Soininen Pasi, Kangas Antti J, Würtz Peter, Suna Teemu, Ala-Korpela Mika
From the Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (P.S., A.J.K., P.W., T.S., M.A.-K.); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (P.S., M.A.-K.); Oulu University Hospital, Oulu, Finland (M.A.-K.); and Computational Medicine, School of Social and Community Medicine and Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (M.A.-K.).
Circ Cardiovasc Genet. 2015 Feb;8(1):192-206. doi: 10.1161/CIRCGENETICS.114.000216.
Metabolomics is becoming common in epidemiology due to recent developments in quantitative profiling technologies and appealing results from their applications for understanding health and disease. Our team has developed an automated high-throughput serum NMR metabolomics platform that provides quantitative molecular data on 14 lipoprotein subclasses, their lipid concentrations and composition, apolipoprotein A-I and B, multiple cholesterol and triglyceride measures, albumin, various fatty acids as well as on numerous low-molecular-weight metabolites, including amino acids, glycolysis related measures and ketone bodies. The molar concentrations of these measures are obtained from a single serum sample with costs comparable to standard lipid measurements. We have analyzed almost 250 000 samples from around 100 epidemiological cohorts and biobanks and the new international set-up of multiple platforms will allow an annual throughput of more than 250 000 samples. The molecular data have been used to study type 1 and type 2 diabetes etiology as well as to characterize the molecular reflections of the metabolic syndrome, long-term physical activity, diet and lipoprotein metabolism. The results have revealed new biomarkers for early atherosclerosis, type 2 diabetes, diabetic nephropathy, cardiovascular disease and all-cause mortality. We have also combined genomics and metabolomics in diverse studies. We envision that quantitative high-throughput NMR metabolomics will be incorporated as a routine in large biobanks; this would make perfect sense both from the biological research and cost point of view - the standard output of over 200 molecular measures would vastly extend the relevance of the sample collections and make many separate clinical chemistry assays redundant.
由于定量分析技术的最新发展以及其在理解健康与疾病方面应用所取得的诱人成果，代谢组学在流行病学中变得越来越普遍。我们的团队开发了一个自动化的高通量血清核磁共振代谢组学平台，该平台可提供有关14种脂蛋白亚类、其脂质浓度和组成、载脂蛋白A-I和B、多种胆固醇和甘油三酯测量值、白蛋白、各种脂肪酸以及众多低分子量代谢物（包括氨基酸、糖酵解相关测量值和酮体）的定量分子数据。这些测量值的摩尔浓度可从单个血清样本中获得，成本与标准脂质测量相当。我们已经分析了来自约100个流行病学队列和生物样本库的近25万个样本，新的多平台国际设置将使年通量超过25万个样本。这些分子数据已被用于研究1型和2型糖尿病的病因，以及表征代谢综合征、长期体育活动、饮食和脂蛋白代谢的分子反映。研究结果揭示了早期动脉粥样硬化、2型糖尿病、糖尿病肾病、心血管疾病和全因死亡率的新生物标志物。我们还在不同的研究中结合了基因组学和代谢组学。我们设想，定量高通量核磁共振代谢组学将作为常规方法纳入大型生物样本库；从生物学研究和成本角度来看，这都将是非常合理的——超过200种分子测量的标准输出将极大地扩展样本收集的相关性，并使许多单独的临床化学检测变得多余。
