Supplementary Methods - Imperial College London · Web viewn=1,864 cases n=19,087 total cohort Country: USA (Health and Retirement Study) Age: 50 years 8-item CES-D 3 All fatal/non-fatal

SUPPLEMENTAL MATERIAL

Genetically determined risk of depression and functional outcome after ischemic stroke: Mendelian randomization study

I. Supplementary MethodsII. Supplementary TablesIII. Supplementary FiguresIV. Supplementary ReferencesV. Author contributionsVI. GISCOME co-investigator contributions

I. Supplementary Methods

SNP-Depression Association EstimatesData from a GWAS meta-analysis investigating variants associated with depression were used to identify instruments for MR analysis1. This study compiled data on 246,363 patients with depression and 561,190 control subjects in discovery analysis, with replication performed in 474,574 depression cases and 1,032,579 controls1. Participants were of European ancestry1, and the considered cohorts are detailed in Supplementary Table I. Instruments for risk of depression were 56 genome-wide significant (p<5x10-8) single-nucleotide polymorphisms (SNPs) identified in the discovery meta-analysis that also replicated at genome-wide significance (Supplementary Table II)1. Estimates were taken from the replication cohort to avoid Winner’s curse bias. F statistics were calculated as a measure of the strength of the instruments using the Chi-squared approximation2, 3.

SNP-Ischemic Stroke Association EstimatesThe association of the SNPs with ischemic stroke risk were obtained from the MEGASTROKE consortium’s multi-ethnic GWAS of 60,341 patients with ischemic stroke and 454,450 control subjects4. Of these, 34,217 cases and 400,201 controls were of European ancestry4.

SNP-Post-Ischemic Stroke Outcome Association EstimatesThe associations of the instrument SNPs with functional outcome after ischemic stroke were obtained from a GWAS meta-analysis performed by the GISCOME collaboration. This study considered several analytical models5, however we restricted our MR estimates from the GISCOME model with the largest sample size to maximize statistical power. In addition, we did not adopt the GWAS models that used an ordinal scale, as the implications of this within an MR framework are not known. We used functional outcome measured using the modified Rankin Scale (mRS) as close as possible to 90 days post-stroke5. An mRS score of ≤2 denoted good functional outcome (3,741 patients), and a score of ≥3 (2,280 patients) indicated poor functional outcome post-stroke5. Approximately 20% of the population had recurrent stroke5. The analysis was adjusted for age, sex, ancestry, and baseline stroke severity (as assessed by the National Institutes of Health Stroke Scale at 0-10 days after stroke onset)5. The adjustment for baseline stroke severity was required to allow for assessment of functional outcome with respect to the degree of baseline impairment. Participants were of European ancestry and cohort details are provided in Supplementary Table III.

Mendelian randomization sensitivity analysesHeterogeneity between ratio method MR estimates produced for individual SNPs was assessed using the Cochran’s Q test as a proxy for the presence of pleiotropy6, where the genetic instruments affect the outcome independently of predisposition to depression to bias the MR analysis6-8. To further investigate possible bias related to any pleiotropic effects, we performed the weighted median and MR-PRESSO statistical sensitivity analyses7, 8. Weighted median MR orders the MR estimates for each SNP by their magnitude weighted for their precision, and remains valid when at least half the information for the analysis comes from valid instruments7. The 95% confidence interval (CI) for this was calculated using the parametric bootstrap method7. The MR pleiotropy residual sum and outlier (MR-PRESSO) test performs a regression of the SNP-outcome association estimates against the SNP-

exposure association estimates8. By comparing the residuals from this against those that would be expected by chance, outlier SNPs can be identified8.

II. Supplementary Tables

Supplementary Table I. Details of the cohorts used to obtain genetic instruments for risk of depression1.

Cohort Location Cases Controls Case diagnosis criteria

Discovery

23andMe International 75,607 231,747 Self-reported diagnosis or treatment

UK Biobank UK 127,552 233,763

(i) Broad depression, defined as patients who self-reported seeking help for their mental health from a psychiatrist or GP (ii) Probable major depressive disorder, characterised by depressive symptoms and resultant impairment reported by

the subject(iii) Formally diagnosed major depressive disorder, defined as

admission records detailing a relevant mood disorder using ICD-10

Psychiatric Genomics

Consortium

Europe and USA 43,204 95,680 ICD-9, ICD-10 and DSM-IV criteria

Replication 23andMe International 474,574 1,032,579 Self-reported diagnosis or treatment

Supplementary Table II. Instruments for genetically determined risk of depression1.SNP Chromosome Position Effect allele Other allele Effect allele

frequency Effect Standard error P value F statistic

rs1568452 2 58012833 t c 0.3851 0.0321 0.0028 2.78E-30 131

rs30266 5 103972357 a g 0.3296 0.0332 0.0029 9.71E-30 131

rs2568958 1 72765116 a g 0.6156 0.0313 0.0028 3.69E-28 125

rs61902811 11 113370758 a g 0.3682 -0.0308 0.0028 2.36E-27 121

rs1095626 3 157977962 t c 0.5799 -0.0299 0.0028 3.58E-27 114

rs5995992 22 41487218 t c 0.7155 -0.0323 0.0031 4.35E-26 109

rs9592461 13 66941792 a g 0.4874 0.0260 0.0028 3.72E-21 86

rs10890020 1 73668836 a g 0.5156 -0.0260 0.0028 6.84E-21 86

rs301799 1 8489302 t c 0.5694 -0.0256 0.0028 2.43E-20 84

rs200949 6 27835435 a g 0.8744 0.0400 0.0044 5.95E-20 83

rs3823624 7 2110346 t c 0.8067 0.0308 0.0035 7.26E-19 77

rs7227069 18 50731802 a g 0.4326 0.0244 0.0028 9.00E-19 76

rs61990288 14 42074726 a g 0.5083 -0.0243 0.0027 9.21E-19 81

rs7030813 9 36999369 t c 0.3736 0.0249 0.0028 1.83E-18 79

rs7200826 16 13066833 t c 0.2551 0.0264 0.0031 4.98E-17 73

rs12923444 16 21639710 a c 0.5625 -0.0255 0.0031 2.37E-16 68

rs11135349 5 164523472 a c 0.4713 -0.0222 0.0028 9.30E-16 63

rs913930 9 120484009 a g 0.6433 -0.0225 0.0029 4.13E-15 60

rs113188507 1 80809636 a g 0.2838 0.0243 0.0032 1.41E-14 58

rs60157091 5 61509655 t c 0.5150 0.0209 0.0028 3.50E-14 56

rs1045430 14 75130235 t g 0.4792 -0.0209 0.0028 4.19E-14 56

rs45510091 4 123186393 a g 0.9472 0.0473 0.0063 6.36E-14 56

rs12052908 2 22503044 a t 0.5325 -0.0206 0.0028 1.03E-13 54

rs12967143 18 53099012 c g 0.6984 -0.0221 0.0030 1.85E-13 54

rs7198928 16 7666402 t c 0.6159 0.0207 0.0028 2.30E-13 55

rs10061069 5 93071630 c g 0.2212 -0.0235 0.0033 1.85E-12 51

rs3793577 9 23737627 a g 0.4665 -0.0199 0.0028 2.14E-12 51

rs7807677 7 117502574 t c 0.5505 0.0194 0.0028 2.24E-12 48

rs7932640 11 88744425 t c 0.4417 0.0192 0.0028 3.95E-12 47

rs4772087 13 99115041 t c 0.3732 0.0197 0.0029 8.56E-12 46

rs2043539 7 12253880 a g 0.4177 0.0186 0.0028 2.47E-11 44

rs3213572 12 121205078 a g 0.4745 0.0183 0.0027 2.62E-11 46

rs1933802 6 105365891 c g 0.4536 -0.0182 0.0028 4.43E-11 42

rs1343605 13 53647048 a c 0.3840 0.0181 0.0028 1.18E-10 42

rs1226412 2 157111313 t c 0.7917 0.0217 0.0034 2.12E-10 41

rs7193263 16 6315880 a g 0.6679 -0.0187 0.0029 2.50E-10 42

rs1021363 10 106610839 a g 0.3547 0.0179 0.0029 4.08E-10 38

rs143186028 20 39997404 t g 0.1778 0.0224 0.0036 4.96E-10 39

rs13084037 3 49214066 a g 0.7740 -0.0200 0.0032 6.83E-10 39

rs10149470 14 104017953 a g 0.4869 -0.0169 0.0027 7.46E-10 39

rs2029865 6 165121844 a t 0.4534 -0.0173 0.0028 7.88E-10 38

rs11579246 1 50559162 a g 0.9067 0.0284 0.0047 1.11E-09 37

rs1354115 9 2983774 a c 0.6243 0.0171 0.0028 1.44E-09 37

rs1890946 1 52342427 t c 0.4671 -0.0167 0.0028 1.57E-09 36

rs3099439 5 87545318 t c 0.5288 -0.0165 0.0028 2.84E-09 35

rs8037355 15 37643831 t c 0.5556 -0.0166 0.0028 2.96E-09 35

rs10913112 1 175913828 t c 0.3767 -0.0179 0.0030 3.12E-09 36

rs1002656 1 37192741 t c 0.7033 -0.0175 0.0030 5.31E-09 34

rs12967855 18 35138245 a g 0.3295 0.0171 0.0029 5.56E-09 35

rs2509805 11 57650796 t c 0.3209 0.0173 0.0030 6.62E-09 33

rs58104186 7 109099919 a g 0.4689 0.0160 0.0028 8.04E-09 33

rs17641524 1 197704717 t c 0.2091 -0.0195 0.0034 8.83E-09 33

rs2876520 6 142996618 c g 0.5271 -0.0161 0.0028 9.20E-09 33

rs34653192 9 31124452 c g 0.3196 -0.0174 0.0031 1.43E-08 32

rs7685686 4 3207142 a g 0.5753 0.0155 0.0028 3.28E-08 31

rs56314503 12 84465022 t g 0.7487 -0.0172 0.0031 4.25E-08 31

Supplementary Table III. GISCOME collaboration cohort details, adapted from Soderholm et al. 20195.

Center Australia Barcelona Boston Cincinnati Gothenburg Helsinki Leuven Lund Malmö Oxford Washington

Cohort abbreviation Australia HdM VdH-1 VdH-2 MGH-1 MGH-2 MGH-3 Cincinnati SAHLSIS SAHLSIS

RS Finland Leuven LSR-1 LSR-2 MDC Oxford WashU

Gender Age NIHSS mRS

mRS 0-2 vs. 3-6Cohort Male/Female Mean(SD

) Median(IQR) 0 1 2 3 4 5 6

Australia 270/232 71.5(13.1) 4(2-8) 24 190 132 64 39 16 37 332 vs 145

Cincinnati 194/160 69.5(13.1) 4(2-8) 26 71 73 71 67 18 28 168 vs 179

Finland 212/130 63.9(12.6) 5(2-10) 63 97 77 47 43 3 12 237 vs 105

HdM 483/423 75.1(11.2) 5(3-12) 153 151 149 125 138 38 152 448 vs 451

Leuven 269/189 67.5(14.5) 4(2-8) 114 130 98 53 39 6 18 342 vs 116

LSR-1 256/233 74.3(12.8) 3(2-7.25) - 260 - 100 43 38 48 260 vs 229

LSR-2 263/215 71.7(13.0) 4(2-8) - 259 - 73 64 31 51 259 vs 219

MDC 170/178 78.4(6.1) 2(0-7) - 136 - 104 47 28 33 136 vs 212

MGH-1 28/21 69.6(16.5) 4(2-10.5) 10 11 4 5 5 0 14 25 vs 23

MGH-2 48/28 66.9(13.1) 3(1-7) 22 24 10 10 5 0 5 56 vs 19

MGH-3 143/82 64.9(15.6) 3(1-8) 55 70 38 23 16 2 21 157 vs 53

Oxford 218/235 74.6(12.3) 2(0-4) 67 127 73 89 35 19 43 264 vs 184

SAHLSIS 487/273 55.8(10.7) 2(1-7) 97 174 330 94 52 2 11 593 vs 156

SAHLSIS-RS 258/140 56.1(10.7) 2(0-6) - 317 - 49 21 8 3 277 vs 66

VdH-1 68/62 71.2(10.5) 15(9-18) 22 32 17 20 24 15 0 71 vs 59

VdH-2 78/27 68.2(11.4) 3(1.75-8) 30 25 16 12 12 4 6 67 vs 34

WashU 52/40 67.2(14.3) 8(4-12) 23 24 9 14 9 8 5 49 vs 30

TOTAL 3497/2668 68.7(14.1) 4(2-8) 706 2098 1026 953 659 236 487 3741 vs 2280

NIHSS; National Institutes of Health Stroke Scale, mRS; modified Rankin Scale, SD; standard deviation, IQR; interquartile range

Supplementary Table IV. Summary of observational studies investigating the association between depression and stroke incidence.

Study Population (cases indicate participants with stroke)

Assessment of depression

Ascertainment of stroke

Duration of follow-up

Confounder adjustment Result

Sun et al, 20169

n==27,623 casesn=487,377 total cohort

Country: China (China Kadoorie Biobank)

Age (mean):MDE – 50.4 years

No MDE – 51.1 years

WHO CIDI-SF (MDE

according to DSM-IV)

Fatal/non-fatal subarachnoid,

ischemic, hemorrhagic, and other or unknown

stroke type; residential records, medical records, death certificates

9 years, 2004 – 2013 Age, sex, region (rural or urban), BMI, marital status,

education level, annual household income, smoking

status, alcohol use, total physical activity (calculated as metabolic equivalent task hours per day spent on work, transportation, housework, non-sedentary recreation,

and sedentary leisure), blood pressure, hypertension,

diabetes mellitus, chronic hepatitis or cirrhosis, peptic

ulcer

Past year MDE was marginally associated with a 15% increased risk of

stroke (adjusted HR=1.15; 95% CI: 0.99-1.33) in the fully adjusted model.

Association was steeper and statistically significant in individuals aged <50 years, smokers, drinkers, those with higher education degree,

body mass index <24.0 kg/m2, and no history of diabetes mellitus. There was a positive dose-response relationship

between the number of depression symptoms and increased stroke risk

(p=0.011).

Brunner et al, 201410

n=168 casesn=10,036 total cohort

Country: UK (Whitehall II Study)

Age (meanSD):Phase 1 to 3 (1985-1993):

44.46.1 yearsPhase 7 to 9 (2002-2009):

61.06.0 years

GHQ-30 5, validated

against clinical interview in Whitehall II; 20-item CES-

D 16 at phase 7

Fatal/non-fatal ischemic and

hemorrhagic stroke; hospital records or general practitioner information, death

certificates

24 years, 1985 – 2009

Age, sex, ethnicity GHQ-30 caseness predicted stroke over 0–5 years (age-, sex- and ethnicity-

adjusted HR=1.60, 95% CI: 1.1–2.3) but not over 5–10 years (HR=0.94,

95% CI: 0.6–1.4). Prospective associations of depressive symptoms

with stroke appeared to arise wholly or partly through reverse causation.

Everson-Rose et al,

201411

n=195 cases (147 strokes, 48 TIA)

n=6,749 total cohortCountry: USA (Multi-Ethnic

Study of Atherosclerosis)Age (meanSD):

Cases – 68.39.4 yearsControls – 62.010.2 years

20-item CES-D 16

All fatal/non-fatal stroke and TIA;

clinical diagnosis

8.5 years (median), 2000 – 2012

Age, sex, race, education, study site, systolic blood

pressure, alcohol use, smoking status, physical

activity, BMI, height, use of antihypertensives, diabetes

mellitus/fasting blood glucose, high-density

lipoprotein cholesterol, triglycerides

Adjusted HRs of stroke or TIA incidence by CES-D scores (p=0.03):

Group 1 (0-2): 1.00Group 2 (3-5): 1.09 (95% CI: 0.71-

1.67)Group 3 (6-10): 1.15 (95% CI: 0.76-

1.74)Group 4 (11-15): 1.26 (95% CI: 0.77-

2.08)Group 5 (16): 1.73 (95% CI: 1.08-

2.77)

Hamano et al, 201412

n=4,718 casesn=326,229 total cohort

Country: Sweden (Nationwide sample of primary healthcare

centres)Age: 30 years

Clinical diagnosis by primary care, inpatient or outpatient registries

(depressive disorders

according to ICD-10)


hemorrhagic stroke; medical records

3 years, 2005 – 2007 Age, sex, country of origin, educational attainment, family income, family

history and comorbidities (stroke, CHD, hypertension,

COPD, obesity)

Depression was associated with significantly greater odds of stroke after

adjustment for potential confounding factors (OR=1.22, 95% CI: 1.08-1.38). Interaction tests showed that the effect of depression on stroke was higher in

men compared with women (the difference in OR between men and

women was 1.30, 95% CI: 1.01-1.68), i.e. the association between depression

and stroke was modified by gender.

Gafarov et al, 201313

n=35 casesn=560 total cohort, females only

Country: Russia (WHO “MONICA-psychosocial”

programme)Age: 25-64 years

15-item MOPSY

questionnaire


hemorrhagic stroke; medical records, death certificates

16 years, 1995 – 2010

Age Risk of stroke for the entire duration of follow-up was more than 4 times higher

for participants with depression than without depression (HR=4.63; 95%

CI:1.03-20.89; p<0.05).

Jackson et al, 201314

n=177 casesn=10,547 total cohort, females

onlyCountry: Australia (Australian

Longitudinal Study on Women’s Health)

Age (meanSD):Depression – 52.51.5 years

No depression – 52.51.5 years

10-item CES-D 10


hemorrhagic stroke; self-reports (non-

fatal), death certificates

12 years, 1998 – 2010

Age, education, homeownership,

hypertension, diabetes mellitus, heart disease,

hysterectomy/oophorectomy, smoking, alcohol use, physical activity, BMI

Depression was associated with a >2-fold increased odds of stroke

(OR=2.41; 95% CI: 1.78-3.27), which attenuated after adjusting for age,

socioeconomic status, lifestyle, and physiological factors (OR=1.94; 95%

CI: 1.37-2.74; p<0.001). Findings were robust to sensitivity analyses

addressing methodological issues, including definition of depression,

antidepressant use, and missing covariate data.

Péquignot et al, 201315


Country: France (The Three City Study)

Age (meanSD):CES-D <16 – 73.65.3 yearsCES-D 16 – 74.35.5 years

20-item CES-D 16


hemorrhagic stroke; hospital records, death certificates

5.3 years (median), from 1999

Age, sex, study center, educational level, living

alone, hypertension, impaired fasting glycemia or

diabetes mellitus, alcohol consumption, smoking

status, MMSE (continuous), hypercholesterolemia

After adjustment for study center, baseline socio-demographic

characteristics, and conventional risk factors, depressive symptoms (CES-D

≥16) were associated withfatal stroke alone (n=25; HR=3.27;

95% CI: 1.42-7.52). This association was even stronger in depressed subjects

receiving antidepressants (HR=4.17; 95% CI: 1.84-9.46) and in depressed

subjects with impaired IADL (HR=8.93; 95% CI: 4.60-17.34).

Rahman et al, 201316


Country: Sweden (Swedish national patient registers, the

Swedish prescribed drug registry, and the Swedish twin

registry)Age (mean):

Depression – 63 yearsNo depression – 63 years

Clinical diagnosis by psychiatrist

(any depression

according to ICD-7, ICD-8, ICD-9, ICD-

10)

Fatal/non-fatal ischemic stroke and TIA; death register, hospital discharge

register

3.860.58 years (meanSD), 2006 –

2009

Birth year, sex, smoking status, educational level, hypertension, diabetes,

alcohol intake, BMI

Significant association between depression and stroke (HR=1.76; 95% CI: 1.14-2.71; p=0.010), adjusted for birth year, gender, smoking status,

educational level, hypertension, diabetes, alcohol intake and BMI.

Li et al, 201217


Country: Taiwan (Nationwide database of the National Health

Insurance Program)Age (meanSD):

MDD (ETT) – 40.8214.80 years

MDD (ITT) – 42.3914.45 years

MDD (DTT) – 41.8712.18 years

Controls – 41.2014.50 years


(MDD according to

ICD-9)

All fatal/non-fatal stroke; hospital

records

9 years, 2001 – 2009 Age, sex, diabetes mellitus, hypertension,

hyperlipidemia, substance comorbidities

Patients with MDD had significantly higher rates of stroke (4.3% vs. 2.8%,

p<0.05) during the follow-up. Mediation regression analyses revealed

that MDD was associated with significantly greater odds of stroke (OR=1.546; 95% CI: 1.080-2.211;

p=0.017).

Majed et al, 201218

n=136 casesn=9,601 total cohort, males only

Countries: Northern Ireland, France (Prospective

Epidemiological Study of Myocardial Infarction Study)

Age: 50-59 years

Fourth quartile of 13-item

modified CES-D compared

with first quartile


hemorrhagic stroke; hospital or general practitioner records

10 years (median), from 1991

Age, study centers, education level, employment

status, marital status, physical activity, smoking,

alcohol intake, systolic blood pressure, use of

antihypertensives, BMI, total and high-density lipoprotein

cholesterol, treatment for diabetes, use of antidepressants

Depressive symptoms at baseline were associated with stroke in the second 5-

year of follow-up period (HR=1.96; 95% CI: 1.21-3.19) after adjustment for

age, study centers, baseline socioeconomic factors, traditional

vascular risk factors, and antidepressant treatment. The association was even stronger for ischemic stroke (n=108;

HR=2.48; 95% CI: 1.45-4.25).

Pan et al, 201119

n=1,033 casesn=80,574 total cohort, females

onlyCountry: USA (Nurses’ Health

Study)Age (mean):

Baseline depression: 65.0 yearsBaseline no depression: 66.3

years

MHI-5 52 Fatal/non-fatal ischemic and

hemorrhagic stroke; self-report, medical

records, death certificates

6 years, 2000 – 2006 Age, marital status, parental history of myocardial infarction, ethnicity,

physical activity level, BMI, alcohol consumption,

smoking status, menopausal status, postmenopausal

hormone therapy, current aspirin use, current

multivitamin use, Dietary Approaches to Stop

Hypertension dietary score, history of hypertension, hypercholesterolemia, diabetes, cancer, heart

diseases

Having a history of depression was associated with a multivariate-adjusted

HR of 1.29 (95% CI: 1.13-1.48) for total stroke. Participants who reported

current depression had an increased risk of stroke (HR=1.41; 95% CI: 1.18-

1.67), whereas individuals who only had a history of depression were at non-

significantly elevated risk (HR=1.23; 95% CI: 0.97-1.56) compared with

women who never reported a diagnosis of depression or antidepressant

medication use.

Glymour et al, 201020

n=1,864 casesn=19,087 total cohort

Country: USA (Health and Retirement Study)

Age: 50 years

8-item CES-D 3

All fatal/non-fatal stroke; self- or proxy-reporting

8.1 years (mean), 1996 – 2006

Age, race, education, income, wealth, marital

status, overweight, obese, alcohol use, smoking, hypertension, diabetes,

history of cardiac disease

After adjustment for sociodemographic and cardiovascular risk factors, elevated depressive symptoms (HR=1.25; 95% CI: 1.12-1.39)

predicted stroke incidence.

Nabi et al, 201021


Country: Finland (The Health and Social Support Prospective

Study Cohort)Age: 20-54 years

21-item BDI 10


hemorrhagic stroke; hospital discharge

register or mortality records

7 years, 1998 – 2005 Age, sex, education, alcohol use, sedentary lifestyle,

smoking, obesity, hypertension, diabetes, incident cardiac disease

Sex-age-education-adjusted HR for CBVD was 1.01 (95% CI: 0.67-1.53) for participants with mild to severe

depressive symptoms, i.e. those scoring 10 on the 21-item BDI, and 1.77 (95% CI: 0.95-3.29) for those who filled antidepressant prescriptions

compared with those without depression markers in 1998, i.e. at

study baseline.

Bos et al, 200822


Country: Netherlands (The Rotterdam Study)Age: 61 years

20-item CES-D 16; clinical

diagnosis by psychiatrist

(major depression,

dysthymia and minor

depressive disorder

according to DSM-IV)


hemorrhagic stroke; medical records,

hospital or general practitioner records

At least 8 years, from 1997-1999 to

2005

Age, sex, systolic blood pressure, diabetes mellitus, cigarette smoking, intima-media thickness, history of

myocardial infarction, history of PTCA or CABG,

history of TIA, antithrombotic drug use,

antihypertensive use, cholesterol-lowering drug use, psycholeptic drug use and psychoanaleptic drug

use

Men with depressive symptoms (n = 73) were at increased risk of stroke

(adjusted HR=2.17; 95% CI: 1.11-4.23) and ischaemic stroke (adjusted HR=3.21; 95% CI: 1.62-6.38).

Conversely, there was no association between presence of depressive

symptoms and risk of stroke in women.

Liebetrau et al, 200823

n=56 casesn=401 total cohortCountry: Sweden

Age: 85 years


(major depression and

dysthymia according to

DSM-III)

All fatal/non-fatal stroke; hospital

discharge register, death certificates, self-reports, key

informants

3 years, 1986 – 1990 Sex, depression at baseline, blood pressure

Depression at baseline (HR= 2.7; 95% CI: 1.5-4.7; p=0.0006) was related to

increased incidence of first-ever stroke during follow-up. Depression increased stroke risk both among demented and

nondemented individuals.

Surtees et al, 200824


Country: UK (United Kingdom European Prospective

Investigation into Cancer-Norfolk Study)

Age: 41-80 years

HLEQ (MDD according to

DSM-IV)

All fatal/non-fatal stroke; clinical

diagnosis or death certificate

8.5 years (median), 1996 – 2006

Age, sex, smoking, systolic blood pressure, cholesterol,

obesity, preexisting myocardial infarction, diabetes, social class,

education, antihypertensive use, family history of stroke,

antidepressant use

Neither past year nor lifetime MDD was associated with stroke.

Wouts et al, 200825

n=176 casesn=2,965 total cohortCountry: Netherlands

(Longitudinal Aging Study Amsterdam)

Age (meanSD): 70.58.7 years

DIS (major depression

according to DSM-III)


hemorrhagic stroke

7.73.1 years (meanSD), 1992 –

2002

Age, sex, MMSE score, smoking

In participants with pre-existent cardiac disease, clinically relevant depressive symptoms at baseline (HR=2.18; 95%

CI: 1.17-4.09) and the severity (range=0-60; HR=1.08; 95% CI: 1.02-1.13) and chronicity (HR=3.51; 95% CI: 1.13-10.93) of symptoms during

follow-up were associated with stroke.

Arbelaez et al, 200726


Country: USA (Cardiovascular Health Study)

Age (meanSD):Cases – 74.35.7 years

Controls – 72.55.5 years

10-item CES-D 8

Fatal/non-fatal ischemic stroke and

TIA; medical records, death certificates, Medicare healthcare

utilization database for hospitalizations

11 years (median), 1989 – 2000

Age, sex, race, occupation, income, education level,

marital status, hypertension, diabetes, smoking, CHD

status, high- and low-density lipoprotein cholesterol, BMI

Greater depressive symptoms associated with risk of ischemic stroke (unadjusted HR=1.32, 95% CI: 1.09-1.59; HR=1.26, 95% CI=1.03-1.54, adjusted for traditional risk factors).

Salaycik et al, 200727


Country: USA (Framingham Heart Study)

Age: 29-100 years

20-item CES-D 16

All fatal/non-fatal stroke and TIA; medical records

8 years, from 1990 and 1996

Age, sex, blood pressure, diabetes, atrial fibrillation,

CVD, left ventricular hypertrophy on ECG,

smoking status

In participants <65 years, the risk of developing stroke/TIA was 4.21 times

greater (p=<0.001) in those with symptoms of depression. After adjusting for components of the Framingham Stroke Risk Profile

(HR=3.43, 95% CI: 1.60-7.36, p=0.002) and education

(HR=4.89, 95% CI: 2.19-10.95), similar results were obtained. In

subjects aged 65 and older, depressive symptoms were not associated with an

increased risk of stroke/TIA.

Avendano et al, 200628


Country: USA (Established Populations for the

Epidemiologic Studies of the Elderly)

Age: 65 years

20-item CES-D 21


hemorrhagic stroke; self-reports (non-fatal) and death

certificates

12 years, 1982 – 1994

Age, sex, race, education, income

HRs of stroke incidence by educational level among men and women aged 65-

74 years:Low (0-7): 1.52 (95% CI: 0.73-3.15)Middle (8-10): 1.46 (95% CI: 0.72-

2.95)High (10-12): 1.55 (95% CI: 0.79-.04)

Highest (13): 1.00

HRs of stroke incidence by income level among men and women aged 65-

74 years:Low (0-4,999): 1.61 (95% CI: 0.78-

3.32)5,000-9,999: 1.28 (95% CI: 0.63-2.58)

10,000-14,999: 1.27 (95% CI: 0.54-2.98)

High (15,000): 1.00

Kamphuis et al, 200629

n=66 casesn=699 total cohort, males only

Countries: Finland, Italy, Netherlands (The Finland, Italy and Netherlands Elderly Study)

Age: 70-90 years

20-item Zung SDS 60

All fatal stroke; death certificates

7.4 years (mean), 1989 – 2000

Age, country, education, BMI, smoking, alcohol intake, blood pressure,

cholesterol, physical activity

Adjusted HRs of stroke mortality by country-specific tertiles of depressive

symptoms:Low: 1.00

Middle: 1.65 (95% CI: 0.78-3.47)High: 3.41 (95% CI: 1.69-6.90)

No significant differences in HRs between northern and southern Europe.

Stürmer et al, 200630

n=62 casesn=4,267 total cohortCountry: GermanyAge: 40-65 years

Personality scale: high category vs

medium category


hemorrhagic stroke; medical records

(treating doctors), death certificates

8.5 years (median), 1992 – 2003

Age, sex, BMI, smoking status, alcohol consumption,

exercise, comorbidity (cancer, hypertension,

hyperlipidemia, diabetes), family history of stroke,

education

Fully adjusted RRs of stroke incidence (morbidity and mortality) by symptoms

of depression according to the personality scale:

Low: 0.91 (95% CI: 0.43-1.94)Medium: 1.00

High: 1.53 (95% CI: 0.83-2.80)

Gump et al, 200531

n=167 casesn=11,216 total cohort, males

onlyCountry: USA (Multiple Risk

Factor Intervention Trial)Age: 35-57 years

20-item CES-D 16

Fatal ischemic and hemorrhagic stroke;

death certificates

18.43 years (median), 1981 –

1999

Age, intervention group, race, education, smoking, systolic blood pressure, alcohol use, cholesterol, history of cardiac disease

Adjusted HRs of stroke incidence by CES-D quintile (scores) (p<0.002):

First (0-1): 1.00Second (2-4): 1.24 (95% CI: 0.73-2.11)Third (5-7): 1.22 (95% CI: 0.71-2.09)

Fourth (8-12): 1.75 (95% CI: 1.06-2.87)

Fifth (13-60): 2.03 (95% CI: 1.20-3.44)

Wassertheil-Smoller et al, 200432

n=464 casesn=93,676 total cohort, females

onlyCountry: USA (The Women’s Health Initiative Observational

Study)Age: 50-79 years

6-item CES-D 5; 2-item

DIS


hemorrhagic stroke; self-report, medical

records

4.1 years (mean), until 2001

Age, race, education, income, BMI, cholesterol,

diabetes, smoking, hormone therapy, physical activity,

hypertension status

Baseline depression and RRs adjusted for age, race, education and CVD risk

factors, of subsequent stroke:No history of CVD: 1.01 (95% CI:

0.78-1.30)History of CVD: 1.45 (95% CI: 1.11-

1.90)

Larson et al, 200133


Country: USA (Baltimore Epidemiologic Catchment Area

Study)Age: 18 years

DIS (Major depression

according to DSM-III)

All fatal/non-fatal stroke; self-reports (non-fatal), death

certificates

13 years, 1981 – 1993 and 1996

Age, sex, education, diabetes, blood pressure,

smoking, history of cardiac disease

Individuals with a history of depressive disorder were 2.6 times more likely to report stroke than those without this disorder after controlling for heart

disease, hypertension, diabetes, and current and previous use of tobacco

(HR=2.67, 95% CI: 1.08-6.63). Medications used in the treatment of

depressive disorder at baseline did not alter this finding.

Ohira et al, 200134

n=69 casesn=901 total cohort

Country: JapanAge, mean (SE):

Cases – 62 (8) yearsControls – 56 (9) years

Zung SDS 35

All fatal/non-fatal stroke; death

certificates, medical records, or clinical

diagnosis

10.3 years (mean), 1985 – 1996

Age, sex, BMI, systolic blood pressure, serum total cholesterol levels, alcohol intake, cigarette smoking,

antihypertensive medication, diabetes mellitus

Multivariate-adjusted RRs of stroke and stroke subtypes according to

tertiles of SDS scores:i) Total stroke:

30: 1.031-34: 1.2 (95% CI: 0.6-2.2)35: 1.9 (95% CI: 1.1-3.5)

ii) Ischemic stroke:30: 1.0

31-34: 1.1 (95% CI: 0.4-2.7)35: 2.7 (95% CI: 1.2-6.0)

iii) Hemorrhagic stroke:

30: 1.031-34: 1.7 (95% CI: 0.6-4.8)35: 0.9 (95% CI: 0.3-3.1)

Depressive symptoms predict the risk of stroke, specifically ischemic stroke

among Japanese.

Ostir et al, 200135


Country: USAAge: 65 years

20-item CES-D 9

All fatal/non-fatal stroke; clinical

diagnosis or death certificates

6 years, 1986 – 1992 Age, sex, race, marital status, education, BMI,

smoking, diabetes, hypertension, history of

cardiac disease

Increasing scores on the modified version of the CES-D were

significantly associated with stroke incidence for the overall sample (RR

=1.04 for each one-point increase, 95% CI:1.01-1.09) over the 6-year follow-up

period after adjusting for sociodemographic characteristics, blood pressure, body mass index,

smoking status, and selected chronic diseases.

Jonas et al, 200036


Country: USA (NHANES I Epidemiologic Followup Study)

Age: 25-74 years

GWB-D 12 Fatal/non-fatal ischemic and

hemorrhagic stroke; hospital records, death certificates

16 years (median), 1971 – 1992

Age, sex, race, education, smoking, BMI, alcohol use,

nonrecreational physical activity, serum cholesterol level, history of diabetes, history of heart disease, systolic blood pressure

In age-adjusted models for all persons, white men, white women, and black

persons of both sexes, depression was predictive of stroke. In risk-adjusted

models for all persons (RR=1.73, 95% CI:1.30-2.31) and for white men (RR=1.68, 95% CI: 1.02-2.75),

depression remained predictive of stroke. For white women, depression

(RR=1.52, 95% CI: 0.97-2.38) reached borderline significance (p = 0.07). For black persons, depression (RR=2.60, 95% CI: 1.40-4.80) demonstrated a

higher risk of stroke.

Everson et al, 199837


Country: USA (The Alameda County Study)

Age (meanSD):Depressed (5 depressive

symptoms) – 45.617.5 yearsNondepressed – 43.015.6 years

18-item HPL Depression Scale 5


hemorrhagic stroke; death certificates

29 years, 1965 – 1994

Age, sex, race, education, alcohol use, smoking, BMI,

hypertension, diabetes

Reporting 5 depressive symptoms at baseline was associated with increased

risk of stroke mortality (adjusted HR=1.66; 95% CI: 1.16-

2.39; p<0.006). This association remained significant after additional adjustments for education, alcohol consumption, smoking, body mass index, hypertension, and diabetes

(HR=1.54; 95% CI: 1.06-2.22; p<0.02). Time-dependent covariate models, which allowed changes in reported

depressive symptoms and risk factor levels during follow-up, revealed the

same pattern of associations.

Vogt et al, 199438

n=2,573 total cohortCountry: USA (Adult members

of the Northwest Region of Kaiser Permanente)

Age: 18 years

Depressive Index, upper

tertile vs bottom tertile

All fatal/non-fatal stroke; death index,

vital records

15 years, 1970 – 1985

Age, sex, smoking status, socioeconomic status, self-

reported health status, duration of health plan

membership

No relationship between baseline depression and first incidence of stroke. Adjusted RH=0.84 (95% CI: 0.57-1.22;

p=0.34).

WHO: World Health Organization; CIDI-SF: Composite International Diagnostic Interview- Short Form; MDE: Major depressive episodes; DSM-IV: Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition; BMI: Body mass index; HR: Hazard ratio; CI: Confidence intervals; SD: Standard deviation; GHQ: General Health Questionnaire; CES-D: Center for Epidemiological Studies – Depression; TIA: Transient ischemic attack; ICD-10: International Classification of Diseases, Tenth Edition; OR: Odds ratio; MONICA: Multinational Monitoring of Trends and Determinants of Cardiovascular Disease; MOPSY: “MONICA-psychosocial”; MMSE: Mini Mental State Examination; ICD-7: International Classification of Diseases, Seventh Edition; MDD: Major depressive disorder; ETT: Easy to treat; ITT: Intermediate difficult to treat, DTT: Difficult to treat; ICD-9: International Classification of Diseases, Ninth Edition; MHI: Mental Health Inventory; DSM-III: Diagnostic and Statistical Manual of Mental Disorders, Third Edition; HLEQ: Health and Life Experiences Questionnaire; DIS: Diagnostic Interview Schedule; SDS: Self-Rating Depression Scale; RR: Relative risk; CVD: Cardiovascular disease; NHANES: National Health and Nutrition Examination Survey; GWB-D: General Wellbeing – Depression; HPL: Human Population Laboratory; RH: Relative hazard

III. Supplementary Figures

Supplementary Figure I. A forest plot of individual SNP MR estimates for the effect of genetically determined risk of depression on functional outcome after ischemic stroke.

Supplementary Figure II. A radial plot of individual SNP MR estimates for the effect of genetically determined risk of depression on functional outcome after ischemic stroke. Results are in log odds ratios.

Supplementary Figure III. A funnel plot of individual SNP MR estimates for the effect of genetically determined risk of depression on functional outcome after ischemic stroke. Results are in log odds ratios.

IV. Supplementary References1. Howard DM, Adams MJ, Clarke TK, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide

meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci. 2019;22:343-352

2. Li BB, Martin EB. An approximation to the f distribution using the chi-square distribution. Comput Stat Data An. 2002;40:21-26

3. Palmer TM, Lawlor DA, Harbord RM, Sheehan NA, Tobias JH, Timpson NJ, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res. 2012;21:223-242

4. Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet. 2018;50:524-537

5. Soderholm M, Pedersen A, Lorentzen E, Stanne TM, Bevan S, Olsson M, et al. Genome-wide association meta-analysis of functional outcome after ischemic stroke. Neurology. 2019;92:e1271-1283

6. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658-665

7. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:304-314

8. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693-698

9. Sun J, Ma H, Yu C, Lv J, Guo Y, Bian Z, et al. Association of major depressive episodes with stroke risk in a prospective study of 0.5 million chinese adults. Stroke. 2016;47:2203-2208

10. Brunner E, Shipley M, Britton A, Stansfeld S, Heuschmann P, Rudd A, et al. Depressive disorder, coronary heart disease, and stroke: Dose-response and reverse causation effects in the Whitehall II cohort study. Eur J Prev Cardiol. 2014;21:340-346

11. Everson-Rose S, Roetker N, Lutsey P, Kershaw K, Longstreth WJ, Sacco R, et al. Chronic stress, depressive symptoms, anger, hostility, and risk of stroke and transient ischemic attack in the multi-ethnic study of atherosclerosis. Stroke. 2014;45:2318-2323

12. Hamano T, Li X, Lönn S, Nabika T, Shiwaku K, Sundquist J, et al. Depression, stroke and gender: Evidence of a stronger association in men. J Neurol Neurosurg Psychiatry. 2014;86:319-323

13. Gafarov V, Panov D, Gromova E, Gagulin I, Gafarova A. The influence of depression on risk development of acute cardiovascular diseases in the female population aged 25-64 in russia. Int J Circumpolar Health. 2013;72:21223

14. Jackson C, Mishra G. Depression and risk of stroke in midaged women: A prospective longitudinal study. Stroke. 2013;44:1555-1560

15. Péquignot R, Tzourio C, Péres K, Ancellin M, Perier M, Ducimetière P, et al. Depressive symptoms, antidepressants and disability and future coronary heart disease and stroke events in older adults: The three city study. Eur J Epidemiol. 2013;28:249-256

16. Rahman I, Humphreys K, Bennet A, Ingelsson E, Pedersen N, Magnusson P. Clinical depression, antidepressant use and risk of future cardiovascular disease. Eur J Epidemiol. 2013;28:589-595

17. Li C, Bai Y, Tu P, Lee Y, Huang Y, Chen T, et al. Major depressive disorder and stroke risks: A 9-year follow-up population-based, matched cohort study. PLoS One. 2012;7:e46818

18. Majed B, Arveiler D, Bingham A, Ferrieres J, Ruidavets J, Montaye M, et al. Depressive symptoms, a time-dependent risk factor for coronary heart disease and stroke in middle-aged men: The prime study. Stroke. 2012;43:1761-1767

19. Pan A, Okereke O, Sun Q, Logroscino G, Manson J, Willett W, et al. Depression and incident stroke in women. Stroke. 2011;42:2770-2705

20. Glymour M, Maselko J, Gilman S, Patton K, Avendaño M. Depressive symptoms predict incident stroke independently of memory impairments. Neurology. 2010;75:2063-2070

21. Nabi H, Kivimäki M, Suominen S, Koskenvuo M, Singh-Manoux A, Vahtera J. Does depression predict coronary heart disease and cerebrovascular disease equally well? The health and social support prospective cohort study. Int J Epidemiol 2010;39:1016-1024

22. Bos M, Lindén T, Koudstaal P, Hofman A, Skoog I, Breteler M, et al. Depressive symptoms and risk of stroke: The rotterdam study. J Neurol Neurosurg Psychiatry. 2008;79:997-1001

23. Liebetrau M, Steen B, Skoog I. Depression as a risk factor for the incidence of first-ever stroke in 85-year-olds. Stroke. 2008;39:1960-1965

24. Surtees PG, Wainwright NW, Luben RN, Wareham NJ, Bingham SA, Khaw KT. Psychological distress, major depressive disorder, and risk of stroke. Neurology. 2008;4:788-794

25. Wouts L, Oude Voshaar R, Bremmer M, Buitelaar J, Penninx B, Beekman A. Cardiac disease, depressive symptoms, and incident stroke in an elderly population. Arch Gen Psychiatry. 2008;65:596-602

26. Arbelaez J, Ariyo A, Crum R, Fried L, Ford D. Depressive symptoms, inflammation, and ischemic stroke in older adults: A prospective analysis in the cardiovascular health study. J Am Geriatr Soc. 2007;55:1825-1830

27. Salaycik K, Kelly-Hayes M, Beiser A, Nguyen A, Brady S, Kase C, et al. Depressive symptoms and risk of stroke: The framingham study. Stroke. 2007;38:16-21

28. Avendano M, Kawachi I, Van Lenthe F, Boshuizen H, Mackenbach J, Van den Bos G, et al. Socioeconomic status and stroke incidence in the us elderlythe role of risk factors in the epese study. Stroke. 2006;37:1368-1373

29. Kamphuis M, Kalmijn S, Tijhuis M, Geerlings M, Giampaoli S, Nissinen A, et al. Depressive symptoms as risk factor of cardiovascular mortality in older european men: The Finland, Italy and Netherlands elderly (FINE) study. Eur J Cardiovasc Prev Rehabil. 2006;13:199-206

30. Stürmer T, Hasselbach P, Amelang M. Personality, lifestyle, and risk of cardiovascular disease and cancer: Follow-up of population based cohort. BMJ. 2006;332:1359

31. Grump BB, Matthews KA, Eberly LE, Chang YF, MRFIT Research Group. Depressive symptoms and mortality in men: Results from the multiple risk factor intervention trial. Stroke. 2005;36:98-102

32. Wassertheil-Smoller S, Shumaker S, Ockene J, Talavera G, Greenland P, Cochrane B, et al. Depression and cardiovascular sequelae in postmenopausal women. The women's health initiative (whi). Arch Intern Med. 2004;164:289-298

33. Larson S, Owens P, Ford D, Eaton W. Depressive disorder, dysthymia, and risk of stroke: Thirteen-year follow-up from the baltimore epidemiologic catchment area study. Stroke. 2001;32:1979-1983

34. Ohira T, Iso H, Satoh S, Sankai T, Tanigawa T, Ogawa Y, et al. Prospective study of depressive symptoms and risk of stroke among japanese. Stroke. 2001;32:903-908

35. Ostir G, Markides K, Peek M, Goodwin J. The association between emotional well-being and the incidence of stroke in older adults. Psychosom Med. 2001;63:210-215

36. Jonas B, Mussolino M. Symptoms of depression as a prospective risk factor for stroke. Psychosom Med. 2000;62:463-471

37. Everson S, Roberts R, Goldberg D, Kaplan G. Depressive symptoms and increased risk of stroke mortality over a 29-year period. Arch Intern Med. 1998;158:1133-1138

38. Vogt T, Pope C, Mullooly J, Hollis J. Mental health status as a predictor of morbidity and mortality: A 15-year follow-up of members of a health maintenance organization. Am J Public Health. 1994;84:227-231

V. Author contributions

Name Location Contribution

Dipender Gill MD

Imperial College London, United

Kingdom

Designed the study, analyzed the data, interpreted the results and drafted the

manuscript

Nicole Ellen James BSc


Kingdom

Interpreted the results and drafted the manuscript

Grace Monori BSc


Kingdom

Interpreted the results and drafted the manuscript

Erik Lorentzen MSc

University of Gothenburg, Sweden

Interpreted the results and revised the manuscript

Israel Fernandez-Cadenas MSc

PhD

Hospital de Sant Pau, Spain

Acquired the data, interpreted the results and revised the manuscript

Robin Lemmens MD PhD

University of Leuven, Belgium


Vincent Thijs MD PhD

University of Melbourne, Australia


Natalia Rost MD PhD

Harvard Medical School, Boston MA,

USA


Rodney Scott MSc PhD

University of Newcastle, Australia


Graeme J Hankey MD PhD

The University of Western Australia,

Australia


Arne Lindgren MD PhD

Lund University, Sweden


Christina Jern MD PhD

University of Gothenburg, Sweden


Jane Maguire RN PhD

University of Technology Sydney,

Australia

Acquired the data, interpreted the results and drafted the manuscript

VI. Co-investigator contributions All individuals listed did not qualify for authorship and are co-investigators on behalf of the GISCOME network. Their contributions refer to the related GISCOME GWAS publication5, summary data from which are used in the current work.

Name Location ContributionMartin Söderholm

MD PhDLund University, Lund, Sweden Skane University

Hospital, Lund and Malmö, SwedenAnalysis and interpretation of the data, drafting

the manuscript for intellectual content

Annie Pedersen MD University of Gothenburg, Gothenburg, Sweden Analysis and interpretation of the data, drafting the manuscript for intellectual content

Tara M Stanne PhD University of Gothenburg, Gothenburg, Sweden Analysis of the data, revising the manuscript for intellectual content

Steve Bevan MSc PhD University of Lincoln, Lincoln, UK Analysis of the data, revising the manuscript

for intellectual contentMaja Olsson MSc

PhD University of Gothenburg, Gothenburg, Sweden Analysis of the data, revising the manuscript for intellectual content

John Cole MD PhD University of Maryland School of Medicine and Baltimore VAMC, Baltimore MD, USA Major role in the acquisition of data

Jordi Jimenez-Conde MD PhD

Institut Hospital del Mar d’Investigació Mèdica (IMIM), Barcelona, Spain

Universitat Autònoma de Barcelona, Barcelona, Spain

Design or conceptualization of the study, major role in the acquisition of data

Katarina Jood MD PhD University of Gothenburg, Gothenburg, Sweden Major role in the acquisition of data, revising the

manuscript for intellectual content

Jin-Moo Lee MD PhD

Washington University Schoolof Medicine, St. Louis MO, USA

Design or conceptualization of the study, major role in the acquisition of data, revising the

manuscript for intellectual content

Christopher Levi MD PhD

University of Technology Sydney, Sydney, Australia

University of Newcastle, Newcastle, AustraliaMajor role in the acquisition of data

Braxton Mitchell MSc PhD

University of Maryland, Baltimore MD, USAVeterans Affairs Medical Center, Baltimore, MD,

USA

Major role in the acquisition of data, revising the manuscript for intellectual content

Bo Norrving MD PhD

Lund University, Lund, SwedenSkane University Hospital, Lund and Malmö,

Sweden

Major role in the acquisition of data, revising the manuscript for intellectual content, revising the

manuscript for intellectual contentKristiina

Rannikmäe MD PhD

University of Edinburgh, Edinburgh, UK Major role in the acquisition of data

Jonathan Rosand MD PhD

Harvard Medical School, Boston MA, USABroad Institute of MIT and Harvard, Cambridge

MA, USAMassachusetts General Hospital, Boston MA,

USA

Major role in the acquisition of data, revising the manuscript for intellectual content

Peter M RothwellMD PhD University of Oxford, Oxford, UK Major role in the acquisition of data

Daniel StrbianMD PhD Helsinki University Hospital, Helsinki, Finland

Design or conceptualization of the study, major role in the acquisition of data, revising the

manuscript for intellectual contentJonathan Sturm

MD PhD University of Newcastle, Australia Major role in the acquisition of data, revising the manuscript for intellectual content

Cathie SudlowMD PhD University of Edinburgh, Edinburgh, UK Design or conceptualization of the study

Matthew TraylorMSc PhD University of Cambridge, Cambridge, UK Acquisition of data

Turgut TatlisumakMD PhD

University of Gothenburg, Gothenburg, SwedenHelsinki University Hospital, Helsinki, Finland Major role in the acquisition of data

Daniel Woo, MD PhD

University of Cincinnati College of Medicine, Cincinnati OH, USA

Design or conceptualization of the study, major role in the acquisition of data

Bradford B Worrall MD PhD University of Virginia, Charlottesville VA, USA Design or conceptualization of the study, major

role in the acquisition of data

Documents

Supplementary Methods - Imperial College London · Web viewn=1,864 cases n=19,087 total cohort Country: USA (Health and Retirement Study) Age: 50 years 8-item CES-D 3 All fatal/non-fatal