Investigators discovered a total of 23 metabolites that are associated with incident ischemic stroke in women, 4 of which were validated in independent cohorts.
Women with metabolomic profiles that include N6-acetyllysine, methionine sulfoxide, sucrose/lactose/trehalose, or glucuronate are at an increased risk for incident ischemic stroke, new findings suggest. A score comprising the 4 validated metabolites resulted in significant improvement in stroke risk protection.1
Senior author Kathyrn Rexrode, MD, associate professor, Harvard Medical School, and colleagues first examined 519 plasma metabolites from the Nurses’ Health Study (NHS) using liquid chromatography–tandem mass spectrometry. NHS included 454 incident ischemic stroke cases and matched controls. To validate results, data from 2 independent, prospective cohorts were used: Prevencion con Dieta Mediterranea (PREDIMED; 118 stroke cases, 791 controls) and NHS 2 (NHS2; 49 ischemic stroke cases, 49 controls).
"Our prospective study of the metabolomics of incident ischemic stroke in women fills an important gap in the literature, providing the largest study of the metabolomics of stroke from a single cohort, validating the metabolites in an independent dataset and evaluating metabolites in risk prediction," the study authors wrote.
Rexrode et al found 73 of the 519 metabolites to be significantly associated with incident ischemic stroke (q <.05) after controlling for matching variables alone, such as age, race, hormone therapy (HT) use, smoking status, history of cardiovascular disease, and fasting status. Of these, 23 metabolites remained significant after additionally adjusting for stroke risk factors that included body mass index (BMI), history of elevated cholesterol, history of hypertension, history of diabetes, HTuse, aspirin use, total cholesterol, HDL cholesterol, and hemoglobin AIC (q <.05).
All metabolites that met the threshold for statistical significance were then evaluated individually in the PREDIMED and NHS2 datasets. Of the 23 metabolites, 14 were available to test in PREDIMED. Of these, 3 metabolites—N6-acetyllysine, methionine sulfoxide, and sucrose/lactose/trehalose—were each associated with an increased risk of incident stroke in fully adjusted models, with odds ratios (ORs) for a 1 SD increase in metabolite level of 1.30 (95% CI, 1.07-1.59), 1.53 (95% CI, 1.28-1.83), and 1.31 (95% CI, 1.08-1.59), respectively.
In NHS2, glucuronate was the only metabolite of the discovered group that was significantly associated with incident stroke, with a fully adjusted OR of 2.35 (95% CI, 1.13-4.89) for a 1 SD increase in metabolite levels. Additionally, the statistical significance of sebacate was close to the threshold for validation (q = .08) after controlling for matching variables and was associated with risk in the fully adjusted model with an OR of 2.85 (95% CI, 1.09-7.47) for a 1 SD increase in levels.
Investigators also estimated stroke metabolite stroke (SMS) using a conditional logistic regression model that simultaneously adjusted for the 4 validated metabolites, in addition to the full set of stroke risk factors. The SMS, treated as a composite measure of the NHS dataset of validated metabolites, was used to quantify improvements in risk prediction in the PREDIMED cohort. At the end of the analysis, the SMS was associated with incident stroke in PREDIMED with an OR of 4.12 (95% CI, 2.26-7.51), corresponding to a 1 SD increase in the SMS, after adjusting for traditional risk factors.
In the logistic regression model that included the SMS and traditional risk factors in PREDIMED, there was a recorded area under the curve (AUC) of 0.70 (95% CI, 0.75-0.79) compared to an AUC of 0.65 (95% CI, 0.70-0.75) in a logistic regression model that only included traditional factors. This 5% absolute increase in AUC attributable to the SMS above and beyond traditional risk factors was significant (P <.005).
Rexrode et al noted that the strengths of the study "include a well-validated LC-MS metabolomics platform, detailed covariate information, and a large number of carefully adjudicated endpoints. Our study employed a robust, 2-stage discovery and validation framework in 3 independent datasets."