Abstract
Background
Telomere length is a surrogate marker of biological aging. Whether telomere length predicts the risk of atrial fibrillation (AF) independently of biological aging is controversial. We conducted a cohort study to examine the relationship between telomere length and paroxysmal AF (PAF), followed by a systematic review and meta‐analysis of the published literature, incorporating our own data.
Methods
DNA was extracted from peripheral blood. Leucocyte telomere length was measured by a real‐time polymerase chain reaction‐based method, normalized to a single copy gene, and presented as telomere/single gene ratio (t/s).
Results
A total of 100 non‐AF patients and 50 PAF patients (mean age: 61.0 ± 9.4 and 64.0 ± 10.7 years, respectively) were included. T/s for subjects without AF tended to be shorter than for those with AF (0.21 [0.06‐0.36] vs 0.28 [0.11‐0.71], P = .077). T/s was associated with a 1.60‐fold increase in the risk of AF but this was not significant (95% CI: 0.988‐2.597, P = .056). Our meta‐analysis confirms no difference in telomere length between AF and non‐AF patients and t/s was not associated with higher risk of AF in multivariate analysis.
Conclusions
Our prospective data showed that leucocyte telomere length was similar between AF and non‐AF patients but was significantly longer in male patients with PAF than those without AF in our subgroup analysis. Our meta‐analysis found that t/s did not predict AF. These findings support the notion that chronological aging, but not markers of biological aging, predicts the risk of AF.
Keywords: atrial fibrillation, telomere length, telomere/single gene ratio
1. INTRODUCTION
Advancing age is considered to be a significant risk factor for atrial fibrillation (AF). Several studies have reported an increasing prevalence of AF with age, taking a value of 4% in individuals older than 60 years1 and 8% in those older than 80 years.1, 2 However, the underlying mechanisms underlying aging‐related AF remain incompletely elucidated. AF is an important clinical condition as it shows a strong association with higher hospitalization rate, and higher mortality, and a poorer quality of life, as well as displaying a slight preponderance toward males.1, 3 It is therefore important to identify the patients who are at risk of developing AF.
Telomeres are tandem repeats of TTAGGG at the ends of chromosomes and shorten with each successful cell division, which is considered a sign of biological aging.1, 2, 4, 5, 6, 7 Short leucocyte telomere length (LTL) is a consequence of cell senescence. High oxidative stress or a pro‐inflammatory state can result in telomere shortening.8 LTL has been associated with diseases involving increased oxidative stress.9, 10 However, its association with AF has not been addressed. A retrospective study performed by Carlquist et al1 has shown that subjects with paroxysmal AF rather than persistent AF or permanent AF have significantly shorter telomere length than those without AF. In the present study, we conducted a prospective cohort study to determine the association between LTL and PAF, followed by a systematic review and meta‐analysis of published literature on this issue.
2. METHODS
2.1. Prospective study
2.1.1. Study design and subjects
The study was approved by the Second Hospital of Tianjin Medical University Ethics Committee. All subjects provided informed consent for enrolment into this study. Patients who were diagnosed with non‐rheumatic, non‐valvular AF were consecutively selected from inpatients in Department of Cardiology, the Second Hospital of Tianjin Medical University as the PAF group between May 2014 and January 2016. The exclusion criteria were listed as follows: 1 ≥ 80 years,2 persistent or permanent atrial fibrillation,3 established diagnosis of coronary artery disease by coronary angiogram,4 dilated or hypertrophic cardiomyopathy,5 malignant arrhythmias,6 secondary hypertension,7 hepatic insufficiency,8 renal dysfunction,9 stroke,10 malignancy,11 acute or chronic inflammation. Baseline characteristics including clinical profiles, laboratory parameters, and echocardiographic variables were recorded. Body mass index (BMI) was calculated from the measurement of weight and height. Patients without AF were enrolled as non‐AF subjects. Paroxysmal AF (PAF) was defined as recurrent episodes that spontaneously terminate in ≤ 7 days; this was determined by self‐reporting or retrieved from the medical records (electrophysiology or 24‐hour Holter monitoring) after admission.
2.1.2. Leucocyte telomere length (LTL) measurements
Genomic DNA was extracted from peripheral blood leucocytes. LTL was determined with a quantitative real‐time polymerase chain reaction (PCR)‐based technique method.11 It was calculated as the ratio of telomere repeat copy number (T) to single‐copy gene copy number (S) (T/S ratio). All samples were processed within 24 hours. In brief, the Ct values of telomere and single‐copy gene (β‐globin) were tested using the Bio‐Rad DNA Engine Opticon 2 Real‐time PCR Detector (Bio‐Rad Ltd, Hercules, CA, USA). For telomere amplification, the PCR program was 95°C for 5 minutes, 33 cycles of 95 for 15 second, 54.3 for 1 minutes, 72 for 30 second; the forward primer sequence was 5′‐ CGGTTTGTTTGGGTTTGGGTTTGGGTTTGGGTTTGGGTT‐3′; and reverse primary sequence was 5′‐ GGCTTGCCTTACCCTTACCCTTACCCTTACCCTTACCCT‐3′. For β‐globin amplification, the PCR program was 95°C for 5 minutes, 37 cycles of 95 for 15 second, 56 for 30 second, 72 for 30 second; the forward primer sequence was 5′‐ GCTTCTGACACAACTGTGTTCACTAGC‐3′; and reverse primary sequence was 5′‐CACCAACTTCATCCACGTTCACC‐3′. The genomic DNA from human embryonic kidney 293 (HEK293S) was used as the standard sample. A dilution series from 3.125 to 200.00 ng (2‐fold dilution; 7 points) was added to the amplification solution for the telomere and the β‐globin PCRs. Good linearity (R 2>0.9) between Ct numbers and log (amount of input DNA) should be ensured.
2.1.3. Statistical analysis
Continuous variables were reported as means ± SD or median (interquartile range) and compared using Student's t test or the Mann‐Whitney U test as appropriate. For categorical variables, percentages were presented and the χ2 test was used for comparisons. A non‐conditional logistic regression model was used to evaluate the association between LTL and PAF before and after adjustment for confounding factors. P < .05 was considered statistically significant. SPSS (Version 19.0) was used for all analyses of this study.
2.2. Systematic review and meta‐analysis
2.2.1. Search strategy, inclusion, and exclusion criteria
This systematic review and meta‐analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISM) statement.12 PubMed and Embase were searched for studies that examined the relationship between TL and AF. The following search terms were used: (telomere length AND atrial fibrillation). The search period was from the beginning of the database to 7th January 2018 without language restrictions. The following inclusion criteria were used: (i) the study was conducted in humans and (ii) mean difference in TL between AF and non‐AF patients, or hazard ratios for AF, was reported or could be calculated from the published data.
The Newcastle‐Ottawa Quality Assessment Scale (NOS) was used for quality assessment of the included studies.13 The NOS point score system evaluated the categories of study participant selection, comparability of the results, and quality of the outcomes. The following characteristics were assessed: (i) representativeness of the exposed cohort; (ii) selection of the non‐exposed cohort; (iii) ascertainment of exposure; (iv) demonstration that outcome of interest was not present at the start of study; (v) comparability of cohorts based on study design or analysis; (vi) assessment of outcomes; (vii) follow‐up periods that were sufficiently long for outcomes to occur; and (viii) adequacy of follow‐up of cohorts. This scale ranged from zero to nine stars, which indicated that studies were graded as poor quality if the score was < 5, fair if the score was 5 to 7, and good if the score was > 8.
2.2.2. Data extraction and statistical analysis
Data from the different studies were entered in pre‐designed spreadsheets using Microsoft Excel. All abstracts were retrieved as complete manuscripts and assessed against the inclusion criteria. The data extracted include: (i) publication details: last name of first author, publication year and locations; (ii) study design; (iii) follow‐up duration; (iv) endpoint(s); (v) quality score; and (vii) characteristics of the population including sample size, gender, age and number of subjects. Two reviewers (GT and CW) reviewed each included study independently. Disagreements were resolved by adjudication with input from a third reviewer (TL).
Statistical analysis was performed using Comprehensive Meta‐Analysis software (Version 2). Heterogeneity between studies was determined using Cochran's Q value, the weighted sum of squared differences between individual study effects and the pooled effect across studies, and the I 2 statistic determined from the standard chi‐square test, which describes the proportion of total variance‐explained heterogeneity. I 2 > 50% was considered to reflect significant statistical heterogeneity. A fixed effects model was used if I 2 < 50%, otherwise the random‐effects model using the inverse variance heterogeneity method was used. To identify the source of the heterogeneity, sensitivity analysis using the leave‐one‐out method was preferred. Funnel plots, Begg and Mazumdar rank correlation test and Egger's test were used to assess for possible publication bias.
3. RESULTS
3.1. Prospective study
A total of 170 subjects who met the inclusion and exclusion criteria were finally included in our study. DNA was then extracted from all subjects. We eliminated 16 samples (8 in the non‐AF group, 8 in the PAF group) since the telomere qPCR Ct values exceeded standard, and we ruled out 4 abnormal values (1 in the non‐AF group, 3 in the PAF group) of the samples (relative t/s > 6). One hundred and fifty patients (54.7% for male and 45.3% for female) were eligible for further analysis, of whom 50 (62.0% for male) were diagnosed with non‐rheumatic paroxysmal AF and 100 (51.0% for male) without AF.
The characteristics of the study population are listed in Table 1. There were differences in the baseline characteristics between the non‐AF group and PAF group for heart failure (55.7% vs 78.0%, P = .009), BMI (23.92 ± 2.99 vs 26.65 ± 5.26, P = .017), previous use of anticoagulants (10.2% vs 62.0%, P < .001), cardiotonic agents (1.1% vs 22.0%, P < .001) or statins (86.4% vs 72.0%, P = .038), D‐dimer (0.2 [0.18‐0.30] vs 0.4 [0.20‐0.67], P = .007), fibrinogen (4.31 ± 2.25 vs 5.54 ± 1.55, P = .002), and left atrial diameter (34.71 ± 4.26 vs 37.70 ± 6.74, P = .014). Interestingly, no significant difference in t/s was found between the non‐AF group and the PAF group (0.21 [0.06‐0.36] vs 0.28 [0.11‐0.71], P = .077).
Table 1.
Baseline characteristics of population included in this study
| Variables | Non‐AF group (n = 100) | Paroxysmal AF group (n = 50) | P value |
|---|---|---|---|
| Clinical profiles | |||
| Years (y) | 61.03 ± 9.40 | 64.02 ± 10.70 | .082 |
| Male, n (%) | 51 (51.0) | 31 (62.0) | .202 |
| Diabetes mellitus, n (%) | 13/100 (13.0) | 8/50 (16.0) | .618 |
| Hypertension, n (%) | 60/100 (60.0) | 29/50 (58.0) | .814 |
| Heart failure, n (%) | 49/88 (55.7) | 39/50 (78.0) | .009a |
| BMI‐kg/m2 | 23.92 ± 2.99 | 26.65 ± 5.26 | .017a |
| Smokers, n (%) | 24/100 (24.0) | 19/50 (38.0) | .074 |
| Previous medication, n (%) | |||
| Anticoagulants | 9/88 (10.2) | 31/50 (62.0) | <.001a |
| Aspirin | 63/100 (63.0) | 32/50 (64.0) | .905 |
| Cardiotonic agents | 1/88 (1.1) | 11/50 (22.0) | <.001a |
| β receptor blockers | 40/88 (45.5) | 24/50 (48.0) | .773 |
| CCB | 44/88 (50.0) | 21/50 (42.0) | .365 |
| Statins | 76/88 (86.4) | 36/50 (72.0) | .038a |
| Laboratory parameters | |||
| BUN (mmol/L) | 5.53 ± 1.81 | 6.19 ± 2.63 | .157 |
| CR (µmol/L) | 70.58 ± 18.02 | 71.62 ± 28.44 | .800 |
| UA (µmol/L) | 312.54 ± 89.50 | 341.47 ± 98.87 | .097 |
| D‐dimer (mg/L) | 0.20 (0.18‐0.30) | 0.40 (0.20‐0.67) | .007a |
| APTT (s) | 26.37 ± 3.43 | 27.65 ± 3.84 | .063 |
| Fibrinogen (g/L) | 4.31 ± 2.25 | 5.54 ± 1.55 | .002a |
| RDW‐cv (fL) | 12.75 ± 0.65 | 12.87 ± 0.56 | .341 |
| WBC (×109/L) | 6.79 ± 1.87 | 6.25 ± 1.82 | .123 |
| Echocardiogram variables | |||
| LAD (mm) | 34.71 ± 4.26 | 37.70 ± 6.74 | .014a |
| LVEDD (mm) | 46.54 ± 6.83 | 46.94 ± 4.85 | .745 |
| LVPWT (mm) | 8.48 ± 1.19 | 8.80 ± 1.54 | .230 |
| IVST (mm) | 8.74 ± 1.45 | 9.19 ± 1.41 | .113 |
| LVEF (%) | 60.73 ± 6.69 | 60.51 ± 6.25 | .863 |
Data are reported as mean ± standard deviation, median (interquartile range) or numbers (percentile).
AF, atrial fibrillation; APTT, activated partial thromboplastin time; BMI, body mass index; BUN, blood urine nitrogen; CR, cretinine; IVST, interventricular septem thickness; LAD, left atrial diameter; LVEDD, left ventricular end‐diastolic diameter; LVEF, left ventricular ejection fraction; LVESD, left ventricular end‐systolic diameter; LVPWT, left ventricular posterior wall thickness; RDW‐cv, red blood cells volume distribution width; UA, urine acid; WBC, white blood count.
Statistical significance for comparisons between patients with PAF and patients without AF.
Subgroup analyses were performed for gender and age (Figure 1). In males, t/s values in the quartiles were significantly longer in subjects with PAF (n = 31, 37.8%) than those without AF (n = 51, 62.2%) (0.40 [0.13‐0.95] vs 0.21 [0.07‐0.32], P = .02). The remaining three subgroups, namely females (0.20 [0.01‐0.29] vs 0.20 [0.05‐0.44], P = .80), age ≥ 60 years old (0.29 [0.16‐0.88] vs 0.22 [0.07‐0.48], P = .12), and age < 60 years old (0.13 [0.05‐0.43] vs 0.18 [0.05‐0.24], P = .59), did not demonstrate significant difference (Figure 1). Logistic regression attributed LTL as a continuous variable that displayed no statistically significant association between t/s and PAF (odds ratio [OR] 1.60, 95% confidence interval [CI]: 0.99‐2.60, P = .056). Further adjustment for age alone (OR 1.54, 95% CI: 0.95‐2.50, P = .078) or for age, BMI, fibrinogen, D‐dimer, and left atrial diameter (OR 1.431, 95% CI: 0.146‐14.064, P = .76) did not further alter the OR, though the association between t/s and PAF was proven to be statistically significant in male patients (OR 1.92, 95% CI: 1.03‐3.55, P = .04). Following multivariate adjustment, no correlation was found between telomere length and atrial fibrillation in males, females, age ≥ 60 years old, or age < 60 years old (Table 2).
Figure 1.
Leucocyte telomere length: subgroup analysis by gender and age
Table 2.
The non‐conditional logistic regression analysis on the association between TL and AF before or after adjustment for confounding factors
| B | SE | Wals | P | OR | OR (95% CI) | ||
|---|---|---|---|---|---|---|---|
| Unadjusted model in total patients | 0.471 | 0.247 | 3.647 | .056 | 1.602 | 0.988 | 2.597 |
| Unadjusted model in male patients | 0.650 | 0.315 | 4.265 | .039 | 1.916 | 1.034 | 3.550 |
| Unadjusted model in female patients | −0.149 | 0.556 | 0.071 | .789 | 0.862 | 0.290 | 2.561 |
| Unadjusted model in patients with age ≥ 60 Y/O | 0.270 | 0.283 | 0.910 | .340 | 1.310 | 0.752 | 2.280 |
| Unadjusted model in patients with age < 60 Y/O | 1.137 | 0.731 | 2.421 | .120 | 3.118 | 0.744 | 13.055 |
| Adjustment for age in total patients | 0.435 | 0.247 | 3.106 | .078 | 1.544 | 0.952 | 2.504 |
| Adjustment for age and BMI in total patients | 0.120 | 0.751 | 0.025 | .873 | 1.127 | 0.259 | 4.914 |
| Adjustment for age, BMI, and fibrinogen in total patients | −0.216 | 0.796 | 0.074 | .786 | 0.805 | 0.169 | 3.837 |
| Adjustment for age, BMI, fibrinogen, and D‐dimer in total patients | −0.041 | 1.042 | 0.002 | .969 | 0.960 | 0.124 | 7.404 |
| Adjustment for age, BMI, fibrinogen, D‐dimer, and LAD in total patients | 0.358 | 1.166 | 0.094 | .759 | 1.431 | 0.146 | 14.064 |
AF, atrial fibrillation; BMI, body mass index; LAD, left atrial diameter; TL, telomere length; Y/O, years old.
3.2. Systematic review and meta‐analysis
A flow diagram of the study identification and selection process is shown in Figure 2. A total of 22 entries were retrieved from the databases, of which 5 studies met our inclusion criteria.1, 2, 6, 14, 15 After including the data from this study, a total of 6 studies were included in the final meta‐analysis. The details of the NOS quality assessment are shown in Table S1. A total of 14794 patients with a mean age of 56.1 ± 11.8 years, 52.1% male, were included.
Figure 2.
Flow diagram for systematic review of the existing literature
Telomere length tended to be shorter in patients with AF than those without AF but no statistical significance was reached (standard mean difference = −0.11 ± 0.09, P = .24; I2 = 77%; Figure 3). However, telomere shortening was not associated with a significant increase in the risk of incident AF in univariate analysis (hazard ratio: 1.34, 95% confidence interval: 0.93 to 1.93; P = .11; I2 = 65%; Figure 4) or in multivariate analysis when analyzed as a continuous variable (hazard ratio: 0.98, 95% confidence interval: 0.85 to 1.12; I2 = 0%; P = .71; Figure 5) or categorical variable (hazard ratio: 1.32, 95% confidence interval: 0.86 to 2.05; I2 = 74%; P = .21; Figure 6). Sensitivity analyses by removing one study at a time did not report significant alterations in the effective estimates (P > .05) (Figures S1‐S4). Visual examination of funnel plots did not reveal significant publication bias (Figures S5‐S8).
Figure 3.
Mean difference in telomere length between AF and non‐AF groups
Figure 4.
Telomere length and risk of AF in univariate analysis
Figure 5.
Telomere length as a continuous variable and risk of AF in multivariate analysis
Figure 6.
Telomere length as a categorical variable and risk of AF in multivariate analysis
4. DISCUSSION
The main finding of our study is that no significant difference in leucocyte telomere length was observed between patients with paroxysmal AF (PAF) and those without PAF. No association between leucocyte telomere length and PAF was found following adjusting for age, BMI, fibrinogen, D‐dimer, and left atrial diameter with an odds ratio of 1.4 (95% confidence interval: 0.15 to 14.06). However, subgroup analysis found that leucocyte telomere length values were significantly longer in male patients with PAF than in male ones without PAF (P = .026), but not in females, or when stratified by age < 60 or ≥ 60 years old. Moreover, our meta‐analysis shows that leucocyte telomere length was not significantly different between AF and non‐AF groups, and t/s was not a significant predictor of AF whether in univariate or multivariate analysis.
Biological aging is a significant risk factor for AF, which has been associated with increasing morbidity and mortality due to strokes.16 The prevalence of AF increases with age17 and the majority of patients with AF are over 65 years old.18, 19 Telomere shortening is a consequence of inflammation and high oxidative stress, and has been associated with biological aging. Changes in telomere biology result in abnormal DNA repair. Shorter leucocyte telomere length has been linked to premature onset of coronary artery disease,20 myocardial infarction,21 and declining renal function in subjects with cardiovascular disease22 and chronic heart failure.23 A high oxidative stress environment can lead to electrophysiological and structural remodelling,24 thereby predisposing to AF. A recent meta‐analysis compared the shortest with longest third of leucocyte telomere length of 24 studies, demonstrating a pooled relative risk for coronary heart disease of 1.54.7 Telomere length is inversely associated with higher risk of coronary heart disease, independent of conventional vascular risk factors, an association that remained consistent across study characteristics and study types. By contrast, controversies exist as to whether shortened telomere length is associated with AF and of its role as an early biomarker for aging.25
The Cardiovascular Health Study, a prospective population‐based cohort study, revealed no statistically significant association between leucocyte telomere length and AF. There was no association between allele rs2736100, a single nucleotide polymorphism linked to lower leucocyte telomere length, and incident AF. Further exploration was performed in a cohort of individuals who had undergone left atrial appendage excision. Atrial cell telomere length (ATL) was longer than leucocyte telomere length among the entire cohort and within the subgroup of individuals with AF, demonstrating that aging increases AF risk through biologic pathways independent of LTL.6 According to the PREVEND (Prevention of Renal and Vascular End‐stage Disease) study, a community‐based cohort study, when dividing individuals into quartiles based on telomere length, the number of individuals with incident AF was inversely related to telomere length. After age‐adjusted analysis, no significant relationship between short telomere length and incident AF was observed.2
Furthermore, in the Intermountain Heart Collaborative Study, a significant association between t/s and increasing age was found. Subjects with a history of AF had shorter telomeres compared to those without AF, a stepwise decrease in prevalence with increasing telomere length quintile. Additionally, shortened telomeres were involved with the onset of AF but were independent of its progression.1 In another retrospective case‐control study, the load‐of‐short telomeres and telomerase activity were much higher in cases and demonstrated a significant association with the occurrence of ICD therapy. The area under curve data indicated that these parameters were better at determining appropriate ICD therapy than left ventricular ejection fraction (LVEF). Fatal ventricular arrhythmias had a strong positive association with the number of shortest telomeres.5
Our study did not show a significant difference of leucocyte telomere length between the two groups in the overall analysis, in contrast to previous studies. The reason is that telomere length signals cell senescence but not necessarily the state of the fibrillation trigger or reentrant substrate. Atrial myocytes show less frequent cell division than leucocytes, and the presence of modifying factors for telomere shortening could have confounded the results. Left ventricular compliance, the main reason for increase in AF prevalence in older age, cannot be directly related to cell senescence. Cell senescence depends on age but this is similar between the control and the AF groups. Nevertheless, two particular groups may present with unusual senescence and this may have important consequences: young patients and octogenerians‐nonogenerians.26, 27 In these cases, telomere length may be predictive of AF but these issues remain to be elucidated.
Of note, the mean t/s measures of telomere length of 0.28 in AF versus 0.20 in non‐AF group are in or below the very lowest quintile of ranges reported in the Intermountain Heart Collaborative Study (0.21 to 3.02).1 This would suggest much shorter telomere lengths in our study population, which was restricted to cardiology in‐patients over half of whom in both groups had heart failure. The latter has previously been associated with short telomere length.
Finally, our meta‐analysis of the published studies with presented data is difficult to interpret given the marked differences in patient populations and study designs. For example, confounding factors include one study when telomere data were obtained years before onset of AF6; lack of comparable subgroups for paroxysmal, persistent, and permanent AF, lack of inclusion of PAF2, 6; use of different methods for telomere length estimations.
4.1. Limitations
Several limitations should be noted. Firstly, our study included only a relatively small sample size of 150 patients compared to larger studies from previous studies. Nevertheless, our findings that TL was not a significant predictor of AF are confirmed by our meta‐analysis of 14 794 patients. Secondly, some differences in baseline characteristics were observed in our study, which could have confounded our results. Nevertheless, the findings of our cohort study were not altered following multivariate adjustment. Thirdly, sleep apnea can predispose to AF28 but this was not investigated in our study. Nevertheless, this is the first study, to the best of our knowledge, to explore the association between TL and AF, and investigate the gender difference for such associations. Future studies should examine these relationships with larger sample sizes.
5. CONCLUSION
Telomere length was similar between AF and non‐AF patients and it is not an independent predictor for AF. These findings support the notion that chronological aging, but not markers of biological aging, is associated with higher risk of AF. Further studies are needed to determine the value of telomere length as a predictor or risk marker.
Supporting information
Zhang N, Fan C, Gong M, et al. Leucocyte telomere length and paroxysmal atrial fibrillation: A prospective cohort study and systematic review with meta‐analysis. J Clin Lab Anal. 2018;32:e22599 10.1002/jcla.22599
Funding information
GT thanks the Croucher Foundation of Hong Kong for funding support. This work was supported by by grants (81570298, 81270245 to T.L.) from the National Natural Science Foundation of China.
Nixiao Zhang and Chong Fan: Joint first authors.
Contributor Information
Gary Tse, Email: tseg@cuhk.edu.hk.
Tong Liu, Email: liutongdoc@126.com.
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