Plasma Metabolomics to Identify and Stratify Patients With Impaired Glucose Tolerance

Characterizing substrate utilization during the fasted state using plasma high-resolution metabolomics

OBJECTIVES

High-resolution metabolomics enables global assessment of metabolites and molecular pathways underlying physiologic processes, including substrate utilization during the fasted state. The clinical index for substrate utilization, respiratory exchange ratio (RER), is measured via indirect calorimetry. The aim of this pilot study was to use metabolomics to identify metabolic pathways and plasma metabolites associated with substrate utilization in healthy, fasted adults.

METHODS

This cross-sectional study included 33 adults (mean age 27.7 ± 4.9 y, mean body mass index 24.8 ± 4 kg/m2). Participants underwent indirect calorimetry to determine resting RER after an overnight fast. Untargeted metabolomics was performed on fasted plasma samples using dual-column liquid chromatography and ultra-high-resolution mass spectrometry. Linear regression and pathway enrichment analyses identified pathways and metabolites associated with substrate utilization measured with indirect calorimetry.

RESULTS

RER was significantly associated with 1389 metabolites enriched within 13 metabolic pathways (P < 0.05). Lipid-related findings included general pathways, such as fatty acid activation, and specific pathways, such as C21-steroid hormone biosynthesis and metabolism, butyrate metabolism, and carnitine shuttle. Amino acid pathways included those central to metabolism, such as glucogenic amino acids, and pathways needed to maintain reduction-oxidation reactions, such as methionine and cysteine metabolism. Galactose and pyrimidine metabolism were also associated with RER (all P < 0.05).

CONCLUSIONS

The fasting plasma metabolome reflects the diverse macronutrient pathways involved in carbohydrate, amino acid, and lipid metabolism during the fasted state in healthy adults. Future studies should consider the utility of metabolomics to profile individual nutrient requirements and compare findings reported here to clinical populations.

Characterisation of Fasting and Postprandial NMR Metabolites: Insights from the ZOE PREDICT 1 Study

BACKGROUND

Postprandial metabolomic profiles and their inter-individual variability are not well characterised. Here, we describe postprandial metabolite changes, their correlations with fasting values and their inter- and intra-individual variability, following a standardised meal in the ZOE PREDICT 1 cohort.

METHODS

In the ZOE PREDICT 1 study (n = 1002 (NCT03479866)), 250 metabolites, mainly lipids, were measured by a Nightingale NMR panel in fasting and postprandial (4 and 6 h after a 3.7 MJ mixed nutrient meal, with a second 2.2 MJ mixed nutrient meal at 4 h) serum samples. For each metabolite, inter- and intra-individual variability over time was evaluated using linear mixed modelling and intraclass correlation coefficients (ICC) were calculated.

RESULTS

Postprandially, 85% (of 250 metabolites) significantly changed from fasting at 6 h (47% increased, 53% decreased; Kruskal-Wallis), with 37 measures increasing by >25% and 14 increasing by >50%. The largest changes were observed in very large lipoprotein particles and ketone bodies. Seventy-one percent of circulating metabolites were strongly correlated (Spearman's rho >0.80) between fasting and postprandial timepoints, and 5% were weakly correlated (rho <0.50). The median ICC of the 250 metabolites was 0.91 (range 0.08-0.99). The lowest ICCs (ICC <0.40, 4% of measures) were found for glucose, pyruvate, ketone bodies (β-hydroxybutyrate, acetoacetate, acetate) and lactate.

CONCLUSIONS

In this large-scale postprandial metabolomic study, circulating metabolites were highly variable between individuals following sequential mixed meals. Findings suggest that a meal challenge may yield postprandial responses divergent from fasting measures, specifically for glycolysis, essential amino acid, ketone body and lipoprotein size metabolites.

Several Metabolite Families Display Inflexibility during Glucose Challenge in Patients with Type 2 Diabetes: An Untargeted Metabolomics Study

Metabolic inflexibility is a hallmark of insulin resistance and can be extensively explored with high-throughput metabolomics techniques. However, the dynamic regulation of the metabolome during an oral glucose tolerance test (OGTT) in subjects with type 2 diabetes (T2D) is largely unknown. We aimed to identify alterations in metabolite responses to OGTT in subjects with T2D using untargeted metabolomics of both plasma and subcutaneous adipose tissue (SAT) samples. Twenty subjects with T2D and twenty healthy controls matched for sex, age, and body mass index (BMI) were profiled with untargeted metabolomics both in plasma (755 metabolites) and in the SAT (588) during an OGTT. We assessed metabolite concentration changes 90 min after the glucose load, and those responses were compared between patients with T2D and controls. Post-hoc analyses were performed to explore the associations between glucose-induced metabolite responses and markers of obesity and glucose metabolism, sex, and age. During the OGTT, T2D subjects had an impaired reduction in plasma levels of several metabolite families, including acylcarnitines, amino acids, acyl ethanolamines, and fatty acid derivates (p < 0.05), compared to controls. Additionally, patients with T2D had a greater increase in plasma glucose and fructose levels during the OGTT compared to controls (p < 0.05). The plasma concentration change of most metabolites after the glucose load was mainly associated with indices of hyperglycemia rather than insulin resistance, insulin secretion, or BMI. In multiple linear regression analyses, hyperglycemia indices (glucose area under the curve (AUC) during OGTT and glycosylated hemoglobin (HbA1c)) were the strongest predictors of plasma metabolite changes during the OGTT. No differences were found in the adipose tissue metabolome in response to the glucose challenge between T2D and controls. Using a metabolomics approach, we show that T2D patients display attenuated responses in several circulating metabolite families during an OGTT. Besides the well-known increase in monosaccharides, the glucose-induced lowering of amino acids, acylcarnitines, and fatty acid derivatives was attenuated in T2D subjects compared to controls. These data support the hypothesis of inflexibility in several metabolic pathways, which may contribute to dysregulated substrate partitioning and turnover in T2D. These findings are not directly associated with changes in adipose tissue metabolism; therefore, other tissues, such as muscle and liver, are probably of greater importance.

Response of circulating metabolites to an oral glucose challenge and risk of cardiovascular disease and mortality in the community

Background

New biomarkers to identify cardiovascular disease (CVD) risk earlier in its course are needed to enable targeted approaches for primordial prevention. We evaluated whether intraindividual changes in blood metabolites in response to an oral glucose tolerance test (OGTT) may provide incremental information regarding the risk of future CVD and mortality in the community.

Methods

An OGTT (75 g glucose) was administered to a subsample of Framingham Heart Study participants free from diabetes (n = 361). Profiling of 211 plasma metabolites was performed from blood samples drawn before and 2 h after OGTT. The log2(post/pre) metabolite levels (Δmetabolites) were related to incident CVD and mortality in Cox regression models adjusted for age, sex, baseline metabolite level, systolic blood pressure, hypertension treatment, body mass index, smoking, and total/high-density lipoprotein cholesterol. Select metabolites were related to subclinical cardiometabolic phenotypes using Spearman correlations adjusted for age, sex, and fasting metabolite level.

Results

Our sample included 42% women, with a mean age of 56 ± 9 years and a body mass index of 30.2 ± 5.3 kg/m². The pre- to post-OGTT changes (Δmetabolite) were non-zero for 168 metabolites (at FDR ≤ 5%). A total of 132 CVD events and 144 deaths occurred during median follow-up of 24.9 years. In Cox models adjusted for clinical risk factors, four Δmetabolites were associated with incident CVD (higher glutamate and deoxycholate, lower inosine and lysophosphatidylcholine 18:2) and six Δmetabolites (higher hydroxyphenylacetate, triacylglycerol 56:5, alpha-ketogluturate, and lower phosphatidylcholine 32:0, glucuronate, N-monomethyl-arginine) were associated with death (P < 0.05). Notably, baseline metabolite levels were not associated with either outcome in models excluding Δmetabolites. The Δmetabolites exhibited varying cross-sectional correlation with subclinical risk factors such as visceral adiposity, insulin resistance, and vascular stiffness, but overall relations were modest. Significant Δmetabolites included those with established roles in cardiometabolic disease (e.g., glutamate, alpha-ketoglutarate) and metabolites with less defined roles (e.g., glucuronate, lipid species).

Conclusions

Dynamic changes in metabolite levels with an OGTT are associated with incident CVD and mortality and have potential relevance for identifying CVD risk earlier in its development and for discovering new potential therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01647-w.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Comprehensive metabolomic characterization of atrial fibrillation

Background

Using human humoral metabolomic profiling, we can discover the diagnostic biomarkers and pathogenesis of disease. The specific characterization of atrial fibrillation (AF) subtypes with metabolomics may facilitate effective and targeted treatment, especially in early stages.

Objectives

By investigating disturbed metabolic pathways, we could evaluate the diagnostic value of biomarkers based on metabolomics for different types of AF.

Methods

A cohort of 363 patients was enrolled and divided into a discovery and validation set. Patients underwent an electrocardiogram (ECG) for suspected AF. Groups were divided as follows: healthy individuals (Control), suspected AF (Sus-AF), first diagnosed AF (Fir-AF), paroxysmal AF (Par-AF), persistent AF (Per-AF), and AF causing a cardiogenic ischemic stroke (Car-AF). Serum metabolomic profiles were determined by gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Metabolomic variables were analyzed with clinical information to identify relevant diagnostic biomarkers.

Results

The metabolic disorders were characterized by 16 cross-comparisons. We focused on comparing all of the types of AF (All-AFs) plus Car-AF vs. Control, All-AFs vs. Car-AF, Par-AF vs. Control, and Par-AF vs. Per-AF. Then, 117 and 94 metabolites were identified by GC/MS and LC-QTOF-MS, respectively. The essential altered metabolic pathways during AF progression included D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism, etc. For differential diagnosis, the area under the curve (AUC) of specific metabolomic biomarkers ranged from 0.8237 to 0.9890 during the discovery phase, and the predictive values in the validation cohort were 78.8–90.2%.

Conclusions

Serum metabolomics is a powerful way to identify metabolic disturbances. Differences in small–molecule metabolites may serve as biomarkers for AF onset, progression, and differential diagnosis.

Diagnostic Performance of Sex-Specific Modified Metabolite Patterns in Urine for Screening of Prediabetes

AIMS/HYPOTHESIS

Large-scale prediabetes screening is still a challenge since fasting blood glucose and HbA_1c as the long-standing, recommended analytes have only moderate diagnostic sensitivity, and the practicability of the oral glucose tolerance test for population-based strategies is limited. To tackle this issue and to identify reliable diagnostic patterns, we developed an innovative metabolomics-based strategy deviating from common concepts by employing urine instead of blood samples, searching for sex-specific biomarkers, and focusing on modified metabolites.

METHODS

Non-targeted, modification group-assisted metabolomics by liquid chromatography-mass spectrometry (LC-MS) was applied to second morning urine samples of 340 individuals from a prediabetes cohort. Normal (n = 208) and impaired glucose-tolerant (IGT; n = 132) individuals, matched for age and BMI, were randomly divided in discovery and validation cohorts. ReliefF, a feature selection algorithm, was used to extract sex-specific diagnostic patterns of modified metabolites for the detection of IGT. The diagnostic performance was compared with conventional screening parameters fasting plasma glucose (FPG), HbA_1c, and fasting insulin.

RESULTS

Female- and male-specific diagnostic patterns were identified in urine. Only three biomarkers were identical in both. The patterns showed better AUC and diagnostic sensitivity for prediabetes screening of IGT than FPG, HbA_1c, insulin, or a combination of FPG and HbA_1c. The AUC of the male-specific pattern in the validation cohort was 0.889 with a diagnostic sensitivity of 92.6% and increased to an AUC of 0.977 in combination with HbA_1c. In comparison, the AUCs of FPG, HbA_1c, and insulin alone reached 0.573, 0.668, and 0.571, respectively. Validation of the diagnostic pattern of female subjects showed an AUC of 0.722, which still exceeded the AUCs of FPG, HbA_1c, and insulin (0.595, 0.604, and 0.634, respectively). Modified metabolites in the urinary patterns include advanced glycation end products (pentosidine-glucuronide and glutamyl-lysine-sulfate) and microbiota-associated compounds (indoxyl sulfate and dihydroxyphenyl-gamma-valerolactone-glucuronide).

CONCLUSIONS/INTERPRETATION

Our results demonstrate that the sex-specific search for diagnostic metabolite biomarkers can be superior to common metabolomics strategies. The diagnostic performance for IGT detection was significantly better than routinely applied blood parameters. Together with recently developed fully automatic LC-MS systems, this opens up future perspectives for the application of sex-specific diagnostic patterns for prediabetes screening in urine.

Baseline serum amino acid levels predict treatment response to augmentation with N-acetylcysteine (NAC) in a bipolar disorder randomised trial

N-acetylcysteine (NAC) acts on glutamatergic and redox systems, two systems implicated in the pathophysiology of bipolar disorder (BD). This has led to the investigation of NAC as a potential candidate for the treatment of BD. The aim of this study was to investigate metabolomic markers to identify predictors of NAC response in a cohort of BD participants. This study is a secondary analysis of a 16-week, multi-site, randomized, double-blinded, parallel-group, placebo-controlled trial in BD participants with a current acute depressive episode. This study included trial participants who received either NAC 2000 mg/day, or placebo. Participants (NAC: n = 31, placebo: n = 29) were assessed at baseline and week 16 using the Montgomery Åsberg Depression Rating Scale (MADRS) and were dichotomised into "responders" (MADRS at week 16 < 50% of MADRS at baseline) and "non-responders" (MADRS at week 16 > 50% at baseline). Untargeted gas chromatography-mass spectrometry analysis was performed to analyse baseline levels of 68 serum metabolites. Of the nine metabolites that differentiated placebo and NAC groups, five were amino acids with lower levels in the NAC responder group compared with the NAC non-responders. Further analysis generated a predictive model of MADRS improvement including glycine, norleucine, threonine, proline, phenylalanine, tyrosine, glutamic acid, lysine and leucine (R² = 0.853; adjusted R² = 0.733). This prediction model predicted 85% of the variance in MADRS outcome after adjunctive treatment with NAC. BD participants with lower serum levels of free amino acids at baseline may be more likely to respond to adjunctive treatment with NAC.

Early overnutrition in male mice negates metabolic benefits of a diet high in monounsaturated and omega-3 fats

Overconsumption of saturated fats promotes obesity and type 2 diabetes. Excess weight gain in early life may be particularly detrimental by promoting earlier diabetes onset and potentially by adversely affecting normal development. In the present study we investigated the effects of dietary fat composition on early overnutrition-induced body weight and glucose regulation in Swiss Webster mice, which show susceptibility to high-fat diet-induced diabetes. We compared glucose homeostasis between a high-fat lard-based (HFL) diet, high in saturated fats, and a high-fat olive oil/fish oil-based (HFO) diet, high in monounsaturated and omega-3 fats. We hypothesized that the healthier fat profile of the latter diet would improve early overnutrition-induced glucose dysregulation. However, early overnutrition HFO pups gained more weight and adiposity and had higher diabetes incidence compared to HFL. In contrast, control pups had less weight gain, adiposity, and lower diabetes incidence. Plasma metabolomics revealed reductions in various phosphatidylcholine species in early overnutrition HFO mice as well as with diabetes. These findings suggest that early overnutrition may negate any beneficial effects of a high-fat diet that favours monounsaturated and omega-3 fats over saturated fats. Thus, quantity, quality, and timing of fat intake throughout life should be considered with respect to metabolic health outcomes.

Identification and quantification of honeybee venom constituents by multiplatform metabolomics

Honeybee (Apis mellifera) venom (HBV) has been a subject of extensive proteomics research; however, scarce information on its metabolite composition can be found in the literature. The aim of the study was to identify and quantify the metabolites present in HBV. To gain the highest metabolite coverage, three different mass spectrometry (MS)-based methodologies were applied. In the first step, untargeted metabolomics was used, which employed high-resolution, accurate-mass Orbitrap MS. It allowed obtaining a broad overview of HBV metabolic components. Then, two targeted metabolomics approaches, which employed triple quadrupole MS, were applied to quantify metabolites in HBV samples. The untargeted metabolomics not only confirmed the presence of amines, amino acids, carbohydrates, and organic acids in HBV, but also provided information on venom components from other metabolite classes (e.g., nucleosides, alcohols, purine and pyrimidine derivatives). The combination of three MS-based metabolomics platforms facilitated the identification of 214 metabolites in HBV samples, among which 138 were quantified. The obtaining of the wide free amino acid profiles of HBV is one of the project’s achievements. Our study contributed significantly to broadening the knowledge about HBV composition and should be continued to obtain the most comprehensive metabolite profile of HBV.

Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes

PURPOSE OF THE REVIEW

Proteins are the central layer of information transfer from genome to phenome and represent the largest class of drug targets. We review recent advances in high-throughput technologies that provide comprehensive, scalable profiling of the plasma proteome with the potential to improve prediction and mechanistic understanding of type 2 diabetes (T2D).

RECENT FINDINGS

Technological and analytical advancements have enabled identification of novel protein biomarkers and signatures that help to address challenges of existing approaches to predict and screen for T2D. Genetic studies have so far revealed putative causal roles for only few of the proteins that have been linked to T2D, but ongoing large-scale genetic studies of the plasma proteome will help to address this and increase our understanding of aetiological pathways and mechanisms leading to diabetes. Studies of the human plasma proteome have started to elucidate its potential for T2D prediction and biomarker discovery. Future studies integrating genomic and proteomic data will provide opportunities to prioritise drug targets and identify pathways linking genetic predisposition to T2D development.

Changes in Metabolites During an Oral Glucose Tolerance Test in Early and Mid-Pregnancy: Findings from the PEARLS Randomized, Controlled Lifestyle Trial

The oral glucose tolerance test (OGTT) is used to diagnose gestational and other types of diabetes. We examined metabolite changes during an OGTT, and how a comprehensive diet and physical activity intervention may influence these changes in a population of overweight/obese Hispanic pregnant women. Integration of changes in metabolites during an OGTT may help us gain preliminary insights into how glucose metabolism changes during pregnancy. Among women from the Pregnancy and EARly Lifestyle improvement Study (PEARLS), we measured metabolites during a multipoint OGTT (fasting, 30, 60 and 120 min) at early and mid-pregnancy. Metabolite levels were measured by liquid chromatography-mass spectrometry in plasma samples in the lifestyle intervention (n = 13) and control (n = 16) arms of the study. A total of 65 candidate metabolites were selected that displayed changes during an OGTT in previous studies. Paired and unpaired t-tests were used to examine differences in Δfast-120 min: (1) at early and mid-pregnancy; and (2) by intervention assignment. We applied principal component analysis (PCA) to identify those metabolites that differed by intervention assignment and OGTT time points. Most of the characteristic changes in metabolites post-OGTT were similar at both gestational time points. PCA identified characteristic metabolite patterns associated with OGTT time points at both early and mid-pregnancy. These metabolites included ketone bodies, tryptophan, acyl carnitines, polyunsaturated fatty acids, and biomarkers related to bile acid, urea cycle, arginine, and proline metabolism. PCA identified distinct Δfast-120 min in fatty acid, acyl carnitine, bile acid, ketone body, and amino acid levels at mid- compared to early pregnancy. Participants in the intervention group did not display mean decreases in Δfast-120 min of several long-chain acyl carnitines that were observed in the control group. These findings provide preliminary insight into metabolites, whose role in increased insulin resistance during pregnancy, should be explored further in future studies.