Abstract
Background
Endometriosis (EM) is a prevalent gynecological condition impacting roughly 10% of women of reproductive age worldwide, causing chronic pain, infertility, and menstrual irregularities. Traditional diagnosis typically relies on invasive surgical methods, and non-invasive diagnostic techniques remain underdeveloped. This study seeks to investigate the association between creatinine levels and endometriosis through cross-sectional analysis and mendelian randomization (MR) analysis, while also developing and assessing diagnostic models.
Method
This research integrates data from the NHANES database (1999–2006) and the affiliated hospital of Jining Medical College in China. The study cohort consists of women aged 20–60, with data collection covering age, race, education level, marital status, family income, weight, height, body mass index(BMI), and serum creatinine levels. Logistic regression models were used for univariate and multivariate analyses. Bidirectional MR analysis, utilizing genetic variation data from Large Genome Association Studies (GWAS), was performed to evaluate causal relationships using the inverse variance weighted (IVW) method, complemented by sensitivity analysis. A diagnostic model based on data from top-tier hospitals in China was constructed and its performance assessed through receiver operating characteristic(ROC) curves, area under the curve(AUC) values, and calibration curves.
Result
In the NHANES dataset, univariate analysis indicated a significant correlation between creatinine levels and endometriosis (OR = 1.01, 95% CI: 1.00–1.01, P = 0.0048), while multivariate analysis maintained significant results after adjustment (OR = 1.00, 95% CI: 1.00–1.01, P = 0.0431). Bidirectional MR analysis demonstrated a causal relationship between creatinine levels and endometriosis, with a positive IVW result of 1.001 (95% CI: 1.00–1.002, P = 0.0350). In the chinese tertiary hospital dataset, the AUC for the diagnostic model on both training and validation sets were 0.721 and 0.730, respectively. An increase of 10 μmol/L in creatinine levels raised the risk of endometriosis by approximately 8% (OR = 1.08, 95% CI: 1.07–1.09, P < 0.001).
Conclusion
This study establishes a significant link between creatinine levels and endometriosis, confirming creatinine as an independent risk factor. Elevated creatinine levels could be used as non-invasive biomarkers for the early detection and diagnosis of endometriosis. Future research should aim to validate these findings in larger, multicenter studies and delve into the specific biological mechanisms, paving the way for novel therapeutic strategies.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12905-025-03808-x.
Keywords: Endometriosis, Serum creatinine, Mendelian randomization, Diagnostic model
Background
Endometriosis (EM) is a prevalent and severe gynecological condition that affects about 10% of women of reproductive age globally [1]. This disorder is marked by the presence of endometrial-like tissue growing outside the uterus, causing chronic pain, infertility, irregular menstruation, and other associated symptoms [2, 3]. This condition significantly impacts patients' quality of life and increases the use of medical resources and economic burden. The exact cause of endometriosis remains unclear, though it is believed to arise from a complex interplay of genetic, immune, hormonal, and environmental factors [4]. Traditional diagnosis mainly relies on clinical symptoms and invasive procedures such as laparoscopy, with non-invasive diagnostic methods still lacking, making early detection and treatment difficult [5, 6]. Moreover, existing treatments, including medication and surgery, can alleviate symptoms but are often challenging to cure and come with a risk of recurrence. Therefore, finding new biomarkers and therapeutic targets is crucial for improving the management of endometriosis.
Serum creatinine(SCR) is an important metabolic byproduct primarily generated by muscle metabolism and excreted through the kidneys [7, 8]. It is commonly used as an indicator of renal function, but recent studies have begun to explore its potential role in other diseases [8]. Studies have suggested that creatinine levels might be linked to various chronic inflammatory conditions, including chronic kidney disease and cardiovascular diseases [9–11]. In terms of endometriosis, research has shown that inflammation and immune responses play significant roles in the occurrence and development of the disease [12, 13]. Given that creatinine is closely related to metabolic and inflammatory states in the body, it is hypothesized that it may have some association with the pathophysiological processes of endometriosis. However, current research on the relationship between creatinine and endometriosis is still very limited, lacking large-scale epidemiological evidence and mechanistic exploration.
This study aims to investigate the relationship between creatinine and endometriosis through cross-sectional analysis and mendelian randomization(MR) analysis, and to construct and evaluate related diagnostic models based on data from tertiary hospitals in China. We hope that through this research, we can further reveal the potential role of creatinine in endometriosis, providing new insights for diagnosis and treatment.
Methods
Research design and data sources
This study integrates cross-sectional research with bidirectional Mendelian randomization (MR) analysis to investigate the relationship between creatinine levels and endometriosis (EM). The data sources include two main parts: the NHANES database and datasets from tertiary hospitals in China.
NHANES Dataset
Data source
The National Health and Nutrition Examination Survey (NHANES), conducted by the National Center for Health Statistics (NCHS), is a continuous cross-sectional survey that assesses the health and nutritional status of the U.S. population. This study utilizes data collected from NHANES between 1999 and 2006.
Study population
Inclusion criteria encompass all women aged 20–60 who participated in the NHANES survey from 1999 to 2006. Exclusion criteria include:1)participants with missing key variable data, 2)participants previously diagnosed with malignant tumors or severe chronic diseases, 3)pregnant or lactating women.
Variable collection
Variables collected include age, race, education level, marital status, household income, weight, height, body mass index(BMI), and serum creatinine(SCR) levels. Menstrual pattern was derived from self-reported questionnaire responses. It was classified as regular if participants indicated having menstrual cycles every 21–35 days with minimal variability, and irregular if they reported inconsistent or abnormal cycle intervals. The diagnosis of endometriosis is based on self-reported physician diagnosis.
Statistical analysis
Chi-square tests and t-tests compare baseline characteristics across different endometriosis statuses. Logistic regression models adjust for confounding factors to assess the independent association between creatinine levels and endometriosis. Logistic regression models were employed to assess the association between creatinine levels and endometriosis, adjusting for potential confounders. The selection of covariates was based on the following principles. Firstly, we considered the theoretical basis. Covariates such as age, weight, body mass index (BMI), and socioeconomic factors (e.g., race, education level, income) were selected based on existing literature and biological understanding, as these factors may influence both creatinine levels and the risk of endometriosis. Research indicates that these variables likely play an important role in the relationship between the two, and thus they were included in the model as adjusting variables. Secondly, we selected known potential confounders. Age, race, and socioeconomic factors are recognized as confounding factors that could affect both creatinine levels and the risk of endometriosis. Since these factors may influence variations in creatinine levels and the occurrence of endometriosis, they were included in the models for adjustment to reduce their potential confounding effects on the causal relationship. For variable selection, we combined stepwise regression with expert judgment. Stepwise regression helped identify variables that have a significant impact on the relationship between creatinine levels and endometriosis, while expert knowledge ensured that all biologically relevant variables were appropriately considered. The covariates included in the final models were based not only on their theoretical relevance but also on their statistical significance. Finally, to ensure model robustness, we performed collinearity checks. For covariates that exhibited high collinearity, we made adjustments or removed them to avoid interference with model results due to collinearity, thus ensuring the stability and reliability of the regression model. Through these methods, we ensured that the selected covariates adequately accounted for potential confounding effects, providing reliable support for assessing the independent relationship between creatinine levels and endometriosis.
Chinese tertiary hospital dataset
Data source
This study also utilizes data from 14,474 EM cases and healthy controls collected at the Affiliated Hospital of Jining Medical University between 2016 and 2023. The study was approved by the Ethics Committee of the Affiliated Hospital of Jining Medical University (Approval No.: 2022C064). Informed consent was not required due to the retrospective nature of the study.
Study population
Inclusion criteria for the EM group include all women aged 20–60 who underwent surgical treatment for endometriosis and were pathologically diagnosed during the specified period. Exclusion criteria include:1)patients with a history of malignant tumors, liver or kidney disease, hematologic diseases, autoimmune diseases, or metabolic disorders,2)patients on long-term immunomodulatory drugs or glucocorticoids.
Variable collection
Collected variables include age, weight, BMI, serum creatinine levels, and other routine blood indicators. Menstrual pattern was derived from self-reported questionnaire responses. It was classified as regular if participants indicated having menstrual cycles every 21–35 days with minimal variability, and irregular if they reported inconsistent or abnormal cycle intervals.
Diagnostic model construction and evaluation
Model construction
A logistic regression model was constructed incorporating key factors such as creatinine, age, weight, and BMI, which were selected based on existing literature and biological relevance. To refine the model, we performed stepwise regression to identify the most significant predictors, ensuring that only those variables with the highest statistical significance were included. A logistic regression model was chosen for the analysis due to its well-established ability to estimate the probability of binary outcomes and its interpretability, especially when considering the clinical relevance of the model’s predictors.
Collinearity check
We performed collinearity checks using variance inflation factor (VIF) analysis. Variables with a VIF greater than 10 were considered to have high collinearity and were either removed or combined to minimize redundancy and improve model stability.
Overfitting mitigation
To mitigate the risk of overfitting, we employed cross-validation methods by splitting the dataset into training and validation sets. This approach allowed us to assess model performance and generalizability. Additionally, regularization techniques such as Lasso or Ridge regression were applied to penalize the inclusion of less significant predictors, further reducing the potential for overfitting and enhancing the robustness of the model.
Model evaluation
The model's predictive ability and fit were evaluated using receiver operating characteristic(ROC) curves, area under the curve(AUC) values, and calibration curves. Decision curve analysis assessed the model's clinical application potential across different risk thresholds, demonstrating its suitability for real-world clinical use.
Mendelian randomization analysis
In selecting the single nucleotide polymorphisms(SNPs) used for MR analysis, we ensured that the SNPs were strongly associated with serum creatinine levels and had been validated in previous studies. The genetic instruments we used were obtained from large-scale genome-wide association studies (GWAS), where SNPs associated with creatinine levels had been validated in large populations, ensuring a reliable relationship with creatinine levels. To address whether these SNPs were exclusively related to creatinine levels, we specifically checked for pleiotropic effects. SNPs associated with creatinine levels were selected from the Phenoscanner database, and we carefully screened them for any potential pleiotropic effects that might influence other phenotypes, such as kidney function or metabolic traits, which could confound the relationship with endometriosis. Additionally, we employed MR-Egger regression and MR-PRESSO methods to detect and adjust for any potential pleiotropic effects, ensuring the robustness of our MR analysis results. These steps ensure that the SNPs used in our MR analysis are strongly associated with creatinine levels and minimize the influence of pleiotropy, thereby enhancing the validity and reliability of our findings.
This single-sample Mendelian randomization study strictly adhered to three core assumptions: Assumption I: There is a strong association between genetic variants and the exposure (SCR levels). Instrumental variables with strong correlations (P < 1 × 10^−5) and F-statistics greater than 10 were used to minimize bias. Assumption II: Confounding variables influencing the relationship between the exposure and genetic variants should be insignificant. To address this, the sample population was restricted to individuals of European ancestry, and phenotypic information was screened from Phenoscanner to exclude SNPs influencing phenotypes related to the outcome. Assumption III: Genetic variants should only affect the outcome through their association with the exposure. Finally, SNPs that exclude confounding variables will be used for subsequent MR analysis. The genetic instrumental variables (IVs) ultimately used for MR analysis are described in detail in Supplementary Table 1. To maintain this assumption, MR-Egger regression and MR-PRESSO methods were employed to reduce potential horizontal pleiotropy and ensure robust sensitivity analyses.
Data source
The Large Genome Association Study (GWAS) data used in this study was only obtained from the IEU OpenGWAS online database, which provides a wide range of genetic data. The use of data is authorized by the ethics committees of each participating center or relevant country, and all participants have provided written informed consent. Firstly, the data on creatinine comes from a large-scale population genetics study involving 110,051 European participants with a total of 11,590,399 SNPs [14]. Secondly, obtain the GWAS data of EM from the UK Biobank database. The EM study included 1121 cases and 461,889 controls, with a total of 9,851,867 SNPs. In positive MR studies, creatinine is considered an exposure factor, while EM is considered an outcome factor. In reverse MR studies, EM is considered an exposure factor, while creatinine is considered a outcome factor.
Instrumental variable selection
Single nucleotide polymorphisms (SNPs) significantly linked to serum creatinine levels are chosen as instrumental variables. The selection criteria are:1)genome-wide significance level of P < 5 × 10^−5, 2)exclusion of SNPs with linkage disequilibrium (r^2 < 0.005, 1,000 kb) and significant associations with potential confounders.
Statistical analysis
The inverse variance weighted (IVW) method is employed to evaluate the causal relationship between genetically predicted creatinine levels and the risk of endometriosis[15]. Sensitivity analyses, including MR-Egger, weighted median, weighted mode, and MR-PRESSO, are performed to confirm the robustness of the IVW results[16, 17]. Heterogeneity is assessed using Cochrane’s Q test, with adjustments made for detected heterogeneity using random effects IVW analysis[18]. Horizontal pleiotropy of genetic variations is evaluated through the MR-Egger intercept and MR-PRESSO global tests[16].
Statistical software
All analyses are conducted using R software (version 4.1.3) and EmpowerStats software (X&Y Solutions Inc., Boston, MA, USA). A two-sided P-value of less than 0.05 is considered statistically significant.
Results
Cross-sectional analysis
The inclusion and exclusion criteria for the NHANES 1999–2006 dataset are shown in Fig. 1A. Univariate analysis identified a significant link between creatinine levels and endometriosis (OR = 1.01, 95% CI: 1.00–1.01, P = 0.0048, see Table 1). After controlling for confounders such as age, race/ethnicity, education level, marital status, household income, weight, height, and BMI, multivariate analysis confirmed that creatinine is an independent risk factor for endometriosis (adjusted OR = 1.00, 95% CI: 1.00–1.01, P = 0.0431, see Table 2). Further analysis revealed that the association was most pronounced among non-Hispanic whites (OR = 5.07, 95% CI: 2.99–8.61, P < 0.0001), and also significant in non-Hispanic blacks (OR = 2.23, 95% CI: 1.17–4.25, P = 0.0144, see Fig. 2D). This association remained significant across various education levels and marital statuses, suggesting the broad relevance of creatinine as a risk factor.
Fig. 1.
Research flowchart. A is a screening of the population studied in the NHANES dataset. B is a screening of the population studied in the dataset of Jining Medical College Affiliated Hospital. C is a dual sample Mendelian randomization positive study of SCR and EM. D is a dual sample Mendelian randomization reverse study of SCR and EM
Table 1.
Baseline description and univariate analysis of NHANES population
| non-EM Mean ± SD/% |
EM Mean ± SD/% |
Total Mean + SD/% |
EM OR (95%CI) Pvalue |
|
|---|---|---|---|---|
| N | 2460 | 178 | 2638 | |
| Age, years | 35.55 ± 10.10 | 39.72 ± 8.71 | 35.83 ± 10.07 | 1.04 (1.03, 1.06) < 0.0001 |
| Race/Ethnicity, % | ||||
| Mexican American | 682 (27.72%) | 16 (8.99%) | 698 (26.46%) | Reference |
| Other Hispanic | 157 (6.38%) | 3 (1.69%) | 160 (6.07%) | 0.81 (0.23, 2.83) 0.7467 |
| Non-Hispanic White | 1067 (43.37%) | 127 (71.35%) | 1194 (45.26%) | 5.07 (2.99, 8.61) < 0.0001 |
| Non-Hispanic Black | 458 (18.62%) | 24 (13.48%) | 482 (18.27%) | 2.23 (1.17, 4.25) 0.0144 |
| Other Race | 96 (3.90%) | 8 (4.49%) | 104 (3.94%) | 3.55 (1.48, 8.52) 0.0045 |
| Veteran/Military Status, % | ||||
| Yes | 42 (1.71%) | 7 (3.93%) | 49 (1.86%) | Reference |
| No | 2418 (98.29%) | 171 (96.07%) | 2589 (98.14%) | 0.42 (0.19, 0.96) 0.0393 |
| Education Level, % | ||||
| Less than high school | 671 (27.32%) | 21 (11.80%) | 692 (26.27%) | Reference |
| Greater than or equal to high school | 1785 (72.68%) | 157 (88.20%) | 1942 (73.73%) | 2.81 (1.77, 4.47) < 0.0001 |
| Marital Status, % | ||||
| Married | 1303 (56.19%) | 118 (67.82%) | 1421 (57.00%) | Reference |
| Other | 1016 (43.81%) | 56 (32.18%) | 1072 (43.00%) | 0.61 (0.44, 0.85) 0.0030 |
| PIR | 2.61 ± 1.66 | 3.18 ± 1.61 | 2.65 ± 1.66 | 1.23 (1.12, 1.35) < 0.0001 |
| Weight, kg | 75.18 ± 19.61 | 78.31 ± 20.91 | 75.40 ± 19.71 | 1.01 (1.00, 1.01) 0.0412 |
| Height, cm | 161.84 ± 7.10 | 163.69 ± 5.68 | 161.97 ± 7.02 | 1.04 (1.02, 1.06) 0.0007 |
| BMI, kg/m2 | 28.68 ± 7.10 | 29.17 ± 7.34 | 28.71 ± 7.11 | 1.01 (0.99, 1.03) 0.3772 |
| Age at menarche, years | 12.61 ± 1.65 | 12.42 ± 1.66 | 12.60 ± 1.65 | 0.93 (0.85, 1.02) 0.1390 |
| Menstrual pattern, % | ||||
| Yes | 1117 (45.41%) | 43 (24.16%) | 1160 (43.97%) | Reference |
| No | 1343 (54.59%) | 135 (75.84%) | 1478 (56.03%) | 2.61 (1.84, 3.71) < 0.0001 |
| Pregnancy frequency, times | 3.13 ± 1.80 | 2.75 ± 1.42 | 3.11 ± 1.78 | 0.87 (0.78, 0.97) 0.0103 |
| Production frequency, times | 2.32 ± 1.44 | 1.85 ± 1.15 | 2.28 ± 1.43 | 0.76 (0.67, 0.88) 0.0001 |
| The child weighs less than 5.5 pounds at birth, % | ||||
| Yes | 214 (11.23%) | 11 (8.15%) | 225 (11.03%) | Reference |
| No | 1691 (88.77%) | 124 (91.85%) | 1815 (88.97%) | 1.43 (0.76, 2.69) 0.2711 |
| Hysterectomy, % | ||||
| Yes | 198 (17.57%) | 78 (66.10%) | 276 (22.17%) | Reference |
| No | 929 (82.43%) | 40 (33.90%) | 969 (77.83%) | 0.11 (0.07, 0.16) < 0.0001 |
| Oophorectomy, % | ||||
| Yes | 138 (5.62%) | 70 (39.33%) | 208 (7.90%) | Reference |
| No | 2316 (94.38%) | 108 (60.67%) | 2424 (92.10%) | 0.09 (0.07, 0.13) < 0.0001 |
| Tubal surgery, % | ||||
| Yes | 629 (25.62%) | 57 (32.02%) | 686 (26.05%) | Reference |
| No | 1826 (74.38%) | 121 (67.98%) | 1947 (73.95%) | 0.73 (0.53, 1.01) 0.0611 |
| Uterine fibroids, % | ||||
| Yes | 256 (10.44%) | 63 (35.59%) | 319 (12.14%) | Reference |
| No | 2195 (89.56%) | 114 (64.41%) | 2309 (87.86%) | 0.21 (0.15, 0.29) < 0.0001 |
| Use of estrogen and progesterone, % | ||||
| Yes | 266 (10.91%) | 71 (39.89%) | 337 (12.88%) | Reference |
| No | 2173 (89.09%) | 107 (60.11%) | 2280 (87.12%) | 0.18 (0.13, 0.26) < 0.0001 |
| SCR, umol/L | 55.68 ± 22.37 | 69.98 ± 86.28 | 56.65 ± 31.29 | 1.01 (1.00, 1.01) 0.0048 |
Table 2.
Multivariate adjusted logistic regression of SCR on EM risk in NHANES
| Exposure | Model I OR (95% CI) P-value |
Model II OR (95% CI) P-value |
Model III OR (95% CI) P-value |
|---|---|---|---|
| SCR | 1.01 (1.00, 1.01) 0.0048 | 1.00 (1.00, 1.01) 0.0154 | 1.00 (1.00, 1.01) 0.0431 |
Model I no adjusted
Model II adjusted for age(smooth), race/ethnicity, formerly enlisted, education level, marital status, ratio of family income to poverty(smooth), weight(smooth), height(smooth) and BMI(smooth)
Model III adjusted for age at menarche(smooth), menstrual pattern, the child weighs less than 5.5 pounds at birth, hysterectomy, oophorectomy, tubal surger and use of estrogen and progesterone
Fig. 2.
Correlation analysis results between SCR and EM. A presented the NHANES cross-sectional analysis and the relationship between SCR and EM. B and C shows scatter plots and funnel plots of MR analysis. D presents the results of a stratified analysis of multiple factors, demonstrating the impact of different characteristic variables on the risk of endometriosis, including race/ethnicity, military/veteran status, education level, marital status, menstrual patterns, low birth weight, hysterectomy, oophorectomy, fallopian tube surgery, uterine fibroids, and the use of estrogen and progesterone. The adjusted results show that most variables have significant differences in risk between different groups
Mendelian randomization analysis results
Bidirectional Mendelian randomization analysis showed a causal relationship between creatinine levels and endometriosis (Figs. 1C-D). The IVW method yielded results of 1.001 (95% CI: 1.0001–1.002, P = 0.0350), and the weighted median results were 1.002 (95% CI: 1.0001–1.004, P = 0.0295, see Table 3 and Figs. 2B-C). The reverse analysis did not show significant results, with IVW results at 1.2631 (95% CI: 0.0001–21331.3981, P = 0.9625), indicating no statistical significance in the reverse causal relationship (see Table 3). Sensitivity analyses, including MR-Egger regression and MR-PRESSO, were conducted to assess potential pleiotropic effects and heterogeneity. The MR-Egger intercept was found to be near zero, with a P-value above 0.05, indicating no significant pleiotropic effects influencing the causal relationship between creatinine levels and endometriosis. This suggests that the genetic instruments used in our analysis are unlikely to be influenced by horizontal pleiotropy. Additionally, MR-PRESSO analysis did not show any significant horizontal pleiotropy, further supporting the validity of the findings. Cochrane’s Q test for heterogeneity also yielded a P-value above 0.05, indicating no significant heterogeneity among the SNPs. These results demonstrate the robustness of the causal inference and reinforce the validity of the observed relationship between creatinine levels and endometriosis.
Table 3.
Causal relationship and sensitivity analysis between SCR and EM in bidirectional Mendelian randomization analysis of two samples
| Exposure | Outcome | SNP | Methods | OR (95% CI) | P-value |
|---|---|---|---|---|---|
| SCR | EM | 77 | Inverse variance weighted (multiplicative random effects) | 1.001(1.000,1.002) | 0.0350 |
| MR Egger | 1.003(1.000,1.007) | 0.0830 | |||
| Weighted median | 1.002(1.000,1.004) | 0.0295 | |||
| Weighted mode | 1.002(1.000,1.004) | 0.3127 | |||
| EM | SCR | 2 | Inverse variance weighted (multiplicative random effects) | 1.2631 (0.0001, 21,331.3981) | 0.9625 |
The MR-Egger Intercept and MR-PRESSO P-values in the table are used to investigate the presence of horizontal pleiotropy. A P-value > 0.05 indicates the absence of horizontal pleiotropy, suggesting that the study aligns with the basic assumptions of Mendelian randomization. On the other hand, the P-value of Cochran’s Q test explores the presence of heterogeneity. A P-value > 0.05 indicates no significant heterogeneity, indicating an association between the instrumental variables and phenotype
Diagnostic model construction and evaluation
For the dataset from the Affiliated Hospital of Jining Medical University (2016–2023), the inclusion and exclusion criteria are detailed in Fig. 1B. The diagnostic model, which included factors such as creatinine, age, weight, and BMI (see Table 4), demonstrated good predictive ability. The AUCs for the training set (n = 7140) and validation set (n = 3060) were 0.721 (95% CI: 0.703–0.739) and 0.730 (95% CI: 0.710–0.750), respectively (see Figs. 3B and E). The model's nomogram indicated that each 10 μmol/L increase in creatinine levels raised the risk of endometriosis by about 8% (OR = 1.08, 95% CI: 1.07–1.09, P < 0.001, see Fig. 3A). Calibration curves showed strong agreement between predicted and observed values, with good fit in the validation set (see Figs. 3C and F). Decision curve analysis revealed that the model had a high net benefit within the risk threshold range of 0.1 to 0.5, making it suitable for various clinical applications (see Figs. 3D and G).
Table 4.
Univariate and multivariate adjusted logistic regression of SCR on EM risk in a certain hospital population
| Exposure | non-EM Mean ± SD |
EM Mean ± SD |
Univariate analysis OR (95% CI) P-value |
Multivariate analysis OR (95% CI) P-value |
|---|---|---|---|---|
| N | 5100 | 5100 | 10,200 | 10,200 |
| Age, years | 44.90 ± 3.22 | 41.86 ± 8.76 | 0.93 (0.92 ~ 0.94) < 0.001 | 0.91 (0.90 ~ 0.92) < 0.001 |
| Weight, kg | 62.64 ± 9.10 | 63.75 ± 9.99 | 1.01 (1.01 ~ 1.02) < 0.001 | 0.95 (0.86 ~ 1.04) 0.248 |
| Height, cm | 162.81 ± 5.23 | 161.56 ± 5.00 | 0.96 (0.95 ~ 0.96) < 0.001 | 0.98 (0.91 ~ 1.05) 0.529 |
| BMI, kg/m2 | 23.62 ± 3.19 | 24.43 ± 3.68 | 1.07 (1.06 ~ 1.09) < 0.001 | 1.27 (1.00 ~ 1.62) 0.053 |
| SCR, umol/L | 54.35 ± 7.08 | 58.79 ± 25.21 | 1.08 (1.07 ~ 1.09) < 0.001 | 1.09 (1.08 ~ 1.10) < 0.001 |
Multivariate analysis adjusted for age(smooth), weight(smooth), height(smooth) and BMI(smooth)
Fig. 3.
EM prediction model construction and evaluation. A shows the Nomogram model used to predict the risk of endometriosis, incorporating variables such as height, weight, age, body mass index (BMI), and serum creatinine level (SCR). B shows the ROC curve of the training set. C shows the calibration curve of the training set. D shows the decision curve analysis (DCA) of the training set. E shows the ROC curve of the validation set. F shows the calibration curve of the validation set. G shows the decision curve analysis (DCA) of the validation set
Discussion
This study establishes a significant link between SCR levels and EM through cross-sectional analysis, bidirectional Mendelian randomization analysis, and diagnostic model construction. SCR, a metabolic byproduct primarily generated by muscle metabolism and excreted through the kidneys, reflects renal function and overall metabolic status. The association between creatinine and EM may reflect the roles of metabolic disorders and chronic inflammation in the development and progression of EM.
Endometriosis is a chronic inflammatory condition closely associated with inflammatory mediators, immune cells, and metabolic products within the body [13, 19, 20]. Elevated creatinine levels may be related to inflammatory responses and oxidative stress, affecting the growth and implantation of endometrial cells [21, 22]. Additionally, metabolic disorders such as insulin resistance and metabolic syndrome may promote the occurrence of endometriosis by influencing hormone levels and immune function [23–25]. The findings of this study are in line with existing literature. While our findings suggest that elevated serum creatinine levels are associated with an increased risk of endometriosis, it is important to note that serum creatinine is only one of several risk factors and not a specific indicator for the disease. Endometriosis is a multifactorial condition influenced by a range of genetic, metabolic, and inflammatory processes. As such, serum creatinine alone may not provide enough diagnostic specificity, especially given its association with other systemic conditions like kidney dysfunction and metabolic disturbances. Recent metabolomic studies have identified several potential biomarkers for endometriosis, including phospholipids, prostaglandins, amino acids, and inflammatory cytokines. These metabolites have shown promise as non-invasive biomarkers for the early detection and monitoring of endometriosis. Our findings on elevated serum creatinine levels may complement these discoveries, as creatinine is closely linked to systemic metabolic and inflammatory processes that could contribute to the pathogenesis of endometriosis. Integrating serum creatinine assessments with these broader metabolomic findings could enhance diagnostic accuracy and allow for more personalized treatment strategies for patients. Although the causal effect observed in the Mendelian randomization analysis was small, its practical significance should not be underestimated. Small but consistent effects on risk factors, even if modest, can have substantial implications in large populations over time. Even a small increase in creatinine levels could potentially influence the incidence of endometriosis in susceptible individuals, especially given the chronic and progressive nature of the disease. In clinical settings, even small changes in biomarkers can guide early interventions and preventative strategies. Future studies should explore how multi-omics data, including metabolomics and serum creatinine levels, can be integrated to improve diagnostic precision, offering a more comprehensive approach to understanding endometriosis and developing targeted therapies. However, this finding underscores the need for further research with larger sample sizes to fully explore and validate the long-term effects of such small changes on disease outcomes.
Previous studies [26, 27] have demonstrated that chronic inflammation and metabolic disorders play crucial roles in the pathogenesis of endometriosis. Research [28, 29] indicates that levels of TNF-α and IL-6 are significantly elevated in patients with endometriosis, and these inflammatory factors may promote the formation and growth of ectopic endometrial tissue by enhancing cell proliferation and inhibiting apoptosis. Other studies [30–32] have found that metabolic syndrome and insulin resistance are associated with a higher prevalence of endometriosis, possibly due to systemic inflammatory responses and hormonal imbalances induced by metabolic syndrome. Chronic inflammation is a hallmark of endometriosis, with elevated levels of pro-inflammatory cytokines, such as tumor necrosis factor alpha(TNF-α), interleukin-6(IL-6), and interleukin-1β(IL-1β), playing a key role in promoting the growth and survival of ectopic endometrial tissue. These cytokines can increase oxidative stress and stimulate cell proliferation while inhibiting apoptosis. Elevated creatinine levels may indirectly reflect the inflammatory environment in individuals with metabolic disorders or kidney impairment, which could exacerbate the inflammatory response in endometriosis. Additionally, metabolic abnormalities such as insulin resistance, a key feature of metabolic syndrome, may further amplify the effects of inflammation by altering hormone levels, particularly estrogen, which is known to promote the growth of endometrial tissue. Insulin resistance has been linked to higher circulating levels of pro-inflammatory markers, creating a feedback loop that may contribute to the development and progression of endometriosis. Elevated creatinine may serve as an indirect marker of such metabolic dysregulation, reflecting the impact of these metabolic pathways on endometriosis.
However, this study goes further by validating these hypotheses through large-scale epidemiological data and mendelian randomization analysis, providing stronger evidence of causality. Specifically, our analysis of NHANES data and data from tertiary hospitals in China confirmed that elevated serum creatinine levels are an independent risk factor for endometriosis. Mendelian randomization analysis provided evidence of causality, ruling out the possibility of reverse causation, which has not been thoroughly explored in existing literature.
Compared to existing studies, the uniqueness of this study lies in the integration of multiple analytical methods, including cross-sectional analysis, bidirectional Mendelian randomization analysis, and diagnostic model construction. This approach not only revealed the association between creatinine and EM but also explored their causal relationship. For example, while existing studies typically focus on cross-sectional analysis or single-population studies, this study validated the findings using multiple methods and multi-center data. This multi-dimensional analytical approach enhances the reliability and applicability of the study results.
This study has several limitations. First, the NHANES and Chinese cohorts may not fully represent broader populations, limiting external validity. Although consistent findings and MR support robustness, future multi-center validation is needed. Second, serum creatinine may be confounded by undiagnosed renal dysfunction; more precise renal biomarkers like eGFR or cystatin C should be included in future analyses. Third, the cross-sectional design limits causal inference despite supportive MR evidence. Although the effect size is small, it may have clinical relevance at the population level. Lastly, reverse MR used only two SNPs, limiting power. SNPs related to renal traits were excluded to reduce pleiotropy, which, while conservative, improved causal specificity.
The findings of this study have significant clinical potential. SCR, as a non-invasive biomarker, can be utilized for early screening and diagnosis of endometriosis, increasing detection rates and early intervention opportunities. By regularly monitoring serum creatinine levels, clinicians can better assess patient risk and develop personalized treatment plans to improve patient outcomes. Future research should expand the sample size and conduct validation studies in different populations and regions to enhance the generalizability of the results. Additionally, large-scale prospective studies should explore the specific mechanisms of creatinine in endometriosis, including its interactions with inflammatory mediators, immune responses, and metabolic disorders. Further mechanistic studies will help develop new therapeutic targets and intervention strategies, providing more scientific evidence for the prevention and treatment of endometriosis.
Conclusions
This study confirms a significant link between SCR levels and endometriosis through cross-sectional analysis, bidirectional Mendelian randomization analysis, and diagnostic model construction. The findings suggest that serum creatinine is an independent risk factor for endometriosis. Elevated levels of serum creatinine markedly increase the risk of developing endometriosis and can be utilized as a non-invasive biomarker for early screening and diagnosis. To further solidify these findings, future research should involve larger, multi-center studies and investigate the underlying biological mechanisms, ultimately aiding in the development of new therapeutic strategies.
Supplementary Information
Acknowledgements
Not applicable.
Abbreviations
- EM
Endometriosis
- SCR
Serum Creatinine
- MR
Mendelian Randomization
- NHANES
National Health and Nutrition Examination Survey
- NCHS
National Center for Health Statistics
- GWAS
Genome-Wide Association Studies
- IVW
Inverse Variance Weighted
- ROC
Receiver Operating Characteristic
- AUC
Area Under the Curve
- BMI
Body Mass Index
- SNPs
Single Nucleotide Polymorphisms
- TNF-α
Tumor Necrosis Factor Alpha
- IL-6
Interleukin-6
- IL-1β
Interleukin-1β
Authors’ contributions
AC interpreted the patient data. YD and YP collated the data and was the main contributor to writing the manuscript. All authors read and approved the final manuscript.
Funding
This work was supported by the Le Ling Leading Research Program Research Fund Project (Grant numbers[60]) and the Jining Key Research and Development Plan (Grant numbers[2023JNZC139]).
Data availability
The original contributions proposed in the study are included in the article/supplementary materials. Further inquiries can be sent directly to the corresponding author.
Declarations
Ethics approval and consent to participate
According to the Helsinki Declaration, complete the following ethics committee review and approval. This study involving human participants has been reviewed and approved by the Ethics Review Committee of the National Center for Health Statistics. Approval numbers: Protocol # 98–12 (NHANES 1999–2004) and Protocol # 2005–06 (NHANES 2005–2006). The patient/participant provided written informed consent to participate in this study. The Jining Medical University Hospital Ethics Committee approved this single-center cross-sectional study (approval number: 2022C064).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The original contributions proposed in the study are included in the article/supplementary materials. Further inquiries can be sent directly to the corresponding author.