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. 2025 May 7;20(5):e0311711. doi: 10.1371/journal.pone.0311711

Arsenic exposure is associated with elevated sweat chloride concentration and airflow obstruction among adults in Bangladesh: A cross-sectional study

Mi-Sun S Lee 1,#, Crystal M North 1,2,#, Irada Choudhuri 3, Subrata K Biswas 4, Abby F Fleisch 5,6, Afifah Farooque 7, Diane Bao 7, Sakila Afroz 8, Sadia Mow 8, Nazmul Husain 8, Fuadul Islam 8, Md Golam Mostofa 8, Partha Pratim Biswas 9, David S Ludwig 10, Subba R Digumarthy 11, Christopher Hug 12, Quazi Quamruzzaman 8, David C Christiani 1,2, Maitreyi Mazumdar 1,7,*
Editor: Aaron Specht13
PMCID: PMC12057939  PMID: 40333927

Abstract

Arsenic is associated with lung disease and experimental models suggest that arsenic-induced degradation of the chloride channel CFTR (cystic fibrosis transmembrane conductance regulator) is a mechanism of arsenic toxicity. We examined associations between arsenic exposure, sweat chloride concentration (measure of CFTR function), and pulmonary function among 269 adults in Bangladesh. Participants with sweat chloride ≥ 60 mmol/L had higher arsenic exposures than those with sweat chloride < 60 mmol/L (water: median 77.5 µg/L versus 34.0 µg/L, p = 0.025; toenails: median 4.8 µg/g versus 3.7 µg/g, p = 0.024). In linear regression models, a one-unit µg/g increment in toenail arsenic was associated with a 0.59 mmol/L higher sweat chloride concentration, p < 0.001. Among the entire study population, after adjusting for covariates including age, sex, smoking, education, and height, toenail arsenic concentration was associated with increased odds of airway obstruction (OR: 1.97, 95%: 1.06, 3.67, p = 0.03); however, sweat chloride concentration did not mediate this association. Our findings suggest that sweat chloride concentration may serve as novel biomarker for arsenic exposure, warranting further investigation in diverse populations, and that arsenic likely acts on the lung through mechanisms other than inducing CFTR dysfunction. Alternative mechanisms by which environmental arsenic exposure may lead to obstructive lung disease, such as arsenic-induced direct lung injury and/or increase lung proteinase activity, require additional exploration in future work.

Introduction

Up to 220 million people in as many as 70 countries are exposed to elevated arsenic levels through contaminated groundwater [1,2]. Arsenic exposure has been associated with lung dysfunction [3–8], respiratory symptoms [9–11], bronchiectasis [12], and increased tuberculosis-related [13] and respiratory-related mortality [14]. One proposed mechanism for arsenic’s effects on the lung is impairment of mucociliary clearance.

The cystic fibrosis transmembrane conductance regulator (CFTR) is a cyclic adenosine monophosphate-regulated chloride channel in the apical membrane of airway epithelial cells. CFTR plays an essential role in mucociliary clearance by establishing an osmotic gradient across the airway epithelium that promotes fluid secretion [15–17]. Mutations in CFTR, the gene that encodes CFTR, cause cystic fibrosis (CF), a rare autosomal recessive disease that affects the lungs and digestive system. Cystic fibrosis is also associated with a diminished immune response to some gram-negative bacteria; a similarly diminished immune response is induced by arsenic exposure [18,19].

Recent studies of CFTR in killifish, an environmental model organism, demonstrate that arsenic induces CFTR ubiquitinylation and degradation [20,21]. Subsequent studies translate the killifish model into humans and use human airway epithelial cell culture to demonstrate that arsenic induces an increase in multiubiquitinlyated CFTR, which results in CFTR degradation and reduced CFTR-mediated chloride secretion [22]. Together, these reports demonstrate that arsenic impairs CFTR function and support the hypothesis that there is a shared mechanism driving both cystic fibrosis and arsenic-induced lung disease. Our study aims to further translate these findings to humans and test the hypothesis that arsenic induces CFTR degradation in humans who are exposed to arsenic through the environment.

In humans, CFTR function is directly measured by a sweat test, which is the gold standard diagnostic test for cystic fibrosis [23]. CFTR mediates chloride resorption from sweat; in the absence or impaired function of CFTR, sweat chloride concentration is elevated. In a recent study of 100 adults in Bangladesh—a country with among the highest arsenic exposure in the world—water and toenail arsenic concentrations were higher among adults with increased sweat conductivity (a marker for elevated sweat chloride concentrations), and those with the highest sweat conductivity did not have CFTR mutations consistent with a genetic diagnosis of cystic fibrosis [24]. This finding in humans supports the hypothesis that arsenic induces CFTR degradation, as was seen in experimental models, and that CFTR degradation may be a mechanism of arsenic toxicity.

Although sweat conductivity is correlated with sweat chloride concentration, the latter remains the gold standard for identifying CFTR dysfunction [25]. Therefore, to evaluate the relationship between arsenic exposure, CFTR dysfunction, and lung function, we measured arsenic levels, sweat chloride concentrations, and lung function among adults in Bangladesh. Our study aimed to test the specific hypothesis that higher arsenic exposures lead to degradation of the CFTR protein in cell membranes; evidence of CFTR degradation is elevated sweat chloride. Furthermore, we hypothesized that CFTR dysfunction mediates the relationship between arsenic exposure and lung dysfunction.

Methods

Ethics statement

All protocols were reviewed and approved by the institutional review boards (IRBs) of Boston Children’s Hospital (BCH) (Protocol number: IRB-P00024501), the Dhaka Community Hospital Trust (DCHT), and the Bangabandhu Sheikh Mujib Medical University (BSMMU) (Protocol number: BSMMU/2019/3739). BSMMU received de-identified samples only and did not receive participant information. The Harvard T.H. Chan School of Public Health (HSPH) relied on BCH IRB review. All participants provided written informed consent.

Inclusivity in global research

Additional information regarding the ethical, cultural, and scientific considerations specific to inclusivity in global research is included in the Supporting Information (S1 Checklist).

Study participants

From 13/05/2018 through 15/11/2022, we enrolled 300 adults at Dhaka Community Hospital. These participants were recruited from a total of 1800 adults who had participated in studies of arsenic and skin lesions between 2001 and 2003 in Pabna, Bangladesh [26]. We paused recruitment and study visits for one week between 20/12/2020 and 27/12/2020 while renewal of IRB applications was under review. No participants were enrolled from BSMMU. BSMMU laboratories were used to evaluate de-identified samples. We began by creating a list of potential participants using random digit assignment and contacted each potential participant in order on the list until we had reached our target enrollment. All participants from the 2001–2003 study were initially considered eligible for recruitment. Exclusion criteria included evidence of lung cancer or active pulmonary tuberculosis on chest radiography.

Arsenic exposure measurements

We collected drinking water samples from each participant’s home. Water arsenic concentrations were measured in the Environmental Engineering Laboratory at the Bangladesh University of Engineering and Technology using graphite furnace atomic absorption spectrometry (GF-AAS) [27]. The limit of detection (LOD) for arsenic was 1 μg/L. Drinking water arsenic concentrations from the 2001–2003 study were measured using inductively coupled plasma mass spectrometry (ICP-MS) by the Environmental Laboratory Services (North Syracuse, NY, USA) [26].

We collected toenail clippings from all ten toes on a white sheet of paper, placed them in a small coin envelope (ULINE Model No. S-7798) and stored them at room temperature. The LOD for arsenic was 0.002 μg/g. The Dartmouth Trace Element core facility measured arsenic in toenails using ICP-MS [28]. Arsenic concentrations in toenail clippings from the 2001–2003 study were measured at the Harvard T.H. Chan School of Public Health using similar ICP-MS protocols [29].

Sweat chloride measurements

We performed sweat collection and sweat chloride testing according to established guidelines [30]. Briefly, we obtained sweat from the ventral surface of both forearms of each participant. After cleaning the skin with distilled water, we stimulated sweat secretion by iontophoresis (total applied current 1.5 mA, 50 µ A/cm2) for five minutes using 0.5% pilocarpine gel disks (Pilogels®) and sweat was collected for 30 minutes in a Macroduct® Sweat Collection System (Wescor Biomedical Systems, Logan, UT). The laboratory measured sweat chloride concentrations within 48 hours at the Department of Biochemistry, Bangabandhu Sheikh Mujib Medical University (BSMMU) using a Dimension® RxL Max® Clinical Chemistry Analyzer (Siemens Healthcare Diagnostics Inc., Newark, DE) with QuikLYTE® Integrated Multisensor Technology (IMT). The LOD and coefficient of variation (CV) were 10 mmol/L and 1.1%, respectively. Quality control measures included not analyzing samples with sweat volumes less than 15 µ L and including sweat controls with standardized chloride concentrations in each analytical batch.

DNA isolation and CFTR sequence analysis

For participants with sweat chloride concentrations ≥ 60 mmol/L, we retrieved archived DNA samples that were obtained in the 2001–2003 study. DNA had been extracted from whole blood samples using the Puregene DNA Isolation kit (Gentra Systems, Minneapolis, MN) and then stored at -80°C. We sent retrieved DNA samples to Ambry Genetics® (Ambry Test: CF; Aliso Viejo, CA) for CFTR sequence analysis and deletion/duplication analysis [31].

Pulmonary function testing (PFT) and questionnaires

We measured lung function using EasyOne® Air handheld spirometers (Medical Technologies, Andover, MA). All participants had abstained from smoking for at least 45 minutes prior to testing and completed testing in the seated position after loosening any tight-fitting clothing. Spirometry was conducted in accordance with American Thoracic Society (ATS) guidelines by trained study staff, and participants were given up to seven tries to achieve three ATS acceptable and reproducible maneuvers [32]. Measured parameters included forced expiratory volume in one second (FEV1) and forced vital capacity (FVC), and all participants repeated pulmonary function tests (PFTs) after receiving four puffs of salbutamol (Beximco Pharmaceuticals, Bangladesh) and waiting for 10 minutes. Study staff monitored data quality control per ATS guidelines by assessing the accuracy of spirometer measurements with volume and multi-flow calibration using a 3-litre syringe (Medical Technologies, Andover, MA) each day before study procedures began to ensure that measured flow and volumes remained within ± 3.5%, per ATS guidelines [32]. We stopped performing PFTs in March 2020, midway through participant enrollment, for the safety of participants and study staff because of the COVID-19 pandemic.

Two study investigators manually reviewed all PFTs for ATS acceptability and reproducibility standards using ATS guidelines for spirometry standardization and for interpretation of lung function tests [32,33]. We included PFTs in the analytic cohort if there were at least two acceptable maneuvers and both the FEV1 and FVC from the two best trials were within 200mL of one another. Per the Global Initiative for Chronic Obstructive Lung Disease (GOLD), we defined airflow obstruction as a post-bronchodilator FEV1/FVC < 0.7 [34].

We used structured questionnaires to collect information on age, education, occupation, smoking history, and medications. We characterized respiratory symptoms using the American Thoracic Society’s Division of Lung Disease questionnaire (ATS-DLD 1978) [35]. We measured height and weight at the study visit.

Chest radiographs

All participants underwent posteroanterior and lateral chest radiographs performed by a trained radiology technician at Dhaka Community Hospital (DCH). All radiographs were evaluated by a DCH staff radiologist who was unaware of arsenic exposure, sweat chloride, or PFT results. The staff radiologist evaluated the radiographs for the presence of parenchymal, bony, or cardiac abnormalities. For participants who were diagnosed with airflow obstruction, a senior radiologist provided additional imaging review using a structured data collection form.

Statistical analysis

We first examined all variables using descriptive statistics and assessed potential differences in characteristics between groups using t-tests for continuous variables and Chi-square tests for categorical variables. We computed Spearman correlation coefficients (r) between water and toenail arsenic concentrations. For samples below the LOD, we estimated arsenic concentrations as LOD/2. We used the average of the toenail arsenic concentrations from the 2001–2003 and 2018–2022 visits to estimate long-term arsenic exposure. Our primary exposure of interest was toenail arsenic concentration. We modeled toenail arsenic using untransformed values and interquartile ranges (IQR).

We used multivariable linear regression to characterize the relationship between arsenic and sweat chloride. We evaluated for potential confounding effects by age, sex, smoking status (current, former, and never), and education (able to write, primary education, middle school or above) by placing each variable in the models individually and assessing for change in the regression coefficient for the arsenic measure.

We also used logistic regression to estimate the relationship between toenail arsenic concentration and the odds of having a sweat chloride concentration ≥ 60 mmol/L. We chose this threshold to be consistent with diagnostic criteria for cystic fibrosis. We considered toenail arsenic concentration our primary measure of arsenic exposure. In sensitivity analyses, we tested the above-described relationships using lower sweat chloride thresholds (≥ 50 mmol/L, ≥ 40 mmol/L, and ≥ 30 mmol/L), as these lower thresholds are sometimes used to identify those who need additional evaluation for cystic fibrosis [25].

We then characterized the relationships between toenail arsenic concentration and lung function using multivariable linear regression (FEV1, FVC, FEV1/FVC) or logistic regression (FEV1/FVC < 0.7), adjusting for age, sex, smoking status, education, and height (in centimeters). Lastly, we employed mediation analyses to estimate the degree to which sweat chloride mediates the relationship between long-term arsenic exposure and lung function, adjusting for age, sex, smoking status, education, and height. Results are presented as regression coefficients (β with 95% confidence interval [95% CI]) for continuous outcomes and odds ratio (OR with 95% CI) for dichotomous outcomes per IQR increment in toenail arsenic concentrations. In sensitivity analyses, we restricted analyses to never smokers to rule out the potential masking effects of smoking on the relationship between arsenic and lung function.

We performed statistical analyses using SAS (version 9.4; SAS Institute, Inc.), R (version 4.2.2; R Development Core Team), and RStudio (version 2022.12.0 + 353) software.

Results

Study population

Of the 300 enrolled participants, 31 participants were excluded due to missing toenail clippings (n = 10) or insufficient sweat volume (n = 21), for a total of 269 (90%) participants. Participant characteristics are shown in Table 1. Of the 300 enrolled participants 166 (55.3%) completed PFTs that met ATS acceptability and reproducibility criteria prior to the COVID19-related shutdown. The 166 participants who underwent PFTs were more frequently women (53% vs 33.6% p < 0.001) and generally less educated (p = 0.02) than the 134 participants for whom PFTs were not performed; otherwise there were no significant differences between these groups (S1 Table).

Table 1. Characteristics of study population.

Variables All Participants (n = 269) Participants with PFTs (n = 166)
All
(n = 269)		 Men
(n = 147)		 Women
(n = 122)		 All
(n = 166)		 Men
(n = 78)		 Women
(n = 88)
Age (y) 51.4 ± 10.4 50.8 ± 11.6 52.2 ± 8.9 51.3 ± 9.9 50.9 ± 11.0 51.6 ± 8.8
BMI (kg/m2) 22.8 ± 3.7 22.6 ± 3.6 23.1 ± 3.8 22.7 ± 3.8 22.0 ± 3.6 23.2 ± 3.9
Education
Able to write 105 (39.0) 39 (26.5) 66 (54.1) 71 (42.8) 22 (28.2) 49 (55.7)
Primary education 68 (25.3) 37 (25.2) 31 (25.4) 45 (27.1) 27 (34.6) 18 (20.5)
Middle school or above 96 (35.7) 71 (48.3) 25 (20.5) 50 (30.1) 29 (37.2) 21 (23.9)
Occupation
Agriculture 65 (24.2) 65 (44.2) – 31 (18.7) 31 (39.7) –
Business 46 (17.1) 46 (31.3) – 28 (16.9) 28 (35.9) –
Service 27 (10.0) 25 (17.0) 2 (1.6) 11 (6.6) 10 (12.8) 1 (1.1)
Laborer 11 (4.1) 9 (6.1) 2 (1.6) 10 (6.0) 8 (10.3) 2 (2.3)
Retired 2 (0.7) 2 (1.4) – 1 (0.6) 1 (1.3) –
Housewife 118 (43.9) – 118 (96.7) 85 (51.2) – 85 (96.6)
Smoking status
Current smoker 49 (18.2) 49 (33.3) – 31 (18.7) 31 (39.7) –
Former 32 (11.9) 32 (21.8) – 20 (12.1) 20 (25.6) –
Never 188 (69.9) 66 (44.9) 122 (100.0) 115 (69.3) 27 (34.6) 88 (100.0)
Respiratory symptoms
Cough 66 (24.5) 40 (27.2) 26 (21.3) 47 (28.3) 23 (29.5) 24 (27.3)
Shortness of breath 37 (13.8) 20 (13.6) 17 (13.9) 27 (16.3) 14 (17.9) 13 (14.8)
Any cough or shortness of breath 77 (28.6) 45 (30.6) 32 (26.2) 54 (32.5) 26 (33.3) 28 (31.8)
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All results are presented as n(%), or mean ± standard deviation (SD).

BMI, body mass index.

Arsenic concentrations

The distributions of drinking water and toenail arsenic concentrations are shown in Table 2. Overall, drinking water and toenail arsenic concentrations in 2018–2022 were lower than those observed in 2001–2003 (water median 41.0 µg/L in 2018–2021 compared with 71.2 µg/L in 2001–2003), reflecting the impact of arsenic awareness and reduction activities. We observed no significant sex differences in drinking water or toenail arsenic concentrations. All toenail arsenic concentrations were above the LOD of 0.002 µg/g. Comprehensive visual inspection of toenail arsenic concentrations using histograms and kernel density plots, along with residual distribution analysis (data not shown) suggested that the data appeared approximately normally distributed. Toenail arsenic concentrations were moderately correlated with drinking water arsenic concentrations (r = 0.66, p < 0.0001).

Table 2. Distribution of arsenic concentrations in drinking water and toenails.

Arsenic concentration Mean ± SD Min 25th percentile Median 75th percentile Max IQR
All subjects (n = 269)
Drinking water, μg/L
2001-2003 234.3 ± 300.4 <LOD 16.8 71.2 410.5 1190.0 393.7
2018-2021 138.5 ± 196.3 <LOD 9.5 41.0 202.0 1239.0 192.5
Toenails, μg/g
2001-2003 6.9 ± 6.7 0.3 2.3 4.8 9.4 45.9 7.1
2018-2021 5.8 ± 5.8 0.2 1.4 4.1 8.3 31.6 6.9
Long-term† 6.3 ± 5.4 0.3 2.2 4.7 8.8 29.1 6.6
Subjects with PFTs (n = 166)
Drinking water, μg/L
2001-2003 293.3 ± 328.7 <LOD 33.6 99.0 484.5 1190.0 450.9
2018-2021 166.9 ± 206.8 <LOD 24.0 82.5 247.0 1239.0 223.0
Toenails, μg/g
2001-2003 8.2 ± 6.8 0.3 3.7 6.9 10.0 45.9 6.3
2018-2021 7.0 ± 6.3 0.3 2.3 5.4 9.8 31.6 7.5
Long-term† 7.6 ± 5.3 0.4 3.7 6.9 10.4 29.1 6.7
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†

Average of toenail arsenic concentrations in 2001–2003 and 2018–2021.

IQR, interquartile range; SD, standard deviation.

LOD, limit of detection.

Sweat chloride concentrations

Mean ± standard deviation (SD) sweat chloride concentration was 49.1 ± 16.8 (median: 48.0 mmol/L, range: 18.0 to 98.0 mmol/L; IQR: 24.0 mmol/L). A total of 69 (25.7%) participants had sweat chloride concentration ≥ 60 mmol/L. Sweat chloride concentrations were higher in men than women (51.6 ± 16.8 vs. 46.1 ± 16.4, p = 0.007) and higher among smokers vs. never smokers (52.6 ± 16.5 vs. 47.6 ± 16.8). There were no significant associations between sweat chloride concentration, participant’s age and education (S2 Table).

CFTR sequence analysis

Of the 69 participants with sweat chloride ≥ 60 mmol/L, 45 (65.2%) underwent CFTR sequence analysis. None of the DNA samples tested revealed homozygous pathogenic mutations in CFTR. Six participants had heterozygous findings consistent with classification as cystic fibrosis carriers (S3 Table). These findings suggest that the elevated sweat chloride concentrations we observed were not caused by biallelic CFTR mutations that lead to absent or abnormal CFTR protein production.

Pulmonary function tests, respiratory symptoms, and chest imaging

No participant was found to have lung cancer, active pulmonary tuberculosis, cardiac or bony abnormalities on chest films. Of the 166 who underwent PFTs, mean ± SD was 1.97 ± 0.54 L for FEV1, 2.57 ± 0.67 for FVC, and 0.77 ± 0.09 for FEV1/FVC (Table 3). Airflow obstruction was present among 19.9% (n = 33) of the study cohort. Most of the men with airflow obstruction (n = 23, 85%) had a history of smoking, while none of the women with airflow obstruction had a history of smoking (p < 0.001). A total of 55 participants (33.1%) endorsed respiratory symptoms, which included wheezing, shortness of breath or coughing. Respiratory symptoms were more prevalent among those with airflow obstruction; 57.6% of people with airflow obstruction reported respiratory symptoms compared to 27.1% of people without airflow obstruction (data not shown). Among the 33 participants with airflow obstruction identified on PFTs, additional review of chest imaging showed evidence of bronchiectasis in 5 individuals.

Table 3. Pulmonary function tests after bronchodilator treatment (n = 166).

Measures All Men Women
FEV1, L 1.97 ± 0.54 2.27 ± 0.58 1.70 ± 0.33
FVC, L 2.57 ± 0.67 3.05 ± 0.61 2.14 ± 0.35
FEV1/FVC 0.77 ± 0.09 0.74 ± 0.11 0.80 ± 0.06
Airflow obstruction† 33 (19.9) 27 (34.6) 6 (6.8)
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n (%) or Mean ± SD.

†

Any participant whose FEV1/FVC < 0.7 was considered an individual with airflow obstruction.

SD, standard deviation

Arsenic and sweat chloride

To evaluate sweat chloride’s potential use as a biomarker of arsenic exposure, we present the most parsimonious models including modeling arsenic on a natural scale for ease of interpretation. Our decisions to use untransformed arsenic values and parametric models are supported by a comprehensive visual inspection using kernel density plots, along with residual distribution analysis (data not shown). These analyses suggested that our data appeared normally distributed and the adequacy of our study’s sample size [36,37]. In our primary analyses, we found that concurrent drinking water and toenail arsenic concentrations were higher among participants with sweat chloride ≥ 60 mmol/L compared to < 60 mmol/L (water: median 77.5 µg/L versus 34.0 µg/L, p = 0.025; toenails: median 4.8 µg/g versus 3.7 µg/g, p = 0.024) (Fig 1). A similar pattern was seen for long-term arsenic concentration (S1 Fig). In models adjusted for sex and smoking status, a one-unit µg/g increment in toenail arsenic concentration was associated with a 0.59 mmol/L higher sweat chloride concentration, p < 0.001, and a one-unit µg/g increment in toenail arsenic concentration was associated with greater odds of having a sweat chloride concentration ≥ 60 mmol/L (OR: 1.06, 95% CI, 1.01, 1.11). We found similar results at lower thresholds of sweat chloride. Models examining varying sweat chloride cutoffs are shown in S2–3 Figs. In models fully adjusted for covariates, an IQR (6.6 µg/g) increment in long-term exposure to toenail arsenic was associated with a 3.2 mmol/L (95% CI: 0.57, 5.85, p = 0.017) higher sweat chloride concentration (S4 Fig).

Fig 1. Arsenic concentrations in drinking water and toenails by sweat chloride concentration.

Fig 1

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Distribution of concurrent drinking water and toenail arsenic concentrations among participants with normal/intermediate (<60 mmol/L) and abnormal (≥60 mmol/L) sweat chloride concentrations.

Arsenic and pulmonary function tests

In adjusted models, an IQR increment in toenail arsenic concentration at each timepoint was associated with nonsignificant reductions in FEV1, FVC, and FEV1/FVC (Table 4). An IQR increment in toenail arsenic concentration from 2001–2003 was associated with greater odds of airflow obstruction (toenail arsenic 2001–2003 OR: 1.57, 95% CI: 1.03, 2.40, p = 0.03; long-term toenail arsenic OR: 1.97, 95%: 1.06, 3.67, p = 0.03). When restricting the analytic cohort to the 115 never smokers, relationships between arsenic exposure and the odds of airflow obstruction were even more pronounced (toenail arsenic 2001–2003 OR: 1.97, 95% CI 1.13, 3.45, p = 0.02; toenail arsenic 2018–2021 OR: 2.40, 95% CI 1.02, 5.65, p = 0.05; long-term toenail arsenic OR: 3.97, 95% CI 1.44, 10.90, p = 0.008; S4 Table).

Table 4. Toenail arsenic (μg/g) and measures of lung function. FEV1, FVC, and FEV1/FVC are modeled using multivariable linear regression. Airflow obstruction is modeled using logistic regression. β represents change per IQR toenail arsenic concentration.

Toenail arsenic FEV1 FVC FEV1/FVC Airflow obstruction
β (95% CI) † P-value β (95% CI) † P-value β (95% CI) † P-value OR (95% CI) † P-value
2001-2003 –0.05 (–0.11, 0.02) 0.15 –0.03 (–0.10, 0.03) 0.30 –0.01 (–0.02, 0.002) 0.11 1.57 (1.03, 2.40) 0.04
2018-2021 –0.05 (–0.13, 0.03) 0.23 –0.03 (–0.11, 0.06) 0.54 –0.01 (–0.03, 0.005) 0.17 1.41 (0.82, 2.45) 0.22
Long-term‡ –0.08 (–0.16, 0.01) 0.09 –0.05 (–0.14, 0.04) 0.29 –0.02 (–0.03, 0.001) 0.06 1.97 (1.06, 3.67) 0.03
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†

Models adjusted for age, sex, smoking status (current, former or never), education (able to write, primary education, middle school or above), and height.

‡

Average of toenail arsenic concentrations in 2001–2003 and 2018–2021.

Mediation analysis

Mediation analyses indicate that there is no evidence of sweat chloride as a mediator of the relationship between toenail arsenic concentrations and airflow obstruction (S4 Fig).

Discussion

Our study demonstrates that 1) elevated sweat chloride concentration, the diagnostic hallmark of cystic fibrosis, is found among individuals exposed to arsenic through drinking water, 2) individuals with sweat chloride concentrations ≥ 60 mmol/L did not have a genetic diagnosis of cystic fibrosis, 3) long-term arsenic exposure as estimated by toenail arsenic concentration is associated with airflow obstruction, particularly among those who have never smoked, and 4) sweat chloride concentration did not mediate the association between arsenic and airflow obstruction. These findings suggest that high sweat chloride may be a novel biomarker of arsenic exposure, but that CFTR dysfunction is unlikely to be the primary mechanism of arsenic-associated lung disease.

We used toenail arsenic concentrations to estimate internal arsenic dose. Toenail arsenic is considered good biomarker for long-term exposure when external exposures are consistent [38]. In secondary analyses, despite the modest correlation between toenail and drinking water arsenic, we found similar patterns between drinking water arsenic and sweat chloride concentration. We were unable to assess how differences in the efficiency of arsenic metabolism between individuals, as might be demonstrated by relative distribution of urine arsenic species, affects associations between arsenic and sweat chloride concentration.

Our studies translate studies from experimental models [20,22] into humans and suggest that one mechanism of arsenic toxicity might include inducing the degradation of CFTR. In these models, CFTR dysfunction is associated with decreased of surface liquid volume, similar to what is seen in the genetic disease cystic fibrosis, where the absence or dysfunction of CFTR in airway epithelium leads to thickened secretions and reduced mucociliary transport, resulting in mucus retention and plugging of airways, favoring persistent bacterial infections and inflammation [39].

Our findings showing associations between arsenic and airway obstruction are consistent with previous studies that describe a relationship between arsenic exposure and lung dysfunction [3,5–8]. Cross-sectional and cohort studies among childhood and adult populations in China, Mexico, and the United States consistently demonstrate relationships between arsenic exposure and restrictive patterns on lung function testing that are suggestive of restrictive lung disease [40–43]. Indeed, in a meta-analysis of nine studies totaling 4,699 participants in Bangladesh, Chile, India, Mexico, and Pakistan, higher arsenic exposure as measured by urine or drinking water concentrations was associated with reduced FEV1 and FVC, but not FEV1/FVC [44]. In contrast, we found a relationship between arsenic and airflow obstruction (reduced FEV1/FVC), and statistically insignificant reductions in FEV1 and FVC. Relying upon spirometry to diagnose restrictive lung disease must be done with caution: total lung capacity (TLC) is the diagnostic gold standard to diagnose restrictive lung disease, and although FVC and TLC are related lung measurements, FVC-based algorithms unreliably identify restrictive lung disease [45]. Importantly, the odds of airflow obstruction among those with higher arsenic exposure were more pronounced among participants in our study who had never smoked, which suggests that non-smoking populations are particularly vulnerable to the lung toxicity of long-term arsenic exposure.

The magnitude of observed association between toenail arsenic and airflow obstruction is considerable when compared with the estimated effect of other covariates. For example, the estimated effect of toenail arsenic on airflow obstruction [β = 0.677 (OR 1.97, 95% CI: 1.057, 3.670) per IQR] is comparable to, and even greater than, the estimated effect of a 10-year increase in age [β = 0.604 (OR 1.83, 95% CI: 1.05, 3.18) per 10 years] in regression model adjusted for covariates (S5 Table), suggesting that arsenic exposure has a significant impact on airflow obstruction.

We did not find evidence that sweat chloride mediates the relationship between arsenic exposure and airflow obstruction, which suggests that there are mechanisms other than CFTR dysfunction that may explain arsenic’s associations with lung health. For example, arsenic has been shown to induce direct lung tissue injury in tracheal epithelial cells and alveolar macrophages through the generation of reactive oxygen species and stress proteins [46,47]. Arsenic exposure is also associated with decreased serum CC16 levels [48], which is a lung protective protein secreted by alveolar epithelial cells that modulates inflammatory lung injury. Lastly, arsenic exposure is associated with increased matrix metalloproteinase-9, one of a class of proteins that regulate airway inflammation and have been implicated in the pathogenesis of obstructive lung disease [49–51].

Our study has multiple implications for screening of arsenic-related disease in countries such as Bangladesh. Sweat tests may be an effective, inexpensive, point-of-care screening test for arsenic exposure. As we have demonstrated here and previously [24], it is possible to perform sweat tests in rural areas without expensive laboratory equipment, although the costs of commercially available instruments and consumables remain high for widespread implementation in low and middle income countries (LMICs). Another implication of our finding is that diagnoses of cystic fibrosis made only by sweat tests (i.e., without genetic testing, as is done in LMICs including Bangladesh [52]) may be false positives – that is, individuals’ elevated sweat chloride concentrations may be a result of high arsenic exposure and not genetic disease. In countries with high arsenic exposure, when cystic fibrosis is suspected because of a constellation of symptoms such as lung disease, fat malabsorption, and diabetes, evaluating for arsenic exposure might complement sweat tests, and genetic confirmation of cystic fibrosis may be warranted.

In a similar manner, better understanding of the mechanisms of arsenic toxicity may benefit individuals with genetically-determined cystic fibrosis. Not all the individuals in our study who were exposed to high concentrations of arsenic had elevated sweat chloride concentrations or lung dysfunction, suggesting that other factors may be associated with preserved epithelial chloride transport. These factors might reveal protective mechanisms that could be exploited to develop therapies for genetically determined cystic fibrosis. The recent work in killifish and cell culture described above [20–22] suggests such mechanisms might include suppressed cellular response of ubiquitinylation.

The strengths of our study include our direct measurements of individual-level long-term arsenic exposure using toenail clippings, sweat chloride concentrations, lung function, and – among those with elevated sweat chloride concentrations – genotyping to exclude pathogenic CFTR variants. None of the samples tested revealed a genetic diagnosis of cystic fibrosis, that is, no participant had homozygous or compound heterozygous mutations in CFTR in variants known to be associated with a cystic fibrosis phenotype. Our study also has important limitations. First, as with all cross-sectional studies, because sweat chloride concentrations were measured at the same time that water and toenail samples were collected, we cannot confirm that arsenic exposure preceded their elevated sweat chloride concentrations. However, the prevalence of CFTR mutations in South Asian populations is low [53]. This, in combination with the bench literature supporting CFTR dysfunction as a plausible mechanism through which arsenic exposure causes high sweat chloride concentrations, we believe our observations likely represent a true association with arsenic exposure. Future longitudinal studies are needed to establish a causal relationship between arsenic exposure, sweat chloride concentrations, and lung function. We also obtained PFTs in only a subset of the total study population because the COVID-19 pandemic interrupted PFT testing. However, there were no substantive differences in the participants who completed PFTs as compared to those who did not (S1 Table), so we do not anticipate that this biased our results.

We were only able to perform CFTR sequencing on 45 of the 69 participants with sweat chloride ≥ 60 mmol/L because of laboratory closures during the COVID-19 pandemic. Given the low rates of pathogenetic CFTR mutations in Bangladeshi populations and our sequencing results from the large subset, we do not anticipate that we missed ascertaining genetic diagnoses of cystic fibrosis among the individuals who did not undergo sequencing. We were not able to evaluate the role of heterozygous CFTR mutations on sweat chloride concentrations or arsenic-variant interactions, and this is an additional limitation of our study. Finally, assessment of CFTR function on the skin may not be wholly representative of activity in other tissues or cell types.

Air pollution is an important contributor to lung disease in Bangladesh [54,55]. Unfortunately, we do not have direct or surrogate measures of air quality in our study, so we are unable to adjust these in our analyses, and this is another important limitation of our study. Arsenic is also present in fine particulate matter (PM2.5), much of it in potentially bioavailable forms [56,57], thus air pollution may also be an important source of arsenic exposure for some populations. Studies from China, for example, found high concentrations of arsenic in ambient PM2.5 samples [56–58], and studies in rats using ambient PM2.5 samples from Tangshan, China suggest that arsenic accumulates in blood and lung tissue following repeated respiratory exposure [58]. In Bangladesh, however, reports of arsenic concentrations in PM2.5 are more than 10 times lower than those found in China [59], suggesting that the high levels of arsenic in contaminated drinking water is the main source of arsenic exposure for our study population. Future work will be needed to characterize the role of air pollution in the relationships between arsenic, sweat chloride, and lung function. Additionally, although we were able to adjust for sex, education, and height, there also may be residual confounding from unmeasured confounders such as nutritional status and occupation.

We were unable to characterize other pulmonary features of cystic fibrosis such as bronchiectasis, a finding that would be most evident on computed tomography (CT) imaging as compared to plain films, or non-pulmonary features of cystic fibrosis such as pancreatic dysfunction. Future work directly measuring CFTR function in tissues including lung epithelium, neutrophils, and pancreatic islet cells may be provide more insight into how arsenic exposure may affect CFTR function and potentially cause disease. Finally, our study population is from a specific region in Bangladesh with exposures to high arsenic concentrations, which may limit the generalizability of our findings to other countries. However, the arsenic measurements at the most recent assessment were within the range of those seen globally, which suggests that even moderately elevated arsenic concentrations may affect CFTR function.

Conclusions

Environmental arsenic exposure is associated with impaired CFTR function, as demonstrated by elevated sweat chloride concentrations, and airway obstruction. Potential applications of our study include future work aimed at development of a point-of-care sweat chloride test as a biomarker of arsenic exposure.

Supporting information

S1 Table. Comparison of study population characteristics among those included in the PFT analysis compared to those for whom PFTs were not available.

(DOCX)

pone.0311711.s001.docx (17.2KB, docx)
S2 Table. Relationship between sweat chloride and age, sex, education and smoking status.

(DOCX)

pone.0311711.s002.docx (17.2KB, docx)
S3 Table. CFTR sequencing results.

(DOCX)

pone.0311711.s003.docx (20.9KB, docx)
S4 Table. Adjusted effect estimates (β and 95% CIs) † from multivariable regression models for lung function and odds ratios (ORs and 95% CIs) † from logistic regression models for airflow obstruction associated with an IQR increase in toenail arsenic among never smokers (n = 115).

(DOCX)

pone.0311711.s004.docx (16.8KB, docx)
S5 Table. Association between airflow obstruction and age, sex, height, and smoking status.

(DOCX)

pone.0311711.s005.docx (17.4KB, docx)
S1 Fig. Distribution of long-term drinking water and toenail arsenic concentrations among participants with normal/intermediate (<60 mmol/L) and abnormal (≥60 mmol/L) sweat chloride concentration.

(DOCX)

pone.0311711.s006.docx (28.7KB, docx)
S2 Fig. Odds ratios (OR) and 95% confidence intervals (CI) for abnormal sweat chloride at different cutoff levels associated with concurrent toenail arsenic concentration.

All models are adjusted for sex and smoking status.

(DOCX)

pone.0311711.s007.docx (31.1KB, docx)
S3 Fig. Odds ratios (OR) and 95% confidence intervals (CI) for abnormal sweat chloride at different cutoff levels associated with long-term toenail arsenic concentration.

All models are adjusted for sex and smoking status.

(DOCX)

pone.0311711.s008.docx (30.7KB, docx)
S4 Fig. Mediation analysis of the long-term arsenic-lung function by sweat chloride.

(A) Mediation for FEV1; (B) Mediation for FVC; (C) Mediation for FEV1/FVC; (D) Mediation for airway obstruction. Models were adjusted for age, sex, height, smoking status, and education.

(DOCX)

pone.0311711.s009.docx (315.1KB, docx)

Data Availability

We have uploaded a minimal anonymized dataset to the Harvard Dataverse. The dataset can cam be found at the following location: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OLY5P2

Funding Statement

We received funding from the National Institute of Environmental Health Sciences (www.niehs.nih.gov) ((NIEHS-R01-ES027825) M.M., (NIEHS-R01-ES011622) D.C.C., (NIEHS-P30-ES000002) M.M.) and from the National Heart, Lung, and Blood Institute (www.nhlbi.nih.gov) (NHLBI-K23-HL154863) C.M.N. The contents are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health.

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PONE-D-24-41161Arsenic exposure is associated with elevated sweat chloride concentration and airflow obstruction among adults in Bangladesh: a cross sectional studyPLOS ONE

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Partly

Reviewer #2: No

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: N/A

Reviewer #3: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Major Comment:

This study investigates the relationship between arsenic exposure, sweat chloride levels (a marker of cystic fibrosis transmembrane conductance regulator [CFTR] function), and pulmonary function among adults in Bangladesh. The study presents intriguing findings suggesting that elevated sweat chloride may be a novel biomarker for arsenic exposure while raising questions about the role of CFTR dysfunction in arsenic-induced lung disease. The study is generally well-conducted, with a clear focus, appropriate methodology, and comprehensive data analysis. However, there are areas where the manuscript could be improved in terms of clarity, precision, and discussion of the limitations and implications of the findings.

Advantage:

1) The study addresses a significant public health issue: arsenic exposure and its potential impact on lung health in a highly exposed population. However, PM2.5 and other co-morbidities with air pollutants should be adjusted and discussed as confounding factors.

2) The study design, including the recruitment of participants, collection of biological samples, and measurement of variables, is appropriate for addressing the research question. The multiple exposures should reveal the relationship and interaction between lung function and CFTR in the design of further studies.

3) The statistical analysis is comprehensive and appropriate for the data collected. The authors have explored the relationships between the variables of interest using various statistical methods, including linear regression, logistic regression, and mediation analysis. The internal and external doses of arsenic should be discussed comprehensively, and distribution should fit the assumption of normal distribution. Thus, the un-transformation and transformation of indicators should be listed in tables, and the non-parametric methods should even be chosen.

4) The study provides novel evidence suggesting that elevated sweat chloride may be a potential biomarker for arsenic exposure. This finding has important implications for the screening and diagnosing arsenic-related health issues. The concordance of arsenic exposure between sweat chloride and blood or urine arsenic metabolites, such as DMA and MMA levels, should be discussed.

5) The manuscript is generally well-written and organized, making it easy to follow the study's rationale, methods, results, and conclusions; however, the correlation between sweat chloride and arsenic exposure should also identify the time-lapse changes.

6) The study population is drawn from a specific region in Bangladesh with high arsenic exposure. The authors need to explicitly acknowledge that the findings may need to be more generalizable to other populations with different levels of arsenic exposure or genetic backgrounds.

7) The study's cross-sectional nature limits the ability to draw definitive conclusions about causality. The authors must emphasize this limitation and suggest future longitudinal studies to establish a temporal relationship between arsenic exposure, sweat chloride levels, and lung function.

8) The manuscript needs more detailed information on the types of lung function tests conducted and the criteria used to define airflow obstruction. More specific information about the lung function assessment would strengthen the study's validity.

9) The study adjusts for several potential confounders. Still, other unmeasured factors, such as nutritional status, socioeconomic status, and exposure to different environmental pollutants, may influence the relationship between arsenic and lung function. The authors should address this limitation.

10) While the study did not find evidence of CFTR mutations in participants with elevated sweat chloride, it does not fully explore other potential mechanisms of CFTR dysfunction that may be induced by arsenic exposure. Expanding the discussion on this aspect would enhance the study's contribution to understanding arsenic toxicity.

Minor Comments:

1) Introduction: The introduction provides a good overview of the problem and the rationale for the study. However, it could be strengthened by briefly mentioning the specific aims and hypotheses of the study.

2) Methods: The methods section is generally well-described. However, providing more details on the quality control measures used for the sweat chloride and lung function testing would be helpful.

3) Results: The results are presented clearly and concisely. However, more information on the distribution of sweat chloride levels in the study population, such as the interquartile range or the 95% confidence interval, would be helpful.

4) Discussion: The discussion reasonably interprets the findings and their implications. However, it could be improved by discussing the study's limitations more explicitly and suggesting future research directions.

This study provides impressive insights into the relationship between arsenic exposure, sweat chloride levels, and lung function. The findings have important implications for public health, particularly in regions with high arsenic contamination. By addressing the negative comments and incorporating specific suggestions, the authors can significantly enhance their manuscript's clarity, precision, and impact.

Reviewer #2: 

- Please provide the IRB approval information of this study.

- In Figure 1, I may assume the comparison did not consider the impact of other factors on levels of sweat chloride. If this is true, then how to convince the readers the difference was mainly driven by arsenic exposure?

- Please verify the linearity assumption for association between As exposure and levels of sweat chloride.

- In mediation analysis, please specify what covariates were adjusted for mediator and outcome models. Usually the confounding factors in these two models should be different.

-

Reviewer #3: The manuscript titled "Arsenic exposure is associated with elevated sweat chloride concentration and airflow obstruction among adults in Bangladesh: a cross-sectional study" investigates the link between arsenic exposure, CFTR dysfunction (as measured by sweat chloride levels), and pulmonary function in Bangladeshi adults. The authors hypothesize that arsenic exposure impairs CFTR function, reflected in elevated sweat chloride concentrations, and that this dysfunction may mediate arsenic’s impact on lung function. Through comprehensive measurements of arsenic levels in toenails and water, sweat chloride testing, pulmonary function testing, and CFTR genotyping, the study aims to establish sweat chloride as a biomarker for arsenic exposure and explore the mechanisms underlying arsenic-associated lung disease. This study is significant due to its focus on an underexplored potential biomarker (sweat chloride) for arsenic exposure and its implications for screening arsenic-related diseases in low-resource settings. However, the manuscript should only be considered after proper adjustments and corrections have been made including clarity in methodology, coherence in data interpretation, and justification of certain scientific claims. Kindly see attachement for all comments.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Attachment

Submitted filename: response - arsenic exposure and sweat chloride.pdf

pone.0311711.s010.pdf (459.8KB, pdf)
PLoS One. 2025 May 7;20(5):e0311711. doi: 10.1371/journal.pone.0311711.r003

Author response to Decision Letter 1

3 Feb 2025

29 January 2025

Dr. Aaron Specht

Academic Editor

PLOS One

RE: PONE-D_24-41161 REVISION “Arsenic exposure is associated with elevated sweat chloride concentration and airflow obstruction among adults in Bangladesh: a cross-sectional study.”

Dear Dr. Specht,

I am writing to submit a revised version of our manuscript titled, “Arsenic exposure is associated with elevated sweat chloride concentration and airflow obstruction among adults in Bangladesh: a cross-sectional study.” We greatly appreciate the constructive feedback provided by the reviewers and the opportunity to revise our work considering their comments.

In response to the reviewers’ suggestions, we have made several changes to improve manuscript. Below is a point-by-point reponse to the issues raised in the review.

Journal requirements:

1. Please ensure that your manuscript meets PLOSOne’s style requirements, including those for file naming.

Response: We have reformatted our manuscript to be consistent with PLOSOne’s style requirements.

2. Please include a complete copy of PLOSOne’s questionnaire on inclusivity in global research.

Response: We have included a complete copy of PLOSOne’s questionnaire on inclusivity in global research and have uploaded it as Supporting Information.

3. Data availability on request

Response: We have uploaded a minimal anonymized dataset to the Harvard Dataverse. The dataset can cam be found at the following location: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OLY5P2

4. Please remove any funding-related text from the manuscript and let us know how you would like to update your funding statement.

Response: We have removed funding information from the manuscript. Our funding statement should read as follows: “We received funding from the National Institute of Environmental Health Sciences (niehs.nih.gov) - R01 ES027825 (Mazumdar), R01 ES011622 (Christiani), P30 ES000002 (Mazumdar) and the National Heart, Lung, and Blood Institute (nhlbi.nih.gov) K23 HL154863(North). The contents are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health.“

5. Please include a caption for Figure 1.

Response: We have added a caption for Figure 1, p. 17, line 438

6. Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables (should remain/ be uploaded) as separate "supporting information" files.

Response: We have included the tables in the manuscript and the supplementary tables remain in a separate file.

7. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly.

Response: We have updated the captions and in-text citations for supporting information as directed.

Reviewer #1:

1) The study addresses a significant public health issue: arsenic exposure and its potential impact on lung health in a highly exposed population. However, PM2.5 and other co-morbidities with air pollutants should be adjusted and discussed as confounding factors.

Response: We expand our discussion of the this limitation. Below is a key paragraph from this discussion that addresses the concerns of the reviewer.

p. 24, line 472. Air pollution is an important contributor to lung disease in Bangladesh[1, 2]. We did not measure air quality, thus are unable to adjust for PM2.5 exposure and other components of air pollution. However, we do not have reason to suspect that those exposed to higher as compared to lower arsenic levels would have differential air pollution exposure, and would therefore expect that not adjusting for air pollution exposure would bias our results towards the null, further emphasizing the importance of our findings. That we found associations between arsenic and airflow exposure despite the absence of air pollution data suggests that the relationship between arsenic and lung function might be stronger than what is observed in our study. In fact, the presence of airflow obstruction among the largely non-smoking women in our cohort further underscores the importance of air pollution as a cause of obstructive lung disease in the region. Additionally, although we were able to adjust for sex, education, and height, there also may be residual confounding from unmeasured confounders such as nutritional status and occupation.

2) The study design, including the recruitment of participants, collection of biological samples, and measurement of variables, is appropriate for addressing the research question. The multiple exposures should reveal the relationship and interaction between lung function and CFTR in the design of further studies.

Response: We have not made any changes in response to this comment.

3) The statistical analysis is comprehensive and appropriate for the data collected. The authors have explored the relationships between the variables of interest using various statistical methods, including linear regression, logistic regression, and mediation analysis. The internal and external doses of arsenic should be discussed comprehensively, and distribution should fit the assumption of normal distribution. Thus, the un-transformation and transformation of indicators should be listed in tables, and the non-parametric methods should even be chosen.

Response: Thank you for the insightful comments. In many observational studies, biomarker data often do not follow a normal distribution. However, linear regression still can be used even when the data are not perfectly normal. Non-parametric methods typically have lower power to detect true effects and often require larger sample sizes (Li et al. 2012). They generally provide less information about the data as they typically test for differences in ranks rather than actual values, potentially limiting the depth of information about the data. In our study, we examined a comprehensive visual inspection using histograms and kernel density plots, along with residual distribution analysis (see Figure below). These analyses suggested that the data appeared approximately normal, with no major outliers or pattern that might affect the model’s validity. Using untransformed values in regression analysis allow the coefficients to represent the effect of a one-unit change in the predictor on the outcome, which can make the results easier to interpret. According to the Central Limit Theorem (CLT) with the adequacy of sample size (Li et al. 2012), the estimated regression coefficients remain reliable. In addition, a simulation study even with heavily skewed data, the cofficients, study power, and confidence interval were fairly reliable at least with a sample size of 100 or larger (Knief and Forstmeier 2021). Based on these considerations, we chose to use a parametric approach in our analysis.

References (added to the manuscript): Li X, Wong W, Lamoureux EL, Wong TY. Are linear regression techniques appropriate for analysis when the dependent (outcome) variable is not normally distributed? Invest Ophthalmol Vis Sci. 2012;53(6):3082-3. doi: 10.1167/iovs.12-9967. PMID: 22618757.

Knief U, Forstmeier W. Violating the normality assumption may be the lesser of two evils. Behav Res Methods. 2021;53(6):2576-2590. doi: 10.3758/s13428-021-01587-5. PMID: 33963496.

Figure: (Please see attached cover letter) Histogram and kernel density of sweat chloride concentration superimposed. The blue solid line illustrates the normal distribution; the red solid line illustrates the kernel distribution; the blue dashed line presents median of sweat chloride (48 mmol/L). (right) Residual distribution

We have made the following additions to our manuscript to clarify these points.

1). We add comments and citations about the use of toenails to estimate internal doses of arsenic as below:

p. 19, line 371. We used toenail arsenic concentrations to estimate internal arsenic dose. Toenail arsenic is considered good biomarker for long-term exposure when external exposures are consistent[3]. In secondary analyses, despite the modest correlation between toenail and drinking water arsenic, we found similar patterns between drinking water arsenic and sweat chloride concentration. We were unable to assess how differences in the efficiency of arsenic metabolism between individuals, as demonstrated by relative distribution of urine arsenic species, affects associations between arsenic and sweat chloride concentration.

2) We summarize our tests of normality and implications for choice of modeling as below:

p.16, line 310. Our decisions to use untransformed arsenic values and parametric models are supported by a comprehensive visual inspection using histograms and kernel density plots, along with residual distribution analysis (data not shown), suggested that the data appeared approximately normally distributed and the adequacy of our study’s sample size [36, 37].

4) The study provides novel evidence suggesting that elevated sweat chloride may be a potential biomarker for arsenic exposure. This finding has important implications for the screening and diagnosing arsenic-related health issues. The concordance of arsenic exposure between sweat chloride and blood or urine arsenic metabolites, such as DMA and MMA levels, should be discussed.

Response: We do not have measures of arsenic metabolites and have added this to the discussion below:

p. 20, line 375. We were unable to assess how differences in the efficiency of arsenic metabolism between individuals, as demonstrated by relative distribution of urine arsenic species, affects associations between arsenic and sweat chloride concentration.

5) The manuscript is generally well-written and organized, making it easy to follow the study's rationale, methods, results, and conclusions; however, the correlation between sweat chloride and arsenic exposure should also identify the time-lapse changes.

Response: We clarify that our primary analysis uses drinking water, toenails and sweat chloride that were measured on the same day.

p.16, line 314. In our primary analyses, we found that concurrent drinking water and toenail arsenic concentrations were higher among participants with sweat chloride ≥ 60 mmol/L compared to < 60 mmol/L

p.23, line 451. First, as with all cross-sectional studies, because sweat chloride concentrations were measured at the same time that water and toenail samples were collected, we cannot confirm that arsenic exposure preceded their elevated sweat chloride concentrations.

Secondary analyses assess measures of chronic arsenic exposure and are now placed in the Supporting material.

6) The study population is drawn from a specific region in Bangladesh with high arsenic exposure. The authors need to explicitly acknowledge that the findings may need to be more generalizable to other populations with different levels of arsenic exposure or genetic backgrounds.

Response: We added a statement to our limitation section acknowledging that our population is drawn from a specific region of Bangladesh with high arsenic exposure and this may limit generalizability.

p. 25, line 491. Finally, our study population is from a specific region in Bangladesh with exposures to high arsenic concentrations, which may limit the generalizability of our findings to other countries. However, the arsenic measurements at the most recent assessment were within the range of those seen globally, which suggests that even moderately elevated arsenic concentrations may affect CFTR function.

7) The study's cross-sectional nature limits the ability to draw definitive conclusions about causality. The authors must emphasize this limitation and suggest future longitudinal studies to establish a temporal relationship between arsenic exposure, sweat chloride levels, and lung function.

Response: We added a text to emphasize the cross-sectional nature of the study and need for longitudinal studies on p. 23, line 451.

First, as with all cross-sectional studies, because sweat chloride concentrations were measured at the same time that water and toenail samples were collected, we cannot confirm that arsenic exposure preceded their elevated sweat chloride concentrations.

8) The manuscript needs more detailed information on the types of lung function tests conducted and the criteria used to define airflow obstruction. More specific information about the lung function assessment would strengthen the study's validity.

Response: We provide more information about lung function tests on page 9, line 166, including the criteria used to define airflow obstruction. We follow (and provide citations for) the American Thoracic Society (ATS) guidelines for spirometry and the Global Initative for Chronic Obstructive Lung Disease (GOLD)’s criteria for airflow obstruction.

We measured lung function using EasyOne® Air handheld spirometers (Medical Technologies, Andover, MA). All participants had abstained from smoking for at least 45 minutes prior to testing and completed testing in the seated position after loosening any tight-fitting clothing. Spirometry was conducted in accordance with American Thoracic Society (ATS) guidelines by trained study staff, and participants were given up to seven tries to achieve three ATS acceptable and reproducible maneuvers[6]. Measured parameters included forced expiratory volume in one second (FEV1) and forced vital capacity (FVC), and all participants repeated PFTs after receiving four puffs of salbutamol (Beximco Pharmaceuticals, Bangladesh) and waiting for 10 minutes. Study staff monitored data quality control per ATS guidelines by assessing the accuracy of spirometer measurements with volume and multi-flow calibration using a 3-litre syringe (ndd Medical Technologies, Andover, MA) each day before study procedures began to ensure that measured flow and volumes remained within ± 3.5%, per ATS guidelines[6].

9) The study adjusts for several potential confounders. Still, other unmeasured factors, such as nutritional status, socioeconomic status, and exposure to different environmental pollutants, may influence the relationship between arsenic and lung function. The authors should address this limitation.

Response: We have added text to our discussion about this limitation.

p. 24, line 483. Although we were able to adjust for sex, education, and height, there also may be residual confounding from unmeasured confounders such as nutritional status and occupation.

10) While the study did not find evidence of CFTR mutations in participants with elevated sweat chloride, it does not fully explore other potential mechanisms of CFTR dysfunction that may be induced by arsenic exposure. Expanding the discussion on this aspect would enhance the study's contribution to understanding arsenic toxicity.

Response: We have made our hypothesis that arsenic degrades the CFTR protein after translation more explicit in our introduction. We do not suggest that arsenic causes mutations in CFTR.

p. 5 line 78. Our study aims to further translate these findings to humans and test the hypothesis that arsenic induces CFTR degradation in humans who are exposed to arsenic through the environment.

p. 5 line 95. Our study aimed to test the specific hypothesis that higher arsenic exposures lead to degradation of the CFTR protein in cell membranes; evidence of CFTR degradation is elevated sweat chloride.

Minor Comments:

1) Introduction: The introduction provides a good overview of the problem and the rationale for the study. However, it could be strengthened by briefly mentioning the specific aims and hypotheses of the study.

Response: We have made our hypothesis that arsenic degrades the CFTR protein more explicit in our introduction.

p. 5 line 78. Our study aims to further translate these findings to humans and test the hypothesis that arsenic induces CFTR degradation in humans who are exposed to arsenic through the environment.

p. 5 line 95. Our study aimed to test the specific hypothesis that higher arsenic exposures lead to degradation of the CFTR protein in cell membranes; evidence of CFTR degradation is elevated sweat chloride.

2) Methods: The methods s

Decision Letter 1

Aaron Specht

17 Mar 2025

<p>Arsenic exposure is associated with elevated sweat chloride concentration and airflow obstruction among adults in Bangladesh: a cross sectional study

PONE-D-24-41161R1

Dear Dr. Mazumdar,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager®  and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Aaron Specht

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors would not discuss the PM2.5 and arsenic exposure interaction, and the comprehensive view was not standing on the solid evidence, so the previous study demonstrated the PM2.5 existing arsenic fraction and represented elevation of risk (https://doi.org/10.1016/j.envpol.2019.113881), which authors should not omitted; thus, the biases should not null and need to discuss the PM2.5 and arsenic exposure interaction, even the cumulative external dose might be played as fallacy for assessment risk for arsenic exposure, which represented why authors need to convince us on scientific view. From a comprehensive point of view, the overall evidence might not stand on solid ground; authors need to discuss this in as much detail as possible. So the previous study demonstrated that the PM2.5 existing arsenic fraction and represented elevation of risk was not a coincidence; thus, the biases should not be null. I encourage these authors to find surrogate markers to adjust PM2.5 levels and re-analyse the risk of external arsenic exposure. Even with no more evidence, they should be humble enough to face the weakness in the present study.

Reviewer #3: The in-text citations should be formatted consistently throughout the manuscript. Currently, there are inconsistencies, such as in line 120 and line 420, and throughout the manuscript, the citations are placed directly after the last word without a space. To ensure clarity and adherence to proper citation formatting, the authors should include a single space between the last word and the citation number in all instances.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

Reviewer #3: No

**********

Acceptance letter

Aaron Specht

PONE-D-24-41161R1

PLOS ONE

Dear Dr. Mazumdar,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Comparison of study population characteristics among those included in the PFT analysis compared to those for whom PFTs were not available.

    (DOCX)

    pone.0311711.s001.docx (17.2KB, docx)
    S2 Table. Relationship between sweat chloride and age, sex, education and smoking status.

    (DOCX)

    pone.0311711.s002.docx (17.2KB, docx)
    S3 Table. CFTR sequencing results.

    (DOCX)

    pone.0311711.s003.docx (20.9KB, docx)
    S4 Table. Adjusted effect estimates (β and 95% CIs) † from multivariable regression models for lung function and odds ratios (ORs and 95% CIs) † from logistic regression models for airflow obstruction associated with an IQR increase in toenail arsenic among never smokers (n = 115).

    (DOCX)

    pone.0311711.s004.docx (16.8KB, docx)
    S5 Table. Association between airflow obstruction and age, sex, height, and smoking status.

    (DOCX)

    pone.0311711.s005.docx (17.4KB, docx)
    S1 Fig. Distribution of long-term drinking water and toenail arsenic concentrations among participants with normal/intermediate (<60 mmol/L) and abnormal (≥60 mmol/L) sweat chloride concentration.

    (DOCX)

    pone.0311711.s006.docx (28.7KB, docx)
    S2 Fig. Odds ratios (OR) and 95% confidence intervals (CI) for abnormal sweat chloride at different cutoff levels associated with concurrent toenail arsenic concentration.

    All models are adjusted for sex and smoking status.

    (DOCX)

    pone.0311711.s007.docx (31.1KB, docx)
    S3 Fig. Odds ratios (OR) and 95% confidence intervals (CI) for abnormal sweat chloride at different cutoff levels associated with long-term toenail arsenic concentration.

    All models are adjusted for sex and smoking status.

    (DOCX)

    pone.0311711.s008.docx (30.7KB, docx)
    S4 Fig. Mediation analysis of the long-term arsenic-lung function by sweat chloride.

    (A) Mediation for FEV1; (B) Mediation for FVC; (C) Mediation for FEV1/FVC; (D) Mediation for airway obstruction. Models were adjusted for age, sex, height, smoking status, and education.

    (DOCX)

    pone.0311711.s009.docx (315.1KB, docx)
    Attachment

    Submitted filename: response - arsenic exposure and sweat chloride.pdf

    pone.0311711.s010.pdf (459.8KB, pdf)

    Data Availability Statement

    We have uploaded a minimal anonymized dataset to the Harvard Dataverse. The dataset can cam be found at the following location: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OLY5P2

    Articles from PLOS One are provided here courtesy of PLOS

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