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Stroke Among Young West Africans: Evidence from the SIREN Large Multi-site Case-Control Study
Associated Data
Abstract
Background and Purpose
Stroke in Lower and Middle Income Countries (LMICs) affects a young and productive age group. Data on factors associated with stroke in the young are sorely lacking from LMIC. Our objective is to characterize the nature of stroke and its risk factors among young West Africans aged <50years old.
Methods
The Stroke Investigative Research and Educational Network (SIREN) is a multicenter, case-control study involving 15 sites in Nigeria and Ghana. Cases included adults aged ≥18 years with CT/MRI-confirmed stroke. Controls were age-and gender-matched stroke-free adults recruited from the communities in catchment areas of cases. Comprehensive evaluation for vascular, lifestyle and psychosocial factors was performed. We used conditional logistic regression to estimate odds ratios (OR) and population attributable risks (PAR) with 95% CIs.
Results
515 (24.3%) out of 2,118 cases enrolled were <50 years old. Among subjects <50 years old, hemorrhagic stroke proportion was 270 (52.5%) vs 245 (47.5%) for ischemic strokes. Etiologic subtypes of ischemic strokes included large artery atherosclerosis (40.0%), small vessel disease (28.6%), cardio-embolism (11.0%), and undetermined (20.4%). Hypertension (91.7%), structural lesions (3.4%) and others (4.9%) were causally associated with hemorrhagic stroke. Six topmost modifiable factors associated with stroke in descending order of PAR (95% Confidence Interval) were hypertension: 88.7% (82.5–94.8%), dyslipidemia: 48.2% (30.6–65.9%), diabetes mellitus: 22.6% (18.7–26.5%), low green vegetable consumption: 18.2% (−6.8–43.2), stress: 14.5% (4.9–24.1%), and cardiac disease: 8.4% (5.8–11.1%).
Conclusion
The high and rising burden of stroke among young Africans should be curtailed via aggressive, population-wide vascular risk factor control.
INTRODUCTION
Recent trends suggest that sub-Saharan Africa (SSA) now bears the highest burden of stroke worldwide with age-standardized stroke incidence rates of up to 316 per 100,000, prevalence rates of up to 14 per 1000 population and 1-month fatality rates of up to 40%.1–4 Stroke in these settings is characterized by a younger age of onset with poor long-term outcomes.5 Stroke in the young has been defined by an age cut-off of <50 years and contributes approximately 10–14% of ischemic strokes in High Income Countries.6,7 Stroke among young adults has devastating consequences due to the longer lasting impact of stroke-related disability on quality of life and productivity.8
Very little is known about the burden, risk factors and features of stroke among young West Africans. Given that stroke in SSA levies a heavy economic toll by affecting a relatively younger age group, it is necessary to stem the rising tide of stroke by identifying risk factors for stroke among this productive segment of the population. Such data is crucial in designing evidence-based, context-specific public health interventions aimed at stroke prevention in a region at the throes of an epidemiological transition. Our aim for this study is to characterize the nature of strokes and quantify the contributions of modifiable risk factors for stroke among young West Africans within the context of the Stroke Investigative Research and Educational Network (SIREN), the largest study of stroke in Africa to date. 9
METHODS
Study Design
The SIREN study is a multicenter case-control study involving several sites in northern and southern belts of Nigeria and Ghana. The study protocol has been previously published.9 Briefly, stroke cases included consecutive consenting (in unconscious or aphasic patients, consent was obtained from next of kin) adults aged ≥18 years with first clinical stroke presenting within 8 days of current symptom onset or ‘last seen without deficit’. All cases had neuroimaging confirmation with CT or MRI scan within 10 days of symptom onset. Although the stroke patients were recruited from hospitals to ensure accurate phenotyping, a robust community engagement core incorporated community sensitization programs to enhance early presentation at SIREN hospitals and minimize referral bias.9 Controls were consenting stroke-free adults, mostly from the communities in the catchment areas of the SIREN hospitals where cases were recruited. Stroke-free status was confirmed with the 8-item questionnaire for verifying stroke-free status (QVSFS) which has 98% negative predictive value.10 Controls were matched by age (+/− 5 years), sex and ethnicity to minimize the potential confounding effect of these variables on the relationship between stroke and the main environmental risk factors. Ethical approval was obtained from all study sites and informed consent was obtained from all subjects.9 Data supporting the findings of this study are available on request from the corresponding author (M.O.).
Stroke Phenotyping
Stroke diagnosis and phenotyping were based on clinical evaluation and brain neuroimaging (CT or MRI), ECG, transthoracic echocardiography, and carotid Doppler ultrasound performed according to standardized protocols (SOP) at each site. Presumed etiological sub-types of ischemic stroke were defined using the Trial of Org 10172 in Acute Stroke Treatment (TOAST)11 and intracerebral hemorrhage was classified etiologically into Structural, Medication-related, Amyloid angiopathy, Systemic/other disease, Hypertension and Undetermined causes (SMASH-U).12
Definition of risk factors
We collected basic demographic and lifestyle data including, socioeconomic status, cardiovascular risk profile, dietary patterns, routine physical activity, stress using a validated INTERSTROKE instrument, depression, cigarette smoking, and alcohol use.13 Hypertension: Blood pressure was recorded at baseline and daily for 7 days. Hypertension was defined as a sustained elevation of blood pressure ≥140/90 mmHg >72 hours after stroke, a premorbid history of hypertension, use of antihypertensive drugs before stroke or >72 hours after stroke onset. Adjustments to systolic BP (SBP) based on reported associations between pre-morbid BP and acute post-stroke BP in the Oxford Vascular Study (OXVASC) were also applied in sensitivity analyses.14 Definition of hypertension in controls was self-reported history of hypertension or use of antihypertensive drugs or average of 3 recorded BP at first clinical encounter ≥140/90mmHg.13 Diabetes mellitus was defined based on history of diabetes mellitus, use of medications for DM, an HBA1c >6.5% or a fasting blood glucose (FBG) levels > 7.0mmol/l at first encounter in controls or measured after the post-acute phase in cases due to the known acute transient elevation of glucose as a stress response after stroke.15 Dyslipidemia was defined as fasting total cholesterol ≥5.2 mmol/L, HDL cholesterol ≤1.03 mmol/l, triglyceride ≥ 1.7 mmol/l or LDL cholesterol ≥ 3.4 mmol/l according to NCEP guidelines16 or use of statin prior to stroke onset. Based on distribution of the LDL/HDL ratio in the present study, the LDL/HDL ratio was dichotomized using the lowest two tertiles (≤1.97 and 1.98–2.95) as normal versus highest tertile (≥2.96) as high. Cardiac disease was defined after evaluation by study cardiologists based on history or current diagnosis of atrial fibrillation, cardiomyopathy, heart failure, ischemic heart disease, rheumatic heart disease or valvular heart diseases. For obesity, we assessed both waist-to-hip ratio (WHR) and body-mass index. Subjects were classified individually either using the WHO guidelines cutoffs of 0.90 (men) and 0.85 (women) for WHR or 30 kg/m2 for BMI (Obesity). 17 Individuals were classified as physically active if they were regularly involved in moderate exercise (walking, cycling, or gardening) or strenuous exercise (jogging, football, and vigorous swimming) for 4 hours or more per week.13 Dietary history included regularity of intake of food items such as meat, fish, green leafy vegetables, addition of salt at table, nuts, sugar and other local staple food items. Regular intake was defined as intake on daily, weekly or at least once monthly versus none in a month. Alcohol use was categorized into current users (users of any form of alcoholic drinks) or never/former drinker while alcohol intake was categorized as low drinkers (1–2 drinks per day for female and 1–3 drinks per day for male) and high drinker (>2 drinks per day for female and >3 drinks per day for male. 1 drink or 1 unit of alcohol = 8g of alcohol).13 Smoking status was defined as current smoker (individuals who smoked any tobacco in the past 12 months) or never/former smoker.13 We adapted the measures of psychosocial stress and depression in the INTERSTROKE study for assessment of psychosocial risk factors.13 Psychosocial stress combined measures of stress at home/work (e.g. irritability, anxiety or sleeping difficulties) and life events, experienced in the 2 weeks preceding the stroke. Depression combined depressed mood and a checklist of other depression symptoms experienced in the 4 weeks preceding the stroke. Additional details on these assessments are presented in the Appendix. Family history of cardiovascular risk/diseases was defined based on self-reported history of any of hypertension, diabetes, dyslipidemia, stroke, cardiac disease or obesity in participants’ father, mother, sibling or second degree relative.
Statistical Analysis
We assessed the bivariate association between risk factors and stroke status (case versus control) using McNemar test for paired categorical outcomes with stratification by age (<50 years versus ≥50 years). Further analyses to determine the adjusted associations between the risk factors and stroke occurrence for the total sample and stratified by stroke types were made. We used conditional logistic regression to estimate the adjusted odds ratio (OR) and 95% confidence intervals for the association between risk factors and odds of stroke . The adjusted models included selected covariates depending on whether or not they are confirmed confounders in the bivariate analysis and considerations from the literature on stroke risk factors. Additionally, the final adjusted models were assessed for collinearity using variance inflation factor (VIF) and goodness of fit using residual analysis. We calculated the adjusted PARs with their respective 95%CI for each exposure variable included in the best-fitted adjusted models. The PARs were estimated as the proportion of the risk of the stroke in the population that is attributable to the individual risk factors (i.e. the proportion of cases that would not occur in the population if the factor were eliminated).18 The 95% CI for the PAR were obtained using the AF R-package19 where the variance is estimated via the delta method. Composite PARs for the dominant risk factors for stroke, stroke subtypes and age <50 years vs ≥50 years were calculated using the ATTRIBRISK R package with its 95% CI computed via the bootstrap method. All statistical tests of hypotheses were two-sided. Statistical analyses and graphics were performed with SAS 9.4 and R statistical program (version 3.4.2).
RESULTS
Demographic & clinical characteristics of stroke subjects by age
Of the 2,118 stroke cases in the SIREN cohort, 515 (24.3%) were below 50 years and 306 (59.4%) were males. Compared with age- and gender-matched controls, stroke subjects <50 years had higher income levels, were more likely to be hypertensive, dyslipidemic, diabetic, have cardiac diseases and higher waist-to-hip ratios as shown in Table 1. Furthermore, young stroke subjects were less physically active, smoked cigarette more commonly compared with age-matched controls. They were less likely to consume green vegetables and fruits, with higher rates of reported stress than controls. (Table 1)
Table 1
Study Participant characteristics by case-control status for each age group.
| Age < 50 | Age ≥ 50 | |||||
|---|---|---|---|---|---|---|
|
|
| |||||
| Variable | Control (n = 515) | Case (n = 515) | p-value | Control (n = 1603) | Case (n = 1603) | p-value |
| Age, mean ± SD | 40.12 ± 6.53 | 40.95 ± 6.58 | <0.0001 | 63.48 ± 10.08 | 64.79 ± 10.00 | <0.0001 |
| Monthly Income >100 USD, % | 48 | 60 | <0.0001 | 43 | 56 | <0.0001 |
| Primary Education or higher, % | 92 | 94 | 0.222 | 77 | 80 | 0.004 |
| Hypertension, n(%) | 35 | 90 | <0.0001 | 65 | 97 | <0.0001 |
| Dyslipidemia, n(%) | 55 | 75 | <0.0001 | 63 | 79 | <0.0001 |
| Diabetes, n(%) | 5 | 26 | <0.0001 | 16 | 42 | <0.0001 |
| Cardiac Disease, n(%) | 4 | 10 | <0.0001 | 6 | 12 | <0.0001 |
| HDL-Cholesterol, mmol/l, mean ± SD | 1.35 ± 0.40 | 1.31 ± 0.51 | 0.181 | 1.37 ± 0.46 | 1.25 ± 0.48 | <0.0001 |
| HDL-Cholesterol, ≤ 1.03mmol/l, n (%) | 23 | 29 | 0.069 | 24 | 36 | <0.0001 |
| LDL-Cholesterol, mmol/l, mean ± SD | 3.14 ± 1.15 | 3.24 ± 1.27 | 0.232 | 3.19 ± 1.22 | 3.24 ± 1.32 | 0.309 |
| LDL-Cholesterol ≥ 3.4mmol/l, n (%) | 37 | 42 | 0.080 | 41 | 41 | 1.000 |
| LDL/HDL ratio, mean ± SD | 2.56 ± 1.30 | 2.86 ± 2.10 | 0.010 | 2.59 ± 1.43 | 2.96 ± 1.74 | <0.0001 |
| LDL/HDL ratio >2.96, n (%) | 31 | 35 | 0.333 | 32 | 40 | <0.0001 |
| Total Cholesterol mmol/l, mean ± SD | 5.0 ± 1.2 | 5.1 ± 1.5 | 0.200 | 5.1 ± 1.3 | 5.1 ± 1.5 | 0.986 |
| Total Cholesterol ≥ 5.2mmol/l, n (%) | 38 | 46 | 0.006 | 43 | 45 | 0.310 |
| Triglyceride mmol/l, mean ± SD | 1.1 ± 0.6 | 1.5 ± 1.0 | 0.0001 | 1.2 ± 0.6 | 1.5 ± 1.0 | <0.0001 |
| Triglyceride ≥ 1.7mmol/l, n (%) | 10 | 28 | <0.0001 | 16 | 26 | <0.0001 |
| Waist-to-hip Ratio, mean ± SD | 0.89 ± 0.10 | 0.94 ± 0.08 | <0.0001 | 0.92 ± 0.09 | 0.94 ± 0.08 | <0.0001 |
| Waist-to-hip Ratio raised, n(%) | 49 | 78 | <0.000 | 75 | 83 | <0.000 |
| BMI, mean±SD (kg/m2) | 26.9 ± 5.9 | 26.7 ± 5.8 | 0.644 | 26.3 ± 5.8 | 26.5 ± 4.9 | 0.326 |
| BMI>25 kg/m2, n(%) | 26 | 25 | 0.928 | 22 | 20 | 0.465 |
| Physical Activity (some activity), n(%) | 99 | 97 | 0.012 | 97 | 95 | 0.001 |
| Tobacco use in past 12 months, n(%) | 1 | 5 | <0.0001 | 1 | 3 | 0.013 |
| Tobacco (any use), n(%) | 4 | 9 | 0.001 | 9 | 10 | 0.345 |
| Alcohol (current user), n(%) | 16 | 25 | <0.0001 | 15 | 17 | 0.113 |
| Alcohol (any use), n(%) | 29 | 36 | 0.010 | 33 | 37 | 0.017 |
| Stress, n(%) | 20 | 26 | 0.032 | 14 | 23 | <0.0001 |
| Cancer, n(%) | 0 | 0 | 1.000 | 0 | 1 | 0.092 |
| Depression, n(%) | 7 | 8 | 0.795 | 7 | 9 | 0.077 |
| Family History of CVD 1, n(%) | 35 | 44 | 0.006 | 27 | 40 | <0.0001 |
| Table added Salt, n(%) | 8 | 9 | 0.561 | 5 | 8 | <0.0001 |
| Green vegetable consumption, n(%) | 82 | 69 | <0.0001 | 82 | 71 | <0.0001 |
| Whole grains consumption, n(%) | 85 | 86 | 0.773 | 82 | 86 | 0.009 |
| Legumes consumption, n(%) | 67 | 71 | 0.274 | 64 | 65 | 0.290 |
| Fruit consumption, n(%) | 89 | 82 | 0.001 | 86 | 83 | 0.033 |
| Sugar consumption or otherwise, n(%) | 46 | 43 | 0.374 | 33 | 28 | 0.005 |
| Meat consumption or otherwise, n(%) | 87 | 90 | 0.256 | 77 | 81 | 0.002 |
| Fish consumption or other wise, n(%) | 94 | 94 | 0.683 | 93 | 93 | 0.654 |
Values are means ± standard deviation or percentages.
Values of polytomous variables may not sum to 100% due to rounding.
Age of stroke case determines age group for matched control.
p-values reported for Paired t-test, McNemar χ2, or Test of Marginal Homogeneity.
Stroke types and sub-types by age
Among subjects <50 years old, hemorrhagic stroke occurred at a higher frequency than ischemic stroke 270 (52.5%) vs 245 (47.5%). Among stroke subjects aged ≥50 years, there were significantly more ischemic strokes 1,174 (73.5%) than hemorrhagic stroke 423 (26.5%), p<0.0001. Six subjects with both ischemic and hemorrhagic stroke were excluded. Among young subjects <50 years, the etiologic subtypes of ischemic strokes included large artery atherosclerosis (40.0%), small vessel disease (28.6%), cardio-embolism (11.0%), and undetermined (20.4%). Hypertension (91.7%), structural lesions (3.4%) and others (4.9%) were causally associated with hemorrhagic stroke in the young. Hemorrhagic stroke was non-lobar 155 (68.3%) and lobar 48 (21.1%) in location in the <50 years group with intraventricular extension in 93 (35.9%) cases. (Table 2).
Table 2
Ischemic and Hemorrhagic stroke subtype distribution by Age Group
| TOAST Ischemic Stroke subtypes | Age <50 N(%) | Age ≥50 N(%) | P-value |
|---|---|---|---|
| Large artery-atherosclerosis | 84(40.0) | 330(32.6) | 0.04 |
| Cardio-embolism | 23(11.0) | 79(7.8) | 0.13 |
| Small vessel disease | 60(28.6) | 396(39.2) | 0.004 |
| Undetermined(two or more causes) | 1(0.4) | 0(0.0) | 0.03 |
| Undetermined(negative evaluation) | 42(20.0) | 206(20.4) | 0.90 |
| Undetermined(incomplete evaluation) | 0(0.0) | 0(0.0) | |
| Missing | 35 | 163 | |
| SMASH-U Hemorrhagic Subtypes | |||
| Structural | 7 (3.4) | 14 (3.8) | 0.81 |
| Medication-associated | 2 (1.0) | 1 (0.3) | 0.29 |
| Cerebral Amyloid Angiopathy | 0 (0.0) | 6 (1.6) | 0.09 |
| Systemic illness | 1 (0.5) | 0 (0.0) | 0.36 |
| Hypertensive | 188 (91.7) | 340 (92.6) | 0.74 |
| Undetermined | 7 (3.4) | 6 (1.6) | 0.24 |
| Missing | 65 | 56 | |
| Location of Hemorrhagic Strokes | |||
| Lobar | 48(21.1) | 61(16.5) | 0.15 |
| Non-lobar | 155(68.3) | 278(75.1) | 0.07 |
| Both | 24(10.6) | 31(8.4) | 0.90 |
| Missing | 32 | 53 | |
| Intraventricular hemorrhage | |||
| Yes | 93 (35.9) | 157 (37.1) | 0.48 |
Factors Associated with stroke among young Africans
The adjusted ORs and PAR (95%CI) of the 6 topmost modifiable risk factors associated with stroke occurrence under 50 years in decreasing order of magnitude by PAR were hypertension: (30.84, 11.37 – 83.61; 88.7%, 82.5–94.8%), dyslipidemia: (2.75, 1.34–5.61; 48.2%, 30.6–65.9%), diabetes mellitus: (5.80, 2.05–16.36; 22.6%, 18.7–26.5%), low green vegetable consumption: 2.31 (1.02–5.22; 18.2%, −6.8–43.2), stress:(2.26, 1.04–4.93; 14.5%, 4.9–24.1%), and cardiac disease:(8.03, 1.91–33.82; 8.4%, 5.8–11.1%). Having no education was independently associated with stroke with adjusted OR of 3.68 (1.01–13.41) and a PAR of 68.9% (36.0–101.7%). Altogether these 7 factors compositely accounted for 98.1% (95%CI, 96.0–99.8%) of PAR associated with stroke. (Table 3, Supplementary Figure I). Four factors were independently associated with ischemic stroke occurrence, namely hypertension, cardiac disease, raised waist-to-hip ratio, and stress whilst hypertension, dyslipidemia and diabetes mellitus were associated with hemorrhagic stroke occurrence (Table 3). Factors associated with stroke occurrence on univariate analyses are presented as supplementary table I. In sensitivity analyses, the effect size of the association between hypertension and stroke varied depending upon the definition of hypertension used and whether adjustments were made for the acute rise in systolic blood pressure after stroke onset (Supplemental table II). For instance, an adjusted OR of 8.57 (4.52–16.26) for hypertension was found if BP was adjusted for the acute rise and hypertension was defined based on BP measured on the morning after admission and 36.70 (12.98–103.76) if new antihypertensive medications were introduced after stroke occurrence.
Table 3
Adjusted Odds Ratio (OR), Population Attributable Risk and 95% CI using Conditional Logistic Regression for stroke and its sub-types among Africans aged <50 years.
| Predictor | Stroke overall | Hemorrhagic Stroke | Ischemic Stroke | |||
|---|---|---|---|---|---|---|
|
| ||||||
| Adjusted OR (95% CI) | PAR% (95% CI) | Adjusted OR (95% CI) | PAR% (95% CI) | Adjusted OR (95% CI) | PAR% (95% CI) | |
| Education, Some vs. none | 3.68(1.01 – 13.41) | 68.9(36.0 – 101.7) | 3.26(0.74–14.35) | 66.2(31.2 – 101.2) | 1.30(0.24–6.89) | 21.5(−129.75 – 172.84) |
| Monthly income > $100 (USD) | 1.37 (0.69 – 2.75) | 17.0(−44.1 – 78.0) | 0.92(0.39–2.16) | −5.3(−58.9 – 48.3) | 1.62(0.73–3.55) | 24.5(−17.9 – 66.8) |
| Hypertension | 30.84(11.37 – 83.61) | 88.7(82.5 – 94.8) | 120.0(16.0–898.13) | 96.0(92.6 – 99.4) | 11.23(4.16–30.32) | 77.5(68.1 – 86.9) |
| Dyslipidemia | 2.75 (1.34 – 5.61) | 48.2 (30.6 – 65.9) | 2.52(1.06–6.01) | 43.8(−12.9 – 100.5) | 2.26(0.99 – 5.16) | 42.0(−2.3 – 86.2) |
| Diabetes Mellitus | 5.80(2.05 – 16.36) | 22.6(18.7 – 26.5) | 8.71(1.86 – 40.8) | 21.4(11.0 – 31.8) | 3.16(0.94 – 10.59) | 19.1(11.4 – 26.8) |
| Cardiac Disease | 8.03 (1.91 – 33.82) | 8.4(5.8 – 11.1) | 2.67(0.37–18.91) | 3.1(0.4 – 5.8) | 11.05(2.11–57.89) | 11.8 (8.0 – 15.6) |
| Raised Waist-to-hip ratio | 1.99(0.95 – 4.18) | 38.5(7.8 – 69.2) | NA | NA | 2.70(1.03–7.07) | 49.9 (11.7 – 88.1) |
| Physical Activity | 7.42(0.59 – 93.98) | 2.8(0.8 – 4.7) | NA | NA | NA | NA |
| History of tobacco use | 5.52(0.36 – 84.59) | 3.7(−3.4 – 10.7) | NA | NA | NA | NA |
| Stress in the last 2 weeks | 2.26(1.04 – 4.93) | 14.5(4.9 – 24.1) | NA | NA | 2.84(1.04–7.80) | 20.2 (8.5 – 31.9) |
| Family History of CVD | 1.27(0.60 – 2.70) | 9.4(−30.6 – 49.3) | 1.31(0.55–3.08) | 11.7(−53.6 – 77.0) | 0.89(0.37–2.14) | −4.5 (−62.1 – 53.2) |
| Sprinkle salt at table | 1.23(0.36 – 4.13) | 1.8(−7.4 – 11.1) | NA | NA | 4.17(0.78–22.37) | 6.9 (1.8 – 12.0) |
| Low consumption of green leafy vegetables | 2.31(1.02 – 5.22) | 18.2(−6.8 – 43.2) | 2.15(0.91–5.10) | 17.1(−9.0 – 43.2) | 1.74(0.59–5.19) | 12.2 (−15.2 – 39.6) |
| Regular sugar consumption | 1.36(0.65 – 2.85) | 11.7(−13.5 – 37.0) | NA | NA | NA | NA |
| Meat consumption | 1.50(0.52 – 4.35) | 30.4(−38.8 – 99.5) | 1.66(0.52–5.29) | 36.1(−40.7 – 113.0) | NA | NA |
|
| ||||||
| Composite PAR | 98.1 (96.0 – 99.8) | |||||
NA=Not computed
DISCUSSION
Burden of stroke
Approximately 25% of strokes in this West African cohort occurred among young adults <50 years old. Reports from HICs in North America and Europe indicate a slightly lower frequency of stroke burden in this age group ranging between 5–20%. 4–7 This would concur with findings from the Global Burden of Disease study where 31% of the global burden of stroke was contributed to by children aged <20years and young and middle adults (20–64 years) with 78% of the young and middle aged stroke cases emanating from LMICs.1 Hence the contribution of stroke between ages 18–50 is quite substantial among West Africans.
Stroke types
The most striking finding of our study is the overwhelming preponderance of hemorrhagic stroke accounting for 52.5% of all strokes in this young population relative the middle-aged to elderly cohort. This contrasts sharply with published studies from HICs such as Germany20 where primary intracerebral hemorrhage among adults <55 years old contributed only 5.5% and from a review of 29 studies (with age cut-off of 45 years) mostly involving European and Northern American countries where the proportions of hemorrhagic stroke ranged between 3.7% and 38.5%.21 A report from the Korean Stroke Statistics collaboration identified 34.3% of stroke <55 years were of hemorrhagic variety.22 Among subjects with ischemic stroke, large artery atherosclerosis was the commonest etiologic subtype in agreement with findings from a Chinese young stroke cohort between 18 and 45 years.23 Although cardioembolic stroke account for up to a third of ischemic stroke among young Europeans23 and up to 47% among a young US cohort24, we found a lower rate of 12.4% highlighting possible differences in etiologic profile of ischemic stroke among young Africans or a limited gamut of investigative capabilities in our study sites.
Factors Associated with Stroke occurrence
Six topmost modifiable factors in decreasing order by rank namely hypertension, dyslipidemia, diabetes mellitus, low consumption of green leafy vegetables, psychosocial stress and cardiac disease were associated with stroke occurrence among young west Africans. Among the sub-cohort aged <55 years in the global INTERSTROKE case-control study, hypertension, physical inactivity, dyslipidemia, central obesity, and current smoking were the leading six factors associated with stroke.13 Furthermore, hypertension, low physical activity, smoking and alcohol consumption explained 78% of stroke among young Germans.20 A common emerging theme is that the established traditional vascular risk factors are now potent contributors to stroke risk among young and middle age adults globally. The differences observed in PARs for specific risk factors in between studies may be due to regional differences in prevailing behavioral and lifestyle practices. For instance, cigarette smoking is a less commonly reported risk factor for stroke among Africans perhaps because smoking is an expensive habit to sustain in a resource-limited setting. Racial differences in vascular risk factor predisposition and expression has been shown to account for some of the differences in stroke burden in the US and indigenous Africans.25
Socio-economic factors may play differential underpinning roles for cardiovascular disease risk in diverse regional blocks worldwide. We identified a potent independent association between no educational attainment and stroke occurrence in the young. A prospective cross-sectional survey among >200,000 Chinese demonstrated an inverse relationship between educational status and stroke.26 Low educational status associated with poor functional health literacy may contribute to a lack of awareness, detection and control of vascular risk factors in particular hypertension which is often asymptomatic. Consequently, the contribution of hypertension to stroke occurrence among young Africans is quite astronomical with a PAR of 88.7% for stroke overall and explained 100% of all hemorrhagic strokes. Additionally, novel psychosocial factors such as stress-recently demonstrated to be causally associated with stroke occurrence27 via elaboration of pro-atherogenic cytokines-was identified to be independently associated with ischemic stroke risk among young West Africans in this study. Interestingly, our group has identified associations between interleukin-6 (IL6) rs1800796 single nucleotide polymorphisms and ischemic stroke in men with hypertension in the model but not in women. 28
Strengths & Limitations
This is one of the largest studies to examine factors associated with stroke risk among young west Africans. Previous studies in this population have been limited by sample size and had no control group.29 A limitation of case-control studies is that causality between putative risk factors and event/outcome measure of interest cannot be conclusively established. Indeed, it has been suggested that the validity of case-control studies is contingent on selecting controls independently of risk factor status and could be compromised by matching.30 Hence, we performed individual matching of cases to controls (age, gender and ethnicity not risk factor status) in a 1:1 fashion and used conditional logistic regression analysis to attain unbiased ORs. Furthermore, although the study paid for the cost of investigation of all study participants, specific investigations such as thrombophilia screening, bubble echocardiographic studies and others investigations pertinent in establishing causes of stroke in the young were unavailable at study sites.
Implications & Future directions
The cluster of factors identified in the present study strongly support urgent population wide interventions aimed at primary prevention of stroke across the lifespan of continental Africans. Further studies are clearly warranted to characterize the outcomes of stroke among young Africans over the longer term including mortality, control of vascular risk factors and re-integration back into a society with limited social support services.
Conclusions
The high and rising burden of stroke among young Africans should be curtailed via aggressive, population-wide vascular risk factor control.
Acknowledgments
Source of Funding
SIREN was funded by the NIH Grant U54 HG007479 under the H3Africa initiative.
Footnotes
Disclosures
All authors have none to declare