Research Article

Association between the Modified Cardiometabolic Index and Endometriosis in an American Cohort: Insights from a Cross-sectional Analysis

DOI:

10.3791/69723

⸱

February 20th, 2026

 ,  , 
1Department of Ultrasound, Jiaxing Hospital of Traditional Chinese Medicine, 2School of Medicine, Hunan Normal University, 3Department of Cardiology, The Affiliated Hospital, Southwest Medical University

In This Article

Summary

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This study investigates the association between the modified cardiometabolic index (MCMI) and the risk of endometriosis, aiming to evaluate MCMI's potential as a non-invasive marker.

Abstract

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The modified cardiometabolic index (MCMI), an integrated measure of visceral fat and lipid metabolism, has been widely applied in metabolic disease research. However, its relationship with endometriosis has not been thoroughly investigated. Data were obtained from the 1999-2006 NHANES surveys. Endometriosis was identified through self-reported diagnosis. MCMI was calculated as ln (TG × glucose / HDL-C) × waist / height. Weighted multivariable logistic regression models were used to examine the association between MCMI and endometriosis. Restricted cubic spline (RCS) models were applied to assess potential non-linear relationships. Subgroup analyses were performed to assess whether the association was consistent across population subgroups. A sensitivity analysis using propensity score matching (PSM) was also conducted to evaluate the robustness of the results. After adjustment for all covariates, higher MCMI was significantly associated with the presence of endometriosis (OR = 1.58, 95% CI: 1.16-2.15; P = 0.004). Participants in the highest MCMI tertile had 99% higher odds of endometriosis compared with those in the lowest tertile (OR = 1.99, 95% CI: 1.10-3.62; P = 0.02). A significant increasing trend was observed across tertiles (P for trend = 0.03). RCS analysis confirmed a positive linear association (P for overall effect = 0.004; P for nonlinearity = 0.20). Subgroup analyses revealed no significant interactions, and the sensitivity analysis confirmed the robustness of the association. Higher MCMI was significantly associated with endometriosis, supporting its potential utility as a simple, non-invasive biomarker. Further research using clinically confirmed diagnoses and longitudinal designs is needed to clarify this association.

Introduction

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Endometriosis affects approximately 10% of women of reproductive age globally1,2 and is characterized by the presence of endometrial-like tissue outside the uterus3. This condition leads to chronic inflammation, pelvic pain, and infertility4,5, with a substantial impact on quality of life. Although laparoscopy remains the diagnostic gold standard, its invasive nature limits early detection, particularly among asymptomatic individuals. Reliable non-invasive biomarkers for identifying or stratifying women at risk are still lacking6,7,8.

Recent evidence suggests that metabolic disturbances involving lipid and glucose pathways may be relevant to endometriosis. Epidemiologic studies have linked endometriosis with body mass index (BMI), and genetic loci associated with waist-to-hip ratio (WHR)-adjusted BMI overlap with endometriosis loci, indicating shared pathways related to fat distribution9. Large population studies from the United States and the United Kingdom have also reported higher triglyceride (TG) levels in women with endometriosis10,11. In addition, high-fat dietary patterns have been associated with increased peritoneal inflammation and more severe abdominal pain12. Visceral fat accumulation promotes a state of chronic low-grade inflammation, creating a microenvironment that may facilitate the adhesion, invasion, and survival of ectopic endometrial tissue13,14.

Glucose metabolism may also contribute. Evidence suggests that women with endometriosis exhibit signs of impaired glucose regulation15. Mendelian randomization analyses further indicate that genetically predicted higher fasting insulin is associated with increased endometriosis risk16. Composite metabolic markers that integrate TG and glucose, such as the triglyceride-glucose (TyG) index, have also shown positive associations with endometriosis17. Taken together, these findings point to a broader pattern of metabolic imbalance. Combined disturbances in lipid and glucose metabolism may better characterize the metabolic milieu associated with endometriosis than lipid abnormalities alone.

MCMI was developed as an expanded version of the earlier cardiometabolic index (CMI). It incorporates TG, fasting blood glucose (FBG), high-density lipoprotein cholesterol (HDL-C), waist circumference (WC), and height, thereby adding glycemic status to CMI's original lipid-based components. This expansion allows MCMI to more comprehensively reflect lipid-glucose metabolism and visceral adiposity18. All components of MCMI are obtained from routine laboratory and anthropometric measurements, making it a practical and non-invasive metric for use in both clinical and epidemiologic settings. As disturbances in glucose regulation have also been linked to endometriosis, incorporating glycemic status enables MCMI to capture metabolic features that are biologically relevant to the condition. Although CMI has been associated with endometriosis19 and with metabolic outcomes such as liver disease, cardiovascular disease, and all-cause mortality20,21,22, the relationship between MCMI and endometriosis has not yet been examined.

This study aimed to examine the association between the MCMI and the prevalence of endometriosis in the U.S. population using National Health and Nutrition Examination Survey (NHANES) 1999-2006 data. By evaluating whether MCMI is related to the presence of endometriosis, this study seeks to clarify its potential relevance as a non-invasive indicator and to establish a foundation for future research incorporating clinically confirmed diagnoses.

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Protocol

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Data source

NHANES is a nationally representative surveillance program that evaluates health and nutrition among individuals in the United States. The survey is conducted by the National Center for Health Statistics (NCHS) and relies on a stratified, multistage sampling framework to recruit a population-based cohort. Information is collected through structured interviews, standardized physical examinations, and laboratory assessments. All survey procedures are approved by the NCHS Ethics Review Board, and informed consent is obtained from all participants. Because NHANES releases fully de-identified public-use files, additional institutional ethical approval is not required for secondary analyses such as the present study.

Study population

Data were drawn from the 1999-2006 NHANES cycles because endometriosis information was collected only during these survey years (Figure 1). The initial dataset included 41,474 participants. We first excluded 20,264 male participants, leaving 21,210 eligible participants. We then excluded 15,653 subjects without endometriosis data, 3,088 without MCMI data, and 156 without weight measurements, resulting in a final analytic sample of 2,313 subjects.

Assessment of endometriosis

Endometriosis was assessed through self-report. Participants were asked, "Have you ever been diagnosed with endometriosis by a doctor?" Those who answered "yes" were classified as having endometriosis.

Assessment of MCMI

In this study, MCMI was the independent variable. It was calculated using TG (mg/dL), FBG (mg/dL), HDL-C (mg/dL), WC (cm), and height (cm) according to the following formula18.

MCMI formula; logarithmic calculation; TG, FBG, HDL-C, WC variables; health metric analysis.

Covariates

Following prior literature17, this analysis adjusted for age (years), poverty-income ratio (PIR), race (White, Black, Hispanic, and other), BMI (kg/m²), education level (less than high school, high school graduate, more than high school), smoking status (yes/no), alcohol consumption (yes/no), history of diabetes (yes/no), history of hypertension (yes/no), marital status (married, never married, separated), age at menarche (years), parity (times), and exogenous hormone use (yes/no). BMI was computed as weight in kg divided by height in m2. PIR represented the ratio of annual household income to the federal poverty threshold. Smoking was defined as having smoked ≥100 cigarettes in one's lifetime, and alcohol consumption as having consumed ≥12 alcoholic drinks. Diabetes and hypertension were identified from self-reported physician diagnoses. Exogenous hormone use was defined as the use of female hormonal agents.

Statistical analysis

The NHANES complex sampling framework was taken into account, and survey weights were applied to generate population-representative estimates. Missing covariate values were addressed through multiple imputation. Continuous variables were presented as means with standard errors, whereas categorical variables were summarized as counts and percentages. The relationship between MCMI and endometriosis was examined using weighted multivariable logistic regression, and results were expressed as adjusted odds ratios (ORs) with 95% confidence intervals (CIs).

Potential non-linear associations were examined using weighted restricted cubic spline (RCS) models23,24 fitted with the rms package. Consistent with the default knot placement rule of rms, three knots were positioned at the 10th, 50th, and 90th percentiles of the MCMI distribution. P-values were reported for both the overall association and the non-linearity test. Subgroup analyses were conducted across predefined covariates.

For sensitivity analyses, propensity score matching (PSM) was performed with the MatchIt package using 1:3 nearest-neighbor matching25,26. The propensity score model included age, PIR, race, BMI, education, smoking status, alcohol consumption, diabetes, hypertension, marital status, age at menarche, parity, and exogenous hormone use. No caliper was specified. Matching quality was evaluated based on post-matching covariate balance, and weighted logistic regression was repeated in the matched cohort. Two-sided P-values < 0.05 were considered statistically significant. The R packages used in the analyses are listed in the Table of Materials.

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Results

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Participant characteristics

Table 1 presents the baseline characteristics of participants, categorized into the control group (n = 2,139) and the endometriosis group (n = 174). Analysis revealed that, compared with the control group, the endometriosis group had significantly higher MCMI and mean age. A notable racial difference was observed, with a higher proportion of White individuals in the endometriosis group. Participants in the endometriosis group also h...

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Discussion

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This study is the first to explore the relationship between the MCMI and endometriosis in U.S. women using NHANES data from 1999 to 2006. Our results demonstrate a significant positive association between higher MCMI levels and the prevalence of endometriosis, even after adjusting for multiple confounding factors. These findings indicate a consistent association between higher MCMI levels and endometriosis, suggesting that MCMI may reflect metabolic features characteristic of the condition.

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Disclosures

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The authors declare no competing interests.

Materials

List of materials used in this article
NameCompanyCatalog NumberComments
forestploter package (version 1.1.2)CRANN/AR package used for generating forest plots.
MatchIt package (version 4.5.5)CRANN/AR package used for 1:3 nearest-neighbor propensity score matching.
mice package (version 3.16.0)CRANN/AR package used for multiple imputation of missing covariate data.
NHANES dataset (1999–2006)Centers for Disease Control and Prevention (CDC), USAN/APublic dataset from the National Health and Nutrition Examination Survey (NHANES). Accessed from: https://www.cdc.gov/nchs/nhanes
R software (version 4.5.1)R Foundation for Statistical ComputingRRID:SCR_001905Statistical computing environment used for all analyses.
rms package (version 6.8-1)CRANN/AR package used for restricted cubic spline modeling.
survey package (version 4.4-2)CRANN/AR package used for NHANES survey-weighted regression analyses.

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Tags

Cardiometabolic IndexEndometriosis RiskVisceral FatLipid MetabolismNHANES CohortLogistic RegressionRestricted Cubic SplinePropensity Score MatchingBiomarker AssociationMetabolic Disease

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