Correlation and predictive value of systemic immune-inflammation index for dyslipidemia in patients with polycystic ovary syndrome

Abstract

Background

Polycystic ovary syndrome(PCOS) is one of the main factors leading to infertility in women of reproductive age, which is often accompanied by metabolic changes such as obesity and chronic low-grade inflammation. Chronic inflammation may play an important role in the occurrence and development of metabolic diseases. Therefore, it is of great significance to explore the relationship between abnormal lipid metabolism and inflammation in PCOS patients. This study aims to analyze the correlation between systemic immune-inflammatory(SII) markers and dyslipidemia in patients with PCOS and their value in early diagnosis.

Methods

A total of 617 PCOS patients aged 20–35 years (according to the Rotterdam diagnostic criteria) who visited the Reproductive Center of the First Hospital of Lanzhou University from January 2020 to December 2022 were included. According to the presence or absence of dyslipidemia, the patients were divided into normal lipid metabolism group and abnormal lipid metabolism group. The clinical data of the patients were collected and analyzed by SPSS software.

Results

There were 454 patients with normal lipid metabolism and 163 patients with abnormal lipid metabolism. The SII level of the abnormal lipid metabolism group was higher than that of the normal group. As the SII quartile increased, TC, TG and LDL increased, while HDL decreased accordingly. The SII level was positively correlated with TC, TG and LDL, and negatively correlated with HDL (all P < 0.05). Among them, SII had the best predictive efficiency for dyslipidemia of polycyctic ovary syndrome at 489.375 (AUC: 0.718, 95%CI: 0.672–0.764), and SII was still associated with the increased occurrence of PCOS dyslipidemia after excluding confounding factors (P < 0.05).

Conclusion

The high level of SII has a correlation with the occurrence of dyslipidemia in PCOS patients, and it has a value in the early diagnosis of PCOS.

Introduction

Polycystic ovary syndrome (PCOS) is a leading cause of infertility in reproductive-age women. PCOS is characterized by ovulation disorders, hyperandrogenism, and polycystic ovarian changes. Additionally, metabolic alterations such as insulin resistance (IR), hyperinsulinemia, and obesity often accompany PCOS [1]. Dyslipidemia, a common metabolic disorder, is prevalent in women with PCOS. Compared to weight-matched controls, PCOS women exhibit elevated levels of Triglyceride(TG), Low-density lipoprotein(LDL), apolipoprotein apoB, and apoB/apoAI ratio. Conversely, the levels of apoAI and High-density lipoprotein (HDL) are decreased [2]. Chronic low-grade inflammation is frequently observed in patients with PCOS, leading to endocrine dysfunction and metabolic disturbances. This inflammation is marked by increased levels of inflammatory factors such as Interleukin (IL) and C reactive protein (CRP) [3, 4]. Recent studies have found that PCOS patients may have chronic low-grade inflammation, which may lead to ovulation disorders [5]. In addition, metabolic alterations such as obesity and hyperinsulinemia exacerbate this chronic inflammatory state [5].Systemic immune inflammation index (SII) is a derived inflammatory index based on blood routine examination, which has the advantages of being simple and convenient and can objectively reflect the balance of the patient's immune response and inflammatory response. SII was originally proposed by Hu [6] et al., as a prognostic indicator for cancer patients. Recent studies have pointed out that SII has a certain predictive value for pregnancy outcomes of PCOS [7]. However, there are few reports on the chronic inflammatory and abnormal lipid metabolism in PCOS patients. As a non-invasive indicator, SII can reflect the level of inflammation in the body, study its predictive value for dyslipidemia in PCOS patients can help early diagnosis of metabolic abnormalities in PCOS patients and play a positive role in the prevention of long-term complications in PCOS patients. Therefore, this study used a case–control trial to retrospectively analyze the correlation and predictive value of SII level with dyslipidemia in PCOS patients, aiming to provide guidance for clinical diagnosis, evaluation of metabolic abnormalities and intervention of PCOS.

Materials and methods

Study design

From January 2020 to December 2022, a study was conducted at the Reproductive Center of the First Hospital of Lanzhou University. The study focused on 617 PCOS patients aged between 20 and 35 years. These patients were categorized based on whether they exhibited dyslipidemia or not. Approval for this study was obtained from the ethics Review Board of the First Hospital of Lanzhou University.Informed consent was waived by the institutional ethics committee of the First Hospital of Lanzhou University due to the retrospective nature of this study.

Patient inclusion

Inclusion criteria: in accordance with the "Rotterdam PCOS Conference revised diagnostic criteria" of PCOS diagnostic criteria [8]: ① Serum total testosterone (T) ≥ 0.7 μg/L or accompanied by acne, hirsutism and other hyperandrogenism clinical manifestations; ② Oligomenorrhea or delayed menstruation; ③ The number of follicles with a diameter of 2-9 mm on the same plane under ultrasound ≥ 12, or the ovarian volume ≥ 10 mL. PCOS was diagnosed if at least 2 items were present.

Exclusion criteria: for this study encompassed individuals who had received hormone drugs, immunosuppressants, glucocorticoids, contraceptives, hypoglycemic drugs, or treatment that influenced lipid levels within a three-month period prior to enrollment. Additionally, individuals with other endocrine diseases characterized by high androgen production, such as ovarian or adrenal tumors, Cushing's syndrome, etc. Furthermore, individuals with systemic lupus erythematosus, ulcerative colitis and other inflammatory diseases, as well as those with blood system diseases, severe dysfunction of vital organs, coagulopathy, or thyroid dysfunction related diseases were also excluded from the study.

Grouping criteria: Dyslipidemia was determined according to the diagnostic criteria for dyslipidemia [9]: Increased total cholesterol(TC) level was defined as TC ≥ 6.2 mmol/L, increased TG level as TG ≥ 2.3 mmol/L, increased LDL level as LDL ≥ 4.1 mmol/L, and decreased HDL level as HDL < 1.0 mmol/L; One of the above conditions must be met.

The patient's personal information was collected using the medical record system at the First Hospital of Lanzhou University., including cycle number, medical card number, nationality, age, height, weight, etc. Blood indicators: The peripheral elbow venous blood of the patients who came to our center was drawn by the specialist nurses of our center using the corresponding suction vessels, and tested by the laboratory Department of the Lanzhou First Hospital in strict accordance with the laboratory procedures and standards. Serum Estradiol (E2), Follicle-stimulating hormone (FSH) and Luteinizing hormone (LH) were collected on the 2nd to 5th day of menstruation (or when no dominant follicle was found by B-ultrasound in amenorrhea patients). Total testosterone (TT), Prolactin (PRL) levels, Anti-Mullerian hormone (AMH), Thyroid Stimulating Hormone (TSH), Hemoglobin(Hb), blood cell volume, Erythrocyte sedimentation rate(ESR), TC, HDL, LDL, TG, Fasting plasma glucose (FPG). Imaging data: Ultrasound report of the uterus and bilateral adnexa was performed on days 2–5 of the menstrual phase(Amenorrhea patients were collected when no dominant follicle was found by B-ultrasound)..

The formula for calculating other derived indicators is as follows:

• ①BMI [7] = weight (kg)/Square of height (m); ② Systemic immune inflammation index (SII) [10] = platelet * neutrophil count/lymphocyte count.

Statistical analysis

SPSS 27.0 statistical analysis software was used in this study. The continuous variables were evaluated by the normal distribution. The measurement data with normal distribution were expressed as mean ± standard deviations, and the independent sample T-test was used. The measurement data not in line with normal distribution were expressed as M(P25, P75), and the independent sample nonparametric test (Mann–Whitney U test) was used. The non-parametric Kruskal–Wallis H test was used to compare the data between multiple groups. Variables with non-normal distribution were log-transformed before statistical analysis, and the Pearson correlation coefficient was used to analyze the correlation between SII and metabolic indicators of PCOS. A multivariate risk model was established and binary logistic regression was used to determine whether SII could be used as a predictor of PCOS-related lipid metabolism disorders. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic value of SII for PCOS. Two-sided tests were used, and P < 0.05 was considered statistically significant. GraphPad Prism 9.5.1 was used to draw correlation curves and pictures.

Results

Comparison of clinical data between the two groups

There were 617 patients enrolled in this study, 454 of whom had normal lipid metabolism and 163 of whom had abnormal lipid metabolism (Table 1)..The age, BMI, basic sex hormones, hemoglobin, blood cell volume, erythrocyte sedimentation rate, and blood cell count of the two groups were collected and analyzed. There were no significant differences between the two groups in age, FSH, LH, and AMH hemoglobin, blood cell volume, and erythrocyte sedimentation rate (P > 0.05). However, there were significant differences in E2, PRL,, white blood cell count, platelet count, neutrophil count, and monocyte count between the two groups (P < 0.05). Compared to the control group, the abnormal lipid metabolism group had lower E2 and PRL levels, but higher T, white blood cell count, platelet count, neutrophil count, and monocyte count levels. (Table 1).

Table 1.

General characteristics and metabolic indicators of patients with different lipid levels were analyzed

Variable	Dyslipidemia group(n = 163)	Normal lipid group (n = 454)	Z	P value
Age	29.00 (27.00,32.00)	30.00 (27.00,32.00)	-1.02	0.308
BMI	25.71 (23.44,27.48)	22.17 (20.34,24.61)	-9.367	< 0.001
E2	32.90 (24.10,41.90)	34.70 (27.55,46.35)	-2.204	0.028
FSH	5.80 (4.90,6.70)	5.90 (5.20,6.78)	-1.468	0.142
LH	7.80 (4.80,12.00)	7.00 (5.20,11.00)	-0.356	0.722
AMH	7.50 (4.68,9.94)	7.91 (5.18,10.44)	-1.008	0.313
PRL	16.50 (13.00,23.00)	19.30 (13.88,25.90)	-2.935	0.003
T	55.60 (30.20,64.51)	41.15 (27.90,61.07)	-2.87	0.004
Hb	144.00 (137.00,149.00)	141.00 (134.00,148.00)	-1.94	0.052
Blood cell volume	43.00 (41.30,44.70)	42.60 (40.60,44.70)	-1.408	0.159
ESR	7.00 (5.00,11.00)	7.00 (5.00,10.00)	-1.386	0.166
SII	579.41 (394.87,763.03)	394.74 (289.06,521.46)	-8.249	< 0.001
HDL	0.99 (0.90,1.16)	1.31 (1.16,1.50)	-12.753	< 0.001
LDL	3.10 (2.55,4.10)	2.63 (2.20,2.99)	-7.916	< 0.001
TC	4.96 (4.00,5.85)	4.33 (3.91,4,89)	-5.948	< 0.001
TG	2.39 (1.44,2.95)	1.04 (0.74,1.43)	-13.779	< 0.001
FPG	4.72 (4.34,5.22)	4.66 (4.37,4.99)	-1.736	0.083

Results showed no statistically significant difference between the two groups in FPG levels (P > 0.05). However a statistically significant difference in blood lipid levels was observed between the two groups (P < 0.05), HDL levels in the abnormal lipid metabolism group were lower than those in the control group, while LDL, TC, and TG levels in the abnormal lipid metabolism group were higher than those in the control group. Besides, there was a statistically significant difference in SII levels between the two groups (P < 0.05), with the abnormal lipid metabolism group having a higher SII level than the control group (Table 1).

Comparison of lipid metabolism indexes in peripheral blood inflammatory parameter level groups of PCOS quartiles

To further evaluate the relationship between SII and dyslipidemia in PCOS patients, PCOS patients were grouped according to the quartile of SII level.The SII level was divided into SII quartiles 1 (SII < 306.01), SII quartiles 2 (306.01 ≤ SII < 422.4), SII quartiles 3 (422.4 ≤ SII < 585.9), and SII quartiles 1 (SII < 306.01). SII quartiles 4 (SII ≥ 585.9). With the increase of the SII quartile, TC, TG and LDL increased, while HDL decreased (P < 0.05), and the overall statistical difference was statistically significant (P < 0.05) (Fig. 1).

Fig. 1.

Changes in lipid levels at different SII quartiles

Correlation between peripheral blood inflammatory indexes and lipid metabolism indexes in PCOS patients

As shown in Table 2, SII in PCOS patients was positively correlated with BMI, TC, TG, and LDL (all P < 0.05), and negatively correlated with HDL (all P < 0.05).

Table 2.

Correlation coefficient between SII index and clinical indicators in patients with polycystic ovary syndrome

	BMI	HDL	LDL	TG	TC	FPG	E2	FSH	LH	T	Hb	ESR
SII	0.367***	-0.275***	0.435***	0.327***	0.317***	0.071	-0.099*	-0.115**	-0.085*	0.029	0.128**	0.152***

^*P < 0.001

^**P < 0.001

^***P < 0.001

Diagnostic efficacy of SII for PCOS with dyslipidemia

The ROC curve was drawn using whether PCOS patients had abnormal lipid metabolism as a state variable, and the results showed that the sensitivity and specificity of SII in predicting abnormal lipid metabolism in PCOS were shown in Table 3, respectively. SII had the best AUC (0.718, 95%CI: 0.672–0.764) at 489.375, and the sensitivity and specificity were 0.626 and 0.714, respectively. In addition, SII combined with BMI and FPG can improve the AUC of predicting abnormal lipid metabolism in PCOS.(Fig. 2).

Table 3.

Efficacy analysis of SII in predicting the occurrence of dyslipidemia in PCOS

	AUC	cutoff value	Sensitivity	Specificity	Standard error	95% CI	P value
SII	0.718	489.375	0.626	0.714	0.023	0.672–0.764	< 0.001
SII + BMI	0.762	-	-	-	0.021	0.720–0.804	< 0.001
SII + BMI + FPG	0.766	-	-	-	0.021	0.724–0.807	< 0.001

Fig. 2.

ROC Curve for predicting abnormal lipid metabolism in PCOS. *P< 0.05, ** P< 0.01, *** P < 0.001

Comprehensive predictive ability of peripheral blood inflammatory parameters to identify PCOS with dyslipidemia

Taking the occurrence of abnormal lipid metabolism in PCOS patients as the dependent variable and relevant clinical indicators as independent variables (both continuous variables), the binary Logistic regression analysis model showed that SII (OR = 1.003, 95%CI: 1.002, 1.004) were independent predictors of abnormal lipid metabolism in PCOS patients (Table 4).

Table 4.

Clinical factors associated with dyslipidemia in PCOS patients

Variable	β	OR	95%CI	P
Age	-0.029	0.971	0.917 -1.029	0.325
BMI	0.236	1.266	1.196 -1.339	< 0.001
E2	0.001	1.000	0.993 -1.007	0.960
FSH	-0.111	0.895	0.791 -1.012	0.078
LH	0.015	1.016	0.981 -1.051	0.376
LH/FSH	0.162	1.176	0.949 -1.457	0.138
T	0.008	1.008	1.000 -1.017	0.051
AMH	-0.027	0.973	0.928 -1.020	0.257
Blood cell volume	0.030	1.030	0.976 -1.088	0.278
ESR	0.019	1.019	0.989 -1.049	0.222
FPG	0.375	1.455	1.117 -1.897	0.005
SII	0.003	1.003	1.002 -1.004	< 0.001
Adjusted SII	0.004	1.004	1.002 -1.006	0.001

After adjusting for these confounding factors (BMI, fasting blood glucose), the risk of SII on dyslipidemia in PCOS was analyzed by multivariate logistic regression. The results showed that SII level was closely related to the reduction of dyslipidemia risk in PCOS (P = 0.001; OR = 1.004; 95%CI:1.002–1.006).

Discussion

The international evidence-based guidelines for polycystic ovary syndrome released in 2023 proposed that PCOS is not an isolated reproductive disease, but a systemic disease affecting psychology, metabolism and reproductive endocrinology, emphasizing more optimized and safer fertility management for patients [11]. Lipid metabolism disorder is an independent risk factor for cardiovascular diseases. Ovulation disorder, menstrual disorder, poor oocyte quality, abortion, and decreased embryo quality caused by dyslipidemia are some of the causes of female infertility [11]. Elevated blood lipids in PCOS patients lead to increased susceptibility to cardiovascular disease and type II diabetes mellitus and also increase the incidence of a series of pregnancy complications such as gestational diabetes mellitus and gestational hypertension, which may cause adverse reproductive outcomes [12]. High lipid levels in the body increase the risk of nonalcoholic fatty liver disease in PCOS patients, and are directly involved in the pathogenesis of decompensated cirrhosis, hepatocellular carcinoma and other chronic liver diseases [13]. It further suggests that lipid metabolism disorder in PCOS is harmful to endocrine diseases and cardiovascular diseases, and seriously affects the long-term prognosis and quality of life of PCOS women.

Therefore, it is necessary to diagnose or predict the risk of dyslipidemia in patients in advance. Recent studies have found that chronic inflammation plays an important role in the occurrence and development of metabolic diseases [14]. Kelly et al. first proposed that PCOS patients may have chronic low-grade inflammation. Inflammatory factors are abnormally increased in PCOS patients, which affect normal follicle development and oocyte quality [15]. Furthermore, chronic low-grade inflammation is associated with insulin resistance and hypothalamic-pituitary-ovarian axis secretion and function in PCOS women [16, 17]. Therefore, it is speculated that chronic inflammation can be used for the diagnosis and evaluation of PCOS. It is therefore important to investigate the relationship between inflammatory markers and abnormal lipid metabolism in PCOS.

In this study, we investigated the differences in SII level, a novel inflammatory parameter, and other clinical indicators in PCOS patients based on lipid metabolism grouping. Previous studies have shown that obese PCOS women have more serious endocrine hormone disorders and abnormal lipid metabolism [18]. In this study, the results also support the strong association between age and BMI with hyperlipidemia. Furthermore, PCOS patients with abnormal lipid metabolism had higher testosterone levels than PCOS patients with normal lipid metabolism, according to the present study. Previous studies have suggested a significant correlation between inflammation and hyperandrogenism in PCOS [19, 20]. The higher the testosterone level, the higher the risk of hyperlipidemia in PCOS patients, suggesting that both clinical and biochemical hyperandrogenism are related to hyperlipidemia in PCOS patients, which is consistent with previous studies [21]. When lipid metabolism is abnormal in the body, cholesterol, the precursor of androgen synthesis, will produce a large amount of androgens under the action of 17α-hydroxylase and 17, 20-lyase, and androgens will be converted into estrone in peripheral adipose tissue. The high level of estrogen acts on the hypothalamus and pituitary gland to promote the continuous secretion of LH, which fails to form the LH peak and causes oligo-ovulation or anovulation, leading to a prolonged menstrual period or amenorrhea [22]. However, only total serum testosterone levels were measured in this study. according to the current International Evidence-based Guideline for the assessment and management of polycystic ovary syndrome 2023, Serum free testosterone can better reflect the level of androgen in the body, which is also the deficiency of this study, and the specific mechanisms underlying the interaction between hyperandrogenism and inflammatory and lipid metabolic processes still require further investigation.

SII is an inflammation-related index that integrates neutrophil, platelet and lymphocyte counts. It can serve as a simple and readily available index of the body's immune and inflammatory status. Neutrophils have strong phagocytic ability, can secrete matrix metalloproteinases, chemokines and cytokines at the lesion, and participate in the regulation of the initiation process of inflammation. It causes immune cell infiltration and tissue damage and plays a significant role in the regulation of inflammation. Lymphocytes have functions such as secreting antibodies and regulating immunity and can secrete a variety of inflammatory factors. By activating platelets, they can release tissue factors and stimulate other cells, such as neutrophils, to secrete inflammatory factors [23, 24]. Additionally, chemotactic proteins and inflammatory factors secreted by adipose tissue may induce macrophage infiltration into adipose tissue, leading to insulin resistance and hepatic steatosis [25]. In our study, the SII level of the abnormal lipid metabolism group was higher than that of the control group, suggesting that the inflammation level is increased in PCOS women with dyslipidemia. This may be due to the fact that lipid metabolism disorders and obesity can trigger chronic inflammatory responses in the body, and the increased SII level means that there is chronic inflammation in PCOS patients, which in turn can promote lipid metabolism disorders and endocrine changes. However, the specific mechanism by which dyslipidemia and chronic inflammation are mutually initiated and causal still needs further research [26, 27]. Using quartiles of peripheral blood inflammatory parameters levels, this study found that as SII levels increased, TC, TG, and LDL-C levels increased, whereas HDL-C levels declined. Further correlation analysis showed that SII was positively correlated with BMI, TC, TG, and LDL-C in PCOS patients (all P < 0.05), and negatively correlated with HDL-C (P < 0.05). Other studies have shown that NLR, PLR, MLR, dNLR and SII are positively correlated with BMI, FPG, IR, TC, TG, LDL, AMH, LH and T, and negatively correlated with HDL in PCOS women, which is consistent with the results of our study [28]. In addition, in the multivariate analysis, SII, BMI and FPG were found to be the influencing factors of dyslipidemia in women with PCOS. After adjusting for confounding factors, SII was still associated with an increased incidence of dyslipidemia in PCOS. However, the odds ratio is not so high, We hope that more studies with larger sample sizes will provide support in the future.

SII is determined by the relationship between the number of neutrophils, platelets, and lymphocytes. As a comprehensive evaluation index, SII not only has a preferable predictive effect on dyslipidemia in PCOS. In addition, the advantage of SII is that for patients who are unable to undergo invasive examination due to underlying diseases or are afraid of invasive examination, the non-invasive detection index SII can be used for disease diagnosis and prognosis prediction. Therefore, the clinical application of SII has important guiding significance for the diagnosis and treatment of PCOS patients and the evaluation of metabolic complications.

The limitation of this study is that a healthy control group was not set up, and the characteristics of abnormal lipid metabolism in healthy women and PCOS women cannot be further compared. Moreover, this study is a retrospective study with a limited number of patients, and larger patient cohorts and prospective studies are needed to verify the results of this study.

Conclusions

In conclusion, chronic inflammation and dyslipidemia interact. SII, as a clinically stable, easily available, and reproducible detection index, may have a certain significance in the early prediction of dyslipidemia in PCOS. The change in inflammation level in PCOS patients not only affects endocrine and metabolism but also relates to ovarian function and long-term complications of PCOS. However, its mechanism and clinical significance still need more exploration.PCOS women with elevated SII are advised to adopt a lifestyle that reduces inflammation if they are found to have elevated SII during a physical examination, as well as quitting smoking, losing weight, regulating emotions, exercising regularly, getting adequate rest, and reducing saturated fat and refined carbohydrates in their diet..Moreover, it is recommended that PCOS women with high SII levels undergo regular medical screenings so that they can benefit from secondary prevention treatments and avoid lipid metabolism disorders and other related complications.

Abbreviations

IR

Insulin resistance

TG

Triglyceride

LDL

Low density lipoprotein

HDL

High density lipoprotein

IL

Interleukin

CRP

C reactive protein

SII

Systemic immune inflammation index

TC

Total cholesterol

E2

Estradiol

LH

Luteinizing hormone

FSH

Folliclestimulating hormone

TT

Total testosterone

PRL

Prolactin

AMH

Anti-Mullerian hormone

TSH

Thyroid Stimulating Hormone

FPG

Fasting plasma glucose

FINS

Fasting insulin

2hBG

2-Hour postprandial plasma glucose

2hINS

2-Hour postprandial insulin

OGTT

Oral glucose tolerance test

Hb

Hemoglobin

ESR

Erythrocyte sedimentation rate

BMI

Body mass index

ROC

Receiver operating characteristic

OR

Odds ratio

CI

Confidence interval

Authors’ contributions

ZXY performed the data processing and analysis and was a major contributor in writing the manuscript and collecting data. TYX was responsible for the revision of the manuscript. ZXH designed the study and was responsible for the revision of the manuscript and providing financial.

Funding

This work was supported by the National Natural Science Foundation of China. (No: 81960273).

Data availability

The data used and/or analyzed during the current study available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Ethics approval for this study was obtained from the ethics Review Board of the First Hospital of Lanzhou University.Informed consent was waived by the institutional ethics committee of the First Hospital of Lanzhou University due to the retrospective nature of this study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.