Abstract
Purpose
Sperm progressive motility (PR) is correlated with the sperm DNA fragmentation index (SDF). Moreover, abnormalities in sperm tails are closely associated with the quality of sperm PR. However, the correlation of abnormal sperm tails with the SDF remains unknown.
Methods
Routine semen examination was performed according to the World Health Organization guidelines with a computer-assisted semen analysis system. SDF analysis was conducted via the sperm chromatin structure assay method. Spearman correlation was used to test the correlation of the SDF with age and conventional semen parameters. The relationship between the SDF and abnormal tails was explored by binary logistic regression analysis, restricted cubic spline (RCS) analyses and subgroup analysis. ROC curves were used to assess the predictive capacity of age, abnormal tails and PR for the SDF.
Results
The SDF was associated with sperm morphology, with a significant correlation observed with abnormal sperm tails (r = 0.491, P < 0.001). Binary regression analysis revealed that abnormal sperm tails were an independent influencing factor of the SDF. RCS analyses and Trend analysis suggested that as the number of abnormal sperm tails increased, the risk of an abnormal SDF also increased, and the trend was statistically significant (P for trend < 0.001). The ROC curve demonstrated that abnormal sperm tails had good discriminatory ability for the SDF (AUC: 0.757, P < 0.001).
Conclusion
Abnormal sperm tails are an independent influencing factor of the SDF, and a higher number of abnormal tails is associated with an increased risk of an abnormal SDF.
Keywords: Abnormal sperm tails, Sperm DNA fragmentation index, Sperm morphology
Introduction
The structural integrity of sperm chromatin is crucial for normal fertilization and embryo development [1]. Various factors, such as age, reproductive tract infections, varicocele, reactive oxygen species (ROS), systemic diseases, environmental pollutants, and lifestyle choices, can affect sperm DNA integrity [1, 2]. The sperm DNA fragmentation index (SDF) is utilized to evaluate male fertility by assessing sperm DNA integrity [3, 4].
Previous studies have explored the connection between the SDF and conventional semen parameters, and have shown that higher SDF values are associated with notable abnormalities in semen parameters [5–8]. Notably, the progressive motility (PR) of sperm has emerged as a key indicator of sperm DNA fragmentation [9].
However, few studies have examined the relationship between the SDF and sperm morphology [10, 11], especially on a large scale [12, 13]. During sperm development, the way DNA is tightly packaged within the sperm head is believed to influence its shape. Some studies have suggested a link between abnormal sperm head shapes and the SDF, whereas previous research has shown a connection between sperm tail abnormalities and PR, indicating a relationship with the SDF [14], and revealing close ties to the SDF. This study sought to more deeply investigate the correlation between the SDF and abnormal sperm tails, aiming to reveal the connections and differences between the SDF and conventional semen parameters for more reliable semen analysis.
Materials and methods
Study population
The study was approved by the Research Ethics Board at Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China (File No. IRB-20200152-R). This retrospective study involved 5125 Chinese men who visited the reproductive center of Women's Hospital from January 2021 to December 2023. Patients whose sperm concentrations were less than 2 million per ml, which is insufficient for SDF analysis by the sperm chromatin structure assay (SCSA) were excluded.
Semen analysis and SDF assay
Semen samples were collected by masturbation in the clinic after 2–7 days of sexual abstinence. After semen liquefaction, semen volume, pH, liquefaction time, appearance, sperm concentration, total sperm number, and motility were analyzed by two independent technicians using computer-assisted semen analysis (CASA) in compliance with the World Health Organization (WHO) guidelines [15] and the Björndahl guidelines [16]. Sperm morphology was assessed by Papanicolaou staining. SDF analysis was conducted via the SCSA method with a specialized commercial kit from Cell Pro Biotech Co., Ltd., China, following the kit instructions [5]. In brief, approximately one million spermatozoa were treated with a membrane-permeabilizing solution on ice for 30 s and then incubated with a staining solution at room temperature. A minimum of 5,000 sperm cells per sample were analyzed by flow cytometry.
Statistical analysis
Statistical analysis was performed with SPSS software version 20. P < 0.05 was considered to indicate a statistically significant difference.
Data with a distribution that was deemed nonnormal according to the Kolmogorov–Smirnov test were presented as medians and quartile intervals. The Kruskal–Wallis test was used to compare groups. The participants were divided into 4 groups according to age: 20–29 years, 30–39 years, 40–49 years, and over 50 years. Additionally, the participants were divided into 3 groups based on their SDF levels (SDF < 20%, 20% ≤ SDF < 30% and SDF ≥ 30%) according to previous research [17]. And the participants were divided into 4 groups based on their PR levels according to previous research [18, 19]: PR < 10%, 10% ≤ PR < 20%, 20% ≤ PR < 32%, and PR ≥ 32%. The age and PR groups were compared among groups via chi-square tests. Spearman correlation was used to assess the correlation of the SDF with age and conventional semen parameters.
Binary logistic regression analysis was chosen due to the rejection of the parallel lines assumption. Three models were constructed to control for confounders: Model 1 excluded confounders, while Model 2 included age and PR, and Model 3 included the sperm concentration, total sperm count, normal forms, abnormal heads, and abnormal midpieces. The relationship between the SDF and abnormal tails was explored via restricted cubic spline (RCS) analyses.
Subgroup analysis focused on the independent influence of abnormal tails on the SDF, excluding potential effects of PR, and binary logistic regression analysis and trend tests were conducted on each PR subgroup. P values for trends in the correlation between abnormal tails and the SDF across PR groups were evaluated.
Finally, ROC curves were used to assess the predictive capacity of age, abnormal tails and PR for the SDF. The optimal predictive indicator was identified with the Youden index and area under the curve (AUC) values, with AUC comparisons performed with the DELONG method to determine the best predictor.
Results
This study included a total of 5125 Chinese men. Table 1 displays the clinical characteristics of the patients included in the study.
Table 1.
Comparison of semen parameters among different SDF groups
| Variables | Total | SDF < 20% | 20% ≤ SDF < 30% | SDF ≥ 30% | P |
|---|---|---|---|---|---|
| (n = 5125) | (n = 3338) | (n = 1072) | (n = 715) | ||
| SDF (%), M (Q₁, Q₃) | 15.81 (10.67, 23.50) | 12.05 (9.07, 15.55) | 23.78 (21.71, 26.37) | 37.41 (33.27, 45.58) | |
| Age (years), M (Q₁, Q₃) | 33.00 (30.00, 38.00) | 33.00 (30.00, 37.00) | 35.00 (31.00, 40.00) | 37.00 (32.00, 43.00) | < 0.001# |
| Categorical Age, n (%) | < 0.001* | ||||
| 20–29 (years) | 971 (18.95) | 761 (22.80) | 143 (13.34) | 67 (9.37) | |
| 30–39 (years) | 3119 (60.86) | 2089 (62.58) | 653 (60.91) | 377 (52.73) | |
| 40–49 (years) | 920 (17.95) | 459 (13.75) | 240 (22.39) | 221 (30.91) | |
| ≥ 50 (years) | 115 (2.24) | 29 (0.87) | 36 (3.36) | 50 (6.99) | |
| Abstinence time (days), M (Q₁, Q₃) | 4.00 (3.00, 5.00) | 3.00 (3.00, 5.00) | 4.00 (3.00, 5.00) | 4.00 (3.00, 5.00) | < 0.001# |
| Concentration (10^6per ml), M (Q₁, Q₃) | 55.80 (31.50, 87.10) | 57.55 (34.40, 88.27) | 53.40 (28.20, 85.03) | 46.70 (21.90, 80.55) | < 0.001# |
| Total number (10^6 per ejaculate), M (Q₁, Q₃) | 185.60 (100.70, 290.10) | 189.05 (108.23, 290.58) | 185.50 (96.90, 295.95) | 159.80 (69.50,277.80) | < 0.001# |
| PR (%), M (Q₁, Q₃) | 38.10 (27.90, 49.60) | 44.20 (34.40, 53.30) | 31.60 (22.90, 39.50) | 20.20 (10.75, 30.10) | < 0.001# |
| Categorical PR, n (%) | < 0.001* | ||||
| PR < 10% | 246 (4.80) | 30 (0.90) | 46 (4.29) | 170 (23.78) | |
| 10% ≤ PR < 20% | 487 (9.50) | 136 (4.07) | 166 (15.49) | 185 (25.87) | |
| 20% ≤ PR < 32% | 1040 (20.29) | 470 (14.08) | 353 (32.93) | 217 (30.35) | |
| PR ≥ 32% | 3352 (65.40) | 2702 (80.95) | 507 (47.29) | 143 (20.00) | |
| Normal forms (%), M (Q₁, Q₃) | 3.50 (2.30, 5.00) | 3.80 (2.40, 5.30) | 3.20 (1.90, 4.50) | 2.80 (1.50, 3.90) | < 0.001# |
| Abnormal heads (%), M (Q₁, Q₃) | 94.20 (91.60, 96.50) | 94.00 (91.30, 96.10) | 94.60 (92.10, 96.60) | 95.40 (92.50, 97.50) | < 0.001# |
| Abnormal midpieces (%), M (Q₁, Q₃) | 17.10 (13.70, 20.80) | 16.60 (13.30, 20.30) | 17.70 (14.40, 21.20) | 18.00 (14.90, 22.20) | < 0.001# |
| Abnormal tails (%), M (Q₁, Q₃) | 19.60 (14.60, 25.70) | 17.30 (13.30, 22.40) | 23.30 (18.08, 29.63) | 27.50 (21.45, 35.20) | < 0.001* |
| Cytoplasmic residue (%), M (Q₁, Q₃) | 2.00 (1.00, 3.00) | 2.00 (1.00, 3.00) | 2.00 (1.40, 3.00) | 2.30 (1.50, 3.50) | < 0.001# |
#Kruskal–Wallis test; *: Chi-square test
M median, Q quartile, PR progressive motility, SDF sperm DNA fragmentation index
Comparison of semen parameters among different SDF groups
The results of the Kruskal–Wallis test revealed that there were statistically significant differences among the different SDF groups in terms of age, sperm concentration, total sperm number, PR, normal forms, abnormal heads, abnormal midpieces and abnormal tails (P < 0.001) (Table 1).
Correlation analysis of the SDF with age and conventional semen parameters
All variables were consistent with a skewed distribution, so Spearman correlation analysis was used. The results revealed that the SDF was positively correlated with age (r = 0.264, P < 0.001), abnormal heads (r = 0.156, P < 0.001), abnormal midpieces (r = 0.150, P < 0.001), abnormal tails (r = 0.491, P < 0.001), and total sperm number (r = 0.067, P < 0.001), and the strongest correlation was that with abnormal tails (Fig. 1). Moreover, the results also revealed that the SDF was negatively correlated with sperm concentration (r = −0.112, P < 0.001), PR (r = −0.605, P < 0.001), and normal forms (r = −0.234, P < 0.001), and the strongest correlation was that with PR (Fig. 1). There was also a strong correlation between PR and abnormal tails (r = −0.57, P < 0.001) (Fig. 1).
Fig. 1.
Correlation analysis between the SDF and semen parameters. The SDF was positively associated with age, abnormal sperm heads, abnormal midpieces, abnormal tails, and total sperm number, with the strongest correlation found for abnormal tails. Conversely, The SDF was negatively correlated with sperm concentration, PR, and normal sperm morphology, with the most pronounced negative correlation observed for PR. Furthermore, a strong correlation was noted between PR and abnormal tails. Abbreviations: SDF, sperm DNA fragmentation index; EA, ejaculatory abstinence; SC, sperm concentration; TSC, total sperm count; PR, progressive motility; NM, percentage of normal morphological sperm; AH, abnormal heads; AM, abnormal midpieces; AT, abnormal tails; CR, cytoplasmic residue
Correlation analysis between the SDF and abnormal sperm tails
Although the correlation analysis revealed that the above conventional semen parameters were related to the SDF, some variables may act as confounding factors. Therefore, we conducted a binary logistic regression analysis to compare abnormal tails between the SDF < 20% and 20% ≤ SDF < 30% groups and to compare abnormal tails between the SDF < 20% and SDF ≥ 30% groups. The results revealed differences in all pairwise comparisons (P < 0.001) (Table 2).
Table 2.
Comparison of semen parameters among different SDF groups
| Variables | SDF = 1 + 2 (n = 4410) | P | SDF = 1 + 3 (n = 4053) | P | |
|---|---|---|---|---|---|
| OR (95%CI) | OR (95%CI) | ||||
| M1 | Abnormal tails | 1.09 (1.08—1.10) | < 0.001 | 1.14 (1.12—1.15) | < 0.001 |
| Q1 | 1.00 (Reference) | 1.00 (Reference) | |||
| Q2 | 1.78 (1.41—2.26) | < 0.001 | 2.35 (1.63—3.39) | < 0.001 | |
| Q3 | 3.57 (2.86—4.46) | < 0.001 | 5.23 (3.72—7.36) | < 0.001 | |
| Q4 | 7.21 (5.76—9.03) | < 0.001 | 20.93 (15.11—29.00) | < 0.001 | |
| P-Trend | < 0.001 | < 0.001 | |||
| M2 | Abnormal tails | 1.06 (1.05—1.07) | < 0.001 | 1.07 (1.05—1.08) | < 0.001 |
| Q1 | 1.00 (Reference) | 1.00 (Reference) | |||
| Q2 | 1.49 (1.17—1.90) | 0.001 | 1.55 (1.04—2.31) | 0.03 | |
| Q3 | 2.51 (1.99—3.16) | < 0.001 | 2.42 (1.66—3.54) | < 0.001 | |
| Q4 | 3.70 (2.90—4.72) | < 0.001 | 4.91 (3.39—7.12) | < 0.001 | |
| P-Trend | < 0.001 | < 0.001 | |||
| Progressive motility | |||||
| PR < 10% | 1.00 (Reference) | 1.00 (Reference) | |||
| 10% ≤ PR < 20% | 0.94(0.55–1.60) | < 0.001 | 0.31(0.19–0.50) | < 0.001 | |
| 20% ≤ PR < 32% | 0.66(0.40–1.09) | < 0.001 | 0.12(0.07–0.18) | < 0.001 | |
| PR ≥ 32% | 0.24(0.14–0.39) | < 0.001 | 0.02(0.01–0.03) | < 0.001 | |
| M3 | Abnormal tails | 1.06 | < 0.001 | 1.08 | < 0.001 |
| Q1 | 1.00 (Reference) | 1.00 (Reference) | |||
| Q2 | 1.56 (1.22—1.99) | 0.001 | 1.74 (1.15—2.61) | 0.008 | |
| Q3 | 2.72 (2.13—3.46) | < 0.001 | 3.03 (2.03—4.51) | < 0.001 | |
| Q4 | 4.05 (3.12—5.25) | < 0.001 | 6.31 (4.22—9.44) | < 0.001 | |
| P-Trend | < 0.001 | < 0.001 | |||
SDF = 1 + 2: the groups with SDF < 20% and of 20% ≤ SDF < 30%; SDF = 1 + 3: the groups with SDF < 20% and of SDF ≥ 30%
M1: no variables adjusted; M2: adjusted for PR and age; M3: M2 additionally adjusted for sperm concentration, total sperm number, normal forms, abnormal heads, and abnormal midpieces
Q quartile, PR progressive motility, SDF sperm DNA fragmentation index
RCS analysis of Model 1 and Model 3 revealed a positive relationship between abnormal tails and the SDF; as the number of abnormal tails increased, the value of the SDF also increased (P < 0.001) (Figure 2). To eliminate the confounding effect of PR, we conducted binary logistic regression analysis and trend tests on each PR subgroup, excluding poorly fitting parts. Binary regression analysis revealed that abnormal sperm tails are an independent influencing factor of the SDF. P-Trend analysis suggested that when the number of abnormal sperm tails increased, the risk of an abnormal SDF also increased, and the trend was statistically significant (P < 0.001) (Figure 3).
Fig. 2.
The relationship between abnormal tails and the SDF according to RCS analysis. A positive relationship between abnormal tails and the SDF; as the number of abnormal tails increased, the value of the SDF also increased. A. Comparison of abnormal tails between the SDF < 20% and 20% ≤ SDF < 30% groups. B. Comparison of abnormal tails between the SDF < 20% and 20% ≤ SDF < 30% groups adjusted for age, sperm concentration, total sperm count, PR, percentage of normal morphological sperm, abnormal heads, abnormal midpieces and abnormal tails. C. Comparison of abnormal tails between the SDF < 20% and SDF ≥ 30% groups. D. Comparison of abnormal tails between the SDF < 20% and SDF ≥ 30% groups adjusted for age, sperm concentration, total sperm count, PR, percentage of normal morphological sperm, abnormal heads, abnormal midpieces and abnormal tails
Fig. 3.
Relationships between abnormal tails and the SDF according to subgroup analysis. Binary regression analysis revealed that abnormal sperm tails are an independent influencing factor of the SDF. Binary logistic regression analysis and trend tests were conducted on each PR subgroup. The subgroup with PR < 10% was excluded from the model because of the small sample size.*: Comparison between the SDF < 20% and 20% ≤ SDF < 30% groups; #: Comparison between the SDF < 20% and SDF ≥ 30% groups. Abbreviations: PR, progressive motility; Q, quartile
Abnormal sperm tails had a discriminatory ability for an abnormal SDF
Owing to the significant correlations of age, abnormal tails and PR with the SDF in the regression analysis, we conducted ROC analysis on these three variables. Participants were categorized into a nonabnormal group (SDF < 30%) and an abnormal group (SDF ≥ 30%) according to their SDF values. The results indicated that PR (AUC: 0.839, cut-off value: 30.95%, P < 0.001), abnormal tails (AUC: 0.757, cut-off value: 22.65%, P < 0.001) and age (AUC: 0.657, cut-off value: 34.5%, P < 0.001) had good discriminatory ability for the SDF. Moreover, the combination of PR, age, and abnormal tails had superior predictive value (AUC: 0.860, P < 0.001) (Table 3).
Table 3.
The predictive capacity of PR, abnormal tails and age for SDF
| Variables | AUC (95% CI) | threshold | Sensitivity | Specificity | P |
|---|---|---|---|---|---|
| PR | 0.839 (0.824, 0.855) | 30.95% | 0.756 | 0.780 | < 0.001 |
| Abnormal tails | 0.757 (0.738, 0.776) | 22.65% | 0.690 | 0.701 | < 0.001 |
| Age | 0.657 (0.635, 0.679) | 34.5 years | 0.600 | 0.629 | < 0.001 |
| Total | 0.860 (0.846, 0.874) | / | / | / | < 0.001 |
The AUCs of PR, abnormal tails, age and total variables were tested via the Delong method
PR progressive motility
Discussion
The structure of sperm chromatin is remarkably complex, and its capacity to decondense at the correct stage of fertilization is a key factor in determining sperm fertility [20]. Sperm DNA integrity plays a pivotal role in the faithful transmission of genetic information and the subsequent progression of embryo development [21]. Compelling evidence has established a connection between abnormal sperm parameters, including DNA fragmentation, and issues such as fertilization failure or delay, as well as compromised embryo development [22, 23].
The detection of the SDF serves as an important supplement to conventional semen analysis and has been widely used in clinical testing [24]. However, it is still unclear whether the routine use of SDF is beneficial for male infertility [25–28]. Increasing understanding of the correlation and variances between the SDF and conventional semen parameters may help elucidate the benefits of the SDF and maximize its utility [29]. Approximately 15% of infertile men have semen parameters within normal reference ranges [30, 31]. A subset of patients in this study also presented elevated SDF levels, despite having conventional semen parameters within normal reference ranges. It has been suggested that there may be underlying factors that evade conventional semen analysis but could significantly impact male factor infertility [31].
Most previous studies have reported the relationships between the SDF and age as well as conventional semen parameters [32–34]. Advanced paternal age was significantly associated with an increased risk of spontaneous miscarriage and birth complications, even after adjusting for maternal age [35]. However, it remains unclear at which age should be considered a cautionary threshold for evaluating the implications of advanced paternal age on reproduction and children's health. An interesting discovery from this study was that paternal age above 34.5 years was significantly associated with an increased risk of abnormal SDF values [36]. Notably, this threshold is close to 35 years, which signifies a transition to advanced maternal age [35].
Consistent with other research findings [37, 38], our study also revealed a significant negative correlation between PR and the SDF, suggesting that sperm progressive motility is an important indicator of the SDF. Interestingly, a PR value less than 30.95% exhibited a strong discriminative ability for abnormal SDF values in this study. Notably, this threshold closely aligns with 32%, the normal limit for sperm progressive motility [39]. While PR is closely linked to the SDF, a subgroup of patients in this study exhibited normal PR values but elevated SDF levels. This indicates that SDF is only partially linked to PR and that SDF independently predicts male fertility, beyond the scope of conventional semen parameters. As such, the measurement of SDF provides additional diagnostic insights that significantly augment the information obtained from routine semen analysis.
In our study, we noted a negative correlation between sperm morphology (comprising normal forms, abnormal heads, abnormal midpieces, and abnormal tails) and SDF values, with abnormal tails showing the strongest association with the SDF values. There are few reports on the relationship between abnormal tails and the SDF, especially those based on large sample sizes [38]. Previous research has mainly highlighted the connection between normal sperm morphology and SDF, yet the specific relationship between sperm tail defects and SDF remains underexplored [38, 40]. Nonetheless, these studies have consistently suggested that tail defects are most likely associated with impaired SDF levels. The sperm tail has a complex anatomical structure that plays a vital role in sustaining sperm motility and is therefore indispensable for sperm migration within the female genital tract and ultimately facilitating fertilization [41, 42]. Our study revealed that abnormal tails were an independent influencing factor for the SDF, with higher abnormal tails associated with elevated SDF values. While emphasis was previously given to the form of the head [43, 44], it is also crucial to consider the tails in this regard. The independent influence of abnormal tails on the SDF suggests that abnormal tails are associated with the SDF, regardless of PR, highlighting the necessity of elucidating the underlying mechanism(s). One potential hypothesis is abnormal tails, such as short or coiled tails, may be associated with exposure to toxic substances, chemicals [45], and elevated levels of ROS [46], which also affect sperm DNA integrity [1, 2], as mentioned above. Another relevant issue is that tail defects, such as known primary ciliary dyskinesia (PCD), are of possible genetic origin, which may affect sperm DNA integrity.
Despite the importance of the results here presented here, we are aware of the limitations of this study. Assessment of sperm morphology is associated with a number of difficulties related to a lack of objectivity and variation in interpretation [39]. However, compared with the assessment of the sperm head and midpieces, the evaluation of the tails is simpler, especially when a computer is used to measure the sperm dimensions correctly. In the morphology assessment of human sperm, there are only three main abnormal forms of tails: short, bent, and coiled [47].
Conclusion
In summary, the analysis of the SDF and routine semen parameters in 5125 male individuals revealed that abnormal sperm tails were an independent influencing factor for the SDF. The greater the number of abnormal sperm tails, the greater the risk of an abnormal SDF, irrespective of progressive motility.
Authors contributions
LJL and MHH conceived and designed research. YJW and HPL conducted the collection and analysis of clinical data. LJL and XZL wrote the manuscript. All authors read and approved the manuscript.
Funding
This study was supported by grants from the National Natural Science Foundation of China (82171596, 82471638).
Data availability
The corresponding author will share the data underlying this article upon reasonable request.
Declarations
Ethical approval
The study was approved by the Research Ethics Board at Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China (File No. IRB-20200152-R).
Consent to participate
Due to the retrospective nature of the study, informed consent could not be obtained from the patients involved. But, clinical data were analyzed anonymously.
Conflict of interest
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Agarwal A, et al. Sperm DNA fragmentation: a new guideline for clinicians. World J Mens Health. 2020;38(4):412–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Szabo A, et al. Lifestyle-, environmental-, and additional health factors associated with an increased sperm DNA fragmentation: a systematic review and meta-analysis. Reprod Biol Endocrinol. 2023;21(1):5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Andrabi SW, et al. Sperm DNA Fragmentation: causes, evaluation and management in male infertility. JBRA Assist Reprod. 2024;28(2):306–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Bjorndahl L, et al. The sixth edition of the WHO Laboratory Manual for the Examination and Processing of Human Semen: ensuring quality and standardization in basic examination of human ejaculates. Fertil Steril. 2022;117(2):246–51. [DOI] [PubMed] [Google Scholar]
- 5.Zhang F, et al. Sperm DNA fragmentation and male fertility: a retrospective study of 5114 men attending a reproductive center. J Assist Reprod Genet. 2021;38(5):1133–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Campos LGA, et al. Correlation between sperm DNA fragmentation index and semen parameters in 418 men seen at a fertility center. JBRA Assist Reprod. 2021;25(3):349–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Lu JC, et al. Analysis of human sperm DNA fragmentation index (DFI) related factors: a report of 1010 subfertile men in China. Reprod Biol Endocrinol. 2018;16(1):23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Dai Y, et al. Relationship among traditional semen parameters, sperm DNA fragmentation, and unexplained recurrent miscarriage: a systematic review and meta-analysis. Front Endocrinol (Lausanne). 2021;12: 802632. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Belloc S, et al. Which isolated sperm abnormality is most related to sperm DNA damage in men presenting for infertility evaluation. J Assist Reprod Genet. 2014;31(5):527–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Ferrigno A, et al. Correlation between the DNA fragmentation index (DFI) and sperm morphology of infertile patients. J Assist Reprod Genet. 2021;38(4):979–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Nguyen HTT, et al. Correlations between abnormalities of morphological details and DNA fragmentation in human sperm. Clin Exp Reprod Med. 2022;49(1):40–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Avendano C, et al. Fragmentation of DNA in morphologically normal human spermatozoa. Fertil Steril. 2009;91(4):1077–84. [DOI] [PubMed] [Google Scholar]
- 13.Park YS, et al. Abnormal human sperm parameters contribute to sperm DNA fragmentation in men with varicocele. World J Mens Health. 2018;36(3):239–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Lehti MS, Sironen A. Formation and function of sperm tail structures in association with sperm motility defects. Biol Reprod. 2017;97(4):522–36. [DOI] [PubMed] [Google Scholar]
- 15.Cooper TG, et al. World Health Organization reference values for human semen characteristics. Hum Reprod Update. 2010;16(3):231–45. [DOI] [PubMed] [Google Scholar]
- 16.Bjorndahl L, et al. “How to count sperm properly”: checklist for acceptability of studies based on human semen analysis. Hum Reprod. 2016;31(2):227–32. [DOI] [PubMed] [Google Scholar]
- 17.Agarwal A, et al. Technical aspects and clinical limitations of sperm DNA fragmentation testing in male infertility: a global survey, current guidelines, and expert recommendations. World J Mens Health. 2024;42(1):202–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Pang H, et al. Exploring the link between chromosomal polymorphisms and reproductive abnormalities. Reprod Health. 2024;21(1):130. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Lu YH, et al. Different sperm sources and parameters can influence intracytoplasmic sperm injection outcomes before embryo implantation. J Zhejiang Univ Sci B. 2012;13(1):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Agarwal A, Said TM. Role of sperm chromatin abnormalities and DNA damage in male infertility. Hum Reprod Update. 2003;9(4):331–45. [DOI] [PubMed] [Google Scholar]
- 21.Barroso G, et al. Developmental sperm contributions: fertilization and beyond. Fertil Steril. 2009;92(3):835–48. [DOI] [PubMed] [Google Scholar]
- 22.Simon L, et al. Paternal influence of sperm DNA integrity on early embryonic development. Hum Reprod. 2014;29(11):2402–12. [DOI] [PubMed] [Google Scholar]
- 23.Vallet-Buisan M, et al. Contribution of semen to early embryo development: fertilization and beyond. Hum Reprod Update. 2023;29(4):395–433. [DOI] [PubMed] [Google Scholar]
- 24.Agarwal A, et al. The Society for Translational Medicine: clinical practice guidelines for sperm DNA fragmentation testing in male infertility. Transl Androl Urol. 2017;6(Suppl 4):S720–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Practice Committee of the American Society for Reproductive, M. The clinical utility of sperm DNA integrity testing: a guideline. Fertil Steril. 2013;99(3):673–7. [DOI] [PubMed] [Google Scholar]
- 26.Simon L, Emery B, Carrell DT. Sperm DNA Fragmentation: Consequences for Reproduction. Adv Exp Med Biol. 2019;1166:87–105. [DOI] [PubMed] [Google Scholar]
- 27.Le MT, et al. Does sperm DNA fragmentation correlate with semen parameters? Reprod Med Biol. 2019;18(4):390–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Malhotra V. Should sperm DNA fragmentation testing be routinely used in assessing male infertility? Transl Androl Urol. 2017;6(Suppl 4):S699–701. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Gill K, et al. Utility and predictive value of human standard semen parameters and sperm DNA dispersion for fertility potential. Int J Environ Res Public Health. 2019;16(11):2004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Agarwal A, Allamaneni SS. Sperm DNA damage assessment: a test whose time has come. Fertil Steril. 2005;84(4):850–3. [DOI] [PubMed] [Google Scholar]
- 31.Borges E Jr, et al. Sperm DNA fragmentation is correlated with poor embryo development, lower implantation rate, and higher miscarriage rate in reproductive cycles of non-male factor infertility. Fertil Steril. 2019;112(3):483–90. [DOI] [PubMed] [Google Scholar]
- 32.Luo Y, et al. Sperm DNA integrity is critically impacted by male age but does not influence outcomes of artificial insemination by husband in the Chinese infertile couples. Aging (Albany NY). 2022;14(10):4326–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Pino V, et al. The effects of aging on semen parameters and sperm DNA fragmentation. JBRA Assist Reprod. 2020;24(1):82–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Lu R, et al. Analysis of age-associated alternation of SCSA sperm DNA fragmentation index and semen characteristics of 1790 subfertile males in China. J Clin Lab Anal. 2020;34(12): e23548. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.du Fosse NA, et al. Advanced paternal age is associated with an increased risk of spontaneous miscarriage: a systematic review and meta-analysis. Hum Reprod Update. 2020;26(5):650–69. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Vaughan DA, et al. DNA fragmentation of sperm: a radical examination of the contribution of oxidative stress and age in 16 945 semen samples. Hum Reprod. 2020;35(10):2188–96. [DOI] [PubMed] [Google Scholar]
- 37.Liu K, et al. Correlation analysis of sperm DNA fragmentation index with semen parameters and the effect of sperm DFI on outcomes of ART. Sci Rep. 2023;13(1):2717. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Chua SC, et al. How well do semen analysis parameters correlate with sperm DNA fragmentation? A retrospective study from 2567 semen samples analyzed by the halosperm test. J Pers Med. 2023;13(3):518. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.World Health Organization. WHO laboratory manual for the examination and processing of human semen. 5th ed. Geneva: World Health Organization; 2010. [Google Scholar]
- 40.Jakubik-Uljasz J, et al. Relationship between sperm morphology and sperm DNA dispersion. Transl Androl Urol. 2020;9(2):405-415.41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Leung MR, et al. Structural specializations of the sperm tail. Cell. 2023;186(13):2880-2896 e17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Toure A, et al. The genetic architecture of morphological abnormalities of the sperm tail. Hum Genet. 2021;140(1):21–42. [DOI] [PubMed] [Google Scholar]
- 43.Majzoub A, et al. Oxidation-reduction potential and sperm DNA fragmentation, and their associations with sperm morphological anomalies amongst fertile and infertile men. Arab J Urol. 2018;16(1):87–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Agarwal A, et al. Sperm morphology assessment in the era of intracytoplasmic sperm injection: reliable results require focus on standardization quality control, and training. World J Mens Health. 2022;40(3):347–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Aziz N, et al. Novel association between sperm reactive oxygen species production, sperm morphological defects, and the sperm deformity index. Fertil Steril. 2022;81(2):349–54. [DOI] [PubMed] [Google Scholar]
- 46.Zini A, et al. Sperm head morphology is related to high deoxyribonucleic acid stainability assessed by sperm chromatin structure assay. Fertil Steril. 2009;91(6):2495–500. [DOI] [PubMed] [Google Scholar]
- 47.Moretti E, et al. Characterisation of three systematic sperm tail defects and their influence on ICSI outcome. Andrologia. 2018;50(10): e13128. [DOI] [PubMed] [Google Scholar]
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Data Availability Statement
The corresponding author will share the data underlying this article upon reasonable request.