Urinary Bisphenol Mixtures at Population-Exposure Levels Are Associated with Diabetes Prevalence: Evidence from Advanced Mixture Modeling

Abstract

Background/Objectives: There is a ubiquitous presence of plastics worldwide, and recent data highlight the continuous growth in their production and usage—a trend paralleled by the rise in chronic diseases like diabetes. The multifactorial nature of these diseases suggests that environmental exposure, notably to bisphenol A (BPA), could be a contributing factor. This study investigates the potential correlation between emerging BPA substitutes, bisphenol S and F (BPS and BPF), and diabetes in a cohort of the general adult population. Methods: A retrospective cohort study was conducted using data from the U.S. National Health and Nutrition Examination Survey (NHANES) 2013–2014 and 2015–2016 cycles. Basic comparative analyses and Pearson correlation tests were performed, followed by logistic regression models. Advanced statistical approaches, including Weighted Quantile Sum (WQS) regression and quantile g-computation, were subsequently applied to evaluate the combined effects of bisphenol exposures. Results: Findings reveal a positive association between combined bisphenols (BPs) and glycated hemoglobin (HbA1c), with binomial logistic regression demonstrating an odds ratio (OR) of 1.103 (1.002–1.214) between BP levels corrected for creatinine (crucial due to glomerular filtration variations) and diabetes. weighted quantile sum (WQS) and quantile G-computation analyses showed a combined positive effect on diabetes, glucose levels, and HbA1c. Individual effect analysis identifies BPS as a significant monomer warranting attention in future diabetes-related research. Conclusions: Replacing BPA with new molecules like BPS or BPF may pose a greater risk in the context of diabetes.

1. Introduction

The contemporary era, often referred to as the “Anthropocene” or the “Plastic Age,” owes its name to the extremely close relationship that humans have developed with plastic polymers [1]. Plastics have become a fundamental element of modern industry because of their countless applications and cost-effectiveness, but they pose a major threat to the environment and to global population health. The centrality of plastic in modern society extends from its use in food packaging to clothing, exposing us directly to xenobiotic compounds [2,3,4]. At the core of this plastic surge is bisphenol A (BPA), a monomer and endocrine disruptor found in epoxy resins and polycarbonates, enhancing the physical properties of diverse polymers [5]. Over time, plastic production has surged dramatically, reaching 368 million tons in 2019 [6], with projections indicating a doubling within the next two decades [7]. This surge, coupled with challenges in waste management and the perpetual cycle of polymer production and recycling, raises concerns about potential far-reaching impacts across various trophic levels [8], posing a substantial threat of chronic human exposure.

In response to increasing scientific and regulatory concern, several countries have implemented legislation restricting or banning the use of BPA. The European Union, for example, has introduced multiple regulations limiting BPA in food contact materials, especially those intended for infants, and has classified BPA as a category 1B reproductive toxicant [9]. Countries such as France and Spain have enacted broader bans on BPA in all food packaging. Likewise, the United States, Canada, China, Taiwan, and India have implemented restrictions, particularly in baby bottles and infant products [10]. As a result, the plastics industry has largely shifted to structural analogs such as bisphenol S (BPS) and bisphenol F (BPF), which are less regulated but share similar molecular structures and endocrine-disrupting potential. Recent research unequivocally confirms the widespread presence of plastic monomers, including the emerging BPS and BPF monomers, in water bodies, soil, and atmosphere worldwide [4,11,12].

In addition to their ubiquitous presence, bisphenols pose significant environmental and health concerns due to their persistence, mobility, and bioactivity. Improper disposal and industrial wastewater discharge lead to the leaching of BPA into the environment, where it contaminates surface water, sediments, and groundwater. Although wastewater treatment can remove up to 98% of BPA, residual amounts persist and accumulate in aquatic ecosystems [13,14]. BPS may represent an even greater environmental hazard. Studies show that BPS is significantly more bioavailable—up to 250 times more than BPA in porcine models [15]—and is resistant to biodegradation in marine environments, unlike BPA and BPF [16]. These characteristics raise critical concerns about its long-term persistence and potential for bioaccumulation. Moreover, the presence of bisphenols in groundwater, with BPA levels detected up to 228 µg/L, underscores the risk of human exposure through drinking water [14]. Taken together, these findings reinforce the importance of including bisphenols—particularly BPS—in environmental monitoring and regulatory strategies due to their role as endocrine-disrupting contaminants of emerging concern.

Parallel to the increased production and use of this class of monomers, there has been a significant increase in the incidence of diabetes worldwide. Diabetes mellitus (DM) emerges as a prominent global health crisis in the 21st century [17], with a substantial increase in prevalence over recent decades. Back in 1980, approximately 108 million adults aged 20–79 years were affected. Presently, DM affects 10.5% of the global population (536 million individuals), and projections anticipate a potential rise to 12.2% by 2045 (783.2 million) [18]. The risk factors associated with DM encompass a broad spectrum of environmental and genetic elements, including variables such as age, weight, diet, and smoking [19]. This prompts a consideration of the plausible role that environmental pollutants might play in the development or progression of the disease. Existing literature offers insights into this intriguing intersection. Studies suggest a conceivable relationship between DM and environmental pollutants, providing a nuanced perspective on the complex factors contributing to this escalating health challenge [20].

Bisphenols, including BPA and its common analogs BPS and BPF, are widely recognized as endocrine-disrupting chemicals (EDCs), capable of interfering with endogenous hormone action [21,22]. BPA binds to estrogen receptors with lower affinity than 17β-estradiol (E2) but may exhibit similar potency via non-nuclear pathways. Additionally, it can exert antiestrogenic and antiandrogenic effects by competitively binding to steroid hormone receptors [21]. Animal studies suggest that BPA and its analogs can alter glycemic responses by mimicking E2, inducing rapid, dose-dependent changes in insulinemia and glucose regulation [23]. These compounds may also impair insulin secretion by modulating β-cell ion channels involved in glucose-stimulated insulin secretion (GSIS), such as ATP-sensitive K⁺ channels and voltage-dependent Ca²⁺ channels. Such alterations can disrupt the physiological oscillatory patterns necessary for effective insulin exocytosis. While mechanistic evidence is robust in animal models, growing concern surrounds the metabolic effects of chronic low-dose exposure in humans, particularly given the non-monotonic dose–response behavior observed with many EDCs [23,24].

A recent meta-analysis demonstrated the need for further analysis of new bisphenols used in industry as substitutes for BPA in view of the increasingly restrictive regulations being promoted in different countries [25]. The results of the meta-analysis showed a statistically significant positive association only with BPS. Nevertheless, it is evident that co-exposure to bisphenols does occur, as demonstrated in human cohorts where mixtures of bisphenols have been quantified in urine [26]. However, the paucity of publications studying the possible combined effect of these monomers in the context of DM is striking. In other variations of DM such as gestational DM (GDM), recent research shows that BPS and BPF could be potential risk factors for its development [27,28].

Consequently, the present work aims to analyze the possible combined effect of the mixture of the three most relevant bisphenols in modern industry with the risk of DM in one of the largest global cohorts analyzing urinary bisphenols, the “National Health and Nutrition Examination Survey” (NHANES) cohort. Thus, using correlations, logistic regressions, weighted quantile sum, and quantile G-computational analysis, the possible relationship between combined bisphenol exposure and DM in adults will be explored.

2. Materials and Methods

2.1. Data Extraction from the NHANES Cohort

The NHANES cohort databases from all years in which urinary bisphenol levels were quantified were utilized. Currently, the 2013–2014 and 2015–2016 cohorts have data on the quantification of urinary BPA, BPF, and BPS. All data are accessible on the official website of the Centers for Disease Control and Prevention [29] (accessed on 13 November 2023). After consolidating and organizing the data by patient code, a total of 20,146 subjects were obtained. The data were then filtered based on subjects aged 18 and above (adults), urinary bisphenol values, and urinary creatinine values, resulting in a total of 3699 subjects included in the database for analysis. The study population was further subdivided based on the pathology of interest (diabetes), and participants with data on all relevant covariates (to be detailed later) were selected, yielding 3658 study subjects (2965 non-diabetics and 693 diabetics) (Figure 1). Individuals with physician-diagnosed diabetes, as well as those with fasting glucose values ≥ 126 mg/dL or hemoglobin A1c ≥ 6.5%, were included in the diabetic group [30].

2.2. Combined Urinary Bisphenol Exposure Study

In the combined study of urinary bisphenols, we first used an approach based on the analysis of the sum of these phenolic compounds. Initially, analyses were conducted based on “Unsupervised summary scores or USS,” as outlined by Li et al. [31]. This involved calculating the molar sum of bisphenols (ΣBPs/Mol) by dividing the concentration of each metabolite by its respective molecular weight and subsequently summing them up:

$$\mathsf{\Sigma }\mathrm{B}\mathrm{P}\mathrm{s}/\mathrm{M}\mathrm{o}\mathrm{l}=({\displaystyle \frac{\left[\mathrm{B}\mathrm{P}\mathrm{A},\frac{\mathrm{n}\mathrm{g}}{\mathrm{m}\mathrm{L}}\right]}{228.29 \mathrm{g}﹡\mathrm{m}\mathrm{o}\mathrm{l}}}+{\displaystyle \frac{\left[\mathrm{B}\mathrm{P}\mathrm{S},\frac{\mathrm{n}\mathrm{g}}{\mathrm{m}\mathrm{L}}\right]}{250.27 \mathrm{g}﹡\mathrm{m}\mathrm{o}\mathrm{l}}}+{\displaystyle \frac{\left[\mathrm{B}\mathrm{P}\mathrm{F},\frac{\mathrm{n}\mathrm{g}}{\mathrm{m}\mathrm{L}}\right]}{200.23 \mathrm{g}﹡\mathrm{m}\mathrm{o}\mathrm{l}}})$$

In the subsequent step, the total concentration of bisphenols, adjusted by the urinary creatinine value (Creat), was employed to mitigate potential result discrepancies stemming from variations in the glomerular filtration capacity among individual study subjects (ΣBPs/Creat).

$${\displaystyle \frac{\mathsf{\Sigma }\mathrm{B}\mathrm{P}\mathrm{s}}{\mathrm{C}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}}}(\mathrm{\mu }\mathrm{g}/\mathrm{g})=\left({\displaystyle \frac{\left[\mathrm{B}\mathrm{P}\mathrm{A},\frac{\mathrm{\mu }\mathrm{g}}{\mathrm{L}}\right]}{\mathrm{C}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t},\frac{\mathrm{m}\mathrm{g}}{\mathrm{d}\mathrm{L}}}}+{\displaystyle \frac{\left[\mathrm{B}\mathrm{P}\mathrm{S},\frac{\mathrm{\mu }\mathrm{g}}{\mathrm{L}}\right]}{\mathrm{C}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t},\frac{\mathrm{m}\mathrm{g}}{\mathrm{d}\mathrm{L}}}}+{\displaystyle \frac{\left[\mathrm{B}\mathrm{P}\mathrm{F},\frac{\mathrm{\mu }\mathrm{g}}{\mathrm{L}}\right]}{\mathrm{C}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t},\frac{\mathrm{m}\mathrm{g}}{\mathrm{d}\mathrm{L}}}}\right)\times 100$$

The quantification of bisphenols was conducted using an online solid-phase extraction coupled with high-performance liquid chromatography and tandem mass spectrometry (online SPE-HPLC-Isotope dilution-MS/MS) method. The respective detection limits were 0.2, 0.2, and 0.1 for BPA, BPF, and BPS. For more information on the protocols, refer to the NHANES website [32,33].

Basic descriptive statistics were conducted to analyze the variables of interest within each pathological subgroup and across bisphenol quartiles. The distribution of the data was assessed using the D’Agostino–Pearson, Shapiro–Wilk, and Kolmogorov–Smirnov normality tests. As the majority of the variables exhibited a non-parametric distribution, quantitative data were expressed as geometric mean with 95% confidence interval (GM [95% CI]). An exception was the poverty ratio, which was reported as median and interquartile range due to the presence of zero values.

For group comparisons, the Kruskal–Wallis test was applied, followed by Dunn’s post hoc test for multiple comparisons. In the case of two-group comparisons, the Mann–Whitney test was used. Categorical (dichotomous) variables were analyzed using Fisher’s exact test. All statistical analyses and graphical representations were performed using GraphPad Prism 7.0 software (GraphPad Software Inc., San Diego, CA, USA). p-values presented in figures and tables correspond to post hoc tests, and statistical significance was set at p < 0.05.

Next, Pearson correlation analysis was employed to assess potential relationships between urinary bisphenols and quantitative variables such as age, body mass index, cotinine levels, serum glucose, and glycosylated hemoglobin. In the subsequent step, a study model involving binomial and multinomial logistic regression was implemented. The aim of the logistic regression analysis was to identify patterns that could reveal potential associations between the pathological subgroups and the composite of urinary bisphenols, considering the multifactorial origin of the study pathologies. Subsequently, each analysis was conducted in three distinct manners: first, individually (1); second, adjusted for age, gender, and body mass index (BMI) (2); and third, adjusted for (2) + race/ethnicity, poverty-income ratio, hypertension, dyslipidemia, and smoking (3) (inclusion of anthropometric and demographic variables as per relevant literature [31,34,35]). Individuals classified as hypertensive comprised those diagnosed by a healthcare professional and those with systolic pressure ≥ 140 mmHg or diastolic pressure ≥ 90 mmHg. The category of patients with dyslipidemia included those diagnosed with cholesterol disorders or those with fasting total cholesterol levels ≥ 240 mg/dL [36]. Regarding smoking, individuals were included if they answered affirmatively to the question “have you smoked more than 100 cigarettes in your life?” or if they had serum cotinine values exceeding 10 mg/dL [37]. Log-transformation of urinary concentrations of bisphenol metabolites was applied to achieve normalized distributions. The Cochran q test and regression models were performed using IBM SPSS Statistics for Windows software, version 27 (IBM Corp, Armonk, NY, USA).

Following this, the weighted quantile sum (WQS) was constructed utilizing the R package “gWQS”, version R.4.2.3. [38] to investigate associations between the exposures and the outcome, concurrently providing a comprehensive summary of the intricate exposure to the particular mixture of interest [39,40]. In essence, WQS regression condenses the overall exposure to the mixture by calculating a singular weighted index, considering the individual contribution of each component through assigned weights [41]. In the combined analysis, the individual value of each of the bisphenols corrected by the urinary creatinine value (BPA/creat, BPS/creat, and BPF/creat) was entered.

Subsequently, a quantile G-computation model was executed using the R package “qgcomp” [42] to estimate the collective impact of the bisphenols mixture on heart disease. The QG-comp method evaluates the effect of the bisphenol mixture on the outcome by measuring the change in outcome for each quantile increase in the concentration of all bisphenols in the mixture. Furthermore, it calculates the relative contribution of each bisphenol to the overall effect, discerning whether it has a positive or negative direction. The estimated overall mixture effect, denoted as ψ, signifies the alteration in the outcome associated with a quantile increase in the concentration of all bisphenols in the mixture. In essence, ψ captures the joint effect of all bisphenols in the mixture on the outcome, allowing for an assessment of the overall impact of the bisphenol mixture on the outcome of interest.

3. Results

The initial stage in the statistical analysis of subpopulations within the NHANES cohort involved conducting descriptive statistics. As illustrated in

Table 1. Descriptive examination of the urinary bisphenols mixture and relevant study covariates in relation to diabetes. Quantitative parameters are presented as geometric mean (95% confidence interval). Dichotomous variables are expressed as percentages. Statistical analysis of quantitative variables involved the use of the Mann–Whitney test, while Fisher’s exact test was applied to dichotomous variables.

Variable	Healthy (n = 2965)	Diabetes (n = 693)	p-Value
Gender, men (%)	1389 (46.85)	343 (49.50)	0.112
Age	41.07 (40.43–41.71)	57.08 (55.88–58.31)	0.000
BMI (kg/m2)	27.8 (27.58–28.03)	31.2 (30.69–31.72)	0.000
Mexican American	434 (14.64)	130 (18.76)	0.000
Other Hispanic	317 (10.69)	88 (12.70)
Non-Hispanic White	1125 (37.94)	195 (28.14)
Non-Hispanic Black	640 (21.59)	174 (25.11)
Other Race—Including Multi-Racial	449 (15.14)	106 (15.30)
Poverty-Income Ratio $	1.79 (0.83–3.81)	1.53 (0.78–3.27)	0.013
Hypertension	1043 (35.18)	472 (68.11)	0.000
Dyslipidemia	993 (33.19)	397 (57.29)	0.000
Smoking	1266 (42.70)	337 (48.63)	0.003
ΣBPs/Creat (µg/g)	2.87 (2.78–2.97)	3.2 (2.98–3.43)	0.017
ΣBP/Mol (nM)	2.13 (2.03–2.23)	2.21 (2.02–2.43)	0.587
BPA/Creat (µg/g)	1.16 (1.12–1.19)	1.2 (1.12–1.28)	0.216
BPS/Creat (µg/g)	0.50 (0.48–0.53)	0.57 (0.52–0.62)	0.018
BPF/Creat (µg/g)	0.41 (0.39–0.43)	0.44 (0.40–0.50)	0.522

Abbreviations: BMI, body mass index; BPs, the mixture of urinary bisphenols BPA, BPS, and BPF; Creat, creatinine. $ represents use of median (interquartile range).

, most of the study covariates employed to adjust the statistical models exhibited noteworthy differences between individuals with and without the pathology of interest. Subjects in the diabetic category were older and had a higher BMI, as is logical in this pathology. In addition, the pathological subgroup presented a higher percentage of subjects with hypertension and dyslipidemia and smokers. With respect to urinary bisphenol levels, there were significant differences between the creatinine-corrected values, while the USS showed no differences between groups. Finally, ethnic differences were also observed, with an increase in the percentage of Mexican American, other Hispanic, and non-Hispanic black subjects among healthy and diabetic subjects, as well as a lower percentage of non-Hispanic white diabetics compared to the non-diabetic group.

The subsequent comparative analysis of the BPmix quartiles (

Table 2. Descriptive examination of covariates associated with urinary bisphenols [ΣBPs/Creat (µg/g)], categorized by quartiles. The sample size (n) is specified for each subgroup. Geometric mean (95% confidence interval) is used to present quantitative parameters, while dichotomous variables are expressed as percentages. Statistical analysis for quantitative variables involved the application of Kruskal–Wallis followed by Dunn’s test, and Fisher’s exact test was employed for dichotomous variables.

Variable	Q1 (n = 914)	Q2 (n = 915)	Q3 (n = 915)	Q4 (n = 914)	p-Value
Diabetes (%)	157 (17.2)	169 (18.5)	175 (19.1)	192 (21.0)	0.208
Gender, men (%)	533 (58.3)	420 (45.9)	386 (42.2)	393 (43.0)	0.000
Age	41.6 (40.5–42.8)	44.6 (43.4–45.8) **	44.1 (42.8–45.3) **	44.61 (43.4–45.8) **
BMI (kg/m2)	28.3(27.9–28.7)	28.2 (27.7–28.6)	28.5 (28.1–28.9)	28.7 (28.2–29.1)
Mexican American	139 (15.2)	150 (16.4)	144 (15.7)	131 (14.3)	0.000
Other Hispanic	86 (9.4)	102 (11.1)	110 (12.0)	107 (11.7)
Non-Hispanic White	307 (33.6)	325 (35.5)	344 (37.6)	344 (37.6)
Non-Hispanic Black	192 (21.0)	204 (22.3)	199 (21.7)	219 (24.0)
Other Race—Including Multi-Racial	190 (20.8)	134 (14.6)	118 (12.9)	113 (12.4)
Poverty-Income Ratio $	2 (0.9–4.1)	1.8 (0.8–3.6)	1.7 (0.8–3.6)	1.6 (0.8–3.4) **
Hypertension	338 (37.0)	396 (43.3)	378 (41.3)	403 (44.1)	0.010
Dyslipidemia	327 (35.8)	352 (38.5)	341 (37.3)	370 (40.5)	0.204
Smoking	360 (239.4)	370 (40.4)	419 (45.8)	454 (49.7)	0.000

Abbreviations: BMI, body mass index. $ represents use of median (interquartile range). ** is equivalent to a p-value ≤ 0.01.

and

Table 3. Descriptive examination of covariates associated with urinary bisphenols [ΣBPs/Mol (nM)], categorized by quartiles. The sample size (n) is specified for each subgroup. Geometric mean (95% confidence interval) is used to present quantitative parameters, while dichotomous variables are expressed as percentages. Statistical analysis for quantitative variables involved the application of Kruskal–Wallis followed by Dunn’s test, and Fisher’s exact test was employed for dichotomous variables.

Variable	Q1 (n = 916)	Q2 (n = 915)	Q3 (n = 912)	Q4 (n = 915)	p-Value
Diabetes (%)	158 (17.2)	195 (21.3)	174 (19.1)	166 (18.1)	0.142
Gender, men (%)	416 (45.4)	444 (48.5)	450 (49.3)	422 (46.1)	0.274
Age	45.4 (44.2–46.7)	44.8 (43.6–46)	42.4 (41.2–43.6) **	42.4 (41.2–43.6) ***
BMI (kg/m2)	27.1 (26.7–27.5)	28.2 (27.8–28.6) ****	29 (28.6–29.5) ****	29.3 (28.9–29.8) ****
Mexican American	122 (13.3)	131 (14.3)	159 (17.4)	152 (16.6)	0.000
Other Hispanic	69 (7.5)	110 (12.0)	119 (13.0)	107 (11.7)
Non-Hispanic White	424 (46.3)	342 (37.4)	294 (32.2)	260 (28.4)
Non-Hispanic Black	131 (14.3)	183 (20.0)	213 (23.4)	287 (31.4)
Other Race—Including Multi-Racial	170 (18.6)	149 (16.3)	127 (13.9)	109 (11.9)
Poverty-Income Ratio $	2.4 (1–4.5)	1.8 (0.9–3.5) ****	1.5 (0.7–3.2) ****	1.5 (0.7–3.3) ****
Hypertension	364 (39.7)	395 (43.2)	362 (39.7)	394 (43.1)	0.225
Dyslipidemia	353 (38.2)	366 (40.0)	347 (38.0)	324 (35.4)	0.234
Smoking	372 (40.6)	417 (45.6)	409 (44.8)	405 (44.3)	0.142

Abbreviations: BMI, body mass index. $ represents use of median (interquartile range). ** is equivalent to a p-value ≤ 0.01; *** is equivalent to a p-value ≤ 0.001; **** is equivalent to a p-value ≤ 0.0001.

) revealed intriguing ethnic differences, particularly pronounced in values normalized by molecular weight (ΣBPs/Mol). Notably, a negative trend was observed in the “Non-Hispanic White” population, contrasting with a reverse trend in the “Non-Hispanic Black” population. Interestingly, the analysis did not reveal significant results regarding diabetes in either of the two approaches used with corrected bisphenol values. However, ΣBPs/Creat exhibited a significant increase in hypertensive and smoking subjects and, intriguingly, a higher percentage of women in Q4 compared to Q1. Furthermore, in both approaches, a significant decrease in the poverty-income ratio is observed, and in the case of ΣBPs/Mol, there is an increase in BMI.

The next step involved Pearson correlation analysis between quantitative variables of interest, such as parameters related to diabetes (glucose, glycated hemoglobin), as well as cholesterol and cotinine. In this case, the results showed a significant and positive relationship with HbA1c but not with glucose, both in the creatinine-corrected and molecular weight-corrected models. It is worth noting that, as indicated in

Table 4. Pearson correlation coefficient analysis of quantitative variables.

	ΣBPs/Creat	ΣBPs/Mol	Glucose	HbA1c	Cholesterol	Cotinine
ΣBPs/creat	1	**	-	*	-	**
ΣBPs/Mol	0.391 **	1	-	*	-	**
Glucose	0.033	0.031	1	**	-	*
HbA1c	0.035 *	0.36 *	0.757 **	1	-	*
Cholesterol	0.009	−0.017	0.007	0.029	1	-
Cotinine	0.085 **	0.078 **	−0.040 *	−0.033 *	−0.015	1

Abbreviations: BPs, bisphenols (BPA + BPF + BPS); HbA1c, glycated hemoglobin or glycosylated hemoglobin. * is equivalent to a p-value ≤ 0.05; ** is equivalent to a p-value ≤ 0.01. Quantitative values for the variables of interest were available for 3530 patients for cotinine, 3521 for glucose, and 3546 for HbA1c.

, the exact numbers of patients with quantitative values for each of the variables of interest were 3530 for cotinine, 3521 for glucose, and 3546 for HbA1c.

In this case, the results were only statistically significant with the ΣBPs/creat approach. As depicted in

Table 5. Association between urinary bisphenol mix and diabetes (odds ratio)—binomial logistic regression analyses. Analysis were performed individually (1), adjusted for age, gender, and BMI (2), and further adjusted for (2) + race/ethnicity, poverty-income ratio, hypertension, dyslipidemia, and smoking (3). Log-transformed urinary concentrations of bisphenol metabolites for normalized distributions.

BP Mixture	Covariates	OR (95% CI)	p-Value
ΣBPs/creat	1	1.132 (1.038–1.235)	0.005
	2	1.102 (1.003–1.211)	0.043
	3	1.103 (1.002–1.214)	0.045
ΣBPs/Mol	1	1.024 (0.960–1.092)	0.468
	2	1.024 (0.955–1.097)	0.509
	3	0.989 (0.920–1.064)	0.773

Abbreviations: BPs, bisphenols (BPA + BPF + BPS); OR, odds ratio; CI, confidence interval.

, both individually and corrected for all relevant covariates, including ethnic group (as recommended by NHANES), significant findings were observed. The results suggest that with each one-unit increase in the log-transformed urinary bisphenol mixture corrected for urinary creatinine—accounting for variations in individual glomerular filtration capacity—there is a diabetes risk of 1.103 (1.002–1.214). This risk remains independent of other covariates associated with the condition, including demographic and pathological variables. This result provides new evidence justifying further exploration of potential relationships between the combined action of bisphenols and the risk of diabetes in adults. However, in the case of multinomial logistic regression (

Table 6. Multinomial logistic regression of diabetes and bisphenols quartile. Quartile 1 serves as the reference group in the statistical model. Analyses were conducted in three ways: Individually (1), adjusted for age, gender, and BMI (2), and further adjusted for (2) + race/ethnicity, poverty-income ratio, hypertension, dyslipidemia, and smoking (3).

	Q1	Q2	Q3	Q4
Variable	Ref.	OR (95% CI)	OR (95%CI)	OR (95%CI)
1 Diabetes (1)	Ref.	1.09 (0.86–1.39)	1.14 (0.90–1.45)	1.28 (1.01–1.62) *
1 Diabetes (2)	Ref.	1.01 (0.78–1.30)	1.05 (0.81–1.36)	1.16 (0.90–1.50)
1 Diabetes (3)	Ref.	1.03 (0.80–1.34)	1.10 (0.85–1.43)	1.21 (0.93–1.56)
2 Diabetes (1)	Ref.	1.30 (1.03–1.64) *	1.13 (0.89–1.44)	1.06 (0.84–1.35)
2 Diabetes (2)	Ref.	1.27 (0.99–1.62)	1.12 (0.87–1.45)	1.04 (0.80–1.35)
2 Diabetes (3)	Ref.	1.14 (0.89–1.48)	0.98 (0.76–1.28)	0.89 (0.68–1.17)

OR, odds ratio; CI, confidence interval. ¹ ΣBPs/creat. ² ΣBPs/Mol. * is equivalent to a p-value ≤ 0.05.

), no significant results were observed between combined bisphenol quartiles and the risk of diabetes in any of the approaches used in the statistical analysis.

Due to the relatively limited significant results obtained using more conventional statistical methodologies, weighted quantile sum (WQS) and quantile G-computation analyses were employed. In this case, three independent analyses were conducted, utilizing different variables of interest. Firstly, the diabetes parameter (n = 3658) was examined, incorporating the same covariates used in the regression models (age, gender, BMI, ethnicity, poverty-income ratio, hypertension, dyslipidemia, and smoking). In this instance, as depicted in Figure 2A,B, the WQS analysis unveiled that BPS carries significant weight in the diabetes-related effect. Quantile G-computation analysis demonstrates that all factors have a positive impact, yet BPS continues to hold greater weight, leading to a clear positive association with diabetes when combined, as illustrated in Figure 2D.

Subsequent statistical analyses were performed on the quantitative variables of glucose and HbA1c. As can be seen in Figure 3 and Figure 4, BPS had a strong positive effect (Figure 3C and Figure 4C), while BPA had a negative effect, which was particularly striking in serum glucose (Figure 3C). In the case of BPF, the impact on serum glucose is minimal, and markedly negative on HbA1c (Figure 4C). In fact, across all results, it is consistently observed that BPS exerts the greatest influence, and the combined effect is consistently positive (Figure 2D, Figure 3D and Figure 4D).

4. Discussion

This manuscript, for the first time, analyzes the potential impact of the three most commonly used bisphenols in the plastic industry on the risk of diabetes in an adult human population cohort. The traditional approach to study xenobiotic compounds has generally been developed under the premise of “one compound—one disease.” Clearly, in the context of studying multifactorial conditions like diabetes, influenced by lifestyle habits, individual genetics, and even exposure to pollutants, the fundamental axiom used to formulate the methodology must be reconfigured. Therefore, the combined approach to the bisphenol mixture introduces novelty and realism to the findings of this manuscript, which also focuses on a cohort of the general population, the NHANES cohort.

The initial hypothesis, drawn from previous research [43,44,45], suggests a potential additive (or synergistic) influence of BPS and BPF on BPA in the context of diabetes. Considering the substantial structural homology and shared hormonal activity among these three phenolic molecules (evidence of comparable hormonal activity among BPA, BPF, and BPS exists [43]), it is justifiable to treat the urinary bisphenol pool as a unified quantitative variable.

Descriptive statistics have revealed specific differences depending on the approach used to analyze the urinary bisphenol pool. Nevertheless, the most noteworthy findings are the common convergences, specifically the poverty-income ratio. In both models, a significant decrease in the ratio is observed with higher bisphenol exposure (Q4 vs. Q1 is significant in both models). This statement suggests that higher bisphenol exposure may be a secondary consequence of socio-economic disadvantages, which are often linked to greater consumption of processed foods [46]. Although BPA content in packaging materials has been increasingly regulated, the exposure to BPS may have risen due to its use as a substitute. Processed foods, which are more commonly consumed in low-resource settings, may also contribute to bisphenol exposure and tend to have lower nutritional value. Additionally, limited access to information about healthy lifestyle habits may exacerbate these effects.

This fact, in turn, has an impact on the healthcare system by increasing non-communicable diseases and the associated morbidity related to conditions such as diabetes mellitus [47,48]. This issue is particularly pertinent in low-income communities where educational resources may be limited. Additionally, heightened consumption of canned foods, frequently lined with bisphenol-containing resins, can contribute to an elevated bisphenol exposure [49]. Similarly, the preparation and heating of food in plastic containers can lead to the leaching of bisphenols into the food, further increasing exposure [50,51].

The use of creatinine normalization is essential for correcting urinary bisphenol concentrations, as glomerular filtration rates can vary significantly among individuals. This adjustment is significant in diabetic patients, who often experience renal impairment, leading to altered filtration and potentially misleading analyte concentrations. Incorporating urinary creatinine ensures the clinical relevance of bisphenol level interpretation.

In our study, the binomial logistic regression model revealed a significant and positive association between the combined bisphenol burden (BPA, BPS, and BPF) and the risk of diabetes. However, this association was not confirmed in the multinomial model when comparing the highest exposure quartile (Q4) to the lowest (Q1). This discrepancy may be attributed to reduced statistical power due to exposure categorization, as well as increased variability within quartile groups. Additionally, adjustment for covariates such as age, BMI, ethnicity, poverty-income ratio, hypertension, dyslipidemia, and smoking led to a modest attenuation of the odds ratio, which is consistent with their known role as independent risk factors for diabetes. Nevertheless, the association remained significant in the adjusted binomial model. Furthermore, more complex statistical approaches such as weighted quantile sum (WQS) regression and G-computation reinforced the findings, demonstrating a consistent positive effect of bisphenol mixture exposure not only on diabetes risk, but also on glycemia and HbA1c levels.

Therefore, the body of evidence in this manuscript suggests a relationship between the exposure to the mixture of bisphenols and the risk of diabetes in the general adult population. At the individual level, the analysis revealed that the predominant impact of the combined effect consistently rests on BPS. The findings of our study align with those of Shankar and Teppala [52], who first demonstrated a significant association between urinary BPA levels and diabetes, independent of traditional risk factors. Building upon this, our study evaluates the combined effect of BPA, BPS, and BPF—the most commonly detected bisphenols in human urine—on diabetes risk. Notably, BPS emerged as the most significant contributor to this association, consistent with recent systematic reviews and meta-analyses showing a significant link between BPS (but not necessarily BPA or BPF) and type 2 diabetes mellitus (T2DM) [25].

Furthermore, the results align with the statistical analysis model conducted by Tang et al. [28] in the context of gestational diabetes. Despite observing a collectively negative impact of BPS, BPA, BPF, TBBPA, and BPB, the most prominent positive effect individually was found with BPS in the quantile-based G-computation model. Three potential factors for comparison arise. First, differences between general population diabetes mellitus and gestational diabetes; second, variations in the analyzed monomers, limited in this study to BPA, BPS, and BPF; and third, the adjustment for creatinine levels could significantly influence the approach to studying the exposure to xenobiotic compounds excreted in urine. In addition, a longitudinal study conducted in 2022 found a positive correlation between the use of BPS and the risk of developing GDM during the first trimester of pregnancy, so perhaps the emerging use of BPS as a substitute for BPA does not guarantee safety, and more research is needed to raise awareness of the possible adverse health effects [53]. Despite the lack of scientific evidence, certain research studies confirm that these alternatives to BPA could have an effect by causing other types of disorders such as those developed during embryonic development [54] and may be related to other pathologies such as obesity [55] and even interfere in the development of ADHD in childhood [56].

To understand the potential molecular aspects involved in the pathogenesis of the disease, evidence has been developed in experimental cellular and animal models using zebrafish, mice, and rats. The evidence suggests that BPS may involve the estrogen receptor beta (ERβ), altering key cellular events in pancreatic cells. Marroqui et al. [57] utilized pancreatic β-cells from both wild-type (WT) and ERβ knockout (BERKO) C57BL/6J mice. Following a 48 h treatment with BPS, they observed enhanced insulin release, reduced ATP-sensitive K + (KATP) channel activity, and a decreased expression of several ion channel subunits exclusively in β-cells from WT mice, not in those from BERKO mice. This suggests the involvement of an extranuclear-initiated pathway linked to ERβ.

Beyond pancreatic β-cell function, recent studies suggest that BPS may exert systemic effects on glucose homeostasis through multiple mechanisms. For instance, Ahmed et al. [58] described how BPS can interfere with insulin signaling and mitochondrial function in skeletal muscle, a key tissue responsible for postprandial glucose uptake. Chronic bisphenol exposure has been associated with reduced insulin receptor substrate 1 (IRS1) and Akt phosphorylation, which impairs glucose uptake. In adipose tissue, BPS has been shown to increase lipid accumulation and the expression of adipogenic markers by activating the nuclear receptor PPARγ. Notably, BPS demonstrates a stronger binding affinity to PPARγ than BPA, suggesting a potentially more potent role in adipogenesis and insulin resistance.

In vivo studies support these results. Zhao et al. [59] demonstrated that exposure of male zebrafish to environmentally relevant BPS concentrations significantly increased fasting blood glucose levels and decreased plasma insulin. These alterations were accompanied by stimulated hepatic gluconeogenesis and glycogenolysis and reduced glycolysis and glycogen synthesis in both liver and muscle. At higher doses, BPS further disrupted glucose metabolic pathways without significantly affecting blood insulin levels, reinforcing the hypothesis that BPS hampers insulin function and promotes systemic glucose dysregulation. Complementing this, Duan et al. [60] found significant associations between urinary BPS levels and altered serum metabolites in humans with type 2 diabetes. In particular, they reported that BPS was linked to deregulation of pyridoxal 5′-phosphate (PLP), a cofactor implicated in glucose metabolism and adipocyte gene expression, which may in turn affect insulin sensitivity. Although direct comparative studies of BPS versus BPA on diabetes development remain limited, a comprehensive review by Alharbi et al. [55] summarized the endocrine and metabolic disruptions mediated by BPS in skeletal muscle and adipose tissue. Notably, the authors highlight that BPS may exert stronger obesogenic effects than BPA, potentially through mechanisms involving estrogenic and androgenic signaling, dysregulation of adipogenic gene expression, induction of oxidative stress, and promotion of inflammatory pathways.

For all these reasons, it is crucial that the relevant institutional authorities in each country consider evidence such as that presented in this manuscript when implementing intervention measures to ensure the safety of citizens. While future studies are needed to conclusively establish the existing causal relationship between the combination of phenolic monomers and diabetes mellitus, the growing body of evidence on the potential deleterious effects of BPA, along with the increasing number of findings related to its substitute molecules in recent decades, underscores the urgency of promoting the application of the precautionary principle. BPA and other substitutes may remain in water sediments [61,62], posing a risk to humans and wild flora and fauna. Through water flows, contaminants can reach terrestrial soil, altering ecological processes and potentially entering agricultural lands [63] that could be incorporated into the food chain. There are promising studies focusing on the application of bioremediation to degrade microplastics [64,65], although further research is needed to scale up the recycling of these particles.

Health policies should be developed with special attention to population groups that are particularly susceptible to endocrine disruptors but should also be extrapolated to the general population. We must be aware that we live in the “Plastic Age” [66] and act accordingly if we want future generations to lead a life free from endocrine disruptors.

5. Conclusions

The evidence shows the existence of a statistical relationship between the combination of bisphenols in the general population and diabetes mellitus. The results also suggest that BPS could be the monomer with the greatest weight in the context of metabolic disease, indicating that the replacement of BPA by the new BPS and BPF molecules could pose a greater risk to population health, at least in the context of diabetes. Therefore, based on the results obtained and scientific evidence of the last decades regarding BPA, the need to apply the precautionary principle to this class of emerging molecules is evident, since the health of future generations depends on the actions we take today.