Metabolomic patterns associated to QTc interval in shiftworkers: an explorative analysis

Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives

INTRODUCTION

Work-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites.

OBJECTIVES

The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021.

RESULTS

Most of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures.

CONCLUSION

Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.

Association of N-Acetyl Asparagine with QTc in Diabetes: A Metabolomics Study

Changes in the cardio-metabolomics profile and hormonal status have been associated with long QT syndrome, sudden cardiac death and increased mortality. The mechanisms underlying QTc duration are not fully understood. Therefore, an identification of novel markers that complement the diagnosis in these patients is needed. In the present study, we performed untargeted metabolomics on the sera of diabetic patients at a high risk of cardiovascular disease, followed up for 2.55 [2.34–2.88] years (NCT02431234), with the aim of identifying the metabolomic changes associated with QTc. We used independent weighted gene correlation network analysis (WGCNA) to explore the association between metabolites clusters and QTc at T1 (baseline) and T2 (follow up). The overlap of the highly correlated modules at T1 and T2 identified N-Acetyl asparagine as the only metabolite in common, which was involved with the urea cycle and metabolism of arginine, proline, glutamate, aspartate and asparagine. This analysis was confirmed by applying mixed models, further highlighting its association with QTc. In the current study, we were able to identify a metabolite associated with QTc in diabetic patients at two chronological time points, suggesting a previously unrecognized potential role of N-Acetyl asparagine in diabetic patients suffering from long QTc.

Multiomics, virtual reality and artificial intelligence in heart failure

Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography-mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85-0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10^-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.

Association of industrial work schedules with development of metabolic syndrome, insulin resistance, and serum adipokine concentrations

Background

Association of work schedule in industrial workers with the progression of metabolic syndrome, insulin resistance, and serum adipokine concentrations is incompletely explored.

Objective

To determine the association of work schedule with the progression of metabolic syndrome, insulin resistance, and adipokine concentrations in industrial workers.

Methods

In a cross-sectional study design of industrial workers we compared metabolic syndrome, insulin resistance, and adipokines concentration between workers in the day shift (n = 52), rotational shift (n = 21), and night shift (n = 15). The international Diabetes Federation criteria were used to diagnose metabolic syndrome. We used a homeostatic model assessment of insulin resistance (HOMA-IR). Serum insulin, leptin, and adiponectin concentrations were measured using enzyme-linked immunosorbent assays. Serum glucose, triglyceride, and high-density lipoprotein cholesterol (HDL-C) concentrations were monitored using Prietest clinical chemistry reagents.

Results

The proportional difference in metabolic syndrome (0.31, 95% confidence interval [CI] 0.036-0.587, P = 0.026), median difference of leptin (0.61, 95% CI 0.186-1.034, P = 0.005), and leptin-to-adiponectin ratio (LAR; 0.45, 95% CI 0.235-0.665, P < 0.001) was significantly higher, and serum adiponectin was lower (-2.00, 95% CI -4.197 to 0.197, P = 0.07) in the night-shift workers compared with that of day-shift workers. Among rotational-shift workers, the proportional difference between metabolic syndrome (0.14, 95% CI -0.098 to 0.378, P = 0.25), median difference of leptin (0.25, 95% CI -0.124 to 0.624, P = 0.19), and LAR (0.09, 95% CI -0.099 to 0.279, P = 0.35) was higher, and serum adiponectin concentration was lower (-0.73, 95% CI -2.660 to 1.208, P = 0.46) compared with that of day-shift workers; however, the altered differences were not significant. We observed a higher proportion of difference in HOMA-IR in shift workers (night and rotation) than in day-shift workers.

Conclusion

Night-shift workers are vulnerable to a higher risk of metabolic syndrome, HOMA-IR, and adipokine changes.

A Comparative Study of Rat Urine 1H-NMR Metabolome Changes Presumably Arising from Isoproterenol-Induced Heart Necrosis Versus Clarithromycin-Induced QT Interval Prolongation

Cardiotoxicity remains a challenging concern both in drug development and in the management of various clinical situations. There are a lot of examples of drugs withdrawn from the market or stopped during clinical trials due to unpredicted cardiac adverse events. Obviously, current conventional methods for cardiotoxicity assessment suffer from a lack of predictivity and sensitivity. Therefore, there is a need for developing new tools to better identify and characterize any cardiotoxicity that can occur during the pre-clinical and clinical phases of drug development as well as after marketing in exposed patients. In this study, isoproterenol and clarithromycin were used as prototypical cardiotoxic agents in rats in order to evaluate potential biomarkers of heart toxicity at very early stages using ¹H-NMR-based metabonomics. While isoproterenol is known to cause heart necrosis, clarithromycin may induce QT interval prolongation. Heart necrosis and QT prolongation were validated by histological analysis, serum measurement of lactate dehydrogenase/creatine phosphate kinase and QTc measurement by electrocardiogram (ECG). Urine samples were collected before and repeatedly during daily exposure to the drugs for ¹H-NMR based-metabonomics investigations. Specific metabolic signatures, characteristic of each tested drug, were obtained from which potential predictive biomarkers for drug-induced heart necrosis and drug-induced QT prolongation were retrieved. Isoproterenol-induced heart necrosis was characterized by higher levels of taurine, creatine, glucose and by lower levels of Krebs cycle intermediates, creatinine, betaine/trimethylamine N-oxide (TMAO), dimethylamine (DMA)/sarcosine. Clarithromycin-induced QT prolongation was characterized by higher levels of creatinine, taurine, betaine/TMAO and DMA/sarcosine and by lower levels of Krebs cycle intermediates, glucose and hippurate.

Association between Work-Related Stress and QT Prolongation in Male Workers

Background: Work-related stress is a potential cardiovascular risk factor, but the underlying mechanism is not fully explained. The autonomic nervous system control of cardiac function might play a specific role; therefore, monitoring the QT interval in the electrocardiogram can highlight an autonomic imbalance induced by occupational stressors. The aim of our study was to explore the QT interval parameters as early indicators of imbalance of the autonomic cardiac function in relation to work-related stress. Methods: During 2015–2016 annual workplace health surveillance, we measured work-related stress in 484 workers of a logistic support company using the Health and Safety Executive (HSE) tool. We assessed the frequency-corrected QT (QTc) interval and the QT index (QTi) on the electrocardiogram of each participant, and collected demographic and clinical data. We compared the QTc values by the four Karasek’s categories (active/passive jobs, low/high strain job), and by job support (present/lacking), and conducted multivariate analysis to adjust for possible confounders. Results: The results of the multivariate regression analysis showed that QTc was prolonged among workers operating at a specific site where stress level was found to be elevated. Regular physical activity showed a beneficial effect against QTc prolongation. We did not observe an effect on QTc length by the cross-combined Karasek’s categories of job control, job demand, and job support. Conclusions: Our study suggests subclinical effects of conditions associated with work-related stress on the autonomic regulation of cardiac function. Further research is warranted to elucidate the combined effect of work organization and lifestyle factors on autonomic cardiac function.

Urinary 1H NMR metabolomics profile of Italian citizens exposed to background levels of arsenic: a (pre)cautionary tale

Objectives: Arsenic is a toxic metal ubiquitous in the environment and in daily life items. Long-term arsenic exposure is associated with severe adverse health effects involving various target organs. It would be useful to investigate the existence of metabolic alterations associated with lifestyle and/or with the environment. For this purpose, we studied the correlation between urinary arsenic levels and urinary proton nuclear magnetic resonance spectroscopy (¹H NMR) metabolomics profiles in a non-occupationally nor environmentally arsenic exposed general population.Methods: Urine samples were collected from 86 healthy subjects. Total and non-alimentary urinary arsenic (U-naAs) levels, namely the sum of arsenite, arsenate, monomethylarsonate and dimethylarsinate, were measured and ¹H NMR analysis was performed. Orthogonal Projection to Latent Structures was applied to explore the correlation between the metabolomics profiles and U-naAs levels.Results: Despite the extremely low U-naAs levels (mean value = 6.13 ± 3.17 µg/g creatinine) of our studied population a urinary metabolomics profile related to arsenic was identified.Conclusion: The identified profile could represent a fingerprint of early arsenic biological effect and could be used in further studies as an indicator of susceptibility, also in subjects exposed to a low arsenic dose, with implications in occupational health, toxicology, and public health.