Pentraxin 3 levels in polycystic ovary syndrome: a meta-analysis

Abstract

Background & aim

Pentraxin 3 (PTX3) is an immunomodulatory molecule involved in inflammation and ovarian physiology. However, its circulating levels in polycystic ovary syndrome (PCOS) remain uncertain due to inconsistent findings. This meta-analysis aimed to clarify the association between PTX3 levels and PCOS.

Methods

A systematic search of PubMed, Embase, Scopus, Web of Science, CNKI, and Google Scholar was conducted up to April 2025 without language restrictions. Observational studies comparing circulating PTX3 levels between patients with PCOS and healthy controls were included. Effect sizes were calculated as standardized mean differences (SMDs) with 95% confidence intervals (CIs) using a random-effects model.

Results

Nineteen studies (25 datasets) involving 1,230 women with PCOS and 1,179 controls were included. Pooled analysis showed significantly higher PTX3 levels in PCOS compared with controls (SMD = 0.993; 95% CI: 0.076 to 1.909; p = 0.034). In subgroup analysis by BMI, the most pronounced elevation in PTX3 concentrations was observed in women with a BMI between 25 and 30 kg/m² (SMD = 2.102; p = 0.008). Meta-regression analysis also identified age (β = 0.37, p = 0.026) as a positive and luteinizing hormone levels (β = − 0.33, p = 0.028) as a negative moderator of PTX3 concentrations.

Conclusions

The observed increase in PTX3 concentrations in PCOS supports its role as a candidate biomarker for disease identification and monitoring. Future studies should clarify its relationship with infertility status, explore its role across different PCOS phenotypes, and address methodological heterogeneity to refine its clinical utility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-026-02091-0.

Introduction

Polycystic ovary syndrome (PCOS) is one of the most prevalent endocrine disorders in women of reproductive age [1]. It is widely recognized as a leading contributor to female infertility, primarily due to chronic anovulation and related ovulatory dysfunction [2]. Its global prevalence ranges from 6.8% to 12.5%, largely influenced by differences in diagnostic criteria and population characteristics [3]. The heterogeneous clinical manifestations of PCOS complicate both diagnosis and management, imposing substantial demands on healthcare systems [4].

PCOS is defined by a complex interplay of hormonal, reproductive, and metabolic abnormalities [5]. Its hallmark features include elevated androgen levels, ovarian dysfunction, and polycystic ovarian morphology [6]. Evidence suggests that both intrinsic factors, such as genetic predisposition and hormonal dysregulation, and extrinsic contributors, including excess body weight, dietary patterns, and environmental exposures contribute to the syndrome’s onset and progression [7]. Clinically, PCOS manifests through a wide array of symptoms. Common presentations include menstrual irregularities, hirsutism, acne, androgenic alopecia, and metabolic dysfunction, notably insulin resistance [6]. The Rotterdam criteria remain the most widely accepted diagnostic framework, requiring the presence of at least two of the following: oligo- or anovulation, clinical or biochemical evidence of hyperandrogenism, and polycystic ovarian morphology on ultrasound [8]. Based on these features, four phenotypic subtypes (A–D) have been identified, each reflecting varying degrees of clinical expression and associated long-term risks [9].

Pentraxin 3 (PTX3) is a prototypical member of the long pentraxin family and functions as an acute-phase protein involved in innate immunity and inflammation [10, 11]. Unlike short pentraxins such as C-reactive protein, which are primarily synthesized in the liver, PTX3 is produced locally at sites of inflammation by immune, endothelial, and stromal cells. In addition to its immunological functions, PTX3 contributes to tissue remodeling, vascular integrity, and regulation of cell survival [12]. In reproductive biology, PTX3 plays an important role in shaping the ovarian microenvironment and modulating localized inflammatory responses, both of which are essential for normal follicular development and ovulation [13]. These functions have led to growing interest in PTX3 as a potential biomarker for PCOS [14]. However, current findings on circulating PTX3 levels in women with PCOS are inconsistent. While several studies have demonstrated elevated PTX3 concentrations in association with hyperandrogenism and ovarian dysfunction, others have reported reduced levels in specific phenotypic subgroups [15, 16]. Furthermore, investigations assessing the diagnostic accuracy of PTX3 have yielded conflicting findings. The reported sensitivity and specificity values vary considerably across studies, and the overall clinical utility of PTX3 as a discriminative biomarker remains uncertain [17, 18].

Given the absence of prior meta-analyses in this domain and persistent inconsistencies across individual studies, we conducted this meta-analysis to assess circulating PTX3 levels in women with PCOS. By synthesizing the available data, this study aims to clarify its potential diagnostic relevance and provide a more unified understanding of PTX3’s role in the pathophysiology of PCOS.

Materials and methods

Protocol and registration

All methodological stages, including the planning, execution, and synthesis phases of this systematic review and meta-analysis, followed the standards set forth by the PRISMA guidelines [19]. Furthermore, the study protocol was registered in the PROSPERO database under registration number CRD420261300213 [20].

Search strategy and study selection

A systematic search was performed in PubMed, Embase, Scopus, Web of Science, CNKI, and Google Scholar, up to April 2025. For PTX3, the following terms were used: “Pentraxin 3,” “PTX3,” “Pentraxin-3,” “PTX-3,” “Long pentraxin,” “Pentraxin-related protein 3,” “TNF-stimulated gene 14,” and “TSG-14.” For PCOS keywords included: “polycystic ovary syndrome,” “polycystic ovarian syndrome,” “PCOS,” “polycystic ovary,” “polycystic ovaries,” “PCO,” “ovary syndrome,” “sclerocystic ovarian degeneration,” “sclerocystic ovary syndrome,” “sclerocystic ovaries,” “Stein-Leventhal syndrome,” “ovarian cysts,” “hyperandrogenic anovulation,” “hyperandrogenism,” and “anovulation.” Boolean operators “AND” and “OR” were used to refine and structure the search queries. The reference lists of all included studies and relevant review articles were examined manually to identify additional eligible studies. No restrictions were applied regarding language, publication date, or geographic location, allowing for the inclusion of all potentially relevant evidence.

The process of study selection was carried out independently by two reviewers (R.M.F. and M.N.), beginning with the screening of titles and abstracts to identify potentially relevant studies. Full-text articles were then retrieved and assessed in detail based on predefined inclusion and exclusion criteria. Discrepancies between the reviewers were discussed and resolved with input from a third investigator (S.S.B.).

Eligibility criteria

Studies were included if they met the following criteria: (1) observational design; (2) conducted on adult women diagnosed with PCOS; (3) included a healthy control group; (4) measured PTX3 levels in blood samples; and (5) reported data as mean ± standard deviation or provided sufficient information to calculate these values.

Exclusion criteria comprised: case reports, review articles, conference abstracts, editorials, and interventional studies; studies conducted on animals or cell lines; studies lacking adequate data to determine PTX3 levels; those measuring PTX3 in non-blood specimens (e.g., follicular fluid); and duplicate publications based on the same population.

Data extraction and quality assessment

Two reviewers (R.M.F. and M.N.) independently extracted data using a standardized form. The extracted variables included study characteristics (first author, publication year, region, design, sample size, and diagnostic criteria), participant information (age, body mass index (BMI), homeostatic model assessment of insulin resistance (HOMA-IR), luteinizing hormone (LH), follicle-stimulating hormone (FSH) and testosterone levels), and PTX3-related data (concentration, sample source, and assay method). When PTX3 levels were reported as ranges or interquartile values, established statistical methods were applied to convert them to mean and standard deviation [21].

The methodological quality of the included studies was assessed via the Newcastle-Ottawa Scale (NOS). This tool examines three key domains: the adequacy of the selection of study groups, the comparability of groups based on important confounding variables, and the reliability of the ascertainment of outcome. Based on these criteria, each study received a score ranging from 0 to 9, with a score of 7 or higher indicating high methodological quality. The quality appraisal was independently conducted by two reviewers (R.M.F. and M.N.), and any discrepancies in scoring were resolved through discussion with a third reviewer (S.S.B.).

Data synthesis and statistical analysis

The Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated to compare circulating levels of PTX3 between women with PCOS and healthy controls. Heterogeneity across studies was assessed using the Chi-square (Q) test and the I-squared statistic, with I-squared values above 50% indicating significant heterogeneity. Depending on the degree of heterogeneity, either a fixed-effects or random-effects model was applied.

Subgroup analyses were performed on the basis of geographic region, sample type, study design, and BMI. Additionally, meta-regression analyses were carried out to assess the potential influence of clinical and demographic covariates, including age, BMI, HOMA-IR, LH, FSH, and testosterone levels on circulating PTX3 concentrations. Sensitivity analyses were carried out by excluding individual studies and re-estimating the pooled effect size. Publication bias was examined via Egger’s test and visualized through a funnel plot. All statistical analyses were performed using Comprehensive Meta-Analysis (CMA) software, version 4.

Results

Study selection

A total of 149 articles were initially identified through the systematic literature search. After removing 76 duplicate records, 73 articles remained for title and abstract screening. Based on the eligibility criteria, 49 studies were excluded at this stage. The full texts of the remaining 24 articles were then reviewed in detail, leading to the exclusion of 5 studies for reasons such as focusing on gene expression [22] or measuring PTX3 levels in follicular fluid [14] rather than circulation. Ultimately, 19 studies satisfied all inclusion criteria and were included in the final quantitative synthesis. The whole process of study identification, screening, and selection is summarized in the PRISMA flow diagram (Fig. 1).

Fig. 1.

Flow diagram of study selection adjusted by PRISMA

Characteristics of the included studies

A total of 19 eligible studies were included in the present meta-analysis, comprising 25 independent data records and involving 2,409 female participants. Of these, 1,230 were diagnosed with PCOS and 1,179 were healthy controls. The studies were published between 2012 and 2025, and consisted of nine case-control and ten cross-sectional designs.

Geographically, the studies were conducted across a diverse range of countries, with seven each from China [15, 23–28] and Turkey [16, 17, 29–33], two from Iraq [18, 34], and one each from Spain [35], Poland [36], and Italy [37]. Regarding BMI, 13 data records involved participants with BMI < 25 kg/m², eight with BMI between 25 and 30 kg/m², and four with BMI > 30 kg/m². In terms of sample type, 20 records utilized serum for PTX3 quantification, whereas five employed plasma samples. A comprehensive summary of study characteristics and quality scores are presented in Table 1.

Table 1.

Characteristics of the studies included in the systematic review and meta-analysis

Author, year (Ref)	Country	Study design	Population	Sample Size	Age (yr)	BMI (kg/m2)	Diagnostic criteria	Sample type	Assay method	NOS Score
Pan et al., 2025 [24]	China	Case-control	PCOS	Cases: 69 Controls: 138	32.69 ± 7.17; 33.51 ± 6.84	21.47 ± 1.39; 21.59 ± 1.44	Rotterdam	Serum	ELISA	7
Shnain et al., 2024 [18]	Iraq	Case-control	PCOS with infertility	Cases: 30 Controls: 33	35–45	28.66 ± 1.53; 28.26 ± 2.51	Rotterdam	Serum	ELISA	7
Cagiran et al., 2024 [16]	Turkey	Case-control	PCOS with infertility	Cases: 35 Controls: 15	25 ± 3.86; 29.5 ± 4.49	25.4 ± 2.17; 26.9 ± 1.39	Rotterdam	Serum	ELISA	6
Song et al., 2024 A [23]	China	Case-control	PCOS without IR	Cases: 82 Controls: 82	28.35 ± 5.24; 27.50 ± 5.32	25.25 ± 5.67; 24.34 ± 5.12	Rotterdam	Serum	ELISA	8
Song et al., 2024 B [23]	China	Case-control	PCOS with IR	Cases: 68 Controls: 68	28.63 ± 5.41; 27.50 ± 5.32	26.13 ± 5.75; 24.34 ± 5.12	Rotterdam	Serum	ELISA	8
Essa et al., 2024 A [34]	Iraq	Case-control	PCOS with normal fertility	Cases: 30 Controls: 15	28.21 ± 4.83; 27.20 ± 5.05	28.61 ± 6.09; 27.30 ± 5.42	Rotterdam	Serum	ECLIA	8
Essa et al., 2024 B [34]	Iraq	Case-control	PCOS with infertility	Cases: 30 Controls: 15	27.97 ± 5.06; 27.20 ± 5.05	27.50 ± 5.6; 27.30 ± 5.42	Rotterdam	Serum	ECLIA	8
Li et al., 2023 A [25]	China	Cross-sectional	Lean PCOS	Cases: 42 Controls: 31	26.02 ± 5.24; 26.12 ± 5.19	22.52 ± 0.62; 22.46 ± 0.51	Rotterdam	Serum	ELISA	7
Li et al., 2023 B [25]	China	Cross-sectional	Overweight PCOS	Cases: 40 Controls: 29	26.25 ± 5.54; 26.33 ± 5.25	24.52 ± 0.45; 24.42 ± 0.25	Rotterdam	Serum	ELISA	7
Yesil et al., 2022 [17]	Turkey	Cross-sectional	PCOS	Cases: 45 Controls: 42	22.11 ± 6.16; 23.76 ± 5.34	25.37 ± 6.21; 22.10 ± 3.44	Rotterdam	Serum	ELISA	7
Song et al., 2022 [26]	China	Case-control	PCOS	Cases: 90 Controls: 40	27.6 ± 3.54; 28.12 ± 3.78	25.37 ± 6.21; 22.86 ± 4.05	Rotterdam	Serum	ELISA	6
Guo et al., 2022 [27]	China	Cross-sectional	PCOS	Cases: 62 Controls: 50	34.49 ± 2.32; 34.52 ± 2.21	23.11 ± 3.21; 23.21 ± 2.98	Rotterdam	Serum	ELISA	7
Jin et al., 2021 [15]	China	Case-control	PCOS with infertility	Cases: 120 Controls: 240	28.33 ± 0.27; 29.57 ± 0.20	22.54 ± 0.30; 22.38 ± 0.22	Rotterdam	Plasma	ELISA	8
Martinez-Garcia et al., 2020 A [35]	Spain	Case-control	Lean PCOS	Cases: 9 Controls: 9	24 ± 8; 26 ± 5	24 ± 2; 23 ± 2	NIH	Serum	Luminex	6
Martinez-Garcia et al., 2020 B [35]	Spain	Case-control	Overweight PCOS	Cases: 8 Controls: 8	30 ± 4; 27 ± 6	37 ± 5; 36 ± 4	NIH	Serum	Luminex	6
Wyskida et al., 2020 [36]	Poland	Cross-sectional	PCOS	Cases: 99 Controls: 61	26.9 ± 5.8; 26.6 ± 5.0	33.76 ± 9.55; 26.66 ± 8.27	Rotterdam	Plasma	ELISA	7
Zhang et al., 2016 A [28]	China	Cross-sectional	Lean PCOS	Cases: 38 Controls: 39	26.61 ± 3.47; 26.26 ± 2.09	20.88 ± 2.18; 21.11 ± 1.67	Rotterdam	Serum	ELISA	6
Zhang et al., 2016 B [28]	China	Cross-sectional	Overweight PCOS	Cases: 40 Controls: 28	26.55 ± 3.75; 27.25 ± 3.75	29.50 ± 4.68; 27.68 ± 2.70	Rotterdam	Serum	ELISA	6
Deveer et al., 2015 [29]	Turkey	Cross-sectional	PCOS	Cases: 25 Controls: 25	27.24 ± 5.34; 31.8 ± 8.11	25.86 ± 4.61; 25.44 ± 5.10	Rotterdam	Serum	ELISA	7
Tosi et al., 2014 [37]	Italy	Cross-sectional	PCOS	Cases: 66 Controls: 51	23 ± 6.06; 27.83 ± 6.10	28.43 ± 8.33; 21.06 ± 2.13	Rotterdam	Plasma	ELISA	8
Sari et al., 2014 A [30]	Turkey	Cross-sectional	Lean PCOS	Cases: 20 Controls: 20	24.50 ± 3.44; 26.70 ± 4.24	22.00 ± 2.00; 20.31 ± 4.00	Rotterdam	Serum	ELISA	8
Sari et al., 2014 B [30]	Turkey	Cross-sectional	Overweight PCOS	Cases: 20 Controls: 20	25.85 ± 5.94; 25.05 ± 4.72	35.80 ± 7.00; 35.80 ± 2.00	Rotterdam	Serum	ELISA	8
Guducu et al., 2014 [31]	Turkey	Cross-sectional	PCOS	Cases: 58 Controls: 34	25.84 ± 5.3; 28.26 ± 6.78	23.70 ± 5; 22,41 ± 3.17	Rotterdam	Serum	ELISA	7
Sahin et al., 2014 [32]	Turkey	Cross-sectional	PCOS	Cases: 64 Controls: 46	22.9 ± 4.3; 21.9 ± 4.5	30.3 ± 8.7; 29.7 ± 5.2	Rotterdam	Plasma	ELISA	7
Aydogdu et al., 2012 [33]	Turkey	Case-control	PCOS	Cases: 40 Controls: 40	21.3 ± 4.7; 21.9 ± 3.5	24.7 ± 5.3; 22.5 ± 2.4	Rotterdam	Plasma	ELISA	8

PCOS Polycystic ovary syndrome, IR Insulin resistance, BMI Body mass index, NIH National institutes of health, ELISA Enzyme-linked immunosorbent assay, ECLIA Electrochemiluminescence immunoassay, NOS Newcastle-Ottawa scale

PTX3 levels in patients with PCOS and healthy controls

Meta-analysis

As shown in Fig. 2, the pooled estimate revealed that circulating PTX3 levels were significantly elevated in the PCOS group compared to the control group (SMD = 0.993; 95% CI: 0.076 to 1.909; p = 0.034). However, the analysis revealed substantial heterogeneity among the included studies (I² = 96.67%, p < 0.01), indicating considerable variation in effect estimates.

Fig. 2.

Forest plot illustrating the pooled standardized mean difference (SMD) for circulating PTX3 levels in women with PCOS compared with healthy controls. Individual study estimates with 95% confidence intervals (CIs) are shown, and the diamond represents the overall pooled effect

Subgroup analysis

To explore potential sources of heterogeneity, subgroup analyses were conducted based on key study and participant characteristics (Fig. 3). When stratified by BMI, the highest PTX3 levels were observed in participants with a BMI between 25 and 30 kg/m² (SMD = 2.102; 95% CI: 0.549 to 3.655; p = 0.008). In contrast, no significant differences were detected in participants with BMI < 25 kg/m² (SMD = 0.619; 95% CI: −0.924 to 2.161; p = 0.432) or in those with BMI ≥ 30 kg/m² (SMD = 0.015; 95% CI: −0.752 to 0.781; p = 0.970). Sample type also appeared to influence the magnitude of the effect. Studies utilizing plasma samples reported higher PTX3 concentrations in PCOS cases (SMD = 2.280; 95% CI: −0.120 to 4.680; p = 0.063) compared to those using serum samples (SMD = 0.628; 95% CI: −0.340 to 1.597; p = 0.204).

Fig. 3.

Forest plots of subgroup analyses examining differences in circulating PTX3 levels between women with PCOS and healthy controls according to (A) body mass index (BMI), (B) biological sample type (serum vs. plasma), (C) study design (case–control vs. cross-sectional), and (D) geographical region. Effect sizes are expressed as standardized mean differences with 95% confidence intervals

Regarding study design, case-control studies demonstrated a stronger association between PTX3 levels and PCOS (SMD = 2.533; 95% CI: 0.531 to 4.535; p = 0.013) than cross-sectional studies, which did not indicate a significant difference (SMD = −0.077; 95% CI: −0.773 to 0.619; p = 0.828). Geographic region also contributed to the observed variability, as studies conducted in East Asia reported higher PTX3 levels (SMD = 1.474; 95% CI: −0.654 to 3.601; p = 0.175) compared with studies from the Middle East (SMD = 0.380; 95% CI: −0.441 to 1.201; p = 0.364) and Europe (SMD = −0.033; 95% CI: −0.884 to 0.819; p = 0.940). However, none of these subgroup differences reached statistical significance.

Meta-regression analysis

Among the variables assessed, age and LH levels emerged as significant moderators of circulating PTX3 concentrations. Age showed a positive correlation with PTX3 levels (β = 0.37, p = 0.026), whereas LH levels demonstrated a significant negative correlation (β = −0.33, p = 0.028). Other variables, including BMI (β = −0.04, p = 0.663), HOMA-IR (β = −0.06, p = 0.805), testosterone (β = −0.31, p = 0.453), and FSH (β = 0.01, p = 0.535) did not show statistically significant associations with PTX3 levels (Fig. 4).

Fig. 4.

Meta-regression plots assessing the relationship between circulating PTX3 levels and clinical and demographic variables, including age, body mass index (BMI), homeostasis model assessment of insulin resistance (HOMA-IR), testosterone, luteinizing hormone (LH), and follicle-stimulating hormone (FSH). Each circle represents an individual study and the regression line depicts the direction and magnitude of the association

Publication bias and sensitivity analysis

The potential for publication bias was evaluated using the funnel plot and Egger’s regression test. Visual examination of the funnel plot (Supplementary Fig. 1) did not indicate considerable asymmetry, suggesting a low risk of publication bias. This was further supported by the results of Egger’s regression test, which yielded a non-significant intercept (regression intercept = 9.52; 95% CI: −3.39 to 22.45; p = 0.070).

A sensitivity analysis was performed to assess the robustness of the pooled effect. By systematically removing each study and recalculating the pooled SMD, the effect size was found to range from 0.454 to 1.157. The corresponding 95% confidence intervals ranged from − 0.422 to 0.258 at the lower boundary and from 1.202 to 2.063 at the upper boundary. Although some variability was observed, the direction of the association remained consistent (Supplementary Fig. 2).

Discussion

Interpretation of findings

This meta-analysis was performed to resolve the existing uncertainty regarding the association between circulating PTX3 concentrations and PCOS, given the conflicting outcomes reported across studies. By integrating data from 19 eligible studies encompassing 25 data records, the primary analysis revealed that circulating PTX3 levels were significantly elevated in women with PCOS compared to healthy controls. Our result is in line with several studies reporting heightened PTX3 expression in PCOS, particularly in association with inflammatory markers and ovarian dysfunction. The elevated concentrations of PTX3, as an anti-inflammatory and tissue-remodeling mediator, may signify a compensatory mechanism aimed at counteracting the chronic low-grade inflammation, endothelial dysfunction, and altered ovarian milieu that characterize PCOS.

Subgroup analysis also offered further insight into potential sources of variability by examining participant characteristics and methodological factors. Among BMI categories, the most pronounced elevation in PTX3 levels was observed in women with a BMI ranging between 25 and 30 kg/m², while no significant differences were detected in those with lower or higher BMI ranges. This finding suggests that the inflammatory and metabolic dysregulation associated with moderate adiposity may augment PTX3 expression more substantially than either lean or severely obese states. In terms of specimen type, higher PTX3 concentrations were detected in studies using plasma samples compared to serum, although this difference did not reach statistical significance. This discrepancy may stem from differences in sample sizes or analyte stability, protein-binding dynamics, and detection sensitivity across sample types.

The type of study design also appeared to influence effect sizes, with case-control studies demonstrating a markedly stronger association than cross-sectional designs. This may reflect the ability of case-control studies to better match participants and control for confounding variables, thus yielding more robust effect estimates. Geographic variability was also examined, revealing higher PTX3 levels in studies conducted in East Asia compared to those from the Middle East and Europe. While these regional differences did not achieve statistical significance, they may hint at underlying ethnic, genetic, dietary, or environmental modifiers that warrant further exploration.

Furthermore, meta-regression analysis revealed that age and LH concentrations significantly moderated PTX3 levels. Specifically, advancing age was associated with higher circulating PTX3 concentrations, a finding consistent with the known age-related increase in systemic inflammatory tone and vascular remodeling, both of which may stimulate PTX3 expression. Conversely, LH levels demonstrated a significant inverse association with PTX3 levels. Given the association of severe PCOS with markedly elevated LH and infertility, this finding contrasts with the established role of PTX3 in supporting fertilization. While PTX3 elevation in PCOS may serve as a compensatory response to ovarian dysfunction, this adaptive mechanism may be diminished in advanced phenotypes with high LH levels, thereby explaining the observed inverse relationship. Other metabolic and hormonal parameters, including BMI, HOMA-IR, FSH, and testosterone, also showed no significant relationships, indicating that PTX3 alterations in PCOS may be partially independent of these traditional endocrine and metabolic markers.

The role of PTX3 in the pathophysiology of PCOS

PCOS is a complex endocrine disorder driven by hormonal imbalances, metabolic dysregulation, and low-grade chronic inflammation [38]. At the pathophysiological level, dysfunction of the hypothalamic-pituitary-ovarian axis leads to a relative excess of LH secretion compared with FSH, resulting in hyperstimulation of theca cells and increased androgen synthesis [39]. These disturbances are further amplified by pro-inflammatory mediators originating from adipose tissue and ovarian immune cell infiltration, which create a local inflammatory microenvironment and contribute to disrupted folliculogenesis and anovulation [40, 41]. Insulin resistance, compensatory hyperinsulinemia, and altered adipokine profiles are commonly observed in PCOS and closely interact with underlying endocrine disturbances [42, 43]. Progressively, these abnormalities establish a pathological loop that drives structural remodeling of the ovary, intensifies hyperandrogenic manifestations, and exacerbates infertility [44].

Within the altered endocrine and inflammatory milieu of PCOS, PTX3 emerges as a pivotal immunomodulatory and matrix-stabilizing molecule with a distinct role in ovarian physiology [13]. It is synthesized by various cell types, including monocytes, macrophages, dendritic cells, endothelial cells, smooth muscle cells, fibroblasts, and ovarian cells, such as granulosa, theca, and cumulus cells [45, 46]. In the ovary, PTX3 expression markedly increases in cumulus cells in response to LH/hCG stimulation [45]. As a member of the hyaladherin family, PTX3 plays a critical structural role in stabilizing the hyaluronan-rich extracellular matrix of the cumulus oophorus during follicular maturation [47]. This stabilization occurs through binding to inter-α-inhibitor (IαI) and tumor necrosis factor-stimulated gene-6 (TSG-6) and creating a molecular “node” that preserves the integrity of the hyaluronan network and supports proper expansion of the oocyte–cumulus complex [47, 48]. Following ovulation, PTX3 synthesis is sustained by endothelial and stromal cells of the corpus luteum, where it may contribute to vascular involution during luteal regression by binding to fibroblast growth factor-2 (FGF-2) and suppressing its pro-angiogenic activity [49, 50].

In addition to the hormonal imbalance characteristic of PCOS, particularly the sustained activation of LH signaling, the chronic inflammatory milieu markedly amplifies PTX3 expression. Elevated concentrations of pro-inflammatory cytokines, such as tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β), stimulate PTX3 transcription through activation of the nuclear factor kappa-B (NF-κB) signaling pathway [51]. This increased synthesis may serve a compensatory role, as PTX3 exerts potent anti-inflammatory effects that help counterbalance the underlying chronic inflammation [52]. Mechanistically, PTX3 has been implicated in the regulation of immune cell recruitment to sites of inflammation. It interacts with P-selectin, a key adhesion molecule that mediates leukocyte–endothelium interactions, and modulates the infiltration of immune cells into inflamed tissues [53]. Furthermore, PTX3 reduces the activity of pro-inflammatory leukocytes, particularly M1 macrophages, and promotes their polarization toward the anti-inflammatory M2 phenotype [54]. By fostering this immunoregulatory shift, PTX3 contributes to the preservation of a local ovarian microenvironment that supports optimal oocyte quality and fertilization potential, even in the presence of persistent inflammation in PCOS [13].

Clinical implications and future directions

The significant elevation of PTX3 concentrations in women with PCOS, together with its established involvement in ovulatory processes, underscores its potential value as an adjunctive biomarker. PTX3 assessment may offer complementary information on inflammatory activity and reproductive status when interpreted alongside established and emerging biomarkers, such as anti-Müllerian hormone (AMH), rather than as a standalone diagnostic tool [55]. However, the clinical application of PTX3 remains constrained by assay variability, lack of standardized cut-off values, and confounding influences of age, ethnicity, and adiposity. Collectively, these considerations support a multimarker approach, in which PTX3 may add pathophysiological insight related to ovarian inflammation and tissue remodeling, thereby enhancing diagnostic accuracy and personalized clinical assessment in PCOS.

Future investigations should focus on standardizing PTX3 measurement methodologies and defining clinically relevant thresholds across diverse populations. Large-scale studies are required to evaluate its expression across different PCOS phenotypes and to assess its value in disease classification. Longitudinal studies with adequate follow-up are particularly important to determine whether elevated PTX3 levels can predict infertility risk or therapeutic response across PCOS subtypes. Finally, mechanistic and experimental research elucidating PTX3-mediated pathways in ovarian and systemic tissues may uncover novel therapeutic targets, supporting the development of targeted immunomodulatory or matrix-focused interventions.

Strengths and limitations

This is the first meta-analysis to systematically assess circulating PTX3 concentrations in women with PCOS. By synthesizing data from a substantial number of studies without restrictions on language, we minimized the risk of language bias and ensured broad coverage of the existing literature. Additionally, the use of subgroup and meta-regression analyses allowed for the identification of potential moderators, such as BMI, age, and LH levels, that may influence PTX3 expression.

Despite these strengths, certain limitations warrant consideration. Substantial heterogeneity was observed across studies (I² = 96.67%), which may reflect differences in study populations, particularly in ethnic and racial composition, as most included studies were conducted in Asian populations. Additional sources of heterogeneity include variation in PCOS phenotypes, infertility status, and study design. Methodological variability, particularly differences in biological sample type (serum versus plasma) and PTX3 assay techniques, may have influenced measured concentrations and limited comparability across studies. Furthermore, key confounding variables, including medication use and comorbid conditions such as diabetes or cardiovascular disease, were not consistently reported or adjusted for, limiting the ability to determine the independent association between PTX3 levels and PCOS.

Conclusion

Based on pooled evidence from 19 studies, this meta-analysis demonstrates a significant elevation of circulating PTX3 concentrations in women with PCOS compared with healthy controls. Given the established role of PTX3 in ovarian physiology, extracellular matrix stabilization, and immune regulation, these findings highlight its potential as an adjunctive biomarker for disease identification, monitoring, and possibly prognostication. Future research should clarify its relationship with infertility outcomes, delineate its expression across different PCOS phenotypes, and address methodological variability to refine its clinical applicability.

Abbreviations

PCOS

Polycystic ovary syndrome

PTX3

Pentraxin 3

BMI

Body mass index

HOMA-IR

Homeostatic model assessment of insulin resistance

LH

Luteinizing hormone

FSH

Follicle-stimulating hormone

IαI

Inter-α-inhibitor

TSG-6

Tumor necrosis factor-stimulated gene-6

FGF-2

Fibroblast growth factor-2

TNF-α

Tumor necrosis factor-α

IL-1β

Interleukin-1β

NF-κB

Nuclear factor kappa-B

NOS

Newcastle-Ottawa scale

SMD

Standardized mean difference

CI

Confidence interval

CMA

Comprehensive meta-analysis

Authors’ contributions

Reza Mohammadpour Fard: Conceptualization, Methodology, Investigation, Data Curation, Writing - Original Draft, Visualization, Supervision. Mohadese Nadi & Tannaz Sakhavarz: Methodology, Data Curation, Writing - Original Draft. Zahra Mansouri: Writing - Original Draft. Razieh Kazemzadeh & Vahid Radmehr: Writing - Original Draft. Mostafa Hosseinpour: Writing - Original Draft, Formal analysis. Seyed Sobhan Bahreiny: Methodology, Formal analysis, Resources, Writing - Original Draft, Visualization, Project administration.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.