IFN-γ and IL-22 in premature ovarian insufficiency correlation with ovarian reserve markers and pathogenic implications: a cross-sectional study

Abstract

Background

This case-control study aimed to investigate serum immunocytokine profiles and their clinical relevance in premature ovarian insufficiency (POI), with a particular focus on the potential of these cytokines as diagnostic biomarkers.

Methods

This case-control study investigated serum immunocytokine profiles in premature ovarian insufficiency (POI) using 28 patients and 17 controls from Guangxi Reproductive Hospital (2023–2024). Hormones (FSH, LH, E2, AMH, PRL, T) were measured by electrochemical luminescence and cytokines (IFN-γ, IL-1β, IL-6, IL-9, IL-17 A, IL-22, TGF-β1) by ELISA.

Results

POI patients exhibited higher FSH/LH and lower E2/AMH/PRL/T (all P < 0.05) versus controls, with increased menstrual irregularity (28.57% vs. 0%, P = 0.043). Distinct cytokine elevation occurred in IFN-γ and IL-22 (both P < 0.05), while other cytokines showed no differences. Menstrual regularity subgroups displayed comparable cytokine profiles despite divergent FSH/LH (P < 0.05), suggesting independent hypothalamic-pituitary-ovarian and immune dysregulation. IFN-γ/IL-22 positively correlated with FSH (r = 0.431/0.476) and POI status (r = 0.314/0.395), while inversely relating to AMH (r=-0.298/-0.345) and PRL (r=-0.382/-0.323) (all P < 0.05). ROC analysis demonstrated diagnostic potential: IFN-γ (AUC = 0.687, 95%CI:0.522–0.852, P = 0.037) and IL-22 (AUC = 0.735, 95%CI:0.574–0.897, P = 0.009).

Conclusion

POI features a unique immunocytokine signature with elevated IFN-γ/IL-22 that correlates with ovarian reserve depletion and shows diagnostic promise. The dissociation between menstrual cyclicity and cytokine patterns implies distinct endocrine-immune pathophysiological mechanisms, positioning these cytokines as potential biomarkers for POI diagnosis and mechanistic research.

Introduction

Premature ovarian insufficiency (POI), clinically defined as ovarian dysfunction occurring before the age of 40, is characterized by menstrual irregularities (amenorrhea or oligomenorrhea) accompanied by elevated gonadotropin levels and fluctuating decreases in estrogen levels [1, 2]. POI not only impairs women’s reproductive function but also increases the risk of long-term health complications, such as cardiovascular diseases, osteoporosis, and psychological distress [1, 3]. Data analysis from 8 databases between 1946 and 2021 shows that the overall prevalence of POI among women worldwide was 3.5% [4]. Notably, recent epidemiological studies have indicated a rising trend in POI prevalence, particularly among younger women, highlighting its growing public health significance [4].

The etiology of POI is multifactorial and may involve genetic predispositions, autoimmune disorders, iatrogenic injuries (e.g., chemotherapy or radiotherapy), viral infections, and environmental or lifestyle factors [5, 6]. Despite advances in research, the precise pathogenesis of POI remains incompletely understood. Proposed mechanisms include genetic deletions or mutations, oxidative stress, dysregulation of germ cell apoptosis or autophagy, and metabolic disturbances [7, 8]. These mechanisms collectively contribute to the premature depletion of ovarian follicles and the disruption of ovarian function.

Immunocytokines, including interleukins, transforming growth factors, interferons, and others, play critical roles in mediating inflammatory responses and regulating immune homeostasis [9, 10]. Specifically, IL-6 modulates hormone release from pituitary cells and regulates cellular proliferation [11], while TGF-β and IFN-γ are critically involved in follicular atresia as key intraovarian regulators [12, 13]. In ovarian cortical cultures, inflammatory mediators such as IL-1β and IL-6 accumulate in a LPS-dependent manner [14], and altered levels of IL-9, IL-17 A, and IL-22 have been documented in ovarian dysfunction models including polycystic ovary syndrome [15, 16]. Furthermore, cytokines may influence folliculogenesis, steroidogenesis, and ovarian reserve maintenance, making them key players in the pathophysiology of POI [17].

Given the potential link between cytokine dysregulation and POI, this study aimed to investigate changes in the levels of relevant cytokines in POI patients, analyze their correlation with sex hormone levels, and explore their potential application in elucidating POI pathogenesis. The findings may provide valuable insights for future scientific research and clinical interventions, ultimately improving the diagnosis and management of POI.

Materials and methods

Patients

The subjects included in this study were 28 patients with POI admitted to the Reproduction Hospital of Guangxi Zhuang Autonomous Region from January 2023 to September 2024, and 17 healthy individuals (control group) undergoing physical examinations during the same period were selected. The diagnostic criteria referred to the POI management guidelines outlined in the Guidelines for POI Management [1, 2]. Data regarding age, age at menarche, menstrual cycle regularity, gravidity, and parity were collected from all participants. This study has been approved by the hospital ethics committee (Ethics Approval Number: KY-LW-2025-03).

Observation Group

Enrollment Criteria: (a) Primary or secondary amenorrhea lasting for more than 4 months; (b) Age less than 40 years old; (c) Follicle-Stimulating Hormone (FSH) levels above 25IU/L on two occasions more than one month apart during menstrual cycles; (d) Individuals who agreed to participate in the study and signed the informed consent form. Exclusion Criteria: (a) Individuals with abnormal karyotypes; (b) Those with autoimmune diseases; (c) Those who have undergone ovarian surgery, radiotherapy, or chemotherapy; (d) Individuals with other diseases related to the onset of POI (Premature Ovarian Insufficiency); (e) Those who refused to participate in the study.

Control Group

Enrollment Criteria: (a) Females of childbearing age, ranging from 25 to 35 years old; (b) Regular menstrual cycles with a biphasic basal body temperature pattern; (c) Normal results on basic endocrine examinations, with a basal serum FSH level below 10IU/L; (d) Five to ten antral follicles in both ovaries; (e) Individuals who agreed to participate in the study and signed the informed consent form. Exclusion Criteria: (a) Those with medical comorbidities and complications; (b) Individuals who refused to participate in the study.

Blood sample collection and processing

Fasting peripheral blood specimens (5 mL each) were obtained from patients diagnosed with premature ovarian insufficiency (POI) and age-matched healthy female controls (during their menstrual period) through venipuncture. The samples were collected into serum separator tubes (SST, BD Vacutainer^®, BD Biosciences, Franklin Lakes, NJ, USA). Following collection, the tubes were left undisturbed at room temperature for 30 min to allow for complete blood clotting. Subsequently, the samples underwent centrifugation at 2000 × g for 15 min at 4 °C to separate the serum. The clarified serum was then carefully aliquoted into sterile cryovials and stored at −80 °C until further analysis.

Detection of serum sex hormones

The baseline concentrations of reproductive endocrine hormones, including follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E2), anti-Müllerian hormone (AMH), prolactin (PRL), and testosterone (T), were determined using the Elecsys^® electrochemiluminescence immunoassay (ECLIA) system (Roche Diagnostics, Mannheim, Germany). The assays were performed in strict accordance with the manufacturer’s instructions, utilizing the following dedicated reagent kits: Elecsys^® FSH II assay, Elecsys^® LH II assay, Elecsys^® Estradiol III assay, Elecsys^® AMH Plus assay, Elecsys^® Prolactin II assay, and Elecsys^® Testosterone II assay.

The assays demonstrated intra-assay coefficients of variation (CVs) ≤ 5% and inter-assay CVs < 8%, as stipulated by the manufacturer.

Detection of immunocytokines in serum

The serum levels of cytokines (IFN-γ, IL-1β, IL-6, IL-9, IL-17 A, IL-22, and TGF-β1) were quantified using enzyme-linked immunosorbent assay (ELISA) kits (Jianglai Biotechnology, Shanghai, China) according to the manufacturer’s protocols. The specific kits employed included the Human Interferon-gamma (IFN-γ) ELISA Kit, Human Interleukin-1 beta (IL-1β) ELISA Kit, Human Interleukin-6 (IL-6) ELISA Kit, Human Interleukin-9 (IL-9) ELISA Kit, Human Interleukin-17 A (IL-17 A) ELISA Kit, Human Interleukin-22 (IL-22) ELISA Kit, and Human Transforming Growth Factor-beta 1 (TGF-β1) ELISA Kit.

All assays followed a standardized workflow: Standard and Sample Preparation: Ultra-pure water was used to prepare solutions. Standard curves were generated using serial dilutions of the provided standards. Wash buffer was prepared by diluting the 20× concentrate with ultra-pure water (1:20 ratio). Plate Setup: Each plate included standard wells, blank wells, and sample wells. Detection: Fifty microliters of standards or samples were added to respective wells, followed by 100 µL of HRP-conjugated detection antibody. Plates were incubated at 37 °C for 60 min. Washing: Wells were washed five times with 350 µL of wash buffer. Substrate Reaction: Fifty microliters each of substrates A and B were added, and plates were incubated at 37 °C in the dark for 15 min. Termination and Measurement: The reaction was stopped with 50 µL of stop solution, and optical density (OD) values were measured at 450 nm within 15 min using a Thermo microplate reader (Model: Multiskan Fc).

Cytokine concentrations were calculated based on standard curves.

Statistical methods

Statistical analysis was performed using SPSS version 26.0 software. Measurement data were expressed as M (P25, P75), and nonparametric tests were used for comparisons between groups. Spearman’s rank correlation analysis was employed for correlation analysis, where r ≤ 0.3 indicated a weak correlation, 0.3 < r ≤ 0.6 indicated a moderate correlation, and 0.6 < r ≤ 1 indicated a strong correlation. A P-value < 0.05 was considered statistically significant.

Results

Baseline characteristics of patients

Baseline demographic and clinical characteristics are summarized in Table 1. The two groups were comparable in age and age at menarche (both P > 0.05). Although overall gravidity and parity distributions did not differ significantly (P = 0.186 and P = 0.081, respectively), subgroup analysis revealed that the observation group had a higher proportion of patients with gravidity = 1 (32.14% vs. 5.88%, P = 0.043) and parity = 1 (21.43% vs. 0%, P = 0.032). The detailed results are shown in Table 1.

Table 1.

Baseline characteristics of patients [M (P25, P75)]

Baseline Characteristics		Control Group (n = 17)	Observation Group (n = 28)	Overall P-value	Subgroup P-values
Age (year)		32.00 (30.00, 34.50)	36.00 (31.25, 37.75)	0.084
Menarche (year)		13.00 (12.00, 13.50)	13.00 (12.00, 14.00)	0.818
Menstrual cycle	Irregular	0 (0)	8 (71.43)	0.043
	Regular	17 (100)	20 (71.43)
Gravidity (> 0)	0	11 (64.71)	15 (53.57)	0.186	0.547
	1	1 (5.88)	9 (32.14)		0.043
	2	3 (17.65)	2 (7.14)		0.347
	3	2 (11.76)	2 (7.14)		0.629
Parity (> 0)	0	17 (100)	21 (75.00)	0.081	0.032
	1	0 (0)	6 (21.43)		0.032
	2	0 (0)	1 (3.57)		1

Comparison of serum sex hormone levels between the two groups

Comparison of serum sex hormone levels between the observation group and the control group revealed significantly elevated levels of FSH and LH in the observation group, with statistically significant differences (P < 0.05). Additionally, the levels of E2, AMH, PRL, and T were significantly decreased in the observation group, with statistically significant differences (P < 0.05). The detailed results are shown in Table 2.

Table 2.

Comparison of serum sex hormone levels between the two groups [M (P25, P75)]

Hormone	Control Group (n = 17)	Observation Group (n = 28)	P-value
FSH (IU/L)	6.55 (5.41, 7.75)	45.17 (30.17, 53.29)	<0.001
LH (IU/L)	3.84 (3.15, 5.89)	19.24 (11.50, 26.57)	<0.001
E2 (pmol/L)	32.12 (24.46, 38.97)	14.32 (5.23, 26.52)	<0.001
AMH (ng/mL)	2.64 (1.77, 4.76)	0.01 (0.01, 0.23)	<0.001
PRL (µg/L)	17.60 (11.07, 27.58)	11.33 (9.27, 13.74)	0.017
T (nmol/L)	0.44 (0.26, 0.53)	0.29 (0.16, 0.38)	0.030

Comparison of serum levels of immunocytokines between the two groups

Compared with the control group, the observation group had significantly elevated levels of serum IFN-γ and IL-22, with statistically significant differences (P < 0.05). However, there were no statistically significant differences in the levels of IL-1β, IL-6, IL-9, IL-17 A, and TGF-β1 between the two groups. The detailed results are shown in Table 3; Fig. 1.

Table 3.

Comparison of serum levels of immunocytokines between the two groups [M (P25, P75)]

Cytokine	Control Group (n = 17)	Observation Group (n = 28)	P-value
IFN-γ(pg/ml)	14.14 (8.56, 32.16)	40.48 (14.32, 58.42)	0.037
IL-6(pg/ml)	2.11 (1.79, 2.60)	2.26 (1.82, 2.59)	0.512
IL-9 (pg/ml)	3.90 (2.55, 5.61)	4.95 (2.98, 6.92)	0.190
IL-17 A(pg/ml)	2.52 (2.05, 3.74)	3.25 (1.00, 5.95)	0.725
IL-22 (pg/ml)	13.27 (7.54, 15.18)	22.48 (14.20, 26.60)	0.009
TGF-β1 (ng/ml)	2.54 (2.07, 2.84)	2.31 (1.66, 2.88)	0.426
IL-1β (ng/ml)	373.60 (316.58, 442.72)	390.97 (270.31, 695.62)	0.815

Fig. 1.

The levels of Interferon-gamma (IFN-γ),Interleukin-22 (IL-22) in the serum of POI patients and controls ((A): IFN-γ; (B): IL-22)

Comparison of serum Immunocytokine levels between regular and irregular menstrual cycle subgroups in POI patients

In healthy women, the hypothalamic-pituitary-ovarian (HPO) axis maintains menstrual cyclicity. In POI, however, cycle regularity (regular vs. irregular) shows no association with immunocytokine levels—despite significant FSH/LH differences—implying independent roles for HPO dysfunction and immune dysregulation in POI. The detailed results are shown in Table 4.

Table 4.

Comparison of serum Immunocytokine levels between regular and irregular menstrual cycle subgroups in POI patients [M (P25, P75)]

Hormone/Cytokine	Regular (n = 20)	Irregular (n = 8)	P-value
FSH (IU/L)	36.31 (28.70,49.44)	65.96 (47.71,90.00)	0.004
LH (IU/L)	17.20 (10.61,20.67)	30.58 (21.20,39.79)	0.017
E2 (pmol/L)	14.32 (6.29,26.52)	12.01 (4.90,29.78)	0.557
AMH (ng/mL)	0.01 (0.01,0.36)	0.01 (0.01,0.02)	0.149
PRL (µg/L)	12.16 (9.38,15.49)	10.85 (8.49,12.30)	0.222
T (nmol/L)	0.30 (0.19,0.49)	0.22 (0.13,0.34)	0.098
IFN-γ(pg/ml)	37.28 (10.74,53.13)	47.59 (29.86,78.24)	0.127
IL-6(pg/ml)	380.33 (265.59,695.62)	481.54 (299.66,696.36)	0.647
IL-9 (pg/ml)	2.23 (1.69,2.62)	2.32 (2.13,2.46)	0.780
IL-17 A(pg/ml)	5.11 (4.18,6.92)	4.39 (2.58,8.39)	0.760
IL-22 (pg/ml)	2.67 (0.86,4.47)	4.69 (1.68,9.70)	0.195
TGF-β1 (ng/ml)	21.02 (12.83,27.24)	23.00 (18.08,25.99)	0.387
IL-1β (ng/ml)	2.37 (2.02,2.88)	1.93 (1.56,4.62)	0.647

Correlation between serum levels of IFN-γ, IL-22, and sex hormones

Serum IFN-γ was positively correlated with FSH and POI diagnosis (r = 0.431 and 0.314, respectively, both P < 0.05) and negatively correlated with AMH and PRL (r = −0.298 and − 0.382, respectively, both P < 0.05). Similarly, serum IL-22 was positively correlated with FSH and POI diagnosis (r = 0.476 and 0.395, respectively, both P < 0.05) and negatively correlated with AMH and PRL (r = −0.345 and − 0.323, respectively, both P < 0.05). The detailed results are shown in Table 5.

Table 5.

Correlation between serum levels of IFN-γ, IL-22, and sex hormones

Hormone/Cytokine	IFN-γ		IL-22
	r	P-value	r	P-value
FSH	0.431	0.003	0.476	0.001
LH	0.258	0.088	0.282	0.061
E2	−0.155	0.309	−0.134	0.378
AMH	−0.298	0.047	−0.345	0.02
PRL	−0.382	0.01	−0.323	0.03
T	−0.222	0.143	−0.206	0.175
POI Diagnosis	0.314	0.036	0.395	0.007

Diagnostic potential of serum IFN-γ and IL-22 levels for POI

Significantly elevated serum IFN-γ and IL-22 levels in POI patients versus controls prompted ROC curve analysis of their diagnostic discriminatory capacity. The results are shown in Fig. 2.

Fig. 2.

ROC curve of serum IFN-γ and IL-22 levels for premature ovarian insufficiency

For IFN-γ, the area under the curve (AUC) was 0.687 (95% confidence interval [CI]: 0.522–0.852) with an asymptotic significance of P = 0.037, indicating statistically significant discriminatory power superior to random chance (AUC > 0.5) under the null hypothesis of true area = 0.5.

For IL-22, the AUC reached 0.735 (95% CI: 0.574–0.897) with a highly significant P-value of 0.009, demonstrating even stronger diagnostic differentiation capability compared to IFN-γ.

Discussion

Premature ovarian insufficiency (POI), a prevalent reproductive disorder in women of reproductive age, is characterized by multifactorial pathogenesis and often idiopathic etiology [18, 19]. Emerging evidence suggests that aberrant cytokine expression and chronic ovarian inflammation may constitute critical mechanisms underlying POI development [20]. This study systematically investigates cytokine profiles in POI patients and their interplay with endocrine dysfunction, providing novel insights into disease pathophysiology. Notably, the elevated prevalence of menstrual irregularity (28.57% vs. 0%, P = 0.043) in POI patients despite comparable baseline demographics reinforces the clinical specificity of this phenotype.

The hormonal alterations observed in our cohort align with established POI diagnostic criteria. Significantly elevated FSH and LH levels accompanied by diminished E2, AMH, PRL, and T concentrations reflect profound ovarian dysfunction. As key regulators of folliculogenesis, FSH and LH surges typically indicate compensatory pituitary response to diminished ovarian feedback [21]. The marked E2 reduction mirrors the collapse of follicular steroidogenesis in failing ovaries [22, 23], while AMH depletion reflects accelerated primordial follicle pool exhaustion [18]. Intriguingly, our subgroup analysis revealed preserved cytokine homogeneity between regular and irregular menstrual cycles despite divergent FSH/LH profiles (P < 0.05), suggesting that immune dysregulation operates independently of hypothalamic-pituitary-ovarian (HPO) axis dysfunction in driving POI progression. The concurrent decline in PRL and T levels further suggests systemic endocrine disruption, potentially linking hypothalamic-pituitary-ovarian axis dysregulation to broader metabolic consequences [24]. These findings corroborate previous reports emphasizing FSH, LH, E2, and AMH as pivotal ovarian reserve markers, while highlighting T and PRL as potential adjunct diagnostic parameters.

Notably, our study reveals significant serum elevation of IFN-γ and IL-22 in POI patients. As a prototypical Th1 cytokine, IFN-γ orchestrates innate and adaptive immune responses through macrophage activation and antigen presentation modulation [25]. Its pathogenic role in ovarian dysfunction may involve dual mechanisms: direct induction of granulosa cell apoptosis via caspase activation [26], and indirect disruption of steroidogenesis through STAT1-mediated inhibition of aromatase expression. Similarly, IL-22, a member of the IL-10 cytokine family, exhibits paradoxical roles in tissue protection and inflammation [27]. While primarily produced by lymphoid cells (Th17, γδ T cells, and ILC3s), its overexpression in POI may activate the JAK1/STAT3 pathway through IL-22R1/IL-10R2 receptor complexes [28, 29], potentially disrupting follicular microenvironment homeostasis. The robust diagnostic performance of these cytokines (IFN-γ AUC = 0.687, IL-22 AUC = 0.735) underscores their translational potential as complementary biomarkers to conventional hormonal assessments. These cytokines’ dysregulation aligns with their documented involvement in autoimmune disorders and reproductive pathologies [30–32], suggesting shared mechanisms between POI and systemic inflammatory conditions.

The observed cytokine-hormone correlations provide mechanistic insights into POI progression. The positive associations between IFN-γ/IL-22 and FSH (r = 0.431/0.476) mirror escalating pituitary stimulation in deteriorating ovarian function. Conversely, their inverse correlations with AMH (r=−0.298/−0.345) and PRL (r=−0.382/−0.323) suggest immunoedocrine crosstalk where inflammatory mediators accelerate follicular depletion and disrupt lactotropic function. The diagnostic threshold analysis further bridges these molecular associations to clinical applicability, with IL-22 demonstrating superior discriminatory capacity (P = 0.009 vs. IFN-γ P = 0.037). This cytokine-hormone network may form a self-perpetuating cycle: granulosa cell apoptosis induced by IFN-γ/IL-22 exacerbates estrogen deficiency, which in turn promotes Th1/Th17 polarization and cytokine overproduction [32]. Such multidirectional interactions could explain the rapid ovarian function decline characteristic of POI.

Several limitations warrant consideration. The modest sample size (n = 28) and single-center design may limit generalizability, particularly given geographic homogeneity in our cohort. The cross-sectional nature precludes causal inference between cytokine dysregulation and ovarian failure. While ROC analyses indicate diagnostic promise, future validation studies must establish optimal cytokine thresholds across diverse populations. Longitudinal studies tracking cytokine dynamics during POI progression and therapeutic interventions are crucial to establish clinical utility.

In conclusion, this study establishes IFN-γ and IL-22 as key immunologic players in POI pathogenesis, demonstrating their close associations with characteristic endocrine disturbances. The dissociation between menstrual cyclicity patterns and cytokine profiles unveils distinct pathological axes - endocrine disruption and immune activation - that may require targeted therapeutic strategies. These cytokines may serve dual roles as pathogenic mediators and clinically actionable biomarkers. Our findings support the growing evidence linking POI to immunometabolic dysregulation, suggesting that targeting the cytokine-hormone axis could be explored for future diagnostic and therapeutic strategies.

Abbreviations

abbreviation

full name

POI

premature ovarian insufficiency

FSH

Follicle-Stimulating Hormone

LH

Luteinising Hormone

E2

Estradiol

AMH

Anti-Mullerian Hormone

PRL

Prolactin

T

Testosterone

IFN-γ

Interferon-gamma

IL-1β

Interleukin-1 beta

IL-6

Interleukin-6

IL-9

Interleukin-9

IL-17A

Interleukin-17 A

IL-22

Interleukin-22

TGF-β1

Transforming Growth Factor-beta 1

OD

optical density

Authors’ contributions

MH participated in the research design of this study and mainly wrote the manuscript. FL, YS, QL screening and collection of clinical data from included individuals. FL, ML, XL, and XJ conducted specific experimental operations. JL conducts statistical analysis on the data. RH conducted research design for this study, analyzed and interpreted the results, and ensured the overall quality of the research. All authors participated in the manuscript writing and agreed to publish it.

Funding

This work was supported by the Guangxi Health Commission Self-funded Research Projects (Grant Nos. Z20190849 and Z20200626).

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the ethics committee of the Reproduction Hospital of Guangxi Zhuang Autonomous Region (Ethical approval number: KY-LW-2025-03) and was in compliance with the Helsinki Declaration (https://www.wma.net/policies-post/wma-declaration-of-helsinki/). All the experiments were performed with informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.