Circulating metabolites are biomarker candidates for stroke risk prediction as assed in a europe-wide population cohort (biomarcare)

Abstract

Abstract Importance Risk stratification for stroke remains difficult. Classic risk factors seem to only capture a fraction of the complex causative mechanisms. Metabolomic profiling offers the potential to detect new biomarkers and improve risk assessment of incident stroke. Objective and Methods To evaluate the association between circulating metabolites and incident stroke in a large European population cohort (BiomarCaRE). This study utilized the BiomarCaRE case-cohort to measure circulating metabolites. Metabolites were measured in serum samples from 10 299 individuals without a history of stroke. The cohort consisted of a weighted, random subcohort of the original cohort of more than 70 000 individuals. The case-cohort design was applied to 6 European cohorts. Associations with time to stroke onset were assessed individually by applying weighted and adjusted Cox proportional hazard models. The association of metabolites with incident stroke was examined using Cox regressions and C statistics. Results In 10 299 individuals (4069 women (39.5%); median (interquartile range) age, 56.8 (49.5, 62.4), 1516 incident stroke (14.7%) occurred over a median (interquartile range) follow-up time of 8.9 (4.4, 14.7) years. 141 metabolites were analyzed and 26 were significantly associated with incident stroke at a nominal P value of .05, including phosphatidylcholines (PCs), lysoPCs, amino acids, and sphingolipids. Six metabolites (2 phosphatidylcholines, 2 lysophosphatidylcholines, 1 hydroxysphingomyeline and glutamic acid) were significantly associated with incident stroke: diacyl-PC C32:1 (hazard ratio (HR), 1.26 [95% CI, 1.15, 1.38]), SM (OH) C14:1 (HR, 0.77 [95% CI, 0.68, 0.87]), lysoPC a C18:2 (HR, 0.74 [95% CI, 0.64, 0.86]), glutamic acid (HR, 1.28 [95% CI, 1.13, 1.44]), lysoPC a C17:0 (HR, 0.77 [95% CI, 0.68, 0.88]) and diacyl-PC C34:1 (HR, 1.24 [95% CI, 1.11, 1.39]). The strength of the associations competes with those of classic risk factors (C statistics: diacyl-PC C32:1, 0.795 [95% CI, 0.762, 0.828], SM (OH) C14:1, 0.792 [95% CI, 0.759, 0.825], lysoPC a C18:2, 0.791 [95% CI, 0.757, 0.824], Glu, 0.794 [95% CI, 0.760, 0.827]), lysoPC a C17:0, 0.791 [95% CI, 0.758, 0.824], diacyl-PC C34:1, 0.793 [95% CI, 0.760, 0.826]). Adding metabolites to a base risk model including classic risk factors, high-sensitivity C-reactive protein and high-sensitivity troponin I did not improve discrimination by C statistics. Conclusions and Relevance This is the first study assessing >100 metabolites in a large population cohort of >10,000 individuals for their association with stroke risk prediction. Six metabolites were significantly associated with risk of incident stroke and showed comparable discrimination with individual classic risk factors. Although these metabolites may not improve stroke risk assessment beyond that of classic risk factors, these findings hold promise for an improved understanding of the early pathophysiology of stroke.