Association between uric acid to high-density lipoprotein cholesterol ratio (UHR) and female infertility: insights from a cross-sectional study.

NHANES database is a nationwide, representative survey conducted by the National Center for Health Statistics (NCHS) to gather comprehensive information on the United States civilian population, including demographic characteristics, socioeconomic indicators, nutritional intake patterns, and various health parameters. The NHANES adopted a stratified and multistage probability sampling method to achieve nationwide population representation [ 14 ]. The NHANES protocols received ethical approval from the NCHS Ethics Review Board (Protocols #2018–01 and #2011–17), and all participants have signed informed consent forms. The information about infertility was only collected in the NHANES 2013–2014, 2015–2016, and 2017–2018 cycles, which included 4175 reproductive-aged women (18–44 years old) [ 15 ]. Initially, women with missing infertility status ( n  = 638), serum UA ( n  = 205), and serum HDL-C ( n  = 1) were excluded from the analysis. Subsequently, women with pregnancy ( n  = 127), history of hysterectomy ( n  = 116), and those with both ovaries removed ( n  = 1) were excluded. Finally, 3087 women were included in this study ( Fig.  1 ) . Fig. 1 Participants selection flowchart. Participants were collected in the NHANES 2013–2014, 2015–2016, and 2017–2018 cycles. After screening, a total of 3087 participants were finally included in the study Participants selection flowchart. Participants were collected in the NHANES 2013–2014, 2015–2016, and 2017–2018 cycles. After screening, a total of 3087 participants were finally included in the study The UA and HDL-C concentrations of participants were quantified from fasting venous blood samples. Complete laboratory protocols for biochemical assays are recorded in the NHANES methodology resources [ 16 ]. The UHR index was calculated using the following formula: UHR = serum UA [mg/dL] ÷ HDL-C [mg/dL]) × 100 11 . Participants were grouped by UHR quartiles (Q1:  75th), with Q1 as the reference. The study outcome was female infertility status, which was confirmed through self-reporting by participants, using two standardized items from the Reproductive Health Questionnaire (RHQ) module of the NHANES: (1) RHQ074, “Have you ever attempted to become pregnant over at least a year without becoming pregnant”; and (2) RHQ076, “Have you ever been to a doctor or other medical provider because you have been unable to become pregnant”. Women who responded “yes” to either of these two items were categorized as infertile [ 6 , 17 ]. The covariates included socioeconomic and demographic factors, health behaviors and measurements, chronic diseases, and medication use. Socioeconomic and demographic factors included marital status (married/living with partner, widowed/divorced/separated, and never married), race (Mexican–American, non-Hispanic White, non-Hispanic Black, other race), poverty-to-income ratio (PIR,  3.5) [ 18 ], and educational level ( high school). The body mass index (BMI) was calculated as the ratio of weight (in kilograms) to height (in meters) squared and categorized into normal/underweight (< 25 kg/m 2 ) and overweight (≥ 25 kg/m 2 ). Smoking patterns were classified as never smoking (lifetime consumption of < 100 cigarettes), former smoking (prior consumption of ≥ 100 cigarettes with current abstinence), and current smoking [ 19 ]. Alcohol consumption was categorized dichotomously based on whether the annual intake exceeded 12 standard drinks [ 20 ]. Physical activity levels were classified into three categories: inactive, moderate, or active, according to the definition of national cardiovascular health promotion and disease prevention goals [ 21 ]. Diabetes was ascertained by self-reported doctor’s diagnosis, glucose-lowering medications or insulin usage, or fasting blood glucose of ≥ 126 mg/dL (7.0 mmol/L), or glycosylated hemoglobin (HbA1c) of ≥ 6.5% [ 22 ]. Hypertension was defined as systolic blood pressure of ≥ 140 mmHg and/or diastolic blood pressure of ≥ 90 mmHg, self-reported doctor’s diagnosis, or antihypertensive drugs usage [ 23 ]. Hyperlipidemia was ascertained by self-reported high-cholesterol level, cholesterol-lowering medicines usage, or laboratory total cholesterol of ≥ 200 mg/dL, triglyceride of ≥ 150 mg/dL, low-density lipoprotein cholesterol of ≥ 130 mg/dL, and HDL-C of < 50 mg/dL [ 24 ]. The diagnosis of pelvic inflammatory disease (PID), use of contraceptive drugs, hormone drugs, and lipid-lowering drugs were obtained through affirmative responses in the questionnaire survey completed by participants. Renal function was staged by the estimated glomerular filtration rate (eGFR) derived from the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) Eq.  [ 25 ]. Hepatic function was evaluated using the albumin–bilirubin (ALBI) grade, which was a three-tier objective method for assessing liver impairment severity [ 26 ]. Our analysis took into account sample weights, stratification, and clustering of the NHANES. Normality of data distribution was assessed using the Kolmogorov–Smirnov test. Continuous variables with a normal distribution are expressed as mean (standard error, SE), and intergroup comparisons were performed using Student's t test. Continuous variables that are not normally distributed are represented by the median (interquartile range) and compared between groups using the Kruskal–Wallis test. Categorical data are summarized as frequency counts (percentages) and compared between groups using the χ 2 test or the rank sum test. The weighted logistic regression analysis was performed to examine the association between UHR and female infertility. To comprehensively assess potential confounding factors, multiple covariates were included based on the existing knowledge and previous literature [ 6 , 15 , 17 , 27 ]. The collinearity was checked using generalized variance-inflation factors (GVIFs), and considered noncollinearity when GVIF^[1 ÷ (2 × degrees of freedom)] was < 5. Model 1 was unadjusted. Model 2 was adjusted for age and race. Model 3 was further adjusted for BMI, marital status, smoking patterns, alcohol consumption, hypertension, diabetes, dyslipidemia, CKD stage, ALBI grade, PID, hormone drug use, and oral contraceptive use. The odds ratios (ORs) with 95% confidence intervals (CIs) are reported for the associations between UHR and infertility. To investigate whether a linear relationship existed between UHR and infertility, the weighted logistic regression with restricted cubic spline (RCS) analysis was conducted, adjusting for the variables in Model 3. Stratified analysis based on Model 3 explored whether there were interactions between age group, BMI category, educational level, marital status, menstrual cycle regularity, diabetes, hypertension, and hyperlipidemia. Data analyses were conducted using R software (version 4.3.0), with a two-sided p value of < 0.05 considered statistically significant.

The study included 3087 women, of whom 357 (11.56%) had infertility ( Table  1 ) . Among all women, the median (Q1, Q3) UHR was 8.0 (6.1, 10.5), and the mean (SE) age was 30.8 (7.8) years. There were 1918 (62%) women who had a BMI ≥ 25 kg/m 2 , 505 (18%) were current smokers, and 1336 (70.1%) were drinkers. Table 1 Characteristics of the included females Characteristics Missing Overall N  = 3087 Noninfertility N  = 2730 Infertility N  = 357 p value Uric acid, mg/dL 0 4.5 (3.8, 5.2) 4.4 (3.7, 5.1) 4.7 (4.0, 5.4) 0.004 HDL-C, mg/dL 0 55.0 (46.0, 65.0) 56.0 (46.0, 66.0) 52.0 (43.0, 63.0) 0.021 UHR 0 8.0 (6.1, 10.5) 7.9 (6.0, 10.4) 8.9 (6.6, 1.3) 0.004 Age, year 0 30.8 (7.8) 30.2 (7.8) 34.4 (6.8)  < 0.001 Marital status 368  < 0.001  Married/living with partner 1550 (58%) 1305 (56%) 245 (73%)  Widowed/divorced/separated 281 (10%) 239 (9.2%) 42 (12%)  Never married 888 (32%) 824 (35%) 64 (15%) Race 0 0.207  Mexican–American 569 (13%) 507 (12.5%) 62 (11%)  Non-Hispanic White 986 (55%) 858 (55%) 128 (60%)  Non-Hispanic Black 658 (13%) 579 (13.5%) 79 (13%)  Other race 874 (19%) 786 (20%) 88 (16%) Poverty-to-income ratio 254 0.367   < 1.3 1080 (30%) 963 (30%) 117 (26%)   ≥ 3.5 712 (34%) 614 (33%) 98 (37%)  1.3–3.49 1041 (36%) 919 (36%) 122 (37%) Educational level 369 0.939   High school 1776 (69%) 1549 (69%) 227 (68%) BMI group 29 0.006   < 25 kg/m 2 1140 (38%) 1040 (39%) 100 (29%)   ≥ 25 kg/m 2 1918 (62%) 1663 (61%) 255 (71%) Smoking status 0 0.094  Never 2284 (70%) 2050 (71%) 234 (64%)  Former 298 (12%) 249 (11%) 49 (15%)  Current 505 (18%) 431 (18%) 74 (21%) Drinker 943 1336 (70.1%) 1156 (69.2%) 180 (76.3%) 0.068 Gout 369 11 (0.4%) 8 (0.3%) 3 (1.0%) 0.078 Hyperlipidemia 0 1720 (54.3%) 1491 (52.6%) 229 (65.5%) 0.001 Lipid-lowering drug 0 40 (1.2%) 32 (1.0%) 8( 2.6%) 0.062 Diabetes mellitus 0 182 (5.1%) 146 (4.5%) 36 (9.1%) 0.002 Hypertension 0 2164 (67.0%) 1902 (66.4%) 262 (71.6%) 0.188 Chronic kidney disease stage 1 0.366 Normal (Stage 1) 2658 (83.6%) 2362 (83.8%) 296 (81.9%) Decreased (> Stage1) 428 (16.4%) 367 (16.2%) 61 (18.1%) Albumin–bilirubin grade 2 0.039 Grade 1 2799 (91.7%) 2486 (92.1%) 313 (88.4%)  > Grade 1 286 (8.3%) 242 (7.9%) 44 (11.6%) Pelvic inflammatory disease 17 137 (4.2%) 101 (3.4%) 36 (10%)  < 0.001 Oral contraceptive use 1 1952 (71%) 1690 (70%) 262 (78%) 0.009 Hormone drug use 372 70 (3.5%) 54 (2.9%) 16 (7.0%) 0.028 Age at menarche, year 18 12.6 (1.8) 12.6 (1.7) 12.5 (1.9) 0.411 Regular menstrual cycle 0 2888 (93%) 2552 (93%) 336 (91%) 0.246 Physical activity 0 0.280  Inactive 1391 (40%) 1212 (39%) 179 (45%)  Active 872 (30%) 779 (30%) 93 (29%)  Moderate 824 (30%) 739 (31%) 85 (26%) Median (interquartile range), mean (standard error), or unweighted number (weighted percent) are represented depending on the type of variables. The Kruskal–Wallis test or the chi-square test was used depending on the type of variables. The boldface indicates statistical significance ( p  < 0.05) N number, UHR uric acid to high-density lipoprotein cholesterol ratio, HDL-C high-density lipoprotein cholesterol Characteristics of the included females Median (interquartile range), mean (standard error), or unweighted number (weighted percent) are represented depending on the type of variables. The Kruskal–Wallis test or the chi-square test was used depending on the type of variables. The boldface indicates statistical significance ( p  < 0.05) N number, UHR uric acid to high-density lipoprotein cholesterol ratio, HDL-C high-density lipoprotein cholesterol When compared with fertile women, infertile women were older (34.4 years vs. 30.2 years, p  < 0.001) and were more likely to be overweight (71% vs. 61%, p  = 0.006). Infertile women had higher UA concentrations (4.7 mg/dL vs. 4.4 mg/dL, p  = 0.004), lower HDL-C concentrations (52.0 mg/dL vs. 56.0 mg/dL, p  = 0.021), and higher UHR (8.9 vs. 7.9, p  = 0.004). Moreover, infertile women tended to have more comorbidities, such as PID (10.0% vs. 3.4%, p   grade 1: 11.6% vs. 7.9%, p  = 0.039). The relationship between UHR and infertility is exhibited in Table  2 . There was no significant collinearity between the variables (Tables S1 and S2). When UHR was continuous, women with higher UHR concentrations were associated with higher odds of infertility (OR = 1.07, 95% CI: 1.02–1.12) after comprehensive adjustments in Model 3. Likewise, when compared with the lowest UHR level (UHR ≤ 6.14), women with the highest UHR level (UHR > 10.49) were associated with a higher risk of infertility (OR = 1.80, 95% CI: 1.03–3.14) in Model 3. Table 2 Association between UHR and female infertility UHR Total, N (%) Infertility, N (%) Model 1 Model 2 Model 3 OR (95%CI) p value OR (95%CI) p value OR (95%CI) p value Continuous 3087 357(11.56%) 1.07(1.03–1.11) 0.001 1.07(1.03–1.11)  < 0.001 1.07(1.02–1.12) 0.010 Quartiles  Quartile 1(< 6.14) 768 60(7.81%) Reference Reference Reference  Quartile 2(6.14–8.00) 724 83(11.46%) 1.38(0.93–2.03) 0.112 1.51(1.00–2.29) 0.050 1.41(0.92–2.18) 0.115  Quartile 3(8.00–10.49) 797 91(11.42%) 1.40(0.94–2.08) 0.103 1.54(1.03–2.29) 0.036 1.40(0.89–2.22) 0.142  Quartile 4(> 10.49) 798 123(15.41%) 1.95(1.27–2.98) 0.003 2.06(1.32–3.21) 0.002 1.80(1.03–3.14) 0.041 The UHR was evaluated as a continuous or quartile (with quartile 1 as the reference) variable in the weighted logistic regression analysis. Model 1 was unadjusted. Model 2 was adjusted for age and race. Model 3 was adjusted for age, race, body mass index, marital status, smoking patterns, alcohol consumption, hypertension, diabetes mellitus, dyslipidemia, chronic kidney disease stage, albumin–bilirubin grade, pelvic inflammation disease, hormone drug use, and oral contraceptive use N number, UHR uric acid to high-density lipoprotein cholesterol ratio, OR odds ratio, CI confidence interval Association between UHR and female infertility The UHR was evaluated as a continuous or quartile (with quartile 1 as the reference) variable in the weighted logistic regression analysis. Model 1 was unadjusted. Model 2 was adjusted for age and race. Model 3 was adjusted for age, race, body mass index, marital status, smoking patterns, alcohol consumption, hypertension, diabetes mellitus, dyslipidemia, chronic kidney disease stage, albumin–bilirubin grade, pelvic inflammation disease, hormone drug use, and oral contraceptive use N number, UHR uric acid to high-density lipoprotein cholesterol ratio, OR odds ratio, CI confidence interval To explore the potential nonlinear relationship between UHR and infertility, logistic regression with RCS was performed based on Model 3, and the number of knots for RCS was set to three. We did not discover a nonlinear association between UHR and infertility ( p -overall < 0.001, p -nonlinear = 0.391) ( Fig.  2 ). Fig. 2 Weighted histogram of UHR distribution and restricted cubic spline analysis between UHR and infertility. The histogram shows the distribution of UHR, and the red solid curve with a light red area represents the adjusted OR with 95% CI. The number of knots for the restricted cubic spline was three. The analysis was adjusted for age, race, body mass index, marital status, smoking patterns, alcohol consumption, hypertension, diabetes mellitus, dyslipidemia, chronic kidney disease stage, albumin–bilirubin grade, pelvic inflammation disease, hormone drug use, and oral contraceptive use. UHR uric acid to high-density lipoprotein cholesterol ratio, OR odds ratio, CI confidence interval Weighted histogram of UHR distribution and restricted cubic spline analysis between UHR and infertility. The histogram shows the distribution of UHR, and the red solid curve with a light red area represents the adjusted OR with 95% CI. The number of knots for the restricted cubic spline was three. The analysis was adjusted for age, race, body mass index, marital status, smoking patterns, alcohol consumption, hypertension, diabetes mellitus, dyslipidemia, chronic kidney disease stage, albumin–bilirubin grade, pelvic inflammation disease, hormone drug use, and oral contraceptive use. UHR uric acid to high-density lipoprotein cholesterol ratio, OR odds ratio, CI confidence interval Figure  3 illustrates stratified analyses of the relationship between UHR and infertility, categorized by age, educational level, BMI, marital status, diabetes, hypertension, hyperlipidemia, and menstrual cycle regularity. The association between UHR and infertility was significant in the age group of 18–30 years (OR = 1.12, 95% CI: 1.02–1.23), obese women (OR = 1.06, 95% CI: 1.01–1.12), women without diabetes (OR = 1.08, 95% CI: 1.03–1.14), and women with regular menstrual cycles (OR = 1.07, 95% CI: 1.02–1.13). The association between UHR and infertility was not statistically significant in the age group of 31–44 years, nonobese women, women with diabetes, and women with irregular menstrual cycles. No statistically significant interactions were observed across any of the stratified variables in the analysis (all p values for interaction were > 0.05). Fig. 3 Stratified analyses for UHR and female infertility. All regressions were adjusted for age, race, body mass index, marital status, smoking patterns, alcohol consumption, hypertension, diabetes mellitus, dyslipidemia, chronic kidney disease stage, albumin–bilirubin grade, pelvic inflammation disease, hormone drug use, and oral contraceptive use, except for the subgroup variables themselves. In the forest plot, an OR greater than one is represented to the right of the vertical line, indicating higher odds of infertility. The horizontal segments represent the 95% CIs of the ORs. N number; UHR uric acid to high-density lipoprotein cholesterol ratio, BMI body mass index, OR odds ratio, CI confidence interval Stratified analyses for UHR and female infertility. All regressions were adjusted for age, race, body mass index, marital status, smoking patterns, alcohol consumption, hypertension, diabetes mellitus, dyslipidemia, chronic kidney disease stage, albumin–bilirubin grade, pelvic inflammation disease, hormone drug use, and oral contraceptive use, except for the subgroup variables themselves. In the forest plot, an OR greater than one is represented to the right of the vertical line, indicating higher odds of infertility. The horizontal segments represent the 95% CIs of the ORs. N number; UHR uric acid to high-density lipoprotein cholesterol ratio, BMI body mass index, OR odds ratio, CI confidence interval To mitigate the effect of the left-skewed UHR distribution, we applied a log transformation to UHR and repeated the weighted logistic regression analysis (Table S3). When UHR was log-transformed and evaluated as a continuous variable, women with higher UHR concentrations were associated with higher odds of infertility (OR = 1.94, 95% CI: 1.18–3.20) in Model 3. Compared with the lowest UHR level [log(UHR) ≤ 1.81], women with the highest UHR level [log(UHR) > 2.35] were associated with a higher risk of infertility (OR = 1.80, 95% CI: 1.03–3.14) in Model 3. A linear relationship was found between log(UHR) and infertility probability ( p -overall = 0.001, p -nonlinear = 0.991) (Figure S1). The predictive mean matching (PMM) method was used to impute missing values of covariates in Model 3, and the results supported the association between higher UHR and higher odds of infertility (Table S4).

This study offers the following advantages: (1) we utilized the NHANES database, which is a large-scale, nationally representative sample; and (2) we used UHR, a novel composite indicator, to thoroughly explore its relationships with female infertility, as echoed by sensitivity analyses (log-transformed UHR, PMM method for imputation). However, there still exist limitations: (1) owing to the cross-sectional study design, we cannot clarify the causal relationship between UHR and infertility, and the interpretation and application of the conclusions should be approached with caution; (2) the infertility information in the NHANES was collected by self-reporting, which may have introduced possible bias; (3) the lack of data on specific etiologies of infertility (e.g., ovulatory disorders or tubal disease) limited our further mechanistic explorations; (4) the UHR index for the population was measured only once, so we are unable to determine the relationship between dynamic changes in UHR and infertility; (5) the results of the subgroup analyses were exploratory (limited by factors such as sample size) and required further validation; and (6) despite adjustment for many covariates, the presence of residual confounders of unmeasured factors (such as genetic factors) should be acknowledged.

This cross-sectional study examined the relationships between UHR and female infertility using data from the NHANES 2013–2018 cycles. The main findings showed that a higher UHR level was related to a higher risk of infertility, whether UHR was continuous or categorized into quartiles. The relationship between UHR and infertility was linear, with no threshold effect. No interactions were found in the stratified analyses, and all sensitivity analyses supported the positive associations between UHR and infertility. It is essential to note that this study cannot establish causality, and all conclusions should be interpreted with caution. Infertility has caused significant distress to individuals of reproductive age worldwide. One study showed a positive correlation between the cardiometabolic index [CMI, calculated as waist-to-height ratio × (triglyceride ÷ HDL-C)] and female infertility (OR = 2.41, 95% CI: 1.42–4.11), suggesting that a disorder of lipid metabolism was an essential factor in female infertility [ 27 ]. Our study combined UA and HDL-C to form a composite indicator, referred to as UHR. The relationship between this new indicator and other diseases has been partially discovered. Exploring its relationship with female infertility can provide new insights into the role of metabolic factors in reproductive dysfunction. One previous study had discovered a positive relationship between elevated serum UA levels and impaired female infertility. For women with a BMI < 25 kg/m 2 , increase in UA levels was associated with increase in the risk of infertility (OR = 1.41, 95% CI: 1.04–1.93, p  = 0.030); however, no significant trend was observed in women with a BMI ≥ 25 kg/m 2 (OR = 1.17, 95% CI: 1.00–1.38, p  = 0.056). Women over 30 years old had a stronger association (OR = 1.23, 95% CI: 1.04–1.45, p  = 0.016) between UA and infertility than younger women (OR = 1.11, 95% CI: 0.78–1.59, p  = 0.556) [ 6 ]. However, the association between UHR and infertility was significant in the age group of 18–30 years and obese women in our study. These differences between subgroups may reflect the modulatory role of HDL-C. It was suggested that the association between UHR and infertility may be influenced by adiposity and age-related metabolic changes. There was controversy regarding the relationship between HDL-C and infertility. One study found that women with the highest HDL-C levels had a lower risk of infertility compared to those with the lowest HDL-C levels (OR = 0.56, 95% CI: 0.38–0.84) [ 17 ]. However, no significant correlation was found between HDL-C levels and the risk of female infertility in a Mendelian randomization study [ 28 ]. Additionally, the relationship between UA and lipid metabolism has been investigated in another study, which found that a one-unit increase in serum UA was associated with a 1.38-fold increase in the risk of metabolic syndrome [ 29 ]. The impact of lipid metabolism on infertility cannot be overlooked, both in women and in men. A review demonstrated that metabolic dysfunction—particularly obesity, diabetes mellitus, and metabolic syndrome—affected male fertility, including sperm concentration, motility, morphology, and the integrity of sperm chromatin at the molecular level [ 30 ]. In our subgroup analyses, the sample sizes were relatively small in specific subgroups, such as patients with diabetes (182 cases) or women with irregular menstruation (199 cases), which may have limited the ability to detect significant interactions. Infertility was a complex and multifactorial disease, and its pathogenesis may vary depending on an individual's biological characteristics. Different biological mechanisms may influence the relationship between UHR and infertility across specific subgroups. For example, in diabetic patients, hyperglycemia can affect the reproductive system through multiple pathways, which may not fully align with the mechanism of action of UHR [ 31 ]. Therefore, metabolic factors inherent to diabetes may weaken the association between UHR and infertility in the diabetic subgroup. In contrast, in the nondiabetic subgroup, the relationship between UHR and infertility was more pronounced. Similarly, no statistically significant association was found between UHR and infertility in women with menstrual irregularities in our study. This may reflect the complex etiology of menstrual irregularities, such as the presence of conditions like endometriosis and PCOS, which are strongly associated with infertility and may mask the role of UHR [ 32 ]. It is worth noting that patients with PCOS are often accompanied by hyperuricemia and low HDL-C levels (associated with obesity and metabolic abnormalities), which may lead to elevated UHR [ 33 ]. In addition, hormone therapy may affect the lipid profile or UA metabolism [ 1 ]. Therefore, there may be a reverse causal relationship between UHR and infertility, and prospective studies are needed to longitudinally track the dynamic changes in UHR. The oocytes are unable to synthesize cholesterol on their own, as they lack the ability to produce both cholesterol and HDL-C receptors. They obtain cholesterol through “cumulus cells” (support cells surrounding the oocytes) by gap junction. Typically, HDL facilitates the removal of excess unesterified cholesterol (UC) by oocytes [ 34 ]. In mice lacking the HDL-C receptor gene (Scarb1−/−), mice had excessive levels of circulating HDL-C that couldn’t be used and UC that couldn’t be removed. The accumulation of UC in oocytes leads to the disruption of cell death and survival signaling, which can block cell division and impair oocyte development [ 10 ]. However, by administering HDL-C-lowering drugs to Scarb1-/- mice, its serum HDL-C concentrations can be reduced, thereby restoring ovarian morphology and rescuing fertility [ 34 ]. UA serves as a crucial endogenous antioxidant in physiological conditions. Paradoxically, excessive UA concentrations can provoke sterile inflammatory responses through the extracellular formation of monosodium urate (MSU). This dual-natured biochemical process establishes a pro-oxidative microenvironment that potentially disrupts ovarian physiology and reduces endometrial receptivity [ 5 ]. The pathological significance of oxidative stress is particularly evident in prevalent gynecological diseases, including PCOS and preeclampsia [ 35 , 36 ]. Women with PCOS displayed significantly higher circulating UA levels than non-PCOS women, which is associated with excess androgen and IR. This creates a reciprocal metabolic relationship where hyperinsulinemia reduces urate excretion, and consequent hyperuricemia exacerbates both hypertriglyceridemia and IR [ 5 , 33 ]. At the molecular level, MSU crystals activate the inflammasome cascade in placental tissues, leading to interleukin-1β-mediated trophoblast apoptosis [ 37 ]. In addition to its pro-oxidant effects, pathological UA concentrations disrupt lipid metabolism through multiple mechanisms, including the activation of mitogenic protein kinases [ 38 , 39 ]. These findings collectively illustrate the intricate interplay between UA metabolism and lipid homeostasis in the pathogenesis of fertility disorders. The dual metabolic disorders of UA and lipids form a self-perpetuating cycle, resulting in persistent dysfunction of the reproductive system. Therefore, compared to measuring UA or HDL-C alone, UHR can more comprehensively capture the dual aspects of oxidative stress and lipid metabolism dysfunction, allowing for a more detailed understanding of the potential metabolic disturbance underlying infertility. Additionally, UHR acquisition is cost-effective and easy to perform, and has potential as a screening tool, but further validation in prospective studies is needed.

UHR is a novel composite marker of oxidative stress and lipid metabolism. In this cross-sectional study, we found that elevated UHR levels were significantly associated with an increased risk of infertility. This association was linear and showed no threshold response. Further prospective studies are required to validate these findings, explore the potential role of UHR as a biomarker for infertility risk and refine risk stratification based on specific causes of infertility.

Infertility refers to the failure to achieve pregnancy after at least one year of regular unprotected sexual intercourse [ 1 ], which poses significant psychological and social challenges for reproductive-aged individuals. Meta-analytic data revealed divergent prevalence rates, with community-based studies reporting infertility rates of 10.4% compared with 79.3% in the hospital setting [ 2 ]. Fallopian tube abnormalities and ovulation disorders represent predominant etiologies, often coexisting with metabolic dysregulation, including insulin resistance (IR), obesity, and hyperandrogenism [ 1 , 3 ]. Therefore, research into the connection between metabolic disorders and female infertility has become a focus of attention. As the terminal metabolite in human purine catabolism, uric acid (UA) serves dual physiological roles. Although exhibiting antioxidant capacities under certain conditions, UA paradoxically demonstrates pro-oxidant effects contingent upon its biochemical microenvironment [ 4 ]. Elevated serum UA levels not only promote gout pathogenesis via urate crystal deposition but also correlate with cardiometabolic disorders, including hypertension, atherosclerotic progression, IR, and diabetes mellitus [ 4 ]. Clinical evidence has indicated that UA concentrations are elevated in patients with polycystic ovary syndrome (PCOS) and endometriosis [ 5 ]. One study has found a positive relationship between elevated serum UA concentrations and the risk of female infertility [ 6 ]. In addition, serum UA was related to maternal complications and even poor fetal outcome [ 5 , 7 ]. Disordered lipid metabolism significantly contributes to diminished endometrial receptivity and subsequent fertility impairment [ 8 ]. High-density lipoprotein cholesterol (HDL-C), often described as “beneficial cholesterol,” is an important biomarker of cardiovascular health. In addition to its ability to reverse cholesterol transport, HDL-C has anti-inflammatory, antioxidant, and endothelial-protective functions [ 9 ]. Biochemical characterization of follicular fluid has identified HDL-C as the exclusive lipoprotein that reaches physiologically relevant concentrations, and the structural parameters of HDL-C can serve as biomarkers for evaluating embryo quality [ 10 ]. The dysregulation of HDL-C homeostasis has been proven to be associated with impaired female reproductive capacity [ 10 ]. By combining UA and HDL-C, the uric acid to high-density lipoprotein cholesterol ratio (UHR) serves as a novel composite indicator capable of simultaneously assessing inflammatory load and oxidative stress levels [ 11 ]. Emerging evidence has established significant associations between elevated UHR and certain diseases, including metabolic syndrome [ 11 ], nonalcoholic fatty liver disease [ 12 ], and hypertension in women of childbearing age [ 13 ]. Nevertheless, the current studies concerning the association between UHR and female infertility remain insufficient. Therefore, this cross-sectional study utilized the National Health and Nutrition Examination Survey (NHANES) data to explore the potential association between UHR and female infertility. Given that UHR can reflect both UA and lipid metabolism, our study aims to provide a novel perspective on metabolic contributions to reproductive dysfunction.

Supplementary Table 1. Assessment of collinearity among covariates when UHR was in quartiles. Supplementary Table 2. Assessment of collinearity among covariates when UHR was continuous. Supplementary Table 3. Association between logand female infertility. Supplementary Table 4. Association between UHR and female infertility after interpolation using predictive mean matching. Supplementary Figure 1. Weighted histogram of logdistribution and restricted cubic spline analysis between logand infertility Supplementary Table 1. Assessment of collinearity among covariates when UHR was in quartiles. Supplementary Table 2. Assessment of collinearity among covariates when UHR was continuous. Supplementary Table 3. Association between logand female infertility. Supplementary Table 4. Association between UHR and female infertility after interpolation using predictive mean matching. Supplementary Figure 1. Weighted histogram of logdistribution and restricted cubic spline analysis between logand infertility