|RUIZ-CANELA, MIGUEL - University Of Navarra|
|HRUBY, ADELA - Jean Mayer Human Nutrition Research Center On Aging At Tufts University|
|CLISH, CLARY - Broad Institute Of Mit/harvard|
|LIANG, LIMING - Harvard School Of Public Health|
|MARTINEZ-GONZALEZ, MIGUEL - University Of Navarra|
|HU, FRANK - Harvard School Of Public Health|
Submitted to: Journal of the American Heart Association
Publication Type: Review Article
Publication Acceptance Date: 6/5/2017
Publication Date: 8/1/2017
Citation: Ruiz-Canela, M., Hruby, A., Clish, C., Liang, L., Martinez-Gonzalez, M.A., Hu, F.B. 2017. Comprehensive metabolomic profiling and incident cardiovascular disease: a systematic review. Journal of the American Heart Association. 6(10):e005705. https://doi.org/10.1161/JAHA.117.005705.
Technical Abstract: Background: Metabolomics is a promising tool of cardiovascular biomarker discovery. We systematically reviewed the literature on comprehensive metabolomic profiling in association with incident cardiovascular disease (CVD). Methods and Results: We searched MEDLINE and EMBASE from inception to January 2016. Studies were eligible if they pertained to adult humans; followed an agnostic and/or comprehensive approach; used serum or plasma (not urine or other biospecimens); metabolite profiling was conducted at baseline in the context of examining prospective disease; myocardial infarction, stroke, and/or CVD death were included in the CVD outcome definition. We identified 12 original articles (9 cohort and 3 nested case-control studies); participant numbers ranged from 67 to 7,256. Mass spectrometry was the predominant analytical method. The number and chemical diversity of metabolites was very heterogeneous, ranging from 31 to >10,000 features. Four studies used untargeted profiling. Different types of metabolites were associated with CVD risk: acylcarnitines, dicarboxylacylcarnitines, and several amino acids and lipid classes. Only tiny improvements in CVD prediction beyond traditional risk factors were observed using these metabolites (C index improvement ranged from 0.006 to 0.05). Conclusions: There are a limited number of longitudinal studies assessing associations between comprehensive metabolomic profiles and CVD risk. Quantitatively synthesizing the literature is challenging owing to the widely varying analytical tools, and diversity of methodological and statistical approaches. Although some results are promising, more research is needed; notably, standardization of metabolomic techniques and statistical approaches. Replication and combinations of novel and holistic methodological approaches would move the field toward the realization of its promise.