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The underlying biological mechanisms causing persistent fatigue complaints after colorectal cancer treatment need further investigation. We investigated longitudinal associations of circulating concentrations of 138 metabolites with total fatigue and subdomains of fatigue between 6 weeks and 2 years after colorectal cancer treatment. Among stage I-III colorectal cancer survivors (n = 252), blood samples were obtained at 6 weeks, and 6, 12 and 24 months posttreatment. Total fatigue and fatigue subdomains were measured using a validated questionnaire. Tandem mass spectrometry was applied to measure metabolite concentrations (BIOCRATES AbsoluteIDQp180 kit). Confounder-adjusted longitudinal associations were analyzed using linear mixed models, with false discovery rate (FDR) correction. We assessed interindividual (between-participant differences) and intraindividual longitudinal associations (within-participant changes over time). In the overall longitudinal analysis, statistically significant associations were observed for 12, 32, 17 and three metabolites with total fatigue and the subscales "fatigue severity," "reduced motivation" and "reduced activity," respectively. Specifically, higher concentrations of several amino acids, lysophosphatidylcholines, diacylphosphatidylcholines, acyl-alkylphosphatidylcholines and sphingomyelins were associated with less fatigue, while higher concentrations of acylcarnitines were associated with more fatigue. For "fatigue severity," associations appeared mainly driven by intraindividual associations, while for "reduced motivation" stronger interindividual associations were found. We observed longitudinal associations of several metabolites with total fatigue and fatigue subscales, and that intraindividual changes in metabolites over time were associated with fatigue severity. These findings point toward inflammation and an impaired energy metabolism due to mitochondrial dysfunction as underlying mechanisms. Mechanistic studies are necessary to determine whether these metabolites could be targets for intervention.

Background: Recent evidence indicates that insulin resistance (IR) in obesity may develop independently in different organs, representing different etiologies toward type 2 diabetes and other cardiometabolic diseases. The aim of this study was to investigate whether IR in the liver and IR in skeletal muscle are associated with distinct metabolic profiles. Methods: This study includes baseline data from 634 adults with overweight or obesity (BMI ≥ 27 kg/m 2) (≤65 years; 63% women) without diabetes of the European Diogenes Study. Hepatic insulin resistance index (HIRI) and muscle insulin sensitivity index (MISI), were derived from a five-point OGTT. At baseline 17 serum metabolites were identified and quantified by nuclear-magnetic-resonance spectroscopy. Linear mixed model analyses (adjusting for center, sex, body mass index (BMI), waist-to-hip ratio) were used to associate HIRI and MISI with these metabolites. In an independent sample of 540 participants without diabetes (BMI ≥ 27 kg/m 2; 40–65 years; 46% women) of the Maastricht Study, an observational prospective population-based cohort study, 11 plasma metabolites and a seven-point OGTT were available for validation. Results: Both HIRI and MISI were associated with higher levels of valine, isoleucine, oxo-isovaleric acid, alanine, lactate, and triglycerides, and lower levels of glycine (all p < 0.05). HIRI was also associated with higher levels of leucine, hydroxyisobutyrate, tyrosine, proline, creatine, and n-acetyl and lower levels of acetoacetate and 3-OH-butyrate (all p < 0.05). Except for valine, these results were replicated for all available metabolites in the Maastricht Study. Conclusions: In persons with obesity without diabetes, both liver and muscle IR show a circulating metabolic profile of elevated (branched-chain) amino acids, lactate, and triglycerides, and lower glycine levels, but only liver IR associates with lower ketone body levels and elevated ketogenic amino acids in circulation, suggestive of decreased ketogenesis. This knowledge might enhance developments of more targeted tissue-specific interventions to prevent progression to more severe disease stages.

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., Author summary Cellular metabolism is a highly complex and interconnected system. As many lifestyle diseases in humans have a strong metabolic component, it is important to understand metabolic differences between healthy and diseased states. In systems biology, metabolic behaviours are investigated using genome-scale metabolic models. In addition to the sheer size and complexity of the genome-scale metabolic models of human systems, using existing analysis methods is challenging and the parameter selection is not straightforward. Therefore, novel methodological frameworks are necessary for analysing metabolic conditions despite the challenges posed by human models. Particularly, an ongoing challenge has been that of comparing several phenotypes for identifying condition- or disease-specific metabolic signatures. We address this significant challenge by developing a scalable and model-driven approach, ComMet (Comparison of Metabolic states). ComMet enables an in-depth investigation and comparison of metabolic phenotypes in large models while also identifying the underlying functional differences. Novel hypotheses can be generated using ComMet for not only understanding known metabolic phenotypes better but also for guiding the design of new experiments to validate the processes predicted by ComMet.

We investigated longitudinal associations of moderate-to-vigorous physical activity (MVPA) and light-intensity physical activity (LPA) with plasma concentrations of 138 metabolites after colorectal cancer (CRC) treatment. Self-reported physical activity data and blood samples were obtained at 6 weeks, and 6, 12 and 24 months post-treatment in stage I-III CRC survivors (n = 252). Metabolite concentrations were measured by tandem mass spectrometry (BIOCRATES AbsoluteIDQp180 kit). Linear mixed models were used to evaluate confounder-adjusted longitudinal associations. Inter-individual (between-participant differences) and intra-individual associations (within-participant changes over time) were assessed as percentage difference in metabolite concentration per 5 h/week of MVPA or LPA. At 6 weeks post-treatment, participants reported a median of 6.5 h/week of MVPA (interquartile range:2.3,13.5) and 7.5 h/week of LPA (2.0,15.8). Inter-individual associations were observed with more MVPA being related (FDR-adjusted q-value

BACKGROUND: Cholesterol Efflux Capacity (CEC) is considered to be a key atheroprotective property of high-density lipoproteins (HDL). However, the role of HDL-CEC in atherosclerosis and cardiovascular (CV) risk is still controversial, and data in individuals with diabetes are limited.OBJECTIVE: In this study, we have investigated the relationship of CEC and other HDL characteristics with clinical and subclinical atherosclerosis in subjects with elevated cardiovascular diseases (CVD) risk and Type 2 Diabetes Mellitus (T2DM).METHODS: Using multiple linear regression analyses, we determined the relationship of HDL-CEC with carotid intima-media thickness (cIMT, Z-Score), an endothelial dysfunction (EnD) Score (Z-Score), prevalent CVD (n = 150 cases) and history of CV events (CVE, n = 85 cases) in an observational cohort (CODAM, n = 574, 59.6 ± 0.3 yr, 61.3% men, 24.4% T2DM). Stratified analyses were performed to determine if the associations differed between individuals with normal glucose metabolism (NGM) and those with disturbed glucose metabolism.RESULTS: HDL-CEC was not associated with either marker of atherosclerosis (cIMT, EnD Score) nor with CVD or CVE. In contrast, other HDL characteristics that is, HDL-Cholesterol (HDL-C, Z-Score), apolipoprotein A-I (apoA-I, Z-Score), HDL size (Z-Score) and HDL particle number (HDL-P, Z-Score) were inversely and significantly associated with the EnD Score (s -0.226 to -0.097, P < .05) and CVE (ORs 0.61 to 0.68, P < .05). In stratified analyses, HDL size and HDL-P were significantly associated with the EnD Score in individuals with NGM (Pinteraction .039 and .005, respectively), but not in those with (pre)diabetes. HDL-C and apoA-I were inversely associated with prevalent CVD in individuals with (pre)diabetes (Pinteraction = .074 and .034, respectively), but not in those with NGM.CONCLUSION: HDL-CEC is not associated with clinical or subclinical atherosclerosis, neither in the whole population nor in individuals with (pre)diabetes, while other HDL characteristics show atheroprotective associations. The atheroprotective associations of HDL-size and HDL-P are lost in (pre)diabetes, while higher concentrations of HDL-C and apoA-I are associated with a lower prevalence of CVD in (pre)diabetes.

Obesity-related insulin resistance (IR) may develop in multiple organs, representing various etiologies for cardiometabolic diseases. We identified abdominal subcutaneous adipose tissue (ScAT) transcriptome profiles in liver or muscle IR by means of RNA sequencing in overweight or obese participants of the Diet, Obesity, and Genes (DiOGenes) (NCT00390637, ClinicalTrials.gov) cohort (n = 368). Tissue-specific IR phenotypes were derived from a 5-point oral glucose tolerance test. Hepatic and muscle IR were characterized by distinct abdominal ScAT transcriptome profiles. Genes related to extracellular remodeling were upregulated in individuals with primarily hepatic IR, while genes related to inflammation were upregulated in individuals with primarily muscle IR. In line with this, in two independent cohorts, the Cohort on Diabetes and Atherosclerosis Maastricht (CODAM) (n = 325) and the Maastricht Study (n = 685), an increased systemic low-grade inflammation profile was specifically related to muscle IR but not to liver IR. We propose that increased ScAT inflammatory gene expression may translate into an increased systemic inflammatory profile, linking ScAT inflammation to the muscle IR phenotype. These distinct IR phenotypes may provide leads for more personalized prevention of cardiometabolic diseases.

Purpose Type 2 diabetes mellitus (T2DM) is characterized by both impaired pancreatic β-cell function (BCF) and insulin resistance. In the etiology of T2DM, BCF basically determines whether a person with a certain degree of insulin resistance develops T2DM, as β-cells are able to compensatorily increase insulin secretion. The effects of dietary intake on BCF are largely unknown. Our study aim was to investigate whether dietary macronutrient intake predicts BCF. Methods Prospective data (median follow-up 7 years) of 303 individuals recruited from the CODAM study population (aged 40–70 years, 39% women) were analyzed. BCF was measured by C-peptide deconvolution and physiological modeling of data from a 5-point, 75-g, 2-h oral glucose tolerance test. Macronutrient intake was estimated by a 178-item Food Frequency Questionnaire. Results Associations adjusted for relevant covariates of baseline macronutrient intake with model-derived parameters describing BCF (glucose sensitivity, rate sensitivity or potentiation) or C-peptidogenic index were detected for trans fat [standardized regression coefficient (95%-CI) glucose sensitivity − 0.14 (− 0.26, − 0.01)] per g, cholesterol [potentiation 0.20 (0.02, 0.37)] per 100 mg, dietary fiber [glucose sensitivity 0.21 (0.08, 0.33)] per 10 g, MUFA glucose sensitivity 0.16 (0.02, 0.31) per 10 g, and polysaccharide [potentiation − 0.24 (− 0.43, − 0.05), C-peptidogenic index − 0.16 (− 0.29 − 0.03); odds ratio lowest versus highest tertile (95%-CI) rate sensitivity 1.51 (1.06, 2.15)) per 50 g. Conclusions In this population at high risk for developing T2DM, polysaccharide and trans fat intake were associated with worse BCF, whereas increased intake of MUFA, dietary cholesterol, and fiber were associated with better BCF. Electronic supplementary material The online version of this article (10.1007/s00394-018-1644-y) contains supplementary material, which is available to authorized users.

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., Author summary The postprandial glucose and insulin responses to an identical meal can vary greatly across individuals. Certain dynamic features of these response curves have been shown to be indicative of the state of the glucose homeostasis and therefore relevant for targeted lifestyle intervention. In this study, we implement personalized computational models of the insulin regulated glucose homeostasis that take advantage of the complete time-courses of postprandial glucose and insulin trajectories following an oral glucose challenge in a large population of overweight/obese individuals. We show that the personalized models capture the responses more accurately compared to population-level models. In addition, the physiological basis of the models provides insight into the variability in the glucose homeostasis of individuals. The model parameters represent relevant features of the metabolic health such as insulin secretion or insulin mediated disposal of glucose into tissues that also correlated with independent measures of insulin secretion and whole-body insulin resistance, respectively. Furthermore, the models can be quantified from comparatively non-invasive sampling techniques and may be readily transferable to the clinic.

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., To study the effects of different fat sources in infant formula on energy and lipid metabolism, a proof‐of‐principle study with young adults is conducted. Replacing 67% of vegetable fat in infant formula with bovine milk fat did not affect postprandial energy metabolism nor the lipemic response, the initial difference in fatty acid profile of the formulas is reflected in the chylomicrons, and serum ketone bodies increase with consumption of bovine milk fat. Whether the exact same effects occur in infants requires experimental verification.

BACKGROUND: Plasma lipoproteins contain heterogeneous subclasses. Previous studies on the associations of the complement system with lipids and lipoproteins are mainly limited to the major lipid classes, and associations of complement with lipoprotein subclass characteristics remain unknown.OBJECTIVE: We investigated the associations of C3 and other components of the alternative complement pathway with plasma lipoprotein subclass profile.METHODS: Plasma complement concentrations (complement component 3 [C3], properdin, factor H, factor D, MASP-3, C3a, Bb), and lipoprotein subclass profile (as measured by nuclear magnetic resonance spectroscopy) were obtained in 523 participants (59.6 +/- 6.9 years, 60.8% men) of the Cohort on Diabetes and Atherosclerosis Maastricht (CODAM) study. Multiple linear regression was used to investigate the associations of C3 (primary determinant) and other alternative pathway components (secondary determinants) with characteristics (particle concentration and size [main outcomes], and lipid contents [secondary outcomes]) of 14 lipoprotein subclasses, ranging from extremely large VLDL to small HDL (all standardized [std] values).RESULTS: Participants with higher C3 concentrations had more circulating VLDL (std beta s ranging from 0.27 to 0.36), IDL and LDL (std beta s ranging from 0.14 to 0.17), and small HDL (stdb = 0.21). In contrast, they had fewer very large and large HDL particles (std beta s = 20.36). In persons with higher C3 concentrations, all lipoprotein subclasses were enriched in triglycerides. Similar but weaker associations were observed for properdin, factor H, factor D, and MASP-3, but not for C3a and Bb.CONCLUSIONS: The alternative complement pathway, and most prominently C3, is associated with an adverse lipoprotein subclass profile that is characterized by more triglyceride-enriched lipoproteins but fewer large HDL. (C) 2021 National Lipid Association. Published by Elsevier Inc.

Different amino acids (AAs) may exert distinct effects on postprandial glucose and insulin concentrations. A quantitative comparison of the effects of AAs on glucose and insulin kinetics in humans is currently lacking. PubMed was queried to identify intervention studies reporting glucose and insulin concentrations after acute ingestion and/or intravenous infusion of AAs in healthy adults and those living with obesity and/or type 2 diabetes (T2DM). The systematic literature search identified 55 studies that examined the effects of l-leucine, l-isoleucine, l-alanine, l-glutamine, l-arginine, l-lysine, glycine, l-proline, l-phenylalanine, l-glutamate, branched-chain AAs (i.e., l-leucine, l-isoleucine, and l-valine), and multiple individual l-AAs on glucose and insulin concentrations. Oral ingestion of most individual AAs induced an insulin response, but did not alter glucose concentrations in healthy participants. Specific AAs (i.e., leucine and isoleucine) co-ingested with glucose exerted a synergistic effect on the postprandial insulin response and attenuated the glucose response compared to glucose intake alone in healthy participants. Oral AA ingestion as well as intravenous AA infusion was able to stimulate an insulin response and decrease glucose concentrations in T2DM and obese individuals. The extracted information is publicly available and can serve multiple purposes such as computational modeling.

Being comprehensive knowledge bases of cellular metabolism, Genome-scale metabolic models (GEMs) serve as mathematical tools for studying cellular flux states in various organisms. However, analysis of large-scale (human) GEMs, still presents considerable challenges with respect to objective selection and reaction flux constraints. In this study, we introduce a model-based method, ComMet (Comparison of Metabolic states), for comprehensive analysis of large metabolic flux spaces and comparison of various metabolic states. ComMet allows (a) an in-depth characterisation of achievable flux states, (b) comparison of flux spaces from several conditions of interest and (c) identification and visualization of metabolically distinct network modules. As a proof-of-principle, we employed ComMet to extract the biochemical differences in the human adipocyte network (iAdipocytes1809) arising due to unlimited/blocked uptake of branched-chain amino acids. Our study opens avenues for exploring several metabolic conditions of interest in both microbe and human models. ComMet is open-source and is available at https://github.com/macsbio/commet.

Context There is a need for novel biomarkers and better understanding of the pathophysiology of diabetic kidney disease. Objective To investigate associations between plasma metabolites and kidney function in people with type 2 diabetes (T2D). Design 3089 samples from individuals with T2D, collected between 1999 and 2015, from 5 independent Dutch cohort studies were included. Up to 7 years follow-up was available in 1100 individuals from 2 of the cohorts. Main outcome measures Plasma metabolites (n = 149) were measured by nuclear magnetic resonance spectroscopy. Associations between metabolites and estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), and eGFR slopes were investigated in each study followed by random effect meta-analysis. Adjustments included traditional cardiovascular risk factors and correction for multiple testing. Results In total, 125 metabolites were significantly associated (PFDR = 1.5×10–32 − 0.046; β = −11.98-2.17) with eGFR. Inverse associations with eGFR were demonstrated for branched-chain and aromatic amino acids (AAAs), glycoprotein acetyls, triglycerides (TGs), lipids in very low-density lipoproteins (VLDL) subclasses, and fatty acids (PFDR < 0.03). We observed positive associations with cholesterol and phospholipids in high-density lipoproteins (HDL) and apolipoprotein A1 (PFDR < 0.05). Albeit some metabolites were associated with UACR levels (P < 0.05), significance was lost after correction for multiple testing. Tyrosine and HDL-related metabolites were positively associated with eGFR slopes before adjustment for multiple testing (PTyr = 0.003; PHDLrelated < 0.05), but not after. Conclusions This study identified metabolites associated with impaired kidney function in T2D, implying involvement of lipid and amino acid metabolism in the pathogenesis. Whether these processes precede or are consequences of renal impairment needs further investigation.

BackgroundHighly specialized cells work in synergy forming tissues to perform functions required for the survival of organisms. Understanding this tissue-specific cellular heterogeneity and homeostasis is essential to comprehend the development of diseases within the tissue and also for developing regenerative therapies. Cellular subpopulations in the adipose tissue have been related to disease development, but efforts towards characterizing the adipose tissue cell type composition are limited due to lack of robust cell surface markers, limited access to tissue samples, and the labor-intensive process required to identify them.ResultsWe propose a framework, identifying cellular heterogeneity while providing state-of-the-art cellular markers for each cell type present in tissues using transcriptomics level analysis. We validate our approach with an independent dataset and present the most comprehensive study of adipose tissue cell type composition to date, determining the relative amounts of 21 different cell types in 779 adipose tissue samples detailing differences across four adipose tissue depots, between genders, across ranges of BMI and in different stages of type-2 diabetes. We also highlight the heterogeneity in reported marker-based studies of adipose tissue cell type composition and provide novel cellular markers to distinguish different cell types within the adipose tissue.ConclusionsOur study provides a systematic framework for studying cell type composition in a given tissue and valuable insights into adipose tissue cell type heterogeneity in health and disease.

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.

Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/).

Objective: In the management of chronic gout, a large proportion of patients need long-term management with urate lowering therapy (ULT). This study reviews medication adherence to ULT and summarizes factors associated with adherence. Methods: We performed a systematic literature search for studies on adherence to ULT among gout patients in PubMed, Embase, CINAHL, and PsycINFO. We conducted meta-analysis, with a random effect model, for the studies reporting the proportion of patients considered adherent to at least 80% of prescribed medication or time taken. We explored potential sources of heterogeneity, including geographic area and measure of adherence. Narrative summaries were made for data on adherence assessed/defined by Medication Event Monitoring System (MEMS)/pill-count or patient-reported, occurrence of a gap in therapy >= 30 days (non-persistence), and factors associated with adherence. Results: Of the 24 studies, 16 assessed adherence using prescription/claims data, two by the MEMS or pill count, and six by patient-reported data. The pooled proportion of adherent patients (n = 13) was 46% (95% CI: 41-51); 45% across studies conducted in the USA (n = 8) and 48% in other countries (n = 5). Adherence assessed by MEMS/pill count and patient-reported was much higher than by studies using prescription/claims data. Non-persistence (n = 6) ranged from 54% to 87%. Factors associated with adherence were investigated in 18 studies. Strong evidence for a positive association with older age, more comorbidities, and the presence of diabetes or hypertension was found. Conclusion: Medication adherence to ULT among gout patients was poor. Better insight into reasons and consequences or poor adherence is needed. (C) 2018 Elsevier Inc. All rights reserved.

Objective: Accumulation of reactive oxygen species by increased uric acid production has been suggested as a possible underlying mechanism for the association between uric acid and high blood pressure (BP). We, therefore, investigated the association between serum uric acid concentration and 24-h urinary uric acid excretion, as proxy for uric acid production, with ambulatory 24-h blood pressure and hypertension. Methods: Cross-sectional analyses were conducted among 2555 individuals [52% men, mean age 60.0 ± 8.2 years; 27% type 2 diabetes (by design)] from The Maastricht Study. Multivariable regression analyses were performed to investigate the association of serum uric acid and 24-h urinary uric acid excretion with 24-h pulse pressure, 24-h mean arterial pressure (MAP), and hypertension. Results: After adjustment for traditional hypertension risk factors, serum uric acid concentration (per SD of 81 μmol/l) was associated with higher 24-h MAP [β 0.63 mmHg; confidence interval (CI) 0.27-1.00] and positively associated with hypertension (odds ratio 1.43; CI 1.27-1.61). Urinary uric acid excretion (per SD of 140 mg/day/1.73 m) was associated with higher 24-h MAP (β 0.79 mmHg; CI 0.46-1.12) and with hypertension (odds ratio 1.13; CI 1.02-1.25). There was no significant association between serum and 24-h urinary uric acid excretion with 24-h pulse pressure. There was no interaction with sex or age for the aforementioned associations. Conclusion: Higher serum and urinary uric acid concentrations were associated with higher 24-h MAP and hypertension. These results suggest that serum and 24-urinary uric acid concentrations, the latter as proxy for uric acid production are, independent of each other, associated with BP and hypertension.

Objective— The classical complement pathway has been assigned both protective and pathological effects in cardiovascular disease (CVD), but human data are lacking. We determined the associations of the pattern recognition factor C1q and the regulator C1-INH (C1-inhibitor) with incident CVD, carotid intima–media thickness, endothelial dysfunction, and low-grade inflammation. Approach and Results— Baseline concentrations of C1q and C1-INH were measured in the CODAM study (Cohort on Diabetes and Atherosclerosis Maastricht; n=574; 61% men; age, 60±7 years). The 7-year incidence of CVD in participants free of CVD at baseline was evaluated using logistic regression analyses (n=342; 73 cases). The lowest incidence of CVD was observed in the middle tertile of C1q (T low compared with T middle : odds ratio, 2.38 [95% confidence interval, 1.14–4.95]; T high compared with T middle : odds ratio, 1.96 [95% confidence interval, 0.94–4.07]). C1-INH was not associated with CVD. During the 7-year follow-up period, C1q and C1-INH were not, or inconsistently, associated with carotid intima–media thickness or with biomarker scores reflecting endothelial dysfunction and low-grade inflammation. Conclusions— Our results suggest a nonlinear association between C1q and incident CVD. This supports the concept that early steps in classical pathway activation may have both protective and pathological effects on human CVD.

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 β-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.

Given the association of disturbances in non-esterified fatty acid (NEFA) metabolism with the development of Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease, computational models of glucose-insulin dynamics have been extended to account for the interplay with NEFA. In this study, we use arteriovenous measurement across the subcutaneous adipose tissue during a mixed meal challenge test to evaluate the performance and underlying assumptions of three existing models of adipose tissue metabolism and construct a new, refined model of adipose tissue metabolism. Our model introduces new terms, explicitly accounting for the conversion of glucose to glyceraldehye-3-phosphate, the postprandial influx of glycerol into the adipose tissue, and several physiologically relevant delays in insulin signalling in order to better describe the measured adipose tissues fluxes. We then applied our refined model to human adipose tissue flux data collected before and after a diet intervention as part of the Yoyo study, to quantify the effects of caloric restriction on postprandial adipose tissue metabolism. Significant increases were observed in the model parameters describing the rate of uptake and release of both glycerol and NEFA. Additionally, decreases in the model’s delay in insulin signalling parameters indicates there is an improvement in adipose tissue insulin sensitivity following caloric restriction., Author summary The adipose tissue is no longer considered a metabolically quiescent tissue. Disturbances in non-esterified fatty acid (NEFA) metabolism leading to ectopic fat deposition have been associated with the development of insulin resistance. In recent years, the use of stable isotope tracers coupled with arteriovenous sampling across tissue depots has greatly improved our knowledge of postprandial NEFA dynamics. In this study, we make use of arteriovenous measurements collected across the abdominal subcutaneous adipose tissue in humans during a high fat mixed meal to evaluate three existing computational models of adipose tissue metabolism. As the three models included in this study were not capable of fully describing the measured adipose tissue fluxes, we present a new model of human in vivo adipose tissue metabolism, introducing novel terms such as the postprandial uptake of glycerol. We then utilised our refined model to quantify the effect of caloric restriction on adipose tissue metabolism by fitting the model to mixed meal challenge test data collected before and after a weight-loss intervention. Parameter estimates indicate an increase in the rates of glycerol and NEFA release coupled with a decrease in the delay of insulin signalling for all reactions, suggesting improved insulin sensitivity following caloric restriction.

Background Metabolic flexibility is the ability of an organism to switch between substrates for energy metabolism, in response to the changing nutritional state and needs of the organism. On the cellular level, metabolic flexibility revolves around the tricarboxylic acid cycle by switching acetyl coenzyme A production from glucose to fatty acids and vice versa. In this study, we modelled cellular metabolic flexibility by constructing a logical model connecting glycolysis, fatty acid oxidation, fatty acid synthesis and the tricarboxylic acid cycle, and then using network analysis to study the behaviours of the model. Results We observed that the substrate switching usually occurs through the inhibition of pyruvate dehydrogenase complex (PDC) by pyruvate dehydrogenase kinases (PDK), which moves the metabolism from glycolysis to fatty acid oxidation. Furthermore, we were able to verify four different regulatory models of PDK to contain known biological observations, leading to the biological plausibility of all four models across different cells and conditions. Conclusion These results suggest that the cellular metabolic flexibility depends upon the PDC-PDK regulatory interaction as a key regulatory switch for changing metabolic substrates.

Background Psoriasis and atopic dermatitis are two inflammatory skin diseases with a high prevalence and a significant burden on the patients. Underlying molecular mechanisms include chronic inflammation and abnormal proliferation. However, the cell types contributing to these molecular mechanisms are much less understood. Recently, deconvolution methodologies have allowed the digital quantification of cell types in bulk tissue based on mRNA expression data from biopsies. Using these methods to study the cellular composition of the skin enables the rapid enumeration of multiple cell types, providing insight into the numerical changes of cell types associated with chronic inflammatory skin conditions. Here, we use deconvolution to enumerate the cellular composition of the skin and estimate changes related to onset, progress, and treatment of these skin diseases. Methods A novel signature matrix, i.e. DerM22, containing expression data from 22 reference cell types, is used, in combination with the CIBERSORT algorithm, to identify and quantify the cellular subsets within whole skin biopsy samples. We apply the approach to public microarray mRNA expression data from the skin layers and 648 samples from healthy subjects and patients with psoriasis or atopic dermatitis. The methodology is validated by comparison to experimental results from flow cytometry and immunohistochemistry studies, and the deconvolution of independent data from isolated cell types. Results We derived the relative abundance of cell types from healthy, lesional, and non-lesional skin and observed a marked increase in the abundance of keratinocytes and leukocytes in the lesions of both inflammatory dermatological conditions. The relative fraction of these cells varied from healthy to diseased skin and from non-lesional to lesional skin. We show that changes in the relative abundance of skin-related cell types can be used to distinguish between mild and severe cases of psoriasis and atopic dermatitis, and trace the effect of treatment. Conclusions Our analysis demonstrates the value of this new resource in interpreting skin-derived transcriptomics data by enabling the direct quantification of cell types in a skin sample and the characterization of pathological changes in tissue composition. Electronic supplementary material The online version of this article (10.1186/s12920-019-0567-7) contains supplementary material, which is available to authorized users.

The Muscle Insulin Sensitivity Index (MISI) has been developed to estimate muscle-specific insulin sensitivity based on oral glucose tolerance test (OGTT) data. To date, the score has been implemented with considerable variation in literature and initial positive evaluations were not reproduced in subsequent studies. In this study, we investigate the computation of MISI on oral OGTT data with differing sampling schedules and aim to standardise and improve its calculation. Seven time point OGTT data for 2631 individuals from the Maastricht Study and seven time point OGTT data combined with a hyperinsulinemic-euglycaemic clamp for 71 individuals from the PRESERVE Study were used to evaluate the performance of MISI. MISI was computed on subsets of OGTT data representing four and five time point sampling schedules to determine minimal requirements for accurate computation of the score. A modified MISI computed on cubic splines of the measured data, resulting in improved identification of glucose peak and nadir, was compared with the original method yielding an increased correlation (ρ = 0.576) with the clamp measurement of peripheral insulin sensitivity as compared to the original method (ρ = 0.513). Finally, a standalone MISI calculator was developed allowing for a standardised method of calculation using both the original and improved methods.

Recent evidence indicates that insulin resistance (IR) in obesity may develop independently in different organs, representing different etiologies toward type 2 diabetes and other cardiometabolic diseases. The aim of this study was to investigate whether IR in the liver and IR in skeletal muscle are associated with distinct metabolic profiles.This study includes baseline data from 634 adults with overweight or obesity (BMI ≥ 27 kg/mBoth HIRI and MISI were associated with higher levels of valine, isoleucine, oxo-isovaleric acid, alanine, lactate, and triglycerides, and lower levels of glycine (all p 0.05). HIRI was also associated with higher levels of leucine, hydroxyisobutyrate, tyrosine, proline, creatine, and n-acetyl and lower levels of acetoacetate and 3-OH-butyrate (all p 0.05). Except for valine, these results were replicated for all available metabolites in the Maastricht Study.In persons with obesity without diabetes, both liver and muscle IR show a circulating metabolic profile of elevated (branched-chain) amino acids, lactate, and triglycerides, and lower glycine levels, but only liver IR associates with lower ketone body levels and elevated ketogenic amino acids in circulation, suggestive of decreased ketogenesis. This knowledge might enhance developments of more targeted tissue-specific interventions to prevent progression to more severe disease stages.

Scope Understanding biological functions of different free human milk oligosaccharides (HMOs) in shaping gastrointestinal tract microbiota during infancy is of great interest. We examined a link between HMOs in maternal milk and infant faecal microbiota composition and investigated the role of microbiota in degrading HMOs within the GI tract of healthy, breastfed, one-month old infants. Methods and results Maternal breast milk and corresponding infant faeces originated from the KOALA Birth Cohort. HMOs were quantified in milk and infant faecal samples using PGC-UPLC-MS and HPAEC-PAD. Faecal microbiota composition was characterised using Illumina HiSeq amplicon 16S rRNA sequencing. The composition associated with gender, mode of delivery, and milk HMOs: Lacto-N-fucopentaose I and 2'-Fucosyllactose. Overall, Bifidobacterium, Bacteroides, Escherichia-Shigella and Parabacteroides were predominating genera. We detected three different patterns in the infant faecal microbiota structure. Gastrointestinal degradation of HMOs was strongly associated with faecal microbiota composition, and there was a link between utilisation of specific HMOs and abundance of various phylotypes (OTUs). Conclusions HMOs in maternal milk are among important factors shaping GI tract microbiota composition in one-month old breastfed infants. Infant's ability to metabolise different HMOs strongly correlate with faecal microbiota composition, and with phylotypes within genera Bifidobacterium, Bacteroides and Lactobacillus. This article is protected by copyright. All rights reserved.

Contains fulltext : 201327.pdf (Publisher’s version ) (Open Access) Gastrointestinal (GI) microbiota composition differs between breastfed and formula-fed infants. Today's infant formulas are often fortified with prebiotics to better mimic properties of human milk with respect to its effect on GI microbiota composition and function. We used Illumina HiSeq sequencing of PCR-amplified 16S rRNA gene fragments to investigate the composition of faecal microbiota in 2-12 week old infants receiving either breastmilk, infant formulas fortified with prebiotics, or mixed feeding. We compared these results with results from infants fed traditional formulas used in the Netherlands in 2002-2003, which contained no added prebiotics. We showed that today's formulas supplemented with either scGOS (0.24-0.50 g/100 ml) or scGOS and lcFOS (at a 9:1 ratio; total 0.6 g/100 ml) had a strong bifidogenic effect as compared to traditional formulas, and they also resulted in altered patterns of microbial colonisation within the developing infant gastrointestinal tract. We identified three microbial states (or developmental stages) in the first 12 weeks of life, with a gradual transition pattern towards a bifidobacteria dominated state. In infants receiving only fortified formulas, this transition towards the bifidobacteria dominated state was accelerated, whereas in infants receiving mixed feeding the transition was delayed, as compared to exclusively breastfed infants. 13 p.

Background The gut microbiota evolves from birth and is in early life influenced by events such as birth mode, type of infant feeding, and maternal and infant antibiotics use. However, we still have a gap in our understanding of gut microbiota development in older children, and to what extent early events and pre-school lifestyle modulate the composition of the gut microbiota, and how this impinges on whole body metabolic regulation in school-age children. Results Taking advantage of the KOALA Birth Cohort Study, a long-term prospective birth cohort in the Netherlands with extensive collection of high-quality host metadata, we applied shotgun metagenomics sequencing and systematically investigated the gut microbiota of children at 6–9 years of age. We demonstrated an overall adult-like gut microbiota in the 281 Dutch school-age children and identified 3 enterotypes dominated by the genera Bacteroides, Prevotella, and Bifidobacterium, respectively. Importantly, we found that breastfeeding duration in early life and pre-school dietary lifestyle correlated with the composition and functional competences of the gut microbiota in the children at school age. The correlations between pre-school dietary lifestyle and metabolic phenotypes exhibited a striking enterotype dependency. Thus, an inverse correlation between high dietary fiber consumption and low plasma insulin levels was only observed in individuals with the Bacteroides and Prevotella enterotypes, but not in Bifidobacterium enterotype individuals in whom the gut microbiota displayed overall lower microbial gene richness, alpha-diversity, functional potential for complex carbohydrate fermentation, and butyrate and succinate production. High total fat consumption and elevated plasma free fatty acid levels in the Bifidobacterium enterotype are associated with the co-occurrence of Streptococcus. Conclusions Our work highlights the persistent effects of breastfeeding duration and pre-school dietary lifestyle in affecting the gut microbiota in school-age children and reveals distinct compositional and functional potential in children according to enterotypes. The findings underscore enterotype-specific links between the host metabolic phenotypes and dietary patterns, emphasizing the importance of microbiome-based stratification when investigating metabolic responses to diets. Future diet intervention studies are clearly warranted to examine gut microbe-diet-host relationships to promote knowledge-based recommendations in relation to improving metabolic health in children. Electronic supplementary material The online version of this article (10.1186/s40168-018-0608-z) contains supplementary material, which is available to authorized users.

Objective— Previous studies suggested that the lectin-complement pathway plays a complex role in cardiovascular disease (CVD). To date, no prospective human studies have investigated the relationship between the initiating factor of the lectin pathway, that is, mannose-binding lectin (MBL), and low-grade inflammation, endothelial dysfunction, or carotid intima–media thickness (cIMT). Moreover, MBL-associated proteases (MASPs) and MBL-associated proteins (MAps), which mediate downstream complement activation, have not been studied in the development of CVD. Approach and Results— In a prospective cohort (n=574; age 60±7 years; 7-year follow-up), we investigated longitudinal associations of plasma MBL, MASP-1, MASP-2, MASP-3, and MAp44 with biomarker scores that reflect low-grade inflammation and endothelial dysfunction, respectively, and with cIMT. We also investigated their associations with incident CVD (n=73). In adjusted analyses, low-grade inflammation was lowest in the middle tertile (T Middle ) of MBL, that is, T Middle was 0.19 SD (0.03 to 0.34) lower than T Low , and 0.15 SD (−0.02 to 0.31) lower than T High . cIMT was 28 μm (−50 to −5) lower in the highest MBL tertile (T High ) than in T Middle and did not differ between T Low and T Middle . MBL was not associated with endothelial dysfunction or CVD. MASP-1 and MASP-2 were not associated with any cardiovascular outcomes. MASP-3 and MAp44 were, independently of MBL levels, associated with endothelial dysfunction (per 1 SD higher MASP-3: β=0.10 SD [0.02 to 0.18]; per 1 SD higher MAp44 β=0.12 SD [0.04 to 0.20]) but not with low-grade inflammation, cIMT, or CVD. Conclusions— High MBL may contribute to low cIMT, whereas the association of MBL with low-grade inflammation was nonlinear. MASP-1 and MASP-2 were not associated with adverse cardiovascular outcomes. MASP-3 and MAp44 may play a role in endothelial dysfunction, potentially independent of lectin-pathway activation.

SummaryThe alternative pathway of complement activation is highly reactive and can be activated spontaneously in the vasculature. Activation may contribute to vascular damage and development of cardiovascular disease (CVD). We aimed to investigate functional components of the alternative pathway in cardiovascular risk. We studied 573 individuals who were followed-up for seven years. At baseline, we measured the enhancer properdin; the rate-limiting protease factor D (FD); and a marker of systemic activation, Bb. Using generalised estimating equations, we investigated their longitudinal associations with cardiovascular events (CVE, N=89), CVD (N=159), low-grade inflammation (LGI), endothelial dysfunction (ED) and carotid intima-media thickness (cIMT). Furthermore, we investigated associations with incident CVE (N=39) and CVD (N=73) in 342 participants free of CVD at baseline. CVE included myocardial infarction, stroke, cardiac angioplasty and/or cardiac bypass. CVD additionally included ischaemia on an electrocardiogram and/or ankle-brachial index < 0.9. In adjusted analyses, properdin was positively associated with CVE (per 1SD, longitudinal OR=1.36 [1.07; 1.74], OR for incident CVE=1.53 [1.06; 2.20]), but not with CVD. Properdin was also positively associated with ED (β=0.13 [95 %CI 0.06; 0.20]), but not with LGI or cIMT. FD and Bb were positively associated with LGI (per 1SD, FD: β=0.21 [0.12; 0.29], Bb: β=0.14 [0.07; 0.21]), and ED (FD: β=0.20 [0.11; 0.29], Bb: β=0.10 [0.03; 0.18]), but not with cIMT, CVE or CVD. Taken together, this suggests that the alternative complement pathway contributes to processes of vascular damage, and that in particular a high potential to enhance alternative pathway activation may promote unfavourable cardiovascular outcomes in humans.Supplementary Material to this article is available online at www.thrombosis-online.com.

ObjectiveMethodsThis study aimed to examine the intestinal microbiota composition of school-aged children in association with (over)weight.The fecal microbiota composition of 295 children was analyzed using the Human Intestinal Tract Chip. Anthropometric outcomes (overweight [BMI85th percentile], age- and sex-standardized BMI and weight z scores) were measured at 6 to 7 years of age, and elastic net was used to select genus-like bacterial groups related to all anthropometric outcomes. Subsequently, multiple linear and logistic regression models were used to model associations between selected bacterial groups and anthropometric measures while controlling for confounders.ResultsConclusionsPrevotella melaninogenica, Prevotella oralis, Dialister, and uncultured Clostridiales II (UCII) accounted for 26.1% of the variation in microbiota composition. Several bacterial groups were inversely associated with the anthropometric outcomes: Sutterella wadsworthensis, Marvinbryantia formatexigens, Prevotella melanogenica, P oralis, Burkholderia, uncultured Clostridiales II, and Akkermansia, while Streptococcus bovis was positively associated with overweight. Microbial diversity and richness, and Bacteroidetes to Firmicutes ratio, were not significantly associated with any of the outcomes.In the largest population-based study on childhood gut microbiota and body weight so far, both new and previously identified bacterial groups were found to be associated with overweight. Further research should elucidate their role in energy metabolism.

Objectives. When urate lowering therapy is indicated in patients with gout, medication adherence is essential. This study assesses non-persistence and non-adherence in patients with newly diagnosed gout, and identifies factors associated with poor medication adherence. Methods. A retrospective data analysis was performed within the UK Clinical Practice Research Datalink (1987-2014) among incident gout patients, aged540 years and starting allopurinol (n = 48 280). The proportion of patients non-persistent (a first medication gap of 590 days) after 1 and 5 years, and median time until a first 90-day gap was estimated using Kaplan-Meier statistics in those starting allopurinol and restarting after a first interruption. Non-adherence (proportion of days covered < 80%) over the full observation period was calculated. Multivariable Cox-or logistic regressions assessed factors associated with non-persistence or non-adherence, respectively. Results. Non-persistence increased from 38.5% (95% CI: 38.1, 38.9) to 56.9% (95% CI: 56.4, 57.4) after 1 and 5 years of initiation. Median time until a first 90-day gap was 1029 days (95% CI: 988, 1078) and 61% were non-adherent. After a first gap, 43.3% (95% CI: 42.7, 43.9) restarted therapy within 1 year, yet only 52.3% (95% CI: 51.4, 53.1) persisted for 1 year. Being female and a current smoker increased the risk for non-persistence and non-adherence, while older age, overweight, receiving anti-hypertensive medication or colchicine and suffering from dementia, diabetes or dyslipidaemia decreased the risk. Conclusion. Medication adherence among gout patients starting allopurinol is poor, particularly among females and younger patients and patients with fewer comorbidities. Medication adherence remains low in those reinitiating after a first gap.

Background Metabolic flexibility is the ability of cells to change substrates for energy production based on the nutrient availability and energy requirement. It has been shown that metabolic flexibility is impaired in obesity and chronic diseases such as type 2 diabetes mellitus, cardiovascular diseases, and metabolic syndrome, although, whether it is a cause or an effect of these conditions remains to be elucidated. Main body In this paper, we have reviewed the literature on metabolic flexibility and curated pathways and processes resulting in a network resource to investigate the interplay between these processes in the subcutaneous adipose tissue. The adipose tissue has been shown to be responsible, not only for energy storage but also for maintaining energy homeostasis through oxidation of glucose and fatty acids. We highlight the role of pyruvate dehydrogenase complex–pyruvate dehydrogenase kinase (PDC-PDK) interaction as a regulatory switch which is primarily responsible for changing substrates in energy metabolism from glucose to fatty acids and back. Baseline gene expression of the subcutaneous adipose tissue, along with a publicly available obesity data set, are visualised on the cellular network of metabolic flexibility to highlight the genes that are expressed and which are differentially affected in obesity. Conclusion We have constructed an abstracted network covering glucose and fatty acid oxidation, as well as the PDC-PDK regulatory switch. In addition, we have shown how the network can be used for data visualisation and as a resource for follow-up studies. Electronic supplementary material The online version of this article (10.1186/s12263-018-0609-3) contains supplementary material, which is available to authorized users.