As a common economic stress and public health event, chronic kidney disease (CKD) has a significant impact on global health and has been recognized as a leading public health problem worldwide (1). The estimated glomerular filtration rate (eGFR) reflects the kidney’s ability to filter blood. It has the characteristics of stable results and high reproducibility. It is an important clinical indicator for evaluating human renal function (2, 3). Not only is it of great value in the prevention, diagnosis and treatment of renal function, but eGFR is also related to other functions of the body. European Society of Cardiology states that the prognostic impact of eGFR on heart failure has been well established, decreased eGFR is a better predictor of adverse outcome than decreased left ventricular ejection fraction (4). A large meta-analysis illustrated that the assessment and inclusion of eGFR will provide some support for cardiovascular risk in the general population (5). Furthermore, eGFR were associated with all-cause mortality among US adults with obstructive lung function (6). Therefore, additional studies of eGFR related factors and development of prevention strategies are necessary to ensure optimal health care. Diabetes and hypertension are the prior factors of abnormal eGFR, but other factors, including environmental toxins, also cause abnormal changes in eGFR (7). Yufen and colleagues studied the relationship between serum concentrations of per-and polyfluoroalkyl substances (PFAS) and kidney damage in 1,700 people over 18 years. The results show that PFAS has a combined effect on eGFR. Perfluorooctane sulfonate (PFOS) concentration is negatively correlated with eGFR, while perfluorohexane sulfonate (PFHS) is positively correlated (8).

Phenolic compounds represented by bisphenol A (BPA) and its substitutes are widely detected in various foods, consumer products, human and animal bodies, and are widely found in multi environmental components such as soil, water and air. It is widely distributed in areas with high levels of urbanization and industrialization and is a typical environmental endocrine disruptor (9). Even at low doses, it can stimulate cellular responses and affect body functions (10). In addition to the self-toxicity of phenolic compounds, their transformation metabolites in vivo may have more complex endocrine disrupting and toxic effects than their own compounds (11). Some animal studies have suggested that kidney may be adversely affected by phenolic compounds. BPA deregulates autophagy flux and redox protection mechanisms, exacerbating chronic kidney injury (12). Kapil et al. showed that bisphenol S (BPS) exposure significantly disrupted rat kidney tissue structure, changed kidney injury marker levels, and affected kidney metabolic pathways (13). Epidemiological studies have also found that phenolic compounds may have adverse effects on renal function. Kang evaluated the effect of exposure to phthalates and environmental phenols on eGFR in 9008 adults from 2005 to 2016 National Health and Nutrition Examination Survey (NHANES). Moreover, exposure to BPA may be responsible for declined eGFR and increased albumin-to-creatinine ratio (ACR) (14). However, phenolic compounds are a large class of substances, and previous studies have focused on the analysis of BPA. The association between other phenolic compounds and eGFR lacks support from population data.

Most traditional statistical models have certain requirements or assumptions for the data. However, in most cases, people cannot make any assumptions about the distribution of real-world data, and are prone to over-fitting or under-fitting, making them unrepresentative. Machine learning (ML) does not make any assumptions about the data, and the generated results are judged by the cross-validation method, getting rid of the classic statistical process of assuming distribution, model fitting, hypothesis testing, and p-value comparison. It has the advantages of good model prediction effect and cross-validation results that are easily understood by practical workers. Currently commonly used “black box” models, such as one-hot coding mlp and random forest (RF), have been widely used to build medical risk prediction models and determine potential determinants (15, 16).

Therefore, to provide new insights into the potential influencing factors of eGFR, this study utilized data from the 2013–2016 NHANES, fitted a ML interpretability model, to explore the relationship between phenolic exposure and eGFR.

In our research, we examined the use of a transparent ML technique in predicting eGFR based on environmental phenols’ exposure between 2013 and 2016, using data from the US NHANES. We evaluated five ML models and found that the RF model was the most effective, and therefore selected it for eGFR prediction. The RF model performed exceptionally well with an R score of 0.998, indicating high efficiency and stability for the regression model, and it showed a lower error rate of 0.68. We also employed the SHAP game theory approach to highlight each feature’s importance in the model, and the decision plot confirmed the RF model’s accuracy and robustness. In summary, our study suggests that the RF model, along with environmental phenols’ exposure data, has significant potential in predicting eGFR.

The NHANES, a cornerstone in US public health research, provides comprehensive and representative health and nutritional status data. It is imperative to consider both its methodological strengths and limitations. The survey’s comprehensive data collection strategy, combining interviews with physical examinations, offers a robust and detailed assessment of the health and nutritional status of the US population (29). This approach not only enhances the depth of the data but also contributes to its reliability and validity. Additionally, NHANES employs a sophisticated, multi-tiered probability sampling methodology, resulting in a sample that accurately reflects the broader US civilian non-institutionalized population. This approach enhances the applicability of its discoveries. Moreover, the survey’s structure supports longitudinal research, granting valuable perspectives into the ever-changing landscape of health and nutrition (30).

However, its effectiveness could be improved by high costs, potential biases, and time-consuming data collection and analysis processes (31). Integrating ML methodologies offers a transformative opportunity to address these challenges and enhance the utility of NHANES. ML algorithms’ ability to efficiently analyze large datasets can reveal intricate patterns and correlations, offering more profound insights into public health trends and facilitating more accurate predictive modeling. This enhancement is pivotal for refining public health strategies and ensuring timely responses to health crises. Simultaneously, the application of ML can mitigate NHANES’s logistical and financial constraints by automating data processing tasks, thus improving cost-effectiveness (32). ML’s proficiency in handling missing data also bolsters the dataset’s robustness, enhancing the survey’s reliability. Furthermore, ML can identify and correct sampling biases, ensuring broader applicability and representativeness of the findings (33).

Our methodology involved training and evaluating ML models using this extensive dataset, focusing particularly on assessing individual exposure to environmental phenols through urinary analysis. To enhance the robustness of our models against potential biases arising from temporal changes in phenols exposure levels, we excluded the average exposure data of study participants from the training dataset. This approach ensures that the models are not influenced by the downward trend in exposure following the legislative ban and increased public awareness (34, 35). Consequently, our models provide a more stable and reliable analysis of bisphenol exposure’s impact on health, unaffected by external temporal factors.

We employed five ML methods to predict eGFR based on environmental phenols exposure, which have been previously shown to be effective in predicting various diseases in other contemporary ML studies (36–38). Then, we conducted a comprehensive assessment of the predictive capabilities of the ML models, by utilizing test datasets to evaluate each model’s discrimination abilities. Our findings revealed that the R score of the RF model was 0.998, indicating good stability for our model for all range exposure level of environmental phenols. Other models, however, are less impressive. KNN suffers from high computational cost in large datasets as it requires computing and comparing distances for all data points during prediction (39). SVM perform poorly and are computationally intensive with large datasets or datasets with a high number of features, due to their reliance on solving quadratic programming problems (40). DT are prone to overfitting, especially with complex datasets, as they tend to learn too much from the training data, including its noise and outliers (41). AdaBoost can be sensitive to noisy data and outliers, as it tends to focus excessively on hard-to-classify instances, which can decrease its overall performance (42). At last, the RF model exhibited the highest classification robustness. Specifically, the discrimination characteristics provided a comprehensive indication of the ML models’ performance. We recognized the challenge of accurately comprehending the ML methodology and visually presenting the identity results in a practical way. We made the decision to incorporate SHAP values in combination with the RF model to achieve the most efficient means of assessing identity impact and improving interpretability. A negative SHAP value indicates that the feature’s associated values resulted in a lower eGFR value, while a positive SHAP value suggests a higher value. The SHAP by tree-regressor explainer is a helpful tool that assists individuals in visualizing the model’s regression process (43).

The results obtained by applying SHAP values are consistent with the results of earlier studies. A study using the Korean National Environmental Health Survey found that BPA and eGFR showed a significant negative correlation (44). A joint effect model based on quantile g calculations showed that quartile increases in EDC mixtures corresponded to decreases in eGFR, with BPA identified as the major contributor to this effect (45). However, some studies have reported a positive correlation between BPA and eGFR, which is contrary to the observations of this study (46). This may be because a decrease in eGFR is accompanied by a decrease in the excretion of chemicals in the urine, thus producing conflicting results (47, 48). For BPF, its impact on eGFR has not yet been found at the population level, but a result based on metabolomics and lipidomics shows: BPF exposure will disrupt the metabolome and lipid profile of the liver and kidneys, causing renal tissue membrane homeostasis and cell dysfunction by disrupting biosynthesis and glycolysis metabolism in liver and kidney tissues, thereby causing renal function damage (49). In addition, results from a rat experiment showed that genetic background modifies the effect of BPF exposure on kidney weight (50). When linear regression analysis was applied, we observed a positive correlation between BPS and TCS and eGFR, an observation that suggests these chemicals may have a protective effect. A study that also used NHANES data showed that the excretion of triclosan in urine decreased with the decline of renal function (48), which was similar to the observations of this study. In fact, TCS is a common broad-spectrum antibacterial agent that inhibits the growth of bacteria, fungi and some viruses by inhibiting bacterial fatty acid synthesis (51). This may be one of the reasons for its protective effect on the kidneys. Previous studies have found that exposure to BPS affects the oxidative stress, cell viability, apoptosis levels and catalase (CAT) activity of mouse kidney cells, thereby causing kidney damage (13, 52). However, this study focuses on the general population. The exposure level of BPS in urine is low, which may produce a toxic hormesis effect on the body (53). Therefore, the potential effects of BPS and TCS on renal function still require further study.

It’s crucial to recognize that our validation approach was limited by a cross-sectional design, preventing us from establishing a causal link between environmental exposure and eGFR decline. However, the potential of future studies utilizing longitudinal data and prospective designs to confirm the temporal relationship observed in this study is truly intriguing. Further validation of our findings could be robustly achieved through a prospective cohort study, incorporating repeated measurements of environmental phenol exposure and eGFR over an extended period. This comprehensive approach would provide a more robust assessment of the temporal relationship between exposure and kidney function decline. While our analysis diligently controlled for several potential confounders, it’s crucial to underscore the potential impact of unmeasured factors. For instance, dietary habits, a known contributor to both environmental phenol exposure and kidney health, were not fully captured in our study (54). The inclusion of detailed dietary assessments in future research could be instrumental in clarifying the independent role of environmental phenols, thereby enriching our understanding of their effects on kidney health. An alternative explanation could involve genetic factors influencing both individual susceptibility to phenol toxicity and predisposition to kidney disease. Future studies investigating gene–environment interactions, particularly those involving genes implicated in phenol metabolism and organ function, such as 16S rRNA, could provide valuable insights (55).

Since the United States Food and Drug Administration announced legislation to ban BPA in 2012, public health has attached great importance to the impact of BPA and its substitutes on the population. Initially, concerns grew over BPA’s estrogen-mimicking properties, which have been linked to a variety of health problems, including endocrine disruption and developmental issues. Therefore, BPS and BPF have become alternatives to BPA (56). However, recent scientific scrutiny reveals that BPS and BPF share a striking chemical similarity to BPA, casting doubt on their safety (57). Detailly, like BPA, both BPS and BPF exhibit estrogenic activity, potentially leading to similar adverse health effects (58). However, with the deepening of research, the complex nonlinear relationship between independent variables and outcomes has posed great challenges to the application of traditional linear statistical methods, and the impact of synergistic effects between independent variables is still a controversial issue. In this study, we use a SHAP game theory approach to highlight the importance of each selected feature in the model, using a combination of traditional statistics and machine learning. A better fitting effect was achieved, and the potential impact of environmental phenol exposure on eGFR was also explored. In the coming years, the introduction of machine learning algorithms in medicine will provide professionals with more comprehensive insights, allowing them to make more informed decisions.

In the research conducted, the RF algorithm demonstrated effectiveness, precision, and resilience when exploring the links between phenols exposure and estimated eGFR in participants of the United States NHANES from 2013 to 2016. The findings indicate that BPA, BPF, and BPS are inversely associated with eGFR.

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author/s.

The studies involving humans were approved by Research Ethics Review Board (ERB) of the United States National Center for Healthcare Statistics (NCHS) authorized the 2013–2016 NHANES. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

LL: Conceptualization, Funding acquisition, Investigation, Writing – original draft. HZ: Conceptualization, Writing – original draft. XW: Formal analysis, Visualization, Writing – original draft. FW: Formal analysis, Visualization, Writing – original draft. GZ: Formal analysis, Visualization, Writing – original draft. JY: Formal analysis, Visualization, Writing – original draft. HS: Formal analysis, Visualization, Writing – original draft. RH: Funding acquisition, Project administration, Writing – review & editing.