Dietary nutrient intake and nutritional status in women with infertility: a cross-sectional study.

A descriptive, cross-sectional study was conducted. The study population consisted of women attending the Gynecology–Infertility Clinic at Marina Salud Hospital in Denia (Spain), who had experienced an unmet reproductive desire for more than one year. Initially, 100 patients were enrolled; however, three were excluded due to inconsistencies in their dietary questionnaires that compromised data reliability, resulting in a final sample of 97 participants. These participants represented the total number of patients who attended the assisted reproduction unit at the hospital during the study period, met the established inclusion and exclusion criteria, and provided informed consent to participate in the research. Recruitment took place between February 1, 2022, and November 1, 2024. The sample size was calculated using G*Power software (version 3.1.9.7). For a multiple linear regression analysis with a medium-small effect size (ƒ 2 = 0.12), an alpha level of 0.05, and a statistical power of 0.85, a minimum of 92 participants was required. Our final sample of 97 participants, representing the total number of patients who attended the assisted reproduction unit during the study period and met the criteria, ensures sufficient power to support the core association analyses. Moreover, the sample was stratified proportionally to the number of patients attending the infertility clinic. The study was approved by the Ethics Committee for Research with Medicinal Products (CEIm) of the Department of Health of Alicante – General Hospital, affiliated with the Alicante Institute for Health and Biomedical Research (ISABIAL) [Approval code: PI2021/113]. All procedures involving human participants were carried out in accordance with Good Clinical Practice guidelines and with the ethical principles set out in the Declaration of Helsinki and its latest revision. Participants were informed about the study’s objectives and procedures, emphasizing the voluntary nature of their participation and their right to withdraw at any time without consequences. Informed consent was obtained from all participants prior to data collection. The inclusion and exclusion criteria were established according to the guidelines of the Department of Health of the Valencian Community for care in assisted reproduction units. Inclusion criteria: Women aged between 18 and 40 years who had not achieved pregnancy after at least 12 months of vaginal intercourse without the use of contraceptive methods. For women over 35 years of age, the required period of active attempts to conceive was reduced to six months. Exclusion criteria: (1) Women with at least one healthy, live-born child, (2)Women who had voluntarily chosen not to conceive, (3) Women with documented medical conditions contraindicating pregnancy or fertility treatments, (4) Women with a confirmed diagnosis of Polycystic Ovary Syndrome (PCOS) or Endometriosis, to avoid confounding effects related to their specific metabolic and inflammatory profiles, (5) Women with diagnosed endocrine pathologies requiring specific pharmacological treatment (e.g., severe thyroid or adrenal disorders) at the time of recruitment, which could independently interfere with the hormonal markers under study, (6) Women with conditions that could severely impact offspring development, (7) Women unable to undergo treatment due to health, family, or social circumstances, (8) Women reporting the use of vitamin or mineral supplements within the 6 months prior to the study, to ensure that nutrient intake reflected dietary sources only, and (9) Women who had undergone significant changes in their dietary patterns or lacked stable and consistent habits during the 6 months preceding the recruitment. A questionnaire specifically designed for this study using the Google Forms tool was employed. Sociodemographic variables included: age, nationality, marital status, employment status, educational level, and annual income range. Regarding lifestyle, the following aspects were assessed: alcohol consumption (never/rarely or regular), smoking habits (non-smoker, smoker, or former smoker), frequency of physical activity (≤ 2 times/week or > 2 times/week), and daily hours of sleep (less than 7, between 7 and 9, or more than 9 h). Anthropometric variables were assessed by trained personnel following standardized procedures. Height, recorded in centimeters, was measured with a vertical stadiometer accurate to 0.2 cm. Waist and hip circumferences were determined using a non-stretchable measuring tape, and the average of three measurements was recorded. Waist circumference was measured at the narrowest point between the rib cage and the navel, while hip circumference was taken at the point of maximum projection of the buttocks. The waist-to-hip ratio (WHR) was calculated based on these measurements. Body weight and composition parameters (body fat percentage, visceral fat, and muscle mass) were evaluated using a bioelectrical impedance analysis (BIA) scale (OMRON HBF-212-EW, Omron Healthcare Co., Ltd., Kyoto, Japan). To ensure the accuracy of the BIA measurements, a strict standardization protocol was followed: all assessments were conducted in the early morning (between 8:00 and 10:00 AM) in a fasting state (minimum 8 h) and after spontaneous voiding. Participants were instructed to remove all footwear and metallic objects (jewelry, watches, or piercings) and to refrain from vigorous physical exercise for 24 h prior to the assessment to minimize fluctuations in hydration status. A semiquantitative 137-item Food Frequency Questionnaire (FFQ), previously validated in Spain and developed within the PREDIMED (Prevención con Dieta Mediterránea) study framework 14 , was used to collect dietary and nutritional data. Validation and reproducibility studies for this FFQ reported Pearson correlation coefficients (r) for food groups, energy, and nutrient intake ranging from 0.50 to 0.82 for reproducibility, and from 0.24 to 0.72 for relative validity compared to dietary records. Additionally, 55–75% of individuals were classified in the same or adjacent quintile for energy and nutrient intake, demonstrating that the FFQ has good reproducibility and relative validity, consistent with dietary assessment tools used in other large prospective studies. The questionnaire was designed to assess habitual dietary intake over the previous 12 months, thereby accounting for seasonal variations in food consumption. The questionnaire included detailed questions about the type and portion size of foods consumed, such as bread (white or whole grain), fats (olive oil, margarine, etc.), and various types of meat, dairy products, or beverages. Consumption frequency referred to the previous year and was categorized as follows: daily intake (once a day, 2–3 times a day, 4–6 times, or more than 6 times a day), weekly intake (once a week, 2–4 times, or 5–6 times per week), monthly intake (1–3 times per month), and no or occasional intake. For seasonally available foods, such as fruits and vegetables, participants were asked to report how often they consumed them during their respective seasons, which was then pro-rated to calculate a representative annual average. Hormonal markers were assessed through peripheral blood sampling performed by trained nursing staff. To ensure the standardization and comparability of the results, all blood samples were collected between 8:00 and 10:00 AM after an overnight fast (minimum 8 h) and strictly during the early follicular phase (days 2–5 of the menstrual cycle). The variables analyzed included anti-Müllerian hormone (AMH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), thyroid-stimulating hormone (TSH), estradiol, calcidiol, and prolactin. Hormonal concentrations were determined at the hospital’s clinical laboratory using automated chemiluminescent immunoassay (CLIA) techniques. In addition, antral follicle count (AFC) was evaluated through transvaginal ultrasound performed by a gynecology specialist. As part of the clinical assessment, resting blood pressure was also measured using a validated digital sphygmomanometer. Data collection was carried out by experienced personnel from the Gynecology and Obstetrics Department. During the initial clinical interview, dietary and lifestyle questionnaires were administered by the researchers using a computerized system to minimize recording errors. Anthropometric measurements and blood pressure were taken during this same visit. Subsequently, participants were scheduled for the standardized hormonal analysis within the following one to two menstrual cycles, ensuring that the blood draw coincided with the aforementioned early follicular phase. Quantitative variables were described using means and standard deviations (SD), while qualitative variables were presented as absolute frequencies and percentages (%). The normality of the data was assessed using the Kolmogorov–Smirnov test, and variance homogeneity was verified with Levene’s test. Responses from the FFQ regarding consumption frequency were transformed into continuous daily frequencies using the midpoint of each category as the numerator and the period in days as the denominator, a standard procedure in nutritional epidemiology. The applied coefficients were: 0.0 (never); 0.07 (1–3 times/month, calculated as 2/30 days); 0.14 (once/week, 1/7); 0.43 (2–4 times/week, 3/7); 0.78 (5–6 times/week, 5.5/7); and 1.0 (once/day). For higher frequencies, the specific reported midpoint was used (e.g., 2–3 times/day = 2.5). These coefficients were multiplied by the portion sizes (in grams) to calculate daily food intake. Finally, individual energy and nutrient intakes were estimated using the Spanish Food Composition Database (BEDCA) 15 , adjusting for the edible portion. To assess associations between variables, Spearman’s correlation coefficient was used to analyze relationships between nutrients and anthropometric and hormonal parameters, due to the non-parametric nature of some data. Significant associations found in the bivariate analysis were further tested using multiple linear regression to adjust for total energy intake, BMI, smoking status, and physical activity. Age was not included in the final multivariate models, as preliminary analyses showed no significant association with the primary outcomes in this specific cohort. The assumptions of the multiple linear regression models were rigorously tested. Linearity and homoscedasticity were verified through the visual inspection of residual plots. The normality of residuals was confirmed using P-P plots and Shapiro–Wilk tests. Furthermore, multicollinearity among predictors was assessed using the Variance Inflation Factor (VIF), with values < 5 (averaging 1.05–1.45) considered acceptable. Model fit was evaluated using the adjusted R-squared (R 2 ). Statistical analyses were performed using SPSS v.25 for Windows (SPSS Inc., Chicago, IL, USA), and results were expressed with a 95% confidence interval (CI). Statistical significance was set at p  < 0.05, applying the Bonferroni correction to adjust p-values for multiple comparisons where appropriate.

The participants ( n  = 97) had a mean age of 33.08 years (± 4.12), and the majority (78.4%; n  = 76) were Spanish nationals. Regarding employment status, 83.5% ( n  = 81) were employed, 9.3% ( n  = 9) were unemployed, and 7.2% ( n  = 7) reported other employment situations. In terms of education level, most participants (68.0%; n  = 66) had completed higher education, 29.9% ( n  = 29) had completed secondary education, and only 2.1% ( n  = 2) reported no formal education. Concerning annual income, 64.9% ( n  = 63) reported earnings between €10,000 and €30,000, 23.7% ( n  = 23) reported earnings below €10,000, and 11.3% ( n  = 11) had earnings above €30,000. These data are summarized in Table  1 . Table 1 Sociodemographic characteristics of the sample ( n  = 97). Variable Category n (%) Age (years) Mean ± SD 33.08 ± 4.12 Nationality Spanish 76 (78.4%) Other 21 (21.6%) Employment status Employed 81 (83.5%) Unemployed 9 (9.3%) Other 7 (7.2%) Educational level Higher education 66 (68.0%) Secondary education 29 (29.9%) No formal education 2 (2.1%) Annual income €30.000 11 (11.3%) SD, standard deviation. Sociodemographic characteristics of the sample ( n  = 97). SD, standard deviation. Table 2 summarizes the anthropometric and clinical characteristics of the sample. The mean BMI of the participants was 25.5 ± 5.6 kg/m², falling within the overweight range. Overall, 23.7% ( n = 23) were classified as overweight (BMI 25–29.9 kg/m²), and 17.5% ( n = 17) as obese (BMI ≥ 30 kg/m²). Regarding body composition, the average body fat percentage was 35.9 ± 9.3%, exceeding the reference value (< 33%) for women of reproductive age 16 . The average muscle mass percentage was 26.4 ± 5.1%, at the lower limit of the normal range (≥ 27%) 16 . Visceral fat accounted for 5.5 ± 2.3%, slightly above the recommended threshold (< 5%) 16 . Clinical markers showed mean levels of estradiol (54.2 ± 39.3 pg/ml), calcidiol (24.2 ± 8.4 ng/ml), and prolactin (16.7 ± 9.3 ng/ml), with considerable individual variability (Table 2 ). Table 2 Anthropometric and clinical characteristics of the sample ( n  = 97). Variable Participants ( n  = 97) Mean ± SD Observed range Weight (kg) 69.32 ± 15.17 44.0–117.0 BMI (kg/m²) 25.51 ± 5.68 17.4–40.7 Body fat (%) 35.96 ± 9.36 17.2–56.4 Visceral fat (%) 5.48 ± 2.26 2.0–15.0 Muscle mass (%) 26.40 ± 5.13 12.6–45.4 Waist circumference (cm) 81.67 ± 13.06 63.0–125.0 Hip circumference (cm) 102.23 ± 12.82 81.0–140.0 Waist-to-hip ratio 0.79 ± 0.07 0.66–1.15 FSH (mIU/ml) 7.70 ± 2.23 2.3–12.7 LH (mIU/ml) 6.23 ± 4.00 2.1–32.0 Estradiol (pg/ml) 54.28 ± 39.39 11.8–355.5 Calcidiol (ng/ml) 24.23 ± 8.48 9.9–62.1 AMH (ng/ml) 2.67 ± 1.84 0.01–7.93 TSH (µU/ml) 2.49 ± 1.70 0.34–13.10 Prolactin (ng/ml) 16.75 ± 9.32 3.80–59.20 AFC (units) 19.00 ± 13.51 2.0–80.0 Mean blood pressure (mmHg) 96.66 ± 8.82 79.0–117.5 SD, standard deviation; BMI, body mass index; FSH, follicle-stimulating hormone; LH, luteinizing hormone; Calcidiol, vitamin D metabolite; AMH, anti-Müllerian hormone; TSH, thyroid-stimulating hormone; AFC, antral follicle count. Anthropometric and clinical characteristics of the sample ( n  = 97). SD, standard deviation; BMI, body mass index; FSH, follicle-stimulating hormone; LH, luteinizing hormone; Calcidiol, vitamin D metabolite; AMH, anti-Müllerian hormone; TSH, thyroid-stimulating hormone; AFC, antral follicle count. Table  3 shows the associations between dietary nutrient intake and anthropometric parameters. In the crude analysis, muscle mass percentage (MMP) was positively correlated with riboflavin ( r  = 0.237, p  = 0.022) and calcium intake ( r  = 0.226, p  = 0.029). These associations remained significant in the multivariate model adjusted for energy intake, physical activity, and smoking status for both riboflavin (B = 0.665; 95% CI 0.026 to 1.305; p  = 0.042) and calcium (B = 0.004; 95% CI 0.001 to 0.007; p  = 0.012). Regarding fat distribution, hip circumference was inversely associated with dietary vitamin E intake ( r = − 0.230, p  = 0.050), showing a stronger association in the adjusted model (B = − 0.773; 95% CI − 1.296 to − 0.250; p  = 0.004). Other initial correlations, specifically riboflavin with body fat percentage and saturated fatty acids with waist-to-hip ratio, did not reach statistical significance after adjustment ( p  > 0.05). Table 3 Multivariate linear regression analysis between anthropometric parameters and dietary nutrient intake in women with infertility ( n  = 97). Anthropometric parameter Associated nutrient Spearman’s r Crude p -value Adjusted B 95% CI (for B) Adjusted p -value MMP (%) Riboflavin (mg) 0.237 0.022 0.665 (0.026 to 1.305) 0.042 Calcium (mg) 0.226 0.029 0.004 (0.001 to 0.007) 0.012 Hip circumference (cm) Vitamin E (mg) − 0.230 0.050 −  0.773 (− 1.296 to − 0.250) 0.004 MMP, Muscle Mass Percentage; B, unstandardized regression coefficient; CI, Confidence Interval. *Adjusted model: Regression coefficients are adjusted for total energy intake (kcal), physical activity, and smoking status. Adjusted R 2 for the multivariate models in this table ranged from 0.029 to 0.117. Variance Inflation Factor (VIF) values for all predictors were < 2.0, confirming the absence of significant multicollinearity. Multivariate linear regression analysis between anthropometric parameters and dietary nutrient intake in women with infertility ( n  = 97). MMP, Muscle Mass Percentage; B, unstandardized regression coefficient; CI, Confidence Interval. *Adjusted model: Regression coefficients are adjusted for total energy intake (kcal), physical activity, and smoking status. Adjusted R 2 for the multivariate models in this table ranged from 0.029 to 0.117. Variance Inflation Factor (VIF) values for all predictors were < 2.0, confirming the absence of significant multicollinearity. Table  4 details the associations between dietary nutritional intake and clinical/hormonal markers. In the crude analysis, prolactin (PRL) showed an inverse correlation with dietary vitamin E intake ( r = − 0.308; p  = 0.005). This association remained robust in the multivariate model after adjusting for total energy intake, smoking status, physical activity, and BMI (B = − 0.501; 95% CI − 0.870 to − 0.132; p  = 0.008). In contrast, the initial correlations detected for anti-Müllerian hormone (AMH) and thyroid-stimulating hormone (TSH) with various nutrients did not persist after adjusting for the aforementioned covariates ( p  > 0.05). Table 4 Multivariate linear regression analysis between hormonal parameters and dietary nutrient intake in women with infertility ( n  = 97). Clinical marker Associated nutrient Spearman’s r Crude p -value Adjusted B 95% CI (for B) Adjusted p -value PRL (ng/mL) Vitamin E (mg) − 0.308 0.005 −  0.501 (− 0.870 to − 0.132) 0.008 PRL, Prolactin; B, unstandardized regression coefficient; CI, Confidence Interval. *Adjusted model: Regression coefficients are adjusted for total energy intake (kcal), physical activity, smoking status, and Body Mass Index (BMI). Adjusted R 2 for the multivariate model: 0.091. Variance Inflation Factor (VIF) values for all predictors were < 2.0. Multivariate linear regression analysis between hormonal parameters and dietary nutrient intake in women with infertility ( n  = 97). PRL, Prolactin; B, unstandardized regression coefficient; CI, Confidence Interval. *Adjusted model: Regression coefficients are adjusted for total energy intake (kcal), physical activity, smoking status, and Body Mass Index (BMI). Adjusted R 2 for the multivariate model: 0.091. Variance Inflation Factor (VIF) values for all predictors were < 2.0.

In conclusion, our study identifies significant associations between the dietary intake of specific micronutrients and key reproductive and anthropometric markers in women seeking fertility treatment. Specifically, higher dietary vitamin E intake is inversely associated with prolactin levels and hip circumference, while riboflavin intake shows a nominal positive association with muscle mass percentage. These findings suggest that specific dietary antioxidant patterns may be linked to variations in the neuroendocrine axis and body composition. Given the cross-sectional nature of this study, these results should be interpreted as hypothesis-generating and do not establish causal relationships. Future longitudinal research is warranted to evaluate the potential role of these nutritional factors in the clinical management of female fertility.

This cross-sectional study analyzed the relationship between dietary nutrient intake and anthropometric and hormonal parameters in women with infertility. After adjusting for total energy intake, BMI, smoking status, and physical activity, and applying the Bonferroni correction for multiple comparisons, our results highlight two primary findings: (1) a robust inverse association between dietary vitamin E intake and prolactin levels (β = − 0.501; 95% CI − 0.870 to − 0.132; p  = 0.008); and (2) an inverse association between vitamin E intake and hip circumference (β = − 0.773; 95% CI − 1.296 to − 0.250; p  = 0.004). Additionally, a nominal positive association was observed between riboflavin intake and muscle mass percentage (β = 0.665; 95% CI 0.026 to 1.305; p  = 0.042), although this relationship reached only marginal significance after stringent multiple-testing adjustment. Notably, other associations—such as those involving AMH—were fully attenuated, emphasizing the need for rigorous statistical control in nutritional epidemiology. Among our results, the association between dietary vitamin E and prolactin levels (β = − 0.501; p  = 0.008) stands out as clinically relevant, suggesting a potential regulatory role of this antioxidant in the neuroendocrine axis. To ensure the clinical validity of this association, we confirmed through medical record reviews that no participants had diagnosed prolactinomas or were receiving pharmacological treatment for hyperprolactinemia. To date, evidence directly linking vitamin E to prolactin regulation in reproductive-aged women is limited. One clinical study in uremic patients undergoing hemodialysis found that oral vitamin E supplementation (300 mg/day) for eight weeks significantly reduced serum prolactin levels without affecting other reproductive hormones 17 . Although this study was conducted in a non-fertility clinical population, it supports the plausibility that vitamin E may influence pituitary prolactin output. Mechanistically, vitamin E is a potent lipid-soluble antioxidant capable of scavenging reactive oxygen species (ROS) and stabilizing cellular membranes, thereby mitigating oxidative stress—a known disruptor of neuroendocrine signaling 18 . Oxidative stress has been implicated in impairing dopaminergic inhibition within the hypothalamic–pituitary axis, and preservation of dopaminergic tone could favor maintenance of prolactin within physiological ranges. Experimental evidence from animal models also indicates that vitamin E supplementation can prevent hormone dysregulation induced by oxidative stress in hypothalamic–pituitary tissues, further supporting a role for antioxidants in modulating endocrine axes 19 . Additionally, studies in women with reproductive disorders such as PCOS have shown that vitamin E supplementation improves oxidative stress profiles and some aspects of hormonal regulation, although effects on prolactin specifically have not been the primary focus in these trials 20 . In the context of infertility, a condition frequently associated with increased oxidative stress and endocrine dysregulation, antioxidant status may be particularly relevant 21 . Elevated ROS have been shown to negatively impact ovulation, oocyte quality, follicular development, and overall reproductive function in women, highlighting oxidative imbalance as a contributor to infertility and reproductive disorders such as PCOS and endometriosis 21 , 22 . Although direct mechanistic evidence linking oxidative stress with altered dopaminergic inhibition of prolactin secretion is limited, oxidative stress has been implicated in impaired neuroendocrine signaling in other contexts, providing a biologically plausible framework for how antioxidant nutrients like vitamin E may support regulatory mechanisms under oxidative challenge. Beyond the hormonal associations, our study identified a significant inverse relationship between dietary vitamin E intake and hip circumference (β = − 0.773; p = 0.004). To our knowledge, this is a novel finding in women seeking fertility treatment. Vitamin E, as a major lipid-soluble antioxidant, is known to accumulate in adipose tissue, where it plays a critical role in mitigating obesity-associated oxidative stress and chronic low-grade inflammation 23 . Mechanistically, experimental evidence suggests that certain vitamin E isoforms, particularly tocotrienols, can modulate adipogenesis and reduce lipid accumulation in specific fat depots. Animal models have shown that γ-tocotrienol can decrease body fat by inhibiting PPARγ-mediated adipocyte differentiation, a master regulator of fat storage 24 . In humans, gluteofemoral fat (reflected by hip circumference) is metabolically distinct from visceral fat; however, an excessive accumulation in this region can still be associated with systemic metabolic imbalances that may indirectly affect reproductive health 25 . Our results raise the hypothesis that a higher intake of antioxidant-rich nutrients like vitamin E may influence regional fat distribution or the metabolic quality of adipose tissue. Nevertheless, given the lack of previous human studies specifically examining gluteofemoral adiposity and vitamin E, this finding should be interpreted with caution and warrants replication in prospective cohorts to confirm its clinical implications. Additionally, our study observed a nominal positive association between dietary riboflavin intake and muscle mass percentage (β = 0.665; p = 0.042), although this relationship reached only marginal significance after the stringent Bonferroni adjustment. Biochemically, riboflavin serves as a precursor for FAD, an essential cofactor for mitochondrial ATP production and fatty acid β-oxidation 26 , while also supporting the glutathione redox cycle to mitigate systemic oxidative stress 27 , 28 . Although direct evidence in women with infertility is scarce, our results align with findings in the general population where higher vitamin B2 intake was linked to a lower risk of muscle mass loss 29 . Our findings expand this evidence by suggesting that adequate riboflavin intake may play a role in preserving lean mass—a key component of metabolic health. This is particularly relevant given recent evidence highlighting the importance of the fat-to-muscle ratio (FMR) in female reproductive health. For instance, a recent study in US women found that a higher FMR was significantly associated with an increased prevalence of infertility, emphasizing that the balance between adipose and muscle tissues may be a better predictor of reproductive outcomes than isolated adiposity markers 30 . Therefore, the potential of riboflavin to support muscle mass could have indirect positive implications for the fertility profile of these women. In addition to riboflavin, our study identified a significant positive association between calcium intake and muscle mass (β = 0.004, p = 0.012). Beyond its well-established role in bone health, calcium is a fundamental mediator of excitation-contraction coupling in skeletal muscle and regulates mitochondrial oxidative metabolism through calcium-dependent activation of key dehydrogenases 31 . Adequate calcium availability has been associated with preservation of muscle mass and reduced risk of sarcopenia in young adults 32 . Given that skeletal muscle plays a central role in metabolic homeostasis, these findings may be particularly relevant in women with infertility, a condition often characterized by metabolic disturbances. Our results support the hypothesis that a synergistic intake of minerals and B-vitamins contributes to a healthier body composition, potentially favoring a more optimal reproductive environment. One of the primary strengths of this study is its comprehensive approach, simultaneously analyzing nutritional, anthropometric, and hormonal parameters using validated methodologies, including standardized dietary questionnaires and energy intake adjustment. To our knowledge, this represents the first scientific evidence on these specific associations within an infertility clinic population—a group with distinct metabolic needs. Based on our findings, we suggest that nutritional counseling for these women should prioritize the intake of foods rich in vitamin E, with a particular emphasis on extra virgin olive oil, nuts, and seeds, to support neuroendocrine balance. Furthermore, encouraging the consumption of riboflavin-rich sources—such as dairy products (yogurt and cheese), eggs, lean meats, and legumes—could be beneficial for maintaining an optimal fat-to-muscle ratio, which has been recently linked to lower infertility prevalence. However, several limitations must be considered. As a cross-sectional study, causal relationships cannot be established; therefore, these associations should be viewed as hypothesis-generating. Regarding dietary assessment, although the FFQ is a validated tool, it relies on self-reported data and is subject to recall bias. Furthermore, it is important to note that most validation studies for this instrument were conducted in older adult populations, which may not fully capture the specific dietary patterns and portion sizes of younger women seeking fertility treatment. Differences in appetite and eating habits between age groups could introduce measurement errors in total energy and nutrient estimation. Moreover, while dietary intake was thoroughly assessed, our study did not systematically record the use of over-the-counter nutritional supplements, which could influence total nutrient exposure. Furthermore, serum nutrient levels were not measured to complement the FFQ findings. Additionally, while we adjusted for major confounders such as BMI, smoking, and physical activity, the influence of other unmeasured lifestyle factors cannot be entirely ruled out. Finally, since all data were obtained from a single hospital center, caution is required when generalizing these findings to other populations.

Infertility, defined as the inability to achieve pregnancy after 12 months of regular unprotected intercourse, affects approximately 17.5% of reproductive-age couples worldwide 1 . This condition represents a major global public health concern, with significant social and economic impacts. Recent estimates suggest that infertility prevalence is comparable across regions and shows no substantial differences between high-, middle-, and low-income countries, reinforcing its relevance as a universal health issue 1 . In this context, identifying its causes and risk factors is essential. Among these, modifiable factors such as nutrient intake and nutritional status have emerged as key targets for the prevention and management of infertility 2 – 4 . Nutritional status, assessed through anthropometric indicators such as body mass index (BMI), body fat percentage, visceral fat percentage, and muscle mass, appears to significantly influence female fertility 4 . Weight-related alterations, including underweight and obesity, have been associated with dysfunctions in the hypothalamic–pituitary–ovarian axis, which may lead to anovulatory cycles and reduced oocyte quality 4 . Indeed, several studies have demonstrated a J-shaped relationship between BMI and infertility, in which the effect of body mass on fecundity appears to be bimodal 2 , 5 , 6 . However, while BMI is a widely used marker, it may not fully account for the complex metabolic influence of specific body compartments, such as the ratio between adiposity and lean mass, which are increasingly recognized as critical for endocrine homeostasis 7 , 8 . Furthermore, the impact of nutritional status on fertility appears to be more pronounced in women, suggesting a distinct sensitivity of the female reproductive system to metabolic signals 8 . Obesity, for example, has been associated with increased levels of insulin and androgens, which interfere with folliculogenesis and oocyte maturation 9 , 10 . Likewise, low body weight can alter gonadotropin secretion due to reduced levels of leptin, a key hormone involved in energy signaling for reproductive function 10 . Despite these observations, there is still limited consensus on how specific regional fat distribution or muscle mass maintenance interacts with dietary factors to influence the reproductive environment. On the other hand, adequate intake of specific nutrients appears to play a key role in regulating essential physiological processes involved in female fertility, such as folliculogenesis, embryo implantation, and sex hormone production. Several studies have shown that higher intake of vitamins such as A, C, B12, and folic acid, as well as minerals like magnesium and iron, is associated with a reduced risk of infertility 10 – 13 . For instance, folic acid is involved in DNA methylation and nucleotide synthesis—processes that are fundamental for cell division and the development of viable oocytes—which may explain its relationship with higher live birth rates in assisted reproductive techniques (ART) 11 , 12 . Additionally, a cross-sectional study using data from the National Health and Nutrition Examination Surveys (NHANES), including 1713 women, found that those with higher dietary intake of complex carbohydrates, vitamins A and C, magnesium, iron, lycopene, and total folate had a lower risk of infertility, with a particularly notable association among women with a BMI in the healthy range (18.5–24.9 kg/m²) 10 . In women with overweight (BMI > 24.9 kg/m²), nutrients such as magnesium, iron, and total folate showed a more pronounced protective association, suggesting that they may help mitigate the negative effects of excess adiposity on ovarian function 10 . Despite these insights, most evidence stems from general population surveys, and the role of other critical metabolic cofactors remains less explored in clinical infertility cohorts. Identifying how these specific nutrients relate to objective hormonal and anthropometric markers is essential to better understand their potential role in this population. These results emphasize the importance of maintaining adequate nutritional status and optimal dietary nutrient intake, as they play a fundamental role in regulating hormonal and metabolic processes critical for female fertility. However, much of the current literature on the impact of diet and nutritional status on infertility is based on studies conducted in general populations or specific treatment settings, leaving a gap in knowledge regarding how these variables affect women with a confirmed infertility diagnosis. In this context, the present study was designed under the hypothesis that a significant association exists between dietary nutrient intake and the hormonal and anthropometric profile of women seeking fertility treatment. Therefore, the objective of this study was to address this gap by analyzing the association between a wide range of dietary nutrients, objective nutritional status, and various reproductive markers in a well-characterized clinical cohort, providing evidence-based insights into modifiable factors that may influence reproductive health.