Abstract
This study aims to investigate the effect of endometriosis (EMs) on fertility and to assess the causal relationship between EMs and fertility using two-sample Mendelian randomization (MR). We conducted an observational study using data from 1999 to 2006, based on the National Health and Nutrition Examination Survey database. The effect of EMs on fertility was assessed using the chi-square test or t test. The genome-wide association study collected 147,343 single-nucleotide polymorphisms associated with EMs and 7,974,415 single-nucleotide polymorphisms for the number of children. The number of children was used as the outcome variable, and EMs as the exposure factor. The inverse variance weighted method was used to evaluate the association between EMs and the number of children. The maximum likelihood ratio method, Mendelian randomization pleiotropy residual sum and outlier test, and MR-Egger regression were used for sensitivity analysis. A total of 4933 women were included in the observational study based on whether participants had EMs. There were 337 women in the EMs group and 4596 in the non-EMs group. The number of children born to women in the EMs group was 1.91 ± 1.078, which was significantly lower than that of the non-EMs group (2.26 ± 1.438; t = −4.287, P < .001). However, there did not appear to be a significant difference in the number of pregnancies and pregnancy rate between the 2 groups (P > .05). The MR study also confirmed that EMs was associated with the ratio of live births (odds ratio = 0.989, 95% confidence interval = 0.981–0.996, P < .05). MR-Egger test analysis found no pleiotropy (P > .05). EMs reduces pregnancies resulting in live births. Our results provide some reference significance for revealing the impact of EMs on fertility.
Keywords: endometriosis, fertility, live births, Mendelian randomization, MR, NHANES, number of children
1. Introduction
Endometriosis (EMs) refers to diseases in which the endometrial glands and interstitium appear outside the uterus and are characterized by endometriotic growth.[1] Chronic pelvic pain, dysmenorrhea, abnormal menstruation, and infertility are the main manifestations of EMs. About 30% to 50% of women with EMs have infertility,[2] and up to 50% of infertile women are diagnosed with EMs.[3] Infertility and EMs interact with each other, and EMs can cause infertility or spontaneous miscarriage by affecting all aspects of pregnancy. The spontaneous pregnancy rate of patients with EMs is significantly lower than that of the normal population (2%–10% vs 15%–25%),[4] and the medical cost of the effect of EMs on fertility accounts for 83.6% of the total cost of EMs.[5] EMs-related fertility problems have become an important public health issue in the field of obstetrics and gynecology. Studies have found that people with undiagnosed EMs have far fewer children than their peers in the years before they are diagnosed.[6] The purpose of this study was to explore the impact of EMs on fertility based on the National Health and Nutrition Examination Survey (NHANES) database. Mendelian randomization (MR) is a causal inference method that has been widely used in recent years, which takes advantage of the randomness of alleles at meiosis and the irreversibility of genetic variations before the occurrence of disease and uses genetic variation as an instrumental variable to infer the causal relationship between the exposure factor and the study outcome, which largely reduces biases and confounding factors. The causal relationship between EMs and pregnancies resulting in live births was examined using publicly available genome-wide association study (GWAS) summary statistics.
2. Materials and methods
2.1. The study population in NHANES
NHANES is a population-based, cross-sectional survey designed to assess the health and nutritional status of the US population. The website address is https://www.cdc.gov/nchs/nhanes. The NHANES research protocol was approved by the National Center for Health Statistics Ethics Review Committee. In this study, we collected data for a total of 8 years from 1999 to 2006. We explored the effects of EMs on pregnancy in participants. The following exclusion criteria for this study were male subjects, missing/no data on the prevalence of EMs (n = 13,886), and subjects who are postmenopausal or have future menstruation. Therefore, the population included in this study was all women of childbearing age.
2.2. Assessment of EMs exposure and fertility in NHANES
For the definition of EMs disease, all participants were asked to answer “Told by doctor had endometriosis?” Answer “Yes” or “No” to classify participants into “EMs” and “Non-EMs.” For the definition of fertility, participants first need to answer this question “Ever been pregnant?” Those who answered “no” were marked as “nullimate,” while those who answered “yes” were asked to further answer the following questions: “How many times have been pregnant?” You need to answer the exact number of times you have been pregnant, and in addition, you need to answer pregnancies resulting in live births? That is, the number of current children must be answered.
2.3. Covariate definition in NHANES
Through previously published studies, we screened for factors associated with EMs and pregnancy. These factors include age, ethnicity, marital status, education, household income, uterine fibroids, timing of menarche, menstrual abnormalities and causes, premature infants, low birth weight, and hormone medications. Continuous variables include age, family income, and time to menarche. Disaggregated variables included sex, ethnicity, marital status, educational attainment, uterine fibroids, menstrual changes and causes, preterm birth, low birth weight, and hormone use. Variables with or without menstrual changes, uterine fibroids, and hormone medication use were classified by answering the corresponding questionnaire “yes” or “no.”
2.4. Genetically instrumental variables for EMs in MR
Genetically associated data for EMs came from the GWAS database (GWAS Catalog [ebi.ac.uk]). The ID is GCST004873. The study with GWAS data included 171 European ancestry cases and 2934 European ancestry controls.[7]
2.5. Genetic summary data on number of children in MR
The genetically associated data for the number of children in this study were from the GWAS (IEU OpenGWAS project [mrcieu.ac.uk]). The ID is ieu-b-4828. This study included 60,430 participants.
This study was a reanalysis of previously collected and published public data, and therefore, no additional ethical approval was required.
2.6. Selection of instrumental variables
EMs and number of children-related single-nucleotide polymorphisms (SNPs) were extracted from the published GWAS database as instrumental variables, with P < 5 × 10−6 as the primary screening criteria. To ensure that EMs is independent of SNPs for number of children, SNPs within a linkage disequilibrium (r2 < 0.001 and physical distance between each 2 genes >10,000 kb) were excluded. The heterogeneity test excluded significant heterogeneous SNPs and finally obtained valid SNPs related to EMs and number of children as instrumental variables. Then, instrumental variables in GWAS were extracted from outcome data GWAS based on SNPs from the previous exposure data. The F-statistic was used to evaluate the bias of weak instrumental variables, and F < 10 indicates that the instrumental variable may be subject to weak instrument bias, and it is then eliminated to avoid affecting the results.
2.7. Statistical analyses
Baseline characteristics of participants are described. Categorical and continuous variables were expressed as percentages and means ± standard deviations, respectively, and P-values were calculated using a weighted chi-square test. Participants were subjected to EMs-based univariate analysis to determine the correlation of covariates with EMs.
In MR analysis, after harmonizing the effect alleles in GWAS for exposure data and outcome data, the inverse variance weighted method (IVW) was used as the main MR analysis, which was characterized by regression without considering the existence of intercept terms and fitting with the reciprocal of outcome variance as a weight. Among them, the IVW fixed-effects model is mainly used in the absence of any potential horizontal pleiotropic heterogeneity. Second, the weighted median method is used to further supplement the above conclusions. The weighted median method is defined as the weighted median of the ratio estimate, and causality can be evaluated if at least 50% of the information in the analysis comes from valid tools.
A variety of methods were used for sensitivity analysis. First, the Cochran Q test was used to assess the heterogeneity between SNP estimates, and a statistically significant Cochran Q test demonstrated significant heterogeneity in the analysis results. Second, the Mendelian randomization pleiotropy residual sum and outlier test was used to verify the results in the IVW model and correct the influence of outliers, and if there are outliers, they were eliminated and reanalyzed. Third, the MR-Egger intercept test was used to test the horizontal pleiotropy of SNPs, and if the intercept term in the MR-Egger intercept test analysis was statistically significant, it indicated that the MR analysis had obvious horizontal pleiotropy. Fourth, the “leave-one-method” sensitivity analysis was achieved by removing a single SNP each time to assess whether the variant drives the association between the exposure and outcome variables. Fifth, funnel plots and forest plots were constructed to visually detect whether MR analysis had horizontal pleiotropy. P < .05 suggested that there was a potential causal relationship in MR analysis, which was statistically significant. All statistical analyses were performed using the “TwoSampleMR” package in R software version 4.1.2 (The R Foundation for Statistical Computing, Vienna, Austria, https://www.r-project.org).
3. Results
3.1. Baseline characteristics of the study participants in NHANES
A total of 4933 women were included in this study (Fig. 1). The participants were divided into 2 groups: the “EMs group” and the “non-EMs group,” with 337 people in the EMs group with a mean age of 40.12 ± 8.68 years, and 4596 people in the non-EMs group, with a mean age of 35.37 ± 10.10 years. The study found that participants in the EMs group had a higher risk of uterine fibroids, abnormal uterine bleeding, and a higher risk of oral contraceptives and estrogen use compared with the control group. In addition, in terms of ethnicity, the risk of EMs in non-Hispanic White (68.55%) was significantly higher than that in the control group (44.41%; P < .001). In terms of education level, the incidence of EMs in the highly educated population was significantly higher than that in the control group (P < .001). In terms of marriage, there were also differences in EMs among different marital statuses (P < .001), the divorce rate of EMs seemed to be higher, and the proportion of EMs among unmarried people was also higher. There were no significant differences in age at menarche, preterm birth, low body weight, or family income level (see Table 1 for details).
Figure 1.
Flowchart of NHANES. NHANES = National Health and Nutrition Examination Survey.
Table 1.
Demographic and clinical characteristics.
| EMs groups | EMs | Non-EMs | P-value |
|---|---|---|---|
| Number | 337 | 4596 | |
| Age (yr) | 40.12 ± 8.68 | 35.37 ± 10.10 | <.001 |
| First menstrual (yr) | 12.49 ± 1.73 (n = 331) | 12.60 ± 1.70 (n = 4514) | .247 |
| Had regular periods in the past 12 mo? | |||
| Yes | 37 (24.83%) | 922 (44.26%) | <.001 |
| No | 112 (75.17%) | 1116 (55.74%) | |
| Reason not having regular periods | <.001 | ||
| Pregnant now | 13 (11.81%) | 329 (30.05%) | |
| Breast feeding | 0 (00.00%) | 16 (1.46%) | |
| Pregnant in past year | 5 (4.55%) | 111 (10.14%) | |
| Periods usually irregular | 12 (10.91%) | 130 (34.43%) | |
| Going-gone through menopause | 54 (49.09%) | 377 (12.05%) | |
| Medical conditions – treatments | 26 (23.64%) | 132 (11.81%) | |
| Breastfed any of your children? | |||
| Yes | 144 (59.50%) | 2036 (61.94%) | .748 |
| No | 98 (40.50%) | 1251 (38.06%) | |
| Any child weight < 5.5 lbs at birth? | |||
| Yes | 25 (10.12%) | 385 (11.24%) | .748 |
| No | 222 (89.88%) | 3039 (88.76%) | |
| Number of children born preterm | 0.92 ± 0.862 (n = 25) | 0.88 ± 0.700 (n = 385) | .761 |
| Told by doctor had uterine fibroids | |||
| Yes | 116 (25.84%) | 500 (10.91%) | <.001 |
| No | 217 (74.16%) | 4083 (89.09%) | |
| Ever taken birth control pills | |||
| Yes | 307 (91.37%) | 3418 (74.42%) | <.001 |
| No | 29 (8.63%) | 1175 (25.58%) | |
| Ever use female hormones? | |||
| Yes | 130 (38.69%) | 466 (10.19%) | <.001 |
| No | 206 (61.30%) | 4105 (89.81%) | |
| Race/ethnicity | |||
| Mexican American | 28 (8.31%) | 1129 (24.56%) | <.001 |
| Other Hispanic | 6 (1.78%) | 235 (5.11%) | |
| Non-Hispanic White | 231 (68.55%) | 2041 (44.41%) | |
| Non-Hispanic Black | 59 (17.51%) | 976 (21.24%) | |
| Other race – including Multi-Racial | 13 (3.86%) | 215 (4.68%) | |
| Race/ethnicity | |||
| Non-Hispanic White | 170 (72.96%) | 1456 (45.09%) | <.001 |
| Non-Hispanic Black | 30 (12.88%) | 682 (2.11%) | |
| Mexican American | 24 (10.30%) | 799 (24.74%) | |
| Other Race – including Multi-Racial | 7 (3.00%) | 110 (3.41%) | |
| Other Hispanic | 2 (0.86%) | 182 (0.55%) | |
| Education level | |||
| Less than ninth grade | 7 (2.08%) | 404 (8.80%) | <.001 |
| 9th–11th grade | 35 (10.39%) | 745 (16.22%) | |
| High school grade/GED/equivalent | 88 (26.11%) | 1018 (22.17%) | |
| Some college or AA degree | 116 (34.42%) | 1435 (31.25%) | |
| College graduate or above | 91 (27.00%) | 990 (21.56%) | |
| Education – recode (old version) | |||
| Less than high school | 32 (13.73%) | 842 (26.10%) | <.001 |
| High school diploma | 67 (28.76%) | 730 (22.63%) | |
| More than high school | 134 (57.51%) | 1654 (51.27%) | |
| Marital status | |||
| Married | 201 (60.36%) | 2416 (54.32%) | <.001 |
| Widowed | 5 (1.50%) | 65 (1.46%) | |
| Divorced | 52 (15.62%) | 399 (8.97%) | |
| Separated | 13 (3.90%) | 202 (4.54%) | |
| Never married | 43 (12.91%) | 964 (21.67%) | |
| Living with partner | 19 (5.70%) | 402 (9.04%) | |
| Annual family income | |||
| $0–$4999 | 5 (2.19%) | 141 (4.59%) | .053 |
| $5000–$9999 | 10 (4.39%) | 192 (6.25%) | |
| $10,000–$14,999 | 14 (6.14%) | 253 (8.23%) | |
| $15,000–$19,999 | 14 (6.14%) | 219 (7.12%) | |
| $20,000–$24,999 | 15 (6.58%) | 271 (8.82%) | |
| $25,000–$34,999 | 26 (11.40%) | 372 (12.10%) | |
| $35,000–$44,999 | 19 (8.33%) | 313 (10.18%) | |
| $45,000–$54,999 | 23 (10.09%) | 246 (8.00%) | |
| $55,000–$64,999 | 16 (7.02%) | 192 (6.25%) | |
| $65,000–$74,999 | 17 (7.46%) | 161 (5.24%) | |
| $75,000 and over | 64 (28.07%) | 588 (19.13%) | |
| Over $20,000 | 4 (1.75%) | 71 (2.31%) | |
| Under $20,000 | 1 (0.44%) | 55 (1.79%) | |
EMs = endometriosis, GED = general educational development.
3.2. Associations of EMs concentrations with the risk of pregnancy in NHANES
The study found that participants in the EMs group pregnancies resulted in live births (1.91 ± 1.078 vs 2.26 ± 1.438; P < .001) in the non-EMs group, but there was no significant difference in the number of pregnancies and pregnancy rate (P > .001; Figure 2; see Table 2 for details).
Figure 2.
Flowchart of Mendelian randomization study. GWAS = genome-wide association study, LD = linkage disequilibrium, MR = Mendelian randomization, SNP = single-nucleotide polymorphisms.
Table 2.
Effect of endometriosis on fertility.
| EMs groups | EMs | Non-EMs | P-value |
|---|---|---|---|
| Ever been pregnant? | |||
| Yes | 126 (84.56%) | 1748 (85.77%) | .920 |
| No | 23 (15.44%) | 290 (14.23%) | |
| How many times have been pregnant? | 2.91 ± 1.58 (n = 282) | 3.05 ± 1.76 (n = 3873) | .208 |
| Pregnancies resulting in live births | 1.91 ± 1.078 (n = 194) | 2.26 ± 1.438 (n = 2596) | <.001 |
EMs = endometriosis.
3.3. A causal association between EMs and number of children in MR
After removing SNPs with linkage disequilibrium in EMs, a total of 13 SNPs related to EMs were included as instrumental variables, and this study was not affected by weak instrumental variables (F = 20.0–32.9). After EMs was matched to the number of children, a total of 12 SNPs were finally included in the study as instrumental variables (see Table 3).
Table 3.
SNP information related to endometriosis.
| Number | SNP | Effect_allele | Other_allele | β | SE | P-value |
|---|---|---|---|---|---|---|
| 1 | rs7942368 | T | C | −0.012 | 0.008 | .149 |
| 2 | rs74781061 | G | A | −0.004 | 0.010 | .647 |
| 3 | rs1432089 | T | C | −0.007 | 0.008 | .347 |
| 4 | rs2300795 | G | A | −0.056 | 0.036 | .114 |
| 5 | rs75801644 | A | G | −0.048 | 0.031 | .112 |
| 6 | rs644045 | G | A | 0.007 | 0.008 | .352 |
| 7 | rs6578185 | G | A | 0.012 | 0.007 | .067 |
| 8 | rs10131751 | A | C | −0.005 | 0.013 | .715 |
| 9 | rs427244 | G | A | 0.011 | 0.009 | .187 |
| 10 | rs6540 | A | G | 0.006 | 0.020 | .784 |
| 11 | rs1750034 | A | C | −0.010 | 0.012 | .382 |
| 12 | rs62355900 | C | T | 0.005 | 0.009 | .570 |
EMs = endometriosis, SE = standard error, SNP = single-nucleotide polymorphisms.
IVW showed that EMs was associated with the risk ratio of number of children (odds ratio = 0.989, 95% confidence interval = 0.981–0.996, P = .004 < .05; see Fig. 3).
Figure 3.
MR_forest. CI = confidence interval, IVW = inverse variance weighted, MR = Mendelian randomization, OR = odds ratio.
The weighted median method also showed that EMs decreased the risk of number of children (odds ratio = 0.987, 95% confidence interval = 0.972–1.002, P = .129 > .05). However, there was no statistical difference (see Fig. 3).
The MR-Egger intercept test did not show horizontal pleiotropism in MR analysis (intercept value 0.993, P = .784 > .05; see Fig. 3).
The scatterplot results showed that SNPs, which are closely related to EMs and the number of children, were stable (see Fig. 4). The funnel chart results show that SNP is symmetrical and MR analysis has no pleiotropy (see Fig. 5).
Figure 4.
MR_scatter_plot. IVW = inverse variance weighted, MR = Mendelian randomization, SNP = single-nucleotide polymorphism.
Figure 5.
The funnel chart results. MR = Mendelian randomization, SE = standard error.
4. Discussion
Previous observational studies have shown that the impact of EMs on fertility is multifactorial. However, to date, the causal relationship between EMs and fertility remains unclear. Patients with EMs have a higher incidence of infertility, and conversely, patients with infertility have a higher incidence of EMs. The causal relationship between EMs and infertility is uncertain.[8] Based on the above questions, this study used the NHANES database and conducted a large sample retrospective analysis to explore the specific effects of EMs on fertility, including whether to become pregnant, the number of pregnancies, and the number of children born. EMs did not appear to affect whether there had been a pregnancy or the number of pregnancies. However, it showed a significant difference in the number of births. In this study, the MR method was used to demonstrate the risk of EMs reducing the number of births, and positive results were also obtained. This study genetically confirms EMs as a risk factor for reducing the number of births. Although it is unclear exactly how EMs plays this role, previous studies have proposed several potential mechanisms, including infertility due to EMs, ectopic pregnancy, miscarriage, etc.[9]
This study has the following advantages: for the first time, a retrospective analysis of the effect of EMs on fertility based on the NHANES database found that EMs significantly reduced the number of children born. In addition, this study also used MR to further confirm the causal relationship between EMs and the number of children born, that is, EMs reduces the number of children born to patients. Although there have been a large number of observational studies on the effects of EMs on fertility, there may be many ethical issues involved, and even if the relevant variables are balanced, there may be bias caused by some unknown variables. The application of MR methods in this study can minimize the lack of observational studies.
There are also some limitations to this study: First, because all data are from populations of European ancestry, the results do not represent a truly random sample and may not be applicable to other ethnic groups. Second, there may be some overlap between participants in terms of exposure and outcomes in this study, which could reduce the quality of the data. Third, although various sensitivity analyses have been performed to test the hypotheses of MR studies, it is difficult to completely rule out the horizontal pleiotropy of instrumental variables. Finally, the sample size of the current GWAS data is not large enough, and more GWAS data need to be studied in the future.
In this study, the relationship between EMs and pregnancy outcome was comprehensively explored using multiple methods, including observational studies and MR analysis. This multimethod study design can better assess causality and overcome the limitations of a single-study method. There are some shortcomings of this study: First, the results of the study may not be well applicable to other populations because all the data were from a specific population. Second, the data of the study are older, and the results of the study may not be well adapted to the current population. Third, although this study has conducted multiple sensitivity analyses to test the hypotheses of the MR study, it is difficult to completely rule out the horizontal pleiotropy of instrumental variables. Fourth, the sample size of this study is not large enough, and more in-depth studies using more GWAS data are needed. Fifth, this study only analyzed the causal association between EMs and pregnancy outcomes and failed to explore the mechanism of action in depth.
In conclusion, this study uses clinical observation in conjunction with MR to show that EMs may reduce pregnancies, leading to live births (Fig. 6). Our findings provide significant reference for revealing the impact of EMs on fertility.
Figure 6.
Results of endometriosis on pregnancy outcomes. MR = Mendelian randomization, NHANES = National Health and Nutrition Examination Survey.
Author contributions
Conceptualization: Luo Xi.
Methodology: Fan Xiaoying.
Formal analysis: Deng Liling, Chen Yun.
Data curation: Huang Ziwei, Luo Xi.
Software: Liu Ting.
Supervision: Yuan Jiejiao, Zou Lisha.
Project administration: Zhang Lingli.
Resources: Zhang Lingli.
Visualization: Luo Xi.
Writing – review & editing: Fan Xiaoying.
Writing—original draft: Luo Xi.
Abbreviations:
- EMs
- endometriosis
- GWAS
- genome-wide association study
- IVW
- inverse variance weighted
- MR
- Mendelian randomization
- NHANES
- National Health and Nutrition Examination Survey
- SNP
- single-nucleotide polymorphism
This study was supported by the Hunan Provincial Traditional Chinese Medicine Research Project (grant number: B2024123), the Zhuzhou Socialized Funding Project (grant number: 2023-81), and the Hunan University of Traditional Chinese Medicine Joint University Fund Project (grant number: 2024XYLH180).
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
How to cite this article: Liling D, Ziwei H, Yun C, Xiaoying F, Ting L, Jiejiao Y, Lisha Z, Lingli Z, Xi L. The effect of endometriosis on fertility: Results of the National Health and Nutrition Examination and Mendelian randomization analysis, 1999 to 2006. Medicine 2026;105:10(e43663).
Contributor Information
Deng Liling, Email: dengliling234@163.com.
Huang Ziwei, Email: 2473193740@qq.com.
Chen Yun, Email: 3491744635@qq.com.
Fan Xiaoying, Email: 2994396142@qq.com.
Liu Ting, Email: 516829412@qq.com.
Yuan Jiejiao, Email: 1447716472@qq.com.
Zou Lisha, Email: 390859049@qq.com.
Zhang Lingli, Email: 1053914246@qq.com.
References
- [1].Saunders PTK, Horne AW. Endometriosis: etiology, pathobiology, and therapeutic prospects. Cell. 2021;184:2807–24. [DOI] [PubMed] [Google Scholar]
- [2].Missmer SA, Hankinson SE, Spiegelman D, Barbieri RL, Marshall LM, Hunter DJ. Incidence of laparoscopically confirmed endometriosis by demographic, anthropometric, and lifestyle factors. Am J Epidemiol. 2004;160:784–96. [DOI] [PubMed] [Google Scholar]
- [3].Meuleman C, Vandenabeele B, Fieuws S, Spiessens C, Timmerman D, D’Hooghe T. High prevalence of endometriosis in infertile women with normal ovulation and normospermic partners. Fertil Steril. 2009;92:68–74. [DOI] [PubMed] [Google Scholar]
- [4].Armour M, Lawson K, Wood A, Smith CA, Abbott J. The cost of illness and economic burden of endometriosis and chronic pelvic pain in Australia: a national online survey. PLoS One. 2019;14:e0223316. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [5].Leone Roberti Maggiore U, Chiappa V, Ceccaroni M, et al. Epidemiology of infertility in women with endometriosis. Best Pract Res Clin Obstet Gynaecol. 2024;92:102454. [DOI] [PubMed] [Google Scholar]
- [6].Tuominen A, Saavalainen L, Niinimäki M, et al. First live birth before surgical verification of endometriosis-a nationwide register study of 18324 women. Hum Reprod. 2023;38:1520–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Sobalska-Kwapis M, Smolarz B, Słomka M, et al. New variants near RHOJ and C2, HLA-DRA region and susceptibility to endometriosis in the Polish population-the genome-wide association study. Eur J Obstet Gynecol Reprod Biol. 2017;217:106–12. [DOI] [PubMed] [Google Scholar]
- [8].Khan Z. Fertility-related considerations in endometriosis. Abdom Radiol (NY). 2020;45:1754–61. [DOI] [PubMed] [Google Scholar]
- [9].Hamilton KM, VanHise K, Truong MD, Wright KN, Siedhoff MT. Surgical management of endometriosis to optimize fertility. Curr Opin Obstet Gynecol. 2023;35:389–94. [DOI] [PubMed] [Google Scholar]