Associations between cardiovascular health and female infertility: A national population-based study.

Female infertility, affecting an estimated 8–12% of couples globally, presents a complex interplay of factors involving both partners. While male factors contribute to approximately 50% of infertility cases, female factors alone are responsible for 20–30% [ 1 , 2 ]. Studies have underscored the impact of early-life conditions on reproductive health, with a higher ponderal index at birth being associated with increased risk of infertility in female, suggesting the significance of in-utero development on long-term fertility outcomes [ 3 ]. Furthermore, secondary infertility is prevalent worldwide, often linked to infections and reproductive tract issues. The probability of conception decreases with age and duration of non-conception, with a notable decline in female fertility beginning around 25–30 years of age, intensifying after 35 [ 4 ]. Emerging evidence links female infertility with cardiovascular diseases (CVD), suggesting that female with infertility may have an increased risk of CVD later in life [ 5 , 6 ]. For instance, factors like polycystic ovary syndrome (PCOS) and endometriosis, which are associated with infertility, have been observed to correlate with higher rates of CVD risk factors such as hypertension and metabolic syndrome [ 7 ]. Elevated levels of triglycerides and lower levels of high-density lipoprotein, markers of cardiovascular risk, have been observed in female with unexplained infertility, indicating a potential for atherogenic lipid profiles in this group [ 8 ]. Additionally, female infertility may act as a biomarker for future health risks, with reproductive health intricately tied to overall and long-term health outcomes [ 9 ]. The relationship between PCOS and cardiovascular risk illustrates the nuanced nature of this association. While PCOS is commonly associated with metabolic syndrome—a known CVD risk factor—the direct link to primary cardiovascular events such as stroke or myocardial infarction is more speculative. Studies provide mixed results, with some indicating a slight increase in cardiovascular events among female with menstrual irregularity, yet little evidence supports a strong association between hyperparathyroidism and cardiovascular events [ 10 , 11 ]. This complexity underscores the importance of considering a wide array of reproductive health factors when assessing cardiovascular risk in female. The American Heart Association’s Life’s Essential 8 (LE8) score encompasses a broad spectrum of cardiovascular health metrics, from diet and physical activity to blood pressure and blood glucose levels [ 12 , 13 ]. Understanding how each component of the LE8 score relates to female infertility can provide insights into potential preventative strategies and therapeutic interventions [ 14 ]. The primary objective of this study is to elucidate the relationship between cardiovascular health (CVH), as gauged by the LE8 score, and female infertility, leveraging the comprehensive data from the National Health and Nutrition Examination Survey (NHANES).

A total of 3,969 female participants with a mean age of 39.97 ± 11.42 years were included in this analysis, of which 476 (11.99%) were diagnosed with infertility. The mean LE8 score with its standard deviation (SD) was 66.76 (11.32). Of note, 16.00% had poor CVH (LE8 <50), 60.07% had moderate CVH (50 ≤ LE8 <80), and 23.94% were classified as having high CVH (LE8≥80). Participants with higher CVH scores had a lower prevalence of infertility compared to those with lower CVH scores (10.11% vs. 15.28%). In addition, participants with higher CVH scores were more likely to be younger and have older menarche, have high proportion of married or living with partner, have higher socioeconomic status (level of education and family income), and have a lower prevalence of diabetes mellitus and pelvic infection disease (P < 0.01) ( Table 1 ). Characteristics of the participants aged 20–49 years are presented in S2 Table , and the results are generally consistent. Mean (SD) for continuous variables: the P value was calculated by the weighted linear regression model. Percentages for categorical variables: the P value was calculated by the weighted chi-square test. Cardiovascular health (CVH) is categorized into 3 grades, low: LE8 score <50, mederate:50≤LE8 score <80, high: LE8 score ≥80. Abbreviation: AHA, American Heart Association; LE8, Life’s Essential 8; CVH, cardiovascular health; DASH, Dietary Approaches to Stop Hypertension; PIR, The ration of family income to poverty. Table 2 demonstrates the association of CVH score and its 8 subscores with infertility. In all models, there was an inverse association between total CVH score and infertility, with each 10-point increase in CVH associated with 7% lower odds of infertility after adjusting for all covariates [OR = 0.93, 95% CI: 0.90–0.96]. Participants with moderate CVH [OR = 0.76, 95% CI: 0.58–1.00] and higher CVH levels [OR = 0.59, 95% CI: 0.41–0.84] had 24% and 41% lower odds of infertility compared with the low CVH group, and there was a significant linear trend ( P for trend < 0.01). For the association of the 8 subscores with infertility, also in the fully adjusted model, our results showed that it was mainly 3 of the subscores (physical activity score, body mass index score, and blood glucose score) that maintained a statistically significant negative association with infertility ( P < 0.05). Associations for participants aged 20–49 years are presented in S3 Table , with generally consistent results. The dose-response relationship between LE8 score and infertility was assessed using smooth curve fitting. A negative association between LE8 score and infertility was observed in the fully adjusted model, but the non-linear relationship was not significant (total P value < 0.001, non-linear P-value = 0.793) ( Fig 2 ). The solid red line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit. Model 1 was unadjusted for covariates; Model 2 enhanced Model 1 by including age and ethnicity; and Model 3 further augmented Model 2 by integrating education level, family income-to-poverty ratio, marital status, age of menarche and pelvic infection disease. Abbreviation: CVH, cardiovascular health; DASH, Dietary Approaches to Stop Hypertension. *Tests for linear trends across three categories of cardiovascular health metrics scores were performed by modeling the median value within each category as a continuous variable Subgroup analyses based on different groups further demonstrated the robustness of the association between CVH and infertility across populations ( Table 3 ). The results showed that the association between CVH and infertility was generally consistent across participants of different age, marital status, education, PIR, and diabetes status, all of which were inversely associated and not significantly different (all P for interaction > 0.05). The results of the subgroup analyses for participants aged 20–49 years are presented in S4 Table . Age, race, education level, family income-to-poverty ratio, marital status, age of menarche and pelvic infection disease were adjusted. Abbreviation: CVH, cardiovascular health.

Our study reveals a significant inverse association between cardiovascular health, as indicated by the LE8 score, and female infertility. This underscores the importance of maintaining optimal cardiovascular health for improving reproductive outcomes in female, highlighting a need for integrated health strategies that focus on both cardiovascular and reproductive wellness.

Our study is based on an original cross-sectional analysis of the NHANES database, which serves as the setting for our research. The NHANES, conducted by the National Center for Health Statistics, is a pivotal program that aims to assess the health and nutritional status of the U.S. population [ 15 , 16 ]. It provides critical data on the prevalence of major diseases and associated risk factors through extensive data collection. NHANES employs sophisticated multi-stage, probability-based sampling methods to ensure a nationally representative sample [ 17 ]. The program’s protocols have received approval from the National Center for Health Statistics (NCHS) Research Ethics Review Board, and all participants provided written informed consent by NHANES project team. This study utilized data from the 2013–2018 NHANES cycle. The study focused on female participants aged 20 years and older. From the initial pool of 10,375 females in the 2013–2018 cycle with reproductive questionnaire, the study excluded those with missing infertility data (n = 5,021), along with participants with missing or incomplete CVH data (n = 1,465), resulting in a final sample of 3,969 female participants for analysis ( Fig 1 ). NHANES, National Health and Nutrition Examination Survey. Infertility status was assessed based on responses to two queries (RHQ074 and RHQ076) within the Reproductive Health Questionnaire. RHQ074 inquired if participants had tried to conceive for a year or more without success, and RHQ076 asked if they had ever sought medical advice for difficulties in becoming pregnant [ 18 , 19 ]. Affirmative responses to either question classified participants as having infertility. Cardiovascular health is evaluated through the LE8 score, which includes diet (aligned with the Dietary Approaches to Stop Hypertension (DASH) guidelines), physical activity, tobacco use, sleep, body mass index (BMI), lipid levels excluding high-density lipoprotein (HDL), blood glucose, and blood pressure. Standard surveys collect lifestyle data, while physical and biochemical measurements are taken following standardized methods. BMI is calculated as weight in kilograms divided by height in meters squared. Blood pressure is averaged from initial readings, non-HDL cholesterol is obtained by subtracting HDL from total cholesterol, and glycated hemoglobin is determined by liquid chromatography (detailed calculation see S1 Table ). The LE8 score ranges from 0 to 100, with an aggregate score derived from these components, categorizing CVH into high, moderate, or low according to American Heart Association benchmarks [ 20 , 21 ]. The selection of covariates for this study was guided by their potential influence on the connections between infertility and CVH. The covariates included age, race, education level, family income-to-poverty ratio (PIR), age of menarche and pelvic infection disease. The PIR is divided into three categories: low (PIR ≤1.3), medium (PIR > 1.3–3.5) and high (PIR > 3.5) [ 22 ]. These covariates were chosen based on their established relevance in epidemiological research and their potential confounding effects on the study outcomes [ 13 ]. The LE 8 score was used to stratify cardiovascular health into three categories: low (scores below 50), intermediate (scores between 50 and 79), and high (scores 80 or above). Percentage distributions were used for categorical data, while continuous data were expressed in terms of means and standard deviations. To compensate for any incomplete data, we utilized the mice package to perform multiple imputation using chained equations with ten imputation sets. The relationship between cardiovascular health status and infertility was investigated through a series of logistic regression models. The initial model did not adjust for additional variables; the second model accounted for age and ethnicity, and the third model further incorporated educational background, PIR, age of menstruation and history of pelvic inflammatory disease. Adjustment for PIR is a continuous variable in all models. Tests for linear trends across three categories of cardiovascular health metrics scores were performed by modeling the median value within each category as a continuous variable. To explore potential non-linear relationships between CVH and infertility, we employed smooth curve fitting techniques using generalized additive models (GAMs). GAMs allow for the flexible modeling of relationships between a predictor and an outcome variable by fitting a smooth curve instead of assuming a linear relationship. This approach is particularly useful for identifying and illustrating complex patterns in the data that might not be evident under linear analysis. The smoothness of the curve is determined by a smoothing parameter, which controls the trade-off between the model’s fit and its complexity. We assessed the appropriateness of the curve using the generalized cross-validation score as a criterion for selecting the smoothing parameter [ 23 – 25 ]. Interaction tests were conducted to assess whether the association between CVH and infertility differed across various subgroups, such as age, ethnicity, and socioeconomic status. For this purpose, we introduced interaction terms into our logistic regression models, which represent the product of CVH scores and subgroup identifiers. In addition, the same sensitivity analyses were conducted by restricting the age to the conventional age range for fertility (less than 50 years) to test the robustness of the results. All analyses were performed using the statistical software R, version 4.3.0, with a predetermined significance threshold set at a P -value of less than 0.05 for all two-tailed tests.

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