Electrocardiogram (ECG) indices, encompassing parameters such as P wave duration, PR interval, QRS duration, and QT interval, serve as crucial indicators of cardiac electrical activity and provide valuable insights into the functioning of the heart's electrical conduction system [[1], [2], [3], [4]]. By assessing these indices, clinicians can identify abnormalities in heart rhythms, detect ischemic changes, and monitor the effects of the use of medications on the heart, thereby guiding therapeutic decisions and interventions. Therefore, these indices play a pivotal role in diagnosing and assessing various cardiovascular conditions, making them fundamental components of clinical cardiology.

Amidst the intricate web of factors influencing cardiovascular health, body mass index (BMI) [5] has emerged as a central focus of research due to its well-established association with obesity. Obesity, in turn, is recognized as a significant risk factor for a wide range of cardiovascular diseases, including atrial [6]/ventricular [7] arrhythmias, heart failure [8,9], and coronary artery disease [10,11]. While previous observational studies [[12], [13], [14], [15]] have reported links between BMI and ECG indices, there is a persistent need to discern whether these associations are causal or merely coincidental. This uncertainty is compounded by the fact that previous prospective or retrospective cohort studies may be influenced by various potential confounding factors, such as age, smoking, alcohol consumption, and comorbidities, which can obscure the true nature of the relationship between BMI and ECG indices. Therefore, it is necessary to further elucidate the causal relationship between BMI and ECG indices, in order to provide more accurate evidence for clinical practice.

Mendelian randomization [[16], [17], [18]] (MR) is a novel and robust analytical approach, which enables us to investigate the potential causal relationship between BMI and ECG indices by leveraging genetic variants. These genetic variants, which are highly associated with BMI and independent of traditional confounding factors, serve as instrumental variables (IVs) for BMI. By doing so, we can disentangle the intricate web of confounding factors and obtain unbiased insights into whether BMI directly influences the aforementioned ECG parameters and, if so, the potential clinical implications of such associations. Therefore, this study employs MR to investigate the causal association between BMI and ECG indices, providing insights into potential pathways linking obesity to cardiac diseases.