Fertility Treatment Reshapes Semen Parameter Networks in Retrospective Study of 112 Men

Fertility treatment in infertile men may do more than lift individual semen measurements: it appears to reorganize the relationships among those measurements, according to a retrospective study published in Basic and Clinical Andrology. Researchers analyzed paired semen samples collected before and after routine clinical treatment from 112 infertile men attending the in vitro fertilization center at Faruk Medical City in Sulaymaniyah, Kurdistan Region of Iraq, between April 2015 and May 2024. Using Spearman’s rank correlations and principal component analysis, they found that correlations between normal sperm morphology and other core parameters strengthened substantially after treatment, while the overall structure linking concentration, total sperm number and progressive motility remained largely stable.

The most striking changes involved morphology, the proportion of sperm with normally shaped heads and bodies. Before treatment, normal morphology correlated moderately with progressive motility (a Spearman coefficient of 0.453) and more weakly with sperm concentration (0.327) and total sperm number (0.270). After treatment, those coefficients rose to 0.597, 0.494 and 0.476 respectively — increases of 32 percent, 51 percent and 76 percent. In other words, men whose sperm showed better shape quality after treatment were also more likely to show better motility and higher sperm counts, a coordinated pattern that was weaker before therapy began.

Principal component analysis, a statistical technique that condenses many correlated variables into a few underlying axes of shared variation, supported this picture. Before treatment, the first principal component was dominated by sperm quantity and motility and explained 34 percent of the variance; the second component captured structural defects and morphology. After treatment, the first component again captured quantity, now explaining 35 percent of variance, but the second component shifted in character, combining normal morphology (loading 0.81) with progressive motility (loading 0.59). The authors describe this as a transition from a defect-dominated component toward a pattern consistent with a functional integration axis coupling sperm form and function.

Not every relationship changed. The strong pretreatment correlation between sperm concentration and total sperm number (rs = 0.93) persisted after treatment (rs = 0.91), and concentration remained strongly associated with motility in both analyses. Progressive motility’s loading on the first principal component decreased from 0.79 to 0.58 while it emerged as a strong contributor to the second component, a redistribution the authors interpret as consistent with functional reorganization rather than a simple quantitative gain. They emphasize, however, that this interpretation remains hypothesis-generating given the observational design.

The analysis also uncovered age-related patterns. Age was excluded from the pretreatment component model because it correlated only weakly with the semen parameters, but after treatment it loaded negatively (-0.544) on the morphology-motility component and positively (0.445) on a component involving liquification time and neck defects. Younger men in the cohort showed stronger morphology-motility coordination after treatment, while older men appeared to retain persistent problems with semen viscosity and structural sperm defects. The authors caution that these age-stratified observations rest on continuous age loadings within the component analysis rather than formal subgroup comparisons, and should be treated as exploratory.

A further signal concerned inflammation. White blood cell counts in semen showed weak negative associations with semen quality before treatment, and the correlation between leukocytes and head defects weakened from -0.387 to -0.225 after treatment. White blood cells also lost factorial significance and were excluded from the posttreatment component model. The authors read this as a pattern consistent with reduced inflammatory contributions to sperm abnormalities following treatment, and they suggest it may support integrating anti-inflammatory strategies into treatment protocols — though they stress that the mechanistic role of such interventions requires prospective confirmation.

The study’s methods reflect the messy reality of clinical semen data. Except for age, all twelve variables deviated significantly from normality on Shapiro-Wilk tests, so the researchers applied logarithmic and square-root transformations, and winsorization at the fifth and 95th percentiles, before running principal component analysis; the Spearman correlations themselves used untransformed ranked data. Variable subsets for each component model were selected iteratively from twelve clinically reasonable candidates, judged by sampling adequacy, explained variance, structural simplicity and retention of the core World Health Organization parameters. The pretreatment model achieved a Kaiser-Meyer-Olkin measure of 0.655 with three components explaining 71 percent of variance; the posttreatment model reached 0.610 with three components explaining 69 percent. Both models retained concentration, motility and morphology, allowing meaningful comparison, but the differing variable sets mean the two models are best read as independent characterizations of each timepoint rather than a fully matched longitudinal structural analysis.

Important caveats temper the findings. The retrospective design precluded control over unmeasured confounders: smoking, alcohol consumption, body mass index, physical activity and diet were not systematically recorded. Treatment modalities were heterogeneous and individualized, and detailed regimens were not systematically documented, so the observed changes cannot be attributed to any specific intervention. There was also no concurrent control group of untreated infertile men followed over the same period, meaning some posttreatment changes could reflect natural biological variation, regression to the mean, or the effects of repeated semen analysis. The median interval between the two analyses was 163 days, roughly one full spermatogenesis cycle, and abstinence periods were standardized to two to seven days before both samples.

Perhaps the most significant limitation is the absence of clinical fertility outcomes. The study captured no data on natural conception rates, fertilization success in assisted reproduction, or live births, so the biomarker-level reorganization it documents cannot yet be linked to improved reproductive results. The authors frame their results as hypothesis-generating observations that pave the way for prospective studies tracking component-derived parameters against clinical endpoints. Within those limits, they argue the work supports a shift from single-parameter monitoring toward multivariate, systems-based assessment of semen profiles, with morphology potentially meriting greater weight in treatment monitoring as a candidate early marker of response — a proposition that future prospective studies with defined treatment protocols, lifestyle data, age strata and clinical follow-up will need to test directly.

Subject of Research: Retrospective analysis of pre- and posttreatment semen parameter networks in 112 infertile men using correlation and principal component analysis

Article Title: Assessing the treatment of male infertility through principal component analysis: a retrospective study

Article References: Kamal, M. A., & Rashid, K. M. J. (2026). Assessing the treatment of male infertility through principal component analysis: a retrospective study. Basic and Clinical Andrology, 36(1), Article 19. https://doi.org/10.1186/s12610-026-00317-1

Image Credits: AI Generated

DOI: 10.1186/s12610-026-00317-1

Keywords: male infertility, semen analysis, sperm morphology, sperm motility, principal component analysis, retrospective study, Spearman correlation, Kurdistan Region of Iraq, treatment response, biomarkers, inflammation, age-stratified analysis