GISCOME – Genetics of Ischaemic Stroke Functional Outcome network: A protocol for an international multicentre genetic association study

Abstract

Introduction

Genome-wide association studies have identified several novel genetic loci associated with stroke risk, but how genetic factors influence stroke outcome is less studied. The Genetics of Ischaemic Stroke Functional outcome network aims at performing genetic studies of stroke outcome. We here describe the study protocol and methods basis of Genetics of Ischaemic Stroke Functional outcome.

Methods

The Genetics of Ischaemic Stroke Functional outcome network has assembled patients from 12 ischaemic stroke projects with genome-wide genotypic and outcome data from the International Stroke Genetics Consortium and the National Institute of Neurological Diseases Stroke Genetics Network initiatives. We have assessed the availability of baseline variables, outcome metrics and time-points for collection of outcome data.

Results

We have collected 8831 ischaemic stroke cases with genotypic and outcome data. Modified Rankin score was the outcome metric most readily available. We detected heterogeneity between cohorts for age and initial stroke severity (according to the NIH Stroke Scale), and will take this into account in analyses. We intend to conduct a first phase genome-wide association outcome study on ischaemic stroke cases with data on initial stroke severity and modified Rankin score within 60–190 days. To date, we have assembled 5762 such cases and are currently seeking additional cases meeting these criteria for second phase analyses.

Conclusion

Genetics of Ischaemic Stroke Functional outcome is a unique collection of ischaemic stroke cases with detailed genetic and outcome data providing an opportunity for discovery of genetic loci influencing functional outcome. Genetics of Ischaemic Stroke Functional outcome will serve as an exploratory study where the results as well as the methodological observations will provide a basis for future studies on functional outcome. Genetics of Ischaemic Stroke Functional outcome can also be used for candidate gene replication or assessing stroke outcome non-genetic association hypotheses.

Introduction

Globally, stroke is one of the principal causes of adult disability and the global burden of stroke is increasing.^1,2 After 1 year, up to 28% of stroke survivors are dependent on others for help with self-care and personal activities of everyday living.³ Even though last decades have shown significant reductions in stroke incidence in high-income countries, this has not been observed in low- or middle-income countries and with population aging and improved stroke survival, the absolute number of people who survive a stroke and experience varying levels of impairment continues to rise.^1,2 A deeper understanding of the biology of recovery after stroke is needed to identify new therapeutic targets for this affected group of patients.

Animal models demonstrate that following an acute ischaemic insult, the brain undergoes spontaneous recovery, repair and remodelling.⁴ However, efforts to translate these findings to improve stroke outcomes in the clinical setting have been limited. Furthermore, the difficulty of predicting individual outcome poses a substantial challenge for ongoing post-stroke management strategies. Clinical parameters related directly to the acute event, such as age, stroke severity, etiologic stroke subtype, infarct size and location are predictors of outcomes,^5–9 but predictive models based on these factors are imprecise.^10–12 Other prognostic factors may include socioeconomic and social factors, post-stroke depression and type and degree of treatment and rehabilitation¹³ and there is a need for consensus on description of rehabilitation measures.¹⁴ Improvement of neurological function following the initial event is likely dependent on several of the above-mentioned factors combined with environmental and genetic influences.¹⁵

A genetic role in disease risk and susceptibility has been reported for many complex diseases including stroke,^16–18 but the contribution of genetic factors to stroke outcomes is less clear. There is substantial heritability reported for both intracerebral haemorrhage (ICH) and ischaemic stroke (IS).^15,19,20 Preliminary evidence from individual candidate gene studies suggests that the functional outcome after stroke may also be determined by genetic factors in addition to clinical factors,^21–25 however replication in larger cohorts is still outstanding. Genome wide association studies (GWASs) use designs that are hypothesis generating and have led to discovery of disease-associated loci across multiple phenotypes and subsequent new knowledge of genetic architecture of diseases.²⁶ The Genetics of Ischaemic Stroke Functional outcome(GISCOME) effort therefore aims at detecting and describing genetic factors influencing IS outcomes, using data from already performed GWASs.

Here we describe the creation of the GISCOME network as the first international multi-centre collection of IS cases with data on outcomes, genome-wide genotypes and salient baseline variables and the study protocol for future genetic analysis. We include a description of the process of selecting variables, outcome measures and the potential future role of this collaboration network.

Methods

Twelve centres or joint projects have agreed to participate and provide data for analysis (Supplementary Table 1) and are already contributing to the International Stroke Genetics Consortium (ISGC) and the National Institute of Neurological Diseases Stroke Genetics Network (NINDS-SiGN) Consortium efforts studying genetics of stroke risk. Some centres contributed more than one cohort of patients (e.g. Barcelona) and some centres used multiple genotyping platforms (e.g. Boston). This resulted in a total of 18 cohorts for which baseline characteristics, data availability and genotyping platform are outlined in Supplementary Table 1. The majority of the cohorts were hospital based with detailed phenotyping, including imaging. Supplementary Table 2 describes inclusion, recruitment period and follow-up methods for each cohort. We have retrospectively collected phenotype data available for the 18 cohorts, selecting variables as outlined below.

Process of variable selection

The variables considered for inclusion in our study had already been collected in the individual cohorts by use of different study protocols. We conducted an initial survey across the cohorts to ascertain: (a) time-points when information on functional outcomes had been recorded; (b) what outcome measures had been utilised and (c) all accessible baseline variables. We sought information on factors known or suspected to influence outcomes and these included: age, sex, living situation, stroke severity measured by National Institute of Health Stroke Scale (NIHSS),²⁶ IS subtype, medical history/comorbid conditions, risk factors (including prior stroke or transient ischaemic attack, coronary artery disease, atrial fibrillation, diabetes mellitus, hypertension, hyperlipidaemia, smoking and alcohol use), pre-stroke physical functioning (measured with pre-stroke modified Rankin score (mRS)), medications and impairments and consequences of stroke such as cognitive impairment and depression.²⁷ We identified 71 variables and grouped them into (a) demographics, (b) baseline characteristics, (c) pre-stroke characteristics, (d) risk factors, (e) post-stroke treatments and (f) outcome measures. This provided us with a comprehensive overview of all variables available in at least one of the cohorts.

Next, we dropped the variables with unavailable data in more than one third of subjects. We selected mRS^27,28 at 60–190 days as the most readily available functional outcome variable, after having observed that the majority of mRS values had been collected at 90 days ± 2 weeks (81%) and that most of the remaining mRS observations were within the 60–190 day time span. The mRS values had already been scored by trained assessors at face-to-face or telephone follow-up for the majority of cohorts (for cohort specific details, please see Supplementary Table 2). The Lund Stroke Register and the Sahlgrenska Academy Study on Ischaemic Stroke (SAHLSIS) phase 2 cohorts patients had been assessed with data from the 3-month follow-up in the Swedish National Register Riksstroke. A validated algorithm for transforming answers on Riksstroke outcome questions into mRS grades was used even though this method prevented a differentiation between the mRS grades 0, 1, 2.²⁹ Baseline NIHSS was the selected measure for initial stroke severity. When multiple NIHSS scores were available, we selected the score taken as close to 24 h after stroke onset as possible (within 0–10 days).

Availability of IS subtype classification data measured by Trial of ORG 10172 in Acute Stroke Treatment (TOAST),³⁰ Causative Classification System (CCS)^31,32 or both, varied across the studies. Agreement between TOAST and CCS subtyping has been previously determined^31,33 and there is significant genetic overlap between these two methods,³⁴ which suggests pooling cases with either classification may be beneficial in subsequent GISCOME studies.

Following this selection process, the phenotypic and genotypic data for each cohort were uploaded to central FTP secure servers located in Cambridge, UK, providing access to computational packages and file storage for this large-scale study. Further interrogation of the dataset led to a decision to remove additional variables not having a clear and homogeneous definition between cohorts or less than 50% availability. This included, e.g. pre-stroke living and housing situation, ICH transformation after tPA, stroke to death interval and recurrent stroke (Supplementary Table 3).

Meta-analysis plan

Several assumptions were made by investigators and included consideration of the retrospective data from multiple cohorts and the subsequent limitations introduced by this. Thus, our planned analyses are considered as exploratory analyses to inform future prospective studies. We plan to analyse the primary outcome as mRS within the 60–190 days window, using first binary and then ordinal scales. The binary analyses will include both mRS 0–2 vs 3–6 and mRS 0–1 vs 2–6. For the analyses of mRS as an ordinal variable, ordinal logistic regression will be used. Simulated power calculations based on the currently available data are depicted in the Supplemental Figure. The ordinal model provides greater power. In this model with the available data set, the minimal odds ratios detectable at 80% power with a p-value < 5 × 10⁻⁸ are 1.15 for MAF 30%, 1.24 for MAF 10% and 1.35 for MAF 5%. Age, sex and initial NIHSS score are known to affect post-stroke outcome. To determine whether the expected associations were present in our data, we performed regression analyses. As expected, all three of these variables were highly significant predictors of outcome in all three mRS models described above (Supplementary Table 4). Therefore, in all analyses, we will adjust for age and sex, and subsequently adjust for baseline NIHSS. We will adopt a standard GWAS significance threshold of 5 × 10⁻⁸ for all primary analyses. Because outcome results may depend on when evaluated after stroke onset, we intend to do a sensitivity analysis for the majority group of our subjects with mRS outcome data available at 90 days ± 2 weeks (81%). A separate secondary analysis including only subjects with baseline NIHSS available within 0–1 days is also planned.

Results

Characteristics of the GISCOME collection

We assembled a total of 8831 IS cases with phenotypic and genotype information in GISCOME. There were slightly fewer women (41.2%) than men, and cardiovascular risk factor frequencies were as expected in a stroke event group (Table 1). All cases included were of European ancestry and all cases were ≥18 years of age. Across all sites, stroke severity recorded at baseline was often mild strokes (NIHSS median 3; interquartile range (IQR) 1–7). Stroke severity was similar across the included cohorts, with the exception of three cohorts: VISP (median NIHSS 1, IQR 0–2); Val de Hebron-1 (median NIHSS 17, IQR 11–20) and Washington University (median NIHSS 8, IQR 4–12). It is of note that the median time of NIHSS scores for VISP were 70 days (IQR 45–98). As only 0.3% of VISP fulfilled the NIHSS time window criteria of 0–10 days, this data set will not be included in the primary analysis. The distribution of TOAST subtypes was as follows: cardioembolic (CE) stroke 31.7%, large artery atherosclerotic (LAA) 17.9%, small vessel disease (SVD) 19.2% and other/undetermined 30.2% (Table 1). For CCS classification the distribution was: CE stroke 33.9%, LAA 16.4%, SVD 12.3% and other/underdetermined 37.4%. Loss to follow up ranged from 0 to 21% with the exception of the Massachusetts General Hospital Genes Affecting Stroke Risk and Outcomes Study (MGH-GASROS) study (69% loss to follow up) (Supplementary Table 2). The Edinburgh cohort subjects will not be included in the primary analysis because >90% lacked mRS outcome data within the 60–190 day window.

Table 1.

Characteristics of the 18 GISCOME cohorts.

Characteristics	Ischaemic stroke cases (number = 8831)	Missing data number (%)
mRS available (60–190 days), number (%)	7416 (84.0)	1415 (16.0)
mRS taken at day, median (IQR)	90 (81–90)
Sex, female number (%)	3658 (41.4)	0 (0)
Age, years mean (SD)	68.4 (13.5)	0 (0)
NIHSS available (0–10 days), number (%)a	6820 (77.2)	2011 (22.8)
NIHSS, median (IQR)	3 (1–7)
NIHSS taken at day, median (IQR)	0 (0–1)
Rehabilitation measures registered, number/available (%)	1638/3387 (48.4)	5444 (61.6)
TOAST Stroke subtypes, number/available (%)
Cases with TOAST data	6437 (72.9)	2394 (27.1)
Large artery atherosclerosis	1155/6437 (17.9)
Cardioembolic	2038/6437 (31.7)
Small vessel disease	1235/6437 (19.2)
Other/undetermined	2009/6437 (31.2)
CCS stroke subtypes, number/available (%)
Cases with CCS data	4694 (53.2)	4137 (46.8)
Large artery atherosclerosis	770/4694 (16.4)
Cardioembolic	1593/4694 (33.9)
Small vessel disease	576/4694 (12.3)
Other/undetermined	1755/4694 (37.4)
Cardiovascular risk factors, number/available (%)b
Hypertension	5891/8787 (67.0)	44 (0.5)
Hypercholesterolemia	4715/8530 (55.3)	301 (3.4)
Diabetes mellitus	1940/8622 (22.5)	209 (2.4)
Atrial fibrillation	1746/8799 (19.8)	32 (0.4)
Ischaemic Heart Disease	1589/7474 (21.3)	1357 (15.4)
Current smoker	2007/8683 (23.1)	148 (1.7)
Pharmacological intervention,
Cases treated with Alteplase, number/available (%)	689/4886 (14.1)	3945 (44.7)
Premorbid impaired functional status, number (%)	772/6867 (77.8)	1964 (22.2)
Pre-stroke living situation, number/available (%)		6095 (69)
Alone	897/2736 (32.8)
Divorced	64/2736 (2.3)
Widowed	17/2736 (0.6)
With someone	1758/2736 (64.3)
First or recurrent stroke, number/available (%)		477 (5.4)
First	6797/8354 (81.4)
Recurrent	1557/8354 (18.6)
Pre-stroke housing, number/available (%)		6430 (72.8)
Assisted living	5/2401 (0.2)
Institution	55/2401 (2.3)
Nursing home	13/2401 (0.5)
Own house/flat	2318/2401 (96.5)
Other	10/2401 (0.4)

^aOnly n = 5/1723 (0.3%) of individuals in VISP fulfilled the NIHSS time window criteria of 0–10 days.

^bAvailability across cohorts. Numbers vary per cohort.

mRS: modified Rankin Scale; IQR: interquartile range; SD: standard deviation; NIHSS: NIH stroke scale; TOAST: Trial of Org 10172 in Acute Stroke Treatment stroke sub classification; CCS: Causative Classification System.

GISCOME subjects to be included in primary analyses

Given the considerations discussed above and the time windows selected for the primary GWA analysis (mRS day 60–190; NIHSS day 0–10), 5762 individuals from 16 cohorts are available with mRS, NIHSS and genotyping data for the primary analyses. Characteristics of this data set are summarised in Table 2. We intend to use the current dataset to conduct the first phase GWAS and then to expand to the second phase of this effort with data we expect to obtain from new cases from our existing studies and joint projects as well as from new contributing studies. A minimum set of variables required for phase 2 will include age, sex, stroke severity at 0–10 days and mRS at 60–190 days and available GWAS or DNA. Apart from the GWAS, we anticipate to specifically investigate known and putative genetic determinants of stroke outcome that include but are not limited to APOE and BDNF, both to validate these candidates and to demonstrate the viability of our cohort to replicate existing literature. We also plan to conduct the first GWAS based assessment for heritability of stroke outcome using a GWAS trait analysis approach using methods similar to those previously described regarding stroke risk.¹⁹ Insufficient sample size currently prevents the conduct of detailed subtype analyses at this stage, however we continue to seek additional cohorts to address this.

Table 2.

The mRS distribution of ischaemic stroke patients in 60–190 day window for GISCOME cohorts intended for the primary analyses.

Characteristics	16 cohorts Ischaemic stroke cases (number = 5762)	14 cohortsa Ischemic stroke cases (number = 4421)
Sex, females number (%)	2472 (42.9)	1894 (42.8)
Age, years mean (SD)	68.6 (14.0)	68.7 (13.9)
mRS dichotomised 0–2 vs 3–6
Poor outcome,  number (%)	2131 (37.0)	N/A
mRS dichotomised 0–1 vs 2–6
Poor outcome,  number (%)	N/A	2567 (58.1)
mRS, ordinal scale
0	718 (12.5)	718 (16.2)
1	1953 (33.9)b	1136 (25.7)
2	960 (16.7)	960 (21.7)
3	847 (14.7)	628 (14.2)
4	605 (10.5)	479 (10.8)
5	215 (3.7)	138 (3.1)
6	464 (8.1)	362 (8.2)
NIHSS (0–10 days), median (IQR)	4 (2–9)	4 (2–9)
NIHSS taken at day, median (IQR)	0 (0–1)	0 (0–1)

^aLSR and SAHLSIS phase 2 not included in this distribution because these cohorts used a collapsed score for mRS 0–2.

^bIncluding LSR and SAHLSIS phase 2, where collapsed mRS scores of 0–2 (number = 519 and number = 298 subjects, respectively) are assigned as having mRS 1.

SD: standard deviation; mRS: modified Rankin Scale; LSR: Lund Stroke Register; SAHLSIS: Sahlgrenska Academy Study on Ischemic Stroke; NIHSS: NIH stroke scale; N/A: not applicable; IQR: interquartile range.

Discussion

This study protocol describes the GISCOME network which aims at conducting the first international multicentre large-scale GWAS on post-stroke outcomes. Within the GISCOME cohorts, the most commonly used outcome metric was the mRS. Fortunately, this is one of the preferred functional outcome measures of choice in contemporary stroke trials.²⁷ The mRS demonstrates strong test–retest and moderate inter-rater reliability, which may possibly be enhanced by structured interviews and training.^35–37 The clinical sensitivity or meaningful responsiveness to change in different outcome measures has been extensively studied.³⁶ While mRS may not be the most sensitive scale to changes in functional activity, a one-point change in the scale is deemed to be clinically significant based on the range of activities captured by the scale.³⁶

Notably, the timing of outcome measures is equally important to the determination of outcome as the measure itself. By introducing time into consideration of outcome, two important derivative metrics emerge – the rate of change in outcome (rate of ‘recovery’) and maximal extent of outcome (extent of ‘recovery’). Rate of recovery refers to improvement per time unit. Extent of recovery refers to the functional ability, assessed by a metric such as mRS that captures the degree of functional ability. The biologic mechanism that underpins both of these is not well understood. However, because outcome metrics were not uniformly collected in several cohorts within GISCOME, we cannot currently study the rate of recovery. Improvement in functional outcome occurs most rapidly in the first days to weeks after ischaemic brain injury; however, over the ensuing months, the degree of improvement plateaus.³⁸ We chose to define mRS to encompass 60–190 days, but acknowledge that it is possible that some functional recovery may occur at earlier or later time points and this may not be accounted for in this investigation. The sensitivity analysis we propose will serve to determine how this may affect our results.

We selected age, sex and initial stroke severity (as measured by baseline NIHSS within 0–10 days) as covariates in this analyses based on previous reports and our own observation that these variables influence functional outcome post-stroke. Study cohort also needs to be considered due to potential variability in outcomes due to differences in clinical practice specific to each stroke care system at the individual study sites. Other known determinants of post-stroke outcomes including pre-morbid status, acute stroke interventions (i.e. intravenous thrombolysis), neuroimaging characteristics of stroke (i.e. infarct size and location) will most likely not be included in this analysis due to lack of current data availability; however, ongoing studies within the ISGC such as MRI-GENIE³⁹ and TOTO⁴⁰ aim to provide additional information as to the role of specific stroke-related characteristics on genetics of functional outcomes in the future.

Our study has several strengths. We have assembled the largest sample of detailed stroke outcome phenotypic and genotypic data. The GISCOME network and proposed study will add to the understanding of genetic variants associated with neurological outcomes after the acute phase of IS using individual level genetic data. Our retrospective design is largely pragmatic, taking advantage of existing datasets collected to examine stroke risk. The driving aim of GISCOME is to meta-analyse individual level data and identify novel genetic variants that influence the mechanistic pathways of functional outcomes post stroke. This parallels the efforts of other international consortia, several of which have extended the initial aim of identifying genetic risk factors associated with complex neurological disease to the investigation of genetic determinants of outcome, e.g. Parkinson’s Disease.⁴¹

The retrospective design is a clear limitation, and introduces both selection and attrition bias since data included were previously collected under a variety of study protocols over a broad time frame with notable loss to follow up in some cohorts. We thus had to derive our phenotypic data set from these heterogeneous sources. We selected the mRS at 60–190 days post index stroke as the primary outcome measure based on availability, and this metric is both acceptable and reliable in clinical stroke research.^28,35–37 However, while the mRS is widely acknowledged as the standard outcome measure in stroke clinical trials, we accept it is a relatively crude measure of functional recovery and the timing of mRS collection was not consistent across all contributing datasets. Even though data about mortality among the included subjects is available for the time of the primary outcome evaluation (i.e. as close to 90 days as possible), we do not have details about at the exact time point when deaths occurred. There was also heterogeneity between the individual cohorts regarding age and initial stroke severity and our total study sample has a bias towards milder strokes with median NIHSS of 3 which may hamper the detection of factors influencing the outcome in subjects with more severe stroke symptoms. We lacked data and/or clear definitions on several clinical variables known to influence outcome such as co-morbid depression, use of particular drugs, e.g. selective serotonin reuptake inhibitors or anticonvulsants, and measures of social support (Supplementary Table 3). We also lack data on the volume of infarct, but as infarct volume is known to correlate with NIHSS, we will be able to partly account for this. Finally, all cases were of European ancestry and do not represent a global stroke population. Therefore, specific genetic factors influencing outcome after stroke in subjects with other ethnic backgrounds will not be detected. We aim to address this in future efforts. In phase 2, we will seek and invite sites that are derived from more diverse ethno-geographic groups. In the future, an expansion of the number of study subjects is also needed to improve the power of detecting genetic variants related to IS outcome. Despite these limitations, a major strength of our planned analysis is the detailed description of the methods used and careful selection of a much needed repository for novel investigation into genetic determinants of stroke outcome.

Conclusion

The GISCOME study protocol describes an exploratory effort providing an excellent opportunity to detect genetic influence on stroke outcomes and to inform future studies within this important field of stroke research. The GISCOME sample size will increase through identification of additional sites and recruitment of cases within existing studies. We anticipate that this will increase our capability to explore other avenues of inquiry, for example, variants of smaller effect sizes.

We also strongly advocate for future prospective cohorts to utilise measures of functional capacity, quality of life and neuropsychological function. We therefore urge the stroke community to characterise stroke cases using standardised definitions⁴² and follow-up stroke patients in their acute and rehabilitation phases with consistent documentation of functional ability. Co-operation within, e.g. the ISGC, is an effective method to increase the availability of studies for this type of research. These efforts will provide a stable platform for identifying genetic variants that are associated with functional outcome.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Robin Lemmens is a senior clinical investigator of FWO Flanders. All other authors had no disclosures.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Arne Lindgren was funded by Region Skåne, Lund University, the Swedish Heart and Lung Foundation, the Freemasons Lodge of Instruction EOS Lund and the Swedish Stroke Association. Christina Jern was funded by the Swedish Research Council (K2014-64X-14605-12-5), the Swedish state and Region Västra Götaland (ALFGBG-429981), the Swedish Heart and Lung Foundation (20130315) and the Swedish Stroke Association. The Oxford Vascular Study has been funded by Wellcome Trust, Wolfson Foundation, UK Stroke Association, British Heart Foundation, Dunhill Medical Trust and the NIHR Oxford Biomedical Research Centre. Helsinki-2000 Study was partly funded by grants from the Helsinki University Central Hospital and the Sigrid Juselius Foundation. Turgut Tatlisumak: Helsinki-2000 ethics permit: 27-Oct-2010, 266, 287/13/03/01/2010. Peter Rothwell: The Oxford Vascular Study has been funded by Wellcome Trust, Wolfson Foundation, UK Stroke Association, British Heart Foundation, Dunhill Medical Trust, and the NIHR Oxford Biomedical Research Centre. Professor Rothwell was in receipt of an NIHR Senior Investigator Award and a Wellcome Trust Senior Investigator Award. Robin Lemmens was a senior clinical investigator of FWO Flanders. Daniel Strbian was supported with a grant from the Finnish Medical Foundation and Helsinki University Hospital governmental subsidiary funds for clinical research.

Ethical approval

All studies included in this manuscript were granted approval by their local ethics committees.

Informed consent

Written informed consent was obtained from the patient(s) for their anonymised information to be published in this article.

Guarantor

Jane Maguire. All co-authors, confirmed by email ‘I consent that Dr Jane Maguire submit the article for publication and I give her the right to sign the Contributor Agreement on my behalf’.

Contributorship

JM, AL and CJ researched literature and conceived the study; JM, JML, RL, CS, RS, BR, CL, VT, EL, BN and CJ was involved in protocol development; JR, CJ, VT, RL, TT, TW, CL, JM, TS and VT gaining ethical approval; JM, TS, EL, VT, SB, BBW and JR patient recruitment and data analysis. JM, AL and CJ wrote the first draft of the manuscript. All authors reviewed and edited the manuscript and approved the final version of the manuscript.