Associations of urinary bisphenol A and its emerging substitutes with subfecundity in preconception couples: a prospective nested case-control study - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. 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Learn more: PMC Disclaimer | PMC Copyright Notice Environ Health . 2026 Mar 4;25:33. doi: 10.1186/s12940-026-01276-w Search in PMC Search in PubMed View in NLM Catalog Add to search Associations of urinary bisphenol A and its emerging substitutes with subfecundity in preconception couples: a prospective nested case-control study Anxin Yin Anxin Yin 1 Department of Maternal and Child Health, School of Public Health, NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, 200032 China Find articles by Anxin Yin 1 , Lap Ah Tse Lap Ah Tse 2 JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China Find articles by Lap Ah Tse 2 , An Chen An Chen 3 School of Public Health, Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053 China 4 Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, 00290 Finland Find articles by An Chen 3, 4 , Lu Cheng Lu Cheng 5 Institute of Biomedicine, University of Eastern Finland, Kuopio, 70211 Finland 6 Department of Computer Science, Aalto University, Espoo, 02150 Finland Find articles by Lu Cheng 5, 6 , Yuyang Chen Yuyang Chen 1 Department of Maternal and Child Health, School of Public Health, NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, 200032 China Find articles by Yuyang Chen 1 , Shiqi Zhang Shiqi Zhang 1 Department of Maternal and Child Health, School of Public Health, NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, 200032 China Find articles by Shiqi Zhang 1 , Zhiqi Lin Zhiqi Lin 7 Department of Environmental Health, School of Public Health, NHC Key Laboratory of Health Technology Assessment, Key Laboratory of Public Health Safety of the Ministry of Education, Fudan University, Shanghai, 200032 China Find articles by Zhiqi Lin 7 , Xingying Li Xingying Li 1 Department of Maternal and Child Health, School of Public Health, NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, 200032 China Find articles by Xingying Li 1 , Zhuoyan Zhu Zhuoyan Zhu 1 Department of Maternal and Child Health, School of Public Health, NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, 200032 China Find articles by Zhuoyan Zhu 1 , Hanxiao Zhang Hanxiao Zhang 1 Department of Maternal and Child Health, School of Public Health, NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, 200032 China Find articles by Hanxiao Zhang 1 , Jiufeng Li Jiufeng Li 7 Department of Environmental Health, School of Public Health, NHC Key Laboratory of Health Technology Assessment, Key Laboratory of Public Health Safety of the Ministry of Education, Fudan University, Shanghai, 200032 China Find articles by Jiufeng Li 7, ✉ , Hong Jiang Hong Jiang 1 Department of Maternal and Child Health, School of Public Health, NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, 200032 China Find articles by Hong Jiang 1, ✉ Author information Article notes Copyright and License information 1 Department of Maternal and Child Health, School of Public Health, NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, 200032 China 2 JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China 3 School of Public Health, Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053 China 4 Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, 00290 Finland 5 Institute of Biomedicine, University of Eastern Finland, Kuopio, 70211 Finland 6 Department of Computer Science, Aalto University, Espoo, 02150 Finland 7 Department of Environmental Health, School of Public Health, NHC Key Laboratory of Health Technology Assessment, Key Laboratory of Public Health Safety of the Ministry of Education, Fudan University, Shanghai, 200032 China ✉ Corresponding author. Received 2025 Oct 7; Accepted 2026 Feb 18; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13085263 PMID: 41781991 Abstract Background Global infertility rates have noticeably increased in recent decades. The effects of environmental exposures on human fertility have been well-noted. However, Evidence on couple-based preconception exposure to bisphenols (BPs), particularly emerging substitutes, and fecundity remains limited. This study aimed to investigate the exposure profiles of 14 classic and emerging BPs in the preconception period as well as their impacts on couple fecundity, to assess both the individual effects of specific compounds and the combined effects of mixtures in males and females, and to evaluate the health risks induced by BPs exposure after tolerable daily intake (TDI) value was tightened in 2023. Methods This nested case-control study involved 1934 preconception individuals in two research sites of China, from October 2016 to May 2025. The case and control groups were comprised of 318 and 649 couples with time to pregnancy (TTP) > 12 and ≤ 12 menstrual cycles, respectively. BPs were quantified in preconception urine samples using ultra-performance liquid chromatography-tandem mass spectrometry, including novel substitutes that had not been measured and reported in human beings, such as 2,2’-diallyl bisphenol A (DBA) and 4-hydroxy-4’-isopropoxydiphenylsulfone (D8). Generalized linear, weighted quantile sum and Bayesian kernel machine regression models were employed to assess individual and mixture effects of BPs on subfecundity. Results The positive associations of BPs and subfecundity risks were observed in both females and males. Mixed BPs were positively correlated with subfecundity in females (OR = 2.84, 95% CI: 2.23–3.61), males (OR = 4.21, 95% CI: 3.24–5.47), and couples (OR = 6.73, 95% CI: 4.81–9.41), with DBA and bisphenol AF (BPAF) identified as major contributors. Health risk assessment indicated that although few participants had an estimated daily intake (EDI) exceeding the 2015 TDI (4000 ng/kg bw/day), most (81.8%-100%) surpassed the 2023 TDI (0.2 ng/kg bw/day). Conclusions This study provides the first evidence linking novel bisphenols, BPAF and DBA, to increased subfecundity risk and highlights significant health risks in a population with an intention to conceive, underscoring the urgency to regulate a broader range of bisphenols to protect reproductive health. Supplementary Information The online version contains supplementary material available at 10.1186/s12940-026-01276-w. Keywords: Prospective nested case-control study, Couple-based exposure to bisphenols, Subfecundity, Time to pregnancy, Health risk assessment Introduction Global infertility rates have noticeably increased in recent decades [ 1 ]. The effects of environmental exposures on human fertility have been well-noted [ 2 ]. However, existing research has often focused predominantly on single-partner exposure, rather than examining the combined effects of both partners during the critical preconception period [ 3 ]. Emerging epidemiological evidence indicates that bisphenol A (BPA) is among the most important environmental pollutants impacting reproductive health [ 4 ]. BPA is a synthetic additive that has been extensively used since the 1960s in the production of polycarbonate plastics and epoxy resins [ 5 ]. Due to the significant health risks posed by BPA [ 4 ], bisphenol analogues (BPs) such as bisphenol S (BPS), bisphenol F (BPF), bisphenol AF (BPAF), and 2,2’-diallyl bisphenol A (DBA) are now widely used as BPA substitutes in food containers, resin, plasticizers, medical equipment, and thermal paper receipts [ 6 – 8 ]. As a result of their widespread application, BPA and its substitutes have become ubiquitous in the environment, leading to human exposure through multiple routes, including ingestion, inhalation, dermal contact, and vertical transmission [ 9 ]. Animal and in vitro studies have shown that BPA analogues had endocrine potencies similar to BPA and demonstrated reproductive toxicity in different species, including Caenorhabditis elegans, zebrafish, and rodents [ 10 – 12 ]. However, only a limited number of studies reported effects of BPA analogues, such as BPS or BPF, on female oocyte health [ 13 ], fecundity [ 14 ], and male sperm quality [ 11 ]. Population-based studies on fecundity related to BPA substitutes remain limited [ 15 , 16 ], especially human exposure data of the novel BPA substitutes such as DBA are unavailable. Although restrictions on BPA have been implemented in certain industries in some countries [ 17 , 18 ], the use of its substitutes has continued to expand. Since the adverse health effects of BPA have been well-documented [ 5 ], the European Food Safety Authority (EFSA) has tightened the tolerable daily intake (TDI) from 4000 ng/kg bw/day in 2015 to 0.2 ng/kg bw/day in 2023 [ 19 ]. Yet, few studies have assessed the human health risks using the new threshold value and their relationships to subfecundity risks. To address these identified knowledge gaps, we carried out a prospective nested case-control study based on a well-established preconception cohort across two distinct research sites in China. This study aimed to investigate the exposure profiles of 14 BPs in the preconception period as well as their impacts on couple fecundity, to assess both the individual effects of specific compounds and the combined effects of mixtures in males and females, and to evaluate the health risks induced by BP exposure after the TDI value was tightened in 2023. Method Study design and population This nested case-control study was conducted based on the Fudan Preconceptional Offspring Trajectory Study (PLOTS) cohort [ 20 ]. PLOTS recruited preconception couples of childbearing age who have been attempting conception since October 2016 and followed them throughout pregnancy, delivery, and for three years postpartum to monitor their offspring. The research sites for PLOTS included Jiading District Maternal and Child Health Hospital in Shanghai and Leping Maternal and Child Health Hospital in Jiangxi Province, China. From October 2016 to May 2025, PLOTS observed fecundity outcome events (defined as either achieving pregnancy or completing at least one year of follow-up without pregnancy) in 2589 couples (1756 in Jiading and 833 in Leping). Among these participants, 515 couples had a time to pregnancy (TTP) greater than 12 menstrual cycles and were classified as cases, while 2074 couples with TTP ≤ 12 cycles served as controls. In this study, 341 cases and 682 matched controls were selected using 1:2 propensity score matching (PSM) based on baseline characteristics such as age, body mass index (BMI), education level, income, permanent residence registration, and lifestyle factors (e.g. smoking, alcohol, etc.). After excluding 56 couples due to missing or invalid baseline urinary bisphenol data from either partner, 1934 participants (967 couples) were involved in the study, with 636 cases and 1298 controls. Inclusion criteria for the study population at enrollment included: (1) married couples of childbearing age; (2) intention to conceive within one year; (3) planning to deliver at the research site hospitals; (4) consent to be followed up. Exclusion criteria included infertility due to non-endocrine causes, such as female with congenital reproductive tract malformations or tubal occlusion, and male with sexual dysfunction. This study was conducted in accordance with the Declaration of Helsinki and approved by the School of Public Health, Fudan University, Shanghai, China (IRB#2016-10-0601, IRB#2017-01-0609, IRB#2022-08-0993). Informed written consent was obtained from all participants. Data collection At baseline, information on anthropometric measurements and sociodemographic characteristics for both males and females was collected, including height, weight, age, residence registration, education level, income, parity, smoking (including smoking and passive smoking), alcohol consumption, renovation of home or workplace within the last two years, and exposure to everyday chemicals. Exposure to everyday chemicals was assessed using a set of questionnaire items. Participants were asked whether they were exposed to any of the following chemicals in their daily life or work: cooking oil fumes, gasoline/petroleum/exhaust, rubber or plastic products, pesticides, disinfectants/detergents, coatings/decoration materials, leather manufacturing, printing/fuels/textiles, heavy metals, electroplating, and other chemicals. If participants reported routine exposure to any of the listed substances, they were classified as having daily chemical exposure; otherwise, they were classified as not or rarely exposed. Additionally, urinary specific gravity (SG) values for both males and females were drawn from the hospital information system using the baseline urine samples at enrollment. After enrollment, participants were prospectively followed up until they achieved a clinically confirmed pregnancy or had attempted conception for 12 menstrual cycles. Trained research investigators conducted telephone interviews with participants every three months after the recruitment, during which they completed TTP questionnaires to obtain fecundity information. The TTP questionnaire collected information such as intentions to conceive, the date of conception attempt initiation, contraceptive practices, the last menstrual period (LMP) date before conception, any medical diagnosis of infertility, and whether assisted reproductive technologies had been used. For participants who achieved conception during the folow-up period, TTP was calculated as (the LMP date before conception - the date of conception attempt initiation - time of contraception during follow-up)/average menstrual cycle length + 1. For participants who did not conceive during follow-up, TTP was calculated as (the LMP date before conception - the date of conception attempt initiation - time of contraception during follow-up)/average menstrual cycle length [ 20 ]. Participants with subfecundity (TTP > 12 menstrual cycles) were classified as cases [ 20 ]. Measurements of urinary BPs Single-spot urine samples of both husband and wife were collected at enrollment, rapidly stored at -80°C until laboratory assessment. The following fourteen bisphenol analogues were measured: BPA, BPS, BPF, BPAF, bisphenol Z (BPZ), bisphenol E (BPE), bisphenol M (BPM), bisphenol B (BPB), bisphenol P (BPP), bisphenol AP (BPAP), tetrachlorobisphenol A (TCBPA), 3,3’,5,5’-tetramethylbiphenol A (TMBPA), 4-hydroxy-4’-isopropoxydiphenylsulfone (D8), and DBA. Quantification was carried out using ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS; Waters XEVO TQ-S MICRO), based on previously established methods with minor modifications [ 21 ]. Briefly, urine aliquots were incubated overnight with β-glucuronidase and internal standards, followed by repeated liquid-liquid extraction. The combined extracts were evaporated to dryness under nitrogen, reconstituted with acetonitrile solution and injected for instrumental analysis. Quality assurance was achieved by including blank samples and quality control samples at low, medium, and high concentrations in each batch of analyses to ensure the reliability and stability of the results. Urinary bisphenol concentrations were adjusted for urinary dilution using SG values according to the following formula: Pc = P × [(SGm − 1)/(SGi − 1)] [ 22 ]. Pc is the SG-corrected concentration, P is the measured concentration, SGi is the individual sample’s specific gravity, and SGm is the median SG value for all samples. For statistical analyses, bisphenol concentrations below the limit of detection (LOD, 0.0011–0.0477 ng/mL) were substituted with LOD divided by the square root of two [ 20 ], and 99th percentile Winsorization was applied to minimize the influence of outliers [ 23 ]. Calculation of health risk The calculation of the daily estimated intake (EDI) for target analytes among preconceptional couples was performed using the following formula: In this formula, is the estimation of the daily intake of BPs. is the measured urinary concentration of bisphenols, and is the body weight of the participant. is the typical 24-hour urine excretion volume, set at 2000 mL for preconception couples [ 24 ]. represents the compound’s molar mass. indicates the fraction of ingested bisphenol dose excreted in urine within 24 h. For BPA and BPS, is set as 100% and 70%, respectively; for other bisphenols, is assigned the same value as BPA (100%) due to insufficient pharmacokinetic data [ 25 ]. To assess potential health risks, the hazard quotient (HQ) for each compound was calculated as the ratio of its estimated daily intake (EDI) to the tolerable daily intake (TDI). For BPA, two TDI values established by the European Food Safety Authority (EFSA) were applied: 4000 ng/kg bw/day (EFSA, 2015) and 0.2 ng/kg bw/day (EFSA, 2023) [ 19 ]. Due to the structural and toxicological similarities among bisphenols, for other bisphenol analogues lacking TDI values, a TDI was calculated based on BPA’s TDI and adjusted for each compound’s molecular weight [ 25 ]. The hazard index (HI) was calculated as the sum of the HQs for the bisphenols included in the analysis, and an HI value exceeding 1 indicates potential health risks [ 26 ]. Statistical analysis For baseline characteristics, continuous variables were described using mean ± SD, and categorical variables were summarized using frequency and percentages. The t-test was conducted to compare means between case and control groups, whereas the χ² test was applied to assess differences in categorical variables between groups. The BPs with detection frequencies exceeding 75% were included in analyses. The total bisphenols (∑BPs) were calculated as the sum of the molar concentrations of these BPs (nmol/mL). Distributions of urinary bisphenol concentrations and ∑BPs were described by detection rates, median and interquartile range (IQR). Pearson correlation analysis was performed to examine the relationships among the bisphenol analyte concentrations. The associations between individual BPs exposure and subfecundity were evaluated using generalized linear models (GLM), adjusting for potential confounders. For each bisphenol analogue, exposure categories were defined by tertiles (T1, T2, T3), and analyses were conducted separately for males and females. The restricted cubic spline (RCS) analysis was conducted to evaluate the potential non-linear relationship between BPs and subfecundity. Both weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) models utilizing the binomial family were applied to investigate the joint effects of mixed bisphenol exposure on subfecundity among preconception couples. The WQS regression also estimated the individual bisphenol’s weighted contribution to the overall effect, while the BKMR provided visualization of the joint effects of mixed bisphenol exposure on subfecundity [ 20 ]. Potential confounding variables, also used for PSM between the case and control group, included in the above models were age, preconception BMI (calculated as weight in kg divided by height in m²), permanent residence registration (non-local/local), education level (high school and below/junior or vocational college/college and above), annual household income per capita (< 100,000 RMB/100,000-200,000 RMB/ ≥200,000 RMB), parity (nulliparous/multiparous), smoking or passive smoking (yes/no), alcohol consumption (yes/no), renovation of home or workplace within two years (yes/no), and exposure to everyday chemicals (yes/no) of females, and same variables of males except for parity. Covariates were selected based on the directed acyclic graph (DAG) using the web-based tool dagitty.net (Fig. S1) [ 27 ]. Sensitivity analyses were conducted by conducting stratified analyses according to female and male age (categorized as < 30 and ≥ 30 years), BMI (categorized by the median value), and female parity (nulliparous subgroup) to assess the robustness of the findings. GLM, WQS, and BKMR analyses were conducted in these subgroups. In addition, the health risk assessment was conducted by calculating the EDI and HQ for each bisphenol and the overall HI. All data analyses were conducted using SPSS (version 22.0, IBM Corporation, Chicago, USA) and R (version 4.5.0, R Foundation for Statistical Computing, Vienna, Austria). P values < 0.05 were considered statistically significant. Results Characteristics of participants This study comprised 1934 preconception participants (967 couples), including 636 cases and 1298 controls (Table 1 ). The average age was 28.45 ± 3.99 years for females and 29.76 ± 4.38 years for males, and the mean BMI values for females and males were 21.57 ± 2.94 kg/m² and 23.72 ± 3.45 kg/m², respectively. The majority of these couples had local permanent residence registration (62.3% for females and 77.2% for males), attained a college education or above (56.5% for females and 55.4% for males), and had an annual household income per capita of less than 200,000 RMB (approximately 27,900 USD) (77.2%). Most females were nulliparous (81.5%), and neither smoked (including passively) (71.3%) nor consumed alcohol (75.3%). Meanwhile, more than half of the male participants reported smoking/passive smoking (53.6%) and alcohol consumption (53.3%). Approximately half of the couples reported renovation of their home or workplace within the past two years (54.8% for females and 49.2% for males). Exposure to everyday chemicals was reported by 45.3% of females and 65.5% of males. There were no statistically significant differences for these demographic or lifestyle factors between the TTP groups among either females or males (all P > 0.05), as shown in Table 1 . Table 1. Characteristics of preconception female and male ( n = 1934) Variable Female Male TTP ≤ 12 TTP > 12 statistics a P value TTP ≤ 12 TTP > 12 statistics a P value Age (years) 28.38 ± 3.88 28.59 ± 4.21 -0.73 0.468 29.66 ± 4.25 29.97 ± 4.64 -1.01 0.315 BMI (kg/m²) b 21.60 ± 2.94 21.52 ± 2.96 0.39 0.694 23.73 ± 3.38 23.72 ± 3.58 0.04 0.971 Permanent residence registration Non-local 241 (37.1%) 124 (39.0%) 0.24 0.624 145 (22.3%) 75 (23.6%) 0.12 0.725 Local 408 (62.9%) 194 (61.0%) 504 (77.7%) 243 (76.4%) Education level High school and below 126 (19.4%) 64 (20.1%) 0.41 0.813 129 (19.9%) 64 (20.1%) 0.04 0.979 Junior or vocational college 159 (24.5%) 72 (22.6%) 161 (24.8%) 77 (24.2%) College and above 364 (56.1%) 182 (57.2%) 359 (55.3%) 177 (55.7%) Annual household income per capita (RMB) < 100,000 266 (41.0%) 117 (36.8%) 2.31 0.316 266 (41.0%) 117 (36.8%) 2.31 0.316 100,000-200,000 234 (36.1%) 130 (40.9%) 234 (36.1%) 130 (40.9%) ≥ 200,000 149 (23.0%) 71 (22.3%) 149 (23.0%) 71 (22.3%) Parity Nulliparous 529 (81.5%) 259 (81.4%) 0.00 1.000 - - - - Multiparous 120 (18.5%) 59 (18.6%) - - Smoking or passive smoking No 463 (71.3%) 226 (71.1%) 0.00 0.990 297 (45.8%) 152 (47.8%) 0.28 0.598 Yes 186 (28.7%) 92 (28.9%) 352 (54.2%) 166 (52.2%) Alcohol consumption No 486 (74.9%) 242 (76.1%) 0.11 0.739 302 (46.5%) 150 (47.2%) 0.01 0.906 Yes 163 (25.1%) 76 (23.9%) 347 (53.5%) 168 (52.8%) Renovation of home or workplace No 290 (44.7%) 147 (46.2%) 0.15 0.701 329 (50.7%) 162 (50.9%) 0.00 0.996 Yes 359 (55.3%) 171 (53.8%) 320 (49.3%) 156 (49.1%) Exposure to everyday chemicals No 360 (55.5%) 169 (53.1%) 0.38 0.539 228 (35.1%) 106 (33.3%) 0.23 0.631 Yes 289 (44.5%) 149 (46.9%) 421 (64.9%) 212 (66.7%) Open in a new tab a For continuous variables, the t-test was conducted; for categorical variables, the chi-square test was conducted b BMI: body mass index Urinary concentrations of bisphenol analogues BPAF had the highest detection rate (97.98%) in preconception couples, followed by BPA (95.60%), BPS (90.85%), BPF (87.07%), and DBA (86.97%) (Table 2 ). Other measured bisphenols (BPZ, BPP, BPAP, TCBPA, D8, TMBPA, BPE, BPM, BPB) showed detection frequencies of 0.00-47.36% with LODs between 0.0026 and 0.0226 ng/mL (Table 2 ) and were not included in subsequent analyses. The median concentrations (ng/mL) of BPA, BPS, BPF, BPAF, and DBA were 2.64, 0.42, 1.92, 0.52, and 0.92, respectively. The median ΣBPs concentration was 0.05 nmol/mL in the study population. After SG correction, the median urinary concentrations of BPA, BPS, BPF, BPAF, and DBA were 2.75, 0.46, 1.96, 0.59, and 1.06 ng/mL, and the median SG-adjusted ΣBPs concentration was 0.05 nmol/mL (Table 2 ). Spearman’s correlation analysis revealed statistically significant correlations among the SG-adjusted concentrations of these bisphenols, with correlation coefficients ranging from 0.04 to 0.50 (Fig. S2). The highest correlation was observed between BPAF and DBA ( r = 0.50, P < 0.001), followed by BPS and DBA ( r = 0.37, P < 0.001) (Fig. S2). Table 2. Descriptive statistics of urinary bisphenols in preconception female and male ( n = 1934) BPs (ng/mL) a Total Female Male LOD b DF c Median (IQR) DF c Median (IQR) DF c Median (IQR) Raw concentrations BPA 0.0011 95.60 2.64 (0.82,9.46) 96.17 2.24 (0.88,5.51) 95.04 3.54 (0.77,18.70) BPS 0.0106 90.85 0.42 (0.14,1.11) 92.45 0.62 (0.19,1.53) 89.25 0.29 (0.11,0.74) BPF 0.0037 87.07 1.92 (0.58,4.72) 83.35 1.78 (0.44,4.58) 90.80 2.04 (0.71,4.88) BPAF 0.0477 97.98 0.52 (0.28,1.18) 97.31 0.64 (0.34,1.28) 98.66 0.41 (0.26,1.03) BPZ 0.0192 1.40 < LOD (< LOD,< LOD) 1.45 < LOD (< LOD,< LOD) 1.34 < LOD (< LOD,< LOD) BPP 0.0055 17.74 < LOD (< LOD,< LOD) 16.03 < LOD (< LOD,< LOD) 19.44 < LOD (< LOD,< LOD) BPAP 0.0038 33.61 < LOD (< LOD,0.52) 35.68 < LOD (< LOD,0.56) 31.54 < LOD (< LOD,0.49) TCBPA 0.0116 0.00 < LOD (< LOD,< LOD) 0.00 < LOD (< LOD,< LOD) 0.00 < LOD (< LOD,< LOD) D8 0.0026 42.40 < LOD (< LOD,0.38) 48.40 < LOD (< LOD,0.42) 36.40 < LOD (< LOD,0.36) DBA 0.0016 86.97 0.92 (0.34,2.63) 92.14 1.39 (0.57,3.55) 81.80 0.63 (0.23,1.74) TMBPA 0.0103 4.03 < LOD (< LOD,< LOD) 4.96 < LOD (< LOD,< LOD) 3.10 < LOD (< LOD,< LOD) BPE 0.0100 47.36 < LOD (< LOD,1.88) 48.40 < LOD (< LOD,1.95) 46.33 < LOD (< LOD,1.69) BPM 0.0044 21.56 < LOD (< LOD,< LOD) 20.68 < LOD (< LOD,< LOD) 22.44 < LOD (< LOD,< LOD) BPB 0.0226 10.55 < LOD (< LOD,< LOD) 9.41 < LOD (< LOD,< LOD) 11.69 < LOD (< LOD,< LOD) ∑BPs (nmol/mL) - - 0.05 (0.02,0.10) - 0.04 (0.02,0.08) - 0.05 (0.02,0.13) SG-adjusted concentrations d BPA - - 2.75 (0.82,11.16) - 2.52 (0.91,6.23) - 3.90 (0.68,21.13) BPS - - 0.46 (0.15,1.20) - 0.69 (0.24,1.69) - 0.29 (0.12,0.73) BPF - - 1.96 (0.60,5.34) - 1.94 (0.52,5.26) - 1.99 (0.65,5.44) BPAF - - 0.59 (0.30,1.36) - 0.73 (0.39,1.56) - 0.48 (0.25,1.15) DBA - - 1.06 (0.33,2.99) - 1.58 (0.64,4.20) - 0.60 (0.21,1.85) ∑BPs (nmol/mL) - - 0.05 (0.02,0.11) - 0.05 (0.03,0.10) - 0.05 (0.02,0.14) Open in a new tab a BPs, bisphenol analogues; BPA, bisphenol A; BPS, bisphenol S; BPF, bisphenol F; BPAF, bisphenol AF; BPZ, bisphenol Z; BPE, bisphenol E; BPM, bisphenol M; BPB, bisphenol B; BPP, bisphenol P; BPAP, bisphenol AP; TCBPA, tetrachlorobisphenol A; TMBPA, 3,3’,5,5’-tetramethylbiphenol A; D8, 4-hydroxy-4’-isopropoxydiphenylsulfone; DBA, 2,2’-diallyl bisphenol A b LOD: limit of detection c DF: detection frequency (%) d SG: specific gravity Individual BPs exposure and subfecundity After adjusting for potential confounders, the GLM analysis showed that the highest tertile (T3) of ∑BPs exposure concentration was associated with an increased risk of subfecundity among both females and males when comparing to the lowest tertile (T1) (T3 vs. T1: aOR = 1.69, 95% CI: 1.21–2.36, P = 0.002 in female; aOR = 3.87, 95% CI: 2.71–5.58, P < 0.001 in male) (Table 3 ) or the middle tertile (T2) (T3 vs. T2: aOR = 1.40, 95% CI: 1.01–1.96, P = 0.044 in female; aOR = 2.81, 95% CI: 2.01–3.96, P < 0.001 in male) (Table S1). In preconception females, higher exposure to BPF (T2 and T3), BPAF (T3) and DBA (T2 and T3) was linked to increased subfecundity risk, while for BPA and BPS, the lowest exposure group (T1) had a higher risk compared to higher tertiles (Table 3 , Table S1). In preconception males, the highest exposure group (T3) of BPA, BPS, BPAF, and DBA was associated with an increased risk of subfecundity compared to lower exposure groups (T1 and T2) (Table 3 , Table S1). Similarly, the RCS analysis demonstrated a positive association between ∑BPs exposure and subfecundity risk in both preconception females and males, with DBA showing the most significant positive trend among the assessed bisphenols (Fig. S3, S4). Table 3. Association between individual urinary bisphenol analogue exposure and subfecundity by gender among preconception couples ( n = 1934) BPs d Crude model a Adjusted model 1 b Adjusted model 2 c OR 95% CI P value OR 95% CI P value OR 95% CI P value Female BPA (ref: T1) e T2 0.47 0.34–0.65 < 0.001 0.45 0.32–0.63 < 0.001 0.45 0.32–0.63 < 0.001 T3 0.62 0.45–0.85 0.004 0.60 0.43–0.84 0.003 0.60 0.43–0.84 0.003 BPS (ref: T1) T2 0.47 0.34–0.66 < 0.001 0.42 0.30–0.59 < 0.001 0.42 0.30–0.59 < 0.001 T3 0.45 0.32–0.62 < 0.001 0.40 0.28–0.57 < 0.001 0.40 0.28–0.57 < 0.001 BPF (ref: T1) T2 1.63 1.17–2.30 0.005 1.64 1.16–2.31 0.005 1.65 1.17–2.33 0.004 T3 1.77 1.26–2.48 < 0.001 1.75 1.25–2.47 0.001 1.76 1.25–2.47 0.001 BPAF (ref: T1) T2 1.25 0.87–1.80 0.225 1.26 0.88–1.82 0.214 1.28 0.89–1.85 0.188 T3 3.49 2.49–4.92 < 0.001 3.56 2.52–5.06 < 0.001 3.61 2.55–5.14 < 0.001 DBA (ref: T1) T2 1.56 1.07–2.29 0.021 1.66 1.13–2.47 0.011 1.69 1.15–2.52 0.008 T3 5.65 3.96–8.14 < 0.001 6.35 4.37–9.36 < 0.001 6.50 4.46–9.60 < 0.001 ∑BPs (ref: T1) T2 1.20 0.86–1.68 0.290 1.19 0.85–1.68 0.307 1.20 0.85–1.69 0.294 T3 1.66 1.20–2.32 0.003 1.69 1.21–2.36 0.002 1.69 1.21–2.36 0.002 Male BPA (ref: T1) T2 0.90 0.63–1.27 0.550 0.93 0.65–1.33 0.698 0.94 0.66–1.34 0.719 T3 2.12 1.53–2.95 < 0.001 2.36 1.68–3.33 < 0.001 2.41 1.71–3.42 < 0.001 BPS (ref: T1) T2 1.17 0.83–1.65 0.369 1.17 0.82–1.66 0.385 1.18 0.83–1.68 0.363 T3 2.02 1.45–2.81 < 0.001 2.00 1.43–2.82 < 0.001 2.03 1.45–2.87 < 0.001 BPF (ref: T1) T2 0.72 0.51–0.99 0.047 0.72 0.51–1.00 0.052 0.72 0.51–1.01 0.058 T3 0.89 0.64–1.23 0.475 0.91 0.65–1.26 0.570 0.91 0.65–1.27 0.579 BPAF (ref: T1) T2 2.12 1.41–3.25 < 0.001 2.14 1.41–3.27 < 0.001 2.14 1.42–3.28 < 0.001 T3 11.43 7.75–17.18 < 0.001 11.77 7.94–17.80 < 0.001 11.80 7.96–17.86 < 0.001 DBA (ref: T1) T2 1.43 0.93–2.21 0.102 1.66 1.07–2.61 0.025 1.69 1.09–2.67 0.021 T3 13.46 9.13–20.21 < 0.001 17.10 11.21–26.68 < 0.001 17.55 11.47–27.48 < 0.001 ∑BPs (ref: T1) T2 1.29 0.90–1.84 0.166 1.35 0.94–1.95 0.103 1.38 0.96–1.98 0.087 T3 3.26 2.33–4.60 < 0.001 3.80 2.67–5.46 < 0.001 3.87 2.71–5.58 < 0.001 Open in a new tab Bold indicates < 0.05 (statistically significant) a Crude model: unadjusted b Adjusted model 1: adjusted for age, BMI, permanent residence registration, education level, income, parity in preconception female, and the same factors for preconception male excluding parity c Adjusted model 2: adjusted for age, BMI, permanent residence registration, education level, income, parity, smoking or passive smoking, alcohol consumption, renovation of home or workplace, exposure to everyday chemicals in preconception female, and the same factors for preconception male excluding parity d BPs, bisphenol analogues; BPA, bisphenol A; BPS, bisphenol S; BPF, bisphenol F; BPAF, bisphenol AF; DBA, 2,2’-diallyl bisphenol A e T1, T2, T3: the lowest, middle, and highest tertiles of concentration. T1 was used as the reference group Mixture BPs exposure and subfecundity Figure 1 A shows the results of the mixture analysis of BP exposure in females. The WQS regression indicated that mixed BPs exposure level was positively associated with subfecundity risk in preconception females (OR = 2.84, 95% CI: 2.23–3.61, P < 0.001). DBA made the largest single contribution in preconception females (58.72%), followed by BPAF (33.74%) and BPF (7.54%) (Fig. 1 A). The permutation tests confirmed the above results (Fig. S5A). Consistently, compared to the mixture at the 50th percentile, the BKMR model revealed a positive association between BP mixture exposure and subfecundity in women when the mixture level was set at the 55th percentile or above (Fig. 1 A). A U-shaped exposure-response relationship was observed in the BKMR model. The subfecundity risk was lowest at intermediate levels of BP mixture exposure, while both lower and higher exposure levels were associated with increased risk (Fig. 1 A). Fig. 1. Open in a new tab A WQS and BKMR models of bisphenol mixture and subfecundity in preconception females. B WQS and BKMR models of bisphenol mixture and subfecundity in preconception males. C WQS and BKMR models of bisphenol mixture and subfecundity in preconception couples. Note: Models for preconception females ( A ) were adjusted for age, BMI, permanent residence registration, education level, income, parity, smoking or passive smoking, alcohol consumption, renovation of home or workplace, and exposure to everyday chemicals of females. Models for preconception males ( B ) were adjusted for corresponding covariates in males (excluding parity). Models for couples ( C ) were adjusted for all these factors in both females and males Figure 1 B displays the results of the mixture analysis for preconception males. The WQS regression showed that exposure to a mixture of five BPs was positively associated with a higher risk of subfecundity in males (OR = 4.21, 95% CI: 3.24–5.47, P < 0.001). DBA (60.98%), BPAF (30.71%), and BPA (7.97%) contributed the largest weights to the WQS index (Fig. 1 B, Fig. S5B). Similarly, the BKMR model demonstrated that BP mixture levels were positively associated with subfecundity in males (Fig. 1 B). As shown in Fig. 1 C, WQS regression demonstrated that the combined exposure to BPs in both partners was positively associated with an increased risk of subfecundity (OR = 6.73, 95% CI: 4.81–9.41, P < 0.001). The main contributors in the mixture were male DBA (33.67%), female DBA (27.64%), male BPAF (18.17%), male BPA (13.62%), and female BPAF (5.26%) (Fig. 1 C, Fig. S5C). The BKMR model also showed a positive dose-response relationship between BP mixture exposure and subfecundity among couples (Fig. 1 C). Sensitivity analysis Sensitivity analyses robustly confirmed the positive association between the highest tertile of total BPs exposure and subfecundity risk in both genders across all stratified groups (Table S2-S4). WQS regression showed that mixed BPs exposure increased the risk of subfecundity, with males aged ≥ 30 years identified as the most susceptible group (Fig. 2 ). In addition, WQS results identified DBA and BPAF as the two major contributors to the mixture effects of bisphenols on subfecundity across all subgroups (Fig. S6-S8). In most groups, DBA consistently accounted for the highest weight. However, among females aged ≥ 30 years, BPAF became the dominant contributor (48.47%), followed by BPF (29.63%) and DBA (21.58%), indicating possible age-specific differences in BPs reproductive toxicity. BKMR models consistently showed that exposure to bisphenol mixtures was associated with subfecundity risk, presenting a U-shaped relationship in females and a positive exposure-response association in males (Fig. S6-S8). Fig. 2. Open in a new tab Forest plot of WQS results for bisphenol mixtures and subfecundity in subgroup analyses Health risk assessment The median EDI values for bisphenols ranged from 0.076 to 0.391 nmol/kg bw/day in females and 0.040 to 0.474 nmol/kg bw/day in males (Table S5). The median total EDI of bisphenol analogues (∑BPs) was 1.709 nmol/kg bw/day in females and 1.419 nmol/kg bw/day in males, and BPA was found to be the primary contributor to the total EDI of bisphenols in both females and males (Table S5). Health risk assessment was conducted using the TDI for BPA set by the EFSA in both 2015 (4,000 ng/kg bw/day, equivalent to 17.5 nmol/kg bw/day) and 2023 (0.2 ng/kg bw/day, equivalent to 0.0009 nmol/kg bw/day) [ 19 ], with reference doses for other analogues converted by molecular weight. When assessing health risks based on the 2015 TDI for BPA, only a small proportion of participants had an EDI exceeding the TDI threshold (1.65% of females and 5.79% of males for BPA) (Table S5, Fig. 3 ). The HI exceeded 1 in 2.38% of females and 7.14% of males. However, when applying the TDI proposed in 2023, the majority of participants’ EDIs greatly exceeded the new reference value, ranging from 81.80% to 100.0%, and all preconception couples had an HI exceeding 1 (Table S5, Fig. 3 ). In addition, RCS analyses revealed a positive association between total BPs EDI and the risk of subfecundity in both male and female partners (Fig. S9, S10). Fig. 3. Open in a new tab A The violin plot for estimated daily intake of urinary bisphenols in preconception females. B The violin plot for estimated daily intake of urinary bisphenols in preconception males Discussion In this study, we systematically investigated the association between preconception exposure to BPA and its analogues and subfecundity in both males and females. Our findings revealed that preconception couples with the highest tertile of total BPs (T3) exposure had a significantly increased risk of subfecundity compared to those in the lowest tertile (T1). Both WQS and BKMR models indicated that mixed BP exposure had a joint effect in elevating subfecundity risk, with the greatest contributions attributed to DBA and BPAF. To the best of our knowledge, this is the first population-based study to report associations between exposure to emerging BPs, particularly BPAF and DBA, and couple fecundity. In the present study, we measured urinary concentrations of fourteen BPs in preconception couples. BPA, BPS, BPF, and BPAF showed high detection rates (95.60%, 90.85%, 87.07%, 97.98%, respectively). For emerging substitutes with limited human biomonitoring data, DBA and D8 were detected in 86.97% and 42.40% of samples. Our study provides the first population data on these compounds, confirming exposure and suggesting potential reproductive risks. The levels of bisphenol exposure observed in our study were comparable with previous studies. The median urinary BPA concentration in our participants was 2.64 ng/mL (2.24 ng/mL in females; 3.54 ng/mL in males), which exceeded the median values reported in the Environment and Reproductive Health (EARTH) Study from a prospective cohort at Massachusetts General Hospital Fertility Center (1.2 ng/mL in females; 1.7 ng/mL in males) [ 28 ]. Whereas, the Home Observation of Peri-conceptional Exposures (HOPE) Study observed higher mean BPA levels in both women (3.8 ± 2.7 ng/mL) and men (4.4 ± 4.6 ng/mL) [ 29 ]. Consistent with some previous epidemiological studies [ 14 , 30 ], our data revealed positive associations between preconception bisphenol exposure and reduced fecundity in both males and females. Among males, we observed that higher BP exposure was linked to lower fecundity. Potential mechanisms proposed in previous studies included influence on the hypothalamic-pituitary-gonadal axis, impairment of spermatogenesis and steroidogenesis, testicular dysfunction, disruption of the blood-testis barrier (BTB), and defects in sperm and semen quality [ 16 , 31 , 32 ]. For females, BPs may interfere with hormone secretion, ovarian reserve, folliculogenesis, and the hypothalamic-pituitary-ovarian axis, thus affecting female fecundity [ 14 , 33 ]. In females of our study, a notable U-shaped relationship was found between BP exposure and subfecundity. This finding was consistent with a New York City pregnancy cohort study, in which BPA levels in the first and fourth quartile group were associated with a higher risk of subfecundity [ 34 ]. The observed U-shaped curve reflected the non-monotonic dose-response (NMDR) effects of BPs, which were recognized as a common feature among environmental endocrine disruptors [ 35 ]. This might result from cytotoxicity, receptor down-regulation and desensitization, receptor selectivity, receptor competition, cell and tissue specific receptors and co-factors, negative feedback loops, and tissue interactions [ 35 ]. Evidence from studies such as the CLARITY-BPA project showed that BPA can induce significant adverse effects at very low doses [ 36 , 37 ]. This could explain that women in our study with BPA or BPS at the lowest exposure tertile (T1) were associated with a higher likelihood of subfecundity compared to the middle tertile (T2). In our couple-based BPs mixture analyses, male BPs exposure demonstrated a stronger association with subfecundity than female, echoing previous reports that men were susceptible to endocrine-disrupting chemicals [ 38 ]. DBA and BPAF were identified as the major contributors to the risk of subfecundity. BPAF, with a fluorinated form of BPA, has been found to be a more potent endocrine disruptor than BPA, exhibiting stronger estrogenic binding potency and a high affinity for estrogen receptors [ 15 ]. DBA, as a derivative of BPA modified with allyl groups, may share comparable endocrine-disrupting potential due to this structural similarity. However, its specific mechanisms of reproductive toxicity remain unexplored, warranting future investigation. Our findings indicated that BPAF became the dominant contributor to mixture effects among females aged ≥ 30 years, which may be linked to age-related changes in hormonal susceptibility. Prior literature has suggested that with advancing age, the reproductive system became more sensitive to hormones such as gonadotrophins [ 39 ]. Given that BPAF could affect sex hormone levels as well as ERα and ERβ estrogen receptors [ 40 , 41 ], its heightened impact in females of high age group may result from this increased vulnerability, explaining the age-specific difference in toxicity contributions. Based on EFSA’s updated standard, our risk assessment showed that a large proportion of the population exceeded the new safety threshold. While only 1.65% of females and 5.79% of males exceeded the 2015 TDI, the vast majority (81.80–100.0%) had EDIs above the 2023 TDI of 0.2 ng/kg bw/day. These results were consistent with recent studies showing that a substantial proportion of the population was exposed to BPA and its analogues at levels exceeding the updated TDI, with some values being up to 100 times higher than the recommended threshold [ 25 , 42 ]. Our observation of subfecundity risks at low bisphenol concentrations, including low-dose effects observed for BPA and BPS in females, provided scientific support for the stricter TDI by demonstrating that adverse reproductive effects could occur at exposure levels previously considered safe. The alignment between the updated EFSA standard and our findings provided mutual validation, reinforcing both the credibility of our risk assessment and the necessity of stricter thresholds. These results underscored the importance of updating regulatory standards to better reflect health risks associated with low-dose endocrine disruption. Notably, our study was among the few studies that have simultaneously evaluated preconception BPs and TTP in both partners, providing a more comprehensive perspective on couple-based fecundity outcomes. In addition, this study was the first to reveal the associations between the BPA substitutes BPAF, DBA and couple subfecundity, thereby extending the assessment to emerging chemicals and highlighting the risks of both established and novel BPs. Moreover, the large sample size (1934 participants) and prospective nested case-control design ensured the reliability of our findings and enabled the causal inference. Given the persistent decline in human fertility [ 1 , 43 ], our study provided scientific evidence for chemical regulation and fertility protection. Nevertheless, bisphenol exposure was evaluated using a single spot urine sample. Although previous studies have shown no significant difference in bisphenol levels between morning urine samples and 24-hour pooled urine [ 44 ], this could lead to possible misclassification bias. In addition, although our analysis adjusted for several lifestyle and sociodemographic factors, detailed dietary information was not included. This might lead to potential bias on the observed associations. Conclusion This nested case-control study demonstrates a positive association between preconception exposure to BPs and the risk of subfecundity in both males and females. Notably, mixed bisphenol exposures showed a joint effect, with emerging analogues DBA and BPAF contributing primarily to the observed association. Most participants’ EDIs exceeded the 2023 TDI threshold, indicating widespread bisphenol exposure at levels of health concern. These results underscore the reproductive toxicity of novel BPA substitutes and suggest that greater attention should be paid to their regulation and management. Supplementary Information 12940_2026_1276_MOESM1_ESM.docx (8.3MB, docx) Supplementary Material 1. Table S1. Association between individual urinary bisphenol analogue exposure and subfecundity by gender among preconception couples. (T2 reference). Table S2. Association between individual urinary bisphenol analogue exposure and subfecundity by age group. Table S3. Association between individual urinary bisphenol analogue exposure and subfecundity by BMI group. Table S4. Association between individual urinary bisphenol analogue exposure and subfecundity in nulliparous group. Table S5. Estimated daily intake and health risk assessment for bisphenol analogue exposure in preconception couples. Figure S1. The directed acyclic graph of the association between BPs exposure and subfecundity. Figure S2. Correlations between bisphenol analogues by Spearman’s correlation analysis. Figure S3. Restricted cubic spline analysis of the association between bisphenol exposure and subfecundity in females. Figure S4. Restricted cubic spline analysis of the association between bisphenol exposure and subfecundity in males. Figure S5. (A) WQS regression results with permutation tests for bisphenol mixture and subfecundity in preconception females. (B) WQS regression results with permutation tests for bisphenol mixture and subfecundity in preconception males. (C) WQS regression results with permutation tests for bisphenol mixture and subfecundity in preconception couples.Figure S6. (A) WQS and BKMR results for BPs mixtures and subfecundity in females of low age group. (B) WQS and BKMR results for BPs mixtures and subfecundity in females of high age group. (C) WQS and BKMR results for BPs mixtures and subfecundity in males of low age group. (D) WQS and BKMR results for BPs mixtures and subfecundity in males of high age group. Figure S7. (A) WQS and BKMR results for BPs mixtures and subfecundity in females of low BMI group. (B) WQS and BKMR results for BPs mixtures and subfecundity in females of high BMI group. (C) WQS and BKMR results for BPs mixtures and subfecundity in males of low BMI group. (D) WQS and BKMR results for BPs mixtures and subfecundity in males of high BMI group. Figure S8. (A) WQS and BKMR results for BPs mixtures and subfecundity in nulliparous females. (B) WQS and BKMR results for BPs mixtures and subfecundity in male partners of nulliparous females. Figure S9. Restricted cubic spline analysis of the association between estimated daily intake of urinary BPs and subfecundity in females. Figure S10. Restricted cubic spline analysis of the association between estimated daily intake of urinary BPs and subfecundity in males. Acknowledgements Not applicable. Authors’ contributions Anxin Yin was involved in the data curation, formal analysis, investigation, methodology, visualization, and writing of the original draft. Lap Ah Tse contributed to the revision and editing of the manuscript and supervision. An Chen and Lu Cheng participated in the funding acquisition and revised the manuscript. Yuyang Chen, Shiqi Zhang, and Zhiqi Lin were involved in the data curation, investigation, and methodology. Xingying Li, Zhuoyan Zhu, and Hanxiao Zhang contributed to the data curation and investigation. Jiufeng Li participated in the funding acquisition, methodology, project administration, resources, supervision, and the revision and editing of the manuscript. Hong Jiang was involved in the conceptualization, funding acquisition, methodology, project administration, resources, supervision, and the revision and editing of the manuscript. Funding This work was supported by the Shanghai “Science and Technology Innovation Action Plan” Intergovernmental International Science and Technology Cooperation Program (Grant number: 22410712700), the National Natural Science Foundation of China (Grant number: 82574106), Key Discipline and Project of High-Quality Development of Public Health of School of Public Health, Fudan University-Jiading District Health Commission (Grant number: GWGZLXK-2023-04), Key Discipline Program of Sixth Round of the Three-year Public Health Action Plan (Year 2023-2025) of Shanghai, China (Grant number: GWVI-11.1-32), 2025 Fudan University Shanghai Medical School Distinguished Educator Program, Shanghai Magnolia talent plan Pujiang project (Grant number: 24PJA020), Shanghai 3-year Public Health Action Plan (Grant number: GWVI-11.1-39). Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and approved by the School of Public Health, Fudan University, Shanghai, China (IRB#2016-10-0601, IRB#2017-01-0609, IRB#2022-08-0993). Informed written consent was obtained from all participants. 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. Contributor Information Jiufeng Li, Email: [email protected]. Hong Jiang, Email: [email protected]. References 1. GBD 2021 Fertility and Forecasting Collaborators. Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2057–99. 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[ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials 12940_2026_1276_MOESM1_ESM.docx (8.3MB, docx) Supplementary Material 1. Table S1. Association between individual urinary bisphenol analogue exposure and subfecundity by gender among preconception couples. (T2 reference). Table S2. Association between individual urinary bisphenol analogue exposure and subfecundity by age group. Table S3. Association between individual urinary bisphenol analogue exposure and subfecundity by BMI group. Table S4. Association between individual urinary bisphenol analogue exposure and subfecundity in nulliparous group. Table S5. Estimated daily intake and health risk assessment for bisphenol analogue exposure in preconception couples. Figure S1. The directed acyclic graph of the association between BPs exposure and subfecundity. Figure S2. Correlations between bisphenol analogues by Spearman’s correlation analysis. Figure S3. Restricted cubic spline analysis of the association between bisphenol exposure and subfecundity in females. Figure S4. Restricted cubic spline analysis of the association between bisphenol exposure and subfecundity in males. Figure S5. (A) WQS regression results with permutation tests for bisphenol mixture and subfecundity in preconception females. (B) WQS regression results with permutation tests for bisphenol mixture and subfecundity in preconception males. (C) WQS regression results with permutation tests for bisphenol mixture and subfecundity in preconception couples.Figure S6. (A) WQS and BKMR results for BPs mixtures and subfecundity in females of low age group. (B) WQS and BKMR results for BPs mixtures and subfecundity in females of high age group. (C) WQS and BKMR results for BPs mixtures and subfecundity in males of low age group. (D) WQS and BKMR results for BPs mixtures and subfecundity in males of high age group. Figure S7. (A) WQS and BKMR results for BPs mixtures and subfecundity in females of low BMI group. (B) WQS and BKMR results for BPs mixtures and subfecundity in females of high BMI group. (C) WQS and BKMR results for BPs mixtures and subfecundity in males of low BMI group. (D) WQS and BKMR results for BPs mixtures and subfecundity in males of high BMI group. Figure S8. (A) WQS and BKMR results for BPs mixtures and subfecundity in nulliparous females. (B) WQS and BKMR results for BPs mixtures and subfecundity in male partners of nulliparous females. Figure S9. Restricted cubic spline analysis of the association between estimated daily intake of urinary BPs and subfecundity in females. Figure S10. Restricted cubic spline analysis of the association between estimated daily intake of urinary BPs and subfecundity in males. Data Availability Statement The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. 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