L-arginine: an ultradian-regulated substrate coupled with insulin oscillations in healthy volunteers

Co-expression of diurnal and ultradian rhythms in the plasma metabolome of common voles (Microtus arvalis)

Metabolic rhythms include rapid, ultradian (hourly) dynamics, but it remains unclear what their relationship to circadian metabolic rhythms is, and what role meal timing plays in coordinating these ultradian rhythms in metabolism. Here, we characterized widespread ultradian rhythms under ad libitum feeding conditions in the plasma metabolome of the vole, the gold standard animal model for behavioral ultradian rhythms, naturally expressing ~2-h foraging rhythms throughout the day and night. These ultradian metabolite rhythms co-expressed with diurnal 24-h rhythms in the same metabolites and did not align with food intake patterns. Specifically, under light-dark entrained conditions we showed twice daily entrainment of phase and period of ultradian behavioral rhythms associated with phase adjustment of the ultradian cycle around the light-dark and dark-light transitions. These ultradian activity patterns also drove an ultradian feeding pattern. We used a unique approach to map this behavioral activity/feeding status to high temporal resolution (every 90 min) measures of plasma metabolite profiles across the 24-h light-dark cycle. A total of 148 known metabolites were detected in vole plasma. Supervised, discriminant analysis did not group metabolite concentration by feeding status, instead, unsupervised clustering of metabolite time courses revealed clusters of metabolites that exhibited significant ultradian rhythms with periods different from the feeding cycle. Two clusters with dissimilar ultradian dynamics, one lipid-enriched (period = 3.4 h) and one amino acid-enriched (period = 4.1 h), both showed co-expression with diurnal cycles. A third cluster solely comprised of glycerophospholipids (specifically ether-linked phosphatidylcholines) expressed an 11.9 h ultradian rhythm without co-expressed diurnal rhythmicity. Our findings show coordinated co-expression of diurnal metabolic rhythms with rapid dynamics in feeding and metabolism. These findings reveal that ultradian rhythms are integral to biological timing of metabolic regulation, and will be important in interpreting the impact of circadian desynchrony and meal timing on metabolic rhythms.

Changes in arginine are inversely associated with type 2 diabetes: A case-cohort study in the PREDIMED trial

The associations between arginine-based metabolites and incident type 2 diabetes (T2D) are unknown. We employed a case-cohort design, nested within the PREDIMED trial, to examine six plasma metabolites (arginine, citrulline, ornithine, asymmetric dimethylarginine [ADMA], symmetric dimethylarginine [SDMA] and N-monomethyl-l-arginine [NMMA]) among 892 individuals (251 cases) for associations with incident T2D and insulin resistance. Weighted Cox models with robust variance were used. The 1-year changes in arginine (adjusted hazard ratio [HR] per SD 0.68, 95% confidence interval [CI] 0.49, 0.95; Q4 vs. Q1 0.46, 95% CI 0.21, 1.04; P trend = 0.02) and arginine/ADMA ratio (adjusted HR per SD 0.73, 95% CI 0.51, 1.04; Q4 vs. Q1 0.52, 95% CI 0.22, 1.25; P trend = 0.04) were associated with a lower risk of T2D. Positive changes of citrulline and ornithine, and negative changes in SDMA and arginine/(ornithine + citrulline) were associated with concurrent 1-year changes in homeostatic model assessment of insulin resistance. Individuals in the low-fat-diet group had a higher risk of T2D for 1-year changes in NMMA than individuals in Mediterranean-diet groups (P interaction = 0.02). We conclude that arginine bioavailability is important in T2D pathophysiology.

Evidence for a Coupled Oscillator Model of Endocrine Ultradian Rhythms

Whereas long-period temporal structures in endocrine dynamics have been well studied, endocrine rhythms on the scale of hours are relatively unexplored. The study of these ultradian rhythms (URs) has remained nascent, in part, because a theoretical framework unifying ultradian patterns across systems has not been established. The present overview proposes a conceptual coupled oscillator network model of URs in which oscillating hormonal outputs, or nodes, are connected by edges representing the strength of node-node coupling. We propose that variable-strength coupling exists both within and across classic hormonal axes. Because coupled oscillators synchronize, such a model implies that changes across hormonal systems could be inferred by surveying accessible nodes in the network. This implication would at once simplify the study of URs and open new avenues of exploration into conditions affecting coupling. In support of this proposed framework, we review mammalian evidence for (1) URs of the gut-brain axis and the hypothalamo-pituitary-thyroid, -adrenal, and -gonadal axes, (2) UR coupling within and across these axes; and (3) the relation of these URs to body temperature. URs across these systems exhibit behavior broadly consistent with a coupled oscillator network, maintaining both consistent URs and coupling within and across axes. This model may aid the exploration of mammalian physiology at high temporal resolution and improve the understanding of endocrine system dynamics within individuals.

Investigation of glucose fluctuations by approaches of multi-scale analysis

Glucose variability provides detailed information on glucose control and fluctuation. The aim of this study is to investigate the glucose variability by multi-scale analysis approach on 72-h glucose series captured by continuous glucose monitoring system (CGMS), gaining insights into the variability and complexity of the glucose time series data. Ninety-eight type 2 DM patients participated in this study, and 72-h glucose series from each subject were recorded by CGMS. Subjects were divided into two subgroups according to the mean amplitude of glycemic excursions (MAGE) value threshold at 3.9 based on Chinese standard. In this study, we applied two types of multiple scales analysis methods on glucose time series: ensemble empirical mode decomposition (EEMD) and refined composite multi-scale entropy (RCMSE). With EEMD, glucose series was decomposed into several intrinsic mode function (IMF), and glucose variability was examined on multiple time scales with periods ranging from 0.5 to 12 h. With RCMSE, complexity of the structure of glucose series was quantified at each time scale ranging from 5 to 30 min. Subgroup with higher MAGE value (>3.9) presented higher glycemic baseline and variability. There were significant differences in glycemic variability on IMFs3-5 between subgroups with MAGE>3.9 and MAGE < = 3.9 (p<0.001), but no significant differences in variability on IMFs1-2. The complexity of glucose series quantified by RCMSE showed statistically difference on each time scale from 5 to 30 min between subgroups (p<0.05). Glucose series from subjects with higher MAGE value represented higher variability but lower complexity on multiple time scales. Compared with traditional matrices measuring the glucose variability, approaches of EEMD and RCMSE can quantify the dynamic glycemic fluctuation in multiple time scales and provide us more detailed information on glycemic variability and complexity.

Multi-scale glycemic variability: a link to gray matter atrophy and cognitive decline in type 2 diabetes

OBJECTIVE

Type 2 diabetes mellitus (DM) accelerates brain aging and cognitive decline. Complex interactions between hyperglycemia, glycemic variability and brain aging remain unresolved. This study investigated the relationship between glycemic variability at multiple time scales, brain volumes and cognition in type 2 DM.

RESEARCH DESIGN AND METHODS

Forty-three older adults with and 26 without type 2 DM completed 72-hour continuous glucose monitoring, cognitive tests and anatomical MRI. We described a new analysis of continuous glucose monitoring, termed Multi-Scale glycemic variability (Multi-Scale GV), to examine glycemic variability at multiple time scales. Specifically, Ensemble Empirical Mode Decomposition was used to identify five unique ultradian glycemic variability cycles (GVC1-5) that modulate serum glucose with periods ranging from 0.5-12 hrs.

RESULTS

Type 2 DM subjects demonstrated greater variability in GVC3-5 (period 2.0-12 hrs) than controls (P<0.0001), during the day as well as during the night. Multi-Scale GV was related to conventional markers of glycemic variability (e.g. standard deviation and mean glycemic excursions), but demonstrated greater sensitivity and specificity to conventional markers, and was associated with worse long-term glycemic control (e.g. fasting glucose and HbA1c). Across all subjects, those with greater glycemic variability within higher frequency cycles (GVC1-3; 0.5-2.0 hrs) had less gray matter within the limbic system and temporo-parietal lobes (e.g. cingulum, insular, hippocampus), and exhibited worse cognitive performance. Specifically within those with type 2 DM, greater glycemic variability in GVC2-3 was associated with worse learning and memory scores. Greater variability in GVC5 was associated with longer DM duration and more depression. These relationships were independent of HbA1c and hypoglycemic episodes.

CONCLUSIONS

Type 2 DM is associated with dysregulation of glycemic variability over multiple scales of time. These time-scale-dependent glycemic fluctuations might contribute to brain atrophy and cognitive outcomes within this vulnerable population.