Abstract
The intra-individual variability of the human serum metabolome over a period of 4 weeks and its dependence on metabolic health and nutritional status was investigated in a single-center study under tightly controlled conditions in healthy controls, pre-diabetic individuals and patients with type-2 diabetes mellitus (T2DM, n = 10 each). Untargeted metabolomics in serum samples taken at three different days after overnight fasts and following intake of a standardized mixed meal showed that the human serum metabolome is remarkably stable: The median intra-class correlation coefficient (ICC) across all metabolites and all study participants was determined as 0.65. ICCs were similar for the three different health groups, before and after meal intake, and for different metabolic pathways. Only 147 out of 1438 metabolites (10%) had an ICC below 0.4 indicating poor stability over time. In addition, we confirmed previously identified metabolic signatures differentiating healthy, pre-diabetic and diabetic individuals. To our knowledge, this is the most comprehensive study investigating the temporal variability of the human serum metabolome under such tightly controlled conditions.
Highlights
The intra-individual variability of the human serum metabolome over a period of 4 weeks and its dependence on metabolic health and nutritional status was investigated in a single-center study under tightly controlled conditions in healthy controls, pre-diabetic individuals and patients with type-2 diabetes mellitus (T2DM, n = 10 each)
Assignment to the three groups was done based on fasting glucose, and glycated hemoglobin (HbA1c) according to ADA criteria[7] as well as the results of the oral glucose tolerance test (OGTT)-challenged 1-h and 2-h glucose, intact proinsulin and C-peptide, and the intact proinsulin/C-peptide (PC) ratio
Differences were mainly driven by the first principal component highly correlated with triacylglycerides (TAG). These results suggest that TAGs were increased in T2DM patients compared to pre-diabetic and healthy subjects
Summary
There is an increasing body of evidence for using metabolomics in medicine, such that the field is poised to discover clinically useful biomarkers and therapeutic targets in nephrology, cancer, and other medical fields[1,2] With this growing interest to perform metabolomics analyses for biomarker identification, it is crucial to accurately understand the diurnal variation, the course over time per subject, as well as the intra- and inter-subject variability dependent on metabolic condition or nutritional status among other factors[3]. Samples are often collected at different clinical centers and by different investigators, and potentially in subjects with unclear nutritional status This may lead to biased estimates of the intra- and inter-subject variability and has the inherent weakness that markers showing high variability are more likely to be identified as “significantly changing” just by chance, in comparison to markers which are stable over time. If the purpose is to test the ability of a biomarker to predict disease modification or progression, samples from different populations (with/without disease) should be collected in addition
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
