Atrial Fibrillation (AF)
Conditions
Keywords
Polygenic Risk Score, Atrial Fibrillation Screening, Handheld ECG, SNP Array, Holter Monitoring, Genetic Risk
Brief summary
The goal of this clinical trial is to learn whether a genetic risk score can help identify undiagnosed atrial fibrillation (AF) in adults who may have it. AF is an irregular heartbeat that raises the risk of stroke if not treated early. The main questions it aims to answer are: Can a polygenic risk score (PRS) - a score based on a person's genes - identify who is more likely to have AF? Does combining PRS with a person's medical history predict AF better than using medical history alone? Participants will: Wear a continuous ECG patch for 7 days to record heart rhythm Give a blood sample for genetic testing to calculate their PRS Use a six-lead handheld ECG device (HATIV® P30, VUNO Inc., Seoul, Republic of Korea) to check their own heart rhythm at home once or twice a week for 1 year Visit the clinic 5 times over 1 year Researchers will use the genetic and clinical information collected to build a scoring system that predicts who is at risk for AF.
Detailed description
Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide, affecting approximately 34 million individuals. While early diagnosis and treatment can significantly reduce the risk of stroke, paroxysmal AF often goes undetected due to its intermittent nature. Current screening guidelines lack clear recommendations on which populations benefit most from AF screening and what screening strategies are optimal. Genome-wide association studies have identified numerous genetic loci associated with AF susceptibility, and the SNP heritability of AF has been estimated at approximately 22%. A polygenic risk score (PRS) aggregates the effects of thousands of common genetic variants across the genome to estimate an individual's genetic predisposition to AF. Prior studies have demonstrated that PRS can stratify AF risk independently of conventional clinical risk factors, suggesting its potential utility in targeted screening strategies. Genetic Assessment At enrollment, a blood sample of 5cc is collected for DNA extraction via centrifugation. A SNP array is performed using a commercially available SNP chip kit. The resulting genotype data are used to calculate a weighted PRS for AF based on previously published genome-wide association study results. ECG Monitoring In addition to standard 12-lead ECG and 7-day continuous ECG patch monitoring performed at enrollment, all participants are provided with a six-lead handheld ECG device (HATIV® P30, VUNO Inc., Seoul, Republic of Korea). Participants perform self-ECG recordings at least once or twice per week and additionally upon symptom onset for 1 year after enrollment. Prediction Model Development At the end of follow-up, participants are classified into AF-diagnosed and non-AF groups. A scoring system integrating clinical history and PRS will be developed to predict AF occurrence and its predictive performance will be evaluated.
Interventions
DEVICE7-day continuous ECG patch monitor
Continuous ECG patch worn for 7 days at enrollment to detect atrial fibrillation and other arrhythmias.
DEVICESix-lead handheld electrocardiogram device
Six-lead ECG device used for self-monitoring at least once or twice weekly and upon symptoms for 1 year.
GENETICSNP array genotyping
DNA extracted from a 5cc blood sample is used to perform SNP array genotyping. The resulting data are used to calculate a polygenic risk score (PRS) for atrial fibrillation based on previously published genome-wide association study results.
Sponsors
Seoul St. Mary's Hospital
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
20 Years to 79 Years
Healthy volunteers
No
Inclusion criteria
* Patients with symptoms suggestive of paroxysmal atrial fibrillation, such as intermittent palpitations or chest discomfort * Asymptomatic patients aged 60 or older with at least one of the following risk factors for atrial fibrillation: hypertension, diabetes, coronary artery disease, valvular heart disease, cardiomyopathy, sleep apnea, hyperthyroidism, obesity (BMI greater than 30), or chronic alcohol dependence (drinking more than 3 times per week)
Exclusion criteria
* Age under 20 years or over 80 years * Moderate or severe cognitive impairment * Previously diagnosed with atrial fibrillation prior to study enrollment * Does not consent to participate in the study
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Incidence of newly diagnosed atrial fibrillation | 1 year | New diagnosis of atrial fibrillation confirmed by 7-day continuous ECG patch monitor, six-lead handheld ECG device, or standard 12-lead ECG during the 1-year follow-up period. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Incidence of newly diagnosed atrial flutter | 1 year | New diagnosis of atrial flutter confirmed by ECG monitoring during the 1-year follow-up period. |
| Predictive performance of the AF prediction model | 1 year | Discrimination and calibration of a scoring system integrating polygenic risk score and clinical risk factors for predicting atrial fibrillation, assessed by area under the receiver operating characteristic curve (AUC). |
Countries
South Korea
Contacts
CONTACTYoung Choi, MD, PhD
PRINCIPAL_INVESTIGATORYoung Choi, MD, PhD
The Catholic University of Korea
Outcome results
None listed