A systematic review and meta-analysis of human population studies on the association between exposure to toxic environmental chemicals and left ventricular dysfunction (LVD)

BACKGROUND

Exposure to environmental chemicals has been associated with an elevated risk of heart failure (HF). However, the impact on early markers of HF, such as left ventricular dysfunction (LVD), remains limited.

OBJECTIVE

To establish a foundation of evidence regarding early HF markers and their association with environmental pollutants, a systematic review and meta-analysis was conducted.

METHODS

The search, conducted on October 13th, 2023, encompassed PubMed, Embase, and Web of Science without filters, focusing on observational studies reporting myocardial geometrical, structural, or functional alterations in individuals without a history of heart disease. This included the general adult population, workers, young people, and the elderly. The risk of bias was assessed using the ROBINS-I tool at both study and item levels.

RESULTS

The systematic review included 17 studies involving 43.358 individuals exposed to air pollution and 2038 exposed to heavy metals. Approximately 41% of the effect measures of associations reported significant abnormalities in myocardial structure or function. The metanalyses by pollutants categories indicated positive associations between LV systolic and diastolic abnormalities and exposure to PM2.5 [-0.069 (-0.104, -0.033); -0.044 (-0.062, -0.025)] and PM10 [-0.055 (-0.087, -0.022); -0.030 (-0.050, -0.010)] and NO2 [-0.042 (-0.071, -0.013); -0.021 (-0.037, -0.004)], as well as positive associations between lead exposure and LV systolic abnormalities [-0.033 (-0.051, -0.016)].

CONCLUSIONS

Existing evidence shows that specific early markers of HF may be associated with exposure to chemical pollutants. It is recommended to include such endpoints in new longitudinal and case-control studies to confirm further risk associations. These studies should consider co-exposures, account for vulnerable groups, and identify cardiotoxic compounds that may require regulation. When examining the link between myocardial abnormalities and environmental exposure, it is also advisable to explore the supportive use of Adverse Outcome Pathway (AOP) approaches to confirm a causal relationship.

Artificial intelligence algorithms for the recognition of Brugada type 1 pattern on standard 12-leads ECG

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): This research project is funded by Tuscany Region

Background/Introduction

Electrocardiograms (ECGs) are rapidly moving from analog to digital versions. Consequently, a series of automatic analyses of standard 12-lead ECGs are attracting interest for their ability to support clinicians in the automatic recognition of specific features associated with different cardiac diseases [2]. Artificial Intelligence applications and Machine Learning (ML) algorithms have gained much attention in the last years for their ability to figure out patterns from data independently, without being explicitly taught rules. Peculiar features define the ECGs of patients with Brugada Syndrome (BrS); however, ambiguities still exist for the correct diagnosis of BrS and discrimination with respect to other pathologies.

Purpose

The BrAID (Brugada syndrome and Artificial Intelligence applications to Diagnosis) project aims to develop an innovative system for diagnosing Type 1 BrS based on ECG pattern recognition through the application of ML algorithms. In this work, an application of Echo State Networks (ESN), a type of Recurrent Neural Network (RNN), for the diagnosis of BrS from ECG is presented.

Methods

After approval from the Local Ethical Committees, 12-lead ECGs were obtained in patients enrolled in 5 Centers diagnosed with typical spontaneous Type 1 pattern (coved) (group A, 81 patients). Baseline ECG was also collected in patients undergoing the ajmaline test, classified as positive (group B, 37 patients) or negative (group C, 14 patients) according to test results. 174 patients with no clinical and familial history of arrhythmias were considered controls (group D). Data were collected from 4 beats extracted from the ECGs as input to the ESN. The datasets obtained in the different groups were used for the ESN model’s training and assessment (testing) through a double cross-validation approach.

Results

As shown in Table 1, the performances using three leads (V1, V2, V3) or V2 only were compared.

The algorithm performance was assessed in all the datasets (group A+B+C+D) and in spontaneous BrS (group A) and controls (group D). A good accuracy (79.21%) was seen when the three leads were considered for groups A and D only; the best test set accuracy (80.20%) was obtained in the case in which V2 only was used as input in all the datasets.

Conclusion(s)

In this work, a novel system for diagnosing Type 1 BrS using an ESN approach was developed. Our preliminary results show that this ML model is able to detect ECG patterns associated with Type 1 BrS with good and comparable accuracy both when three leads (79.21% ) or V2 only (80.20%) were analyzed. The future availability of larger datasets could improve the model performance, increasing the ESN potentialities as a clinical support system tool to be used in everyday clinical practice.

Table 1. The accuracy, specificity, and sensitivity reported for each dataset group are obtained through double cross-validation.

The BrAID study protocol: integration of machine learning and transcriptomics for brugada syndrome recognition

BACKGROUND

Type 1 Brugada syndrome (BrS) is a hereditary arrhythmogenic disease showing peculiar electrocardiographic (ECG) patterns, characterized by ST-segment elevation in the right precordial leads, and risk of Sudden Cardiac Death (SCD). Furthermore, although various ECG patterns are described in the literature, different individual ECG may show high-grade variability, making the diagnosis problematic. The study aims to develop an innovative system for an accurate diagnosis of Type 1 BrS based on ECG pattern recognition by Machine Learning (ML) models and blood markers analysis trough transcriptomic techniques.

METHODS

The study is structured in 3 parts: (a) a retrospective study, with the first cohort of 300 anonymized ECG obtained in already diagnosed Type 1 BrS (75 spontaneous, 150 suspected) and 75 from control patients, which will be processed by ML analysis for pattern recognition; (b) a prospective study, with a cohort of 11 patients with spontaneous Type 1 BrS, 11 with drug-induced Type 1 BrS, 11 suspected BrS but negative to Na + channel blockers administration, and 11 controls, enrolled for ECG ML analysis and blood collection for transcriptomics and microvesicles analysis; (c) a validation study, with the third cohort of 100 patients (35 spontaneous and 35 drug-induced BrS, 30 controls) for ML algorithm and biomarkers testing.

DISCUSSION

The BrAID system will help clinicians improve the diagnosis of Type 1 BrS by using multiple information, reducing the time between ECG recording and final diagnosis, integrating clinical, biochemical and ECG information thus favoring a more effective use of available resources. Trial registration Clinical Trial.gov, NCT04641585. Registered 17 November 2020, https://clinicaltrials.gov/ct2/show/NCT04641585.

Modulated molecular markers of restenosis and thrombosis byin-vitrovascular cells exposed to bioresorbable scaffolds

Drug-eluting bioresorbable vascular scaffolds (BVSs) have emerged as a potential breakthrough for the treatment of coronary artery stenosis, providing mechanical support and drug delivery followed by complete resorption. Restenosis and thrombosis remain the primary limitations in clinical use. The study aimed to identify potential markers of restenosis and thrombosis analyzing the vascular wall cell transcriptomic profile modulation triggered by BVS at different values of shear stress (SS). Human coronary artery endothelial cells and smooth muscle cells were cultured under SS (1 and 20 dyne cm^-2) for 6 h without and with application of BVS and everolimus 600 nM. Cell RNA-Seq and bioinformatics analysis identified modulated genes by direct comparison of SS conditions and Gene Ontology (GO). The results of different experimental conditions and GO analysis highlighted the modulation of specific genes as semaphorin 3E, mesenchyme homeobox 2, bone morphogenetic protein 4, (heme oxygenase 1) and selectin E, with different roles in pathological evolution of disease. Transcriptomic analysis of dynamic vascular cell cultures identifies candidate genes related to pro-restenotic and pro-thrombotic mechanisms in anin-vitrosetting of BVS, which are not adequately contrasted by everolimus addition.

P6167Low leptin plasma levels are associated with progression of coronary atherosclerosis in patients with stable coronary artery disease from the SMARTool Study

Background

Leptin is an adipokine involved in energy homeostasis and has been related with established vascular risk factors. However, studies on the association of leptin plasma levels with coronary artery disease (CAD) have yielded conflicting results.

Purpose

Aim of the present study was to evaluate the association between leptin plasma levels and presence, severity and progression of coronary atherosclerosis in patients with suspected stable CAD.

Methods

In a cohort of 257 patients with symptoms of stable CAD enrolled in the SMARTool study, coronary computed tomography angiography (CTA), plasma leptin levels and clinical and bio-humoral CAD risk profile (including glucose, lipid and inflammation variables) were obtained at enrolment and after 6±1yrs of follow-up. Sixty-four patients were revascularized and the remaining 193 represent the population for the present study. CTA findings were categorised as no-minimal CAD (<30% stenosis), non-obstructive CAD (30%-50% stenosis) and obstructive CAD (≥50% stenosis in at least one major coronary vessel). A CTA risk score (based on plaque extent, severity, composition, and location) was calculated at baseline and at follow-up to assess coronary atherosclerotic burden and its progression (Δ CTA score≥5).

Results

CTA findings showed obstructive CAD in 11% of patients at baseline and in 15% at follow-up (p<0.0001). CTA risk score, was 8.03±7.80 at baseline and increased to 10.33±8.17 at follow-up (p<0.0001) with CAD progression in 20% of patients. Leptin plasma levels were inversely related with CTA findings both at baseline and follow-up (Figure). In a Cox model, baseline plasma leptin was an independent predictor of CAD progression, after adjustment for clinical risk factors, biomarkers, and treatment (HR 0.572, 95% CI 0.393–0.834, P=0.0037).

Figure 1

Conclusion

Plasma leptin is inversely associated with coronary atherosclerotic burden and disease progression in patients with stable CAD. This association is independent of known factors affecting leptin levels. These results could prompt further investigations on the pathophysiological mechanisms of this association.

Acknowledgement/Funding

EU H2020 research and innovation program under grant agreement No 689068

Patient specific multiscale modelling for plaque formation and progression

We present a three-dimensional model of plaque formation and progression that was tested in a set of patients who underwent coronary Computed Tomography angiography (CTA) for anginal symptoms. The 3D blood flow is described by the Navier-Stokes equations, together with the continuity equation. Mass transfer within the blood lumen and through the arterial wall is coupled with the blood flow and is modeled by a convection-diffusion equation. The Low Density Lipoprotein (LDL) transports in lumen of the vessel and through the vessel tissue (which has a mass consumption term) are coupled by Kedem-Katchalsky equations. The inflammatory process is modeled using three additional reaction-diffusion partial differential equations. A full three-dimensional model was created. Furthermore, features potentially affecting plaque growth, such as patient risk score, circulating biomarkers, localization and composition of the initial plaque, and coronary vasodilating capability were also investigated. The proof of concept of the model effectiveness was assessed 6 months after the baseline evaluation.

Expression of C-type natriuretic peptide and its receptor NPR-B in cardiomyocytes

C-type natriuretic peptide (CNP) was recently found in myocardium at the mRNA and protein levels, but it is not known whether cardiomyocytes are able to produce CNP. The aim of this study was to determine the expression of CNP and its specific receptor NPR-B in cardiac cells, both in vitro and ex vivo. CNP, brain natriuretic peptide (BNP) and natriuretic peptide receptor (NPR)-B mRNA expression were examined by RT-PCR in the H9c2 rat cardiac myoblast cell line, in neonatal rat primary cardiomyocytes and in human umbilical vein endothelial cells (HUVECs) as control. CNP protein expression was probed in cardiac tissue sections obtained from adult male minipigs by immunohistochemistry, and in H9c2 cells both by immunocytochemistry and by specific radioimmunoassay. The results showed that cardiac cells as well as endothelial cells were able to produce CNP. Unlike cardiomyocytes, as expected, in endothelial cells expression of BNP was not detected. NPR-B mRNA expression was found in both cell types. Production of CNP in the heart muscle cells at protein level was confirmed by radioimmunological determination (H9c2: CNP=0.86 ± 0.083 pg/mg) and by immunocytochemistry studies. By immunostaining of tissue sections, CNP was detected in both endothelium and cardiomyocytes. Expression of CNP in cardiac cells at gene and protein levels suggests that the heart is actively involved in the production of CNP.

A new library of HEMET model: Insulin effects on hepatic metabolism

Prediction and simulation of cell culture behaviour, under different chemical and physical stimuli by a mathematical model, represent an innovative way to create a virtual cell laboratory, where it is possible to perform and optimize experimental protocol, saving time and money. In silico experiments permit to reproduce pathological and physiological situations and make toxicological tests. In this paper we introduce a new library of HEMET (HEpatocyte METabolism) software that allows the insulin effects on hepatic metabolism to be simulated. This new set of nonlinear differential equations, derived from biochemical reactions which involve this pancreatic hormone, allows the catabolites concentration in hepatic cell culture after insulin infusion to be predicted. The validation procedures were carried out using data obtained from specifically designed cell experiments and from literature. A user friendly interface allows to easily change model parameters, rate constants and inputs simulating a wide range of physiological and pathological scenarios.

HEMET: mathematical model of biochemical pathways for simulation and prediction of HEpatocyte METabolism

Many computer studies and models have been developed in order to simulate cell biochemical pathways. The difficulty of integrating all the biochemical reactions that occur in a cell in a single model is the main reason for the poor results in the prediction and simulation of cell behaviour under different chemical and physical stimuli. In this paper we have translated biochemical reactions into differential equations for the development of modular model of metabolism of a hepatocyte cultured in static and standard conditions (in a plastic multiwell placed in an incubator at 37 degrees C with 5% of CO(2)). Using biochemical equations and energetic considerations a set of non-linear differential equations has been derived and implemented in Simulink. This set of equations mimics some of the principal metabolic pathways of biomolecules present in the culture medium. The software platform developed is subdivided into separate modules, each one describing a different metabolic pathway; they constitute a library which can be used for developing new modules and models to project, predict and validate cell behaviour in vitro.

Consecutive exposure to lamotrigine and citalopram during pregnancy

We describe the case of a pregnancy healthy outcome after in utero consecutive exposure to lamotrigine and citalopram.