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

The metabolomic profiling of total fat and fat distribution in a multi-cohort study of women and men

Currently studies aiming for the comprehensive metabolomics profiling of measured total fat (%) as well as fat distribution in both sexes are lacking. In this work, bioimpedance analysis was applied to measure total fat (%) and fat distribution (trunk to leg ratio). Liquid chromatography-mass spectrometry-based untargeted metabolomics was employed to profile the metabolic signatures of total fat (%) and fat distribution in 3447 participants from three Swedish cohorts (EpiHealth, POEM and PIVUS) using a discovery-replication cross-sectional study design. Total fat (%) and fat distribution were associated with 387 and 120 metabolites in the replication cohort, respectively. Enriched metabolic pathways for both total fat (%) and fat distribution included protein synthesis, branched-chain amino acids biosynthesis and metabolism, glycerophospholipid metabolism and sphingolipid metabolism. Four metabolites were mainly related to fat distribution: glutarylcarnitine (C5-DC), 6-bromotryptophan, 1-stearoyl-2-oleoyl-GPI (18:0/18:1) and pseudouridine. Five metabolites showed different associations with fat distribution in men and women: quinolinate, (12Z)-9,10-dihydroxyoctadec-12-enoate (9,10-DiHOME), two sphingomyelins and metabolonic lactone sulfate. To conclude, total fat (%) and fat distribution were associated with a large number of metabolites, but only a few were exclusively associated with fat distribution and of those metabolites some were associated with sex*fat distribution. Whether these metabolites mediate the undesirable effects of obesity on health outcomes remains to be further investigated.

Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies

BACKGROUND

Due to the rapidly increasing availability of metabolomics data in prospective studies, an update of the meta evidence on metabolomics and type 2 diabetes risk is warranted.

PURPOSE

To conduct an updated systematic review and meta-analysis of plasma, serum, and urine metabolite markers and incident type 2 diabetes.

DATA SOURCES

We searched PubMed and Embase until 6 March 2021.

STUDY SELECTION

We selected prospective observational studies where investigators used high-throughput techniques to investigate the relationship between plasma, serum, or urine metabolites and incident type 2 diabetes.

DATA EXTRACTION

Baseline metabolites per-SD risk estimates and 95% CIs for incident type 2 diabetes were extracted from all eligible studies.

DATA SYNTHESIS

A total of 61 reports with 71,196 participants and 11,771 type 2 diabetes cases/events were included in the updated review. Meta-analysis was performed for 412 metabolites, of which 123 were statistically significantly associated (false discovery rate-corrected P < 0.05) with type 2 diabetes risk. Higher plasma and serum levels of certain amino acids (branched-chain, aromatic, alanine, glutamate, lysine, and methionine), carbohydrates and energy-related metabolites (mannose, trehalose, and pyruvate), acylcarnitines (C4-DC, C4-OH, C5, C5-OH, and C8:1), the majority of glycerolipids (di- and triacylglycerols), (lyso)phosphatidylethanolamines, and ceramides included in meta-analysis were associated with higher risk of type 2 diabetes (hazard ratio 1.07-2.58). Higher levels of glycine, glutamine, betaine, indolepropionate, and (lyso)phosphatidylcholines were associated with lower type 2 diabetes risk (hazard ratio 0.69-0.90).

LIMITATIONS

Substantial heterogeneity (I2 > 50%, τ2 > 0.1) was observed for some of the metabolites.

CONCLUSIONS

Several plasma and serum metabolites, including amino acids, lipids, and carbohydrates, are associated with type 2 diabetes risk.

Changes in metabolomics profiles over ten years and subsequent risk of developing type 2 diabetes: Results from the Nurses' Health Study

BACKGROUND

Metabolomics profiles were consistently associated with type 2 diabetes (T2D) risk, but evidence on long-term metabolite changes and T2D incidence is lacking. We examined the associations of 10-year plasma metabolite changes with subsequent T2D risk.

METHODS

We conducted a nested T2D case-control study (n=244 cases, n=244 matched controls) within the Nurses' Health Study. Repeated metabolomics profiling (170 targeted metabolites) was conducted in participant blood specimens from 1989/1990 and 2000/2001, and T2D occurred between 2002 and 2008. We related 10-year metabolite changes (Δ-values) to subsequent T2D risk using conditional logistic models, adjusting for baseline metabolite levels and baseline levels and concurrent changes of BMI, diet quality, physical activity, and smoking status.

FINDINGS

The 10-year changes of thirty-one metabolites were associated with subsequent T2D risk (false discovery rate-adjusted p-values [FDR]<0.05). The top three high T2D risk-associated 10-year changes were (odds ratio [OR] per standard deviation [SD], 95%CI): Δisoleucine (2.72, 1.97-3.79), Δleucine (2.53, 1.86-3.47), and Δvaline (1.93, 1.52-2.44); other high-risk-associated metabolite changes included alanine, tri-/diacylglycerol-fragments, short-chain acylcarnitines, phosphatidylethanolamines, some vitamins, and bile acids (ORs per SD between 1.31and 1.82). The top three low T2D risk-associated 10-year metabolite changes were (OR per SD, 95% CI): ΔN-acetylaspartic acid (0.54, 0.42-0.70), ΔC20:0 lysophosphatidylethanolamine (0.68, 0.56-0.82), and ΔC16:1 sphingomyelin (0.68, 0.56-0.83); 10-year changes of other sphingomyelins, plasmalogens, glutamine, and glycine were also associated with lower subsequent T2D risk (ORs per SD between 0.66 and 0.78).

INTERPRETATION

Repeated metabolomics profiles reflecting the long-term deterioration of amino acid and lipid metabolism are associated with subsequent risk of T2D.

Instability of Variable-selection Algorithms Used to Identify True Predictors of an Outcome in Intermediate-dimension Epidemiologic Studies

BACKGROUND

Machine-learning algorithms are increasingly used in epidemiology to identify true predictors of a health outcome when many potential predictors are measured. However, these algorithms can provide different outputs when repeatedly applied to the same dataset, which can compromise research reproducibility. We aimed to illustrate that commonly used algorithms are unstable and, using the example of Least Absolute Shrinkage and Selection Operator (LASSO), that stabilization method choice is crucial.

METHODS

In a simulation study, we tested the stability and performance of widely used machine-learning algorithms (LASSO, Elastic-Net, and Deletion-Substitution-Addition [DSA]). We then assessed the effectiveness of six methods to stabilize LASSO and their impact on performance. We assumed that a linear combination of factors drawn from a simulated set of 173 quantitative variables assessed in 1,301 subjects influenced to varying extents a continuous health outcome. We assessed model stability, sensitivity, and false discovery proportion.

RESULTS

All tested algorithms were unstable. For LASSO, stabilization methods improved stability without ensuring perfect stability, a finding confirmed by application to an exposome study. Stabilization methods also affected performance. Specifically, stabilization based on hyperparameter optimization, frequently implemented in epidemiology, increased the false discovery proportion dramatically when predictors explained a low share of outcome variability. In contrast, stabilization based on stability selection procedure often decreased the false discovery proportion, while sometimes simultaneously lowering sensitivity.

CONCLUSIONS

Machine-learning methods instability should concern epidemiologists relying on them for variable selection, as stabilizing a model can impact its performance. For LASSO, stabilization methods based on stability selection procedure (rather than addressing prediction stability) should be preferred to identify true predictors.

Plasma metabolites associated with exposure to perfluoroalkyl substances and risk of type 2 diabetes - A nested case-control study

Perfluoroalkyl substances (PFAS) are widespread persistent environmental pollutants. There is evidence that PFAS induce metabolic perturbations in humans, but underlying mechanisms are still unknown. In this exploratory study, we investigated PFAS-related plasma metabolites for their associations with type 2 diabetes (T2D) to gain potential mechanistic insight in these perturbations. We used untargeted LC-MS metabolomics to find metabolites related to PFAS exposures in a case-control study on T2D (n = 187 matched pairs) nested within the Västerbotten Intervention Programme cohort. Following principal component analysis (PCA), six PFAS measured in plasma appeared in two groups: 1) perfluorononanoic acid, perfluorodecanoic acid and perfluoroundecanoic acid and 2) perfluorohexane sulfonic acid, perfluorooctane sulfonic acid and perfluorooctanoic acid. Using a random forest algorithm, we discovered metabolite features associated with individual PFAS and PFAS exposure groups which were subsequently investigated for associations with risk of T2D. PFAS levels correlated with 171 metabolite features (0.16 ≤ |r| ≤ 0.37, false discovery rate (FDR) adjusted p < 0.05). Out of these, 35 associated with T2D (p < 0.05), with 7 remaining after multiple testing adjustment (FDR < 0.05). PCA of the 35 PFAS- and T2D-related metabolite features revealed two patterns, dominated by glycerophospholipids and diacylglycerols, with opposite T2D associations. The glycerophospholipids correlated positively with PFAS and associated inversely with risk for T2D (Odds Ratio (OR) per 1 standard deviation (1-SD) increase in metabolite PCA pattern score = 0.2; 95% Confidence Interval (CI) = 0.1-0.4). The diacylglycerols also correlated positively with PFAS, but they associated with increased risk for T2D (OR per 1-SD = 1.9; 95% CI = 1.3-2.7). These results suggest that PFAS associate with two groups of lipid species with opposite relations to T2D risk.

Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC-MS based untargeted metabolomics practitioners

INTRODUCTION

The metabolomics quality assurance and quality control consortium (mQACC) evolved from the recognized need for a community-wide consensus on improving and systematizing quality assurance (QA) and quality control (QC) practices for untargeted metabolomics.

OBJECTIVES

In this work, we sought to identify and share the common and divergent QA and QC practices amongst mQACC members and collaborators who use liquid chromatography-mass spectrometry (LC-MS) in untargeted metabolomics.

METHODS

All authors voluntarily participated in this collaborative research project by providing the details of and insights into the QA and QC practices used in their laboratories. This sharing was enabled via a six-page questionnaire composed of over 120 questions and comment fields which was developed as part of this work and has proved the basis for ongoing mQACC outreach.

RESULTS

For QA, many laboratories reported documenting maintenance, calibration and tuning (82%); having established data storage and archival processes (71%); depositing data in public repositories (55%); having standard operating procedures (SOPs) in place for all laboratory processes (68%) and training staff on laboratory processes (55%). For QC, universal practices included using system suitability procedures (100%) and using a robust system of identification (Metabolomics Standards Initiative level 1 identification standards) for at least some of the detected compounds. Most laboratories used QC samples (>86%); used internal standards (91%); used a designated analytical acquisition template with randomized experimental samples (91%); and manually reviewed peak integration following data acquisition (86%). A minority of laboratories included technical replicates of experimental samples in their workflows (36%).

CONCLUSIONS

Although the 23 contributors were researchers with diverse and international backgrounds from academia, industry and government, they are not necessarily representative of the worldwide pool of practitioners due to the recruitment method for participants and its voluntary nature. However, both questionnaire and the findings presented here have already informed and led other data gathering efforts by mQACC at conferences and other outreach activities and will continue to evolve in order to guide discussions for recommendations of best practices within the community and to establish internationally agreed upon reporting standards. We very much welcome further feedback from readers of this article.

Branched-chain and aromatic amino acids and cardiometabolic risk in Black African and Asian Indian populations

INTRODUCTION

Studies have shown that systemic levels of branched-chain amino acids (BCAAs) and aromatic amino acids (AAAs) are elevated in cardiometabolic diseases (CMDs) in populations resident in high income countries. However, little is known about the association of BCAAs and AAAs with metabolic syndrome and its components in Asian Indian (AI) and Black African (BA) populations.

OBJECTIVE

The aim of this study was to describe the association of BCAAs and AAAs with the metabolic syndrome, its individual components and insulin resistance in AI and BA populations.

METHODS

Serum samples collected from AI (n = 349) and BA (n = 369) subjects were used to measure levels of BCAAs and AAAs by ultra-pressure liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Anthropometric, demographic and cardiometabolic variables were measured in all subjects.

RESULTS

The sum of BCAAs and AAAs was higher in AIs compared to BAs. The BCAAs and AAAs were positively associated with insulin resistance, metabolic syndrome and its individual components. This was particularly the case for AI subjects, in unadjusted regression models. However, these associations were non-significant after adjusting for co-variates, particularly visceral adipose tissue (VAT). Triglyceride levels were significantly associated with valine and leucine levels in BAs even after adjustment for co-variates. Lastly, we found that fasting circulatory BCAA and AAA levels are strongly correlated with VAT in both populations.

CONCLUSION

This study identified specific associations of serum valine and leucine levels with triglycerides in BAs. The association of amino acids with CMDs was observed in AIs, but was found to be the result of confounding by VAT. Further studies are required to determine whether BCAAs and AAAs are aetiological factors in CMDs and how VAT modulates their serum levels.

The chemical exposome of type 2 diabetes mellitus: Opportunities and challenges in the omics era

Type 2 diabetes mellitus (T2DM) is a global silent killer, with > 450 million affected adults worldwide. A diverse array of non-modifiable risk factors such as family history, age (> 45 yrs), race/ethnicity, genetics, and history of gestational diabetes and modifiable risk factors such as physical inactivity, high body fat, body weight, high blood pressure, and high cholesterol for progression of prediabetes to T2DM. Given, that the modern world human population is constantly exposed to multiple stressors in the form of physical (i.e., sound, weather etc.) and chemical environment (i.e., diet, pollutants etc.), industrialization, and modernization has led to form a basis for exposomal correlation with T2DM incidence. Over the past decade, there have been emerging reports on association of levels of persistent organic pollutants (POPs), phthalates, antibiotics, drugs, air pollution, pesticides, and heavy metals with T2DM. In this review, we discuss the well known chemical exposome that has been associated with T2DM; the tools and approaches to capture this chemical exposome, and future opportunities and challenges in this exciting area of research. We further provide a window of thoughts, whether omics technologies can help fill in the gaps to help provide high throughput exposomics datasets in an unbiased manner to help understand T2DM pathophysiology in the context of industrialization, drastic lifestyle changes, urbanization, and pollution. We also discuss and provide guidelines/call to action for future exposomics studies investigating the association of T2DM with exposomes in the context of both epidemiological and experimental approaches.

Genotoxic, cytotoxic, and cytopathological effects in rats exposed for 18 months to a mixture of 13 chemicals in doses below NOAEL levels

The present study investigates the genotoxic and cytotoxic effects of long term exposure to low doses of a mixture consisting of methomyl, triadimefon, dimethoate, glyphosate, carbaryl, methyl parathion, aspartame, sodium benzoate, EDTA, ethylparaben, buthylparaben, bisphenol A and acacia gum in rats. Four groups of ten Sprangue Dawley rats (5 males and 5 females per group) were exposed for 18 months to the mixture in doses of 0xNOAEL, 0.0025xNOAEL, 0.01xNOAEL and 0.05xNOAEL (mg/kg bw/day). After 18 months of exposure, the rats were sacrificed and their organs were harvested. Micronuclei frequency was evaluated in bone marrow erythrocytes whereas the organs were cytopathologically examined by the touch preparation technique. The exposure to the mixture caused a genotoxic effect identified only in females. Cytopathological examination showed specific alterations of tissue organization in a tissue-type dependent manner. The observed effects were dose-dependent and correlated to various tissue parameters. Specifically, testes samples revealed degenerative and cellularity disorders, liver hepatocytes exhibited decreased glycogen deposition whereas degenerative changes were present in gastric cells. Lung tissue presented increased inflammatory cells infiltration and alveolar macrophages with enhanced phagocytic activity, whereas brain tissue exhibited changes in glial and astrocyte cells' numbers. In conclusion, exposure to very low doses of the tested mixture for 18 months induces genotoxic effects as well as monotonic cytotoxic effects in a tissue-dependent manner.

Understanding glycaemic control and current approaches for screening antidiabetic natural products from evidence-based medicinal plants

Type 2 Diabetes Mellitus has reached epidemic proportions as a result of over-nutrition and increasingly sedentary lifestyles. Current therapies, although effective, are not without limitations. These limitations, the alarming increase in the prevalence of diabetes, and the soaring cost of managing diabetes and its complications underscores an urgent need for safer, more efficient and affordable alternative treatments. Over 1200 plant species are reported in ethnomedicine for treating diabetes and these represents an important and promising source for the identification of novel antidiabetic compounds. Evaluating medicinal plants for desirable bioactivity goes hand-in-hand with methods in analytical biochemistry for separating and identifying lead compounds. This review aims to provide a comprehensive summary of current methods used in antidiabetic plant research to form a useful resource for researchers beginning in the field. The review summarises the current understanding of blood glucose regulation and the general mechanisms of action of current antidiabetic medications, and combines knowledge on common experimental approaches for screening plant extracts for antidiabetic activity and currently available analytical methods and technologies for the separation and identification of bioactive natural products. Common in vivo animal models, in vitro models, in silico methods and biochemical assays used for testing the antidiabetic effects of plants are discussed with a particular emphasis on in vitro methods such as cell-based bioassays for screening insulin secretagogues and insulinomimetics. Enzyme inhibition assays and molecular docking are also highlighted. The role of metabolomics, metabolite profiling, and dereplication of data for the high-throughput discovery of novel antidiabetic agents is reviewed. Finally, this review also summarises sample preparation techniques such as liquid-liquid extraction, solid phase extraction, and supercritical fluid extraction, and the critical function of nuclear magnetic resonance and high resolution liquid chromatography-mass spectrometry for the dereplication, putative identification and structure elucidation of natural compounds from evidence-based medicinal plants.