Abstract
Metabolic profiling of individuals with type 2 diabetes mellitus (T2DM) has previously been limited to single-time-point samples, ignoring time-of-day variation. Here, we tested our hypothesis that body mass and T2DM affect daily rhythmicity and concentrations of circulating metabolites across a 24-h day in 3 age-matched, male groups—lean, overweight/obese (OW/OB), and OW/OB with T2DM—in controlled laboratory conditions, which were not confounded by large meals. By using targeted liquid chromatography/mass spectrometry metabolomics, we quantified 130 plasma metabolites every 2 h over 24 h, and we show that average metabolite concentrations were significantly altered by increased body mass (90 of 130) and T2DM (56 of 130). Thirty-eight percent of metabolites exhibited daily rhythms in at least 1 study group, and where a metabolite was rhythmic in >1 group, its peak time was comparable. The optimal time of day was assessed to provide discriminating biomarkers. This differed between metabolite classes and study groups—for example, phospholipids showed maximal difference at 5:00 AM (lean vs. OW/OB) and at 5:00 PM (OW/OB vs. T2DM). Metabolites that were identified with both robust 24-h rhythms and significant concentration differences between study groups emphasize the importance of controlling the time of day for diagnosis and biomarker discovery, offering a significant improvement over current single sampling.—Isherwood, C. M., Van der Veen, D. R., Johnston, J. D., Skene, D. J. Twenty-four-hour rhythmicity of circulating metabolites: effect of body mass and type 2 diabetes. It is widely accepted that obesity is the main risk factor for type 2 diabetes mellitus (T2DM) (1). The progression from obesity to T2DM is largely a result of comorbidities, such as systemic inflammation and insulin resistance. Metabolic profiling by using targeted metabolomics, which enables the quantification of more than 100 low-MW intermediates of metabolism, is increasingly used to characterize (pre)diabetic phenotypes and has identified differences in metabolite profiles between those individuals who are obese and those with T2DM (2–5). Recent work by our group and others has shown a 24-h variation in the human metabolome in healthy individuals, analyzed by using a range of analytical platforms (6–12), which has demonstrated that an estimated 15–20% of the metabolome is rhythmic in blood (6, 7). Transgenic mice that carry targeted genetic manipulation of circadian clock genes also exhibit a phenotype that involves defective metabolism, and associations between the circadian timing system and metabolic responses have been reported in humans (13). Reviews of these studies, including the higher incidence of obesity, T2DM, and related disorders in shift workers, have recently been published (14, 15). Existing metabolomics studies in T2DM have been restricted to the analysis of single-time-point, mostly fasting, samples, which cannot characterize the effect of increased body mass and T2DM on rhythmic metabolites. Characterizing 24-h metabolite rhythms in T2DM compared with age- and body mass–matched controls may therefore provide novel insights into the etiology and progression of T2DM. Identification of the optimal time of day for blood sampling—when metabolite levels show the biggest difference between T2DM and controls—would also provide more discriminating diagnostic biomarkers, rather than taking a single morning fasting sample. We thus assessed the effect of increased body mass [overweight/obese (OW/OB)] and T2DM on 24-h rhythms of circulating metabolites in men by using a quantitative targeted liquid chromatography/mass spectrometry (LC/MS) metabolomics approach. As T2DM is often accompanied by obesity, we set out to distinguish the effects of T2DM from those of increased body mass by incorporating both a lean and an OW/OB control group into the current study design.