HMT and Hirosaki University COI have jointly published a method for large-scale metabolome analysis which can be used to detect predictive signs of disease.
HMT is pleased to announce that the results of our joint research conducted with Hirosaki University COI, have been published in Metabolites, a journal affiliated with the Metabolomics Society (MetSoc).
Metabolomics is known to be affected by variable factors such as the condition of analytical instruments during sample measurement. In particular, for large samples in cohort studies, the measurement is performed over a long period of time and thus, the measured values may fluctuate, causing difficulties in data handling. To reduce the effect of these variations, HMT has been conducting research on data correction in metabolome analysis for some time and working towards solving this problem so that we can meet the needs of large-scale testing, i.e., hundreds to thousands of samples per project.
HMT has established a joint course, “Department of Metabolomics Innovation”, at the Graduate School of Medicine, Hirosaki University, in May 2019 for performing metabolome analysis of plasma and urine samples obtained from participants of the Iwaki Health Promotion Project. In this paper, we present an analysis method for the correction of large-scale metabolome analysis data using plasma samples of healthy individuals.
Read the full paper here:
Capillary Electrophoresis Mass Spectrometry-Based Metabolomics of Plasma Samples from Healthy Subjects in a Cross-Sectional Japanese Population Study.
Yamamoto et al. Metabolites 2021, 11(5), 314
Keywords: metabolomics; capillary electrophoresis–mass spectrometry; large-scale sampling; normalization; quality control; oxidative stress
Information on Hirosaki University COI
Hirosaki University is one of the 18 centers of excellence selected by the Japan Science and Technology Agency (JST) for its Center of Innovation (COI) program. Its Iwaki Health Promotion Project has been carried out for more than 10 years, and over 2,000 categories of health data have been acquired. Analyzing this multi-faceted data will enable the early detection of dementia, lifestyle-related diseases, etc., as well as the development and verification of preventive methods, and implementation of these results in society.