Dietary Intake of α-Linolenic Acid Is Not Appreciably Associated with Risk of Ischemic Stroke among Middle-Aged Danish Men and Women

Dietary Intake of α-Linolenic Acid Is Not Appreciably Associated with Risk of Ischemic Stroke... Abstract Background Intake of the plant-derived omega-3 (n–3) fatty acid α-linolenic acid (ALA) may reduce the risk of ischemic stroke. Objective We have investigated the associations between dietary intake of ALA and the risk of ischemic stroke and ischemic stroke subtypes. Methods This was a follow-up study. A total of 57,053 participants aged 50–64 y were enrolled into the Danish Diet, Cancer and Health cohort between 1993 and 1997. Intake of ALA was assessed by a validated semiquantitative food frequency questionnaire. Potential incident cases of ischemic stroke were identified in the Danish National Patient Register, validated, and classified into subtypes based on assumed etiology. Statistical analyses were performed via Cox proportional hazard regression with adjustment for established ischemic stroke risk factors. Results A total of 1859 ischemic stroke cases were identified during a median of 13.5 y of follow-up. In multivariable analyses using restricted cubic splines adjusting for traditional risk factors for ischemic stroke, we observed no clear associations between dietary intake of ALA and the risk of total ischemic stroke or any of its subtypes including ischemic stroke due to large artery atherosclerosis, ischemic stroke due to small-vessel occlusion, and ischemic stroke due to cardio-embolism. Conclusion Dietary intake of ALA was neither consistently nor appreciably associated with the risk of ischemic stroke or ischemic stroke subtypes among middle-aged Danish men and women. This study was registered at clinicaltrials.gov as NCT03258983. alpha-linolenic acid, omega-3 fatty acids, ischemic stroke, cohort study, diet Introduction The plant-derived omega-3 fatty acid α-linolenic acid (ALA) may be associated with a lower risk of cardiovascular disease (1, 2). However, data on the associations between ALA intake and the risk of ischemic stroke are sparse and knowledge on the associations with ischemic stroke subtypes is lacking. ALA is the most abundant ω-3 fatty acid in the typical Western diet and has been suggested to be an important part of the Mediterranean diet (3). ALA is found in high concentrations in plant oils, seeds, and walnuts, but can also be acquired from foods such as green leafy vegetables, whole-grain cereals, dairy products, and meat (4–6). Previous studies have reported that ALA may have anti-atherosclerotic, anti-inflammatory, anti-arrhythmic, and anti-thrombotic properties (1), but the clinical impact of these potential beneficial cardiovascular properties on development of ischemic stroke remains unclear. The majority of previous studies investigating the association between ALA intake and cardiovascular disease have focused on coronary heart disease (CHD) and some cohort studies have reported negative associations between ALA intake and the risk of incident CHD (6–11), but the results have not been consistent (5, 9, 12–15). Less emphasis has been given to a possible effect of ALA on stroke, but in 2 studies intake of ALA was inversely associated with the risk of stroke (11, 12), although other studies have not been able to confirm these findings (13, 16–19). Few of the studies have investigated the association between ALA intake and the risk of ischemic stroke and the results have been inconsistent (12, 13, 17, 19). However, previous cohort studies have not distinguished between ischemic stroke subtypes although the underlying etiology of subtypes of ischemic stroke may be different (20, 21). The objective of this study was to investigate the associations between intake of ALA and the risk of ischemic stroke and ischemic stroke subtypes. We hypothesized that intake of ALA would be inversely associated with development of ischemic stroke and ischemic stroke subtypes. Methods Study population and study design This study was based on data from the Diet, Cancer and Health cohort, which has previously been described in detail (22). Briefly, native citizens, aged 50–64 y, who were living in the greater areas of Copenhagen and Aarhus without a previous diagnosis of cancer, were between 1993 and 1997 invited to participate in the study. Potential eligible cohort members were retrieved through the Danish Civil Registration System in which every person living in Denmark is provided with a unique 10-digit identification number. The current study was conducted as a cohort study to investigate the associations between intake of ALA and the risk of ischemic stroke and ischemic stroke subtypes. We excluded participants with a cancer diagnosis before baseline that, due to a processing delay, was not registered in the Danish Cancer Registry at the time of invitation. Also, participants registered with a diagnosis of stroke before enrollment as well as participants with missing information on ischemic stroke risk factors were excluded. At inclusion all participants gave written informed consent, including permission for prospective data collection from national registries, and the study was approved by the relevant scientific Ethical Committees and the Danish Data Protection Agency. Exposure assessment Habitual diet over the past 12 mo was assessed at baseline through the use of a 192-item semiquantitative FFQ. The average consumption of foods and beverages was reported within 12 categories ranging from never to ≥8 times/d. The FFQ was processed by optical scanning and checked for omissions by a trained interviewer (22). The FFQ was developed for the Diet, Cancer and Health study, and a validation study confirmed that it was a useful instrument for categorizing individuals according to their intake of nutrients and energy when validated against 7-d weighed diet records (23, 24). Daily ALA intake averages were calculated from Danish food composition tables with the use of the software program FoodCalc (www.ibt.ku.dk/jesper/foodcalc) based on estimated intakes of foods and beverages from the FFQ. The dietary intake of ALA was expressed as energy-adjusted intake in g/d via the residual method (25), because ALA is metabolized in approximate proportion to total energy intake. Covariates Participants completed a detailed lifestyle questionnaire at baseline which included questions on health status, social, and lifestyle factors such as length of schooling, smoking habits, physical activity, history of hypercholesterolemia and/or use of lipid-lowering medication, hypertension and/or use of antihypertensive medication, and diabetes mellitus. The lifestyle questionnaire was processed by optical scanning and was checked for reading errors and omissions. Anthropometric measurements including height, weight, and waist circumference were undertaken by a trained technician (22). Information on alcohol consumption, total energy intake, and intake of other nutrients was obtained from the FFQ. Information regarding history of atrial fibrillation or atrial flutter before baseline was obtained by record linkage with the National Patient Register according to the International Classification of Diseases (ICD) (ICD-8: 42,793, 42,794; ICD-10: I48). All potential confounders were selected a priori based on the literature on risk factors for ischemic stroke. Identification of cases The outcome measures were ischemic stroke and ischemic stroke subtypes. Information on potential incident stroke cases was obtained by linkage with the Danish National Patient Register. This nationwide register includes discharge diagnoses from somatic hospitals since 1977, and since 1995 data on outpatient and emergency patients have been included as well (26). Participants who were registered with either a primary or secondary discharge diagnosis of stroke (ICD-8: 430, 431, 433, 434, 436.01, or 436.90; ICD-10: I60, I61, I63, or I64) were identified through the Danish National Patient Register. Stroke was defined as a disease with rapid onset of focal or global neurologic deficit of vascular origin that persisted beyond 24 h or leading to death. However, patients with neurologic deficits of shorter duration were also considered as stroke cases if computed tomography or MRI showed a lesion suggestive of stroke. All stroke cases have been validated by a physician with neurologic experience and classified according to the trial of ORG 10172 in acute stroke treatment (TOAST)-classification (20) based on review of medical records (27). The TOAST-classification separates cerebral infarctions into 5 groups: large artery atherosclerosis, small-vessel occlusion, cardio-embolism, stroke of other etiology, and stroke of undetermined etiology based on clinical findings, brain imaging, imaging of extracranial arteries, laboratory tests, electrocardiograms, and echocardiography. Participants were followed from baseline until the first registration of stroke, death, emigration, or end of follow-up in November 2009. Statistical analyses The associations between intake of ALA and the rate of ischemic stroke and ischemic stroke subtypes were investigated with HRs. HRs and 95% CIs were calculated with age as the underlying timescale via sex-stratified Cox proportional hazard regression, allowing the baseline hazard to differ between men and women. The regression models were also adjusted for baseline age through the use of restricted cubic splines with 3 knots (model 1A). Multivariable regression models were adjusted for baseline information regarding the following established ischemic stroke risk factors: length of schooling (≤7, 8–10, or >10 y), smoking (never, former, current 1–14, 15–24, or >24 g tobacco/d), physical activity [Cambridge index (28): inactive, moderately inactive, moderately active, or active], waist circumference adjusted for BMI (cm, continuous), and alcohol intake (g/d, continuous) (model 1B). In further analyses, we also adjusted for the following clinical characteristics, which may be considered potential intermediate variables: self-reported history of hypercholesterolemia or use of lipid-lowering medication (yes, no, or unknown), self-reported history of hypertension or use of antihypertensive medication (yes, no, or unknown), self-reported history of diabetes mellitus (yes, no, or unknown), or a diagnosis of atrial fibrillation or flutter recorded in the Danish National Patient Register at baseline (yes, no) (model 2). Dietary intake of ALA was analyzed as a continuous variable using restricted cubic splines with 3 knots with the median intake as reference, and as a categorical variable in quintiles with the lowest quintile of ALA intake as reference, respectively. The spline plots were restricted to the 2.5–97.5 percentile range and presented with 95% CIs. The spline curves were tested against a horizontal line. In sensitivity analyses, we plotted the whole exposure range and modified the number and the location of the knots. In additional analyses, we added the following potential dietary risk factors to model 1B: total energy intake (kJ/d, continuous), intake of fiber (g/d, continuous), glycemic load (unit-less), and intakes of MUFAs, SFAs, linoleic acid, and marine ω-3 fatty acids (g/d, continuous) (model 3). All continuous variables were included as restricted cubic splines with 3 knots placed at the 10th, 50th, and 90th percentiles as recommended by Harrell (29). The proportional hazard assumption was evaluated by plotting the scaled Schoenfeld residuals against age. Potential differences in the underlying dietary pattern related to ALA intake were assessed graphically in a radar plot comparing the median intake among participants in the highest and lowest quintiles of ALA for selected foods and beverages. Data were analyzed with Stata statistical software (version 14; StataCorp LP) and P < 0.05 was considered statistically significant. Results A total of 160,725 eligible participants were invited, of whom 57,053 individuals accepted to participate. We excluded 2035 participants because they had a diagnosis of cancer (n = 569) or stroke (n = 597) before entry into the study or had missing information on 1 or more covariates (n = 961) (Supplemental Figure 1). Among the remaining 55,018 participants, we identified 1859 participants with ischemic stroke including 316 ischemic stroke cases due to large artery atherosclerosis, 835 ischemic stroke cases due to small-vessel occlusion, 102 ischemic stroke cases due to cardio-embolism, 98 ischemic stroke cases of other etiology, and 508 ischemic stroke cases of undetermined etiology during a median follow-up of 13.5 y (95% central range: 3.7–15.2 y). Baseline characteristics of the participants in the cohort and among cases are shown in Table 1 and Supplemental Table 1, respectively. Ischemic stroke risk factors were generally more prevalent among participants who later became cases compared with the full cohort. Accordingly, we observed larger proportions of men, participants with a short length of schooling, higher age, waist circumference, and alcohol intake, the physically inactive, and current smokers among subsequent cases. A self-reported history of hypercholesterolemia, hypertension, and diabetes mellitus was also more prevalent in cases. A history of atrial fibrillation or flutter was more prevalent among cases classified with total ischemic stroke, cardio-embolism, stroke of other etiology, and stroke with undetermined etiology compared with the full cohort. Dietary characteristics of the participants are given in Supplemental Table 2. TABLE 1 Baseline characteristics according to quintiles of ALA intake among middle-aged men and women included in the Diet, Cancer and Health cohort1 Quintiles of energy-adjusted dietary intake of ALA 1 (≤1.35 g/d) 2 (1.36–1.62 g/d) 3 (1.63–1.90 g/d) 4 (1.91–2.26 g/d) 5 (≥2.27 g/d) Sex, %  Men 16.3 25.3 45.7 68.9 82.0  Women 83.7 74.7 54.3 31.1 18.0 Age at enrollment, y 55.8 (50.5, 64.7) 56.2 (50.5, 64.8) 56.3 (50.5, 64.7) 56.3 (50.5, 64.7) 56.0 (50.5, 64.7) Length of schooling, %  ≤7 y 25.6 30.5 33.8 36.1 37.7  8–10 y 46.5 47.8 45.7 45.3 45.4  >10 y 27.9 21.7 20.6 18.6 16.9 Smoking, %  Never 43.5 40.2 35.5 31.6 25.9  Former 29.4 28.1 28.4 29.8 28.3  Current <15 g/d 13.3 13.3 13.5 12.3 12.6  Current 15–25 g/d 10.7 14.1 16.6 17.6 21.2  Current >25 g/d 3.1 4.3 5.9 8.7 12.0 Physical activity,2 %  Inactive 8.3 9.7 11.2 12.3 12.2  Moderately inactive 27.5 31.8 32.3 30.9 29.1  Moderately active 25.4 25.2 23.9 23.2 23.2  Active 38.8 33.3 32.7 33.6 35.4 Waist circumference,3 cm 82.6 (72.0, 100.8) 83.8 (72.5, 102.1) 88.4 (73.5, 103.7) 93.4 (74.8, 104.7) 94.9 (76.8, 105.3) Alcohol intake, g/d 10.7 (0.0, 67.8) 11.4 (0.2, 73.1) 12.6 (0.2, 81.1) 15.4 (0.4, 85.9) 16.9 (0.2, 86.7) Clinical characteristics, %  Hypercholesterolemia 6.6 7.2 7.5 8.1 7.4  Hypertension 15.7 17.5 16.0 15.9 14.7  Diabetes 1.7 1.9 2.2 2.0 2.2  Atrial fibrillation or flutter 0.5 0.6 0.9 0.9 0.8 Quintiles of energy-adjusted dietary intake of ALA 1 (≤1.35 g/d) 2 (1.36–1.62 g/d) 3 (1.63–1.90 g/d) 4 (1.91–2.26 g/d) 5 (≥2.27 g/d) Sex, %  Men 16.3 25.3 45.7 68.9 82.0  Women 83.7 74.7 54.3 31.1 18.0 Age at enrollment, y 55.8 (50.5, 64.7) 56.2 (50.5, 64.8) 56.3 (50.5, 64.7) 56.3 (50.5, 64.7) 56.0 (50.5, 64.7) Length of schooling, %  ≤7 y 25.6 30.5 33.8 36.1 37.7  8–10 y 46.5 47.8 45.7 45.3 45.4  >10 y 27.9 21.7 20.6 18.6 16.9 Smoking, %  Never 43.5 40.2 35.5 31.6 25.9  Former 29.4 28.1 28.4 29.8 28.3  Current <15 g/d 13.3 13.3 13.5 12.3 12.6  Current 15–25 g/d 10.7 14.1 16.6 17.6 21.2  Current >25 g/d 3.1 4.3 5.9 8.7 12.0 Physical activity,2 %  Inactive 8.3 9.7 11.2 12.3 12.2  Moderately inactive 27.5 31.8 32.3 30.9 29.1  Moderately active 25.4 25.2 23.9 23.2 23.2  Active 38.8 33.3 32.7 33.6 35.4 Waist circumference,3 cm 82.6 (72.0, 100.8) 83.8 (72.5, 102.1) 88.4 (73.5, 103.7) 93.4 (74.8, 104.7) 94.9 (76.8, 105.3) Alcohol intake, g/d 10.7 (0.0, 67.8) 11.4 (0.2, 73.1) 12.6 (0.2, 81.1) 15.4 (0.4, 85.9) 16.9 (0.2, 86.7) Clinical characteristics, %  Hypercholesterolemia 6.6 7.2 7.5 8.1 7.4  Hypertension 15.7 17.5 16.0 15.9 14.7  Diabetes 1.7 1.9 2.2 2.0 2.2  Atrial fibrillation or flutter 0.5 0.6 0.9 0.9 0.8 1Values are percentages or medians (2.5–97.5th percentiles). ALA, α-linolenic acid. 2Cambridge index. 3Adjusted for BMI. View Large TABLE 1 Baseline characteristics according to quintiles of ALA intake among middle-aged men and women included in the Diet, Cancer and Health cohort1 Quintiles of energy-adjusted dietary intake of ALA 1 (≤1.35 g/d) 2 (1.36–1.62 g/d) 3 (1.63–1.90 g/d) 4 (1.91–2.26 g/d) 5 (≥2.27 g/d) Sex, %  Men 16.3 25.3 45.7 68.9 82.0  Women 83.7 74.7 54.3 31.1 18.0 Age at enrollment, y 55.8 (50.5, 64.7) 56.2 (50.5, 64.8) 56.3 (50.5, 64.7) 56.3 (50.5, 64.7) 56.0 (50.5, 64.7) Length of schooling, %  ≤7 y 25.6 30.5 33.8 36.1 37.7  8–10 y 46.5 47.8 45.7 45.3 45.4  >10 y 27.9 21.7 20.6 18.6 16.9 Smoking, %  Never 43.5 40.2 35.5 31.6 25.9  Former 29.4 28.1 28.4 29.8 28.3  Current <15 g/d 13.3 13.3 13.5 12.3 12.6  Current 15–25 g/d 10.7 14.1 16.6 17.6 21.2  Current >25 g/d 3.1 4.3 5.9 8.7 12.0 Physical activity,2 %  Inactive 8.3 9.7 11.2 12.3 12.2  Moderately inactive 27.5 31.8 32.3 30.9 29.1  Moderately active 25.4 25.2 23.9 23.2 23.2  Active 38.8 33.3 32.7 33.6 35.4 Waist circumference,3 cm 82.6 (72.0, 100.8) 83.8 (72.5, 102.1) 88.4 (73.5, 103.7) 93.4 (74.8, 104.7) 94.9 (76.8, 105.3) Alcohol intake, g/d 10.7 (0.0, 67.8) 11.4 (0.2, 73.1) 12.6 (0.2, 81.1) 15.4 (0.4, 85.9) 16.9 (0.2, 86.7) Clinical characteristics, %  Hypercholesterolemia 6.6 7.2 7.5 8.1 7.4  Hypertension 15.7 17.5 16.0 15.9 14.7  Diabetes 1.7 1.9 2.2 2.0 2.2  Atrial fibrillation or flutter 0.5 0.6 0.9 0.9 0.8 Quintiles of energy-adjusted dietary intake of ALA 1 (≤1.35 g/d) 2 (1.36–1.62 g/d) 3 (1.63–1.90 g/d) 4 (1.91–2.26 g/d) 5 (≥2.27 g/d) Sex, %  Men 16.3 25.3 45.7 68.9 82.0  Women 83.7 74.7 54.3 31.1 18.0 Age at enrollment, y 55.8 (50.5, 64.7) 56.2 (50.5, 64.8) 56.3 (50.5, 64.7) 56.3 (50.5, 64.7) 56.0 (50.5, 64.7) Length of schooling, %  ≤7 y 25.6 30.5 33.8 36.1 37.7  8–10 y 46.5 47.8 45.7 45.3 45.4  >10 y 27.9 21.7 20.6 18.6 16.9 Smoking, %  Never 43.5 40.2 35.5 31.6 25.9  Former 29.4 28.1 28.4 29.8 28.3  Current <15 g/d 13.3 13.3 13.5 12.3 12.6  Current 15–25 g/d 10.7 14.1 16.6 17.6 21.2  Current >25 g/d 3.1 4.3 5.9 8.7 12.0 Physical activity,2 %  Inactive 8.3 9.7 11.2 12.3 12.2  Moderately inactive 27.5 31.8 32.3 30.9 29.1  Moderately active 25.4 25.2 23.9 23.2 23.2  Active 38.8 33.3 32.7 33.6 35.4 Waist circumference,3 cm 82.6 (72.0, 100.8) 83.8 (72.5, 102.1) 88.4 (73.5, 103.7) 93.4 (74.8, 104.7) 94.9 (76.8, 105.3) Alcohol intake, g/d 10.7 (0.0, 67.8) 11.4 (0.2, 73.1) 12.6 (0.2, 81.1) 15.4 (0.4, 85.9) 16.9 (0.2, 86.7) Clinical characteristics, %  Hypercholesterolemia 6.6 7.2 7.5 8.1 7.4  Hypertension 15.7 17.5 16.0 15.9 14.7  Diabetes 1.7 1.9 2.2 2.0 2.2  Atrial fibrillation or flutter 0.5 0.6 0.9 0.9 0.8 1Values are percentages or medians (2.5–97.5th percentiles). ALA, α-linolenic acid. 2Cambridge index. 3Adjusted for BMI. View Large Association between ALA and ischemic stroke and ischemic stroke subtypes The median energy-adjusted intake of ALA in the cohort was 1.8 g/d (95% central range: 0.9–3.3 g/d). Multivariable analyses conducted using restricted cubic splines and adjusting for established ischemic stroke risk factors (model 1B) showed a weak positive association between ALA intake and total ischemic stroke, but this association was not statistically significant (P = 0.431) (Figure 1). Multivariable analyses of ischemic stroke subtypes (model 1B) showed no statistically significant associations between intake of ALA and large artery atherosclerosis (P = 0.908), small-vessel occlusion (P = 0.173), or cardio-embolism (P = 0.696) (Figure 1). The associations between intake of ALA and risk of stroke of other etiology and between ALA intake and risk of stroke of undetermined etiology are given in Supplemental Figure 2. FIGURE 1 View largeDownload slide Intake of ALA among middle-aged men and women and the risk of incident ischemic stroke (A), ischemic stroke due to large artery atherosclerosis (B), ischemic stroke due to small-vessel occlusion (C), and ischemic stroke due to cardio-embolism (D). Analyses were adjusted for established ischemic stroke risk factors (model 1B) with median intake as reference (solid vertical line). The 20th, 40th, 60th, and 80th percentiles of ALA are marked by dashed lines. The shaded grey areas represent the 95% CIs of HRs of ischemic stroke and subtypes (curves) which were calculated via Cox proportional hazard regression. Only 2.5–97.5th percentiles of ALA are shown. The statistical analyses included 55,018 participants including 1859 cases of total ischemic stroke, 316 ischemic stroke cases due to large artery atherosclerosis, 835 ischemic stroke cases due to small-vessel occlusion, and 102 ischemic stroke cases due to cardio-embolism. ALA, α-linolenic acid. FIGURE 1 View largeDownload slide Intake of ALA among middle-aged men and women and the risk of incident ischemic stroke (A), ischemic stroke due to large artery atherosclerosis (B), ischemic stroke due to small-vessel occlusion (C), and ischemic stroke due to cardio-embolism (D). Analyses were adjusted for established ischemic stroke risk factors (model 1B) with median intake as reference (solid vertical line). The 20th, 40th, 60th, and 80th percentiles of ALA are marked by dashed lines. The shaded grey areas represent the 95% CIs of HRs of ischemic stroke and subtypes (curves) which were calculated via Cox proportional hazard regression. Only 2.5–97.5th percentiles of ALA are shown. The statistical analyses included 55,018 participants including 1859 cases of total ischemic stroke, 316 ischemic stroke cases due to large artery atherosclerosis, 835 ischemic stroke cases due to small-vessel occlusion, and 102 ischemic stroke cases due to cardio-embolism. ALA, α-linolenic acid. Analyses of energy-adjusted intake of ALA in quintiles and the risk of ischemic stroke and ischemic stroke subtypes are shown in Table 2. In analyses including adjustment for established ischemic stroke risk factors (model 1B), the individual hazards for ischemic stroke and subtypes in the second to fifth quintiles were not statistically different from the reference in the first quintile. Additional adjustments for self-reported history of hypercholesterolemia, hypertension, diabetes mellitus, and atrial fibrillation or flutter at baseline (model 2) also showed individual hazards in the second to fifth quintiles that were not statistically different from the reference in the first quintile. TABLE 2 Quintiles of ALA intake among middle-aged men and women in the Diet, Cancer and Health cohort and HRs for total ischemic stroke and ischemic stroke subtypes1 ALA intake Cases, n Model 1A2 HR (95% CI) Model 1B3 HR (95% CI) Model 24 HR (95% CI) Model 35 HR (95% CI) Total ischemic stroke  ≤1.35 g/d 265 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 307 1.08 (0.92, 1.27) 1.03 (0.87, 1.21) 1.02 (0.86, 1.20) 0.95 (0.80, 1.13)  1.63–1.90 g/d 391 1.22 (1.04, 1.43) 1.12 (0.95, 1.31) 1.12 (0.95, 1.31) 1.01 (0.85, 1.20)  1.91–2.26 g/d 415 1.16 (0.98, 1.37) 1.03 (0.87, 1.22) 1.04 (0.88, 1.23) 0.89 (0.74, 1.08)  ≥2.27 g/d 481 1.30 (1.10, 1.54) 1.10 (0.93, 1.31) 1.12 (0.95, 1.32) 0.92 (0.75, 1.13)  P-trend 0.002 0.318 0.224 0.333 Large artery atherosclerosis  ≤1.35 g/d 50 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 59 1.11 (0.76, 1.62) 1.03 (0.71, 1.51) 1.02 (0.70, 1.50) 0.94 (0.63, 1.39)  1.63–1.90 g/d 60 1.03 (0.70, 1.51) 0.91 (0.62, 1.34) 0.91 (0.62, 1.35) 0.79 (0.52, 1.20)  1.91–2.26 g/d 68 1.07 (0.72, 1.58) 0.90 (0.61, 1.34) 0.91 (0.61, 1.35) 0.76 (0.48, 1.18)  ≥2.27 g/d 79 1.22 (0.82, 1.80) 0.96 (0.65, 1.44) 0.97 (0.65, 1.44) 0.82 (0.50, 1.35)  P-trend 0.414 0.706 0.725 0.336 Small-vessel occlusion  ≤1.35 g/d 115 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 138 1.13 (0.88, 1.45) 1.08 (0.84, 1.39) 1.07 (0.84, 1.37) 1.01 (0.78, 1.31)  1.63–1.90 g/d 180 1.34 (1.05, 1.70) 1.23 (0.97, 1.56) 1.24 (0.97, 1.57) 1.14 (0.88, 1.48)  1.91–2.26 g/d 180 1.21 (0.94, 1.56) 1.09 (0.84, 1.40) 1.09 (0.85, 1.40) 0.98 (0.74, 1.30)  ≥2.27 g/d 222 1.46 (1.14, 1.87) 1.25 (0.98, 1.61) 1.27 (0.98, 1.63) 1.08 (0.79, 1.47)  P-trend 0.004 0.123 0.096 0.799 Cardio-embolism  ≤1.35 g/d 12 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 18 1.32 (0.64, 2.76) 1.30 (0.62, 2.72) 1.30 (0.62, 2.71) 1.14 (0.53, 2.42)  1.63–1.90 g/d 21 1.27 (0.61, 2.63) 1.23 (0.59, 2.56) 1.21 (0.58, 2.52) 1.00 (0.46, 2.20)  1.91–2.26 g/d 22 1.11 (0.53, 2.35) 1.08 (0.51, 2.29) 1.10 (0.52, 2.33) 0.82 (0.35, 1.90)  ≥2.27 g/d 29 1.39 (0.67, 2.89) 1.31 (0.63, 2.76) 1.37 (0.65, 2.87) 0.96 (0.39, 2.36)  P-trend 0.572 0.691 0.590 0.682 Stroke of other etiology  ≤1.35 g/d 13 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 17 1.24 (0.60, 2.57) 1.23 (0.59, 2.54) 1.22 (0.59, 2.52) 1.13 (0.53, 2.40)  1.63–1.90 g/d 22 1.46 (0.72, 2.95) 1.39 (0.68, 2.81) 1.38 (0.68, 2.80) 1.18 (0.55, 2.54)  1.91–2.26 g/d 23 1.39 (0.67, 2.87) 1.29 (0.62, 2.69) 1.30 (0.63, 2.70) 1.05 (0.46, 2.38)  ≥2.27 g/d 23 1.34 (0.64, 2.81) 1.17 (0.55, 2.48) 1.19 (0.56, 2.52) 0.94 (0.38, 2.35)  P-trend 0.480 0.768 0.722 0.788 Stroke of undetermined etiology  ≤1.35 g/d 75 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 75 0.91 (0.66, 1.26) 0.87 (0.63, 1.19) 0.86 (0.62, 1.18) 0.82 (0.59, 1.14)  1.63–1.90 g/d 108 1.12 (0.83, 1.52) 1.03 (0.76, 1.40) 1.03 (0.76, 1.39) 0.94 (0.68, 1.30)  1.91–2.26 g/d 122 1.10 (0.81, 1.50) 0.98 (0.72, 1.34) 0.99 (0.72, 1.35) 0.86 (0.60, 1.21)  ≥2.27 g/d 128 1.11 (0.81, 1.52) 0.94 (0.68, 1.29) 0.95 (0.69, 1.31) 0.77 (0.53, 1.14)  P-trend 0.317 0.948 0.944 0.292 ALA intake Cases, n Model 1A2 HR (95% CI) Model 1B3 HR (95% CI) Model 24 HR (95% CI) Model 35 HR (95% CI) Total ischemic stroke  ≤1.35 g/d 265 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 307 1.08 (0.92, 1.27) 1.03 (0.87, 1.21) 1.02 (0.86, 1.20) 0.95 (0.80, 1.13)  1.63–1.90 g/d 391 1.22 (1.04, 1.43) 1.12 (0.95, 1.31) 1.12 (0.95, 1.31) 1.01 (0.85, 1.20)  1.91–2.26 g/d 415 1.16 (0.98, 1.37) 1.03 (0.87, 1.22) 1.04 (0.88, 1.23) 0.89 (0.74, 1.08)  ≥2.27 g/d 481 1.30 (1.10, 1.54) 1.10 (0.93, 1.31) 1.12 (0.95, 1.32) 0.92 (0.75, 1.13)  P-trend 0.002 0.318 0.224 0.333 Large artery atherosclerosis  ≤1.35 g/d 50 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 59 1.11 (0.76, 1.62) 1.03 (0.71, 1.51) 1.02 (0.70, 1.50) 0.94 (0.63, 1.39)  1.63–1.90 g/d 60 1.03 (0.70, 1.51) 0.91 (0.62, 1.34) 0.91 (0.62, 1.35) 0.79 (0.52, 1.20)  1.91–2.26 g/d 68 1.07 (0.72, 1.58) 0.90 (0.61, 1.34) 0.91 (0.61, 1.35) 0.76 (0.48, 1.18)  ≥2.27 g/d 79 1.22 (0.82, 1.80) 0.96 (0.65, 1.44) 0.97 (0.65, 1.44) 0.82 (0.50, 1.35)  P-trend 0.414 0.706 0.725 0.336 Small-vessel occlusion  ≤1.35 g/d 115 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 138 1.13 (0.88, 1.45) 1.08 (0.84, 1.39) 1.07 (0.84, 1.37) 1.01 (0.78, 1.31)  1.63–1.90 g/d 180 1.34 (1.05, 1.70) 1.23 (0.97, 1.56) 1.24 (0.97, 1.57) 1.14 (0.88, 1.48)  1.91–2.26 g/d 180 1.21 (0.94, 1.56) 1.09 (0.84, 1.40) 1.09 (0.85, 1.40) 0.98 (0.74, 1.30)  ≥2.27 g/d 222 1.46 (1.14, 1.87) 1.25 (0.98, 1.61) 1.27 (0.98, 1.63) 1.08 (0.79, 1.47)  P-trend 0.004 0.123 0.096 0.799 Cardio-embolism  ≤1.35 g/d 12 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 18 1.32 (0.64, 2.76) 1.30 (0.62, 2.72) 1.30 (0.62, 2.71) 1.14 (0.53, 2.42)  1.63–1.90 g/d 21 1.27 (0.61, 2.63) 1.23 (0.59, 2.56) 1.21 (0.58, 2.52) 1.00 (0.46, 2.20)  1.91–2.26 g/d 22 1.11 (0.53, 2.35) 1.08 (0.51, 2.29) 1.10 (0.52, 2.33) 0.82 (0.35, 1.90)  ≥2.27 g/d 29 1.39 (0.67, 2.89) 1.31 (0.63, 2.76) 1.37 (0.65, 2.87) 0.96 (0.39, 2.36)  P-trend 0.572 0.691 0.590 0.682 Stroke of other etiology  ≤1.35 g/d 13 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 17 1.24 (0.60, 2.57) 1.23 (0.59, 2.54) 1.22 (0.59, 2.52) 1.13 (0.53, 2.40)  1.63–1.90 g/d 22 1.46 (0.72, 2.95) 1.39 (0.68, 2.81) 1.38 (0.68, 2.80) 1.18 (0.55, 2.54)  1.91–2.26 g/d 23 1.39 (0.67, 2.87) 1.29 (0.62, 2.69) 1.30 (0.63, 2.70) 1.05 (0.46, 2.38)  ≥2.27 g/d 23 1.34 (0.64, 2.81) 1.17 (0.55, 2.48) 1.19 (0.56, 2.52) 0.94 (0.38, 2.35)  P-trend 0.480 0.768 0.722 0.788 Stroke of undetermined etiology  ≤1.35 g/d 75 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 75 0.91 (0.66, 1.26) 0.87 (0.63, 1.19) 0.86 (0.62, 1.18) 0.82 (0.59, 1.14)  1.63–1.90 g/d 108 1.12 (0.83, 1.52) 1.03 (0.76, 1.40) 1.03 (0.76, 1.39) 0.94 (0.68, 1.30)  1.91–2.26 g/d 122 1.10 (0.81, 1.50) 0.98 (0.72, 1.34) 0.99 (0.72, 1.35) 0.86 (0.60, 1.21)  ≥2.27 g/d 128 1.11 (0.81, 1.52) 0.94 (0.68, 1.29) 0.95 (0.69, 1.31) 0.77 (0.53, 1.14)  P-trend 0.317 0.948 0.944 0.292 1Values are HRs with 95% CIs calculated via Cox proportional hazard regression allowing for separate baseline hazards among men and women. The analyses included 55,018 middle-aged men and women including 1859 cases of total ischemic stroke, 316 ischemic stroke cases due to large artery atherosclerosis, 835 ischemic stroke cases due to small-vessel occlusion, 102 ischemic stroke cases due to cardio-embolism, 98 ischemic stroke cases of other etiology, and 508 ischemic stroke cases of undetermined etiology. ALA, α-linolenic acid. 2Model 1A included baseline age. 3Model 1B included the variables of model 1A and the following risk factors for ischemic stroke: length of schooling, smoking, physical activity, waist circumference adjusted for BMI, and alcohol intake. 4Model 2 included the variables of model 1B and the following potential intermediate variables: self-reported history of hypercholesterolemia and/or use of lipid-lowering medication, hypertension and/or use of antihypertensive medication, diabetes mellitus, and diagnoses of atrial fibrillation or flutter recorded in the Danish National Patient Register at baseline. 5Model 3 included the variables of model 1B and the following potential dietary risk factors: total energy intake, intake of fiber, glycemic load, and intake of SFAs, MUFAs, linoleic acid, and marine ω-3 fatty acids. View Large TABLE 2 Quintiles of ALA intake among middle-aged men and women in the Diet, Cancer and Health cohort and HRs for total ischemic stroke and ischemic stroke subtypes1 ALA intake Cases, n Model 1A2 HR (95% CI) Model 1B3 HR (95% CI) Model 24 HR (95% CI) Model 35 HR (95% CI) Total ischemic stroke  ≤1.35 g/d 265 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 307 1.08 (0.92, 1.27) 1.03 (0.87, 1.21) 1.02 (0.86, 1.20) 0.95 (0.80, 1.13)  1.63–1.90 g/d 391 1.22 (1.04, 1.43) 1.12 (0.95, 1.31) 1.12 (0.95, 1.31) 1.01 (0.85, 1.20)  1.91–2.26 g/d 415 1.16 (0.98, 1.37) 1.03 (0.87, 1.22) 1.04 (0.88, 1.23) 0.89 (0.74, 1.08)  ≥2.27 g/d 481 1.30 (1.10, 1.54) 1.10 (0.93, 1.31) 1.12 (0.95, 1.32) 0.92 (0.75, 1.13)  P-trend 0.002 0.318 0.224 0.333 Large artery atherosclerosis  ≤1.35 g/d 50 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 59 1.11 (0.76, 1.62) 1.03 (0.71, 1.51) 1.02 (0.70, 1.50) 0.94 (0.63, 1.39)  1.63–1.90 g/d 60 1.03 (0.70, 1.51) 0.91 (0.62, 1.34) 0.91 (0.62, 1.35) 0.79 (0.52, 1.20)  1.91–2.26 g/d 68 1.07 (0.72, 1.58) 0.90 (0.61, 1.34) 0.91 (0.61, 1.35) 0.76 (0.48, 1.18)  ≥2.27 g/d 79 1.22 (0.82, 1.80) 0.96 (0.65, 1.44) 0.97 (0.65, 1.44) 0.82 (0.50, 1.35)  P-trend 0.414 0.706 0.725 0.336 Small-vessel occlusion  ≤1.35 g/d 115 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 138 1.13 (0.88, 1.45) 1.08 (0.84, 1.39) 1.07 (0.84, 1.37) 1.01 (0.78, 1.31)  1.63–1.90 g/d 180 1.34 (1.05, 1.70) 1.23 (0.97, 1.56) 1.24 (0.97, 1.57) 1.14 (0.88, 1.48)  1.91–2.26 g/d 180 1.21 (0.94, 1.56) 1.09 (0.84, 1.40) 1.09 (0.85, 1.40) 0.98 (0.74, 1.30)  ≥2.27 g/d 222 1.46 (1.14, 1.87) 1.25 (0.98, 1.61) 1.27 (0.98, 1.63) 1.08 (0.79, 1.47)  P-trend 0.004 0.123 0.096 0.799 Cardio-embolism  ≤1.35 g/d 12 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 18 1.32 (0.64, 2.76) 1.30 (0.62, 2.72) 1.30 (0.62, 2.71) 1.14 (0.53, 2.42)  1.63–1.90 g/d 21 1.27 (0.61, 2.63) 1.23 (0.59, 2.56) 1.21 (0.58, 2.52) 1.00 (0.46, 2.20)  1.91–2.26 g/d 22 1.11 (0.53, 2.35) 1.08 (0.51, 2.29) 1.10 (0.52, 2.33) 0.82 (0.35, 1.90)  ≥2.27 g/d 29 1.39 (0.67, 2.89) 1.31 (0.63, 2.76) 1.37 (0.65, 2.87) 0.96 (0.39, 2.36)  P-trend 0.572 0.691 0.590 0.682 Stroke of other etiology  ≤1.35 g/d 13 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 17 1.24 (0.60, 2.57) 1.23 (0.59, 2.54) 1.22 (0.59, 2.52) 1.13 (0.53, 2.40)  1.63–1.90 g/d 22 1.46 (0.72, 2.95) 1.39 (0.68, 2.81) 1.38 (0.68, 2.80) 1.18 (0.55, 2.54)  1.91–2.26 g/d 23 1.39 (0.67, 2.87) 1.29 (0.62, 2.69) 1.30 (0.63, 2.70) 1.05 (0.46, 2.38)  ≥2.27 g/d 23 1.34 (0.64, 2.81) 1.17 (0.55, 2.48) 1.19 (0.56, 2.52) 0.94 (0.38, 2.35)  P-trend 0.480 0.768 0.722 0.788 Stroke of undetermined etiology  ≤1.35 g/d 75 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 75 0.91 (0.66, 1.26) 0.87 (0.63, 1.19) 0.86 (0.62, 1.18) 0.82 (0.59, 1.14)  1.63–1.90 g/d 108 1.12 (0.83, 1.52) 1.03 (0.76, 1.40) 1.03 (0.76, 1.39) 0.94 (0.68, 1.30)  1.91–2.26 g/d 122 1.10 (0.81, 1.50) 0.98 (0.72, 1.34) 0.99 (0.72, 1.35) 0.86 (0.60, 1.21)  ≥2.27 g/d 128 1.11 (0.81, 1.52) 0.94 (0.68, 1.29) 0.95 (0.69, 1.31) 0.77 (0.53, 1.14)  P-trend 0.317 0.948 0.944 0.292 ALA intake Cases, n Model 1A2 HR (95% CI) Model 1B3 HR (95% CI) Model 24 HR (95% CI) Model 35 HR (95% CI) Total ischemic stroke  ≤1.35 g/d 265 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 307 1.08 (0.92, 1.27) 1.03 (0.87, 1.21) 1.02 (0.86, 1.20) 0.95 (0.80, 1.13)  1.63–1.90 g/d 391 1.22 (1.04, 1.43) 1.12 (0.95, 1.31) 1.12 (0.95, 1.31) 1.01 (0.85, 1.20)  1.91–2.26 g/d 415 1.16 (0.98, 1.37) 1.03 (0.87, 1.22) 1.04 (0.88, 1.23) 0.89 (0.74, 1.08)  ≥2.27 g/d 481 1.30 (1.10, 1.54) 1.10 (0.93, 1.31) 1.12 (0.95, 1.32) 0.92 (0.75, 1.13)  P-trend 0.002 0.318 0.224 0.333 Large artery atherosclerosis  ≤1.35 g/d 50 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 59 1.11 (0.76, 1.62) 1.03 (0.71, 1.51) 1.02 (0.70, 1.50) 0.94 (0.63, 1.39)  1.63–1.90 g/d 60 1.03 (0.70, 1.51) 0.91 (0.62, 1.34) 0.91 (0.62, 1.35) 0.79 (0.52, 1.20)  1.91–2.26 g/d 68 1.07 (0.72, 1.58) 0.90 (0.61, 1.34) 0.91 (0.61, 1.35) 0.76 (0.48, 1.18)  ≥2.27 g/d 79 1.22 (0.82, 1.80) 0.96 (0.65, 1.44) 0.97 (0.65, 1.44) 0.82 (0.50, 1.35)  P-trend 0.414 0.706 0.725 0.336 Small-vessel occlusion  ≤1.35 g/d 115 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 138 1.13 (0.88, 1.45) 1.08 (0.84, 1.39) 1.07 (0.84, 1.37) 1.01 (0.78, 1.31)  1.63–1.90 g/d 180 1.34 (1.05, 1.70) 1.23 (0.97, 1.56) 1.24 (0.97, 1.57) 1.14 (0.88, 1.48)  1.91–2.26 g/d 180 1.21 (0.94, 1.56) 1.09 (0.84, 1.40) 1.09 (0.85, 1.40) 0.98 (0.74, 1.30)  ≥2.27 g/d 222 1.46 (1.14, 1.87) 1.25 (0.98, 1.61) 1.27 (0.98, 1.63) 1.08 (0.79, 1.47)  P-trend 0.004 0.123 0.096 0.799 Cardio-embolism  ≤1.35 g/d 12 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 18 1.32 (0.64, 2.76) 1.30 (0.62, 2.72) 1.30 (0.62, 2.71) 1.14 (0.53, 2.42)  1.63–1.90 g/d 21 1.27 (0.61, 2.63) 1.23 (0.59, 2.56) 1.21 (0.58, 2.52) 1.00 (0.46, 2.20)  1.91–2.26 g/d 22 1.11 (0.53, 2.35) 1.08 (0.51, 2.29) 1.10 (0.52, 2.33) 0.82 (0.35, 1.90)  ≥2.27 g/d 29 1.39 (0.67, 2.89) 1.31 (0.63, 2.76) 1.37 (0.65, 2.87) 0.96 (0.39, 2.36)  P-trend 0.572 0.691 0.590 0.682 Stroke of other etiology  ≤1.35 g/d 13 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 17 1.24 (0.60, 2.57) 1.23 (0.59, 2.54) 1.22 (0.59, 2.52) 1.13 (0.53, 2.40)  1.63–1.90 g/d 22 1.46 (0.72, 2.95) 1.39 (0.68, 2.81) 1.38 (0.68, 2.80) 1.18 (0.55, 2.54)  1.91–2.26 g/d 23 1.39 (0.67, 2.87) 1.29 (0.62, 2.69) 1.30 (0.63, 2.70) 1.05 (0.46, 2.38)  ≥2.27 g/d 23 1.34 (0.64, 2.81) 1.17 (0.55, 2.48) 1.19 (0.56, 2.52) 0.94 (0.38, 2.35)  P-trend 0.480 0.768 0.722 0.788 Stroke of undetermined etiology  ≤1.35 g/d 75 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 75 0.91 (0.66, 1.26) 0.87 (0.63, 1.19) 0.86 (0.62, 1.18) 0.82 (0.59, 1.14)  1.63–1.90 g/d 108 1.12 (0.83, 1.52) 1.03 (0.76, 1.40) 1.03 (0.76, 1.39) 0.94 (0.68, 1.30)  1.91–2.26 g/d 122 1.10 (0.81, 1.50) 0.98 (0.72, 1.34) 0.99 (0.72, 1.35) 0.86 (0.60, 1.21)  ≥2.27 g/d 128 1.11 (0.81, 1.52) 0.94 (0.68, 1.29) 0.95 (0.69, 1.31) 0.77 (0.53, 1.14)  P-trend 0.317 0.948 0.944 0.292 1Values are HRs with 95% CIs calculated via Cox proportional hazard regression allowing for separate baseline hazards among men and women. The analyses included 55,018 middle-aged men and women including 1859 cases of total ischemic stroke, 316 ischemic stroke cases due to large artery atherosclerosis, 835 ischemic stroke cases due to small-vessel occlusion, 102 ischemic stroke cases due to cardio-embolism, 98 ischemic stroke cases of other etiology, and 508 ischemic stroke cases of undetermined etiology. ALA, α-linolenic acid. 2Model 1A included baseline age. 3Model 1B included the variables of model 1A and the following risk factors for ischemic stroke: length of schooling, smoking, physical activity, waist circumference adjusted for BMI, and alcohol intake. 4Model 2 included the variables of model 1B and the following potential intermediate variables: self-reported history of hypercholesterolemia and/or use of lipid-lowering medication, hypertension and/or use of antihypertensive medication, diabetes mellitus, and diagnoses of atrial fibrillation or flutter recorded in the Danish National Patient Register at baseline. 5Model 3 included the variables of model 1B and the following potential dietary risk factors: total energy intake, intake of fiber, glycemic load, and intake of SFAs, MUFAs, linoleic acid, and marine ω-3 fatty acids. View Large In analyses including adjustment for established risk factors for ischemic stroke and dietary factors (model 3), HRs were generally somewhat lower compared with the observed HRs in model 1B, but the hazards in the second to fifth quintiles were not statistically different from the reference in the first quintile. No linear trends across quintiles were observed in either of the multivariable adjusted models. Sensitivity analyses indicated that the models with the use of restricted cubic splines were robust when the location and number of knots were modified. No evidence of deviation from the proportionality assumption was observed. A Radar plot of the underlying dietary pattern in the cohort showed several differences in the energy-adjusted median intake of selected foods among participants in different quintiles of ALA intake (Supplemental Figure 3). Participants in the highest quintile of energy-adjusted ALA intake thus had higher intakes of refined cereals, potatoes, vegetable oils and mayonnaises, margarines, butter and other animal fat, eggs, processed and red meat, fish, poultry, snacks and fatty potatoes, soft drinks and juices, and alcohol, and lower intakes of fruits, vegetables, and lean dairy products. Discussion In this large cohort study, indications of a weak positive association between ALA intake and the risk of total ischemic stroke, a weak inverse association between ALA intake and the risk of large artery atherosclerosis, a weak positive association between ALA intake and the risk of small-vessel occlusion, and a weak inverse U-shaped association between ALA intake and the risk of cardio-embolism were shown, but none of these associations was statistically significant. Given the relatively weak and statistically nonsignificant associations observed, the current study suggested that intake of ALA was not associated with the risk of ischemic stroke or ischemic stroke subtypes to any degree relevant to public health. It is important to emphasize that our study did not evaluate the potential effect of a Mediterranean diet on ischemic stroke, but it suggests that if such an effect exists it is unlikely to be related to ALA intake. Some strengths and limitations should be mentioned. The current study holds the advantage of a follow-up design and nearly complete follow-up, limiting the concern of selection bias. Information bias derived from potential differential misclassification of the outcomes is an unlikely explanation for the observed results because cases were identified independently of the baseline dietary assessment through the nationwide Danish National Patient Register and subsequently validated and classified in subtypes according to the TOAST-classification. However, random measurement error of ALA intake was likely because the diet was self-reported by a FFQ and not designed specifically to measure ALA intake, which may have attenuated the observed associations toward the null. Another possible limitation is that changes in dietary habits during follow-up might have occurred and repeated dietary measurements would have been preferable. Detailed information on ischemic stroke risk factors was included in the analyses, but residual confounding from known or unknown ischemic stroke risk factors may still be of importance for the observed associations. Adjustment for established ischemic stroke risk factors (model 1B) generally weakened the observed associations, indicating confounding from these risk factors. Additional adjustment for potential intermediate variables (model 2) showed similar patterns of associations. However, the interpretation of this model is complicated because these clinical characteristics may represent intermediate steps in the causal pathway between ALA intake and the risk of ischemic stroke and adjustment for these variables potentially could introduce bias. We evaluated by radar plots the underlying dietary patterns to explore further the intake of ALA as a marker of specific dietary patterns in the study population. Although several differences were seen at baseline, the radar plots did not indicate that ALA intake solely reflects a healthy dietary pattern within this cohort. Additional adjustments for dietary factors revealed somewhat weaker associations compared to model 1B and confounding from dietary factors cannot be excluded. However, the interpretation of measures of association from models including diet is complicated because adjustment for dietary factors may introduce restrictions in the underlying dietary pattern that are not comparable with the ordinary dietary pattern. Thus, findings from analyses with and without adjustment for dietary factors should not be directly compared. Given the interpretational complexities of models 2 and 3, we consider model 1B the most appropriate model for interpretation. The classification of ischemic stroke into subtypes did not allow for gender-specific analyses due to the limited number of cases. We performed analyses on stroke of other etiology and stroke of undetermined etiology, but these analyses were not commented on further because they were without clear interpretation. Previous cohort studies on ALA intake have not differentiated between ischemic stroke subtypes and the results on total ischemic stroke have been inconsistent (12, 13, 17, 19). A small nested case-control study reported an inverse association between ALA content in cholesterol esters and the risk of total stroke (30), but other biomarker studies investigating the association between ALA content in cholesterol ester or plasma phospholipids and total ischemic stroke have generally reported modest inverse statistically nonsignificant associations (31, 32) or inconsistent results (13, 33–36). Further studies are warranted to investigate the associations between ALA exposure and the risk of ischemic stroke subtypes. A previous cohort study has suggested that ALA in particular may reduce CHD risk when intake of marine ω-3 fatty acids is low (8). This could be important because the intake of marine ω-3 fatty acids in our study was markedly higher than compared with previous cohort studies that have reported inverse associations between ALA intake and the risk of cardiovascular disease (6, 9–11). In conclusion, in this large cohort study, intake of ALA was neither consistently nor statistically significantly associated with the risk of ischemic stroke or ischemic stroke subtypes among middle-aged Danish men and women. Acknowledgments The authors’ responsibilities were as follows—AT and KO: conceived the study concept and contributed to the data acquisition; CSB: conducted the statistical analyses, prepared the tables and figures, and wrote the manuscript; CSB, SKV, MUJ, SL-C, EBS, and KO: contributed to the study design, planning of the statistical analyses, interpretation of data, and writing of the manuscript; SL-C: supervised the conduct of the statistical analyses; AT: contributed to the critical interpretation of the manuscript. All authors read and approved the final manuscript. Notes Supported by The Danish Heart Foundation grant 17-R115-A7415-22060. The Danish Cancer Society funded the Diet, Cancer and Health study. The funding agencies had no influence on the design, analysis, or writing of this article. Author disclosures: CSB, SKV, SL-C, MUJ, AT, EBS, and KO, no conflicts of interest. Supplemental Tables 1 and 2 and Supplemental Figures 1–3 are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/jn/. Abbreviations used: ALA, α-linolenic acid; CHD, coronary heart disease; ICD, International Classification of Diseases; TOAST, trial of ORG 10172 in acute stroke treatment. References 1. Rajaram S . Health benefits of plant-derived alpha-linolenic acid . Am J Clin Nutr 2014 ; 100 : 443 – 8 . Google Scholar CrossRef Search ADS 2. Fleming J , Kris-Etherton P . The evidence for alpha-linolenic acid and cardiovascular disease benefits: comparisons with eicosapentaenoic acid and docosahexaenoic acid . Adv Nutr 2014 ; 5 : 863 – 76 . Google Scholar CrossRef Search ADS 3. de Lorgeril M , Salen P . Mediterranean diet and n-3 fatty acids in the prevention and treatment of cardiovascular disease . J Cardiovasc Med 2007 ; 8 ( Suppl 1 ): 38 – 41 . Google Scholar CrossRef Search ADS 4. Gebauer S , Psota T , Harris W , Kris-Etherton P . n-3 fatty acid dietary recommendations and food sources to achieve essentiality and cardiovascular benefits . Am J Clin Nutr 2006 ; 83 : 1526 – 35 . Google Scholar CrossRef Search ADS 5. Bork C , Jakobsen M , Lundbye-Christensen S , Tjønneland A , Schmidt E , Overvad K . Dietary intake and adipose tissue content of alpha-linolenic acid and risk of myocardial infarction: a Danish cohort study . Am J Clin Nutr 2016 ; 104 : 41 – 8 . Google Scholar CrossRef Search ADS PubMed 6. Hu F , Stampfer M , Manson J , Rimm E , Wolk A , Colditz G , Hennekens C , Willett W . Dietary intake of alpha-linolenic acid and risk of fatal ischemic heart disease among women . Am J Clin Nutr 1999 ; 69 : 890 – 7 . Google Scholar CrossRef Search ADS PubMed 7. Ascherio A , Rimm E , Giovannucci E , Spiegelman D , Stampfer M , Willett W . Dietary fat and risk of coronary heart disease in men: cohort follow up study in the United States . BMJ 1996 ; 313 : 84 – 90 . Google Scholar CrossRef Search ADS PubMed 8. Mozaffarian D , Ascherio A , Hu F , Stampfer M , Willett W , Siscovick D , Rimm E . Interplay between different polyunsaturated fatty acids and risk of coronary heart disease in men . Circulation 2005 ; 111 : 157 – 64 . Google Scholar CrossRef Search ADS PubMed 9. Vedtofte M , Jakobsen M , Lauritzen L , O'Reilly E , Virtamo J , Knekt P , Colditz G , Hallmans G , Buring J , Steffen L et al. Association between the intake of alpha-linolenic acid and the risk of CHD . Br J Nutr 2014 ; 112 : 735 – 43 . Google Scholar CrossRef Search ADS PubMed 10. Dolecek T . Epidemiological evidence of relationships between dietary polyunsaturated fatty acids and mortality in the multiple risk factor intervention trial . Proc Soc Exp Biol Med 1992 ; 200 : 177 – 82 . Google Scholar CrossRef Search ADS PubMed 11. Koh A , Pan A , Wang R , Odegaard A , Pereira M , Yuan J , Koh W . The association between dietary omega-3 fatty acids and cardiovascular death: the Singapore Chinese Health Study . Eur J Prev Cardiol 2015 ; 22 : 364 – 72 . Google Scholar CrossRef Search ADS PubMed 12. de Goede J , Verschuren W , Boer J , Kromhout D , Geleijnse J . Alpha-linolenic acid intake and 10-year incidence of coronary heart disease and stroke in 20,000 middle-aged men and women in the Netherlands . PLoS One 2011 ; 6 : e17967 . Google Scholar CrossRef Search ADS PubMed 13. Fretts A , Mozaffarian D , Siscovick D , Sitlani C , Psaty B , Rimm E , Song X , McKnight B , Spiegelman D , King I et al. Plasma phospholipid and dietary α-linolenic acid, mortality, CHD and stroke: the Cardiovascular Health Study . Br J Nutr 2014 ; 112 : 1206 – 13 . Google Scholar CrossRef Search ADS PubMed 14. Albert C , Oh K , Whang W , Manson J , Chae C , Stampfer M , Willett W , Hu F . Dietary alpha-linolenic acid intake and risk of sudden cardiac death and coronary heart disease . Circulation 2005 ; 112 : 3232 – 8 . Google Scholar CrossRef Search ADS PubMed 15. Oomen C , Ocké M , Feskens E , Kok F , Kromhout D . Alpha-linolenic acid intake is not beneficially associated with 10-y risk of coronary artery disease incidence: the Zutphen Elderly Study . Am J Clin Nutr 2001 ; 74 : 457 – 63 . Google Scholar CrossRef Search ADS PubMed 16. He K , Rimm E , Merchant A , Rosner B , Stampfer M , Willett W , Ascherio A . Fish consumption and risk of stroke in men . JAMA 2002 ; 288 : 3130 – 6 . Google Scholar CrossRef Search ADS PubMed 17. Rhee J , Kim E , Buring J , Kurth T . Fish consumption, omega-3 fatty acids, and risk of cardiovascular disease . Am J Prev Med 2017 ; 52 : 10 – 19 . Google Scholar CrossRef Search ADS PubMed 18. Sala-Vila A , Guasch-Ferré M , Hu F , Sánchez-Tainta A , Bulló M , Serra-Mir M , López-Sabater C , Sorlí J , Arós F , Fiol M et al. Dietary alpha-linolenic acid, marine omega-3 fatty acids, and mortality in a population with high fish consumption: findings from the PREvención con DIeta MEDiterránea (PREDIMED) study . J Am Heart Assoc 2016 ; 5 : e002543 . Google Scholar CrossRef Search ADS PubMed 19. Larsson S , Virtamo J , Wolk A . Dietary fats and dietary cholesterol and risk of stroke in women . Atherosclerosis 2012 ; 221 : 282 – 6 . Google Scholar CrossRef Search ADS PubMed 20. Adams H , Bendixen B , Kappelle L , Biller J , Love B , Gordon D , Marsh E . Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment . Stroke 1993 ; 24 : 35 – 41 . Google Scholar CrossRef Search ADS PubMed 21. Kolominsky-Rabas P , Weber M , Gefeller O , Neundoerfer B , Heuschmann P . Epidemiology of ischemic stroke subtypes according to TOAST criteria: incidence, recurrence, and long-term survival in ischemic stroke subtypes: a population-based study . Stroke 2001 ; 32 : 2735 – 40 . Google Scholar CrossRef Search ADS PubMed 22. Tjønneland A , Olsen A , Boll K , Stripp C , Christensen J , Engholm G , Overvad K . Study design, exposure variables, and socioeconomic determinants of participation in Diet, Cancer and Health: a population-based prospective cohort study of 57,053 men and women in Denmark . Scand J Public Health 2007 ; 35 : 432 – 41 . Google Scholar CrossRef Search ADS PubMed 23. Overvad K , Tjønneland A , Haraldsdóttir J , Ewertz M , Jensen O . Development of a semiquantitative food frequency questionnaire to assess food, energy and nutrient intake in Denmark . Int J Epidemiol 1991 ; 20 : 900 – 5 . Google Scholar CrossRef Search ADS PubMed 24. Tjønneland A , Overvad K , Haraldsdóttir J , Bang S , Ewerts M , Jensen O . Validations of a semiquantitative food frequency questionnaire developed in Denmark . Int J Epidemiol 1991 ; 20 : 906 – 12 . Google Scholar CrossRef Search ADS PubMed 25. Willett W , Stampfer M . Total energy intake: implications for epidemiologic analyses . Am J Epidemiol 1986 ; 124 : 17 – 27 . Google Scholar CrossRef Search ADS PubMed 26. Andersen T , Madsen M , Jørgensen J , Mellemkjær L , Olsen J . The Danish National Hospital Register. A valuable source of data for modern health sciences . Dan Med Bull 1999 ; 46 : 263 – 8 . Google Scholar PubMed 27. Lühdorf P , Overvad K , Schmidt E , Johnsen S , Bach F . Predictive value of stroke discharge diagnoses in the Danish National Patient Register . Scand J Public Health 2017 ; 45 : 630 – 6 . Google Scholar CrossRef Search ADS PubMed 28. Wareham N , Jakes W , Rennie K , Schuit J , Mitchell J , Hennings S , Day N . Validity and repeatability of a simple index derived from the short physical activity questionnaire used in the European Prospective Investigation into Cancer and Nutrition (EPIC) study . Public Health Nutr 2003 ; 6 : 407 – 13 . Google Scholar CrossRef Search ADS PubMed 29. Harrell FE . Regression modeling strategies. With applications to linear models, logistic and ordinal regression, and survival analysis . 2nd ed . New York : Springer ; 2015 . 30. Simon J , Fong J , Bernert JT Jr , Browner W . Serum fatty acids and the risk of stroke . Stroke 1995 ; 26 : 778 – 82 . Google Scholar CrossRef Search ADS PubMed 31. Daneshmand R , Kurl S , Tuomainen T , Virtanen J . Associations of serum n-3 and n-6 PUFA and hair mercury with the risk of incident stroke in men: the Kuopio Ischaemic Heart Disease risk factor study (KIHD) . Br J Nutr 2016 ; 115 : 1851 – 9 . Google Scholar CrossRef Search ADS PubMed 32. Yaemsiri S , Sen S , Tinker L , Robinson W , Evans R , Rosamond W , Wasserthiel-Smoller S , He K . Serum fatty acids and incidence of ischemic stroke among postmenopausal women . Stroke 2013 ; 44 : 2710 – 17 . Google Scholar CrossRef Search ADS PubMed 33. Yamagishi K , Folsom A , Steffen L . Plasma fatty acid composition and incident ischemic stroke in middle-aged adults: the Atherosclerosis Risk in Communities (ARIC) study . Cerebrovasc Dis 2013 ; 36 : 38 – 46 . Google Scholar CrossRef Search ADS PubMed 34. Wiberg B , Sundström J , Árnlöv J , Terént A , Vessby B , Zethelius B , Lind L . Metabolic risk factors for stroke and transient ischemic attacks in middle-aged men: a community-based study with long-term follow-up . Stroke 2006 ; 37 : 2898 – 903 . Google Scholar CrossRef Search ADS PubMed 35. De Goede J , Verschuren WMM , Boer JMA , Kromhout D , Geleijnse JM . N-6 and n-3 fatty acid cholesteryl esters in relation to incident stroke in a Dutch adult population: a nested case-control study . Nutr Metab Cardiovasc Dis 2013 ; 23 : 737 – 43 . Google Scholar CrossRef Search ADS PubMed 36. Iso H , Sato S , Umemura U , Kudo M , Koike K , Kitamura A , Imano H , Okamura T , Naito Y , Shimamoto T . Linoleic acid, other fatty acids, and the risk of stroke . Stroke 2002 ; 33 : 2086 – 93 . Google Scholar CrossRef Search ADS PubMed © 2018 American Society for Nutrition. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Nutrition Oxford University Press

Dietary Intake of α-Linolenic Acid Is Not Appreciably Associated with Risk of Ischemic Stroke among Middle-Aged Danish Men and Women

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Oxford University Press
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© 2018 American Society for Nutrition.
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0022-3166
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1541-6100
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10.1093/jn/nxy056
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Abstract

Abstract Background Intake of the plant-derived omega-3 (n–3) fatty acid α-linolenic acid (ALA) may reduce the risk of ischemic stroke. Objective We have investigated the associations between dietary intake of ALA and the risk of ischemic stroke and ischemic stroke subtypes. Methods This was a follow-up study. A total of 57,053 participants aged 50–64 y were enrolled into the Danish Diet, Cancer and Health cohort between 1993 and 1997. Intake of ALA was assessed by a validated semiquantitative food frequency questionnaire. Potential incident cases of ischemic stroke were identified in the Danish National Patient Register, validated, and classified into subtypes based on assumed etiology. Statistical analyses were performed via Cox proportional hazard regression with adjustment for established ischemic stroke risk factors. Results A total of 1859 ischemic stroke cases were identified during a median of 13.5 y of follow-up. In multivariable analyses using restricted cubic splines adjusting for traditional risk factors for ischemic stroke, we observed no clear associations between dietary intake of ALA and the risk of total ischemic stroke or any of its subtypes including ischemic stroke due to large artery atherosclerosis, ischemic stroke due to small-vessel occlusion, and ischemic stroke due to cardio-embolism. Conclusion Dietary intake of ALA was neither consistently nor appreciably associated with the risk of ischemic stroke or ischemic stroke subtypes among middle-aged Danish men and women. This study was registered at clinicaltrials.gov as NCT03258983. alpha-linolenic acid, omega-3 fatty acids, ischemic stroke, cohort study, diet Introduction The plant-derived omega-3 fatty acid α-linolenic acid (ALA) may be associated with a lower risk of cardiovascular disease (1, 2). However, data on the associations between ALA intake and the risk of ischemic stroke are sparse and knowledge on the associations with ischemic stroke subtypes is lacking. ALA is the most abundant ω-3 fatty acid in the typical Western diet and has been suggested to be an important part of the Mediterranean diet (3). ALA is found in high concentrations in plant oils, seeds, and walnuts, but can also be acquired from foods such as green leafy vegetables, whole-grain cereals, dairy products, and meat (4–6). Previous studies have reported that ALA may have anti-atherosclerotic, anti-inflammatory, anti-arrhythmic, and anti-thrombotic properties (1), but the clinical impact of these potential beneficial cardiovascular properties on development of ischemic stroke remains unclear. The majority of previous studies investigating the association between ALA intake and cardiovascular disease have focused on coronary heart disease (CHD) and some cohort studies have reported negative associations between ALA intake and the risk of incident CHD (6–11), but the results have not been consistent (5, 9, 12–15). Less emphasis has been given to a possible effect of ALA on stroke, but in 2 studies intake of ALA was inversely associated with the risk of stroke (11, 12), although other studies have not been able to confirm these findings (13, 16–19). Few of the studies have investigated the association between ALA intake and the risk of ischemic stroke and the results have been inconsistent (12, 13, 17, 19). However, previous cohort studies have not distinguished between ischemic stroke subtypes although the underlying etiology of subtypes of ischemic stroke may be different (20, 21). The objective of this study was to investigate the associations between intake of ALA and the risk of ischemic stroke and ischemic stroke subtypes. We hypothesized that intake of ALA would be inversely associated with development of ischemic stroke and ischemic stroke subtypes. Methods Study population and study design This study was based on data from the Diet, Cancer and Health cohort, which has previously been described in detail (22). Briefly, native citizens, aged 50–64 y, who were living in the greater areas of Copenhagen and Aarhus without a previous diagnosis of cancer, were between 1993 and 1997 invited to participate in the study. Potential eligible cohort members were retrieved through the Danish Civil Registration System in which every person living in Denmark is provided with a unique 10-digit identification number. The current study was conducted as a cohort study to investigate the associations between intake of ALA and the risk of ischemic stroke and ischemic stroke subtypes. We excluded participants with a cancer diagnosis before baseline that, due to a processing delay, was not registered in the Danish Cancer Registry at the time of invitation. Also, participants registered with a diagnosis of stroke before enrollment as well as participants with missing information on ischemic stroke risk factors were excluded. At inclusion all participants gave written informed consent, including permission for prospective data collection from national registries, and the study was approved by the relevant scientific Ethical Committees and the Danish Data Protection Agency. Exposure assessment Habitual diet over the past 12 mo was assessed at baseline through the use of a 192-item semiquantitative FFQ. The average consumption of foods and beverages was reported within 12 categories ranging from never to ≥8 times/d. The FFQ was processed by optical scanning and checked for omissions by a trained interviewer (22). The FFQ was developed for the Diet, Cancer and Health study, and a validation study confirmed that it was a useful instrument for categorizing individuals according to their intake of nutrients and energy when validated against 7-d weighed diet records (23, 24). Daily ALA intake averages were calculated from Danish food composition tables with the use of the software program FoodCalc (www.ibt.ku.dk/jesper/foodcalc) based on estimated intakes of foods and beverages from the FFQ. The dietary intake of ALA was expressed as energy-adjusted intake in g/d via the residual method (25), because ALA is metabolized in approximate proportion to total energy intake. Covariates Participants completed a detailed lifestyle questionnaire at baseline which included questions on health status, social, and lifestyle factors such as length of schooling, smoking habits, physical activity, history of hypercholesterolemia and/or use of lipid-lowering medication, hypertension and/or use of antihypertensive medication, and diabetes mellitus. The lifestyle questionnaire was processed by optical scanning and was checked for reading errors and omissions. Anthropometric measurements including height, weight, and waist circumference were undertaken by a trained technician (22). Information on alcohol consumption, total energy intake, and intake of other nutrients was obtained from the FFQ. Information regarding history of atrial fibrillation or atrial flutter before baseline was obtained by record linkage with the National Patient Register according to the International Classification of Diseases (ICD) (ICD-8: 42,793, 42,794; ICD-10: I48). All potential confounders were selected a priori based on the literature on risk factors for ischemic stroke. Identification of cases The outcome measures were ischemic stroke and ischemic stroke subtypes. Information on potential incident stroke cases was obtained by linkage with the Danish National Patient Register. This nationwide register includes discharge diagnoses from somatic hospitals since 1977, and since 1995 data on outpatient and emergency patients have been included as well (26). Participants who were registered with either a primary or secondary discharge diagnosis of stroke (ICD-8: 430, 431, 433, 434, 436.01, or 436.90; ICD-10: I60, I61, I63, or I64) were identified through the Danish National Patient Register. Stroke was defined as a disease with rapid onset of focal or global neurologic deficit of vascular origin that persisted beyond 24 h or leading to death. However, patients with neurologic deficits of shorter duration were also considered as stroke cases if computed tomography or MRI showed a lesion suggestive of stroke. All stroke cases have been validated by a physician with neurologic experience and classified according to the trial of ORG 10172 in acute stroke treatment (TOAST)-classification (20) based on review of medical records (27). The TOAST-classification separates cerebral infarctions into 5 groups: large artery atherosclerosis, small-vessel occlusion, cardio-embolism, stroke of other etiology, and stroke of undetermined etiology based on clinical findings, brain imaging, imaging of extracranial arteries, laboratory tests, electrocardiograms, and echocardiography. Participants were followed from baseline until the first registration of stroke, death, emigration, or end of follow-up in November 2009. Statistical analyses The associations between intake of ALA and the rate of ischemic stroke and ischemic stroke subtypes were investigated with HRs. HRs and 95% CIs were calculated with age as the underlying timescale via sex-stratified Cox proportional hazard regression, allowing the baseline hazard to differ between men and women. The regression models were also adjusted for baseline age through the use of restricted cubic splines with 3 knots (model 1A). Multivariable regression models were adjusted for baseline information regarding the following established ischemic stroke risk factors: length of schooling (≤7, 8–10, or >10 y), smoking (never, former, current 1–14, 15–24, or >24 g tobacco/d), physical activity [Cambridge index (28): inactive, moderately inactive, moderately active, or active], waist circumference adjusted for BMI (cm, continuous), and alcohol intake (g/d, continuous) (model 1B). In further analyses, we also adjusted for the following clinical characteristics, which may be considered potential intermediate variables: self-reported history of hypercholesterolemia or use of lipid-lowering medication (yes, no, or unknown), self-reported history of hypertension or use of antihypertensive medication (yes, no, or unknown), self-reported history of diabetes mellitus (yes, no, or unknown), or a diagnosis of atrial fibrillation or flutter recorded in the Danish National Patient Register at baseline (yes, no) (model 2). Dietary intake of ALA was analyzed as a continuous variable using restricted cubic splines with 3 knots with the median intake as reference, and as a categorical variable in quintiles with the lowest quintile of ALA intake as reference, respectively. The spline plots were restricted to the 2.5–97.5 percentile range and presented with 95% CIs. The spline curves were tested against a horizontal line. In sensitivity analyses, we plotted the whole exposure range and modified the number and the location of the knots. In additional analyses, we added the following potential dietary risk factors to model 1B: total energy intake (kJ/d, continuous), intake of fiber (g/d, continuous), glycemic load (unit-less), and intakes of MUFAs, SFAs, linoleic acid, and marine ω-3 fatty acids (g/d, continuous) (model 3). All continuous variables were included as restricted cubic splines with 3 knots placed at the 10th, 50th, and 90th percentiles as recommended by Harrell (29). The proportional hazard assumption was evaluated by plotting the scaled Schoenfeld residuals against age. Potential differences in the underlying dietary pattern related to ALA intake were assessed graphically in a radar plot comparing the median intake among participants in the highest and lowest quintiles of ALA for selected foods and beverages. Data were analyzed with Stata statistical software (version 14; StataCorp LP) and P < 0.05 was considered statistically significant. Results A total of 160,725 eligible participants were invited, of whom 57,053 individuals accepted to participate. We excluded 2035 participants because they had a diagnosis of cancer (n = 569) or stroke (n = 597) before entry into the study or had missing information on 1 or more covariates (n = 961) (Supplemental Figure 1). Among the remaining 55,018 participants, we identified 1859 participants with ischemic stroke including 316 ischemic stroke cases due to large artery atherosclerosis, 835 ischemic stroke cases due to small-vessel occlusion, 102 ischemic stroke cases due to cardio-embolism, 98 ischemic stroke cases of other etiology, and 508 ischemic stroke cases of undetermined etiology during a median follow-up of 13.5 y (95% central range: 3.7–15.2 y). Baseline characteristics of the participants in the cohort and among cases are shown in Table 1 and Supplemental Table 1, respectively. Ischemic stroke risk factors were generally more prevalent among participants who later became cases compared with the full cohort. Accordingly, we observed larger proportions of men, participants with a short length of schooling, higher age, waist circumference, and alcohol intake, the physically inactive, and current smokers among subsequent cases. A self-reported history of hypercholesterolemia, hypertension, and diabetes mellitus was also more prevalent in cases. A history of atrial fibrillation or flutter was more prevalent among cases classified with total ischemic stroke, cardio-embolism, stroke of other etiology, and stroke with undetermined etiology compared with the full cohort. Dietary characteristics of the participants are given in Supplemental Table 2. TABLE 1 Baseline characteristics according to quintiles of ALA intake among middle-aged men and women included in the Diet, Cancer and Health cohort1 Quintiles of energy-adjusted dietary intake of ALA 1 (≤1.35 g/d) 2 (1.36–1.62 g/d) 3 (1.63–1.90 g/d) 4 (1.91–2.26 g/d) 5 (≥2.27 g/d) Sex, %  Men 16.3 25.3 45.7 68.9 82.0  Women 83.7 74.7 54.3 31.1 18.0 Age at enrollment, y 55.8 (50.5, 64.7) 56.2 (50.5, 64.8) 56.3 (50.5, 64.7) 56.3 (50.5, 64.7) 56.0 (50.5, 64.7) Length of schooling, %  ≤7 y 25.6 30.5 33.8 36.1 37.7  8–10 y 46.5 47.8 45.7 45.3 45.4  >10 y 27.9 21.7 20.6 18.6 16.9 Smoking, %  Never 43.5 40.2 35.5 31.6 25.9  Former 29.4 28.1 28.4 29.8 28.3  Current <15 g/d 13.3 13.3 13.5 12.3 12.6  Current 15–25 g/d 10.7 14.1 16.6 17.6 21.2  Current >25 g/d 3.1 4.3 5.9 8.7 12.0 Physical activity,2 %  Inactive 8.3 9.7 11.2 12.3 12.2  Moderately inactive 27.5 31.8 32.3 30.9 29.1  Moderately active 25.4 25.2 23.9 23.2 23.2  Active 38.8 33.3 32.7 33.6 35.4 Waist circumference,3 cm 82.6 (72.0, 100.8) 83.8 (72.5, 102.1) 88.4 (73.5, 103.7) 93.4 (74.8, 104.7) 94.9 (76.8, 105.3) Alcohol intake, g/d 10.7 (0.0, 67.8) 11.4 (0.2, 73.1) 12.6 (0.2, 81.1) 15.4 (0.4, 85.9) 16.9 (0.2, 86.7) Clinical characteristics, %  Hypercholesterolemia 6.6 7.2 7.5 8.1 7.4  Hypertension 15.7 17.5 16.0 15.9 14.7  Diabetes 1.7 1.9 2.2 2.0 2.2  Atrial fibrillation or flutter 0.5 0.6 0.9 0.9 0.8 Quintiles of energy-adjusted dietary intake of ALA 1 (≤1.35 g/d) 2 (1.36–1.62 g/d) 3 (1.63–1.90 g/d) 4 (1.91–2.26 g/d) 5 (≥2.27 g/d) Sex, %  Men 16.3 25.3 45.7 68.9 82.0  Women 83.7 74.7 54.3 31.1 18.0 Age at enrollment, y 55.8 (50.5, 64.7) 56.2 (50.5, 64.8) 56.3 (50.5, 64.7) 56.3 (50.5, 64.7) 56.0 (50.5, 64.7) Length of schooling, %  ≤7 y 25.6 30.5 33.8 36.1 37.7  8–10 y 46.5 47.8 45.7 45.3 45.4  >10 y 27.9 21.7 20.6 18.6 16.9 Smoking, %  Never 43.5 40.2 35.5 31.6 25.9  Former 29.4 28.1 28.4 29.8 28.3  Current <15 g/d 13.3 13.3 13.5 12.3 12.6  Current 15–25 g/d 10.7 14.1 16.6 17.6 21.2  Current >25 g/d 3.1 4.3 5.9 8.7 12.0 Physical activity,2 %  Inactive 8.3 9.7 11.2 12.3 12.2  Moderately inactive 27.5 31.8 32.3 30.9 29.1  Moderately active 25.4 25.2 23.9 23.2 23.2  Active 38.8 33.3 32.7 33.6 35.4 Waist circumference,3 cm 82.6 (72.0, 100.8) 83.8 (72.5, 102.1) 88.4 (73.5, 103.7) 93.4 (74.8, 104.7) 94.9 (76.8, 105.3) Alcohol intake, g/d 10.7 (0.0, 67.8) 11.4 (0.2, 73.1) 12.6 (0.2, 81.1) 15.4 (0.4, 85.9) 16.9 (0.2, 86.7) Clinical characteristics, %  Hypercholesterolemia 6.6 7.2 7.5 8.1 7.4  Hypertension 15.7 17.5 16.0 15.9 14.7  Diabetes 1.7 1.9 2.2 2.0 2.2  Atrial fibrillation or flutter 0.5 0.6 0.9 0.9 0.8 1Values are percentages or medians (2.5–97.5th percentiles). ALA, α-linolenic acid. 2Cambridge index. 3Adjusted for BMI. View Large TABLE 1 Baseline characteristics according to quintiles of ALA intake among middle-aged men and women included in the Diet, Cancer and Health cohort1 Quintiles of energy-adjusted dietary intake of ALA 1 (≤1.35 g/d) 2 (1.36–1.62 g/d) 3 (1.63–1.90 g/d) 4 (1.91–2.26 g/d) 5 (≥2.27 g/d) Sex, %  Men 16.3 25.3 45.7 68.9 82.0  Women 83.7 74.7 54.3 31.1 18.0 Age at enrollment, y 55.8 (50.5, 64.7) 56.2 (50.5, 64.8) 56.3 (50.5, 64.7) 56.3 (50.5, 64.7) 56.0 (50.5, 64.7) Length of schooling, %  ≤7 y 25.6 30.5 33.8 36.1 37.7  8–10 y 46.5 47.8 45.7 45.3 45.4  >10 y 27.9 21.7 20.6 18.6 16.9 Smoking, %  Never 43.5 40.2 35.5 31.6 25.9  Former 29.4 28.1 28.4 29.8 28.3  Current <15 g/d 13.3 13.3 13.5 12.3 12.6  Current 15–25 g/d 10.7 14.1 16.6 17.6 21.2  Current >25 g/d 3.1 4.3 5.9 8.7 12.0 Physical activity,2 %  Inactive 8.3 9.7 11.2 12.3 12.2  Moderately inactive 27.5 31.8 32.3 30.9 29.1  Moderately active 25.4 25.2 23.9 23.2 23.2  Active 38.8 33.3 32.7 33.6 35.4 Waist circumference,3 cm 82.6 (72.0, 100.8) 83.8 (72.5, 102.1) 88.4 (73.5, 103.7) 93.4 (74.8, 104.7) 94.9 (76.8, 105.3) Alcohol intake, g/d 10.7 (0.0, 67.8) 11.4 (0.2, 73.1) 12.6 (0.2, 81.1) 15.4 (0.4, 85.9) 16.9 (0.2, 86.7) Clinical characteristics, %  Hypercholesterolemia 6.6 7.2 7.5 8.1 7.4  Hypertension 15.7 17.5 16.0 15.9 14.7  Diabetes 1.7 1.9 2.2 2.0 2.2  Atrial fibrillation or flutter 0.5 0.6 0.9 0.9 0.8 Quintiles of energy-adjusted dietary intake of ALA 1 (≤1.35 g/d) 2 (1.36–1.62 g/d) 3 (1.63–1.90 g/d) 4 (1.91–2.26 g/d) 5 (≥2.27 g/d) Sex, %  Men 16.3 25.3 45.7 68.9 82.0  Women 83.7 74.7 54.3 31.1 18.0 Age at enrollment, y 55.8 (50.5, 64.7) 56.2 (50.5, 64.8) 56.3 (50.5, 64.7) 56.3 (50.5, 64.7) 56.0 (50.5, 64.7) Length of schooling, %  ≤7 y 25.6 30.5 33.8 36.1 37.7  8–10 y 46.5 47.8 45.7 45.3 45.4  >10 y 27.9 21.7 20.6 18.6 16.9 Smoking, %  Never 43.5 40.2 35.5 31.6 25.9  Former 29.4 28.1 28.4 29.8 28.3  Current <15 g/d 13.3 13.3 13.5 12.3 12.6  Current 15–25 g/d 10.7 14.1 16.6 17.6 21.2  Current >25 g/d 3.1 4.3 5.9 8.7 12.0 Physical activity,2 %  Inactive 8.3 9.7 11.2 12.3 12.2  Moderately inactive 27.5 31.8 32.3 30.9 29.1  Moderately active 25.4 25.2 23.9 23.2 23.2  Active 38.8 33.3 32.7 33.6 35.4 Waist circumference,3 cm 82.6 (72.0, 100.8) 83.8 (72.5, 102.1) 88.4 (73.5, 103.7) 93.4 (74.8, 104.7) 94.9 (76.8, 105.3) Alcohol intake, g/d 10.7 (0.0, 67.8) 11.4 (0.2, 73.1) 12.6 (0.2, 81.1) 15.4 (0.4, 85.9) 16.9 (0.2, 86.7) Clinical characteristics, %  Hypercholesterolemia 6.6 7.2 7.5 8.1 7.4  Hypertension 15.7 17.5 16.0 15.9 14.7  Diabetes 1.7 1.9 2.2 2.0 2.2  Atrial fibrillation or flutter 0.5 0.6 0.9 0.9 0.8 1Values are percentages or medians (2.5–97.5th percentiles). ALA, α-linolenic acid. 2Cambridge index. 3Adjusted for BMI. View Large Association between ALA and ischemic stroke and ischemic stroke subtypes The median energy-adjusted intake of ALA in the cohort was 1.8 g/d (95% central range: 0.9–3.3 g/d). Multivariable analyses conducted using restricted cubic splines and adjusting for established ischemic stroke risk factors (model 1B) showed a weak positive association between ALA intake and total ischemic stroke, but this association was not statistically significant (P = 0.431) (Figure 1). Multivariable analyses of ischemic stroke subtypes (model 1B) showed no statistically significant associations between intake of ALA and large artery atherosclerosis (P = 0.908), small-vessel occlusion (P = 0.173), or cardio-embolism (P = 0.696) (Figure 1). The associations between intake of ALA and risk of stroke of other etiology and between ALA intake and risk of stroke of undetermined etiology are given in Supplemental Figure 2. FIGURE 1 View largeDownload slide Intake of ALA among middle-aged men and women and the risk of incident ischemic stroke (A), ischemic stroke due to large artery atherosclerosis (B), ischemic stroke due to small-vessel occlusion (C), and ischemic stroke due to cardio-embolism (D). Analyses were adjusted for established ischemic stroke risk factors (model 1B) with median intake as reference (solid vertical line). The 20th, 40th, 60th, and 80th percentiles of ALA are marked by dashed lines. The shaded grey areas represent the 95% CIs of HRs of ischemic stroke and subtypes (curves) which were calculated via Cox proportional hazard regression. Only 2.5–97.5th percentiles of ALA are shown. The statistical analyses included 55,018 participants including 1859 cases of total ischemic stroke, 316 ischemic stroke cases due to large artery atherosclerosis, 835 ischemic stroke cases due to small-vessel occlusion, and 102 ischemic stroke cases due to cardio-embolism. ALA, α-linolenic acid. FIGURE 1 View largeDownload slide Intake of ALA among middle-aged men and women and the risk of incident ischemic stroke (A), ischemic stroke due to large artery atherosclerosis (B), ischemic stroke due to small-vessel occlusion (C), and ischemic stroke due to cardio-embolism (D). Analyses were adjusted for established ischemic stroke risk factors (model 1B) with median intake as reference (solid vertical line). The 20th, 40th, 60th, and 80th percentiles of ALA are marked by dashed lines. The shaded grey areas represent the 95% CIs of HRs of ischemic stroke and subtypes (curves) which were calculated via Cox proportional hazard regression. Only 2.5–97.5th percentiles of ALA are shown. The statistical analyses included 55,018 participants including 1859 cases of total ischemic stroke, 316 ischemic stroke cases due to large artery atherosclerosis, 835 ischemic stroke cases due to small-vessel occlusion, and 102 ischemic stroke cases due to cardio-embolism. ALA, α-linolenic acid. Analyses of energy-adjusted intake of ALA in quintiles and the risk of ischemic stroke and ischemic stroke subtypes are shown in Table 2. In analyses including adjustment for established ischemic stroke risk factors (model 1B), the individual hazards for ischemic stroke and subtypes in the second to fifth quintiles were not statistically different from the reference in the first quintile. Additional adjustments for self-reported history of hypercholesterolemia, hypertension, diabetes mellitus, and atrial fibrillation or flutter at baseline (model 2) also showed individual hazards in the second to fifth quintiles that were not statistically different from the reference in the first quintile. TABLE 2 Quintiles of ALA intake among middle-aged men and women in the Diet, Cancer and Health cohort and HRs for total ischemic stroke and ischemic stroke subtypes1 ALA intake Cases, n Model 1A2 HR (95% CI) Model 1B3 HR (95% CI) Model 24 HR (95% CI) Model 35 HR (95% CI) Total ischemic stroke  ≤1.35 g/d 265 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 307 1.08 (0.92, 1.27) 1.03 (0.87, 1.21) 1.02 (0.86, 1.20) 0.95 (0.80, 1.13)  1.63–1.90 g/d 391 1.22 (1.04, 1.43) 1.12 (0.95, 1.31) 1.12 (0.95, 1.31) 1.01 (0.85, 1.20)  1.91–2.26 g/d 415 1.16 (0.98, 1.37) 1.03 (0.87, 1.22) 1.04 (0.88, 1.23) 0.89 (0.74, 1.08)  ≥2.27 g/d 481 1.30 (1.10, 1.54) 1.10 (0.93, 1.31) 1.12 (0.95, 1.32) 0.92 (0.75, 1.13)  P-trend 0.002 0.318 0.224 0.333 Large artery atherosclerosis  ≤1.35 g/d 50 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 59 1.11 (0.76, 1.62) 1.03 (0.71, 1.51) 1.02 (0.70, 1.50) 0.94 (0.63, 1.39)  1.63–1.90 g/d 60 1.03 (0.70, 1.51) 0.91 (0.62, 1.34) 0.91 (0.62, 1.35) 0.79 (0.52, 1.20)  1.91–2.26 g/d 68 1.07 (0.72, 1.58) 0.90 (0.61, 1.34) 0.91 (0.61, 1.35) 0.76 (0.48, 1.18)  ≥2.27 g/d 79 1.22 (0.82, 1.80) 0.96 (0.65, 1.44) 0.97 (0.65, 1.44) 0.82 (0.50, 1.35)  P-trend 0.414 0.706 0.725 0.336 Small-vessel occlusion  ≤1.35 g/d 115 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 138 1.13 (0.88, 1.45) 1.08 (0.84, 1.39) 1.07 (0.84, 1.37) 1.01 (0.78, 1.31)  1.63–1.90 g/d 180 1.34 (1.05, 1.70) 1.23 (0.97, 1.56) 1.24 (0.97, 1.57) 1.14 (0.88, 1.48)  1.91–2.26 g/d 180 1.21 (0.94, 1.56) 1.09 (0.84, 1.40) 1.09 (0.85, 1.40) 0.98 (0.74, 1.30)  ≥2.27 g/d 222 1.46 (1.14, 1.87) 1.25 (0.98, 1.61) 1.27 (0.98, 1.63) 1.08 (0.79, 1.47)  P-trend 0.004 0.123 0.096 0.799 Cardio-embolism  ≤1.35 g/d 12 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 18 1.32 (0.64, 2.76) 1.30 (0.62, 2.72) 1.30 (0.62, 2.71) 1.14 (0.53, 2.42)  1.63–1.90 g/d 21 1.27 (0.61, 2.63) 1.23 (0.59, 2.56) 1.21 (0.58, 2.52) 1.00 (0.46, 2.20)  1.91–2.26 g/d 22 1.11 (0.53, 2.35) 1.08 (0.51, 2.29) 1.10 (0.52, 2.33) 0.82 (0.35, 1.90)  ≥2.27 g/d 29 1.39 (0.67, 2.89) 1.31 (0.63, 2.76) 1.37 (0.65, 2.87) 0.96 (0.39, 2.36)  P-trend 0.572 0.691 0.590 0.682 Stroke of other etiology  ≤1.35 g/d 13 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 17 1.24 (0.60, 2.57) 1.23 (0.59, 2.54) 1.22 (0.59, 2.52) 1.13 (0.53, 2.40)  1.63–1.90 g/d 22 1.46 (0.72, 2.95) 1.39 (0.68, 2.81) 1.38 (0.68, 2.80) 1.18 (0.55, 2.54)  1.91–2.26 g/d 23 1.39 (0.67, 2.87) 1.29 (0.62, 2.69) 1.30 (0.63, 2.70) 1.05 (0.46, 2.38)  ≥2.27 g/d 23 1.34 (0.64, 2.81) 1.17 (0.55, 2.48) 1.19 (0.56, 2.52) 0.94 (0.38, 2.35)  P-trend 0.480 0.768 0.722 0.788 Stroke of undetermined etiology  ≤1.35 g/d 75 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 75 0.91 (0.66, 1.26) 0.87 (0.63, 1.19) 0.86 (0.62, 1.18) 0.82 (0.59, 1.14)  1.63–1.90 g/d 108 1.12 (0.83, 1.52) 1.03 (0.76, 1.40) 1.03 (0.76, 1.39) 0.94 (0.68, 1.30)  1.91–2.26 g/d 122 1.10 (0.81, 1.50) 0.98 (0.72, 1.34) 0.99 (0.72, 1.35) 0.86 (0.60, 1.21)  ≥2.27 g/d 128 1.11 (0.81, 1.52) 0.94 (0.68, 1.29) 0.95 (0.69, 1.31) 0.77 (0.53, 1.14)  P-trend 0.317 0.948 0.944 0.292 ALA intake Cases, n Model 1A2 HR (95% CI) Model 1B3 HR (95% CI) Model 24 HR (95% CI) Model 35 HR (95% CI) Total ischemic stroke  ≤1.35 g/d 265 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 307 1.08 (0.92, 1.27) 1.03 (0.87, 1.21) 1.02 (0.86, 1.20) 0.95 (0.80, 1.13)  1.63–1.90 g/d 391 1.22 (1.04, 1.43) 1.12 (0.95, 1.31) 1.12 (0.95, 1.31) 1.01 (0.85, 1.20)  1.91–2.26 g/d 415 1.16 (0.98, 1.37) 1.03 (0.87, 1.22) 1.04 (0.88, 1.23) 0.89 (0.74, 1.08)  ≥2.27 g/d 481 1.30 (1.10, 1.54) 1.10 (0.93, 1.31) 1.12 (0.95, 1.32) 0.92 (0.75, 1.13)  P-trend 0.002 0.318 0.224 0.333 Large artery atherosclerosis  ≤1.35 g/d 50 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 59 1.11 (0.76, 1.62) 1.03 (0.71, 1.51) 1.02 (0.70, 1.50) 0.94 (0.63, 1.39)  1.63–1.90 g/d 60 1.03 (0.70, 1.51) 0.91 (0.62, 1.34) 0.91 (0.62, 1.35) 0.79 (0.52, 1.20)  1.91–2.26 g/d 68 1.07 (0.72, 1.58) 0.90 (0.61, 1.34) 0.91 (0.61, 1.35) 0.76 (0.48, 1.18)  ≥2.27 g/d 79 1.22 (0.82, 1.80) 0.96 (0.65, 1.44) 0.97 (0.65, 1.44) 0.82 (0.50, 1.35)  P-trend 0.414 0.706 0.725 0.336 Small-vessel occlusion  ≤1.35 g/d 115 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 138 1.13 (0.88, 1.45) 1.08 (0.84, 1.39) 1.07 (0.84, 1.37) 1.01 (0.78, 1.31)  1.63–1.90 g/d 180 1.34 (1.05, 1.70) 1.23 (0.97, 1.56) 1.24 (0.97, 1.57) 1.14 (0.88, 1.48)  1.91–2.26 g/d 180 1.21 (0.94, 1.56) 1.09 (0.84, 1.40) 1.09 (0.85, 1.40) 0.98 (0.74, 1.30)  ≥2.27 g/d 222 1.46 (1.14, 1.87) 1.25 (0.98, 1.61) 1.27 (0.98, 1.63) 1.08 (0.79, 1.47)  P-trend 0.004 0.123 0.096 0.799 Cardio-embolism  ≤1.35 g/d 12 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 18 1.32 (0.64, 2.76) 1.30 (0.62, 2.72) 1.30 (0.62, 2.71) 1.14 (0.53, 2.42)  1.63–1.90 g/d 21 1.27 (0.61, 2.63) 1.23 (0.59, 2.56) 1.21 (0.58, 2.52) 1.00 (0.46, 2.20)  1.91–2.26 g/d 22 1.11 (0.53, 2.35) 1.08 (0.51, 2.29) 1.10 (0.52, 2.33) 0.82 (0.35, 1.90)  ≥2.27 g/d 29 1.39 (0.67, 2.89) 1.31 (0.63, 2.76) 1.37 (0.65, 2.87) 0.96 (0.39, 2.36)  P-trend 0.572 0.691 0.590 0.682 Stroke of other etiology  ≤1.35 g/d 13 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 17 1.24 (0.60, 2.57) 1.23 (0.59, 2.54) 1.22 (0.59, 2.52) 1.13 (0.53, 2.40)  1.63–1.90 g/d 22 1.46 (0.72, 2.95) 1.39 (0.68, 2.81) 1.38 (0.68, 2.80) 1.18 (0.55, 2.54)  1.91–2.26 g/d 23 1.39 (0.67, 2.87) 1.29 (0.62, 2.69) 1.30 (0.63, 2.70) 1.05 (0.46, 2.38)  ≥2.27 g/d 23 1.34 (0.64, 2.81) 1.17 (0.55, 2.48) 1.19 (0.56, 2.52) 0.94 (0.38, 2.35)  P-trend 0.480 0.768 0.722 0.788 Stroke of undetermined etiology  ≤1.35 g/d 75 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 75 0.91 (0.66, 1.26) 0.87 (0.63, 1.19) 0.86 (0.62, 1.18) 0.82 (0.59, 1.14)  1.63–1.90 g/d 108 1.12 (0.83, 1.52) 1.03 (0.76, 1.40) 1.03 (0.76, 1.39) 0.94 (0.68, 1.30)  1.91–2.26 g/d 122 1.10 (0.81, 1.50) 0.98 (0.72, 1.34) 0.99 (0.72, 1.35) 0.86 (0.60, 1.21)  ≥2.27 g/d 128 1.11 (0.81, 1.52) 0.94 (0.68, 1.29) 0.95 (0.69, 1.31) 0.77 (0.53, 1.14)  P-trend 0.317 0.948 0.944 0.292 1Values are HRs with 95% CIs calculated via Cox proportional hazard regression allowing for separate baseline hazards among men and women. The analyses included 55,018 middle-aged men and women including 1859 cases of total ischemic stroke, 316 ischemic stroke cases due to large artery atherosclerosis, 835 ischemic stroke cases due to small-vessel occlusion, 102 ischemic stroke cases due to cardio-embolism, 98 ischemic stroke cases of other etiology, and 508 ischemic stroke cases of undetermined etiology. ALA, α-linolenic acid. 2Model 1A included baseline age. 3Model 1B included the variables of model 1A and the following risk factors for ischemic stroke: length of schooling, smoking, physical activity, waist circumference adjusted for BMI, and alcohol intake. 4Model 2 included the variables of model 1B and the following potential intermediate variables: self-reported history of hypercholesterolemia and/or use of lipid-lowering medication, hypertension and/or use of antihypertensive medication, diabetes mellitus, and diagnoses of atrial fibrillation or flutter recorded in the Danish National Patient Register at baseline. 5Model 3 included the variables of model 1B and the following potential dietary risk factors: total energy intake, intake of fiber, glycemic load, and intake of SFAs, MUFAs, linoleic acid, and marine ω-3 fatty acids. View Large TABLE 2 Quintiles of ALA intake among middle-aged men and women in the Diet, Cancer and Health cohort and HRs for total ischemic stroke and ischemic stroke subtypes1 ALA intake Cases, n Model 1A2 HR (95% CI) Model 1B3 HR (95% CI) Model 24 HR (95% CI) Model 35 HR (95% CI) Total ischemic stroke  ≤1.35 g/d 265 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 307 1.08 (0.92, 1.27) 1.03 (0.87, 1.21) 1.02 (0.86, 1.20) 0.95 (0.80, 1.13)  1.63–1.90 g/d 391 1.22 (1.04, 1.43) 1.12 (0.95, 1.31) 1.12 (0.95, 1.31) 1.01 (0.85, 1.20)  1.91–2.26 g/d 415 1.16 (0.98, 1.37) 1.03 (0.87, 1.22) 1.04 (0.88, 1.23) 0.89 (0.74, 1.08)  ≥2.27 g/d 481 1.30 (1.10, 1.54) 1.10 (0.93, 1.31) 1.12 (0.95, 1.32) 0.92 (0.75, 1.13)  P-trend 0.002 0.318 0.224 0.333 Large artery atherosclerosis  ≤1.35 g/d 50 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 59 1.11 (0.76, 1.62) 1.03 (0.71, 1.51) 1.02 (0.70, 1.50) 0.94 (0.63, 1.39)  1.63–1.90 g/d 60 1.03 (0.70, 1.51) 0.91 (0.62, 1.34) 0.91 (0.62, 1.35) 0.79 (0.52, 1.20)  1.91–2.26 g/d 68 1.07 (0.72, 1.58) 0.90 (0.61, 1.34) 0.91 (0.61, 1.35) 0.76 (0.48, 1.18)  ≥2.27 g/d 79 1.22 (0.82, 1.80) 0.96 (0.65, 1.44) 0.97 (0.65, 1.44) 0.82 (0.50, 1.35)  P-trend 0.414 0.706 0.725 0.336 Small-vessel occlusion  ≤1.35 g/d 115 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 138 1.13 (0.88, 1.45) 1.08 (0.84, 1.39) 1.07 (0.84, 1.37) 1.01 (0.78, 1.31)  1.63–1.90 g/d 180 1.34 (1.05, 1.70) 1.23 (0.97, 1.56) 1.24 (0.97, 1.57) 1.14 (0.88, 1.48)  1.91–2.26 g/d 180 1.21 (0.94, 1.56) 1.09 (0.84, 1.40) 1.09 (0.85, 1.40) 0.98 (0.74, 1.30)  ≥2.27 g/d 222 1.46 (1.14, 1.87) 1.25 (0.98, 1.61) 1.27 (0.98, 1.63) 1.08 (0.79, 1.47)  P-trend 0.004 0.123 0.096 0.799 Cardio-embolism  ≤1.35 g/d 12 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 18 1.32 (0.64, 2.76) 1.30 (0.62, 2.72) 1.30 (0.62, 2.71) 1.14 (0.53, 2.42)  1.63–1.90 g/d 21 1.27 (0.61, 2.63) 1.23 (0.59, 2.56) 1.21 (0.58, 2.52) 1.00 (0.46, 2.20)  1.91–2.26 g/d 22 1.11 (0.53, 2.35) 1.08 (0.51, 2.29) 1.10 (0.52, 2.33) 0.82 (0.35, 1.90)  ≥2.27 g/d 29 1.39 (0.67, 2.89) 1.31 (0.63, 2.76) 1.37 (0.65, 2.87) 0.96 (0.39, 2.36)  P-trend 0.572 0.691 0.590 0.682 Stroke of other etiology  ≤1.35 g/d 13 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 17 1.24 (0.60, 2.57) 1.23 (0.59, 2.54) 1.22 (0.59, 2.52) 1.13 (0.53, 2.40)  1.63–1.90 g/d 22 1.46 (0.72, 2.95) 1.39 (0.68, 2.81) 1.38 (0.68, 2.80) 1.18 (0.55, 2.54)  1.91–2.26 g/d 23 1.39 (0.67, 2.87) 1.29 (0.62, 2.69) 1.30 (0.63, 2.70) 1.05 (0.46, 2.38)  ≥2.27 g/d 23 1.34 (0.64, 2.81) 1.17 (0.55, 2.48) 1.19 (0.56, 2.52) 0.94 (0.38, 2.35)  P-trend 0.480 0.768 0.722 0.788 Stroke of undetermined etiology  ≤1.35 g/d 75 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 75 0.91 (0.66, 1.26) 0.87 (0.63, 1.19) 0.86 (0.62, 1.18) 0.82 (0.59, 1.14)  1.63–1.90 g/d 108 1.12 (0.83, 1.52) 1.03 (0.76, 1.40) 1.03 (0.76, 1.39) 0.94 (0.68, 1.30)  1.91–2.26 g/d 122 1.10 (0.81, 1.50) 0.98 (0.72, 1.34) 0.99 (0.72, 1.35) 0.86 (0.60, 1.21)  ≥2.27 g/d 128 1.11 (0.81, 1.52) 0.94 (0.68, 1.29) 0.95 (0.69, 1.31) 0.77 (0.53, 1.14)  P-trend 0.317 0.948 0.944 0.292 ALA intake Cases, n Model 1A2 HR (95% CI) Model 1B3 HR (95% CI) Model 24 HR (95% CI) Model 35 HR (95% CI) Total ischemic stroke  ≤1.35 g/d 265 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 307 1.08 (0.92, 1.27) 1.03 (0.87, 1.21) 1.02 (0.86, 1.20) 0.95 (0.80, 1.13)  1.63–1.90 g/d 391 1.22 (1.04, 1.43) 1.12 (0.95, 1.31) 1.12 (0.95, 1.31) 1.01 (0.85, 1.20)  1.91–2.26 g/d 415 1.16 (0.98, 1.37) 1.03 (0.87, 1.22) 1.04 (0.88, 1.23) 0.89 (0.74, 1.08)  ≥2.27 g/d 481 1.30 (1.10, 1.54) 1.10 (0.93, 1.31) 1.12 (0.95, 1.32) 0.92 (0.75, 1.13)  P-trend 0.002 0.318 0.224 0.333 Large artery atherosclerosis  ≤1.35 g/d 50 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 59 1.11 (0.76, 1.62) 1.03 (0.71, 1.51) 1.02 (0.70, 1.50) 0.94 (0.63, 1.39)  1.63–1.90 g/d 60 1.03 (0.70, 1.51) 0.91 (0.62, 1.34) 0.91 (0.62, 1.35) 0.79 (0.52, 1.20)  1.91–2.26 g/d 68 1.07 (0.72, 1.58) 0.90 (0.61, 1.34) 0.91 (0.61, 1.35) 0.76 (0.48, 1.18)  ≥2.27 g/d 79 1.22 (0.82, 1.80) 0.96 (0.65, 1.44) 0.97 (0.65, 1.44) 0.82 (0.50, 1.35)  P-trend 0.414 0.706 0.725 0.336 Small-vessel occlusion  ≤1.35 g/d 115 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 138 1.13 (0.88, 1.45) 1.08 (0.84, 1.39) 1.07 (0.84, 1.37) 1.01 (0.78, 1.31)  1.63–1.90 g/d 180 1.34 (1.05, 1.70) 1.23 (0.97, 1.56) 1.24 (0.97, 1.57) 1.14 (0.88, 1.48)  1.91–2.26 g/d 180 1.21 (0.94, 1.56) 1.09 (0.84, 1.40) 1.09 (0.85, 1.40) 0.98 (0.74, 1.30)  ≥2.27 g/d 222 1.46 (1.14, 1.87) 1.25 (0.98, 1.61) 1.27 (0.98, 1.63) 1.08 (0.79, 1.47)  P-trend 0.004 0.123 0.096 0.799 Cardio-embolism  ≤1.35 g/d 12 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 18 1.32 (0.64, 2.76) 1.30 (0.62, 2.72) 1.30 (0.62, 2.71) 1.14 (0.53, 2.42)  1.63–1.90 g/d 21 1.27 (0.61, 2.63) 1.23 (0.59, 2.56) 1.21 (0.58, 2.52) 1.00 (0.46, 2.20)  1.91–2.26 g/d 22 1.11 (0.53, 2.35) 1.08 (0.51, 2.29) 1.10 (0.52, 2.33) 0.82 (0.35, 1.90)  ≥2.27 g/d 29 1.39 (0.67, 2.89) 1.31 (0.63, 2.76) 1.37 (0.65, 2.87) 0.96 (0.39, 2.36)  P-trend 0.572 0.691 0.590 0.682 Stroke of other etiology  ≤1.35 g/d 13 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 17 1.24 (0.60, 2.57) 1.23 (0.59, 2.54) 1.22 (0.59, 2.52) 1.13 (0.53, 2.40)  1.63–1.90 g/d 22 1.46 (0.72, 2.95) 1.39 (0.68, 2.81) 1.38 (0.68, 2.80) 1.18 (0.55, 2.54)  1.91–2.26 g/d 23 1.39 (0.67, 2.87) 1.29 (0.62, 2.69) 1.30 (0.63, 2.70) 1.05 (0.46, 2.38)  ≥2.27 g/d 23 1.34 (0.64, 2.81) 1.17 (0.55, 2.48) 1.19 (0.56, 2.52) 0.94 (0.38, 2.35)  P-trend 0.480 0.768 0.722 0.788 Stroke of undetermined etiology  ≤1.35 g/d 75 1 (reference) 1 (reference) 1 (reference) 1 (reference)  1.36–1.62 g/d 75 0.91 (0.66, 1.26) 0.87 (0.63, 1.19) 0.86 (0.62, 1.18) 0.82 (0.59, 1.14)  1.63–1.90 g/d 108 1.12 (0.83, 1.52) 1.03 (0.76, 1.40) 1.03 (0.76, 1.39) 0.94 (0.68, 1.30)  1.91–2.26 g/d 122 1.10 (0.81, 1.50) 0.98 (0.72, 1.34) 0.99 (0.72, 1.35) 0.86 (0.60, 1.21)  ≥2.27 g/d 128 1.11 (0.81, 1.52) 0.94 (0.68, 1.29) 0.95 (0.69, 1.31) 0.77 (0.53, 1.14)  P-trend 0.317 0.948 0.944 0.292 1Values are HRs with 95% CIs calculated via Cox proportional hazard regression allowing for separate baseline hazards among men and women. The analyses included 55,018 middle-aged men and women including 1859 cases of total ischemic stroke, 316 ischemic stroke cases due to large artery atherosclerosis, 835 ischemic stroke cases due to small-vessel occlusion, 102 ischemic stroke cases due to cardio-embolism, 98 ischemic stroke cases of other etiology, and 508 ischemic stroke cases of undetermined etiology. ALA, α-linolenic acid. 2Model 1A included baseline age. 3Model 1B included the variables of model 1A and the following risk factors for ischemic stroke: length of schooling, smoking, physical activity, waist circumference adjusted for BMI, and alcohol intake. 4Model 2 included the variables of model 1B and the following potential intermediate variables: self-reported history of hypercholesterolemia and/or use of lipid-lowering medication, hypertension and/or use of antihypertensive medication, diabetes mellitus, and diagnoses of atrial fibrillation or flutter recorded in the Danish National Patient Register at baseline. 5Model 3 included the variables of model 1B and the following potential dietary risk factors: total energy intake, intake of fiber, glycemic load, and intake of SFAs, MUFAs, linoleic acid, and marine ω-3 fatty acids. View Large In analyses including adjustment for established risk factors for ischemic stroke and dietary factors (model 3), HRs were generally somewhat lower compared with the observed HRs in model 1B, but the hazards in the second to fifth quintiles were not statistically different from the reference in the first quintile. No linear trends across quintiles were observed in either of the multivariable adjusted models. Sensitivity analyses indicated that the models with the use of restricted cubic splines were robust when the location and number of knots were modified. No evidence of deviation from the proportionality assumption was observed. A Radar plot of the underlying dietary pattern in the cohort showed several differences in the energy-adjusted median intake of selected foods among participants in different quintiles of ALA intake (Supplemental Figure 3). Participants in the highest quintile of energy-adjusted ALA intake thus had higher intakes of refined cereals, potatoes, vegetable oils and mayonnaises, margarines, butter and other animal fat, eggs, processed and red meat, fish, poultry, snacks and fatty potatoes, soft drinks and juices, and alcohol, and lower intakes of fruits, vegetables, and lean dairy products. Discussion In this large cohort study, indications of a weak positive association between ALA intake and the risk of total ischemic stroke, a weak inverse association between ALA intake and the risk of large artery atherosclerosis, a weak positive association between ALA intake and the risk of small-vessel occlusion, and a weak inverse U-shaped association between ALA intake and the risk of cardio-embolism were shown, but none of these associations was statistically significant. Given the relatively weak and statistically nonsignificant associations observed, the current study suggested that intake of ALA was not associated with the risk of ischemic stroke or ischemic stroke subtypes to any degree relevant to public health. It is important to emphasize that our study did not evaluate the potential effect of a Mediterranean diet on ischemic stroke, but it suggests that if such an effect exists it is unlikely to be related to ALA intake. Some strengths and limitations should be mentioned. The current study holds the advantage of a follow-up design and nearly complete follow-up, limiting the concern of selection bias. Information bias derived from potential differential misclassification of the outcomes is an unlikely explanation for the observed results because cases were identified independently of the baseline dietary assessment through the nationwide Danish National Patient Register and subsequently validated and classified in subtypes according to the TOAST-classification. However, random measurement error of ALA intake was likely because the diet was self-reported by a FFQ and not designed specifically to measure ALA intake, which may have attenuated the observed associations toward the null. Another possible limitation is that changes in dietary habits during follow-up might have occurred and repeated dietary measurements would have been preferable. Detailed information on ischemic stroke risk factors was included in the analyses, but residual confounding from known or unknown ischemic stroke risk factors may still be of importance for the observed associations. Adjustment for established ischemic stroke risk factors (model 1B) generally weakened the observed associations, indicating confounding from these risk factors. Additional adjustment for potential intermediate variables (model 2) showed similar patterns of associations. However, the interpretation of this model is complicated because these clinical characteristics may represent intermediate steps in the causal pathway between ALA intake and the risk of ischemic stroke and adjustment for these variables potentially could introduce bias. We evaluated by radar plots the underlying dietary patterns to explore further the intake of ALA as a marker of specific dietary patterns in the study population. Although several differences were seen at baseline, the radar plots did not indicate that ALA intake solely reflects a healthy dietary pattern within this cohort. Additional adjustments for dietary factors revealed somewhat weaker associations compared to model 1B and confounding from dietary factors cannot be excluded. However, the interpretation of measures of association from models including diet is complicated because adjustment for dietary factors may introduce restrictions in the underlying dietary pattern that are not comparable with the ordinary dietary pattern. Thus, findings from analyses with and without adjustment for dietary factors should not be directly compared. Given the interpretational complexities of models 2 and 3, we consider model 1B the most appropriate model for interpretation. The classification of ischemic stroke into subtypes did not allow for gender-specific analyses due to the limited number of cases. We performed analyses on stroke of other etiology and stroke of undetermined etiology, but these analyses were not commented on further because they were without clear interpretation. Previous cohort studies on ALA intake have not differentiated between ischemic stroke subtypes and the results on total ischemic stroke have been inconsistent (12, 13, 17, 19). A small nested case-control study reported an inverse association between ALA content in cholesterol esters and the risk of total stroke (30), but other biomarker studies investigating the association between ALA content in cholesterol ester or plasma phospholipids and total ischemic stroke have generally reported modest inverse statistically nonsignificant associations (31, 32) or inconsistent results (13, 33–36). Further studies are warranted to investigate the associations between ALA exposure and the risk of ischemic stroke subtypes. A previous cohort study has suggested that ALA in particular may reduce CHD risk when intake of marine ω-3 fatty acids is low (8). This could be important because the intake of marine ω-3 fatty acids in our study was markedly higher than compared with previous cohort studies that have reported inverse associations between ALA intake and the risk of cardiovascular disease (6, 9–11). In conclusion, in this large cohort study, intake of ALA was neither consistently nor statistically significantly associated with the risk of ischemic stroke or ischemic stroke subtypes among middle-aged Danish men and women. Acknowledgments The authors’ responsibilities were as follows—AT and KO: conceived the study concept and contributed to the data acquisition; CSB: conducted the statistical analyses, prepared the tables and figures, and wrote the manuscript; CSB, SKV, MUJ, SL-C, EBS, and KO: contributed to the study design, planning of the statistical analyses, interpretation of data, and writing of the manuscript; SL-C: supervised the conduct of the statistical analyses; AT: contributed to the critical interpretation of the manuscript. All authors read and approved the final manuscript. Notes Supported by The Danish Heart Foundation grant 17-R115-A7415-22060. The Danish Cancer Society funded the Diet, Cancer and Health study. The funding agencies had no influence on the design, analysis, or writing of this article. Author disclosures: CSB, SKV, SL-C, MUJ, AT, EBS, and KO, no conflicts of interest. Supplemental Tables 1 and 2 and Supplemental Figures 1–3 are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/jn/. Abbreviations used: ALA, α-linolenic acid; CHD, coronary heart disease; ICD, International Classification of Diseases; TOAST, trial of ORG 10172 in acute stroke treatment. References 1. Rajaram S . Health benefits of plant-derived alpha-linolenic acid . Am J Clin Nutr 2014 ; 100 : 443 – 8 . Google Scholar CrossRef Search ADS 2. Fleming J , Kris-Etherton P . The evidence for alpha-linolenic acid and cardiovascular disease benefits: comparisons with eicosapentaenoic acid and docosahexaenoic acid . Adv Nutr 2014 ; 5 : 863 – 76 . Google Scholar CrossRef Search ADS 3. de Lorgeril M , Salen P . Mediterranean diet and n-3 fatty acids in the prevention and treatment of cardiovascular disease . J Cardiovasc Med 2007 ; 8 ( Suppl 1 ): 38 – 41 . Google Scholar CrossRef Search ADS 4. Gebauer S , Psota T , Harris W , Kris-Etherton P . n-3 fatty acid dietary recommendations and food sources to achieve essentiality and cardiovascular benefits . Am J Clin Nutr 2006 ; 83 : 1526 – 35 . Google Scholar CrossRef Search ADS 5. Bork C , Jakobsen M , Lundbye-Christensen S , Tjønneland A , Schmidt E , Overvad K . Dietary intake and adipose tissue content of alpha-linolenic acid and risk of myocardial infarction: a Danish cohort study . Am J Clin Nutr 2016 ; 104 : 41 – 8 . Google Scholar CrossRef Search ADS PubMed 6. Hu F , Stampfer M , Manson J , Rimm E , Wolk A , Colditz G , Hennekens C , Willett W . Dietary intake of alpha-linolenic acid and risk of fatal ischemic heart disease among women . Am J Clin Nutr 1999 ; 69 : 890 – 7 . Google Scholar CrossRef Search ADS PubMed 7. Ascherio A , Rimm E , Giovannucci E , Spiegelman D , Stampfer M , Willett W . Dietary fat and risk of coronary heart disease in men: cohort follow up study in the United States . BMJ 1996 ; 313 : 84 – 90 . Google Scholar CrossRef Search ADS PubMed 8. Mozaffarian D , Ascherio A , Hu F , Stampfer M , Willett W , Siscovick D , Rimm E . Interplay between different polyunsaturated fatty acids and risk of coronary heart disease in men . Circulation 2005 ; 111 : 157 – 64 . Google Scholar CrossRef Search ADS PubMed 9. Vedtofte M , Jakobsen M , Lauritzen L , O'Reilly E , Virtamo J , Knekt P , Colditz G , Hallmans G , Buring J , Steffen L et al. Association between the intake of alpha-linolenic acid and the risk of CHD . Br J Nutr 2014 ; 112 : 735 – 43 . Google Scholar CrossRef Search ADS PubMed 10. Dolecek T . Epidemiological evidence of relationships between dietary polyunsaturated fatty acids and mortality in the multiple risk factor intervention trial . Proc Soc Exp Biol Med 1992 ; 200 : 177 – 82 . Google Scholar CrossRef Search ADS PubMed 11. Koh A , Pan A , Wang R , Odegaard A , Pereira M , Yuan J , Koh W . The association between dietary omega-3 fatty acids and cardiovascular death: the Singapore Chinese Health Study . Eur J Prev Cardiol 2015 ; 22 : 364 – 72 . Google Scholar CrossRef Search ADS PubMed 12. de Goede J , Verschuren W , Boer J , Kromhout D , Geleijnse J . Alpha-linolenic acid intake and 10-year incidence of coronary heart disease and stroke in 20,000 middle-aged men and women in the Netherlands . PLoS One 2011 ; 6 : e17967 . Google Scholar CrossRef Search ADS PubMed 13. Fretts A , Mozaffarian D , Siscovick D , Sitlani C , Psaty B , Rimm E , Song X , McKnight B , Spiegelman D , King I et al. Plasma phospholipid and dietary α-linolenic acid, mortality, CHD and stroke: the Cardiovascular Health Study . Br J Nutr 2014 ; 112 : 1206 – 13 . Google Scholar CrossRef Search ADS PubMed 14. Albert C , Oh K , Whang W , Manson J , Chae C , Stampfer M , Willett W , Hu F . Dietary alpha-linolenic acid intake and risk of sudden cardiac death and coronary heart disease . Circulation 2005 ; 112 : 3232 – 8 . Google Scholar CrossRef Search ADS PubMed 15. Oomen C , Ocké M , Feskens E , Kok F , Kromhout D . Alpha-linolenic acid intake is not beneficially associated with 10-y risk of coronary artery disease incidence: the Zutphen Elderly Study . Am J Clin Nutr 2001 ; 74 : 457 – 63 . Google Scholar CrossRef Search ADS PubMed 16. He K , Rimm E , Merchant A , Rosner B , Stampfer M , Willett W , Ascherio A . Fish consumption and risk of stroke in men . JAMA 2002 ; 288 : 3130 – 6 . Google Scholar CrossRef Search ADS PubMed 17. Rhee J , Kim E , Buring J , Kurth T . Fish consumption, omega-3 fatty acids, and risk of cardiovascular disease . Am J Prev Med 2017 ; 52 : 10 – 19 . Google Scholar CrossRef Search ADS PubMed 18. Sala-Vila A , Guasch-Ferré M , Hu F , Sánchez-Tainta A , Bulló M , Serra-Mir M , López-Sabater C , Sorlí J , Arós F , Fiol M et al. Dietary alpha-linolenic acid, marine omega-3 fatty acids, and mortality in a population with high fish consumption: findings from the PREvención con DIeta MEDiterránea (PREDIMED) study . J Am Heart Assoc 2016 ; 5 : e002543 . Google Scholar CrossRef Search ADS PubMed 19. Larsson S , Virtamo J , Wolk A . Dietary fats and dietary cholesterol and risk of stroke in women . Atherosclerosis 2012 ; 221 : 282 – 6 . Google Scholar CrossRef Search ADS PubMed 20. Adams H , Bendixen B , Kappelle L , Biller J , Love B , Gordon D , Marsh E . Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment . Stroke 1993 ; 24 : 35 – 41 . Google Scholar CrossRef Search ADS PubMed 21. Kolominsky-Rabas P , Weber M , Gefeller O , Neundoerfer B , Heuschmann P . Epidemiology of ischemic stroke subtypes according to TOAST criteria: incidence, recurrence, and long-term survival in ischemic stroke subtypes: a population-based study . Stroke 2001 ; 32 : 2735 – 40 . Google Scholar CrossRef Search ADS PubMed 22. Tjønneland A , Olsen A , Boll K , Stripp C , Christensen J , Engholm G , Overvad K . Study design, exposure variables, and socioeconomic determinants of participation in Diet, Cancer and Health: a population-based prospective cohort study of 57,053 men and women in Denmark . Scand J Public Health 2007 ; 35 : 432 – 41 . Google Scholar CrossRef Search ADS PubMed 23. Overvad K , Tjønneland A , Haraldsdóttir J , Ewertz M , Jensen O . Development of a semiquantitative food frequency questionnaire to assess food, energy and nutrient intake in Denmark . Int J Epidemiol 1991 ; 20 : 900 – 5 . Google Scholar CrossRef Search ADS PubMed 24. Tjønneland A , Overvad K , Haraldsdóttir J , Bang S , Ewerts M , Jensen O . Validations of a semiquantitative food frequency questionnaire developed in Denmark . Int J Epidemiol 1991 ; 20 : 906 – 12 . Google Scholar CrossRef Search ADS PubMed 25. Willett W , Stampfer M . Total energy intake: implications for epidemiologic analyses . Am J Epidemiol 1986 ; 124 : 17 – 27 . Google Scholar CrossRef Search ADS PubMed 26. Andersen T , Madsen M , Jørgensen J , Mellemkjær L , Olsen J . The Danish National Hospital Register. A valuable source of data for modern health sciences . Dan Med Bull 1999 ; 46 : 263 – 8 . Google Scholar PubMed 27. Lühdorf P , Overvad K , Schmidt E , Johnsen S , Bach F . Predictive value of stroke discharge diagnoses in the Danish National Patient Register . Scand J Public Health 2017 ; 45 : 630 – 6 . Google Scholar CrossRef Search ADS PubMed 28. Wareham N , Jakes W , Rennie K , Schuit J , Mitchell J , Hennings S , Day N . Validity and repeatability of a simple index derived from the short physical activity questionnaire used in the European Prospective Investigation into Cancer and Nutrition (EPIC) study . Public Health Nutr 2003 ; 6 : 407 – 13 . Google Scholar CrossRef Search ADS PubMed 29. Harrell FE . Regression modeling strategies. With applications to linear models, logistic and ordinal regression, and survival analysis . 2nd ed . New York : Springer ; 2015 . 30. Simon J , Fong J , Bernert JT Jr , Browner W . Serum fatty acids and the risk of stroke . Stroke 1995 ; 26 : 778 – 82 . Google Scholar CrossRef Search ADS PubMed 31. Daneshmand R , Kurl S , Tuomainen T , Virtanen J . Associations of serum n-3 and n-6 PUFA and hair mercury with the risk of incident stroke in men: the Kuopio Ischaemic Heart Disease risk factor study (KIHD) . Br J Nutr 2016 ; 115 : 1851 – 9 . Google Scholar CrossRef Search ADS PubMed 32. Yaemsiri S , Sen S , Tinker L , Robinson W , Evans R , Rosamond W , Wasserthiel-Smoller S , He K . Serum fatty acids and incidence of ischemic stroke among postmenopausal women . Stroke 2013 ; 44 : 2710 – 17 . Google Scholar CrossRef Search ADS PubMed 33. Yamagishi K , Folsom A , Steffen L . Plasma fatty acid composition and incident ischemic stroke in middle-aged adults: the Atherosclerosis Risk in Communities (ARIC) study . Cerebrovasc Dis 2013 ; 36 : 38 – 46 . Google Scholar CrossRef Search ADS PubMed 34. Wiberg B , Sundström J , Árnlöv J , Terént A , Vessby B , Zethelius B , Lind L . Metabolic risk factors for stroke and transient ischemic attacks in middle-aged men: a community-based study with long-term follow-up . Stroke 2006 ; 37 : 2898 – 903 . Google Scholar CrossRef Search ADS PubMed 35. De Goede J , Verschuren WMM , Boer JMA , Kromhout D , Geleijnse JM . N-6 and n-3 fatty acid cholesteryl esters in relation to incident stroke in a Dutch adult population: a nested case-control study . Nutr Metab Cardiovasc Dis 2013 ; 23 : 737 – 43 . Google Scholar CrossRef Search ADS PubMed 36. Iso H , Sato S , Umemura U , Kudo M , Koike K , Kitamura A , Imano H , Okamura T , Naito Y , Shimamoto T . Linoleic acid, other fatty acids, and the risk of stroke . Stroke 2002 ; 33 : 2086 – 93 . Google Scholar CrossRef Search ADS PubMed © 2018 American Society for Nutrition. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Journal of NutritionOxford University Press

Published: May 15, 2018

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