Abstract
Background
Infertility is a significant challenge in women’s reproductive health, but the relationship between ultra-processed food (UPF) intake and female infertility remains unclear. This study aims to investigate the association between UPF intake and female infertility.
Methods
Using data from 3601 women in the NHANES database, UPF intake was defined as the percentage of energy consumed by UPF in each participant’s daily total food intake energy. Propensity score matching (PSM) and logistic regression were applied to control for potential confounders and analyze the relationship between UPF intake and female infertility. In addition, the restricted cubic splines (RCS) were used to model the potential non-linear relationships.
Results
After PSM, 1645 participants were included in the final analysis, comprising 417 in the infertility group and 1228 in the control group. Compared to the first quartile (Q1), UPF intake in the fourth quartile (Q4) was significantly associated with infertility in both unadjusted and adjusted logistic regression models (unadjusted OR: 1.43, 95% CI: 1.05–1.96, P = 0.025; adjusted OR: 1.43, 95% CI: 1.03–2.00, P = 0.033, respectively). No significant non-linear relationship was observed between UPF intake and infertility. However, the curve showed an overall upward trend, with a notable increase in infertility risk when UPF intake exceeded 40.8%.
Conclusion
Excessive UPF intake is significantly associated with an increased risk of female infertility. Future prospective cohort studies with larger sample sizes are still needed to provide more stable evidence of the relationship between UPF intake and female infertility.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-025-23458-w.
Keywords: Processed food, Infertility, Risk factor, Cross-sectional study, Dietary intake
Introduction
Infertility refers to the failure of a couple to conceive for more than one year without taking any contraceptive measures despite regular sexual intercourse [1]. Infertility has become a widespread public health issue, affecting women’s mental health [2, 3]. The etiology of female infertility is multifactorial, with common causes including ovulation disorders and reproductive organ diseases [1]. Identifying and addressing the causes and risk factors of infertility are crucial for reducing its incidence. Recent research suggests that certain environmental factors may also contribute to female infertility [4–6]. Ultra-processed food (UPF) intake, a prominent aspect of the modern diet, is not unclearly associated with female infertility.
UPFs are foods and beverages that have undergone complex industrial processing. They usually contain high levels of salt, sugar, unhealthy fats, and artificial additives to extend expiration date and improve palatability [7]. They are widely prevalent in society and consumed by a large population. Recent studies have found that metabolic disorders associated with UPF may contribute to obesity [8–10], endocrine disorders [11] and immune dysfunction [12]. Epidemiological studies have found that a high body mass index (BMI) in women is correlated with an increased risk of infertility [13, 14]. Furthermore, UPFs may contain harmful chemicals [15, 16] that could potentially affect the reproductive function of childbearing-age women [17].
While excessive UPF consumption may lead to female infertility, strong evidence to support this link is currently lacking. This study used large-scale data from the National Health and Nutrition Examination Survey (NHANES) database to investigate the relationship between UPF intake and female infertility using logistic regression models and restricted cubic splines (RCS). We aim to provide novel insights and encourage women of childbearing age to adopt healthier dietary habits.
Materials and methods
Data extraction and participants
In this cross-sectional study, data were obtained from the NHANES database from 2013 to 2014, 2015–2016, and 2017-March 2020 pre-pandemic. NHANES is a complex, multi-stage stratified probability sampling survey conducted by the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS) of the United States. It has been conducted regularly since the 1960s and provides available survey data to assess the health and nutritional status of the non-institutional population in the United States.
Initially, there were 35,706 adults in the database. We included women aged 18 to 44 years, and excluded women who did not answer the reproductive health questions (RHQ74 and RHQ76), had no dietary intake information and BMI information, had a previous hysterectomy, had a previous bilateral oophorectomy, or were pregnant during the investigation. The NCHS Institutional Review Board (IRB) reviewed and approved all NHANES protocols. All extracted data were de-identified and numbered with a unique identifier “SEQN”, and all participants provided written informed consent to the CDC.
Dietary assessment
The dietary intake information used in this study was obtained from 24-hour dietary recall interviews conducted face-to-face in mobile examination centers (MECs). The interviews were completed by trained interviewers in fluent English or Spanish and collected using a validated United States Department of Agriculture (USDA) dietary data collection tool, the Automated Multiple-Pass Method (AMPM). The NOVA food classification system generally divides foods into four categories based on the degree and purpose of food processing [18]: (1) Unprocessed and minimally processed foods; (2) processed culinary ingredients; (3) processed foods; and (4) ultra-processed foods (See Supplement Table S1 for details). Based on this classification method and our research protocols, we categorized the foods reported in NHANES into two categories: UPF and non-UPF. UPF corresponds to the fourth category in the NOVA classification system.
The “Main Food Description” information in NHANES provides a brief description of the food, and the “Additional Food Description” information provides a detailed description of the food, specifically the basic ingredients and methods of the food. Through these two descriptive variables, we can preliminarily identify UPF in the participants’ 24-hour dietary recall. Some combination food types, such as “Lunchables” (a trademark registered by Kraft Foods, which refers to a convenient, pre-packaged lunch combination product, usually including small portions of meat, cheese and biscuits, etc., for children to use in school lunches) and “Frozen meals” (i.e., meals that are pre-made and frozen and heated when needed) are considered UPF. At the same time, we also consider the source of the food. Food was also considered UPF if it came from sources like “Restaurant fast food/pizza” (e.g., creamy dressing, chicken wings with hot sauce) or “Vending machine” (e.g., energy drinks, cookies). For each food, NHANES provides the corresponding energy value. The energy values of UPF consumed daily by each participant were summed, and the total energy intake from all foods consumed that day was also calculated. The relative contribution of UPF intake to the total daily energy intake (percentage of total energy intake) was calculated to reflect the UPF intake (%UPF). Two authors independently reviewed the classification of each item. Discrepant classifications were resolved by discussion.
Infertility diagnosis
All participants completed the Reproductive Health Questionnaire (RHQ). Two questions in the questionnaire were related to infertility diagnosis: “Have you ever attempted to become pregnant over a period of at least a year without becoming pregnant?” (RHQ74) and “Have you ever been to a doctor or other medical provider because you have been unable to become pregnant?” (RHQ76). Women who answered “Yes” to at least one of these two questions were considered to be in the infertility group; women who answered “No” to both questions were considered to be in the control group. Questionnaire information on infertility diagnosis was only available during 2013 to March 2020, and therefore, only data from the corresponding period were included in the study.
Covariates
We also selected the following variables from the database as covariates, including the continuous variables age and BMI, and the categorical variables race, education level, ratio of family income to poverty (RFIP), pelvic infection disease (PID), physical activity, smoking and alcohol. Race was categorized as Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other Race (including multi-racial). Education level was categorized as Less than 9th grade, 9-11th grade (including 12th grade with no diploma), High school graduate/GED or equivalent, Some college or AA degree, College graduate or above. RFIP was categorized as poor or not poor, and RFIP greater than or equal to 1 was considered not to be poor. Participants were categorized as smokers or nonsmokers. Smoking was defined as blood cotinine concentrations more than or equal to 3 ng/mL. Similarly, participants were categorized as drinkers or nondrinkers. Drinking alcohol was defined as consuming at least 12 drinks of any type of alcoholic beverage in any one year. Physical activity was converted into weekly energy expenditure using the formula: weekly energy expenditure (MET-h/week) = recommended metabolic equivalent (MET) scores×weekly exercise time (h) of corresponding activity, and all participants were divided into no physical activity group, low-intensity physical activity group, and high-intensity physical activity group according to < 1 MET-h/week, 1–48 MET-h/week, and > 48 MET-h/week, respectively.
For categorical variables, if the answer was “reject” or “don’t know”, we considered this part of the data missing. For all missing data, we used multiple imputation to fill in. Akaike Information Criterion (AIC) was used for model selection [19]. After multiple imputations, we selected the imputation result with the minimum AIC value as the final analysis data. Because the data distribution of the infertility group and the control group was different, we imputed the two groups of data separately (Supplement Figures S1-S4). The final complete data set was used for subsequent statistical analysis.
Statistical analysis
The Shapiro-Wilk test was used to assess the normality of continuous variables. Normally distributed data are presented as mean ± standard deviation, while non-normally distributed data are presented as median and interquartile range. For group comparisons, the student’s t-test was used for normally distributed data, and the Mann-Whitney U test was used for non-normally distributed data. As previous studies have shown that advanced age, high BMI and a history of PID are significantly associated with female infertility [20–22], we used the 1:3 nearest neighbor matching algorithms to match the infertile group with the control group. The caliper value was set to 0.05, and it was measured in standard deviations of the propensity score. The standardized mean difference (SMD) was used to effectively assess the balance of covariates after matching, with absolute values less than 0.1 indicating good balance and values between 0.1 and 0.2 indicating moderate balance. Subsequently, the data after PSM were statistically analyzed. After the Shapiro-Wilk test, the intake of UPF in this study did not conform to the normal distribution (P < 0.001). Therefore, the UPF intake (percentage of total energy intake) was classified according to quartiles (Q1, Q2, Q3, and Q4), with Q1 serving as a reference. The logistic regression analysis was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to find the relationship between the UPF intake and female infertility. No covariates were adjusted in Model 1. Model 2 was adjusted for all covariates, including race, education level, ratio of family income to poverty (RFIP), smoking, alcohol, and physical activity. In addition, RCS were used to investigate the potential nonlinear relationship between UPF intake and infertility. All analyses were considered statistically significant at p value < 0.05. All statistical processes were performed using R project (version 4.4.1), and the main R packages included “haven”, “survey”, “ggplot2” and “rms”.
Results
Characteristics of participants
We included 4236 participants aged 18 to 44 years who completed the reproductive health questionnaire from the NHANES database from 2013 to March 2020 pre-pandemic. Participants were excluded if they had undergone a hysterectomy (n = 143), bilateral oophorectomy (n = 2), were pregnant at the survey time (n = 1), had missing dietary information (n = 356), or had missing BMI data (n = 133). This resulted in a final sample of 3601 eligible participants, comprising 423 in the infertility group and 3178 in the control group. After PSM analysis, 1645 women (including 417 infertile participants and 1228 control participants) were included for subsequent analysis. The detailed participant screening process is shown in Fig. 1. Prior to PSM, there were significant differences in age, BMI, PID, and RFIP between the two groups. The SMD of age and BMI after matching were 0.048 and 0.066, respectively, indicating a balance between the groups. The SMD of PID was 0.116, suggesting moderate balance. However, RFIP remained different between the groups after PSM. More detailed demographic characteristics are presented in Table 1.
Fig. 1.
Flowchart of participants in the study
Table 1.
Baseline characteristics of participants before PSM and after PSM
| Characteristics | Before PSM | After PSM | ||||||
|---|---|---|---|---|---|---|---|---|
| Total (n = 3601) | Control (n = 3178) | Infertility (n = 423) | p | Total (n = 1645) | Control (n = 1228) | Infertility (n = 417) | p | |
| Age (years, median [IQR]) | 30 [23, 37] | 29 [23, 37] | 35 [29, 39] | <0.001 | 35 [29, 40] | 35 [29, 40] | 35 [29, 39] | 0.425 |
| BMI (kg/m2, median [IQR]) | 26.62 [22.35, 32.69] | 26.52 [22.31, 32.27] | 29.23 [23.85, 35.98] | <0.001 | 28.35 [23.80, 34.75] | 28.34 [23.91, 34.45] | 29.12 [23.79, 35.77] | 0.341 |
| PID (%) | <0.001 | 0.042 | ||||||
| No | 3450 (95.8) | 3068 (96.5) | 382 (90.3) | 1543 (93.8) | 1161 (94.5) | 382 (91.6) | ||
| Yes | 151 (4.2) | 110 (3.5) | 41 (9.7) | 102 (6.2) | 67 (5.5) | 35 (8.4) | ||
| Race (%) | 0.824 | 0.452 | ||||||
| Mexican American | 582 (16.2) | 515 (16.2) | 67 (15.8) | 272 (16.5) | 206 (16.8) | 66 (15.8) | ||
| Other Hispanic | 366 (10.2) | 327 (10.3) | 39 (9.2) | 156 (9.5) | 117 (9.5) | 39 (9.4) | ||
| Non-Hispanic White | 1155 (32.1) | 1009 (31.7) | 146 (34.5) | 510 (31.0) | 366 (29.8) | 144 (34.5) | ||
| Non-Hispanic Black | 887 (24.6) | 785 (24.7) | 102 (24.1) | 434 (26.4) | 334 (27.2) | 100 (24.0) | ||
| Other Race (Including Multi-Racial) | 611 (17.0) | 542 (17.1) | 69 (16.3) | 273 (16.6) | 205 (16.7) | 68 (16.3) | ||
| Education (%) | 0.205 | 0.168 | ||||||
| Less than 9th grade | 132 (3.7) | 121 (3.8) | 11 (2.6) | 76 (4.6) | 65 (5.3) | 11 (2.6) | ||
| 9-11th grade (Includes 12th grade with no diploma) | 331 (9.2) | 281 (8.8) | 50 (11.8) | 174 (10.6) | 125 (10.2) | 49 (11.8) | ||
| High school graduate/ GED or equivalent | 728 (20.2) | 644 (20.3) | 84 (19.9) | 308 (18.7) | 225 (18.3) | 83 (19.9) | ||
| Some college or AA degree | 1356 (37.7) | 1193 (37.5) | 163 (38.5) | 618 (37.6) | 457 (37.2) | 161 (38.6) | ||
| College graduate or above | 1054 (229.3) | 939 (29.5) | 115 (27.2) | 469 (28.5) | 356 (29.0) | 113 (27.1) | ||
| RFIP (%) | 0.001 | 0.007 | ||||||
| ≥ 1 | 2641 (73.3) | 2301 (72.4) | 340 (80.4) | 1238 (75.3) | 903 (73.5) | 335 (80.3) | ||
| <1 | 960 (26.7) | 877 (27.6) | 83 (19.6) | 407 (24.7) | 325 (26.5) | 82 (19.7) | ||
| Smoking (%) | 0.108 | 0.392 | ||||||
| No | 2739 (76.1) | 2431 (76.5) | 308 (72.8) | 1231 (74.8) | 926 (75.4) | 305 (73.1) | ||
| Yes | 862 (23.9) | 747 (23.5) | 115 (27.2) | 414 (25.2) | 302 (24.6) | 112 (26.9) | ||
| Alcohol (%) | 0.053 | 0.102 | ||||||
| No | 3579 (99.4) | 3162 (99.5) | 417 (98.6) | 1633 (99.3) | 1222 (99.5) | 411 (98.6) | ||
| Yes | 22 (0.6) | 16 (0.5) | 6 (1.4) | 12 (0.7) | 6 (0.5) | 6 (1.4) | ||
| Physical activity (MET-h/week, %) | 0.167 | 0.250 | ||||||
| <1 | 749 (20.8) | 649 (20.4) | 100 (23.6) | 385 (23.4) | 287 (23.4) | 98 (23.5) | ||
| 1–48 | 1576 (43.8) | 1407 (44.3) | 169 (40.0) | 710 (43.2) | 543 (44.2) | 167 (40.0) | ||
| >48 | 1276 (35.4) | 1122 (35.3) | 154 (36.4) | 550 (33.4) | 398 (32.4) | 152 (36.5) | ||
| Total Energy (kcal, median [IQR]) | 1839.0 [1371.0, 2362.0] | 1838.5 [1367.3, 2355.8] | 1879.0 [1428.5, 2405.5] | 0.221 | 1856.0 [1397.0, 2364.0] | 1852.5 [1394.5, 2348.0] | 1879.0 [1428.0, 2398.0] | 0.539 |
IQR, interquartile range; BMI, body mass index; RFIP, ratio of family income to poverty; PID, pelvic infection disease; MET, metabolic equivalent; PSM, propensity score matching. The bold represents the p-values are less than 0.05
Association between UPF intake and infertility
In the unadjusted logistic regression model (Model 1), UPF intake at Q4 was significantly associated with infertility (Table 2, OR: 1.43, 95% CI: 1.05–1.96, P = 0.025). In the logistic regression model adjusted for all covariates (Model 2), UPF intake at Q4 remained significantly associated with infertility (Table 2, aOR: 1.43, 95% CI: 1.03–2.00, P = 0.033).
Table 2.
Logistic regression for the association between UPF intake (%UPF) and infertility
| %UPF | Model 1 | Model 2 | ||
|---|---|---|---|---|
| OR (95% CI) | p | aOR (95% CI) | p | |
| Q1 | Reference | Reference | Reference | Reference |
| Q2 | 1.14 (0.83, 1.56) | 0.430 | 1.17 (0.85, 1.61) | 0.347 |
| Q3 | 0.96 (0.69, 1.32) | 0.789 | 0.95 (0.68, 1.32) | 0.760 |
| Q4 | 1.43 (1.05, 1.96) | 0.025 | 1.43 (1.03, 2.00) | 0.033 |
%UPF, ultra-processed food intake, the percentage of energy consumed by ultra-processed food in each participant’s daily total food intake energy
Model 1 was adjusted for no covariates. Model 2 was adjusted for all covariates, including race, education, ratio of family income to poverty, smoking, alcohol and physical activity
aOR, adjusted OR
Regardless of covariate adjustment, we did not find a significant nonlinear relationship between UPF intake and female infertility (Model 1, P for overall = 0.230; Model 2, P for overall = 0.244). However, the overall curve showed an upward trend (Fig. 2), with a significant increase in the risk of infertility when UPF intake exceeded 40.8% (OR = 1).
Fig. 2.
Restricted cubic splines of the relationship between ultra-processed foods intake and female infertility. (A) model 1: unadjusted; (B) model 2: adjusted for all covariates. The red line and red area represent odds ratios (ORs) and their associated 95% confidence intervals (CIs), respectively
Discussion
This is the first study to explore the relationship between UPF intake and female infertility. We found a positive relationship between UPF intake and infertility in women of childbearing age. These findings provide evidence suggesting that UPF intake is a significant risk factor for female infertility and offer valuable suggestions for improving women’s dietary structure.
Our analysis revealed a significant association between UPF intake and female infertility. Given that age, BMI and PID were closely associated with female infertility, we conducted a 1:3 matching analysis between the infertility group and the control group based on these three factors. After PSM, the logistic regression model demonstrated that higher UPF intake was associated with an increased risk of female infertility. Furthermore, using RCS regression, we found that as UPF intake increased, the risk of infertility gradually rose. Notably, when UPF intake exceeded 40.8% of total energy intake, the risk of infertility increased significantly, suggesting a potential dose-response relationship.
UPFs typically have higher energy density, which means that they contain more calories, and people eating these foods may inadvertently consume excess energy, leading to weight gain and higher BMI. Higher UPF intake is positively associated with weight gain [23], and high BMI may be a potential cause of infertility [24, 25]. High BMI disrupts the hypothalamic-pituitary-ovarian axis and interferes with oocyte maturation [26, 27]. Previous studies have shown that women with high BMI have an increased risk of subfertility and infertility, as well as poorer pregnancy outcomes [28]. Therefore, BMI may be an important confounder in the study of the association between UPF intake and infertility. To observe the direct impact of UPF intake on infertility, we eliminated important confounders such as BMI through PSM, enhancing the reliability of our results.
The world’s food system has undergone significant changes in recent decades due to advancements in food processing technology, with industrial and prepared food increasingly replacing traditional diets. Traditional diets emphasize minimal processing and home cooking, whereas UPF generally undergo complex processing and contain high levels of sugar, fat, salt, and various additives and preservatives. The intake of UPF may increase the risk of infertility through various mechanisms. Trans fatty acids and other ingredients in UPF may adversely affect oocytes quality by altering membrane lipid composition, leading to disrupted metabolic pathways and impaired reproductive health [29]. Additives and preservatives in UPF may promote excess free radical production, increase oxidative stress and damage oocytes [30, 31]. Some chemicals harmful to reproductive health may leach from packaging materials into food ingredients. UPF intake may expose individuals to higher levels of phthalates [32, 33]. Phthalates can reach the follicular fluid through thecal capillaries in the ovary, where they can induce DNA damage in oocytes, inhibit follicle growth, and impair embryo development [34].
Other factors may also contribute to the observed relationship between UPF intake and female infertility. High intake of UPF will lead to poor diet quality and deficiencies in micronutrients such as vitamins and minerals [35], which are crucial for female fertility and may increase the risk of infertility when these micronutrients are lacking [36, 37]. UPF negatively impacts the gut microbiota [38], which plays a role in gamete development, fertilization, and embryo development, thereby potentially contributing to infertility [39, 40]. Moreover, excessive consumption of UPF can trigger inflammatory responses in the body, which may also adversely affect female fertility [41].
This study has several strengths and limitations. First, to our knowledge, this is the first study to investigate the relationship between UPF intake and female infertility. Second, the study’s findings are generalizable because the study analyzed a large, nationally representative sample of American women of childbearing age.
However, we must acknowledge some limitations. First, as a cross-sectional study, we could not establish a causal relationship between UPF intake and infertility. Second, the diversity of food processing techniques makes it challenging to precisely measure the degree of processing, and accurately classifying some foods remains difficult. Third, while the 24-hour dietary recall interview is a commonly used dietary assessment tool, it has limitations. Individual dietary intake is influenced by numerous factors, and single-day data may not accurately reflect usual eating patterns. Moreover, the 24-hour dietary recall relies on participants’ memory and self-reporting, making it susceptible to memory bias and subjective judgment, leading to potential measurement errors. Fourth, we recognize the possibility of unmeasured variables, such as mental health status or male infertility factors. Although we have controlled for known confounders to the best of our ability, we cannot completely rule out the possibility of residual confounding.
Conclusion
Excessive intake of UPF is significantly associated with an increased risk of female infertility. Therefore, to improve reproductive health, women of childbearing age are advised to modify their diets and reduce UPF consumption. However, future prospective cohort studies with larger sample sizes are still needed to provide more robust evidence further elucidating the relationship between UPF intake and female infertility.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
Thanks for the establishment and sharing of NHANES database. We also would like to thank the participants in the NHANES.
Abbreviations
- AIC
Akaike Information Criterion
- AMPM
Automated Multiple-Pass Method
- BMI
Body mass index
- CDC
Centers for Disease Control and Prevention
- CI
Confidence interval
- IVF
In vitro fertilization
- MEC
Mobile examination center
- MET
Metabolic equivalent
- NCHS
National Center for Health Statistics
- NHANES
National Health and Nutrition Examination Survey
- OR
Odds ratio
- PID
Pelvic infection disease
- PSM
Propensity score matching
- RCS
Restricted cubic splines
- RHQ
Reproductive Health Questionnaire
- RFIP
Ratio of family income to poverty
- SMD
Standardized mean difference
- UPF
Ultra-processed food
- USDA
United States Department of Agriculture
Author contributions
YNH and XXS conceived and designed the study. GC and XXS extracted the data. XXS, YNH and GC analyzed the data. YNH, XXS, GC and SLS wrote the manuscript. HJS and YS contributed to the writing of final version of the manuscript. All authors agreed and reviewed the final version of the manuscript.
Funding
Not applicable.
Data availability
The data extracted from a public free database, NHANES (https://www.cdc.gov/nchs/nhanes/index.htm).
Declarations
Ethics approval and consent to participate
This study data was obtained from publicly available data set (NHANES) and the NCHS Institutional Review Board (IRB) reviewed and approved all NHANES protocols. All participants signed informed consent. This study does not contain individual information about participants, so no need to apply for ethical approval. The study was conducted in accordance with the Declaration of Helsinki and its later amendments.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Xiaoxiao Su and Ge Chen contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data extracted from a public free database, NHANES (https://www.cdc.gov/nchs/nhanes/index.htm).