The association between high-density lipoprotein-related inflammation index and female infertility prevalence: Insight from a cross-sectional study of NHANES.

Objective Monocyte‐to‐high‐density lipoprotein cholesterol ratio (MHR), lymphocyte‐to‐high‐density lipoprotein cholesterol ratio (LHR), and neutrophil‐to‐high‐density lipoprotein cholesterol ratio (NHR) are novel and convenient biomarkers. However, the association between these high‐density lipoprotein‐related inflammation indices and the odds of female infertility remains unclear. The present study aimed to investigate potential relationships.

In this study, 2408 participants were the focus of an analysis of data from the National Health and Nutrition Examination Survey (NHANES) in 2013–2018. Multivariate logistic regression, smooth curve fitting, and subgroup analyses were employed to explore the correlation between MHR, LHR, NHR and female infertility.

As indicated by our research, there was no obvious correlation between NHR and female infertility, whereas MHR and LHR showed an association with increased odds of infertility. A multiple logistic regression model demonstrated a positive linear relationship between MHR, LHR and the incidence of infertility after adjusting for all potential confounders. Then, compared to the minimum MHR and LHR, individuals in the highest quartile had a 70% and 54% separately higher chance of female infertility prevalence (MHR: odds ratio [OR] = 1.70, 95% confidence interval [CI]: 1.13–2.56, P = 0.011; LHR: OR = 1.54, 95% CI: 1.03–2.31, P = 0.036). Furthermore, smooth curve fitting showed the linear relationship between MHR, LHR and infertility. The results of subgroup analysis demonstrated that the correlation between infertility odds and MHR, LHR. In the population under 30 years, MHR and LHR were more prominent.

Female infertility prevalence is positively correlated with elevated MHR and LHR, suggesting that they may be potential indicators for evaluating the association with female infertility.

cross‐sectional study, female infertility, high‐density lipoprotein‐related inflammation index, NHANES 1. INTRODUCTION Infertility is known as the inability to conceive after over 12 months of regular, uncovered sexual behavior. 1 It can be influenced by the emotional state of an individual, as well as personal and social factors. 2 The report shows that approximately 10%–15% of women experience infertility. 3 As a result, infertility has emerged as a significant public health concern that places a huge burden on the community. 4 Therefore, identifying simple, cost‐effective, and reliable biomarkers are crucial for exploring the risk factors of female infertility. High density lipoprotein (HDL) has beneficial effects such as antithrombosis, anti‐inflammatory, antioxidant, and immune regulation. HDL interacts complexly with blood cells and mutually with different immune cells. 5 Given the positive relationships of HDL, researchers have been focusing on the potential value of HDL‐related inflammatory markers. Monocyte‐to‐high‐density lipoprotein cholesterol ratio (MHR), lymphocyte‐to‐high‐density lipoprotein cholesterol ratio (LHR), and neutrophil‐to‐high‐density lipoprotein cholesterol ratio (NHR) have been novel biomarkers recently. They can be simply obtained and computed from routine blood tests. They have been observed to be early indicators of a range of medical conditions, including chronic obstructive pulmonary disease, metabolic syndrome, non‐alcoholic fatty liver disease, and cardiovascular disease. 6 Nevertheless, it remains unclear whether there is a potential correlation between high‐density lipoprotein‐related inflammatory indicators and female infertility. In summary, regarding the complicated interactions between inflammatory cells and HDL, as well as the evidence confirming that MHR, LHR, and NHR are strongly associated with several systemic disorders, we hypothesized that the above‐mentioned HDL‐related inflammatory biomarkers are related to female infertility. Our study utilized data from the National Health and Nutrition Examination Survey (NHANES) to investigate the relationships between MHR, LHR, NHR and the prevalence of female infertility. It aimed to provide simple and economical inflammatory markers for exploring the association with infertility and offer a basis for the exploration of female infertility. 2. MATERIALS AND METHODS 2.1. Data sources and study population NHANES is a comprehensive research project that conducts a large‐scale national epidemiological survey in community and clinical settings across the USA. It assesses the nutritional status and overall health of the American population through physical examinations and interviews. Every year, it selects a nationally representative sample of approximately 5000 individuals using an advanced, multistage sampling design. This study adopted a cross‐sectional design, utilizing data from the 2013–2018 cycles of NHANES. The NHANES public data provided the basis for this study, and all data were gathered from the official website (https://wwwn.cdc.gov/nchs/nhanes/). 7 The data sources were the cycle datasets from 2013 to 2014, 2015 to 2016, and 2017 to 2018. The data files were merged using the unique participant number (SEQN) to ensure data consistency. The NHANES research program for the years 2013–2018 received official approval from the National Center for Health Statistics (NCHS). 8 The NHANES protocol was revised and approved by the Ethics Review Committee of the NCHS (protocol NCHS ERB nos: #2011‐17 and 2018‐01). All participants provided written informed consent. Groups were retrospectively classified by NHANES RHQ074 responses: no randomization, matching, or intervention was implemented in this observational comparison. RHQ074 specifically corresponds to the survey question “Have you attempted to become pregnant for at least one year without success?” with “Yes” coded as infertile and “No” as fertile. In this study, the cohort consisted of individuals who participated in NHANES from 2013 to 2018 and had complete MHR, LHR, NHR and reproductive data records. A total of 29 400 participants were included. Of these, people with the following characteristics were excluded: (1) men, (2) women under the age of 18 years or those over the age of 44 years, (3) females with incomplete fertility information, (4) missing information on covariates, (5) missing information on high‐density lipoprotein‐related inflammation index. The detailed exclusion process is illustrated in Figure 1 (the participant screening flow chart). Ultimately, the final sample size comprised 2408 individuals. 2.2. Diagnosis of female infertility Responses from the reproductive health questionnaire were gathered to calculate the infertile dependent variable (variable name: RHQ074). Those who responded positively to the questionnaires' question were considered “infertile,” whereas women who answered “No” were considered “fertile.” 9 It also limited the assessment to women who were not pregnant at the time. 2.3. Assessment of MHR, LHR and NHR The exposure variables MHR, LHR, and NHR were calculated from the NHANES whole blood count database. The following is an outline of the calculation formula. MHR = monocyte count (1000 cells/μL)/HDL‐C (mmol/L). LHR = lymphocyte count (1000 cells/μL)/HDL‐C (mmol/L). NHR = neutrophil count (1000 cells/μL)/HDL‐C (mmol/L). Exposure variables (MHR, LHR, NHR) were measured synchronously with infertility status and quartile stratification was based on overall sample distribution. 2.4. Covariables This analysis addressed a variety of covariates selected based on previous studies. 10 , 11 The considered demographic factors were age, ethnicity, poverty income ratio (PIR), educational level, and marital status. The gynecologic condition was the age of menarche (years). Behavioral variables included alcohol use and smoking status. Clinical indicators were considered specifically body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters), hypertension, and diabetes. Races were divided into non‐Hispanic White, Non‐Hispanic Black, Mexican American and other race. Each of the three reported levels of education: below high school, high school, and above high school. Marital status was classified as married/living with partners, widowed/divorced/separated and never married. “RHQ10” questionnaires were used to gather the age of menarche. “Age when first menstrual period occurred” was the question posed to the participants. The mobile examination center (MEC) examined additional health‐related factors, including diabetes, hypertension, alcohol use, and smoking status. The above indicators were all based on self‐reports of the participants. There was a yes/no option for alcohol use, hypertension and smoking. Smokers were those who have smoked at least 100 cigarettes in their lifetime. Covariates were chosen for their established dual associations with infertility and HDL‐related inflammation in prior studies, but key reproductive history variables such as polycystic ovary syndrome status, ovarian reserve markers, thyroid function, parity, pelvic inflammatory disease history, and ovarian surgery record were mostly missing or unavailable in NHANES, representing unmeasured confounders. 2.5. Subgroup analysis All subgroup analyses in this study were exploratory, designed to generate hypotheses for future confirmatory research. We examined potential heterogeneity in the exposure–outcome association across predefined strata: age, ethnicity, education level, marital status, PIR, BMI, drinking or smoking status, and hypertension. To evaluate consistency between subgroup‐specific findings and the overall study result, interaction P values were calculated. 2.6. Statistical analysis Categorical variables are given as percentages, and continuous variable data shown as mean ± standard deviations (SD). The chi‐square test (categorical variables) and the Kruskal‐Wallis H test (continuous variables) were performed to evaluate differences in baseline characteristics between the non‐fertility and fertility groups. BMI, MHR, LHR and NHR were analyzed as continuous variables. Neither MHR nor LHR was standardized, and the “unit rise” for both indices was defined as a per 1‐unit increment in their original values, corresponding to a 1.0‐fold change in the ratio of immune cell count to HDL‐C concentration. Based on the directed acyclic graph (DAG, Figure S1), the minimal and sufficient adjustment set for confounder control was age, ethnicity, BMI, educational level, age of menarche, hypertension, alcohol use, and smoking status. The relationship between high‐density lipoprotein‐related inflammation index and female fertility was then assessed using logistic regression models, where MHR, LHR, and NHR were categorized into quartiles. The lowest quartile (Q1) was considered the reference group. In our study, three models were built. In Model 1, no variables were adjusted. Age and ethnicity were adjusted in Model 2. Model 3 was adjusted for all potential confounding variables below. Smooth curve fitting was utilized to describe the trend between high‐density lipoprotein‐related inflammation indices and female infertility. In addition, odds ratios (ORs) were computed for each unit rise in MHR and LHR, with subgroup analyses performed according to age, race, BMI, educational level, marital status, PIR, age of menarche, alcohol use, smoking status, and hypertension. An interaction P value <0.05 was considered statistically significant, indicating heterogeneous associations across subgroups. All statistical analyses were conducted using the R package version 3.4.3 and the EmpowerStats software. Significant outcomes were characterized by P values below 0.05. 3. RESULTS 3.1. Baseline characteristics The research population's baseline characteristics are displayed based on whether they had infertility (Table 1). Among them, 283 patients (11.75%) reported infertility, while 2125 cases (88.25%) served as the control group. Compared with the non‐infertile group, women in the infertile group were significantly older, had a higher BMI, and more of them were married or living with partners. They also exhibited higher rates of hypertension and diabetes (all P < 0.001). Particularly, the individuals in the infertile group displayed higher levels of MHR, LHR, and NHR (P 0.05). TABLE 1. | Characteristics | Fertility (n = 2125) | Infertility (n = 283) | P value | |---|---|---|---| | Age (years) | 31.29 ± 7.21 | 34.47 ± 6.53 | <0.001 | | PIR | 2.67 ± 1.65 | 2.74 ± 1.65 | 0.463 | | BMI | 28.98 ± 8.32 | 32.16 ± 9.13 | <0.001 | | MHR | 0.41 ± 0.18 | 0.45 ± 0.22 | <0.001 | | LHR | 1.72 ± 0.80 | 1.93 ± 0.91 | <0.001 | | NHR | 3.37 ± 1.80 | 3.77 ± 2.02 | <0.001 | | Age of menarche (years) | 12.61 ± 1.74 | 12.42 ± 1.84 | 0.072 | | Race (n, %) | ||| | Non‐Hispanic White | 57.02 | 59.34 | 0.700 | | Non‐Hispanic Black | 12.58 | 12.74 | | | Mexican American | 11.63 | 12.05 | | | Other race | 18.76 | 15.87 | | | Education (n, %) | ||| | Below high school | 10.00 | 10.90 | 0.335 | | High school | 19.18 | 22.43 | | | Above high school | 70.83 | 66.67 | | | Marital status (n, %) | ||| | Married/living with partners | 56.77 | 74.04 | <0.001 | | Widowed/divorced/separated | 9.06 | 11.76 | | | Never married | 34.17 | 14.19 | | | Alcohol use (n, %) | ||| | Yes | 79.80 | 80.73 | 0.713 | | No | 20.20 | 19.27 | | | Diabetes (n, %) | ||| | Yes | 2.55 | 7.01 | <0.001 | | No | 96.03 | 90.41 | | | Borderline | 1.42 | 2.58 | | | Hypertension (n, %) | ||| | Yes | 11.34 | 18.27 | <0.001 | | No | 88.66 | 81.73 | | | Smoking status (n, %) | ||| | Yes | 31.63 | 36.51 | 0.097 | | No | 68.37 | 63.49 | Note: BMI, calculated as weight in kilograms divided by the square of height in meters. P value <0.05 is in bold. Abbreviations: BMI, body mass index; LHR, lymphocyte‐to‐high‐density lipoprotein cholesterol ratio; MHR, monocyte‐to‐high‐density lipoprotein cholesterol ratio; NHR, neutrophil‐to‐high‐density lipoprotein cholesterol ratio; PIR, poverty income ratio. 3.2. Association between high‐density lipoprotein‐related inflammation index and infertility Data presented a skewed distribution when the HDL‐related inflammation index was exhibited as a continuous variable. Considering all potential covariates in Model 3, excluding NHR, it was observed that MHR and LHR demonstrated a positive association with female infertility (MHR: OR = 2.15, 95% confidence interval [CI]: 1.11–4.16, P = 0.023; LHR: OR = 1.19, 95% CI: 1.03–1.39, P = 0.021) (Table 2). TABLE 2. | Characteristic | Model 1 OR (95% CI) | P value | Model 2 OR (95% CI) | P value | Model 3 OR (95% CI) | P value | |---|---|---|---|---|---|---| | MHR | 2.82 (1.57, 5.06) | <0.001 | 2.82 (1.57, 5.10) | <0.001 | 2.15 (1.11, 4.16) | 0.023 | | MHR quartile | |||||| | Q1 | 1.0 (1.00,1.00) | 1.0 (1.00, 1.00) | 1.0 (1.00, 1.00) | ||| | Q2 | 1.32 (0.89, 1.97) | 0.167 | 1.36 (0.91, 2.03) | 0.130 | 1.33 (0.89, 2.01) | 0.167 | | Q3 | 1.67 (1.14, 2.45) | 0.008 | 1.69 (1.15, 2.48) | 0.007 | 1.54 (1.03, 2.31) | 0.034 | | Q4 | 1.93 (1.33, 2.79) | <0.001 | 1.96 (1.35, 2.85) | <0.001 | 1.70 (1.13, 2.56) | 0.011 | | LHR | 1.30 (1.14, 1.48) | <0.001 | 1.31 (1.15, 1.50) | <0.001 | 1.19 (1.03, 1.39) | 0.021 | | LHR quartile | |||||| | Q1 | 1.0 (1.00, 1.00) | 1.0 (1.00, 1.00) | 1.0 (1.00, 1.00) | ||| | Q2 | 1.28 (0.87, 1.87) | 0.207 | 1.33 (0.90, 1.95) | 0.151 | 1.21 (0.81, 1.80) | 0.353 | | Q3 | 1.40 (0.96, 2.03) | 0.080 | 1.48 (1.01, 2.16) | 0.043 | 1.29 (0.87, 1.92) | 0.209 | | Q4 | 1.87 (1.30, 2.67) | <0.001 | 1.92 (1.34, 2.77) | <0.001 | 1.54 (1.03, 2.31) | 0.036 | | NHR | 1.08 (1.02, 1.15) | 0.013 | 1.08 (1.01, 1.14) | 0.021 | 1.01 (0.94, 1.09) | 0.704 | | NHR quartile | |||||| | Q1 | 1.0 (1.00, 1.00) | 1.0 (1.00, 1.00) | 1.0 (1.00, 1.00) | ||| | Q2 | 1.32 (0.91, 1.91) | 0.149 | 1.40 (0.96, 2.04) | 0.083 | 1.30 (0.88, 1.92) | 0.181 | | Q3 | 1.36 (0.94, 1.98) | 0.099 | 1.42 (0.97, 2.07) | 0.071 | 1.24 (0.83, 1.84) | 0.292 | | Q4 | 1.60 (1.12, 2.30) | 0.010 | 1.65 (1.13, 2.39) | 0.009 | 1.27 (0.84, 1.93) | 0.259 | Note: Model 1 adjusts for: none. Model 2 adjusts for: age and race. Model 3 adjusts for: age, race, BMI, educational level, marital status, PIR, age of menarche, alcohol use, diabetes, smoking status and hypertension. P value<0.05 is in bold. Abbreviations: BMI, body mass index; CI, confidence interval; LHR, lymphocyte‐to‐high‐density lipoprotein cholesterol ratio; MHR, monocyte‐to‐high‐density lipoprotein cholesterol ratio; NHR, neutrophil‐to‐high‐density lipoprotein cholesterol ratio; OR, odds ratio; PIR, poverty income ratio. Additionally, the HDL‐related inflammatory index was divided into quartiles in the models. In fully adjusted Model 3, it is worth noting that individuals in the highest quartile of MHR and LHR exhibit an elevated incidence of infertility in comparison with those in the lowest quartile, with increases of 0.7 and 0.54 times, respectively (MHR: OR = 1.70, 95% CI: 1.13–2.56, P = 0.011; LHR: OR = 1.54, 95% CI: 1.03–2.31, P = 0.036). As for NHR, there was no significant correlation between them and the possibility of female infertility (P>0.05) (Table 2). The results of the smoothed curve fitting further revealed that a positive correlation exists between MHR, LHR and the incidence of female infertility (Figures 2 and 3). 3.3. Subgroup analyses To further explore the correlation between MHR, LHR and female infertility, subgroup analysis and interaction testing were conducted. This was applied across subgroups stratified by age, ethnicity, BMI, educational level, marital status, PIR, age of menarche, alcohol use, smoking status, and hypertension (Figures 4 and 5). Notably, the LHR subgroup with age <30 years old showed an extreme OR of 7.00, accompanied by a wide confidence interval, reflecting unstable estimates due to small sample sizes. Given multiple testing and wide CIs, these subgroups findings were only exploratory and hypothesis‐generating, and no correction for multiple comparisons was applied. Statistically significant interactions were observed for MHR with age (P = 0.012), race (P = 0.032), and LHR with BMI (P = 0.049), indicating effect modification in these subgroups. For all other subgroups, including race, educational level, marital status, PIR, alcohol and smoking status, and hypertension, the P value of the interaction was > 0.05, which confirmed that the overall study effect was consistent in most stratifications. These findings were consistent with the overall results. 4. DISCUSSION The present study demonstrated no apparent correlation between NHR with female infertility, whereas there was a positive correlation between MHR, LHR and the odds of female infertility. After adjusting for all potential confounding factors, the trend persisted. When converting continuous MHR and LHR into categorical variables by quartiles (Q1–Q4), compared with the lowest quartile (Q1) of LHR and MHR, the odds of infertility relative to Q1 increased by 54% and 70% for the third (Q3) and fourth (Q4) quartiles, respectively. For continuous LHR, each unit increase was associated with higher odds of infertility. When stratified by quartiles, the highest LHR quartile (Q4) showed a 54% increase in infertility odds compared to the lowest quartile (Q1). Subgroup analyses were applied to determine whether the relationship between MHR, LHR and infertility was robust. To our knowledge, it is the first study to explore the connection between MHR, LHR and female infertility. These findings demonstrated that MHR and LHR may serve as helpful indicators for evaluating the association with the likelihood of female infertility. These ratios were selected as exploratory biomarkers for infertility risk, as they synergistically reflect the crosstalk between immune cell activation and HDL‐mediated lipid metabolism. 12 , 13 Two pathways are closely linked to reproductive function regulation. Inflammation and immunity are crucial causes of female infertility, such as endometriosis, reproductive tract and pelvic inflammatory diseases, and unexplained infertility. Furthermore, the balance of fetal immune tolerance and resistance to infection is key to successful pregnancy, and changes in the number or function of immune cells are the pathogenesis of reproductive adverse events. 14 Currently, diagnostic procedures for infertility are costly, intricate, and potentially uncomfortable for patients. HDL interacts with different immune cells in both directions. 5 Considering the numerous beneficial effects of HDL, researchers have conducted studies into the value of HDL‐related inflammatory indicators. However, limited evidence of the connection between these novel markers and infertility has been explored. Thus, our study investigated the possible association of HDL‐related inflammatory biomarkers in assessing the association with female infertility odds. The results demonstrated that MHR and LHR were positively associated with infertility after controlling for possible covariates. MHR serves as a novel, comprehensive biomarker for lipid metabolism and systemic inflammation. Prior studies found a robust relationship between MHR and metabolic conditions, including non‐alcoholic fatty disease, metabolic syndrome, and polycystic ovary syndrome. 15 , 16 , 17 Moreover, MHR is also an independent risk factor for cardiovascular diseases. 18 , 19 The immunological and inflammatory responses can be impacted by a variety of complex interactions between monocytes and HDL‐C. HDL‐C regulates the immunological response and is essential for lipid metabolism through its direct effects. 20 Additionally, it is vital in reducing pro‐oxidative and proinflammatory actions of monocytes, largely by inhibiting inflammatory factors and monocyte migration and release, oxidation of low‐density lipoprotein cholesterol, and promoting these cells outflow of cholesterol. 20 , 21 Existing literature has further confirmed that HDL‐C can effectively inhibit the proliferation and differentiation of mononuclear progenitor cells. 22 According to our study, the logistic regression model revealed a positive relationship between MHR and the odds of female infertility. This tendency persisted after a full adjustment (OR = 2.15, 95% CI: 1.11–4.16, P = 0.023). When the MHR was divided into quartiles, individuals in the highest quartile had a 70% higher incidence of infertility (OR = 1.70, 95% CI: 1.13–2.56, P = 0.011). After illustrating subgroup analysis and interaction testing, it was found that this correlation was most prominent among participants below the age of 30 years and without hypertension. Mechanistically, it is plausible that elevated MHR may impair endometrial receptivity by promoting proinflammatory monocyte infiltration into the endometrium, disrupting the cytokine balance required for embryo implantation. 23 For LHR, increased lymphocyte‐HDL dysregulation could contribute to ovulatory dysfunction in polycystic ovary syndrome (PCOS) by altering ovarian steroidogenesis and folliculogenesis. 24 These pathways may also affect tubal patency through chronic low‐grade inflammation, a known cause of infertility. The possible underlying mechanism behind this connection may require further research on the mechanism. The ratio of lymphocytes to high‐density lipoprotein cholesterol (LHR) is an emerging indicator that involves two conditions, immunological activation and abnormalities of lipid metabolism, making it a helpful indicator of inflammatory metabolism. 25 Previous research revealed that LHR can be considered a marker of inflammatory conditions. 17 , 26 , 27 An elevated LHR can serve as a stand‐alone biomarker for the onset of diabetes and is significantly linked with an increased cardiovascular risk factor. 28 Furthermore, in the follow‐up survey conducted by Yu et al 27 on Chinese residents, it was determined that LHR is an effective predictor of MetS. In our study, continuous LHR is positively related to the odds of female infertility, with a 19% increase in infertility incidence per unit elevated in LHR of a fully adjusted model (OR = 1.19, 95% CI: 1.03–1.39, P = 0.021). For sensitivity analysis using quartile‐stratified categorical LHR, the highest quartile (Q4) had 54% higher odds of infertility compared to the lowest quartile (Q1) (OR = 1.54, 95% CI: 1.03–2.31, P = 0.036). Subgroup analysis and interaction testing demonstrated that the relationship between LHR and infertility was statistically different across those under 30 years, 1.35 < PIR < 3.0 and with a status of drinking, and without hypertension. This prominent association in the <30 years subgroup was solely for exploratory hypothesis generation due to multiple testing and sparse data‐induced wide confidence intervals, with no multiple comparison correction, and thus could not be overinterpreted. The stronger association observed in women under 30 years old may stem from the distinct etiological profile of infertility in this group, where ovulatory dysfunction such as PCOS and unexplained infertility, is more prevalent than age‐related decline in ovarian reserve. Younger women with elevated MHR/LHR may experience heightened inflammatory disruption of folliculogenesis or endometrial receptivity, which are reversible in early stages but contribute to infertility risk before age‐related factors dominate. These findings suggest that LHR may serve as a potential correlate warranting further validation in longitudinal studies. The strengths of our study first included the ability to examine the correlation between MHR, LHR and female infertility in a sizable and nationally representative sample. Second all potential confounding covariates were modified to minimize the impact of bias on findings. Third, to determine whether there is a specific difference in the relationship between MHR, LHR and infertility prevalence, subgroup analysis was conducted. However, the present study had some limitations. First, as a cross‐sectional study, it only reflects the association between MHR, LHR and female infertility at a specific time point, and cannot establish causal relationships or infer the direction of the association. Meanwhile, this design precludes the determination of whether elevated MHR and LHR precede the development of infertility or are a consequence of infertility‐related pathological processes, thus reverse causation cannot be excluded. Second, we failed to confirm that the impacts of every possible confounder had been eliminated, even after considering and integrating many variables. Lipid‐modifying medication use may confound the relationship between HDL‐based ratios and infertility, and critical confounders such as polycystic ovary syndrome status, ovarian reserve markers, pelvic inflammatory disease history, and ovarian surgery records. And lipid‐modulating factors were mostly missing or unavailable in NHANES, introducing potential unmeasured bias. Third, infertility can be assessed via questionnaire surveys, which lack more precise diagnostic and categorization standards. Specifically, defining infertility solely through self‐reported data (RHQ074) without clinical validation may introduce misclassification bias, including overreporting or underreporting due to subjective cognition differences. It is worth noting that RHQ074 failed to distinguish between primary infertility and secondary infertility, overlooked cases of infertility caused by male factors, and did not record the duration of infertility, treatment history, or clinician's diagnosis information. All these factors led to a directional error in classification bias. Confusing cases of primary and secondary infertility and attributing infertility caused by male factors to females may underestimate the true association between MHR/LHR and female‐specific infertility. Meanwhile, self‐reported data are prone to over‐reporting or under‐reporting, causing the effect estimates to shift in an invalid direction. This excessive reliance on a single self‐reported question also hinders stratification of infertility subgroups, alters the observed correlations, and reduces the general applicability of the results to the clinical‐defined cohort. Subgroup analyses also reveal unstable estimates in sparse strata, such as the LHR subgroup with age below 30 years, which should be interpreted cautiously. 5. CONCLUSION In summary, we highlighted a significant positive correlation between MHR, LHR and the incidence of female infertility among women in the USA. These findings suggest that MHR and LHR are positively correlated with an increased risk of female infertility. Since these conclusions were derived based on the self‐reported definition of infertility, future studies should adopt clinical validation standards and subgroup classification to verify the observed associations. AUTHOR CONTRIBUTIONS Jing Zhe was responsible for data curation, investigation, methodology, and drafting the primary manuscript. Dan Zheng reviewed and revised the manuscript. All authors approved the final manuscript. FUNDING INFORMATION This study was supported by the Seventh Batch of High‐level Innovative Talent Projects of the Guizhou Provincial Department of Science and Technology (GCC 2023‐025), the Science and Technology Program of Guiyang Health Bureau (no. 2021030), General Project of Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders (NCRCCHD‐2022‐GP‐18), National Key R&D Program of China(2019YFC0840703) and Guizhou Science and Technology Plan Project (no. [2022]4‐11). CONFLICT OF INTEREST STATEMENT The authors declare no conflict of interest. Supporting information ACKNOWLEDGMENTS We would like to thank the NHANES for the database. DATA AVAILABILITY STATEMENT In this study, publicly accessible datasets were analyzed. This data is available at https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.

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