Investigating the association between systemic immune-inflammation index and female fertility: a retrospective cohort study.

This retrospective cohort study included 24,703 infertile patients who underwent their first IVF/ICSI-ET cycle at the Centre for Assisted Reproduction, Sichuan Jinxin Xinan Women & Children’s Hospital, between January 2018 and January 2024, and had complete blood routine test results and corresponding records. Exclusions were made based on the following criteria: (a) missing essential data, (b) loss to follow-up, and (c) chromosomal abnormalities, resulting in the exclusion of 3,840 cases. The study was approved by the Ethics Committees of both Sichuan Jinxin Xinan Women & Children’s Hospital (No. 2021014) and Chongqing Medical University (No. 2021060). Ultimately, 19,282 female participants were included in the analysis. Figure. S1 illustrates the patients’ inclusion and exclusion process. Neutrophil count (NC), platelet count (PC), and lymphocyte count (LC) were measured through complete blood count analysis of blood specimens using a Beckman Coulter automated blood analyzer at Sichuan Jinxin Xinan Women & Children’s Hospital. Blood samples for complete blood count (CBC) were collected on menstrual cycle days 2–4 (early follicular phase) of a spontaneous cycle or withdrawal bleed, concurrently with baseline hormonal assays (follicle-stimulating hormone [FSH], luteinizing hormone [LH], estradiol [E₂], anti-Müllerian hormone [AMH]) and antral follicle count (AFC) assessment, before initiation of controlled ovarian stimulation [ 22 ]. This standardized timing minimizes hormonal influence on leukocyte and platelet parameters, as estradiol and progesterone levels are at their nadir during this phase. Multiple studies have confirmed that inflammatory markers derived from CBC, such as NLR and PLR, exhibit minimal fluctuation in the early follicular phase compared to other cycle phases [ 23 , 24 ]. Results were expressed as ×10³ cells/µL. Inflammatory markers were calculated based on these cell counts: the systemic SII was computed as PC × NC/LC; the PPN as PC × NC; the PLR as PC/LC; and the NLR as NC/LC [ 25 ]. The outcome measures in this study included anti-Müllerian hormone (AMH), basal antral follicle count (AFC), and the number of oocytes retrieved, all treated as continuous variables. Fertility evaluation focused on two main aspects: ovarian reserve and ovarian response. Ovarian reserve was assessed by measuring AMH levels on days 2–3 of the menstrual cycle and counting AFC using transvaginal ultrasound. Ovarian response was evaluated by the number of oocytes retrieved [ 26 ]. AMH levels (quantification range: 0.01–23 ng/mL; intra-assay variability: 2.9%) were measured using electrochemiluminescent immunoassays (Roche, Switzerland) following the manufacturer’s protocol. AFC was determined via transvaginal ultrasound to ensure precision. Ovulation was induced using human chorionic gonadotropin (hCG) (Merck Serono, Switzerland or Lizhu Pharmaceutical, China) and/or a GnRH agonist (Ferring Pharmaceuticals, Switzerland) once at least three follicles reached a diameter of ≥ 17 mm. Oocyte retrieval was then performed 34–36 h post-trigger [ 27 ]. Descriptive statistics for continuous variables were presented as means with standard deviations (SD) or medians with interquartile ranges (IQR), depending on the distribution of the data. Categorical variables were reported as counts and percentages. Missing data for covariates (with less than 20% missingness) were imputed using the random forest method. Inflammatory markers were grouped into quartiles, with the lowest quartile serving as the reference group. Examine the correlations between inflammatory makers through a correlation analysis chart, and analyze the degree of association among the independent variables. General linear model was employed to examine the association between increasing concentrations of these markers and ovarian reserve and ovarian response. Histograms were used to visually assess the distributions of AMH, AFC, and the number of oocytes retrieved, revealing that all three variables were skewed. Square root transformations were subsequently applied for the regression analyses. Associations with ovarian reserve (AMH, AFC) and response (oocytes retrieved) were assessed using multivariable linear regression. Two models were constructed: Model 1: unadjusted; Model 2: adjusted for age, BMI, duration of infertility, baseline FSH, LH, estradiol, progesterone, prolactin, testosterone, treatment protocol (long GnRH agonist/antagonist/others), and infertility etiology (tubal, male factor, ovulation disorder, unexplained, mixed).This covariate selection was based on established clinical relevance and prior literature [ 28 ]. To investigate potential non-linear relationships between inflammatory markers and ovarian reserve and ovarian response, restricted cubic splines (RCS) were used. Various knot positions (ranging from 3 to 7) were tested, and the model with the lowest Akaike information criterion (AIC) value was selected, ultimately using 3 knots. For variables with non-linear relationships, we also use threshold effect analysis to determine critical points, allowing inflammatory makers to undergo segmented linear regression before and after the inflection points. Stratified analyses were conducted to identify sensitive subpopulations. Age was categorized into three groups (18–29 years, 30–35 years, 36–55 years), BMI was classified into three categories (< 18.5 kg/m², 18.5–24 kg/m², ≥ 24 kg/m²), and ovulation induction protocols were categorized as antagonist or agonist. To assess the robustness of the findings, sensitivity analyses were performed by excluding patients with a history of ovarian surgery or ovarian diseases (a total of 14571 cases), and the associations were re-evaluated. Data processing and statistical analysis were performed using R version 4.4.1 and Zstats v1.0 (available at www.zstats.net ). A two-sided P value of less than 0.05 was considered statistically significant.

Table 1 summarizes the baseline characteristics of the 19,282 female participants. The mean age was 31.44 years (SD, 4.50), the median BMI was 21.77 (range, 14.02–30.08), and the median duration of infertility was 3 years (range, 0–9 years). The mean endometrial thickness was 9.77 mm (SD, 2.47). Hormonal levels showed variability, with a median LH of 3.99 mIU/mL, PRL of 247.30 µIU/mL, T of 38.48 ng/dL, E 2 of 2402.50 pg/mL, P of 0.92 ng/mL, and FSH of 13.83 mIU/mL. Table 1 Characteristics of 19, 282 participants at baseline All patients ( N  = 19, 282) a Age 31.44 (4.50) b BMI 21.77 (14.02, 30.08) b Duration of infertility 3.00 (0.00, 9.00) a FSH 7.72 (2.02) b LH 3.99 (0.00, 9.25) b PRL 247.30 (0.00, 601.10) b T 38.48 (0.00, 93.65) b E 2 43.00 (0.00, 99.00) b P 0.55 (0.00, 1.50) Stimulation protocol (n, %) GnRH Antagonistc 8668 (44.95) GnRH agonist 6706 (34.78) Other protocols 3908 (20.27) Cause of infertility (n, %) Tubal factor 10,175 (52.8) Mixed infertility factors 5148 (26.7) Other factor 3959 (20.5) b SII 470.67 (48.38, 3157.89) b PPN 807.30 (90.90, 2671.80) b PLR 123.53 (23.86, 663.27) b NLR 2.22 (0.53, 13.72) a AMH_sqrt 1.72 (0.64) a AFC_sqrt 3.74 (1.14) a Number of retrieved oocytes_sqrt 2.99 (1.07) Abbreviations BMI body mass index a Cited as mean (standard deviation, SD) b Cited as medians (interquartile range, IQR) Characteristics of 19, 282 participants at baseline Abbreviations BMI body mass index a Cited as mean (standard deviation, SD) b Cited as medians (interquartile range, IQR) Inflammatory markers also varied among participants, with a median SII of 470.67, PNR of 807.30, PLR of 123.53, and NLR of 2.22. Square root-transformed measures of AMH, AFC, and the number of oocytes retrieved had means of 1.72 (SD, 0.64), 3.74 (SD, 1.14), and 2.99 (SD, 1.07), respectively. Figure S2 shows that there is a general positive correlation among the inflammatory markers. The strongest correlations are observed between SII and both PLR and NLR, indicating a close relationship between SII and the inflammatory or immune status reflected by PLR and NLR. The correlations of PPN with the other inflammatory markers are relatively weaker, which may suggest that PPN plays a different role among these variables, or its influencing factors differ from those of SII, PLR, and NLR. Table 2 shows the correlation between inflammatory markers and AMH. In Model 1, AMH levels decreased as SII, PPN, PLR, and NLR increased. After adjusting for covariates in Model 2, these associations remained significant. Specifically, compared to the reference group (Q1), higher levels of inflammatory markers were associated with lower AMH: SII Q4 (β: −0.037, 95% CI: −0.055, −0.018), PPN Q4 (β: −0.023, 95% CI: −0.042, −0.004), PLR Q4 (β: −0.001, 95% CI: −0.019, −0.018), and NLR Q4 (β: −0.057, 95% CI: −0.075, −0.038). Significant linear trends were found for SII and NLR ( P for trend < 0.05). Table 2 Association between inflammatory markers and AMH Inflammatory markers Model 1 (unadjusted) Model 2 (adjusted) β (95% CI) P P for trend β (95% CI) P P for trend SII < 0.001 < 0.001 Q1 [48.38, 347.63] ref ref Q2 [347.63, 470.67] −0.003 (−0.028, 0.023) 0.831 −0.001 (−0.020, 0.017) 0.905 Q3 [470.67, 637.57] −0.015 (−0.041, 0.010) 0.235 −0.001 (−0.019, 0.018) 0.923 Q4 [637.57, 3157.89] −0.077 (−0.102, −0.051) < 0.001 −0.037 (−0.055, −0.018) < 0.001 PPN 0.831 0.087 Q1 [90.90, 579.81] ref ref Q2 [579.81, 807.29] −0.012 (−0.037, 0.014) 0.360 −0.019 (−0.038, 0.000) 0.040 Q3 [807.29, 1107.39] 0.015 (−0.010, 0.041) 0.238 −0.001 (−0.020, 0.017) 0.903 Q4 [1107.39, 2671.80] −0.012 (−0.037, 0.013) 0.356 −0.023 (−0.042, −0.004) 0.018 PLR < 0.001 0.860 Q1 [23.86, 100.00] ref ref Q2 [100.00, 123.53] −0.010 (−0.035, 0.015) 0.458 0.007 (−0.011, 0.026) 0.431 Q3 [123.53, 152.57] −0.010 (−0.035, 0.015) 0.441 0.008 (−0.012, 0.026) 0.421 Q4 [152.57, 663.27] −0.058 (−0.083, −0.033) < 0.001 −0.001 (−0.019, 0.018) 0.978 NLR < 0.001 < 0.001 Q1 [0.53, 1.74] ref ref Q2 [1.74, 2.22] −0.041 (−0.067, −0.016) 0.001 −0.009 (−0.028, 0.016) 0.357 Q3 [2.22, 2.88] −0.071 (−0.096, −0.045) < 0.001 −0.020 (−0.039, −0.002) 0.032 Q4 [2.88, 13.72] −0.125 (−0.150, −0.100) (−0.075, −0.038) < 0.001 Model 1: Unadjusted for any covariates Model 2: Adjusted for age, BMI, duration of infertility, FSH, LH, PRL, T, E 2 , P, stimulation protocol, and cause of infertility Association between inflammatory markers and AMH Model 1: Unadjusted for any covariates Model 2: Adjusted for age, BMI, duration of infertility, FSH, LH, PRL, T, E 2 , P, stimulation protocol, and cause of infertility Table 3 presents the association between inflammatory markers and AFC. In Model 1, we observed a decrease in AFC levels with higher concentrations of SII, PPN, PLR, and NLR. After adjusting for covariates in Model 2, the significant associations persisted. Specifically, compared to the reference group (Q1), elevated levels of inflammatory markers were linked to lower AFC values: SII Q4 (β: −0.077, 95% CI: >−0.111, −0.043), PPN Q4 (β: −0.034, 95% CI: −0.069, 0.001), PLR Q4 (β: −0.009, 95% CI: −0.043, −0.025), and NLR Q4 (β: −0.116, 95% CI: −0.150, −0.082). Table 3 Association between inflammatory markers and AFC inflammatory markers Model 1 (unadjusted) Model 2 (adjusted) β (95% CI) P P for trend β (95% CI) P P for trend SII <0.001 <0.001 Q1 [48.38, 347.63] ref ref Q2 (347.63, 470.67] −0.017 (−0.062, 0.028) 0.466 −0.024 (−0.057, 0.010) 0.172 Q3 (470.67, 637.57] −0.044 (−0.089, 0.002) 0.058 −0.036 (−0.070, −0.002) 0.036 Q4 (637.57, 3157.89] −0.124 (−0.170, −0.079) <0.001 −0.077 (−0.111, −0.043) <0.001 PPN 0.114 0.073 Q1 [90.90, 579.81] ref ref Q2 (579.81, 807.29] 0.007 (−0.039, 0.052) 0.773 −0.019 (−0.053, 0.015) 0.269 Q3 (807.29, 1107.39] 0.038 (−0.007, 0.084) 0.098 −0.018 (−0.052, 0.017) 0.315 Q4 (1107.39, 2671.80] 0.028 (−0.017, 0.073) 0.226 −0.034 (−0.069, 0.001) 0.054 PLR <0.001 0.571 Q1 [23.86, 100.00] ref ref Q2 (100.00, 123.53] 0.007 (−0.038, 0.052) 0.756 0.032 (−0.001, 0.066) 0.060 Q3 (123.53, 152.57] −0.004 (−0.049, 0.041) 0.867 0.028 (−0.006, 0.061) 0.108 Q4 (152.57, 663.27] −0.114 (−0.160, −0.069) <0.001 −0.009 (−0.043, 0.025) 0.610 NLR <0.001 <0.001 Q1 [0.53, 1.74] ref ref Q2 (1.74, 2.22] −0.074 (−0.119, −0.029) 0.001 −0.025 (−0.059, 0.009) 0.148 Q3 (2.22, 2.88] −0.136 (−0.181, −0.090) <0.001 −0.057 (−0.091, −0.023) <0.001 Q4 (2.88, 13.72] −0.233 (−0.278, −0.188) <0.001 −0.116 (−0.150, −0.082) <0.001 Model 1: Unadjusted for any covariates Model 2: Adjusted for age, BMI, duration of infertility，FSH，LH，PRL，T，E 2 ，P，stimulation protocol，and cause of infertility Association between inflammatory markers and AFC Model 1: Unadjusted for any covariates Model 2: Adjusted for age, BMI, duration of infertility，FSH，LH，PRL，T，E 2 ，P，stimulation protocol，and cause of infertility Table 4 illustrates the relationship between various inflammatory markers and the number of oocytes retrieved. In Model 1, we observed a consistent decrease in the number of oocytes retrieved as the concentrations of SII, PPN, PLR, and NLR increased. After adjusting for relevant covariates in Model 2, these associations remained statistically significant. Specifically, compared to the reference group (Q1), higher levels of inflammatory markers were linked to a lower number of oocytes retrieved: SII Q4 (β: −0.066, 95% CI: −0.099, −0.033), PPN Q4 (β: −0.072, 95% CI: −0.106, −0.039), PLR Q4 (β: −0.023, 95% CI: −0.075, 0.024), and NLR Q4 (β: −0.069, 95% CI: −0.102, −0.036). Table 4 Association between inflammatory markers and number of retrieved oocytes inflammatory markers Model 1 (unadjusted) Model 2 (adjusted) β (95% CI) P P for trend β (95% CI) P P for trend SII <0.001 <0.001 Q1 [48.38, 347.63] ref ref Q2 (347.63, 470.67] −0.010 (−0.052, 0.033) 0.651 −0.009 (−0.042, 0.023) 0.577 Q3 (470.67, 637.57] −0.039 (−0.082, 0.003) 0.069 −0.014 (−0.047, 0.019) 0.412 Q4 (637.57, 3157.89] −0.126 (−0.168, −0.083) <0.001 −0.066 (−0.099, −0.033) <0.001 PPN 0.043 <0.001 Q1 [90.90, 579.81] ref ref Q2 (579.81, 807.29] −0.014 (−0.056, 0.029) 0.530 −0.024 (−0.057, 0.009) 0.148 Q3 (807.29, 1107.39] 0.019 (−0.023, 0.062) 0.376 0.002 (−0.035, 0.031) 0.903 Q4 (1107.39, 2671.80] −0.057 (−0.100, −0.015) 0.008 −0.072 (−0.106, −0.039) <0.001 PLR <0.001 0.144 Q1 [23.86, 100.00] ref ref Q2 (100.00, 123.53] −0.018 (−0.060, 0.025) 0.420 0.017 (−0.017, 0.033) 0.310 Q3 (123.53, 152.57] −0.028 (−0.071, 0.014) 0.196 0.009 (−0.045, 0.006) 0.608 Q4 (152.57, 663.27] −0.119 (−0.161, −0.077) <0.001 −0.023 (−0.075, 0.024) 0.169 NLR <0.001 <0.001 Q1 [0.53, 1.74] ref ref Q2 (1.74, 2.22] −0.047 (−0.090, −0.005) 0.030 0.002 (−0.031, 0.035) 0.902 Q3 (2.22, 2.88] −0.100 (−0.142, −0.057) <0.001 −0.025 (−0.057, 0.008) 0.141 Q4 (2.88, 13.72] −0.163 (−0.205, −0.120) <0.001 −0.069 (−0.102, −0.036) <0.001 Model 1: Unadjusted for any covariates. Model 2: Adjusted for age, BMI, duration of infertility，FSH，LH，PRL，T，E 2 ，P，stimulation protocol，and cause of infertility. Association between inflammatory markers and number of retrieved oocytes Model 1: Unadjusted for any covariates. Model 2: Adjusted for age, BMI, duration of infertility，FSH，LH，PRL，T，E 2 ，P，stimulation protocol，and cause of infertility. Figures 1 ,  2  and 3 display the exposure-response relationships between inflammatory markers and ovarian reserve, represented by AMH and AFC, as well as ovarian reactivity, measured by the number of eggs retrieved. The results reveal a declining trend in both ovarian reserve and reactivity with increasing concentrations of nearly all inflammatory markers. Notably, a nonlinear relationship was observed between PLR levels and both ovarian reserve and ovarian reactivity (P for nonlinear < 0.05). Additionally, a significant nonlinear association was identified between NLR levels and AFC (P for nonlinear < 0.05). Fig. 1 The exposure-response relationship between inflammatory markers and AFC. (A) SII; (B) PPN; (C) PLR; (D) NLR. Models adjusted for age, BMI, duration of infertility, FSH, LH, PRL, T, E 2 , P, stimulation protocol, and cause of infertility The exposure-response relationship between inflammatory markers and AFC. (A) SII; (B) PPN; (C) PLR; (D) NLR. Models adjusted for age, BMI, duration of infertility, FSH, LH, PRL, T, E 2 , P, stimulation protocol, and cause of infertility Fig. 2 The exposure-response relationship between inflammatory markers and AMH. (A) SII; (B) PPN; (C) PLR; (D) NLR. Models adjusted for age, BMI, duration of infertility, FSH, LH, PRL, T, E 2 , P, stimulation protocol, and cause of infertility The exposure-response relationship between inflammatory markers and AMH. (A) SII; (B) PPN; (C) PLR; (D) NLR. Models adjusted for age, BMI, duration of infertility, FSH, LH, PRL, T, E 2 , P, stimulation protocol, and cause of infertility Fig. 3 The exposure-response relationship between inflammatory markers and the number of retrieved oocytes. (A) SII; (B) PPN; (C) PLR; (D) NLR. Models adjusted for age, BMI, duration of infertility, FSH, LH, PRL, T, E 2 , P, stimulation protocol, and cause of infertility The exposure-response relationship between inflammatory markers and the number of retrieved oocytes. (A) SII; (B) PPN; (C) PLR; (D) NLR. Models adjusted for age, BMI, duration of infertility, FSH, LH, PRL, T, E 2 , P, stimulation protocol, and cause of infertility Threshold effect analysis indicates that there are inflection points at PLR = 123.53, PLR = 123.59, and NLR = 2.22, respectively. Tables S1-S3 present the segmented general linear analyses of ovarian reserve and ovarian responsiveness before and after the inflection points. The results between NLR levels and AFC show that on the left side of the inflection point (NLR  0.05). However, when NLR > 2.22, for each one-unit increase in NLR, there is a significant decrease in ovarian reserve capacity (β=−0.001, 95% CI=−0.002, 0.000). Stratified analysis (Table S4-S15) revealed a trend of declining AMH, AFC, and oocyte yield with increasing inflammatory marker levels across all age groups, although the differences between groups did not reach statistical significance. Similarly, patients with a high BMI appeared more susceptible to the adverse effects of inflammatory markers, resulting in reductions in ovarian reserve and responsiveness; however, these differences between BMI groups were also not statistically significant. In contrast, when stratified by treatment regimen, patients receiving antagonist protocols exhibited significantly greater susceptibility to inflammatory markers ( P for interaction < 0.05). Specifically, elevated levels of SII and PPN were associated with significant declines in AFC, highlighting a differential impact of inflammatory markers in this treatment subgroup. Table S16-S18 present the results of the sensitivity analysis, which were consistent with the primary findings. In Model 1, an overall downward trend was observed in AMH levels, AFC levels, and oocyte retrieval numbers with increasing levels of SII, PPN, PLR, and NLR. These associations remained significant after adjusting for relevant covariates in Model 2. Specifically, higher inflammatory marker levels were associated with significantly lower AMH levels, AFC levels, and oocyte retrieval numbers compared to the reference group (Q1).

This study provides compelling evidence linking systemic inflammation to reduced ovarian reserve and impaired ovarian response. Elevated levels of markers such as SII, NLR, PLR, and PPN are associated with lower AMH levels, AFC values, and oocyte yield, emphasizing the critical role of inflammation in female reproductive health. These findings advocate for the integration of inflammatory marker assessments into routine reproductive care and underscore the importance of addressing inflammation to optimize fertility outcomes.

This study highlights the significant relationship between systemic inflammatory markers and measures of ovarian reserve and ovarian response in a large cohort of 19,282 female participants. Our findings suggest that elevated levels of inflammatory markers, such as SII, PPN, PLR, and NLR, are associated with diminished ovarian reserve, evidenced by reductions in AMH levels and AFC, as well as decreased ovarian responsiveness, measured by the number of oocytes retrieved. These observations provide critical insights into the interplay between systemic inflammation and female reproductive health. Inflammation plays a vital role in defending against infections and promoting tissue repair. However, when it becomes chronic, it can lead to tissue damage and the onset of autoimmune diseases [ 29 ]. This dual nature of inflammation makes it a critical area of research, especially in understanding its contribution to disease progression and developing effective treatments [ 30 ]. In the context of reproductive health, chronic low-grade inflammation, as evidenced by elevated systemic inflammatory markers, may disrupt the ovarian microenvironment by impairing follicular development, compromising oocyte quality, altering steroidogenesis, and perturbing immune signaling mechanisms [ 31 ]. This disruption occurs through the production of pro-inflammatory cytokines such as Interleukin-1(IL-1), IL-6, IL-18, interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α), which have been observed in patients with primary ovarian insufficiency (POI), diminished ovarian reserve (DOR) and PCOS [ 32 – 34 ]. In our study, AMH levels—a key marker of ovarian reserve—showed significant inverse associations with all inflammatory markers examined. Particularly strong correlations were observed with SII and NLR. These results are consistent with prior research on ovarian dysfunction, where elevated NLR levels have been linked to conditions like ovarian hyperstimulation syndrome (OHSS) [ 35 ]. Moreover, a positive correlation between NLR and POI has also been documented [ 36 ]. The persistence of these associations, even after adjusting for covariates, reinforces the role of inflammation as an independent factor influencing ovarian health. Notably, nonlinear relationships between PLR and ovarian reserve, as well as between NLR and AFC, suggest that the impact of inflammation on ovarian function is not a simple linear relationship. This variability in both intensity and direction aligns with earlier studies indicating that chronic inflammation can disrupt follicular development [ 37 ]. Additionally, our identification of thresholds emphasizes the need to consider the dosage-dependent effects of inflammation on ovarian health. Below these threshold values, elevated PLR and NLR levels appear to have minimal or nonsignificant effects on ovarian reserve. However, once these thresholds are exceeded, the negative impact on ovarian function becomes more pronounced. This finding supports the hypothesis that chronic low-grade inflammation progressively impairs ovarian physiology, particularly when inflammatory markers surpass a critical threshold [ 38 ]. In parallel, ovarian response, as measured by the number of oocytes retrieved, demonstrated significant negative correlations with elevated levels of SII, PPN, PLR, and NLR. These findings suggest that inflammation, as indicated by elevated levels of these markers, may impair ovarian response and reduce oocyte yield during ART procedures. The link between inflammation and ovarian response is supported by existing literature. Chronic low-grade inflammation can disrupt ovarian function by inducing oxidative stress, promoting tissue fibrosis, and impairing folliculogenesis. Specifically, inflammation leads to the production of pro-inflammatory cytokines such as TNF-α, IL-6, and IL-1β, which are known to negatively affect granulosa cell function and follicular development [ 39 , 40 ]. In the context of chronic low-grade inflammation, conditions such as PCOS, endometriosis, and aging further exacerbate the inflammatory environment, which contributes to oxidative stress, impaired folliculogenesis, and reduced oocyte quality [ 41 ]. In addition, recent research has shown that immune cells such as dendritic cells (DCs) play an important role in the inflammatory processes within the ovarian follicular fluid, with their maturation correlating with ovarian response to gonadotropins [ 42 ]. These findings suggest that the immune profile within the ovarian microenvironment can influence follicular development and oocyte quality, further supporting the idea that inflammation impacts ovarian function. Our results reinforce the notion that systemic inflammation is a critical factor in ovarian dysfunction and impaired oocyte retrieval. Given these findings, future research should focus on elucidating the precise mechanisms through which chronic inflammation affects ovarian reserve and response. Investigating therapeutic strategies, such as anti-inflammatory treatments or immune modulation, could offer potential avenues for reducing the negative impact of inflammation on ovarian health and improving fertility outcomes in ART. The study has several advantages. First, it is the first to examine the relationship between markers of systemic inflammation and ovarian reserve and ovarian response, yielding novel and powerful results. Second, the large sample size increases the statistical validity of the findings, making the results more reliable. Third, the use of robust statistical models, with appropriate covariate adjustments and exposure-response models, further enhances the reliability and validity of the findings. Fourth, stratified analyses identified more sensitive subpopulations, providing valuable insights for developing targeted interventions. However, some limitations should be acknowledged. First, as this study is a retrospective cohort design, there is a potential for selection bias. Second, although blood sampling was standardized to cycle days 2–4, minor deviations in exact timing or the presence of anovulatory cycles in some patients cannot be entirely ruled out in a large retrospective setting [ 23 , 24 , 43 ]. Third, while systemic inflammatory markers provide practical indicators of overall inflammatory status, we did not directly assess the underlying sources of inflammation within our cohort, which may stem from heterogeneous and multifactorial etiologies. Finally, future studies incorporating follicular fluid analysis and advanced biomarkers, such as cytokine profiling, are needed to provide deeper mechanistic insights into the role of inflammation in ovarian function.

Infertility has become a significant global health challenge with women bearing a substantial portion of the social and emotional burden [ 1 ]. Female fertility naturally declines with age, especially after the early 30 s [ 2 ]. One of the primary factors contributing to this decline is the loss of ovarian reserve, which leads to a reduction in both the quantity and quality of oocytes [ 3 ]. A diminished ovarian reserve is closely associated with poor ovarian response to controlled ovarian hyperstimulation during ART therapy, resulting in fewer oocytes being retrieved and lower chances of successful fertilization and implantation [ 4 ]. Additionally, older women face an increased risk of miscarriage and pregnancy complications, often due to a combination of reduced ovarian reserve and higher rates of chromosomal abnormalities in the oocytes [ 5 ]. In addition to age-related decline in ovarian function, infertility can also be influenced by a range of other factors, such as hormonal imbalances, ovulatory dysfunction, and environmental factors. Conditions like polycystic ovary syndrome (PCOS), thyroid disorders, and conditions that reduce ovarian reserve can further impair ovarian response, leading to compromised fertility [ 6 , 7 ]. Environmental stressors, including high levels of stress, obesity, smoking, and exposure to environmental factors, have been shown to interfere with reproductive health by disrupting hormonal regulation and negatively impacting ovarian function and response [ 8 – 12 ]. Emerging evidence highlights the potential of systemic inflammatory markers as predictors of fertility and reproductive outcomes. Peripheral blood inflammatory markers, such as the Systemic Immune-Inflammation Index (SII), the Product of Platelet and Neutrophil Count (PPN), the Platelet-Lymphocyte Ratio (PLR), and the Neutrophil-Lymphocyte Ratio (NLR), are increasingly recognized for their utility in disease prognosis and status prediction [ 13 ]. For instance, SII has been associated with inflammatory and reproductive disorders, including infertility and PCOS [ 13 , 14 ]. Elevated SII levels are correlated with poorer outcomes in in vitro fertilization (IVF) treatments following the GnRH antagonist protocol, suggesting its potential role as a predictive marker in reproductive medicine [ 15 ]. Importantly, SII is considered more stable than individual blood cell counts, which are susceptible to fluctuations caused by dehydration or fluid overload [ 16 ]. Similarly, PPN—calculated as the product of peripheral platelet and neutrophil counts—is positively associated with estradiol levels and linked to sex hormone regulation [ 17 ]. While no significant differences in log2-PPN were observed between infertile and non-infertile women, the lack of information on infertility causes limits the interpretation of this finding [ 13 ]. In contrast, both NLR and PLR have been identified as markers of inflammatory status and show a positive correlation with infertility, suggesting a potential link between inflammation and reproductive health [ 13 , 18 , 19 ]. Elevated NLR and PLR levels have been associated with placental dysfunction and early miscarriage, making them significant markers for predicting adverse pregnancy outcomes [ 18 , 20 ]. Beyond fertility, NLR has demonstrated value as a prognostic marker for inflammatory and infectious pathologies, as well as postoperative complications, underscoring its broader utility in clinical settings [ 21 ]. Although chronic inflammation is increasingly recognized for its impact on reproductive health, the precise relationships between systemic inflammatory markers—specifically SII, PPN, PLR, and NLR—and key aspects of female fertility, such as ovarian reserve and ovarian response, remain poorly defined. This study aims to examine the associations between systemic inflammatory markers and these reproductive parameters, providing novel insights into their potential clinical relevance in infertility assessment and management.

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