 0.91–1.22). Risk of preeclampsia was somewhat higher among participants with migraine with aura (RR vs no migraine = 1.51; 95% CI = 1.22–1.88) than migraine without aura (RR vs no migraine = 1.30; 95% CI = 1.04–1.61;p-heterogeneity = 0.32), whereas other outcomes were similar by migraine phenotype. Participants with migraine who reported regular prepregnancy aspirin use had lower risks of preterm delivery (p-interaction < 0.01) and preeclampsia (p-interaction = 0.39); however, power for these stratified analyses was limited.DiscussionMigraine history, and to a lesser extent migraine phenotype, appear to be important considerations in obstetric risk assessment and management. Future research should determine whether aspirin prophylaxis may be beneficial for preventing adverse pregnancy outcomes among pregnant individuals with a history of migraine.

Ambient air pollution has been associated with bone damage. However, no studies have evaluated the metabolomic response to air pollutants and its potential influence on bone health in postmenopausal women. We analyzed data from WHI participants with plasma samples. Whole-body, total hip, femoral neck, and spine BMD at enrollment and follow-up (Y1, Y3, Y6). Daily particulate matter NO, NO2, PM10 and SO2 were averaged over 1-, 3-, and 5-year periods before metabolomic assessments. Statistical analyses included multivariable-adjusted linear mixed models, pathways analyses, and mediation modeling. NO, NO2, and SO2, but not PM10, were associated with taurine, inosine, and C38:4 phosphatidylethanolamine (PE), at all averaging periods. We found a partial mediation of C38:4 PE in the association between 1-year average NO and lumbar spine BMD (p-value: 0.032). This is the first study suggesting that a PE may partially mediate air pollution-related bone damage in postmenopausal women.

OBJECTIVE The present study aims to prospectively examine the association between post-diagnosis low carbohydrate diet (LCD) patterns and mortality among individuals with type 2 diabetes (T2D). RSEARCH DESIGN AND METHODS Among participants with incident diabetes identified in the Nurses’ Health Study and Health Professionals Follow-Up Study, an overall total low carbohydrate diet score (TLCDS) was calculated based on the percentage of energy as total carbohydrates. In addition, vegetable (VLCDS), animal (ALCDS), healthy (HLCDS), and unhealthy LCDS (ULCDS) were further derived that emphasized different sources and quality of macronutrients. Multivariable-adjusted Cox model were used to assess the association between the LCDS and mortality. RESULTS Among 10,101 incident T2D cases contributing 139,407 person-years during follow-up, we documented 4,595 deaths of which 1,389 cases were attributed to cardiovascular disease (CVD) and 881 to cancer. The pooled multivariable-adjusted hazard ratios (HRs, 95% CIs) of total mortality per 10 points increment of post-diagnosis LCDS were 0.87 (0.82,0.92) for TLCDS, 0.76 (0.71,0.82) for VLCDS, and 0.78 (0.73,0.84) for HLCDS. Both VLCDS and HLCDS were also associated with significantly lower CVD and cancer mortality. Each 10 points increase of TLCDS, VLCDS, and HLCDS from pre-diagnosis to post-diagnosis period was associated with 12% (7%, 17%), 25% (19%, 30%), and 25% (19%, 30%) lower total mortality, respectively. No significant associations were observed for ALCDS and ULCDS. CONCLUSIONS Among people with T2D, greater adherence to LCD patterns that emphasize high quality sources of macronutrients was significantly associated with lower total, cardiovascular, and cancer mortality.

Short and long sleep duration are associated with adverse metabolic outcomes, such as obesity and diabetes. We evaluated cross-sectional differences in metabolite levels between women with self-reported habitual short (

We examined whether urinary concentrations of phthalate and phthalate replacement metabolites were associated with lipid biomarker levels among pregnant women. This cross-sectional study included 175 women who enrolled in the Environment and Reproductive Health (EARTH) Study (2005–2017). We used linear regression models to assess the relationship between urinary phthalates and lipid biomarkers [triglycerides, total cholesterol, high density lipoprotein (HDL), non-HDL, and low-density lipoprotein (LDL) cholesterol] levels while adjusting for confounders. Pregnant women in the highest quartile of urinary mono(2-ethyl-5-carboxypentyl) phthalate (MECPP) had, overall, 14% [31 (95% CI = 6.56) mg/dL], 21% [33 (95% CI = 9.57) mg/dL] and 25% [30 (95% CI = 8.53) mg/dL] higher serum total, non-HDL and LDL cholesterol, respectively, compared to women in the lowest quartile of MECPP. Similar positive associations were found for urinary concentrations of other metabolites of di(2-ethylhexyl) phthalate, mono(2-ethylhexyl) phthalate, and mono(2-ethyl-5-oxohexyl) phthalate. Pregnant women with urinary mono-n-butyl phthalate (MBP) in the highest quartile had higher triglycerides and non-HDL cholesterol compared to women with MBP in the lowest quartile. Women with detectable concentrations of two phthalate replacement metabolites had lower HDL cholesterol compared to women with non-detectable concentrations. Gestational urinary concentrations of certain phthalate and phthalate replacement metabolites were associated with lipid levels among these women.

Background and Objectives:Subarachnoid hemorrhage from cerebral aneurysm remains a devastating disease with high mortality and morbidity. Cerebral aneurysm and its rupture are more prevalent in post-menopausal women and have been postulated to be hormonally influenced. The goal of this study was to investigate the associations of female-specific factors, including reproductive life span, age at menarche, and age at menopause, with the incidence of aneurysmal subarachnoid hemorrhage (aSAH) in women.Methods:Participants in the Nurses’ Health Study were followed from 1980 or the time of reaching menopause until 2018, Only women with natural menopause or surgical menopause due to bilateral oophorectomy were included. Reproductive life span was defined by subtracting the age at menarche from the age at menopause. Multivariable stratified proportional hazards models were used to study reproductive life span, age at menarche and age at menopause with the incidence of aSAH. Multivariable models were adjusted for age, race, smoking, hysterectomy, hypertension, hyperlipidemia, body mass index, hormone therapy use, oral contraceptive use, and parity.Results:A total of 97,398 postmenopausal women with reproductive lifespan data were included, with 138 participants developed aneurysmal subarachnoid hemorrhage which was confirmed on medical record review by a physician. A shorter reproductive lifespan (≤35 years) was associated with a two-fold higher incidence of aSAH after multivariable adjustment (HR=2.0 [95%CI 1.4-2.8]). Early age at menopause (ageDiscussion:An earlier age at menopause, as well as a shorter reproductive life span duration (≤35 years), were associated with a higher risk of incident aSAH in women. No associations were noted for age at menarche, parity, oral contraceptive use or postmenopausal therapy use.

To describe the imaging findings of intimate partner violence (IPV)-related injury and to evaluate the role of longitudinal imaging review in detecting IPV.Radiology studies were reviewed in chronological order and IPV-related injuries were recorded among 400 victims of any type of abuse (group 1) and 288 of physical abuse (group 2) from January 2013 to June 2018. The likelihood of IPV was assessed as low/moderate/high based on the review of (1) current and prior anatomically related studies only and (2) longitudinal imaging history consisting of all prior studies. The first radiological study date with moderate/high suspicion was compared to the self-reported date by the victim.A total of 135 victims (33.8%) in group 1 and 144 victims (50%) in group 2 demonstrated IPV-related injuries. Musculoskeletal injury was most common (58.2% and 44.5% in groups 1 and 2, respectively; most commonly lower/upper extremity fractures), followed by neurologic injury (20.9% and 32.9% in groups 1 and 2, respectively; most commonly facial injury). With longitudinal imaging history, radiologists were able to identify IPV in 31% of group 1 and 46.5% of group 2 patients. Amongst these patients, earlier identification by radiologists was provided compared to the self-reported date in 62.3% of group 1 (median, 64 months) and in 52.6% of group 2 (median, 69.3 months).Musculoskeletal and neurological injuries were the most common IPV-related injuries. Knowledge of common injuries and longitudinal imaging history may help IPV identification when victims are not forthcoming.• Musculoskeletal injuries were the most common type of IPV-related injury, followed by neurological injuries. • With longitudinal imaging history, radiologists were able to better raise the suspicion of IPV compared to the selective review of anatomically related studies only. • With longitudinal imaging history, radiologists were able to identify IPV earlier than the self-reported date by a median of 64 months in any type of abuse, and a median of 69.3 months in physical abuse.

Although vitamin D inhibits breast tumor growth and development in experimental settings, the findings from population-based studies remain inconclusive. Our goals were to investigate the association between prediagnostic plasma 25-hydroxyvitamin D [25(OH)D] concentration and breast cancer recurrence in prospective epidemiologic studies and to explore the molecular underpinnings linking 25(OH)D to slower progression of breast cancer. Our study included 659 women with invasive breast cancer from the Nurses’ Health Studies (NHS) diagnosed from 1989 to 2010. Plasma 25(OH)D was measured with a high-affinity protein binding assay or a radioimmunoassay. We profiled transcriptome-wide gene expression in breast tumors using microarrays. Hazard ratios (HRs) of breast cancer recurrence were estimated from covariate-adjusted Cox proportional hazards models. We examined differential gene expression regulation in association with 25(OH)D with covariate-adjusted linear regression and employed pathway analysis. We derived a gene expression score for 25(OH)D, and further assessed associations between the standardized score and breast cancer recurrence. Although 25(OH)D was not associated with breast cancer recurrence overall (hazard ratio (HR)=0.96; 95% confidence interval (CI): 0.86, 1.08) (median follow-up=12.8 years), the association varied by estrogen receptor (ER) status (p for interaction=0.005). Importantly, among ER-positive stage I to III cancers, every 5 ng/ml increase in prediagnostic plasma 25(OH)D was associated with a 13% lowered risk of recurrence (HR=0.87; 95% CI: 0.76; 0.99). No association was observed in women with stage I to III ER-negative cancers (HR=1.07; 95% CI: 0.91; 1.27). Pathway analysis identified multiple gene sets that were significantly (FDR Citation Format: Yujing Heng, Donghao Lu, Natalie DuPre, Kimberly Glass, Peter Kraft, David Feldman, Susan Hankinson, Kathryn Rexrode, Heather Eliassen, Rulla Tamimi, Cheng Peng. Prediagnostic 25-hydroxyvitamin D concentrations in relation to tumor molecular alterations and risk of breast cancer recurrence [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr A85.

Objective: Genome-wide association studies (GWAS) have been successful at identifying associations with stroke and stroke subtypes, but have not yet identified any associations solely with small vessel stroke (SVS). SVS comprises one quarter of all ischemic stroke and is a major manifestation of cerebral small vessel disease, the primary cause of vascular cognitive impairment. Studies across neurological traits have shown that younger-onset cases have an increased genetic burden. We leveraged this increased genetic burden by performing an age-at-onset informed GWAS meta-analysis, including a large younger-onset SVS population, to identify novel associations with stroke. Methods: We used a three-stage age-at-onset informed GWAS to identify novel genetic variants associated with stroke. On identifying a novel locus associated with SVS, we assessed its influence on other small vessel disease phenotypes, as well as on messenger RNA (mRNA) expression of nearby genes, and on DNA methylation of nearby CpG sites in whole blood and in the fetal brain. Results: We identified an association with SVS in 4,203 cases and 50,728 controls on chromosome 16q24.2 (odds ratio [OR; 95% confidence interval {CI}] = 1.16 [1.10–1.22]; p = 3.2 × 10−9). The lead single-nucleotide polymorphism (rs12445022) was also associated with cerebral white matter hyperintensities (OR [95% CI] = 1.10 [1.05–1.16]; p = 5.3 × 10−5; N = 3,670), but not intracerebral hemorrhage (OR [95% CI] = 0.97 [0.84–1.12]; p = 0.71; 1,545 cases, 1,481 controls). rs12445022 is associated with mRNA expression of ZCCHC14 in arterial tissues (p = 9.4 × 10−7) and DNA methylation at probe cg16596957 in whole blood (p = 5.3 × 10−6). Interpretation: 16q24.2 is associated with SVS. Associations of the locus with expression of ZCCHC14 and DNA methylation suggest the locus acts through changes to regulatory elements. Ann Neurol 2017;81:383–394.

Introduction: The associations of individual seafood n-3 polyunsaturated fatty acids with incident ischemic stroke, and its subtypes (atherothrombotic and cardioembolic stroke), are not well-established. Individual n-3 fatty acids, which are best assessed by circulating biomarkers, may also have differential effects on atherothrombotic vs. cardioembolic stroke. Objective: To prospectively investigate the relationship of circulating eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), and docosahexaenoic acid (DHA) with risk of total ischemic, atherothrombotic, and cardioembolic stroke. Methods: We measured circulating phospholipid fatty acids (erythrocyte or plasma) at baseline in the Cardiovascular Health Study (CHS), Nurses’ Health Study (NHS) and Health Professionals Follow-Up Study (HPFS). Ischemic stroke was prospectively adjudicated and classified into atherothrombotic (large and small vessel infarctions) or cardioembolic (cardiac mural thrombi, valvular heart disease, atrial fibrillation, other sources) stroke by imaging studies and medical records. Risk was assessed using conditional logistic regression (for prospective nested case-control studies: NHS, HPFS) or Cox proportional hazards model (full cohort: CHS). Findings were pooled by fixed-effects meta-analysis. Results: A total of 953 incident ischemic strokes were identified that included 408 atherothrombotic and 256 cardioembolic strokes, during mean follow-up of 11.4 (CHS) and 8.3 (NHS,HPFS) years (the remaining 289 ischemic strokes could not be sub-classified). After adjustment for age, race, sex, smoking, physical activity, alcohol intake, family history of MI and diabetes, menopausal status, BMI, aspirin use and intakes of fruits, vegetables, and meats, lower risk of total ischemic stroke was seen with DPA (highest vs. lowest-quartiles, pooled HR=0.74, 95%CI=0.58-0.92) and DHA (0.80, 0.64-1.00), but not EPA (0.94, 0.77-1.19). Among stroke subtypes, DHA was associated with substantially lower risk of atherothrombotic (0.53, 0.34-0.83), but not cardioembolic stroke; and DPA with substantially lower risk of cardioembolic (0.58, 0.37-0.92), but not atherothrombotic stroke. Findings in each individual cohort were similar to the pooled results. Conclusions: In 3 US cohorts, higher circulating levels of DHA were inversely associated with incident atherothrombotic stroke, and DPA, with cardioembolic stroke. EPA was not associated with ischemic stroke. These novel findings highlight the importance of separately evaluating ischemic stroke subtypes as well as specific individual fatty acids, and suggest differential pathways of benefit for DHA, DPA, and EPA.

Introduction: The trimethylamine-containing nutrient phosphatidylcholine is the major dietary source for gut microbiota metabolite trimethylamine-N-oxide, which has been related to cardiovascular diseases (CVD) and mortality. We recently found higher dietary phosphatidylcholine intake was associated with an increased risk of diabetes. No study has analyzed whether phosphatidylcholine intake is related to all-cause and CVD mortality in diabetic patients, who have a higher risk of CVD and premature mortality than the general population. Methods: In total, we included 10061 women and 3668 men with type 2 diabetes in the Nurses’ Health Study and Health Professionals Follow-up Study, respectively. Dietary intakes and potential confounders were assessed with regularly administered questionnaires. Results: We documented 2427 all-cause and 807 CVD deaths during up to 30-year follow-up. After multivariate adjustment for potential confounders, including age, smoking, body mass index, physical activity, dietary intakes and duration of diabetes, higher phosphatidylcholine intake was associated with an increased risk of all-cause and CVD mortality. The pooled relative risk comparing top to bottom quartiles of phosphatidylcholine intake was 1.22 (95%CI, 1.07-1.39; P for trend across quartiles=0.003) for all-cause mortality and 1.33 (1.04-1.69; P for trend=0.03) for CVD mortality. Conclusions: These data indicate that higher phosphatidylcholine consumption is associated with higher all-cause and CVD mortality in diabetic population, independent of other risk factors. Our findings suggest dietary modulation may be a potential interventional strategy to reduce mortality related to gut microbiota metabolites.

Introduction: Recent evidence suggests that gut microbiota functions like a metabolically active ‘organ’ and influences cardiovascular risk. Gallstone patients are characterized by gut microbiota dysbiosis and distorted metabolism; however, the relation between gallstones and coronary heart disease (CHD) is less well established. Methods: We examined the association between a history of gallstones and risk of CHD in 3 large prospective cohorts, and conducted a meta-analysis of published data with our result. A prospective study of 225,430 women and 44,637 men without CHD at baseline from the Nurses’ Health Study (NHS) (112,509 women; 1980-2010), NHS II (112,921 women; 1989-2011) and the Health Professionals Follow-up Study (HPFS) (44,637 men; 1986-2010). Other prospective cohort studies on a history of gallstones and risk of CHD were identified in searches of PUBMED and EMBASE up to March 2015. Results: After adjustment for potential confounders, the hazard ratio (HR) for the participants with a gallstone history as compared with those without was 1.16 (95% confidence interval [CI] 1.10-1.22) for NHS, 1.33 (95% CI 1.17-1.51) for NHS II and 1.11 (95% CI 1.03-1.20) for HPFS (all: p-value < 0.01). The associations appeared to be stronger in individuals with BMI Conclusions: Findings from prospective cohort studies consistently support a history of gallstones as an independent predictor of CHD risk.

Background: Prior observational studies suggest that self-reported consumption of dairy foods is associated with lower risk of stroke. Few studies have used circulating biomarkers that provide objective measure of dairy consumption. AIM: To test the hypothesis that plasma fatty acid biomarkers of dairy, 15:0, 17:0 and 16:1 n-7t, are associated with lower risk of incident stroke, especially ischemic stroke. Methods: We performed nested case-control studies in two prospective cohorts, the Nurses Health Study (NHS) and Health Professionals Follow Up Study (HPFS). Blood collected in 1989-90 (NHS) and 1993-94 (HPFS) was used to measure plasma 15:0, 17:0 and 16:1 n-7t biomarkers that are not endogenously synthesized. Stroke was prospectively adjudicated using medical records through 2004. A total of 597 stroke cases (NHS:475, HPFS:122), including 454 ischemic, 122 hemorrhagic and 21 unknown type, were matched 1:1 with controls on age, race, smoking, blood collection date and follow-up time (risk set sampling). Conditional logistic regression was used and cohort findings pooled by fixed-effects meta-analysis. Results: 15:0 was correlated with self-reported intakes of whole fat dairy foods (r=0.18 in NHS, r=0.18 in HPFS), low fat dairy (r=0.13, 0.11) and dairy fat (r=0.20, 0.30). 17:0 and 16:1 n-7t were less strongly correlated. After confounder adjustment, comparing the top vs. bottom quartiles, 15:0, 17:0 and 16:1 n-7t were not significantly associated with total or ischemic stroke in either cohort or overall ( Table 1 ). Results were similar after simultaneous adjustment for the 3 dairy fat biomarkers and for potential mediators. Conclusion: In these 2 cohorts, circulating dairy fat biomarkers were not significantly associated with stroke. Possible explanations include no true association, insufficient statistical power or measurement error due to changes in exposure over time. This study emphasizes need for further research on dairy and stroke using objective biomarkers, including studies with serial measures.

Summary Background The discovery of disease-associated loci through genome-wide association studies (GWAS) is the leading genetic approach to the identification of novel biological pathways underlying diseases in humans. Until recently, GWAS in ischaemic stroke have been limited by small sample sizes and have yielded few loci associated with ischaemic stroke. We did a large-scale GWAS to identify additional susceptibility genes for stroke and its subtypes. Methods To identify genetic loci associated with ischaemic stroke, we did a two-stage GWAS. In the first stage, we included 16 851 cases with state-of-the-art phenotyping data and 32 473 stroke-free controls. Cases were aged 16 to 104 years, recruited between 1989 and 2012, and subtypes of ischaemic stroke were recorded by centrally trained and certified investigators who used the web-based protocol, Causative Classification of Stroke (CCS). We constructed case-control strata by identifying samples that were genotyped on nearly identical arrays and were of similar genetic ancestral background. We cleaned and imputed data by use of dense imputation reference panels generated from whole-genome sequence data. We did genome-wide testing to identify stroke-associated loci within each stratum for each available phenotype, and we combined summary-level results using inverse variance-weighted fixed-effects meta-analysis. In the second stage, we did in-silico lookups of 1372 single nucleotide polymorphisms identified from the first stage GWAS in 20 941 cases and 364 736 unique stroke-free controls. The ischaemic stroke subtypes of these cases had previously been established with the Trial of Org 10 172 in Acute Stroke Treatment (TOAST) classification system, in accordance with local standards. Results from the two stages were then jointly analysed in a final meta-analysis. Findings We identified a novel locus (G allele at rs12122341) at 1p13.2 near TSPAN2 that was associated with large artery atherosclerosis-related stroke (first stage odds ratio [OR] 1·21, 95% CI 1·13–1·30, p=4·50 × 10 −8 ; joint OR 1·19, 1·12–1·26, p=1·30 × 10 −9 ). Our results also supported robust associations with ischaemic stroke for four other loci that have been reported in previous studies, including PITX2 (first stage OR 1·39, 1·29–1·49, p=3·26 × 10 −19 ; joint OR 1·37, 1·30–1·45, p=2·79 × 10 −32 ) and ZFHX3 (first stage OR 1·19, 1·11–1·27, p=2·93 × 10 −7 ; joint OR 1·17, 1·11–1·23, p=2·29 × 10 −10 ) for cardioembolic stroke, and HDAC9 (first stage OR 1·29, 1·18–1·42, p=3·50 × 10 −8 ; joint OR 1·24, 1·15–1·33, p=4·52 × 10 −9 ) for large artery atherosclerosis stroke. The 12q24 locus near ALDH2 , which has previously been associated with all ischaemic stroke but not with any specific subtype, exceeded genome-wide significance in the meta-analysis of small artery stroke (first stage OR 1·20, 1·12–1·28, p=6·82 × 10 −8 ; joint OR 1·17, 1·11–1·23, p=2·92 × 10 −9 ). Other loci associated with stroke in previous studies, including NINJ2 , were not confirmed. Interpretation Our results suggest that all ischaemic stroke-related loci previously implicated by GWAS are subtype specific. We identified a novel gene associated with large artery atherosclerosis stroke susceptibility. Follow-up studies will be necessary to establish whether the locus near TSPAN2 can be a target for a novel therapeutic approach to stroke prevention. In view of the subtype-specificity of the associations detected, the rich phenotyping data available in the Stroke Genetics Network (SiGN) are likely to be crucial for further genetic discoveries related to ischaemic stroke. Funding US National Institute of Neurological Disorders and Stroke, National Institutes of Health.

Lipoprotein-Associated Phospholipase A 2 (Lp-PLA 2 ) is an enzyme that hydrolyzes oxidized phospholipids in low-density lipoprotein, creating pro-inflammatory molecules that are hypothesized to play a role in the development of coronary heart disease. Many of the previous studies that have shown Lp-PLA 2 activity to be associated with a moderate increase in risk for CHD have been conducted mostly among men. The one previous study among women was small and reported a modest nonsignificant increase in risk. Thus, the association of Lp-PLA 2 with CHD risk in women warrants exploration. We conducted a nested case-control study among participants in the Nurses’ Health Study who provided a blood sample in 1989 –1990. With approximately 15 years of follow-up through 2004 we identified 438 incident cases of fatal and non-fatal MI and used incidence density sampling to match controls to cases (2:1) based on age, smoking status, date of blood draw, and fasting status at the time of blood draw. Lp-PLA 2 activity was measured using a colorimetric activity assay. We used conditional logistic regression in multivariate models. Compared to women in the lowest tertile, the relative risk of CHD for women in the second and third tertiles of Lp-PLA 2 activity were 1.75 (95% CI 1.26 –2.44) and 2.67 (1.94 – 3.69), respectively (p for trend = 0.002). After further adjustment for LDL cholesterol, c-reactive protein,body mass index, alcohol intake, aspirin use, physical activity, and post-menopausal hormone use, and histories of hypercholesterolemia, diabetes, and hypertension, the relative risks were similar: 1.81 (1.26 – 2.60) comparing the second to the first tertile, and 2.26 (1.54 – 3.30) comparing the third to the first tertile (p trend < 0.001). Further adjustment for HDL cholesterol modestly attenuated the relative risks to 1.62 (1.13 – 2.35) and 1.81 (1.20 – 2.71) (p trend = 0.007). The significantly elevated relative risks were not appreciably modified by HDL or LDL. In conclusion, we found that Lp-PLA 2 activity was associated with increased risk for MI among women. Adjustment for standard risk factors modestly attenuated this relationship, but Lp-PLA 2 remained a strong independent predictor of MI in this population of women healthy at baseline.

Publisher's version (útgefin grein), Objective: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. Methods We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n=20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. Results: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p[BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p[BI]= 4.4 × 10-10; p [SSBI] = 1.2 × 10 -4), diabetes (p[BI] = 1.7 × 10 -8; p [SSBI] = 2.8 × 10 -3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10 -24), and MRI-defined white matter hyperintensity burden (p [BI]=1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. Conclusion: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI., CHAP: R01-AG-11101, R01-AG-030146, NIRP-14-302587. SMART: This study was supported by a grant from the Netherlands Organization for Scientific Research–Medical Sciences (project no. 904-65–095). LBC: The authors thank the LBC1936 participants and the members of the LBC1936 research team who collected and collated the phenotypic and genotypic data. The LBC1936 is supported by Age UK (Disconnected Mind Programme grant). The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative (MR/K026992/1). The brain imaging was performed in the Brain Research Imaging Centre (https://www.ed.ac.uk/clinical-sciences/edinburgh-imaging), a center in the SINAPSE Collaboration (sinapse.ac.uk) supported by the Scottish Funding Council and Chief Scientist Office. Funding from the UK Biotechnology and Biological Sciences Research Council (BBSRC) and the UK Medical Research Council is acknowledged. Genotyping was supported by a grant from the BBSRC (ref. BB/F019394/1). PROSPER: The PROSPER study was supported by an investigator-initiated grant obtained from Bristol-Myers Squibb. Prof. Dr. J.W. Jukema is an Established Clinical Investigator of the Netherlands Heart Foundation (grant 2001 D 032). Support for genotyping was provided by the seventh framework program of the European commission (grant 223004) and by the Netherlands Genomics Initiative (Netherlands Consortium for Healthy Aging grant 050-060-810). SCES and SiMES: National Medical Research Council Singapore Centre Grant NMRC/CG/013/2013. C.-Y.C. is supported by the National Medical Research Council, Singapore (CSA/033/2012), Singapore Translational Research Award (STaR) 2013. Dr. Kamran Ikram received additional funding from the Singapore Ministry of Health's National Medical Research Council (NMRC/CSA/038/2013). SHIP: SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs, as well as the Social Ministry of the Federal State of Mecklenburg–West Pomerania, and the network “Greifswald Approach to Individualized Medicine (GANI_MED)” funded by the Federal Ministry of Education and Research (grant 03IS2061A). Genome-wide data have been supported by the Federal Ministry of Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthineers, Erlangen, Germany, and the Federal State of Mecklenburg–West Pomerania. Whole-body MRI was supported by a joint grant from Siemens Healthineers, Erlangen, Germany, and the Federal State of Mecklenburg–West Pomerania. The University of Greifswald is a member of the Caché Campus program of the InterSystems GmbH. OATS (Older Australian Twins Study): OATS was supported by an Australian National Health and Medical Research Council (NHRMC)/Australian Research Council (ARC) Strategic Award (ID401162) and by a NHMRC grant (ID1045325). OATS was facilitated via access to the Australian Twin Registry, which is supported by the NHMRC Enabling Grant 310667. The OATS genotyping was partly supported by a Commonwealth Scientific and Industrial Research Organisation Flagship Collaboration Fund Grant. NOMAS: The Northern Manhattan Study is funded by the NIH grant “Stroke Incidence and Risk Factors in a Tri-Ethnic Region” (NINDS R01NS 29993). TASCOG: NHMRC and Heart Foundation. AGES: The study was funded by the National Institute on Aging (NIA) (N01-AG-12100), Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament), with contributions from the Intramural Research Programs at the NIA, the National Heart, Lung, and Blood Institute (NHLBI), and the National Institute of Neurological Disorders and Stroke (NINDS) (Z01 HL004607-08 CE). ERF: The ERF study as a part of European Special Populations Research Network (EUROSPAN) was supported by European Commission FP6 STRP grant no. 018947 (LSHG-CT-2006-01947) and also received funding from the European Community's Seventh Framework Programme (FP7/2007–2013)/grant agreement HEALTH-F4-2007-201413 by the European Commission under the programme “Quality of Life and Management of the Living Resources” of 5th Framework Programme (no. QLG2-CT-2002-01254). High-throughput analysis of the ERF data was supported by a joint grant from Netherlands Organization for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). Exome sequencing analysis in ERF was supported by the ZonMw grant (project 91111025). Najaf Amin is supported by the Netherlands Brain Foundation (project no. F2013[1]-28). ARIC: The Atherosclerosis Risk in Communities study was performed as a collaborative study supported by NHLBI contracts (HHSN268201100005C, HSN268201100006C, HSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL70825, R01HL087641, R01HL59367, and R01HL086694; National Human Genome Research Institute contract U01HG004402; and NIH contract HHSN268200625226C. Infrastructure was partly supported by grant no. UL1RR025005, a component of the NIH and NIH Roadmap for Medical Research. This project was also supported by NIH R01 grant NS087541 to M.F. FHS: This work was supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (contracts no. N01-HC-25195 and no. HHSN268201500001I), and its contract with Affymetrix, Inc. for genotyping services (contract no. N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. This study was also supported by grants from the NIA (R01s AG033040, AG033193, AG054076, AG049607, AG008122, and U01-AG049505) and the NINDS (R01-NS017950, UH2 NS100605). Dr. DeCarli is supported by the Alzheimer's Disease Center (P30 AG 010129). ASPS: The research reported in this article was funded by the Austrian Science Fund (FWF) grant nos. P20545-P05, P13180, and P20545-B05, by the Austrian National Bank Anniversary Fund, P15435, and the Austrian Ministry of Science under the aegis of the EU Joint Programme–Neurodegenerative Disease Research (JPND) (jpnd.eu). LLS: The Leiden Longevity Study has received funding from the European Union's Seventh Framework Programme (FP7/2007–2011) under grant agreement no. 259679. This study was supported by a grant from the Innovation-Oriented Research Program on Genomics (SenterNovem IGE05007), the Centre for Medical Systems Biology, and the Netherlands Consortium for Healthy Ageing (grant 050-060-810), all in the framework of the Netherlands Genomics Initiative, Netherlands Organization for Scientific Research (NWO), UnileverColworth, and by BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO 184.021.007). CHS: This CHS research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N01HC15103, and HHSN268200960009C and grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, R01HL085251, and R01HL130114 from the NHLBI with additional contribution from NINDS. Additional support was provided through R01AG023629 from the NIA. A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Rotterdam Study: The generation and management of GWAS genotype data for the Rotterdam Study is supported by the Netherlands Organisation of Scientific Research (NWO) Investments (no. 175.010.2005.011, 911-03-012). This study is funded by the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/NWO project no. 050-060-810. The Rotterdam Study is funded by Erasmus MC Medical Center and Erasmus MC University, Rotterdam, Netherlands Organization for Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. M.A.I. is supported by an NWO Veni grant (916.13.054). The 3-City Study: The 3-City Study is conducted under a partnership agreement among the Institut National de la Santé et de la Recherche Médicale (INSERM), the University of Bordeaux, and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study is also supported by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, Mutuelle Générale de l’Education Nationale (MGEN), Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, and Ministry of Research–INSERM Programme “Cohortes et collections de données biologiques.” C.T. and S.D. have received investigator-initiated research funding from the French National Research Agency (ANR) and from the Fondation Leducq. S.D. is supported by a starting grant from the European Research Council (SEGWAY), a grant from the Joint Programme of Neurodegenerative Disease research (BRIDGET), from the European Union's Horizon 2020 research and innovation programme under grant agreements No 643417 & No 640643, and by the Initiative of Excellence of Bordeaux University. Part of the computations were performed at the Bordeaux Bioinformatics Center (CBiB), University of Bordeaux. This work was supported by the National Foundation for Alzheimer's Disease and Related Disorders, the Institut Pasteur de Lille, the Labex DISTALZ, and the Centre National de Génotypage. ADGC: The Alzheimer Disease Genetics Consortium is supported by NIH. NIH-NIA supported this work through the following grants: ADGC, U01 AG032984, RC2 AG036528; NACC, U01 AG016976; NCRAD, U24 AG021886; NIA LOAD, U24 AG026395, U24 AG026390; Banner Sun Health Research Institute, P30 AG019610; Boston University, P30 AG013846, U01 AG10483, R01 CA129769, R01 MH080295, R01 AG017173, R01 AG025259, R01AG33193; Columbia University, P50 AG008702, R37 AG015473; Duke University, P30 AG028377, AG05128; Emory University, AG025688; Group Health Research Institute, UO1 AG06781, UO1 HG004610; Indiana University, P30 AG10133; Johns Hopkins University, P50 AG005146, R01 AG020688; Massachusetts General Hospital, P50 AG005134; Mayo Clinic, P50 AG016574; Mount Sinai School of Medicine, P50 AG005138, P01 AG002219; New York University, P30 AG08051, MO1RR00096, UL1 RR029893, 5R01AG012101, 5R01AG022374, 5R01AG013616, 1RC2AG036502, 1R01AG035137; Northwestern University, P30 AG013854; Oregon Health & Science University, P30 AG008017, R01 AG026916; Rush University, P30 AG010161, R01 AG019085, R01 AG15819, R01 AG17917, R01 AG30146; TGen, R01 NS059873; University of Alabama at Birmingham, P50 AG016582, UL1RR02777; University of Arizona, R01 AG031581; University of California, Davis, P30 AG010129; University of California, Irvine, P50 AG016573, P50, P50 AG016575, P50 AG016576, P50 AG016577; University of California, Los Angeles, P50 AG016570; University of California, San Diego, P50 AG005131; University of California, San Francisco, P50 AG023501, P01 AG019724; University of Kentucky, P30 AG028383, AG05144; University of Michigan, P50 AG008671; University of Pennsylvania, P30 AG010124; University of Pittsburgh, P50 AG005133, AG030653; University of Southern California, P50 AG005142; University of Texas Southwestern, P30 AG012300; University of Miami, R01 AG027944, AG010491, AG027944, AG021547, AG019757; University of Washington, P50 AG005136; Vanderbilt University, R01 AG019085; and Washington University, P50 AG005681, P01 AG03991. The Kathleen Price Bryan Brain Bank at Duke University Medical Center is funded by NINDS grant NS39764, NIMH MH60451, and by GlaxoSmithKline. Genotyping of the TGEN2 cohort was supported by Kronos Science. The TGen series was also funded by NIA grant AG041232, the Banner Alzheimer's Foundation, The Johnnie B. Byrd Sr. Alzheimer's Institute, the Medical Research Council, and the state of Arizona and also includes samples from the following sites: Newcastle Brain Tissue Resource (funding via the Medical Research Council [MRC], local NHS trusts, and Newcastle University), MRC London Brain Bank for Neurodegenerative Diseases (funding via the Medical Research Council), South West Dementia Brain Bank (funding via numerous sources including the Higher Education Funding Council for England [HEFCE], Alzheimer's Research Trust [ART], BRACE, as well as North Bristol NHS Trust Research and Innovation Department and DeNDRoN), The Netherlands Brain Bank (funding via numerous sources including Stichting MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, Internationale Stiching Alzheimer Onderzoek), Institut de Neuropatologia, Servei Anatomia Patologica, and Universitat de Barcelona). ADNI: Funding for ADNI is through the Northern California Institute for Research and Education by grants from Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson & Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., Alzheimer's Association, Alzheimer's Drug Discovery Foundation, the Dana Foundation, and the National Institute of Biomedical Imaging and Bioengineering and NIA grants U01 AG024904, RC2 AG036535, and K01 AG030514. Support was also provided by the Alzheimer's Association (LAF, IIRG-08-89720; MAP-V, IIRG-05-14147) and the US Department of Veterans Affairs Administration, Office of Research and Development, Biomedical Laboratory Research Program. SiGN: Stroke Genetic Network (SiGN) was supported in part by award nos. U01NS069208 and R01NS100178 from NINDS. Genetics of Early-Onset Stroke (GEOS) Study was supported by the NIH Genes, Environment and Health Initiative (GEI) grant U01 HG004436, as part of the GENEVA consortium under GEI, with additional support provided by the Mid-Atlantic Nutrition and Obesity Research Center (P30 DK072488); and the Office of Research and Development, Medical Research Service, and the Baltimore Geriatrics Research, Education, and Clinical Center of the Department of Veterans Affairs. Genotyping services were provided by the Johns Hopkins University Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the NIH to Johns Hopkins University (contract no. HHSN268200782096C). Assistance with data cleaning was provided by the GENEVA Coordinating Center (U01 HG 004446; PI Bruce S. Weir). Study recruitment and assembly of datasets were supported by a Cooperative Agreement with the Division of Adult and Community Health, Centers for Disease Control and Prevention, and by grants from NINDS and the NIH Office of Research on Women's Health (R01 NS45012, U01 NS069208-01). METASTROKE: ASGC: Australian population control data were derived from the Hunter Community Study. This research was funded by grants from the Australian National and Medical Health Research Council (NHMRC Project Grant ID: 569257), the Australian National Heart Foundation (NHF Project Grant ID: G 04S 1623), the University of Newcastle, the Gladys M Brawn Fellowship scheme, and the Vincent Fairfax Family Foundation in Australia. E.G.H. was supported by a Fellowship from the NHF and National Stroke Foundation of Australia (ID: 100071). J.M. was supported by an Australian Postgraduate Award. BRAINS: Bio-Repository of DNA in Stroke (BRAINS) is partly funded by a Senior Fellowship from the Department of Health (UK) to P.S., the Henry Smith Charity, and the UK-India Education Research Institutive (UKIERI) from the British Council. GEOS: Genetics of Early Onset Stroke (GEOS) Study, Baltimore, was supported by GEI Grant U01 HG004436, as part of the GENEVA consortium under GEI, with additional support provided by the Mid-Atlantic Nutrition and Obesity Research Center (P30 DK072488), and the Office of Research and Development, Medical Research Service, and the Baltimore Geriatrics Research, Education, and Clinical Center of the Department of Veterans Affairs. Genotyping services were provided by the Johns Hopkins University Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the NIH to the Johns Hopkins University (contract no. HHSN268200782096C). Assistance with data cleaning was provided by the GENEVA Coordinating Center (U01 HG 004446; PI Bruce S. Weir). Study recruitment and assembly of datasets were supported by a Cooperative Agreement with the Division of Adult and Community Health, Centers for Disease Control and Prevention, and by grants from NINDS and the NIH Office of Research on Women's Health (R01 NS45012, U01 NS069208-01). HPS: Heart Protection Study (HPS) (ISRCTN48489393) was supported by the UK MRC, British Heart Foundation, Merck and Co. (manufacturers of simvastatin), and Roche Vitamins Ltd. (manufacturers of vitamins). Genotyping was supported by a grant to Oxford University and CNG from Merck and Co. J.C.H. acknowledges support from the British Heart Foundation (FS/14/55/30806). ISGS: Ischemic Stroke Genetics Study (ISGS)/Siblings With Ischemic Stroke Study (SWISS) was supported in part by the Intramural Research Program of the NIA, NIH project Z01 AG-000954-06. ISGS/SWISS used samples and clinical data from the NIH-NINDS Human Genetics Resource Center DNA and Cell Line Repository (ccr.coriell.org/ninds), human subjects protocol nos. 2003-081 and 2004-147. ISGS/SWISS used stroke-free participants from the Baltimore Longitudinal Study of Aging (BLSA) as controls. The inclusion of BLSA samples was supported in part by the Intramural Research Program of the NIA, NIH project Z01 AG-000015-50, human subjects protocol no. 2003-078. The ISGS study was funded by NIH-NINDS Grant R01 NS-42733 (J.F.M.). The SWISS study was funded by NIH-NINDS Grant R01 NS-39987 (J.F.M.). This study used the high-performance computational capabilities of the Biowulf Linux cluster at the NIH (biowulf.nih.gov). MGH-GASROS: MGH Genes Affecting Stroke Risk and Outcome Study (MGH-GASROS) was supported by NINDS (U01 NS069208), the American Heart Association/Bugher Foundation Centers for Stroke Prevention Research 0775010N, the NIH and NHLBI's STAMPEED genomics research program (R01 HL087676), and a grant from the National Center for Research Resources. The Broad Institute Center for Genotyping and Analysis is supported by grant U54 RR020278 from the National Center for Research resources. Milan: Milano–Besta Stroke Register Collection and genotyping of the Milan cases within CEDIR were supported by the Italian Ministry of Health (grant nos.: RC 2007/LR6, RC 2008/LR6; RC 2009/LR8; RC 2010/LR8; GR-2011-02347041), FP6 LSHM-CT-2007-037273 for the PROCARDIS control samples. WTCCC2: Wellcome Trust Case-Control Consortium 2 (WTCCC2) was principally funded by the Wellcome Trust, as part of the Wellcome Trust Case Control Consortium 2 project (085475/B/08/Z and 085475/Z/08/Z and WT084724MA). The Stroke Association provided additional support for collection of some of the St George's, London cases. The Oxford cases were collected as part of the Oxford Vascular Study, which is funded by the MRC, Stroke Association, Dunhill Medical Trust, National Institute of Health Research (NIHR), and the NIHR Biomedical Research Centre, Oxford. The Edinburgh Stroke Study was supported by the Wellcome Trust (clinician scientist award to C.L.M.S.) and the Binks Trust. Sample processing occurred in the Genetics Core Laboratory of the Wellcome Trust Clinical Research Facility, Western General Hospital, Edinburgh. Much of the neuroimaging occurred in the Scottish Funding Council Brain Imaging Research Centre (https://www.ed.ac.uk/clinical-sciences/edinburgh-imaging), Division of Clinical Neurosciences, University of Edinburgh, a core area of the Wellcome Trust Clinical Research Facility, and part of the SINAPSE (Scottish Imaging Network: A Platform for Scientific Excellence) collaboration (sinapse.ac.uk), funded by the Scottish Funding Council and the Chief Scientist Office. Collection of the Munich cases and data analysis was supported by the Vascular Dementia Research Foundation. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements no. 666881, SVDs@target (to M.D.) and no. 667375, CoSTREAM (to M.D.); the DFG as part of the Munich Cluster for Systems Neurology (EXC 1010 SyNergy) and the CRC 1123 (B3) (to M.D.); the Corona Foundation (to M.D.); the Fondation Leducq (Transatlantic Network of Excellence on the Pathogenesis of Small Vessel Disease of the Brain) (to M.D.); the e:Med program (e:AtheroSysMed) (to M.D.) and the FP7/2007-2103 European Union project CVgenes@target (grant agreement no. Health-F2-2013-601456) (to M.D.). M.F. and A.H. acknowledge support from the BHF Centre of Research Excellence in Oxford and the Wellcome Trust core award (090532/Z/09/Z). VISP: The GWAS component of the Vitamin Intervention for Stroke Prevention (VISP) study was supported by the US National Human Genome Research Institute (NHGRI), grant U01 HG005160 (PI Michèle Sale and Bradford Worrall), as part of the Genomics and Randomized Trials Network (GARNET). Genotyping services were provided by the Johns Hopkins University Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the NIH to Johns Hopkins University. Assistance with data cleaning was provided by the GARNET Coordinating Center (U01 HG005157; PI Bruce S. Weir). Study recruitment and collection of datasets for the VISP clinical trial were supported by an investigator-initiated research grant (R01 NS34447; PI James Toole) from the US Public Health Service, NINDS, Bethesda, MD. Control data obtained through the database of genotypes and phenotypes (dbGAP) maintained and supported by the United States National Center for Biotechnology Information, US National Library of Medicine. WHI: Funding support for WHI-GARNET was provided through the NHGRI GARNET (grant no. U01 HG005152). Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GARNET Coordinating Center (U01 HG005157). Funding support for genotyping, which was performed at the Broad Institute of MIT and Harvard, was provided by the GEI (U01 HG004424). R.L. is a senior clinical investigator of FWO Flanders. F.W.A. is supported by a Dekker scholarship-Junior Staff Member 2014T001–Netherlands Heart Foundation and UCL Hospitals NIHR Biomedical Research Centre.