Weight, height, weight change, and risk of incident atrial fibrillation in middle‐aged men and women

Johansson, Cecilia; Lind, Marcus; Eriksson, Marie; Johansson, Lars

doi:10.1002/joa3.12409

Cited by 7 publications

(9 citation statements)

References 28 publications

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“…Fourth, the questionnaire used to collect data on several lifestyle factors has not been separately validated. On the other hand, several previous studies using the data from the same VIP health examinations have found associations between the recorded risk factors (e.g., studies regarding the association between alcohol consumption and risk of AF, and anthropometric factors and risk of AF) that are consistent with findings from other cohorts [40,41]. We also lack information on some conditions associated with both hyperglycemia and AF, for example, heart failure and obstructive sleep apnea [29,42,43], and we do not have information about the duration or severity of hypertension.…”

Section: Discussionsupporting

confidence: 81%

Diabetes, prediabetes, and atrial fibrillation—A population‐based cohort study based on national and regional registers

et al. 2023

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BackgroundPrevious studies have shown an increased risk for atrial fibrillation and atrial flutter (AF) in people with type 2 diabetes and prediabetes. It is unclear whether this increase in AF risk is independent of other risk factors for AF.ObjectiveTo investigate the association between diabetes and different prediabetic states, as independent risk factors for the onset of AF.MethodsWe performed a population‐based cohort study in Northern Sweden, including data on fasting plasma glucose, oral glucose tolerance test, major cardiovascular risk factors, medical history, and lifestyle factors. Participants were divided into six groups depending on glycemic status and followed through national registers for AF diagnosis. Cox proportional hazard model was used to assess the association between glycemic status and AF, using normoglycemia as reference.ResultsThe cohort consisted of 88,889 participants who underwent a total of 139,661 health examinations. In the model adjusted for age and sex, there was a significant association between glycemic status and development of AF in all groups except the impaired glucose tolerance group, with the strongest association for the group with known diabetes (p‐value <0.001). In a model adjusted for sex, age, systolic blood pressure, body mass index, antihypertensive drugs, cholesterol, alcohol, smoking, education level, marital status, and physical activity, there was no significant association between glycemic status and AF.Conclusions/interpretationThe association between glycemic status and AF disappears upon adjustment for potential confounders. Diabetes and prediabetes do not appear to be independent risk factors for AF.

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Section: Discussionsupporting

confidence: 81%

Diabetes, prediabetes, and atrial fibrillation—A population‐based cohort study based on national and regional registers

et al. 2023

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“…Greater height has been identified as a risk factor for AF in several studies [2,9,[62][63][64] and in both males [10,65] and females [65,66]. Some studies report that taller people have greater heart chamber size [9,10,63,65,66], meaning a larger left atrial size, which may be potential explanation albeit not a very robust one as AF is driven by left atrial stretch and fibrosis. Two other anthropometric traits, weight and waist circumference, ranked just below standing height.…”

Section: Af Resultsmentioning

confidence: 99%

“…Two other anthropometric traits, weight and waist circumference, ranked just below standing height. Obesity is associated with increased risk of left atrial enlargement, atrial fibrosis, electrical derangements of the atria, impaired diastolic function, inflammation and accumulation of pericardial fat, which are all key mechanisms in the pathogenesis of AF [10, 62, 65, 66]. The ranking of sex as the 7 th most significant feature in the model is also in agreement with epidemiological studies reporting sex differences in AF, males are at higher risk which is in agreement with our results, along with the electrophysiologic properties of the atria and structural remodelling[59, 60].…”

Section: Discussionmentioning

confidence: 99%

Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank

Papadopoulou

Harding

Slabaugh

et al. 2022

Preprint

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We employed machine learning (ML) approaches to evaluate 2,199 clinical features and disease phenotypes available in the UK Biobank as predictors for Atrial Fibrillation (AF) risk. After quality control, 99 features were selected for analysis in 21,279 prospective AF cases and equal number of controls. Different ML methods were employed, including LightGBM, XGBoost, Random Forest (RF), Deep Neural Network (DNN),) and Logistic Regression with L1 penalty (LR). In order to eliminate the black box character of the tree-based ML models, we employed Shapley-values (SHAP), which are used to estimate the contribution of each feature to AF prediction. The area-under-the-roc-curve (AUROC) values and the 95% confidence intervals (CI) per model were: 0.729 (0.719, 0.738) for LightGBM, 0.728 (0.718, 0.737) for XGBoost, 0.716 (0.706,0.725) for DNN, 0.715 (0.706, 0.725) for RF and 0.622 (0.612, 0.633) for LR. Considering the running time, memory and stability of each algorithm, LightGBM was the best performing among those examined. DeLongs test showed that there is statistically significant difference in the AUROCs between penalised LR and the other ML models. Among the top important features identified for LightGBM, using SHAP analysis, are the genetic risk score (GRS) of AF and age at recruitment. As expected, the AF GRS had a positive impact on the model output, i.e. a higher AF GRS increased AF risk. Similarly, age at recruitment also had a positive impact increasing AF risk. Secondary analysis was performed for the individuals who developed ischemic stroke after AF diagnosis, employing 129 features in 3,150 prospective cases of people who developed ischemic stroke after AF, and equal number of controls in UK Biobank. The AUC values and the 95% CI per model were: 0.631 (0.604, 0.657) for XGBoost, 0.620 (0.593, 0.647) for LightGBM, 0.599 (0.573, 0.625) for RF, 0.599 (0.572, 0.624) for SVM, 0.589 (0.562, 0.615) for DNN and 0.563 (0.536, 0.591) for penalised LR. DeLongs test showed that there is no evidence for significant difference in the AUROCs between XGBoost and all other examined ML models but the penalised LR model (pvalue=2.00 E-02). Using SHAP analysis for XGBoost, among the top important features are age at recruitment and glycated haemoglobin. DeLongs test showed that there is evidence for statistically significant difference between XGBoost and the current clinical tool for ischemic stroke prediction in AF patients, CHA2DS2-VASc (pvalue=2.20E-06), which has AUROC and 95% CI of 0.611 (0.585, 0.638).

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“…Large body size at a young age anticipated development of AF in further life. Higher BMI and BSA in adolescence are strongly related with AF in future [6]. Obesity is also associated with an increased risk of transition from paroxysmal AF to persistent AF.…”

Section: Predictors and Risk Factorsmentioning

confidence: 98%

Obesity and Atrial Fibrillation – Bariatric Surgery as a Method of Af Risk Decrease

et al. 2021

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The prevalence of obesity is growing at alarming rate worldwide. Obesity has reached the proportion of a global epidemic in both developed and developing countries. Growing number of obese patients with atrial fibrillation requires a closer look at how excessive body fat leads to AF. Excessive adipose tissue is the source of many cardiovascular diseases, like hypertension, ischemic heart disease, and arrhythmias such as atrial fibrillation. The prevalence of atrial fibrillation in the general population is estimated at 1-2%. Obesity and overweight occur in 25% of patients with AF. Pathophysiology of obesity and the relationship between atrial fibrillation, diabetes, obstructive sleep apnea and metabolic syndrome are presented in this article as well as the benefit of bariatric surgery in qualifying patients. Paroxysmal AF and its risk factors should be carefully assessed in all patients referred for bariatric surgery. Proper diagnosis allows physicians to introduce appropriate anticoagulant prophylaxis, and significantly lower complication rate. Antiarrhythmic, respiratory, and metabolic therapy should also be considered in preoperative and postoperative care.

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Weight, height, weight change, and risk of incident atrial fibrillation in middle‐aged men and women

Cited by 7 publications

References 28 publications

Diabetes, prediabetes, and atrial fibrillation—A population‐based cohort study based on national and regional registers

Diabetes, prediabetes, and atrial fibrillation—A population‐based cohort study based on national and regional registers

Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank

Obesity and Atrial Fibrillation – Bariatric Surgery as a Method of Af Risk Decrease

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