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Year : 2021  |  Volume : 32  |  Issue : 4  |  Page : 157-163

Sperm motility is the best semen parameter to predict sperm DNA fragmentation

Department of Urology, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan

Date of Submission23-Dec-2020
Date of Decision25-Mar-2021
Date of Acceptance06-May-2021
Date of Web Publication14-Dec-2021

Correspondence Address:
Dr. Hong-Chiang Chang
Department of Urology, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei; No. 7, Chung-Shan South Road, Zhongzheng Dist., Taipei City 100
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/UROS.UROS_175_20

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Purpose: Sperm DNA fragmentation (SDF) is associated with male infertility and abnormal semen parameters. However, the effect of SDF on each parameter may differ. In this study, we evaluated the correlation between different semen parameters and SDF to identify the most suitable predictor for abnormal SDF. Materials and Methods: We conducted a retrospective review from a prospective database. Enrollees who underwent conventional semen analysis and an SDF test for medical purposes or elective examinations were enrolled. SDF ≥20% was regarded as abnormal. Spearman correlation coefficient was used to determine the correlations. Area under the receiver operating characteristic curve area under the curve (AUC) was calculated to determine the predictive value. Youden index was used to determine the optimal cutoff value of conventional semen parameters to predict abnormal SDF. Results: In total, 90 men were enrolled, of whom 44 (48.89%) visited for infertility and 51 (56.67%) had abnormal semen analysis. Immotile sperm (IM) and nonprogressive sperm (NPS, NPS = nonprogressive motility + IM) were significantly correlated with SDF (r = 0.50, P < 0.001 for NPS; r = 0.49, P < 0.001 for IM) and were the most predictive for abnormal SDF (AUC = 0.77 for NPS; AUC = 0.78 for IM). By using Youden index, the cutoff values for the prediction of abnormal SDF were 66.37% for NPS and 48.73% for IM. Conclusion: Sperm motility is the most predictive and relevant parameter for the prediction of abnormal SDF. Suboptimal sperm motility should be considered an indication for SDF testing.

Keywords: DNA fragmentation, infertility, male, semen analysis, sperm motility, spermatozoa

How to cite this article:
Huang WL, Chang YK, Tung SY, Peng BH, Chang HC. Sperm motility is the best semen parameter to predict sperm DNA fragmentation. Urol Sci 2021;32:157-63

How to cite this URL:
Huang WL, Chang YK, Tung SY, Peng BH, Chang HC. Sperm motility is the best semen parameter to predict sperm DNA fragmentation. Urol Sci [serial online] 2021 [cited 2023 Feb 7];32:157-63. Available from: https://www.e-urol-sci.com/text.asp?2021/32/4/157/332407

  Introduction Top

Approximately 48.5 million couples worldwide experience infertility, and approximately 20%–70% of infertility cases can be attributed to male-related factors.[1],[2] Conventional semen analysis, which includes the evaluation of semen pH, semen volume, sperm concentration, total sperm count, sperm motility, and sperm morphology, is the primary test utilized for the evaluation of male infertility. Conventional semen analysis provides data on microscopic semen parameters but lacks information on molecular architecture, such as the presence of immature chromatin and DNA damage; therefore, this approach alone is not sufficient for comprehensive evaluation.[3]

Importantly, sperm DNA integrity has become a central concern in male infertility. Abnormal sperm DNA fragmentation (SDF) is associated with abnormal conventional semen parameters, infertility, poor outcomes of assisted reproductive techniques, miscarriage, and low live-birth rate.[4],[5],[6],[7],[8],[9] Studies have demonstrated improvement in SDF in association with the prevention or resolution of risk factors related to abnormal SDF.[10] The indications for SDF analysis include varicocele, unexplained infertility, recurrent failure of assisted reproductive techniques, obesity, smoking, and history of exposures to certain toxicants.[10] Nevertheless, whether patients with abnormal semen parameters should undergo SDF analysis remains unclear. Therefore, we performed a study to evaluate correlations between various semen parameters and SDF analysis to identify the most suitable predictor for abnormal SDF.

  Materials and Methods Top

Ethical approval

The present study followed all ethical standards concerning experimentation and research in accordance with the World Medical Association Declaration of Helsinki. The institutional review board of the study hospital approved the study (approval number: 202003013RINB) and waived the requirement of informed consent due to the retrospective study design.

Study design and participant selection

Based on a retrospective review of prospectively collected data, all individuals who underwent conventional semen analysis for infertility-related or elective purposes between June 2017 and December 2019 in the study center were invited for SDF analysis. Individuals who agreed to participate were included in the current study. Data on participant demographics were collected from the medical records, and those with a history of azoospermia, orchiectomy, urinary tract infection in the preceding 3 months, cancer diagnosis, systemic anticancer therapy, or radiotherapy were excluded from the study.

Semen collection and analysis

Protocols for semen collection and analysis were based on the recommendations of the 2010 World Health Organization (WHO) manual for the examination of human semen.[11] All participants were requested to abstain from sexual activity for 3–7 days before semen collection, which was conducted in a private room next to the study laboratory. All semen samples were obtained by masturbation and directly ejaculated into a clean and wide-mouthed plastic container. Collected samples were immediately sent for analysis. All smears were prepared by the same, well-trained technician and viewed under a bright light microscope at ×1000 magnification (Eclipse e200; Nikon Instruments, New York, NY, USA). A minimum of 100 spermatozoa were counted for each test. Sperm concentration, motility, and morphology were analyzed using the computer-aided sperm analysis (CASA) system (SCA Motility and Concentration, SCA Morphology; Microptic S. L., Barcelona, Spain). According to the 2010 WHO manual, fifth-percentile cutoff values were used to define normal ranges for semen parameters as follows: semen volume ≥1.5 mL, sperm concentration ≥15 million/mL, total sperm count ≥39 million, progressive motility (PR) ≥32%, total motility ≥40%, Immotile sperm (IM) ≤59%, vitality ≥58%, and morphologically normal spermatozoa ≥4%.[11] Moreover, we determined the ratio of sperm without PR, i.e. nonprogressive sperm (NPS), which included spermatozoa with nonprogressive (NP) motility and IM (NPS = NP + IM). According to the 2010 WHO manual, normozoospermia was defined according to the following criteria: sperm concentration ≥15 million/mL or total sperm number ≥39 million, sperm PR ≥32%, and morphology ≥4%.[11]

Sperm DNA fragmentation analysis

The sperm chromatin dispersion (SCD) test was used to evaluate SDF. All smears were prepared by the same technician using a GoldCyto sperm DNA kit (GoldCyto Biotech, GuangZhou, China). The SCD test, first reported by Fernández et al.,[12] is based on the treatment of spermatozoa with a hydrochloric acid solution for DNA denaturation, which is followed by the addition of lysis buffer to remove nuclear proteins. The presence of DNA dispersion loops in spermatozoa indicates nonfragmented DNA, whereas the minimal presence or the absence of loops in spermatozoa indicates DNA fragmentation. The procedure conformed to the standard manufacturer protocol included in the GoldCyto Sperm DNA kit (http://www.goldcyto.com/UploadFile/UP_201561111209846.pdf). The smears were viewed under a bright light microscope at ×1000 magnification (Eclipse e200) and analyzed using the CASA system (SCA DNA Fragmentation). A minimum of 100 spermatozoa were counted for each test. Spermatozoa with dispersed DNA loops were classified into those with large halo (halo width similar to or greater than the diameter of the sperm head), medium halo (halo width more than one-third of but less than the diameter of the sperm head), small halo (halo width similar to or less than one-third of the diameter of the sperm head), and no halo and degraded spermatozoa (spermatozoa with an irregular or weakly stained sperm head). Spermatozoa with large or medium halos were defined as normal, whereas those with small or no halo and degraded spermatozoa were defined as fragmented spermatozoa [Figure 1]. The SCD test results were expressed as ratios of spermatozoa with fragmented DNA, and an SDF of ≥20% was defined as abnormal based on a previous study.[6]
Figure 1: Sperm chromatin dispersion test with assistance of the computer-aided sperm analysis system. Spermatozoa with large halo or medium halo (normal, triangle arrow) and small halo (abnormal, arrow) were identified with the computer-aided sperm analysis system

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Statistical analysis

Data were analyzed using SPSS (version 22; SPSS, Chicago, IL, USA). Categorical data were analyzed using the Chi-square test. The Mann–Whitney U and Kruskal–Wallis tests were used to compare differences in continuous data between two and three or more groups, respectively. The Kolmogorov–Smirnov test was used to determine whether numerical variables were normally distributed. Pearson's correlation coefficient and Spearman's rank-order correlation coefficient were used to evaluate the correlation of normally and nonnormally distributed variables, respectively. The predictive values of conventional semen parameters were analyzed using the receiver operating characteristic (ROC) curve and area under the ROC area under the curve (AUC). The Youden index was used to determine the cutoff values of conventional semen parameters for the prediction of abnormal SDF. In all analyses, a two-tailed P < 0.05 was considered to indicate statistical significance.

  Results Top

The characteristics of the study participants are presented in [Supplementary Table 1]. The study cohort comprised 90 participants, including 44 (48.89%), 19 (21.22%), 2 (2.22%), 10 (11.11%), 4 (4.44%), and 11 (12.22%) participants visiting the study institution for infertility, varicocele, hypogonadism, elective examination, sexual dysfunction, and miscellaneous complaints, respectively. All participants visiting for infertility failed to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse and therefore met the criteria for infertility. In total, 39 (43.33%) participants had normozoospermia, whereas the remaining 51 (56.67%) participants had at least one abnormal semen parameter, including 22 (24.44%), 20 (22.22%), 41 (45.56%), 29 (32.22%), and 23 (25.56%) participants with abnormal sperm concentration, abnormal sperm count, abnormal PR, abnormal total sperm motility, and teratospermia, respectively. Conversely, 19 of the 39 participants (48.72%) had abnormal SDF. The median values for sperm concentration and total sperm count and the rates of sperms with PR, total motility, normal morphology, and SDF were 41.68 million/mL, 135.30 million, 35.68%, 51.28%, 6.00%, and 30.92%, respectively [Supplementary Table 2]. The Chi-square test revealed that all abnormal conventional semen parameters were significantly associated with a higher rate of abnormal SDF [Table 1]. The Kolmogorov–Smirnov test revealed that the distribution of SDF was not normal (P < 0.001). Thus, Spearman's rank-order correlation coefficient was used to evaluate correlations, which revealed that NPS ratio, IM ratio, and age exhibited significant positive correlations with SDF (r = 0.50, P < 0.001; r = 0.49, P < 0.001; and r = 0.33, P = 0.002, respectively; [Table 2] and [Figure 2]. As shown in [Supplementary Table 3], our analyses to determine the effect of age on semen quality and the correlation between conventional semen parameters and SDF revealed that older participants had worse sperm motility and higher SDF. In participants younger than 40 years of age, sperm motility exhibited a significant correlation with SDF, which was not observed in those who were 40 years or older. The ROC analysis [Figure 3] revealed that age, NPS ratio, and IM ratio were predictive of abnormal SDF (AUC, 0.65, 0.77, and 0.78, respectively). By the Youden index, the cutoff values for age, NPS ratio, and IM ratio for predicting abnormal SDF were 34 years (sensitivity, 73.58%; specificity, 59.38%), 66.37% (sensitivity, 63.80%; specificity, 81.28%), and 48.73% (sensitivity, 67.24%; specificity, 84.38%), respectively. In contrast, sperm concentration, total sperm count, total sperm motility, and sperm morphology were not predictors of SDF. The effects of environmental factors on SDF are shown in [Supplementary Table 4].
Figure 2: Scatterplots of distribution between sperm DNA fragmentation, nonprogressive sperm, and IM. Nonprogressive sperm and IM are positively correlated with sperm DNA fragmentation

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Figure 3: Receiver operating characteristic curve analysis for abnormal sperm DNA fragmentation. Nonprogressive sperm and IM exhibited the most suitable area under the curve in receiver operating characteristic curve analysis. Area under the curve = 0.5 (no discrimination), 0.7–0.8 (acceptable discrimination), 0.8–0.9 (excellent discrimination), and 0.9–1.0 (outstanding discrimination)

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Table 1: Chi-square test results for sperm DNA fragmentation and conventional semen parameters

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Table 2: Spearman correlation coefficients between various parameters and sperm DNA fragmentation

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  Discussion Top

The present study evaluating the utility of SDF analysis in participants with abnormal semen parameters revealed that high SDF was associated with low semen quality. Based on the SDF cutoff value of 20%, we found that participants with abnormal SDF were at higher risk of abnormal conventional semen parameters. Notably, among all the conventional semen parameters used in the present study, only sperm motility, based on the PR and IM ratios, was significantly correlated with SDF. Both the parameters could also predict abnormal SDF, with an AUC of 0.77 and 0.78. In contrast, sperm concentration, total sperm count, and sperm morphology were not predictive of abnormal SDF. Moreover, age had a significant correlation with SDF and older participants were at higher risk for abnormal SDF.

Since the 1970s, several semen analysis guidelines have been published by the WHO. The normal ranges for semen parameters defined by the WHO are based on the analysis of more than 4500 men from 14 countries, which included participants with proven fertility, unknown fertility, and normozoospermia.[13] Therefore, the utility for semen analysis is mainly for couple counseling rather than fertility prediction. Furthermore, conventional semen analysis can only provide microscopic information on spermatozoa but lacks data on molecular architecture and DNA integrity. Studies have reported that up to 37% of couples in the infertile population have unexplained infertility, in whom the conventional semen analyses are normal.[14],[15] Guzick et al.[3] found that the cutoff values for conventional semen parameters could be used to classify men as subfertile, of indeterminate fertility, or fertile; nevertheless, none of these measures can facilitate the diagnosis of infertility. In the previous study as well as the current study [Supplementary Table 1], nearly half of men with normozoospermia had abnormal SDF, suggesting that the conventional semen analysis might be insufficient in determining sperm health.[7] These data implicate that male fertility cannot be determined based on conventional semen analysis alone.

The predictive power of SDF for infertility is superior to those of conventional semen parameters. A recent meta-analysis including 28 studies, which comprised 2883 men with infertility and 1294 men defined as fertile, found that sperm SDF was higher in the infertile group and that it was predictive of infertility.[6] A cross-sectional study evaluating the ability of SDF and conventional semen parameters in predicting infertility in 114 participants reported that the AUC of SDF was better than those of conventional semen parameters.[16]

Several studies have reported a negative correlation between SDF and conventional semen parameters, particularly sperm motility, indicating the negative impact of the DNA abnormalities on gross parameters.[6],[7],[17],[18] The underlying cause of this correlation may be multifactorial. First, abnormal spermatogenesis results in defective DNA integrity, lower sperm count and vitality, and abnormal morphology. Defects in sperm function are related to impaired mechanisms of progression.[7],[19],[20],[21] Second, SDF is negatively correlated with sperm vitality;[7] therefore, IM may increase because of an increase in the number of immotile dead sperms in samples with higher SDF. Third, environmental insults such as reactive oxygen species may directly damage both DNA integrity and regulatory mechanisms involved in sperm motility.[19] However, a study found that the correlation between conventional semen analysis and SDF might not be observed in the infertile population.[22] In the present study, the cohort size was small and nearly half of the participants were infertile. Thus, it is possible that only the strongest predictor, motility, exhibited a statistically significant correlation with SDF. Nevertheless, the risk of abnormal sperm concentration, total sperm count, and morphology was higher in participants with abnormal SDF in the present study.

Aging is a risk factor for an abnormal semen profile. Older men are at higher risk of damage to sperm DNA because of prolonged exposure to oxidative stress, defective sperm chromatin packaging, and dysregulated apoptosis.[10] In the present study, older participants had worse sperm motility and higher SDF. We also found a negative correlation between sperm motility and SDF in participants younger than 40 years of age but did not observe a similar correlation in those who were 40 years or older. In fact, the sperm quality was very poor in participants who were older than 40 years (median PR, 19.22%; median IM, 70.59%, and SDF, 48.28%), resulting in the narrow distribution of the semen parameters. Thus, the correlation between the semen parameters and SDF might not be statistically significant in such a small cohort with narrow distribution. Moreover, other age-related factors such as higher oxidative stress might directly influence sperm progression.[19]

Abnormal SDF is associated with poor outcomes in intrauterine insemination and in vitro fertilization.[4],[5],[6],[9],[23],[24],[25] In contrast, several studies have reported that the extent of SDF does not influence outcomes in couples who receive intracytoplasmic sperm injection (ICSI).[4],[23] In addition, SDF is more limited in testicular spermatozoa than in the ejaculated sperm; therefore, the combination of testicular sperm extraction (TESE) with ICSI, termed TESE-ICSI, has been reported to be beneficial in couples with infertility and high SDF.[26] Furthermore, advanced sperm selection techniques based on surface charge, features of apoptosis, membrane maturity, and morphology are reported to be helpful in selecting spermatozoa with better DNA integrity and are beneficial to outcomes of assisted reproductive techniques.[27],[28]

Routine SDF testing during initial fertility evaluation, which can be more informative, might however be unnecessary and even impossible in some settings, such as financial concerns or lack of equipment for testing. Indications for SDF testing remain a focus of debate. Agarwal et al.[10] suggested that men with varicocele and several lifestyle risk factors and couples with unexplained infertility, recurrent pregnancy loss, or recurrent failure of intrauterine insemination, in vitro fertilization, or ICSI should undergo SDF testing. However, the present study did not reveal an effect of these factors, such as smoking and varicocele, on SDF, which might have been due to the small subgroups of participants with these characteristics. In contrast, although semen analysis results can predict SDF,[7] few studies have examined whether abnormal semen parameters might be utilized as an indication for SDF testing.

The present study revealed that the sperm motility was the most important semen parameter in predicting sperm DNA integrity. By using the Youden index, we also determined the cutoff values for nonprogressive sperm and IM ratios for predicting abnormal SDF (66.37% and 48.73%, respectively). These findings indicate that suboptimal sperm motility should be considered as an indication for SDF analysis. Specifically, clinicians should consider SDF analysis in men with poor sperm motility based on a PR ratio of <33.63% or an IM ratio of >48.73% to facilitate the identification of clinically abnormal SDF and to improve fertility planning. For men who are infertile and have abnormal SDF, ICSI or TESE-ICSI might be considered for potentially more favorable fertility outcomes. Conversely, for men with abnormal SDF without proven infertility, the risk of infertility should be noted and fertility treatment should be prepared accordingly. Considering that some lifestyle factors are associated with SDF, lifestyle modifications are recommended to improve SDF for at-risk individuals.[10]

The present study has several limitations that should be acknowledged. First, the SCD test was used to evaluate SDF. Despite its proven utility, the SCD test might be relatively less predictive than the alkaline Comet, terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL), and sperm chromatin structure assays used for infertility.[8],[29] In addition, vitality was not routinely tested in conventional semen analysis. Second, the participants who visited the study clinic for concerns not related to fertility might have been infertile and their reproductive history and reason for semen analysis were not routinely recorded. Therefore, the cutoff SDF value to predict infertility could not be determined using the study cohort and a cutoff value of 20% was used based on the results of a meta-analysis.[6] Third, most patients visiting for infertility were referred from gynecologists after the consultation of their female partners with a gynecologist for infertility. Most of the participants did not return to the study clinic after their evaluation of male infertility; therefore, obtaining data regarding their reproductive outcomes was difficult. However, the present study has several notable strengths. First, the errors of interpretation of both the conventional semen analysis and the SCD test were minimized by using the CASA. As reported by Fernández et al.,[12] correlations between the CASA results and those obtained by manual evaluation are excellent. Second, before the interpretation of samples using the CASA, all smears were prepared by the same well-trained technician using standard protocols; therefore, interobserver variation was minimized. Third, only fresh semen samples were used for all analyses to avoid sample degradation. All participants were requested to collect their semen by using masturbation in a private room next to the study laboratory, and all samples were processed immediately after.

In summary, the present study reveals that sperm motility is negatively correlated with SDF and is the best semen parameter for predicting abnormal SDF, using a PR cutoff value of <33.63% or an IM cutoff value of >48.73%. For clinical purposes, suboptimal sperm motility should be considered as an indication for SDF testing. Nevertheless, larger studies are required to confirm the cutoff values for sperm motility for the prediction of abnormal SDF.


We are thankful to consultants and specialist registrars of the study department for their contribution in the database construction and study consultation. We are also grateful for the support of the medical staff in the study hospital and English editing provided by Wallace academic editing.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

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  [Figure 1], [Figure 2], [Figure 3]

  [Table 1], [Table 2]


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