Comprehensive risk factor analysis of sick sinus syndrome: a genetic, sociodemographic, clinical and laboratory investigation using the UK Biobank data.

Kim HJ, Yang PS, Park H, +5 more, Joung B.

Research Support, Non-U.S. Gov'tAbstract only — full text under license

AI summary — for doctors, generated from the abstract only

In a large UK Biobank study, a genetic risk score for atrial fibrillation, along with age, male sex, prior myocardial infarction, hypertension, and the kidney biomarker cystatin C, was associated with increased risk of developing sick sinus syndrome, though adding genetic and biomarker data provided only modest improvement in prediction.

What was studied

A prospective cohort study of 405,869 UK Biobank participants aged 38-73, followed for a median of 11.9 years, without prior atrial fibrillation or valvular disease, evaluating sociodemographic, clinical, laboratory, and genetic risk factors, using an atrial fibrillation polygenic risk score since no dedicated score exists for sick sinus syndrome, for incident sick sinus syndrome using Cox regression.

Key findings

769 sick sinus syndrome events occurred during follow-up; incidence increased with age (hazard ratio 1.52 per 5 years), male sex (hazard ratio 1.39), prior myocardial infarction (hazard ratio 1.54), and hypertension (hazard ratio 1.21).
A high atrial fibrillation polygenic risk score was associated with an 82% higher sick sinus syndrome risk (hazard ratio 1.82), and an intermediate risk score with a 32% higher risk (hazard ratio 1.32).
Cystatin C, a kidney function biomarker, was also associated with increased risk (hazard ratio 1.71).
The model's discrimination improved only modestly, from a C-index of 0.752 using sociodemographic and clinical variables alone to 0.760 with the addition of genetic and biomarker data.

Clinical relevance

This suggests that genetic susceptibility to atrial fibrillation and kidney function markers are linked to sick sinus syndrome risk beyond traditional clinical factors, supporting the potential value of integrating genetic and biomarker information for more comprehensive risk assessment, though the practical improvement in prediction was modest.

Who should care

Cardiology

Limitations

The study used an atrial fibrillation polygenic risk score as a proxy since no dedicated sick sinus syndrome risk score exists, which may not fully capture syndrome-specific genetic risk; the improvement in predictive discrimination from adding genetic and biomarker data was small.

Genetic predisposition to atrial fibrillation and the kidney biomarker cystatin C are linked to higher risk of developing sick sinus syndrome, alongside traditional factors like age and hypertension, though combining this information only modestly improves risk prediction.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

Background

Sinoatrial node dysfunction and atrial fibrillation (AF) often coexist, and each potentially initiates or perpetuates the other. Consequently, genetic risk factors for AF may be associated with sick sinus syndrome (SSS). This study evaluated clinical, sociodemographic, laboratory and genetic factors in predicting the SSS incidence by using a polygenic risk score (PRS) for AF, given the absence of a dedicated PRS for SSS.

Methods

A total of 502 421 individuals aged 38-73 years across 22 centres from 2006 to 2010 were enrolled in the UK Biobank. After those with incomplete data, prior AF or valvular disease were excluded, 405 869 participants (median age 58.0 (IQR, 50.0-64.0) years; 213 684 (45.2%) male) were analysed. AF PRS categorised the participants into low (first quintile), intermediate (middle three quintiles) and high (fifth quintile) risks. SSS incidence was recorded, and associations were evaluated using Cox regression with sociodemographic, clinical, laboratory and genetic variables.

Results

During the median follow-up of 11.9 years, 769 SSS events occurred. Their incidence increased with age (HR 1.52, 95% CI 1.43 to 1.62 per 5 years), male sex (HR 1.39, 95% CI 1.03 to 1.88), prior myocardial infarction (HR 1.54, 95% CI 1.08 to 2.21) and hypertension (HR 1.21, 95% CI 1.02 to 1.42). The high-PRS and intermediate-PRS groups were related to 82% (HR 1.82, 95% CI 1.45 to 2.29) and 32% (HR 1.32, 95% CI 1.07 to 1.62) higher SSS risks, respectively. Cystatin C (HR 1.71, 95% CI 1.33 to 2.22) was also associated with the increased risk. The model's C-index was 0.752 with sociodemographic and clinical variables, but it increased to 0.760 with genetic and biomarker data.

Conclusion

In addition to sociodemographic and clinical risk factors, AF PRS and cystatin C were linked to the SSS incidence. Although their inclusion modestly improved prediction, these findings highlighted the potential of integrating genetic and biomarker information for comprehensive SSS risk assessments.

DOI

10.1136/heartjnl-2025-327272

PMID

41605626

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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