Skip main navigation

Genetic Susceptibility to Mood Disorders and Risk of Stroke: A Polygenic Risk Score and Mendelian Randomization Study

and Regeneron Genetics Center
Originally publishedhttps://doi.org/10.1161/STROKEAHA.122.041026Stroke. 2023;54:1340–1346

Abstract

Background:

Mood disorders and strokes are often comorbid, and their health toll worldwide is huge. This study characterizes prognostic and causal roles of mood disorders in stroke.

Methods:

We tested if genetic susceptibilities for mood disorders were associated with all strokes, ischemic strokes in the Malmö Diet and Cancer cohort (24 631 individuals with a median follow-up of 21.3 (interquartile range: 16.6–23.2) years. We further examined the causal effects for mood disorders on all strokes and ischemic strokes using summary statistics from large genome-wide association studies of mood disorders (up to 609 424 individuals, Psychiatric Genomics Consortium), all strokes and ischemic strokes (up to 446 696 individuals, MEGASTROKE Consortium).

Results:

Among 24 366 stroke-free participants at baseline, 2632 individuals developed strokes, 2172 of them ischemic, during follow-up. After properly adjusting for well-known risk factors, participants in the highest quintile of polygenic risk scores for mood disorders had 1.45× (95% CI, 1.21–1.74) higher risk of strokes and 1.44× (95% CI, 1.18–1.76) higher risk of ischemic strokes compared with the lowest quintile in women. Mendelian randomization analyses suggested that mood disorders had a causal effect on strokes (odds ratio, 1.07 [95% CI, 1.03–1.11]) and ischemic strokes (odds ratio, 1.09 [95% CI, 1.04–1.13]).

Conclusions:

Our results suggest a causal role of mood disorders in the risk of stroke. High-risk women could be identified early in life using polygenic risk scores to ultimately prevent mood disorders and strokes.

Mood disorders and cardiovascular disease are frequently comorbid conditions.1,2 Approximately, 15% to 20% of cardiovascular disease patients suffer from major depressive disorder (MDD).1,2 The cardiovascular disease prevalence among subjects with bipolar disorder (BD) is about twice greater than in those subjects with MDD.3 Mood disorders are a class of mental illnesses, being the most common forms, MDD, and BD, affecting 10% to 20% of the world’s population at some point in their lifetime4 and 14% of patients in the first 6 months following a diagnosis of COVID-19.5 Previous epidemiologic studies reported that people with MDD or BD had 1.5 to 4 times higher risk of cardiovascular disease3,6–8 and stroke9 compared with the general population. Recently, a large population study (about 5.9 million persons) from the Danish national registries reported that the highest subsequent medical conditions after the diagnosis of mood disorders were circulatory conditions.10 Among these circulatory conditions, a time-dependent risk of stroke was consistently observed within the first 15 years since the diagnosis of the mood disorder. However, due to the intrinsic limitations of observational studies, the causal associations between mood disorders and stroke have been unknown.

Large-scale genome-wide association studies (GWAS) have greatly advanced our understanding of the genetic underpinnings for mood disorders.11–13 To date, 73 loci had been identified showing risk for mood disorders and a genetic susceptibility to MDD was suggested to have a role in the development of coronary artery disease.13 An interesting cross-sectional study has reported that polygenic risk scores (PRS) for depression showed a significant correlation with prevalent stroke.14 Casual relationships between depression and stroke were also studied using Mendelian randomization (MR).15,16 All these findings are shedding light using genetic susceptibility to infer causal relationships among common disorders.17 Therefore, MR and PRS, summing up an individual’s genetic liability for a condition, are promising tools to examine the relationship between mood disorders and stroke,17 as well as for stroke risk prediction.18 To date, no studies on mood disorders and stroke have been reported.

In this study, we aimed to examine the relations between mood disorders in a broad spectrum and the risk of stroke. We constructed the PRS for mood disorders using the latest GWAS of mood disorder.13 We examined the PRS ability to predict the incidence of strokes and, particularly, ischemic strokes, using the MDC (Malmö Diet and Cancer) study. Finally, we studied the causal relations between the 2 disorders using MR, with the analysis based on summary statistics from the latest GWAS.13,19

Methods

MDC data can be accessed upon application (https://www.malmo-kohorter.lu.se). Links to the publicly available GWAS summary statistics can be found in the Table S1.

Participants

The MDC is a population-based prospective cohort study in Sweden. Details of the cohort and the recruitment are described elsewhere.20 Briefly, baseline examinations on 30 446 individuals were conducted between 1991 and 1996, which included peripheral venous blood samples collection, physical examination, and a self-administered survey. Details of genotyping and quality controls were described in the Supplemental Material. A total of 24 631 unrelated European participants remained for polygenic risk scores construction (Figure S1). The MDC study was approved by the Ethics Committee of Lund University (LU 51–90, LU 2009/633, LU 2011/356) and all patients provided written informed consent. Data use for the current study was approved by the MDC committee.

Ascertainment of Incident Stroke

In the MDC study, stroke was defined as acute onset of neurological deficits that persisted longer than 24 hours or was interrupted by death within 24 hours and exclusion of nonvascular causes.21,22 Information on stroke was retrieved from the Stroke Register of Malmö23 and Swedish Hospital Discharge Register.24 Strokes were defined as codes 430, 431, 434, and 436 for the Ninth version of the International Classification of Diseases. Ischemic stroke, the most common type of stroke, was defined by 434 in ICD9 codes. Other types of strokes were not examined in the present study due to a small sample size (haemorrhagic stroke) or unspecified.

All MDC participants were followed until incident stroke, emigration from Sweden, death or the end of follow-up (December 31, 2016), whichever came first. Participants with previous history of stroke or missing phenotype data were excluded prior to survival analysis. In total, 2632 incidences of strokes including 2172 ischemic strokes were reported in the follow-up of up to 25 years.

Polygenic Risk Scores for Mood Disorders

Mood disorders are a broad spectrum including the 2 most common types MDD and BD. The current largest GWAS on mood disorders was conducted on a meta-analysis on MDD and BD, and diagnoses were defined according to DSM-4, DSM-5, ICD-9, or ICD-10 (Table S2), respectively; the inclusion and exclusion criteria were described elsewhere.11–13 Based on this, the PRS, a continuous score, represented the genetic liability to mood disorders for an individual.

To construct PRS for MDC samples, we applied a Bayesian regression framework PRS-continuous shrinkage (PRS-CS).25 PRS-CS inferred posterior single-nucleotide polymorphisms (SNPs) effect sizes under continuous shrinkage priors using GWAS summary statistics and an external linkage disequilibrium reference panel. Here, effect sizes were taken from a downsampled GWAS of mood disorders (N cases=95 418, N controls=192 514), which did not include participants from 23andMe13 and the MDC study. The linkage disequilibrium reference was prebuilt using 1000 Genomes Project European sample (N=503) and shrinkage parameter phi was set to 0.02 as recommended by PRS-CS assuming a highly polygenic architecture. Once posterior single-nucleotide polymorphism (SNP) effect sizes were obtained from PRS-CS, PLINK (version 1.90)26 was used to construct PRS for mood disorders on MDC samples using the obtained posterior effect sizes for 995 772 SNPs. These SNPs explained 7.6% of the variance in mood disorders.

Other Clinical Measurements

At the baseline of MDC, systolic blood pressure was measured after 5 minutes of rest in a supine position; white blood cells count (WBC) was analyzed in fresh and heparinized blood using the SYSMEX K1000 hematology analyzer (TOA Medical Electronics, Kobe, Japan); apolipoprotein A (ApoA) and apolipoprotein B (ApoB) were reported from Quest Diagnostics (San Juan Capistrano, CA). Hypertension was defined as a diastolic blood pressure ≥95 or a systolic blood pressure ≥160 mmHg or undergoing antihypertensive medication. Smoking status was either never smoking or former and current smoking (where the latter two were grouped together). Information on previous history of type 2 diabetes (T2D), atrial fibrillation (AF), and coronary events was retrieved from the Swedish Hospital Discharge Register.24 To ascertain T2D, diabetic patients with specific cause like type 1 diabetes, latent autoimmune diabetes in adults, secondary diabetes, patients lacking screening date or age of onset of diabetes younger than 40 years were discarded from further analysis.18

Statistical Analysis

Characteristics of participants were compared by quintile of PRS for mood disorders using analysis of variance for continuous variables, and Chi-squared tests for categorical variables. Characteristics of associations between PRS for mood disorders and variables were determined by generalized linear model using PRS as predictor. Statistical significance was defined as P<0.05.

We explored the associations between the PRS for mood disorders (continuous or quintiles) and strokes, as well as ischemic strokes using Cox proportional hazards regressions. Hazard ratios (HRs) were calculated in 3 separate models: (1) unadjusted, (2) adjusting for age and sex and principal components 1 to 5 of population structure, and (3) adjusting for covariates in the model 2 plus body mass index, smoking initiation, WBC, hypertension, and history of T2D and AF at baseline. Individuals with prevalent AF and those diagnosed with AF before the stroke event were excluded for the sensitivity analysis.

The interaction between PRS and sex in predicting stroke was examined accounting for all other covariates used in the model 3. To explore sex-specific effects, we performed Cox regressions on men and women using those 3 models excluding sex as a covariate, separately.

Heritability and Genetic Correlation

SNP-based heritability and genetic correlations were estimated from GWAS summary statistics using linkage disequilibrium score regression.27 GWAS summary statistics on mood disorders (N=609 424, N cases=95 418, N controls=192 514),13 strokes (N=446 696, N cases=40 585, N controls=406 111) and ischemic strokes (N=440 328, N cases=34 217, N controls=406 111)19 were retrieved from Psychiatric Genomics Consortium and MEGASTROKE consortium (Table S1), respectively. SNP-based heritability of mood disorders was estimated using GWAS summary statistics restricted to SNPs used for PRS construction.

Mendelian Randomization

MR analysis was conducted to estimate causal effects for mood disorders on stroke using the same GWAS summary statistics as used in genetic correlation analyses. Genomic positions for all variants were harmonized in human genome build 19. Variants with mismatches between GWAS summary datasets and 1000 Genomes Phase 3 European panel regarding dbSNP reference SNP number, effect and alternative alleles, or effect allele frequency differences >0.2, were excluded prior to MR analysis.

MR analysis was implemented using the robust adjusted profile score (MR-RAPS) which can account for weak instrument bias, pleiotropy, and extreme outliers.28 Independent variants (linkage disequilibrium r2<0.001 in a window of 500 kb) were chosen as instruments when the cutoffs of P values were 10−4, 10−5, and 10−6 in SNP-Mood disorders association. Proxy variants were not used if variants were not available in the outcome GWAS. Heterogeneity in effect size between instrumental variants was assessed by Cochran’s Q statistic. To account for potential horizontal pleiotropy, a sensitivity analysis was implemented using MR-Egger.29 MR analysis was also conducted to examine causal effects for strokes, ischemic strokes on mood disorders using summary statistics from strokes and a downsampled GWAS of mood disorders (N=287 932)13 using the same settings. For strokes, 252, 63, and 28 variants were chosen as instruments when cutoff of P values were 10−4, 10−5, and 10−6 in SNP-stroke association. For ischemic strokes, 227, 60, and 26 variants were chosen as instruments when cutoff of P values were 1×10−4, 1×10−5, and 1×10−6.

MR was implemented using the R package TwoSampleMR.30

Results

Baseline Characteristics

Demographic characteristics of MDC unrelated participants at baseline were presented in Table 1. The median follow-up duration was 21.3 years (interquartile range, 16.6–23.2), with a maximum of 25 years by the end of 2016. We observed that PRS for mood disorders were associated with multiple risk factors for stroke at baseline (Table 1). PRS for mood disorders were significantly associated with smoking, body mass index, white blood cells count (WBC), prevalent T2D and prevalent AF at baseline. No significant association was found for PRS and ApoA, ApoB or prevalent coronary events at baseline. Subject characteristics in relation to sex were shown in Table S3.

Table 1. Baseline Characteristics in Relation to PRS for Mood Disorders

Q1 (N=4927)Q2 (N=4926)Q3 (N=4926)Q4 (N=4926)Q5 (N=4926)P*BSEP value
Age, y; mean ±SD58.6±7.758.3±7.758.1±7.557.8±7.557.5±7.61.0×10−12−0.050.011.0×10−15
Sex, men, N (%)2019 (41.0%)1963 (39.8%)1965 (39.9%)1933 (39.2%)1968 (40.0%)0.29−0.020.010.08
BMI, kg/m2; mean±SD25.6±4.025.7±4.025.8±4.025.9±4.026.1±4.22.5×10−80.040.013.7×10-10
Former or current smokers, N (%)2852 (57.9%)2806 (57.0%)2875 (58.4%)2933 (59.5%)2923 (59.3%)1.7×10−60.080.012.2×10−9
ApoA, mg/dL; mean±SD157.2±28.8157.2±28.5156.6±28.2157.8±28.4156.1±27.90.06−0.010.010.34
ApoB, mg/dL; mean±SD107.2±25.6106.8±26.3107.1±25.9107.4±26.0106.9±26.30.52−0.010.010.61
WBC, ×109 cells/L; mean±SD6.3±1.96.4±2.76.4±1.86.5±3.56.5±1.82.5×10−30.020.015.4×10−4
Hypertension, N (%)1815 (36.8%)1736 (35.2%)1738 (35.3%)1728 (35.1%)1689 (34.3%)8.2×10−3−0.030.010.02
T2D, N (%)158 (3.2%)192 (3.9%)203 (4.1%)190 (3.9%)200 (4.1%)0.020.100.030.002
CE, N (%)107 (2.2%)97 (2.0%)86 (1.7%)98 (2.0%)91 (1.8%)0.25−0.060.050.20
AF, N (%)45 (0.9%)43 (0.9%)53 (1.1%)54 (1.1%)59 (1.2%)0.170.130.060.04

AF indicates atrial fibrillation; ApoA, apolipoprotein A; ApoB, apolipoprotein B; BMI, body mass index; CE, coronary event; PRS, polygenic risk scores; Q1, first quintile; Q2, second quintile; Q3, third quintile; Q4, fourth quintile; Q5, fifth quintile; T2D, type 2 diabetes; and WBC, white blood cells count.

* P values are from ANOVA for continuous variable or Chi-squared test for categorical variable (Q5 vs Q1).

† Beta coefficient (B), SE of beta, and P value is from generalized linear model using PRS for mood disorders as predictor.

‡ Hypertension refers to a participant with systolic blood pressure ≥160 or diastolic blood pressure ≥95 mmHg or undergoing antihypertensive treatment.

Polygenic Risk Scores for Mood Disorders and Risk of Future Stroke

PRS for mood disorders were consistently associated with incidence of both all strokes and ischemic strokes (Figure 1; Table S4; Figure S2). The largest difference in cumulative incidence of strokes was found between the fifth PRS quintile and the first PRS quintile (HR, 1.27 [95% CI, 1.11–1.45]; P=3.4×10−4; Table S4) from the Cox proportional hazards model. The fifth PRS quintile showed substantial differences in cumulative incidence of stroke (log-rank test, P=0.01, Figure S3) compared with the first quintile from Kaplan-Meier analyses. We obtained similar results for ischemic strokes: HRs for strokes and ischemic strokes were 1.08 ([95% CI, 1.04–1.12]; P=3.5×10−4) and 1.07 ([95% CI, 1.02–1.12]; P=0.004) per 1 SD increase of PRS, respectively. Including history of T2D and AF in the model did not alter the strength of observed associations (Figure 1; Table S4). The associations remained significant when excluding individuals with AF at baseline and with AF before the stroke event.

Figure 1.

Figure 1. Hazard ratios (HRs, 95% CI) for incident stroke per polygenic risk scores (PRS) quintile relative to the first quintile. Horizontal dashed line corresponds to HR of 1. PRS: polygenic risk scores for mood disorders; M1: Model 1 is unadjusted; M2: Model 2 adjusts for age, sex, and principal components 1-5 of population structure; M3: Model 3 adjusts for covariates in model 2, body mass index, smoking initiation, white blood cells count, hypertension, history disease of type 2 diabetes and atrial fibrillation.

The PRS for mood disorders showed significant differences between sex in the association with incident strokes (P=0.02 for PRS-sex interaction term) and incident ischemic strokes (P=0.03 for PRS-sex interaction term) adjusted for all other covariates used in model 3, respectively. Sex-stratified analysis suggested that the associations between PRS and strokes, as well as ischemic strokes were significant in women, but not in men (Figure 2; Figures S3 and S4). For women, HRs were 1.45 ([95% CI, 1.21–1.74]; P=5.5×10−5) for stroke and 1.44 ([95% CI, 1.18–1.76]; P=3.3×10−4) for ischemic stroke comparing fifth PRS quintile to the first PRS quintile. HR per 1 SD increase of PRS for mood disorders were associated with incidence of strokes (HR, 1.13 [95% CI, 1.07–1.19]; P=4.0×10−5) and ischemic strokes (HR, 1.12 [95% CI, 1.05–1.19]; P=6.2×10−4) in women.

Figure 2.

Figure 2. Adjusted hazard ratios for association between polygenic risk scores (PRS) for mood disorders and incident stroke, ischemic stroke by sex. Model 3 is used which controls for age, body mass index, smoking initiation, white blood cells count, hypertension, and history disease of type 2 diabetes, atrial fibrillation, and principal components 1 to 5 of population structure. Hazard ratios (HRs) and 95% CI are shown in quintiles of PRS for mood disorders compared with first quintile (Q1), as well as HR per 1 SD increase of PRS for mood disorders. Q2 indicates second quintile; Q3, third quintile; Q4, fourth quintile; Q5, fifth quintile; and Ref, reference.

Genetic Correlation and MR Analyses

The genetic correlations between mood disorders and all strokes, mood disorders and ischemic strokes were 0.10 (P=0.03) and 0.11 (P=0.02), respectively, suggesting that mood disorders and strokes may have shared genetic basis or causal relationships. We therefore moved on to examine causal relationships between mood disorders and strokes in a case-control consortium MEGASTROKE.19 As very low variance (0.002–0.006) in mood disorders was explained by SNPs showing genome-wide significance (P<5×10−8),11,12 861, 376, and 185 variants (linkage disequilibrium r2<0.001) were chosen as instruments when the cutoffs of P values were 1×10−4, 1×10−5, and 1×10−6 in SNP-mood disorders association, respectively. Accordingly, average F-statistic was 21.2, 26.7, and 31.9 for the respective cutoff. MR analysis showed a causal effect for mood disorders on all strokes (odds ratio, 1.07 [95% CI, 1.03–1.11]; P=1.5×10−4), and ischemic strokes (odds ratio, 1.09 [95% CI, 1.04–1.13]; P=6.0×10−5; Figure 3; Table S5). These causal effects were consistently observed in MR analyses with varying cutoffs of P values to select instruments (Table S5). There was heterogeneity in the SNP effects (Table S6). However, no directional horizontal pleiotropy was detected (Table S7). Results from MR-Egger analysis did not align with MR-RAPS, possibly due to violation of the “no measurement error assumption” as low values of I2 statistic were observed (Table S5). In this analyses, casual effects for all strokes, ischemic strokes on mood disorders were not observed (Figure 3; Table S5).

Figure 3.

Figure 3. Forest plots showing results from Mendelian randomization analyses. Causal effects (odds ratio and 95% CIs) are examined using mood disorders as (A) exposure and (B) outcome.

Discussion

In this study, we showed that the PRS for mood disorders were associated with strokes and ischemic strokes particularly, in a large population-based prospective cohort with up to 25 years of follow-up. Furthermore, a causal effect for mood disorders on stroke was suggested by MR using GWAS summary statistics from large case-control consortia.

Mood disorders have often an early onset,31 whereas strokes are often diagnosed at later ages.32 Early studies have suggested that the identification of mood disorders at younger ages might help with the prevention of strokes.10,33 Our results, by showing that mood disorders were causally linked with risk of stroke, bring forth the importance of an early screening of mood disorders, facilitating not only the risk stratification, identification of possibly undiagnosed, faster and earlier treatment of individuals of mood disorders, but also ultimately preventing the substantial consequences of strokes for the subjects and society in general. PRS, is therefore an ideal and simple approach to identify high-risk subjects at very early stages as germline DNA is measurable even before birth.

We found that the association between PRS for mood disorders and stroke is specific for women, and not men. This may be explained by women being more likely than men to have a history of or an undergoing medication for depression at the time of stroke.34 A subsequent question that might be raised in this study is whether mood disorders cause stroke or vice visa. To answer that, we used MR to examine causal relations using genetic variants identified from GWAS. In line with our findings that the PRS for mood disorders were associated with risk for stroke in a large prospective study, our MR analysis using GWAS from case-control consortia conclude a causal role of mood disorders on stroke. MR analysis was usually restricted to genome-wide significant variants (P<5×10−8), which might lack statistical power17 although recent MR methods such as MR-RAPS were proposed to consider weak instruments.28 The PRS, on the other hand, uses a large number of genetic variants and a greater proportion of variance is expected to become explained. Additionally, PRS can explore temporal relationship between mood disorders and stroke together with follow-up data. Taking this into account, the utility of PRS in disease prediction may further improve the detection rates for causal relationships and should thus be applied in complement to MR.

PRS and MR are less likely influenced by confounding factors over time, since the germline DNA is also relatively stable across the lifespan. Thereby, the path of reverse causations from outcomes to instruments is very unlikely. This is an important strength of this study. Another strength of this large-scale PRS study was its prospective design with a very long follow-up time. Our study’s PRS were constructed using summary statistics from the latest and most modern GWAS13 that can provide a great power to examine the association in our sample. Our cohort had relatively large numbers of stroke cases, and over 90% of the diagnoses were validated by the review of hospital records. To avoid the risk of losing power and due to the lack of detailed data and lower number, we could not analyze the various subtypes of strokes separately, and rather studied them as a broader group, as ischemic stroke. It is also noteworthy that our PRS were derived from GWAS of European populations, as well as the MR analysis, which may misestimate the risk if applied to other populations.35 The reported associations between the mood disorders and strokes may differ in other ethnicities or ancestries. There might be sample overlaps between GWAS of mood disorders and GWAS of stroke. However, these overlapping samples (if any) only accounted for 1.9% of the mood disorders study and 2.6% of the MEGASTROKE. Bias of the effect is likely negligible as estimated by the previously published method (https://sb452.shinyapps.io/overlap).36 At last, no summary statistics from sex-specific GWAS on mood disorders were currently available to use. This may limit the interpretation of results from sex stratified PRS analyses even though sex can be accounted for in the association between PRS and stroke.

In conclusion, our results show that PRS for mood disorders are associated with future strokes in women. The novel knowledge brought forth by this study is highly relevant and applicable for the development of directed and concrete measures of prevention against mood disorders and strokes, with subsequent improved public health.

Article Information

Acknowledgments

Part of the computation was performed on the Norwegian high-performance computation resources, sigma2, through project no. NN9769K/NS9769K. We utilize GWAS summary statistics on strokes and ischemic strokes from the MEGASTROKE. The MEGASTROKE project received funding from sources specified at http://www.megastroke.org-/acknowledgments.html.

Supplemental Material

Supplemental Methods

Figures S1–S4

Tables S1–S7

References 37–39

Nonstandard Abbreviations and Acronyms

BD

bipolar disorder

GWAS

genome-wide association study

HR

hazard ratio

MDC study

The Malmö Diet and Cancer study

MDD

major depressive disorder

MR

Mendelian randomization

PRS

polygenic risk score

SNP

single-nucleotide polymorphism

Disclosures None.

Footnotes

*A list of all Regeneron Genetics Center members is given in the Supplemental Material.

For Sources of Funding and Disclosures, see page 1345.

Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/STROKEAHA.122.041026.

Correspondence to: Isabel Gonçalves, MD, PhD, Jan Waldenströms gata 35, 205 02 Malmö, Sweden. Email

References

  • 1. Colquhoun DM, Bunker SJ, Clarke DM, Glozier N, Hare DL, Hickie IB, Tatoulis J, Thompson DR, Tofler GH, Wilson A, et al. Screening, referral and treatment for depression in patients with coronary heart disease.Med J Aust. 2013; 198:483–484. doi: 10.5694/mja13.10153CrossrefGoogle Scholar
  • 2. Disease GBD, Injury I, Prevalence C. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017.Lancet. 2018; 392:1789–1858. doi: 10.1016/S0140-6736(18)32279-7CrossrefMedlineGoogle Scholar
  • 3. Goldstein BI, Fagiolini A, Houck P, Kupfer DJ. Cardiovascular disease and hypertension among adults with bipolar I disorder in the United States.Bipolar Disord. 2009; 11:657–662. doi: 10.1111/j.1399-5618.2009.00735.xCrossrefMedlineGoogle Scholar
  • 4. Steel Z, Marnane C, Iranpour C, Chey T, Jackson JW, Patel V, Silove D. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980-2013.Int J Epidemiol. 2014; 43:476–493. doi: 10.1093/ije/dyu038CrossrefGoogle Scholar
  • 5. Taquet M, Geddes JR, Husain M, Luciano S, Harrison PJ. 6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records.Lancet Psychiatry. 2021; 8:416–427. doi: 10.1016/S2215-0366(21)00084-5CrossrefGoogle Scholar
  • 6. Correll CU, Solmi M, Veronese N, Bortolato B, Rosson S, Santonastaso P, Thapa-Chhetri N, Fornaro M, Gallicchio D, Collantoni E, et al. Prevalence, incidence and mortality from cardiovascular disease in patients with pooled and specific severe mental illness: a large-scale meta-analysis of 3,211,768 patients and 113,383,368 controls.World Psychiatry. 2017; 16:163–180. doi: 10.1002/wps.20420CrossrefMedlineGoogle Scholar
  • 7. Lambert AM, Parretti HM, Pearce E, Price MJ, Riley M, Ryan R, Tyldesley-Marshall N, Avsar TS, Matthewman G, Lee A, et al. Temporal trends in associations between severe mental illness and risk of cardiovascular disease: a systematic review and meta-analysis.PLoS Med. 2022; 19:e1003960. doi: 10.1371/journal.pmed.1003960CrossrefGoogle Scholar
  • 8. Pan A, Sun Q, Okereke OI, Rexrode KM, Hu FB. Depression and risk of stroke morbidity and mortality: a meta-analysis and systematic review.JAMA. 2011; 306:1241–1249. doi: 10.1001/jama.2011.1282CrossrefMedlineGoogle Scholar
  • 9. Salaycik KJ, Kelly-Hayes M, Beiser A, Nguyen AH, Brady SM, Kase CS, Wolf PA. Depressive symptoms and risk of stroke: the Framingham Study.Stroke. 2007; 38:16–21. doi: 10.1161/01.STR.0000251695.39877.caLinkGoogle Scholar
  • 10. Momen NC, Plana-Ripoll O, Agerbo E, Benros ME, Borglum AD, Christensen MK, Dalsgaard S, Degenhardt L, de Jonge P, Debost JCPG, et al. Association between mental disorders and subsequent medical conditions.New Engl J Med. 2020; 382:1721–1731. doi: 10.1056/NEJMoa1915784CrossrefGoogle Scholar
  • 11. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, Adams MJ, Agerbo E, Air TM, Andlauer TMF, et al; eQTLGen. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression.Nat Genet. 2018; 50:668–681. doi: 10.1038/s41588-018-0090-3CrossrefMedlineGoogle Scholar
  • 12. Stahl EA, Breen G, Forstner AJ, McQuillin A, Ripke S, Trubetskoy V, Mattheisen M, Wang Y, Coleman JRI, Gaspar HA, et al; eQTLGen Consortium. Genome-wide association study identifies 30 loci associated with bipolar disorder.Nat Genet. 2019; 51:793–803. doi: 10.1038/s41588-019-0397-8CrossrefGoogle Scholar
  • 13. Coleman JRI, Gaspar HA, Bryois J, Breen G, Consortium PG. The genetics of the mood disorder spectrum: genome-wide association analyses of more than 185,000 cases and 439,000 controls.Biol Psychiat. 2020; 88:169–184. doi: 10.1016/j.biopsych.2019.10.015CrossrefGoogle Scholar
  • 14. Wassertheil-Smoller S, Qi QB, Dave T, Mitchell BD, Jackson RD, Liu SM, Park K, Salinas J, Dunn EC, Leira EC, et al. Polygenic risk for depression increases risk of ischemic stroke: from the stroke genetics network study.Stroke. 2018; 49:543–548. doi: 10.1161/STROKEAHA.117.018857LinkGoogle Scholar
  • 15. Gill D, James NE, Monori G, Lorentzen E, Fernandez-Cadenas I, Lemmens R, Thijs V, Rost NS, Scott R, Hankey GJ, et al; International Stroke Genetics Consortium and the GISCOME Network. Genetically determined risk of depression and functional outcome after ischemic stroke.Stroke. 2019; 50:2219–2222. doi: 10.1161/STROKEAHA.119.026089LinkGoogle Scholar
  • 16. Cai H, Cai B, Zhang H, Sun W, Wang Y, Zhou S, Ye Z, Zhang Z, Liang J. Major depression and small vessel stroke: a Mendelian randomization analysis.J Neurol. 2019; 266:2859–2866. doi: 10.1007/s00415-019-09511-wCrossrefMedlineGoogle Scholar
  • 17. Richardson TG, Harrison S, Hemani G, Smith GD. An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome.Elife. 2019; 8:e43657. doi: 10.7554/eLife.43657CrossrefMedlineGoogle Scholar
  • 18. Sun J, Wang Y, Folkersen L, Borne Y, Amlien I, Buil A, Orho-Melander M, Borglum AD, Hougaard DM, Regeneron Genetics C, et al. Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction.Nat Commun. 2021; 12:5276. doi: 10.1038/s41467-021-25014-7CrossrefMedlineGoogle Scholar
  • 19. Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, Rutten-Jacobs L, Giese AK, van der Laan SW, Gretarsdottir S, et al; AFGen Consortium. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes.Nat Genet. 2018; 50:524–537. doi: 10.1038/s41588-018-0058-3CrossrefMedlineGoogle Scholar
  • 20. Berglund G, Elmstahl S, Janzon L, Larsson SA. The Malmo Diet and Cancer Study. Design and feasibility.J Intern Med. 1993; 233:45–51. doi: 10.1111/j.1365-2796.1993.tb00647.xCrossrefMedlineGoogle Scholar
  • 21. WHO MONICA Project Principal Investigators. The World Health Organization MONICA Project (monitoring trends and determinants in cardiovascular disease): a major international collaboration.J Clin Epidemiol. 1988; 41:105–114. doi: 10.1016/0895-4356(88)90084-4CrossrefMedlineGoogle Scholar
  • 22. Zia E, Hedblad B, Pessah-Rasmussen H, Berglund G, Janzon L, Engstrom G. Blood pressure in relation to the incidence of cerebral infarction and intracerebral hemorrhage. Hypertensive hemorrhage: debated nomenclature is still relevant.Stroke. 2007; 38:2681–2685. doi: 10.1161/STROKEAHA.106.479725LinkGoogle Scholar
  • 23. Jerntorp P, Berglund G. Stroke registry in Malmo, Sweden.Stroke. 1992; 23:357–361. doi: 10.1161/01.str.23.3.357LinkGoogle Scholar
  • 24. Ludvigsson JF, Andersson E, Ekbom A, Feychting M, Kim JL, Reuterwall C, Heurgren M, Olausson PO. External review and validation of the Swedish national inpatient register.BMC Public Health. 2011; 11:450. doi: 10.1186/1471-2458-11-450CrossrefMedlineGoogle Scholar
  • 25. Ge T, Chen CY, Ni Y, Feng YCA, Smoller JW. Polygenic prediction via Bayesian regression and continuous shrinkage priors.Nat Commun. 2019; 10:1776. doi: 10.1038/s41467-019-09718-5CrossrefMedlineGoogle Scholar
  • 26. Shaun Purcell CC. PLINK v1.90b6.9.www.cog-genomics.org/plink/1.9/. 2019. Accessed December 1, 2019.Google Scholar
  • 27. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N, Daly MJ, Price AL, Neale BM; Schizophrenia Working Group of the Psychiatric Genomics Consortium. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.Nat Genet. 2015; 47:291–295. doi: 10.1038/ng.3211CrossrefMedlineGoogle Scholar
  • 28. Zhao Q, Wang J, Hemani G, Bowden J, Small DS. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score.Ann Stat. 2020; 48:1742–1769. doi: 10.1214/19-AOS1866CrossrefGoogle Scholar
  • 29. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression.Int J Epidemiol. 2015; 44:512–525. doi: 10.1093/ije/dyv080CrossrefMedlineGoogle Scholar
  • 30. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, et al. The MR-Base platform supports systematic causal inference across the human phenome.Elife. 2018; 7:e34408. doi: 10.7554/eLife.34408CrossrefMedlineGoogle Scholar
  • 31. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication.Arch Gen Psychiatry. 2005; 62:593–602. doi: 10.1001/archpsyc.62.6.593CrossrefMedlineGoogle Scholar
  • 32. Feigin VL, Lawes CM, Bennett DA, Anderson CS. Stroke epidemiology: a review of population-based studies of incidence, prevalence, and case-fatality in the late 20th century.Lancet Neurol. 2003; 2:43–53. doi: 10.1016/s1474-4422(03)00266-7CrossrefMedlineGoogle Scholar
  • 33. Carod-Artal FJ. Are mood disorders a stroke risk factor?Stroke. 2007; 38:1–3. doi: 10.1161/01.STR.0000251674.14708.9bLinkGoogle Scholar
  • 34. Dong L, Sanchez BN, Skolarus LE, Stulberg E, Morgenstern LB, Lisabeth LD. Sex difference in prevalence of depression after stroke.Neurology. 2020; 94:e1973–e1983. doi: 10.1212/WNL.0000000000009394CrossrefMedlineGoogle Scholar
  • 35. Kim MS, Patel KP, Teng AK, Berens AJ, Lachance J. Genetic disease risks can be misestimated across global populations.Genome Biol. 2018; 19:179. doi: 10.1186/s13059-018-1561-7CrossrefMedlineGoogle Scholar
  • 36. Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization.Genet Epidemiol. 2016; 40:597–608. doi: 10.1002/gepi.21998CrossrefMedlineGoogle Scholar
  • 37. Abraham G, Qiu Y, Inouye M. FlashPCA2: principal component analysis of Biobank-scale genotype datasets.Bioinformatics. 2017; 33:2776–2778. doi: 10.1093/bioinformatics/btx299CrossrefMedlineGoogle Scholar
  • 38. Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM. Robust relationship inference in genome-wide association studies.Bioinformatics. 2010; 26:2867–2873. doi: 10.1093/bioinformatics/btq559CrossrefMedlineGoogle Scholar
  • 39. McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, Kang HM, Fuchsberger C, Danecek P, Sharp K, et al; Haplotype Reference Consortium. A reference panel of 64,976 haplotypes for genotype imputation.Nat Genet. 2016; 48:1279–1283. doi: 10.1038/ng.3643CrossrefMedlineGoogle Scholar

eLetters(0)

eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.

Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.