What is the clinical evidence from this study?

Study design: Case-Control. Population: Atrial fibrillation (n=1840). Intervention: Serum uric acid (SUA) vs. Control group (sinus rhythm, non-AF). Primary outcome: Atrial fibrillation in normoglycemic pattern (OR 1.313, 95% CI 1.120-1.539, p=0.001).

What does this research mean for the field?

Serum uric acid is independently associated with an increased risk of atrial fibrillation across normoglycemic, impaired fasting glucose, and diabetes mellitus patterns. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

January 23, 2023Open Access

Association between serum uric acid levels and atrial fibrillation in different fasting glucose patterns: A case-control study

XZXia ZhongPeking University HJHuachen JiaoShandong University of Traditional Chinese Medicine DZDongsheng ZhaoNantong University

Key Result

Serum uric acid was independently associated with an increased risk of atrial fibrillation across normoglycemic (OR 1.313), impaired fasting glucose (OR 1.386), and diabetes mellitus (OR 1.505) patterns.

Study Design

Type

Case-Control (n=1,840)

Multicenter

Structured PICO

Is elevated serum uric acid associated with atrial fibrillation across different fasting glucose patterns?

Population

1,840 adults (aged 28-85 years) including 920 with atrial fibrillation (AF) and 920 age- and sex-matched non-AF controls with sinus rhythm. Patients were divided into normoglycemic (n=1203), impaired fasting glucose (IFG, n=283), and diabetes mellitus (DM, n=354) groups. Excluded patients with congenital heart disease, valvular disease, heart failure, cardiac surgery, severe infection, malignant tumor, autoimmune disease, severely impaired liver function, hyperthyroidism, impaired renal function (eGFR <60), pregnant/lactating women, and those on anticoagulants, diuretics, non-statin lipid-lowering drugs, uric acid lowering drugs, or antidiabetic drugs.

Comparator

920 age- and sex-matched non-AF patients with sinus rhythm

Outcome

Association between serum uric acid (SUA) levels and atrial fibrillation (AF) in different fasting glucose patterns, and diagnostic efficiency of SUA for diagnosing AF

Main Result

Effect estimate: OR 1.313 (95% CI 1.120-1.539)

p-value: p=0.001

Limitations

Retrospective design preventing knowledge of causality
Limited population size and single research center
SUA and FBG at a single point are not representative of overall status
HbA1c not routinely analyzed
Failed to focus on subgroup analyses of AF type, sex, and age
Markers of inflammation and oxidative stress were not evaluated
Cannot exclude residual confounding factors, including BMI, physical activity, smoking, and drinking
Only focused on the population diagnosed with AF on admission

Abstract

Background: Previous studies have shown both dysglycaemia and hyperuricemia are associated with an increased risk of atrial fibrillation (AF), while the relationship between serum uric acid (SUA) levels and AF in different fasting glucose patterns (FBG) is unclear. Therefore, this study aimed to determine the association between SUA and AF in different FBG patterns. Methods: A total of 1840 patients in this case-control study were enrolled, including 920 AF patients and 920 controls. Patients were divided into three groups according to the different FBG patterns: normoglycemic, impaired fasting glucose (IFG), and diabetes mellitus (DM). Multivariate logistic regression models were performed to evaluate the relationship between SUA and AF in different FBG patterns. Pearson correlation analysis was used to explore the correlation between SUA and metabolic factors. Receiver operating characteristic (ROC) curve models indicated the diagnostic efficiency of SUA for diagnosing AF. Results: SUA was independently associated with AF after adjusting for all confounding factors in different FBG patterns(normoglycemic: OR=1.313, 95% CI:1.120-1.539; IFG: OR=1.386, 95% CI:1.011-1.898; DM: OR=1.505, 95% CI:1.150-1.970). Pearson's correlation analysis suggested that SUA in AF patients was correlated with several different metabolic factors in different FBG patterns (p<0.05). ROC curve analysis showed that SUA in the normoglycemic group combined with CHD and APOB AUC: 0.906 (95% CI: 0.888-0.923), in the IFG group combined with CHD and Scr AUC: 0.863 (95% CI: 0.820-0.907), in the DM group combined with CHD and SBP AUC: 0.858 (95% CI: 0.818-0.898) had the highest AUC for predicting AF. Conclusion: Findings implied a significant association between SUA and AF in different FBG patterns and provide specific models combined with other factors (CHD, APOB, SCr, SBP), which might contribute to the diagnosis of AF.

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