Association between shortened telomere length and systemic lupus erythematosus: a meta-analysis

Abstract

Objective

We aimed to evaluate the relationship between telomere length and systemic lupus erythematosus (SLE).

Methods

PUBMED and EMBASE databases were searched; meta-analyses were performed comparing telomere length in SLE patients and healthy controls, and on SLE patients in subgroups based on ethnicity, sample type, assay method and data type.

Results

Eight studies including 472 SLE patients and 365 controls were ultimately selected which showed that telomere length was significantly shorter in the SLE group than in the control group (standardized mean difference (SMD) = −0.835, 95% confidence interval (CI) = −1.291 to −0.380, p = 3.3 × 10⁻⁴). Stratification by ethnicity showed significantly shortened telomere length in the SLE group in Caucasian, Asian and mixed populations (SMD = −0.455, 95% CI = −0.763 to −0.147, p = 0.004; SMD = −0.887, 95% CI = −1.261 to −0.513, p = 3.4 × 10⁻⁴; SMD = −0.535, 95% CI = −0.923 to −0.147, p = 0.007; respectively). Furthermore, telomere length was significantly shorter in the SLE group than in the control group in whole blood and peripheral blood mononuclear cell groups (SMD = −0.361, 95% CI = −0.553 to −0.169, p = 2.3 × 10⁻⁴; SMD = −1.546, 95% CI = −2.583 to −0.510, p = 0.003; respectively); a similar trend was observed in leukocyte groups (SMD = −0.699, 95% CI = −1.511 to −0.114, p = 0.092). Meta-analyses based on assay method or data type revealed similar associations.

Conclusions

Our meta-analysis demonstrated that telomere length was significantly shorter in patients with SLE, regardless of ethnicity, sample type or assay method evaluated.

Keywords

Telomere length systemic lupus erythematosus

Introduction

Systemic lupus erythematosus (SLE) is a prototypic autoimmune disease, characterized by B cell hyperactivity, immune-complex deposition, high level of autoantibody production and multiple organ damage. Although the etiology of SLE has been known to be multifactorial, caused by interactions between genetic and environmental factors, findings including significant familial aggregation and multiple genetic linkages in SLE demonstrate that the etiology of SLE is genetic in nature.¹

Telomeres are DNA–protein complexes composed of TTAGGG-rich repeats capped at the ends of chromosomes in eukaryotic cells, and play an essential role in maintaining the stability and integrity of chromosomal architecture during duplication.² Telomeres shorten during cell divisions by loss of the hexameric repeats, resulting in shortening of telomeres.³ Thus, telomere length reflects the replicative history of cells, and can be considered as a mitotic or biological clock.³ Environmental factors such as inflammation and oxidative stress are associated with accelerated telomere shortening.⁴ If cells reach a critically short size of telomere length, cellular senescence or apoptosis is triggered.⁵ Telomere shortening has been involved in genetic instability of cells that are suspected to be implicated in the pathogenesis of various diseases, including autoimmune diseases, cancer, type 2 diabetes, neuropsychiatric disorders, as a possible risk factor.^6–9

Studies have shown that shortened telomere length is associated with SLE, but, on the other hand, other reports found no such associations.^10–17 These disparities are probably caused by small sample sizes, low statistical power, and/or clinical heterogeneity; therefore, to overcome the limitations of individual studies, resolve inconsistencies and reduce the likelihood that random errors are responsible for false-positive or false-negative associations, we turned to meta-analysis.^18-20 The aim of the present study was to determine the relationship between telomere length and SLE by using a meta-analytic approach.

Materials and methods

Identification of eligible studies and data extraction

We performed a literature search for studies that examined telomere length in SLE patients and controls. PUBMED and EMBASE databases were searched to identify all available past articles (up to February 2016). The following keywords and subject terms were used in the search: ‘telomere’, ‘systemic lupus erythematosus’ and ‘SLE’. All references cited were also reviewed to identify additional studies not covered by the above-mentioned electronic databases. Studies were considered eligible if (1) they were case-control, cross-sectional or cohort studies; (2) they provided data on telomere length in case and control groups; and (3) they provided sufficient data on the association between telomere length and SLE. Studies were excluded if (1) they contained overlapping or insufficient data or (2) they were reviews or case reports. The following information was extracted from each study: primary author, year of publication, country, ethnicity, number of participants, sample type, assay method, and mean and standard deviation (SD) of telomere length. If the standard error of mean was reported, we calculated the SD using the appropriate statistical formula. When the data were in terms of median, range or p-value, we computed the mean and SD using previously described formulae.^21,22 We conducted a sensitivity test on imputed values.

Evaluation of statistical associations

We performed a meta-analysis examining the relationship between telomere length and SLE. For continuity of data, results were presented as standardized mean differences (SMDs) and 95% confidence intervals (CIs). SMDs were calculated by dividing the mean difference between two groups by the pooled SD, and were used when different scales were integrated to measure the same concept. This measure compares case and control arms in terms of standardized scores. The magnitude of SMD was considered as follows: 0.2–0.5, small effect; 0.5–0.8, medium effect; ≥ 0.8, large effect.²³ We assessed within-study and between-study variations and heterogeneities using Cochran’s Q-statistics.²⁴ The heterogeneity test was used to assess the null hypothesis that all studies were evaluating the same effect. When the significant Q-statistic (p < 0.10) indicated heterogeneity across studies, the random effects model was used for the meta-analysis.²⁵ If not, the fixed effects model was used, which assumed that all studies estimated the same underlying effect, and considered within-study variation only.²⁴ We quantified the effect of heterogeneity using I²= 100% × (Q–df)/Q,²⁶ where I² measured the degree of inconsistency between studies and determined whether the percentage total variation across studies was due to heterogeneity rather than due to chance. I² ranged between 0% and 100%; I² values of 25%, 50% and 75% were referred to as low, moderate and high estimates, respectively.²⁶ Statistical manipulations were undertaken using the Comprehensive Meta-Analysis computer program (Biostat Inc., Englewood, NJ, USA).

Evaluation of heterogeneity, sensitivity test and publication bias

To examine potential sources of heterogeneity observed in the meta-analysis, meta-regression analysis was performed using the following variables: ethnicity, sample type, assay method, publication year, sample size and data type. A sensitivity test was performed to assess the inﬂuence of each individual study on the pooled odds ratio (OR) by omitting each study individually. Although funnel plots are often used to detect publication bias, they require diverse study types of varying sample sizes, and their interpretation involves subjective judgment. Therefore, we evaluated publication bias using Egger’s linear regression test,²⁷ which measured funnel plot asymmetry using a natural logarithm scale of ORs. When asymmetry was indicated, we used the ‘trim and fill’ method to adjust summary estimates for observed bias.²⁸ This method removes small studies until funnel plot symmetry is achieved by recalculating the center of the funnel before removed studies are replaced with their missing mirror-image counterparts. A revised summary estimate was then calculated using all original studies and hypothetical ‘filled’ studies.

Results

Studies included in the meta-analysis

We identified 109 studies by using electronic and manual search methods, 12 of which were selected for full-text review based on the title and abstract and four of which were excluded because they had no data on telomere length, duplicate data or data on review. Thus, eight articles met the inclusion criteria.^10-17 One report contained data on four different groups¹² and another on two different groups,¹⁷ and we analyzed these studies independently. Therefore, in total, 12 separate studies were considered in the meta-analysis, which contained 472 SLE patients and 365 controls, and two Caucasian, three Asian, one African-American and six mixed ethnicity populations (Table 1). Selected characteristics of these studies related to the association between the telomere length and SLE are summarized in Table 1.

Table 1

Characteristics of individual studies included in the meta-analysis

Primary authors	Country	Ethnicity	Number		Sample	Method	Matched	Results
Primary authors	Country	Ethnicity	SLE	Control	Sample	Method	Matched	SMD	Magnitude^a	p-value
Hoffecker, 2013¹⁰	USA	African-American	59	59	PBMC	qPCR	Age, gender, race, number	−2.798	Large	0.000
Haque, 2013¹¹	UK	Caucasian	63	63	WB	qPCR	Age, number	−0.583	Medium	0.001
Skamra-1, 2013¹²	USA	Mixed	35	20	WB	qPCR	Age	−0.544	Medium	0.056
Skamra-2, 2013¹²	USA	Mixed	53	52	WB	qPCR	Age	−0.258	Small	0.188
Skamra-3, 2013¹²	USA	Mixed	44	49	WB	qPCR	Age	−0.274	Small	0.189
Skamra-4, 2013¹²	USA	Mixed	22	31	WB	qPCR	Age	−0.027	No effect	0.923
Beier, 2007¹³	Germany	Caucasian	22	19	Lymphocyte	FISH	Age	−0.075	No effect	0.811
Wu, 2007¹⁴	Taiwan	Asian	53	24	PBMC	TRF	Age, gender, race	−0.860	Large	0.001
Kurosaka, 2006¹⁵	Japan	Asian	34	17	Lymphocyte	FISH	Age^b, race	−0.769	Medium	0.012
Lin, 2005¹⁶	China	Asian	5	4	Lymphocyte	TRF	Age^b, race	−2.016	Large	0.014
Honda-1, 2001¹⁷	USA	Mixed	41	17	PBMC	TRF	Age	−0.719	Medium	0.015
Honda-2, 2001¹⁷	USA	Mixed	41	10	PBMC	TRF	Age	−1.814	Large	0.000

SLE: systemic lupus erythematosus; SMD: standard mean difference; WB: whole blood; PBMC: peripheral blood mononuclear cell; qPCR: quantitative polymerase chain reaction; TRF: terminal restriction fragment; FISH: fluorescence in situ hybridization.

Magnitude of Cohen’s d effect size: 0.2–0.5: small effect; 0.5–0.8: medium effect; ≥0.8: large effect.

Not age-matched, but no statistical difference in age between SLE and control groups.

Meta-analysis of telomere length in SLE patients compared with controls

Meta-analysis was performed on all SLE patients, and on SLE patients in each ethnic group. Telomere length was significantly shorter in the SLE group than in the control group (SMD = −0.835, 95% CI = −1.291 to −0.380, p = 3.3 × 10⁻⁴) (Table 2, Figure 1). In addition, stratification by ethnicity showed a significantly shortened telomere length in SLE group in Caucasian, Asian and mixed populations (SMD = −0.455, 95% CI = −0.763 to −0.147, p = 0.004; SMD = −0.887, 95% CI = −1.261 to −0.513, p = 3.4 × 10⁻⁴; SMD = −0.535, 95% CI =−0.923 to −0.147, p = 0.007; respectively) (Table 2, Figure 2). A single African-American study showed a significantly shortened telomere length in SLE group (SMD = −2.798, 95% CI = −3.306 to −2.290, p < 1.0 × 10^–8).

Table 2

Meta-analysis of the association between telomere length and systemic lupus erythematosus

Groups	Population	No. of studies	Test of association			Test of heterogeneity
Groups	Population	No. of studies	SMD^a	95% CI	p-value	Model	p-value	I ²
All	Overall	12	−0.835	−1.291 to −0.380	3.3 × 10⁻⁴	R	0.000	88.5
Ethnicity	Caucasian	2	−0.455	−0.763 to −0.147	0.004	F	0.161	49.1
	Asian	2	−0.887	−1.261 to −0.513	3.4 × 10⁻⁶	F	0.360	2.21
	Mixed	6	−0.535	−0.923 to −0.147	0.007	R	0.005	70.1
Sample type	WB	5	−0.361	−0.553 to −0.169	2.3 × 10⁻⁴	F	0.436	0
	PBMC	4	−1.546	−2.583 to −0.510	0.003	R	0.000	92.2
	Leukocyte	3	−0.699	−1.511 to −0.114	0.092	R	0.050	66.5
Assay method	qPCR	6	−0.743	−1.478 to −0.009	0.047	R	0.000	93.8
	TRF	4	−1.163	−1.721 to −0.604	4.6 × 10⁻⁵	R	0.075	56.5
	FISH	2	−0.429	−0.859 to 0.000	0.050	F	0.113	60.0
Data type	Original	9	−0.890	−1.538 to −0.242	0.007	R	0.000	91.6
Data type	Calculated	3	−0.694	−0.955 to 0.432	<1.0 × 10⁻⁸	F	0.653	0

SMD: standard mean difference; CI: confidence interval; WB: whole blood; PBMC: peripheral blood mononuclear cell; qPCR: quantitative polymerase chain reaction; TRF: terminal restriction fragment; FISH: fluorescence in situ hybridization; F: fixed effects model; R: random effects model.

Magnitude of Cohen’s d effect size (SMD): 0.2–0.5: small effect; 0.5–0.8: medium effect; ≥0.8: large effect.

Meta-analysis of telomere length in SLE patients compared with controls in subgroup

Meta-analysis was performed on SLE patients in each subgroup based on sample type, assay method and data type. Telomere length was significantly shorter in the SLE group than in the control group in whole blood and peripheral blood mononuclear cell groups (SMD = −0.361, 95% CI = −0.553 to −0.169, p = 2.3 × 10⁻⁴; SMD = −1.546, 95% CI = −2.583 to −0.510, p = 0.003; respectively), and a similar trend was observed in the leukocyte group (SMD = −0.699, 95% CI = −1.511 to −0.114, p = 0.092). Subgroup analysis by the assay method showed a significantly shortened telomere length in the SLE group by the quantitative polymerase chain reaction (qPCR) and terminal restriction fragment methods (SMD = −0.743, 95% CI = −1.478 to −0.009, p = 0.047; SMD = −1.163, 95% CI = −1.721 to −0.604, p = 4.6 × 10^–5; respectively); a similar association was observed by the fluorescence in situ hybridization (FISH) method (SMD = −0.429, 95% CI = −0.859 to −0.000, p = 0.050). Stratification by the input data type revealed a significantly shortened telomere length in the original data and imputed data groups (Table 2).

Heterogeneity, sensitivity test and publication bias

Between-study heterogeneity was identified during the meta-analyses of telomere length in SLE patients (Table 2). Meta-regression analysis showed that ethnicity (p < 0.001), sample type (p < 0.001) and sample size (p = 0.012), but not publication year, assay method and data type (p > 0.05), had significant impacts on the heterogeneity in the meta-analysis of telomere length in SLE patients. Sensitivity analysis showed that no individual study signiﬁcantly affected the pooled OR, indicating that the results of this meta-analysis are robust. It was difficult to correlate the funnel plot, which is usually used to detect publication bias, because the number of studies included in the analysis was relatively small. Egger’s regression test showed no evidence of publication bias (Egger’s regression test p-values > 0.1), but the funnel plot showed asymmetry; therefore, the ‘trim and fill’ method was used to adjust for publication bias (Figure 3). However, ORs that had been significant before this adjustment remained significant after the adjustment (SMD = −0.914, 95% CI = −1.363 to −0.465).

Discussion

Immune aging is associated with loss of immune functions, leading to autoimmunity, infection and malignancy.^6–9 Critically shortened telomeres lead to cell senescence or apoptotic death and, thus, susceptibility to autoimmune diseases increases in the condition of accelerated immunosenescence.⁵ Telomere shortening causes chromosomal instability that may lead to development of autoimmune diseases, including SLE.⁷

Telomere length has been studied as a possible marker for SLE risk, showing mixed results. In this meta-analysis, we combined the evidence on the relationship between the telomere length and SLE. This meta-analysis of 12 comparative studies involving 472 SLE patients and 365 controls showed that telomere length was significantly shorter in the SLE group than in the control group. The significantly shortened telomere length in the SLE group was found in Caucasian, Asian and mixed populations. In addition, the telomere length is significantly shorter in patients with SLE, regardless of sample type or assay method evaluated. Accelerated immune aging, represented by telomere length reduction, has been involved in a variety of rheumatic diseases, including SLE. Immunosenescence by telomere shortening is related to increased reactivity to self-antigens, and loss of immune tolerance. Our meta-analysis showed that there was a significant association between shortened telomere length and SLE, independent of potential confounders such as age or ethnicity. It is well known that African-American patients with SLE have generally severe disease that is difficult to manage. A single African-American study showed a significantly shortened telomere length in SLE group (SMD = −2.798, 95% CI = −3.306 to −2.290, p < 1.0 × 10^–8. Telomere length was lower in African-American patients with SLE than in Caucasian patients (SMD –2.798 vs. –0.445). Thus, the results of our study were consistent with the known finding.

It is not known whether this association is the cause or the consequence of shortened telomere length. There are some possible explanations. First, telomere shortening results from repeated mitotic divisions, exposure to oxidative stress, or DNA repair mechanisms.⁴ SLE is associated with increased oxidative stress and can lead directly to accelerated telomere attrition. Immune activation observed in SLE may accelerate aging of the immune system. Telomere shortening is also influenced by environmental factors, lifestyle and socioeconomic factors.²⁹ Second, SLE patients may have congenitally shorter telomeres with decreased functionality, thus resulting in disease.³⁰ Telomere shortening is prevented via up-regulation of telomerase expression.³¹ Genetic variation in telomerase, a regulator of telomere length, may be associated with shortened telomere length.³² Third, initial autoantibodies developed in SLE may result in high leukocyte activation, leading to shortened telomeres;³³ although a mechanism by which either autoantibodies are produced or telomeres are congenitally shorter needs to be elucidated. A previous literature review investigated whether accelerated telomere shortening is a cause or a consequence of SLE.³⁰ The review indicated that accelerated telomere shortening may be a cause and not a consequence of disease development, because healthy relatives of patients have similarly short telomere length compared with patients and there seems to be a prelude of accelerated telomere shortening, which increases risk for disease development.³⁰

Multiple methods have been used for measurement of telomeres.³⁴ Terminal restriction fragment (TRF) is a convenient tool used for determining telomere length and, thus, is often called as the ‘gold standard’ method. Genomic DNA is exhaustively digested using frequent cutting restriction enzymes that lack recognition sites in the telomeric and subtelomeric regions. However, the restriction enzymes used can lead to the inclusion of subtelomeric DNA that is contiguous with the telomere, thereby possibly causing overestimation of the true telomere length. In addition, TRF requires a large amount of DNA and is labor-intensive. qPCR is a popular method for estimating telomere length because of the requirement for smaller amounts of DNA, its low cost and amenability for high-throughput testing. However, qPCR does not provide absolute length estimate. FISH uses fluorescent probes to quantify not only mean telomere lengths but also chromosome-specific telomere lengths, However, it requires mitotically active cells for metaphase chromosomes and cannot be used in cells that are not mitotically active.

The present study has some shortcomings that should be considered. First, most of the studies included in this meta-analysis had small sample sizes; thus, many of the individual studies that constitute this meta-analysis may have been underpowered. Second, the studies included in the meta-analysis were heterogeneous in demographic characteristics and clinical features. The heterogeneity and confounding factors such as disease activity and drugs used (e.g. immunosuppressive agents, hydroxychloroquine, corticosteroids) may have affected our results, which may be compounded by the limited information provided on clinical status and disease activity. This limited data did not allow further analysis, although we performed a sensitivity test and a meta-regression analysis.

Nevertheless, this meta-analysis has its strengths. The number of patients from individual studies ranged from five to 63, but our pooled analysis included 472 patients. Compared with individual studies, our study was able to provide more accurate data on the relation between the telomere length and SLE by increasing the statistical power and resolution through pooling of the results of independent analyses.

Our meta-analysis demonstrates that the telomere length is significantly shorter in patients with SLE, regardless of ethnicity, sample type and assay method evaluated, thereby suggesting that the shortened telomere length may be a risk factor for SLE or a consequence of SLE. Our meta-analysis indicated that telomere length likely plays an important role in the pathogenesis of SLE. Further studies are necessary to elucidate that a shortened telomere length directly contributes to the pathogenesis of SLE.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by a Korea University grant.

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