As a novel prognostic and inflammatory marker, the systemic immune-inflammation index (SII) has come to the foreground in recent years. SII may be used as an indicator reflecting the progressive inflammatory process in patients with polycystic ovary syndrome (PCOS). This study aimed to evaluate the correlation between SII and assisted reproductive outcomes in PCOS patients.
Results
A total of 966 women undergoing in vitro fertilization (IVF) procedure with PCOS were included in the study. The SII was calculated as platelet count (/L) × neutrophil count (/L)/lymphocyte count (/L). Participants were divided into four groups according to SII quartiles calculated at baseline, and the differences of clinical and laboratory outcomes between these four groups were compared. Moreover, a univariate linear regression model was used to evaluate the associations between SII and the outcomes. Patients in the highest SII quartile (Q4) had lower antral follicle count (AFC), estradiol (E2), and progesterone (P) levels on the day of human chorionic gonadotropin (HCG) start compared with the lower three SII quartiles (Q1-Q3). Moreover, our analysis demonstrated that women in the lower SII quartiles had a higher rate of available embryos and blastocyst formation compared with those in the highest SII quartile. Logarithm of SII correlated negatively with available embryo rate, but not with number of available embryos. Additionally, the results of our multivariate logistic regression analyses indicated that the highest SII quartile was negatively associated with biochemical pregnancy rate (BPR), clinical pregnancy rate (CPR), live birth rate (LBR), and implantation rate (IR). A non-linear relationship between the SII and number of available embryos, with a negative relationship seen to the right of the inflection point was also found.
Conclusions
The interplay among thrombocytosis, inflammation, and immunity could influence assisted reproductive outcomes in PCOS patients. In this regard, SII may serve as a valuable marker for exploring potential correlations.
Xin Li and Ting Luan contribute to this paper equally.
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Introduction
Polycystic ovary syndrome (PCOS) is a primary contributor to female infertility, playing a pivotal role in reproductive challenges for women. It affects a substantial proportion of women of reproductive age worldwide, with prevalence rates ranging from 6 to 20% [1, 2]. PCOS is generally characterized by anovulation or oligo-anovulation, androgen excess and ovarian polycystic morphology [3]. The diagnosis of PCOS, as per the Rotterdam criteria, requires the presence of a minimum of two out of the following three features: oligoanovulation, clinical and/or biochemical hyperandrogenism, and polycystic ovaries observed through ultrasonography, while excluding other endocrinopathies. The clinical manifestations of PCOS are complicated and individualized and the precise etiology remains elusive. Current research suggests that the etiology of PCOS may be multifactorial, possibly related to reactive oxygen species (ROS) [4], inflammatory reactions [5, 6], genetic predisposition [7], excessive exposure to embryonic androgens [8], unhealthy lifestyle factors [9], and hormonal disorders[10]. In addition, there is growing recognition that low-grade chronic inflammation plays a crucial role as both a symptom and a contributing factor [11, 12]. In recent years, an increasing number of studies have focused on the critical role of inflammation in PCOS. Previous studies reported that significantly higher concentrations of inflammatory cells were detected in the peripheral blood of PCOS patients. Leukocytosis, neutrophilia, and platelet aggregation have been frequently detected in PCOS patient blood profile [13, 14]. The systemic immune-inflammation index (SII) is a novel index based on the lymphocyte, neutrophil, and platelet counts. A mounting body of evidence illustrates that SII is a useful index to reflect the systemic immune and inflammatory status of the human body [15‐17]. SII has been reported to be associated with various inflammatory and reproductive disorders, such as endometrial cancer, ovarian cancer [18, 19]. To the best of our knowledge, no study has explored the association between SII and in vitro fertilization (IVF) outcomes in women with PCOS. First introduced by Hu et al. [20], this marker has been demonstrated to gauge the severity of systemic inflammation in carcinoma patients [21] and offers high prognostic value across various cancer types [22], where local and systemic inflammation are key features [23].
Women with PCOS often seek the help of assisted reproductive technology (ART) to get pregnant due to disorders of oocyte maturation or any other male or female factors. In recent years, gonadotropin releasing hormone antagonist (GnRH-ant) protocol has been widely used among PCOS patients, due to its short stimulation duration, low gonadotropin consumption and significantly lower incidence of ovarian hyperstimulation syndrome (OHSS) [24]. As SII integrates the information of neutrophil, platelet and lymphocyte counts, the analysis of these three types of blood cells together could elucidate their interaction in the pathological process of PCOS, though their possible opposite roles in the process may not be appreciated. Furthermore, it would be of greater value to elaborate the potential interactive effects of platelets, lymphocytes and neutrophils on PCOS women. To address these issues, our study aimed to investigate the relationship between the SII on the day before oocyte retrieval and the outcomes of assisted conception in women diagnosed with PCOS who underwent treatment with GnRH-ant protocol.
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Materials and methods
Study design and participants
This study was a hospital-based cohort study, carried out at Women’s Hospital of Nanjing Medical University between January 2018 and September 2020. PCOS patients who underwent their first IVF/ Intracytoplasmic sperm injection (ICSI)-embryos transfer (ET) were evaluated. Inclusion criteria: (1) Patients aged between 20 and 40 years, diagnosed with PCOS according to the Rotterdam diagnostic criteria (3), therefore fulfilling ≥ 2 of the following: oligo- or anovulation; clinical or biochemical signs of hyperandrogenism; and polycystic ovaries and exclusion of other etiologies (i.e., congenital adrenal hyperplasia, androgen-secreting tumors, or Cushing syndrome; (2) IVF or ICSI was used for insemination; (3) PCOS protocols were GnRH-ant protocol. Exclusion criteria: (1) Infertility patients caused by non-PCOS ovulatory disorder or other factors; (2) Patients with history of ovarian surgery or complication with endometriosis or pelvic adhesion; (3) Patients complicated with liver, kidney or thyroid dysfunction; (4) Recurrent spontaneous abortion (defined as three or more previous spontaneous pregnancy losses), congenital or acquired uterine malformations, abnormal parental karyotypes or medical conditions that contraindicated ART and/or pregnancy.
Ovarian stimulation
Participants underwent a flexible GnRH-ant protocol, briefly, patients were injected daily with 150–225 IU recombinant FSH (rFSH, Gonal-F, Merck Serono, Italy) from day 2 or 3 of the menstrual cycle, with daily 0.125 mg-0.25 mg GnRH-ant (Cetrorelix, Merck Serono, Darmstadt, Germany) being initiated once the largest follicle was > 12–14 mm in size. We monitored follicular growth by ultrasound scan and sex hormone levels [follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol(E2), and progesterone(P)] and adjusted the dose of gonadotropin (Gn). When at least two follicles are larger than 18 mm, a 10,000 IU human chorionic gonadotrophin (hCG, Lizhu, China) injection was given to achieve final oocyte maturation, and oocyte retrieval was scheduled 36 h later. For women at high risk for OHSS, low doses of hCG (5,000 IU) were used to trigger ovulation, a minority of patients opt for a double trigger. Serum sex hormone levels and ET were measured on the trigger day.
Oocyte retrieval and embryo transfer
The oocytes and embryos were cultured according to our previously published article [25]. Based on sperm quality, conventional IVF or ICSI was performed. All the embryos were in vitro fertilization and cultured for 3 days (D3) or 5 days (D5). Ultrasound guidance was used for all ET and performed 3 or 5 days after oocyte retrieval, with a maximum of 2 embryos to be transferred. During the luteal phase, 20 mg progesterone injection was injected twice a day from the first day after oocyte retrieval.
Freeze-all
Indications for the freeze-all policy included a high risk of developing OHSS (women who were younger than 35 years old, use hCG for ovarian stimulation or had a high response to Gn, the number of follicles > 14 mm was more than 20 or E2 level was more than 5000 pg/ml on trigger day), inadequate endometrial thickness (< 7 mm), high P level (> 2.0 ng/ml) and some other conditions (such as high blood pressure, fever, individual preference).
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Definition of SII
Peripheral venous blood samples were collected one day before oocyte retrieval in all patients. The counts of peripheral neutrophils, lymphocytes and platelets were measured and analyzed by an automatic blood analyzer (five-category hematological analyzer XT 2000i, SYSMEX, Japan). The definition of SII was shown as follow: SII = platelet × neutrophil/lymphocyte counts [26]. SII was designed as exposure variable in our analysis. The SII value was also transformed to a logarithmic scale (Log SII) to minimize skewness of the underlying distribution.
Outcome assessment
Clinical outcomes
The delivery of a viable infant was considered as the live birth. A biochemical pregnancy was defined as a positive hCG level without a gestational sac. The presence of a gestational sac with or without fetal heart activity, ectopic pregnancy and heterotopic pregnancy was regarded as clinical pregnancy. The implantation rate (IR) was defined as the number of gestational sacs divided by the number of embryos transferred. The early pregnancy loss rate (EPLR) was defined as the proportion of patients with a spontaneous termination of pregnancy.
Laboratory outcomes
The following oocyte and embryo parameters were analyzed: number of oocytes retrieved, number of two pronucleus (2PN), number of 2PN cleavage embryos, cleavage rate, number of cleavage embryos, number of available embryos, available embryo rate, number of superior-quality embryos and blastocyst formation rate.
Statistical analysis
In this study, participants were divided into four groups according to SII quartiles calculated at baseline. Continuous variables were presented as mean ± standard deviation for normal distribution and median (quartile) for skewed distribution, while categorical variables were described using frequency or percentage. To determine the statistical differences between means and proportions of the groups, the normal distribution was analyzed using the One-Way ANOVA, while the skewed distribution was analyzed using the Kruskal Wallis H test and categorical variables were analyzed using the chi-square test. Moreover, we used a univariate linear regression model to evaluate the associations between SII and the outcomes. The results were presented for both unadjusted, minimally adjusted, and fully adjusted analyses. For investigating the non-linear relationship between SII on clinical and laboratory outcomes, we used Generalized Additive Models (GAM). If the non-linear correlation was observed, a two-piecewise linear regression model was performed to calculate the threshold effect of the SII on clinical and laboratory outcomes in terms of the smoothing plot. When the ratio between SII on clinical and laboratory outcomes appears obvious in smoothed curve, recursive method calculates automatically the inflection point, where the maximum model likelihood will be used [27]. P < 0.05 was used for statistical significance. All statistical analyses were performed using statistical software packages R (http://www.R-project.org, The R Foundation) and EmpowerStats software (http://www.mpowerstats.com, X&Y Solution, Inc., Boston, MA).
Results
Baseline characteristics of four groups
Figure 1 shows a flow chart of the study population. SII was divided into quartiles based on the distribution of baseline SII in the participants ((quartile 1 (Q1): < 776.63, quartile 2 (Q2): 776.63–1006.63, quartile 3 (Q3): 1006.63–1330.43, and quartile 4 (Q4): ≥ 1330.43). The baseline characteristics of the four groups according to SII levels were shown in Table 1. Compared with high level of SII group (Q4), patients had a significantly lower follicle count (AFC), E2 on HCG start day, P on HCG start day in other three groups (Q1-Q3).
Fig. 1
Flow-chart of the study cohort characteristics. PCOS polycystic Ovary Syndrome, SII Systematic immune-inflammation index
Table 1
Baseline characteristics of participants by quartiles of SII
Characteristics
Quantile 1 (N = 242)
Quantile 2 (N = 241)
Quantile 3 (N = 241)
Quantile 4 (N = 242)
P-value
Age
28.32 ± 3.18
28.54 ± 3.23
28.20 ± 3.33
28.18 ± 2.92
0.581
Primary infertility
164 (67.8%)
173 (71.8%)
183 (75.9%)
176 (72.7%)
0.254
78 (32.2%)
68 (28.2%)
58 (24.1%)
66 (27.3%)
Duration of infertility(y)
3.12 ± 2.00
3.37 ± 1.96
3.21 ± 2.04
3.45 ± 2.19
0.281
Smoking history, n (%)
No
240 (99.2%)
240 (99.6%)
240 (99.6%)
240 (99.2%)
1.000
Yes
2 (0.8%)
1 (0.4%)
1 (0.4%)
2 (0.8%)
Gravidity
0.00 (0.00 to 1.00)
0.00 (0.00 to 1.00)
0.00 (0.00 to 1.00)
0.00 (0.00 to 1.00)
0.363
Parity
0.00 (0.00 to 0.00)
0.00 (0.00 to 0.00)
0.00 (0.00 to 0.00)
0.00 (0.00 to 0.00)
0.662
Number of abortions
0.00 (0.00 to 1.00)
0.00 (0.00 to 0.00)
0.00 (0.00 to 0.00)
0.00 (0.00 to 0.00)
0.661
Basal FSH(IU/L)
6.84 ± 1.58
6.74 ± 3.03
6.60 ± 1.39
6.37 ± 1.65
0.060
Basal E2(pg/mL)
55.11 ± 10.20
44.41 ± 20.33
50.26 ± 37.80
46.47 ± 20.89
0.182
Basal LH(IU/L)
7.72 ± 4.01
7.11 ± 4.02
7.71 ± 4.02
7.98 ± 4.69
0.139
Basla T(ng/dl)
0.56 (0.43 to 0.70)
0.55 (0.43 to 0.72)
0.56 (0.42 to 0.70)
0.57 (0.42 to 0.75)
0.997
AMH (ng/ml)
11.16 ± 5.28
11.48 ± 5.48
11.52 ± 5.32
12.27 ± 5.67
0.155
AFC
14.23 ± 4.27
15.07 ± 4.79
16.16 ± 5.28
16.46 ± 5.17
< .001
BMI (Kg/m2)
0.680
< 18.5
11 (4.55%)
8 (3.32%)
8 (3.32%)
11 (4.55%)
18.5–25
144 (59.50%)
128 (53.11%)
143 (59.34%)
132 (54.55%)
25–30
65 (26.86%)
77 (31.95%)
71 (29.46%)
69 (28.51%)
≥ 30
22 (9.09%)
28 (11.62%)
19 (7.88%)
30 (12.40%)
Starting dose of Gn (IU)
188.21 ± 33.11
192.71 ± 39.46
188.68 ± 35.15
188.42 ± 36.61
0.466
Total Gn dose (IU)
1834.29 ± 590.33
1887.87 ± 659.64
1852.45 ± 712.67
1795.18 ± 625.93
0.464
Duration of Gn (day)
9.29 ± 1.72
9.24 ± 1.72
9.28 ± 2.01
9.18 ± 1.48
0.910
Starting dose of GnRH-ant (IU)
0.25 (0.12 to 0.25)
0.25 (0.12 to 0.25)
0.25 (0.12 to 0.25)
0.25 (0.12 to 0.25)
0.258
Total GnRH-ant dosea(IU)
1.12 (0.75 to 1.50)
1.00 (0.75 to 1.50)
1.25 (0.88 to 1.50)
1.25 (0.88 to 1.50)
0.155
Duration of GnRH-ant (day)
5.19 ± 2.16
4.82 ± 1.75
5.10 ± 1.87
5.17 ± 1.82
0.121
Triggered with hCG
217(89.67%)
209(86.72%)
206(85.48%)
197(81.40%)
0.072
Triggered with GnRH-a + HCG
25(10.33%)
32(13.28%)
35(14.52%)
45(18.59%)
E2 on HCG start day (pg/ml)
5769.50 (4257.00 to 8217.00)
5612.00 (4395.00 to 8325.00)
6142.00 (4555.00 to 9117.00)
6871.00 (4805.37 to 9928.00)
0.019
P on HCG start day(ng/ml)
1.25 (0.91 to 1.69)
1.29 (0.94 to 1.70)
1.31 (0.92 to 1.84)
1.42 (1.05 to 2.00)
0.017
LH on HCG start day (IU/L)
2.69 (1.67 to 3.97)
2.65 (1.66 to 3.96)
2.67 (1.75 to 4.77)
2.59 (1.64 to 4.64)
0.907
P on HCG start day(ng/ml)
1.25 (0.91 to 1.69)
1.29 (0.94 to 1.70)
1.31 (0.92 to 1.84)
1.42 (1.05 to 2.00)
0.017
LH on HCG start day (IU/L)
2.69 (1.67 to 3.97)
2.65 (1.66 to 3.96)
2.67 (1.75 to 4.77)
2.59 (1.64 to 4.64)
0.907
Data are expressed as median (interquartile range) for non-normally distributed continuous variables
Data are expressed as mean + SD for normally distributed continuous variables
Categorical variables were expressed in frequency or as a percentage
Abbreviations: SII Systemic immune-inflammation index, BMI Body mass index, FSH Follicle-stimulating hormone, LH Luteinizing hormone, T Testosterone, AMH Anti-müllerian hormone, P Progesterone, AFC Antral follicle count, Gn Gonadotropin, GnRH-ant Gonadotropin releasing hormone antagonist, HCG Human chorionic gonadotropin, GnRH-a Gonadotropin releasing hormone agonist
×
Comparing the laboratory outcomes, we observed that patients in the lower three SII groups (Q1-Q3) had significantly lower numbers of oocytes retrieved, 2PN cleavage, 2PN and cleavage embryos than those in the highest SII group (Q4). However, the rate of available embryos and blastocyst formation in the three lower SII groups were much higher than that in the highest SII group (Q4) Table 2.
Table 2
Comparison of laboratory data between the four groups
Characteristics
Quantile 1 (N = 242)
Quantile 2 (N = 241)
Quantile 3 (N = 241)
Quantile 4 (N = 242)
P-value
No. of oocytes retrieved(n)
14.30 ± 5.31
14.89 ± 5.63
15.70 ± 6.36
16.80 ± 6.91
< 0.001
No. of 2PN cleavage
10.00 (7.00 to 15.00)
12.00 (8.00 to 15.00)
11.00 (8.00 to 16.00)
13.00 (9.00 to 17.00)
< 0.001
No. of 2PN(n)
10.00 (7.00 to 15.00)
12.00 (8.00 to 15.00)
12.00 (8.00 to 16.00)
13.00 (9.00 to 18.00)
< 0.001
cleavage rate(%)
99.66 (2675/2684)
99.65 (2837/2847)
99.62 (2894/2905)
99.72 (3238/3247)
0.137
No. of cleavage embryos(n)
11.42 ± 5.72
12.18 ± 5.61
12.39 ± 6.24
13.70 ± 6.82
< 0.001
No. of available embryos(n)
7.08 ± 4.22
7.94 ± 4.41
7.84 ± 4.73
7.19 ± 4.19
0.064
Available embryo rate(%)
67 (50 to 82)
70 (55 to 83)
67 (47 to 85)
54 (43 to 67)
< 0.001
High-quality embryos(n)
2.00 (1.00 to 4.00)
3.00 (1.00 to 5.00)
2.00 (1.00 to 5.00)
3.00 (1.00 to 6.00)
0.329
Blastocyst formation rate(%)
56.03(1291/2304)
60.11(1495/2487)
58.78(1475/2868)
57.04(1636/2868)
0.019
OHSS rate(%)
3.75(9/242)
5.8(14/241)
9.1 (22/241)
6.6 (16/242)
0.106
Data are expressed as median (interquartile range) for non-normally distributed continuous variables
Data are expressed as mean + SD for normally distributed continuous variables
Categorical variables were expressed in frequency or as a percentage
Abbreviations: 2PN Two pronucleus, OHSS Ovarian hyperstimulation syndrome
Univariate analysis
The results of univariate analysis were shown in Table 3, Supplementary Tables 1 & 2. Table 3 showed that anti-mullerian hormone (AMH), LH, AFC were positively correlated with numbers of available embryos. We also found that basal FSH, body mass index (BMI) ≥ 30, starting dose of Gn, duration of Gn were negatively associated with numbers of available embryos, whereas age, duration of infertility, basal E2, SII and Log SII were not associated with numbers of available embryos. The univariate analysis of available embryo rate and high-quality embryos were presented in Supplementary Tables 1 & 2.
Table 3
The results of uivariate analysis
No. of available embryos
Statistics
β(95%CI), P value
Age
28.31 ± 3.17
-0.05 (-0.13, 0.04), 0.298
Duration of infertility(y)
3.29 ± 2.05
-0.10 (-0.24, 0.03), 0.133
BMI(Kg/m2)
< 18.5
38 (3.93%)
0
18.5–25
547 (56.63%)
-0.35 (-1.78, 1.07), 0.626
25–30
282 (29.19%)
-1.39 (-2.86, 0.07), 0.063
≥ 30
99 (10.25%)
-2.71 (-4.33, -1.08), 0.001
AMH(ng/ml)
Low (< 7.44)
321 (33.23%)
0
Middle (7.44–10.66)
323 (33.44%)
1.43 (0.75, 2.10), < 0.0001
High (≥ 10.66)
322 (33.33%)
1.98 (1.30, 2.66), < 0.0001
Basal FSH(IU/L)
6.64 ± 2.02
-0.20 (-0.34, -0.06), 0.004
Basal E2(pg/mL)
49.07 ± 57.43
0.00 (-0.00, 0.01), 0.861
Basal LH(IU/L)
7.63 ± 4.20
0.12 (0.06, 0.19), 0.0003
AFC
15.48 ± 4.96
0.40 (0.35, 0.45), < 0.0001
Starting dose of Gn(IU)
189.50 ± 36.14
-0.02 (-0.03, -0.01), < 0.0001
Duration of Gn(IU)
9.25 ± 1.74
-0.21 (-0.37, -0.05), 0.011
SII
1100.36 ± 463.46
0.00 (-0.00, 0.00), 0.569
SII quartile
Q1
242 (25.05%)
0
Q2
241 (24.95%)
0.86 (0.08, 1.64), 0.031
Q3
241 (24.95%)
0.76 (-0.02, 1.54), 0.057
Q4
242 (25.05%)
0.11 (-0.68, 0.89), 0.787
SII quartile continuous
1.50 ± 1.12
0.02 (-0.23, 0.27), 0.860
Log SII
6.92 ± 0.41
0.27 (-0.42, 0.95), 0.445
Data is represented as β(95%CI), P value
SII: Quantile 1 Quantile 2 Quantile 3 Quantile 4
Abbreviations: SII Systemic immune-inflammation index, BMI Body mass index, FSH Follicle-stimulating hormone, LH Luteinizing hormone, T Testosterone, AMH Anti-müllerian hormone, P Progesterone, AFC Antral follicle count, Gn Gonadotropin, GnRH-ant Gonadotropin releasing hormone antagonist; HCG human chorionic gonadotropin
The relationship between SII quartiles and laboratory outcomes
The association between SII quartiles and laboratory outcomes was shown in Table 4. We used univariate linear regression model to evaluate the associations between SII quartiles and laboratory outcomes. Meanwhile, we showed the non-adjusted and adjusted models. In crude model, Log SII showed no correlation with number of available embryos and high-quality embryos, but a negative correlation was found in Log SII and available embryo rate. In the minimally adjusted model (adjusted age, AMH, BMI, AFC), the highest quartiles of SII showed negative associations with number of available embryos (β = -0.72, 95% CI -1.45 to 0.01; P for trend = 0.0195), available embryo rate (β = -0.10, 95% CI -0.15 to -0.06; P for trend < 0.0001), when compared with the lowest quartiles of SII. No significant associations were observed between any quartiles of SII and high-quality embryos. Consistent results were also observed in the fully adjusted model when adjusted for age, AMH, BMI, AFC, basal FSH, basal E2, basal LH, total Gn dose, duration of Gn, E2 on HCG start day, P on HCG start day.
Table 4
Association between quartiles of SII and laboratory data among the whole participants (N = 966)
Variable
Crude model (β, 95%CI, P)
Minimally adjusted model(β, 95%CI, P)
Fully adjusted model(β, 95%CI, P)
No. of available embryos
SII quartile
Q1
Ref
Ref
Ref
Q2
0.86 (0.08, 1.64), 0.032
0.64 (-0.08, 1.36), 0.082
0.74 (0.02, 1.46), 0.046
Q3
0.76 (-0.02, 1.54), 0.058
0.07 (-0.64, 0.79), 0.838
0.12 (-0.60, 0.84), 0.748
Q4
0.11 (-0.68, 0.89), 0.788
-0.72 (-1.45, 0.01), 0.052
-0.71 (-1.44, 0.02), 0.056
Log SII
0.27 (-0.42, 0.95), 0.445
-0.56 (-1.19, 0.08), 0.089
-0.57 (-1.21, 0.07), 0.079
P for trenda
0.8601
0.0195
0.0187
Available embryo rate
SII quartile
Q1
Ref
Ref
Ref
Q2
0.03 (-0.01, 0.07) 0.163
0.02 (-0.02, 0.07) 0.247
0.03 (-0.02, 0.07) 0.239
Q3
0.01 (-0.03, 0.06) 0.507
0.01 (-0.03, 0.06) 0.525
0.01 (-0.03, 0.06) 0.541
Q4
-0.10 (-0.14, -0.06) < 0.0001
-0.10 (-0.15, -0.06) < 0.0001
-0.11 (-0.15, -0.06) < 0.0001
SII Log
-0.08 (-0.11, -0.04) < 0.0001
-0.08 (-0.12, -0.04) < 0.0001
-0.08 (-0.12, -0.04) < 0.0001
P for trenda
< 0.0001
< 0.0001
< 0.0001
High-quality embryos
SII quartile
Q1
Ref
Ref
Ref
Q2
0.46 (-0.15, 1.08) 0.136
0.31 (-0.28, 0.89) 0.303
0.33 (-0.25, 0.92) 0.266
Q3
0.54 (-0.07, 1.15) 0.084
0.04 (-0.55, 0.62) 0.903
0.05 (-0.53, 0.64) 0.856
Q4
0.41 (-0.20, 1.02) 0.185
-0.17 (-0.76, 0.42) 0.569
-0.17 (-0.76, 0.43) 0.583
Log SII
0.48 (-0.05, 1.01) 0.0789
-0.09 (-0.60, 0.42) 0.733
-0.09 (-0.60, 0.43) 0.745
P for trenda
0.182
0.411
0.417
Tests for linear trend were conducted by assigning median values of each quartile of systemic immune-inflammation index as a continuous variable in the models
Crude model: did not adjust other covariants
Minimally adjusted model: adjusted age; AMH; BMI; AFC
Fully adjusted model: adjusted age; Basal FSH; Basal E2; Basal LH; AMH;AFC; Total Gn dose; Duration of Gn; E2 on HCG start day; P on HCG start day
Abbreviations: CI Confidence interval, Ref Reference, SII Systemic immune-inflammation index
The analyses of non-linear relationship
In the present study, we analysed the non-linear relationship between Log SII and outcomes because Log SII was a continuous variable (Fig. 2). We found that the relationship between Log SII and number of available embryos was non-linear (after adjusting age, AMH, BMI, AFC, basal FSH, basal E2, basal LH, total Gn dose, duration of Gn, E2 on HCG start day, P on HCG start day). By using a two-piecewise linear regression model, we calculated that the inflection point was 6.72. On the left of the inflection point, the effect size, 95%CI and P value were 1.10, − 0.40 to 2.66 and 0.168, respectively (Table 5). However, we also observed a negative relationship between Log SII and number of available embryos on the right side of the inflection point (-1.37, -2.30 to -0.43, 0.004). Consistent results were also observed in the relationship between Log SII and available embryos rate (Supplementary Table 3 & Fig. 3). We also found that while the number of high-quality embryos appeared to decline with an increase in Log SII levels, this observed trend lacked statistical significance (Supplementary Fig. 1). The clinical significance of this finding is that Log SII has a non-linear relationship with the ovarian response and embryo quality in PCOS patients undergoing IVF/ICSI treatment, and that this relationship changes at an inflection point of 6.72.
Fig. 2
The relationship between number of available embryo and Log SH. A nonlinear relationship between them was detected after adjusting for age, AMH, BMI, AFC, basal FSH, basal E2, basal LH, total Gn dose, duaration of Gn, E2 on HCG start day, P on HCG start day
Table 5
The results of two-piecewise linear regression model
Inflection point of Log SII
Effect size (β)
95%CI
P
< 6.72
1.10
-0.40 to 2.66
0.168
≥ 6.72
-1.37
-2.30 to -0.43
0.004
Abbreviations: CI Confidence interval, SII Systemic immune-inflammation index
Effect: No. of available embryos, Cause: Log SII
Adjusted: age, sex, BMI, AMH; AFC; BMI; Gn days
Fig. 3
The relationship between available embryos rate and Log SII. A nonlinear relationship between them was detected after adjusting for age, AMH, BMI, AFC, basal FSH, basal E2, basal LH, total Gn dose, duation of Gn, E2 on HCG start day, P on HCG start day
×
×
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Univariable and multivariate regression analysis of pregnancy outcomes
Table 6 illustrated that both univariate and multivariate logistic regression analyses demonstrate the highest quartiles of SII were negatively associated with biochemical pregnancy rate (BPR), clinical pregnancy rate (CPR), LBR, and IR. Conversely, a positive association was found with EPLR, albeit with no statistically significant difference (P > 0.05).
Table 6
Univariable and Multivariate regression analysis of Pregnancy outcomes among the whole participants
Variable
Outcome
Univariate analysis
Multivariate analysis
OR
P
OR
P
Biochemical pregnancy rate (%)
SII quartile
Q1
58.33(7/12)
Ref
Ref
Q2
68.75(11/16)
1.57 (0.33, 7.48)
0.570
1.21 (0.23, 6.33)
0.818
Q3
50(3/6)
0.71 (0.10, 5.12)
0.738
0.40 (0.04, 3.88)
0.431
Q4
20(1/5)
0.18 (0.02, 2.12)
0.172
0.05 (0.00, 1.14)
0.061
Clinical pregnancy rate (n%)
SII quartile
Q1
58.33(7/12)
Ref
Ref
Q2
68.75(11/16)
1.57 (0.33, 7.48)
0.570
1.21 (0.23, 6.33)
0.818
Q3
50.00(3/6)
0.71 (0.10, 5.12)
0.738
0.40 (0.04, 3.88)
0.431
Q4
20.00(1/5)
0.18 (0.02, 2.12)
0.172
0.05 (0.00, 1.14)
0.061
Early pregnancy loss rate (n%)
SII quartile
Q1
58.33(14/24)
Ref
Ref
Q2
0(0/11)
-
0.997
-
0.997
Q3
33.33(1/3)
2.20 (0.11, 42.74)
0.602
1.36 (0.05, 36.56)
0.854
Q4
100(1/1)
2.75 (0.14, 55.17)
0.509
2.29 (0.07, 76.10)
0.643
Live birth rate (n%)
SII quartile
Q1
50.00(6/12)
Ref
Ref
Q2
62.50(10/16)
1.67 (0.37, 7.61)
0.509
1.30 (0.26, 6.48)
0.747
Q3
16.67(1/16)
0.20 (0.02, 2.27)
0.194
0.08 (0.00, 1.70)
0.105
Q4
0(0/1)
-
0.992
-
0.994
Implantation rate (n%)
SII quartile
Q1
8.33(1/7)
Ref
Ref
Q2
69.70(23/33)
1.64(0.55,5.01)
0.376
1.34(0.21,8.74)
0.762
Q3
54.55(6/11)
0.86 (0.20, 3.73)
0.834
1.49 (0.21, 19.6)
0.751
Q4
20(2/10)
0.18 (0.02, 0.90)
0.054
0.57 (0.00, 49.5)
0.834
Abbreviations: SII Systemic immune-inflammation index, OR, Odds ratio, P value shows significance of entrance in the logistic regression model
SII: Quantile 1 Quantile 2 Quantile 3 Quantile 4
All values are ORs (95% CIs). Values were determined by using logistic regression
Discussion
SII is a promising inflammatory indicator that integrates data deriving from platelet, neutrophil, and lymphocyte counts, thereby potentially reflects three distinct biological pathways: thrombus formation, the inflammatory response, and the adaptive immune response [28, 29]. In contrast to traditional inflammatory factors, SII better reflects the inflammatory state, and a number of studies have demonstrated that SII reflects the inflammatory state preferably and is more prognostic [20, 30, 31]. In addition, SII is a noninvasive, easy-to-access and low-cost method, which is also universally available. Clinical applications are therefore promising.
We hypothesized that SII could be independently associated with IVF outcomes in women with PCOS. To our knowledge, this is the first study that uses data from a cohort study to investigate whether SII is associated with IVF outcomes. In this study, we found that SII was negatively correlated with the number and rate of available embryos in PCOS patients undergoing IVF/ICSI treatment, after adjusting for confounding factors. This suggests that SII may be a useful marker for predicting the ovarian response and embryo quality in this population. The clinical significance of this finding is that SII could be used as a screening tool for individualizing the treatment protocol and improving the pregnancy rate in PCOS patients.
Prior research has indicated that patients with PCOS exhibit significantly higher concentrations of inflammatory cells in their peripheral blood, accompanying these altered leukocyte counts, the levels of some inflammatory factors, such as serum C-reactive protein (CRP) [32], high sensitivity C-reactive protein (hs-CRP) [33] were also found to be significantly increased in the peripheral blood of PCOS patients. Our study offers the opportunity to simultaneously assess the association between each of the three blood cells (including platelets, neutrophils, and lymphocytes) and IVF outcomes. The SII was divided into quartiles, and comparison across the four groups revealed significant differences in several baseline characteristics and laboratory outcomes. Specifically, patients in the highest SII quartile (Q4) had higher AFC, E2, and P levels on the day of HCG start compared to the lower three SII quartiles (Q1-Q3). Furthermore, our analysis demonstrated that women in the lower SII quartiles had a higher rate of available embryos and blastocyst formation compared with those in the highest SII quartile. This suggested of a possible negative correlation between SII and ART outcomes. Similarly, our results showed no significant correlation between the Log SII and the number of available embryos, but a negative correlation was identified between the Log SII and the available embryo rate. These findings were consistent with previous research. While moderate inflammation was a physiological norm during follicle genesis and ovulation, aberrant inflammation may lead to compromised oocyte quality, precipitate oligo-anovulation, and subsequently contribute to infertility [34]. Immunohistochemical staining revealed extensive infiltration of macrophages and lymphocytes throughout the ovaries at PCOS cases [35], which was indicative of a persistent low-grade chronic inflammation.
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Inflammation is deemed a key characteristic of endothelial dysfunction and atherosclerosis [36], and is also associated with prominent features of PCOS, including insulin resistance and cardiovascular diseases [37]. Orio et al. has demonstrated the elevations of white cell count in PCOS women [38]. In PCOS patients, the intercellular adhesion molecule (ICAM)-1, tumor necrosis factor (TNF)-α, and monocyte chemoattractant protein (MCP)-1 have been detected in higher concentration [39]. These parameters are indicative of an inflammatory state within the body. PCOS patients have higher mean platelet volume (MPV) and neutrophil to lymphocyte ratio compared with healthy women [40]. Leukocytosis, neutrophilia, and platelet aggregation are frequently observed phenomena in the blood profiles of patients with PCOS [14]. Increased numbers of lymphocytes and macrophages secrete more inflammatory cytokines, such as TNF-α, lymphokine and Interleukin (IL)-6, which can in turn strengthen the secretory function of these two kinds of cells to produce more inflammatory cytokines [41].
As a heterogeneous syndrome characterized by endocrine abnormalities and metabolic dysfunction, PCOS can affect every stage of reproduction, including folliculogenesis and implantation [42]. Previously, PCOS has been associated with adverse effects on ovarian response and IVF outcomes, as well as a higher rate of miscarriages [43]. The results of our multivariate logistic regression analyses indicate that the highest SII quartile is negatively associated with BPR, CPR, LBR, and IR. One hypothesis is that SII serves as an indicator of systemic immune activation, which has previously been linked to compromised implantation and placentation processes. Another hypothesis is that SII could be a marker for endometrial dysfunction, a condition known to influence endometrial receptivity and the interactions between the embryo and endometrium. This suggests that women with PCOS and higher SII may face greater challenges in achieving successful pregnancy and live birth outcomes.
Interestingly, we observed a non-linear relationship between the SII and number of available embryos, with a negative relationship seen to the right of the inflection point. This non-linearity underscores the complexity of the relationship between inflammation and fertility outcomes in PCOS patients. The possible mechanisms behind the non-linear relationship between SII and reproductive outcomes in PCOS patients are not fully understood, but several hypotheses can be proposed. One possible explanation is that SII reflects the balance between immune activation and suppression in the body, which has been shown to play a crucial role in implantation and pregnancy maintenance. Another possible explanation is that SII reflects the threshold effect of inflammation on ovarian function and embryo quality in PCOS patients, which has been shown to vary depending on the degree of inflammation. A third possible explanation is that SII reflects the interaction between inflammation and other factors that affect reproductive outcomes in PCOS patients, such as oxidative stress, insulin resistance, or hormonal imbalance.
Notably, while our study indicated a trend of declining number of high-quality embryos with increasing Log SII levels, this trend lacked statistical significance, highlighting an area for future research to explore.
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Our study possessed several merits. Foremost among these was that our research emerged as the inaugural cohort study delineating the association of SII with ART outcomes in PCOS-afflicted women. Furthermore, we adjusted for potential confounders to ensure the reliability of our results. However, certain limitations were present. The cohort did not have comprehensive data at the time of blood sample collection, missing markers of acute inflammatory state, such as CRP levels, and information on the use of certain medications, a small fresh transfer sample does. While SII shows promise as a biomarker to predict pregnancy outcomes in PCOS patients undergoing IVF/ICSI treatment, it necessitates further validation to ascertain its clinical relevance. Consequently, to establish causality, prospective studies with more extensive sample sizes remain imperative.
Conclusion
Our research reveals that increased level of SII is correlated with suboptimal reproductive outcomes in females diagnosed with PCOS. Further large-scale prospective studies are still needed to validate our findings.
Acknowledgements
The authors acknowledge the physicians, nurses, and scientific staff of Department of Reproductive Medicine, Nanjing Maternity and Child Health Care Hospital.
Declarations
Ethics of approval and consent to participate
This retrospective cohort study was conducted according to the Declaration of Helsinki and approved by the Ethics Committee of Nanjing Maternity and Child Health Care Hospital (2023KY-083). Informed patient consent was not required as the study was retrospective in nature and analyzed patient data anonymously. A statement from the Ethics Committee of Nanjing Maternity and Child Health Care Hospital waived the need for informed consent.
Consent for publication
The authors affirm that all participants provided informed consent for publication of the data collected for the study.
Competing interests
The authors declare no competing interests.
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