A lipidomic dataset for epidemiological studies of acute myocardial infarction
Cecilia Castro,
Eric L. Harshfield,
Adam S. Butterworth,
Angela M. Wood,
Albert Koulman,
Julian L. Griffin
Affiliations
Cecilia Castro
Rowett Institute, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, UK; Corresponding author.
Eric L. Harshfield
Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
Adam S. Butterworth
Rowett Institute, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, UK; Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK; National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK; Cambridge Centre of Artificial Intelligence in Medicine, UK; British Heart Foundation Data Science Centre, Health Data Research UK, London, UK; Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
Angela M. Wood
Rowett Institute, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, UK; Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK; National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK; Cambridge Centre of Artificial Intelligence in Medicine, UK; British Heart Foundation Data Science Centre, Health Data Research UK, London, UK; Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
Albert Koulman
Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
Julian L. Griffin
Rowett Institute, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, UK
Understanding the cause of coronary heart diseases relies on the analysis of data from a range of techniques on an epidemiological scale. Lipidomics, the identification and quantification of lipid species in a system, is an omic approach increasingly used in epidemiology. The altered concentration of lipids in plasma is one of the recognised risk factors for these diseases. An important first step in the analysis is to profile lipids in healthy volunteers at an epidemiological level to understand how the geneome influences risk factors; for this reason we made use of the control samples within a bigger case-control sample collection in Pakistan from patients with first acute myocardial infarctions. After extraction, the samples were infused into a Thermo Exactive Orbitrap, without any up-front chromatographic separation. The use of direct infusion allowed fast experiment, facilitating the analysis of large sets of samples. The raw data were processed and analysed using scripts within R, to extract all the meaningful information. The data set originated from this study is a valuable resource to both increase our knowledge in lipid metabolism associated with myocardial infarction, and test new methods and strategy in analysing big lipidomic data sets.
