Abstract
Purpose:
Cataract, a major health concern among the elderly, can be influenced by environmental exposures. This study examines the association between per- and polyfluoroalkyl substance (PFAS) exposure and cataract prevalence.
Methods:
Six serum PFAS concentrations were detected among 2119 U.S. adults aged 40 years or older based on the National Health and Nutrition Examination Survey. Multivariable models, including weighted logistic regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression, were used to assess the association between individual and overall PFAS exposure and cataract prevalence. A mediation analysis was conducted for inflammation biomarkers.
Results:
Single exposure to perfluorohexane sulfonic acid (PFHS) and perfluorononanoic acid (PFNA) was found to be markedly associated with cataract prevalence after adjustment for covariates (PFHS: odds ratio [OR], 1.579; 95% CI, 1.003–2.514; PFNA: OR, 1.629; 95% CI, 1.065–2.506). The WQS index was significantly associated with cataract (OR, 1.441; 95% CI, 1.130–1.837). PFNA and PFHS were the most influential exposures in the PFAS mixture. In the Bayesian kernel machine regression model, PFNA and PFHS exhibited the highest group posterior inclusion probability, aligning with the WQS results. Moreover, serum albumin and neutrophil counts were found to mediate the relationship between PFHS and cataract, accounting for −26.20% and −5.95% of the mediation effect, respectively.
Conclusions:
Exposure to PFAS was positively associated with cataract, primarily driven by PFHS and PFNA. Mediation of serum albumin and neutrophil count was observed.
Translational Relevance:
This study links PFAS exposure to cataract prevalence, suggesting reducing exposure could help in cataract prevention.
Statistical analyses were conducted in accordance with NHANES guidelines, taking into account the complex survey design. Continuous variables with normal distribution were presented as mean ± standard deviation and analyzed using t tests. Categorical variables were presented as absolute values (n) or percentages (%) and analyzed using the χ2 test. Given the skewed distribution of PFAS levels, they were log-transformed, and the PFAS levels were grouped into quartiles. Then, to determine the correlations among the six different PFAS contents, Pearson correlation tests were used.
We used weighted logistic regression models to analyze the odds ratios (ORs) and 95% confidence intervals (CIs) for binary cataract outcomes based on each PFAS exposure. Logistic regression was chosen for its ability to evaluate individual PFAS exposures while accounting for key confounders, offering insights into their independent effects. The models were adjusted for the main confounding factors. To enhance model fit and minimize the potential influence of outliers, serum PFAS concentrations were log-transformed.
25 All analyses have been adjusted for various factors, including age, gender, race and ethnicity, educational attainment, smoking status, alcohol use, dyslipidemia, diabetes, hypertension, and cardiovascular disease. Additionally, the model evaluated the linear trend by considering the median value of each quartile as a continuous variable.
The study used WQS regression to investigate the collective impact of PFAS on cataract, as this method is particularly effective for analyzing environmental mixtures with highly correlated exposures.
26 An R package (“gWQS”) can be used to calculate the WQS index empirically. This index is composed of weighted sums of individual PFAS concentrations and ranges from 0 to 1. The WQS index represents the overall level of mixed exposure to PFAS, and non-negligible weights identify the components of concern. The dataset was randomly divided into two sets, with 40% allocated for training and the remaining 60% for validation.
Next, we used a Bayesian variable selection framework to examine the prevalence of cataracts with PFAS. This method accounts for nonlinear relationships and interactions among exposures, offering a comprehensive assessment of PFAS mixtures. Within this model, we analyzed the effects of exposure levels by comparing specific quartiles with their corresponding medians. The contribution of each PFAS to the prevalence of cataracts was assessed by estimating the posterior inclusion probability (PIP), with a significance threshold set at 0.5. Furthermore, both univariate and bivariate exposure–response functions were used to evaluate the individual impact and interaction of PFAS, taking into account the other PFAS compounds at the 25th, 50th, and 75th percentiles simultaneously. The estimation of the BKMR model was obtained by running 25,000 iterations using the “bkmr” R package.
Causal mediation analysis was carried out using the R package (“mediation”) to assess whether the association between PFAS exposure and cataract prevalence operates through intermediate variables. This analysis aimed to identify possible biological pathways underlying the observed association. Restricted cubic splines were used to investigate the nonlinear correlation between PFAS and cataract, with knots at the 5th, 35th, 65th, and 95th centiles.
All analyses were conducted using R software version 4.3.2 and SAS version 9.4 (SAS Institute, Inc., Cary, NC). Statistical significance was defined as a two-sided P value of less than 0.05.
Logistic Regression to Analyze the Association of Single PFAS With Cataract Prevalence
To our understanding, this cross-sectional study is the first to investigate the effects of serum PFAS on cataract prevalence in a large, nationally representative sample using multiple statistical approaches. A weighted logistic regression analysis was conducted using individual environmental pollutants to explore the detrimental health impacts. The research findings revealed a positive correlation between PFHS and PFNA and the presence of cataract. Given that humans are exposed to multiple environmental exposures simultaneously, further exploration of the effects of the mixture of chemicals on health effects is essential. However, traditional regression models are prone to exhibit biases, particularly when dealing with multicollinearity or in scenarios involving chemicals that are highly correlated.
27 Thus, to estimate the impact of mixtures of PFAS on the prevalence of cataract, we used WQS and BKMR models.
The WQS analysis showed significant positive associations between the PFAS mixture and cataract prevalence, with PFNA and PFHS being the most influential. In comparison with WQS, BKMR analysis allows for the identification of the exposure–response relationship between a specific PFAS and cataract prevalence within a mixture while controlling for the levels of other PFAS. During the BKMR analysis, a positive trend was observed in PFNA and PFHS for cataract prevalence. The univariate estimation results are consistent with those of the WQS analysis; PFAS demonstrating positive associations were among the highest weighted compounds in the latter. This coherence reinforces the robustness of our findings in identifying key PFAS contributing to adverse health outcomes. Notably, PFNA and PFHS emerge as primary drivers of the combined effect associated with cataract prevalence, as elucidated by the three models identified in this study. Additionally, BKMR analysis enabled us to assess the overall influence of PFAS on outcomes at specific concentration levels. Results indicated a consistent upward trend in outcomes associated with the PFAS mixture.
When considering health-related issues, the environment encompasses all factors influencing disease causation or outcomes that are nongenetic.
28 Notably, the eye is exposed to the air directly and is susceptible to environmental pollutants. Research has demonstrated associations between cataracts and various environmental pollutants, including air pollutants,
29 heavy metals,
30,31 and exposure to cooking fuels.
7,32 These findings underscore the potential harm these elements can pose to ocular health. Heavy metals and PFAS are both common environmental pollutants. A previous NHANES analysis found that cumulative cadmium exposure may increase the risk of cataract surgery.
23 However, no research has been conducted to investigate the potential association between PFAS and eye disease. PFAS, a pervasive and harmful substance found in daily life and manufacturing processes, can be exposed through inhalation, dietary intake, and contact with household items.
33 These substances have been linked to several chronic conditions, including various forms of cancer and increased mortality rates, highlighting their potential for widespread harm.
34–36 Concurrently, the prevalence of cataract has grown as a major public health issue, drawing attention to the need for a comprehensive understanding of its risk factors. This study addresses a notable gap in the existing literature by investigating the potential link between PFAS exposure and cataract development. The aim is to provide crucial insights into how this pervasive environmental contaminant may contribute to one of the major causes of global visual impairment.
Population research has demonstrated that exposure to PFAS can trigger oxidative stress and inflammation,
18,19 which may contribute to cataract formation. Previous research suggests that PFAS can cause oxidative stress by increasing levels of reactive oxygen species,
37–39 which undergo rapid oxidative reactions and damage surrounding tissues.
40 Furthermore, in addition to disrupting the tricarboxylic acid cycle in mitochondria, PFHS was also found to cause dysregulation of the ubiquinone biosynthesis pathway in zebrafish at 120 hpf.
41 Similarly, PFNA exposure can also induce reactive oxygen species generation, cause mitochondrial hyperpolarization, and impair mitochondrial membrane integrity.
42 The evidence discussed in this article could elucidate the potential mechanisms underlying the effects of two PFAS on cataract formation. This is supported by the fact that oxidative stress has been linked to the etiopathogenesis of age-related cataract, given the increased production of reactive oxygen species and free radicals within the lens.
19,40
In the mediation analysis, we discovered that the impact of PFHS on cataract prevalence may be modulated through serum albumin and neutrophil count, suggesting protective roles for both. Prior research indicated that PFHS exposure correlates with increased serum albumin levels and decreased neutrophil count.
18 Albumin, a primary transporter of various PFAS in plasma,
43 exhibits enhanced binding affinity for longer chain lengths and also plays a vital role in maintaining lens health and protecting against cataracts.
44,45 In contrast, PFAS are immunotoxic and suppress crucial neutrophil functions.
46,47 Considering the association between elevated neutrophil count, increased neutrophil–lymphocyte ratios, and the development of cataract, we found that the effects of PFHS on cataract prevalence could be regulated negatively by neutrophil count, indicating the protection effects of neutrophil count.
48,49
Several limitations should be considered. First, the cross-sectional design limits causal inference. Longitudinal studies that track PFAS exposure at multiple time points are needed to assess chronic PFAS exposure over time and its impact on cataract development. Second, the incidence of cataract may be underestimated when using cataract surgery as a proxy, given the potential years of delay between the onset of cataract and surgical intervention, as well as the fact that not all individuals with cataract undergo surgery. However, we believe that cataract surgery remains a reasonable indicator for assessing the prevalence of clinically significant cataract: those cases that are severe enough to require and have undergone surgical intervention. This subset of patients often represents the more burdensome and medically relevant part of the cataract spectrum. Future studies should use direct assessment of lens opacity through ophthalmic examinations as the gold standard for diagnosing cataract. Third, the study used a single PFAS measurement and analyzed only six compounds, which may not fully capture long-term exposure or the complexity of PFAS mixtures. To enhance the generalizability of the findings, it would be beneficial to conduct studies with repeated measurements at multiple time points and to analyze a broader range of PFAS compounds. Finally, residual confounding from unmeasured variables may persist despite controlling for various factors, and exploring additional mediators may further elucidate the underlying mechanisms. Despite these limitations, our findings provide valuable insights into potential mechanisms, and future research could address these issues to provide more robust evidence.
Our study offers an initial exploration into the relationship between PFAS exposure and cataract prevalence, employing a detailed statistical analysis of a large, nationally representative sample. We found that PFHS and PFNA exposure are associated positively with cataract, with these results being consistent across different analytical methods. This research contributes to a deeper insight into the potential health impacts of PFAS, particularly on ocular health. By highlighting the need for further studies to confirm these findings and explore mechanisms, our study aims to provide epidemiological evidence that can help to guide future research on the association between PFAS exposure and cataract prevalence, ultimately contributing to the development of a theoretical basis for cataract prevention.
The corresponding author thanks all the co-workers for collecting, managing, and maintaining the data used in this analysis. We also appreciate funders for providing the financial support to conduct the present study.
Partially supported by the Zhejiang Provincial Natural Science Foundation of China (grant LTGY23H120001), National Natural Science Foundation of China (grant 81900820), and Foundation of Wenzhou City Science & Technology Bureau (grant Y20210990).
Author Contributions: Yuti Liu, Jiazhen Yao, An-Peng Pan, and Xu Xu: designed the study and had primary responsibility for the final content; An-Peng Pan, and Xu Xu: Funding acquisition; Yuti Liu, and Jiazhen Yao: acquired the data; Yuti Liu, and Jiazhen Yao: analyzed and interpreted the data; Yuti Liu, and Jiazhen Yao: conducted the statistical analysis; Yuti Liu, Jiazhen Yao, Mingxue Ren, Lingxia Ye, An-Peng Pan, and Xu Xu: wrote the paper; and all authors: read and approved the final manuscript.
Ethics Approval and Consent to Participate: Institutional Review Board approval was not required as the NHANES represents an adequately de-identified and publicly available dataset.
Disclosure: Y. Liu, None; J. Yao, None; M. Ren, None; L. Ye, None; A.-P. Pan, None; X. Xu, None