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Thiazolidinedione use and atrial fibrillation in diabetic patients: a meta-analysis

  • Zhiwei Zhang1,
  • Xiaowei Zhang1,
  • Panagiotis Korantzopoulos2,
  • Konstantinos P. Letsas3,
  • Gary Tse4, 5,
  • Mengqi Gong1,
  • Lei Meng1,
  • Guangping Li1 and
  • Tong Liu1Email author
Contributed equally
BMC Cardiovascular DisordersBMC series – open, inclusive and trusted201717:96

DOI: 10.1186/s12872-017-0531-4

Received: 28 October 2016

Accepted: 1 April 2017

Published: 5 April 2017

Abstract

Background

Accumulating evidence suggests that thiazolidinediones (TZDs) may exert protective effects in atrial fibrillation (AF). The present meta-analysis investigated the association between TZD use and the incidence of AF in diabetic patients.

Methods

Electronic databases were searched until December 2016. Of the 346 initially identified records, 3 randomized clinical trials (RCTs) and 4 observational studies with 130,854 diabetic patients were included in the final analysis.

Results

Pooled analysis of the included studies demonstrated that patients treated with TZDs had approximately 30% lower risk of developing AF compared to controls [odds ratio (OR): 0.73, 95% confidence interval (CI): 0.62 to 0.87, p = 0.0003]. This association was consistently observed for both new onset AF (OR =0.77, p = 0.002) and recurrent AF (OR =0.41, p = 0.002), pioglitazone use (OR =0.56, p = 0.04) but not rosiglitazone use (OR =0.78, p = 0.12). The association between TZD use and AF incidence was not significant in the pooled analysis of three RCTs (OR =0.77, 95% CI = 0.53–1.12, p = 0.17), but was significantly in the pooled analysis of the four observational studies (OR =0.71, p = 0.0003).

Conclusions

This meta-analysis suggests that TZDs may confer protection against AF in the setting of diabetes mellitus (DM). This class of drugs can be used as upstream therapy for DM patients to prevent the development of AF. Further large-scale RCTs are needed to determine whether TZDs use could prevent AF in the setting of DM.

Keywords

Atrial fibrillation Diabetes mellitus Thiazolidinediones Pioglitazone Rosiglitazone Meta-analysis

Background

Atrial fibrillation (AF) is the most prevalent arrhythmia observed in clinical practice, and is associated with significant morbidity and mortality in the popuation. The burden of AF increases over time mainly due to an aging population and to the increasing prevalence of cardiovascular comorbidities. However, strategies to predict and prevent AF are not fully effective [1]. Diabetes mellitus (DM) is one of the strongest independent risk factors for AF incidence, conferring an approximate 40% higher risk of subsequent AF development [2, 3]. It also predicts the recurrence of AF following a successful direct current cardioversion [4]. Moreover, DM increases the risk of developing stroke, heart failure, and cardiovascular death in patients with AF [5]. Although the exact pathophysiological mechanisms linking DM and AF remain incompletely elucidated, an increasing body of evidence suggests that inflammation and oxidative stress may play an important role [68].

Thiazolidinediones (TZDs), a class of peroxisome proliferator-activated receptor-γ (PPAR-γ) agonists, are among the most potent insulin-sensitizing drugs [9]. Apart from their anti-diabetic activity, TZDs display several pleiotropic effects including anti-inflammatory and antioxidant actions that may have potential benefits for AF prevention [10, 11]. However, inconsistent results have been reported regarding TZDs use and AF incidence [1218]. In light of such conflicting data, we performed a comprehensive meta-analysis to evaluate the present evidence and investigate whether the use of TZDs confers benefits in preventing AF.

Methods

This systematic review was conducted according to the Quality of Reports of Meta-Analyses of Randomized Controlled Trials (QUOROM) recommendations [19] and the guidelines of the Meta-analysis of Observational Studies in Epidemiology Group (MOOSE) [20].

Inclusion criteria

The studies considered for this meta-analysis were either randomized clinical trials (RCTs) or observational studies that investigated the potential effects of TZDs on AF. The inclusion criteria were as follows: RCTs: 1) randomized controlled human trials with a parallel design; 2) comparison of TZDs with control; 3) collecting data on new or recurrent AF during follow-up. Observational Studies: 1) comparison of TZDs with control; 2) evaluating new or recurrent AF as an outcome. In the studies of interventions with TZDs no limit in the length of follow-up period was set due to the paucity of relevant studies.

Search strategies

A systematic literature search was performed by two investigators (Z. Z. and X. Z.) using the online databases of PubMed and Embase to identify relevant studies published before December 2016. The following key terms were used: “thiazolidinediones”, “pioglitazone”, “rosiglitazone”, “troglitazone”, and “atrial fibrillation”. Both investigators independently evaluated the search results and identified potential studies for further assessment. Disagreements were resolved by a third reviewer (T. L.).

Quality assessment and data extraction

As quality scoring in meta-analyses of RCTs and observational studies is controversial, several key points of study quality were assessed according to a critical review checklist of Wynn et al. [21]. The key points of this checklist and quality assessments of included studies are listed in Table 1.
Table 1

Quality assessments of included studies

Study, year

Study

type

Randomisation

Method

Blinding

Eligibility criteria reported

Study Population representative of normal practice

Method of follow-up properly defined

Equal follow-up between groups

Was loss to follow-up

reported or explained

Prospective

recruitment

Consecutive

recruitment

PROactive, 2005 [12]

RCT

Randomised permuted blocks

Double

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Anglade, 2007 [13]

Case

control

NA

NA

Yes

Yes

Yes

Yes

No loss to follow-up

No

Yes

RECORD, 2009 [14]

RCT

Randompermuted

blocks

None

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Gu, 2011 [15]

Cohort

NA

NA

Yes

Yes

Yes

Yes

No loss to follow-up

Yes

Yes

Chao, 2012 [16]

Case

control

NA

NA

Yes

Yes

Yes

Yes

No loss to follow-up

No

Yes

Liu, 2014 [17]

RCT

Computer

Double

Yes

Yes

Yes

Yes

No loss to follow-up

Yes

Yes

Pallisgaard, 2016 [18]

Cohort

NA

NA

Yes

Yes

Yes

Yes

No loss to follow-up

Yes

Yes

Abbreviations: RCT randomized controlled trial, NA not applicable

Two investigators (Z. Z. and X. Z.) independently extracted the relevant data using a pre-defined spreadsheets. The extracted data elements of the meta-analysis included information on the inclusion criteria, publication details, study design, follow-up duration, daily dosage of TZDs, definition of AF, methods of AF detection, baseline patient characteristics, the variables of multivariate model used in observational studies and results. Disagreements were resolved through discussion or consensus with a third reviewer (T. L.).

Statistical analysis

Results of the AF outcome are expressed as odds ratio (OR) with 95% confidence interval (CI) for each study using generic inverse-variance method. The hazard ratio value using multivariate Cox proportional hazards model in the primary study was directly considered as OR [22]. Raw event numbers were extracted from the RCTs and adjusted effect estimates from the observational studies to calculate the overall effects. Statistical heterogeneity was assessed by the χ2 test and quantified by using the I2 statistic. An I2 > 50% is indicative of at least moderate heterogeneity [23]. A random-effects model was used. Subgroup analyses regarding AF subtypes (new onset AF or recurrent AF), different TZDs (solely pioglitazone or solely rosiglitazone), study designs (RCTs or observational studies), and different follow-up duration (>5 years or ≤5 years) were additionally performed. Sensitivity analysis was done by removing one study at a time and checking the consequent effects on the effect estimate. Publication bias was evaluated using a funnel plot. Two-tailed p values of <0.05 were considered statistically significant. The statistical analysis was performed using the Review Manager (RevMan, version 5.3, Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014).

Results

A total of 346 records were identified initially through our literature search strategy. After careful assessment, seven studies (three RCTs [12, 14, 17] and four observational studies [13, 15, 16, 18]) comprising 130,854 diabetic patients (11,781 in the treatment and 119,073 in the control group) were included in the final meta-analysis (Fig. 1).
Fig. 1

Flow diagram of the study selection process

Three studies [12, 15, 17] examined the relationship between pioglitazone use and AF, while two other [14, 16] studied rosiglitazone use. The remaining two studies [13, 18] reported data regarding the use of pioglitazone, rosiglitazone and troglitazone. The characteristics of each study are listed in Table 2, and the patients’ characteristics in each study are shown in Table 3.
Table 2

The characteristics of 7 included studies

Study, year

Study population

Patients (n)

Comparators

Daily dosage of TZDs

Follow-up

Definition of AF

Methods of AF detection

The variables of multivariate model

PROactive, 2005 [12]

Patients with type 2 diabetes who had evidence of macrovascular disease

5238

Pioglitazone (n = 2605) vs. placebo (n = 2633)

Titrated from 15 to 45 mg

34.5 months

New-onset

AF

NA

NA

Anglade, 2007 [13]

Diabetic patients who underwent CABG and/or valvular surgery

184

Pioglitazone (n = 14), rosiglitazone (n = 24) and troglitazone (n = 2) vs. No TZD (n = 140)

Pioglitazone: average 30 mg

Rosiglitazone: average 6 mg,

Troglitazone: average 525 mg

30 days

Postoperative AF

NA

NA

RECORD, 2009 [14]

Patients with type 2 diabetes

4447

Rosiglitazone + metformin or sulfonylurea (n = 2220) vs. metformin and sulfonylurea (n = 2227)

Titrated from 4 to 8 mg

5.5 years

New-onset

AF

NA

NA

Gu, 2011

Type 2 diabetic patients with paroxysmal AF undergoing catheter ablation

161

Pioglitazone (n = 51) vs. No pioglitazone (n = 99)

30 mg

22.9 ± 5.1 months

Recurrent ATa (AF, AT, AFL)

ECG and Holter recording

Duration of PAF, LAD, treatment with ACEI/ARB

Chao, 2012 [16]

Patients with non-insulin dependent diabetes.

12,065

Rosiglitazone (n = 4137) vs. No rosiglitazone (n = 7928)

NA

63 ± 25 months

New-onset AF

NA

Age, HTN, CAD, chronic renal disease and use of statins or alpha-glucosidase inhibitors

Liu, 2014 [17]

Diabetic patients with the first presence of persistent AF

146

Pioglitazone (n = 70) vs. placebo (n = 76)

30 mg

20.1 months

Recurrent AF

ECG, history of arrhythmia-related symptoms, and Holter monitoring

NA

Pallisgaard, 2016 [18]

Diabetic patients of Danish nationwide registries

108,624

TZD (n = 2658) vs. other second-line antidiabetic drugs (n = 105,966)

NA

12 years

New-onset

AF

NA

Age, sex, stroke, HF, all cancer, hyperthyroidism, IHD, COPD, CKD, liver disease, vascular disease, HTN, statin use, prior CABG, and prior PCI

Abbreviations: AF atrial fibrillation, PAF paroxysmal atrial fibrillation, ATa atrial tachyarrhythmias, AT atrial tachycardia, AFL atrial flutter, ECG electrocardiograph, CABG coronary artery bypass graft, TZDs thiazolidinediones, LAD left atrial diameter, ACEI angiotensin converting enzyme inhibitor, ARB angiotensin receptor blocker, HTN hypertension, CAD coronary arterial disease, IHD ischaemic heart disease, COPD chronic obstructive pulmonary disease, CKD chronic kidney disease, PCI percutaneous coronary intervention, NA not applicable

Table 3

Patients characteristics of 7 included studies

Study, year

Design

Age (years)

T/C

Male

T/C

HF

T/C

HTN

T/C

CAD

T/C

HbA1c (%) T/C

β-blocker

T/C

CCB

T/C

ACEI/ARB

T/C

Statin

T/C

Insulin

T/C

PROactive, 2005 [12]

RCT

61.9/61.6

67%/66%

NA

75%/76%

48%/48%

7.8/7.9

55%/54%

34%/37%

70%/70%

43%/43%

33.2%/34%

Anglade, 2007 [13]

Nested case control study of patients from the AFIST I, II and III trials

65.8/67.2

72.5%/71.5%

15.0%/18.8%

90.0%/75.7%

NA

NA

75.0%/75.0%

12.5%/21.5%

75.0%/56.9%

77.5%/61.8%

NA

RECORD, 2009 [14]

RCT

58.4/58.5

51.4%/51.7%

0.5%/0.4%

NA

NA

7.9/7.9

22.6%/20.9%

19.1%/21.6%

43.1%/42.1%

18%/19.2%

NA

Gu, 2011

Prospective cohort study

59.6/58.7

52.9%/45.5%

0/0

62.7%/72.7%

5.9%/5.1%

6.2/6.4

35.3%/37.4%

35.3%/28.3%

56.9%/45.5%

13.7%/12.1%

3.9%/2.0%

Chao, 2012 [16]

Nested case control study of patients from NHIRD

53.7/54.1

52.9%/53.6%

4.1%/4.7%

38.1%/44.5%

16.9%/18.4%

NA

45.5%/46.4%

NA

68.6%/68.3%

59%/57.4%

0/0

Liu, 2014 [17]

RCT

60.70/62.25

74.3%/ 65.8%

0/0

28.6%/30.3%

28.6%/30.3%

6.41/6.19

41.4%/38.2%

20%/17.1%

NA

31.4%/34.2%

NA

Pallisgaard, 2016 [18]

Prospective cohort study

59.59/62.40

56.7%/ 58.1%

2.3%/4.9%

50.2%/48.4%

NA

NA

31.5%/31.5%

NA

58.8%/55.9%

58.0%/53.0%

NA

Abbreviations: RCT randomized controlled trial, HF heart failure, HTN hypertension, CAD Coronary arterial disease, HbA1c haemoglobin A1c, CCB calcium channel blocker, ACEI angiotensin converting enzyme inhibitor, ARB angiotensin receptor blocker, T/C thiazolidinediones group/control group, NA not applicable

Of the seven studies, four [1518] studies showed that TZDs use attenuated either the risk of new-onset or recurrent AF, whereas the other three [1214] studies did not indicate a statistically significant difference. Overall, the pooled analysis of the seven included studies suggested that patients treated with TZDs have nearly 30% lower risk of AF compared with controls (OR =0.73, 95% CI = 0.62–0.87, p = 0.0003; Fig. 2). No significant heterogeneity between the individual studies was observed (P = 0.36, I2 = 9%).
Fig. 2

Forest plot showing the association association between thiazolidinediones (TZDs) and atrial fibrillation (AF)

Subgroup analyses according to AF types, different TZDs, follow-up duration, and study designs were subsequently performed (Fig. 2, Table 4). TZDs use was associated with a decrease in the risk of both new-onset [12, 14, 16, 18] (OR =0.77, 95% CI = 0.65–0.91, p = 0.002) and recurrent AF [13, 15, 17] (OR =0.41, 95% CI = 0.24–0.72, 0.002) without any heterogeneity across the studies. Regarding different TZDs, pioglitazone use [12, 15, 17] (OR =0.56, 95% CI = 0.32–0.98, p = 0.04; I2 = 54%) was associated with a lower risk of AF incidence, whereas rosiglitazone use [14, 16] was not significantly associated with a decreasing AF incidence (OR =0.78, 95% CI = 0.57–1.07, p = 0.12; I2 = 34%). Regarding the subgroup analysis on different follow-up duration, there was no significant difference between the 3 studies [14, 16, 18] with a follow-up duration >5 years (OR =0.76, 95% CI = 0.63–0.91, p = 0.002; I2 = 0%) and the 4 studies [12, 13, 15, 17] with a follow-up duration ≤5 years (OR =0.62, 95% CI = 0.41–0.94, p = 0.02; I2 = 34%). Finally, the pooled analysis of the 4 [13, 15, 16, 18] observational studies showed a strong association between TZDs use and risk reduction of AF (OR =0.71, 95% CI = 0.59–0.85, p = 0.0003; I2 = 0%), whereas the pooled analysis of the three RCTs showed a non-statistically significant 23% reduction in the odds of developing AF (OR =0.77, 95% CI = 0.53–1.12, p = 0.10; I2 = 40%).
Table 4

Subgroup analyses of the association between TZDs and AF

Subgroup

Study

Number of studies

Heterogeneity

Meta-analysis

I2

P-Value

OR

95% CI

p-Value

AF types

New-onset AF

4

0%

0.64

0.77

0.65–0.91

0.002

Recurrent AF

2

0%

0.54

0.41

0.24–0.72

0.002

TZDs

Solely pioglitazone

3

54%

0.11

0.56

0.32–0.98

0.04

Solely rosiglitazone

2

34%

0.22

0.78

0.57–1.07

0.12

Follow-up duration

≤ 5 years

4

34%

0.21

0.62

0.41–0.94

0.02

> 5 years

3

0

0.47

0.76

0.63–0.91

0.002

Study design

RCTs

3

40%

0.10

0.77

0.53–1.12

0.17

Observational studies

4

0%

0.41

0.71

0.59–0.85

0.0003

Abbreviations: TZDs thiazolidinediones, AF atrial fibrillation, RCTs randomized controlled trials, OR odds ratio, CI confidence interval

Besides, due to different pathophysiologic mechanisms of AF, a sensitivity analysis was performed by removing the studies evaluated post-operation AF [13] and post-AF [15] ablation recurrences, no significant differences were found in the heterogeneity (P = 0.44; I2 = 0%) among the remaining five studies [12, 14, 1618], and the overall outcome remained the same (OR =0.75, 95% CI = 0.64–0.88, p = 0.0003).

Discussion

The main findings of this comprehensive meta-analysis on 130,854 diabetic patients are the following: i. TZDs may confer protection against AF incidence; ii. the beneficial effects of TZDs were consistently observed in both new onset and recurrent AF; iii. Pioglitazone use was associated with a statistically reduced risk of incident AF, whereas rosiglitazone use showed no statistically significant difference; and iv. the protective effects of TZDs were only observed in the pooled analysis of the observational studies rather than the RCTs.

The PROactive [12] and RECORD [14] RCTs showed that pioglitazone or rosiglitazone use does not provide any benefit in preventing AF incidence among high-risk patients with type 2 DM. However, in these two RCTs, AF was reported as an adverse event rather than a predefined endpoint. Furthermore, these trials displayed a very low AF incidence in both intervention and control groups (1.5–2%), and thus AF detection may be underpowered.

Moreover, in the present meta-analysis, we observed that pioglitazone use was associated with beneficial effects on AF prevention compared with rosiglitazone use. Similarly, previous study suggested that pioglitazone has a beneficial effect on cardiovascular disease, whereas rosiglitazone seemed to increase cardiovascular risk [24]. By assembling a diabetic cohort of older than 65 years, Winkelmayer et al. [25] demonstrated greater risk of mortality and congestive heart failure among patients who initiated therapy with rosiglitazone compared with pioglitazone, however, there were no differences in their incidences of myocardial infarction or stroke. Previous data [26] also showed similar effects on glycemic control between pioglitazone and rosiglitazone, as well as on other parameters such as C-reactive protein (CRP), plasminogen activator inhibitor-1 and indices of insulin secretion and sensitivity. However, pioglitazone treatment was associated with greater beneficial changes on plasma lipids than rosiglitazone treatment [26], which may partly explain the advantage of pioglitazone in reducing AF incidence.

Recently, the IRIS trial [27] demonstrated that pioglitazone can prevent fatal or nonfatal stroke or myocardial infarction among patients who have insulin resistance along with cerebrovascular disease. However, the underlying mechanism for these beneficial effects of pioglitazone remains incompletely elucidated. AF is a known risk factor of morbidity and mortality by predisposing to strokes and acute coronary syndrome [28]. Thus, it is possible to postulate that pioglitazone reduces the stroke or MI events partly through the reduction of AF burden.

Accumulating evidence supports the role of inflammation and immune response activation in the genesis and perpetuation of AF in different clinical settings, including cardiac surgery, electrical cardioversion and catheter ablation [29]. Oxidative stress has been suggested to play an important role in AF incidence [30]. Numerous studies have demonstrated that TZDs may attenuate inflammation and oxidative stress as well as atrial electrophysiological and structural remodeling in different animal models.

In a ventricular tachypacing-induced CHF rabbit model, Shimano et al. [31] showed that pioglitazone prevents atrial structural remodeling and inhibits AF promotion. Also, similarly to candesartan, pioglitazone suppresses transforming growth factor-β1 (TGF-β1) and tumor necrosis factor-α (TNF-α) expression in atrial tissue, molecules that are inflammatory mediators related to fibrosis-mediated AF incidence [29]. More recently, Kume et al. [32] suggested that pioglitazone effectively attenuates inflammatory profibrotic signals and vulnerability to AF in a pressure overload AF rat model, possibly via its suppression in monocyte chemoattractant protein (MCP-1) expression. PPAR-γ agonists have been shown to attenuate Angiotensin II (Ang II) -induced atrial electrical and structural remodeling in cellular models [33]. These effects are mediated by prevention of ICa-L remodeling by inhibiting CAMP responsive element binding protein (CREB) phosphorylation, as well as by suppression of connective tissue growth factor (CTGF) expression and cell proliferation via inhibiting TGF-β1/Smad2/3 and TGF-β1/tumor necrosis factor receptor associated factor 6 (TRAF6)/TGF-β-associated kinase 1 (TAK1) signaling pathways. In addition, Pioglitazone exhibits beneficial effects on Ang II-induced potassium channel remodeling [34]. More recently, Chen et al. [35] further indicated that pioglitazone inhibits Ang II-induced atrial fibroblasts proliferation through nuclear factor-κB (NF-κB)/TGF-β1/Toll/IL-1 receptor domain-containing adaptor inducing IFN-β (TRIF)/TRAF6 signaling pathway. Additionally, Xu et al. [36] suggested that pioglitazone prevents age-related arrhythmogenic atrial remodeling and AF incidence by improving heat shock protein (HSP) 70 expression and antioxidant capacity, and by inhibiting the mitochondrial apoptotic signaling pathway. In an alloxan-induced diabetic rabbit model, we have shown that rosiglitazone attenuates arrhythmogenic atrial structural remodeling and AF incidence via anti-inflammatory and antioxidant effects [37]. In keeping with these findings, the IRIS trial found lower CRP levels in the pioglitazone group than in the placebo group. Indeed, increased CRP levels have been associated with greater risk of AF [38].

Finally, the treatment of hyperglycemia may have favorable effects on AF burden. In other words, treatment of DM may ameliorate atrial remodeling [7]. Haemoglobin A1c levels have been associated with the occurrence and recurrence of AF [7, 39, 40], and therefore TZDs may exert their favorable effects through HbA1c level reduction.

Study limitations

The present meta-analysis has potential limitations. Firstly, due to the small number of included studies we analyzed observational studies and RCTs together while 2 included RCTs reported AF as an adverse event rather than a predefined endpoint, and the favorable effects of TZDs use on preventing AF incidence were predominately driven by observational studies, whereas data from the 2 RCTs were unable to draw unanimous conclusion. Secondly, information regarding methods of AF detection, cardiac substrate, ejection fraction and atrial volume were not fully presented in our analysis due to the lack of relative data. Thirdly, the heterogeneous types of patient populations (ranging from uncomplicated type 2 diabetics to post-CABG or post-AF ablation patients) may indicate latent bias in this meta-analysis. Fourthly, “gray” literature (primarily conference abstracts/presentations, ongoing studies, communication with investigators) was not searched. Finally, the results of the funnel plot suggested that publication bias may be present, although the small number of studies made this somewhat difficult to interpret (Fig. 3).
Fig. 3

Funnel plot of meta-analysis

Conclusions

In summary, this meta-analysis suggests that TZDs may be effective in AF prevention in the setting of DM. Therefore, TZDs may be considered as the treatment of choice in diabetic patient with high risk features for AF incidence. Since the overall conclusion was mainly drawn from the observational studies, further large-scale prospective RCTs that assessed AF as a predefined outcome are needed to determine whether TZDs use could prevent AF in the setting of DM.

Notes

Abbreviations

AF: 

Atrial fibrillation

CHF: 

Congestive heart failure

CI: 

Confidence interval

CRP: 

C-reactive protein

DM: 

Diabetes mellitus

OR: 

Odds ratio

PPAR-γ: 

Peroxisome proliferator-activated receptor-γ

RCTs: 

Randomized clinical trials

TZDs: 

Thiazolidinediones

Declarations

Acknowledgments

Not applicable.

Funding

This work was supported by grants (81,570,298, 30,900,618, 81,270,245 to T.L.) from the National Natural Science Foundation of China, Tianjin Natural Science Foundation (16JCZDJC34900 to T.L.).

Availability of data and materials

All data generated or analysed during this study are included in this published article.

Authors’ contributions

ZZ and ZX participated in study design, searched databases, extracted and assessed data, carried out the statistical analysis and drafted the manuscript. LM and MG performed statistical analyses. TL and GL conceived the design of the study, selected the included studies and drafted the review. KP, KPL and GT revised the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

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Authors’ Affiliations

(1)
Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University
(2)
First Department of Cardiology, University of Ioannina Medical School
(3)
Second Department of Cardiology, Laboratory of Cardiac Electrophysiology, “Evangelismos” General Hospital of Athens
(4)
Department of Medicine and Therapeutics, Faculty of Medicine, Chinese University of Hong Kong
(5)
Li KaShing Institute of Health Sciences, Faculty of Medicine, Chinese University of Hong Kong

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