Risk Factors for New-Onset Atrial Fibrillation in Thai Adults with Hypertension

Risk Factors for New-Onset Atrial Fibrillation in Thai Adults with Hypertension | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Risk Factors for New-Onset Atrial Fibrillation in Thai Adults with Hypertension Varisa Limpijankit , View ORCID Profile Thinnakrit Sasiprapha , Htun Teza , Anuchate Pattanateepapon , Sukanya Siriyotha , Suparee Boonmanunt , View ORCID Profile John Attia , Ammarin Thakkinstian doi: https://doi.org/10.1101/2025.05.07.25327197 Varisa Limpijankit a Chulalongkorn University International Medical Program, The Faculty of Medicine, Chulalongkorn University , Bangkok, Thailand Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thinnakrit Sasiprapha b Division of Cardiology, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University , Bangkok, Thailand Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thinnakrit Sasiprapha Htun Teza c Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University , Bangkok, Thailand Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anuchate Pattanateepapon c Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University , Bangkok, Thailand Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sukanya Siriyotha c Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University , Bangkok, Thailand Find this author on Google Scholar Find this author on PubMed Search for this author on this site Suparee Boonmanunt c Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University , Bangkok, Thailand Find this author on Google Scholar Find this author on PubMed Search for this author on this site John Attia d Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, and Hunter Medical Research Institute , New Lambton, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John Attia Ammarin Thakkinstian c Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University , Bangkok, Thailand Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: thinnakrit.sas{at}mahidol.ac.th Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Limited data exist on new-onset atrial fibrillation (NOAF) risk factors in Asian populations with hypertension (HTN). This study identified predictors of NOAF in Thai adults with HTN. Methods We conducted a retrospective cohort study of adults (≥18 years) newly diagnosed with HTN at Ramathibodi Hospital, Bangkok (2010–2023). Patients with prior atrial fibrillation or predisposing conditions (e.g., valvular heart disease, hyperthyroidism) were excluded. Baseline demographics, comorbidities, and medication use were analyzed as time-varying covariates using a multivariate Cox proportional hazards model. Results Of 293,798 HTN patients, 168,441 met inclusion criteria. Over a median 3.7-year follow-up (range: 2.2–8.0), 5,028 developed NOAF (incidence: 5.7 per 1,000 person-years). An age–body mass index (BMI) interaction was observed. In patients <60 years, low BMI increased NOAF risk [hazard ratio (HR) 2.3; 95% CI: 1.4–3.6], while overweight [HR 1.1; 0.8–1.4] and obesity [HR 0.8; 0.6–1.1] showed no significant effect. In patients ≥60 years, NOAF risk rose 2- to 4-fold across BMI categories. Male sex and comorbidities (vascular disease, stroke, heart failure, chronic kidney disease, hyperuricemia) increased risk by 1.2–1.8-fold. Statin use reduced risk [HR 0.8; 0.7–0.9]; sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists showed a non-significant protective trend [HR 0.8; 0.7–1.1]. Conclusions Older age, male sex, abnormal BMI, and the presence of comorbidities are significant risk factors for NOAF in Thai patients with HTN. In contrast, statin use may offer a protective effect. Introduction Systemic arterial hypertension (HTN) is a prevalent chronic non-communicable disease and a leading risk factor for cardiovascular disease and all-cause mortality ( 1 , 2 ). A 2016 World Health Organization (WHO) report estimated that 22% of the global population and 26% of the Southeast Asian population have HTN ( 3 ). In Thailand, the 2019 National Health Survey found that 25% of adults have HTN ( 4 ), a condition responsible for two-thirds of strokes and half of coronary artery disease events ( 5 ). HTN is a well-established major risk factor for atrial fibrillation (AF) ( 6 , 7 ), contributing to approximately 50% of AF cases ( 6 , 8 ). Long-standing uncontrolled HTN can lead to AF through a complex interplay of factors, including cardiac structural changes (such as left ventricular hypertrophy, left atrial enlargement, and fibrosis), electrical disturbances, neurohormonal activation, and increased sympathetic activity ( 9 , 10 ). Additionally, systemic inflammation, oxidative stress, and endothelial dysfunction are commonly observed in individuals with HTN ( 11 – 13 ). These factors together contribute to the formation of an AF substrate, disrupting the electrical and mechanical functions of the atria and promoting the initiation and maintenance of AF. HTN has been shown to increase the risk of developing AF by 1.8-fold and the risk of progression to permanent AF by 1.5-fold ( 9 ). Both HTN and AF are synergistically associated with an increased incidence of heart failure, stroke, and mortality ( 10 , 14 ). Therefore, identifying predictors of new-onset AF (NOAF) in patients with HTN is essential for preventing or delaying the onset of this arrhythmia. Behavioral modifications could help address these risk factors and combined with guideline-directed medical therapy for HTN, significantly reduce the likelihood of developing AF and its associated complications ( 15 , 16 ). Several scoring systems have been developed to assess the risk of AF, using community-based cohorts, primarily from Western populations ( 17 – 19 ). However, only one of these risk scores has been specifically validated for patients with HTN: the risk score developed for the ESCARVAL (Estudio CARdiometabolico VALenciano)-RISK study in Spain ( 20 ). This score identified age, sex, obesity, and heart failure as independent predictors of NOAF. AF is a multifactorial condition influenced by aging, genetics, lifestyle, and comorbidities. Identifying independent predictors of AF across diverse populations is crucial for timely intervention and lifestyle modification. However, the specific etiology of NOAF in individuals with HTN, particularly in Asian populations, remains poorly understood ( 21 ). Therefore, this study aimed to identify individuals with NOAF in a large cohort of Thai adults with HTN and identify the associated factors, including demographics, comorbidities, and medication use. Methods Study design & subjects This retrospective cohort analysis utilized data from the Clinical Epidemiology and Biostatistics (CEB) HTN Data Warehouse at the Faculty of Medicine Ramathibodi Hospital, Mahidol University (Bangkok, Thailand). The database included electronic medical records of routine patients from January 2010 to December 2023. Our analysis focused on demographic information, comorbidities, and medication usage. The study protocol was approved by the Human Research Ethics Committee of the Faculty of Medicine Ramathibodi Hospital (COA.MURA 2024/681). The CEB database for 2010-23 contained 293,798 patients with HTN diagnosed at Ramathibodi Hospital. These patients were initially identified based on ICD-10 codes and/or the use of anti-hypertensive medications, such as angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), calcium channel blockers (CCBs), beta-blockers (BBs), alpha-blockers, and diuretics. Subjects included in our analysis met the following inclusion criteria: ( 1 ) age 18 years or older; ( 2 ) newly diagnosed with HTN without any complications; ( 3 ) no prior medical history of AF; and ( 4 ) at least one follow-up visit more than 30 days post-diagnosis. The exclusion criterion was the presence of pre-existing conditions that could predispose individuals to AF, such as valvular heart disease or hyperthyroidism. Data Collection Covariates potentially associated with NOAF were extracted from the master cohort database, including age, sex, body mass index (BMI, kg/m 2 ), and comorbidities at baseline and follow-up (using ICD-10 codes, as detailed in Supplementary Table 1). The following definitions were used: diabetes mellitus (DM), defined by fasting plasma glucose ≥ 126 mg/dL, HbA1c ≥6.5% on two consecutive tests, or the use of anti-diabetic medications ( 22 ); dyslipidemia, defined by total cholesterol ≥ 200 mg/dL, LDL-C ≥ 130 mg/dL, or the use of statins or other lipid-lowering medications; chronic kidney disease (CKD), defined by an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m 2 on two consecutive occasions within 90 days; a history of vascular diseases (including coronary artery disease, upper and lower extremity peripheral arterial disease, carotid disease, renal artery disease, and aortic disease; heart failure, determined by echocardiogram with a left ventricular ejection fraction 6 mg/dL in women and > 7 mg/dL in men, or the use of uric-lowering medications. These comorbidities were cross-validated with other fields from the CEB Warehouse data set, where available. Medication usage data were retrieved from prescriptions and pharmacy dispensing records. These included antihypertensive drugs (as listed above), mineralocorticoid receptor antagonists (MRAs, e.g., spironolactone), diabetic medications [e.g., sulfonylureas, metformin, thiazolidinediones, dipeptidyl peptidase-4 inhibitors, alpha-glucosidase inhibitors, sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonist (GLP-1RA), and insulin], statins, antiarrhythmic drugs (e.g., amiodarone, digoxin, non-dihydropyridine CCBs such as verapamil and diltiazem), antiplatelet agents (e.g., aspirin, clopidogrel, ticagrelor, prasugrel), and oral anticoagulants (OACs) (e.g., warfarin and direct oral anticoagulants). All medications were classified by their generic names and drug classes. Data on these variables, collected at baseline and during follow-up visits prior to the development of NOAF (treated as time-varying covariates), were included in the analysis. Outcomes of Interest and Follow-Up NOAF was the primary outcome of this analysis. It was identified using the CEB AF data warehouse (Supplementary Figure 1) through the following steps: 1) data were retrieved from three sources: ICD-10 codes (I48*), ECG/Holter reports, and prescriptions of OACs (warfarin, dabigatran, rivaroxaban, apixaban, edoxaban); 2) ICD-10 and ECG/Holter data were linked using hashed hospital number and date (within 90 days); 3) patients with ECG/Holter-confirmed AF (with or without ICD-10 codes) were considered NOAF cases; 4) patients diagnosed with ICD-10 alone were linked to OAC prescriptions and any OAC use was confirmed in NOAF patients. Those without OAC use were verified via electrical medical records. The follow-up end date for each patient was either the NOAF diagnosis date (ICD-10 or ECG/Holter, whichever came first) or December 31, 2023. Time to NOAF or censoring was then calculated by subtracting the end date from the baseline date (the date when HTN was diagnosed). Statistical analysis Continuous data were expressed as means with standard deviations (SDs), while categorical data were presented as counts (n) and percentages. A Cox proportional hazards model with time-varying covariates was used to identify factors associated with NOAF. Initially, a univariate Cox model was constructed by fitting each covariate to NOAF. Covariates with p-values less than 0.20 were then included in a multivariate Cox model. A likelihood ratio test was used to select and retain only the significant covariates for the final model. Potential interaction effects between covariates were also explored where appropriate. The probabilities of NOAF occurrence were estimated, and hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. A sensitivity analysis was subsequently performed, considering death from any cause as a competing risk event. The Fine and Gray sub-hazard model (SHD) with time-varying covariates was applied to minimize survival bias related to the occurrence of NOAF, accounting for the possibility that individuals may have died before developing NOAF. Sub-distribution hazard ratios (SHRs) and 95% CIs were estimated. All statistical analyses were conducted using RStudio and STATA 18, with a p-value of < 0.05 considered statistically significant. Results Baseline characteristics of subjects Of the 293,798 patients initially identified with HTN (Supplementary Figure 2), 168,441 met the inclusion criteria and were included in the cohort for analysis ( Figure 1 ). The baseline characteristics of 63,045 newly diagnosed hypertensive patients were available and are presented in Table 1 . The cohort was predominantly female (58.7%), with a mean age of 59.5±14.6 years. Most patients were aged 60–79 years (48.1%) or over 80 years (8.2%). Based on the BMI cutoffs for Asians, 36.8% of participants were classified as overweight (BMI > 23 kg/m²), and 33.4% were classified as obese (BMI > 27 kg/m²). The prevalences of comorbid conditions were as follows: type 2 DM (20.4%), dyslipidemia (4.5%), CKD (13.2%), vascular diseases (15.0%), heart failure (0.2%), stroke (4.2%), and hyperuricemia (13.1%). Download figure Open in new tab Figure 1. Study Flowchart View this table: View inline View popup Download powerpoint Table 1. Distribution of Baseline and Time-Varying Data At baseline, most patients (61.3%) were treated with antihypertensive medications, with 37.0% receiving monotherapy and 24.3% receiving combination therapy. In contrast, 38.7% of patients were recommended lifestyle modifications alone, without antihypertensive drugs. MRAs were used by only 1.8% of patients, and one-third of patients were treated with statins. All diabetic patients were on medications, with 1.2% receiving SGLT2i or GLP-1RA. Antithrombotic agents were prescribed to 18.4% of patients (13.7% received antiplatelets, and 4.7% received OACs), while 2.2% of patients were using antiarrhythmic drugs. Data from 99,408 patients were available during the follow-up period. When comparing baseline and time-varying data ( Table 1 ), there were increases in the frequencies of older age, overweight/obesity, and comorbidities, including vascular disease (from 15.0% to 25.3%), stroke (from 4.2% to 5.9%), heart failure (from 0.2% to 1.0%), OSA (from 4.0% to 6.7%), and type 2 DM (from 19.1% to 27.2%). Regarding treatments during follow-up, the use of statins and antidiabetic medications increased over time, but there were no significant changes in the use of MRAs, antihypertensive drugs, antithrombotic agents, or antiarrhythmic drugs. Incidence and Predictors of NOAF Of 168,441 patients with HTNs, 5,028 (3.0%) were diagnosed with NOAF during follow-up (median of 3.7 years; range 2.2–8.0), yielding an incidence of 5.7 per 1,000 person-years. Univariate analysis for time-varying covariates (n=99,408) identified age as one of the strongest risk factors for the occurrence of NOAF ( Table 2 ). Patients aged > 80 years had a hazard ratio (HR) of 6.4 (95% CI: 5.6–7.3), while those aged 60-79 years had an HR of 2.4 (2.2–2.7). Male gender was also associated with increased risk [HR 1.4 (1.3–1.5)]. Unexpectedly, low body weight (BMI <18.5 kg/m 2 ) was linked to higher NOAF risk [HR 1.5 (1.2-1.8], while overweight and obesity were associated with lower risk (HRs ∼0.9 vs. normal BMI). View this table: View inline View popup Table 2. Univariate Analysis of Time-varying Covariates Comorbidities associated with higher NOAF risk included pre-existing vascular diseases [HR 2.5 (2.3–2.7)], stroke [HR 1.9 (1.7–2.2)], heart failure [HR 5.2 (4.3–6.4)], CKD [HR 1.0 (4.3–6.4)], OSA [HR 3.2 (2.9–3.5)], and hyperuricemia [HR 1.9 (1.7–2.1)]. Regarding treatments, combination antihypertensive therapy was more strongly associated with NOAF than monotherapy (both compared to no treatment), with HRs of 3.3 (3.0–3.6) and 1.8 (1.6–2.0), respectively. Other medications associated with NOAF included MRAs [HR 3.6 (3.0–4.3)], antidiabetic medications [HR 1.2 (1.1–1.3)], antidiabetic medications combined with SGLT2i or GLP-1RA [HR 1.4 (1.1–1.7)], and statins [HR 1.5 (1.4–1.6)]. All antithrombotic and antiarrhythmic agents were also associated with a higher risk of NOAF, as shown in Table 2 . A multivariate time-varying Cox model ( Table 3 and Figure 2 ) identified 11 factors significantly associated with increased NOAF risk: advanced age, male sex, BMI, five pre-existing comorbidities (vascular disease, heart failure, CKD, hyperuricemia, and stroke), antihypertensive medication use, type 2 diabetes, and statin use. Download figure Open in new tab Figure 2. Forest Plot of factors associated with AF in hypertension: A multivariable time-varying Cox model View this table: View inline View popup Table 3. Factors associated with AF using time-varying data: A Multivariate Cox Proportional Hazards Model An interaction effect between age and BMI was observed (Supplementary Table 2). The mean age (±SD) was lower among obese patients (60.4 ± 13.3 years) and higher among those with low BMI (64.1 ± 18.9 years) compared to patients with normal BMI (62.5 ± 15.5 years). Additionally, the proportion of patients under 60 years of age was highest in the obese group (41.5%), while the proportion of patients over 80 years of age was highest in the low BMI group (24.2%). Based on these findings, an interaction term between age and BMI was included in the final model (Likelihood ratio: Chi-square = 17.1 (df=6), p = 0.009). Among patients with normal BMI, advanced age was the strongest predictor of NOAF, followed by vascular disease, CHF, CKD, hyperuricemia, and stroke. In individuals under 60 with normal BMI, low BMI was significantly associated with increased NOAF risk [HR 2.3 (1.4–3.6)], while overweight showed a non-significant slight increase in risk [HR 1.1 (0.8–1.4)]. In contrast, obesity was associated with a non-significant trend toward reduced risk [HR 0.8 (0.6–1.1)]. In patients aged 60 years or older, the risk of NOAF increased significantly by approximately 2- to 3-fold among those aged 60–79 with low BMI or who were overweight or obese and by about 3- to 4-fold in patients aged 80 and above across all three BMI categories, compared to those with normal BMI. The presence of comorbidities (vascular disease, CHF, CKD, hyperuricemia, or stroke) was associated with a 1.2- to 1.8-fold increased risk of NOAF. Antihypertensive monotherapy and combination therapy were linked to 1.7-fold and 2.4-fold increased risks, respectively. Conversely, statin use was associated with a 20% reduction in NOAF risk [HR 0.8 (0.7–0.9)]. Combined antidiabetic therapy involving SGLT2i or GLP-1RA showed a non-significant trend toward reduced risk [HR 0.8 (0.7–1.1)]. A sensitivity analysis using a competing risk model was conducted. The overall death rate was 1.5 per 1000 patients per year. The SHRs and their corresponding 95% CIs for risk factors were estimated and showed similar effect sizes to those found in the survival model (see Supplementary Table 3). Discussion This large-scale, real-world cohort study of 168,441 hypertensive patients in Thailand, with a median follow-up of 3.7 years, provides critical insights into the incidence and risk factors of NOAF. Our findings identified eight factors associated with increased risk of NOAF, some aligning with current literature, while others revealed novel or unexpected associations. This study provides valuable insights into the specific risk factors for NOAF in Thai and Southeast Asian populations, offering crucial data to guide clinical management for these at-risk groups. Notably, statin use was linked to a reduced risk of NOAF, while the effects of SGLT2i and GLP-1RA were not statistically significant. The overall incidence of NOAF in this cohort was 5.7 per 1,000 person-years, which is lower than published global estimates (6.3-9.9 per 1,000 person-years) ( 17 , 24 , 25 ). This discrepancy may be attributed to factors such as differences in healthcare access, lifestyle, genetic background, HTN management in Thailand, and the relatively short median follow-up period (3.7 years) compared to other studies (7-10 years). Shorter follow-up periods may not capture all NOAF cases, as some cases develop over a longer duration. Despite the lower incidence, NOAF remains a significant concern due to its impact on long-term cardiovascular outcomes, underscoring the need for longer-term studies to better understand lifetime risks. Consistent with previous studies, older age and male gender were identified as significant risk factors for the development of NOAF in hypertensive patients ( 17 , 20 , 21 , 26 ). In older individuals, the mechanisms underlying AF primarily involve structural changes in the atria due to aging, such as atrial fibrosis, reduced cellular connectivity, loss of atrial muscle mass, and electrical remodeling ( 27 ). These changes create a substrate that promotes irregular heart rhythms, often exacerbated by altered autonomic function and comorbidities that are more common in older individuals and men. Both electrical remodeling and the effect of sex hormones, along with structural changes like atrial fibrosis, contribute to gender differences in AF ( 28 ). Further research—including the inclusion of smoking and alcohol use— is needed to fully understand the mechanisms behind these gender-based disparities in AF development. Our findings highlight the importance of early detection and management of AF risk factors in older hypertensive male patients, especially those with multiple comorbidities, to reduce the risk of NOAF and its associated complications. Another important finding in our study was the association between pre-existing comorbidities (i.e., vascular diseases, heart failure, and stroke) and an increased risk of NOAF. These comorbid conditions likely contribute to the development of AF through well-established pathophysiologic mechanisms, including increased left atrial pressure, atrial fibrosis, and altered blood flow dynamics. These risk factors are integral components of the CHA2DS2-VASc score, which is used to predict the risk of stroke in AF patients ( 29 ). The CHA2DS2-VASc score has also been directly associated with the incidence of NOAF in the general population. Elderly patients with significant changes in their CHA2DS2-VASc score are more likely to develop incident AF ( 30 ). These findings emphasize the importance of careful monitoring and comprehensive management of hypertensive patients, addressing not only BP but also these underlying comorbidities to mitigate the risk of NOAF. Similar to previous studies ( 31 , 32 ), we found that CKD and hyperuricemia are additional risk factors for NOAF in hypertensive patients. CKD activates the renin-angiotensin-aldosterone and sympathetic nervous systems while also inducing oxidative stress, systemic inflammation, and volume overload ( 33 ). CKD can lead to hyperuricemia by reducing uric acid excretion, and both conditions often share common underlying pathologies. Hyperuricemia, in turn, contributes to oxidative stress, inflammation, atrial fibrosis, and potential disruption of electrical signaling ( 34 ). These factors strongly correlate with atrial electrical changes and structural remodeling, which can promote and sustain AF. The possible role of uric acid-lowering agents as an upstream therapy for HTN to reduce the burden of AF is an important issue for future research. Effective control of HTN is crucial for reducing the risk of AF. However, we observed an increased risk of NOAF in hypertensive patients using multiple antihypertensive medications. This may be due to factors such as the severity of HTN, comorbidities, clinical inertia, lack of adherence, electrolyte imbalances, autonomic regulation, and/or drug interactions. Some antihypertensive medications, such as diuretics and alpha-blockers, may raise AF risk by affecting electrolyte balance ( 35 ). Alpha-blockers, in particular, can cause orthostatic hypotension, potentially triggering arrhythmias ( 36 ). In contrast, ACEIs, ARBs, and BBs may offer protection against AF ( 37 ). The beneficial effects of ACEIs and ARBs on the development of AF are thought to be related to atrial electrical remodeling, while BBs help lower BP and reduce heart rate, especially in patients with HTN and rapid AF. Further research is needed to evaluate the benefits of different antihypertensive therapies in preventing AF. The Systolic Blood Pressure Intervention Trial (SPRINT) ( 38 ) demonstrated that lowering systolic BP to a target of less than 120 mm Hg (intensive BP lowering) significantly reduced the incidence of cardiovascular events, including AF, compared to a standard target of less than 140 mm Hg in hypertensive patients. This underscores the benefit of more aggressive BP control in reducing cardiovascular events, including AF. In our study, the average number of antihypertensive medications remained unchanged from baseline to follow-up, possibly reflecting a lack of dosage adjustment. Therefore, careful management of HTN—including determining the optimal BP target, selecting appropriate medications, adjusting dosages, and monitoring for adverse effects —particularly in the elderly, may be crucial to minimizing the risk of AF. Our study found that statin use was associated with a reduced risk of NOAF, aligning with previous research that links statin therapy to a lower incidence of both the onset and recurrence of AF ( 39 , 40 ). In addition to their well-known cholesterol-lowering effects, statins possess pleiotropic properties—including anti-inflammatory, anti-fibrotic, and endothelial-protective actions—which may help prevent atrial remodeling and reduce the risk of arrhythmias ( 41 ). These findings suggest that statins may play a role in preventing NOAF in hypertensive patients, offering benefits beyond their traditional cardiovascular effects. However, the evidence remains inconclusive, particularly among individuals without ischemic vascular disease ( 42 ). Although longitudinal studies in population cohorts support the role of statins in the primary prevention of AF ( 43 , 44 ), their effect on reducing NOAF specifically in hypertensive populations remains uncertain. Further randomized controlled trials (RCTs) are needed to clarify this relationship. Nonetheless, statins should be considered for patients who develop NOAF, as their use has been associated with a lower risk of ischemic and hemorrhagic stroke, transient ischemic attack, all-cause mortality, and major cardiovascular events ( 45 ). SGLT2i or GLP-1RA, known for their glucose-lowering and cardiovascular protective effects ( 46 – 48 ), were associated with a reduced risk of NOAF in our study. However, the results were not statistically significant. Both drug classes improve endothelial function, reduce inflammation, and help attenuate heart failure progression—mechanisms that may contribute to a lower incidence of AF ( 49 , 50 ). The lack of statistical significance may be due to these drugs’ novelty and high cost, resulting in low usage (1.2%) and a limited sample size, thus insufficient power to detect protective effects. Recent meta-analyses of RCTs have shown that SGLT2i or GLP-1RA significantly reduces the risk of NOAF in both diabetic and non-diabetic patients ( 51 – 53 ). However, the potential benefit of these drugs in reducing AF risk in hypertensive patients requires further investigation in cohorts with longer periods of follow-up. A novel and clinically relevant finding was the age-dependent association between BMI and NOAF. Among individuals under 60, low BMI was linked to higher NOAF risk. Conversely, in older adults, overweight and obesity were associated with increased NOAF risk, consistent with evidence linking adiposity to atrial enlargement, diastolic dysfunction, and pro-inflammatory states ( 20 , 54 , 55 ). These results underscore the need for age-specific risk stratification, as uniform BMI thresholds may be insufficient for accurate cardiovascular risk prediction. Both underweight and obese individuals are at increased risk of AF, with underweight status emerging as an independent and previously under-recognized risk factor. This finding is supported by a large-scale, nationwide study conducted in Korea, which analyzed health data from 132,063 individuals aged 40 and older over a median follow-up period of 9 years ( 56 ). The study found a U-shaped relationship between BMI and AF risk: each 1.0 kg/m 2 decrease below a BMI of 20 increased the risk of AF by 13%, while each 1.0 kg/m 2 increase above 20 raised the risk by 6%. Compared to individuals of normal weight, those who were underweight had a 23% higher risk of developing AF. The elevated risk in underweight individuals may be associated with factors such as frailty, malnutrition, or chronic illness—conditions that can contribute to autonomic dysregulation and atrial remodeling. Furthermore, a sensitivity analysis using a competing risk model, with death as a competing event, showed similar trends in age-BMI effects, suggesting that survival bias due to death before AF onset did not influence the results. Additional factors, such as thyroid disease, chronic lung disease, cancer, smoking, alcohol use, and physical activity, may also play a role. These findings highlight the importance of maintaining a healthy BMI for the prevention of AF ( 57 ). Another important but potentially underrecognized factor is OSA, which contributes to AF through intermittent hypoxia and sympathetic activation ( 58 , 59 ). While OSA was not significantly associated with NOAF in our study, it may have been underdiagnosed. Prior evidence shows that continuous positive airway pressure therapy can reduce AF recurrence by 42% ( 60 ), underscoring the need for better screening and management of OSA in hypertensive patients. This study fills a crucial knowledge gap by providing region-specific data on NOAF in Asian hypertensive populations. It supports a comprehensive, individualized approach to AF prevention, emphasizing optimal hypertension control, weight management, treatment of comorbidities, and appropriate use of cardiovascular medications. Study Limitations This is the first large cohort study to investigate factors associated with atrial fibrillation (AF) in Asian patients with hypertension. However, several limitations should be acknowledged. First, as a retrospective cohort study conducted at a tertiary care hospital, there is a risk of selection bias, as the study population may not fully represent the broader hypertensive population. Second, our analysis was based on complete data from 99,408 out of 168,441 patients. The reliance on electronic health records introduces the potential for information bias due to inaccuracies or incomplete documentation. Third, although we adjusted for several potential confounders, unmeasured variables—such as lifestyle factors (e.g., physical inactivity, diet, smoking, and alcohol consumption) or other interventions—may have influenced the outcomes. Additionally, while the findings may be generalizable to Thai populations in similar healthcare settings, they may not be applicable to other populations due to differences in genetic background, cultural practices, and healthcare systems. Finally, with a median follow-up of 3.7 years, the study may not have captured cases of late-onset NOAF, highlighting the need for longer-term follow-up in future research. Conclusions Older age, male sex, abnormal BMI (both underweight and overweight/obese), and comorbidities are significant risk factors for NOAF in Thai patients with HTN. Statins may have a protective effect, while the role of SGLT2i or GLP-1RA requires further study. Long-term prospective studies are warranted to confirm these findings and guide prevention strategies. Data Availability Data are not publicly available due to sensitivity but can be requested from the corresponding author, Thinnakrit Sasiprapha (Division of Cardiology, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Ratchathewi, Bangkok, Thailand 10400; email: [insert email]). Data are stored at the CEB Data Warehouse, Ramathibodi Hospital Data availability The data supporting the findings of this study are not publicly available due to their sensitive nature. However, they can be obtained from the corresponding author upon reasonable request. The data are stored in a controlled-access repository at the Department of Clinical Epidemiology and Biostatistics (CEB), Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand. Further information regarding the CEB Data Warehouse can be found at https://www.rama.mahidol.ac.th/ceb/CEBdatawarehouse/Overview . Author Contributions V.L. conceptualizations, methodology, data analysis, interpretation, manuscript writing. T.S. methodology, study design, data acquisition, data analysis, interpretation, manuscript revision. H.T. methodology, study design, data acquisition, data analysis, interpretation, manuscript revision. A.P. methodology, data acquisition. S.S. methodology, data acquisition. S.B. methodology, data acquisition. A.T. methodology, data analysis, interpretation, manuscript revision. All authors read and approved the final manuscript. Completing interests The authors had no relevant financial relationships with the medical or pharmaceutical industries. Funding statement This work was supported by the National Research Council of Thailand (N42A640323). View this table: View inline View popup The RECORD statement – checklist of items, extended from the STROBE statement, that should be reported in observational studies using routinely collected health data Acknowledgments The study team thanks Dr. Arthur Brown for reviewing the manuscript and providing valuable comments, and CEB Data Warehouse Working Group for their expert advice and support throughout this work. References 1. ↵ Mills KT , Stefanescu A , He J . The global epidemiology of hypertension . Nat Rev Nephrol 2020 ; 16 : 223 – 237 . doi: 10.1038/s41581-019-0244-2 OpenUrl CrossRef PubMed 2. ↵ Mills KT , Bundy JD , Kelly TN , Reed JE , Kearney PM , Reynolds K , Chen J , He J . Global disparities of hypertension prevalence and control . Circulation 2016 ; 134 : 441 – 450 . doi: 10.1161/CIRCULATIONAHA.115.018912 OpenUrl Abstract / FREE Full Text 3. ↵ World Heath Organization . Hypertension World Health Organization Newsroom: World Health Organization (WHO) 2023 . Available at: https://www.who.int/news-room/fact-sheets/detail/hypertension (Updated March 16 , 2023 ; Cited March 29, 2023 ). 4. ↵ Sukonthasarn A , Audhya R SS , Chattranukulchai P , Roubsanthisuk W , Saengwattanaroj S et al. Thai Guidelines on the Treatment of Hypertension . 2019 . 5. ↵ World Health Organization . Hypertension care in Thailand: best practices and challenges, 2019 . Bangkok : World Health Organization Country Office for Thailand 2019 . 6. ↵ Aune D , Mahamat-Saleh Y , Kobeissi E , Feng T , Heath AK , Janszky I . Blood pressure, hypertension and the risk of atrial fibrillation: a systematic review and meta-analysis of cohort studies . Eur J Epidemiol 2023 ; 38 : 145 – 178 . doi: 10.1007/s10654-022-00914-0 OpenUrl CrossRef PubMed 7. ↵ Lee SR , Park CS , Choi EK , Ahn HJ , Han K Do , Oh S , Lip GYH . Hypertension burden and the risk of new-onset atrial fibrillation: A nationwide population-based study . Hypertension 2021 ; 77 : 919 – 928 . doi: 10.1161/HYPERTENSIONAHA.120.16659 OpenUrl CrossRef 8. ↵ Healey JS , Connolly SJ . Atrial fibrillation: hypertension as a causative agent, risk factor for complications, and potential therapeutic target . Am J Cardiol 2003 ; 91 : 9 – 14 . doi: 10.1016/S0002-9149(03)00227-3 OpenUrl CrossRef 9. ↵ Ogunsua AA , Shaikh AY , Ahmed M , McManus DD . Atrial fibrillation and hypertension: mechanistic, epidemiologic, and treatment parallels . Methodist DeBakey Cardiovasc J 2015 ; 11 : 228 – 234 . doi: 10.14797/mdcj-11-4-228 OpenUrl CrossRef PubMed 10. ↵ Verdecchia P , Angeli F , Reboldi G . Hypertension and atrial fibrillation: Doubts and certainties from basic and clinical studies . Circ Res 2018 ; 122 : 352 – 368 . doi: 10.1161/CIRCRESAHA.117.311402 OpenUrl Abstract / FREE Full Text 11. ↵ Schulz E , Gori T , Münzel T . Oxidative stress and endothelial dysfunction in hypertension . Hypertens Res 2011 ; 34 : 665 – 673 . doi: 10.1038/hr.2011.39 OpenUrl CrossRef PubMed 12. Ihara K , Sasano T . Role of inflammation in the pathogenesis of atrial Fibrillation . Front Physiol 2022 ; 13 : 862164 . doi: 10.3389/fphys.2022.862164 OpenUrl CrossRef 13. ↵ Maida CD , Vasto S , Di Raimondo D , Casuccio A , Vassallo V , Daidone M , Del Cuore A , Pacinella G , Cirrincione A , Simonetta I , et al. Inflammatory activation and endothelial dysfunction markers in patients with permanent atrial fibrillation: a cross-sectional study . Aging 2020 ; 12 : 8423 – 8433 . doi: 10.18632/aging.103149 OpenUrl CrossRef PubMed 14. ↵ Aronow WS . Hypertension associated with atrial fibrillation . Ann Transl Med 2017 ; 5 : 457 – 457 . doi: 10.21037/atm.2017.10.33 OpenUrl CrossRef PubMed 15. ↵ Healey JS , Baranchuk A , Crystal E , Morillo CA , Garfinkle M , Yusuf S , Connolly SJ . Prevention of atrial fibrillation with angiotensin-converting enzyme inhibitors and angiotensin receptor blockers . J Am Coll Cardiol 2005 ; 45 : 1832 – 1839 . doi: 10.1016/j.jacc.2004.11.070 OpenUrl FREE Full Text 16. ↵ Larstorp ACK , Stokke IM , Kjeldsen SE , Hecht Olsen M , Okin PM , Devereux RB , Wachtell K . Antihypertensive therapy prevents new-onset atrial fibrillation in patients with isolated systolic hypertension: the LIFE study . Blood Press 2019 ; 28 : 317 – 326 . doi: 10.1080/08037051.2019.1633905 OpenUrl CrossRef PubMed 17. ↵ Schnabel RB , Sullivan LM , Levy D , Pencina MJ , Massaro JM , D’Agostino RB , Newton-Cheh C , Yamamoto JF , Magnani JW , Tadros TM , et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study . Lancet 2009 ; 373 : 739 – 745 . doi: 10.1016/S0140-6736(09)60443-8 OpenUrl CrossRef PubMed Web of Science 18. Chamberlain AM , Agarwal SK , Folsom AR , Soliman EZ , Chambless LE , Crow R , Ambrose M , Alonso A . A clinical risk score for atrial fibrillation in a biracial prospective cohort (from the Atherosclerosis Risk In Communities [ARIC] Study) . Am J Cardiol 2011 ; 107 : 85 – 91 . doi: 10.1016/j.amjcard.2010.08.049 OpenUrl CrossRef PubMed Web of Science 19. ↵ Alonso A , Krijthe BP , Aspelund T , Stepas KA , Pencina MJ , Moser CB , Sinner MF , Sotoodehnia N , Fontes JD , Janssens ACJW , et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF Consortium . J Am Heart Assoc 2013 ; 2 : e000102 . doi: 10.1161/JAHA.112.000102 OpenUrl Abstract / FREE Full Text 20. ↵ Orozco-Beltran D , Quesada JA , Bertomeu-Gonzalez V , Lobos-Bejarano JM , Navarro-Perez J , Gil-Guillen VF , Garcia Ortiz L , Lopez-Pineda A , Castellanos-Rodriguez A , Lopez-Domenech A , et al. A new risk score to assess atrial fibrillation risk in hypertensive patients (ESCARVAL-RISK Project . Sci Rep 2020 ; 10 : doi: 10.1038/s41598-020-61437-w OpenUrl CrossRef 21. ↵ Krittayaphong R , Rangsin R , Thinkhamrop B , Hurst C , Rattanamongkolgul S , Sripaiboonkij N , Yindeengam A . Prevalence and associating factors of atrial fibrillation in patients with hypertension: a nation-wide study . BMC Cardiovasc Disord 2016 ; 16 : 57 . doi: 10.1186/s12872-016-0232-4 OpenUrl CrossRef 22. ↵ Looareesuwan P , Boonmanunt S , Siriyotha S , Lukkunaprasit T , Thammasudjarit R , Pattanaprateep O , Nimitphong H , Reutrakul S , Attia J , McKay G , et al. Retinopathy prediction in type 2 diabetes: Time-varying Cox proportional hazards and machine learning models . Inform Med Unlocked 2023 ; 40 : 101285 . doi: 10.1016/j.imu.2023.101285 OpenUrl CrossRef 23. ↵ Lolak S , Attia J , McKay GJ , Thakkinstian A . Application of dragonnet and conformal inference for estimating individualized treatment effects for personalized stroke prevention: Retrospective cohort study . JMIR Cardio 2025 ; 9 : e50627 . doi: 10.2196/50627 OpenUrl CrossRef 24. ↵ Wilke T , Groth A , Mueller S , Pfannkuche M , Verheyen F , Linder R , Maywald U , Bauersachs R , Breithardt G . Incidence and prevalence of atrial fibrillation: an analysis based on 8.3 million patients . Europace 2013 ; 15 : 486 – 493 . doi: 10.1093/europace/eus333 OpenUrl CrossRef PubMed Web of Science 25. ↵ Segan L , Canovas R , Nanayakkara S , Chieng D , Prabhu S , Voskoboinik A , Sugumar H , Ling L-H , Lee G , Morton J , et al. New-onset atrial fibrillation prediction: the HARMS2-AF risk score . Eur Heart J 2023 ; 44 : 3443 – 3452 . doi: 10.1093/eurheartj/ehad375 OpenUrl CrossRef PubMed 26. ↵ Heeringa J , van der Kuip DAM , Hofman A , Kors JA , van Herpen G , Stricker BHCh , Stijnen T , Lip GYH , Witteman JCM . Prevalence, incidence and lifetime risk of atrial fibrillation: the Rotterdam study . Eur Heart J 2006 ; 27 : 949 – 953 . doi: 10.1093/eurheartj/ehi825 OpenUrl CrossRef PubMed Web of Science 27. ↵ Curtis AB , Rich MW . Atrial fibrillation in the elderly: Mechanisms and management . Heart Rhythm 2007 ; 4 : 1577 – 1579 . doi: 10.1016/j.hrthm.2007.07.023 OpenUrl CrossRef PubMed 28. ↵ Westerman S , Wenger N . Gender differences in atrial fibrillation: A review of epidemiology, management, and outcomes . Curr Cardiol Rev 2019 ; 15 : 136 – 144 . doi: 10.2174/1573403X15666181205110624 OpenUrl CrossRef PubMed 29. ↵ Saliba W , Gronich N , Barnett-Griness O , Rennert G . Usefulness of CHADS2 and CHA2DS2-VASc Scores in the prediction of new-onset atrial fibrillation: A population-based study . Am J Med 2016 ; 129 : 843 – 849 . doi: 10.1016/j.amjmed.2016.02.029 OpenUrl CrossRef PubMed 30. ↵ Zhang W , Chen Y , Hu L-X , Xia J-H , Ye X-F , Cheng Y-B , Wang Y , Guo Q-H , Li Y , Lowres N , et al. Changes in the CHA2DS2-VASC score as a predictor of incident atrial fibrillation in older Chinese individuals: the AF-CATCH study . EHJ Open 2022 ; 2 : 1 – 5 . doi: 10.1093/ehjopen/oeac046 OpenUrl CrossRef 31. ↵ Horio T , Iwashima Y , Kamide K , Tokudome T , Yoshihara F , Nakamura S , Kawano Y . Chronic kidney disease as an independent risk factor for new-onset atrial fibrillation in hypertensive patients . J Hypertens 2010 ; 28 : 1738 – 1744 . doi: 10.1097/HJH.0b013e32833a7dfe OpenUrl CrossRef PubMed Web of Science 32. ↵ Gao Z , Shi H , Xu W , Guan Z , Su X , Guo N , Ma H . Hyperuricemia increases the risk of atrial fibrillation: A systematic review and meta-analysis . Int J Endocrinol 2022 ; 2022 : 8172639 . doi: 10.1155/2022/8172639 OpenUrl CrossRef PubMed 33. ↵ Voroneanu L , Ortiz A , Nistor I , Covic A . Atrial fibrillation in chronic kidney disease . Eur J Intern Med 2016 ; 33 : 3 – 13 . doi: 10.1016/j.ejim.2016.04.007 OpenUrl CrossRef PubMed 34. ↵ Deng Y , Liu F , Yang X , Xia Y . The key role of uric acid in oxidative stress, inflammation, fibrosis, apoptosis, and immunity in the pathogenesis of atrial fibrillation . Front Cardiovasc Med 2021 ; 8 : 641136 . doi: 10.3389/fcvm.2021.641136 OpenUrl CrossRef PubMed 35. ↵ Jong G-P , Chen H-Y , Li S-Y , Liou Y . Long-term effect of antihypertensive drugs on the risk of new-onset atrial fibrillation: a longitudinal cohort study . Hypertens Res 2014 ; 37 : 950 – 953 . doi: 10.1038/hr.2014.104 OpenUrl CrossRef PubMed 36. ↵ Haywood LJ , Ford CE , Crow RS , Davis BR , Massie BM , Einhorn PT , Williard A . Atrial fibrillation at baseline and during follow-up in ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial) . J Am Coll Cardiol 2009 ; 54 : 2023 – 2031 . doi: 10.1016/j.jacc.2009.08.020 OpenUrl FREE Full Text 37. ↵ Mancia G , Kreutz R , Brunström M , Burnier M , Grassi G , Januszewicz A , Muiesan ML , Tsioufis K , Agabiti-Rosei E , Algharably EAE , et al. 2023 ESH Guidelines for the management of arterial hypertension . The Task Force for the management of arterial hypertension of the European Society of Hypertension. J Hypertension 2023 ; 41 : 1874 – 2071 . doi: 10.1097/HJH.0000000000003480 OpenUrl CrossRef PubMed 38. ↵ Soliman EZ , Rahman AF , Zhang Z , Rodriguez CJ , Chang TI , Bates JT , Ghazi L , Blackshear JL , Chonchol M , Fine LJ , et al. Effect of intensive blood pressure lowering on the risk of atrial fibrillation . Hypertension 2020 ; 75 : 1491 – 1496 . doi: 10.1161/HYPERTENSIONAHA.120.14766 OpenUrl CrossRef PubMed 39. ↵ Fang W , Li H , Zhang H , Jiang S . The role of statin therapy in the prevention of atrial fibrillation: a meta-analysis of randomized controlled trials . Br J Clin Pharmacol 2012 ; 74 : 744 – 756 . doi: 10.1111/j.1365-2125.2012.04258.x OpenUrl CrossRef PubMed 40. ↵ Liu T , Li L , Korantzopoulos P , Liu E , Li G . Statin use and development of atrial fibrillation: A systematic review and meta-analysis of randomized clinical trials and observational studies . Int J Cardiol 2008 ; 126 : 160 – 170 . doi: 10.1016/j.ijcard.2007.07.137 OpenUrl CrossRef PubMed Web of Science 41. ↵ Hadi HA , Mahmeed W Al , Suwaidi J Al , Ellahham S . Pleiotropic effects of statins in atrial fibrillation patients: the evidence . Vasc Health Risk Manag 2009 ; 5 : 533 – 551 . doi: 10.2147/VHRM.S4841 OpenUrl CrossRef PubMed 42. ↵ Alves-Cabratosa L , García-Gil M , Comas-Cufí M , Ponjoan A , Martí-Lluch R , Parramon D , Blanch J , Elosua-Bayes M , Ramos R . Statins and new-onset atrial fibrillation in a cohort of patients with hypertension . Analysis of electronic health records, 2006–2015 . PLoS One 2017 ; 12 : e0186972 . doi: 10.1371/journal.pone.0186972 OpenUrl CrossRef PubMed 43. ↵ Hung CY , Lin CH , Wang KY , Huang JL , Hsieh YC , Loh el-W , Lan TH , Chou P , Ting CT , Wu TJ . Dosage of statin, cardiovascular comorbidities, and risk of atrial fibrillation: a nationwide population-based cohort study . Int J Cardiol 2013 ; 168 ( 2 ): 1131 – 1136 . doi: 10.1016/j.ijcard.2012.11.087 OpenUrl CrossRef PubMed 44. ↵ Veronese G , Montomoli J , Schmidt M , Horváth-Puhó E , Sørensen HT . Statin use and risk of atrial fibrillation or flutter: A population-based case-control study . Am J Ther 2015 ; 22 ( 3 ): 186 – 194 . doi: 10.1097/MJT.0b013e31827ab488 OpenUrl CrossRef PubMed 45. ↵ Huang T , Yap L , Chen C , Lin H , Lin S , Li Y . Long-term statin use is associated with reduced rates of adverse events in patients with newly diagnosed atrial fibrillation . J Am Heart Assoc 2024 ; 13 : e035827 . doi: 10.1161/JAHA.124.035827 OpenUrl CrossRef PubMed 46. ↵ Patel SM , Kang YM , Im K , Neuen BL , Anker SD , Bhatt DL , Butler J , Cherney DZI , Claggett BL , Fletcher RA , et al. Sodium-glucose cotransporter-2 inhibitors and major adverse cardiovascular outcomes: A SMART-C collaborative meta-analysis . Circulation 2024 ; 149 : 1789 – 1801 . doi: 10.1161/CIRCULATIONAHA.124.069568 OpenUrl CrossRef PubMed 47. He G , Yang G , Huang X , Luo D , Tang C , Zhang Z . SGLT2 inhibitors for prevention of primary and secondary cardiovascular outcomes: A meta-analysis of randomized controlled trials . Heart & Lung 2023 ; 59 : 109 – 116 . doi: 10.1016/j.hrtlng.2023.02.009 OpenUrl CrossRef PubMed 48. ↵ Lincoff AM , Brown-Frandsen K , Colhoun HM , Deanfield J , Emerson SS , Esbjerg S , Hardt-Lindberg S , Hovingh GK , Kahn SE , Kushner RF , et al. Semaglutide and cardiovascular outcomes in obesity without diabetes . N Engl J Med 2023 ; 389 : 2221 – 2232 . doi: 10.1056/NEJMoa2307563 OpenUrl CrossRef PubMed 49. ↵ Wu J , Liu Y , Wei X , Zhang X , Ye Y , Li W , Su X . Antiarrhythmic effects and mechanisms of sodium-glucose cotransporter 2 inhibitors: A mini review . Front Cardiovasc Med 2022 ; 9 : 915455 . doi: 10.3389/fcvm.2022.915455 OpenUrl CrossRef PubMed 50. ↵ Bohne LJ , Jansen HJ , Dorey TW , Daniel IM , Jamieson KL , Belke DD , McRae MD , Rose RA . Glucagon-like peptide-1 protects against atrial fibrillation and atrial remodeling in type 2 diabetic mice . J Am Coll Cardiol Basic Trans Science 2023 ; 8 : 922 – 936 . doi: 10.1016/j.jacbts.2023.01.005 OpenUrl CrossRef 51. ↵ Pandey AK , Okaj I , Kaur H , Belley-Cote EP , Wang J , Oraii A , Benz AP , Johnson LSB , Young J , Wong JA , et al. Sodium-glucose co-transporter inhibitors and atrial fibrillation: A systematic review and meta-analysis of randomized controlled trials . J Am Heart Assoc 2021 ; 10 : e022222 . doi: 10.1161/JAHA.121.022222 OpenUrl CrossRef PubMed 52. Zhang H-D , Ding L , Mi L-J , Zhang A-K , Zhang K , Jiang Z-H , Yu F-Y , Yan X-X , Shen Y-J , Tang M . Sodium–glucose co-transporter-2 inhibitors for the prevention of atrial fibrillation: a systemic review and meta-analysis . Eur J Prev Cardiol 2024 ; 31 : 770 – 779 . doi: 10.1093/eurjpc/zwad356 OpenUrl CrossRef PubMed 53. ↵ Saglietto A , Falasconi G , Penela D , Francia P , Sau A , Ng FS , Dusi V , Castagno D , Gaita F , Berruezo A , et al. Glucagon-like peptide-1 receptor agonist semaglutide reduces atrial fibrillation incidence: A systematic review and meta-analysis . Eur J Clin Invest 2024 ; 54 : e14292 . doi: 10.1111/eci.14292 OpenUrl CrossRef PubMed 54. ↵ Lavie CJ , Pandey A , Lau DH , Alpert MA , Sanders P . Obesity and atrial fibrillation prevalence, pathogenesis, and prognosis . J Am Coll Cardiol 2017 ; 70 : 2022 – 2035 . doi: 10.1016/j.jacc.2017.09.002 OpenUrl FREE Full Text 55. ↵ Wong CX , Ganesan AN , Selvanayagam JB . Epicardial fat and atrial fibrillation: current evidence, potential mechanisms, clinical implications, and future directions . Eur Heart J 2017 ; 38 : 1294 – 1302 . doi: 10.1093/eurheartj/ehw045 OpenUrl CrossRef PubMed 56. ↵ Kang SH , Choi EK , Han KD , Lee SR , Lim WH , Cha MJ , Cho Y , Oh IY , Oh S . Underweight is a risk factor for atrial fibrillation: A nationwide population-based study . Int J Cardiol . 2016 ; 215 : 449 – 456 . doi: 10.1016/j.ijcard.2016.04.036 . OpenUrl CrossRef PubMed 57. ↵ Pathak RK , Middeldorp ME , Meredith M , Mehta AB , Mahajan R , Wong CX , Twomey D , Elliott AD , Kalman JM , Abhayaratna WP , et al. Long-term effect of goal-directed weight management in an atrial fibrillation cohort: A long-term follow-up study (LEGACY) . J Am Coll Cardiol 2015 ; 65 : 2159 – 2169 . doi: 10.1016/j.jacc.2015.03.002 OpenUrl FREE Full Text 58. ↵ Youssef I , Kamran H , Yacoub M , Patel N , Goulbourne C , Kumar S , Kane J , Hoffner H , Salifu M , McFarlane SI . Obstructive sleep apnea as a risk factor for atrial fibrillation: A meta-analysis . J Sleep Disord Ther 2018 ; 7 : 282 . doi: 10.4172/2167-0277.1000282 OpenUrl CrossRef 59. ↵ Linz D , McEvoy RD , Cowie MR , Somers VK , Nattel S , Lévy P , Kalman JM , Sanders P . Associations of obstructive sleep apnea with atrial fibrillation and continuous positive airway pressure treatment . JAMA Cardiol 2018 ; 3 : 532 – 540 . doi: 10.1001/jamacardio.2018.0095 OpenUrl CrossRef PubMed 60. ↵ Shukla A , Aizer A , Holmes D , Fowler S , Park DS , Bernstein S , Bernstein N , Chinitz L . Effect of obstructive sleep apnea treatment on atrial fibrillation recurrence: A meta-analysis . 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Share Risk Factors for New-Onset Atrial Fibrillation in Thai Adults with Hypertension Varisa Limpijankit , Thinnakrit Sasiprapha , Htun Teza , Anuchate Pattanateepapon , Sukanya Siriyotha , Suparee Boonmanunt , John Attia , Ammarin Thakkinstian medRxiv 2025.05.07.25327197; doi: https://doi.org/10.1101/2025.05.07.25327197 Share This Article: Copy Citation Tools Risk Factors for New-Onset Atrial Fibrillation in Thai Adults with Hypertension Varisa Limpijankit , Thinnakrit Sasiprapha , Htun Teza , Anuchate Pattanateepapon , Sukanya Siriyotha , Suparee Boonmanunt , John Attia , Ammarin Thakkinstian medRxiv 2025.05.07.25327197; doi: https://doi.org/10.1101/2025.05.07.25327197 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Cardiovascular Medicine Subject Areas All Articles Addiction Medicine (567) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4411) Dentistry and Oral Medicine (443) Dermatology (380) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1505) Epidemiology (15205) Forensic Medicine (30) Gastroenterology (1119) Genetic and Genomic Medicine (6574) Geriatric Medicine (666) Health Economics (994) Health Informatics (4511) Health Policy (1365) Health Systems and Quality Improvement (1608) Hematology (537) HIV/AIDS (1263) Infectious Diseases (except HIV/AIDS) (15903) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (666) Neurology (6573) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1139) Occupational and Environmental Health (954) Oncology (3319) Ophthalmology (968) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (662) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5422) Public and Global Health (9205) Radiology and Imaging (2191) Rehabilitation Medicine and Physical Therapy (1367) Respiratory Medicine (1191) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9feb57bb4fd6ad07',t:'MTc3OTI4MDEyMg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();