Numerous studies have demonstrated that plasma transforming growth factor-β1 (TGF-β1) may be involved in the pathogenesis of atrial fibrillation (AF), but some discrepancy remained. We performed a meta-analysis to evaluate the association between the plasma level of TGF-β1 and the risk of AF.
Published clinical studies evaluating the association between the plasma level of TGF-β1 and the risk of AF were retrieved from PubMed and EMBASE databases. Two reviewers independently evaluated the quality of the included studies and extracted study data. Subgroup analysis and sensitivity analysis were performed to evaluate for heterogeneity between studies.
Of the 395 studies identified initially, 13 studies were included into our analysis, with a total of 3354 patients. Higher plasma level of TGF-β1 was associated with increased risk of AF when evaluated as both a continuous variable (SMD 0.67; 95%CI 0.29–1.05) and a categorical variable (OR 1.01, 95% CI 1.01–1.02).
Citation: Li J, Yang Y, Ng CY, Zhang Z, Liu T, Li G (2016) Association of Plasma Transforming Growth Factor-β1 Levels and the Risk of Atrial Fibrillation: A Meta-Analysis. PLoS ONE 11(5): e0155275. https://doi.org/10.1371/journal.pone.0155275
Editor: Thomas Hund, The Ohio State University, UNITED STATES
Received: February 4, 2016; Accepted: April 26, 2016; Published: May 12, 2016
Copyright: © 2016 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This work was supported by grants (81270245, 81570298 to Tong Liu, and 81370300 to Guangping Li) from the National Natural Science Foundation of China and the Specialized Research Fund for the Doctoral Program of Higher Education (20121202110004 to Guangping Li). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Atrial fibrillation (AF) is the most common sustained arrhythmia with debilitating consequences such as stroke and heart failure. It is also associated with an increase in overall mortality [1,2,3]. Animal models and studies on patients with AF have confirmed that the development of AF is associated with both structural and electrical remodeling of the atria . Patients with chronic AF have significant myocardial interstitial fibrosis which contributes to the occurrence and perpetuation of AF [5, 6]. Transforming growth factor-β1 (TGF-β1) is an important factor in fibrosis . It is involved in the process of cell proliferation, apoptosis and migration. It promotes the differentiation of cardiac fibroblasts and production of extracellular matrix such as collagen, fibronectin, and protein polysaccharide which leads to cardiac fibrosis . In transgenic mouse models, the activation of TGF-β1 promotes atrial fibrosis and the development of AF . On the other hand, the inhibition of TGF-β1 by pirfenidone (PFD) can significantly reduce the extent of atrial fibrosis . These findings have prompted clinical studies on the relationship between plasma TGF-β1 levels and the development of AF in humans. However, the results generated have been inconsistent. Therefore, we conducted a comprehensive meta-analysis to evaluate the available evidence of whether high plasma TGF-β1 levels are related to the risk of having AF.
Articles were identified by searching PubMed and Embase online databases for articles published up until November 2015. The key terms used are ‘TGF-β1’, ‘transforming growth factor-β1’, ‘transforming growth factor-beta1’, ‘transforming growth factor’ and ‘atrial fibrillation’. We manually searched the bibliographies of original papers and abstracts of the scientific sessions of the past 3 years. In addition, we sought the assistance from potential experts in the field to assess the quality of included articles. We evaluated the titles, abstracts and reference lists of all articles to identify potentially relevant studies.
Trial selection and inclusion criteria
Two reviewers (J. L. and Y. Y.) evaluated the titles and abstracts of all eligible studies. The full text of relevant studies was retrieved and assessed accordingly based on the inclusion criteria. Any disagreements on whether to include any study between the two investigators were resolved through joint review and discussions.
For inclusion, eligible trials should meet the following criteria: (1) the study design was case-control, prospective or retrospective cohort studies; (2) human subjects; (3) included the characteristics of study patients; (4) clearly defined endpoint events, such as AF occurrence or recurrence; (5) evaluated the plasma TGF-β1 levels of AF patients and non-AF patients; (6) reported the plasma level of TGF-β1 using [mean ± standard deviation (SD)] and odds ratio (OR) or hazard ratio (HR) of AF incidence and the corresponding 95% confidence interval (CI) for TGF-β1 levels.
Two independent reviewers (J. L. and Y. Y.) extracted data from included studies using a standard data extraction form. Information on authors and published journals were removed and then independently evaluated according to the described inclusion criteria. Relevant data were extracted from the manuscripts. We extracted and analyzed the plasma concentration of TGF-β1 expressed as mean ± SD from each primary study. Adjusted OR values were selected for the analysis. Additional data collected included study characteristics (first author’s last name, publication year, study design, sample size, AF definition, follow-up duration, end-point events) and patients baseline characteristics (age, sex, BMI, smoking, mean left atria diameter, left ventricular ejection fraction, the presence of CAD, hypertension, diabetes and medication).
The two investigators (J. L. and Y. Y.) independently evaluated the quality of the eligible studies based on the guidelines by the Evidence-Based Medicine Working Group  and the United States Preventive Task Force . Each study was judged in accordance to the 10-item STROBE checklist. We appraised the quality of studies according to the following characteristics: (1) the inclusion and exclusion criteria are clearly defined; (2) sample selection is clearly described; (3) involved population is representative of study sample; (4) the patients’ follow-up period is adequate; (5) reports loss of follow-up; (6) clinical and demographic variables are complete; (7) the definition of AF is clearly defined; (8) the outcomes and outcome assessment are clearly defined; (9) temporality (evaluation of plasma TGF-β1 levels at baseline) and (10) adjustment of possible confounders on the multivariate analysis, especially for categorical variable. If any of the characteristics was not described, we assumed that it had not been performed.
All continuous variables were presented as (mean ± SD). The standard mean difference (SMD) was used to analyze the results in our meta-analysis. SMD method was used as different unit of measurements were presented for TGF-β1 levels. As the studies included in this meta-analysis may have used either a continuous or categorical variable for TGF-β1 levels, we performed a separate meta-analysis for both types of variables to evaluate the association between TGF-β1 levels and the occurrence of AF. The HR values in multivariate Cox proportional hazards model in each primary study were directly considered as OR values. I2 derived from the chi-square test was used to evaluate the heterogeneity across the studies included. I2 of ≤50% indicates that there was no significant heterogeneity . A fixed effects model was used if no significant heterogeneity was found. When pooled effect resulted in significant heterogeneity, the random effects model was used. We conducted random effects meta-analysis using the inverse variance heterogeneity method. In addition, we also performed subgroup analysis based on the patients’ age (≤50y or >50y), study design (cohort study or case control study), duration of follow-up (<12 months or ≥12 months), sample size (<100 or ≥100) and left ventricular ejection fraction (LVEF) (<50% or ≥50%). Sensitivity analysis was performed by sequentially removing each individual study. We assessed for publication bias by constructing a funnel plot. Two-tailed p value of <0.05 was considered statistically significant. All statistical analyses were performed with Review Manager Version 5.3.
Data retrieval and study selection was shown in the flow chart (Fig 1). A total of 395 studies were found using our search criteria. After reviewing title and abstract of each study, we excluded 356 articles because they were either unrelated, review articles or basic science research papers. Then, we evaluated the remaining 39 studies in detail. Of these 39 studies, we excluded 26 studies because: 1 had duplicate data, 17 did not provide the plasma TGF-β1 levels, 6 did not provide (mean ± SD) data of TGF-β1 or OR/HR values, 1 did not provide baseline characteristics of patients and 1 had no control group. Finally, the remaining 13 studies were included into our meta-analysis.
We included 13 studies with a total of 3354 patients of which 1154 patients have AF and 2200 patients have no AF. The main features of the studies are exhibited in Table 1, and the patients’ baseline characteristics are summarized in Table 2. Confounding factors used in multivariate analysis are shown in Table 3. Out of the 13 included studies, 6 studies[14–19] demonstrated that AF patients had higher plasma TGF-β1 levels, regardless of whether it was new-onset AF or recurrent AF, but the remaining 7[20–26] presented no significant correlation between plasma TGF-β1 levels and occurrence of AF. 12[14–26] studies in which the plasma TGF-β1 levels was expressed as (mean ± SD) were analyzed using TGF- β1 as a continuous variable. 4[15, 19, 25, 26] studies with OR/HR values in which the plasma TGF-β1 levels was analyzed as a categorical variable had been included in a separate analysis. Of the 13 included studies, 3 studies [15, 19, 25] analyzed TGF-β1 levels as both a continuous variable and categorical variable.
The pooled analysis of included studies showed that plasma TGF-β1 levels in the patients with AF was significantly higher than those without AF in both analyses; continuous variable (SMD 0.67; 95%CI 0.29–1.05) with significant heterogeneity across studies (I² = 91%, P<0.00001) (Fig 2) and categorical variable (OR 1.01, 95% CI 1.01–1.02) with moderate heterogeneity across studies (I² = 62%, P = 0.05) (Fig 3). Patients with persistent AF had higher TGF-β1 levels than that in paroxysmal AF patients (SMD 0.57; 95%CI 0.22–0.92) without significant heterogeneity (I² = 30%, P = 0.23) across studies (Fig 4).
AF, atrial fibrillation; CI, confidence interval; SD, standard deviation.
AF, atrial fibrillation; CI, confidence interval; OR, odds ratio.
Sensitivity and subgroup analysis
A subgroup analysis was performed based on the type of AF on 5 studies and it showed a positive correlation between high TGF-β1 plasma levels and the risk of new-onset AF (SMD 1.07; 95%CI 0.26–1.89) with significant heterogeneity (I² = 95%, P<0.00001) across studies. However, there was no clear relationship between plasma TGF-β1 levels and the risk of recurrent AF (SMD 0.38; 95%CI (-0.05–0.81) with significant heterogeneity (I² = 83%, P<0.00001) across studies (Fig 2).
A predefined subgroup analysis was performed to investigate the origin of the heterogeneity between studies. In the subgroups with follow-up <12 months, LVEF <50% and sample size ≥100, there were no significant heterogeneity between studies. Therefore, the follow-up duration, LVEF and sample size are likely the origin of the significant heterogeneity in our meta-analysis (Table 4).
Finally, we performed a sensitivity analysis and found that there was no significant difference on the overall heterogeneity regardless of which study was removed. The result of the funnel plot for TGF-β1 in AF patients was asymmetrical, indicating the potential for publication bias (Fig 5). After removing the study with the highest levels of TGF-β1, the result of the funnel plot was symmetrical (Fig 6).
The main finding of this comprehensive meta-analysis is that there is an association between high plasma TGF-β1 levels and risk of AF, especially for new-onset AF. To the best of our knowledge, this is the first comprehensive meta-analysis performed to investigate their relationship.
TGF-β1 is a major factor promoting collagen production in cardiac fibroblasts . It is also considered to be a key factor in the signal cascade reaction during the process of tissue fibrosis [28, 29]. Multiple studies have found increased atrial fibrosis on biopsy or autopsy specimens of patients with AF and concurrently elevated plasma level of TGF-β1 [30, 31, 32]. Previous studies have only investigated the plasma level of TGF-β1 in specific subgroups, such as the patients who developed recurrent AF (following surgical maze procedure, electrical cardioversion and catheter ablation) or patients with new-onset AF after cardiac surgery. In our meta-analysis, different groups were analyzed together to identify the potential role of TGF-β1 in promoting AF. We demonstrated that there is a positive correlation between higher plasma TGF-β1 levels and the development of new onset AF and the overall occurrence of AF. However, it is worth noting that there was no clear relationship between plasma TGF-β1 levels and recurrent AF in the subgroup analysis. This finding could be due to the heterogeneity in study population and AF management strategy between studies.
4 studies included information on the type of AF, whether they were persistent or paroxysmal AF. Persistent AF was defined as AF lasting for more than 7 days while paroxysmal AF was defined as AF with spontaneous termination in less than 7 days. To investigate the relationship between TGF-β1 and the type of AF, we analyzed these 4[14, 16–18] studies which included the plasma level of TGF-β1 in both persistent AF and paroxysmal AF patients. TGF-β1 levels were found to be higher in patients with persistent AF compared to paroxysmal AF. The finding was expected as atrial fibrosis is more extensive with longer duration of AF. It also leads us to the hypothesis that TGF-β1 as an index of atrial fibrosis may inform us of the chronicity of AF.
Despite being the most common sustained arrhythmia, the mechanism of AF is poorly understood. Systemic inflammatory response appear to be the contributing factor to the occurrence and recurrence of AF. A meta-analysis performed by Wu et al  demonstrated that high levels of circulating inflammatory factors especially CRP and IL-6 are associated with greater risk of AF in the general population, occurrence of AF after coronary artery bypass grafting and AF recurrence after electrical cardioversion or catheter ablation. In our analysis, we found that elevated levels of plasma TGF-β1 was associated with the occurrence of new onset AF. Our findings provide important insight into the mechanisms of AF.
The present meta-analysis has several limitations. First, AF duration and methods of AF detection were different among the studies which account for the heterogeneity between the individual studies. Second, the sample size of the meta-analysis was relatively small. Finally, the asymmetrical funnel plot suggests that there may be publication bias.
In conclusion, this meta-analysis suggests an association between high plasma TGF-β1 and the occurrence of new onset AF. Additional studies with larger sample sizes are needed to further investigate the relationship between plasma TGF-β1 and the occurrence of AF.
This work was supported by grants (81270245, 81570298 to T.L. and 81370300, to G.L.) from the National Natural Science Foundation of China and the Specialized Research Fund for the Doctoral Program of Higher Education (20121202110004 to G.L.).
Conceived and designed the experiments: JL YY CN ZZ TL GL. Performed the experiments: JL YY. Analyzed the data: JL YY. Contributed reagents/materials/analysis tools: JL YY. Wrote the paper: JL YY CN.
- 1. Tan AY, Zimetbaum P. Atrial fibrillation and atrial fibrosis. J Cardiovasc Pharmacol 2011; 57:625–9. pmid:21633248
- 2. Kostin S, Klein G, Szalay Z, Hein S, Bauer EP, Schaper J. Structural correlate of atrial fibrillation in human patients. Cardiovasc Res 2002; 54:361–79. pmid:12062341
- 3. Beyerbach DM, Zipes DP. Mortality as an endpoint in atrial fibrillation. Heart Rhythm 2004; 1:B8–19. pmid:15851134
- 4. Abed HS, Samuel CS, Lau DH, Kelly DJ, Royce SG, Alasady M, et al. Obesity results in progressive atrial structural and electrical remodeling: implications for atrial fibrillation. Heart Rhythm 2013; 10:90–100. pmid:23063864
- 5. Everett TH, Olgin JE. Atrial fibrosis and the mechanisms of atrial fibrillation. Heart Rhythm 2007; 4:S24–27. pmid:17336879
- 6. Gramley F, Lorenzen J, Koellensperger E, Kettering K, Weiss C, Munzel T. Atrial fibrosis and atrial fibrillation: the role of the TGF-beta1 signaling pathway. Int J Cardiol 2010; 143:405–413. pmid:19394095
- 7. Redondo S, Navarro-Dorado J, Ramajo M, Medina U, Tejerina T. The complex regulation of TGF-beta in cardiovascular disease. Vasc Health Risk Manag 2012; 8:533–539. pmid:23028232
- 8. Khan R, Sheppard R. Fibrosis in heart disease: understanding the role of transforming growth factor-β in cardiomyopathy, valvular disease and arrhythmia. Immunology 2006; 118:10–24. pmid:16630019
- 9. Verheule S, Sato T, Everett T, Engle SK, Otten D, Rubart-von der Lohe M, et al. Increased vulnerability to atrial fibrillation in transgenic mice with selective atrial fibrosis caused by overexpression of TGF-beta1. Circ Res 2004; 94:1458–1465. pmid:15117823
- 10. Bunch TJ, Mahapatra S, Bruce GK, Johnson SB, Miller DV, Horne BD, et al. Impact of transforming growth factor-beta1 on atrioventricular node conduction modification by injected autologous fibroblasts in the canine heart. Circulation 2006; 113:2485–94. pmid:16717152
- 11. Levine M, Walter S, Lee H, Haines T, Holbrook A, Moyer V. Users' guides to the medical literature. IV. How to use an article about harm. Evidence-Based Medicine Working Group. JAMA 1994; 271:1615–1619. pmid:8182815
- 12. Harris RP, Helfand M, Woolf SH, Lohr KN, Mulrow CD, Teutsch SM, et al. Current methods of the US Preventive Services Task Force: a review of the process. Am J Prev Med 2001; 20:21–35.
- 13. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003; 327:557–560. pmid:12958120
- 14. Lin X, Wu N, Shi Y, Wang S, Tan K, Shen Y, et al. Association between transforming growth factor β1 and atrial fibrillation in essential hypertensive patients. Clin Exp Hypertens 2015; 37:82–87. pmid:25496287
- 15. Wu CH, Hu YF, Chou CY, Lin YJ, Chang SL, Lo LW, et al. Transforming growth factor-β1 level and outcome after catheter ablation for nonparoxysmal atrial fibrillation. Heart Rhythm 2013; 10:10–5. pmid:22982967
- 16. Xiao H, Lei H, Qin S, Ma K, Wang X. TGF-beta1 expression and atrial myocardium fibrosis increase in atrial fibrillation secondary to rheumatic heart disease. Clin Cardiol 2010; 33:149–156. pmid:20235205
- 17. Zhao F, Zhang S, Chen Y, Gu W, Ni B, Shao Y, et al. Increased expression of NF-AT3 and NF-AT4 in the atria correlates with procollagen I carboxyl terminal peptide and TGF-β1 levels in serum of patients with atrial fibrillation. BMC Cardiovasc Disord 2014; 14:167. pmid:25422138
- 18. Mira YE, Muhuyati , Lu WH, He PY, Liu ZQ, Yang YC. TGF-β1 signal pathway in the regulation of inflammation in patients with atrial fibrillation. Asian Pac J Trop Med 2013; 6:999–1003. pmid:24144036
- 19. Canpolat U, Oto A, Hazirolan T, Sunman H, Yorgun H, Sahiner L, et al. A prospective DE-MRI study evaluating the role of TGF-β1 in left atrial fibrosis and implications for outcomes of cryoballoon-based catheter ablation: new insights into primary fibrotic atriocardiomyopathy. J Cardiovasc Electrophysiol 2015; 26:251–9. pmid:25366936
- 20. On YK, Jeon ES, Lee SY, Shin DH, Choi JO, Sung J, et al. Plasma transforming growth factor beta1 as a biochemical marker to predict the persistence of atrial fibrillation after the surgical maze procedure. J Thorac Cardiovasc Surg 2009; 137:1515–1520. pmid:19464473
- 21. Wang Y, Hou X, Li Y. Association between transforming growth factor beta1 polymorphisms and atrial fibrillation in essential hypertensive subjects. J Biomed Sci 2010; 17:23. pmid:20356380
- 22. Kim SK, Pak HN, Park JH, Ko KJ, Lee JS, Choi JI, et al. Clinical and serological predictors for the recurrence of atrial fibrillation after electrical cardioversion. Europace 2009; 11:1632–1638. pmid:19858160
- 23. Kimura T, Takatsuki S, Inagawa K, Katsumata Y, Nishiyama T, Nishiyama N, et al. Serum Inflammation Markers Predicting Successful Initial Catheter Ablation for Atrial Fibrillation. Heart Lung Circ 2014; 23:636–643. pmid:24613042
- 24. Shim J, Joung B, Park JH, Uhm JS, Lee MH, Pak HN. Long duration of radiofrequency energy delivery is an independent predictor of clinical recurrence after catheter ablation of atrial fibrillation: Over 500 cases experience. Int J Cardiol 2013; 167:2667–2672. pmid:22790188
- 25. Smit MD, Maass AH, De Jong AM, Muller Kobold AC, Van Veldhuisen DJ, Van Gelder IC. Role of inflammation in early atrial fibrillation recurrence. Europace 2012; 14:810–817. pmid:22237586
- 26. Rosenberg MA, Maziarz M, Tan AY, Glazer NL, Zieman SJ, Kizer JR, et al. Circulating fibrosis biomarkers and risk of atrial fibrillation: The Cardiovascular Health Study (CHS). Am Heart J 2014; 16:723–8.
- 27. Biernacka A, Dobaczewski M, Frangogiannis NG. TGF-beta signaling in fibrosis. Growth Factors 2011; 29:196–202. pmid:21740331
- 28. Kim SK, Park JH, Kim JY, Choi JI, Joung B, Lee MH, et al. High plasma concentrations of transforming growth factor-β and tissue inhibitor of metalloproteinase-1: potential non-invasive predictors for electroanatomical remodeling of atrium in patients with non-valvular atrial fibrillation. Circ J 2011; 75:557–564. pmid:21186331
- 29. Arujuna A, Karim R, Caulfield D, Knowles B, Rhode K, Schaeffter T, et al. Acute pulmonary vein isolation is achieved by a combination of reversible and irreversible atrial injury after catheter ablation: evidence from magnetic resonance imaging. Circ Arrhythm Electrophysiol 2012; 5:691–700. pmid:22652692
- 30. Rahmutula D, Marcus GM, Wilson EE, Ding CH, Xiao Y, Paquet AC, et al. Molecular basis of selective atrial fibrosis due to overexpression of transforming growth factor-b1. Cardiovasc Res 2013; 99:769–779. pmid:23612580
- 31. Zhao F, Zhang S, Shao Y, Wu Y, Qin J, Chen Y, et al. Calreticulin overexpression correlates with integrin-α5 and transforming growth factor-β1 expression in the atria of patients with rheumatic valvular disease and atrial fibrillation. Int J Cardiol 2013; 168:2177–2185. pmid:23465222
- 32. Zhang YJ, Ma N, Su F, Liu H, Mei J. Increased TRPM6 expression in atrial fibrillation patients contribute to atrial fibrosis. Exp Mol Pathol 2015; 98:486–490. pmid:25796343
- 33. Wu N, Xu B, Xiang Y, Wu L, Zhang Y, Ma X, et al. Association of inflammatory factors with occurrence and recurrence of atrial fibrillation: a meta-analysis. Int J Cardiol 2013; 25; 169:62–72. pmid:24095158