Association of Per- and Polyfluoroalkyl Substance Exposure With Cataract Prevalence Among U.S. Adults: A NHANES Analysis (2005–2008)

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.

Introduction

Cataract, characterized by the loss of lens transparency owing to opacification, has remained a significant public health concern.¹ In 2020, cataracts were identified as the leading cause of blindness in middle-aged and elderly, affecting more than 15 million individuals and accounting for approximately 45% of global blindness.² Although the development of cataract in most patients is age related, it is a multifactorial process that has been recognized increasingly in recent years, with factors such as unhealthy lifestyle, genetics, age, diet, systemic medical problems, and environmental pollution being implicated.^1,3–6 Industrialization advances and synthetic chemicals have become more prevalent, and mounting evidence suggests that environmental pollution may contribute to the risk of cataract significantly.^7,8 

Per-fluoroalkyl and polyfluoroalkyl substances (PFAS), known for their carbon-fluorine structures,⁹ are a group of synthetic compounds that are gaining increasing attention among various environmental pollutants. Their resistance to water and oil makes them useful in industrial and consumer products such as antifouling agents, adhesives, paints, nonstick cookware, carpets, furniture, and convenience food packaging.¹⁰ Owing to their notable persistence, PFAS are widely found in water, air, and soil.^11,12 These compounds have a relatively long biological half-life, and their long-term accumulation may lead to numerous adverse health outcomes,¹³ such as chronic obstructive pulmonary disease, fatty liver disease, and depressive symptoms.^14–16 

According to recent studies, the development of chronic inflammation and oxidative stress can be associated with PFAS exposure.¹⁷ Moreover, increased exposure levels are associated with higher serum concentrations of biomarkers, suggesting a potential correlation between PFAS exposure and the exacerbation of chronic inflammation and oxidative stress.¹⁸ Coinciding with this, oxidative stress and inflammation are widely acknowledged as contributing factors in developing and progressing various anterior segment eye disorders, including dry eye, keratoconus, uveitis, and cataract.¹⁹ Consequently, there is a possibility that PFAS exposure could be a contributing factor to cataract formation. To date, no studies have been conducted to specifically examine the relationship between the levels of PFAS and the prevalence of cataract. 

This study was designed to use weighted logistic regression, weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) to investigate the influence of individual and overall serum PFAS on cataract among U.S. adults. In addition, potential mechanisms that may be present were explained through mediation analyses. 

Methods

Study Participants

The National Health and Nutrition Examination Survey (NHANES) selects nationally representative samples of noninstitutionalized U.S. adults using stratified, multistage probability sampling methods in 2-year cycles since 1999–2000 (https://www.cdc.gov/nchs/nhanes/index.htm). We included two survey cycles from 2005–2006 to 2007–2008 and excluded individuals younger than 40 years of age (n = 13,416), missing complete information on cataracts (n = 3526), without valid serum PFAS (n = 397), or pregnant women (n = 3). Finally, our study consisted of 2119 participants (Supplementary Figure S1). 

Assessment of PFAS

NHANES used online solid-phase extraction combined with high-performance liquid chromatography–turboionspray ionization–tandem mass spectrometry to measure PFAS levels and randomly select one-third of participants aged 12 years or older as outlined on the NHANES website.¹⁸ This test's lower limit of detection (LLOD) was 0.10 ng/mL. Values below the LLOD were imputed by the LLOD divided by the square root of 2.²⁰ The participants who had PFAS detection levels of less than 75% were not included in this study. The analysis was conducted for perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorohexane sulfonic acid (PFHS), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDE), and 2-(N-ethyl-PFOSA) acetate (MPAH). The total amount of PFAS was calculated by adding the quantities of these six substances. 

Ascertainment of Cataract

During household interviews, information regarding cataract surgery status was obtained by asking the following questions: “Have you ever had cataract surgery?” Because cataract surgery is becoming more common in the United States and has a lower visual threshold, self-reported cataract surgery may indicate clinically significant cataracts.²¹ If the answer was yes, the participant was diagnosed with a cataract. 

Covariates

Based on existing literature and theoretical knowledge, we included the following variables as covariates in our analysis^14,22–24: (1) demographic characteristics: age (40–60 years, ≥61 years), gender (male/female), race and ethnicity (non-Hispanic White, non-Hispanic Black, Mexican American, other), educational attainment (less than high school, high school, more than high school), smoking status (yes/no), alcohol use (yes/no); (2) physical examinations parameters: body mass index (BMI) (<25, 25–<30, ≥30); (3) medical conditions: dyslipidemia (yes/no), diabetes (yes/no), hypertension (yes/no) and cardiovascular disease (yes/no); and (4) laboratory findings: C-reactive protein, serum albumin, neutrophil count, and lymphocyte count, which were used as markers to assess inflammation and oxidative stress, which has been widely used in previous studies.¹⁸ 

Statistical Analyses

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. 

Results

Characteristics of the Population

Among the 2119 adults, 325 were diagnosed with cataract. A summary of the baseline characteristics of the study participants, categorized by the presence or absence of cataracts, can be found in Table 1. Overall, individuals with cataracts generally exhibited advanced age, a higher probability of being non-Hispanic White, and alcohol drinkers; they also tended to have lower BMI, lower levels of educational attainment, and higher prevalence of diabetes, hypertension, and cardiovascular disease (all P < 0.05). 

Table 1.

 

Characteristics of Study Participants in NHANES 2005–2008

 

Distribution of Serum PFAS Concentration

All six analyzed PFAS showed detection rates of greater than 75%. Table 2 displays the distribution of serum PFAS concentrations. PFOS, PFOA, PFHS, and PFNA exhibited detection rates of more than 95%. Compared with the cataract group, the noncataract group exhibited lower exposure levels of PFOS, PFHS, and MPAH at the 25th, 50th, and 75th percentiles for PFAS. All PFAS, except for PFDE, which showed higher concentrations in the cataract group. According to Pearson correlation analysis, there were significant correlations among the six PFAS, with correlation coefficients ranging from 0.15 to 0.76 (Supplementary Fig. S2). 

Table 2.

 

Levels of Exposure to PFAS in People With or Without Cataract

 

Logistic Regression to Analyze the Association of Single PFAS With Cataract Prevalence

The study used multivariate logistic regression to evaluate the association between individual PFAS and cataract prevalence while adjusting for all covariates (Table 3). The prevalence of cataract is significantly higher in the highest quartile (Q4) of PFHS and PFNA compared with the lowest quartile (Q1) (PFHS: OR, 1.579; 95% CI, 1.003–2.514; PFNA: OR, 1.629; 95% CI, 1.065–2.506). No significant associations were found between cataract and other PFAS. 

Table 3.

 

Adjusted ORs for Associations Between Serum PFAS Concentrations and Cataract in NHANES 2005–2008

 

WQS Model to Evaluate the Associations of PFAS and Cataract Prevalence

The study applied the WQS model to investigate the relationship between the combined effects of six PFAS and the prevalence of cataract. The results, as presented in Supplementary Table S3, showed that the PFAS mixtures had a positive correlation with the prevalence of cataract (basic model: OR, 1.399; 95% CI, 1.106–1.770; extended model: OR, 1.441; 95% CI, 1.130–1.837). According to Figure 1, PFNA has the highest weight of 0.41 for cataract prevalence in comparison with PFHS, MPAH, PFDE, PFOA, and PFOS, which have weights of 0.34, 0.18, 0.05, 0.01, and 0.01, respectively. All covariates were adjusted, and the weights were in the positive direction. Moreover, The WQS regression analysis in the negative direction did not demonstrate any statistically significant relationship between the PFAS mixtures and the prevalence of cataracts (basic model: OR, 0.973; 95% CI, 0.800–1.184; extended model: OR, 0.984; 95% CI, 0.798–1.214), as shown in Supplementary Table S3 and Supplementary Figure S4. 

Figure 1.

 

The WQS model weights of serum PFAS on the prevalence of cataract in positive direction. This model was adjusted for age, gender, race and ethnicity, BMI, educational attainment, smoking status, alcohol use, dyslipidemia, diabetes, hypertension, and cardiovascular disease.

BKMR Model to Evaluate the Associations of PFAS and Cataract Prevalence

Figure 2B illustrates the combined impact of PFAS on the prevalence of cataract. Although the overall effect was insignificant, there was an upward trend in the prevalence of cataract with increasing PFAS concentration compared with the 50th percentile in PFAS mixtures. A positive association between cataract prevalence and two specific types of PFAS, PFNA and PFHS, while keeping the remaining PFAS mixtures at their median concentrations (Fig. 2A). Additionally, BKMR has identified PFNA (PIP 0.637) and PFHS (PIP 0.533) as crucial factors that affect cataract, which aligns with the WQS findings (Supplementary Table S4). Furthermore, an investigation of the interactions among the six PFAS (Supplementary Fig. S5) revealed no evidence of interactions, as indicated by the parallel exposure–response relationships observed in the study. Upward trends and positive effects were observed in the PFNA and PFHS levels and cataract prevalence, although the correlations did not reach significant levels (Supplementary Fig. S6). 

Figure 2.

 

Relationship between serum log-transformed PFAS and cataract levels estimated by BKMR in NHANES 2005–2008. (A) Exposure-response function for each serum PFAS, with other PFAS fixed at the median. (B) Combined effect of total PFAS on cataract. Models were adjusted for age, gender, race and ethnicity, BMI, educational attainment, smoking status, alcohol use, dyslipidemia, diabetes, hypertension, and cardiovascular disease.

Other Analyses

Mediation analyses were conducted to ascertain whether inflammation and oxidative stress act as mediators in the association between serum PFAS levels and cataract prevalence (see Supplementary Table S1). Supplementary Tables S5 and S6 demonstrate that elevated PFHS quartiles are associated with reduced neutrophil counts and elevated serum albumin levels, both of which are associated significantly with cataract prevalence. Serum albumin and neutrophil count significantly mediated the associations of PFHS with cataract prevalence, with mediation proportions of −26.20% and −5.95%, respectively (Fig. 3), suggesting their potential roles in the observed associations. By restricted cubic spline analysis, we did not find a nonlinear relationship between PFAS and cataract (Supplementary Table S3). As discovered in Supplementary Table S2, no significant interaction was observed between the PFAS and age, gender, race and ethnicity, BMI, smoking status, and drinking status (all P interaction > 0.05). 

Figure 3.

 

Estimated proportion of the association between PFHS and cataract mediated by serum albumin (A), and neutrophil count (B). The models were adjusted for age, gender, race and ethnicity, BMI, educational attainment, smoking status, alcohol use, dyslipidemia, diabetes, hypertension, and cardiovascular disease. DE, the estimate of the direct effect; IE, the estimate of the indirect effect; TE, the estimate of the total effect. Proportion of mediation = IE/DE + IE. *P < 0.05; ***P value < 0.001.

Discussion

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. 

Conclusions

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. 

Acknowledgments

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. 

Availability of Data and Materials: The datasets used and analyzed during the current study are available at https://www.cdc.gov/nchs/nhanes/index.htm. 

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