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

Campagna, Marcello; Locci, Emanuela; Piras, Roberto; Noto, Antonio; Lecca, Luigi Isaia; Pilia, Ilaria; Cocco, Pierluigi; d’Aloja, Ernesto; Scano, Paola

doi:10.3109/1354750x.2016.1171900

Cited by 10 publications

(9 citation statements)

References 34 publications

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“…Nor did we observe in TTNtv carriers an alteration in urinary N-terminal titin fragments previously shown to be a negative prognostic indicator in HF [28]. We were unable to demonstrate any statistically significant correlations between metabolomic biomarkers and AECG; however, metabolomics has previously shown that kynurenine pathway is associated with mental stress-induced LVSD and ketone bodies (acetate and beta hydroxybutyrate) with QTc in shift workers [29,30]. We were, however, able to show an ability to not only discriminate between healthy participants and HF patients using PPSG and EDA, but also identify the presence of mental stress with a high degree of accuracy.…”

Section: Discussioncontrasting

confidence: 71%

Multiomics, Virtual Reality and Artificial Intelligence in Heart Failure

et al. 2021

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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.

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Section: Discussioncontrasting

confidence: 71%

Multiomics, Virtual Reality and Artificial Intelligence in Heart Failure

et al. 2021

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“…As work-related stress has been linked to CVD risk [1,2,3], monitoring QT-related parameters (QTc, QTi, QT variability, QT dispersion) might be useful to prevent a major cause of death, disability, and loss of productivity. By reaching a large fraction of the general population, the periodical occupational health surveillance [17,20,21,30,40] is a point of intervention: in fact, it might allow more effective preventive action through detecting early changes in biological parameters in otherwise healthy subjects. This aspect might have important public health implications, by reducing the direct and indirect costs of cardiovascular disease related to work-related stress.…”

Section: Discussionmentioning

confidence: 99%

“…Workplace factors, such as shiftwork in irregular and unpredictable working hours, can induce changes in the length of the QT interval [20]. QTc changes have also been associated with specific metabolic profiles in a cohort of shift workers engaged in nonstandard working hours [21].…”

Section: Introductionmentioning

confidence: 99%

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

Lecca

Portoghese

Mucci

et al. 2019

IJERPH

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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.

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“…Pathway analysis of these metabolites revealed statistically significant abnormalities in aminoacyl-tRNA biosynthesis, sulphur, taurine and hypotaurine, citric acid cycle, thiamine, pantothenate and CoA biosynthesis, pyruvate metabolism, glutathione, glycine, serine and threonine, glyoxylate and dicarboxylate, cysteine and methionine, arginine and proline metabolism, though these did not exceed the False Discovery Rate (FDR). A prior relationship between pyroglutamic acid and QTc could not be validated (39).…”

Section: Lvsdmentioning

confidence: 99%

Screening for Cardiac Disease with Genetic risk scoring, Advanced ECG, Echocardiography, Protein Biomarkers and Metabolomics

Gladding

Dugo

Wynne

et al. 2021

Preprint

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Introduction Screening patients for cardiovascular disease has not been widely advocated due to cost implications and is reserved for high risk or symptomatic patients. We undertook an exploratory study to evaluate the promising low-cost methods for screening, including genetic risk scoring (GRS), advanced ECG (A-ECG), echocardiography and metabolomics. Methods 78 patients underwent advanced 5-min ECG and echocardiography, including global longitudinal strain (GLS), and echocardiographic calcium scoring (eCS). A GRS of 27 SNPs (GRS27) related to coronary disease and 3 SNPs for atrial fibrillation was used, as well as hs-troponin (Abbott, Singulex, Roche), NTproBNP (Roche) testing and targeted plasma metabolomics using GC-MS. Results were correlated with the presence of coronary artery disease (CAD) (CT coronary angiography (CTCA)), measures of left ventricular hypertrophy (LVH) (echocardiography and CTCA), and LV systolic dysfunction (LVSD) (echocardiography). Results LV dysfunction was accurately identified by using either A-ECG (AUC 0.97, 0.89 to 0.99) or NTproBNP. eCS demonstrated accurate discrimination of CAD (AUC 0.84, 95% CI 0.72 to 0.92, p < 0.0001. Troponin I (Abbott/Singulex) had the highest sensitivity and accuracy for the detection of LVH measured by either CT or echocardiography (AUC 0.85, 95% CI 0.73 to 0.92), however specificity was reduced by the presence of LV systolic dysfunction. Metabolomics and A-ECG identified underlying abnormal mechanisms related to both LVH (glycine metabolism) and LV dysfunction, (Citric Acid cycle). Metabolomics provided incidental utility by identifying metformin adherence and nutritional biomarkers. Conclusion A multi-omic approach to screening can be achieved at relatively low cost, and high accuracy, but will need to be evaluated in larger populations to prove its utility.

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

Cited by 10 publications

References 34 publications

Multiomics, Virtual Reality and Artificial Intelligence in Heart Failure

Multiomics, Virtual Reality and Artificial Intelligence in Heart Failure

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

Screening for Cardiac Disease with Genetic risk scoring, Advanced ECG, Echocardiography, Protein Biomarkers and Metabolomics

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