Association between fatty acids and female infertility: dual evidence from a cross-sectional study and Mendelian randomization analysis

Abstract

Background: Infertility poses a considerable threat to female reproductive health on a global scale. Dietary pattern, as a modifiable lifestyle factor, is frequently recommended as an important intervention for infertility-related diseases. Fatty acids play a crucial role in maintaining the health of the female reproductive system. However, the available evidence on the specific relationship between various types of fatty acids and infertility remains insufficient and controversial. Methods: Initially, a cross-sectional study was conducted utilizing the National Health and Nutrition Examination Survey (NHANES) database to collect data from women aged 18–45 years who met the inclusion criteria across the 2013–2020 cycles. Infertility was defined based on information gleaned from reproductive questionnaires. Fatty acid intake was determined by analyzing two 24 hour dietary recall interviews. Weighted logistic regression and weighted restricted cubic spline (RCS) analyses, incorporating covariate adjustments, were employed to preliminarily delineate the association between various types of fatty acids and proportions of fatty acid intake and female infertility risk. Model performance evaluation was carried out through receiver operating characteristic (ROC) curve analysis, complemented by the utilization of a nomogram diagram to gauge the infertility risk attributed to covariates. Genetic instrumental variables pertinent to diverse fatty acid profiles and female infertility were sourced from genome-wide association studies (GWAS). Mendelian randomization (MR), multivariable MR (MVMR) and reverse MR analyses were subsequently used to ascertain causality and reverse causality between distinct fatty acids and infertility, concurrently assessing for heterogeneity and horizontal pleiotropy. Results: In our NHANES analysis, a total of 3159 women were enrolled in the study, with a self-reported infertility prevalence of 11.49%. Infertile women exhibited significantly elevated intake of total omega-6 and omega-6/total fatty acids (TFA) compared to the controls. Weighted logistic regression models confirmed positive correlations between total omega-6 (continuous) and omega-6/TFA (categorical) and infertility risk, while omega-3 (continuous) intake demonstrated a negative correlation. Model 2, post rigorous multivariate covariate adjustment, showed improved predictive performance according to ROC curve analysis. Subgroup analysis suggested that the positive correlation between omega-6/TFA (continuous) and female infertility risk was not affected by stratification. Total omega-6 (continuous) emerged as a risk factor for infertile women aged 18–34 years. However, total saturated fatty acids (TSFAs, continuous), total omega-3 (continuous) and total polyunsaturated fatty acids (PUFAs, categorical) were protective factors only in the infertile women with a BMI ≥ 25 kg m⁻². The positive associations between total omega-6 (Q4) and omega-6/TFA (continuous and Q3–Q4) and infertility risk were consistent across all BMI subgroups. MR analysis employing inverse variance weighted (IVW) as the primary method and Bonferroni correction revealed that genetically predicted TSFAs, monounsaturated fatty acids (MUFAs), omega-6 and MUFA/TFA were positively associated with female infertility risk, whereas PUFA/TFA showed a negative association. Importantly, the positive associations between MUFAs and omega-6 and infertility risk remained robust even after adjusting for potential confounders using MVMR analyses. Reverse MR analysis did not provide any evidence for reverse causality. The MR-Egger regression intercept and Cochran's Q test did not detect any heterogeneity or horizontal pleiotropy. Conclusions: This study presents compelling evidence to substantiate the link between diverse fatty acids, particularly omega-6 PUFAs, and the risk of female infertility. However, to fully comprehend the potential mechanisms and impact of distinct fatty acids and their compositional ratios on female infertility, extensive future research spanning fundamental and large-scale clinical inquiries is imperative.

---

1 Introduction

Infertility, defined as the inability to achieve a clinical pregnancy outcome following a minimum of 12 months of regular and unprotected intercourse, or 6 months for women aged 35 and older,^1,2 affects an estimated 8–12% of reproductive-age couples globally and represents a significant public health concern.³ The etiology of infertility is multifaceted and intricate, encompassing unilateral or bilateral factors in both sexes.⁴ Of these factors, female-related causes, such as polycystic ovary syndrome (PCOS), pelvic inflammatory disease (PID), endometriosis, and obstructed fallopian tubes, contribute to approximately 43% of cases of infertility.^5,6 In the USA, about 11% of married women exhibit impaired fecundability.⁷ Disturbingly, global statistics show that the age-standardized prevalence of female infertility increased by 14.962% from 1990 to 2017, with an annual change of 0.377%.⁸ The harm of infertility is not only reflected in the huge psychological and social distress it causes, but also extends to the substantial economic burdens placed on patients and healthcare systems. Therefore, it is crucial to reduce the social costs and public health burden associated with infertility, as well as to identify preventable and modifiable risk factors, which hold significant clinical implications and guidance.

As an adjustable factor in daily life, dietary pattern plays an important role in maintaining the normal function of the reproductive system.⁹ Numerous previous studies have highlighted the link between abnormal consumption of certain foods, such as fatty acids (FAs), whole milk, animal protein, carbohydrate-rich diets, alcohol and caffeine, and impaired fertility.^10–13 These dietary choices and their associated nutrients may impact female fertility and ovulation by disrupting endocrine and metabolic balance, inducing oxidative stress, promoting chronic inflammation, and affecting carbohydrate metabolism.¹³ Among them, FAs have garnered much attention due to their significant pathophysiological effects on the reproductive system. FAs belong to carboxylic acids composed of a long carbon chain and at least one carboxyl group. According to the degree of saturation in their hydrocarbon chains, FAs can be categorized into saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs).¹⁴ PUFAs can be further classified into omega-3 PUFAs and omega-6 PUFAs. The former primarily encompasses α-linolenic acid (ALA), docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), while the latter mainly includes linoleic acid (LA) and arachidonic acid (AA).

A previous prospective study indicated that a high intake of ALA was effective in increasing baseline oestradiol (E₂) levels in women undergoing in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI).¹⁵ Conversely, a high intake of EPA and DHA resulted in decreased E₂ response and follicle number after ovarian stimulation.¹⁵ In particular, supplementation with omega-3 PUFAs, especially ALA and DHA, was beneficial for embryonic morphology.¹⁵ Similarly, omega-3 PUFA supplementation was also noticed to significantly reduce follicle-stimulating hormone (FSH) levels and FSH response to gonadotropin-releasing hormone (GnRH) in normal weight women, while in obese women, it led to lower serum levels of interleukin-1β (IL-1β) and tumour necrosis factor-α (TNF-α).¹⁶ Furthermore, maternal preconception intake of PUFAs, particularly omega-6 PUFAs and LA, was associated with improved pregnancy rates in overweight and obese women undergoing IVF.¹⁷ In contrast, a prospective cohort study suggested that MUFAs are linked to increased fertility or a shorter duration of pregnancy, while PUFAs are associated with decreased fertility or a longer duration of pregnancy in normal-weight women.¹⁸ No significant associations were found between fertility and SFAs, trans FAs, omega-3 PUFAs and LA.¹⁸

Despite the inconsistent findings across these studies, it is undeniable that FAs play a crucial role in maintaining the reproductive health of women. To shed light on this matter, we first conducted a cross-sectional study utilizing data from the National Health and Nutrition Examination Survey (NHANES), in order to investigate the association between the intake of various FAs and the risk of infertility in women of childbearing age. Next, we employed Mendelian randomization (MR) analysis, an emerging epidemiological method that can effectively address the limitations of traditional observational studies, such as unknown confounding factors and reverse causality. Through this approach, we aimed to further explore the causal relationship between genetically predicted FA levels and different FA proportions and the likelihood of female infertility. Our study mainly identified that MUFAs and omega-6 PUFAs were associated with an elevated risk of female infertility, whereas omega-3 PUFAs exhibited a protective effect on fertility. Although these findings are generally consistent with previous reports, they also suggest that the underlying biological mechanisms through which these FAs influence the reproductive system may vary depending on specific fertility-related conditions. Additionally, the subclassifications of these FAs also appeared to exert distinct effects on reproductive health. We conducted an in-depth discussion on the effects and mechanisms of these FAs, endeavouring to provide valuable evidence for enhancing clinical prevention and treatment of infertility through lifestyle improvement.

2 Methods

This research comprised of two components. The initial segment involved the utilization of publicly accessible data from the NHANES (https://www.cdc.gov/nchs/nhanes/index.htm) to investigate the correlation between various FA intake levels and the risk of female infertility, while accounting for data weighting and adjusting for potential confounding variables. The subsequent segment involved the execution of MR, MVMR and reverse MR analyses using aggregated genome-wide association studies (GWAS) data to further assess the causal impact of genetically predicted FA levels on female infertility.

2.1 Observational study

2.1.1 Cross-sectional study design and participant screening procedures. The NHANES program is a continuous study that integrates structured interviews with overall physical examinations, administered by highly trained medical personnel. NHANES aims to evaluate the health and nutritional status of a complex and stratified sample of non-institutionalised residents across the United States. The research protocol for NHANES was approved by the Ethics Review Board of the National Centre for Health Statistics, and all participants granted written informed consent prior to their involvement in the study.

The data utilized in this cross-sectional study were sourced from three cycles of the NHANES database spanning the years 2013 to 2020, encompassing five distinct categories: demographic data, dietary data, examination data, laboratory data and questionnaire data. This particular time frame was selected due to the fact that the infertility assessment was exclusively conducted within this timeframe. In particular, we focused on the 18 to 45 age range as this represents the prime reproductive period for women, during which fertility gradually declines, with a sharp decrease after 45, eventually leading to the end of natural fertility.^19,20 Initially, data were gathered for a total of 35 706 participants across the three cycles. Then, the study population was excluded according to the following criteria: (1) male individuals; (2) individuals outside the age range of 18 to 45 years; (3) individuals declining to respond to the reproductive questionnaire, providing responses of “do not know”, or having incomplete information on it; (4) individuals with a history of hysterectomy and/or oophorectomy, or lacking a history of reproductive system surgery; (5) individuals lacking a history of PID; (6) individuals with missing or positive results on a urine pregnancy test; (7) individuals without information on FA intake; and (8) individuals with extreme daily energy intake (600 kilocalories < total daily energy intake < 6000 kilocalories).²¹ A total of 3159 participants were enrolled in our study, with 363 individuals in the infertility group and 2796 in the control group. The comprehensive screening process is illustrated in Fig. 1A.

Fig. 1 Study design of the analysis. (A) The sampling flow during the NHANES data analysis. NHANES: National Health and Nutrition Examination Survey; PID: pelvic inflammatory disease; FAs: fatty acids. (B) Causal relationships between fatty acid intake and female infertility based on Mendelian randomization with three fundamental assumptions. MR: Mendelian randomization; SNP: single nucleotide polymorphism.

2.1.2 Measurement of FA intake. Dietary data were collected through two 24 hour dietary recall interviews, with the initial interview conducted in a private room at the mobile examination centre (MEC) and the follow-up interview conducted via telephone scheduled 3 to 10 days later. Daily FA intake was calculated by aggregating total nutrient intakes and total dietary supplements reported during the recall period. The food energy and 64 nutrients/food components for each food or beverage were calculated from the U.S. Department of Agriculture's Food and Nutrient Database for the corresponding year's dietary studies, and the amounts of 34 nutrients/dietary components for each dietary supplement were calculated via the NHANES Dietary Supplement Database.

This study analysed the average daily intake of various types of fatty acids, including total SFAs (TSFAs), total MUFAs, total PUFAs, total omega-3 PUFAs and total omega-6 PUFAs, for each participant. Specifically, omega-3 PUFAs encompassed ALA, DHA, docosapentaenoic acid (DPA), EPA and stearidonic acid (SDA), while omega-6 PUFAs included AA and LA. Additionally, the ratios of omega-3 to total fatty acids (omega-3/TFA), omega-6 to total fatty acids (omega-6/TFA), monounsaturated fatty acids to total fatty acids (MUFA/TFA) and polyunsaturated fatty acids to total fatty acids (PUFA/TFA) were also calculated.

2.1.3 Assessment of infertility. Infertility was evaluated through responses to specific inquiries in the Reproductive Health Questionnaire module, including questions “RHQ074” and “RHQ076”, which are pertaining to unsuccessful attempts at conception for at least a year and seeking medical assistance due to inability to conceive, respectively. Participants indicating affirmative responses were categorized into the infertility group, while those indicating negative responses were categorized into the control group.

2.1.4 Covariate extraction. The selection of covariates in this study was guided by a comprehensive review of previous literature reports and clinical expertise, aiming to effectively account for potential confounding factors.^22–25 In addition to the widely recognized age-related factors that affect female fertility potential,²⁶ studies have underscored the impact of modifiable lifestyle factors on the female reproductive system, including age of starting family, alcohol misuse, tobacco, depression, obesity, physical exercise, sedentary behaviour, and sleep disturbance.^27–29 Integrating lifestyle intervention programs, which include psychological or behavioural guidance, exercise regimes, nutritional adjustments, weight management, and smoking cessation, have been shown to improve clinical pregnancy and live birth outcomes.³⁰ Moreover, socioeconomic status also plays a significant role in fertility, with lower income levels and educational attainment linked to reduced fecundability.³¹ Additionally, a history of PID, hypertension, diabetes, or hypercholesteremia has been proven to have adverse effects on female reproductive function.^32–35 Given the direct and indirect influence of these factors on fecundability, it was essential to include them as covariates in the analysis to mitigate their potential confounding effects, thereby providing a more accurate exploration of the relationship between fatty acid intake and the risk of female infertility.

The covariates were derived from multiple data modules, namely demographic data, dietary data, examination data, laboratory data and questionnaire data. Firstly, the covariates from the demographic data module consisted of age, race, educational level, marital status, and poverty income ratio (PIR). Race was categorized into six groups: “Mexican American”, “Other Hispanic”, “Non-Hispanic White”, “Non-Hispanic Black”, “Non-Hispanic Asian” and “Other/Multiracial”. Educational level was divided into three categories: “less than high school”, “high school” and “more than high school”. Marital status was categorized as “married/living with partner”, “widowed/divorced/separated” and “never married”. Age and PIR were treated as continuous variables for analysis.

Secondly, covariates obtained from the dietary data module comprised the mean daily calorie intake, which was a continuous variable. The examination data module sourced covariate contained categorical data on body mass index (BMI), which was classified as “underweight (<18.5 kg m⁻²)”, “normal (18.5 to <25 kg m⁻²)”, “overweight (25 to <30 kg m⁻²)” and “obese (30 or greater kg m⁻²)”. The covariate in the laboratory data module included the continuous variable serum cotinine levels. Finally, the questionnaire data module encompassed covariates pertaining to drinking status, smoking status, history of PID, regular periods, depression, sleeplessness, history of diabetes, history of hypertension, history of hypercholesteremia, physical activity and sedentary behaviour, all of which were treated as categorical variables. Physical activity was quantified using the metabolic equivalent (MET) recommended by NHANES multiplied by the duration of the specific activity, and categorized as sufficient or insufficient based on a threshold of 450 MET-min per week.³⁶ Sedentary behaviour was assessed by self-reported daily sedentary time and categorized as either present or absent using a cut-off of 360 minutes per day.³⁶

2.2 Mendelian randomization

2.2.1 Mendelian randomization analysis design. MR analysis was carried out using the genetic instrumental variables (IVs) from the European ancestry in order to minimize the potential confounding bias related to ethnic factors. GWAS data for SFA (GWAS ID: met-d-SFA), MUFA (GWAS ID: met-d-MUFA), PUFA (GWAS ID: met-d-PUFA), omega-3 (GWAS ID: met-d-Omega_3), omega-6 (GWAS ID: met-d-Omega_6), omega-3/TFA (GWAS ID: met-d-Omega_3_pct), omega-6/TFA (GWAS ID: met-d-Omega_6_pct), MUFA/TFA (GWAS ID: met-d-MUFA_pct) and PUFA/TFA (GWAS ID: met-d-PUFA_pct) as exposure factors were obtained from the IEU Open GWAS Project (website: https://gwas.mrcieu.ac.uk/), which consisted of 114 999 participants involving 12 321 875 SNP sites. The GWAS dataset for female infertility was obtained from the FinnGen biobank (GWAS ID: finn-b-N14_FEMALEINFERT, 6481 cases and 68 969 controls involving 16 377 038 SNP sites). No sample overlap was observed between the datasets in this study, as the source populations for all the GWAS data were distinctly diverse.

2.2.2 Genetic instrumental variable selection and evaluation. In this two-sample MR analysis, all genetic IVs chosen for the study fulfilled the following three hypotheses: (1) strong associations exist between the genetic IVs and various types of fatty acids as exposure factors (the relevance assumption); (2) the genetic IVs are independent of potential confounding factors, ensuring that they are not associated with any variables that can potentially mix up the relationship between FAs and female infertility (the independence assumption); and (3) the SNPs only influence the outcome through the exposure factors (the exclusion restriction assumption). Specifically, the genetic IVs do not have an independent effect on female infertility, indicating the absence of horizontal pleiotropy (Fig. 1B).

The detailed procedure for screening IVs is outlined below. Firstly, SNPs associated with FA levels were extracted as IVs at a genome-wide significant level (P < 5 × 10⁻⁸). Secondly, SNPs with linkage disequilibrium (LD, clumping r² = 0.001 and window size = 10 000 kilobases) were excluded. We then calculated the F-statistic for each remaining SNP to quantify the strength of SNP exposure and eliminated weak exposures with an F-statistic <10, aiming to minimize weak instrument bias. The F-statistic was calculated using the formula: F = R² × (N − 2)/(1 − R²), where N represents the sample size of the exposure factor and R² represents the proportion of variance explained by IVs.^37,38R² can be calculated as follows: R² = 2 × EAF × (1 − EAF) × β²/[(2 × EAF × (1 − EAF) × β²) + (2 × EAF × (1 − EAF) × N × SE²)], where EAF is the SNP effect allele frequency, β is the estimated effect size of the SNP on the exposure factor, and SE is the standard error of the genetic effect, representing the degree of variation.^38,39 Ambiguous and palindromic SNPs were then removed during the coordination process. Finally, PhenoScanner (https://www.phenoscanner.medschl.cam.ac.uk/) was visited to obtain the phenotype information for each of the SNPs, and any related phenotypes that can affect outcomes were manually ruled out.

2.2.3 MVMR and reverse MR analysis. Multivariable MR (MVMR) is an analytical approach that extends MR analysis by enabling the concurrent estimation of the impacts of multiple exposure variables on outcome variables.⁴⁰ In light of documented correlations between current smoking, alcohol consumption, BMI, mood disorders and insomnia and female infertility,^41–43 we employed MVMR models to control for the influence of these factors in investigating the causal link between FAs and female infertility. Data for current smoking (GWAS ID: ukb-b-223, 462 434 samples involving 9 851 867 SNP sites), alcohol consumption (GWAS ID: ukb-a-25, 336 965 samples involving 10 894 596 SNP sites), BMI (GWAS ID: ukb-a-248, 336 107 samples involving 10 894 596 SNP sites), mood disorders (GWAS ID: ukb-b-18336, 53 414 cases and 407 288 controls involving 9 851 867 SNP sites) and insomnia (GWAS ID: ukb-a-13, 336 965 samples involving 10 894 596 SNP sites) were acquired from the Neale Lab consortium or the MRC-IEU consortium. In the MVMR analysis, we applied the same stringent quality-control step (clumping r² = 0.001, window size = 10 000 kb) to select independent SNPs that reached the genome-wide significant level (P < 5 × 10⁻⁸) as the exposure factors. The details are shown in Table S1.†

In order to address the concerns regarding reverse causality, we conducted a reverse MR analysis to assess the impact of genetically predicted female infertility on the levels of various fatty acids. This analysis utilized a similar methodology to that described above, with female infertility as the exposure factor and various FA levels as the outcome factors.

2.3 Statistical analysis

Due to the intricate multistage and stratified sampling methodology utilized in NHANES, we adhered to the sample weights provided in the NHANES database during our analysis to make the sample nationally representative. Initially, we analysed the baseline characteristics of the participants. Descriptive statistics on the features of the study population are presented as weighted means with standard deviation (SD) for continuous variables and as unweighted sample size (frequency) for categorical variables. Differences between the infertility and control subjects were assessed by a weighted t-test, a Wilcoxon rank-sum test or a chi-square test with Rao and Scott's second-order correction. Additionally, taking full account of the special effect of age and BMI on female infertility, we also conducted a stratified analysis of these lamination factors (age: 18–34 years and 35–45 years; BMI: <25 kg m⁻² and ≥25 kg m⁻²) to examine whether the association between FA intake and female infertility risk differs by age and BMI.

To further investigate the link between different types of FA intake and the risk of female infertility, multiple weighted logistic regression models were employed, and the odds ratio (OR) and 95% confidence interval (CI) were calculated to determine the effect of each variable on infertility. FA intake was included in the models as a continuous variable and a component variable (Q1–Q4), respectively, where categorical variables were grouped according to quartiles, with the lowest quartile (Q1) serving as the reference group. Missing values were imputed using the mice package in R (version 4.3.0) through a fully conditional chained equations approach for multiple imputation.⁴⁴ The intake of each fatty acid type was normalized to body weight (g kg⁻¹ day⁻¹) and then incorporated into the multivariate regression models. In addition, a directed acyclic graph was established to identify and account for potential confounding factors that may affect the relationship between FA intake and female infertility.⁴⁵ Specifically, the crude model did not adjust for any covariates, while model 1 accounted for age, BMI and race. Model 2 was additionally adjusted for educational level, marital status, PIR, drinking status, smoking status, history of PID, regular periods, depression, sleeplessness, serum cotinine, calorie intake, history of diabetes, history of hypertension, history of hypercholesteremia, physical activity and sedentary behaviour.

Moreover, weighted restricted cubic splines (RCS) were applied to assess the dose–response relationship between varying levels of FA intake and the risk of female infertility.⁴⁶ A nomogram diagram was drawn to quantify the magnitude of the risk of female infertility in relation to covariates. The sensitivity and performance of the models were further assessed through the calculation of the area under the receiver operating characteristic (ROC) curve (AUC).

In the context of MR analysis, SNPs demonstrating a robust association with the exposure and exhibiting independence from LD were chosen as IVs. Inverse variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode approaches were employed to investigate the potential causal relationship between the genetically predicted levels of fatty acids and female infertility. The IVW method, serving as the primary analytical tool in MR analysis, utilizes a meta-analytic framework to amalgamate the Wald estimates from individual SNPs for the derivation of an overall causal estimate.⁴⁷ The remaining four methodologies were considered as supplementary analyses. Sensitivity analyses were performed to evaluate the presence of heterogeneity and horizontal pleiotropy. As the exposures and outcomes come from distinct populations, conducting the heterogeneity test is essential to assess the consistency of various IVs in estimating the relationship between these exposures and outcomes. Significant heterogeneity frequently indicates potential violations of model assumptions or that certain genetic variants do not conform to the IV assumption.^48,49 Horizontal pleiotropy, in which a genetic variant influences the outcome through biological pathways unrelated to the risk factor of interest, contravenes the exclusion restriction assumption fundamental to MR analysis. The presence of horizontal pleiotropy may lead to either a spurious non-causal association or a false negative finding between the genetic predictors of the studied risk factor and the outcome.⁵⁰ In this study, Cochran's Q test was employed to assess the heterogeneity of the genetic IV effect,⁴⁸ while the MR-Egger regression intercept was utilized to test horizontal pleiotropy.⁵¹P > 0.05 indicates the absence of significant heterogeneity and horizontal pleiotropy, suggesting the robustness of the results. Leave-one-out sensitivity analysis was conducted to determine the impact of individual SNPs on MR estimates, thereby verifying the reliability of the results. MR pleiotropy residual sum and outlier (MR-PRESSO) was employed to detect and eliminate potential outliers.⁵² Additionally, funnel plots were constructed to provide a preliminary assessment of potential asymmetry and pleiotropy, and scatter plots were used to visually evaluate the direction of effect and the consistency of the association between exposure and outcome.

MVMR and reverse MR analyses were conducted to mitigate potential influences on the correlation between FAs and infertility and to rule out reverse causality. The MVMR-IVW method was utilized as the primary approach for assessing causal effects, with the MVMR-Lasso method serving as a supplementary analysis technique and the MVMR-Egger intercept test utilized to evaluate pleiotropy. The principles underlying reverse MR analysis were consistent with those of MR. Given the bidirectional nature of the MR analysis examining various FA levels and female infertility in our study, Bonferroni correction was applied, with statistical significance defined as a P value less than 0.00556 (0.05/9). However, the significance of certain results was overlooked due to the low P value obtained after adjusting for multiple comparisons. Consequently, findings that were initially deemed significant (P < 0.05) prior to Bonferroni correction but did not meet the adjusted threshold (P > 0.00556) were still considered in our analysis.^53,54

Statistical analyses of the cross-sectional study and MR analysis mentioned above were performed using R (version 4.3.0). Furthermore, the MR analysis involved the utilization of R packages such as “TwoSampleMR” and “MR-PRESSO”. A two-tailed P value of less than 0.05 was considered statistically significant.

3 Results

3.1 Baseline characteristics of the participants

In three cycles of NHANES spanning from 2013 to 2020, a total of 3159 women were enrolled in the study, with 363 (11.49%) belonging to the infertility group and 2796 (88.51%) to the control group, representing the national populations of 5 207 575 and 38 967 795, respectively, across the United States. The baseline characteristics of the study subjects are summarized in Table 1. Notably, significant differences were observed between the two groups in terms of age, BMI, marital status, drinking status, history of PID, sleeplessness, history of diabetes, history of hypertension and history of hypercholesteremia. Furthermore, infertile women had significantly higher levels of total omega-6 intake and omega-6/TFA compared to the control group. Race, educational level, PIR, smoking status, regular periods, depression, serum cotinine, calorie intake, physical activity and sedentary behaviour showed no significant differences between the two groups. However, compared with the fertile population, the total intake of omega-3, omega-3/TFA and MUFAs in the infertile population tended to be lower, and the total intake of TSFA and PUFA tended to be higher, but the difference was not statistically significant.

Table 1 Baseline clinical characteristics of 363 infertile and 2796 fertile subjects

Characteristics	Total (N = 44 175 370)	Control (N = 38 967 795)	Infertility (N = 5 207 575)	P-value
Abbreviations: BMI: body mass index; PIR: poverty income ratio; PID: pelvic inflammatory disease; TSFAs: total saturated fatty acids; MUFAs: monounsaturated fatty acids; PUFAs: polyunsaturated fatty acids; TFA: total fatty acids.
Age (years)	31.42 ± 7.21	31.06 ± 7.23	34.14 ± 6.42	<0.001
Race				0.84
Mexican American	533 (12.46%)	470 (12.39%)	63 (12.96%)
Other Hispanic	339 (8.24%)	304 (8.34%)	35 (7.47%)
Non-Hispanic White	1028 (54.92%)	901 (54.88%)	127 (55.29%)
Non-Hispanic Black	759 (13.78%)	667 (13.65%)	92 (14.75%)
Non-Hispanic Asian	380 (6.79%)	341 (7.00%)	39 (5.18%)
Other/Multiracial	159 (3.81%)	142 (3.74%)	17 (4.34%)
BMI				<0.001
Underweight (<18.5 kg m^−2)	83 (2.51%)	78 (2.67%)	5 (1.31%)
Normal (18.5 to <25 kg m^−2)	1008 (34.51%)	909 (35.68%)	99 (25.84%)
Overweight (25 to <30 kg m^−2)	762 (24.10%)	696 (24.83%)	66 (18.67%)
Obese (30 or greater kg m^−2)	1331 (38.88%)	1128 (36.82%)	203 (54.18%)
Educational level				0.43
Less than high school	467 (10.29%)	408 (9.97%)	59 (12.68%)
High school	631 (20.53%)	555 (20.53%)	76 (20.54%)
More than high school	2100 (69.18%)	1862 (69.50%)	238 (66.78%)
Marital status				<0.001
Married/living with partner	1776 (57.18%)	1506 (54.70%)	270 (75.72%)
Widowed/divorced/separated	309 (9.08%)	269 (8.83%)	40 (10.97%)
Never married	1113 (33.74%)	1050 (36.47%)	63 (13.31%)
PIR	2.67 ± 1.67	2.66 ± 1.67	2.78 ± 1.60	0.19
Drinking status				0.045
Never drinker	1704 (51.00%)	1505 (50.77%)	199 (52.69%)
Former drinker	835 (23.09%)	724 (22.42%)	111 (28.08%)
Current drinker	659 (25.92%)	596 (26.81%)	63 (19.23%)
Smoking status				0.58
Never smoker	2280 (68.67%)	2033 (69.09%)	247 (65.51%)
Former smoker	349 (12.84%)	303 (12.61%)	46 (14.50%)
Current smoker	567 (18.49%)	487 (18.29%)	80 (19.99%)
History of PID				0.003
Yes	150 (4.46%)	116 (3.85%)	34 (9.01%)
No	3032 (95.54%)	2695 (96.15%)	337 (90.99%)
Regular periods				0.27
Yes	3000 (94.08%)	2649 (94.31%)	351 (92.33%)
No	198 (5.92%)	176 (5.69%)	22 (7.67%)
Depression				0.066
Yes	324 (10.74%)	269 (10.19%)	55 (14.91%)
No	2868 (89.26%)	2550 (89.81%)	318 (85.09%)
Sleeplessness				0.002
Yes	728 (24.73%)	610 (23.46%)	118 (34.22%)
No	2470 (75.27%)	2215 (76.54%)	255 (65.78%)
Cotinine (ng mL ^−1 )	40.69 ± 99.07	39.90 ± 98.15	46.58 ± 105.62	0.56
Calorie intake (kcal)	1860.01 ± 659.45	1860.07 ± 656.98	1859.60 ± 678.56	0.86
Diabetes				<0.001
Yes	190 (4.64%)	149 (3.85%)	41 (10.50%)
No	3008 (95.36%)	2676 (96.15%)	332 (89.50%)
Hypertension				0.022
Yes	455 (11.55%)	385 (10.96%)	70 (16.03%)
No	2743 (88.45%)	2440 (89.04%)	303 (83.97%)
Hypercholesteremia				0.003
Yes	430 (13.10%)	361 (12.07%)	69 (20.79%)
No	2768 (86.90%)	2464 (87.93%)	304 (79.21%)
Physical activity				0.10
Insufficient	938 (24.14%)	812 (23.57%)	126 (28.44%)
Sufficient	2260 (75.86%)	2013 (76.43%)	247 (71.56%)
Sedentary behaviour				0.29
Yes	1572 (48.52%)	1393 (48.12%)	179 (51.52%)
No	1626 (51.48%)	1432 (51.88%)	194 (48.48%)
TSFAs (g)	25.42 ± 11.94	25.38 ± 11.95	25.70 ± 11.90	0.50
Total MUFAs (g)	17.75 ± 9.21	17.75 ± 9.18	17.72 ± 9.47	0.84
Total PUFAs (g)	23.94 ± 11.76	23.91 ± 11.84	24.12 ± 11.18	0.51
Total omega-3 (g)	1.76 ± 1.04	1.76 ± 1.04	1.70 ± 1.07	0.35
Total omega-6 (g)	15.31 ± 8.60	15.11 ± 8.60	16.80 ± 8.44	0.002
Omega-3/TFA	0.03 ± 0.01	0.03 ± 0.01	0.02 ± 0.01	0.15
Omega-6/TFA	0.23 ± 0.08	0.22 ± 0.07	0.25 ± 0.10	<0.001
MUFA/TFA	0.38 ± 0.05	0.38 ± 0.05	0.38 ± 0.04	0.80
PUFA/TFA	0.26 ± 0.07	0.26 ± 0.07	0.26 ± 0.07	0.36

---

3.2 Associations between FA intake and infertility risk

Weighted logistic regression analysis was conducted to further investigate the impact of different types of FA intake on the risk of female infertility (Table 2). Our findings revealed that when TSFA (OR: 0.35, 95% CI: 0.16–0.78, P = 0.011), total MUFAs (OR: 0.40, 95% CI: 0.18–0.89, P = 0.026) and total PUFA (OR: 0.27, 95% CI: 0.09–0.79, P = 0.018) were treated as continuous variables, they displayed a negative correlation with female infertility risk solely in the crude model. Similarly, when considering total omega-3 and total omega-6 as continuous variables, total omega-3 intake exhibited an inverse association with the risk of female infertility in three models (crude model: OR: 0.00, 95% CI: 0.00–0.01, P = 0.008; model 1: OR: 0.00, 95% CI: 0.00–0.13, P = 0.022; model 2: OR: 0.00, 95% CI: 0.00–0.02, P = 0.016), while total omega-6 intake showcased a positive association with the risk of female infertility in both model 1 and model 2 (model 1: OR: 3.54, 95% CI: 1.31–9.57, P = 0.014; model 2: OR = 3.39, 95% CI: 1.28–8.98, P = 0.012). In addition, the third quartile of total omega-3 intake showed a negative association with infertility in all models (crude model: OR: 0.52, 95% CI: 0.34–0.78, P = 0.002; model 1: OR: 0.62, 95% CI: 0.40–0.97, P = 0.038; model 2: OR: 0.55, 95% CI: 0.32–0.93, P = 0.028). Inversely, total omega-6 intake in the third (model 2: OR: 1.75, 95% CI: 1.08–2.84, P = 0.025) and fourth (model 2: OR: 2.22, 95% CI: 1.25–3.96, P = 0.009) quartiles demonstrated a positive association with infertility risk in the fully adjusted models. When categorizing omega-6/TFA and incorporating it into the weighted logistic regression model, we observed a consistent positive association with an increased risk of female infertility in all models.

Table 2 Weighted multivariable adjusted logistic regression analysis with infertility risk for different types of FAs

	Crude model		Model 1		Model 2
	OR (95% CI)	P-value	OR (95% CI)	P-value	OR (95% CI)	P-value
Abbreviations: TSFAs: total saturated fatty acids; MUFAs: monounsaturated fatty acids; PUFAs: polyunsaturated fatty acids; TFA: total fatty acids.
TSFAs (g)
Continuous	0.35 (0.16, 0.78)	0.011	0.84 (0.34, 2.06)	0.696	0.40 (0.10, 1.70)	0.205
Q1	Ref.
Q2	0.93 (0.65, 1.33)	0.680	1.01 (0.69, 1.48)	0.948	0.96 (0.61, 1.52)	0.856
Q3	0.75 (0.48, 1.18)	0.207	0.95 (0.60, 1.51)	0.820	0.85 (0.48, 1.49)	0.551
Q4	0.74 (0.49, 1.11)	0.142	1.09 (0.69, 1.73)	0.690	0.99 (0.51, 1.89)	0.963
Total MUFAs (g)
Continuous	0.40 (0.18, 0.89)	0.026	0.95 (0.41, 2.19)	0.906	0.80 (0.21, 3.08)	0.741
Q1	Ref.
Q2	0.73 (0.46, 1.14)	0.157	0.82 (0.52, 1.30)	0.400	0.75 (0.44, 1.28)	0.278
Q3	0.69 (0.46, 1.02)	0.060	0.86 (0.57, 1.30)	0.471	0.83 (0.49, 1.39)	0.454
Q4	0.71 (0.46, 1.10)	0.120	1.14 (0.72, 1.79)	0.573	1.13 (0.55, 2.31)	0.732
Total PUFAs (g)
Continuous	0.27 (0.09, 0.79)	0.018	0.77 (0.25, 2.40)	0.650	0.77 (0.17, 3.45)	0.719
Q1	Ref.
Q2	0.66 (0.43, 1.01)	0.054	0.73 (0.48, 1.12)	0.149	0.67 (0.43, 1.06)	0.087
Q3	0.71 (0.48, 1.05)	0.082	0.86 (0.56, 1.32)	0.482	0.76 (0.49, 1.17)	0.199
Q4	0.56 (0.37, 0.84)	0.006	0.81 (0.52, 1.26)	0.343	0.68 (0.37, 1.26)	0.213
Total omega-3 (g)
Continuous	0.00 (0.00, 0.01)	0.008	0.00 (0.00, 0.13)	0.022	0.00 (0.00, 0.02)	0.016
Q1	Ref.
Q2	0.76 (0.50, 1.17)	0.206	0.81 (0.52, 1.27)	0.342	0.75 (0.46, 1.23)	0.245
Q3	0.52 (0.34, 0.78)	0.002	0.62 (0.40, 0.97)	0.038	0.55 (0.32, 0.93)	0.028
Q4	0.61 (0.40, 0.93)	0.021	0.84 (0.53, 1.34)	0.465	0.70 (0.38, 1.27)	0.226
Total omega-6 (g)
Continuous	1.04 (0.41, 2.64)	0.927	3.54 (1.31, 9.57)	0.014	3.39 (1.28, 8.98)	0.012
Q1	Ref.
Q2	0.90 (0.57, 1.41)	0.630	1.04 (0.66, 1.62)	0.877	1.10 (0.69, 1.75)	0.674
Q3	1.15 (0.74, 1.78)	0.538	1.44 (0.92, 2.27)	0.110	1.75 (1.08, 2.84)	0.025
Q4	1.02 (0.65, 1.59)	0.937	1.59 (1.00, 2.54)	0.051	2.22 (1.25, 3.96)	0.009
Omega-3/TFA
Continuous	0.96 (0.45, 2.04)	0.921	0.83 (0.46, 1.50)	0.521	0.98 (0.61, 1.55)	0.912
Q1	Ref.
Q2	0.82 (0.47, 1.42)	0.468	0.81 (0.46, 1.44)	0.471	0.85 (0.46, 1.58)	0.591
Q3	0.87 (0.55, 1.37)	0.540	0.81 (0.50, 1.30)	0.367	0.77 (0.46, 1.29)	0.307
Q4	0.81 (0.49, 1.35)	0.408	0.81 (0.49, 1.34)	0.410	0.86 (0.51, 1.43)	0.538
Omega-6/TFA
Continuous	1.96 (0.60, 6.41)	0.252	1.06 (0.57, 1.96)	0.852	1.21 (0.72, 2.06)	0.454
Q1	Ref.
Q2	1.90 (1.17, 3.10)	0.010	1.85 1.10, 3.12	0.021	1.97 (1.11, 3.49)	0.022
Q3	2.06 (1.31, 3.22)	0.002	2.00 1.27, 3.15	0.004	2.28 (1.38, 3.75)	0.002
Q4	2.14 (1.28, 3.57)	0.004	2.17 1.30, 3.61	0.004	2.50 (1.47, 4.27)	0.002
MUFA/TFA
Continuous	1.08 (0.33, 3.58)	0.892	1.15 (0.36, 3.71)	0.802	2.15 (0.63, 7.33)	0.210
Q1	Ref.
Q2	1.20 (0.75, 1.92)	0.451	1.13 (0.68, 1.88)	0.630	1.03 (0.55, 1.90)	0.934
Q3	1.35 (0.93, 1.96)	0.111	1.25 (0.84, 1.86)	0.264	1.12 (0.70, 1.79)	0.630
Q4	1.14 (0.73, 1.80)	0.555	1.11 (0.68, 1.81)	0.674	1.03 (0.58, 1.83)	0.907
PUFA/TFA
Continuous	0.41 (0.04, 3.83)	0.430	0.46 (0.05, 3.94)	0.474	1.04 (0.12, 8.77)	0.973
Q1	Ref.
Q2	0.93 (0.56, 1.55)	0.783	0.88 (0.52, 1.47)	0.611	0.84 (0.47, 1.50)	0.547
Q3	1.11 (0.73, 1.68)	0.628	1.09 (0.72, 1.65)	0.681	1.21 (0.79, 1.87)	0.363
Q4	0.84 (0.53, 1.33)	0.447	0.86 (0.55, 1.34)	0.488	0.94 (0.59, 1.49)	0.787

---

In addition to the aforementioned analyses, we also utilized a nomogram diagram to visually represent the relationship between each covariate and the risk of female infertility, as depicted in Fig. S1.† Furthermore, for the purpose of demonstrating the robustness of the results, we generated the ROC curve and calculated the AUC, as shown in Fig. S2.† The AUC for all types of FA intake in the fully adjusted model exceeded 0.65, indicating a substantial predictive capability of the models. Besides, weighted RCS with five knots at the 5th, 35th, 50th, 65th and 95th centiles were employed to flexibly model the non-linear association of different types of fatty acids with the risk of female infertility. Notably, the non-linear model demonstrated a superior ability than a linear model in elucidating the connection between the intake of omega-6, omega-6/TFA, omega-3/TFA and infertility risk after accounting for all covariates (Fig. S3†).

3.3 Subgroup analysis

Taking into account previous literature detailing the specific effects of BMI and age on female infertility, our study aimed to further investigate the relationship between dietary FA intake and infertility through stratified analyses. Upon analyzing the baseline characteristics of the participants, it was noted that the infertility group exhibited significantly higher levels of total omega-6 intake compared to the control group, regardless of age or BMI stratification factors (Tables S2 and S3†). Additionally, a higher omega-6/TFA ratio was only observed in the infertility group with a BMI ≥ 25 kg m⁻².

Significantly, the results of the weighted logistic regression analysis indicated that, in fully adjusted models, a positive association between total omega-6 intake (as continuous) and the risk of infertility was observed solely within the subgroup of women aged 18 to 34 years. Furthermore, the relationship between omega-6/TFA (as continuous) and infertility risk remained consistent across all subgroups, establishing it as a risk factor for female infertility (Table S4†). Based on model 2 in the BMI stratified analysis, TSFAs, total omega-3 and its third and fourth quantiles, as well as Q2–Q4 of total PUFA were identified as protective factors for infertility in the population with a BMI ≥ 25 kg m⁻². However, these factors did not show the same protective effects in individuals with a BMI < 25 kg m⁻². In addition, the positive associations between the Q4 of total omega-6, omega-6/TFA and its third and fourth quantiles and infertility risk were consistently observed across all subgroups (Table S5†).

3.4 MR analysis of FA levels with the risk of infertility

To further clarify the causal relationship between the results in the cross-sectional study, we additionally conducted an MR analysis in order to elucidate the potential causal relationship between FA levels and female infertility. Following the established selection criteria, a total of 49, 59, 57, 48, 52, 37, 48, 60 and 45 SNPs were ultimately selected as IVs to evaluate the causal relationship between TSFA, MUFA, PUFA, omega-3, omega-6, omega-3/TFA, omega-6/TFA, MUFA/TFA, PUFA/TFA and female infertility, respectively. The specific information on SNPs can be found in Tables S6–S14.†

In the analyses of genetically predicted FA levels on female infertility using MR methodology (Table S15†), we primarily employed the IVW method to examine the causal association between them, which is visually presented through the forest plot in Fig. 2. Our findings revealed significant associations between the levels of five specific types of fatty acids and female infertility. Specifically, TSFA (OR_IVW: 1.158, 95% CI: 1.010–1.328, P = 0.035), MUFA (OR_IVW: 1.189, 95% CI: 1.056–1.339, P = 0.004), omega-6 (OR_IVW: 1.153, 95% CI: 1.019–1.305, P = 0.024) and MUFA/TFA (OR_IVW: 1.175, 95% CI: 1.044–1.324, P = 0.008) showed positive correlations with infertility, with the most significant result being MUFA. Conversely, a negative correlation between PUFA/TFA and infertility (OR_IVW: 0.858, 95% CI: 0.740–0.993, P = 0.040) was noted. However, there was no conclusive evidence of causal relationship between PUFA (OR_IVW: 1.071, 95% CI: 0.964–1.190, P = 0.199), omega-3 (OR_IVW: 1.067, 95% CI: 0.936–1.216, P = 0.334), omega-3/TFA (OR_IVW: 0.936, 95% CI: 0.791–1.107, P = 0.439), omega-6/TFA (OR_IVW: 0.900, 95% CI: 0.780–1.038, P = 0.146) and female infertility. The visual results can be found in Fig. S4.†

Fig. 2 Two-sample MR analysis with IVW as the primary method. The exposure is FA intake and the outcome is female infertility. The orange color represents the causal associations where exposure has a protective or risk effect on the outcome, while the dark blue color represents no causal association between exposure and the outcome.

3.5 Sensitivity analysis for MR analysis

According to the results of the MR-Egger intercept test and Cochran's Q test (IVW and MR-Egger method), there was no significant heterogeneity (P > 0.05) and horizontal pleiotropy (P > 0.05) observed in the MR analysis. Furthermore, the MR-PRESSO method successfully identified and eliminated the outlier SNPs. The summary table of the specific results is presented in Table 3. To visually represent the data, funnel plots were also plotted (Fig. S5†). Additionally, the leave-one-out analysis was performed to assess the stability of the results and no significant changes were observed when individual SNPs were excluded singly, indicating the reliability and stability of all analyses (Fig. S6†). The aforementioned sensitivity analyses provided strong evidence to support the robustness of our MR analysis results.

Table 3 Sensitivity analysis of fatty acids and female infertility in the two-sample MR analysis and reverse MR analysis

Exposure	Outcome	Pleiotropy test			Heterogeneity test						MR-PRESSO
		MR-Egger			MR-Egger			Inverse-variance weighted			Raw			Outlier-corrected
		Intercept	SE	P-value	Q-value	Q-df	Q-pval	Q-value	Q-df	Q-pval	Beta	SD	P-value	Beta	SD	P-value
Abbreviations: MR-PRESSO: Mendelian randomization pleiotropy residual sum and outlier; SE: standard error; Q-value, the statistics of Cochran's Q test; Q-df: the degree of freedom of Cochran's Q test; Q-pval: the P-value of Cochran's Q test; SD: standard deviation.
TSFA	Infertility	0.001	0.007	0.832	51.123	47	0.315	51.172	48	0.350	0.147	0.070	0.040	NA	NA	NA
MUFA		0.005	0.006	0.393	51.183	57	0.692	51.924	58	0.699	0.173	0.057	0.004	NA	NA	NA
PUFA		0.003	0.006	0.636	54.700	55	0.486	54.926	56	0.516	0.069	0.053	0.200	NA	NA	NA
Omega-3		0.000	0.007	0.986	57.460	46	0.120	57.461	47	0.141	0.064	0.067	0.339	NA	NA	NA
Omega-6		−0.001	0.007	0.841	48.935	50	0.516	48.975	51	0.554	0.142	0.062	0.026	NA	NA	NA
Omega-3/TFA		−0.003	0.008	0.726	43.347	35	0.157	43.502	36	0.182	−0.066	0.086	0.444	NA	NA	NA
Omega-6/TFA		−0.005	0.007	0.421	52.568	46	0.235	53.322	47	0.244	−0.106	0.073	0.153	NA	NA	NA
MUFA/TFA		0.011	0.006	0.061	71.321	58	0.112	75.793	59	0.069	0.162	0.061	0.010	NA	NA	NA
PUFA/TFA		−0.012	0.007	0.088	48.403	43	0.264	51.840	44	0.195	−0.154	0.075	0.046	NA	NA	NA
Infertility	TSFA	0.003	0.004	0.540	4.845	8	0.774	5.254	9	0.812	0.002	0.010	0.861	NA	NA	NA
	MUFA	0.003	0.004	0.430	6.495	8	0.592	7.186	9	0.618	−0.005	0.012	0.674	NA	NA	NA
	PUFA	0.004	0.004	0.341	6.548	8	0.586	7.571	9	0.578	0.005	0.012	0.700	NA	NA	NA
	Omega-3	0.007	0.004	0.127	6.276	8	0.616	9.170	9	0.422	0.006	0.013	0.658	NA	NA	NA
	Omega-6	0.003	0.004	0.489	5.276	8	0.728	5.801	9	0.760	0.004	0.011	0.716	NA	NA	NA
	Omega-3/TFA	0.006	0.004	0.165	6.376	8	0.605	8.709	9	0.465	0.007	0.013	0.615	NA	NA	NA
	Omega-6/TFA	−0.004	0.004	0.390	4.906	8	0.768	5.732	9	0.766	0.004	0.010	0.725	NA	NA	NA
	MUFA/TFA	0.003	0.004	0.500	6.271	8	0.617	6.771	9	0.661	−0.012	0.011	0.306	NA	NA	NA
	PUFA/TFA	−0.001	0.004	0.721	5.526	8	0.700	5.663	9	0.773	0.004	0.010	0.726	NA	NA	NA

---

3.6 MVMR and reverse analysis

In the MVMR framework, we accounted for five potential confounders, namely current smoking, alcohol consumption, BMI, mood disorders and insomnia, in order to verify the direct causal effect of FA levels on female infertility. Our findings revealed that the causal relationship between MUFA and female infertility remained robust after adjusting for any factor. In addition, omega-6 (OR_MVMR-IVW: 1.09, 95% CI: 1.02–1.16, P = 0.046) and MUFA/TAF (OR_MVMR-IVW: 1.10, 95% CI: 1.00–1.20, P = 0.031) exhibited a significant causal relationship with female infertility after accounting for all confounding factors. The results obtained from the MVMR-Lasso method were consistent with those of the MVMR-IVW approach, further enhancing the reliability of the findings that higher FA levels increases the risk of female infertility, irrespective of confounding factors (Table S16†). Importantly, the P values for the MVMR-Egger intercept test and Cochran's Q test were all above 0.05, hinting the absence of horizontal pleiotropy.

In the reverse MR analysis, we also applied MR-Egger, weighted median, IVW, simple mode and weighted mode methods to effectively rule out any possible reverse causality. We did not detect any significant association between genetically predicted female infertility and FA levels (Fig. S7†). Furthermore, sensitivity analyses of reverse MR analysis were also conducted, and the results of the MR-Egger intercept test and Cochran's Q test did not indicate any heterogeneity or horizontal pleiotropy (P > 0.05), thus further reinforcing the reliability of our results, as outlined in Table 3.

4 Discussion

In this study, we aimed to elucidate the intricate correlation between various types of fatty acids and their constituent ratios and female infertility, focusing on the underlying mechanisms and causality. Our study provides compelling evidence that partial fatty acids are important contributors to the development of female infertility with a strong genetic correlation. Specifically, analysis of data from the NHANES cross-sectional study revealed a positive association between both omega-6 PUFAs and omega-6/TFA and the risk of infertility, while omega-3 PUFAs exhibited an inverse association with infertility risk. Through MR analysis, we observed that genetically predicted TSFA, MUFA, omega-6 and MUFA/TFA were positively associated with infertility risk, while PUFA/TFA demonstrated a negative association. Furthermore, after accounting for potential confounders using MVMR analysis, the positive associations between MUFA and omega-6 and infertility risk remained robust. Importantly, our study did not uncover any evidence suggesting heterogeneity, horizontal pleiotropy, or reverse causality. These results indicate that dietary interventions targeting these FAs may be a promising strategy to reduce the risk of female infertility, particularly given the modifiable nature of dietary habits, which offers a non-pharmacological option for women seeking to enhance their fertility prospects.

Fatty acids play a crucial role in providing energy for the human body and serving as a vital component of cellular structure, and can be broadly categorized into saturated FAs and unsaturated FAs based on their chemical bond characteristics. While all the PUFAs and other FAs are carried in the bloodstream, only a minor proportion reaches target organs like the ovary in their free state.⁵⁵ Among them, SFAs and MUFAs are primarily utilized by the oocytes and the surrounding cumulus cells as a source of energy and structural elements. On the other hand, PUFAs play diverse roles in oocyte biology and metabolism.⁵⁶ Targeted deletion of Δ6-fatty acid desaturase (FADS2), essential for PUFA synthesis, did not impact the viability or lifespan of fads2^−/− mice but caused infertility in both sexes.⁵⁷ Female fads2^−/− mice exhibited inadequate ovarian blood supply during the cycle, a lack of granulosa cell syncytium and theca folliculi, poorly developed or absent zona pellucida, and dysmorphic follicles.⁵⁷ Besides, the gap junction (GJ) channel system, tagged with Cx43, was severely disordered within the ovarian granulosa cell syncytium, with GJs barely detectable in the plasma membranes of adjacent granulosa cells.⁵⁷ Remarkably, this infertility caused by FADS2 deficiency can be restored through dietary supplementation with specific PUFAs, underscoring the crucial role of PUFAs in intercellular signaling, folliculogenesis, oocyte maturation, and the maintenance of normal reproductive function.

Previous studies have shown that the mean number of total and metaphase II (MII) oocytes is positively correlated with the intake of TFA, SFAs, MUFAs, PUFAs, LA, ALA and, oleic acids (OA), and negatively correlated with the intake of EPA and DHA.⁵⁸ In addition, higher intakes of TFA, SFAs, PUFAs, MUFAs, LA, and OA were associated with lower fertilization rates.⁵⁸ In contrast, higher intakes of ALA and EPAs increased fertilization rates, with PUFA intake directly affecting the proportion of high-quality embryos.⁵⁸ Moreover, in healthy women with regular menstrual cycles, a high intake of PUFAs was correlated with elevated levels of total and free T, while DPA intake was linked to increased progesterone levels during the luteal phase and a reduced risk of anovulation.⁵⁹ These findings suggest that various subtypes of dietary FA intake may have distinct roles in processes such as hormonal regulation, oogenesis, and fertilization, ultimately influencing female reproductive outcomes. However, two cohort studies involving populations from North America and Denmark identified no significant relationship between dietary intake of total fat, SFAs, MUFAs, PUFAs, or omega-6 PUFAs and fertility.⁶⁰ Among North American women, a high intake of trans fatty acids was linked to reduced fertility, whereas greater consumption of omega-3 PUFAs was correlated with enhanced fertility.⁶⁰ These associations were not observed in the Danish cohort, likely due to the generally lower intake of trans fatty acids and higher consumption of omega-3 PUFAs. Interestingly, substituting just 2% of the energy intake of trans fatty acids with MUFAs can reduce the risk of ovulatory infertility by less than half.⁶¹ In PCOS patients, the lowest intake of MUFAs was identified as one of the three key predictors of the highest testosterone (T) level,⁶² while the supplementation of MUFA-rich almonds can significantly reduce the free androgen index,⁶³ suggesting that MUFA intake may be a potential strategy for the management of T levels in PCOS patients, thereby improving hyperandrogenism-related ovulation disorders and infertility. OA, one of the most representative MUFAs, also has a protective effect on oocyte maturation and implantation, which can counteract the adverse effects of higher levels of SFAs and enhance the developmental competence.⁶⁴ These perspectives are generally consistent with ours, underscoring the notable distinctions in the effects of various types and consumption of FAs on female fertility disorders. Hence, it is crucial to ensure an appropriate intake of FAs in terms of both quantity and quality to prevent fertility disorders.

PUFAs are significant sources of cholesterol in the body and important precursors for prostaglandin synthesis. Cholesterol derived from PUFAs is vital for the synthesis of steroid hormones, while prostaglandins are involved in various physiological processes such as steroidogenesis, follicle maturation, ovulation, implantation and pregnancy.⁶⁵ Both omega-3 and omega-6 belong to the subgroup of PUFAs, which can be distinguished by the distance between their first double bond and the terminal methyl group in the aliphatic hydrocarbon chain. Studies have demonstrated that women who took omega-3 supplements were 1.51 times more likely to conceive compared to those who did not.⁶⁶ Additionally, couples who both consumed at least eight servings of omega-3-rich seafood per cycle experienced a 22% increase in sexual frequency and a 61% increase in fertility compared to couples with lower intake.⁶⁷ The intake of omega-3 can also significantly reduce FSH levels in eumenorrheic women of normal weight, whereas no comparable effect was observed in obese women.^16,68 In addition, omega-3 consumption effectively decreased serum levels of the pro-inflammatory cytokines, IL-1β and TNF-α, in obese women, but not in their normal-weight counterparts.¹⁶ Notably, omega-3 intake resulted in a slight elevation of the endoplasmic reticulum stress marker CCAAT-enhancer-binding homologous protein (CHOP) in normal-weight women, while it correspondingly reduced CHOP levels in obese women, with diversity in the mean expression of CHOP between the two groups approaching statistical significance.⁶⁸ These findings suggest that omega-3 may significantly contribute to the delay of ovarian aging in women of normal weight. In contrast, the influence of omega-3 on ovarian function in obese women is more intricate. It appears that obesity may significantly modulate the dietary omega-3 supplementation effects on the reproductive axis, highlighting the need for further investigation into the complex interplay between omega-3, body composition, and reproductive health.

Our study identified omega-3 as a protective factor against female infertility, specifically in women with a BMI ≥ 25 kg m⁻². This finding contrasts somewhat with previous research, potentially due to the different characteristics of populations. While our investigation included both overweight and obese groups, prior research predominantly focused on individuals classified as obese (BMI ≥ 30 kg m⁻²). Overweight and obesity are often linked to complex pathological changes, such as elevated inflammation and more severe metabolic dysfunction, which may impair the bioavailability of omega-3 and diminish the positive effects of it on reproductive health.^69,70 These results highlight the need for further investigation into the mechanisms by which omega-3 affects female infertility, especially in populations with diverse characteristics.

In vivo experiments have shown that exposing oocytes to high levels of omega-3 during maturation in the ovary leads to notable changes in the distribution of active mitochondria and calcium levels, as well as an increase in the generation of reactive oxygen species (ROS).⁷¹ This exposure also adversely affected the morphological characteristics of embryos during in vivo fertilization and diminished their developmental capacity to the blastocyst stage.⁷¹ However, no significant impact of omega-3 supplementation was observed on the number and morphology of matured oocytes retrieved in vivo.⁷¹ Conversely, in vitro studies have revealed that omega-3 and L-carnitine can prevent spindle and chromosome damage in bovine oocytes cultured with FF derived from infertile women with minimal to mild endometriosis (FFEI/II), as well as those with moderate to severe endometriosis (FFEIII/IV).⁷² This dietary intervention also increased the normal MII rate in the FFEI/II and FFEIII/IV groups, while improving the total MII rate in the EIII/IV and endometrioma groups.⁷² Additionally, transgenic Fat-1 mice, which endogenously produce high levels of omega-3, exhibited reduced levels of immune mediators (interleukin 6 and cyclooxygenase-2) and the marker of epithelial cell proliferation (phosphohistone 3) in endometriosis-like lesions,⁷³ suggesting that omega-3 supplementation may inhibit the early establishment and maintenance of ectopic lesions. Similarly, oral administration of omega-3 significantly lowered the levels of interleukin 3, TNF-α, and vascular endothelial growth factor in the peritoneal fluid of endometriosis model rats and decreased glandular tissue and stromal tissue scores.⁷⁴ These results further confirm the anti-proliferative and anti-inflammatory properties of omega-3 in the context of endometriosis, indicating its potential to enhance the reproductive health in patients with endometriosis through both indirect mechanisms, such as mitigating the development of endometriosis lesions and alleviating abdominal inflammation, and direct mechanisms by protecting oocytes from meiotic damage.

PCOS is a common cause of infertility due to ovulation failure. Prior research has indicated that omega-3 supplementation can effectively reverse the elevated levels of luteinizing hormone (LH), LH/FSH, E₂ and T induced by dehydroepiandrosterone (DHEA), decrease the number of cystic follicles, enhance corpus luteum formation, normalize estrous cycles, and ultimately ameliorate ovarian dysfunction in mouse models of PCOS.⁷⁵In vitro investigations have additionally shown that omega-3 can notably accelerate the extrusion of the first polar body, diminish the proportion of abnormal spindle distribution, restore the expression of key antioxidant enzymes (Sirt1 and SOD3) as well as DNA damage repair genes (Brca1 and Msh2), and reduce superoxide dismutase (SOD) levels in oocytes of PCOS mice, thus exerting a protective effect.⁷⁶ Direct evidence comes from a double-blind randomized trial, which suggests that omega-3 intake increases the rate of clinical pregnancy in women with PCOS.⁷⁷ In contrast to the above evidence, Stanhiser et al. found no association between serum concentrations of both omega-3 and omega-6 PUFAs and spontaneous fertility, the risk of miscarriage, or levels of the anti-Müllerian hormone (AMH) among couples attempting natural pregnancy.⁷⁸ This incongruous outcome may be ascribed to the demographic composition of the participants enrolled in the survey, specifically, who were between 30 and 44 years old. This aligns with our own observation that, in the case of older women, no discernible correlation between omega-3 and omega-6 (excluding Q4) and the risk of infertility was observed (Table S2†).

Although both omega-3 and omega-6 belong to a subgroup of PUFAs, they serve different functions and cannot be easily substituted for one another. They compete for the same enzyme system to produce longer and more unsaturated products.^14,79 Therefore, maintaining a proper balance between omega-6 and omega-3 is essential for the homeostasis and normal development of organisms. It has been proven that lifelong consumption of a diet rich in omega-3 can extend the reproductive function of mice until advanced maternal age, while a diet high in omega-6 is linked to reduced reproductive success rate in advanced maternal age.⁸⁰ Moreover, even short-term dietary treatment rich in omega-3, initiated during the rapid age-related decline in reproductive function, was connected with improved oocyte quality, while omega-6 had the opposite effect.⁸⁰ This is consistent with our cross-sectional findings showing the existence of diametrical opposite effects of omega-3 and omega-6 on the reproductive system. Ma et al. discovered that treatment with omega-3/omega-6 at a ratio of 1 : 15 in PCOS rats resulted in a reduced number of large antral follicles, an increased number of cystic follicles, and a decreased T level accompanied by an increase in FSH and E₂, and promoted the expression of hormone-synthesizing enzymes.⁸¹ This also attenuated the apoptosis in ovarian cells accompanied by improved proliferative status of granulosa cells, theca cells and oocytes.⁸¹ Furthermore, an increase in the ratio of omega-3/omega-6 PUFAs led to an improvement in the ovarian structure, including the thickness of the granular and ovalbumin layers.⁸¹ These studies suggest that a balanced omega-6 and omega-3 system is beneficial for improving the reproductive performance of infertile women, including those with PCOS.

ALA and LA are the precursors of omega-3 and omega-6 PUFAs, respectively, and both belong to the essential FAs that must be obtained from food.⁸² Zhang et al. discovered that LA can activate the endoplasmic reticulum and trigger the transcription of Foxo1 gene by binding to estrogen receptors on granulosa cells.⁸³ This process brought about the accumulation of ROS and an inflammatory response, ultimately inducing apoptosis in granulosa cells.⁸³ In contrast, administering a high LA diet for three months to adult female mice resulted in a notable reduction in ALA levels, an increase in the LA/AL ratio, an extended luteal phase, and an elevated concentration of plasma progesterone.⁸⁴ This dietary supplementation also led to a higher percentage of cleaved oocytes, positively influencing ovulation rates and oocyte quality, ultimately enhancing overall fecundity.⁸⁴ Moreover, the administration of Urtica pilulifera L. seed extract, abundant in LA, palmitic acid, and OA, was observed to decrease serum FSH levels, increase LH and E₂ levels, enhance serum SOD activity, and reduce malondialdehyde levels in a dose-dependent manner in DOR model mice.⁸⁵ This intervention resulted in a reduction in the number of primordial and atretic follicles, as well as a decrease in the proportion of apoptotic cells within ovarian tissues,⁸⁵ suggesting that LA-rich Urtica pilulifera L. seed extract may contribute to the preservation of fecundity in patients with DOR by mitigating oxidative stress, modulating steroid hormone levels, and inhibiting apoptosis. LA is converted by desaturase to synthesize γ-linolenic acid (GLA) and dihomo-γ-linolenic acid (DGLA) in the body, which are subsequently metabolized to AA.⁸⁶ It is noteworthy that GLA, as an intermediary metabolite, has been proven to enhance reproductive health by reducing ovarian volume, the levels of DHEA, and the degree of neutrophil infiltration in the ovary induced by DHEA through the PPAR-γ pathway.⁸⁷ Oppositely, DGLA, another intermediary metabolite, was linked to germ cell destruction and infertility.⁸⁸

Although the conversion rate of LA to AA in humans is less than 1%, numerous studies still highlight the important role of AA in follicular growth and development, and ovulation. One study showed that the concentration of AA in FF of normal-weight PCOS patients was significantly higher compared to that in the normal-weight control group.⁸⁹ Subsequent in vitro experiments demonstrated that AA markedly decreased the activities of key antioxidant enzymes (SOD, catalase, glutathione peroxidase, and glutathione reductase), upregulated the levels of oxidative stress markers (malondialdehyde, ROS, and superoxide anion), reduced the total antioxidant capacity, and impaired mitochondrial function and endocrine steroid secretion, ultimately promoting apoptosis in KGN cells.⁸⁹ Li et al. conducted a study to examine the temporal metabolic profiles of mouse oocytes during maturation in vivo,⁹⁰ indicating that the supplementation of AA leads to a heightened occurrence of meiotic defects in metaphase I oocytes, such as spindle disorganization and chromosome aberrations, which signify changes in the meiotic structure. In contrast, a study revealed that 15 oxylipin metabolites were reduced in the follicular fluid (FF) of infertile women with diminished ovarian reserve (DOR), specifically related to the AA metabolic pathway.⁹¹ Notably, the AA oxidative metabolite, 20-COOH-AA, exhibited a significant negative correlation with FSH levels, while showing positive correlations with AMH levels, antral follicle counting, oocyte retrieval, MII oocytes and fertilization.⁹¹ Importantly, only 20-COOH-AA was positively correlated with the number of high-quality embryos.⁹¹ These results suggest that AA and its metabolites may exert different effects on follicle growth, development, and maturation. Modulating the transformation process between AA and its metabolites can be a potential target for improving reproductive outcomes.

ALA can be metabolized to EPA and DHA in the human body, with a higher conversion rate to EPA (approximately 8%) compared to DHA (less than 1%).⁹² Recent studies have elucidated a notable connection between the consumption of DHA and EPA and an increased likelihood of successful live births, juxtaposed by an inverse relationship concerning the incidence of both clinical and total pregnancy loss.⁹³ Conversely, the ingestion of total omega-3 and ALA has not demonstrated any discernible correlation with implantation, clinical pregnancy, or live birth probabilities.⁹³ These findings underscore the pivotal role of long-chain omega-3 PUFAs, specifically EPA and DHA, in dietary regimens, emphasizing their profound impact on fertility outcomes, in contrast to their short-chain counterparts such as ALA. In contrast, Broughton et al. reported that very high consumption of ALA in rats correlates with enhanced ovulation, similar to the effects observed with high intake of EPA and DHA.⁹⁴ Notably, ALA intake significantly increased prostaglandin F synthesis and decreased prostaglandin E synthesis in the ovary, while EPA and DHA did not exert a significant impact on these pathways.⁹⁴ Moreover, EPA/DHA is more effective than ALA in reducing ovarian AA levels, indicating that ALA and EPA/DHA may facilitate ovulation through the regulation of prostaglandin synthesis and AA, though the underlying mechanisms may differ.⁹⁴ Besides, the oral administration of ALA-rich flaxseed oil has been shown to significantly improve ovarian response, enhance oocyte and embryo quality, and increase the rates of MII oocyte, fertilization, cleavage, blastocyst formation, and embryo implantation in DOR patients undergoing assisted reproductive technology.⁹⁵ These findings highlight the need for future research to further explore how ALA affects reproductive processes such as folliculogenesis, fertilization, and implantation across various populations and species. By elucidating its mechanisms of action at different stages of reproduction, it is beneficial for developing evidence-based dietary recommendations.

Furthermore, research has indicated that DHA supplementation exhibits the capacity to ameliorate disrupted estrous cycles, mitigate the reduction in primordial follicle counts, and counteract alterations in ovarian gene expression induced by prolonged exposure to a high-fat diet in murine models.⁹⁶ Similarly, DHA had beneficial effects on ovarian growing follicles in both sedentary and exhaustive exercise rat models, as evidenced by reduced caspase-3 levels and elevated SOD and catalase activities.⁹⁷ Additional support for the protective attributes of AA and DHA is gleaned from investigations involving PUFA synthesis-deficient auxotrophic fads2^−/− mouse mutants. The study revealed the potential of AA and DHA to mitigate pathological changes associated with ovarian cycle disturbances, disruptions in the granulosa cell layers, disassembly of the gap junction network among follicular granulosa cells, stunted folliculogenesis, atresia of oocytes and infertility arising from PUFA synthesis deficiencies.⁹⁸ Nevertheless, high concentrations of DHA and EPA have been postulated to exert deleterious effects on oocyte developmental competence in vitro, with observed disparities potentially stemming from variances in experimental models.^99,100

In this study, we initially explored the potential association between different fatty acids and the risk of infertility in women. However, given that infertility often involves factors from both male and female, it is equally important to investigate the potential effects of male fatty acid intake on reproductive health. For instance, a cross-sectional study demonstrated that each 1 g increase in SFA consumption was significantly correlated with a 38% reduction in the likelihood of producing more than 1.5 mL of semen volume and a 21% decrease in the likelihood of achieving a sperm concentration greater than 15 million.¹⁰¹ However, there was no significant effect of SFA intake on other semen quality parameters, such as sperm motility and morphology.¹⁰¹ Eslamian et al. also identified a positive association between high TSFA intake and asthenozoospermia.¹⁰² These findings align with our preliminary genetic predictive results in the infertile female population, obtained through the MR analysis approach. Furthermore, an increased intake of omega-3 PUFAs and DHA was significantly associated with a reduced risk of asthenozoospermia, with total omega-3 PUFA intake positively correlated with sperm concentration, count, and motility.^95,103 These findings suggest that omega-3 supplementation may confer protective benefits to the male reproductive system, paralleling our cross-sectional results observed in infertile women. Additionally, a study demonstrated that omega-6 PUFA intake in the fourth quantile was positively correlated with total sperm motility and normal sperm morphology.¹⁰⁴ Interestingly, this finding contradicts the results of our cross-sectional and MR analyses in women, where omega-6 intake was identified as a risk factor.

These observed similarities and differences in the impact of fatty acids on male and female reproductive health highlight the necessity of assessing dietary fatty acid intake in both partners when developing infertility prevention and management strategies. Given the dietary convergences in couples’ food choices, a woman's fatty acid consumption often reflects that of the partner. Thus, fertility-enhancing dietary interventions may need to be designed individually for both parties, as this can enhance fertility. Further research is warranted to explore the intricate dynamics between fatty acid intake and reproductive health in both males and females, offering more holistic guidance for nutritional advice and interventions aimed at boosting fertility and reducing the risk of infertility.

Building on these understandings, it becomes evident that assessing the impact of fatty acid intake on reproductive health requires a more comprehensive framework that considers a broader spectrum of factors potentially influencing fecundability. While this study accounted for a variety of potential confounding variables, it is important to acknowledge that certain unmeasured factors, such as lifestyle factors (e.g., stress and anxiety), dietary patterns, occupational exposures, and environmental conditions, may still impact the female reproductive system and contribute to infertility.^105–108 Furthermore, a range of additional dietary components, including herbal extracts and herbal formulae, have been shown to improve reproductive health by enhancing oocyte development, promoting ovulation, improving endometrial receptivity, and restoring hormonal balance.^109,110 However, these components may not have been fully captured in the NHANES dataset, representing potential unmeasured confounders that can impact the assessment of the relationship between fatty acid intake and female infertility. Future studies should incorporate a broader range of aspects to provide a clearer picture of the multifaceted influences on fertility.

Our investigation represents a comprehensive exploration of the interplay between diverse fatty acid profiles and the risk of female infertility, employing a fusion of the NHANES database and MR analysis. Nonetheless, it is crucial to acknowledge the restrictions inherent in our study design. Firstly, we cannot exclude the possibility of infertility due to male factors because of the limitations of the NHANES database. This can potentially overemphasize the role of female factors in infertility research, thereby introducing a potential bias. Additionally, the incomplete or missing data from the Sexual Behavior Questionnaire prevented us from effectively excluding individuals who have not had sexual intercourse in the past 12 months or those without sexual experience. Their potential inclusion in the fertility group may have affected the assessment of the link between fatty acid intake and infertility. Secondly, some unmeasured potential confounders were not taken into account, such as occupational and living environment, as well as other dietary components like Chinese herbal medicine, which may introduce confounding bias. Thirdly, within the framework of a cross-sectional study, the evaluation of fatty acid consumption and the characterization of infertility relied upon questionnaire-based methodologies and dietary recall interviews, potentially engendering recall bias. Fourthly, inherent to the nature of a cross-sectional study, we cannot delineate a definitive causal relationship between fatty acid intake and the incidence of infertility. It remains plausible that individuals diagnosed with infertility may alter their dietary habits post-diagnosis, thus introducing potential confounding variables. Furthermore, food constituents extend beyond mere nutritive value to non-nutritive components, including environmental contaminants and synthetic by-products resultant from food processing and packaging. Regrettably, the design of NHANES precludes the exclusion of potential confounding influences stemming from these factors on infertility. In response, we employed MR analysis to deepen our understanding of the nexus between genetically predicted fatty acid levels and infertility risk, aiming to attenuate the impact of confounding variables, which may elucidate the partial disparities observed between the findings of our cross-sectional study and those derived from the MR analysis.

5 Conclusions

In summary, our analysis provides suggestive evidence supporting the nexus between fatty acids and female infertility risk. Particularly noteworthy is the consistently positive correlation between omega-6 PUFAs and infertility risk, evident across both cross-sectional investigations and MR analyses. However, it is imperative to acknowledge that the veracity, robustness and generalizability of these findings necessitate further validation via comprehensive and large-scale clinical inquiries. Subsequent research should probe into the potential mechanisms and impacts of diverse fatty acid intake profiles, their proportional compositions and various metabolites in the origin and development of female infertility, which will lay a pivotal groundwork for the formulation and implementation of effective clinical interventions aimed at preventing and treating infertility.

Author contributions

Qiaorui Yang: conceptualization, methodology, software, formal analysis, and writing – original draft. Jing Tao: methodology, software, formal analysis, writing – original draft, and writing – review and editing. Shengxiao Jia: validation, investigation, resources, data curation, and writing – review and editing. Zhenliang Fan: visualization and supervision. All the authors have read and approved the published version of the manuscript.

Data availability

This study was carried out using publicly available data from the National Health and Nutrition Examination Survey (NHANES) at https://www.cdc.gov/nchs/nhanes/index.htm and the IEU Open GWAS Project at https://gwas.mrcieu.ac.uk/ (6th March 2024).

Conflicts of interest

There are no conflicts to declare. The graphical abstract and Fig. 1 were created using Figdraw (https://www.figdraw.com).

Acknowledgements

This project was funded by the National Natural Science Foundation of China (No. 82104756) and the Natural Science Foundation of Zhejiang Province (No. LQ22H270002). We gratefully acknowledge the authors and participants of all NHANES and GWAS from which we used the statistical data.

References

1. F. Zegers-Hochschild, G. D. Adamson, S. Dyer, C. Racowsky, J. de Mouzon, R. Sokol, L. Rienzi, A. Sunde, L. Schmidt, I. D. Cooke, J. L. Simpson and S. van der Poel, The International Glossary on Infertility and Fertility Care, 2017, Fertil. Steril., 2017, 108, 393–406.
2. Practice Committee of the American Society for Reproductive Medicine, Definitions of infertility and recurrent pregnancy loss: a committee opinion, Fertil. Steril., 2020, 113, 533–535.
3. M. Vander Borght and C. Wyns, Fertility and infertility: Definition and epidemiology, Clin. Biochem., 2018, 62, 2–10.
4. E. Bouko-Levy, C. Vialaret, C. Sallée, P. Marquet, F. Margueritte, L. Dion, V. Lavoue and T. Gauthier, Estimation of the prevalence of uterine infertility and its different causes in France according to data from a literature review, J. Gynecol. Obstet. Hum. Reprod., 2023, 52, 102684.
5. E. Harris, Infertility Affects 1 in 6 People Globally, J. Am. Med. Assoc., 2023, 329, 1443.
6. A. Omidvar-Mehrabadi, F. Ebrahimi, M. Shahbazi and M. Mohammadnia-Afrouzi, Cytokine and chemokine profiles in women with endometriosis, polycystic ovary syndrome, and unexplained infertility, Cytokine, 2024, 178, 156588.
7. A. Chandra, C. E. Copen and E. H. Stephen, Infertility and impaired fecundity in the United States, 1982-2010: data from the National Survey of Family Growth, Natl. Health Stat. Rep., 2013, 67, 1–18.
8. H. Sun, T. T. Gong, Y. T. Jiang, S. Zhang, Y. H. Zhao and Q. J. Wu, Global, regional, and national prevalence and disability-adjusted life-years for infertility in 195 countries and territories, 1990-2017: results from a global burden of disease study, 2017, Aging, 2019, 11, 10952–10991.
9. M. Maitin-Shepard, E. F. Werner, L. A. Feig, J. E. Chavarro, S. L. Mumford, B. Wylie, O. J. Rando, A. J. Gaskins, D. Sakkas, M. Arora, R. Kudesia, M. E. Lujan, J. Braun and D. Mozaffarian, Food, nutrition, and fertility: from soil to fork, Am. J. Clin. Nutr., 2024, 119, 578–589.
10. T. Silva, M. Jesus, C. Cagigal and C. Silva, Food with Influence in the Sexual and Reproductive Health, Curr. Pharm. Biotechnol., 2019, 20, 114–122.
11. J. Lyngsø, U. S. Kesmodel, B. Bay, H. J. Ingerslev, A. M. N. Andersen and C. H. Ramlau-Hansen, Impact of female daily coffee consumption on successful fertility treatment: a Danish cohort study, Fertil. Steril., 2019, 112, 120–129.
12. J. Lee, C. W. Choo, K. Y. Moon, S. W. Lyu, H. Kim, J. Y. Lee, J. R. Lee, B. C. Jee, K. Hwang, S. H. Kim and S. K. Park, Risk Factors for Infertility in Korean Women, J. Korean Med. Sci., 2024, 39, e85.
13. J. Jurczewska and D. Szostak-Węgierek, The Influence of Diet on Ovulation Disorders in Women-A Narrative Review, Nutrients, 2022, 14, 1556.
14. K. Smolińska, A. Szopa, J. Sobczyński, A. Serefko and P. Dobrowolski, Nutritional Quality Implications: Exploring the Impact of a Fatty Acid-Rich Diet on Central Nervous System Development, Nutrients, 2024, 16, 1093.
15. F. Hammiche, M. Vujkovic, W. Wijburg, J. H. de Vries, N. S. Macklon, J. S. Laven and R. P. Steegers-Theunissen, Increased preconception omega-3 polyunsaturated fatty acid intake improves embryo morphology, Fertil. Steril., 2011, 95, 1820–1823.
16. Z. A. Al-Safi, H. Liu, N. E. Carlson, J. Chosich, M. Harris, A. P. Bradford, C. Robledo, R. H. Eckel and A. J. Polotsky, Omega-3 Fatty Acid Supplementation Lowers Serum FSH in Normal Weight But Not Obese Women, J. Clin. Endocrinol. Metab., 2016, 101, 324–333.
17. L. J. Moran, V. Tsagareli, M. Noakes and R. Norman, Altered Preconception Fatty Acid Intake Is Associated with Improved Pregnancy Rates in Overweight and Obese Women Undertaking in Vitro Fertilisation, Nutrients, 2016, 8, 10.
18. S. L. Mumford, R. W. Browne, K. Kim, C. Nichols, B. Wilcox, R. M. Silver, M. T. Connell, T. L. Holland, D. L. Kuhr, U. R. Omosigho, N. J. Perkins, R. Radin, L. A. Sjaarda and E. F. Schisterman, Preconception Plasma Phospholipid Fatty Acids and Fecundability, J. Clin. Endocrinol. Metab., 2018, 103, 4501–4510.
19. D. García, S. Brazal, A. Rodríguez, A. Prat and R. Vassena, Knowledge of age-related fertility decline in women: A systematic review, Eur. J. Obstet. Gynecol. Reprod. Biol., 2018, 230, 109–118.
20. M. J. Eijkemans, F. van Poppel, D. F. Habbema, K. R. Smith, H. Leridon and E. R. te Velde, Too old to have children? Lessons from natural fertility populations, Hum. Reprod., 2014, 29, 1304–1312.
21. R. Wang, Y. Feng, J. Chen, Y. Chen and F. Ma, Association between polyunsaturated fatty acid intake and infertility among American women aged 20-44 years, Front. Public Health, 2022, 10, 938343.
22. H. Xu, Q. Wen, X. Xing, Y. Chen, Q. Zhu, M. Tan, M. Zhang, T. Pan and S. Wu, High Dietary Inflammatory Index increases the risk of female infertility: An analysis of NHANES 2013-2018, Nutr. Res., 2024, 125, 50–60.
23. Y. Chen, H. Xu, J. Yan, Q. Wen, M. Ma, N. Xu, H. Zou, X. Xing, Y. Wang and S. Wu, Inflammatory markers are associated with infertility prevalence: a cross-sectional analysis of the NHANES 2013-2020, BMC Public Health, 2024, 24, 221.
24. J. Lu, J. Tang, Y. Zou, R. Wu, H. Chen and W. Wang, Association between dietary inflammatory index and self-reported female infertility from the National Health and Nutrition Examination Survey 2013-2020, J. Hum. Nutr. Diet., 2024, 37, 354–364.
25. J. Tang, Y. Xu, Z. Wang, X. Ji, Q. Qiu, Z. Mai, J. Huang, N. Ouyang and H. Chen, Association between metabolic healthy obesity and female infertility: the national health and nutrition examination survey, 2013-2020, BMC Public Health, 2023, 23, 1524.
26. American College of Obstetricians and Gynecologists Committee on Gynecologic Practice and Practice Committee, Female age-related fertility decline. Committee Opinion No. 589, Fertil. Steril., 2014, 101, 633–634.
27. M. A. Emokpae and S. I. Brown, Effects of lifestyle factors on fertility: practical recommendations for modification, Reprod. Fertil., 2021, 2, R13–R26.
28. B. V. Rossi, M. Abusief and S. A. Missmer, Modifiable Risk Factors and Infertility: What are the Connections?, Am. J. Lifestyle Med., 2014, 10, 220–231.
29. J. D. Kloss, M. L. Perlis, J. A. Zamzow, E. J. Culnan and C. R. Gracia, Sleep, sleep disturbance, and fertility in women, Sleep Med. Rev., 2015, 22, 78–87.
30. S. Untaaveesup, B. Chongthanadon, C. Kositamongkol, P. Phisalprapa, K. Panyakhamlerd and V. Titapant, Economic evaluation of lifestyle interventions in infertility management: A systematic review, PLoS One, 2024, 19, e0306419.
31. M. D. Jørgensen, E. M. Mikkelsen, E. E. Hatch, K. J. Rothman, L. A. Wise, H. T. Sørensen and A. S. D. Laursen, Socioeconomic status and fecundability in a Danish preconception cohort, Hum. Reprod., 2023, 38, 1183–1193.
32. N. Andlib, S. Prabha and S. C. Thakur, Unraveling the molecular pathogenesis of Type 2 Diabetes and its impact on female infertility: A bioinformatics and systems biology approach, Comput. Biol. Med., 2024, 180, 108987.
33. W. Xiong, L. Han, X. Tang, R. Li, W. Chen, X. Liu, H. Nie, W. Qin and L. Ling, Maternal Hypertension and Fecundability: A Population-Based Cohort Study, Hypertension, 2024, 81, e173–e184.
34. T. Yang, J. Zhao, F. Liu and Y. Li, Lipid metabolism and endometrial receptivity, Hum. Reprod. Update, 2022, 28, 858–889.
35. S. Hunt and B. Vollenhoven, Pelvic inflammatory disease and infertility, Aust. J. Gen. Pract., 2023, 52, 215–218.
36. Y. Zhang and X. Liu, Effects of physical activity and sedentary behaviors on cardiovascular disease and the risk of all-cause mortality in overweight or obese middle-aged and older adults, Front. Public Health, 2024, 12, 1302783.
37. Q. Wang, Q. Shi, J. Lu, Z. Wang and J. Hou, Causal relationships between inflammatory factors and multiple myeloma: A bidirectional Mendelian randomization study, Int. J. Cancer, 2022, 151, 1750–1759.
38. N. Papadimitriou, N. Dimou, K. K. Tsilidis, B. Banbury, R. M. Martin, S. J. Lewis, N. Kazmi, T. M. Robinson, D. Albanes, K. Aleksandrova, S. I. Berndt, D. T. Bishop, H. Brenner, D. D. Buchanan, B. Bueno-de-Mesquita, P. T. Campbell, S. Castellví-Bel, A. T. Chan, J. Chang-Claude, M. Ellingjord-Dale, J. C. Figueiredo, S. J. Gallinger, G. G. Giles, E. Giovannucci, S. B. Gruber, A. Gsur, J. Hampe, H. Hampel, S. Harlid, T. A. Harrison, M. Hoffmeister, J. L. Hopper, L. Hsu, J. M. Huerta, J. R. Huyghe, M. A. Jenkins, T. O. Keku, T. Kühn, C. La Vecchia, L. Le Marchand, C. I. Li, L. Li, A. Lindblom, N. M. Lindor, B. Lynch, S. D. Markowitz, G. Masala, A. M. May, R. Milne, E. Monninkhof, L. Moreno, V. Moreno, P. A. Newcomb, K. Offit, V. Perduca, P. D. P. Pharoah, E. A. Platz, J. D. Potter, G. Rennert, E. Riboli, M. J. Sánchez, S. L. Schmit, R. E. Schoen, G. Severi, S. Sieri, M. L. Slattery, M. Song, C. M. Tangen, S. N. Thibodeau, R. C. Travis, A. Trichopoulou, C. M. Ulrich, F. J. B. van Duijnhoven, B. Van Guelpen, P. Vodicka, E. White, A. Wolk, M. O. Woods, A. H. Wu, U. Peters, M. J. Gunter and N. Murphy, Physical activity and risks of breast and colorectal cancer: a Mendelian randomisation analysis, Nat. Commun., 2020, 11, 597.
39. X. Liang, L. Liang and Y. Fan, Two-sample mendelian randomization analysis investigates ambient fine particulate matter's impact on cardiovascular disease development, Sci. Rep., 2023, 13, 20129.
40. E. Sanderson, Multivariable Mendelian Randomization and Mediation, Cold Spring Harbor Perspect. Med., 2021, 11, a038984.
41. L. Peng, X. Luo, B. Cao and X. Wang, Unraveling the link: environmental tobacco smoke exposure and its impact on infertility among American women (18-50 years), Front. Public Health, 2024, 12, 1358290.
42. W. Xu, Y. You, T. Yu and J. Li, Insights into Modifiable Risk Factors of Infertility: A Mendelian Randomization Study, Nutrients, 2022, 14, 4042.
43. N. Salari, F. Babajani, A. Hosseinian-Far, R. Hasheminezhad, N. Abdoli, P. Haydarisharaf and M. Mohammadi, Global prevalence of major depressive disorder, generalized anxiety, stress, and depression among infertile women: a systematic review and meta-analysis, Arch. Gynecol. Obstet., 2024, 5, 1833–1846.
44. S. van Buuren and K. Groothuis-Oudshoorn, mice: Multivariate Imputation by Chained Equations in R, J. Stat. Softw., 2011, 45, 1–67.
45. N. Sasaki, L. E. Jones and D. O. Carpenter, Fish consumption and omega-3 polyunsaturated fatty acids from diet are positively associated with cognitive function in older adults even in the presence of exposure to lead, cadmium, selenium, and methylmercury: a cross-sectional study using NHANES 2011-2014 data, Am. J. Clin. Nutr., 2024, 119, 283–293.
46. D. H. Lee, N. Keum, F. B. Hu, E. J. Orav, E. B. Rimm, W. C. Willett and E. L. Giovannucci, Predicted lean body mass, fat mass, and all cause and cause specific mortality in men: prospective US cohort study, Br. Med. J., 2018, 362, k2575.
47. S. Burgess, F. Dudbridge and S. G. Thompson, Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods, Stat. Med., 2016, 35, 1880–1906.
48. J. Bowden, M. F. Del Greco, C. Minelli, Q. Zhao, D. A. Lawlor, N. A. Sheehan, J. Thompson and G. D. Smith, Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption, Int. J. Epidemiol., 2019, 48, 728–742.
49. M. F. Greco, C. Minelli, N. A. Sheehan and J. R. Thompson, Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome, Stat. Med., 2015, 34, 2926–2940.
50. S. C. Larsson, A. S. Butterworth and S. Burgess, Mendelian randomization for cardiovascular diseases: principles and applications, Eur. Heart J., 2023, 44, 4913–4924.
51. J. Bowden, G. D. Smith and S. Burgess, Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression, Int. J. Epidemiol., 2015, 44, 512–525.
52. M. Verbanck, C. Y. Chen, B. Neale and R. Do, Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases, Nat. Genet., 2018, 50, 693–698.
53. P. Zhao, F. Han, X. Liang, L. Meng, B. Yu, X. Liu and J. Tian, Causal Effects of Basal Metabolic Rate on Cardiovascular Disease: A Bidirectional Mendelian Randomization Study, J. Am. Heart Assoc., 2024, 13, e031447.
54. X. Chen, S. Zhang, X. Wu, Y. Lei, B. Lei and Z. Zhao, Inflammatory cytokines and oral lichen planus: a Mendelian randomization study, Front. Immunol., 2024, 15, 1332317.
55. M. Abodi, V. De Cosmi, F. Parazzini and C. Agostoni, Omega-3 fatty acids dietary intake for oocyte quality in women undergoing assisted reproductive techniques: A systematic review, Eur. J. Obstet. Gynecol. Reprod. Biol., 2022, 275, 97–105.
56. R. Zarezadeh, A. Mehdizadeh, J. Leroy, M. Nouri, S. Fayezi and M. Darabi, Action mechanisms of n-3 polyunsaturated fatty acids on the oocyte maturation and developmental competence: Potential advantages and disadvantages, J. Cell. Physiol., 2019, 234, 1016–1029.
57. W. Stoffel, B. Holz, B. Jenke, E. Binczek, R. H. Günter, C. Kiss, I. Karakesisoglou, M. Thevis, A. A. Weber, S. Arnhold and K. Addicks, Delta6-desaturase (FADS2) deficiency unveils the role of omega3- and omega6-polyunsaturated fatty acids, EMBO J., 2008, 27, 2281–2292.
58. M. Jahangirifar, M. Taebi, M. H. Nasr-Esfahani, M. Heidari-Beni and G. H. Asgari, Dietary Fatty Acid Intakes and the Outcomes of Assisted Reproductive Technique in Infertile Women, J. Reprod. Infertil., 2021, 22, 173–183.
59. S. L. Mumford, J. E. Chavarro, C. Zhang, N. J. Perkins, L. A. Sjaarda, A. Z. Pollack, K. C. Schliep, K. A. Michels, S. M. Zarek, T. C. Plowden, R. G. Radin, L. C. Messer, R. A. Frankel and J. Wactawski-Wende, Dietary fat intake and reproductive hormone concentrations and ovulation in regularly menstruating women, Am. J. Clin. Nutr., 2016, 103, 868–877.
60. L. A. Wise, A. K. Wesselink, K. L. Tucker, S. Saklani, E. M. Mikkelsen, H. Cueto, A. H. Riis, E. Trolle, C. J. McKinnon, K. A. Hahn, K. J. Rothman, H. T. Sørensen and E. E. Hatch, Dietary Fat Intake and Fecundability in 2 Preconception Cohort Studies, Am. J. Epidemiol., 2018, 187, 60–74.
61. J. E. Chavarro, J. W. Rich-Edwards, B. A. Rosner and W. C. Willett, Dietary fatty acid intakes and the risk of ovulatory infertility, Am. J. Clin. Nutr., 2007, 85, 231–237.
62. L. Barrea, A. Arnone, G. Annunziata, G. Muscogiuri, D. Laudisio, C. Salzano, G. Pugliese, A. Colao and S. Savastano, Adherence to the Mediterranean Diet, Dietary Patterns and Body Composition in Women with Polycystic Ovary Syndrome (PCOS), Nutrients, 2019, 11, 2278.
63. S. Kalgaonkar, R. U. Almario, D. Gurusinghe, E. M. Garamendi, W. Buchan, K. Kim and S. E. Karakas, Differential effects of walnuts vs almonds on improving metabolic and endocrine parameters in PCOS, Eur. J. Clin. Nutr., 2011, 65, 386–393.
64. P. Mirabi, M. J. Chaichi, S. Esmaeilzadeh, S. G. A. Jorsaraei, A. Bijani, M. Ehsani and S. F. H. Karooee, The role of fatty acids on ICSI outcomes: a prospective cohort study, Lipids Health Dis., 2017, 16, 18.
65. D. C. Wathes, D. R. Abayasekara and R. J. Aitken, Polyunsaturated fatty acids in male and female reproduction, Biol. Reprod., 2007, 77, 190–201.
66. J. Stanhiser, A. M. Z. Jukic, D. R. McConnaughey and A. Z. Steiner, Omega-3 fatty acid supplementation and fecundability, Hum. Reprod., 2022, 37, 1037–1046.
67. A. J. Gaskins, R. Sundaram, G. M. B. Louis and J. E. Chavarro, Seafood Intake, Sexual Activity, and Time to Pregnancy, J. Clin. Endocrinol. Metab., 2018, 103, 2680–2688.
68. J. L. Bauer, K. Kuhn, A. P. Bradford, Z. A. Al-Safi, M. A. Harris, R. H. Eckel, C. Y. Robledo, A. Malkhasyan, J. Johnson, N. R. Gee and A. J. Polotsky, Reduction in FSH Throughout the Menstrual Cycle After Omega-3 Fatty Acid Supplementation in Young Normal Weight but not Obese Women, Reprod. Sci., 2019, 26, 1025–1033.
69. S. Punia, K. S. Sandhu, A. K. Siroha and S. B. Dhull, Omega 3-metabolism, absorption, bioavailability and health benefits–A review, PharmaNutrition, 2019, 10, 100162.
70. K. Albracht-Schulte, N. S. Kalupahana, L. Ramalingam, S. Wang, S. M. Rahman, J. Robert-McComb and N. Moustaid-Moussa, Omega-3 fatty acids in obesity and metabolic syndrome: a mechanistic update, J. Nutr. Biochem., 2018, 58, 1–16.
71. S. L. Wakefield, M. Lane, S. J. Schulz, M. L. Hebart, J. G. Thompson and M. Mitchell, Maternal supply of omega-3 polyunsaturated fatty acids alter mechanisms involved in oocyte and early embryo development in the mouse, Am. J. Physiol.: Endocrinol. Metab., 2008, 294, E425–E434.
72. V. S. I. Giorgi, R. A. Ferriani and P. A. Navarro, Omega-3 fatty acids and L-carnitine prevent meiotic oocyte damage induced by follicular fluid from infertile women with endometriosis: an experimental study, JBRA Assist. Reprod., 2023, 27, 610–618.
73. J. A. Attaman, A. K. Stanic, M. Kim, M. P. Lynch, B. R. Rueda and A. K. Styer, The anti-inflammatory impact of omega-3 polyunsaturated Fatty acids during the establishment of endometriosis-like lesions, Am. J. Reprod. Immunol., 2014, 72, 392–402.
74. A. Akyol, M. Şimşek, R. İlhan, B. Can, M. Baspinar, H. Akyol, H. F. Gül, F. Gürsu, B. Kavak and M. Akın, Efficacies of vitamin D and omega-3 polyunsaturated fatty acids on experimental endometriosis, Taiwan J. Obstet. Gynecol., 2016, 55, 835–839.
75. H. Zhang, L. Zheng, C. Li, J. Jing, Z. Li, S. Sun, T. Xue, K. Zhang, M. Xue, C. Cao, L. Ouyang, Z. Qian, R. Xu, Z. He, R. Ma, L. Chen and B. Yao, Effects of gut microbiota on omega-3-mediated ovary and metabolic benefits in polycystic ovary syndrome mice, J. Ovarian Res., 2023, 16, 138.
76. R. Ma, S. Wang, M. Xue, H. Zhang, Z. He, K. Jueraitetibaike, X. Ge, L. Chen and B. Yao, Effects of n-3 PUFA supplementation on oocyte in vitro maturation in mice with polycystic ovary syndrome, J. Ovarian Res., 2023, 16, 87.
77. S. Trop-Steinberg, E. M. Heifetz, Y. Azar, I. Kafka, A. Weintraub and M. Gal, Omega-3 Intake Improves Clinical Pregnancy Rate in Polycystic Ovary Syndrome Patients: A Double-Blind, Randomized Study, Isr. Med. Assoc. J., 2023, 25, 131–136.
78. J. Stanhiser, A. M. Z. Jukic and A. Z. Steiner, Serum omega-3 and omega-6 fatty acid concentrations and natural fertility, Hum. Reprod., 2020, 35, 950–957.
79. S. Bianconi, M. E. Santillán, M. D. R. Solís, A. C. Martini, M. F. Ponzio, L. M. Vincenti, H. B. Schiöth, V. P. Carlini and G. Stutz, Effects of dietary omega-3 PUFAs on growth and development: Somatic, neurobiological and reproductive functions in a murine model, J. Nutr. Biochem., 2018, 61, 82–90.
80. D. Nehra, H. D. Le, E. M. Fallon, S. J. Carlson, D. Woods, Y. A. White, A. H. Pan, L. Guo, S. J. Rodig, J. L. Tilly, B. R. Rueda and M. Puder, Prolonging the female reproductive lifespan and improving egg quality with dietary omega-3 fatty acids, Aging Cell, 2012, 11, 1046–1054.
81. X. Ma, X. Weng, X. Hu, Q. Wang, Y. Tian, Y. Ding and C. Zhang, Roles of different n-3/n-6 PUFA ratios in ovarian cell development and steroidogenesis in PCOS rats, Food Funct., 2019, 10, 7397–7406.
82. R. K. Saini and Y. S. Keum, Omega-3 and omega-6 polyunsaturated fatty acids: Dietary sources, metabolism, and significance - A review, Life Sci., 2018, 203, 255–267.
83. W. Zhang and F. Wu, Linoleic acid induces human ovarian granulosa cell inflammation and apoptosis through the ER-FOXO1-ROS-NFκB pathway, Sci. Rep., 2024, 14, 6392.
84. L. L. Oliva, M. E. Santillán, L. C. Ryan, S. Bianconi, L. M. Vincenti, A. C. Martini, M. F. Ponzio and G. Stutz, Mouse plasma progesterone levels are affected by different dietary ω6/ω3 ratios, Horm. Metab. Res., 2014, 46, 120–125.
85. S. Hekmat, M. Sharifzadeh, T. Toliyat, R. S. Kouzehkonan, M. M. Ardestani, M. Tabarrai and S. N. S. Lamardi, Urtica pilulifera L. seed extract promotes folliculogenesis and alleviates the diminished ovarian reserve in the Balb/c mice model: An experimental study, Int. J. Reprod. Biomed., 2024, 22, 111–126.
86. J. K. Innes and P. C. Calder, Omega-6 fatty acids and inflammation, Prostaglandins, Leukotrienes Essent. Fatty Acids, 2018, 132, 41–48.
87. Y. D. Prabhu and A. V. Gopalakrishnan, γ-Linolenic acid ameliorates DHEA induced pro-inflammatory response in polycystic ovary syndrome via PPAR-γ signaling in rats, Reprod. Biol., 2020, 20, 348–356.
88. C. M. Webster, M. L. Deline and J. L. Watts, Stress response pathways protect germ cells from omega-6 polyunsaturated fatty acid-mediated toxicity in Caenorhabditis elegans, Dev. Biol., 2013, 373, 14–25.
89. Y. Ma, L. Zheng, Y. Wang, Y. Gao and Y. Xu, Arachidonic Acid in Follicular Fluid of PCOS Induces Oxidative Stress in a Human Ovarian Granulosa Tumor Cell Line (KGN) and Upregulates GDF15 Expression as a Response, Front. Endocrinol., 2022, 13, 865748.
90. L. Li, S. Zhu, W. Shu, Y. Guo, Y. Guan, J. Zeng, H. Wang, L. Han, J. Zhang, X. Liu, C. Li, X. Hou, M. Gao, J. Ge, C. Ren, H. Zhang, T. Schedl, X. Guo, M. Chen and Q. Wang, Characterization of Metabolic Patterns in Mouse Oocytes during Meiotic Maturation, Mol. Cell, 2020, 80, 525–540.
91. C. Liang, X. Zhang, C. Qi, H. Hu, Q. Zhang, X. Zhu and Y. Fu, UHPLC-MS-MS analysis of oxylipins metabolomics components of follicular fluid in infertile individuals with diminished ovarian reserve, Reprod. Biol. Endocrinol., 2021, 19, 143.
92. M. Plourde and S. C. Cunnane, Extremely limited synthesis of long chain polyunsaturates in adults: implications for their dietary essentiality and use as supplements, Appl. Physiol., Nutr., Metab., 2007, 32, 619–634.
93. A. Salas-Huetos, M. Arvizu, L. Mínguez-Alarcón, M. Mitsunami, J. Ribas-Maynou, M. Yeste, J. B. Ford, I. Souter and J. E. Chavarro, Women's and men's intake of omega-3 fatty acids and their food sources and assisted reproductive technology outcomes, Am. J. Obstet. Gynecol., 2022, 227, 246.
94. K. S. Broughton, J. Bayes and B. Culver, High α-linolenic acid and fish oil ingestion promotes ovulation to the same extent in rats, Nutr. Res., 2010, 30, 731–738.
95. Q. Chu, Y. X. Yu, J. Z. Zhang, Y. T. Zhang and J. P. Yu, Effects of flaxseed oil supplementation on metaphase II oocyte rates in IVF cycles with decreased ovarian reserve: a randomized controlled trial, Front. Endocrinol., 2024, 15, 1280760.
96. N. M. Hohos, E. M. Elliott, K. J. Cho, I. S. Lin, M. C. Rudolph and M. E. Skaznik-Wikiel, High-fat diet-induced dysregulation of ovarian gene expression is restored with chronic omega-3 fatty acid supplementation, Mol. Cell. Endocrinol., 2020, 499, 110615.
97. A. F. Mostafa, S. M. Samir and R. M. Nagib, Omega-3 polyunsaturated fatty acid docosahexaenoic acid and its role in exhaustive-exercise-induced changes in female rat ovulatory cycle, Can. J. Physiol. Pharmacol., 2018, 96, 395–403.
98. W. Stoffel, I. Schmidt-Soltau, E. Binczek, A. Thomas, M. Thevis and I. Wegner, Dietary ω3-and ω6-Polyunsaturated fatty acids reconstitute fertility of Juvenile and adult Fads2-Deficient mice, Mol. Metab., 2020, 36, 100974.
99. M. Oseikria, S. Elis, V. Maillard, E. Corbin and S. Uzbekova, N-3 polyunsaturated fatty acid DHA during IVM affected oocyte developmental competence in cattle, Theriogenology, 2016, 85, 1625–1634.
100. N. Nikoloff, A. Campagna, C. Luchetti, A. C. Carranza-Martín, A. M. Pascua, J. M. Anchordoquy, J. P. Anchordoquy, D. M. Lombardo, A. Seoane and C. C. Furnus, Effects of EPA on bovine oocytes matured in vitro with antioxidants: Impact on the lipid content of oocytes and early embryo development, Theriogenology, 2020, 146, 152–161.
101. H. Dadkhah, A. Kazemi, M. H. Nasr-Isfahani and S. Ehsanpour, The Relationship between the Amount of Saturated Fat Intake and Semen Quality in Men, Iran. J. Nurs. Midwifery Res., 2017, 22, 46–50.
102. G. Eslamian, N. Amirjannati, B. Rashidkhani, M. R. Sadeghi, A. R. Baghestani and A. Hekmatdoost, Dietary fatty acid intakes and asthenozoospermia: a case-control study, Fertil. Steril., 2015, 103, 190–198.
103. C. González-Ravina, M. Aguirre-Lipperheide, F. Pinto, D. Martín-Lozano, M. Fernández-Sánchez, V. Blasco, E. Santamaría-López and L. Candenas, Effect of dietary supplementation with a highly pure and concentrated docosahexaenoic acid (DHA) supplement on human sperm function, Reprod. Biol., 2018, 18, 282–288.
104. F. Haeri, M. Nouri, O. Sadrmanesh, M. Shirani and R. Ghiasvand, The relationship between the intake of dietary fatty acids and minerals with sperm parameters in infertile men, Clin. Nutr. ESPEN, 2023, 58, 201–207.
105. A. Salas-Huetos, M. Mitsunami, S. Wang, L. Mínguez-Alarcón, J. Ribas-Maynou, M. Yeste, I. Souter and J. E. Chavarro, Women's Adherence to Healthy Dietary Patterns and Outcomes of Infertility Treatment, JAMA Netw. Open, 2023, 6, e2329982.
106. K. S. Hougaard, H. Hannerz, H. Feveile and J. P. Bonde, Increased incidence of infertility treatment among women working in the plastics industry, Reprod. Toxicol., 2009, 27, 186–189.
107. F. Y. Koochaksaraei, M. Simbar, M. Khoshnoodifar, M. Faramarzi and M. Nasiri, Interventions promoting mental health dimensions in infertile women: a systematic review, BMC Psychol., 2023, 11, 254.
108. M. Sørensen, A. H. Poulsen, B. Nøhr, J. Khan, M. Ketzel, J. Brandt, O. Raaschou-Nielsen and A. Jensen, Long term exposure to road traffic noise and air pollution and risk of infertility in men and women: nationwide Danish cohort study, Br. Med. J., 2024, 386, e080664.
109. J. F. Xia, Y. Inagaki, J. F. Zhang, L. Wang and P. P. Song, Chinese medicine as complementary therapy for female infertility, Chin. J. Integr. Med., 2017, 23, 245–252.
110. M. Masjedi, Y. Izadi, T. Montahaei, R. Mohammadi, M. A. Helforoush and K. R. Rad, An illustrated review on herbal medicine used for the treatment of female infertility, Eur. J. Obstet. Gynecol. Reprod. Biol., 2024, 302, 273–282.

---

Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4fo04020a

---