H3Africa is developing capacity for health-related genomics research in Africa

Summary Background Sub-Saharan Africa has the highest incidence, prevalence, and fatality from stroke globally. Yet, only little information about context-specific risk factors for prioritising interventions to reduce the stroke burden in sub-Saharan Africa is available. We aimed to identify and characterise the effect of the top modifiable risk factors for stroke in sub-Saharan Africa. Methods The Stroke Investigative Research and Educational Network (SIREN) study is a multicentre, case-control study done at 15 sites in Nigeria and Ghana. Cases were adults (aged ≥18 years) with stroke confirmed by CT or MRI. Controls were age-matched and gender-matched stroke-free adults (aged ≥18 years) recruited from the communities in catchment areas of cases. Comprehensive assessment for vascular, lifestyle, and psychosocial factors was done using standard instruments. We used conditional logistic regression to estimate odds ratios (ORs) and population-attributable risks (PARs) with 95% CIs. Findings Between Aug 28, 2014, and June 15, 2017, we enrolled 2118 case-control pairs (1192 [56%] men) with mean ages of 59.0 years (SD 13.8) for cases and 57.8 years (13.7) for controls. 1430 (68%) had ischaemic stoke, 682 (32%) had haemorrhagic stroke, and six (<1%) had discrete ischaemic and haemorrhagic lesions. 98.2% (95% CI 97.2–99.0) of adjusted PAR of stroke was associated with 11 potentially modifiable risk factors with ORs and PARs in descending order of PAR of 19.36 (95% CI 12.11–30.93) and 90.8% (95% CI 87.9–93.7) for hypertension, 1.85 (1.44–2.38) and 35.8% (25.3–46.2) for dyslipidaemia, 1.59 (1.19–2.13) and 31.1% (13.3–48.9) for regular meat consumption, 1.48 (1.13–1.94) and 26.5% (12.9–40.2) for elevated waist-to-hip ratio, 2.58 (1.98–3.37) and 22.1% (17.8–26.4) for diabetes, 2.43 (1.81–3.26) and 18.2% (14.1–22.3) for low green leafy vegetable consumption, 1.89 (1.40–2.54) and 11.6% (6.6–16.7) for stress, 2.14 (1.34–3.43) and 5.3% (3.3–7.3) for added salt at the table, 1.65 (1.09–2.49) and 4.3% (0.6–7.9) for cardiac disease, 2.13 (1.12–4.05) and 2.4% (0.7–4.1) for physical inactivity, and 4.42 (1.75–11.16) and 2.3% (1.5–3.1) for current cigarette smoking. Ten of these factors were associated with ischaemic stroke and six with haemorrhagic stroke occurrence. Interpretation Implementation of interventions targeting these leading risk factors at the population level should substantially curtail the burden of stroke among Africans. Funding National Institutes of Health.

Background The potential of mobile-health (mHealth) technology for the management of hypertension among stroke survivors in Africa remains unexplored. We assessed whether an mHealth technology-enabled, nurse-guided intervention initiated among stroke patients within one month of symptom onset is effective in improving their blood pressure (BP) control. Methods A two-arm pilot cluster randomized controlled trial involving 60 stroke survivors, ≥18 years, with BP ≥140/90 mmHg at screening/enrollment visit at a medical center in Ghana. Participants in the intervention arm (n = 30) received a Blue-toothed BP device and smartphone with an App for monitoring BP measurements and medication intake under nurse guidance for three months after which intervention was withdrawn. Control arm (n = 30) received usual care. Primary outcome measure was proportion with clinic BP < 140/90 mmHg at month 9; secondary outcomes included medication adherence. Findings Mean ± SD age was 55 ± 13 years, 65% males. Two participants on intervention and three in control group were lost to follow-up. At month 9, proportion on the intervention versus controls with BP < 140/90 mmHg was 14/30 (46.7%) versus 12/30 (40.0%), p = 0.79 by intention-to-treat; systolic BP < 140 mmHg was 22/30 (73.3%) versus 13/30 (43.3%), p = 0.035. Mean ± SD medication possession ratio was 0.95 ± 0.16 on intervention versus 0.98 ± 0.24 in the control arm, p = 0.56. Interpretation We demonstrate feasibility and signal of improvement in BP control among stroke survivors in a resource-limited setting via an mHealth intervention. Larger scale studies are warranted. Trial registration NCT02568137. Registered on 13 July 2015 at ClinicalTrials.gov.

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

We write this article amid a global pandemic and a heightened awareness of the underlying structural racism in the United States, unmasked by the recent killing of George Floyd and multiple other unarmed Black Americans (Spring 2020). Our purpose is to highlight the role of social determinants of health (SDOH) on stroke disparities, to inspire dialogue, to encourage research to deepen our understanding of the mechanism by which SDOH impact stroke outcomes, and to develop strategies to address SDOH and reduce stroke racial/ethnic disparities. We begin by defining SDOH and health disparities in today’s context; we then move to discussing SDOH and stroke, particularly secondary stroke prevention, and conclude with possible approaches to addressing SDOH and reducing stroke disparities. These approaches include (1) building on prior work; (2) enhancing our understanding of populations and subpopulations, including intersectionality, of people who experience stroke disparities; (3) prioritizing populations and points along the stroke care continuum when racial/ethnic disparities are most prominent; (4) understanding how SDOH impact stroke disparities in order to test SDOH interventions that contribute to the disparity; (5) partnering with communities; and (6) exploring technological innovations. By building on the prior work and expanding efforts to address SDOH, we believe that stroke disparities can be reduced.