Identification of the Metabolic Signature of Atrial Fibrillation for Personalized Prevention

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

400 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-03-10

Study Completion Date

2027-04-30

Brief Summary

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Atrial fibrillation (AF) is a major public health problem. The efficacy of the existing techniques is limited in the more aggressive forms. It is therefore necessary to develop approaches, in particular the identification of relevant biomarkers, to prevent the onset, recurrence or progression of AF in at-risk patients. The objective of this study is to describe the longitudinal metabolic and biomolecular signature of AF in patients eligible for cardiac ablation.

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Detailed Description

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Atrial fibrillation (AF) is a major public health problem. Its prevalence exceeds 2%. The main aim of drug treatment is to prevent the onset of stroke and heart failure, but side effects often require discontinuation, and contraindications limit their use. Rhythm control strategies based on catheter ablation have led to significant progress in incident AF, improving quality of life. Nevertheless, the efficacy of these techniques is limited in the more aggressive forms. Significant recurrence rates are reported one year after ablation, and access to them is often reserved for symptomatic patients due to their invasive and costly nature.

It is therefore necessary to develop approaches to prevent the onset, recurrence or progression of AF in at-risk patients. While the pathophysiology of AF involves metabolic remodelling that can be observed in animal and human models, no clinically relevant metabolites have been identified as biomarkers of the risk of AF onset or progression, with a view to preventive and personalized management.

In response to this unmet need, this project aims to develop a method for assessing the risk of AF recurrence, combining the identification of a metabolic signature of the arrhythmia and the patient, with a machine learning approach to aggregate conventional risk factors and metabolic biomarkers. A longitudinal clinical study will be conducted on patients scheduled for AF ablation, to monitor changes in their metabolic signature over 12 months, in parallel with arrhythmia progression. Using machine learning, the study team will establish and validate a classifier retrospectively stratifying patients with or without recurrent AF, and compare this method with canonical risk stratification. This will enable to consider personalized management of patients at risk of recurrence, with the aim of reducing human and economic costs.

Conditions

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Atrial Fibrillation (AF)

Study Design

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Allocation Method

NON_RANDOMIZED

Intervention Model

SEQUENTIAL

prospective multicentric study

Primary Study Purpose

PREVENTION

Blinding Strategy

NONE

Study Groups

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Patients with FA

Patient with a documented Fibrillation Atrial within the last 18 months

Group Type OTHER

FA ablation

Intervention Type PROCEDURE

Ablation of the FA

Lab test

Intervention Type BIOLOGICAL

Blood collection

Patients without FA

Patient without Fibrillation Atrial

Group Type OTHER

Lab test

Intervention Type BIOLOGICAL

Blood collection

Interventions

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FA ablation

Ablation of the FA

Intervention Type PROCEDURE

Lab test

Blood collection

Intervention Type BIOLOGICAL

Eligibility Criteria

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Inclusion Criteria

* Age ≥ 18 years, all genders, and ethnic origins
* Free, informed, and written consent signed
* Person affiliated to or benefiting from a social security scheme

Exclusion Criteria

* Age \< 18 years
* Lack of informed consent
* Gestating women (pregnancy test carried out as part of care for FA patients, contraception, or menopause for women in control groups)
* Persons under administrative or judicial protection
* Endocarditis or pericarditis in progress or within the 3 last months
* Active tumor pathology (benign or malignant)
* Chronic inflammation or autoimmune disease
* Chronic liver disease
* Myocardial infarction within the last 8 weeks

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes