The vagus nerve connects the brain and pancreas and enhances postprandial endocrine secretion from the pancreas through cholinergic signaling, such as increasing insulin release immediately after food intake in the cephalic-phase insulin response (CPIR). Here, we investigated how obesity affects vagal regulation of pancreatic endocrine function using designer receptors exclusively activated by designer drugs (DREADDs) to manipulate vagal activity. As expected, the plasma concentration of insulin was increased by vagal activation in mice expressing the excitatory DREADD hM3Dq (M3 mice) and decreased by vagal inactivation in mice expressing inhibitory DREADD hM4Di (M4 mice). However, vagal activation in M3 mice with diet-induced obesity did not elicit an early increase in insulin and instead produced a delayed insulin decrease. Mathematical modeling showed that plasma insulin dynamics in these mice were best explained by a model incorporating both the insulin-increasing and insulin-decreasing effects of the vagus nerve. Furthermore, the insulin-decreasing effect was mediated by nitric oxide (NO)-dependent, noncholinergic signaling and was enhanced in obesity. In obese M3 mice, vagal deficiency of neuronal NO synthase (nNOS) abolished the insulin-decreasing effect and restored insulin release after vagal activation. Vagal nNOS deficiency also enhanced insulin release after voluntary feeding, consistent with the CPIR. These findings suggest that vagal NO action inhibits postprandial insulin release, particularly in obesity.

Impaired glucose homeostasis leads to numerous complications, with coronary artery disease (CAD) being a major contributor to healthcare costs worldwide. Because continuous glucose monitoring (CGM) captures multidimensional features of glucose regulation beyond average glycemia, we evaluated whether CGM-derived indices better predict coronary plaque vulnerability than conventional measures. We examined associations between CGM-derived indices and coronary plaque vulnerability assessed by virtual histology–intravascular ultrasound, focusing on the necrotic core (%NC) in humans. We analyzed 14 CGM-derived indices, including average daily risk ratio (ADRR) and autocorrelation-based metrics (AC_Mean and AC_Var), alongside commonly used measures, such as fasting blood glucose (FBG), hemoglobin A1c (HbA1c), and 120 min plasma glucose during oral glucose tolerance testing (PG120). Factor analysis was used to identify latent components underlying glucose dynamics and to relate these components to %NC. Findings were validated across independent datasets from Japan (n=64), the United States (n=53), and China (n=100). CGM-derived indices, particularly ADRR and AC_Var, demonstrated stronger predictive capability for %NC than FBG, HbA1c, and PG120. Factor analysis identified three independent components of glucose dynamics: mean, variance, and autocorrelation, each showing an independent association with %NC. ADRR reflected both mean and variance components, whereas AC_Var primarily captured the autocorrelation component. In contrast, FBG, HbA1c, and PG120 primarily reflected the mean component alone and were, therefore, insufficient for %NC prediction. CGM-derived indices reflecting the three components of glucose dynamics can serve as more effective screening tools for CAD risk assessment, complementing or possibly replacing traditional diabetes diagnostic methods. This study was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (JP21H04759), CREST, the Japan Science and Technology Agency (JST) (JPMJCR2123), The Uehara Memorial Foundation, and The Takeda Science Foundation.

Ultra-long-acting basal insulins, insulin degludec (IDeg) and insulin glargine U300 (IGlarU300), are widely used in type 1 diabetes mellitus (T1DM), but their differential effects on multidimensional continuous glucose monitoring (CGM) profiles remain unclear. We aimed to identify latent dimensions underlying CGM-derived metrics in T1DM and to compare these dimensions between IDeg and IGlarU300. This predefined secondary analysis used data from the multicenter, randomized, crossover KOBE-BBIS2 trial in C-peptide-negative individuals with T1DM. Participants received IDeg and IGlarU300 for 4 weeks each, and professional CGM was performed during the final week of each treatment period. Exploratory factor analysis was applied to conventional CGM metrics and two autocorrelation-based indices (AC_Mean and AC_Var). Associations between clinical characteristics and factor scores were also examined. Three latent components were identified: high-glucose/high-variability, autocorrelation, and low-glucose/high-variability. The low-glucose/high-variability factor score was higher during IDeg treatment than during IGlarU300 treatment (mean difference 0.40, 95% CI 0.01–0.78). In exploratory analyses, body mass index was inversely correlated with this factor during IDeg treatment (r = − 0.45, 95% CI − 0.73 to − 0.11). These findings require validation in larger independent cohorts.

BackgroundEarly detection of metabolic dysfunction before diabetes onset remains a critical challenge in preventive medicine. Although glucose dynamics provide high-dimensional insights into metabolic states, it remains unclear which combination of glucose dynamics-derived measures most effectively summarises interindividual differences in glucose regulation.MethodsWe analysed continuous glucose monitoring (CGM) data from 8025 adults without diagnosed diabetes, using all recordings of at least seven days' duration, to derive a low-dimensional representation of glucose dynamics. Using exploratory factor analysis, we identified a small set of latent features that accounted for variation between individuals in CGM-derived metrics. We then trained and validated machine-learning models to predict postprandial glucose trajectories from these features in 863 participants who underwent standardised meal tests. We further assessed the generalisability of this representation in an independent oral glucose tolerance test dataset. Finally, we examined associations between the derived features and markers of vascular and liver health in 1784 non-diabetic adults.ResultsThree features, "mean", "variance", and "autocorrelation", explain more than 80% of the interindividual differences in CGM-derived measures. A three-dimensional representation based on these features reconstructs postprandial glucose trajectories with high accuracy and outperforms fasting, mean, and two-hour postprandial glucose values. Each feature shows independent associations with carotid artery intima-media thickness and with hepatic steatosis and stiffness.ConclusionsBy compressing high-dimensional glucose dynamics into three interpretable features with minimal loss of information, this framework provides a simple yet physiologically meaningful representation of glucose regulation that may facilitate a more precise and interpretable assessment of diabetes-related risk.

Starvation induces complex metabolic adaptations in skeletal muscle, a key tissue for maintaining energy homeostasis; however, these adaptations are largely impaired in obesity. How obesity alters global metabolic adaptations to starvation in skeletal muscle remains unclear. Here, we analyzed the metabolic adaptations on a trans-omics scale during starvation in skeletal muscle from wild-type (WT) and leptin-deficient obese (ob/ob) mice. We measured multi-omics data during starvation and constructed global trans-omics networks in WT and ob/ob mice. We found that starvation induces “responsiveness” in WT mice, characterized by increases or decreases in key regulator metabolites, including ATP and AMP, as well as enzyme proteins, leading to global regulation of metabolic pathways, which was lost in ob/ob mice. In contrast, during starvation, ob/ob mice exhibit “difference” in comparison to WT mice, manifested by the persistently elevated expression of metabolic enzymes. These features were similarly found in liver, another key metabolic organ. Thus, global loss of responsiveness and elevated enzyme proteins are systemic features of metabolic dysregulation in ob/ob mice.

BACKGROUND:Efficiently assessing glucose handling capacity is a critical public health challenge. This study assessed the utility of relatively easy-to-measure continuous glucose monitoring (CGM)-derived indices in estimating glucose handling capacities calculated from resource-intensive clamp tests. METHODS:We conducted a prospective study of 64 individuals without prior diabetes diagnosis. The study performed CGM, oral glucose tolerance tests (OGTT), and hyperglycemic and hyperinsulinemic-euglycemic clamp tests. We validated CGM-derived indices characteristics using an independent dataset from another country and mathematical models with simulated data. RESULTS:A CGM-derived index reflecting the autocorrelation function of glucose levels (AC_Var) is significantly correlated with clamp-derived disposition index (DI), a well-established measure of glucose handling capacity and predictor of diabetes onset. Multivariate and machine learning models indicate AC_Var's contribution to predicting clamp-derived DI independent from other CGM-derived indices. The model using CGM-measured glucose standard deviation and AC_Var outperforms models using commonly used diabetes diagnostic indices, such as fasting blood glucose, HbA1c, and OGTT measures, in predicting clamp-derived DI. Mathematical simulations also demonstrate the association of AC_Var with DI. CONCLUSIONS:CGM-derived indices, including AC_Var, serve as valuable tools for predicting glucose handling capacities in populations without prior diabetes diagnosis. We develop a web application that calculates these CGM-derived indices ( https://cgm-ac-mean-std.streamlit.app/ ).

Mammalian liver metabolism undergoes a substantial shift during fasting. Thermodynamic principles impose fundamental constraints on metabolism, and the Gibbs free energy change of reaction (Δ r G ′) indicates the reaction’s direction and distance from equilibrium. However, Δ r G ′ landscapes in intact mammalian organs remain largely uncharacterized. Here, we mapped Δ r G ′ profile of glucose metabolism in mouse liver during fasting, using experimentally measured absolute metabolite concentrations and a newly developed computational method, GLEAM. We found that despite large metabolite fluctuations during fasting, the corresponding Δ r G ′s remained robust, even for reactions reversing the direction between glycolysis and gluconeogenesis. A remarkable thermodynamic robustness is found in maintaining potential candidates of rate-limiting steps during fasting. This robustness is achieved by expending substantial costs for enzyme expressions, contributing to efficient switching from glycolysis to gluconeogenesis. Furthermore, obese mouse liver also showed thermodynamic robustness despite obesity-induced metabolic disruption. Our framework provided a novel thermodynamic perspective on intact organ metabolism, demonstrating that the liver robustly maintains thermodynamic characteristics favorable for metabolic control by buffering metabolite concentration differences.

Adaptation to starvation is a multimolecular and temporally ordered process. We sought to elucidate how the healthy liver regulates various molecules in a temporally ordered manner during starvation and how obesity disrupts this process. We used multiomic data collected from the plasma and livers of wild-type and leptin-deficient obese (ob/ob) mice at multiple time points during starvation to construct a starvation-responsive metabolic network that included responsive molecules and their regulatory relationships. Analysis of the network structure showed that in wild-type mice, the key molecules for energy homeostasis, ATP and AMP, acted as hub molecules to regulate various metabolic reactions in the network. Although neither ATP nor AMP was responsive to starvation in ob/ob mice, the structural properties of the network were maintained. In wild-type mice, the molecules in the network were temporally ordered through metabolic processes coordinated by hub molecules, including ATP and AMP, and were positively or negatively coregulated. By contrast, both temporal order and coregulation were disrupted in ob/ob mice. These results suggest that the metabolic network that responds to starvation was structurally robust but temporally disrupted by the obesity-associated loss of responsiveness of the hub molecules. In addition, we propose how obesity alters the response to intermittent fasting.

ABSTRACTImpaired glucose homeostasis leads to numerous complications, with coronary artery disease (CAD) being a major contributor to healthcare costs worldwide. Given the limited efficacy of current CAD screening methods, we investigated the association between glucose dynamics and a predictor of coronary events measured by virtual histology-intravascular ultrasound (%NC), with the aim of predicting CAD using easy-to-measure indices. We found that continuous glucose monitoring (CGM)-derived indices, particularly average daily risk ratio (ADRR) and AC_Var, exhibited stronger predictive capabilities for %NC compared to commonly used indices such as fasting blood glucose (FBG), hemoglobin A1C (HbA1c), and plasma glucose level at 120 min during oral glucose tolerance tests (PG120). Factor analysis identified three distinct components underlying glucose dynamics – value, variability, and autocorrelation – each independently associated with %NC. ADRR was influenced by the first two components and AC_Var by the third. FBG, HbA1c, and PG120 were influenced only by the value component, making them insufficient for %NC prediction. Our results were validated using data sets from Japan (n=64), America (n=53), and China (n=100). CGM-derived indices reflecting the three components of glucose dynamics can serve as more effective screening tools for CAD risk assessment, complementing or possibly replacing traditional diabetes diagnostic methods.

Starvation induces complex metabolic adaptations in skeletal muscle, a key tissue for maintaining energy homeostasis; however, these adaptations are largely impaired in obesity. How obesity alters global metabolic adaptations to starvation in skeletal muscle remains unclear. Here, we analyzed the metabolic adaptations on a trans-omics scale during starvation in skeletal muscle from wild-type (WT) and leptin-deficient obese ( ob / ob ) mice. We measured multi-omics data during starvation and constructed global trans-omics networks in WT and ob / ob mice. We found that starvation induces "responsiveness" in WT mice, characterized by increases or decreases in key regulator metabolites, including ATP and AMP, as well as enzyme proteins, leading to global regulation of metabolic pathways, which was lost in ob / ob mice. In contrast, during starvation, ob / ob mice exhibit "difference" in comparison to WT mice, manifested by the persistently elevated expression of metabolic enzymes. These features were similarly found in liver, another key metabolic organ. Thus, global loss of responsiveness and elevated enzyme proteins are systemic features of metabolic dysregulation in ob / ob mice. ### Competing Interest Statement The authors have declared no competing interest.

Glucose homeostasis is a fundamental component of human physiology, and its failure underlies diabetes. Existing measures, such as mean glucose levels, provide limited insight into the underlying physiological processes because a quantitative law linking glycemic trajectories directly to regulatory capacity has yet to be established. We derived an equation, F = K · G , that connects glucose inputs ( F ) and measurable features of glucose dynamics ( G ) to the system’s regulatory capacity ( K ). K includes proportional-integral-derivative (PID)-like components from control theory, while G integrates glucose-trajectory characteristics, including area under the curve, amplitude, and temporal distortion. Analyses of clamp and continuous glucose monitoring data from more than 2,000 individuals showed that K was identifiable from glucose dynamics and explained interindividual variation in glucose tolerance and diabetes complication risks beyond conventional metrics. This framework identified four subtypes of impaired glucose regulation, including an underappreciated subtype characterized by deficient insulin-independent glucose-lowering. These results provide a mechanistic interpretation of how biological systems achieve robust glucose regulation and a practical basis for stratifying glucose tolerance and disease risk.

Metabolism, the biochemical reaction network within cells, is crucial for life, health, and disease. Recent advances in multi-omics technologies, enabling the simultaneous measurement of transcripts, proteins, and metabolites, provide unprecedented opportunities to comprehensively analyze metabolic regulation. However, effectively integrating these diverse data types to decipher the complex interplay between enzymes and metabolites remains a significant challenge due to the extensive data requirements of kinetic modeling approaches and the limited interpretability of machine learning approaches. Here, we present MetDeeCINE, a novel explainable deep learning framework that predicts the quantitative relationship between each enzyme and metabolite from proteomic and metabolomic data. We demonstrate that our newly developed Metabolism-informed Graph Neural Network (MiGNN), a core component of MetDeeCINE that is guided by the stoichiometric information of metabolic reactions, outperforms other machine learning models in predicting concentration control coefficients (CCCs) using data obtained from kinetic models of E. coli. Notably, MetDeeCINE, even without explicit information on allosteric regulation, can identify key distant enzymes that predominantly control the steady-state concentrations of specific metabolites. Application of MetDeeCINE to mouse liver multi-omics experimental data further demonstrated its ability to generate biologically meaningful predictions through identifying a rate-limiting enzyme of gluconeogenesis associated with obesity, consistent with existing knowledge. MetDeeCINE offers a scalable and interpretable approach for deciphering complex metabolic regulation from multi-omics data, with broad applications in disease research, drug discovery, and metabolic engineering. ### Competing Interest Statement The authors have declared no competing interest.

An unmet need for preventing diabetes complications is the early detection of metabolic dysregulation. While glucose dynamics provide high-dimensional insights into metabolic states, extracting comprehensive and interpretable information from such data remains challenging. Here we show that the majority of inter-individual variation in glucose dynamics can be captured by just three features - mean, variance and autocorrelation - each independently associated with diabetes-related measures, even in individuals without a prior diabetes diagnosis. Analysis of continuous glucose monitoring data from 8,025 individuals showed that these three measures explained over 80% of the inter-individual variation in glucose dynamics. These measures outperformed conventional measures, including fasting, mean, and 2-hour postprandial glucose levels, in reconstructing postprandial glucose dynamics. Each feature showed independent associations with vascular or hepatic status. By condensing high-dimensional glucose dynamics into three interpretable features with minimal loss of information, this framework provides a basis for a more accurate diabetes risk assessment. ### Competing Interest Statement H.S. and S.K. declare no competing interests. G.S. is an employee of Pheno.AI Ltd. A.K. is a paid consultant to Pheno.AI, Ltd. ### Funding Statement This study was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (JP21H04759), CREST, the Japan Science and Technology Agency (JST) (JPMJCR2123), The Uehara Memorial Foundation and The Takeda Science Foundation. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2005143 https://www.nature.com/articles/s41551-024-01311-6 https://humanphenotypeproject.org/ https://www.nature.com/articles/s41591-020-0934-0 https://www.medrxiv.org/content/10.1101/2023.09.18.23295711v1 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2005143 https://www.nature.com/articles/s41551-024-01311-6 https://humanphenotypeproject.org/ https://www.nature.com/articles/s41591-020-0934-0 https://www.medrxiv.org/content/10.1101/2023.09.18.23295711v1

Alzheimer’s disease (AD), a leading cause of dementia, has been recognized as a disease with profound metabolic dysregulation. However, a systems-level view of metabolic regulation across multiple omic modalities in AD remains elusive. Here, we integrated public multi-omic datasets (transcriptome, proteome, and metabolome) from the dorsolateral prefrontal cortex of AD patients and controls. By leveraging existing molecular biological knowledge, we reconstructed a multi-layered metabolic regulatory network to systematically map the interplay between mRNAs, proteins, and metabolites in AD. Our analysis revealed a coordinated downregulation of energy producing pathways, including the TCA cycle, oxidative phosphorylation, and ketone body metabolism, driven by reduced enzyme abundance and inhibitory allosteric effects. In contrast, the glycolysis/gluconeogenesis pathway appeared to be influenced by opposing enzymatic and allosteric regulations. These findings highlight key metabolic dysregulations that may contribute to the bioenergetic deficits in AD pathology. ### Competing Interest Statement The authors have declared no competing interest. Japan Society for the Promotion of Science, https://ror.org/00hhkn466 KAKENHI, JP21H04759 CREST Japan Science and Technology Agency, JPMJCR2123 Uehara Memorial Foundation, https://ror.org/00gc20a07 Grant-in-Aid for Early-Career Scientists, JP21K15342 AMED, JP21wm0425016

Obesity impairs hepatic functions through abnormal functional protein expression, potentially through DNA methylation, which suppresses gene expression, and changes in transcription factors (TFs) expression. However, the specific protein expression changes associated with DNA methylation in the obese liver remain unclear. To dissect the relative association of DNA methylome and TF-binding with protein expression changes in the obese liver, we used a trans-omic integration approach combining DNA methylome, transcriptome, proteome, and TF-binding data for the livers of wild-type (WT) and obese (ob/ob) mice. We found that gene and protein expression changes were more strongly associated with TF expression changes than with changes in DNA methylation in promoter region. However, decreased protein expression of the complement and coagulation system in obesity was specifically associated with increased DNA methylation together with decreased expression of TF Hnf4a. Our study highlights abnormal protein expression specifically associated with DNA methylation and TF expression changes in obesity.

Context:Sodium-glucose cotransporter 2 (SGLT2) inhibitors lower blood glucose levels by promoting urinary glucose excretion, but their overall effects on hormonal and metabolic status remain unclear.Objective:We here investigated the roles of insulin and glucagon in the regulation of glycemia in individuals treated with an SGLT2 inhibitor using mathematical model analysis.Methods:Hyperinsulinemic-euglycemic clamp and oral glucose tolerance tests were performed in 68 individuals with type 2 diabetes treated with the SGLT2 inhibitor dapagliflozin. Data previously obtained from such tests in 120 subjects with various levels of glucose tolerance and not treated with an SGLT2 inhibitor were examined as a control. Mathematical models of the feedback loops connecting glucose and insulin (GI model) or glucose, insulin, and glucagon (GIG model) were generated.Results:Analysis with the GI model revealed that the disposition index/clearance, which is defined as the product of insulin sensitivity and insulin secretion divided by the square of insulin clearance and represents the glucose-handling ability of insulin, was significantly correlated with glycemia in subjects not taking an SGLT2 inhibitor but not in those taking dapagliflozin. Analysis with the GIG model revealed that a metric defined as the product of glucagon sensitivity and glucagon secretion divided by glucagon clearance (designated production index/clearance) was significantly correlated with blood glucose level in subjects treated with dapagliflozin.Conclusion:Treatment with an SGLT2 inhibitor alters the relation between insulin effect and blood glucose concentration, and glucagon effect may account for variation in glycemia among individuals treated with such drugs.

l-Lactate is a monocarboxylate produced during the process of cellular glycolysis and has long generally been considered a waste product. However, studies in recent decades have provided new perspectives on the physiological roles of l-lactate as a major energy substrate and a signaling molecule. To enable further investigations of the physiological roles of l-lactate, we have developed a series of high-performance (ΔF/F = 15 to 30 in vitro), intensiometric, genetically encoded green fluorescent protein (GFP)-based intracellular l-lactate biosensors with a range of affinities. We evaluated these biosensors in cultured cells and demonstrated their application in an ex vivo preparation of Drosophila brain tissue. Using these biosensors, we were able to detect glycolytic oscillations, which we analyzed and mathematically modeled.

Dysregulation of liver metabolism associated with obesity during feeding and fasting leads to the breakdown of metabolic homeostasis. However, the underlying mechanism remains unknown. Here, we measured multi-omics data in the liver of wild-type and leptin-deficient obese (ob/ob) mice at ad libitum feeding and constructed a differential regulatory trans-omic network of metabolic reactions. We compared the trans-omic network at feeding with that at 16 h fasting constructed in our previous study. Intermediate metabolites in glycolytic and nucleotide metabolism decreased in ob/ob mice at feeding but increased at fasting. Allosteric regulation reversely shifted between feeding and fasting, generally showing activation at feeding while inhibition at fasting in ob/ob mice. Transcriptional regulation was similar between feeding and fasting, generally showing inhibiting transcription factor regulations and activating enzyme protein regulations in ob/ob mice. The opposite metabolic dysregulation between feeding and fasting characterizes breakdown of metabolic homeostasis associated with obesity.

Hepatic glucose metabolism serves dual purposes: maintaining glucose homeostasis and converting glucose into energy sources; however, the underlying mechanisms are unclear. We quantitatively measured liver metabolites, gene expression, and phosphorylated insulin signaling molecules in mice orally administered varying doses of glucose, and constructed a transomic network. Rapid phosphorylation of insulin signaling molecules in response to glucose intake was observed, in contrast to the more gradual changes in gene expression. Glycolytic and gluconeogenic metabolites and expression of genes involved in glucose metabolism including glucose-6-phosphate, G6pc, and Pck1, demonstrated high glucose dose sensitivity. Whereas, glucokinase expression and glycogen accumulation showed low glucose dose sensitivity. During the early phase after glucose intake, metabolic flux was geared towards glucose homeostasis regardless of the glucose dose but shifted towards energy conversion during the late phase at higher glucose doses. Our research provides a comprehensive view of time- and dose-dependent selective glucose metabolism.