Prognostic Impact of Body Mass Index in Atrial Fibrillation
by
, , , , , , , , ,
Maria Nteli
1,†
,
Despoina Nteli
1,†,
Dimitrios V. Moysidis
2
,
Anastasia Foka
1,
Panagiotis Zymaris
1,
Triantafyllia Grantza
1,
Olga Kazarli
1,
Alexis Vagianos
1,
Andreas S. Papazoglou
3,
Anastasios Kartas
1,
Athanasios Samaras
1,
Alexandra Bekiaridou
1,4,
Efstathios Spyridonidis
2,
Antonios Ziakas
1,
Apostolos Tzikas
5 and
George Giannakoulas
1,*
1
First Department of Cardiology, AHEPA University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
2
424 General Military Hospital of Thessaloniki, 56429 Thessaloniki, Greece
3
Athens Naval Hospital, 11521 Athens, Greece
4
Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, USA
5
Interbalkan European Medical Center, 55535 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Clin. Med. 2024, 13(11), 3294; https://doi.org/10.3390/jcm13113294
Submission received: 7 May 2024
/
Revised: 30 May 2024
/
Accepted: 31 May 2024
/
Published: 3 June 2024
(This article belongs to the Special Issue Recent Advances and Emerging Therapeutic Approaches for the Treatment of Heart Failure)
Abstract
:Background/Objectives: Contradictory results have been reported regarding the influence of obesity on the prognosis of atrial fibrillation (AF). The present study aimed to explore the potential association of body mass index (BMI) with the clinical outcomes of hospitalized patients with AF. Methods: In this retrospective, post hoc analysis of the MISOAC-AF randomized trial, 1113 AF patients were included and stratified as the following: underweight (BMI < 18 kg/m2), normal weight (BMI 18–24.9 kg/m2), overweight (BMI 25–29.9 kg/m2), and obese (BMI ≥ 30 kg/m2). The primary outcome was all-cause mortality; the secondary composite outcome was any hospitalization related to AF, heart failure (HF), or stroke. Cox regression analysis, survival analysis, and spline curve models were utilized. Results: Of the patients (median age: 76 years (IQR: 13), male: 54.6%), the majority were overweight (41.4%), followed by obese (33%), normal weight (24%), and underweight (1.6%). During a median 31-month follow-up, 436 (39.2%) patients died and 657 (59%) were hospitalized due to AF, HF, or stroke. Underweight, overweight, and obesity groups were significantly associated with an increased risk of all-cause mortality (p-values 0.02, 0.001, and <0.001, respectively), while overweight and obesity were significantly associated with the composite endpoint (p-values 0.01, <0.001, respectively) compared to normal weight. The spline curve analyses yielded that BMIs > 26.3 and > 25 were incrementally associated with all-cause mortality and the composite endpoint, respectively. A J-shaped relationship between BMI and AF prognosis was deduced. Conclusions: In conclusion, in recently hospitalized AF patients, BMI values outside the normal range were independently associated with poorer prognosis; therefore, it is essential that AF patients maintain a normal weight.
1. Introduction
Atrial fibrillation (AF) constitutes the most frequently observed cardiac arrhythmia [1]. AF is a marker of increased risk for stroke, heart failure (HF), ischemic heart disease, or chronic kidney disease [2,3,4,5]. Obesity, defined by a body mass index (BMI) equal to or greater than 30 kg/m2 [6], has gained epidemic proportions with dramatic consequences worldwide mainly due to lifestyle components such as sedentary behavior, physical inactivity, and dietary habits [7,8]. Many epidemiological studies point out obesity as an independent risk factor for developing AF as well as for increased disease burden [9,10,11,12,13,14,15]. Based on the results of some studies a 10% reduction in weight and a BMI < 27 kg/m2 has been recommended for AF patients to reduce disease burden [16,17]. Nonetheless, the effect of BMI on hard clinical endpoints in patients with AF remains unclear [18]. Several studies indicate that increased BMI might be associated with favorable clinical outcomes in AF and other cardiovascular diseases; a phenomenon known as the “Obesity Paradox” [19,20,21]; however, few publications exist, reaching a contradictory conclusion–namely indicating that overweight/obese individuals with AF do not present with a better prognosis [22,23,24]. Hence, the existing body of evidence on the clinical outcomes of AF in overweight or obese patients warrants further validation. Aiming to add to the existing literature, we investigated the association of obesity (and the whole BMI spectrum) with the risk of all-cause mortality and rehospitalization, using data from a cohort of well-characterized patients with AF.
2. Materials and Methods
2.1. Study Design
This is a retrospective observational study based on the MISOAC-AF (Motivational Interviewing to Support Oral AntiCoagulation adherence in patients with non-valvular Atrial Fibrillation) randomized clinical trial (ClinicalTrial.gov identifier: NCT02941978 accessed on 30 May 2024). The study took place at the cardiology clinic of a tertiary academic hospital located in Thessaloniki, Greece, and its design and main outcomes were detailed in prior publications [25,26]. In summary, MISOAC-AF evaluated the impact of patient–physician interviews and standardized guidance on the adherence of patients with AF to oral anticoagulation (OAC) therapy.
2.2. Data Sources/Study Population
Our analysis included data derived from patients with AF and available BMI values, who were discharged after hospitalization between December 2015 and June 2018. The MISOAC-AF electronic database contained baseline characteristics, medical history, concomitant medication, laboratory and echocardiographic data, discharge diagnoses, and vital status. These data were obtained from electronic hospital records and by physicians who interviewed the patients while they were hospitalized. Every study participant provided written informed consent. The study complied with the 1975 Declaration of Helsinki and received approval from the ethics committee of Aristotle University of Thessaloniki.
2.3. Definitions
AF was defined as an electrocardiographic arrhythmia newly diagnosed or previously documented. Specifically, AF had to be recorded as an irregular heart rhythm lasting more than 30 s, characterized by the absence of sinus P waves and varying R-R intervals. BMI values were derived from the equation weight/(height)2 (kg/m2) of the enrolled patients. Patients were classified according to their BMI at index hospitalization as follows:
- (i)
- under 18: underweight;
- (ii)
- from 18 to 24.9: normal weight;
- (iii)
- from 25 to 29.9: overweight;
- (iv)
- from 30 to 34.9: obese (obese class I);
- (v)
- from 35 to 39.9: severely obese (obese class II);
- (vi)
- equal to or over 40: morbidly obese (obese class III).
2.4. Study Outcomes
The primary outcome was all-cause mortality (death from any cause). Any hospitalization related to AF or HF (e.g., arrhythmia, decompensated HF, pacemaker/defibrillator implantation, hemorrhage, thromboembolism) or stroke constituted the secondary composite outcome. The follow-up process was carried out in the first, second, sixth, and twelfth post-discharge months and then annually until April 2020. The website of the National Insurance System of Greece was utilized for validation of the reported deaths.
2.5. Statistical Analysis
The baseline characteristics of patients were analyzed in relation to BMI. Each continuous and categorical variable was described by mean–standard deviation (SD), median–interquartile range (IQR), and frequency–percentage, respectively. The comparison between continuous variables was conducted using one-way ANOVA or Kruskal–Wallis test, while Pearson’s Chi-squared test or Fisher’s exact test was performed for the comparison between categorical variables. A Kaplan–Meier survival analysis was implemented to examine the association of BMI with the primary and secondary composite outcomes. A log-rank test was utilized to compare the all-cause mortality and hospitalization in different BMI groups. Adjusted hazard ratios (aHR) were calculated through a multivariate Cox regression analysis in which the following—univariately significant or clinically important—variables were included for adjustment: age, gender, smoking, history of HF, history of coronary artery disease, hypertension, diabetes mellitus (DM), CHA2DS2-VASc score, glomerular filtration rate (GFR), left ventricular ejection fraction (LVEF) on transthoracic echocardiogram, and AF subtype (first diagnosed, paroxysmal/atrial flutter, persistent/permanent). The relationship between the continuum of BMI values and the risk of primary and secondary outcomes was illustrated via restricted cubic spline curves. p-values < 0.05 were considered statistically significant. SPSS software, version 26 (IBM SPSS Statistics, IBM Corporation, New York, USA) and R version 3.4.4 (R Foundation for Statistical Computing, Vienna, Austria) were employed for data management, statistical analysis, and graph creation.
3. Results
All of the 1113 patients with AF enrolled in the MISOAC-AF trial fulfilled the inclusion criteria for the present study. Of them, 608 (54.6%) were men. The median age of study participants was 76 years (IQR: 13), while the median BMI of study participants was 27.8 (IQR: 6.3). A total of 18 patients (1.6%) constituted the underweight class (BMI < 18), 267 patients (24%) the normal weight class (BMI 18–24.9), 461 patients (41.4%) the overweight class (BMI 25–29.9), 240 patients (21.6%) the obese class (BMI 30–34.9), 87 patients (7.8%) the severely obese class (BMI 35–39.9), and 40 patients (3.6%) the morbidly obese class (BMI ≥ 40).
Baseline clinical, laboratory, and echocardiographic characteristics are described in Table 1 according to different BMI categories.
In general, underweight patients had no significant differences in comparison to normal-weight patients regarding their baseline characteristics. On the contrary, overweight, obese, severely obese, and morbidly obese patients differed in the prevalence of HF and DM along with the median age when compared to normal-weight patients (p-values < 0.05).
In total, 7 (38.9%) of the 18 underweight patients, 73 (27.3%) of the 267 normal-weight patients, 171 (37.1%) of the 461 overweight patients, 112 (46.7%) of the 240 obese patients, 49 (56.3%) of the 87 severely obese patients, and 24 (60%) of the 40 morbidly obese patients died during a median 2.6-year follow-up. With regards to the secondary outcomes, 11 (61.1%) underweight, 118 (44.2%) normal weight, 269 (58.4%) overweight, 158 (65.8%) obese, 68 (78.2%) severely obese, and 33 (82.5%) morbidly obese patients were hospitalized due to AF-, HF- or stroke-related events.
The Kaplan–Meier curves for all-cause mortality and any hospitalization related to AF, HF, or stroke are shown in Figure 1 (log-rank test: both p-values < 0.001). Multivariate Cox regression analysis yielded that BMI values ≥ 25 were independently associated with higher risk for both the primary and the secondary outcomes (p-values < 0.05) compared to the normal-weight patients, while BMI values < 18 were significantly associated with higher risk for the primary outcome (p-value < 0.05) compared to the normal weight category (Figure 2).
The unadjusted and adjusted spline curves illustrating the risk of death as well as AF-, HF-related hospitalization, or stroke in patients with AF across the BMI spectrum are presented in Figure 3. BMI values from 26.3 to 29.7 and >30.7 positively correlated with the probability for death, while BMI values > 25 emerged as independent predictors of the composite clinical endpoint “any hospitalization related to AF, HF, or stroke”. BMI values < 21.9 were also linked to an enhanced risk of the studied outcomes, albeit not significantly.
4. Discussion
In this real-world analysis, underweight, overweight, and obese patients had an independently increased risk of all-cause mortality and hospitalization related to AF, HF, or stroke when compared to normal-weight patients.
Despite the association between obesity and the risk of AF incidence being already well-established [10,27,28], the exact relationship between BMI and prognosis in AF patients is yet to be fully elucidated, since the relevant studies have reached equivocal results.
Several studies indicate that, unanticipatedly, the medically unfavorable phenotype of BMI ≥ 25 (and especially overweight and obesity class I) conduces to lessened prevalence of all-cause or cardiovascular death and thromboembolism among AF subjects, an observation described in the literature as the “Obesity Paradox” [29,30,31,32]. Our findings are inconsistent with this theory. A couple of meta-analyses affirmed the reduced risk of all-cause death, stroke, and thromboembolism inflicted on overweight and obese AF patients [33,34]. Intriguingly, the observed trends were mainly driven from the secondary analysis of randomized clinical trial data, while observational studies, and especially those with a prolonged follow-up duration, reached more ambiguous conclusions or failed to ascertain the “Obesity Paradox” [32].
Nevertheless, there exists a portion of the bibliography suggesting that BMI does not mediate the AF prognosis [18,35,36] and other researchers yielding results similar to ours, namely demonstrating that elevated BMI is linked to worse prognosis [22,23,24,37]. Specifically, in the latter studies, overweight and obese AF patients seemed to encounter increased thromboembolic risk (a 1 kg/m2 BMI elevation was equivalent to a 9% enhancement of the thromboembolism rates) and even reduced survival rates compared to their thinner counterparts.
There are ongoing discussions over whether the “Obesity Paradox” reflects a factual phenomenon [21,38], although this concept stands true for other heart-related diseases (e.g., hypertension, coronary artery disease, HF) alongside non-cardiovascular maladies (e.g., end-stage renal disease, DM) [21]. Multiple underlying mechanisms have been hypothesized in an attempt to decipher this conundrum. Problems inherent to the methodology utilized by some researchers, such as selection bias, could influence the reliability of the extracted results [39,40]. Moreover, within the overweight/obese groups, common cardiovascular diseases (such as hypertension and dyslipidemia)—including AF itself—may be identified earlier since obesity is a known risk factor for them; therefore, these patients usually receive earlier, more aggressive treatment [39]. Meanwhile, individuals categorized as having a normal or decreased BMI may not be truly healthy but suffer from some illness that leads to catabolism or to a pro-inflammatory state [18,40]. Lastly, there is increasing evidence that the most decisive role when it comes to AF prognosis might be played by cardiorespiratory fitness and not BMI itself [40]. Even in studies confirming the “Obesity Paradox”, it is stated that cardiorespiratory fitness would mitigate—if not neutralize—the paradox, resulting in the so-called “fat but fit” phenomenon [39,40].
The other conclusion of our research, namely that underweight AF patients are at increased risk for mortality and thromboembolism, is in alignment with the preponderance of the existing literature, including recent meta-analyses [32,33,41,42]. Possible pathophysiological mechanisms for this observed “Lean Paradox” include increased frailty and vulnerability to illness due to malnutrition or cachexia; enhanced activation of the renin–angiotensin–aldosterone system in response to stress; worse endothelial function combined with attenuated systemic inflammation; and increased levels of adipokines [33]. It should also be considered that underweight AF patients tend to be older and suffer from other chronic comorbidities (such as cancer) [32].
In light of the aforementioned reasoning, purposeful weight loss seems to be of pivotal importance in patients with established AF (apart from the ones with concomitant HF [43,44,45])—as proposed in the ESC recommendations for AF management [46]—due to alleviating the severity of AF symptomatology and improving recurrence-free survival [47,48]. A multidisciplinary approach should be employed in relation to AF management, incorporating its three traditional pillars (anticoagulation, rhythm control, and rate control) along with a comprehensive risk factor control, which can—among others—be achieved through ensuring that individuals maintain a normal BMI/percentage of lean body mass and fitness level [46,49,50]. However, further research should be conducted to clarify whether dietary interventions indeed yield improvements in long-term hard clinical AF endpoints [38,40] and untangle the conflicting landscape regarding the efficacy of pharmaceutical (such as anorexigenic drugs) and surgical (such as bariatric surgery) methods aiming at weight loss [38].
Limitations
Certain factors place constraints on the generalizability of this study’s conclusions. The small size of our sample (especially regarding the underweight category) may lead to insufficiently powered results and type II errors. Additionally, this subgroup analysis was not prespecified in the MISOAC-AF protocol, thereby suffering from limitations associated with such a retrospective, non-randomized, single-center study. Moreover, in this study, only acutely hospitalized patients were included and most of these patients presented with poor cardiovascular risk factor control on admission. Consequently, our findings might not be reproducible on larger or non-hospitalized populations with AF. BMI, though predominantly utilized in clinical practice, is unable to discriminate between adiposity and lean mass. Alternative measures, such as waist circumference, waist-to-hip ratio, bioimpedance, 3D scanning, and dual-energy X-ray absorptiometry could be implemented as complementary indicators in further investigation. Since BMI fluctuation might influence rhythm control, another potential limitation is that BMI was measured only once (at baseline). Finally, despite the utilization of multivariate adjustment, these results are still vulnerable to unrecognized residual confounding. For instance, it has been previously discussed concerning underweight patients with AF that it is possible that their comorbidities, such as sarcopenia, could play a significant role in their prognosis, rather than the arrhythmia per se. Likewise, an overweight or obese status is associated with increased rates of comorbidities, such as type 2 DM, respiratory disorders, and chronic kidney disease, which are predictors of worse prognosis. This could imply that our findings may be influenced by obesity-related outcomes.
5. Conclusions
In conclusion, the present study demonstrated a higher mortality risk in both overweight/obese and underweight patients, which formed a J-shaped spline curve across the BMI spectrum. Therefore, AF patients should maintain their weight within the normal range. We hope our findings stimulate further investigation of the subject that will allow for more reliable risk assessment and, in practical terms, more effective risk management.
Author Contributions
Conceptualization, M.N., D.N., D.V.M., A.F., P.Z., T.G., O.K., A.V., A.S.P., A.K., A.S., A.B., E.S., A.Z., A.T. and G.G.; methodology, M.N., D.N., D.V.M., P.Z. and A.S.P.; formal analysis, M.N., D.N., D.V.M., P.Z. and A.S.P.; investigation, M.N., D.N., D.V.M., P.Z. and A.S.P.; data curation, D.V.M. and A.S.P.; writing—original draft preparation, M.N., D.N., A.F., P.Z., T.G., O.K. and A.V.; writing—review and editing, M.N., D.N., D.V.M., A.F., P.Z., T.G., O.K., A.V., A.S.P., A.K., A.S., A.B., E.S., A.Z., A.T. and G.G.; visualization, D.V.M., P.Z. and A.S.P.; supervision, A.Z., A.T. and G.G.; project administration, G.G. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board and Ethics Committee of Aristotle University of Thessaloniki (protocol code: 173; date of approval: 30 November 2015).
Informed Consent Statement
Written informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The data used for the present study can be requested from the corresponding author (George Giannakoulas, e-mail: [email protected]).
Acknowledgments
The authors thank Anastasios Papanastasiou, Eleni Vrana, Dimitra Vasdeki, George Dividis, George Fotos, Eleni Paschou, Evropi Forozidou, Paraskevi Tsoukra, Eleni Kotsi, and Ioannis Goulas for their contribution to the initial data collection of the MISOAC-AF trial.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Lippi, G.; Sanchis-Gomar, F.; Cervellin, G. Global Epidemiology of Atrial Fibrillation: An Increasing Epidemic and Public Health Challenge. Int. J. Stroke 2021, 16, 217–221. [Google Scholar] [CrossRef] [PubMed]
- Odutayo, A.; Wong, C.X.; Hsiao, A.J.; Hopewell, S.; Altman, D.G.; Emdin, C.A. Atrial Fibrillation and Risks of Cardiovascular Disease, Renal Disease, and Death: Systematic Review and Meta-Analysis. BMJ 2016, 354, i4482. [Google Scholar] [CrossRef] [PubMed]
- Kartas, A.; Samaras, A.; Akrivos, E.; Vrana, E.; Papazoglou, A.S.; Moysidis, D.V.; Papanastasiou, A.; Baroutidou, A.; Botis, M.; Liampas, E.; et al. Τhe Association of Heart Failure across Left Ventricular Ejection Fraction with Mortality in Atrial Fibrillation. ESC Heart Fail. 2021, 8, 3189–3197. [Google Scholar] [CrossRef] [PubMed]
- Liampas, E.; Kartas, A.; Samaras, A.; Papazoglou, A.S.; Moysidis, D.V.; Vrana, E.; Botis, M.; Papanastasiou, A.; Baroutidou, A.; Vouloagkas, I.; et al. Renal Function and Mortality in Patients with Atrial Fibrillation. J. Cardiovasc. Med. 2022, 23, 430–438. [Google Scholar] [CrossRef] [PubMed]
- Papazoglou, A.S.; Kartas, A.; Moysidis, D.V.; Tsagkaris, C.; Papadakos, S.P.; Bekiaridou, A.; Samaras, A.; Karagiannidis, E.; Papadakis, M.; Giannakoulas, G. Glycemic Control and Atrial Fibrillation: An Intricate Relationship, yet under Investigation. Cardiovasc. Diabetol. 2022, 21, 39. [Google Scholar] [CrossRef]
- Obesity: Preventing and Managing the Global Epidemic: Report of a WHO Consultation. Available online: https://iris.who.int/handle/10665/42330 (accessed on 10 January 2024).
- Dai, H.; Alsalhe, T.A.; Chalghaf, N.; Riccò, M.; Bragazzi, N.L.; Wu, J. The Global Burden of Disease Attributable to High Body Mass Index in 195 Countries and Territories, 1990-2017: An Analysis of the Global Burden of Disease Study. PLoS Med. 2020, 17, e1003198. [Google Scholar] [CrossRef]
- James, P.T.; Leach, R.; Kalamara, E.; Shayeghi, M. The Worldwide Obesity Epidemic. Obes. Res. 2001, 9, 228S–233S. [Google Scholar] [CrossRef] [PubMed]
- Zacharias, A.; Schwann, T.A.; Riordan, C.J.; Durham, S.J.; Shah, A.S.; Habib, R.H. Obesity and Risk of New-Onset Atrial Fibrillation after Cardiac Surgery. Circulation 2005, 112, 3247–3255. [Google Scholar] [CrossRef]
- Foy, A.J.; Mandrola, J.; Liu, G.; Naccarelli, G.V. Relation of Obesity to New-Onset Atrial Fibrillation and Atrial Flutter in Adults. Am. J. Cardiol. 2018, 121, 1072–1075. [Google Scholar] [CrossRef] [PubMed]
- Lee, H.J.; Choi, E.K.; Lee, S.H.; Han, K.D.; Rhee, T.M.; Park, C.S.; Lee, S.R.; Choe, W.S.; Lim, W.H.; Kang, S.H.; et al. Atrial Fibrillation Risk in Metabolically Healthy Obesity: A Nationwide Population-Based Study. Int. J. Cardiol. 2017, 240, 221–227. [Google Scholar] [CrossRef]
- Wang, T.J.; Parise, H.; Levy, D.; D’Agostino, R.B.; Wolf, P.A.; Vasan, R.S.; Benjamin, E.J. Obesity and the Risk of New-Onset Atrial Fibrillation. JAMA 2004, 292, 2471–2477. [Google Scholar] [CrossRef] [PubMed]
- Tedrow, U.B.; Conen, D.; Ridker, P.M.; Cook, N.R.; Koplan, B.A.; Manson, J.A.E.; Buring, J.E.; Albert, C.M. The Long- and Short-Term Impact of Elevated Body Mass Index on the Risk of New Atrial Fibrillation the WHS (Women’s Health Study). J. Am. Coll. Cardiol. 2010, 55, 2319–2327. [Google Scholar] [CrossRef] [PubMed]
- Frost, L.; Hune, L.J.; Vestergaard, P. Overweight and Obesity as Risk Factors for Atrial Fibrillation or Flutter: The Danish Diet, Cancer, and Health Study. Am. J. Med. 2005, 118, 489–495. [Google Scholar] [CrossRef] [PubMed]
- Tsang, T.S.M.; Barnes, M.E.; Miyasaka, Y.; Cha, S.S.; Bailey, K.R.; Verzosa, G.C.; Seward, J.B.; Gersh, B.J. Obesity as a Risk Factor for the Progression of Paroxysmal to Permanent Atrial Fibrillation: A Longitudinal Cohort Study of 21 Years. Eur. Heart J. 2008, 29, 2227–2233. [Google Scholar] [CrossRef] [PubMed]
- Chung, M.K.; Eckhardt, L.L.; Chen, L.Y.; Ahmed, H.M.; Gopinathannair, R.; Joglar, J.A.; Noseworthy, P.A.; Pack, Q.R.; Sanders, P.; Trulock, K.M. Lifestyle and Risk Factor Modification for Reduction of Atrial Fibrillation: A Scientific Statement From the American Heart Association. Circulation 2020, 141, E750–E772. [Google Scholar] [CrossRef] [PubMed]
- Al-Kaisey, A.M.; Kalman, J.M. Obesity and Atrial Fibrillation: Epidemiology, Pathogenesis and Effect of Weight Loss. Arrhythm. Electrophysiol. Rev. 2021, 10, 159–164. [Google Scholar] [CrossRef]
- Bertomeu-Gonzalez, V.; Moreno-Arribas, J.; Esteve-Pastor, M.A.; Roldán-Rabadán, I.; Muñiz, J.; Raña-Míguez, P.; Ruiz-Ortiz, M.; Cequier, Á.; Bertomeu-Martínez, V.; Badimón, L.; et al. Association of Body Mass Index with Clinical Outcomes in Patients with Atrial Fibrillation: A Report From the FANTASIIA Registry. J. Am. Heart Assoc. 2020, 9, e013789. [Google Scholar] [CrossRef] [PubMed]
- Sandhu, R.K.; Ezekowitz, J.; Andersson, U.; Alexander, J.H.; Granger, C.B.; Halvorsen, S.; Hanna, M.; Hijazi, Z.; Jansky, P.; Lopes, R.D.; et al. The “Obesity Paradox” in Atrial Fibrillation: Observations from the ARISTOTLE (Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation) Trial. Eur. Heart J. 2016, 37, 2869–2878. [Google Scholar] [CrossRef] [PubMed]
- Elagizi, A.; Kachur, S.; Lavie, C.J.; Carbone, S.; Pandey, A.; Ortega, F.B.; Milani, R.V. An Overview and Update on Obesity and the Obesity Paradox in Cardiovascular Diseases. Prog. Cardiovasc. Dis. 2018, 61, 142–150. [Google Scholar] [CrossRef]
- Pandey, A.; Gersh, B.J.; McGuire, D.K.; Shrader, P.; Thomas, L.; Kowey, P.R.; Mahaffey, K.W.; Hylek, E.; Sun, S.; Burton, P.; et al. Association of Body Mass Index with Care and Outcomes in Patients with Atrial Fibrillation: Results From the ORBIT-AF Registry. JACC Clin. Electrophysiol. 2016, 2, 355–363. [Google Scholar] [CrossRef]
- Overvad, T.F.; Rasmussen, L.H.; Skjøth, F.; Overvad, K.; Lip, G.Y.H.; Larsen, T.B. Body Mass Index and Adverse Events in Patients with Incident Atrial Fibrillation. Am. J. Med. 2013, 126, 640.e9–640.e17. [Google Scholar] [CrossRef] [PubMed]
- Wang, H.J.; Si, Q.J.; Shan, Z.L.; Guo, Y.T.; Lin, K.; Zhao, X.N.; Wang, Y.T. Effects of Body Mass Index on Risks for Ischemic Stroke, Thromboembolism, and Mortality in Chinese Atrial Fibrillation Patients: A Single-Center Experience. PLoS ONE 2015, 10, e0123516. [Google Scholar] [CrossRef] [PubMed]
- Patti, G.; Pecen, L.; Manu, M.C.; Huber, K.; Rohla, M.; Renda, G.; Siller-Matula, J.; Ricci, F.; Kirchhof, P.; De Caterina, R. Thromboembolic and Bleeding Risk in Obese Patients with Atrial Fibrillation According to Different Anticoagulation Strategies. Int. J. Cardiol. 2020, 318, 67–73. [Google Scholar] [CrossRef] [PubMed]
- Samaras, A.; Kartas, A.; Vasdeki, D.; Dividis, G.; Forozidou, E.; Fotos, G.; Kotsi, E.; Paschou, E.; Tsoukra, P.; Goulas, I.; et al. Rationale and Design of a Randomized Study Comparing Motivational Interviewing to Support Oral Anticoagulation Adherence versus Usual Care in Patients with Nonvalvular Atrial Fibrillation: The MISOAC-AF Trial. Hell. J. Cardiol. 2020, 61, 453–454. [Google Scholar] [CrossRef] [PubMed]
- Tzikas, A.; Samaras, A.; Kartas, A.; Vasdeki, D.; Fotos, G.; Dividis, G.; Paschou, E.; Forozidou, E.; Tsoukra, P.; Kotsi, E.; et al. Motivational Interviewing to Support Oral AntiCoagulation Adherence in Patients with Non-Valvular Atrial Fibrillation (MISOAC-AF): A Randomized Clinical Trial. Eur. Heart J. Cardiovasc. Pharmacother. 2021, 7, F63–F71. [Google Scholar] [CrossRef]
- Berkovitch, A.; Kivity, S.; Klempfner, R.; Segev, S.; Milwidsky, A.; Erez, A.; Sabbag, A.; Goldenberg, I.; Sidi, Y.; Maor, E. Body Mass Index and the Risk of New-Onset Atrial Fibrillation in Middle-Aged Adults. Am. Heart J. 2016, 173, 41–48. [Google Scholar] [CrossRef] [PubMed]
- Goudis, C.A.; Korantzopoulos, P.; Ntalas, I.V.; Kallergis, E.M.; Ketikoglou, D.G. Obesity and Atrial Fibrillation: A Comprehensive Review of the Pathophysiological Mechanisms and Links. J. Cardiol. 2015, 66, 361–369. [Google Scholar] [CrossRef] [PubMed]
- Badheka, A.O.; Rathod, A.; Kizilbash, M.A.; Garg, N.; Mohamad, T.; Afonso, L.; Jacob, S. Influence of Obesity on Outcomes in Atrial Fibrillation: Yet Another Obesity Paradox. Am. J. Med. 2010, 123, 646–651. [Google Scholar] [CrossRef]
- Ardestani, A.; Hoffman, H.J.; Cooper, H.A. Obesity and Outcomes among Patients with Established Atrial Fibrillation. Am. J. Cardiol. 2010, 106, 369. [Google Scholar] [CrossRef]
- Wang, J.; Yang, Y.M.; Zhu, J.; Zhang, H.; Shao, X.H.; Tian, L.; Huang, B.; Yu, L.T.; Gao, X.; Wang, M. Overweight Is Associated with Improved Survival and Outcomes in Patients with Atrial Fibrillation. Clin. Res. Cardiol. 2014, 103, 533–542. [Google Scholar] [CrossRef]
- Grymonprez, M.; Capiau, A.; De Backer, T.L.; Steurbaut, S.; Boussery, K.; Lahousse, L. The Impact of Underweight and Obesity on Outcomes in Anticoagulated Patients with Atrial Fibrillation: A Systematic Review and Meta-analysis on the Obesity Paradox. Clin. Cardiol. 2021, 44, 599. [Google Scholar] [CrossRef] [PubMed]
- Zhu, W.; Wan, R.; Liu, F.; Hu, J.; Huang, L.; Li, J.; Hong, K. Relation of Body Mass Index with Adverse Outcomes among Patients with Atrial Fibrillation: A Meta-Analysis and Systematic Review. J. Am. Heart Assoc. 2016, 5, e004006. [Google Scholar] [CrossRef] [PubMed]
- Zhou, Y.; Ma, J.; Zhu, W. Efficacy and Safety of Direct Oral Anticoagulants Versus Warfarin in Patients with Atrial Fibrillation Across BMI Categories: A Systematic Review and Meta-Analysis. Am. J. Cardiovasc. Drugs 2020, 20, 51–60. [Google Scholar] [CrossRef]
- Kaplan, R.M.; Tanaka, Y.; Passman, R.S.; Fine, M.; Rasmussen-Torvik, L.J.; Vupputuri, S.; Martin, K.; Khan, S.S. Efficacy and Safety of Direct Oral Anticoagulants for Atrial Fibrillation Across Body Mass Index Categories. J. Am. Heart Assoc. 2020, 9, e017383. [Google Scholar] [CrossRef]
- Netley, J.; Howard, K.; Wilson, W. Effects of Body Mass Index on the Safety and Effectiveness of Direct Oral Anticoagulants: A Retrospective Review. J. Thromb. Thrombolysis 2019, 48, 359–365. [Google Scholar] [CrossRef] [PubMed]
- Wang, H.; Wang, H.J.; Chen, Y.D.; Tao, T.; Guo, Y.T.; Zhao, X.N.; Liu, H.B.; Wang, Y.T. Prognostic Factors of Clinical Endpoints in Elderly Patients with Atrial Fibrillation during a 2-Year Follow-up in China: An Observational Cohort Study. Medicine 2017, 96, e7679. [Google Scholar] [CrossRef]
- Javed, S.; Gupta, D.; Lip, G.Y.H. Obesity and Atrial Fibrillation: Making Inroads through Fat. Eur. Heart J. Cardiovasc. Pharmacother. 2021, 7, 59–67. [Google Scholar] [CrossRef]
- Liu, X.; Guo, L.; Xiao, K.; Zhu, W.; Liu, M.; Wan, R.; Hong, K. The Obesity Paradox for Outcomes in Atrial Fibrillation: Evidence from an Exposure-Effect Analysis of Prospective Studies. Obes. Rev. 2020, 21, e12970. [Google Scholar] [CrossRef]
- Vyas, V.; Lambiase, P. Obesity and Atrial Fibrillation: Epidemiology, Pathophysiology and Novel Therapeutic Opportunities. Arrhythm. Electrophysiol. Rev. 2019, 8, 28. [Google Scholar] [CrossRef]
- Wang, L.; Du, X.; Dong, J.Z.; Liu, W.N.; Zhou, Y.C.; Li, S.N.; Guo, X.Y.; Jiang, C.X.; Yu, R.H.; Sang, C.H.; et al. Body Mass Index and All-Cause Mortality in Patients with Atrial Fibrillation: Insights from the China Atrial Fibrillation Registry Study. Clin. Res. Cardiol. 2019, 108, 1371–1380. [Google Scholar] [CrossRef]
- Okumura, K.; Tomita, H.; Nakai, M.; Kodani, E.; Akao, M.; Suzuki, S.; Hayashi, K.; Sawano, M.; Goya, M.; Yamashita, T.; et al. Risk Factors Associated with Ischemic Stroke in Japanese Patients with Nonvalvular Atrial Fibrillation. JAMA Netw. Open 2020, 3, e202881. [Google Scholar] [CrossRef] [PubMed]
- Mozaffarian, D.; Benjamin, E.J.; Go, A.S.; Arnett, D.K.; Blaha, M.J.; Cushman, M.; Das, S.R.; De Ferranti, S.; Després, J.P.; Fullerton, H.J.; et al. Heart Disease and Stroke Statistics-2016 Update: A Report From the American Heart Association. Circulation 2016, 133, e38–e360. [Google Scholar] [CrossRef] [PubMed]
- Guo, L.; Liu, X.; Yu, P.; Zhu, W. The “Obesity Paradox” in Patients with HFpEF with or without Comorbid Atrial Fibrillation. Front. Cardiovasc. Med. 2022, 8, 743327. [Google Scholar] [CrossRef] [PubMed]
- Wang, J.; Yang, Y.M.; Zhu, J.; Zhang, H.; Shao, X.H. Obesity Paradox in Patients with Atrial Fibrillation and Heart Failure. Int. J. Cardiol. 2014, 176, 1356–1358. [Google Scholar] [CrossRef] [PubMed]
- Hindricks, G.; Potpara, T.; Kirchhof, P.; Kühne, M.; Ahlsson, A.; Balsam, P.; Bauersachs, J.; Benussi, S.; Brandes, A.; Braunschweig, F.; et al. 2020 ESC Guidelines for the Diagnosis and Management of Atrial Fibrillation Developed in Collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the Diagnosis and Management of Atrial Fibrillation of the European Society of Cardiology (ESC) Developed with the Special Contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur. Heart J. 2021, 42, 373–498. [Google Scholar] [CrossRef] [PubMed]
- Middeldorp, M.E.; Pathak, R.K.; Meredith, M.; Mehta, A.B.; Elliott, A.D.; Mahajan, R.; Twomey, D.; Gallagher, C.; Hendriks, J.M.L.; Linz, D.; et al. PREVEntion and RegReSsive Effect of Weight-Loss and Risk Factor Modification on Atrial Fibrillation: The REVERSE-AF Study. Europace 2018, 20, 1929–1935. [Google Scholar] [CrossRef] [PubMed]
- Abed, H.S.; Wittert, G.A.; Leong, D.P.; Shirazi, M.G.; Bahrami, B.; Middeldorp, M.E.; Lorimer, M.F.; Lau, D.H.; Antic, N.A.; Brooks, A.G.; et al. Effect of Weight Reduction and Cardiometabolic Risk Factor Management on Symptom Burden and Severity in Patients with Atrial Fibrillation: A Randomized Clinical Trial. JAMA 2013, 310, 2050–2060. [Google Scholar] [CrossRef] [PubMed]
- Lavie, C.J.; Pandey, A.; Lau, D.H.; Alpert, M.A.; Sanders, P. Obesity and Atrial Fibrillation Prevalence, Pathogenesis, and Prognosis: Effects of Weight Loss and Exercise. J. Am. Coll. Cardiol. 2017, 70, 2022–2035. [Google Scholar] [CrossRef]
- Jahangir, E.; De Schutter, A.; Lavie, C.J. Low Weight and Overweightness in Older Adults: Risk and Clinical Management. Prog. Cardiovasc. Dis. 2014, 57, 127–133. [Google Scholar] [CrossRef]
Figure 1.
Kaplan–Meier analysis on (a) all-cause mortality and (b) any hospitalization related to AF, HF, or stroke (AF = atrial fibrillation, HF = heart failure, BMI = body mass index).
Figure 1.
Kaplan–Meier analysis on (a) all-cause mortality and (b) any hospitalization related to AF, HF, or stroke (AF = atrial fibrillation, HF = heart failure, BMI = body mass index).
Figure 2.
Risk of primary (a) and secondary (b) outcomes according to BMI category (BMI = body mass index, 95% CI = 95% confidence interval). The normal-weight BMI category (BMI = 18–24.9) was used as reference (ref.) for all comparisons.
Figure 2.
Risk of primary (a) and secondary (b) outcomes according to BMI category (BMI = body mass index, 95% CI = 95% confidence interval). The normal-weight BMI category (BMI = 18–24.9) was used as reference (ref.) for all comparisons.
Figure 3.
Cubic spline curve for the association between (a) BMI and all-cause death, (b) BMI and any hospitalization related to AF, HF, or stroke, (c) BMI and all-cause death after multivariate adjustment, and (d) BMI and any hospitalization related to AF, HF, or stroke after multivariate adjustment (BMI = body mass index, AF = atrial fibrillation, HF = heart failure).
Figure 3.
Cubic spline curve for the association between (a) BMI and all-cause death, (b) BMI and any hospitalization related to AF, HF, or stroke, (c) BMI and all-cause death after multivariate adjustment, and (d) BMI and any hospitalization related to AF, HF, or stroke after multivariate adjustment (BMI = body mass index, AF = atrial fibrillation, HF = heart failure).
Table 1.
Baseline characteristics stratified by BMI category.
| Total: 1113 | Underweight | Normal Weight | Overweight | Obese | Severely Obese | Morbidly Obese | p-Value |
|---|---|---|---|---|---|---|---|
| Number of Cases | 18 (1.6) | 267 (24.0) | 461 (41.4) | 240 (21.6) | 87 (7.8) | 40 (3.6) | |
| Demographics | |||||||
| Age (years) | 74.6 (10.2) | 78 (12.0) | 76 (14.0) | 75 (15.0) | 72 (16.0) | 68.8 (10.5) | <0.001 |
| Female Gender | 8 (44.4) | 119 (44.6) | 187 (40.6) | 115 (47.9) | 50 (57.5) | 26 (65.0) | 0.006 |
| Cardiovascular Risk Factors and Comorbidities | |||||||
| Smoking | 10 (66.7) | 159 (61.6) | 276 (61.1) | 134 (58.0) | 47 (57.3) | 24 (61.5) | 0.925 |
| Hypertension | 14 (87.5) | 205 (78.2) | 374 (82.0) | 191 (82.7) | 70 (82.4) | 37 (92.5) | 0.344 |
| Dyslipidemia | 4 (26.7) | 99 (37.9) | 227 (49.9) | 132 (57.1) | 44 (53.7) | 21 (52.5) | <0.001 |
| Diabetes Mellitus | 2 (12.5) | 83 (31.4) | 133 (29.3) | 88 (37.9) | 46 (54.8) | 18 (45.0) | <0.001 |
| HF including new cases at discharge HFrEF HFmrEF HFpEF | 9 (52.9) 4 (23.5) 3 (17.6) 2 (11.8) | 144 (56.5) 55 (21.6) 29 (11.4) 60 (23.5) | 221 (48.9) 77 (17.0) 46 (10.2) 98 (21.7) | 110 (47.6) 28 (12.1) 23 (10.0) 59 (25.5) | 52 (61.2) 10 (11.8) 7 (8.2) 35 (41.2) | 28 (73.7) 4 (10.5) 7 (18.4) 17 (44.7) | 0.008 0.050 0.474 <0.001 |
| AMI during or prior to hospitalization | 5 (33.3) | 48 (18.5) | 103 (22.7) | 45 (19.5) | 21 (25.3) | 7 (17.5) | 0.436 |
| CAD | 9 (52.9) | 95 (37.8) | 194 (44.9) | 98 (42.8) | 33 (40.7) | 10 (26.3) | 0.153 |
| Types of AF First-Diagnosed Paroxysmal Persistent Permanent | 1 (7.7) 5 (38.5) 0 (0.0) 7 (53.8) | 22 (8.5) 82 (31.8) 18 (7.0) 136 (52.7) | 68 (15.2) 158 (35.3) 29 (6.5) 192 (43.0) | 24 (10.7) 72 (32.0) 13 (5.8) 116 (51.6) | 13 (15.9) 24 (29.3) 3 (3.7) 42 (51.2) | 6 (15.4) 8 (20.5) 1 (2.6) 24 (61.5) | 0.102 0.436 0.857 0.047 |
| Cardiac Arrest | 1 (7.1) | 10 (3.9) | 11 (2.4) | 5 (2.2) | 4 (4.9) | 0 (0.0) | 0.340 |
| Prior Ablation | 0 (0.0) | 8 (3.1) | 7 (1.5) | 3 (1.3) | 2 (2.4) | 1 (2.6) | 0.577 |
| History of PCI/CABG | 5 (29.4) | 75 (28.8) | 154 (33.8) | 72 (31.0) | 24 (28.2) | 8 (20.0) | 0.432 |
| Pacemaker | 0 (0.0) | 18 (6.9) | 28 (6.2) | 12 (5.2) | 5 (6.0) | 0 (0.0) | 0.613 |
| ICD | 0 (0.0) | 15 (5.7) | 7 (1.5) | 8 (3.4) | 6 (7.2) | 0 (0.0) | 0.012 |
| Prosthetic Valve | 0 (0.0) | 24 (9.2) | 25 (5.5) | 7 (3.0) | 3 (3.6) | 1 (2.5) | 0.068 |
| Vascular Disease | 5 (35.7) | 116 (44.8) | 217 (48.0) | 105 (45.5) | 48 (57.8) | 15 (37.5) | 0.222 |
| Congenital Heart Disease | 0 (0.0) | 9 (3.4) | 10 (2.2) | 4 (1.7) | 1 (1.2) | 1 (2.5) | 0.789 |
| Thyroid Disease | 4 (28.6) | 59 (22.8) | 92 (20.3) | 44 (19.0) | 20 (24.4) | 14 (35.0) | 0.244 |
| COPD | 5 (33.3) | 36 (13.8) | 48 (10.6) | 33 (14.3) | 12 (14.5) | 10 (25.0) | 0.024 |
| Pulmonary Disease | 5 (33.3) | 40 (15.3) | 55 (12.1) | 34 (14.7) | 16 (19.3) | 16 (40.0) | <0.001 |
| CKD | 3 (21.4) | 37 (14.2) | 67 (14.8) | 31 (13.4) | 18 (22.0) | 9 (23.1) | 0.263 |
| History of Stroke/TIA/Unspecified Stroke/Systemic Thromboembolism | 3 (17.6) | 52 (19.9) | 80 (17.5) | 48 (20.6) | 16 (18.6) | 2 (5.0) | 0.226 |
| History of Hemorrhagic Stroke | 0 (0.0) | 2 (0.8) | 5 (1.1) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0.640 |
| History of Major Bleeding | 3 (18.8) | 44 (16.9) | 74 (16.2) | 23 (9.8) | 8 (9.4) | 4 (10.0) | 0.089 |
| Bleeding while under OAC | 9 (56.3) | 81 (31.5) | 131 (29.3) | 56 (24.5) | 23 (28.0) | 13 (32.5) | 0.116 |
| Medication | |||||||
| Antiplatelets on admission Aspirin Clopidogrel Both | 4 (28.6) 2 (14.3) 1 (7.1) 1 (7.1) | 58 (23.4) 26 (10.4) 22 (8.8) 10 (4.0) | 131 (29.4) 69 (15.5) 33 (7.4) 29 (6.5) | 71 (31.1) 35 (15.4) 22 (9.6) 14 (6.1) | 17 (21.8) 9 (11.5) 5 (6.4) 3 (3.8) | 4 (10.8) 1 (2.7) 3 (8.1) 0 (0.0) | 0.044 0.123 0.909 0.425 |
| VKA on admission | 4 (28.6) | 89 (35.7) | 126 (28.3) | 55 (24.0) | 26 (33.3) | 12 (32.4) | 0.105 |
| NOAC on admission Dabigatran Rivaroxaban Abixaban | 3 (21.4) 0 (0.0) 2 (14.3) 1 (7.1) | 81 (32.5) 13 (5.2) 38 (15.3) 30 (12.0) | 139 (31.2) 36 (8.1) 52 (11.7) 51 (11.5) | 87 (38.0) 22 (9.6) 41 (17.9) 24 (10.5) | 31 (39.7) 8 (10.3) 15 (19.2) 8 (10.3) | 16 (43.2) 1 (2.7) 8 (21.6) 7 (18.9) | 0.229 0.295 0.131 0.772 |
| Rate Control Medication on admission Beta-blockers Digoxin Both | 12 (85.7) 11 (78.6) 0 (0.0) 1 (7.1) | 177 (71.1) 138 (55.4) 13 (5.2) 26 (10.4) | 301 (67.6) 270 (60.7) 13 (2.9) 18 (4.0) | 167 (72.9) 147 (64.2) 6 (2.6) 14 (6.1) | 49 (62.8) 46 (59.0) 1 (1.3) 2 (2.6) | 31 (83.8) 26 (70.3) 0 (0.0) 5 (13.5) | 0.107 0.188 0.437 0.005 |
| Rhythm Control Medication on admission Amiodarone Propafenone Sotalol | 3 (21.4) 3 (21.4) 0 (0.0) 0 (0.0) | 49 (19.8) 32 (12.9) 16 (6.5) 1 (0.4) | 80 (18.0) 45 (10.1) 31 (7.0) 4 (0.9) | 32 (14.0) 22 (9.6) 8 (3.5) 2 (0.9) | 12 (15.4) 8 (10.3) 3 (3.8) 1 (1.3) | 4 (10.8) 3 (8.1) 1 (2.7) 0 (0.0) | 0.489 0.574 0.450 0.784 |
| ACEi-ARB at discharge | 8 (44.4) | 110 (41.2) | 203 (44.0) | 109 (45.4) | 40 (46.0) | 15 (37.5) | 0.877 |
| Statin at discharge | 9 (50.0) | 85 (31.8) | 197 (42.7) | 101 (42.1) | 38 (43.7) | 13 (32.5) | 0.043 |
| Clinical Data | |||||||
| Systolic BP (mmHg) | 138.6 (17.1) | 136.6 (27.0) | 140 (31.0) | 143.9 (25.3) | 151.9 (26.0) | 140.4 (22.9) | <0.001 |
| Diastolic BP (mmHg) | 83.5 (11.8) | 78 (22.0) | 80 (19.0) | 80 (20.0) | 83.4 (16.7) | 85 (23.0) | 0.006 |
| NT-pro BNP (pg/mL) | 826 (17,307) | 468 (2087) | 187 (2056) | 519 (1717) | 152 (1788) | 217 (1635) | 0.438 |
| High Sensitivity Troponin (pg/mL) | 47 (73.0) | 25 (38.0) | 27 (40.0) | 26 (38.0) | 28 (41.0) | 28 (40.0) | 0.803 |
| ECG on admission Sinus Rhythm AF Atrial Flutter | 5 (35.7) 7 (50.0) 2 (14.3) | 45 (21.1) 148 (69.5) 7 (3.3) | 78 (20.9) 253 (67.6) 21 (5.6) | 38 (19.9) 139 (72.8) 5 (2.6) | 7 (10.9) 53 (82.8) 1 (1.6) | 2 (6.5) 26 (83.9) 2 (6.5) | 0.079 0.029 0.121 |
| GFR by CKD-EPI (mL/min/1.73 m2) | 54.6 (30.6) | 59.9 (22.2) | 61.9 (36.0) | 62.6 (36.0) | 58.9 (23.8) | 64.5 (25.8) | 0.267 |
| TTE on admission Left Atrium Diameter (cm) LVEF | 5.2 (0.7) 50 (15.0) | 4.5 (0.9) 50 (17.0) | 4.3 (0.9) 55 (15.5) | 4.3 (0.7) 55 (15.0) | 4.5 (0.9) 55 (12.9) | 5.5 (0.7) 50 (14.0) | 0.148 0.071 |
| Hospitalization | |||||||
| Length of hospitalization (days) | 8.7 (5.1) | 6 (7.0) | 6 (6.0) | 6 (6.0) | 6 (7.0) | 4.5 (6.0) | 0.128 |
| Reason for hospitalization AF ACS HF HVD Other | 6 (33.3) 4 (22.2) 5 (27.8) 1 (5.6) 2 (11.1) | 87 (34.9) 22 (8.8) 79 (31.7) 16 (6.4) 45 (18.1) | 175 (41.5) 51 (12.1) 103 (24.4) 21 (5.0) 72 (17.1) | 93 (42.7) 22 (10.1) 57 (26.1) 9 (4.1) 37 (17.0) | 20 (26.7) 5 (6.7) 30 (40.0) 5 (6.7) 15 (20.0) | 16 (43.2) 3 (8.1) 13 (35.1) 0 (0.0) 5 (13.5) | 0.096 0.331 0.051 0.554 0.954 |
| Scores | |||||||
| CHA2DS2-VASc | 4.2 (1.2) | 4 (3.0) | 4 (3.0) | 4 (3.0) | 5 (3.0) | 4 (3.0) | 0.668 |
| HAS-BLED | 2 (2.0) | 2 (1.0) | 2 (1.0) | 2 (1.0) | 2 (1.0) | 1 (1.0) | 0.084 |
Continuous variables: mean (±SD) (for variables with normal distribution), median (IQR = Q3–Q1) (for variables with non-normal distribution), and categorical variables: number (%). The statistically significant results (p-values < 0.05) are in bold. ACEi-ARB = angiotensin-converting enzyme inhibitor–angiotensin receptor blocker; ACS = acute coronary syndrome; AF = atrial fibrillation; AMI = acute myocardial infarction; BMI = body mass index; BP = blood pressure; CABG = coronary artery bypass grafting; CAD = coronary artery disease; CHA2DS2-VASc = congestive heart failure or left ventricular dysfunction, hypertension, age ≥75 (doubled), diabetes, stroke (doubled)–vascular disease, age 65–74, sex category; CKD = chronic kidney disease; CKD-EPI = chronic kidney disease–epidemiology collaboration; COPD = chronic obstructive pulmonary disease; ECG = electrocardiogram; GFR = glomerular filtration rate; HAS-BLED = hypertension, abnormal renal/liver function, stroke–bleeding history or predisposition, labile international normalized ratio, elderly, drugs/alcohol concomitantly; HF = heart failure; HFmrEF = heart failure with mid-range ejection fraction; HFpEF = heart failure with preserved ejection fraction; HFrEF = heart failure with reduced ejection fraction; HVD = heart valve disease; ICD = implantable cardioverter defibrillator; IQR = interquartile range; LVEF = left ventricular ejection fraction; NOAC = non-vitamin K antagonist oral anticoagulants; NT-pro BNP = N-terminal pro-B-type natriuretic peptide; OAC = oral anticoagulant drugs; PCI = percutaneous coronary intervention; SD = standard deviation; TIA = transient ischemic attack; TTE = transthoracic echocardiogram; VKA = vitamin K antagonists.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Nteli, M.; Nteli, D.; Moysidis, D.V.; Foka, A.; Zymaris, P.; Grantza, T.; Kazarli, O.; Vagianos, A.; Papazoglou, A.S.; Kartas, A.; et al. Prognostic Impact of Body Mass Index in Atrial Fibrillation. J. Clin. Med. 2024, 13, 3294. https://doi.org/10.3390/jcm13113294
AMA Style
Nteli M, Nteli D, Moysidis DV, Foka A, Zymaris P, Grantza T, Kazarli O, Vagianos A, Papazoglou AS, Kartas A, et al. Prognostic Impact of Body Mass Index in Atrial Fibrillation. Journal of Clinical Medicine. 2024; 13(11):3294. https://doi.org/10.3390/jcm13113294
Chicago/Turabian StyleNteli, Maria, Despoina Nteli, Dimitrios V. Moysidis, Anastasia Foka, Panagiotis Zymaris, Triantafyllia Grantza, Olga Kazarli, Alexis Vagianos, Andreas S. Papazoglou, Anastasios Kartas, and et al. 2024. "Prognostic Impact of Body Mass Index in Atrial Fibrillation" Journal of Clinical Medicine 13, no. 11: 3294. https://doi.org/10.3390/jcm13113294
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
