The height as an independent risk factor of atrial fibrillation: A review

Sohail, Hamza; Hassan, Syeda Maria; Yaqoob, Uzair; Hassan, Zair

doi:10.1016/j.ihj.2020.11.008

Cited by 7 publications

(6 citation statements)

References 35 publications

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“…Our observation that an increasing body height, to the contrary, has a positive association with AF also has been noted in previous studies in other populations [ 20 – 22 ]. In a large Korean cohort study, data from the Korean National Health Insurance Service-National Sample Cohort (NHIS-NSC) from 2002 to 2015 of more than 300,000 individuals who underwent a medical examination between 2006 and 2009 were analyzed.…”

Section: Discussionsupporting

confidence: 91%

The association between the body height and cardiovascular diseases: a retrospective analysis of 657,310 outpatients in Germany

Krieg

Kostev²,

Luedde

et al. 2022

Eur J Med Res

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Background Cardiovascular disease (CVD) represents the leading cause of death worldwide. The identification of individuals at increased risk of CVD is essential to reduce its morbidity and mortality globally. Based on existing data on a potential association between the individual body height and the risk for CVD, we investigated this association in a large cohort of outpatients in Germany. Methods A total of 657,310 adult outpatients with available body height data from the Disease Analyzer (IQVIA) database were included in Germany between 2019 and 2021. The prevalence of common CVD diagnoses (hypertension, coronary heart disease, atrial fibrillation and flutter, heart failure, ischemic stroke, and venous thromboembolism) was evaluated as a function of the patients’ body height stratified by age and sex. Results In both sexes, the prevalence of hypertension, coronary heart disease, heart failure, and ischemic stroke was higher among patients of smaller body height. In contrast, the prevalence of atrial fibrillation and venous thromboembolism was higher in taller patients. In age- and BMI-adjusted logistic regression analyses, an increased body height was negatively associated with coronary heart disease (OR = 0.91 in women and OR = 0.87 in men per 10-cm increase in height) and strongly positively associated with atrial fibrillation (OR = 1.25 in women and men) and venous thromboembolism (OR = 1.23 in women and OR = 1.24 in men). Conclusion We present the first data from a large cohort of outpatients in Germany providing strong evidence for an association between the body height and common CVD. These data should stimulate a discussion as to how far the body height should be implemented as a parameter in stratification tools to assess CVD risk in order to further reduce cardiovascular morbidity and mortality in the future.

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Section: Discussionsupporting

confidence: 91%

The association between the body height and cardiovascular diseases: a retrospective analysis of 657,310 outpatients in Germany

Krieg

Kostev²,

Luedde

et al. 2022

Eur J Med Res

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“…And in the Danish Diet, Cancer, and Health Study [ 55 ], BMI was found to be significantly associated with AF after adjusting for height, smoking, alcohol consumption, hypertension, diabetes, heart diseases, etc. Similarly, height is also a well-known independent risk factor for AF (as shown by Height → AF) after adjusting for many traditional risk factors as evidenced by many studies [ 56 – 58 ]. Overall, some of our findings here have lent strong support for some existing causal hypotheses drawn from previous observational cohort studies.…”

Section: Resultsmentioning

confidence: 99%

Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data

2023

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Mendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total causal effect between two traits, while it would be useful to infer the direct causal effect between any two of many traits (by accounting for indirect or mediating effects through other traits). For this purpose we propose a two-step approach: we first apply an extended MR method to infer (i.e. both estimate and test) a causal network of total effects among multiple traits, then we modify a graph deconvolution algorithm to infer the corresponding network of direct effects. Simulation studies showed much better performance of our proposed method than existing ones. We applied the method to 17 large-scale GWAS summary datasets (with median N = 256879 and median #IVs = 48) to infer the causal networks of both total and direct effects among 11 common cardiometabolic risk factors, 4 cardiometabolic diseases (coronary artery disease, stroke, type 2 diabetes, atrial fibrillation), Alzheimer’s disease and asthma, identifying some interesting causal pathways. We also provide an R Shiny app (https://zhaotongl.shinyapps.io/cMLgraph/) for users to explore any subset of the 17 traits of interest.

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“…Interestingly, standing height was ranked as the fourth most significant feature in our best performing model for AF prediction. Greater height has been identified as a risk factor for AF in several studies [2, 9, 62-64] 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.…”

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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The height as an independent risk factor of atrial fibrillation: A review

Cited by 7 publications

References 35 publications

The association between the body height and cardiovascular diseases: a retrospective analysis of 657,310 outpatients in Germany

The association between the body height and cardiovascular diseases: a retrospective analysis of 657,310 outpatients in Germany

Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data

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

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