Background: Insulin resistance (IR) and obesity are key drivers of atrial fibrillation (AF). However, the comparative predictive value of the Triglyceride-Glucose (TyG) index versus composite indices combining IR and anthropometric measures such as TyG-BMI, TyG-Waist Circumference (TyG-WC), and Waist-to-Height Ratio (WHtR) remains undefined. We aimed to evaluate these associations and the modifying effect of genetic susceptibility. Methods: We analyzed 293,318 UK Biobank participants free of AF at baseline. Hazard ratios (HRs) were estimated using Cox proportional hazards models, and non-linearity was assessed using restricted cubic splines. Incremental predictive value was evaluated via Net Reclassification Improvement (NRI). Interactions with AF Polygenic Risk Scores (PRS) were examined. Results: During follow-up, 22,707 incident AF cases occurred. While the TyG index was associated with AF in unadjusted models, this association was nullified after full adjustment. In contrast, composite indices (TyG-BMI, TyG-WC) and WHtR showed robust, positive associations (WHtR HR per SD: 1.30, 95% CI 1.28-1.32). Spline analysis identified non-linear threshold effects (e.g., WHtR inflection at 0.556). Adding WHtR or TyG-BMI to baseline models significantly improved risk reclassification (NRI ~10.3-11.8%, P<0.001), whereas TyG alone did not (P=0.73). Elevated metabolic risk increased AF incidence across all genetic categories, with significant interactions suggesting greater relative impact in low-genetic risk groups. Conclusions: Composite indices integrating central obesity and insulin resistance are superior to the TyG index alone in predicting incident AF. The identification of specific risk thresholds and genetic interactions highlights "metabolic health" as a crucial, modifiable target for AF prevention.

The authors have declared no competing interest.

This work was supported by Beijing Anzhen Hospital, Capital Medical University, and Beijing Institute of Heart Lung and Blood Vessel Diseases. This study was supported by the National Natural Science Foundation of China (Grant No. 82100366).

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Data supporting the findings of this study from the UK Biobank team (http://www.ukbiobank.ac.uk/). The data and methods that support the findings of this study are available from the corresponding author on reasonable request.