Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting

Cell-Type-Specific Metabolic Profiling Achieved by Combining Desorption Electrospray Ionization Mass Spectrometry Imaging and Immunofluorescence Staining

Cell-type-specific metabolic profiling in tissue with heterogeneous composition has been of great interest across all mass spectrometry imaging (MSI) technologies. We report here a powerful new chemical imaging capability in desorption electrospray ionization (DESI) MSI, which enables cell-type-specific and in situ metabolic profiling in complex tissue samples. We accomplish this by combining DESI-MSI with immunofluorescence staining using specific cell-type markers. We take advantage of the variable frequency of each distinct cell type in the lateral septal nucleus (LSN) region of mouse forebrain. This allows computational deconvolution of the cell-type-specific metabolic profile in neurons and astrocytes by convex optimization-a machine learning method. Based on our approach, we observed 107 metabolites that show different distributions and intensities between astrocytes and neurons. We subsequently identified 23 metabolites using high-resolution mass spectrometry (MS) and tandem MS, which include small metabolites such as adenosine and N-acetylaspartate previously associated with astrocytes and neurons, respectively, as well as accumulation of several phospholipid species in neurons which have not been studied before. Overall, this method overcomes the relatively low spatial resolution of DESI-MSI and provides a new platform for in situ metabolic investigation at the cell-type level in complex tissue samples with heterogeneous cell-type composition.

Non-targeted urine metabolomics and associations with prevalent and incident type 2 diabetes

Better risk prediction and new molecular targets are key priorities in type 2 diabetes (T2D) research. Little is known about the role of the urine metabolome in predicting the risk of T2D. We aimed to use non-targeted urine metabolomics to discover biomarkers and improve risk prediction for T2D. Urine samples from two community cohorts of 1,424 adults were analyzed by ultra-performance liquid chromatography/mass spectrometry (UPLC-MS). In a discovery/replication design, three out of 62 annotated metabolites were associated with prevalent T2D, notably lower urine levels of 3-hydroxyundecanoyl-carnitine. In participants without diabetes at baseline, LASSO regression in the training set selected six metabolites that improved prediction of T2D beyond established risk factors risk over up to 12 years' follow-up in the test sample, from C-statistic 0.866 to 0.892. Our results in one of the largest non-targeted urinary metabolomics study to date demonstrate the role of the urine metabolome in identifying at-risk persons for T2D and suggest urine 3-hydroxyundecanoyl-carnitine as a biomarker candidate.

Short-term variability of the human serum metabolome depending on nutritional and metabolic health status

The intra-individual variability of the human serum metabolome over a period of 4 weeks and its dependence on metabolic health and nutritional status was investigated in a single-center study under tightly controlled conditions in healthy controls, pre-diabetic individuals and patients with type-2 diabetes mellitus (T2DM, n = 10 each). Untargeted metabolomics in serum samples taken at three different days after overnight fasts and following intake of a standardized mixed meal showed that the human serum metabolome is remarkably stable: The median intra-class correlation coefficient (ICC) across all metabolites and all study participants was determined as 0.65. ICCs were similar for the three different health groups, before and after meal intake, and for different metabolic pathways. Only 147 out of 1438 metabolites (10%) had an ICC below 0.4 indicating poor stability over time. In addition, we confirmed previously identified metabolic signatures differentiating healthy, pre-diabetic and diabetic individuals. To our knowledge, this is the most comprehensive study investigating the temporal variability of the human serum metabolome under such tightly controlled conditions.

Non-Targeted Metabolomic Analysis Reveals Serum Phospholipid Alterations in Patients with Early Stages of Diabetic Foot Ulcer

Diabetic foot ulcer (DFU) is a common complication of type 2 diabetes mellitus (T2DM) characterized by ulcer formation, which can lead to the amputation of lower extremities. However, the metabolic alterations related to this complication are not completely elucidated. Therefore, we carried out a metabolomic analysis of serum samples obtained from T2DM adult patients diagnosed with diabetic foot ulcer in a cross-sectional, observational, and comparative study. Eighty-four volunteers were classified into the following groups: without T2DM (control group, n = 30) and with T2DM and different stages of diabetic foot ulcer according to Wagner-Meggitt classification system: DFU G0 (n = 11), DFU G1 (n = 14), DFU G2 (n = 16), and DFU G3 (n = 13). The non-target metabolomic profile followed by chemometric analysis revealed that lysophosphatidylethanolamine (16:1) could be proposed as key metabolite related to the onset of diabetic foot ulcer; however, this phospholipid was not affected by diabetic foot ulcer progression. Therefore, further studies are necessary to validate these phospholipids as biomarker candidates for the early diagnosis of diabetic foot ulcer in T2DM patients.

TGR5 Signaling in Hepatic Metabolic Health

TGR5 is a G protein-coupled bile acid receptor that is increasingly recognized as a key regulator of glucose homeostasis. While the role of TGR5 signaling in immune cells, adipocytes and enteroendocrine L cells in metabolic regulation has been well described and extensively reviewed, the impact of TGR5-mediated effects on hepatic physiology and pathophysiology in metabolic regulation has received less attention. Recent studies suggest that TGR5 signaling contributes to improvements in hepatic insulin signaling and decreased hepatic inflammation, as well as metabolically beneficial improvements in bile acid profile. Additionally, TGR5 signaling has been associated with reduced hepatic steatosis and liver fibrosis, and improved liver function. Despite the beneficial effects of TGR5 signaling on metabolic health, TGR5-mediated gallstone formation and gallbladder filling complicate therapeutic targeting of TGR5 signaling. To this end, there is a growing need to identify cell type-specific effects of hepatic TGR5 signaling to begin to identify and target the downstream effectors of TGR5 signaling. Herein, we describe and integrate recent advances in our understanding of the impact of TGR5 signaling on liver physiology and how its effects on the liver integrate more broadly with whole body glucose regulation.

Integrating Two-Dimensional Gas and Liquid Chromatography-Mass Spectrometry for Untargeted Colorectal Cancer Metabolomics: A Proof-of-Principle Study

Untargeted metabolomics is expected to lead to a better mechanistic understanding of diseases and thus applications of precision medicine and personalized intervention. To further increase metabolite coverage and achieve high accuracy of metabolite quantification, the present proof-of-principle study was to explore the applicability of integration of two-dimensional gas and liquid chromatography-mass spectrometry (GC × GC-MS and 2DLC-MS) platforms to characterizing circulating polar metabolome extracted from plasma collected from 29 individuals with colorectal cancer in comparison with 29 who remained cancer-free. After adjustment of multiple comparisons, 20 metabolites were found to be up-regulated and 8 metabolites were found to be down-regulated, which pointed to the dysregulation in energy metabolism and protein synthesis. While integrating the GC × GC-MS and 2DLC-MS data can dramatically increase the metabolite coverage, this study had a limitation in analyzing the non-polar metabolites. Given the small sample size, these results need to be validated with a larger sample size and with samples collected prior to diagnostic and treatment. Nevertheless, this proof-of-principle study demonstrates the potential applicability of integration of these advanced analytical platforms to improve discrimination between colorectal cancer cases and controls based on metabolite profiles in future studies.

Targeted Metabolomics for Plasma Amino Acids and Carnitines in Patients with Metabolic Syndrome Using HPLC-MS/MS

Metabolic syndrome (MetS) is a health disorder characterized by metabolic abnormalities that predict an increased risk to develop cardiovascular disease (CVD) and type 2 diabetes. Biomarkers can provide an insight into the novel mechanism for MetS and can be potentially used for personalized response to therapies. We exploited a targeted HPLC-MS/MS method to characterize plasma amino acids and carnitine metabolic profile in MetS patients. A training set (40 cases and 40 controls) and validation set (80 MetS patients and 80 healthy controls) were carried out to find the metabolic profiles. We discovered two carnitine metabolites including hydroxydecanoyl carnitine and methylglutarylcarnitine. Our results indicated that the decreased level of hydroxydecanoyl carnitine and methylglutarylcarnitine may be associated with the risk of MetS. These biomarkers may improve the risk prediction and provide a novel tool for monitoring of the progression of disease and response to treatment in MetS patients.

Altered Metabolome of Lipids and Amino Acids Species: A Source of Early Signature Biomarkers of T2DM

Diabetes mellitus, a disease of modern civilization, is considered the major mainstay of mortalities around the globe. A great number of biochemical changes have been proposed to occur at metabolic levels between perturbed glucose, amino acid, and lipid metabolism to finally diagnoe diabetes mellitus. This window period, which varies from person to person, provides us with a unique opportunity for early detection, delaying, deferral and even prevention of diabetes. The early detection of hyperglycemia and dyslipidemia is based upon the detection and identification of biomarkers originating from perturbed glucose, amino acid, and lipid metabolism. The emerging “OMICS” technologies, such as metabolomics coupled with statistical and bioinformatics tools, proved to be quite useful to study changes in physiological and biochemical processes at the metabolic level prior to an eventual diagnosis of DM. Approximately 300–400 such metabolites have been reported in the literature and are considered as predicting or risk factor-reporting metabolic biomarkers for this metabolic disorder. Most of these metabolites belong to major classes of lipids, amino acids and glucose. Therefore, this review represents a snapshot of these perturbed plasma/serum/urinary metabolic biomarkers showing a significant correlation with the future onset of diabetes and providing a foundation for novel early diagnosis and monitoring the progress of metabolic syndrome at early symptomatic stages. As most metabolites also find their origin from gut microflora, metabolism and composition of gut microflora also vary between healthy and diabetic persons, so we also summarize the early changes in the gut microbiome which can be used for the early diagnosis of diabetes.

Review of methods for detecting glycemic disorders

Prediabetes (intermediate hyperglycemia) consists of two abnormalities, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) detected by a standardized 75-gram oral glucose tolerance test (OGTT). Individuals with isolated IGT or combined IFG and IGT have increased risk for developing type 2 diabetes (T2D) and cardiovascular disease (CVD). Diagnosing prediabetes early and accurately is critical in order to refer high-risk individuals for intensive lifestyle modification. However, there is currently no international consensus for diagnosing prediabetes with HbA1c or glucose measurements based upon American Diabetes Association (ADA) and the World Health Organization (WHO) criteria that identify different populations at risk for progressing to diabetes. Various caveats affecting the accuracy of interpreting the HbA1c including genetics complicate this further. This review describes established methods for detecting glucose disorders based upon glucose and HbA1c parameters as well as novel approaches including the 1-hour plasma glucose (1-h PG), glucose challenge test (GCT), shape of the glucose curve, genetics, continuous glucose monitoring (CGM), measures of insulin secretion and sensitivity, metabolomics, and ancillary tools such as fructosamine, glycated albumin (GA), 1,5- anhydroglucitol (1,5-AG). Of the approaches considered, the 1-h PG has considerable potential as a biomarker for detecting glucose disorders if confirmed by additional data including health economic analysis. Whether the 1-h OGTT is superior to genetics and omics in providing greater precision for individualized treatment requires further investigation. These methods will need to demonstrate substantially superiority to simpler tools for detecting glucose disorders to justify their cost and complexity.

The impact of ageing on lipid-mediated regulation of adult stem cell behavior and tissue homeostasis

Adult stem cells sustain tissue homeostasis throughout life and provide an important reservoir of cells capable of tissue repair in response to stress and tissue damage. Age-related changes to stem cells and/or the specialized niches that house them have been shown to negatively impact stem cell maintenance and activity. In addition, metabolic inputs have surfaced as another crucial layer in the control of stem cell behavior (Chandel et al., 2016; Folmes and Terzic, 2016; Ito and Suda, 2014; Mana et al., 2017; Shyh-Chang and Ng, 2017). Here, we will present a brief review of how lipid metabolism influences adult stem cell behavior under homeostatic conditions and speculate on how changes in lipid metabolism may impact stem cell ageing. This review considers the future of lipid metabolism research in stem cells, with the long-term goal of identifying mechanisms that could be targeted to counter or slow the age-related decline in stem cell function.

Amino acid and lipid metabolism in post-gestational diabetes and progression to type 2 diabetes: A metabolic profiling study

BACKGROUND

Women with a history of gestational diabetes mellitus (GDM) have a 7-fold higher risk of developing type 2 diabetes (T2D) during midlife and an elevated risk of developing hypertension and cardiovascular disease. Glucose tolerance reclassification after delivery is recommended, but fewer than 40% of women with GDM are tested. Thus, improved risk stratification methods are needed, as is a deeper understanding of the pathology underlying the transition from GDM to T2D. We hypothesize that metabolites during the early postpartum period accurately distinguish risk of progression from GDM to T2D and that metabolite changes signify underlying pathophysiology for future disease development.

METHODS AND FINDINGS

The study utilized fasting plasma samples collected from a well-characterized prospective research study of 1,035 women diagnosed with GDM. The cohort included racially/ethnically diverse pregnant women (aged 20-45 years-33% primiparous, 37% biparous, 30% multiparous) who delivered at Kaiser Permanente Northern California hospitals from 2008 to 2011. Participants attended in-person research visits including 2-hour 75-g oral glucose tolerance tests (OGTTs) at study baseline (6-9 weeks postpartum) and annually thereafter for 2 years, and we retrieved diabetes diagnoses from electronic medical records for 8 years. In a nested case-control study design, we collected fasting plasma samples among women without diabetes at baseline (n = 1,010) to measure metabolites among those who later progressed to incident T2D or did not develop T2D (non-T2D). We studied 173 incident T2D cases and 485 controls (pair-matched on BMI, age, and race/ethnicity) to discover metabolites associated with new onset of T2D. Up to 2 years post-baseline, we analyzed samples from 98 T2D cases with 239 controls to reveal T2D-associated metabolic changes. The longitudinal analysis tracked metabolic changes within individuals from baseline to 2 years of follow-up as the trajectory of T2D progression. By building prediction models, we discovered a distinct metabolic signature in the early postpartum period that predicted future T2D with a median discriminating power area under the receiver operating characteristic curve of 0.883 (95% CI 0.820-0.945, p < 0.001). At baseline, the most striking finding was an overall increase in amino acids (AAs) as well as diacyl-glycerophospholipids and a decrease in sphingolipids and acyl-alkyl-glycerophospholipids among women with incident T2D. Pathway analysis revealed up-regulated AA metabolism, arginine/proline metabolism, and branched-chain AA (BCAA) metabolism at baseline. At follow-up after the onset of T2D, up-regulation of AAs and down-regulation of sphingolipids and acyl-alkyl-glycerophospholipids were sustained or strengthened. Notably, longitudinal analyses revealed only 10 metabolites associated with progression to T2D, implicating AA and phospholipid metabolism. A study limitation is that all of the analyses were performed with the same cohort. It would be ideal to validate our findings in an independent longitudinal cohort of women with GDM who had glucose tolerance tested during the early postpartum period.

CONCLUSIONS

In this study, we discovered a metabolic signature predicting the transition from GDM to T2D in the early postpartum period that was superior to clinical parameters (fasting plasma glucose, 2-hour plasma glucose). The findings suggest that metabolic dysregulation, particularly AA dysmetabolism, is present years prior to diabetes onset, and is revealed during the early postpartum period, preceding progression to T2D, among women with GDM.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01967030.

Exploring the Diversity of Sugar Compounds in Healthy, Prediabetic, and Diabetic Volunteers

SCOPE

Diabetes is thought to primarily represent a disturbance of carbohydrate metabolism; however, population studies employing metabolomics have mainly identified plasma amino acids and lipids, or their products, as biomarkers. In this pilot study, the aim is to analyze a wide spectrum of sugar compounds in the fasting state and during an oral glucose tolerance test (OGTT) in healthy, prediabetic, and type 2 diabetic volunteers.

METHODS AND RESULTS

The three volunteer groups underwent a standard OGTT. Plasma samples obtained in the fasting state, 30 and 90 min after the OGTT, are subjected to a semitargeted GC-MS (gas chromatography-mass spectrometry) sugar profiling. Overall, 40 sugars are detected in plasma, of which some are yet unknown to change during an OGTT. Several sugars (e.g., trehalose) reveal significant differences between the volunteer groups both in fasting plasma and in distinct time courses after the OGTT. This suggests an endogenous production from orally absorbed glucose and/or an insulin-dependent production/removal from plasma.

CONCLUSION

It is demonstrated that more sugars than expected can be found in human plasma. Since some of these show characteristic differences depending on health status, it may be worthwhile to assess their usability as biomarkers for diagnosing early-stage insulin resistance and type 2 diabetes.

Metabolomics Signatures in Type 2 Diabetes: A Systematic Review and Integrative Analysis

OBJECTIVE

Metabolic signatures have emerged as valuable signaling molecules in the biochemical process of type 2 diabetes (T2D). To summarize and identify metabolic biomarkers in T2D, we performed a systematic review and meta-analysis of the associations between metabolites and T2D using high-throughput metabolomics techniques.

METHODS

We searched relevant studies from MEDLINE (PubMed), Embase, Web of Science, and Cochrane Library as well as Chinese databases (Wanfang, Vip, and CNKI) inception through 31 December 2018. Meta-analysis was conducted using STATA 14.0 under random effect. Besides, bioinformatic analysis was performed to explore molecule mechanism by MetaboAnalyst and R 3.5.2.

RESULTS

Finally, 46 articles were included in this review on metabolites involved amino acids, acylcarnitines, lipids, carbohydrates, organic acids, and others. Results of meta-analysis in prospective studies indicated that isoleucine, leucine, valine, tyrosine, phenylalanine, glutamate, alanine, valerylcarnitine (C5), palmitoylcarnitine (C16), palmitic acid, and linoleic acid were associated with higher T2D risk. Conversely, serine, glutamine, and lysophosphatidylcholine C18:2 decreased risk of T2D. Arginine and glycine increased risk of T2D in the Western countries subgroup, and betaine was negatively correlated with T2D in nested case-control subgroup. In addition, slight improvements in T2D prediction beyond traditional risk factors were observed when adding these metabolites in predictive analysis. Pathway analysis identified 17 metabolic pathways may alter in the process of T2D and metabolite-related genes were also enriched in functions and pathways associated with T2D.

CONCLUSIONS

Several metabolites and metabolic pathways associated with T2D have been identified, which provide valuable biomarkers and novel targets for prevention and drug therapy.

Glycolysis/gluconeogenesis- and tricarboxylic acid cycle-related metabolites, Mediterranean diet, and type 2 diabetes

BACKGROUND

Glycolysis/gluconeogenesis and tricarboxylic acid (TCA) cycle metabolites have been associated with type 2 diabetes (T2D). However, the associations of these metabolites with T2D incidence and the potential effect of dietary interventions remain unclear.

OBJECTIVES

We aimed to evaluate the association of baseline and 1-y changes in glycolysis/gluconeogenesis and TCA cycle metabolites with insulin resistance and T2D incidence, and the potential modifying effect of Mediterranean diet (MedDiet) interventions.

METHODS

We included 251 incident T2D cases and 638 noncases in a nested case-cohort study within the PREDIMED Study during median follow-up of 3.8 y. Participants were allocated to MedDiet + extra-virgin olive oil, MedDiet + nuts, or control diet. Plasma metabolites were measured using a targeted approach by LC-tandem MS. We tested the associations of baseline and 1-y changes in glycolysis/gluconeogenesis and TCA cycle metabolites with subsequent T2D risk using weighted Cox regression models and adjusting for potential confounders. We designed a weighted score combining all these metabolites and applying the leave-one-out cross-validation approach.

RESULTS

Baseline circulating concentrations of hexose monophosphate, pyruvate, lactate, alanine, glycerol-3 phosphate, and isocitrate were significantly associated with higher T2D risk (17-44% higher risk for each 1-SD increment). The weighted score including all metabolites was associated with a 30% (95% CI: 1.12, 1.51) higher relative risk of T2D for each 1-SD increment. Baseline lactate and alanine were associated with baseline and 1-y changes of homeostasis model assessment of insulin resistance. One-year increases in most metabolites and in the weighted score were associated with higher relative risk of T2D after 1 y of follow-up. Lower risks were observed in the MedDiet groups than in the control group although no significant interactions were found after adjusting for multiple comparisons.

CONCLUSIONS

We identified a panel of glycolysis/gluconeogenesis-related metabolites that was significantly associated with T2D risk in a Mediterranean population at high cardiovascular disease risk. A MedDiet could counteract the detrimental effects of these metabolites.This trial was registered at controlled-trials.com as ISRCTN35739639.

Analysis of Astragalus Polysaccharide Intervention in Heat-Stressed Dairy Cows' Serum Metabolomics

This experiment was conducted to investigate the effects of astragalus polysaccharides (APS) on serum metabolism of dairy cows under heat stress. Thirty healthy Holstein dairy cows were randomly divided into three groups (10 cows in each group). In the experimental group, 30 mL/d (Treatment I) and 50 mL/d (Treatment II) of APS injection were injected into the neck muscle respectively. Each stage was injected with APS for 4 days (8:00 a.m. every day) and stopped for 3 days. Serum hormone and antioxidant indexes of dairy cows were investigated. Through repeated measurement analysis of variance, the results have shown that cortisol (COR) (F = 6.982, p = 0.026), triiodothyronine (T3) (F = 10.005, p = 0.012) and thyroxine (T4) (F = 22.530, p = 0.002) at different time points were significantly different. COR showed a downward trend, T3 and T4 showed an upward trend. At each time point, different concentrations of APS have significant effects on COR (F = 30.298, p = 0.000 < 0.05), T3 (F = 18.122, p = 0.001), and T4 (F = 44.067, p = 0.000 < 0.05). However, there were no significant differences in serum insulin (INS), glucagon (GC) and heat shock protein 70 (HSP70) between different time points (p > 0.05) and at each time point (p > 0.05). Additionally, the results have also shown that there were also no significant differences in serum Superoxide dismutase (SOD), malondialdehyde (MDA) and lactate dehydrogenase (LDH) between different time points (p > 0.05) and at each time point (p > 0.05). However, the injection of APS had a significant impact on glutathione peroxidase (GSH-Px) (F = 9.421, p = 0.014) at different times, and showed a trend of rising first and then falling. At each time point, APS of different concentrations had no significant effect on GSH-Px (p > 0.05). Furthermore, we used gas chromatography-mass spectrometry (GC-MS) non-targeted metabolomics to determine the potential markers of APS for heat-stressed dairy cows. Twenty metabolites were identified as potential biomarkers for the diagnosis of APS in heat-stressed dairy cows. These substances are involved in protein digestion and absorption, glutathione metabolism, prolactin signaling pathway, aminoacyl-tRNA biosynthesis, pentose and glucuronate interconversions, and so on. Our findings suggest that APS have an effect on the serum hormones of heat-stressed dairy cows, and regulate the metabolism of heat-stressed dairy cows through glucose metabolism and amino acid metabolism pathways.

Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment

The metabolic syndrome is a multifactorial disease developed due to accumulation and chronification of several risk factors associated with disrupted metabolism. The early detection of the biomarkers by NMR spectroscopy could be helpful to prevent multifactorial diseases. The exposure of each risk factor can be detected by traditional molecular markers but the current biomarkers have not been enough precise to detect the primary stages of disease. Thus, there is a need to obtain novel molecular markers of pre-disease stages. A promising source of new molecular markers are metabolomics standing out the research of biomarkers in NMR approaches. An increasing number of nutritionists integrate metabolomics into their study design, making nutrimetabolomics one of the most promising avenues for improving personalized nutrition. This review highlight the major five risk factors associated with metabolic syndrome and related diseases including carbohydrate dysfunction, dyslipidemia, oxidative stress, inflammation, and gut microbiota dysbiosis. Together, it is proposed a profile of metabolites of each risk factor obtained from NMR approaches to target them using personalized nutrition, which will improve the quality of life for these patients.

Metabolic profiling of tissue-specific insulin resistance in human obesity: results from the Diogenes study and the Maastricht Study

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/m²) (≤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/m²; 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.

An Integrated Multi-Disciplinary Perspectivefor Addressing Challenges of the Human Gut Microbiome

Our understanding of the human gut microbiome has grown exponentially. Advances in genome sequencing technologies and metagenomics analysis have enabled researchers to study microbial communities and their potential function within the context of a range of human gut related diseases and disorders. However, up until recently, much of this research has focused on characterizing the gut microbiological community structure and understanding its potential through system wide (meta) genomic and transcriptomic-based studies. Thus far, the functional output of these microbiomes, in terms of protein and metabolite expression, and within the broader context of host-gut microbiome interactions, has been limited. Furthermore, these studies highlight our need to address the issues of individual variation, and of samples as proxies. Here we provide a perspective review of the recent literature that focuses on the challenges of exploring the human gut microbiome, with a strong focus on an integrated perspective applied to these themes. In doing so, we contextualize the experimental and technical challenges of undertaking such studies and provide a framework for capitalizing on the breadth of insight such approaches afford. An integrated perspective of the human gut microbiome and the linkages to human health will pave the way forward for delivering against the objectives of precision medicine, which is targeted to specific individuals and addresses the issues and mechanisms in situ.

Bile acids associate with glucose metabolism, but do not predict conversion from impaired fasting glucose to diabetes

OBJECTIVE

Bile acids (BAs) are signaling molecules controlling lipid and glucose metabolism. Since BA alterations are associated with obesity and insulin resistance, plasma BAs have been considered candidates to predict type 2 diabetes (T2D) risk. We aimed to determine (1) the association of BAs with glucose homeostasis parameters and (2) their predictive association with the risk of conversion from prediabetes to new-onset diabetes (NOD) in a prospective cohort study.

DESIGN

205 patients with impaired fasting glucose (IFG) were followed each year during 5 years in the IT-DIAB cohort study. Twenty-one BA species and 7α-hydroxy-4-cholesten-3-one (C4), a marker of BA synthesis, were quantified by LC/MS-MS in plasma from fasted patients at baseline. Correlations between plasma BA species and metabolic parameters at baseline were assessed by Spearman's coefficients and the association between BAs and NOD was determined using Cox proportional-hazards models.

RESULTS

Among the analyzed BA species, total hyocholic acid (HCA) and the total HCA/total chenodeoxycholic acid (CDCA) ratio, reflecting hepatic BA 6α-hydroxylation activity, negatively correlated with BMI and HOMA-IR. The total HCA/total CDCA ratio also correlated negatively with HbA_1C. Conversion from IFG to NOD occurred in 33.7% of the participants during the follow-up. Plasma BA species were not independently associated with the conversion to NOD after adjustment with classical T2D risk factors.

CONCLUSIONS

Fasting plasma BAs are not useful clinical biomarkers for predicting NOD in patients with IFG. However, an unexpected association between 6α-hydroxylated BAs and glucose parameters was found, suggesting a role for this specific BA pathway in metabolic homeostasis. IT-DIAB study registry number: NCT01218061.

OLIVe: A Genetically Encoded Fluorescent Biosensor for Quantitative Imaging of Branched-Chain Amino Acid Levels inside Single Living Cells

Branched-chain amino acids (BCAAs) are essential amino acids, controlling cellular metabolic processes as signaling molecules; therefore, utilization of intracellular BCAAs may be regulated by the availability of nutrients in the environment. However, spatial and temporal regulation of intracellular BCAA concentration in response to environmental conditions has been unclear due to the lack of suitable methods for measuring BCAA concentrations inside single living cells. Here, we developed a Förster resonance energy transfer (FRET)-based genetically encoded biosensor for BCAAs, termed optical biosensor for leucine-isoleucine-valine (OLIVe). The biosensor showed approximately 2-fold changes in FRET values corresponding to BCAA concentrations. Importantly, FRET signals from HeLa cells expressing OLIVe in the cytoplasm and nucleus correlated with bulk intracellular BCAA concentrations determined from populations of cells by a biochemical method, and were decreased by knockdown of L-type amino acid transporter 1 (LAT1), a transporter for BCAAs, indicating that OLIVe can reliably report intracellular BCAA concentrations inside single living cells. We also succeeded in imaging BCAA concentrations in the mitochondria using mitochondria-targeted OLIVe. Using the BCAA imaging technique, we found apparently correlated concentrations between the cytoplasm and the mitochondria. We also found that extracellular non-BCAA amino acids affected intracellular BCAA concentrations. Of these amino acids, extracellular glutamine markedly increased intracellular BCAA concentrations in a LAT1-dependent manner. Unexpectedly, extracellular pyruvate was also found to have significant positive effects on maintaining intracellular BCAA concentrations, suggesting that the cells have pyruvate-dependent systems to import BCAAs and/or to regulate BCAA metabolism.

Metabolomics Identifies a Biomarker Revealing In Vivo Loss of Functional β-Cell Mass Before Diabetes Onset

Identification of individuals with decreased functional β-cell mass is essential for the prevention of diabetes. However, in vivo detection of early asymptomatic β-cell defect remains unsuccessful. Metabolomics has emerged as a powerful tool in providing readouts of early disease states before clinical manifestation. We aimed at identifying novel plasma biomarkers for loss of functional β-cell mass in the asymptomatic prediabetes stage. Nontargeted and targeted metabolomics were applied in both lean β-Phb2^-/- (β-cell-specific prohibitin-2 knockout) mice and obese db/db (leptin receptor mutant) mice, two distinct mouse models requiring neither chemical nor dietary treatments to induce spontaneous decline of functional β-cell mass promoting progressive diabetes development. Nontargeted metabolomics on β-Phb2^-/- mice identified 48 and 82 significantly affected metabolites in liver and plasma, respectively. Machine learning analysis pointed to deoxyhexose sugars consistently reduced at the asymptomatic prediabetes stage, including in db/db mice, showing strong correlation with the gradual loss of β-cells. Further targeted metabolomics by gas chromatography-mass spectrometry uncovered the identity of the deoxyhexose, with 1,5-anhydroglucitol displaying the most substantial changes. In conclusion, this study identified 1,5-anhydroglucitol as associated with the loss of functional β-cell mass and uncovered metabolic similarities between liver and plasma, providing insights into the systemic effects caused by early decline in β-cells.

A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context

Genetic studies of metabolites have identified thousands of variants, many of which are associated with downstream metabolic and obesogenic disorders. However, these studies have relied on univariate analyses, reducing power and limiting context-specific understanding. Here we aim to provide an integrated perspective of the genetic basis of metabolites by leveraging the Finnish Metabolic Syndrome In Men (METSIM) cohort, a unique genetic resource which contains metabolic measurements, mostly lipids, across distinct time points as well as information on statin usage. We increase effective sample size by an average of two-fold by applying the Covariates for Multi-phenotype Studies (CMS) approach, identifying 588 significant SNP-metabolite associations, including 228 new associations. Our analysis pinpoints a small number of master metabolic regulator genes, balancing the relative proportion of dozens of metabolite levels. We further identify associations to changes in metabolic levels across time as well as genetic interactions with statin at both the master metabolic regulator and genome-wide level.

Prospective analysis of circulating metabolites and breast cancer in EPIC

BACKGROUND

Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively investigated the associations between metabolite concentrations in plasma and breast cancer risk.

METHODS

A nested case-control study was established within the European Prospective Investigation into Cancer cohort, which included 1624 first primary incident invasive breast cancer cases (with known estrogen and progesterone receptor and HER2 status) and 1624 matched controls. Metabolites (n = 127, acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose, sphingolipids) were measured by mass spectrometry in pre-diagnostic plasma samples and tested for associations with breast cancer incidence using multivariable conditional logistic regression.

RESULTS

Among women not using hormones at baseline (n = 2248), and after control for multiple tests, concentrations of arginine (odds ratio [OR] per SD = 0.79, 95% confidence interval [CI] = 0.70-0.90), asparagine (OR = 0.83 (0.74-0.92)), and phosphatidylcholines (PCs) ae C36:3 (OR = 0.83 (0.76-0.90)), aa C36:3 (OR = 0.84 (0.77-0.93)), ae C34:2 (OR = 0.85 (0.78-0.94)), ae C36:2 (OR = 0.85 (0.78-0.88)), and ae C38:2 (OR = 0.84 (0.76-0.93)) were inversely associated with breast cancer risk, while the acylcarnitine C2 (OR = 1.23 (1.11-1.35)) was positively associated with disease risk. In the overall population, C2 (OR = 1.15 (1.06-1.24)) and PC ae C36:3 (OR = 0.88 (0.82-0.95)) were associated with risk of breast cancer, and these relationships did not differ by breast cancer subtype, age at diagnosis, fasting status, menopausal status, or adiposity.

CONCLUSIONS

These findings point to potentially novel pathways and biomarkers of breast cancer development. Results warrant replication in other epidemiological studies.

Metabolomics in genetic testing

Metabolomics is an intriguing field of study providing a new readout of the biochemical activities taking place at the moment of sampling within a subject's biofluid or tissue. Metabolite concentrations are influenced by several factors including disease, environment, drugs, diet and, importantly, genetics. Metabolomics signatures, which describe a subject's phenotype, are useful for disease diagnosis and prognosis, as well as for predicting and monitoring the effectiveness of treatments. Metabolomics is conventionally divided into targeted (i.e., the quantitative analysis of a predetermined group of metabolites) and untargeted studies (i.e., analysis of the complete set of small-molecule metabolites contained in a biofluid without a pre-imposed metabolites-selection). Both approaches have demonstrated high value in the investigation and understanding of several monogenic and multigenic conditions. Due to low costs per sample and relatively short analysis times, metabolomics can be a useful and robust complement to genetic sequencing.

Amino acid signature predictive of incident prediabetes: A case-control study nested within the longitudinal pathobiology of prediabetes in a biracial cohort

OBJECTIVE

Circulating branched-chain amino acids (BCAAs, isoleucine, leucine, valine) and aromatic amino acids (AAAs, tyrosine and phenylalanine) predicted type 2 diabetes mellitus (T2DM) risk in a Caucasian population. Here, we assessed amino acid levels in relation to incident prediabetes among initially normoglycemic African Americans (AA) and European Americans (EA).

RESEARCH DESIGN AND METHODS

Using a nested case-control design, we studied 70 adults (35 AA, 35 EA) who developed prediabetes (progressors) and 70 matched participants who maintained normoglycemia (nonprogressors) during 5.5 years of follow-up in the Pathobiology of Prediabetes in a Biracial Cohort study. Assessments included plasma amino acid levels, insulin sensitivity, and beta-cell function.

RESULTS

The total level of all 18 amino acid assayed was significantly associated with lean mass (r = 0.36, P < 0.0001), waist circumference (r = 0.27, P = 0.001), fasting plasma glucose (r = 0.24, P = 0.005), HOMA-IR (r = 0.22, P = 0.01) and HDL cholesterol (r = -0.18, P = 0.03). Individual amino acid levels were significantly associated with insulin sensitivity and insulin secretion. Compared with nonprogressors, progressors had higher baseline levels of asparagine and aspartic acid (P <0.0001), glutamine/glutamic acid (P = 0.005) and phenylalanine (P = 0.02), and lower histidine (P = 0.02) levels. In fully-adjusted logistic regression models, aspartic acid/asparagine (OR 2.72 [95% CI 1.91-3.87]) and histidine (OR 0.90 [95% CI 0.85-0.96]) were the only amino acids that significantly predicted incident prediabetes.

CONCLUSIONS

Baseline plasma aspartic acid and asparagine levels predicted progression to prediabetes, whereas histidine levels were protective of prediabetes risk. Thus, the amino acid signature associated with prediabetes in a diverse population may be distinct from that previously linked to T2DM in Caucasians.

Metabolomics reveals a link between homocysteine and lipid metabolism and leukocyte telomere length: the ENGAGE consortium

Telomere shortening has been associated with multiple age-related diseases such as cardiovascular disease, diabetes, and dementia. However, the biological mechanisms responsible for these associations remain largely unknown. In order to gain insight into the metabolic processes driving the association of leukocyte telomere length (LTL) with age-related diseases, we investigated the association between LTL and serum metabolite levels in 7,853 individuals from seven independent cohorts. LTL was determined by quantitative polymerase chain reaction and the levels of 131 serum metabolites were measured with mass spectrometry in biological samples from the same blood draw. With partial correlation analysis, we identified six metabolites that were significantly associated with LTL after adjustment for multiple testing: lysophosphatidylcholine acyl C17:0 (lysoPC a C17:0, p-value = 7.1 × 10⁻⁶), methionine (p-value = 9.2 × 10⁻⁵), tyrosine (p-value = 2.1 × 10⁻⁴), phosphatidylcholine diacyl C32:1 (PC aa C32:1, p-value = 2.4 × 10⁻⁴), hydroxypropionylcarnitine (C3-OH, p-value = 2.6 × 10⁻⁴), and phosphatidylcholine acyl-alkyl C38:4 (PC ae C38:4, p-value = 9.0 × 10⁻⁴). Pathway analysis showed that the three phosphatidylcholines and methionine are involved in homocysteine metabolism and we found supporting evidence for an association of lipid metabolism with LTL. In conclusion, we found longer LTL associated with higher levels of lysoPC a C17:0 and PC ae C38:4, and with lower levels of methionine, tyrosine, PC aa C32:1, and C3-OH. These metabolites have been implicated in inflammation, oxidative stress, homocysteine metabolism, and in cardiovascular disease and diabetes, two major drivers of morbidity and mortality.

Metabolic profiling of serum and tissue from the rotator interval and anterior capsule in shoulder stiffness: a preliminary study

Background

Using mass spectrometry, we evaluated the metabolic profiles of patients who had rotator cuff tears with shoulder stiffness, or shoulder stiffness only, and compared these with samples from a control group.

Methods

This study enrolled 28 patients, including 10 patients with shoulder stiffness only (group I), nine patients with rotator cuff tear and stiffness (group II), and nine controls selected from patients diagnosed with impingement syndrome or long head of the biceps lesions without evident limitation of joint motion or rotator cuff tears. Serum and tissue from the rotator interval and anterior capsule were collected. In all, 82 samples were analyzed for metabolite profiling using the AbsoluteIDQ™p180 Kit.

Results

Comparison of 186 metabolites revealed that groups I and II had significantly higher concentrations of sphingolipids in serum (SM C24:1; group I = 65.16 μm, group II = 68.07 μm) than controls (55.37 μm, p = 0.005 & 0.015, respectively). Higher concentrations of sphingolipids were also present in the rotator interval tissue (SM C22:3) of groups 1 (0.0197 μm) and 2 (0.0144 μm) than controls (0.0081 μm, p = 0.012 & 0.014, respectively). The concentration of glycerophospholipid (PC aa C30:0) was higher in the anterior capsule tissue of groups I (0.850 μm) and II (1.164 μm) than controls (0.572 μm; p = 0.007) Total cholesterol was positively correlated with sphingolipid concentration in serum (SM C24:1, rho = 0.782, p = 0.008) and rotator interval tissue (SM C22:3, rho = 0.750, p = 0.017). There was no significant difference in the metabolites evaluated in groups I and II.

Conclusion

Metabolic profiling showed that levels of lipid-related metabolites were increased in the anterior capsule tissue and rotator interval tissue of patients with shoulder stiffness. Sphingomyelin (SM C22:3) in the tissue of the rotator interval was positively correlated with the serum level of total cholesterol in patients with shoulder stiffness only. The level of glycerophospholipid (PC30:0) in the anterior capsule was positively correlated with the serum level of total cholesterol in patients who had rotator cuff tear with shoulder stiffness. The results indicate that serum total cholesterol may be related to shoulder stiffness. Future studies are needed to evaluate the role of serum cholesterol in the pathogenesis of shoulder stiffness.

Trial registration

KC12OISI0532. Registered Nov 15, 2012. approval by the Institutional Review Board of Seoul St. Mary’s Hospital, the Catholic University of Korea.

Microbiota-Related Changes in Unconjugated Fecal Bile Acids Are Associated With Naturally Occurring, Insulin-Dependent Diabetes Mellitus in Dogs

Diabetes mellitus (DM) in humans has recently been associated with altered intestinal microbiota. The consequences of intestinal dysbiosis, such as increased intestinal permeability and altered microbial metabolites, are suspected to contribute to the host inflammatory state and peripheral insulin resistance. Human diabetics have been shown to have changes in bile acid (BA) metabolism which may be detrimental to glycemic control. The purpose of this study was to examine BA metabolism in dogs with naturally-occurring, insulin-dependent DM and to relate these findings to changes in the intestinal microbiota. A prospective observational study of adult dogs with a clinical diagnosis of DM (n = 10) and healthy controls (HC, n = 10) was performed. The fecal microbiota were analyzed by 16S rRNA gene next-generation (Illumina) sequencing. Concentrations of fecal unconjugated BA (fUBA) were measured using gas chromatography and mass spectrometry. Analysis of bacterial communities showed no significant difference for any of the alpha-diversity measures between DM vs. HC dogs. Principal coordinate analysis based on unweighted Unifrac distance metric failed to show significant clustering between dog groups (ANOSIM_Unweighted: R = 0.084; p = 0.114). However, linear discriminate analysis effects size (LEfSe) detected differentially abundant bacterial taxa (α = 0.01, LDA score >2.0) on various phylogenetic levels. While Enterobacteriaceae was overrepresented in dogs with DM, the proportions of Erysipelotrichia, Mogibacteriaceae, and Anaeroplasmataceae were increased in HC dogs. Dogs with DM had increased concentration of total primary fUBA compared to HC dogs (p = 0.028). The concentrations of cholic acid and the cholic acid percentage of the total fUBA were increased (p = 0.028 and p = 0.035, respectively) in the feces of DM dogs relative to HC dogs. The levels of lithocholic acid (both absolute value and percentage of the total fUBA) were decreased (p = 0.043 and p < 0.01, respectively) in DM dogs vs. HC dogs. Results indicate that dogs with DM have both intestinal dysbiosis and associated fUBA alterations. The pattern of dysbiosis and altered BA composition is similar to that seen in humans with Type 2 DM. The dog represents a novel large animal model for advancing translational medicine research efforts (e.g., investigating pathogenesis and therapeutics) in DM affecting humans.

Characterization of Bulk Phosphatidylcholine Compositions in Human Plasma Using Side-Chain Resolving Lipidomics

Kit-based assays, such as AbsoluteIDQ^TM p150, are widely used in large cohort studies and provide a standardized method to quantify blood concentrations of phosphatidylcholines (PCs). Many disease-relevant associations of PCs were reported using this method. However, their interpretation is hampered by lack of functionally-relevant information on the detailed fatty acid side-chain compositions as only the total number of carbon atoms and double bonds is identified by the kit. To enable more substantiated interpretations, we characterized these PC sums using the side-chain resolving Lipidyzer^TM platform, analyzing 223 samples in parallel to the AbsoluteIDQ^TM. Combining these datasets, we estimated the quantitative composition of PC sums and subsequently tested their replication in an independent cohort. We identified major constituents of 28 PC sums, revealing also various unexpected compositions. As an example, PC 16:0_22:5 accounted for more than 50% of the PC sum with in total 38 carbon atoms and 5 double bonds (PC aa 38:5). For 13 PC sums, we found relatively high abundances of odd-chain fatty acids. In conclusion, our study provides insights in PC compositions in human plasma, facilitating interpretation of existing epidemiological data sets and potentially enabling imputation of PC compositions for future meta-analyses of lipidomics data.

The Consortium of Metabolomics Studies (COMETS): Metabolomics in 47 Prospective Cohort Studies

The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89).

Bile acid alterations in nonalcoholic fatty liver disease, obesity, insulin resistance and type 2 diabetes: what do the human studies tell?

PURPOSE OF REVIEW

The purpose of this review is to discuss the influence of obesity, insulin resistance, type 2 diabetes (T2D), and nonalcoholic fatty liver disease (NAFLD) on bile acid metabolism and to analyze whether these findings reinforce current beliefs about the role of bile acids in the pathophysiology of these diseases.

RECENT FINDINGS

Discordant results on plasma bile acid alterations in NAFLD patients have been reported. Obesity, insulin resistance, and T2D, common comorbidities of NAFLD, have been associated with bile acid changes, but the individual bile acid species variations differ between studies (summarized in this review), perhaps because of clinicobiological differences between the studied patient populations and the heterogeneity of statistical analyses applied.

SUMMARY

The regulatory role of bile acids in metabolic and cellular homeostasis renders bile acids attractive candidates as players in the pathophysiology of NAFLD. However, considering the complex relationship between NAFLD, obesity, insulin resistance and T2D, it is difficult to establish clear and independent associations between bile acid alterations and these individual diseases. Though bile acid alterations may not drive NAFLD progression, signaling pathways activated by bile acids remain potent therapeutic targets for its treatment. Further studies with appropriate matching or adjustment for potential confounding factors are necessary to determine which pathophysiological conditions drive the alterations in bile acid metabolism.

GC/MS-based metabolomics strategy to analyze the effect of exercise intervention in diabetic rats

Metabolomics was used to explore the effect of exercise intervention on type 2 diabetes. The rat model of type 2 diabetes was induced by an injection of streptozocin (30 mg/kg), after fed with 8-week high-fat diet. The rats were divided into three groups: the control group, the diabetic model group (DM) and the diabetes + exercise group (DME). After exercise for 10 weeks, blood samples were collected to test biomedical indexes, and 24-h urine samples were collected for the metabolomics experiment. In the DME group, fasting blood glucose (FBG), both total cholesterol (TC) and total plasma triglycerides (TG), were decreased significantly, compared with those in the DM group. Based on gas chromatography-mass spectrometry (GC/MS), a urinary metabolomics method was used to study the mechanism of exercise intervention on diabetes mellitus. Based on the principal component analysis (PCA), it was found that the DM group and control group were separated into two different clusters. The DME group was located between the DM group and the control group, closer to the control group. Twelve significantly changed metabolites of diabetes mellitus were detected and identified, including glycolate, 4-methyl phenol, benzoic acid, 1H-indole, arabinitol, threitol, ribonic acid, malic acid, 2,3-dihydroxy-butanoic, aminomalonic acid, l-ascorbic acid and 3-hydroxy hexanedioic acid. After exercise, seven metabolites were significantly changed, compared with the control group, the relative contents of benzoic acid, aminomalonic acid, tetrabutyl alcohol and ribonucleic acid in the diabetic exercise group decreased significantly. The relative contents of 2,3-dihydroxybutyric acid, l-ascorbic acid and 3-hydroxy adipic acid increased significantly. l-ascorbic acid and aminomalonic acid which related with the oxidative stress were significantly regulated to normal. The results showed that exercise could display anti-hyperglycemic and anti-hyperlipidemic effects. The exercise had antioxidation function in preventing the occurrence of complications with diabetes mellitus to some extent. The work illustrates that the metabolomics method is a useful tool to study the mechanism of exercise treatment.

Metabolomics of Type 1 and Type 2 Diabetes

Type 1 and type 2 diabetes mellitus (DM) are chronic diseases that affect nearly 425 million people worldwide, leading to poor health outcomes and high health care costs. High-throughput metabolomics screening can provide vital insight into the pathophysiological pathways of DM and help in managing its effects. The primary aim of this study was to contribute to the understanding and management of DM by providing reliable evidence of the relationships between metabolites and type 1 diabetes (T1D) and metabolites and type 2 diabetes (T2D). Information for the study was obtained from the PubMed, MEDLINE, and EMBASE databases, and leads to additional articles that were obtained from the reference lists of the studies examined. The results from the selected studies were used to assess the relationships between diabetes (T1D and/or T2D) and metabolite markers—such as glutamine, glycine, and aromatic amino acids—in patients. Seventy studies were selected from the three databases and from the reference lists in the records retrieved. All studies explored associations between various metabolites and T1D or T2D. This review identified several plasma metabolites associated with T2D prediabetes and/or T1D and/or T2D in humans. The evidence shows that metabolites such as glucose, fructose, amino acids, and lipids are typically altered in individuals with T1D and T2D. These metabolites exhibit significant predictive associations with T2D prediabetes, T1D, and/or T2D. The current review suggests that changes in plasma metabolites can be identified by metabolomic techniques and used to identify and analyze T1D and T2D biomarkers. The results of the metabolomic studies can be used to help create effective interventions for managing these diseases.

Novel Metabolites Are Associated With Augmentation Index and Pulse Wave Velocity: Findings From the Bogalusa Heart Study

BACKGROUND

Metabolomics study may help identify novel mechanisms underlying arterial stiffening.

METHODS

We performed untargeted metabolomics profiling among 1,239 participants of the Bogalusa Heart Study. After quality control, 1,202 metabolites were evaluated for associations with augmentation index (AI) and pulse wave velocity (PWV), using multivariate linear regression adjusting for age, sex, race, education, smoking, drinking, body weight, body height, physical activity, and estimated glomerular filtration rate. Heart rate, blood pressure and antihypertensive medication usage, lipids, and fasting glucose were sequentially adjusted in the sensitivity analyses for significant metabolites. Weighted correlation network analysis was applied to build metabolite networks.

RESULTS

Six novel metabolites were negatively associated with AI, of which, 3-methyl-2-oxobutyrate had the lowest P value and the largest effect size (β = -6.67, P = 5.99 × 10-6). Heart rate contributed to a large proportion (25%-58%) of the association for each metabolite. Twenty-one novel metabolites were identified for PWV, of which, fructose (β = 0.61, P = 6.18 × 10-10) was most significant, and histidine had the largest effect size (β = -1.09, P = 2.51 × 10-7). Blood pressure played a major contribution (9%-54%) to the association for each metabolite. Furthermore, 16 metabolites were associated with arterial stiffness independent of traditional risk factors. Network analysis identified 2 modules associated with both AI and PWV (P < 8.00 × 10-4). One was composed of metabolites from the glycerolipids synthesis and recycling pathway, and the other was involved in valine, leucine, and isoleucine metabolism. One module related to sphingomyelin metabolism was associated with PWV only (P = 0.002).

CONCLUSIONS

This study has identified novel and important metabolites and metabolic networks associated with arterial stiffness.

The Effects of Age and Reproduction on the Lipidome of Caenorhabditis elegans

Aging is a complex life process, and a unified view is that metabolism plays key roles in all biological processes. Here, we determined the lipidomic profile of Caenorhabditis elegans (C. elegans) using ultraperformance liquid chromatography high-resolution mass spectrometry (UPLC-HRMS). Using a nontargeted approach, we detected approximately 3000 species. Analysis of the lipid metabolic profiles at young adult and ten-day-old ages among wild-type N2, glp-1 defective mutant, and double mutant daf-16;glp-1 uncovered significant age-related differences in the total amount of phosphatidylcholines (PC), sphingomyelins (SM), ceramides (Cer), diglycerides (DG), and triglycerides (TG). In addition, the age-associated lipid profiles were characterized by ratio of polyunsaturated (PUFA) over monounsaturated (MUFA) lipid species. Lipid metabolism modulation plays an important role in reproduction-regulated aging; to identify the variations of lipid metabolites during germ line loss-induced longevity, we investigated the lipidomic profiles of long-lived glp-1/notch receptor mutants, which have reproductive deficiency when grown at nonpermissive temperature. The results showed that there was some age-related lipid variation, including TG 40:2, TG 40:1, and TG 41:1, which contributed to the long-life phenotype. The longevity of glp-1 mutant was daf-16-dependent; the lipidome analysis of daf-16;glp-1 double mutant revealed that the changes of some metabolites in the glp-1 mutant were daf-16-dependent, while other metabolites displayed more complex epistatic patterns. We first conducted a comprehensive lipidome analysis to provide novel insights into the relationships between longevity and lipid metabolism regulated by germ line signals in C. elegans.

Metabolomics toward personalized medicine

Metabolomics, which is the metabolites profiling in biological matrices, is a key tool for biomarker discovery and personalized medicine and has great potential to elucidate the ultimate product of the genomic processes. Over the last decade, metabolomics studies have identified several relevant biomarkers involved in complex clinical phenotypes using diverse biological systems. Most diseases result in signature metabolic profiles that reflect the sums of external and internal cellular activities. Metabolomics has a major role in clinical practice as it represents >95% of the workload in clinical laboratories worldwide. Many of these metabolites require different analytical platforms, such as Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), and Ultra Performance Liquid Chromatography (UPLC), while many clinically relevant metabolites are still not routinely amenable to detection using currently available assays. Combining metabolomics with genomics, transcriptomics, and proteomics studies will result in a significantly improved understanding of the disease mechanisms and the pathophysiology of the target clinical phenotype. This comprehensive approach will represent a major step forward toward providing precision medical care, in which individual is accounted for variability in genes, environment, and personal lifestyle. In this review, we compare and evaluate the metabolomics strategies and studies that focus on the discovery of biomarkers that have "personalized" diagnostic, prognostic, and therapeutic value, validated for monitoring disease progression and responses to various management regimens.

Comparison of the microbiome, metabolome, and lipidome of obese and non-obese horses

Metabolic diseases such as obesity and type 2 diabetes in humans have been linked to alterations in the gastrointestinal microbiota and metabolome. Knowledge of these associations has improved our understanding of the pathophysiology of these diseases and guided development of diagnostic biomarkers and therapeutic interventions. The cellular and molecular pathophysiology of equine metabolic syndrome (EMS) and obesity in horses, however, remain ill-defined. Thus, the objectives of this study were to characterize the fecal microbiome, fecal metabolome, and circulating lipidome in obese and non-obese horses. The fecal microbiota, fecal metabolome, and serum lipidome were evaluated in obese (case) horses (n = 20) and non-obese (control) horses (n = 20) matched by farm of origin (n = 7). Significant differences in metabolites of the mitochondrial tricarboxylic acid cycle and circulating free fatty acids were identified in the obese horses compared to the non-obese horses. These results indicate that the host and bacterial metabolism should be considered important in obese horses. Further studies to determine whether these associations are causal and the mechanistic basis of the association are warranted because they might reveal diagnostic biomarkers and therapeutic interventions to mitigate obesity, EMS, and sequelae including laminitis.

Exposure to disinfection byproducts and risk of type 2 diabetes: a nested case-control study in the HUNT and Lifelines cohorts

INTRODUCTION

Environmental chemicals acting as metabolic disruptors have been implicated with diabetogenesis, but evidence is weak among short-lived chemicals, such as disinfection byproducts (trihalomethanes, THM composed of chloroform, TCM and brominated trihalomethanes, BrTHM).

OBJECTIVES

We assessed whether THM were associated with type 2 diabetes (T2D) and we explored alterations in metabolic profiles due to THM exposures or T2D status.

METHODS

A prospective 1:1 matched case-control study (n = 430) and a cross-sectional 1:1 matched case-control study (n = 362) nested within the HUNT cohort (Norway) and the Lifelines cohort (Netherlands), respectively, were set up. Urinary biomarkers of THM exposure and mass spectrometry-based serum metabolomics were measured. Associations between THM, clinical markers, metabolites and disease status were evaluated using logistic regressions with Least Absolute Shrinkage and Selection Operator procedure.

RESULTS

Low median THM exposures (ng/g, IQR) were measured in both cohorts (cases and controls of HUNT and Lifelines, respectively, 193 (76, 470), 208 (77, 502) and 292 (162, 595), 342 (180, 602). Neither BrTHM (OR = 0.87; 95% CI: 0.67, 1.11 | OR = 1.09; 95% CI: 0.73, 1.61), nor TCM (OR = 1.03; 95% CI: 0.88, 1.2 | OR = 1.03; 95% CI: 0.79, 1.35) were associated with incident or prevalent T2D, respectively. Metabolomics showed 48 metabolites associated with incident T2D after adjusting for sex, age and BMI, whereas a total of 244 metabolites were associated with prevalent T2D. A total of 34 metabolites were associated with the progression of T2D. In data driven logistic regression, novel biomarkers, such as cinnamoylglycine or 1-methylurate, being protective of T2D were identified. The incident T2D risk prediction model (HUNT) predicted well incident Lifelines cases (AUC = 0.845; 95% CI: 0.72, 0.97).

CONCLUSION

Such exposome-based approaches in cohort-nested studies are warranted to better understand the environmental origins of diabetogenesis.

Metabolic and proteomic signatures of hypoglycaemia in type 2 diabetes

AIMS

To determine the biochemical changes that underlie hypoglycaemia in a healthy control group and in people with type 2 diabetes (T2D).

MATERIALS AND METHODS

We report a hypoglycaemic clamp study in seven healthy controls and 10 people with T2D. Blood was withdrawn at four time points: at baseline after an overnight fast; after clamping to euglycaemia at 5 mmol/L; after clamping to hypoglycaemia at 2.8 mmol/L; and 24 hours later, after overnight fast. Deep molecular phenotyping using non-targeted metabolomics and the SomaLogic aptamer-based proteomics platform was performed on collected samples.

RESULTS

A total of 955 metabolites and 1125 proteins were identified, with significant alterations in >90 molecules. A number of metabolites significantly increased during hypoglycaemia, but only cortisol, adenosine-3',5'-cyclic monophosphate (cyclic AMP), and pregnenolone sulphate, were independent of insulin. By contrast, identified protein changes were triggered by hypoglycaemia rather than insulin. The T2D group had significantly higher levels of fatty acids including 10-nonadecenoate, linolenate and dihomo-linoleate during hypoglycaemia compared with the control group. Molecules contributing to cardiovascular complications such as fatty-acid-binding protein-3 and pregnenolone sulphate were altered in the participants with T2D during hypoglycaemia. Almost all molecules returned to baseline at 24 hours.

CONCLUSIONS

The present study provides a comprehensive description of molecular events that are triggered by insulin-induced hypoglycaemia. We identified deregulated pathways in T2D that may play a role in the pathophysiology of hypoglycaemia-induced cardiovascular complications.

Farnesoid X receptor: An important factor in blood glucose regulation

Farnesoid X receptor (FXR) is a transcription factor that can be activated by bile acid as well as influenced bile acid metabolism. β-cell bile acid metabolism is mediated by FXR and closely related to the regulation of blood glucose (BG). FXR can regulate BG through multiple pathways. This review summarises recent studies on FXR regulation of BG balance via bile acid regulation, lowering glucagon-like peptide-1 (GLP-1), inhibiting gluconeogenesis, increasing insulin secretion and enhancing insulin sensitivity. In addition, the current review provides additional insight into the relationship between FXR and BG which may provide a new theoretical basis for further study on the role of FXR.

Taxonomic profiling and populational patterns of bacterial bile salt hydrolase (BSH) genes based on worldwide human gut microbiome

BACKGROUND

Bile salt hydrolase plays an important role in bile acid-mediated signaling pathways, which regulate lipid absorption, glucose metabolism, and energy homeostasis. Several reports suggest that changes in the composition of bile acids are found in many diseases caused by dysbacteriosis.

RESULTS

Here, we present the taxonomic identification of bile salt hydrolase (BSH) in human microbiota and elucidate the abundance and activity differences of various bacterial BSH among 11 different populations from six continents. For the first time, we revealed that bile salt hydrolase protein sequences (BSHs) are distributed in 591 intestinal bacterial strains within 117 genera in human microbiota, and 27.52% of these bacterial strains containing BSH paralogs. Significant variations are observed in BSH distribution patterns among different populations. Based on phylogenetic analysis, we reclassified these BSHs into eight phylotypes and investigated the abundance patterns of these phylotypes among different populations. From the inspection of enzyme activity among different BSH phylotypes, BSH-T3 showed the highest enzyme activity and is only found in Lactobaclillus. The phylotypes of BSH-T5 and BSH-T6 mainly from Bacteroides with high percentage of paralogs exhibit different enzyme activity and deconjugation activity. Furthermore, we found that there were significant differences between healthy individuals and patients with atherosclerosis and diabetes in some phylotypes of BSHs though the correlations were pleiotropic.

CONCLUSION

This study revealed the taxonomic and abundance profiling of BSH in human gut microbiome and provided a phylogenetic-based system to assess BSHs activity by classifying the target sequence into specific phylotype. Furthermore, the present work disclosed the variation patterns of BSHs among different populations of geographical regions and health/disease cohorts, which is essential to understand the role of BSH in the development and progression of related diseases.

LC-MS/MS-Based Metabolomics for Cell Cultures

Metabolomics, a comprehensive analysis of metabolites in biological specimens (e.g., cells, body fluids, tissues, exhaled air, plants), offers promising tools in health, nutrition, biotechnology, and food sciences. Here we describe methods of LC-MS/MS-based analyses for cell metabolomics. Using methods employed in this section, over 1000 endogenous and exogenous metabolites can be detected, annotated, and quantified relatively by nontargeted analysis approach, whereas targeted metabolomics analysis enables us to quantify 188 endogenous metabolites.

Metabolic Signature Differentiated Diabetes Mellitus from Lipid Disorder in Elderly Taiwanese

Aging is a complex progression of biological processes and is the causal contributor to the development of diabetes mellitus (DM). DM is the most common degenerative disease and is the fifth leading cause of death in Taiwan, where the trend of DM mortality has been steadily increasing. Metabolomics, important branch of systems biology, has been mainly utilized to understand endogenous metabolites in biological systems and their dynamic changes as they relate to endogenous and exogenous factors. The purpose of this study was to elucidate the metabolomic profiles in elderly people and its relation to lipid disorder (LD). We collected 486 elderly individuals aged ≥65 years and performed untargeted and targeted metabolite analysis using nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography—mass spectrometry (LC/MS). Several metabolites, including branched-chain amino acids, alanine, glutamate and alpha-aminoadipic acid were elevated in LD compared to the control group. Based on multivariate analysis, four metabolites were selected in the best model to predict DM progression: phosphatidylcholine acyl-alkyl (PC ae) C34:3, PC ae C44:3, SM C24:1 and PCae C36:3. The combined area under the curve (AUC) of those metabolites (0.82) was better for DM classification than individual values. This study found that targeted metabolic signatures not only distinguish the LD within the control group but also differentiated DM from LD in elderly Taiwanese. These metabolites could indicate the nutritional status and act as potential metabolic biomarkers for the elderly in Taiwan.

Interorgan Metabolic Crosstalk in Human Insulin Resistance

Excessive energy intake and reduced energy expenditure drive the development of insulin resistance and metabolic diseases such as obesity and type 2 diabetes mellitus. Metabolic signals derived from dietary intake or secreted from adipose tissue, gut, and liver contribute to energy homeostasis. Recent metabolomic studies identified novel metabolites and enlarged our knowledge on classic metabolites. This review summarizes the evidence of their roles as mediators of interorgan crosstalk and regulators of insulin sensitivity and energy metabolism. Circulating lipids such as free fatty acids, acetate, and palmitoleate from adipose tissue and short-chain fatty acids from the gut effectively act on liver and skeletal muscle. Intracellular lipids such as diacylglycerols and sphingolipids can serve as lipotoxins by directly inhibiting insulin action in muscle and liver. In contrast, fatty acid esters of hydroxy fatty acids have been recently shown to exert a series of beneficial effects. Also, ketoacids are gaining interest as potent modulators of insulin action and mitochondrial function. Finally, branched-chain amino acids not only predict metabolic diseases, but also inhibit insulin signaling. Here, we focus on the metabolic crosstalk in humans, which regulates insulin sensitivity and energy homeostasis in the main insulin-sensitive tissues, skeletal muscle, liver, and adipose tissue.

Cross-Platform Comparison of Untargeted and Targeted Lipidomics Approaches on Aging Mouse Plasma

Lipidomics – the global assessment of lipids – can be performed using a variety of mass spectrometry (MS)-based approaches. However, choosing the optimal approach in terms of lipid coverage, robustness and throughput can be a challenging task. Here, we compare a novel targeted quantitative lipidomics platform known as the Lipidyzer to a conventional untargeted liquid chromatography (LC)-MS approach. We find that both platforms are efficient in profiling more than 300 lipids across 11 lipid classes in mouse plasma with precision and accuracy below 20% for most lipids. While the untargeted and targeted platforms detect similar numbers of lipids, the former identifies a broader range of lipid classes and can unambiguously identify all three fatty acids in triacylglycerols (TAG). Quantitative measurements from both approaches exhibit a median correlation coefficient (r) of 0.99 using a dilution series of deuterated internal standards and 0.71 using endogenous plasma lipids in the context of aging. Application of both platforms to plasma from aging mouse reveals similar changes in total lipid levels across all major lipid classes and in specific lipid species. Interestingly, TAG is the lipid class that exhibits the most changes with age, suggesting that TAG metabolism is particularly sensitive to the aging process in mice. Collectively, our data show that the Lipidyzer platform provides comprehensive profiling of the most prevalent lipids in plasma in a simple and automated manner.

Metabolomics in the study of retinal health and disease

Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease. While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.