Association between nonalcoholic fatty liver disease and infertility in reproductive-aged females

doi:10.21203/rs.3.rs-4963920/v1

Download PDF

Article

Association between nonalcoholic fatty liver disease and infertility in reproductive-aged females

https://doi.org/10.21203/rs.3.rs-4963920/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Context: Findings from observational studies indicate association between non-alcoholic fatty liver disease (NAFLD) and polycystic ovary syndrome. However, the relationship between NAFLD and female infertility remains unclear.

Objective: This study aimed to evaluate the associations between NAFLD and female infertility at the phenotypic and genetic levels.

Methods: Cross-sectional analysis using the 2013-2018 and 2017-2020 (pre-pandemic) National Health and Nutrition Examination Survey (NHANES) was performed. NAFLD was defined by utilizing the Hepatic Steatosis Index (HSI), median value of controlled attenuation parameter (CAP)and liver stiffness measurement (LSM). Multivariable logistic regression and subgroup analyses were used to examine the observational associations of NAFLD related indicesand female infertility. Subgroup analyses were used to explore whether the relationship between female infertility and HSI values was influenced by potential confounders. A bidirectional, two-sample Mendelian randomization analysis was used to determine the potential causal relationship between NAFLD and female infertility.

Results: Higher HSI was associated with progressively higher odds of infertility in women. Increasing CAP and LSM value were positively associated with an increased risk of female infertility. There was an interaction between age and NAFLD indices in relation to the prevalence of female infertility. In MR analyses, the IVW method showed no genetically causal relationship between NAFLD and female infertility.

Conclusion: Reproductive-aged women with NAFLD may have higher prevalence of infertility in the U.S. however, findings from MR analysis did not indicate a causal relationship between NAFLD and female infertility.

Health sciences/Diseases

Health sciences/Endocrinology

Health sciences/Medical research

female infertility

fatty liver

hepatic steatosis and fibrosis

Mendelian randomization study

Infertility is defined as inability to achieve a clinical pregnancy after at least 12 months of regular sexual activity without use of contraception, which afflicted approximately 10–20% of reproductive-aged couples in the United States (U.S.) and the World[1]. Among the common causes of infertility that can be identified, ovulatory dysfunction, tubal disease, and male related factors are the most prevalent. Moreover, environmental factors like obesity can worsen the underlying pathological process that contributes to infertility[2, 3]. Female infertility is a multifaceted condition resulting from a variety of factors including anatomical, immune system, hormonal, and genetic abnormalities, and it is significantly associated with conditions like PCOS, endometriosis, metabolic syndrome (Mets) and cardiovascular disease[4–7]. However, the relationship between infertility with these diseases is complicated, involving multiple interacting factors. Finding the possible trigger of these diseases might be helpful for developing additional preventive and treatment strategies for female infertility, and is also beneficial for women’s life-long health.

Nonalcoholic fatty liver disease, which is highly associated with metabolic diseases including obesity, diabetes, and hyperlipidemia, is the most prevalent chronic liver disease in the world[8]. Generally, NAFLD was considered as the hepatic manifestation of Mets, but more evidence suggests that NAFLD may be a key driver[9]. As the central site of regulating lipid and glucose metabolism, the liver expresses sex steroid receptors and is influenced by both androgen and estrogen level[8]. Meanwhile, the liver modulates the reproductive axis through the hepatic metabolism of sex steroid hormones and production of the sex hormone-binding globulin (SHBG)[10]. Increasing evidences from clinical and preclinical researches has shown the associations between NAFLD and sex steroid hormone levels[11–13]. Several large observational studies have reported the women with hyperandrogenism were likely to have higher risk of fatty liver[14, 15], which has also been documented in women with polycystic ovary syndrome[16], a common cause of female infertility[17]. However, as NAFLD is an asymptomatic until advanced disease, and have lots of similar comorbidities with female infertility, it is possible that fatty liver related hepatic dysfunction plays a role in the etiology of female infertility, but being ignored. So, we conducted this study to investigate the relationship between NAFLD and female infertility.

MR analysis is widely applied to examine causal relationships via using genetic variants as the instrumental variables (IVs) for exposure[18]. As genetic alleles are randomly assigned during meiosis and not affected by environmental factors and disease process, MR is less susceptible to the chance of reverse causation and confounding bias[19].

Thus, in this study, we first conducted parallel association analysis using data from 2013 to 2018 and 2017 to 2020 cycles of NHANES to examine the relationships between NAFLD and risk of female infertility at individual level. We then conducted bidirectional two-sample MR analysis to investigate the causality underlying the observational associations.

Study Population

The individual-level analyses were based on the NHANES data (2013–2018 and 2017–2020 pre-pandemic cycle), which is a national, cross-sectional survey conducted by the National Center for Health Statistics (NCHS) through a combination of questionnaires, physical examinations, and laboratory tests. These data are sampling-probability based and are represented the civilian, noninstitutionalized U.S. population. For the study population, we limited our analysis to women who completed the reproductive health questionnaire which was given to all female participants. Of the women for whom data regarding history of fertility was available, we further excluded those who did not finish the urine pregnant test or who were proven to be pregnant. Additionally, participants those with significant alcohol consumption, viral hepatitis (presence of viral RNA and/or positive plasma hepatitis C antibody and/or positive plasma hepatitis B surface antigen), autoimmune hepatitis were excluded from the study. The amount of daily alcohol consumption was collected in two 24-h recalls and excessive alcohol consumption was defined as an average of > 10g/day for women[20]. Furthermore, any participants with missing data on vibration-controlled transient elastography (VCTE) measurement, plasma aminotransferase (AST), alanine aminotransferase (ALT), body mass index, waist circumference, fasting plasma glucose, hemoglobin A1C concentration, plasma triglyceride concentration, blood pressure, age, race, and education level were also excluded from our analysis. The research Ethics Review Board of the National Center for Health Statistics (NCHS) approved the study of NHANES, and all participants provided informed written consent.

Disease Parameters

Non-alcoholic fatty liver disease (NAFLD) was determined by using either the hepatic steatosis index (HSI), or the median liver stiffness measurement (LSM), or the controlled attenuation parameter (CAP). We calculated the HSI by this equation: HSI = 8*(ALT/AST) + BMI (+ 2, if diabetes), and NAFLD was defined by the published cut-off of 36[21, 22]. Participants with a CAP value of 248 dB/m or higher was considered as hepatic steatosis, which was established by Karlas T et al. in comprehensive meta-analysis evaluating CAP values for the diagnosis of NAFLD[23, 24]. LSM ≥ 8.0 kPa was used as cut-off suggesting clinically relevant fibrosis[25–28]. Type 2 diabetes mellitus (T2DM) was defined as a fasting plasma glucose level > 126 mg/dL or hemoglobin A1C concentration > = 6.5%, or a self-reported history of the use of oral hypoglycemic agents or insulin. Hypertension was defined as a systolic blood pressure ≥ 140mmHg or diastolic blood pressure ≥ 90 mmHg, or if the individuals were currently taking antihypertensive medications. Infertility was defined as a binary variable based on how female participants responded to the questions, “Tried for a year to become pregnant?” and “Have you ever been to a doctor or other medical provider because you have/she has been unable to become pregnant?” One of these two questions answered “Yes” indicate an “infertile” case, and both answered “No” defined “fertility”. No response was considered missing.

Other variables

Based on the available literature, we included a variety of covariates that could potentially bias the association of NAFLD with risk of developing infertility. Variables considered included age, race/ethnicity, education level, marital status, family poverty income ratio (PIR), health insurance, body mass index (BMI), waist circumference, physical activity, smoking status, hypertension, diabetes, fasting triglycerides, fasting plasma glucose, high-density lipoprotein cholesterol. Low-density lipoprotein cholesterol, and glycosylated hemoglobin. We also considered reproductive factors, including previous treatment for pelvic infection/ pelvic inflammatory disease, regular menstrual periods in the past 12 months, to control for bias. All these variables were extracted from NHANES questionnaires and laboratory measurements.

Genetic Variants and External Genome-Wide Association Study Data

Data sources

For the NAFLD GWAS dataset, we used data from Ghodsian N et.al[29], which is a genome-wide meta-analysis of 4 cohorts (eMERGE, FinnGen, UK Biobank, and Estonian Biobank cohorts) of electronic health record-documented NAFLD in participants of European ancestry, and this research include 8434 cases and 770180 controls. Due to the lack of sex-specific GWAS of NAFLD, we used data from the NAFLD GWAS in general population and assumed there were no sex-specific genetic effects for NAFLD, as supported by previous studies[30, 31]. Data on female infertility were obtained from FinnGen[32] and UKBiobank (Phenocode 626.8). The details of each GWAS summary statistic in our study were listed in Table S1.

Genetic instrument selection

A series of quality control steps were conducted to select eligible instrumental single nucleotide polymorphisms (SNPs). Genome-wide significant (p < 5×10^− 8) SNPs were identified in the NAFLD GWAS. After linkage disequilibrium (LD) clumping (a window of 10Mb and r2 < 0.001) using the European samples from the 1000 Genomes Project reference panel, the SNPs with minor allele frequency (MAF) > 0.01 were retained as genetic instruments (Table S2). As all genetic instruments were available in the outcome GWAS summary data, so we did not use any proxy variants in the research. And these instrument variants were harmonized using TwoSampleMR R package.

Statistical analysis

We conducted statistical analysis using R software (version 4.4.0). we mainly used the “survey”, “bruceR”, “TwoSampleMR”, and “MR-PRESSO” packages in the analysis. Before sample weighting, continuous variables were presented as mean ± SD, and group categorical variables were reported as frequency and percentages (%), the two comparison groups identified based on infertility status were assessed using a weighted Student-t-test (for continuous variables) or a weighted chi-square test (for categorical data). Weighted multivariate logistic regression was employed to compute the odds ratio (OR) and 95% confidence intervals (CIs) regarding the risk of infertility associated with HS index or liver median stiffness and CAP. In the analysis, the HS index and median liver stiffness measurement (LSM) were stratified into quartiles (Q1 to Q4) and tertiles (T1 to T3), respectively. Subsequently, multivariable logistic regression was implemented to assess the independent association between NAFLD and infertility. In the logistic models for HS index and infertility, covariates were set as follows: model 1 was crude model without any adjustment, model 2 was adjusted for marital status and ever treated for a pelvic infection/PID, and model 3 was additionally adjusted for age. In the logistic models for LSM and infertility, model 1 was crude model without any adjustment, model 2 was adjusted for marital status and model 3 was additionally adjusted for smoking status. Considering that BMI, HDL cholesterol level, and hypertension may be the mediators between NAFLD and infertility or vice versa, we also performed mediation analysis.

Mendelian randomization analyses

A bidirectional MR analysis was performed to evaluate the causal relationship between NAFLD and female infertility. we used the random-effects inverse-variance weighted (IVW) method in the primary MR analysis and the fixed effects IVW method was used when there were three or fewer IVs available[33, 34]. Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) was conducted to detect the presence of outliers and evaluate the horizontal pleiotropy[35]. F statistics were used to assess the strength of the selected IVs in MR analysis, to examine whether MR estimates are influenced by weak instrument bias. Genetic instruments with F statistics > 10 are considered strong enough for MR analysis[36]. Cochran’s Q test was applied to assess the heterogeneity of individual genetic variants in both MR-Egger and IVW methods, and the heterogeneity and/or pleiotropy were considered absent when P value > 0.05[37]. A set of MR method including MR-Egger regression[18], weighted median[38], simple mode, and weighted mode methods were used for sensitivity analysis. The leave-one-out (LOO) analysis was used to estimates whether the overall results of the MR-Egger and IVW methods were driven by an individual instrument by removing single SNPs out at a time[39].

All statistical analyses were performed using R version 4.4.0 (R Foundation for Statistical Computing, Vienna, Austria). All analyses accounted for the complex survey design using appropriate survey weights in NHANES, with the weighted analysis, P values less than 0.05 were considered statistically significant.

Population Characteristics

The baseline characteristics of the first study participants according to their fertility status are presented in Table 1. A total of 880 participants aged 18 to 45 years were included in the first analysis, of whom 123 were participants with infertility. Self-reported infertility was more prevalent in women who were older, married/cohabiting, had a higher BMI and waist circumference, had high blood pressure and lower HDL cholesterol, had received pelvic inflammatory disease/PID treatment, which were consistent with former researches. In addition, a higher hepatic steatosis index (averaging 41 ± 10.67), as well as nonalcoholic fatty liver disease was also more prevalent among women with self-reported infertility.

Table 1

Characteristics of the study population 1 (n = 880)
	Total (N = 880)	Fertility (N = 757)	Infertility (N = 123)	p value
Age, (years)	32.87 ± 7.02	32.56 ± 7.04	34.74 ± 6.58	< 0.001
Ethnicity (%)				0.537
- Non-Hispanic White	279 (31.7%)	234 (30.9%)	45 (36.6%)
- Non-Hispanic Black	176 (20.0%)	155 (20.5%)	21 (17.1%)
- Other/multiracial	169 (19.2%)	144 (19.0%)	25 (20.3%)
- Mexican American	164 (18.6%)	141 (18.6%)	23 (18.7%)
- Other Hispanic	92 (10.5%)	83 (11.0%)	9 (7.3%)
High education (%)	551 (62.6%)	474 (62.6%)	77 (62.6%)	0.998
Marital status				0.002
- Married or living with partner	511 (58.1%)	424 (56.0%)	87 (70.7%)
- Living alone	369 (41.9%)	333 (44.0%)	36 (29.3%)
Poverty income ratio (PIR)	2.22 ± 1.56	2.20 ± 1.56	2.32 ± 1.57	0.406
Health insurance (%)	657 (74.7%)	569 (75.2%)	88 (71.5%)	0.392
Body mass index (kg/m2)	30.30 ± 8.60	29.84 ± 8.40	33.17 ± 9.54	< 0.001
- Underweight (< 18.5)	17 (2.2%)	3 (2.4%)	20 (2.3%)
- Normal (18.5 to < 25)	231 (30.5%)	29 (23.6%)	260 (29.5%)
- Overweight (25 to < 30)	185 (24.4%)	16 (13.0%)	201 (22.8%)
- Obese (30 or greater)	324 (42.8%)	75 (61.0%)	399 (45.3%)
Waist circumference (cm)	97.49 ± 19.28	96.42 ± 18.81	104.03 ± 20.86	< 0.001
Physical activity per week (%)				0.090
- Vigorous	246 (28.0%)	221 (29.2%)	25 (20.3%)
- Moderate	230 (26.1%)	191 (25.2%)	39 (31.7%)
- Sedentary	404 (45.9%)	345 (45.6%)	59 (48.0%)
Smoking status (%)				0.290
- Never smoker	640 (72.2%)	556 (73.0%)	84 (67.7%)
- Former smoker	107 (12.1%)	87 (11.4%)	20 (16.1%)
- Current smoker	139 (15.7%)	119 (15.6%)	20 (16.1%)
Hypertension (%)	132 (15.0%)	98 (12.9%)	34 (27.6%)	< 0.001
Diabetes (%)	65 (7.4%)	53 (7.0%)	12 (9.8%)	0.279
Fasting Glucose (mg/dl)	101.27 ± 29.13	100.81 ± 27.32	104.12 ± 38.44	0.242
Haemoglobin A1c (%)	5.48 ± 0.88	5.46 ± 0.83	5.57 ± 1.10	0.196
HDL-Cholesterol (mg/dl)	55.06 ± 14.18	55.57 ± 14.18	51.96 ± 13.87	0.009
LDL-Cholesterol (mg/dl)	104.13 ± 29.84	103.39 ± 29.70	108.72 ± 30.37	0.068
Triglyceride (mg/dl)	95.64 ± 153.96	94.96 ± 162.91	99.84 ± 79.41	0.745
Alanine aminotransferase (IU/L)	19.17 ± 15.13	19.13 ± 15.78	19.46 ± 10.30	0.819
Aspartate aminotransferase (IU/L)	20.40 ± 12.44	20.48 ± 13.15	19.91 ± 6.55	0.638
Hepatic Steatosis Index	37.80 ± 9.57	37.28 ± 9.28	41.00 ± 10.67	< 0.001
Nonalcoholic fatty liver disease (%)	456 (51.8%)	374 (49.4%)	82 (66.7%)	< 0.001
Had regular periods in past 12 months (%)				0.717
-Yes	788 (89.5%)	679 (89.7%)	109 (88.6%)
-No	92 (10.5%)	78 (10.3%)	14 (11.4%)
Ever treated for a pelvic infection/PID (%)				0.015
-Yes	41 (4.7%)	30 (4.0%)	11 (8.9%)
-No	839 (95.3%)	727 (96.0%)	112 (91.1%)
Note: The data are shown as the weighted mean ± standard error or weighted proportion (95% confidence interval). Triglycerides and glucose were evaluated only in subjects who had fasted
Abbreviations: HDL-cholesterol: High-density lipoprotein-cholesterol; LDL-cholesterol: Low-density lipoprotein-cholesterol; PID: PID: pelvic inflammatory disease.

As NHANES started vibration controlled transient elastography (VCTE) examination after 2017, so we analysis participants with VCTE results (Cycle 2017 to 2020, pre-pandemic data) separately and presented in Table 2. Unlike the first analysis, the fertile and infertile group did not differ significantly with regards to BMI, waist circumference, HDL-cholesterol, and PID status, except for a higher prevalence of hypertension, as well as the percentage of currenting smokers in the second analysis. Meanwhile, women with infertility had a significantly higher CAP value (266.47 ± 62.90 dB/m), higher median stiffness value (6.06 ± 3.84 kPa), and have significant higher prevalence of liver steatosis and fibrosis diagnosed by elastography evidence.

Table 2

Characteristics of the study population 2 (n = 438)
	Total (N = 438)	Fertility (N = 387)	Infertility (N = 51)	p value
Age, (years)	33.09 ± 6.82	33.04 ± 6.89	33.49 ± 6.30	0.657
Ethnicity (%)				0.395
- Non-Hispanic White	122 (27.9%)	102 (26.4%)	20 (39.2%)
- Non-Hispanic Black	122 (27.9%)	110 (28.4%)	12 (23.5%)
- Other/multiracial	87 (19.9%)	78 (20.2%)	9 (17.6%)
- Mexican American	65 (14.8%)	58 (15.0%)	7 (13.7%)
- Other Hispanic	42 (9.6%)	39 (10.1%)	3 (5.9%)
High education (%)	295 (67.4%)	259 (66.9%)	36 (70.6%)	0.600
Marital status				0.001
- Married/Living with partner	253 (57.8%)	213 (55.0%)	40 (78.4%)
- Living alone	185 (42.2%)	174 (45.0%)	11 (21.6%)
Poverty income ratio (PIR)	2.31 ± 1.59	2.29 ± 1.59	2.48 ± 1.58	0.409
Health insurance (%)	346 (79.0%)	309 (79.8%)	37 (72.5%)	0.229
Body mass index (kg/m2)	31.08 ± 9.20	30.94 ± 9.25	32.10 ± 8.83	0.397
- Underweight (< 18.5)	7 (1.6%)	5 (1.3%)	2 (3.9%)
- Normal (18.5 to < 25)	124 (28.3%)	111 (28.7%)	13 (25.5%)
- Overweight (25 to < 30)	95 (21.7%)	87 (22.5%)	8 (15.7%)
- Obese (30 or greater)	212 (48.4%)	184 (47.5%)	28 (54.9%)
Waist circumference (cm)	98.32 ± 20.63	97.96 ± 20.55	101.06 ± 21.22	0.313
Physical activity per week (%)				0.905
- Vigorous	122 (27.9%)	107 (27.6%)	15 (29.4%)
- Moderate	97 (22.1%)	85 (22.0%)	12 (23.5%)
- Sedentary	219 (50.0%)	195 (50.4%)	24 (47.1%)
Smoking status (%)				0.034
- Never smoker	321 (73.3%)	291 (75.2%)	30 (58.8%)
- Former smoker	52 (11.9%)	44 (11.4%)	8 (15.7%)
- Current smoker	65 (14.8%)	52 (13.4%)	13 (25.5%)
Hypertension (%)	61 (13.9%)	47 (12.1%)	14 (27.5%)	0.003
Diabetes (%)	33 (7.5%)	27 (7.0%)	6 (11.8%)	0.223
Fasting Glucose (mg/dl)	104.07 ± 30.89	103.23 ± 29.51	110.47 ± 39.67	0.116
Haemoglobin A1c (%)	5.54 ± 0.95	5.52 ± 0.93	5.66 ± 1.09	0.322
HDL Cholesterol (mg/dl)	53.23 ± 13.87	53.63 ± 13.84	50.16 ± 13.84	0.092
LDL Cholesterol (mg/dl)	102.55 ± 28.39	102.90 ± 28.13	99.78 ± 30.51	0.465
Triglyceride (mg/dl)	85.56 ± 60.10	84.19 ± 54.99	95.88 ± 89.98	0.192
Alanine aminotransferase (IU/L)	16.43 ± 10.41	16.33 ± 10.37	17.14 ± 10.84	0.605
Aspartate aminotransferase (IU/L)	17.36 ± 6.32	17.32 ± 6.23	17.67 ± 7.03	0.715
Median CAP (dB/m)	245.61 ± 62.35	242.86 ± 61.83	266.47 ± 62.90	0.011
Median stiffness (kPa)	5.05 ± 2.40	4.92 ± 2.11	6.06 ± 3.84	0.001
Hepatic steatosis (%)¹	191 (43.6%)	161 (41.6%)	30 (58.8%)	0.020
Hepatic fibrosis (%)²	25 (5.7%)	18(4.7%)	7 (13.7%)	0.012
Had regular periods in past 12 months (%)				0.344
- Yes	386 (88.1%)	339 (87.6%)	47 (92.2%)
- No	52 (11.9%)	48 (12.4%)	4 (7.8%)
Ever treated for a pelvic infection/PID (%)				0.986
- Yes	26 (5.9%)	23 (5.9%)	3 (5.9%)
- No	412 (94.1%)	364 (94.1%)	48 (94.1%)
Note: The data are shown as the weighted mean ± standard error or weighted proportion (95% confidence interval). Triglycerides and glucose were evaluated only in subjects who had fasted.
Abbreviations: HDL-cholesterol: High-density lipoprotein-cholesterol; LDL-cholesterol: Low-density lipoprotein-cholesterol; PID: PID: pelvic inflammatory disease; CAP: controlled attenuation parameter,
1: CAP ≥ 248 dB/m was diagnosed as hepatic steatosis
2: Median stiffness ≥ 8.0 kPa was diagnosed as hepatic fibrosis

Observational Associations of HS index and liver median stiffness measurement with female infertility

We analyzed the risk of infertility according to the quartiles of HS index and tertiles of liver median stiffness in women (Table 3.). Compared with healthy participants, women with infertility had higher hepatic steatosis index and higher median liver stiffness value. HS index portrayed significant associations in both univariate (Q4 vs Q1: OR = 2.56; 95% CI, 1.22–5.36) and fully adjusted multivariate (Q4 vs Q1: OR = 2.23; 95% CI, 1.12–4.43) regression models. Furthermore, the HS index level had a significant dose-response association with infertility (P _trend <0.01). Higher liver median stiffness was also positively associated with higher odds of female infertility in all three multivariable models (P _trend <0.05 in all three models) (Table 3).

Table 3

Multivariable odds ratios of risk factors for female infertility according to Hepatic Steatosis Index and Median Liver Stiffness
	Crude model (model 1)		Multivariable model 2		Multivariable model 3
	OR (95% CI)	P value	OR (95% CI)	P value	OR (95% CI)	P value
HS Index
Continuous	1.04(1.02, 1.07)	0.001	1.04 (1.01, 1.06)	0.002	1.04 (1.01, 1.06)	0.004
-Quartile 1	Reference	-	Reference	-	Reference	-
-Quartile 2	0.67 (0.32, 1.40)	0.278	0.65 (0.32, 1.34)	0.237	0.57 (0.28, 1.18)	0.128
-Quartile 3	1.40 (0.68, 2.89)	0.358	1.43 (0.69, 2.93)	0.325	1.29 (0.61, 2.73)	0.488
-Quartile 4	2.56 (1.22, 5.36)	0.014	2.46 (1.22, 4.96)	0.013	2.23(1.12, 4.43)	0.023
P for trend		0.003		0.002		0.002
Median stiffness
Continuous	1.17(1.06, 1.28)	< 0.001	1.20 (1.10, 1.32)	< 0.001	1.20 (1.10, 1.31)	< 0.001
-Tertiles 1	Reference		Reference		Reference
- Tertiles 2	0.45(0.14,1.39)	0.157	0.46 (0.15, 1.41)	0.164	0.42 (0.14, 1.25)	0.113
- Tertiles 3	1.64(0.84, 3.21)	0.140	1.83 (0.90, 3.70)	0.090	1.81 (0.82, 3.99)	0.135
P for trend		0.029		0.022		0.022
Note: In sensitivity analysis, the HSI index was converted from a continuous variable to a categorical variable (quartile). Model 1, No covariates were adjusted; Multivariable model 2 was adjusted for marital status and ever treated for a pelvic infection/PID; Multivariable model 3 includes age in addition to the variables addressed in model 2. The median stiffness was converted from a continuous variable to a categorical variable (tertiles). Model 1, No covariates were adjusted; Multivariable model 2 was adjusted for marital status; Multivariable model 3 includes smoking status in addition to the variables addressed in model 2.
Abbreviations: CI, confidence interval; OR, odds ratio; HSI: Hepatic Steatosis Index; OR, odds ratio.

We also performed subgroup analyses with weighting, and the results indicated there is noticeable interaction between the age and HSI (Table 4), and same result was found between the liver median stiffness and age. The Factors such as hypertension, HDL-cholesterol, pelvic inflammatory disease/PID treatment, smoking status, and marital status did not significantly affect the positive correlation between HS index and infertility, as well as median liver stiffness value and infertility.

Table 4

Subgroups Analyses of the effect of HS Index and median liver stiffness on Infertility
Subgroup	Infertility [OR (95% CI)]	p-value
HS index
Age		0.027
- <35	1.07(1.03, 1.10)	< 0.001
-≥35	1.01(0.98, 1.05)	0.400
Hypertension		0.222
- Yes	1.01(0.97, 1.05)	0.600
- No	1.04(1.01, 1.07)	0.022
HDL-Cholesterol		0.368
- <50	1.04 (1.01, 1.07)	0.004
− 50 to < 60	1.02(0.98, 1.07)	0.300
− 60 or greater	1.01(0.93, 1.10)	0.700
Ever treated for a pelvic infection		0.962
- Yes	1.03(0.92, 1.15)	0.500
- No	1.04(1.01, 1.07)	0.003
Marital status		0.235
- Married or living with partner	1.03(1.01, 1.06)	0.018
- Living alone	1.06(1.03, 1.10)	0.001
Median liver stiffness
Hypertension		0.702
- Yes	1.16(0.98, 1.39)	0.081
- No	1.06(0.82, 1.37)	0.700
Age		0.035
- <35	1.55(1.01, 2.37)	0.045
-≥35	1.10(0.98, 1.24)	0.100
Smoking status		0.094
- Never smoker	1.07 (0.91, 1.26)	0.398
- Former smoker	1.29 (1.02, 1.65)	0.039
- Current smoker	1.26 (0.79, 2.02)	0.305
Marital status		0.221
- Married or living with partner	1.15(1.01, 1.32)	0.059
- Living alone	1.31(1.06, 1.63)	0.015
Abbreviations: CI, confidence interval; OR, odds ratio; HSI: Hepatic Steatosis Index; OR, odds ratio; HDL-cholesterol: High-density lipoprotein-cholesterol.

Findings of mediation analysis showed that hypertension and HDL-cholesterol were not the mediator between HS index and infertility (Table S3). In contrast, HS index mediated the association between hypertension and infertility, as well as the association between HDL-cholesterol and infertility, while the proportion of the mediation effects were 19.6% and 50.0%, respectively (Table S4).

Casual Relationships Between Nonalcoholic Fatty Liver Disease and Female Infertility

As hepatic steatosis index and median liver stiffness had been validated and widely used in NAFLD related epidemiologic studies[21, 22, 27], and we found these two indices have significant associations with the risk of female infertility, so we then focused on the causal relationship between NAFLD and female infertility.

In the primary MR analysis, we found that genetically predicted NAFLD did not significantly increase the risk of female infertility (OR = 1.03, 95% CI: 0.74–1.42, P = 0.877) (Fig. 1). Similar finding was observed in the replication analysis employing another independent female infertility datasets as outcome (OR = 1.01, 95% CI: 0.93–1.09, P = 0.824) (Fig. 1). For reversal MR analysis, we found there is only one independent SNP identified in female infertility GWAS from UKB, so we did the reverse MR analysis using GWAS of female infertility from FinnGen and NAFLD GWAS from Ghodsian et al[29]. There was little evidence for a causal effect of genetically predicted female infertility on NAFLD risk (OR = 0.85, 95% CI: 0.65–1.11, P = 0.225). For the sensitivity analysis, no horizontal pleiotropy (P > 0.05) or heterogeneity (Q > 0.05) was discovered among the selected instruments (Table 5), thereby suggesting that these findings are credible. The results of the leave-one-out analysis indicated that none of the single SNPs would affect the overall effect (Fig. S1).

Table 5

Heterogeneity and pleiotropy tests for instrument variants
Traits	MR-PRESSO		Cochran’s Q test (MR-Egger)		Cochran’s Q test (IVW)		Horizontal pleiotropy tests
Traits	RSSobs	P value	Q	P value	Q	P value	Intercept	SE	P value
NAFLD on Female infertility (FinnGen)	3.998	0.517	3.121	0.210	3.121	0.373	0.0003	0.024	0.990
NAFLD on Female infertility (UKB)	9.644	0.322	4.947	0.084	5.026	0.170	0.017	0.098	0.874
Female infertility (FinnGen) on NAFLD	-^a	-	2.741	0.098	3.570	0.168	0.040	0.074	0.680
Abbreviations: NAFLD, nonalcoholic fatty liver disease; SE, standard error; IVW: inverse-variance weighted.
^a MR-PRESSO is not availble as there is not enough intrumental variables (3 instrument variants)

Currently, studies on the association between female infertility and NAFLD with large-scale, representative populations are scarce. In this study, we conducted observational association analyses between HSI, CAP, LSM and female infertility using the NHANES 2013–2018 and 2017–2020 datasets. we found that higher median liver stiffness values were significantly associated with an increased risk of female infertility regardless of age, BMI and diabetes in this nationally representative sample of US women. Meanwhile, the prevalence of hepatic steatosis and fibrosis were noticeably higher in the women facing infertility issues. Multivariable regression analysis between HSI, LSM and female infertility were consistent with former results. Furthermore, we employed IVs from external GWASs to reveal bidirectional causality for the observed associations, but MR analysis results did not support a causal association between genetically predicted NAFLD and female infertility.

The reproductive outcome of women with NAFLD attracted attentions recently, but previous studies mainly focused on the adverse pregnancy outcome of fatty liver[40–42]. few research detects the influence of NAFLD on the female infertility, which is traceable but not verified yet. NAFLD has long been considered as the consequence of PCOS, cohort studies have indicated that women with polycystic ovary syndrome have a higher tendency of NAFLD (34%-70% vs 14–34%)[43, 44]. However, result from a recent bidirectional MR analysis on NAFLD and PCOS indicated that genetically predicted NAFLD is causally correlated with an increased risk of developing PCOS, but not vice versa[31]. NAFLD may influence the female reproductive system through these ways: first, women with NAFLD were found to have higher concentration of circulating testosterone [45], which is detrimental to the development of follicle and the quality of oocyte[46, 47]; besides, low levels of SHBG were commonly observed in women with NAFLD[45], and is correlated with insulin resistance[48]. SHBG is secreted by the liver, and was considered as a protective factor in anovulatory infertility in women through various mechanisms. Third, hepatic steatosis may accompany with altered activity of the steroid hormone-metabolizing P450 system[10], which would influence the hepatic clearance of sex steroids, thus impact the reproductive system. Generally, serum biomarkers score (such as HSI) or imaging methods measuring liver fat content and stiffness via ultrasound (such as CAP and LSM) or magnetic resonance-based methods were the most widely used non-invasive approaches to detect and evaluate the progress of NAFLD[49]. In this study, we found that higher HSI, liver stiffness measurement value, as well as CAP were all correlated with higher risk of female infertility, and the prevalence of NAFLD is also higher in women with infertility. Considering that participants involved in this study are all at reproductive age, and fatty liver is a chronic disease, it is more likely that the dysfunction of liver has happened at least simultaneously or even ahead of infertility, but not the sequelae of infertility at middle age.

Interestingly, we found that elevated HS index as well as LSM were both associated with a higher prevalence of infertility in participants younger than 35, indicating that the participants with a fatty liver change at younger age have higher risk of infertility. A plausible explanation for this is that most women aged 36 years or older had a reduced number of oocytes and an increased risk of infertility[50], so the adverse effect of NAFLD on infertility is confounded in the elder group.

However, across all MR methods, we did not find evidence of a causal relationship between genetically predicted NAFLD and female infertility. The results of sensitive analysis including horizontal pleiotropy (MR-PRESSO), heterogeneity (Cochrane’s Q test) and LOO analysis confirmed the robustness of the findings. Moreover, bidirectional MR analysis was used to eliminate the possibility of reversal causation. No causal relationships between NAFLD and female infertility can be inferred.

There are several strengths as well as some limitations of the present study. First, the data involved in the observation analysis was from NHANES, with a strict quality control and is national representative. Second, we employed above SNPs as genetic instrumental variables in bidirectional MR analysis, to reduce reverse causation and confounding. Third, we also used independent data source to validate results of the primary MR analysis. The main limitation is that our observational study was conducted on a multiethnic US population but the MR analysis only included European ancestral participants, therefore, whether this conclusion can be generalized to populations with different genetic backgrounds is pending for further research. Besides, NAFLD was found to be a sexual dimorphism condition[51], but there was no sex-specific analysis conducted for NAFLD at present. Further sex-specific GWAS on NAFLD might be helpful to elucidate the casual relationship between NAFLD and female infertility.

In conclusion, our findings demonstrated that higher HSI value, as well as higher liver median stiffness are associated with an increasing risk of female infertility. Meanwhile, the prevalence of NAFLD is significantly higher in women with infertility. MR analysis revealed that no genetic cause-and-effect relationship between NAFLD and female infertility, which implying a restrained basis for advocating NAFLD screening among women with infertility. However, the association between NAFLD and female infertility indicated that infertility risk should be considered in women with NAFLD, especially in younger group.

ALT: alanine aminotransferase; AST: aminotransferase; BMI: body mass index; CAP: controlled attenuation parameter; CIs: confidence intervals; GWAS: Genome-Wide Association studies; HDL-cholesterol: High-density lipoprotein-cholesterol; HSI: Hepatic Steatosis Index; IVs: instrumental variables; IVW: inverse-variance weighted; LD: linkage disequilibrium; LDL-cholesterol: Low-density lipoprotein-cholesterol; LSM: liver stiffness measurement; MAF: minor allele frequency; Mets: metabolic syndrome; MR: Mendelian randomization; NAFLD: non-alcoholic fatty liver disease; NCHS: National Center for Health Statistics; NHANES: National Health and Nutrition Examination Survey; OR: odds ratio; PCOS: polycystic ovary syndrome; PID: pelvic inflammatory disease; PIR: poverty income ratio; SHBG: sex hormone-binding globulin; SNPs: single nucleotide polymorphisms; T2DM: Type 2 diabetes mellitus.

Patient Consent Statment

The National Center for Health Statistics obtained informed consent from all participants.

Funding

This work was supported by the Shanghai Sailing Program (22YF1434600)

Disclosures

None

Author Contribution

Jun Zhao: conceptualization, writing and reviewing of the manuscript. Ajuan Liang and Zhenhua Li: digging the data and making the tables and figures.

Acknowledgement

We sincerely appreciate the collaborators, researchers, and participants of NHANES for data collection and management and thank for all the GWAS consortiums and authors for sharing the summary statistics.

Data Availability

The National Health and Nutrition Examination Survey 2013-2018 and 2017-2020 pre-pandemic datasets are publicly available at National Center for Health Statistics of the Center for Disease Control and Prevention (https://www.cdc.gov/nchs/nhanes/index.htm). The GWAS datasets are from GWAS catalog, FinnGen, and Pheweb, all publicly available.

Vander Borght M, Wyns C: Fertility and infertility: Definition and epidemiology. Clin Biochem 2018, 62:2-10.
Carson SA, Kallen AN: Diagnosis and Management of Infertility: A Review. Jama 2021, 326(1):65-76.
Munro MG, Balen AH, Cho S, Critchley HOD, Díaz I, Ferriani R, Henry L, Mocanu E, van der Spuy ZM: The FIGO ovulatory disorders classification system. Int J Gynaecol Obstet 2022, 159(1):1-20.
Nichols AR, Rifas-Shiman SL, Switkowski KM, Zhang M, Young JG, Hivert MF, Chavarro JE, Oken E: History of Infertility and Midlife Cardiovascular Health in Female Individuals. JAMA Netw Open 2024, 7(1):e2350424.
Gleason JL, Shenassa ED, Thoma ME: Self-reported infertility, metabolic dysfunction, and cardiovascular events: a cross-sectional analysis among U.S. women. Fertil Steril 2019, 111(1):138-146.
Obesity and reproduction: a committee opinion. Fertil Steril 2021, 116(5):1266-1285.
Silvestris E, de Pergola G, Rosania R, Loverro G: Obesity as disruptor of the female fertility. Reprod Biol Endocrinol 2018, 16(1):22.
Murag S, Ahmed A, Kim D: Recent Epidemiology of Nonalcoholic Fatty Liver Disease. Gut Liver 2021, 15(2):206-216.
Li AA, Ahmed A, Kim D: Extrahepatic Manifestations of Nonalcoholic Fatty Liver Disease. Gut Liver 2020, 14(2):168-178.
Donato MT, Lahoz A, Jiménez N, Pérez G, Serralta A, Mir J, Castell JV, Gómez-Lechón MJ: Potential impact of steatosis on cytochrome P450 enzymes of human hepatocytes isolated from fatty liver grafts. Drug Metab Dispos 2006, 34(9):1556-1562.
Polyzos SA, Kountouras J, Tsatsoulis A, Zafeiriadou E, Katsiki E, Patsiaoura K, Zavos C, Anastasiadou VV, Slavakis A: Sex steroids and sex hormone-binding globulin in postmenopausal women with nonalcoholic fatty liver disease. Hormones (Athens) 2013, 12(3):405-416.
Kim JJ, Kim D, Yim JY, Kang JH, Han KH, Kim SM, Hwang KR, Ku SY, Suh CS, Kim SH et al: Polycystic ovary syndrome with hyperandrogenism as a risk factor for non-obese non-alcoholic fatty liver disease. Aliment Pharmacol Ther 2017, 45(11):1403-1412.
Kim D, Manikat R, Cholankeril G, Ahmed A: Endogenous sex hormones and nonalcoholic fatty liver disease in US adults. Liver Int 2024, 44(2):460-471.
Yim JY, Kim J, Kim D, Ahmed A: Serum testosterone and non-alcoholic fatty liver disease in men and women in the US. Liver Int 2018, 38(11):2051-2059.
Sarkar M, Wellons M, Cedars MI, VanWagner L, Gunderson EP, Ajmera V, Torchen L, Siscovick D, Carr JJ, Terry JG et al: Testosterone Levels in Pre-Menopausal Women are Associated With Nonalcoholic Fatty Liver Disease in Midlife. Am J Gastroenterol 2017, 112(5):755-762.
Kumarendran B, O'Reilly MW, Manolopoulos KN, Toulis KA, Gokhale KM, Sitch AJ, Wijeyaratne CN, Coomarasamy A, Arlt W, Nirantharakumar K: Polycystic ovary syndrome, androgen excess, and the risk of nonalcoholic fatty liver disease in women: A longitudinal study based on a United Kingdom primary care database. PLoS Med 2018, 15(3):e1002542.
Coyle C, Campbell RE: Pathological pulses in PCOS. Mol Cell Endocrinol 2019, 498:110561.
Bowden J, Davey Smith G, Burgess S: Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 2015, 44(2):512-525.
Davies NM, Holmes MV, Davey Smith G: Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. Bmj 2018, 362:k601.
Zhao Y, Li H: Association of serum vitamin C with liver fibrosis in adults with nonalcoholic fatty liver disease. Scand J Gastroenterol 2022, 57(7):872-877.
Lee JH, Kim D, Kim HJ, Lee CH, Yang JI, Kim W, Kim YJ, Yoon JH, Cho SH, Sung MW et al: Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis 2010, 42(7):503-508.
Meffert PJ, Baumeister SE, Lerch MM, Mayerle J, Kratzer W, Völzke H: Development, external validation, and comparative assessment of a new diagnostic score for hepatic steatosis. Am J Gastroenterol 2014, 109(9):1404-1414.
Karlas T, Petroff D, Sasso M, Fan JG, Mi YQ, de Lédinghen V, Kumar M, Lupsor-Platon M, Han KH, Cardoso AC et al: Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis. J Hepatol 2017, 66(5):1022-1030.
Abeysekera KWM, Fernandes GS, Hammerton G, Portal AJ, Gordon FH, Heron J, Hickman M: Prevalence of steatosis and fibrosis in young adults in the UK: a population-based study. Lancet Gastroenterol Hepatol 2020, 5(3):295-305.
Roulot D, Costes JL, Buyck JF, Warzocha U, Gambier N, Czernichow S, Le Clesiau H, Beaugrand M: Transient elastography as a screening tool for liver fibrosis and cirrhosis in a community-based population aged over 45 years. Gut 2011, 60(7):977-984.
Wong VW, Vergniol J, Wong GL, Foucher J, Chan HL, Le Bail B, Choi PC, Kowo M, Chan AW, Merrouche W et al: Diagnosis of fibrosis and cirrhosis using liver stiffness measurement in nonalcoholic fatty liver disease. Hepatology 2010, 51(2):454-462.
Cassinotto C, Boursier J, de Lédinghen V, Lebigot J, Lapuyade B, Cales P, Hiriart JB, Michalak S, Bail BL, Cartier V et al: Liver stiffness in nonalcoholic fatty liver disease: A comparison of supersonic shear imaging, FibroScan, and ARFI with liver biopsy. Hepatology 2016, 63(6):1817-1827.
Roulot D, Roudot-Thoraval F, G NK, Kouacou N, Costes JL, Elourimi G, Le Clesiau H, Ziol M, Beaugrand M: Concomitant screening for liver fibrosis and steatosis in French type 2 diabetic patients using Fibroscan. Liver Int 2017, 37(12):1897-1906.
Ghodsian N, Abner E, Emdin CA, Gobeil É, Taba N, Haas ME, Perrot N, Manikpurage HD, Gagnon É, Bourgault J et al: Electronic health record-based genome-wide meta-analysis provides insights on the genetic architecture of non-alcoholic fatty liver disease. Cell Rep Med 2021, 2(11):100437.
Lefebvre P, Staels B: Hepatic sexual dimorphism - implications for non-alcoholic fatty liver disease. Nat Rev Endocrinol 2021, 17(11):662-670.
Liu D, Gao X, Pan XF, Zhou T, Zhu C, Li F, Fan JG, Targher G, Zhao J: The hepato-ovarian axis: genetic evidence for a causal association between non-alcoholic fatty liver disease and polycystic ovary syndrome. BMC Med 2023, 21(1):62.
Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, Reeve MP, Laivuori H, Aavikko M, Kaunisto MA et al: FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 2023, 613(7944):508-518.
Patel A, Ye T, Xue H, Lin Z, Xu S, Woolf B, Mason AM, Burgess S: MendelianRandomization v0.9.0: updates to an R package for performing Mendelian randomization analyses using summarized data. Wellcome Open Res 2023, 8:449.
Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R et al: The MR-Base platform supports systematic causal inference across the human phenome. Elife 2018, 7.
Verbanck M, Chen CY, Neale B, Do R: Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 2018, 50(5):693-698.
Burgess S, Thompson SG: Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol 2011, 40(3):755-764.
Greco MF, Minelli C, Sheehan NA, Thompson JR: Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med 2015, 34(21):2926-2940.
Bowden J, Davey Smith G, Haycock PC, Burgess S: Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 2016, 40(4):304-314.
Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG: Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology 2017, 28(1):30-42.
Qian Y, Zhang Y, Fan X, Yan H, Li X, Fan Y, Song Y, Ma S, Hu Z, Gao X et al: Nonalcoholic Fatty Liver Disease and Adverse Pregnancy Outcomes in Women With Normal Prepregnant Weight. J Clin Endocrinol Metab 2023, 108(2):463-471.
Nachshon S, Hadar E, Bardin R, Barbash-Hazan S, Borovich A, Braun M, Shmueli A: The association between chronic liver diseases and preeclampsia. BMC Pregnancy Childbirth 2022, 22(1):500.
Lee SM, Jung YM, Choi ES, Kwak SH, Koo JN, Oh IH, Kim BJ, Kim SM, Kim SY, Kim GM et al: Metabolic Dysfunction-Associated Fatty Liver Disease and Subsequent Development of Adverse Pregnancy Outcomes. Clin Gastroenterol Hepatol 2022, 20(11):2542-2550.e2548.
Paschou SA, Polyzos SA, Anagnostis P, Goulis DG, Kanaka-Gantenbein C, Lambrinoudaki I, Georgopoulos NA, Vryonidou A: Nonalcoholic fatty liver disease in women with polycystic ovary syndrome. Endocrine 2020, 67(1):1-8.
Arvanitakis K, Chatzikalil E, Kalopitas G, Patoulias D, Popovic DS, Metallidis S, Kotsa K, Germanidis G, Koufakis T: Metabolic Dysfunction-Associated Steatotic Liver Disease and Polycystic Ovary Syndrome: A Complex Interplay. J Clin Med 2024, 13(14).
Jaruvongvanich V, Sanguankeo A, Riangwiwat T, Upala S: Testosterone, Sex Hormone-Binding Globulin and Nonalcoholic Fatty Liver Disease: a Systematic Review and Meta-Analysis. Ann Hepatol 2017, 16(3):382-394.
Kim JY, Xue K, Cao M, Wang Q, Liu JY, Leader A, Han JY, Tsang BK: Chemerin suppresses ovarian follicular development and its potential involvement in follicular arrest in rats treated chronically with dihydrotestosterone. Endocrinology 2013, 154(8):2912-2923.
Danfeng D, Ke D, Dengxuan F, Xuelian L, Congjian X: Oocyte quality is impaired in a hyperandrogenic PCOS mouse model by increased Foxo1 expression. Reprod Biol 2023, 23(4):100812.
Feng C, Jin Z, Chi X, Zhang B, Wang X, Sun L, Fan J, Sun Q, Zhang X: SHBG expression is correlated with PI3K/AKT pathway activity in a cellular model of human insulin resistance. Gynecol Endocrinol 2018, 34(7):567-573.
EASL-ALEH Clinical Practice Guidelines: Non-invasive tests for evaluation of liver disease severity and prognosis. J Hepatol 2015, 63(1):237-264.
Mihalas BP, Pieper GH, Aboelenain M, Munro L, Srsen V, Currie CE, Kelly DA, Hartshorne GM, Telfer EE, McAinsh AD et al: Age-dependent loss of cohesion protection in human oocytes. Curr Biol 2024, 34(1):117-131.e115.
Grossmann M, Wierman ME, Angus P, Handelsman DJ: Reproductive Endocrinology of Nonalcoholic Fatty Liver Disease. Endocrine Reviews 2018, 40(2):417-446.

No competing interests reported.

Download PDF

Reviewers agreed at journal
12 Nov, 2024
Reviewers agreed at journal
17 Sep, 2024
Reviews received at journal
15 Sep, 2024
Reviewers agreed at journal
11 Sep, 2024
Reviewers invited by journal
10 Sep, 2024
Editor assigned by journal
03 Sep, 2024
Editor invited by journal
02 Sep, 2024
Submission checks completed at journal
30 Aug, 2024
First submitted to journal
23 Aug, 2024

You are reading this latest preprint version

Association between nonalcoholic fatty liver disease and infertility in reproductive-aged females

Status:

Version 1

Abstract

Figures

Backgroud

Methods

Study Population

Disease Parameters

Other variables

Genetic Variants and External Genome-Wide Association Study Data

Data sources

Genetic instrument selection

Statistical analysis

Mendelian randomization analyses

Results

Population Characteristics

Observational Associations of HS index and liver median stiffness measurement with female infertility

Casual Relationships Between Nonalcoholic Fatty Liver Disease and Female Infertility

Discussion

Abbreviations

Declarations

Patient Consent Statment

Funding

Disclosures

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1