Metabolic Syndrome Predicts Chemotherapy Resistance and Poor Prognosis in Epithelial Ovarian Cancer: A Retrospective Observational Cohort Study

Introduction

Ovarian cancer remains the most lethal gynecological malignancy, characterized by a poor prognosis primarily attributable to late diagnosis and high recurrence rates following initial therapy.^1–3 The standard treatment involves cytoreductive surgery followed by platinum-based chemotherapy; however, approximately 70–80% of patients who initially respond to chemotherapy eventually develop resistance, significantly compromising survival outcomes.^4,5 Chemotherapy resistance and recurrence represent critical clinical challenges in ovarian cancer management, underscoring the urgent need to identify novel prognostic factors and therapeutic targets to enhance clinical decision-making and patient outcomes.^6,7

Metabolic syndrome is characterized by a cluster of interrelated factors, including central obesity, dyslipidemia, hypertension, insulin resistance, and elevated fasting glucose levels. It has emerged as a significant global health concern due to its rising prevalence and its association with multiple malignancies.^8–10 Accumulating evidence from human studies indicates that metabolic syndrome and its individual components are not only associated with increased cancer incidence but may also adversely affect treatment response and survival outcomes.^11–13 For instance, hyperglycemia, insulin resistance, and dyslipidemia have been linked to reduced chemotherapy sensitivity and poorer prognosis in several solid tumors, potentially through mechanisms involving chronic inflammation, oxidative stress, and activation of insulin-like growth factor–related signaling pathways.^14–16 However, despite these findings, the impact of metabolic syndrome on chemotherapy resistance and survival outcomes in epithelial ovarian cancer remains unclear.

Recent studies have investigated the association between metabolic syndrome and ovarian cancer; however, the available evidence remains limited and inconsistent.^17,18 Previous research has largely focused on the role of metabolic syndrome in cancer risk and tumor progression, with relatively few studies examining its impact on chemotherapy response and long-term survival in patients with epithelial ovarian cancer.^19–21 Furthermore, most existing studies have not comprehensively evaluated whether metabolic syndrome independently predicts chemotherapy resistance and prognosis in epithelial ovarian cancer after controlling for confounding factors. Therefore, the present study aimed to evaluate whether metabolic syndrome was independently associated with chemotherapy resistance and adverse survival outcomes in patients with epithelial ovarian cancer after adjustment for clinically relevant confounding factors. This study may extend previous work and provide more robust clinical evidence to clarify the prognostic and therapeutic relevance of metabolic syndrome in ovarian cancer, thereby facilitating individualized treatment approaches.

Materials and Methods

Study Population

A total of 745 ovarian cancer patients treated at the Department of Obstetrics and Gynecology, The First Affiliated Hospital of Chengdu Medical College, between January 2019 and December 2021. At last, 513 patients were included in the study based on the following inclusion and exclusion criteria, and the patient enrollment process was displayed in Figure 1. Inclusion criteria were as follows: (1) confirmed diagnosis of epithelial ovarian cancer through pathological assessment; (2) presence of metabolic syndrome at the time of enrollment; (3) treatment involving cytoreductive surgery followed by standard platinum-based chemotherapy; and (4) availability of complete medical records and follow-up data. Patients were excluded if they had other coexisting malignancies or failed to complete chemotherapy due to severe complications. Written informed consent was obtained from all participants before the commencement of the study. This study was conducted following the ethical principles of the Declaration of Helsinki and received approval from the Ethics Committee of The First Affiliated Hospital of Chengdu Medical College (No. 23463457).

Figure 1 The flow chart of patient enrollment.

Patient Treatment and Data Collection

All included patients underwent cytoreductive surgery followed by 6–8 cycles of platinum-based chemotherapy, and some of them received maintenance therapies. Preoperative fasting venous blood samples were collected 1–3 days before surgery and analyzed within 2 hours. Demographic data, clinicopathological parameters, and preoperative laboratory test results were extracted from medical records.

Diagnosis of Metabolic Syndrome

Metabolic syndrome was assessed at the time of admission, prior to the initiation of ovarian cancer treatment. As defined by the International Diabetes Federation (IDF), a diagnosis of Metabolic Syndrome requires the presence of central obesity in combination with any two of the following four criteria: (1) elevated triglyceride levels, equal to or exceeding 1.7 mmol/L (150 mg/dL); (2) decreased high-density lipoprotein (HDL) levels which are less than 1.03 mmol/L (40 mg/dL) in men and less than 1.29 mmol/L (50 mg/dL) in women, or undergoing specific treatment for dyslipidemia; (3) increased blood pressure, indicated by a systolic reading of at least 140 mmHg or a diastolic reading of at least 90 mmHg, or current use of antihypertensive medication; and (4) elevated fasting plasma glucose levels of 5.6 mmol/L (100 mg/dL) or more, or a previous diagnosis of type 2 diabetes mellitus.⁹ Central obesity was defined using ethnicity-specific IDF thresholds for Asian populations, with waist circumference ≥90 cm in men and ≥80 cm in women.

Definition of Variables

Metabolic syndrome was treated as a binary variable (present vs absent) based on the International Diabetes Federation criteria. Chemotherapy response was categorized as chemotherapy-sensitive versus chemotherapy-resistant according to standard clinical definitions (recurrence ≤6 months). Other covariates, including age and BMI, were analyzed as continuous variables, while clinicopathological characteristics such as FIGO stage, histological subtype, and surgical completeness were included as categorical variables. Surgical completeness was categorized according to the degree of cytoreduction, complete cytoreduction: no macroscopic residual disease; optimal cytoreduction: residual disease ≤1 cm; suboptimal cytoreduction: residual disease >1 cm.

Patient Follow-Up

Patient follow-up was conducted through telephone interviews and outpatient consultations. Assessments during follow-up included physical examinations, imaging studies such as computed tomography, and evaluation of tumor markers as clinically indicated. Overall survival was defined as the duration from the date of diagnosis until death from any cause or the date of the last follow-up. Progression-free survival was measured from the initiation of treatment until disease recurrence, as confirmed by imaging studies or elevated serum tumor marker levels. The last follow-up ended in January 2025, and the median follow-up time was 58 months. The chemotherapy responses were assessed following the guidance of the Response Evaluation Criteria in Solid Tumors.²² Patients were categorized as chemotherapy-sensitive if they demonstrated a significant response to initial platinum-based chemotherapy, achieved clinical remission, or experienced disease recurrence more than six months after treatment completion. Conversely, chemotherapy resistance was defined as disease progression or relapse within six months after completion of initial chemotherapy, despite an initially favorable response.²²

Statistical Analysis

Statistical evaluations were conducted using SPSS software (SPSS Inc., Chicago, IL, USA). Data were expressed as frequencies and percentages for categorical variables, and as means with standard deviations (mean ± SD) for continuous variables. The Chi-square test or Fisher’s exact test was applied for analyzing categorical data, whereas the Student’s t-test or Mann–Whitney U-test was used for continuous data, depending on the distribution.

To identify prognostic indicators, both univariate and multivariate analyses were carried out using the Cox proportional hazards model. Covariates included in the multivariable Cox proportional hazards regression models were selected based on clinical relevance and previously established prognostic importance in epithelial ovarian cancer, including age, histological subtype, FIGO stage, surgical completeness, chemotherapy response, and maintenance therapy. Multicollinearity among candidate variables was assessed using variance inflation factors, and no substantial multicollinearity was observed. The proportional hazards assumption was evaluated for all variables included in the Cox models by inspection of log-minus-log survival plots. We performed a post hoc power assessment for the Cox proportional hazards models using Schoenfeld’s method. Overall survival and progression-free survival were estimated using the Kaplan–Meier method, and differences between groups were assessed with the Log rank test. Statistical significance was defined as a two-tailed p-value less than 0.05.

Results

Patient Characteristics

A total of 513 patients diagnosed with epithelial ovarian cancer were included in this study. Patient demographics and clinical characteristics are summarized in Table 1. The median age at diagnosis was 53 years old, and the median body mass index (BMI) was 27 kg/m². Among participants, 128 (24.95%) had a history of diabetes, 191 (37.23%) had hypertension, and 116 (22.61%) had dyslipidemia. Metabolic syndrome was identified in 180 patients, representing 35.09% of the study cohort.

Table 1 Patient Characteristics

Histopathological analysis showed that 282 patients (54.97%) had serous carcinoma, 126 (24.56%) had mucinous carcinoma, 69 (13.45%) had endometrioid carcinoma, and 36 (7.02%) had clear cell carcinoma. According to FIGO staging, 38 (7.41%) patients were classified as stage I, 128 (24.95%) as stage II, 200 (38.99%) as stage III, and 147 (28.65%) as stage IV. Surgical completeness included complete cytoreduction in 300 patients (58.48%), optimal cytoreduction in 147 (28.65%), and suboptimal cytoreduction in 66 (12.87%). Regarding chemotherapy response, 350 (68.23%) patients were classified as chemotherapy-sensitive, while 163 (31.77%) patients demonstrated chemotherapy resistance. A total of 102 (19.88%) patients received maintenance therapy, while 411 (80.12%) patients did not receive maintenance therapy.

Associations Between Patient Characteristics and Metabolic Syndrome

Based on the presence or absence of metabolic syndrome, the study patients were divided into two groups: the metabolic syndrome group and the non-metabolic syndrome group. Table 2 summarizes the patient characteristics associated with metabolic syndrome. Compared to patients without metabolic syndrome, those with metabolic syndrome exhibited significantly higher BMI (p < 0.0001) and more advanced FIGO stages (p < 0.0001). Additionally, metabolic syndrome was significantly associated with diabetes (p < 0.0001), hypertension (p = 0.035), dyslipidemia (p < 0.0001), and chemotherapy response (p = 0.032). However, no significant associations were found between the two groups regarding age (p = 0.623), tumor histological subtype (p = 0.432), surgical completeness (p = 0.397), or maintenance therapy (p = 0.329).

Table 2 Associations Between Patient Characteristics and Metabolic Syndrome

Associations Between Patient Characteristics and Chemotherapy Response

The study cohort was categorized into two groups, including the chemotherapy-resistant group and the chemotherapy-sensitive group based on chemotherapy response. The results indicated that metabolic syndrome (p = 0.032), histological subtype (p < 0.0001), FIGO stages (p < 0.0001), surgical completeness (p = 0.0002), and maintenance therapy (p = 0.0014) were significantly associated with chemotherapy response (Table 3). However, no significant associations were found between the two groups regarding age (p = 0.302), BMI (p = 0.271), diabetes (p = 0.610), hypertension (p = 0.297), or dyslipidemia (p = 0.348) (Table 3).

Table 3 Associations Between Patient Characteristics and Chemotherapy Response

Identification of Risk Factors for Overall Survival and Progression-Free Survival of Ovarian Cancer Patients

To identify prognostic factors for ovarian cancer patients, univariate and multivariate Cox proportional hazards regression models were employed. The univariate analysis revealed that metabolic syndrome, tumor FIGO stage, surgical completeness, chemotherapy response, and maintenance therapy were all significantly associated with overall survival (Table 4). In the subsequent multivariate analysis, adjusted for potential confounding variables including age, histological subtype, FIGO stage, surgical completeness, and maintenance therapy, it identified metabolic syndrome (HR = 3.16; 95% CI: 2.11–5.24; p = 0.002) and chemotherapy resistance (HR = 4.83; 95% CI: 2.42–6.03; p < 0.0001) as independent risk factors for overall survival (Table 4). Additionally, progression-free survival was evaluated, it also identified metabolic syndrome (HR = 3.31; 95% CI: 2.02–5.53; p = 0.001) and chemotherapy resistance (HR = 2.95; 95% CI: 2.13–5.47; p < 0.0001) as independent risk factors for progression-free survival (Table 5).

Table 4 Identification of Risk Factors for Overall Survival Using Univariate and Multivariate Cox Proportional Hazards Regression Model

Table 5 Identification of Risk Factors for Progression-Free Survival Using Univariate and Multivariate Cox Proportional Hazards Regression Model

Ovarian Cancer Patients with Metabolic Syndrome Exhibit Reduced Overall and Progression-Free Survival

Kaplan–Meier analyses were performed to compare survival outcomes between ovarian cancer patients with and without metabolic syndrome (Figure 2). The results demonstrated that patients with metabolic syndrome had significantly decreased overall survival (HR = 4.13; 95% CI: 2.32–6.25; p = 0.002) and progression-free survival (HR = 3.54; 95% CI: 1.27–5.68; p = 0.001) compared to those without metabolic syndrome (Figure 2).

Figure 2 Kaplan–Meier curves for overall survival (A) and progression-free survival (B) of ovarian cancer patients with metabolic syndrome and without metabolic syndrome. The statistical differences between groups were assessed by the Log rank test and a p-value less than 0.05 was determined as statistical significance.

Discussion

In the present study, we demonstrated a significant association between metabolic syndrome and chemotherapy resistance, as well as poor prognosis, in patients with epithelial ovarian cancer. Specifically, our findings indicated that ovarian cancer patients with metabolic syndrome exhibited significantly shorter overall and progression-free survival, and chemotherapy resistance compared to patients without metabolic syndrome. These results underscore the clinical relevance of metabolic syndrome as a potential prognostic marker and therapeutic target for ovarian cancer management, suggesting it may be considered to integrate metabolic evaluation into clinical practice.

Metabolic syndrome is characterized by central obesity, hypertension, dyslipidemia, insulin resistance, and hyperglycemia, and has increasingly been recognized as a contributor to cancer initiation and progression.^23–26 Epidemiological evidence consistently suggests that metabolic syndrome and its individual components substantially elevate the risk of various malignancies, including breast, colorectal, and pancreatic cancers.^26–28 Mechanistic studies further support these observations, implicating chronic inflammation, oxidative stress, insulin-like growth factor (IGF) signaling alterations, and disruptions in lipid metabolism associated with metabolic syndrome as potential drivers of tumorigenesis and progression.

Our findings align with prior research on the influence of metabolic syndrome on ovarian cancer progression, as evidenced by advanced tumor FIGO stages. A case-control study by Ying Chen et al reported that metabolic syndrome significantly increased the risk of ovarian cancer, advanced disease stages, and tumor aggressiveness, underscoring the complex interaction between metabolic syndrome and cancer progression.¹⁷ Similarly, studies of lipid metabolism have shown that lipid-related indices may be associated with chemoresistance and prognosis in epithelial ovarian cancer.¹⁹ In contrast, a recent meta-analysis summarized by Ziyu Chen et al found no statistically significant association between metabolic syndrome and ovarian cancer risk; however, their analysis included only five studies, potentially introducing selection bias and limiting generalizability.¹⁸ Collectively, these studies, alongside our own, emphasize the importance of metabolic syndrome as a contributing factor in tumorigenesis and ovarian cancer progression.

In this study, we also observed a significant correlation between metabolic syndrome and chemotherapy resistance which is an essential determinant of clinical outcomes in ovarian cancer. The mechanism underlying this association likely involves multiple interconnected pathways. Elevated insulin and IGF-1 levels, common in insulin-resistant states, activate oncogenic signaling cascades such as the phosphoinositide 3-kinase PI3K/Akt pathway, enhancing cell proliferation, promoting survival, and inhibiting apoptosis, thus contributing to chemotherapy resistance.^29–31 Additionally, dyslipidemia in metabolic syndrome may alter cell membrane fluidity and drug uptake, thereby influencing the intracellular drug accumulation and efficacy of chemotherapies.³² Consistent with this hypothesis, previous studies indicated that patients with disrupted lipid metabolism experienced poorer responses to platinum-based chemotherapy, highlighting the potential therapeutic value of addressing metabolic disturbances in conjunction with conventional chemotherapy.

Furthermore, obesity-related chronic inflammation which is characterized by elevated pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and C-reactive protein (CRP) that may exacerbate tumorigenesis and chemotherapy resistance.³³ Inflammatory signaling pathways, including the NF-κB pathway, have been documented to upregulate multidrug resistance genes and anti-apoptotic mechanisms, consequently diminishing chemotherapy efficacy.³⁴ Therefore, the chronic inflammatory environment promoted by obesity and metabolic syndrome likely constitutes an additional critical factor underlying the observed association between metabolic syndrome and chemotherapy resistance in ovarian cancer.

Our multivariate analyses identified metabolic syndrome as an independent predictor of poorer overall survival and progression-free survival, even after adjusting for established confounders, such as age, histological subtype, FIGO stage, surgical completeness, and maintenance therapy. These results support the hypothesis that metabolic syndrome independently modulates treatment responses and clinical outcomes. Early identification and management of metabolic syndrome may thus potentially improve treatment responses and survival outcomes in ovarian cancer patients through personalized therapeutic strategies combining metabolic interventions with conventional oncological treatments.

Our findings hold important implications for the management of ovarian cancer patients. Routine screening for metabolic syndrome at ovarian cancer diagnosis could provide clinicians with crucial prognostic information, facilitating more informed therapeutic decision-making. Furthermore, interventions targeting metabolic syndrome including lifestyle modifications and pharmacological therapies aimed at insulin resistance, obesity, hypertension, and dyslipidemia could potentially enhance chemotherapy sensitivity and improve clinical outcomes. For example, previous studies have demonstrated the beneficial effects of metformin in reducing ovarian cancer risk and improving prognosis in ovarian cancer patients through its insulin-sensitizing and anti-inflammatory properties.^35,36

This study has several important strengths. First, we employed rigorous and standardized diagnostic criteria for metabolic syndrome, ensuring accurate characterization of metabolic disturbances in our patient population. Second, comprehensive multivariate analyses accounted for numerous potential confounders, robustly validating metabolic syndrome as an independent predictor of clinical outcomes. Lastly, our evaluation of both chemotherapy resistance and survival outcomes provides a holistic assessment of the clinical significance of metabolic syndrome in ovarian cancer.

Nevertheless, some limitations of this study should be acknowledged. Its retrospective design could introduce selection biases, limiting the ability to infer causality from our findings. Despite adjustment for related confounding factors in the multivariate analyses, residual confounding cannot be completely excluded. Furthermore, the relatively limited sample size from a single institution may restrict the generalizability of our conclusions to broader populations. Prospective, multicenter studies involving larger, diverse cohorts are therefore necessary to confirm and extend these observations. Additionally, although our study highlights significant associations, detailed mechanistic investigations are warranted to elucidate the precise biological pathways linking metabolic syndrome to chemotherapy resistance and poor prognosis in ovarian cancer.

Conclusion

In conclusion, this single-center retrospective observational study demonstrated that metabolic syndrome was significantly associated with chemotherapy resistance and poorer overall and progression-free survival in patients with epithelial ovarian cancer. These findings suggest that metabolic syndrome may have prognostic value and could help identify patients at increased risk of adverse prognostic outcomes. However, because of the observational study design, causal relationships cannot be established. Further prospective multicenter interventional studies are needed to validate these findings and to determine whether targeted metabolic management can improve chemotherapy response and survival in patients with epithelial ovarian cancer.