Raji Balasubramanian1, Jie Hu2, Marta Guasch-Ferre2, Jun Li2, Farzaneh Sorond2, Yibai Zhao2, Katherine H Shutta2, Jordi Salas-Salvado2, Frank Hu2, Clary B Clish2, Kathryn M Rexrode2. 1. From the Department of Biostatistics and Epidemiology (R.B., Y.Z., K.H.S.), University of Massachusetts-Amherst; Division of Women's Health (J.H., K.M.R.) and Channing Division of Network Medicine, Department of Medicine (M.G.-F., F.H.), Brigham and Women's Hospital, Harvard Medical School; Departments of Nutrition (M.G.-F., J.L., F.H.) and Epidemiology (J.L., F.H.), Harvard T.H. Chan School of Public Health, Boston, MA; Davee Department of Neurology, Division of Stroke and Neurocritical Care (F.S.), Northwestern Feinberg School of Medicine, Chicago, IL; Departament de Bioquímica i Biotecnologia, Unitat de Nutrició (J.S.S.), Universitat Rovira i Virgili, Reus; Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN) (J.S.-S.), Institute of Health Carlos III, Madrid; Nutrition Unit, Pere Virgili Research Institute (IISPV) (J.S.-S.), University Hospital of Sant Joan de Reus, Spain; and Broad Institute of the Massachusetts Institute of Technology and Harvard University (C.B.C.), Cambridge. rbalasub@umass.edu. 2. From the Department of Biostatistics and Epidemiology (R.B., Y.Z., K.H.S.), University of Massachusetts-Amherst; Division of Women's Health (J.H., K.M.R.) and Channing Division of Network Medicine, Department of Medicine (M.G.-F., F.H.), Brigham and Women's Hospital, Harvard Medical School; Departments of Nutrition (M.G.-F., J.L., F.H.) and Epidemiology (J.L., F.H.), Harvard T.H. Chan School of Public Health, Boston, MA; Davee Department of Neurology, Division of Stroke and Neurocritical Care (F.S.), Northwestern Feinberg School of Medicine, Chicago, IL; Departament de Bioquímica i Biotecnologia, Unitat de Nutrició (J.S.S.), Universitat Rovira i Virgili, Reus; Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN) (J.S.-S.), Institute of Health Carlos III, Madrid; Nutrition Unit, Pere Virgili Research Institute (IISPV) (J.S.-S.), University Hospital of Sant Joan de Reus, Spain; and Broad Institute of the Massachusetts Institute of Technology and Harvard University (C.B.C.), Cambridge.
Abstract
BACKGROUND AND OBJECTIVES: Women have higher lifetime risk of stroke than men, and metabolic factors seem more strongly associated with stroke for women than men. However, few studies in either men or women have evaluated metabolomic profiles and incident stroke. METHODS: We applied liquid chromatography-tandem mass spectrometry to measure 519 plasma metabolites in a discovery set of women in the Nurses' Health Study (NHS; 454 incident ischemic stroke cases, 454 controls) with validation in 2 independent, prospective cohorts: Prevención con Dieta Mediterránea (PREDIMED; 118 stroke cases, 791 controls) and Nurses' Health Study 2 (NHS2; 49 ischemic stroke cases, 49 controls). We applied logistic regression models with stroke as the outcome to adjust for multiple risk factors; the false discovery rate was controlled through the q value method. RESULTS: Twenty-three metabolites were significantly associated with incident stroke in NHS after adjustment for traditional risk factors (q < 0.05). Of these, 14 metabolites were available in PREDIMED and 3 were significantly associated with incident stroke: methionine sulfoxide, N6-acetyllysine, and sucrose (q < 0.05). In NHS2, one of the 23 metabolites (glucuronate) was significantly associated with incident stroke (q < 0.05). For all 4 metabolites, higher levels were associated with increased risk. These 4 metabolites were used to create a stroke metabolite score (SMS) in the NHS and tested in PREDIMED. Per unit of standard deviation of SMS, the odds ratio for incident stroke was 4.12 (95% confidence interval [CI] 2.26-7.51) in PREDIMED, after adjustment for risk factors. In PREDIMED, the area under the receiver operating characteristic curve (AUC) for the model including SMS and traditional risk factors was 0.70 (95% CI 0.75-0.79) vs the AUC for the model including the traditional risk factors only of 0.65 (95% CI 0.70-0.75), corresponding to a 5% improvement in risk prediction with SMS (p < 0.005). DISCUSSION: Metabolites associated with stroke included 2 amino acids, a carboxylic acid, and sucrose. A composite SMS including these metabolites was associated with ischemic stroke and showed improvement in risk prediction beyond traditional risk factors. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that a SMS accurately predicts incident ischemic stroke risk.
BACKGROUND AND OBJECTIVES: Women have higher lifetime risk of stroke than men, and metabolic factors seem more strongly associated with stroke for women than men. However, few studies in either men or women have evaluated metabolomic profiles and incident stroke. METHODS: We applied liquid chromatography-tandem mass spectrometry to measure 519 plasma metabolites in a discovery set of women in the Nurses' Health Study (NHS; 454 incident ischemic stroke cases, 454 controls) with validation in 2 independent, prospective cohorts: Prevención con Dieta Mediterránea (PREDIMED; 118 stroke cases, 791 controls) and Nurses' Health Study 2 (NHS2; 49 ischemic stroke cases, 49 controls). We applied logistic regression models with stroke as the outcome to adjust for multiple risk factors; the false discovery rate was controlled through the q value method. RESULTS: Twenty-three metabolites were significantly associated with incident stroke in NHS after adjustment for traditional risk factors (q < 0.05). Of these, 14 metabolites were available in PREDIMED and 3 were significantly associated with incident stroke: methionine sulfoxide, N6-acetyllysine, and sucrose (q < 0.05). In NHS2, one of the 23 metabolites (glucuronate) was significantly associated with incident stroke (q < 0.05). For all 4 metabolites, higher levels were associated with increased risk. These 4 metabolites were used to create a stroke metabolite score (SMS) in the NHS and tested in PREDIMED. Per unit of standard deviation of SMS, the odds ratio for incident stroke was 4.12 (95% confidence interval [CI] 2.26-7.51) in PREDIMED, after adjustment for risk factors. In PREDIMED, the area under the receiver operating characteristic curve (AUC) for the model including SMS and traditional risk factors was 0.70 (95% CI 0.75-0.79) vs the AUC for the model including the traditional risk factors only of 0.65 (95% CI 0.70-0.75), corresponding to a 5% improvement in risk prediction with SMS (p < 0.005). DISCUSSION: Metabolites associated with stroke included 2 amino acids, a carboxylic acid, and sucrose. A composite SMS including these metabolites was associated with ischemic stroke and showed improvement in risk prediction beyond traditional risk factors. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that a SMS accurately predicts incident ischemic stroke risk.
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