Inverse association between cardiometabolic index and endometriosis in women of reproductive age: A cross-sectional, population-based study

Endometriosis is a gynecological condition characterized by the ectopic presence of endometrial-like tissue outside the uterine cavity, 1 triggering a persistent estrogen-mediated inflammatory response 2 and predominantly affecting pelvic structures, 3 particularly the ovaries. This condition arises from retrograde menstruation and the subsequent survival of endometrial fragments within the pelvic cavity, 4 and it manifests primarily as chronic pelvic pain 5 , 6 and infertility. 7 Despite ongoing uncertainties regarding its etiology and natural course, 8 its pathogenesis involves a complex interplay of hormonal, neurogenic, and immunological mechanisms. 2 , 8 Emerging research suggests that endometriosis is a systemic disorder extending beyond the reproductive organs, 9 with potential effects on mental health, 10 metabolic functions, autoimmune diseases, carcinogenic risk, and cardiovascular health. Therapeutic strategies for affected women primarily aim to improve quality of life through pharmacological interventions and surgical procedures. The cardiometabolic index (CMI) is a key metric for assessing central obesity and has substantial relevance to overall health. 11 Zhou et al. introduced the concept of CMI in 2015, 12 providing a novel tool to evaluate obesity-related health risks. CMI is derived by multiplying the triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C) ratio by the waist-to-height ratio (WHtR). 13 – 16 It has proven effective in predicting diabetes risk among Japanese and Chinese populations. 17 , 18 Among various indicators, CMI stands out as a precise and reliable measure for identifying the risk of metabolic syndrome in obese women. 16 Individuals with lipid metabolism disorders often experience progressive declines in metabolic health over time. 12 Therefore, careful monitoring of lipid metabolism abnormalities in women with endometriosis is crucial for reducing disease burden and improving quality of life. 12 Despite extensive research on endometriosis, modifiable and clinically accessible biomarkers that capture both adiposity and lipid-related metabolic dysfunction remain underexplored. Emerging evidence indicates that women with endometriosis have a higher prevalence of central obesity, atherogenic dyslipidemia, and chronic estrogen-mediated inflammation—the components integrated by CMI. Because CMI incorporates the WHtR (reflecting visceral fat) and the TG/HDL-C ratio (reflecting adverse lipid metabolism), it simultaneously quantifies two pathophysiological processes suspected to influence endometrial implantation, angiogenesis, and disease progression. Furthermore, CMI is superior to body mass index (BMI) or single lipid markers in predicting incident diabetes and cardiovascular events, suggesting that it may also more sensitively detect the subtle metabolic perturbations associated with endometriosis. However, no epidemiological study has specifically examined the association between CMI and endometriosis risk. Addressing this gap could identify a readily available and inexpensive screening metric linking systemic metabolism with gynecological disease, opening new avenues for lifestyle-based prevention. To investigate this, we analyzed a nationally representative sample of US women of reproductive age to test the hypothesis that higher CMI is associated with a lower prevalence of endometriosis. 19

As shown in Table 1 , the study cohort included 1156 participants with a mean age of 35.73 ± 9.98 years. Among these, 96 individuals (8.30%) self-reported a diagnosis of endometriosis. Of those with endometriosis, 49.57% were Non-Hispanic White. Compared with the control group, participants with endometriosis had a significantly higher CMI (mean: 0.92 ± 1.80 vs. 0.64 ± 0.72) and a higher prevalence of alcohol consumption (71.88% vs. 61.51%). Additionally, the endometriosis group was characterized by older age, higher PIR, and higher BMI. This group also had a higher proportion of individuals with lower educational level, greater number of individuals with single status, more frequent OC use, and fewer reported pregnancies. However, differences in racial demographics, age at menarche, and body weight were not statistically significant between participants with and without endometriosis. Detailed data are provided in Table 1 . Characteristics of the participants in NHANES (2005–2006). Mean ± standard deviation (SD) for continuous variables; p values were calculated using weighted linear regression models. Percentages are shown for categorical variables; p values were calculated using weighted chi-square tests. NHANES: National Health and Nutrition Examination Survey; BMI: body mass index; PIR: poverty income ratio; CMI: cardiometabolic index. Table 2 presents the associations of CMI, age, race, and PIR with endometriosis risk, estimated using three sequentially weighted logistic regression models with incremental covariate adjustment. Association between the covariates and odds of endometriosis. Model 1: No covariates adjusted. Model 2: Adjusted for age, race, and PIR but not for the covariate itself. Model 3: Adjusted for age, age at menarche, alcohol use, BMI, weight, educational level, marital status, PIR, history of pregnancy, race, birth control pills, and smoking status. p values were calculated using logistic regression analysis. OR: odds ratio; CI: confidence interval; CMI: cardiometabolic index; BMI: body mass index; PIR: poverty income ratio. A consistent, statistically significant inverse association between CMI and endometriosis was observed across all models. In unadjusted Model 1, each 1-unit increase in CMI was associated with an 18% reduction in the odds of endometriosis (odds ratio (OR) = 0.82, 95% confidence interval (CI): 0.70–0.95). This relationship remained robust after adjusting for age, race, and PIR in Model 2 (OR = 0.82, 95% CI: 0.70–0.95), and the protective effect was further strengthened in fully adjusted Model 3 (adjusted for age, age at menarche, alcohol consumption, BMI, weight, educational level, marital status, PIR, pregnancy history, race, OC use, and smoking status) (OR = 0.58, 95% CI: 0.38–0.91, p  < 0.05). CMI quartile stratification (Quartile 1 as reference) demonstrated a graded protective effect, most pronounced in Quartile 4. In Model 3, women in Quartile 4 had 51% lower odds of endometriosis than those in Quartile 1 (OR = 0.49, 95% CI: 0.29–0.83). Associations for Quartiles 2 (OR = 0.85, 95% CI: 0.46–1.57) and 3 (OR = 0.68, 95% CI: 0.36–1.29) were not statistically significant. Age was a positive predictor of endometriosis risk in Model 3. Women aged 30–34 years had the highest risk (OR = 3.21, 95% CI: 1.47–6.93 vs. 20–29 years), followed by those aged 35–40 years (OR = 2.38, 95% CI: 1.09–5.19). The elevated risk for women aged 41–54 years was not statistically significant (OR = 1.71, 95% CI: 0.80–3.67). Regarding race (non-Hispanic White as reference), non-Hispanic Black women had slightly higher odds of endometriosis (OR = 1.29, 95% CI: 0.63–2.65), while Mexican American (OR = 0.55, 95% CI: 0.25–1.22), Other Hispanic (OR = 0.81, 95% CI: 0.41–1.60), and Other races (OR = 0.67, 95% CI: 0.28–1.59) had lower odds. None of these associations were statistically significant. No significant association was observed between PIR and endometriosis risk. Compared with the low PIR group, medium (OR = 1.05, 95% CI: 0.61–1.82) and high (OR = 1.15, 95% CI: 0.67–1.98) PIR groups showed negligible and nonsignificant differences in endometriosis odds ( p  > 0.05 for all). Participants were stratified into subgroups according to agerace, PIR, marital status, alcohol consumption, BMI, history of pregnancy, OC use, educational level, and age at menarche. After adjusting for potential confounders, an inverse association between CMI and endometriosis was consistently observed across all subgroups. This finding highlights a robust relationship that remains stable despite variations in confounding factors, confirming the reliability of the observed association ( Figure 2 ). ORs and 95% CIs for the association between CMI and endometriosis across 10 subgroups: age, race/ethnicity, PIR, marital status, alcohol use, BMI, pregnancy history, OC use, educational level, and age at menarche. All analyses were adjusted for the full covariate set: age, age at menarche, alcohol use, BMI, weight, education, marital status, PIR, pregnancy history, race/ethnicity, OC use, and smoking status. Weighted logistic regression was used to test interaction terms between CMI and each stratifying variable. A p for interaction >0.05 indicates that the inverse CMI–endometriosis association was consistent across subgroups. OR: odds ratio; CI: Confidence interval; CMI: cardiometabolic index; OC: oral contraceptive; PIR: poverty income ratio; BMI: body mass index. Regarding the mediating role of age, the standard error of the mean (SEM) results revealed a complete indirect pathway through which CMI influences endometriosis, with each step showing statistically significant associations. CMI was significantly and negatively correlated with age (β = −0.12, p  < 0.05), indicating that women with higher CMI levels were generally younger. Age was significantly and positively correlated with endometriosis risk (β = 0.21, p  < 0.05). Specifically, as age increased, the odds of developing endometriosis also increased. Using 5000 bootstrap samples, we observed a statistically significant negative indirect effect (indirect effect β = −0.025; 95% CI: −0.042 to −0.006). This confirms that age significantly mediates the relationship between CMI and endometriosis. In other words, higher CMI levels are associated with younger age, which in turn is associated with lower endometriosis risk. Therefore, age acts as a mediating bridge transmitting the protective effect of CMI. OC use significantly moderated the CMI–endometriosis association (β = 0.09, p  < 0.05), suggesting that the strength of the inverse relationship between CMI and endometriosis depends on OC use.

In this cross-sectional observational study, we identified a significant inverse correlation between higher CMI and lower endometriosis prevalence, with findings remaining robust after adjusting for relevant covariates. First proposed by Wakabayashi in 2015, 22 CMI is a precise metric for identifying susceptibility to metabolic syndrome in obese women, 15 thoroughly assessing abdominal adiposity and dyslipidemia. 23 It correlates more closely with metabolic disorders than conventional metrics (e.g. BMI and waist circumference). 24 , 25 Our observed inverse association between CMI and endometriosis aligns with prior evidence linking endometriosis to metabolic abnormalities. 8 , 26 Mu et al. 8 and Chen et al. 27 reported a higher risk of hypercholesterolemia and elevated LDL levels in patients with endometriosis, while Stock et al. 28 linked obesity markers to reproductive anomalies, including endometriosis. CMI, a key obesity-related indicator, 29 , 30 may share pathogenic pathways with endometriosis. 31 A plausible mechanism involves chronic inflammation, as observed in both endometriosis and cardiovascular diseases. 30 , 32 , 33 Long-term disease effects or hormone therapy may exacerbate metabolic perturbations, further modulating endometriosis risk. 34 , 35 Consistent with previous studies, 36 , 37 age may influence the risk of endometriosis through cumulative hormonal exposure and age-related changes in reproductive and immune function, which may partly explain the associations observed in this study. OC use is correlated with higher endometriosis prevalence, likely reflecting symptomatic treatment rather than a causal effect. 38 , 39 The long-term effects of OC use on disease progression or recurrence as well as the impacts of formulation and dosage require further study. 40 The cross-sectional design precludes the establishment of temporal relationships, and treatment bias may exist. Prospective studies recording details of OC initiation are needed to disentangle this association. It is important to emphasize that the findings of this study are preliminary. As a cross-sectional observational study, we identified only an association between CMI and endometriosis and cannot establish a causal relationship or determine the direction of the association. The observed inverse correlation may be influenced by unmeasured confounding factors (e.g. detailed hormonal profiles, genetic predispositions, and long-term lifestyle factors not captured in the NHANES database) that were not fully adjusted for in the analysis. Additionally, the self-reported nature of endometriosis diagnosis may introduce information bias, which could affect the robustness of the observed association. Future prospective cohort studies with long-term follow-up are urgently needed to validate the causal relationship between CMI and endometriosis incidence. Such studies should collect more detailed data on potential confounders, including comprehensive hormonal measurements, genetic markers, and longitudinal lifestyle assessments, to better clarify the underlying mechanisms. Furthermore, experimental studies (e.g. animal models or in vitro experiments) could help elucidate the biological pathways through which CMI-related metabolic perturbations interact with endometrial implantation and disease progression, thereby translating these preliminary epidemiological findings into evidence-based insights for clinical practice. These findings extend prior single-factor studies by highlighting the multifactorial nature of endometriosis risk and underscore the importance of integrative analytical frameworks in future research. Hormonal intervention status, such as OC use, may influence the relationship between metabolic status and endometriosis, reflecting the complexity of its pathogenic mechanisms. In clinical practice, integrating metabolic indicators with reproductive history may help inform more personalized risk assessment strategies. In conclusion, our findings highlight the complex interplay among metabolic status, age, and hormonal interventions in endometriosis risk and pathogenesis. Future prospective studies are warranted to validate causality, explore mechanisms, and inform personalized strategies.

In this cross-sectional, population-based sample, higher CMI was associated with lower odds of self-reported endometriosis. These findings, although novel, are preliminary and suggest that cardiometabolic factors may play a role in endometriosis prevalence. However, because of the inherent limitations of the cross-sectional design, causal inferences are not warranted, and longitudinal studies with rigorous designs are needed before any clinical recommendations can be made.

This study is a cross-sectional, retrospective analysis of publicly available, deidentified National Health and Nutrition Examination Survey (NHANES) data collected from 2003 to 2006. NHANES, conducted by the Centers for Disease Control and Prevention, is a comprehensive repository of health- and nutrition-related data representative of the US population. Analyses were conducted in accordance with the Helsinki Declaration of 1975, as revised in 2024. All NHANES participants provided written informed consent. Participant identifiers were removed prior to analysis, and no information that could enable identification of individual participants is presented. Reporting of this study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. 20 , 21 We analyzed a subset of 1156 women aged 20–54 years, recruited from diverse US communities. Of the 20,470 individuals enrolled in NHANES during this period, males and those outside the specified age range were excluded, reducing the pool to 17,269 participants. Further exclusions were applied to participants without CMI data (n = 1535) and those lacking information on endometriosis (n = 455). Participants with incomplete demographic profiles, unrecorded alcohol consumption, or missing reproductive health data (n = 55) were also excluded. The final analytical cohort comprised 1156 participants, each carefully vetted to ensure data integrity and relevance ( Figure 1 ). Flow chart of participant selection. NHANES: National Health and Nutrition Examination Survey. WHtR is defined as waist circumference in centimeters divided by height in centimeters and serves as a measure of central obesity. The TG/HDL-C ratio quantifies the balance between these two blood lipids. CMI is calculated by multiplying these two metrics. Participants were stratified into four quartiles based on CMI values: (a) the first quartile (Q1) included those with a CMI ≤0.23; (b) the second quartile (Q2) included those with CMI values between 0.23 and 0.38; (c) the third quartile (Q3) included those with CMI values between 0.38 and 0.58; and (d) the fourth quartile (Q4) included those with CMI values >0.82. 18 The presence of endometriosis among participants was determined based on their responses to question “RHQ360,” which was administered as part of the Mobile Examination Center (MEC) protocol. This question was as follows: “Has a physician or healthcare provider ever informed you that you have endometriosis?” Affirmative responses indicated a confirmed diagnosis of endometriosis, while negative responses indicated the absence of the condition. Demographic covariates included age, age at menarche, alcohol use, BMI, weight, educational level, marital status, poverty income ratio (PIR), history of pregnancy, race, oral contraceptive (OC) use, and smoking status. Marital status was categorized into two groups: married or cohabiting with a partner and widowed, divorced, or separated. Race was divided into five groups: Mexican American, Non-Hispanic Black, Non-Hispanic White, Other Hispanic, and Other races. Educational background was recorded, with classifications ranging from less than high school education to high school graduates and individuals with advanced degrees. Alcohol consumption, history of pregnancy, and OC use were dichotomously categorized as yes or no. OC use, a key covariate, was assessed using two items from the NHANES Reproductive Health Questionnaire (RHQ): RHQ131: “Have you ever used birth control pills (oral contraceptives)?” (Responses: Yes/No/Don’t know/Refused) and RHQ420: “How old were you when you first started using birth control pills?” (Responses: Age/Never used/Don’t know/Refused). Participants were classified as OC users if they answered “Yes” to RHQ131, regardless of RHQ420 responses. Those who answered “No” to RHQ131 or “Never used” to RHQ420 were classified as nonusers. Responses of “Don’t know” or “Refused” (n = 3, <0.3%) were excluded to ensure unambiguous classification. This binary grouping (“ever used” vs. “never used”) aligns with the study’s focus on the moderating effect of OC use. PIR was categorized into three NHANES-derived groups: low (<1.35), medium (1.35–2.99), and high (≥3.0). Smoking status was dichotomized as “never smoker” versus “ever smoker,” the latter combining former and current smokers. Alcohol consumption was categorized as “nondrinker” (0 drinks/day) versus “drinker” (any positive daily ethanol intake recorded in the 24-h recall). Due to the complex sampling design of NHANES, accounting for standard errors is essential to accurately reflect differences between the sampled population and the broader US population. Continuous variables were presented as means with their standard errors, whereas categorical variables were reported as counts with corresponding percentages. Comparisons of continuous variables across groups were performed using analysis of variance, and the chi-squared test (χ 2 ) was applied to categorical variables. To ensure the integrity and reliability of the results, all st analyses were conducted using complex sampling–weighted methods with NHANES-recommended weights. Our analytical framework comprised three progressive models to enhance the robustness of the findings: (a) Model 1, an unadjusted model providing a baseline comparison; (b) Model 2, adjusted for a selected set of covariates; and (c) Model 3, a comprehensive model incorporating all covariates. Statistical computations were performed using R software (version 4.2) or the EmpowerStats platform (version 5.0) to ensure accuracy and reproducibility. To investigate complex interrelationships among study variables, mediation and moderation analyses were conducted using the PROCESS macro (Model 5). Endometriosis was specified as the dependent variable and CMI as the independent variable. Age was evaluated as a potential mediator and OC use as a potential moderator. Bootstrap resampling with 5000 iterations was applied to assess the robustness of the estimated effects. Relevant demographic and clinical covariates were included to control for potential confounding. Missing data were addressed using complete-case analysis (list-wise deletion), whereby only participants with complete data for variables included in each analysis were retained. The proportion of missing data was small (<3% for key variables, including CMI, endometriosis diagnosis, and demographic covariates), making it unlikely to materially affect the study results.