Are There Relationships between Seminal Parameters and the Neutrophil-to-Lymphocyte Ratio or the Platelet-to-Lymphocyte Ratio?

Relationship between inflammatory markers, hormonal profiles, and sperm parameters

OBJECTIVE

The aim of this study is to evaluate the relationship between semen parameters, complete blood count, and hormone levels on the day of spermiogram.

METHODS

Semen parameters of 230 patients who were examined for full blood count test and hormone levels on the day of spermiogram were included in the study. Patients were grouped according to the total motile sperm count (TMSC), semen parameters, hemogram, and hormone levels were compared between groups.

RESULTS

No statistically significant difference was found between groups in neutrophil ratios, neutrophil, lymphocyte, platelet counts, neutrophile-to-lymphocyte ratio (N/L), and platelet-to-lymphocyte ratio (P/L). However, white blood cell (WBC) and lymphocyte counts were weakly positively correlated with sperm concentration (p=0.021, p=0.026), and a weakly significant positive correlation was found with WBC and neutrophil count for motility (p=0.038, p=0.004). FSH level was found to be lower in cases with TMSC >20 m than those with TMSC <5 m and 5-10 m (p=0.004, p=0.022). LH was found to be lower in cases with TMSC >20 m than those with TMSC <5 m (p=0.048). A negative correlation was found for both FSH and LH levels with sperm concentration, motility, and TMSC (p<0.001, p=0.014).

CONCLUSION

In this study, a significant negative correlation was demonstrated between FSH, LH levels and sperm concentration, motility, TMSC. N/L and P/L cannot be used as predictive markers of sperm quality. The results of a significant positive correlation between WBC, neutrophil counts, and sperm parameters encourage researchers to conduct prospective randomized controlled trials with larger sample sizes and different inflammatory and hormonal markers.

Systemic immune-inflammation index, neutrophil-lymphocyte ratio and platelet-lymphocyte ratio are predictors of sperm presence in microdissection testicular sperm extraction

The aim of this study is to evaluate the value of the haematologic inflammatory parameters in predicting sperm retrieval rates during microdissection testicular sperm extraction (micro-TESE).159 patients diagnosed with non-obstructive azoospermia were included in the study. After excluding the patients that do not fit the inclusion criteria, age, smoking status, body-mass index, serum luteinizing hormone, follicle-stimulating hormone, total testosterone levels and neutrophil, lymphocyte and platelet counts were recorded. Neutrophil-lymphocyte ratio, platelet-lymphocyte ratio and systemic immune-inflammation index were calculated. The primary outcome was defined as the presence of spermatozoa during the procedure and the association between the candidate predictors and primary endpoint were evaluated by logistic regression analysis. Then, a baseline model from age, smoking, body-mass index and hormonal levels was built. Ratios and indexes were included, respectively, and were compared by multivariate analyses. Each of all three parameters was an independent predictor of obtaining spermatozoa during micro-TESE procedure (all p < 0.001). Even though all three parameters were significant, neutrophil-lymphocyte ratio and systemic immune-inflammation index stood out as more powerful than platelet-lymphocyte ratio (p < 0.08, p < 0.08 respectively). Additionally, adding these parameters individually to the baseline model significantly empowered the predictive value (all p < 0.001). Using haematologic inflammatory parameters in the prediction of sperm presence during microdissection testicular sperm extraction may be helpful when consulting the patient with having a better foresight of the procedural outcomes.

Correlation among isolated teratozoospermia, sperm DNA fragmentation and markers of systemic inflammation in primary infertile men

Aim

To assess the prevalence of isolated teratozoospermia (iTZS) in a cohort of infertile and fertile men; explore the relationship between iTZS, inflammatory parameters and sperm DNA fragmentation index (SDF) in the same cohort.

Materials and methods

1824 infertile men and 103 fertile controls. Semen analysis, the neutrophil-to-lymphocyte ratio (NLR) and serum hormones were investigated. DFI was tested in infertile men only. According to 2010 WHO semen analysis, patients were categorized in 3 sub-groups of isolated sperm defects: isolated oligozoospermia (iOZS), isolated asthenozoospermia (iAZS) and iTZS. Descriptive statistics and linear regression models tested the association between clinical variables and inflammatory markers.

Results

Among infertile men, iAZS, iTZS, and iOZS were found in 13.9%, 11.9% and 4.1% participants, respectively. iTZS was found in 37 (35.9%) fertile men. Infertile men with iTZS had higher NLR values than those with iOZS, iAZS and men with normal semen parameters (all p<0.001). FSH and LH were higher and inhibin B lower in iOZS infertile men compared to all other groups (p≤0.001). Hormonal characteristics were similar between iTZS infertile and fertile men. Similarly, iTZS infertile men had higher SDF than all other groups (all p<0.001). Infertile men with iTZS had higher NLR values than fertile men with iTZS (p<0.01). Linear regression analysis showed that, in infertile men, iTZS was associated with SDF and NLR (all p≤0.01).

Conclusions

iTZS was found in 11.9% of infertile men but it was even more prevalent in fertile controls. Infertile men with iTZS had higher NLR than fertile controls and increased SDF values than infertile participant with iAZS, iOZS, or normal semen parameters. No differences in hormonal characteristics were found between infertile and fertile men with iTZS.

Multilevel approach to male fertility by machine learning highlights a hidden link between haematological and spermatogenetic cells

BACKGROUND

Male infertility represents a complex clinical condition requiring an accurate multilevel assessment, in which machine learning technology, combining large data series in non-linear and highly interactive ways, could be innovatively applied.

METHODS

A longitudinal, observational, retrospective, big data study was carried out, applying for the first time the ML in the context of male infertility. A large database including all semen samples collected between 2010 and 2016 was generated, together with blood biochemical examinations, environmental temperature and air pollutants exposure. First, the database was analysed with principal component analysis and multivariable linear regression analyses. Second, classification analyses were performed, in which patients were a priori classified according to semen parameters. Third, machine learning algorithms were applied in a training phase (80% of the entire database) and in a tuning phase (20% of the data set). Finally, conventional statistical analyses were applied considering semen parameters and those other variables extracted during machine learning.

RESULTS

The final database included 4239 patients, aggregating semen analyses, blood and environmental parameters. Classification analyses were able to recognize oligozoospermic, teratozoospermic, asthenozoospermic and patients with altered semen parameters (0.58 accuracy, 0.58 sensitivity and 0.57 specificity). Machine learning algorithms detected three haematological variables, that is lymphocytes number, erythrocyte distribution and mean globular volume, significantly related to semen parameters (0.69 accuracy, 0.78 sensitivity and 0.41 specificity).

CONCLUSION

This is the first machine learning application to male fertility, detecting potential mathematical algorithms able to describe patients' semen characteristics changes. In this setting, a possible hidden link between testicular and haematopoietic tissues was suggested, according to their similar proliferative properties.

Are There Any Relationships Between Abnormal Seminal Parameters and Neutrophil-Lymphocyte Ratio, Platelet-Lymphocyte Ratio, and Red Blood Cell Distribution-Platelet Ratio?

Objective The aim of this study is to determine the relationship between neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and red blood cell distribution width (RDW)/platelet ratio (RPR) values, which are pro-inflammatory markers, with abnormal sperm parameters, and to evaluate their availability as predictive markers. Materials and methods A total of 160 patients, 80 of whom were the control group, formed with match-pair analysis (Group 1), and 80 patients with abnormal sperm analysis, who met the study criteria (Group 2), were included in the study. Complete blood count results were recorded. NLR, PLR, and RPR values were calculated using hematological parameters, and a comparison was made between the two groups. Results The mean age was 31.23 ± 5.1 years in Group 1 and 31.33 ± 6.4 years in Group 2. NLR values were 1.84 ± 0.57-1.87 ± 0.65 (P =0.77), PLR values were 105.42 ± 23.89-111.42 ± 34.54 (P = 0.62) and RPR values were 0.05 ± 0.009-0.05 ± 0.01 (P =0.45), respectively. There was no statistically significant difference between the groups. Conclusions We investigated whether NLR, PLR, and RPR results can be used as a predictive marker on abnormal sperm parameters. We do not recommend the use of these parameters as a predictive marker.

Male Inflammatory Parameters Are not Useful to Predict the Outcomes of Intracytoplasmic Sperm Injection: Results from a Cross-Sectional Study

Purpose

The relationship between male systemic inflammation and fertility seems intriguing, but no data about its impact on the assisted reproductive technology outcomes has been reported. Here, we aimed to evaluate the prognostic role of male systemic inflammatory parameters in intracytoplasmic sperm injection (ICSI) outcomes prediction, in couples undergoing an ovum donation program.

Materials and Methods

From January 2016 to December 2017, one hundred-ten couples were considered for this cross-sectional study. Neutrophil-to-lymphocyte ratio (NLR), monocyte-to-eosinophil ratio (MER), platelet-to-lymphocyte ratio (PLR), seminal parameters, fertilization rate (FR), cleavage rate (CR), pregnancy rate (PR) were evaluated. Male patients were divided into Group A with FR ≤70%, Group B with FR >70%.

Results

Overall, FR was 74.5%, CR 90.9%, PR 41.8%. Group A included 43 patients, Group B 67 men. Group A showed a median NLR of 1.55, PLR of 106.09, MER of 2.33. Group B reported a median NLR of 1.64, PLR 109.0, MER 2.76. We found no statistically differences between two groups with respect to NLR, PLR, MER (p=0.90, p=0.70, p=0.96, respectively). The age-adjusted linear regression analysis demonstrated only a relationship between NLR and sperm motility count (r=−0.02; p<0.05). Using the univariate logistic regression analysis, we found no significant associations.

Conclusions

We did not find any relationship between ICSI outcomes and male inflammation parameters.

Validation of SwimCount™, a Novel Home-Based Device That Detects Progressively Motile Spermatozoa: Correlation with World Health Organization 5th Semen Analysis

Purpose

We evaluated the usefulness of a home-based device (SwimCount™) compared with World Health Organization (WHO) 5th semen analysis in screening for male fertility in Asian men.

Materials and Methods

One hundred Asian men who visited CHA Seoul Station Fertility Center for evaluation of fertility were included. Semen samples were analyzed and compared with the SwimCount™ results. An aliquot of 0.5 mL of the semen sample was added to the SwimCount™ and a WHO 5th semen analysis was performed. Results were categorized as low (<5×10⁶/mL), and normal to high (≥5×10⁶/mL) total progressively motile sperm concentration. Receiver operating characteristic curve analysis was performed to evaluate the accuracy of the SwimCount™.

Results

The mean total progressively motile sperm concentration was 26.7×10⁶/mL. Semen analysis revealed that 28% of the samples were below the threshold count of 5 million/mL total progressively motile sperm concentration. The mean total progressively motile sperm concentration of the light color SwimCount™ result group determined by semen analysis was 7.5×10⁶/mL, and the mean total progressively motile sperm concentration of the moderate to dark color SwimCount™ result group was 34.2×10⁶/mL. An area under the receiver operating characteristic curve of 0.85 (95% confidence interval, 0.77–0.94; p<0.001) was obtained when the SwimCount™ was compared with semen analysis. The sensitivity and specificity were obtained at a cut off value of 5.0×10⁶/mL total progressively motile sperm concentration, giving a sensitivity and specificity of 87.5% and 73.4%.

Conclusions

We confirmed the reliability of the SwimCount™ as a home-based device for male fertility by evaluating the total progressively motile sperm concentration.