Oncotarget

Meta-Analysis:

Is male infertility associated with increased oxidative stress in seminal plasma? A-meta analysis

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Oncotarget. 2018; 9:24494-24513. https://doi.org/10.18632/oncotarget.25075

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Chao Huang, Xiyue Cao, Dejiang Pang, Chao Li, Qihui Luo, Yuanfeng Zou, Bin Feng, Lixia Li, Anchun Cheng and Zhengli Chen _

Abstract

Chao Huang1,2,*, Xiyue Cao1,2,*, Dejiang Pang3,*, Chao Li1,2, Qihui Luo1,2, Yuanfeng Zou2, Bin Feng4, Lixia Li2, Anchun Cheng1,2 and Zhengli Chen1,2

1Laboratory of Experimental Animal Disease Model, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China

2Key Laboratory of Animal Disease and Human Health of Sichuan Province, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China

3Department of Biochemistry and Molecular Biology, West China School of Preclinical and Forensic Medicine, West China Hospital, Sichuan University, Chengdu 610041, China

4Animal Nutrition Institute, Sichuan Agricultural University, Chengdu 611130, China

*These authors contributed to the work equally

Correspondence to:

Zhengli Chen, email: chzhli75@163.com

Keywords: oxidative stress; antioxidants; male infertility; meta-analysis; markers

Received: June 22, 2017     Accepted: March 22, 2018     Published: May 11, 2018

ABSTRACT

Objectives: We conducted a systematic review and meta-analysis of observational case-control studies to evaluate markers of oxidative stress in seminal plasma of patients with male infertility.

Background: Current evidence links oxidative stress to male infertility, in which multiple markers of oxidative stress have been widely detected, publishing inconsistent results with regard to the role of oxidative stress markers in the evaluation of male infertility. Therefore, a systematic review and meta-analysis on this issue is necessary.

Results: From the 1024 articles initially retrieved, 65 studies were included in our meta-analysis with 11 oxidative stress markers, containing 3819 male infertility patients and 2012 controls. The concentrations of malondialdehyde (SMD = 1.86, p < 0.00001), NO (SMD = 0.89, p = 0.001), carbonyl protein (SMD = 2.09, p < 0.00001) in seminal plasma were significantly higher in infertility patients. The concentrations of GSH (SMD = –1.68, p < 0.00001), vitamin C (SMD = –1.12, p < 0.00001), and vitamin E (SMD = –1.48, p = 0.003), as well as the activities of catalase (SMD = –1.91, p < 0.0001), glutathione peroxidase (SMD = –1.96, p = 0.0002) and glutathione-S-transferase (SMD = –1.62, p = 0.009) declined remarkably, resulting in decreased total antioxidant capacity (SMD = –1.77, p < 0.00001). Besides, the activity of superoxide dismutase showed no statistical difference between infertility patients and controls (SMD = –0.51, p = 0.07).

Conclusions: Our meta-analysis suggests that oxidative stress in seminal plasma resulting from decreased antioxidant defense are associated with male infertility.

Methods: Using PubMed, EMBASE, CNKI, VIP, and Wanfang database, we searched for literature reporting the detection of oxidative stress markers in the seminal plasma of male infertility published up to June 2017. Standardized mean differences (SMDs) and 95% confidence intervals (95%CI) were calculated for the finally analysis.


INTRODUCTION

Infertility has become an increasing medical problem affecting approximately 15% of couples worldwide, of which male factors account for almost 50% cases [1]. Male infertility is common due to deficiencies in the semen, so seminal analysis is a keystone for the assessment of male fertilization potential, according to which the semen deficiencies are often labeled as oligozoospermia, aspermia, hypospermia, azoospermia, teratospermia, asthenozoospermia, or even combinations of these [2]. Genetic problems, immunologic disorders, obstructive lesions, varicocele, cryptorchidism etc. are frequently involved in male infertility, but the exact mechanisms of many cases are not well known, making the therapy face lots of challenges [35]. As a result, growing interests are elicited in identifying more causes of male infertility.

In recent decades, extensive researches have reported an important role of oxidative stress in causing male infertility, suggesting that the pathology of infertility in 30–80% of infertile men is oxidative damage to spermatozoa [610]. Oxidative stress happens as a result of the excessively reactive oxygen species (ROS) or reactive nitrogen species (RNS) accumulation, due to either increased ROS/RNS generation or impaired ROS/RNS clearance. It will cause extensive sperm DNA damage [11], reduced sperm motility [12], decline in sperm fertilising ability [13], and defective sperm membrane integrity via lipid peroxidation [14], all of which are important mechanisms behind sperm dysfunction. The major sources of ROS/RNS in semen include activated leukocytes in the seminal plasma and the mitochondria in the spermatozoa, while leukocytes in seminal plasma produce 1,000 times more ROS/RNS than the spermatozoa does [15].

To counteract the effects of ROS/RNS, semen is proved to possess a large amount of antioxidants, thereby protecting gonadal cells and mature spermatozoa from oxidative damage, especially after leaving the testicles. The antioxidants in semen are present both in spermatozoa and seminal plasma, but most abundant in seminal plasma, because the amount of spermatozoa cytoplasm is low, making the antioxidant defense activity limited [16]. Seminal plasma is enriched with both enzymatic antioxidants, such as superoxide dismutase (SOD), catalase, glutathione peroxidase (GPX), glutathione S-transferase (GST), and non-enzymatic antioxidants such as glutathione, vitamin A, vitamin C, vitamin E, coenzyme Q10 etc. [17]. Deficiencies of enzymatic or non-enzymatic antioxidant systems in seminal plasma are widely associated with male infertility as the absence of any of these systems leads to the accumulation of excessive levels of ROS, resulting in impairment of both the structural and functional integrity of spermatozoa [18, 19]. However, previous studies addressing the oxidative stress status or detecting the markers of oxidative stress in seminal plasma of male infertile patients yielded controversial results, possibly due to the relatively small sample sizes of most of these studies, the different regions where the subjects were from, and the variability in the markers and assays used in them. In order to overcome these limitations, we conducted a systematic review and meta-analysis, and sought to rigorously evaluate current evidence from a large number of studies addressing the differences in markers of oxidative stress (MDA, TAC, SOD, Catalase, GPX, GST, Ve, Vc, NO, GSH and protein carbonyl) in seminal plasma between male infertile patients and controls.

METHODS

This systematic review and meta-analysis was performed according to the preferred reporting items for systematic reviews and meta-analyses.

Search strategies

A systematic review of the literature was conducted using PubMed, EMBASE, Chinese National Knowledge Infrastructure (CNKI), VIP information database, and Wanfang database. Studies reporting biomarkers of oxidative stress in the seminal plasma of infertile patients and controls published up to June 2017 were identified and analyzed. In addition, a hand search of the references of the retrieved articles and relevant reviews was performed to identify other potentially eligible studies. The following search strategy was used for PubMed, and was modified to suit other databases to include potentially relevant reports. Disagreements were resolved by discussion or consensus of a fourth reviewer (Z.C.).

1. infertility/ or infertile/ or subfertile/ or subfertility/ or sterility

2. oxidative stress/ or antioxidant/ or reactive oxygen species/ or reactive nitrogen species/ or free radicals/ or nitric oxide

3. total antioxidant capacity/ or total antioxidant power/ or total oxidant status/ or total antioxidant status

4. oxidative damages/ or protein carbonyl/ or protein carbonylation/ or lipid peroxidation/ or malondialdehyde/ or 8-hydroxydeoxyguanosine/ or thiobarbituric acid reactive substances/ or asymmetric dimethylarginine

5. xanthine oxidase/ or superoxide dismutase/ or glutathione/ or glutathione peroxidase/ or glutathione transferase/ or catalase

6. homocysteine/ or vitamin A/ or vitamin C/ or vitamin E/ or coenzyme Q10

7. 1 and 2 or 3–6

Inclusion and exclusion criteria

Inclusion criteria included: 1) studies where oxidative stress biomarkers were detected in the seminal plasma of infertile patients and fertile controls; 2) the mean and standard deviation of the levels of oxidative stress markers could be obtained or calculated using the data provided therein;

Exclusion criteria included: 1) case reports, abstracts, reviews, or irrelevant to the current analysis; 2) animal studies and female infertility; 3) studies of infertile patients with testicular varicocele, leukocytospermia, infection, or on antioxidant supplement; 4) studies only reporting oxidative stress markers in non-seminal plasma; 5) studies with no control groups; 6) studies with data only in graphic form; 7) duplicate publications or studies with inappropriate outcome measure.

Data extract

Two reviewers (C. H. and X.C.) independently performed data extraction using a standard data extraction form to determine eligibility for inclusion and data extract. Data was extracted for meta-analysis only when the markers were studied in three or more reports. The study with the largest sample size was included in the meta-analysis to avoid over-representation of cases when the same author had published two or more articles using the same or similar data within five years; otherwise, the latest study was included. The extracted data elements of this review included: 1) publication details: first author’s last name, publication year, and country of the studied population; (2) study design; (3) characteristics of the studied population: sample size, age; (4) the mean and standard deviation of the oxidative stress markers. Disagreements were resolved by discussion or consensus with a third reviewer (Z.C.).

If the study provided medians and interquartile ranges instead of means and SDs, we imputed the means and SDs as described by Hozo et al. [20]. The combined mean and standard deviation for several subgroups in a study were computed as described by Borenstein et al. [21].

Missing or ambiguous data were solicited from authors. The corresponding author of each article received a copy of the completed validity and data-abstraction forms and was then requested to correct erroneous assessments and provide missing information when necessary.

Quality assessment

The quality of the studies included in the meta-analysis were assessed according to a modification of the Newcastle-Ottawa Quality Assessment Scale (NOS) for case–control studies as described by Wells et al. [22]. The total possible score for each article is nine points; articles with more than five points were considered of high quality, and the others were considered of low quality.

To assess the quality of the pooled evidence, the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach was adopted [23]. We developed Summary of Findings tables with the use of the GradePro software.

Statistical analysis

Meta-analysis was performed using the Review Manager software (Vision 5.3, The Cochrane Collaboration, Oxford, United Kingdom). We used Hedge’s g standardized mean differences (SMDs) as a measure of effect size because the outcomes of the studies were frequently measured by different assays and techniques. SMD becomes dimensionless and the scales become uniform across the different studies. Results were given as SMD and 95% confidence intervals (95%CI).

The statistical heterogeneity was determined by the value of the I2 statistic using Hedge’s test. When the statistical heterogeneity was notable (I2 > 50%), an integrated effect was calculated with a random-effect model; otherwise, the fixed-effect model was used.

Publication bias was estimated by funnel plot asymmetry and Egger’s regression test performed by Stata 14.0 software, when 10 or more studies were included in the same analysis [24]. A sensitivity analysis was also performed with Stata 14.0 software to evaluate the robustness in the conclusion of each analysis with over 10 or more studies [25]. All P values were evaluated using two-tailed tests, and p < 0.05 was set as statistically significant.

RESULTS

The flow chart of preferred reporting items for Systematic Review and Meta-Analysis was present in Figure 1. 1024 records were retrieved after the primary literature search. However, after screening the titles and abstracts, 829 studies were excluded because they were case reports, abstracts, review articles, animal studies, or irrelevant to the current analysis. The remaining 195 reports were detailed evaluated with the full text, and 130 studies were excluded according to the exclusion criteria and quality assessment. 65 articles were finally included in our analysis, which contained 33 from Asia and Oceania, 20 from Europe, 6 from Africa and 6 from North/South America. These included articles contained 11 oxidative stress markers, 2012 fertile controls and 3819 infertile patients.

Flow diagram of the trial selection process.

Figure 1: Flow diagram of the trial selection process.

The eligible studies for meta-analysis were shown in Table 1, and they were marked with NOS scores for the methodological quality assessment (Supplementary Figure 1). There were 51 studies of high quality (NOS > 5), and the remaining 14 were of low quality (NOS ≤ 5).

Table 1: Characteristics and quality assessment of the studies included in the meta-analysis

Author

Year

Country

Simple size (n)

Mean or range of age (years)

Oxidative stress biomarkers

NOS

Fertility

Infertility

Fertility

Infertility

Atig, et al. [26]

2017

Tunisia

50

100

34.81 ± 5.43

39.40 ± 5.20

MDA, SOD, GPX, CAT

8

Fafula, et al. [27]

2017

Ukraine

20

39

N/A

N/A

MDA, GPX, GST

7

Vatannejad, et al. [28]

2017

Iran

28

25

N/A

N/A

TAC

4

Fazeli, et al. [29]

2016

Iran

34

35

N/A

N/A

MDA, TAC

6

Micheli, et al. [30]

2016

Italy

14

16

22–44

22–40

MDA, GPX, CAT, GSH

8

Riaz, et al. [31]

2016

Pakistan

20

20

N/A

N/A

TAC

5

Roychoudhury, et al. [32]

2016

USA

46

279

37.57 ± 11.37

35.61 ± 6.06

TAC

6

Taken, et al. [33]

2016

Turkey

22

30

30.40 ± 4.97

29.90 ± 4.28

MDA, NO

8

El-Taieb, et al. [34]

2015

Egypt

20

30

33.5 ± 7.69

35.26 ± 10.07

MDA

8

Layali, et al. [35]

2015

Iran

12

25

31.21 ± 4.07

29 ± 3.37

MDA, TAC

8

Tamilselvan, et al. [36]

2015

India

10

10

30.20 ± 2.74

30.10 ± 2.56

SOD, GPX

6

Yousefniapasha, et al. [37]

2015

Iran

33

30

24–38

24–38

TAC, NO

8

zhao, et al. [38]

2015

China

40

40

24–36

25–39

SOD

6

Al-Azzawie, et al. [39]

2014

Iraq

30

50

N/A

23–45

MDA, SOD, GPX, CAT, GSH, VC, VE

7

Collodel, et al. [40]

2014

Italy

14

20

24–45

24–40

MDA

8

Eroglu, et al. [41]

2014

Turkey

15

44

32.5 ± 5.3

31.2 ± 4.9

TAC

8

Mostafa, et al. [13]

2014

Egypt

20

40

30.15 ± 6.8

30.9 ± 5.1

MDA, GPX

8

Türk, et al. [42]

2014

Estonia

27

32

31.0 ± 2.0

31.0 ± 1.5

TAC, GPX

8

Jiao, et al. [43]

2013

China

21

72

25–30

20–35

MDA, TAC

7

Kullisaar, et al. [44]

2013

Estonia

27

32

30.0 ± 5.2

32.2 ± 6.6

NO

8

Mehrotra, et al. [45]

2013

India

15

40

30.79 ± 1.83

36.53 ± 3.39

TAC, MDA

8

Benedetti, et al. [46]

2012

Italy

12

31

40 ± 5

41 ± 6

TAC, MDA, VE

8

Gao, et al. [47]

2012

China

100

120

29 ± 3.1

30 ± 3.3

MDA, SOD

7

Khalil, et al. [48]

2012

Egypt

40

80

20–35

20–35

SOD, GPX, GST

7

Shamsi, et al. [49]

2012

India

76

93

29.14 ± 1.78

31.75 ± 4.82

TAC

8

Shete, et al. [50]

2012

India

30

50

21–58

21–58

GSH

7

You, et al. [51]

2012

China

50

30

27.5 ± 4.7

27 ± 4.5

MDA

6

Badade, et al. [52]

2011

India

30

30

21–45

21–45

MDA, SOD

8

Li, et al. [53]

2011

China

30

30

24–36

24–38

MDA, SOD

5

Tang, et al. [54]

2011

China

30

65

29.2 ± 8.7

26.5 ± 5.6

MDA, NO

8

Abdul-rasbeed, et al. [55]

2010

Iraq

39

60

31.87 ± 3.76

31.94 ± 4.21

MDA

7

Ahmad, et al. [56]

2010

India

75

75

25–40

25–40

MDA, SOD, GPX, CP, GSH, VC, VE

8

Abdallah, et al. [57]

2009

Tunisia

9

93

N/A

N/A

MDA, SOD, CAT

5

Elshal, et al. [58]

2009

Egypt

16

70

<45

<45

MDA, SOD, CAT, GSH

7

El-Taieb, et al. [59]

2009

Austria

10

20

35.6 ± 5.72

38.85 ± 10.52

CP

6

Kumar, et al. [60]

2009

India

30

33

31

30.5

MDA, SOD, GPX, CAT

8

Mahfouz, et al. [61]

2009

USA

55

42

N/A

N/A

TAC

5

Nouri, et al. [62]

2008

Iran

40

60

37.1 ± 4

34.2 ± 3.4

MDA, VC, VE

7

Tavilani, et al. [63]

2008

Iran

15

30

20–40

20–40

SOD, CAT, GPX

8

Amiri, et al. [64]

2007

Iran

70

45

31.40 ± 4.60

33.20 ± 5.50

NO

6

Aydemir, et al. [65]

2007

Turkey

60

52

40.00 ± 7.00

39.12 ± 5.94

MDA, GSH, CP

8

Khosrowbeygi, et al. [8]

2007

Iran

16

46

32.06 ± 3.91

33.81 ± 5.84

TAC, SOD, CAT

8

Kiziler, et al. [66]

2007

Turkey

45

50

40.22 ± 9.0

39.54 ± 6.5

MDA, CP, GSH

8

Murawski, et al. [67]

2007

Poland

5

10

N/A

N/A

SOD

4

Verit, et al. [68]

2006

Turkey

20

30

31.60 ± 5.43

31.66 ± 5.06

TAC

5

Mehraban, et al. [69]

2005

Iran

40

40

27.9 ± 4.44

30.4 ± 5.17

NO

8

Shi, et al. [70]

2005

China

28

113

26–38

25–40

TAC

5

Tavilani, et al. [71]

2005

Iran

15

35

N/A

N/A

MDA

6

Li, et al. [72]

2004

China

18

81

29.4 ± 3.7

30.4 ± 3.1

MDA

7

Koca, et al. [73]

2003

Turkey

20

30

N/A

N/A

TAC

7

Raijmakers, et al. [74]

2003

Netherlands

25

25

34.25 ± 3.75

33.50 ± 3.50

GSH

7

Giannattasio, et al. [75]

2002

Italy

14

20

25–37

28–37

GPX

7

Siciliano, et al. [76]

2001

Italy

25

90

20–40

20–40

TAC, SOD, CAT

6

Pasqualotto, et al. [77]

2000

USA

19

36

N/A

N/A

TAC

6

zini, et al. [6]

2000

Canada

12

105

43 ± 2

36 ± 1

SOD, CAT

8

Sharma, et al. [78]

1999

USA

24

28

N/A

N/A

TAC

7

Zhang, et al. [79]

1999

China

108

350

27.56 ± 2.19

28.15 ± 3.08

SOD

5

Alkan, et al. [80]

1997

Turkey

10

18

28.4 ± 0.7

32.6 ± 1.5

SOD, CAT, GPX

8

Lewis, et al. [81]

1997

UK

18

41

N/A

N/A

VC, VE

5

Kurpisz, et al. [82]

1996

Poland

10

63

N/A

N/A

SOD

6

Smith, et al. [83]

1996

Chile

15

101

N/A

25–45

TAC

4

Fan, et al. [84]

1995

China

15

55

24–34

26–35

SOD

5

Chen, et al. [85]

1994

China

137

245

29.8

32.3

SOD

4

Jeulin, et al. [86]

1989

France

11

14

N/A

N/A

CAT

5

Li, et al. [87]

1989

China

27

75

26–40

22–40

SOD

5

NOS: Newcastle–Ottawa Quality Assessment Scale; MDA: malondialdehyde; TAC: total antioxidant capacity; SOD: superoxide dismutase; CAT: catalase; GPX: glutathione peroxidase; GST: glutathione S-transferase; GSH: glutathione; NO: nitric oxide; CP: carbonyl protein; VC: vitamin C; VE: vitamin E; N/A: not available.

Meta-analysis of malondialdehyde concentration

Lipids are considered to be the most susceptible macromolecules to ROS and are present in sperm plasma membrane. Oxidative damage to lipids results in lipid peroxidation, and malondialdehyde (MDA) is one of the by-products of lipid peroxidation, which has been used in various biochemical assays to monitor the degree of peroxidative damage sustained by spermatozoa [88]. There were 28 articles including 1412 infertile patients and 871 fertile controls that were used to perform the meta-analysis of seminal plasma MDA concentration [13, 26, 27, 29, 30, 3335, 39, 40, 43, 4547, 5158, 60, 62, 65, 66, 71, 72]. The statistical heterogeneity was notable (I2 = 95%), so the random-effect model was chosen to calculate the integrated effect, showing that the seminal plasma MDA concentrations in infertile patients were higher than those in control groups (SMD = 1.86, 95% CI: 1.40, 2.33, Z = 7.85, p < 0.00001; Figure 2). Egger’s regression analysis displayed an evidence of publication bias for this marker, with no small study effect (Egger’s regression intercept 6.907356, t = 2.29042, 95%CI 2.199367~11.61534, P = 0.006 < 0.05; Figure 3). We excluded the studies of low quality (NOS ≤ 5) [53, 57], yielding similar results and publication bias (SMD = 1.96, 95% CI: 1.51, 2.40, Z = 7.85, p < 0.00001; Egger’s regression intercept 7.293577, t = 3.56, 95%CI 3.064078~11.52308, P = 0.002 < 0.05), so this conclusion needs to be interpreted cautiously. Meanwhile, a sensitivity analysis with a random effect model showed that the pooled estimate of the effect did not vary substantially with the exclusion of any of the studies, indicating the robustness of this result (Supplementary Figure 2).

Meta-analysis of studies addressing MDA levels in seminal plasma of infertile patients and control subjects.

Figure 2: Meta-analysis of studies addressing MDA levels in seminal plasma of infertile patients and control subjects. Results are shown as standardized mean differences (SMDs).

Evidence of publication bias in MDA concentration was assessed using funnel plot asymmetry and Egger&#x2019;s regression.

Figure 3: Evidence of publication bias in MDA concentration was assessed using funnel plot asymmetry and Egger’s regression.

Meta-analysis of total antioxidant capacity

The total antioxidant capacity (TAC) of seminal plasma is measured by the levels of enzymatic and non-enzymatic antioxidants. Low TAC in semen is thought to be widely involved in male infertility [89]. Seminal plasma TAC was analyzed in meta-analysis with 21 articles containing 1255 infertile patients and 553 fertile controls [8, 28, 29, 31, 32, 35, 37, 4143, 45, 46, 49, 61, 68, 70, 73, 76-78, 83]. The random-effect model was performed to calculate the integrated effect, because of the notable statistical heterogeneity (I2 = 97%). The TAC in seminal plasma of infertile patients was remarkably lower than that of fertile subjects (SMD = –1.77, 95% CI: –2.49, –1.04, Z = 4.78, p < 0.00001; Figure 4). Egger’s test was performed to detect publication bias, and the results indicated no publication bias or small study effect, confirming the reliability of the conclusions (Egger’s regression intercept –6.005721, t = –1.41, 95% CI –14.89445~2.88301, P = 0.173 > 0.05; Figure 5). A sensitivity analysis with a random effect model was performed to calculate the pooled estimate of the effect after exclusion of each of the studies, which showed no vary substantially results, indicating the robustness of this result (Supplementary Figure 3).

Meta-analysis of studies addressing TAC in seminal plasma of infertile patients and control subjects.

Figure 4: Meta-analysis of studies addressing TAC in seminal plasma of infertile patients and control subjects. Results are shown as standardized mean differences (SMDs).

Evidence of publication bias in TAC was assessed using funnel plot asymmetry and Egger&#x2019;s regression.

Figure 5: Evidence of publication bias in TAC was assessed using funnel plot asymmetry and Egger’s regression.

Meta-analysis of superoxide dismutase activity

Superoxide dismutase (SOD) scavenges both intracellular and extracellular superoxide radicals and prevents the lipid peroxidation of plasma membrane [90]. Tween-one reports were included in the meta-analysis of SOD activity in seminal plasma with 1808 infertile patients and 760 fertile controls [6, 8, 26, 38, 39, 47, 48, 52, 53, 5658, 60, 63, 67, 76, 79, 80, 82, 84, 85, 87]. There exhibited a notable statistical heterogeneity (I2 = 97%), and thus a random-effect model was performed to calculate the integrated effect. The results indicated no statistically significant difference of the superoxide dismutase (SOD) activity in infertile patients and fertile subjects (SMD = –0.51, 95% CI: –1.08, 0.05, Z = 1.78, P = 0.17 > 0.05; Figure 6). Meanwhile, the publication bias was evaluated through the Egger’s test, showing no statistically significant evidence of publication bias or small study effect among these studies (Egger’s regression intercept 0.8459985, t = 0.32, 95% CI –4.621949~6.313946, P = 0.750 > 0.05; Figure 7). The pooled estimate of the effect did not vary substantially displayed by a sensitivity analysis with a random effect model with the exclusion of each one of the studies, indicating the robustness of this result (Supplementary Figure 4).

Meta-analysis of studies addressing SOD activity in seminal plasma of infertile patients and control subjects.

Figure 6: Meta-analysis of studies addressing SOD activity in seminal plasma of infertile patients and control subjects. Results are shown as standardized mean differences (SMDs).

Evidence of publication bias in SOD activity was assessed using funnel plot asymmetry and Egger&#x2019;s regression.

Figure 7: Evidence of publication bias in SOD activity was assessed using funnel plot asymmetry and Egger’s regression.

Meta-analysis of catalase activity

Catalase detoxifies both intracellular and extracellular H2O2 to water and oxygen [91]. In addition, catalase activates NO-induced sperm capacitation, which is a complex mechanism involving H2O2 [92]. Thirteen studies were included in the meta-analysis with 740 infertile patients and 263 fertile controls to analyze the catalase activity in seminal plasma [6, 8, 26, 30, 39, 5658, 60, 63, 76, 80, 86]. The statistical heterogeneity was notable (I2 = 96%), and the integrated effect was calculated from a random-effect model hereby. Statistically lower catalase activity was present in infertile subjects than that in the control ones (SMD = –1.91, 95% CI: –2.79, –1.04, Z = 4.28, p < 0.0001; Figure 8). Egger’s test was also performed to evaluate the publication bias, displaying no statistically significant evidence of publication bias and small study effect among these studies (Egger’s regression intercept –6.19329, t = –2.11, 95%CI –12.66441~0.2778316, P = 0.059 > 0.05; Figure 9). A sensitivity analysis with a random effect model was performed to calculate the pooled estimate of the effect after exclusion of each of the studies, which showed no vary substantially results, indicating the robustness of this result (Supplementary Figure 5).

Meta-analysis of studies addressing catalase activity in seminal plasma of infertile patients and control subjects.

Figure 8: Meta-analysis of studies addressing catalase activity in seminal plasma of infertile patients and control subjects. Results are shown as standardized mean differences (SMDs).

Evidence of publication bias in catalase activity was assessed using funnel plot asymmetry and Egger&#x2019;s regression.

Figure 9: Evidence of publication bias in catalase activity was assessed using funnel plot asymmetry and Egger’s regression.

Meta-analysis of glutathione peroxidase activity

Glutathione peroxidase (GPX) reduces lipid hydroperoxides to their corresponding alcohols and free hydrogen peroxide to water [93]. 468 infertile patients and 290 fertile subjects from 12 studies were included in the meta-analysis to evaluate the GPX activity in seminal plasma [13, 26, 27, 30, 36, 39, 42, 48, 60, 63, 75, 80]. Notable statistical heterogeneity was present (I2 = 97%), so the integrated effect was calculated from a random-effect model. The GPX activity was significantly lower in infertile subjects when compared to fertile controls (SMD = –1.96, 95% CI: –3.00, –0.92, Z = 3.68, P = 0.0002 < 0.05; Figure 10). The publication bias was of significant existence according to an Egger’s test (Egger’s regression intercept –8.752643, t = –2.52, 95% CI –16.50495~-1.000335, P = 0.031 < 0.05; Figure 11), so this conclusion need to be interpreted cautiously. Meanwhile, the pooled estimate of the effect did not vary substantially displayed by a sensitivity analysis with a random effect model with the exclusion of each one of the studies, indicating the robustness of this result (Supplementary Figure 6).

Meta-analysis of studies addressing GPX activity in seminal plasma of infertile patients and control subjects.

Figure 10: Meta-analysis of studies addressing GPX activity in seminal plasma of infertile patients and control subjects. Results are shown as standardized mean differences (SMDs).

Evidence of publication bias in GPX activity was assessed using funnel plot asymmetry and Egger&#x2019;s regression.

Figure 11: Evidence of publication bias in GPX activity was assessed using funnel plot asymmetry and Egger’s regression.

Meta-analysis of reduced glutathione concentration

Reduced glutathione (GSH) is able to react directly with cytotoxic aldehydes produced during lipid peroxidation, and thus protects the free sulphydryl groups on the sperm plasma membrane. Moreover, GSH acts to preserve SH groups of protein in the reduced state by means of disulfide interchange [94]. Eight studies were included in the meta-analysis to evaluate the reduced GSH concentration in seminal plasma, containing 388 infertile patients and 245 fertile controls [30, 39, 50, 56, 58, 65, 66, 74]. Remarkably evidence of heterogeneity was observed (I2 = 93%), so the integrated effect was calculated from a random-effect model. The GSH concentration in infertile patients was lower than that in fertile controls (SMD = –1.68, 95% CI: –2.40, –0.97, Z = 4.63, p < 0.00001; Figure 12).

Meta-analysis of studies addressing reduced GSH concentration in seminal plasma of infertile patients and control subjects.

Figure 12: Meta-analysis of studies addressing reduced GSH concentration in seminal plasma of infertile patients and control subjects. Results are shown as g standardized mean differences (SMDs).

Meta-analysis of nitric oxide concentration

Nitric oxide (NO) is one of the reactive oxygen species that have been implicated in variety of physiologic cell signaling mechanisms in many tissues. Excessive NO could cause sperm hypomotility and reduce fertility in humans [95]. Six studies were included in the meta-analysis to evaluate the NO concentration in seminal plasma, containing 326 infertile patients and 222 fertile controls [33, 37, 44, 54, 64, 69]. The heterogeneity was notable (I2 = 87%), so the integrated effect was calculated from a random-effect model. The NO concentration was found to be much higher in infertile patients when compared with fertile controls (SMD = 0.89, 95% CI: 0.35, 1.43, Z = 3.24, P = 0.001 < 0.05; Figure 13). The controls from the two studies were of unproven fertility [54, 64], and exclusion of these studies yielded the similar results but with much less heterogeneity (NO concentration decreased in infertile patients; SMD = 0.55, 95% CI: 0.31, 0.79, Z = 4.52, p < 0.00001; I 2 = 40%; fixed-effect model), indicating that the fertility of controls may be the source of heterogeneity.

Meta-analysis of studies addressing NO concentration in seminal plasma of infertile patients and control subjects.

Figure 13: Meta-analysis of studies addressing NO concentration in seminal plasma of infertile patients and control subjects. Results are shown as standardized mean differences (SMDs).

Meta-analysis of vitamin E concentration

Vitamin E (VE) is a chain-breaking antioxidant in the sperm membranes and appears to scavenge superoxide, H2O2, and hydroxyl radicals [96]. Five studies were included in the meta-analysis to evaluate the VE concentration in seminal plasma, containing 257 infertile patients and 175 fertile controls [39, 46, 56, 62, 81]. The heterogeneity was notable (I2 = 94%), so a random-effect model was performed to calculate the integrated effect, showing that the VE concentration was statistically lower in infertile patients than that in fertile controls (SMD = –1.48, 95% CI: –2.45, –0.51, Z = 2.98, P = 0.003 < 0.05; Figure 14).

Meta-analysis of studies addressing VE concentration in seminal plasma of infertile patients and control subjects.

Figure 14: Meta-analysis of studies addressing VE concentration in seminal plasma of infertile patients and control subjects. Results are shown as standardized mean differences (SMDs).

Meta-analysis of vitamin C concentration

Vitamin C (VC) is also an important chain-breaking antioxidant that can neutralize hydroxyl, superoxide, and hydrogen peroxide radicals, and prevent sperm agglutination [96]. Four studies were included in the meta-analysis to evaluate the VC concentration in seminal plasma, containing 226 infertile patients and 163 fertile controls [39, 56, 62, 81]. A fixed-effect model was performed as there was no evidence of heterogeneity among these studies (I 2 = 32%), showing that the VC concentration was statistically lower in infertile patients than that in fertile controls (SMD = –1.12, 95% CI: –1.34, –0.90, Z = 10.03, p < 0.00001; Figure 15).

Meta-analysis of studies addressing VC concentration in seminal plasma of infertile patients and control subjects.

Figure 15: Meta-analysis of studies addressing VC concentration in seminal plasma of infertile patients and control subjects. Results are shown as standardized mean differences (SMDs).

Meta-analysis of carbonyl protein concentration

Protein carbonyl content is actually the most universal indicator and by far the most commonly used marker of protein oxidation [97]. Accumulation of protein carbonyls has been observed in many human diseases including Alzheimer’s disease (AD), diabetes, and male infertility [98]. Four studies were included in the meta-analysis to evaluate the carbonyl protein (CP) concentration in seminal plasma, containing 197 infertile patients and 190 fertile controls [56, 59, 65, 66]. A fixed-effect model was performed as there was no evidence of heterogeneity among these studies (I2 = 13%). And, significantly higher CP concentration was observed in the infertile patients than that in controls (SMD = 2.09, 95% CI: 1.84, 2.34, Z = 16.31, p < 0.00001; Figure 16).

Meta-analysis of studies addressing carbonyl protein concentration in seminal plasma of infertile patients and control subjects.

Figure 16: Meta-analysis of studies addressing carbonyl protein concentration in seminal plasma of infertile patients and control subjects. Results are shown as standardized mean differences (SMDs).

Meta-analysis of glutathione S-transferase activity

Glutathione S-transferase (GST) functions to detoxify xenobiotics by catalyzing the nucleophilic which is attacked by GSH on electrophilic carbon, sulfur, or nitrogen atoms of said nonpolar xenobiotic substrates, thereby preventing their interaction with crucial cellular proteins or nucleic acids [99]. Reduced activity of GST may lead to sperm membrane damage, resulting in male infertility [66]. Seminal plasma GST activity was analyzed in meta-analysis with 4 articles containing 221 infertile patients and 145 fertile controls [27, 48, 65, 66]. The heterogeneity was observed (I2 = 95%), so the integrated effect was calculated by a random-effect model. The results indicated that the GST activity was lower in infertile patients than that in fertile controls (SMD = –1.62, 95% CI: –2.83, –0.41, Z = 2.63, P = 0.009 < 0.05; Figure 17).

Meta-analysis of studies addressing GST activity in seminal plasma of infertile patients and control subjects.

Figure 17: Meta-analysis of studies addressing GST activity in seminal plasma of infertile patients and control subjects. Results are shown as standardized mean differences (SMDs).

DISCUSSION

High ROS levels and oxidative stress have been implicated in the pathophysiology of male infertility and correlated with sperm DNA damage, impaired fertilization and embryo development, low rates of implantation and occurrence of miscarriage [100, 101]. In spite of abundant studies related, it is not well defined how changes in different antioxidants in seminal plasma are correlated with male infertility, and contradictory conclusions could always be found. This may due to the low number of patients and different types of infertility included in these published studies. However, a large-scale systematic study that could address this query is not available, so we reviewed existing literatures and performed a meta-analysis on research studies that reported male infertility associated oxidative stress markers in seminal plasma. With 11 oxidative stress markers obtained from 52 individual studies that included a total of 1428 fertile controls and 2563 infertile patients, our meta-analysis may shed some light into this important issue.

ROS/RNS, as major causes of oxidative stress, plays an important role in many physiological cell processes under a certain concentration, and produce adverse modifications to cell components, such as lipids, proteins, and DNAs. Meta-analysis of these studies has shown that the concentration of seminal NO was significantly increased in infertility patient when compared with fertility controls. Few reports addressed the ROS concentration in seminal plasma, but many studies that had detected the ROS levels in spermatozoa indicated its higher concentration in infertility patients when compared with control ones [10, 49, 102105]. Accordingly, we could speculate that the ROS concentration is also higher in the seminal plasma of infertility patients, as ROS could easily diffuses across the sperm plasma membrane [106]. Oxidative damages were evaluated in our meta-analysis, which showed that the by-products of lipid and protein peroxidation, MDA and protein carbonyls, were highly accumulated in seminal plasma of infertility patients. On the other hand, 8-OHdG, a major NDA oxidation product, was rarely reported in seminal plasma. Vatannejad, et al. has noticed an increased level of 8-OHdG in seminal plasma of infertility patients [107], but more studies are required. Similarly, the level of 8-isoprostane, a stable end product of oxidized lipids derived from arachidonic acid, in seminal plasma was reported to increase by Khosrowbeygi et al. [108]. All these findings demonstrate the evidences of oxidative stress be present in seminal plasma of infertility patients.

To counteract the harmful effects of oxidative stress, some strategies like prevention of damage, repair mechanism to alleviate the oxidative damages, physical protection mechanism against damage, and most importantly, the antioxidant defense mechanisms, are present in semen. Semen antioxidant defenses include a network of compartmentalized antioxidant enzymic and nonenzymic molecules that are usually distributed within the spermatozoa and seminal plasma. In our meta-analysis, both enzymic antioxidants and non-enzymic antioxidants were evaluated. The results indicated that the activities of seminal enzymic antioxidants, such as catalase, GPX and GST, were remarkably decreased in infertility patients, and the concentrations of seminal non-enzymic antioxidants, such as GSH, VC and VE, were obviously lower in infertility patients. All of these changes resulted in a significantly lower total antioxidant capacity (TAC) in infertile seminal plasma. SOD catalyzes the dismutation of the superoxide (O2) radical into either ordinary molecular oxygen (O2) or hydrogen peroxide (H2O2) [90]. Surprisingly, as an important antioxidant defense in nearly all living cells exposed to oxygen, it shows no statistically difference of activity in seminal plasma of infertility patients and fertility controls according to our meta-analysis. This result suggested that, compared with other antioxidants, the activity of SOD may vary greatly in different kinds of infertility, or more closely related to the severity of the infertility, so the SOD activity may not a suitable assay for infertility diagnosis. Coenzyme Q10 (CoQ10) appears to play an important role in energy metabolism, as well as function as a liposoluble chain-breaking antioxidant for cell membranes and lipoproteins [109]. The level of CoQ10 in seminal plasma was reported to be decreased in infertile patients [55], yet no differences were found between patients and controls in another study [41]. In general, the deficiencies of both enzymic antioxidants and non-enzymic antioxidants could be the exact causes of oxidative stress in the seminal plasma of infertility patients.

The strengths of our current systematic review and meta-analysis include the wide range of markers in oxidative stress evaluated, and the large number of subjects included. However, our study is not free of limitations. First, the heterogeneity among the studies of most analyzed markers (MDA, TAC, SOD, CAT, GPX, GSH, GST, VE) associated with infertility was modest (>50%) and could not be reduced lower than 50% neither by reasonable exclusion nor subgroup analysis (Data not shown). Such heterogeneity may be related to the following factors: (1) search bias may be present when the inclusion of only English and Chinese articles and the exclusion of articles in other languages; (2) selection bias may be of evidence because no randomized controlled trial was performed in these studies and there were differences in the geographic areas and races of the study subjects, and there were also lack of definition for the review population; (3) measurement bias may occur from the differences in the reagent kits, testing equipment, and assay methods. Second, although the publication bias was evaluated in the markers with more than 10 studies, bias may still be present in the fewer than 10 articles reporting other markers. Such biases can be resolved by increasing the number of included articles in future research. Third, SMD analysis can be applied to data where different measurement scales are reported for the same outcome. However, the limitation of SMD analysis is often confined to <10 subjects per group and more conservative than normalized mean difference (NMD) [110]. Finally, the qualities of the pooled evidence were very low as assessed by the GRADE approach (Supplementary Table 1), thus the results must be interpreted with caution.

In conclusion, as summarized in Table 2, in light of the evidence available at present, our systematic review and meta-analysis indicates that several markers of oxidative stress are abnormal in seminal plasma of men with infertility, indicating these might be associated with male infertility, and more studies addressing these markers should be encouraged. Accordingly, this analysis might guide studies of certain interventions upon male infertility.

Table 2: Summary of the meta-analysis of oxidative stress markers in seminal plasma of male infertility patients

Marker

Action

Changes in male infertility

MDA

By-product of lipid peroxidation

TAC

Prevents oxidation and detoxifies oxidants

SOD

Converts superoxide anions to hydrogen peroxide and molecular oxygen

Catalase

Detoxifies both intracellular and extracellular H2O2 to water and oxygen

GPX

Reduce lipid hydroperoxides to their corresponding alcohols and to reduce free hydrogen peroxide to water

GST

Detoxify xenobiotics by catalyzing the nucleophilic attack by GSH on electrophilic carbon

GSH

Detoxifies hydrogen peroxide and lipid peroxides, prevents protein from oxidation

NO

Promotes reactive nitrogen species

 Carbonyl protein

Products of protein oxidation

VC

Neutralize hydroxyl, superoxide, and hydrogen peroxide radicals

VE

scavenge superoxide, H2O2, and hydroxyl radicals

↓, decreased; ↑, increased; ↔, unchanged.

ACKNOWLEDGMENTS

This work was supported by the Key project of Sichuan Provincial Education Department National (16ZA0029 to C.H) and in part by grants from Natural Science Foundation of China (31501200 to C.H), and National Key Technology Support Program (2014BAI03B01 to Z.C).

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

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