Lars Lind1, Samira Salihovic2, Andrea Ganna3, Johan Sundström4, Corey D Broeckling5, Patrik K Magnusson6, Nancy L Pedersen6, Agneta Siegbahn1, Jessica Prenni5, Tove Fall2, Erik Ingelsson7, Johan Ärnlöv8. 1. Department of Medical Sciences, Uppsala University, Uppsala, Sweden. 2. Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden. 3. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts. 4. Department of Medical Sciences, Uppsala University, Uppsala, Sweden; Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden. 5. Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, Colorado. 6. Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institute, Stockholm, Sweden. 7. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California; Stanford Cardiovascular Institute, Stanford, California. 8. Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Huddinge, Sweden; School of Health and Social Sciences, Dalarna University, Falun, Sweden. Electronic address: johan.arnlov@ki.se.
Abstract
BACKGROUND AND PURPOSE: To search for novel pathophysiological pathways related to ischemic stroke using a metabolomics approach. METHODS: We identified 204 metabolites in plasma by liquid chromatography mass spectrometry in 3 independent population-based samples (TwinGene, Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) and Uppsala Longitudinal Study of Adult Men). TwinGene was used for discovery and the other 2 samples were meta-analyzed as replication. In PIVUS, traditional cardiovascular (CV) risk factors, multiple markers of subclinical CV disease, markers of coagulation/fibrinolysis were measured and analyzed in relation to top metabolites. RESULTS: In TwinGene (177 incident cases, median follow-up 4.3 years), levels of 28 metabolites were associated with incident ischemic stroke at a false discover rate (FDR) of 5%. In the replication (together 194 incident cases, follow-up 10 and 12 years, respectively), only sphingomyelin (32:1) was significantly associated (HR .69 per SD change, 95% CI .57-0.83, P value = .00014; FDR <5%) when adjusted for systolic blood pressure, diabetes, smoking, low density lipoportein (LDL)- and high density lipoprotein (HDL), body mass index (BMI) and atrial fibrillation. In PIVUS, sphingomyelin (32:1) levels were significantly related to both LDL- and HDL-cholesterol in a positive fashion, and to serum triglycerides, BMI and diabetes in a negative fashion. Furthermore, sphingomyelin (32:1) levels were related to vasodilation in the forearm resistance vessels, and inversely to leukocyte count (P < .0069 and .0026, respectively). CONCLUSIONS: An inverse relationship between sphingomyelin (32:1) and incident ischemic stroke was identified, replicated, and characterized. A possible protective role for sphingomyelins in stroke development has to be further investigated in additional experimental and clinical studies.
BACKGROUND AND PURPOSE: To search for novel pathophysiological pathways related to ischemic stroke using a metabolomics approach. METHODS: We identified 204 metabolites in plasma by liquid chromatography mass spectrometry in 3 independent population-based samples (TwinGene, Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) and Uppsala Longitudinal Study of Adult Men). TwinGene was used for discovery and the other 2 samples were meta-analyzed as replication. In PIVUS, traditional cardiovascular (CV) risk factors, multiple markers of subclinical CV disease, markers of coagulation/fibrinolysis were measured and analyzed in relation to top metabolites. RESULTS: In TwinGene (177 incident cases, median follow-up 4.3 years), levels of 28 metabolites were associated with incident ischemic stroke at a false discover rate (FDR) of 5%. In the replication (together 194 incident cases, follow-up 10 and 12 years, respectively), only sphingomyelin (32:1) was significantly associated (HR .69 per SD change, 95% CI .57-0.83, P value = .00014; FDR <5%) when adjusted for systolic blood pressure, diabetes, smoking, low density lipoportein (LDL)- and high density lipoprotein (HDL), body mass index (BMI) and atrial fibrillation. In PIVUS, sphingomyelin (32:1) levels were significantly related to both LDL- and HDL-cholesterol in a positive fashion, and to serum triglycerides, BMI and diabetes in a negative fashion. Furthermore, sphingomyelin (32:1) levels were related to vasodilation in the forearm resistance vessels, and inversely to leukocyte count (P < .0069 and .0026, respectively). CONCLUSIONS: An inverse relationship between sphingomyelin (32:1) and incident ischemic stroke was identified, replicated, and characterized. A possible protective role for sphingomyelins in stroke development has to be further investigated in additional experimental and clinical studies.
Authors: Lars Lind; Samira Salihovic; Johan Sundström; Corey D Broeckling; Patrik K Magnusson; Jessica Prenni; Tove Fall; Johan Ärnlöv Journal: J Am Heart Assoc Date: 2021-01-05 Impact factor: 5.501
Authors: Lars Lind; Daniela Zanetti; Martin Ingelsson; Stefan Gustafsson; Johan Ärnlöv; Themistocles L Assimes Journal: J Am Heart Assoc Date: 2021-11-30 Impact factor: 5.501
Authors: Rebekah Nixon; Ting Hin Richard Ip; Benjamin Jenkins; Ping K Yip; Paul Clarke; Vennila Ponnusamy; Adina T Michael-Titus; Albert Koulman; Divyen K Shah Journal: Nutrients Date: 2021-11-28 Impact factor: 5.717