Genetically-Determined Low-density Lipoprotein in Ischemic Stroke Survivors

Genetically-Determined Low-density Lipoprotein in Ischemic Stroke Survivors | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Genetically-Determined Low-density Lipoprotein in Ischemic Stroke Survivors View ORCID Profile Kane Wu , View ORCID Profile Shufan Huo , View ORCID Profile Cyprien A. Rivier , View ORCID Profile Victor M. Torres-Lopez , Richa Sharma , View ORCID Profile Adam de Havenon , View ORCID Profile Bradford Worrall , View ORCID Profile Kevin N. Sheth , View ORCID Profile Guido J. Falcone doi: https://doi.org/10.1101/2025.02.26.25322908 Kane Wu 1 Department of Neurology, Yale School of Medicine , New Haven, CT, USA BA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kane Wu Shufan Huo 1 Department of Neurology, Yale School of Medicine , New Haven, CT, USA MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shufan Huo Cyprien A. Rivier 1 Department of Neurology, Yale School of Medicine , New Haven, CT, USA MD MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cyprien A. Rivier Victor M. Torres-Lopez 1 Department of Neurology, Yale School of Medicine , New Haven, CT, USA MA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Victor M. Torres-Lopez Richa Sharma 1 Department of Neurology, Yale School of Medicine , New Haven, CT, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Adam de Havenon 1 Department of Neurology, Yale School of Medicine , New Haven, CT, USA MD, MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Adam de Havenon Bradford Worrall 2 Department of Neurology, University of Virginia. MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bradford Worrall Kevin N. Sheth 1 Department of Neurology, Yale School of Medicine , New Haven, CT, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kevin N. Sheth Guido J. Falcone 1 Department of Neurology, Yale School of Medicine , New Haven, CT, USA MD ScD MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Guido J. Falcone For correspondence: guido.falcone{at}yale.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Background and Objectives Polygenic susceptibility to hypertension and diabetes negatively impacts the clinical trajectory of ischemic stroke survivors. We hypothesize that polygenic susceptibility to hyperlipidemia (PSH) negatively impacts cholesterol control in this same population. Methods We conducted a genetic association study using data from the Vitamin Intervention Stroke Prevention (VISP) study, a clinical trial that enrolled survivors of ischemic stroke. PSH was modeled through a polygenic risk score built with 38 independent genetic risk variants for LDL-c that was divided into 80 percentile categories labeled as low, intermediate and high PSH. We used multivariable linear, logistic and Cox regression, as appropriate, to test whether high PSH was associated with risk of uncontrolled hyperlipidemia (LDL-c > 100mg/dl), resistant hyperlipidemia (LDL-c > 100mg/dl despite statin treatment) and clinical outcomes. We replicated our findings in a cohort of ischemic stroke survivors enrolled in the UK Biobank. Results 1,567 ischemic stroke survivors (mean age 68 years, 35% female) enrolled in VISP were included in the study. Stroke survivors with higher versus low PSH had 66% higher risk of uncontrolled hyperlipidemia (OR 1.66, 95%CI 1.17-2.36), 83% higher risk of resistant hyperlipidemia (1.83, 95%CI 1.00-3.37), twice the risk of recurrent stroke (HR 2.03, 95%CI 1.14-3.61) and 80% higher risk of acute coronary events (HR 1.81, 95%CI 1.18-2.78). The association between high PSH and higher risk of uncontrolled and resistant hyperlipidemia were replicated in 1,634 stroke survivors (mean age 61, 32% female sex) enrolled in the UK Biobank (OR 2.34, 95%CI 1.68-3.28 and OR 2.33, 95%CI 1.61-3.38, respectively). Discussion Among acute ischemic stroke survivors, higher PSH is associated with worse lipid control and higher risk of recurrent vascular events. Combined with existing evidence on the role of adverse genetic profiles in blood pressure and blood control in this population, our findings support the comprehensive evaluation of polygenic profiles as a cause of failed risk factor control in stroke survivors. INTRODUCTION Stroke is one of the top five leading causes of death and disability in the United States and worldwide. 1 With advancements in treatment and rehabilitation, the number of stroke survivors has steadily increased. 2 This vulnerable group is at particularly high risk of stroke recurrence, cognitive decline, and dementia. 3 , 4 These secondary events cause a high additional burden of disability, morbidity, and mortality for more than 70% of stroke survivors within five years. 5 Elevated low-density lipoprotein cholesterol (LDL-c) is one of the main treatable risk factors for both first-time stroke and stroke recurrence, acute coronary events and dementia in stroke survivors. 6 , 7 While lipid lowering treatments are generally shown to reduce mortality and morbidity, efficacy and outcomes vary due to polygenetic variations and environmental factors influencing LDL-c elevation. 8 , 9 Current guidelines for antihyperlipidemic therapy considering classical cardiovascular risk factors such as age, blood pressure, and cholesterol measurements often fail to account for these inter-individual differences. 10 Genetic variation explains around 30-40% of the variance of LDL-c levels in the general population. 11 This genetic contribution towards elevated LDL-c is a synergy of many common genetic risk variants. The aggregate burden of these variants carried by an individual determines their overall polygenic susceptibility to hyperlipidemia (PSH). Prior research indicates that high PSH is associated with higher risk of first-ever cardiovascular events; 12 however, research on the role of PSH after stroke remains limited. Describing genetic risk factors for post-stroke hyperlipidemia and secondary events can enable the development of individually tailored prevention strategies in stroke aftercare. We hypothesize that genetically elevated LDL-c levels negatively impact antihyperlipidemic treatment control and clinical trajectories after stroke, leading to higher risk of recurrent stroke, coronary events, and death. METHODS Study design and participant selection We conducted a 2-stage (testing and replication) genetic association study. For the primary stage, we conducted a post-hoc analysis of clinical and genetic data collected for the Vitamin Intervention Stroke Prevention (VISP) trial, which examined high-dose vitamins in ischemic stroke survivors. VISP is a multi-center, double-blind, randomized, controlled clinical trial enrolling patients aged 35 or older with a non-disabling ischemic stroke within 120 days and homocysteine levels above the 25th percentile at screening. Ischemic stroke was defined as a non-cardioembolic ischemic stroke with a neurological deficit persisting for at least 24 hours, or an infarction in the corresponding brain region confirmed by neuroimaging. Non-disabling was defined as a modified Rankin score ≤ 3. Participants were randomized to receive either a low or high dose daily multivitamin containing pyridoxine (Vitamin B6), folic acid (Vitamin B9), and cobalamin (Vitamin B12) and were followed for a two-year period at three-month interval clinic and phone appointments to assess interim recurrent stroke. Incidences of other vascular events (myocardial Infarction, coronary artery bypass grafting, etc.) were additionally tracked by physician assessment, hospital records, and reviewed by committee. The protocols for the VISP trial are further described elsewhere. 13 We evaluated data from the nested genetic cohort study “Genomics and Randomized Trials Network” (GARNET) completed in 2,164 participants of the VISP trial. Because the majority of enrolled participants were of European ancestry, we further limited genetic analysis to participants of European ancestry to minimize population stratification effects and genetic confounding. Genomic data Genome-wide data were generated using the Illumina’s HumanOmni1-Quad array and genotyping was conducted at the Center for Inherited Disease Research, Johns Hopkins University. We performed standard quality control procedures for genome-wide data including genotype level filters testing for departure from Hardy-Weinberg equilibrium, genotype call rates, sex effects, and missingness between cases. 14 Subject level quality checks included relatedness and heterozygosity. Population stratification was conducted to assess for European genetic ancestry. 15 This pipeline yielded 1,761 subjects and 791,723 markers that were imputed to 1000 Genomes integrated reference panels (phase 3 integrated variant set release in NCBI build 37) 16 using SHAPEIT 17 and IMPUTE2 18 after quality control and multidimensional scaling. Exposure of interest Our exposure of interest was PSH modeled through a polygenic risk score for LDL-c levels. Polygenic risk scores (PRS) constitute a well-established tool in population genetics that estimates an individual’s genetic burden across numerous genetic risk variants. 19 For a given study participant, the PRS is the sum of the product of the risk allele counts for each variant multiplied by the allele’s reported effect on LDL-c levels. We built a PRS using genetic information on 38 known genetic risk variants, also called single nucleotide polymorphisms (SNPs), associated with higher LDL-c. To isolate LDL-c specific effects, these genetic variants were only associated with higher LDL-c levels and not with other plasma lipid components or other traits. 11 In addition, these SNPs had a minor allele frequency >1% and were independent (r2 <0.1), biallelic (involve two alleles only) and associated with increased plasma LDL-c at genome-wide levels (p<5×10 -8 ). To represent the PSH as a categorical variable in an intuitive manner, we divided participants into 80 percentile categories labeled as low, intermediate and high PSH. Outcomes of interest We evaluated several primary outcomes representing lipid control in stroke survivors: plasma LDL-c at enrollment, uncontrolled hyperlipidemia, and treatment-resistant hyperlipidemia. We defined uncontrolled hyperlipidemia as baseline LDL > 100 mg/dL at enrollment and resistant hyperlipidemia as LDL > 100 mg/dL despite being on statin treatment according to current therapeutic guidelines. 20 – 22 The detailed description of LDL cholesterol measurements in the VISP trial has been published previously. 13 , 23 Briefly, plasma LDL-c was obtained during the baseline examination. To determine whether patients were on statin treatment, we used data from self-reported information in the baseline interview completed by all participants. Secondary clinical endpoints were stroke recurrence, a composite of stroke recurrence or acute coronary event, and composite of stroke recurrence, acute coronary event, or all-cause death. Qualifying coronary events included hospitalized myocardial infarction, coronary collapse, and coronary artery bypass graft or coronary angioplasty performed. Clinical endpoints and patient deaths were recorded with available information from hospital records, death certificates, coroner’s reports, and physician questionnaires. All clinical outcomes were centrally adjudicated by a panel of clinical experts. Covariates Age at baseline was calculated as the difference between the participant’s birthdate and the date of randomization in the VISP trial and treated as a continuous variable. Sex and ancestry were determined using genetic information. Statin usage, hypertension diagnosis, and diabetes diagnosis as binary categorical variables were identified by combining the baseline forms and physical exam data. Statistical analysis Primary analysis First, we investigated unadjusted associations between PSH and LDL-c levels, uncontrolled hyperlipidemia and treatment-resistant hyperlipidemia using unpaired t, ANOVA, and chi-square tests as appropriate. Second, to account for bias introduced by potential confounders, we also evaluated these same relationships using multivariable linear (LDL-c levels) and logistic regression (uncontrolled and treatment-resistant hyperlipidemia) models that were adjusted for age, sex, hypertension, diabetes, and statin treatment. All variables were evaluated to identify data entry errors and participants with missing exposures or outcomes of interest were excluded from analysis. Replication To maximize the scientific rigor of this study, we replicated our primary analyses using data from the UK Biobank, a population-based cohort study that enrolled around 500,000 participants throughout the United Kingdom. 24 Sociodemographic information, questionnaires, physical measurements, information about medication intake including statins, and bio-samples including blood samples for LDL-c measurements and genotyping were collected at baseline. Clinical data including incident ischemic stroke before and after baseline were retrieved via electronic health records and ICD-9/10 codes. We included participants of European ancestry with ischemic stroke prior to enrollment. The statistical analyses for correlation between PSH and LDL-c, uncontrolled and resistant hyperlipidemia were performed equivalent to the discovery cohort. Secondary analysis We used Cox proportional hazards models and survival analyses via the log rank test to compare the relative risk of recurrent stroke between the intermediate and high PSH categories versus low PSH, repeating the analysis for composite endpoints of ischemic stroke and coronary event, as well as a composite including all-cause mortality. Participants were censored at loss-to-follow-up and the date was set from the VISP trial data. We confirmed the proportional hazard assumption over time by calculating Schoenfeld residuals for all variables in our multivariable models. We adjusted for age, sex, hypertension, diabetes, and statin treatment in multivariable Cox proportional hazards models to test whether higher PSH was associated with stroke recurrence and the composite endpoints. As a sensitivity analysis, we calculated the cox proportional hazards for clinical endpoints while including measured LDL as a covariable. This sensitivity adjusted model would investigate whether the PSH increased the risk of recurrent stroke beyond the risk captured by measured LDL at the time of enrollment. Statistical significance and software We declared statistical significance at p<0.05. We used PLINK (version 1.9) to conduct quality control procedures and polygenic risk scoring, and R (version 3.6) for all other analyses. Standard Protocol Approvals, Registrations, and Patient Consents The VISP trial protocols were approved by the ethics committees of all study institutions and administrative sites with written informed consent obtained from every potential participant prior to screening. Ethics approval for the UK Biobank study was obtained from the North West Centre for Research Ethics Committee and all participants or their legal guardians have provided written informed consent. Data availability The data from GARNET are publicly available on the database of Genotypes and Phenotypes (dbGaP) and was acquired through the accession number phs000343.v3.p1. Data from the UK Biobank are publicly available and was accessed using project application number 58743. RESULTS Of the total 3680 participants enrolled in VISP, 2,164 participants were enrolled in GARNET and had available genetic data. Of these, 19 (0.9%) were excluded during the genomic quality control process and 342 (16.3%) participants were excluded for non-European ancestry. Of the resulting 1761 participants, one was rejected after imputation and ten rejected for ID mismatch (0.6%). Out of 1750 remaining participants, 1567 (89.5%) had a recorded LDL-c baseline measurement on file and were thus included in this study (mean age 68 years [SD 10.7]; 549 (35%) were female, Figure 1 ). The mean LDL-c in this final analytical cohort was 122.6 mg/dL [SD 38] ( Table 1 ). The proportion of uncontrolled and resistant hyperlipidemia in the cohort was 71% (n=1110) and 23% (n=355), respectively (Supplemental Table 2). Download figure Open in new tab Figure 1. Cohort Selection View this table: View inline View popup Download powerpoint Table 1. Baseline Characteristics from selected VISP cohort Polygenic susceptibility to hyperlipidemia and observed LDL Higher PSH, modeled via PRS, was significantly associated with LDL-c levels in stroke survivors. When comparing study participants with low, intermediate, and high PSH, the mean LDL-c was 114.5 [SD 38.8], 123.2 [SD 37.5] and 128.8 [SD 41.9], respectively (unadjusted p<0.001) ( Table 1 ). These associations remained significant in multivariable linear regression analyses comparing intermediate vs low PSH (beta 7.96, SE 2.51) and high versus low PSH (beta 14.29, SE 3.08; test for trend p < 0.001) ( Table 2 ). View this table: View inline View popup Download powerpoint Table 2. Polygenic Susceptibility to Hyperlipidemia and LDL-c control Polygenic susceptibility to hyperlipidemia and uncontrolled/resistant hyperlipidemia A higher PSH was also associated with a higher likelihood of uncontrolled hyperlipidemia in stroke survivors. When comparing study participants with low versus high PSH, the proportion with uncontrolled hyperlipidemia was 63.1% (n=198) versus 74.1% (n=232), respectively (p = 0.003). We observed a similar relationship between PSH categories and resistant hyperlipidemia: In patients with low PSH, 17.3% (n=54) had resistant hyperlipidemia, whereas it was 24.2% (n=76) in patients with high PSH (p = 0.03) (Supplemental Table 2). These associations remained significant in the multivariable modeling even after adjusting for covariates ( Table 2 ). Those in the intermediate risk cohort (OR 1.50, 95% CI 1.14-1.97) and high-risk cohort (OR 1.66, 95% CI 1.17-2.34) were more likely to have uncontrolled hyperlipidemia, and the same was true for the likelihood of resistant hyperlipidemia in the intermediate (OR 1.79, 95% CI 1.11-2.90) and high-risk cohorts (OR 1.83, 95% CI 1.01-3.33). Replication Of 502,369 participants enrolled in the UK Biobank, 1,913 had a history of prior ischemic stroke. Of those, 1,838 had genomic data that passed quality control. 1,731 of these participants were white and 1,634 of these had LDL-c measurements and were included in the final analysis (mean age 61 years [SD 6.7], 31.7% females) (Supplemental Table 1). The mean LDL-c was 105.9mg/dl [SD 6.7]. The proportion of uncontrolled and resistant hyperlipidemia in the cohort was 52% (n=847) and 33% (n=537), respectively (Supplemental Table 2). When comparing participants with low, intermediate, and high PSH, the mean LDL-c was 97.2mg/dl [SD 28.4], 107.3mg/dl [SD 31.5] and 110.2mg/dl [SD 33.1], respectively (unadjusted p<0.001). Participants with intermediate (OR 1.98, 95% CI 1.50-2.60) and high genetic risk (OR 2.34, 95% CI 1.68-3.28) were twice as likely to have uncontrolled hyperlipidemia, and the same relationship was observed for resistant hyperlipidemia in the intermediate (OR 2.07, 95% CI 1.52-2.84) and high-risk cohorts (OR 2.33, 95% CI 1.61-3.38). Thus, all results from the primary analysis could be replicated ( Table 2 ). Polygenic susceptibility to hyperlipidemia and clinical endpoints Over a total follow-up period of 2,866 person-years (1.8 years mean follow-up, SD 0.36 years), 127 recurrent stroke endpoints occurred. Compared to stroke survivors with low PSH, the unadjusted cox-proportional hazards model showed that those with intermediate (HR 1.43, 95% CI 0.85-2.39) and high (HR 1.97, 95% CI 1.11-3.48) PSH had a higher risk of stroke recurrence (p-trend 0.02). Similar associations of higher PSH and stroke recurrence were observed in adjusted multivariable hazard modeling for intermediate (HR 1.52, 95% CI 0.91-2.54) and high (HR 2.03, 95% CI 1.14-3.61) risk categories. Differences for the composite endpoint of stroke and acute coronary events were observed for intermediate (HR 1.31, 95% CI 0.90-1.92) and high (HR 1.81, 95% CI 1.18-2.78) risk categories as well ( Table 3 ). Inclusion of all-cause mortality further confirmed the increased hazard of adverse outcomes for intermediate and high PSH groups. These associations persisted when examining survival curves with the log rank test in adjusted analyses for recurrent stroke and composite endpoints (p<0.001) ( Figure 2 ). Sensitivity analysis showed similar increases in risk of stroke recurrence in intermediate (HR 1.51, 95% CI 0.90-2.53) and high PSH groups (HR 2.00, 95% CI 1.12-3.57) even when including measured baseline LDL in the proportional hazard model, which was confirmed for both composite endpoints ( Table 3 ). Schoenfeld residuals showed that cox proportional hazard modeling assumptions were met in all analyses. Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Figure 2. Survival Curves (A) Recurrent Stroke by Polygenic Susceptibility to Hyperlipidemia. (B) Recurrent Stroke or Coronary Event by Polygenic Susceptibility to Hyperlipidemia. (C) Recurrent Stroke or Coronary Event or Death by Polygenic Susceptibility to Hyperlipidemia. View this table: View inline View popup Download powerpoint Table 3. Clinical Endpoints in VISP cohort DISCUSSION In our study, higher PSH, modeled through PRS, negatively impacted post-stroke LDL-c control, including higher LDL-c values and a greater incidence of uncontrolled and treatment resistant hyperlipidemia. These associations were consistent after adjusting for confounding and when replicated in an independent cohort. Higher PSH also was associated with greater stroke recurrence, cardiovascular morbidity and death. These results were confirmed in our sensitivity analyses, which demonstrated that the predictive value of PSH persisted even when including observed LDL-c measurements of individuals at the time of enrollment in the VISP trial. Clinical care of stroke survivors can be challenging due to the elevated risk of stroke recurrence, acute coronary events, cognitive decline and dementia. 4 , 25 Along with the possibility of stroke-related disabilities, these complications carry substantial additional morbidity and reduction in quality of life. A meta-analysis of randomized control trials concluded that every 1 mmol/L (39 mg/dL) of LDL-c reduction translates into a 20-25% decrease in risk of major cardiovascular events, observed across multiple types of lipid-lowering therapy. 26 As is the case in conventional guidelines regarding primary prevention, vigilant control of cardiometabolic risk factors in stroke survivors constitute an effective way to reduce these morbidity and mortality burdens. 27 Despite compelling evidence showing the beneficial effects of lipid optimization strategies, especially when combined with intensive management with other vascular risk factors, an important proportion of stroke survivors have LDL-c levels that are not at goal. A better understanding of the biology and risk factors for elevated LDL-c resistant to statin treatment may lead to better strategies to identify and address stroke survivors at high risk of dangerous LDL-c trajectories. While the role of PSH in primary occurrence of cerebrovascular diseases has been studied, little is known about its influence on clinical trajectories after stroke. Our findings address this gap in knowledge, not only by highlighting the impact of PSH on LDL-c in stroke patients, but also on the more prominent role of PSH in the subgroups of uncontrolled and resistant hyperlipidemia. Furthermore, we demonstrate that PSH has the capacity to be a greater predictive tool for stroke recurrence than measured LDL-c alone, as PSH represents a person’s lifetime exposure to greater hyperlipidemia risk before and after any single LDL-c measurement from blood. These results, highlighting the adverse influence of PSH on vascular long-term outcome measures, emphasize the need to incorporate genetic factors in risk assessment and treatment decisions after stroke. While treatment guidelines for other cardiovascular risk factors such as hypertension and diabetes have numerical cutoffs, prescription of statins and other lipid-lowering therapies are dependent on cardiovascular risk assessments 28 – 30 . These considerations, however, cannot account for inter-individual differences in risk and treatment response. 31 Integrating genomic data into novel tools could identify stroke survivors who stand to benefit most from earlier and more aggressive treatment for hyperlipidemia. Considering genetic factors is one important component of today’s precision medicine advances. Ultimately, we are moving towards integration of existing approaches for risk assessment using clinical, socioeconomic, and cultural data. Supporting these ideas, genetic studies of the Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Subjects With Elevated Risk (FOURIER) and Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab (ODYSSEY OUTCOMES) trials demonstrated that patients with higher genetic risk of cardiovascular disease have the greatest benefit from aggressive lipid lowering with proprotein convertase subtilisin/kexin type 9 (PCSK-9) inhibitors. 32 , 33 Furthermore, the application of both genetic risk and clinical predictive scores could produce better health outcomes than either approaches alone. 34 Communicating genetic risk in counseling lifestyle interventions addressing cardiovascular diseases could potentially lead to more effective patient education and adherence. 35 As the financial costs of genotyping decrease and the number of persons with available genome-wide data increase, these strategies hold promising potential as an integrative and widely applicable tool in clinical practice. Our study has several limitations. Firstly, therapeutic options for hyperlipidemia have increased since the VISP trial was initially designed and conducted. Our analysis only accounts for the modulation of PSH by statin treatment. Furthermore, our data did not differentiate between types of statins, which could obscure differential susceptibility to genetic resistance. Secondly, medication data in the VISP trial and the UK Biobank are self-reported, and medication doses and adherence could not be assessed, which may inflate the estimate of resistant hyperlipidemia in our cohort. Clinical outcomes analysis was not replicated due to limited data for secondary events. Finally, we could only include participants from European genetic ancestry, which limits the generalizability of our results to other ancestry groups. The importance of external validation in persons from different ancestries cannot be understated, as the underrepresentation of minority populations in genetic studies is widely known. Uncritical use of this data for precision medicine tools could worsen existing health disparities and should therefore be avoided. In summary, PSH is associated with worse lipid control and clinical trajectories in stroke survivors from European ancestry. These findings suggest that precision medicine approaches for stratifying stroke patients according to their genetic risk for elevated LDL-c have unique clinical utility in assessing post-stroke clinical management and should be further pursued. Sources of Funding KW is supported by NIH-NIDDK grant T35DK104689 SH is supported by the Walter-Benjamin Scholarship from the German Research Foundation (DFG, grant number 514143076) CR is supported by the American Heart Association (817874) and the AAN/AHA Ralph L. Sacco Scholars Fellowship ( https://doi.org/10.58275/AHA.24RSSPOST1328228.pc.gr.197089 ). Dr. de Havenon reports NIH/NINDS funding (R21NS138995, UG3NS130228, R01NS130189). GJF is supported by the AHA (24GWTGSIC1341098, 817874 and 23BFHSCP1178409) and the NIH (1U01NS106513 and R01NS093870). Disclosures Dr. de Havenon has received consultant fees from Integra and Novo Nordisk, royalty fees from UpToDate, and has equity in TitinKM and Certus. Acknowledgments We gratefully thank all the participants in the VISP and UK Biobank studies without whom this research would not have been possible. REFERENCES 1. ↵ Feigin VL , Brainin M , Norrving B , et al. World Stroke Organization (WSO): Global Stroke Fact Sheet 2022 . Int J Stroke Off J Int Stroke Soc . 2022 ; 17 ( 1 ): 18 – 29 . doi: 10.1177/17474930211065917 OpenUrl CrossRef PubMed 2. ↵ Feigin VL , Stark BA , Johnson CO , et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019 . Lancet Neurol . 2021 ; 20 ( 10 ): 795 – 820 . doi: 10.1016/S1474-4422(21)00252-0 OpenUrl CrossRef PubMed 3. ↵ Flach C , Muruet W , Wolfe CDA , Bhalla A , Douiri A . Risk and Secondary Prevention of Stroke Recurrence . Stroke . 2020 ; 51 ( 8 ): 2435 – 2444 . doi: 10.1161/STROKEAHA.120.028992 OpenUrl CrossRef PubMed 4. ↵ Pendlebury ST , Rothwell PM . Prevalence, incidence, and factors associated with pre-stroke and post-stroke dementia: a systematic review and meta-analysis . Lancet Neurol . 2009 ; 8 ( 11 ): 1006 – 1018 . doi: 10.1016/S1474-4422(09)70236-4 OpenUrl CrossRef PubMed Web of Science 5. ↵ Sennfält S , Norrving B , Petersson J , Ullberg T. Long-Term Survival and Function After Stroke . Stroke . 2019 ; 50 ( 1 ): 53 – 61 . doi: 10.1161/STROKEAHA.118.022913 OpenUrl CrossRef PubMed 6. ↵ Pan Y , Wangqin R , Li H , et al. LDL-C levels, lipid-lowering treatment and recurrent stroke in minor ischaemic stroke or TIA . Stroke Vasc Neurol . 2022 ; 7 ( 4 ). doi: 10.1136/svn-2021-001317 OpenUrl Abstract / FREE Full Text 7. ↵ Hosomi N , Kitagawa K , Nagai Y , et al. Desirable Low-Density Lipoprotein Cholesterol Levels for Preventing Stroke Recurrence . Stroke . 2018 ; 49 ( 4 ): 865 – 871 . doi: 10.1161/STROKEAHA.117.018870 OpenUrl Abstract / FREE Full Text 8. ↵ Gencer B , Marston NA , Im K , et al. Efficacy and safety of lowering LDL cholesterol in older patients: a systematic review and meta-analysis of randomised controlled trials . The Lancet . 2020 ; 396 ( 10263 ): 1637 – 1643 . doi: 10.1016/S0140-6736(20)32332-1 OpenUrl CrossRef 9. ↵ Klein-Szanto AJP , Bassi DE . Keep recycling going: New approaches to reduce LDL-C . Biochem Pharmacol . 2019 ; 164 : 336 – 341 . doi: 10.1016/j.bcp.2019.04.003 OpenUrl CrossRef PubMed 10. ↵ Cifu AS , Davis AM. Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults . JAMA . 2017 ; 318 ( 21 ): 2132 – 2134 . doi: 10.1001/jama.2017.18706 OpenUrl CrossRef PubMed 11. ↵ Graham SE , Clarke SL , Wu KHH , et al. The power of genetic diversity in genome-wide association studies of lipids . Nature . 2021 ; 600 ( 7890 ): 675 - 679 . doi: 10.1038/s41586-021-04064-3 OpenUrl CrossRef PubMed 12. ↵ Khera AV , Chaffin M , Aragam KG , et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations . Nat Genet . 2018 ; 50 ( 9 ): 1219 – 1224 . doi: 10.1038/s41588-018-0183-z OpenUrl CrossRef 13. ↵ Toole JF , Malinow MR , Chambless LE , et al. Lowering homocysteine in patients with ischemic stroke to prevent recurrent stroke, myocardial infarction, and death: the Vitamin Intervention for Stroke Prevention (VISP) randomized controlled trial . JAMA . 2004 ; 291 ( 5 ): 565 – 575 . doi: 10.1001/jama.291.5.565 OpenUrl CrossRef PubMed Web of Science 14. ↵ Marees AT , de Kluiver H , Stringer S , et al. A tutorial on conducting genome-wide association studies: Quality control and statistical analysis . Int J Methods Psychiatr Res . 2018 ; 27 ( 2 ): e1608 . doi: 10.1002/mpr.1608 OpenUrl CrossRef PubMed 15. ↵ Purcell S , Neale B , Todd-Brown K , et al. PLINK: a tool set for whole-genome association and population-based linkage analyses . Am J Hum Genet . 2007 ; 81 ( 3 ): 559 – 575 . doi: 10.1086/519795 OpenUrl CrossRef PubMed 16. ↵ 1000 Genomes Project Consortium , Abecasis GR , Altshuler D , et al. A map of human genome variation from population-scale sequencing . Nature . 2010 ; 467 ( 7319 ): 1061 – 1073 . doi: 10.1038/nature09534 OpenUrl CrossRef PubMed Web of Science 17. ↵ Delaneau O , Marchini J , Zagury JF . A linear complexity phasing method for thousands of genomes . Nat Methods . 2011 ; 9 ( 2 ): 179 – 181 . doi: 10.1038/nmeth.1785 OpenUrl CrossRef PubMed 18. ↵ Howie BN , Donnelly P , Marchini J . A flexible and accurate genotype imputation method for the next generation of genome-wide association studies . PLoS Genet . 2009 ; 5 ( 6 ): e1000529 . doi: 10.1371/journal.pgen.1000529 OpenUrl CrossRef PubMed 19. ↵ Choi SW , Mak TSH , O’Reilly PF . Tutorial: a guide to performing polygenic risk score analyses . Nat Protoc . 2020 ; 15 ( 9 ): 2759 – 2772 . doi: 10.1038/s41596-020-0353-1 OpenUrl CrossRef PubMed 20. ↵ Expert Panel on Detection E and Treatment of High Blood Cholesterol in Adults . Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) . JAMA . 2001 ; 285 ( 19 ): 2486 – 2497 . doi: 10.1001/jama.285.19.2486 OpenUrl CrossRef PubMed Web of Science 21. Wilson PWF , Polonsky TS , Miedema MD , Khera A , Kosinski AS , Kuvin JT . Systematic Review for the 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines . J Am Coll Cardiol . 2019 ; 73 ( 24 ): 3210 – 3227 . doi: 10.1016/j.jacc.2018.11.004 OpenUrl FREE Full Text 22. ↵ Su L , Mittal R , Ramgobin D , Jain R , Jain R . Current Management Guidelines on Hyperlipidemia: The Silent Killer . J Lipids . 2021 ; 2021 : e9883352 . doi: 10.1155/2021/9883352 OpenUrl CrossRef 23. ↵ Toole JF . Vitamin intervention for stroke prevention . J Neurol Sci . 2002 ; 203-204 : 121 – 124 . doi: 10.1016/S0022-510X(02)00265-4 OpenUrl CrossRef 24. ↵ Bycroft C , Freeman C , Petkova D , et al. The UK Biobank resource with deep phenotyping and genomic data . Nat 2018 5627726 . 2018 ; 562 ( 7726 ): 203 – 209 . doi: 10.1038/s41586-018-0579-z OpenUrl CrossRef PubMed 25. ↵ Mohan KM , Wolfe CDA , Rudd AG , Heuschmann PU , Kolominsky-Rabas PL , Grieve AP . Risk and Cumulative Risk of Stroke Recurrence . Stroke . 2011 ; 42 ( 5 ): 1489 – 1494 . doi: 10.1161/STROKEAHA.110.602615 OpenUrl Abstract / FREE Full Text 26. ↵ Silverman MG , Ference BA , Im K , et al. Association Between Lowering LDL-C and Cardiovascular Risk Reduction Among Different Therapeutic Interventions: A Systematic Review and Meta-analysis . JAMA . 2016 ; 316 ( 12 ): 1289 – 1297 . doi: 10.1001/jama.2016.13985 OpenUrl CrossRef PubMed 27. ↵ Hackam DG , Spence JD . Combining multiple approaches for the secondary prevention of vascular events after stroke: a quantitative modeling study . Stroke . 2007 ; 38 ( 6 ): 1881 – 1885 . doi: 10.1161/STROKEAHA.106.475525 OpenUrl Abstract / FREE Full Text 28. ↵ Unger T , Borghi C , Charchar F , et al. 2020 International Society of Hypertension Global Hypertension Practice Guidelines . Hypertension . 2020 ; 75 ( 6 ): 1334 - 1357 . doi: 10.1161/HYPERTENSIONAHA.120.15026 OpenUrl CrossRef PubMed 29. ElSayed NA , Aleppo G , Aroda VR , et al. 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes—2023 . Diabetes Care . 2023 ; 46 ( Suppl 1 ): S19 – S40 . doi: 10.2337/dc23-S002 OpenUrl CrossRef PubMed 30. ↵ Grundy SM , Stone NJ , Bailey AL , et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines . Circulation . 2019 ; 139 ( 25 ): e1082 - e1143 . doi: 10.1161/CIR.0000000000000625 OpenUrl CrossRef PubMed 31. ↵ Khatib R , Glowacki N , Lauffenburger J , Siddiqi A . Race/Ethnic Differences in Atherosclerotic Cardiovascular Disease Risk Factors Among Patients With Hypertension: Analysis From 143 Primary Care Clinics . Am J Hypertens . 2021 ; 34 ( 9 ): 948 – 955 . doi: 10.1093/ajh/hpab053 OpenUrl CrossRef PubMed 32. ↵ Marston NA , Kamanu FK , Nordio F , et al. Predicting Benefit From Evolocumab Therapy in Patients With Atherosclerotic Disease Using a Genetic Risk Score: Results From the FOURIER Trial . Circulation . 2020 ; 141 ( 8 ): 616 – 623 . doi: 10.1161/CIRCULATIONAHA.119.043805 OpenUrl CrossRef 33. ↵ Damask A , Steg PG , Schwartz GG , et al. Patients With High Genome-Wide Polygenic Risk Scores for Coronary Artery Disease May Receive Greater Clinical Benefit From Alirocumab Treatment in the ODYSSEY OUTCOMES Trial . Circulation . 2020 ; 141 ( 8 ): 624 – 636 . doi: 10.1161/CIRCULATIONAHA.119.044434 OpenUrl CrossRef 34. ↵ Riveros-Mckay F , Weale ME , Moore R , et al. Integrated Polygenic Tool Substantially Enhances Coronary Artery Disease Prediction . Circ Genomic Precis Med . 2021 ; 14 ( 2 ): e003304 . doi: 10.1161/CIRCGEN.120.003304 OpenUrl CrossRef PubMed 35. ↵ Widén E , Junna N , Ruotsalainen S , et al. How Communicating Polygenic and Clinical Risk for Atherosclerotic Cardiovascular Disease Impacts Health Behavior: an Observational Follow-up Study . Circ Genomic Precis Med . 2022 ; 15 ( 2 ): e003459 . doi: 10.1161/CIRCGEN.121.003459 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted February 27, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Genetically-Determined Low-density Lipoprotein in Ischemic Stroke Survivors Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Genetically-Determined Low-density Lipoprotein in Ischemic Stroke Survivors Kane Wu , Shufan Huo , Cyprien A. Rivier , Victor M. Torres-Lopez , Richa Sharma , Adam de Havenon , Bradford Worrall , Kevin N. Sheth , Guido J. Falcone medRxiv 2025.02.26.25322908; doi: https://doi.org/10.1101/2025.02.26.25322908 Share This Article: Copy Citation Tools Genetically-Determined Low-density Lipoprotein in Ischemic Stroke Survivors Kane Wu , Shufan Huo , Cyprien A. Rivier , Victor M. Torres-Lopez , Richa Sharma , Adam de Havenon , Bradford Worrall , Kevin N. Sheth , Guido J. Falcone medRxiv 2025.02.26.25322908; doi: https://doi.org/10.1101/2025.02.26.25322908 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neurology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (298) Cardiovascular Medicine (4421) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15215) Forensic Medicine (30) Gastroenterology (1122) Genetic and Genomic Medicine (6582) Geriatric Medicine (667) Health Economics (996) Health Informatics (4522) Health Policy (1366) Health Systems and Quality Improvement (1611) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15907) Intensive Care and Critical Care Medicine (1103) Medical Education (621) Medical Ethics (144) Nephrology (667) Neurology (6580) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3328) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5436) Public and Global Health (9215) Radiology and Imaging (2193) Rehabilitation Medicine and Physical Therapy (1368) Respiratory Medicine (1194) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ff6dbd01df2ad07',t:'MTc3OTQwMDg3NQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();