Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study

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.

Computational Methods for the Discovery of Metabolic Markers of Complex Traits

Metabolomics uses quantitative analyses of metabolites from tissues or bodily fluids to acquire a functional readout of the physiological state. Complex diseases arise from the influence of multiple factors, such as genetics, environment and lifestyle. Since genes, RNAs and proteins converge onto the terminal downstream metabolome, metabolomics datasets offer a rich source of information in a complex and convoluted presentation. Thus, powerful computational methods capable of deciphering the effects of many upstream influences have become increasingly necessary. In this review, the workflow of metabolic marker discovery is outlined from metabolite extraction to model interpretation and validation. Additionally, current metabolomics research in various complex disease areas is examined to identify gaps and trends in the use of several statistical and computational algorithms. Then, we highlight and discuss three advanced machine-learning algorithms, specifically ensemble learning, artificial neural networks, and genetic programming, that are currently less visible, but are budding with high potential for utility in metabolomics research. With an upward trend in the use of highly-accurate, multivariate models in the metabolomics literature, diagnostic biomarker panels of complex diseases are more recently achieving accuracies approaching or exceeding traditional diagnostic procedures. This review aims to provide an overview of computational methods in metabolomics and promote the use of up-to-date machine-learning and computational methods by metabolomics researchers.

The discovery of novel predictive biomarkers and early-stage pathophysiology for the transition from gestational diabetes to type 2 diabetes

AIMS/HYPOTHESIS

Gestational diabetes mellitus (GDM) affects up to 20% of pregnancies, and almost half of the women affected progress to type 2 diabetes later in life, making GDM the most significant risk factor for the development of future type 2 diabetes. An accurate prediction of future type 2 diabetes risk in the early postpartum period after GDM would allow for timely interventions to prevent or delay type 2 diabetes. In addition, new targets for interventions may be revealed by understanding the underlying pathophysiology of the transition from GDM to type 2 diabetes. The aim of this study is to identify both a predictive signature and early-stage pathophysiology of the transition from GDM to type 2 diabetes.

METHODS

We used a well-characterised prospective cohort of women with a history of GDM pregnancy, all of whom were enrolled at 6-9 weeks postpartum (baseline), were confirmed not to have diabetes via 2 h 75 g OGTT and tested anually for type 2 diabetes on an ongoing basis (2 years of follow-up). A large-scale targeted lipidomic study was implemented to analyse ~1100 lipid metabolites in baseline plasma samples using a nested pair-matched case-control design, with 55 incident cases matched to 85 non-case control participants. The relationships between the concentrations of baseline plasma lipids and respective follow-up status (either type 2 diabetes or no type 2 diabetes) were employed to discover both a predictive signature and the underlying pathophysiology of the transition from GDM to type 2 diabetes. In addition, the underlying pathophysiology was examined in vivo and in vitro.

RESULTS

Machine learning optimisation in a decision tree format revealed a seven-lipid metabolite type 2 diabetes predictive signature with a discriminating power (AUC) of 0.92 (87% sensitivity, 93% specificity and 91% accuracy). The signature was highly robust as it includes 45-fold cross-validation under a high confidence threshold (1.0) and binary output, which together minimise the chance of data overfitting and bias selection. Concurrent analysis of differentially expressed lipid metabolite pathways uncovered the upregulation of α-linolenic/linoleic acid metabolism (false discovery rate [FDR] 0.002) and fatty acid biosynthesis (FDR 0.005) and the downregulation of sphingolipid metabolism (FDR 0.009) as being strongly associated with the risk of developing future type 2 diabetes. Focusing specifically on sphingolipids, the downregulation of sphingolipid metabolism using the pharmacological inhibitors fumonisin B1 (FB1) and myriocin in mouse islets and Min6 K8 cells (a pancreatic beta-cell like cell line) significantly impaired glucose-stimulated insulin secretion but had no significant impact on whole-body glucose homeostasis or insulin sensitivity.

CONCLUSIONS/INTERPRETATION

We reveal a novel predictive signature and associate reduced sphingolipids with the pathophysiology of transition from GDM to type 2 diabetes. Attenuating sphingolipid metabolism in islets impairs glucose-stimulated insulin secretion.

Branched Chain Amino Acids

Branched chain amino acids (BCAAs) are building blocks for all life-forms. We review here the fundamentals of BCAA metabolism in mammalian physiology. Decades of studies have elicited a deep understanding of biochemical reactions involved in BCAA catabolism. In addition, BCAAs and various catabolic products act as signaling molecules, activating programs ranging from protein synthesis to insulin secretion. How these processes are integrated at an organismal level is less clear. Inborn errors of metabolism highlight the importance of organismal regulation of BCAA physiology. More recently, subtle alterations of BCAA metabolism have been suggested to contribute to numerous prevalent diseases, including diabetes, cancer, and heart failure. Understanding the mechanisms underlying altered BCAA metabolism and how they contribute to disease pathophysiology will keep researchers busy for the foreseeable future.

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.

Blood Metabolomic Measures Associate With Present and Future Glycemic Control in Type 2 Diabetes

OBJECTIVE

We studied whether blood metabolomic measures in people with type 2 diabetes (T2D) are associated with insufficient glycemic control and whether this association is influenced differentially by various diabetes drugs. We then tested whether the same metabolomic profiles were associated with the initiation of insulin therapy.

METHODS

A total of 162 metabolomic measures were analyzed using a nuclear magnetic resonance-based method in people with T2D from four cohort studies (n = 2641) and one replication cohort (n = 395). Linear and logistic regression analyses with adjustment for potential confounders, followed by meta-analyses, were performed to analyze associations with hemoglobin A1c levels, six glucose-lowering drug categories, and insulin initiation during a 7-year follow-up period (n = 698).

RESULTS

After Bonferroni correction, 26 measures were associated with insufficient glycemic control (HbA1c >53 mmol/mol). The strongest association was with glutamine (OR, 0.66; 95% CI, 0.61 to 0.73; P = 7.6 × 10-19). In addition, compared with treatment-naive patients, 31 metabolomic measures were associated with glucose-lowering drug use (representing various metabolite categories; P ≤ 3.1 × 10-4 for all). In drug-stratified analyses, associations with insufficient glycemic control were only mildly affected by different glucose-lowering drugs. Five of the 26 metabolomic measures (apolipoprotein A1 and medium high-density lipoprotein subclasses) were also associated with insulin initiation during follow-up in both discovery and replication. The strongest association was observed for medium high-density lipoprotein cholesteryl ester (OR, 0.54; 95% CI, 0.42 to 0.71; P = 4.5 × 10-6).

CONCLUSION

Blood metabolomic measures were associated with present and future glycemic control and might thus provide relevant cues to identify those at increased risk of treatment failure.

Branched Chain Amino Acids in Metabolic Disease

PURPOSE OF REVIEW

Elevations in circulating branched chain amino acids (BCAAs) have gained attention as potential contributors to the development of insulin resistance and diabetes.

RECENT FINDINGS

Epidemiological evidence strongly supports this conclusion. Suppression of BCAA catabolism in adipose and hepatic tissues appears to be the primary drivers of plasma BCAA elevations. BCAA catabolism may be shunted to skeletal muscle, where it indirectly leads to FA accumulation and insulin resistance, via a number of proposed mechanisms. BCAAs have an important role in the development of IR, but our understanding of how plasma BCAA elevations occur, and how these elevations lead to insulin resistance, is still limited.

Altered Asparagine and Glutamate Homeostasis Precede Coronary Artery Disease and Type 2 Diabetes

CONTEXT

Type 2 diabetes mellitus (T2DM) is accompanied by an increased risk for coronary artery disease (CAD), but the overlapping metabolic disturbances preceding both diseases are insufficiently described.

OBJECTIVE

We hypothesized that alterations in metabolism occur years before clinical manifestation of T2DM and CAD and that these alterations are reflected in the plasma metabolome. We thus aimed to identify plasma metabolites that predict future T2DM and CAD.

DESIGN

Through use of targeted liquid chromatography-mass spectrometry, 35 plasma metabolites (amino acid metabolites and acylcarnitines) were quantified in 1049 individuals without CAD and diabetes, drawn from a population sample of 5386 in the Malmö Preventive Project (mean age, 69.5 years; 31% women). The sample included 204 individuals who developed T2DM, 384 who developed CAD, and 496 who remained T2DM and CAD free during a mean follow-up of 6.1 years.

RESULTS

In total, 16 metabolites were significantly associated with risk for developing T2DM according to logistic regression models. Glutamate (OR, 1.96; P = 5.4e-12) was the most strongly associated metabolite, followed by increased levels of branched-chain amino acids. Incident CAD was predicted by three metabolites: glutamate (OR, 1.28; P = 6.6e-4), histidine (OR, 0.76; P = 5.1e-4), and asparagine (OR, 0.80; P = 2.2e-3). Glutamate (OR, 1.48; P = 1.6e-8) and asparagine (OR, 0.75; P = 1.8e-5) were both associated with a composite endpoint of developing T2DM or CAD.

CONCLUSION

Several plasma metabolites were associated with incidence of T2DM and CAD; elevated glutamate and reduced asparagine levels were associated with both diseases. We thus discovered associations that might help shed additional light on why T2DM and CAD commonly co-occur.

Metabolomics-Based Clinical Efficacy and Effect on the Endogenous Metabolites of Tangzhiqing Tablet, a Chinese Patent Medicine for Type 2 Diabetes Mellitus with Hypertriglyceridemia

Tangzhiqing tablet (TZQ) is derived from Tangzhiqing formula, which has been used to regulate glucose and lipid metabolism in China for hundreds of years. However, as a new Chinese patent medicine, its clinical indication is not clear. To explore the clinical indication and effect on the patients with type 2 diabetes mellitus (T2DM), a pilot clinical trial and metabolomics study were carried out. In the clinical study, T2DM patients were divided into three groups and treated with TZQ, placebo, or acarbose for 12 weeks, respectively. The metabolomic study based on UPLC Q-TOF MS was performed including patients with hypertriglyceridemia in TZQ and placebo groups and healthy volunteers. The clinical results showed that TZQ could reduce glycosylated hemoglobin (HbA1c) and fasting insulin. For patients with hypertriglyceridemia in TZQ group, the levels of HbA1c all decreased and were correlated with the baseline level of triglyceride. Metabonomics data showed a significant difference between patients and healthy volunteers, and 17 biomarkers were identified. After 12-week treatment with TZQ, 11 biomarkers decreased significantly (p<0.05), suggesting that TZQ could improve the metabolomic abnormalities in these participants. In conclusion, the clinical indication of TZQ was T2DM with hypertriglyceridemia, and its target was related to glycerophospholipid metabolism.

Dietary Patterns among Asian Indians Living in the United States Have Distinct Metabolomic Profiles That Are Associated with Cardiometabolic Risk

Background

Recent studies, primarily in non-Hispanic whites, suggest that dietary patterns have distinct metabolomic signatures that may influence disease risk. However, evidence in South Asians, a group with unique dietary patterns and a high prevalence of cardiometabolic risk, is lacking.

Objective

We investigated the metabolomic profiles associated with 2 distinct dietary patterns among a sample of Asian Indians living in the United States. We also examined the cross-sectional associations between metabolomic profiles and cardiometabolic risk markers.

Methods

We used cross-sectional data from 145 Asian Indians, aged 45-79 y, in the Metabolic Syndrome and Atherosclerosis in South Asians Living in America (MASALA) pilot study. Metabolomic profiles were measured from fasting serum samples. Usual diet was assessed by using a validated food-frequency questionnaire. We used principal components analysis to derive dietary and metabolomic patterns. We used adjusted general linear regression models to examine associations between dietary patterns, individual food groups, metabolite patterns, and cardiometabolic risk markers.

Results

We observed 2 major principal components or metabolite clusters, the first comprised primarily of medium- to long-chain acylcarnitines (metabolite pattern 1) and the second characterized by branched-chain amino acids, aromatic amino acids, and short-chain acylcarnitines (metabolite pattern 2). A "Western/nonvegetarian" pattern was significantly and positively associated with metabolite pattern 2 (all participants: β ± SE = 0.180 ± 0.090, P = 0.05; participants without type 2 diabetes: β ± SE = 0.323 ± 0.090, P = 0.0005). In all participants, higher scores on metabolite pattern 2 were adversely associated with measures of glycemia (fasting insulin: β ± SE = 2.91 ± 1.29, P = 0.03; 2-h insulin: β ± SE = 22.1 ± 10.3, P = 0.03; homeostasis model assessment of insulin resistance: β ± SE = 0.94 ± 0.42, P = 0.03), total adiponectin (β ± SE = -1.46 ± 0.47, P = 0.002), lipids (total cholesterol: β ± SE = 7.51 ± 3.45, P = 0.03; triglycerides: β ± SE = 14.4 ± 6.67, P = 0.03), and a radiographic measure of hepatic fat (liver-to-spleen attenuation ratio: β ± SE = -0.83 ± 0.42, P = 0.05).

Conclusions

Our findings suggest that a "Western/nonvegetarian" dietary pattern is associated with a metabolomic profile that is related to an adverse cardiometabolic profile in Asian Indians. Public health efforts to reduce cardiometabolic disease burden in this high-risk group should focus on consuming a healthy plant-based diet.

Changes in Plasma Metabolite Concentrations after a Low-Glycemic Index Diet Intervention

SCOPE

To examine whether a low-glycemic index (LGI) diet improves a set of plasma metabolites related to different metabolic diseases, and comparison to a high-glycemic index (HGI) diet and a low-fat (LF) diet.

METHODS AND RESULTS

A parallel, randomized trial with three intervention diets: an LGI diet, an HGI diet, and an LF diet. A total of 122 adult overweight and obese subjects were enrolled in the study for 6 months. Blood samples were collected at baseline and at the end of the intervention. The plasma metabolomic profile of 102 subjects was analyzed using three different approaches: GC/quadrupole-TOF, LC/quadrupole-TOF, and nuclear magnetic resonance. Both univariate and multivariate analysis were performed. Serine levels were significantly higher following the LGI diet compared to both the HGI and LF diets (q = 0.002), whereas leucine (q = 0.015) and valine (q = 0.024) were lower in the LGI diet compared to the LF diet. A set of two sphingomyelins, two lysophosphatidylcholines, and six phosphatidylcholines were significantly modulated after the LGI diet compared to the HGI and LF diets (q < 0.05). Significant correlations between changes in plasma amino acids and lipid species with changes in body weight, glucose, insulin, and some inflammatory markers are also reported.

CONCLUSION

These results suggest that an LGI diet modulates certain circulating amino acids and lipid levels. These findings may explain the health benefits attributed to LGI diets in metabolic diseases such as type 2 diabetes.

NMR-based metabolomics of biofluids in cancer

This review describes the current status of NMR-based metabolomics of biofluids with respect to cancer risk assessment, detection, disease characterization, prognosis, and treatment monitoring. While the metabolism of cancer cells is altered compared with that of non-proliferating cells, the metabolome of blood and urine reflects the entire organism. We conclude that many studies show impressive associations between biofluid metabolomics and cancer progression, but translation to clinical practice is currently hindered by lack of validation, difficulties in biological interpretation, and non-standardized analytical procedures.

Comparative Metabolomics Analysis of Cervicitis in Human Patients and a Phenol Mucilage-Induced Rat Model Using Liquid Chromatography Tandem Mass Spectrometry

Cervicitis is an exceedingly common gynecological disorder that puts women at high risk of sexually transmitted infections and induces a series of reproductive system diseases. This condition also has a significant impact on quality of life and is commonly misdiagnosed in clinical practice due to its complicated pathogenesis. In the present study, we performed non-targeted plasma metabolomics analysis of cervicitis in both plasma samples obtained from human patients and plasma samples from a phenol mucilage induced rat model of cervicitis, using ultra-performance liquid chromatography coupled to quadrupole time-of-flight tandem mass spectrometry. In addition to differences in histopathology, we identified differences in the metabolic profile between the cervicitis and control groups using unsupervised principal component analysis and orthogonal projections to latent structures discriminant analysis. These results demonstrated changes in plasma metabolites, with 27 and 22 potential endogenous markers identified in rat and human samples, respectively. The metabolic pathway analysis showed that linoleic acid, arachidonic acid, ether lipid, and glycerophospholipid metabolism are key metabolic pathways involved in cervicitis. This study showed the rat model was successfully created and applied to understand the pathogenesis of cervicitis.

The Evolving Epidemiology of Atherosclerotic Cardiovascular Disease in People with Diabetes

Atherosclerotic cardiovascular disease (ASCVD) is a leading global cause of death and accounts for most deaths among individuals with diabetes. This article reviews the latest observational and trial data on changes in the relationship between diabetes and ASCVD risk, remaining gaps in how the role of each risk factor is understood, and current knowledge about specific interventions. Differences between high-income countries and low-income and middle-income countries are examined, barriers and facilitators are discussed, and a discussion around the concept of ideal cardiovascular health factors (Life's Simple 7) is focused on.