Leveraging continuous glucose monitoring for personalized modeling of insulin-regulated glucose metabolism.

Continuous glucose monitoring (CGM) is a promising, minimally invasive alternative to plasma glucose measurements for calibrating physiology-based mathematical models of insulin-regulated glucose metabolism, reducing the reliance on in-clinic measurements. However, the use of CGM glucose, particularly in combination with insulin measurements, to develop personalized models of glucose regulation remains unexplored. Here, we simultaneously measured interstitial glucose concentrations using CGM as well as plasma glucose and insulin concentrations during an oral glucose tolerance test (OGTT) in individuals with overweight or obesity to calibrate personalized models of glucose-insulin dynamics. We compared the use of interstitial glucose with plasma glucose in model calibration, and evaluated the effects on model fit, identifiability, and model parameters' association with clinically relevant metabolic indicators. Models calibrated on both plasma and interstitial glucose resulted in good model fit, and the parameter estimates associated with metabolic indicators such as insulin sensitivity measures in both cases. Moreover, practical identifiability of model parameters was improved in models estimated on CGM glucose compared to plasma glucose. Together these results suggest that CGM glucose may be considered as a minimally invasive alternative to plasma glucose measurements in model calibration to quantify the dynamics of glucose regulation.

Hepatic insulin resistance and muscle insulin resistance are characterized by distinct postprandial plasma metabolite profiles: a cross-sectional study.

BACKGROUND: Tissue-specific insulin resistance (IR) predominantly in muscle (muscle IR) or liver (liver IR) has previously been linked to distinct fasting metabolite profiles, but postprandial metabolite profiles have not been investigated in tissue-specific IR yet. Given the importance of postprandial metabolic impairments in the pathophysiology of cardiometabolic diseases, we compared postprandial plasma metabolite profiles in response to a high-fat mixed meal between individuals with predominant muscle IR or liver IR. METHODS: This cross-sectional study included data from 214 women and men with BMI 25-40 kg/m2, aged 40-75 years, and with predominant muscle IR or liver IR. Tissue-specific IR was assessed using the muscle insulin sensitivity index (MISI) and hepatic insulin resistance index (HIRI), which were calculated from the glucose and insulin responses during a 7-point oral glucose tolerance test. Plasma samples were collected before (T = 0) and after (T = 30, 60, 120, 240 min) consumption of a high-fat mixed meal and 247 metabolite measures, including lipoproteins, cholesterol, triacylglycerol (TAG), ketone bodies, and amino acids, were quantified using nuclear magnetic resonance spectroscopy. Differences in postprandial plasma metabolite iAUCs between muscle and liver IR were tested using ANCOVA with adjustment for age, sex, center, BMI, and waist-to-hip ratio. P-values were adjusted for a false discovery rate (FDR) of 0.05 using the Benjamini-Hochberg method. RESULTS: Sixty-eight postprandial metabolite iAUCs were significantly different between liver and muscle IR. Liver IR was characterized by greater plasma iAUCs of large VLDL (p = 0.004), very large VLDL (p = 0.002), and medium-sized LDL particles (p = 0.026), and by greater iAUCs of TAG in small VLDL (p = 0.025), large VLDL (p = 0.003), very large VLDL (p = 0.002), all LDL subclasses (all p < 0.05), and small HDL particles (p = 0.011), compared to muscle IR. In liver IR, the postprandial plasma fatty acid (FA) profile consisted of a higher percentage of saturated FA (p = 0.013), and a lower percentage of polyunsaturated FA (p = 0.008), compared to muscle IR. CONCLUSION: People with muscle IR or liver IR have distinct postprandial plasma metabolite profiles, with more unfavorable postprandial metabolite responses in those with liver IR compared to muscle IR.

Quantifying postprandial glucose responses using a hybrid modeling approach: Combining mechanistic and data-driven models in The Maastricht Study.

Computational models of human glucose homeostasis can provide insight into the physiological processes underlying the observed inter-individual variability in glucose regulation. Modelling approaches ranging from "bottom-up" mechanistic models to "top-down" data-driven techniques have been applied to untangle the complex interactions underlying progressive disturbances in glucose homeostasis. While both approaches offer distinct benefits, a combined approach taking the best of both worlds has yet to be explored. Here, we propose a sequential combination of a mechanistic and a data-driven modeling approach to quantify individuals' glucose and insulin responses to an oral glucose tolerance test, using cross sectional data from 2968 individuals from a large observational prospective population-based cohort, the Maastricht Study. The best predictive performance, measured by R2 and mean squared error of prediction, was achieved with personalized mechanistic models alone. The addition of a data-driven model did not improve predictive performance. The personalized mechanistic models consistently outperformed the data-driven and the combined model approaches, demonstrating the strength and suitability of bottom-up mechanistic models in describing the dynamic glucose and insulin response to oral glucose tolerance tests.

Analysis of high-dimensional metabolomics data with complex temporal dynamics using RM-ASCA.

The intricate dependency structure of biological "omics" data, particularly those originating from longitudinal intervention studies with frequently sampled repeated measurements renders the analysis of such data challenging. The high-dimensionality, inter-relatedness of multiple outcomes, and heterogeneity in the studied systems all add to the difficulty in deriving meaningful information. In addition, the subtle differences in dynamics often deemed meaningful in nutritional intervention studies can be particularly challenging to quantify. In this work we demonstrate the use of quantitative longitudinal models within the repeated-measures ANOVA simultaneous component analysis+ (RM-ASCA+) framework to capture the dynamics in frequently sampled longitudinal data with multivariate outcomes. We illustrate the use of linear mixed models with polynomial and spline basis expansion of the time variable within RM-ASCA+ in order to quantify non-linear dynamics in a simulation study as well as in a metabolomics data set. We show that the proposed approach presents a convenient and interpretable way to systematically quantify and summarize multivariate outcomes in longitudinal studies while accounting for proper within subject dependency structures.

Clustering of Cardiac Transcriptome Profiles Reveals Unique: Subgroups of Dilated Cardiomyopathy Patients.

Dilated cardiomyopathy is a heterogeneous disease characterized by multiple genetic and environmental etiologies. The majority of patients are treated the same despite these differences. The cardiac transcriptome provides information on the patient's pathophysiology, which allows targeted therapy. Using clustering techniques on data from the genotype, phenotype, and cardiac transcriptome of patients with early- and end-stage dilated cardiomyopathy, more homogeneous patient subgroups are identified based on shared underlying pathophysiology. Distinct patient subgroups are identified based on differences in protein quality control, cardiac metabolism, cardiomyocyte function, and inflammatory pathways. The identified pathways have the potential to guide future treatment and individualize patient care.

Elucidating the Corneal Endothelial Cell Proliferation Capacity through an Interspecies Transcriptome Comparison.

The regenerative capacity of corneal endothelial cells (CECs) differs between species; in bigger mammals, CECs are arrested in a non-proliferative state. Damage to these cells can compromise their function causing corneal opacity. Corneal transplantation is the current treatment for the recovery of clear eyesight, but the donor tissue demand is higher than the availability and there is a need to develop novel treatments. Interestingly, rabbit CECs retain a high proliferative profile and can repopulate the endothelium. There is a lack of fundamental knowledge to explain these differences. Gaining information on their transcriptomic variances could allow the identification of CEC proliferation drivers. In this study, human, sheep, and rabbit CECs are analyzed at the transcriptomic level. To understand the differences across each species, a pipeline for the analysis of pathways with different activities is generated. The results reveal that 52 pathways have different activity when comparing species with non-proliferative CECs (human and sheep) to species with proliferative CECs (rabbit). The results show that Notch and TGF-ß pathways have increased activity in species with non-proliferative CECs, which might be associated with their low proliferation. Overall, this study illustrates transcriptomic pathway-level differences that can provide leads to develop novel therapies to regenerate the corneal endothelium.

Allele-specific expression analysis for complex genetic phenotypes applied to a unique dilated cardiomyopathy cohort.

Allele-specific expression (ASE) analysis detects the relative abundance of alleles at heterozygous loci as a proxy for cis-regulatory variation, which affects the personal transcriptome and proteome. This study describes the development and application of an ASE analysis pipeline on a unique cohort of 87 well phenotyped and RNA sequenced patients from the Maastricht Cardiomyopathy Registry with dilated cardiomyopathy (DCM), a complex genetic disorder with a remaining gap in explained heritability. Regulatory processes for which ASE is a proxy might explain this gap. We found an overrepresentation of known DCM-associated genes among the significant results across the cohort. In addition, we were able to find genes of interest that have not been associated with DCM through conventional methods such as genome-wide association or differential gene expression studies. The pipeline offers RNA sequencing data processing, individual and population level ASE analyses as well as group comparisons and several intuitive visualizations such as Manhattan plots and protein-protein interaction networks. With this pipeline, we found evidence supporting the case that cis-regulatory variation contributes to the phenotypic heterogeneity of DCM. Additionally, our results highlight that ASE analysis offers an additional layer to conventional genomic and transcriptomic analyses for candidate gene identification and biological insight.

Cardiometabolic health improvements upon dietary intervention are driven by tissue-specific insulin resistance phenotype: A precision nutrition trial.

Precision nutrition based on metabolic phenotype may increase the effectiveness of interventions. In this proof-of-concept study, we investigated the effect of modulating dietary macronutrient composition according to muscle insulin-resistant (MIR) or liver insulin-resistant (LIR) phenotypes on cardiometabolic health. Women and men with MIR or LIR (n = 242, body mass index [BMI] 25-40 kg/m2, 40-75 years) were randomized to phenotype diet (PhenoDiet) group A or B and followed a 12-week high-monounsaturated fatty acid (HMUFA) diet or low-fat, high-protein, and high-fiber diet (LFHP) (PhenoDiet group A, MIR/HMUFA and LIR/LFHP; PhenoDiet group B, MIR/LFHP and LIR/HMUFA). PhenoDiet group B showed no significant improvements in the primary outcome disposition index, but greater improvements in insulin sensitivity, glucose homeostasis, serum triacylglycerol, and C-reactive protein compared with PhenoDiet group A were observed. We demonstrate that modulating macronutrient composition within the dietary guidelines based on tissue-specific insulin resistance (IR) phenotype enhances cardiometabolic health improvements. Clinicaltrials.gov registration: NCT03708419, CCMO registration NL63768.068.17.

Impact of In Utero Folate Exposure on DNA Methylation and Its Potential Relevance for Later-Life Health-Evidence from Mouse Models Translated to Human Cohorts.

SCOPE: Persistent DNA methylation changes may mediate effects of early-life exposures on later-life health. Human lifespan is challenging for prospective studies, therefore data from longitudinal studies are limited. Projecting data from mouse models of early-life exposure to human studies offers a tool to address this challenge. METHODS AND RESULTS: C57BL/6J mice were fed low/normal folate diets before and during pregnancy and lactation. Genome-wide promoter methylation was measured in male offspring livers at 17.5 days gestation and 28 weeks. Eight promoters were concurrently hypermethylated by folate depletion in fetuses and adults (>1.10 fold-change; p < 0.05). Processes/pathways potentially influenced by global changes, and function of these eight genes, suggest neurocognitive effects. Human observational and randomized controlled trial data were interrogated for translation. Methylation at birth was inversely associated with maternal plasma folate in six genes (-1.15% to -0.16% per nmol L-1 ; p < 0.05), while maternal folic acid supplementation was associated with differential methylation of four genes in adulthood. Three CpGs were persistently hypermethylated with lower maternal folate (p = 0.04). CONCLUSION: Some persistent folate-induced methylation changes in mice are mirrored in humans. This demonstrates utility of mouse data in identifying human loci for interrogation as biomarkers of later-life health.

An interferon-related signature characterizes the whole blood transcriptome profile of insulin-resistant individuals-the CODAM study.

BACKGROUND: Worldwide, the prevalence of obesity and insulin resistance has grown dramatically. Gene expression profiling in blood represents a powerful means to explore disease pathogenesis, but the potential impact of inter-individual differences in a cell-type profile is not always taken into account. The objective of this project was to investigate the whole blood transcriptome profile of insulin-resistant as compared to insulin-sensitive individuals independent of inter-individual differences in white blood cell profile. RESULTS: We report a 3% higher relative amount of monocytes in the insulin-resistant individuals. Furthermore, independent of their white blood cell profile, insulin-resistant participants had (i) higher expression of interferon-stimulated genes and (ii) lower expression of genes involved in cellular differentiation and remodeling of the actin cytoskeleton. CONCLUSIONS: We present an approach to investigate the whole blood transcriptome of insulin-resistant individuals, independent of their DNA methylation-derived white blood cell profile. An interferon-related signature characterizes the whole blood transcriptome profile of the insulin-resistant individuals, independent of their white blood cell profile. The observed signature indicates increased systemic inflammation possibly due to an innate immune response and whole-body insulin resistance, which can be a cause or a consequence of insulin resistance. Altered gene expression in specific organs may be reflected in whole blood; hence, our results may reflect obesity and/or insulin resistance-related organ dysfunction in the insulin-resistant individuals.

Comparison of metabolic states using genome-scale metabolic models.

Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks.

Hepatic Steatosis Contributes to the Development of Muscle Atrophy via Inter-Organ Crosstalk.

Individuals with hepatic steatosis often display several metabolic abnormalities including insulin resistance and muscle atrophy. Previously, we found that hepatic steatosis results in an altered hepatokine secretion profile, thereby inducing skeletal muscle insulin resistance via inter-organ crosstalk. In this study, we aimed to investigate whether the altered secretion profile in the state of hepatic steatosis also induces skeletal muscle atrophy via effects on muscle protein turnover. To investigate this, eight-week-old male C57BL/6J mice were fed a chow (4.5% fat) or a high-fat diet (HFD; 45% fat) for 12 weeks to induce hepatic steatosis, after which the livers were excised and cut into ~200-µm slices. Slices were cultured to collect secretion products (conditioned medium; CM). Differentiated L6-GLUT4myc myotubes were incubated with chow or HFD CM to measure glucose uptake. Differentiated C2C12 myotubes were incubated with chow or HFD CM to measure protein synthesis and breakdown, and gene expression via RNA sequencing. Furthermore, proteomics analysis was performed in chow and HFD CM. It was found that HFD CM caused insulin resistance in L6-GLUT4myc myotubes compared with chow CM, as indicated by a blunted insulin-stimulated increase in glucose uptake. Furthermore, protein breakdown was increased in C2C12 cells incubated with HFD CM, while there was no effect on protein synthesis. RNA profiling of C2C12 cells indicated that 197 genes were differentially expressed after incubation with HFD CM, compared with chow CM, and pathway analysis showed that pathways related to anatomical structure and function were enriched. Proteomics analysis of the CM showed that 32 proteins were differentially expressed in HFD CM compared with chow CM. Pathway enrichment analysis indicated that these proteins had important functions with respect to insulin-like growth factor transport and uptake, and affect post-translational processes, including protein folding, protein secretion and protein phosphorylation. In conclusion, the results of this study support the hypothesis that secretion products from the liver contribute to the development of muscle atrophy in individuals with hepatic steatosis.

Assessing the Contribution of Relative Macrophage Frequencies to Subcutaneous Adipose Tissue.

Background: Macrophages play an important role in regulating adipose tissue function, while their frequencies in adipose tissue vary between individuals. Adipose tissue infiltration by high frequencies of macrophages has been linked to changes in adipokine levels and low-grade inflammation, frequently associated with the progression of obesity. The objective of this project was to assess the contribution of relative macrophage frequencies to the overall subcutaneous adipose tissue gene expression using publicly available datasets. Methods: Seven publicly available microarray gene expression datasets from human subcutaneous adipose tissue biopsies (n = 519) were used together with TissueDecoder to determine the adipose tissue cell-type composition of each sample. We divided the subjects in four groups based on their relative macrophage frequencies. Differential gene expression analysis between the high and low relative macrophage frequencies groups was performed, adjusting for sex and study. Finally, biological processes were identified using pathway enrichment and network analysis. Results: We observed lower frequencies of adipocytes and higher frequencies of adipose stem cells in individuals characterized by high macrophage frequencies. We additionally studied whether, within subcutaneous adipose tissue, interindividual differences in the relative frequencies of macrophages were reflected in transcriptional differences in metabolic and inflammatory pathways. Adipose tissue of individuals with high macrophage frequencies had a higher expression of genes involved in complement activation, chemotaxis, focal adhesion, and oxidative stress. Similarly, we observed a lower expression of genes involved in lipid metabolism, fatty acid synthesis, and oxidation and mitochondrial respiration. Conclusion: We present an approach that combines publicly available subcutaneous adipose tissue gene expression datasets with a deconvolution algorithm to calculate subcutaneous adipose tissue cell-type composition. The results showed the expected increased inflammation gene expression profile accompanied by decreased gene expression in pathways related to lipid metabolism and mitochondrial respiration in subcutaneous adipose tissue in individuals characterized by high macrophage frequencies. This approach demonstrates the hidden strength of reusing publicly available data to gain cell-type-specific insights into adipose tissue function.

Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge.

Plasma glucose and insulin responses following an oral glucose challenge are representative of glucose tolerance and insulin resistance, key indicators of type 2 diabetes mellitus pathophysiology. A large heterogeneity in individuals' challenge test responses has been shown to underlie the effectiveness of lifestyle intervention. Currently, this heterogeneity is overlooked due to a lack of methods to quantify the interconnected dynamics in the glucose and insulin time-courses. Here, a physiology-based mathematical model of the human glucose-insulin system is personalized to elucidate the heterogeneity in individuals' responses using a large population of overweight/obese individuals (n = 738) from the DIOGenes study. The personalized models are derived from population level models through a systematic parameter selection pipeline that may be generalized to other biological systems. The resulting personalized models showed a 4-5 fold decrease in discrepancy between measurements and model simulation compared to population level. The estimated model parameters capture relevant features of individuals' metabolic health such as gastric emptying, endogenous insulin secretion and insulin dependent glucose disposal into tissues, with the latter also showing a significant association with the Insulinogenic index and the Matsuda insulin sensitivity index, respectively.

The Effect of Partly Replacing Vegetable Fat with Bovine Milk Fat in Infant Formula on Postprandial Lipid and Energy Metabolism: A Proof-of-principle Study in Healthy Young Male Adults.

SCOPE: Infant formula (IF) uses besides vegetable fats also bovine milk fat, which differs in triacylglycerol (TAG) structure. Furthermore, it differs in fatty acid (FA) composition. Whether changing fat source in IF affects postprandial energy metabolism, lipemic response, and blood lipid profile is unknown. METHODS AND RESULTS: A proof-of-principle study, with a randomized controlled double-blind cross-over design, is conducted. Twenty healthy male adults consumed drinks with either 100% vegetable fat (VEG) or 67% bovine milk fat and 33% vegetable fat (BOV), on 2 separate days. For a detailed insight in the postprandial responses, indirect calorimetry is performed continuously, and venous blood samples are taken every 30 min, until 5 h postprandially. No differences in postprandial energy metabolism, serum lipids, lipoprotein, or chylomicron concentrations are observed between drinks. After consumption of VEG-drink, C18:2n-6 in serum increased. Observed differences in chylomicron FA profile reflect differences in initial FA profile of test drinks. Serum ketone bodies concentrations increase following consumption of BOV-drink. CONCLUSIONS: The use of bovine milk fat in IF does neither affect postprandial energy metabolism nor lipemic response in healthy adults, but alters postprandial FA profiles and ketone metabolism. Whether the exact same effects occur in infants requires experimental verification.

Phenotypic clustering of dilated cardiomyopathy patients highlights important pathophysiological differences.

AIMS: The dilated cardiomyopathy (DCM) phenotype is the result of combined genetic and acquired triggers. Until now, clinical decision-making in DCM has mainly been based on ejection fraction (EF) and NYHA classification, not considering the DCM heterogenicity. The present study aimed to identify patient subgroups by phenotypic clustering integrating aetiologies, comorbidities, and cardiac function along cardiac transcript levels, to unveil pathophysiological differences between DCM subgroups. METHODS AND RESULTS: We included 795 consecutive DCM patients from the Maastricht Cardiomyopathy Registry who underwent in-depth phenotyping, comprising extensive clinical data on aetiology and comorbodities, imaging and endomyocardial biopsies. Four mutually exclusive and clinically distinct phenogroups (PG) were identified based upon unsupervised hierarchical clustering of principal components: [PG1] mild systolic dysfunction, [PG2] auto-immune, [PG3] genetic and arrhythmias, and [PG4] severe systolic dysfunction. RNA-sequencing of cardiac samples (n = 91) revealed a distinct underlying molecular profile per PG: pro-inflammatory (PG2, auto-immune), pro-fibrotic (PG3; arrhythmia), and metabolic (PG4, low EF) gene expression. Furthermore, event-free survival differed among the four phenogroups, also when corrected for well-known clinical predictors. Decision tree modelling identified four clinical parameters (auto-immune disease, EF, atrial fibrillation, and kidney function) by which every DCM patient from two independent DCM cohorts could be placed in one of the four phenogroups with corresponding outcome (n = 789; Spain, n = 352 and Italy, n = 437), showing a feasible applicability of the phenogrouping. CONCLUSION: The present study identified four different DCM phenogroups associated with significant differences in clinical presentation, underlying molecular profiles and outcome, paving the way for a more personalized treatment approach.

EFMviz: A COBRA Toolbox extension to visualize Elementary Flux Modes in Genome-Scale Metabolic Models.

Elementary Flux Modes (EFMs) are a tool for constraint-based modeling and metabolic network analysis. However, systematic and automated visualization of EFMs, capable of integrating various data types is still a challenge. In this study, we developed an extension for the widely adopted COBRA Toolbox, EFMviz, for analysis and graphical visualization of EFMs as networks of reactions, metabolites and genes. The analysis workflow offers a platform for EFM visualization to improve EFM interpretability by connecting COBRA toolbox with the network analysis and visualization software Cytoscape. The biological applicability of EFMviz is demonstrated in two use cases on medium (Escherichia coli, iAF1260) and large (human, Recon 2.2) genome-scale metabolic models. EFMviz is open-source and integrated into COBRA Toolbox. The analysis workflows used for the two use cases are detailed in the two tutorials provided with EFMviz along with the data used in this study.

Stratifying cellular metabolism during weight loss: an interplay of metabolism, metabolic flexibility and inflammation.

Obesity is a global epidemic, contributing significantly to chronic non-communicable diseases, such as type 2 diabetes mellitus, cardiovascular diseases and metabolic syndrome. Metabolic flexibility, the ability of organisms to switch between metabolic substrates, is found to be impaired in obesity, possibly contributing to the development of chronic illnesses. Several studies have shown the improvement of metabolic flexibility after weight loss. In this study, we have mapped the cellular metabolism of the adipose tissue from a weight loss study to stratify the cellular metabolic processes and metabolic flexibility during weight loss. We have found that for a majority of the individuals, cellular metabolism was downregulated during weight loss, with gene expression of all major cellular metabolic processes (such as glycolysis, fatty acid ß-oxidation etc.) being lowered during weight loss and weight maintenance. Parallel to this, the gene expression of immune system related processes involving interferons and interleukins increased. Previously, studies have indicated both negative and positive effects of post-weight loss inflammation in the adipose tissue with regards to weight loss or obesity and its co-morbidities; however, mechanistic links need to be constructed in order to determine the effects further. Our study contributes towards this goal by mapping the changes in gene expression across the weight loss study and indicates possible cross-talk between cellular metabolism and inflammation.