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. 2025 Dec 31;4(5):954–964. doi: 10.1021/envhealth.5c00286

Disinfection Byproducts, Oxidative Stress, and Sleep Quality among Healthy Chinese Men

Shiyu Xu †,‡, Guangming Li †, Vicente Mustieles §,, Yu Zhang ∥, Carmen Messerlian ∥,⊥, Audrey J Gaskins #, Chengliang Xiong ∇, Tianqing Meng ∇, Susu Pan †,*, Yi-Xin Wang †,*
PMCID: PMC13185059  PMID: 42164873

Abstract

Toxicological studies show that disinfection byproducts (DBPs) adversely affect neurological and psychiatric symptoms, which have been linked to poor sleep quality. This cohort study explored the associations between DBP exposure, oxidative stress, and sleep quality assessed by the Pittsburgh Sleep Quality Index (PSQI) questionnaire among young healthy Chinese men who provided 710 blood and 2647 repeated urinary samples over 3 months. We measured trihalomethanes in blood and haloacetic acids and oxidative stress markers in urine samples. In adjusted models, each 2.7-fold increase in urinary trichloroacetic acid (TCAA) concentrations was associated with a greater PSQI score of 0.29 (95% confidence interval: 0.07, 0.50). A clear dose–response relationship was observed when TCAA concentrations were included as quartile variables (P for trend = 0.01). In the analyses of individual PSQI components, TCAA was associated with a greater risk of difficulty in falling asleep, short sleep duration, and daytime dysfunction (odds ratios = 1.33 (1.04, 1.70), 1.35 (1.03, 1.78), and 1.39 (1.08, 1.78), respectively, per 2.7-fold increase in TCAA concentrations). Mediation analyses showed that 4-hydroxy-2-nonenal-mercapturic acid mediated 22.24% (4.44%, 80.00%) of the association between TCAA concentrations and PSQI scores. In summary, TCAA exposure may affect sleep quality, which is partly mediated by oxidative stress.

Keywords: disinfection byproducts, trichloroacetic acid, sleep quality, oxidative stress, mediation effect

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Introduction

Humans spend approximately one-third of their lives asleep. Adequate sleep is essential not only for immune, cardiovascular, and mental well-being, but also for hormone regulation, particularly testosterone, , which is vital for sperm production. Insufficient sleep quality has been associated with reduced human semen quality, as well as an increased risk of male infertility. Globally, 38.5% of the population experience poor sleep quality. In China, poor sleep quality affects nearly 19.0% of adults aged 28–56 years. Therefore, identifying factors affecting sleep quality among young men is crucial for the prevention of lifelong chronic diseases and the improvement of reproductive health. Besides well-established lifestyle and physiological factors (e.g., depression, smoking, physical activity, and night shift), − growing evidence shows that environmental chemical pollutants may also adversely affect sleep quality.

Disinfection byproducts (DBPs) are a class of ubiquitously existing pollutants in chlorinated tap water, formed when chlorine reacts with natural organic matter. Trihalomethanes (THMs) and haloacetic acids (HAAs) are two leading species among the hundreds of identified DBPs. Humans are exposed to DBPs during daily water-use activities through inhalation, ingestion, and dermal absorption. , Toxicological and population studies suggest that DBP exposure may affect neurological and psychiatric outcomes, which are closely associated with sleep quality. , For instance, toxicological studies have demonstrated the neurotoxic effects of DBP exposure, such as impaired fetal neurodevelopment and autistic-like behaviors. , Additionally, several population studies show that occupational exposure to THMs is associated with neurological and psychiatric symptoms, such as depression, hallucinations, ataxia, and dysarthria. , In the general population, maternal uptake of total THMs (TTHMs) and brominated THMs (Br-THMs) during pregnancy has been inversely associated with cognitive scores among 1855 Spanish children. However, it is unclear whether DBP exposure is associated with sleep quality.

Blood THM concentrations provide an integrated measure of exposure from multiple sources and are sensitive to low-dose exposure events. Although the elimination half-lives of THMs in blood are short (i.e., minutes to hours), they are relatively stable due to the frequent daily water-use activities and their slow release from adipose tissue. Urinary dichloroacetic acid (DCAA) and trichloroacetic acid (TCAA), the two leading species of HAAs, are reliable exposure markers for ingested HAAs in chlorinated water, although their urinary concentrations exhibit considerable within-person variability. Given the invasiveness of blood collection and the feasibility of repeated collections of urine samples, we measured THM concentrations in a single blood sample and DCAA and TCCA concentrations in repeated urine samples and explored their associations with sleep quality among healthy Chinese men screened as sperm donors. Given the well-documented associations between oxidative stress and DBP exposure and sleep disorders, , we also explored the potential mediating role of oxidative stress.

Method

Study Design

In 2017–2018, we recruited 1487 healthy men who volunteered as potential sperm donors from the Hubei Province Human Sperm Bank. , Eligible volunteers met specific criteria, including being aged 22–45 years, having at least a high school diploma, and being free of genetic or sexually transmitted diseases. A single urine sample was obtained from participants during each predefined follow-up period (days 1–15, 16–31, 32–63, and 64–90 since baseline recruitment) to measure HAAs and oxidative stress markers. A single blood sample was collected at baseline to measure THMs. Sleep quality was repeatedly assessed during the first (days 1–15) and third (days 32–63) follow-up periods. We excluded 652 men who did not complete the Pittsburgh Sleep Quality Index (PSQI) questionnaire in either of the two follow-up periods, 125 due to missing data on blood THM concentrations, and 94 without measurements of urinary HAA and creatinine concentrations, leaving 710 and 741 men in the analyses of THMs and HAAs, respectively (Figure ). The Ethics Committee of the Reproductive Medicine Center, Tongji Medical College approved the study protocol (approval No. [2017] IEC [No.1]). All participants provided written informed consent.

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Cohort design and exclusion criteria. Abbreviations: THMs, trihalomethanes; HAAs, haloacetic acids.

DBP Measurements

Detailed sample collection, DBP measurements, and quality control procedures have been described in our previous study. In brief, a single blood sample was collected at the baseline in anticoagulant tubes without THM contamination. Chloroform (TCM), bromodichloromethane (BDCM), dibromochloromethane (DBCM), and bromoform (TBM) concentrations in blood were extracted and then determined by a gas chromatograph. Given the high within-person variability of urinary DCAA and TCAA concentrations, , urine samples were repeatedly collected at baseline and during 4 follow-up periods (days 1–15, 16–31, 32–63, and 64–90 since baseline recruitment). DCAA and TCAA in urine samples were extracted, converted to methyl esters, and eventually determined by a gas chromatograph. The limits of detection (LODs) of THMs and HAAs were 0.45–2.00 and 0.5–1.0 μg/L, respectively. Concentrations lower than the LODs were imputed by LOD/√2. Br-THMs, chlorinated THMs (Cl-THMs), and TTHMs were calculated to reflect the summary of THM measurements. To adjust for the urine dilution effect, we divided raw HAA and oxidative stress marker measurements by urinary creatinine concentrations.

Oxidative Stress Marker Measurements

Detailed measurement processes have been presented in our previous study. Briefly, due to the high within-person variability of oxidative stress markers in urine samples, we pipetted equal volumes of all urine samples collected from each man. Three oxidative stress markers, namely 8-hydroxy-2-deoxyguanosine (8-OHdG), 8-iso-prostaglandin F2α (8-isoPGF2α), and 4-hydroxy-2-nonenal-mercapturic acid (HNE-MA), in within-subject pooled urinary samples were determined by a liquid chromatography-tandem mass spectrometry. , The LODs of 8-OHdG, HNE-MA, and 8-isoPGF2α were 0.03–0.08 μg/L. Spiked recoveries were assessed to confirm the analytical accuracy. Reagent blanks were analyzed to evaluate the potential contamination.

Assessment of Sleep Quality

As described in our previous study, PSQI questionnaire was used to assess sleep quality over a one-month period, which shows good test-retest reliability over short time intervals. However, to capture potential seasonal variation in sleep quality, PSQI scores were repeatedly assessed during the first and third follow-up visits. The PSQI consists of 7 components, including sleep duration, sleep disturbances, sleep quality, sleep latency, daytime dysfunction, habitual sleep efficiency, and use of sleep medication. A binary variable was created for each PSQI component-specific score, with scores greater than 0 indicating poor sleep. A summary score, ranging from 0 to 21, was also generated by summing the scores of each component. According to previous studies, , poor sleep quality was defined as a total PSQI score of ≥ 5.0.

Covariates

Demographic and socioeconomic characteristics, lifestyle factors, and health conditions were collected using structured questionnaires either at recruitment or during follow-up visits. We also collected detailed data on participants’ daily use of electronic devices and water-use activities. Height and weight were measured using a digital height and weight scale and calculated for body mass index (BMI, kg/m2). , Overweight was defined as a BMI ≥ 24 kg/m2. Hip and waist circumferences were measured using a tape measure and calculated for waist-hip ratios (WHR). Depressive symptoms were assessed using the widely validated Beck Depression Inventory questionnaire, with a cutoff score of 4 indicating the presence of depressive symptoms. Night shifts, defined as working between 0:00 and 8:00, were self-reported. Staying up late was defined according to a self-reported question: “In the past 3 months, apart from working night shifts, have you ever gone to bed between 0:00 and 8:00?”

Statistical Analysis

All analyses were conducted using R software, version 4.4.2 (R Development Core Team). Multivariable linear mixed models with an individual-specific random intercept were used to assess the associations between continuous measurements of blood THMs, creatinine-adjusted urinary HAAs (log-transformed and averaged across repeated samples), , and oxidative stress markers (measured in within-person pooled urine samples) and repeated measurements of PSQI scores. Multivariable linear models were used to assess the associations between continuous measurements of blood THMs and creatinine-adjusted urinary HAAs (log-transformed and averaged across repeated samples) and oxidative stress marker concentrations. Because THMs, HAAs, and oxidative stress markers were right-skewed, their concentrations were naturally log-transformed in the analyses requiring normality assumptions. Thus, effect estimates represent the percent change in outcomes per one-unit increase in ln­(exposure), equivalent to approximately a 2.7-fold increase on the original scale. Regression coefficients (βs) for oxidative stress markers in relation to DBPs were converted into percentage changes using the formula: (exp­(β) – 1) × 100%. We also modeled DBP exposure and oxidative stress markers as quartiles to explore dose–response relationships. Tests for trends were estimated by modeling DBP exposure and oxidative stress markers as ordinal variables using the median values within each quartile. Generalized additive models (GAMs) were used to explore nonlinear dose–response relationships. For results showing evidence of an association, we further applied multivariable logistic regression models to explore the odds ratios (ORs) of low PSQI component-specific scores (i.e., ≥ 1 versus 0) and poor sleep quality (overall PSQI scores ≥ 5.0 versus < 5) in relation to DBP exposure. Mediation analysis was performed to assess the extent to which oxidative stress markers mediated the associations between DBP exposure and sleep quality, using the R “mediation” package. To assess the heterogeneity of study findings, stratified analyses were conducted for BMI (≥ 24 versus < 24 kg/m2), moderate-to-vigorous physical activity (< 210 versus ≥ 210 min per week), smoking status (current versus former/never), alcohol consumption (current versus former/never), the use of electronic device (≥ 3 versus < 3 h per day), staying up late (yes versus no), night shift (yes versus no), and depression (yes versus no). We also explored the effect modification by sleep duration, because short sleep duration (≤ 7 h) represents a distinct sleep characteristic and is strongly influenced by various factors, such as lifestyle factors (e.g., staying up late and childcare responsibilities) and occupational patterns (e.g., shift work). Multiplicative interactions between DBP exposure and these covariates were assessed by testing the regression coefficients of the cross-product terms by Wald tests. Covariates with missing data were imputed via the classification and regression trees method (M = 5) for continuous and categorical variables, respectively.

Potential covariates were selected based on previous evidence investigating factors affecting DBP exposure, oxidative stress, or sleep quality, ,,, and eventually identified using a directed acyclic graph (Figure S1). The covariates in the final regression models included age (continuous, years), BMI (continuous, kg/m2), WHR (continuous), education levels (less than undergraduate versus at least undergraduate), living areas (urban versus rural), marital status (married versus other), ever fathered a child (yes versus no), household income (≤ 4000, 4001–8000, or ≥ 8001 CNY/month), smoking status (current versus former/never), alcohol consumption (current versus former/never), tea drinking (yes versus no), and occupation status (employed, student, or unemployed).

Sensitivity Analyses

First, to enhance causal inference, we performed a prospective mixed linear regression model, in which DBP exposure measurements were ensured to precede subsequent sleep quality assessments. Second, we additionally adjusted for recent water-use activities to evaluate whether our results were influenced by these water-use activities. Third, we modeled average creatinine concentrations as covariates in HAA regression models to account for the effect of urinary dilution.

Results

Population Characteristics

Among 710 participants with blood THM measurements, their mean age, BMI, and WHR were 28.30 ± 5.31 years, 22.94 ± 3.23 kg/m2, and 0.85 ± 0.06 (Table ). Most participants were noncurrent smokers (66.06%) and did not drink alcohol (87.32%). A total of 205 (28.87%) men had ever fathered a child, and 219 (30.85%) had a monthly income exceeding 8000 CNY. Only 67 participants (9.44%) bathed or showered within 6 h, and 21 (2.97%) swam in the past week. A total of 634 and 471 men completed the PSQI questionnaire during the first and third follow-up periods, yielding mean scores of 2.55 ± 2.04 and 2.48 ± 1.86, respectively. Overall, 74 (10.42%) men were identified as having poor sleep quality. Similar characteristics were observed among participants with urinary HAA measurements (n = 741).

1. Demographic Characteristics of Study Participants [n (%) or Mean ± SD] .

Characteristics Participants with blood THM measurements (n = 710) Participants with urinary HAA measurements (n = 741)
Age (years) 28.30 ± 5.31 28.28 ± 5.34
BMI (kg/m2) 22.94 ± 3.23 22.99 ± 3.25
WHR 0.85 ± 0.06 0.85 ± 0.06
Duration of electronic device use (hours/day) 6.74 ± 4.90 6.72 ± 4.85
Living in urban areas 688 (96.90) 717 (96.76)
Undergraduate or above 265 (37.32) 275 (37.11)
Married 228 (32.11) 238 (32.12)
Ever fathered a child 205 (28.87) 209 (28.21)
Household income (CNY/month)    
≤4000 229 (32.25) 237 (31.98)
4000–8000 262 (36.90) 273 (36.84)
>8000 219 (30.85) 231 (31.17)
Current cigarette smokers at recruitment 241 (33.94) 257 (34.68)
Current alcohol drinkers at recruitment 90 (12.68) 91 (12.28)
Current tea drinkers at recruitment 196 (27.61) 206 (27.80)
Occupation status    
Employed 559 (78.73) 583 (78.68)
Student 121 (17.04) 127 (17.14)
Unemployed 30 (4.23) 31 (4.18)
Tap water consumption (mL/day)    
0 341 (48.03) 358 (48.31)
1–1000 173 (24.37) 180 (24.29)
>1000 196 (27.61) 203 (27.40)
Time interval since last bathing/showering within 6 h 67 (9.44) 72 (9.72)
Swimming in the pool in the past week 21 (2.97) 23 (3.12)
Overall poor sleep quality 74 (10.42) 80 (10.80)
PSQI in the first survey    
Overall scores 2.55 ± 2.04 2.57 ± 2.05
Subjective sleep quality 0.67 ± 0.58 0.68 ± 0.59
Sleep latency 0.54 ± 0.75 0.54 ± 0.76
Sleep duration 0.25 ± 0.52 0.26 ± 0.54
Habitual sleep efficiency 0.04 ± 0.28 0.04 ± 0.28
Sleep disturbances 0.45 ± 0.52 0.45 ± 0.52
Use of sleeping medication 0.02 ± 0.18 0.02 ± 0.17
Daytime dysfunction 0.58 ± 0.80 0.58 ± 0.81
PSQI in the second survey    
Overall scores 2.48 ± 1.86 2.47 ± 1.85
Subjective sleep quality 0.68 ± 0.57 0.68 ± 0.58
Sleep latency 0.50 ± 0.67 0.50 ± 0.67
Sleep duration 0.24 ± 0.52 0.24 ± 0.52
Habitual sleep efficiency 0.04 ± 0.26 0.04 ± 0.25
Sleep disturbances 0.44 ± 0.52 0.44 ± 0.52
Use of sleeping medication 0.01 ± 0.11 0.01 ± 0.11
Daytime dysfunction 0.55 ± 0.76 0.55 ± 0.77
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A total of 1 man had missing data on education level, 4 on living areas, 4 on number of children, and 1 on household income, which were imputed with the classification and regression trees method. Abbreviations: BMI, body mass index; WHR, waist-to-hip ratio; PSQI, Pittsburgh Sleep Quality Index.

Distribution of DBP Exposure and Oxidative Stress Markers

TCM and BDCM were detectable in 98.59% and 91.27% of 710 blood samples collected at the baseline (Table S1). DCAA and TCAA were detectable in 73.21% and 94.60% of 2647 urine samples, respectively. The detection rates of 8-OHdG, HNE-MA, and 8-isoPGF2α in 739 within-person pool urine samples were 100%, 100%, and 96.75%, respectively. DBCM and TBM were detected in less than 70% of the blood samples and were thus not analyzed further.

DBP Exposure and Sleep Quality

In crude models, a 2.7-fold increase in urinary TCAA concentrations was associated with a higher PSQI score of 0.23 (95% confidence interval (CI): 0.02, 0.44), which was slightly strengthened after adjustment for potential confounders (0.29, 95% CI: 0.07, 0.50; Table ). A clear dose–response relationship was observed when TCAA concentrations were included as a quartile variable (P trend = 0.01), with the extreme quartiles showing a PSQI score difference of 0.58 (95% CI: 0.20, 0.96). Generalized additive models further confirmed that the positive TCAA-PSQI association was monotonically linear (estimated degree of freedom = 1.48; Figure A). TCAA was unrelated to dichotomized PSQI scores (≥ 5.0 versus < 5). However, in the analyses of individual PSQI components, TCAA was associated with a greater risk of difficulty in falling asleep, short sleep duration, and daytime dysfunction (odds ratios = 1.33 (95% CI: 1.04, 1.70), 1.35 (95% CI: 1.03, 1.78), 1.39 (95% CI: 1.08, 1.78), respectively, per 2.7-fold increase in TCAA concentrations; Figure ). Blood THMs were unrelated to PSQI scores.

2. Associations between Blood THM and Urinary HAA Concentrations and PSQI Scores among Healthy Young Men .

    Crude Models
 Adjusted Models
Measurements n Continuous P Q4 vs Q1 P for trend Continuous P Q4 vs Q1 P for trend
Blood THMs (ng/L)                  
TCM 710 0.02 (−0.17, 0.22) 0.82 –0.07 (−0.45, 0.31) 0.65 0.02 (−0.18, 0.21) 0.88 –0.12 (−0.50, 0.27) 0.57
BDCM 710 0.01 (−0.23, 0.25) 0.96 –0.05 (−0.43, 0.33) 0.84 –0.02 (−0.26, 0.22) 0.86 –0.06 (−0.43, 0.32) 0.81
Cl-THMs 710 0.01 (−0.21, 0.23) 0.94 –0.04 (−0.42, 0.34) 0.85 –0.01 (−0.23, 0.21) 0.96 –0.08 (−0.46, 0.30) 0.75
Br-THMs 710 –0.03 (−0.11, 0.05) 0.42 –0.18 (−0.56, 0.19) 0.50 –0.04 (−0.12, 0.04) 0.37 –0.20 (−0.57, 0.18) 0.45
TTHMs 710 –0.04 (−0.17, 0.09) 0.52 –0.11 (−0.49, 0.26) 0.77 –0.05 (−0.18, 0.08) 0.43 –0.12 (−0.50, 0.26) 0.71
Urinary HAAs (μg/g creatinine)                  
DCAA 741 0.07 (−0.08, 0.22) 0.36 0.34 (−0.03, 0.72) 0.10 0.07 (−0.08, 0.22) 0.38 0.33 (−0.04, 0.70) 0.14
TCAA 741 0.23 (0.02, 0.44) 0.03 0.52 (0.15, 0.89) 0.02 0.29 (0.07, 0.50) 0.01 0.58 (0.20, 0.96) 0.01
Urinary oxidative stress markers (μg/g creatinine)                  
8-OHdG 739 0.09 (−0.18, 0.37) 0.50 0.10 (−0.27, 0.47) 0.52 0.08 (−0.20, 0.35) 0.57 0.04 (−0.33, 0.40) 0.70
HNE-MA 739 0.21 (0.08, 0.35) <0.01 0.71 (0.35, 1.08) <0.01 0.21 (0.08, 0.35) <0.01 0.74 (0.36, 1.11) <0.01
8-isoPGF2α 739 0.07 (−0.06, 0.20) 0.27 0.12 (−0.25, 0.49) 0.81 0.09 (−0.04, 0.22) 0.16 0.19 (−0.18, 0.56) 0.56
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a

Urinary HAA concentrations and oxidative stress markers were divided by urinary creatinine concentrations and then ln-transformed; blood THM concentrations were ln-transformed. Estimates represent a one-unit increase in ln­(exposure), corresponding to an approximately 2.7-fold increase in DBP and oxidative stress marker concentrations. Abbreviations: THMs, trihalomethanes; TCM, chloroform; BDCM, bromodichloromethane; Cl-THMs, chlorinated THMs; Br-THMs, brominated THMs; TTHMs, total THMs; HAAs, haloacetic acids; TCAA, trichloroacetic acid; DCAA, dichloroacetic acid; Cr, creatinine; 8-OHdG, 8-hydroxy-2-deoxyguanosine; HNE-MA, 4-hydroxy 2-nonenal-mercapturic acid; 8-isoPGF2α, 8-iso-prostaglandin F2α; BMI, body mass index; WHR, waist-to-hip ratio; PSQI, Pittsburgh Sleep Quality Index.

b

Models were adjusted for age (continuous, years), BMI (continuous, kg/m2), WHR (continuous), education levels (less than undergraduate versus at least undergraduate), living areas (urban versus rural), marital status (married versus other), ever fathered a child (yes versus no), household income (≤ 4000, 4001–8000, or ≥ 8001 CNY/month), smoking status (current versus former/never), alcohol consumption (current versus former/never), tea drinking (yes versus no), and occupation status (employed, student, or unemployed).

c

Beta (95% CI) of associations between exposure and PSQI score.

2.

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(A) Dose–response relationships between urinary TCAA concentrations and PSQI score using generalized additive models (n = 741). (B) Mediation effect of urinary 8-OHdG on the association between TCAA and sleep quality (n = 739). (C) Mediation effect of urinary HNE-MA on the association between TCAA and sleep quality (n = 739). (D) Mediation effect of urinary 8-isoPGF2α on the association between TCAA and sleep quality (n = 739). Urinary HAA concentrations and oxidative stress markers were divided by urinary creatinine concentrations and then ln-transformed. Models were adjusted for age (continuous, years), BMI (continuous, kg/m2), WHR (continuous), education levels (less than undergraduate versus at least undergraduate), living areas (urban versus rural), marital status (married versus other), ever fathered a child (yes versus no), household income (≤ 4000, 4001–8000, or ≥ 8001 CNY/month), smoking status (current versus former/never), alcohol consumption (current versus former/never), tea drinking (yes versus no), and occupation status (employed, student, or unemployed). Abbreviations: HAAs, haloacetic acids; TCAA, trichloroacetic acid; Cr, creatinine; 8-OHdG, 8-hydroxy-2-deoxyguanosine; HNE-MA, 4-hydroxy 2-nonenal-mercapturic acid; 8-isoPGF2α, 8-iso-prostaglandin F2α; BMI, body mass index; WHR, waist-to-hip ratio; PSQI, Pittsburgh Sleep Quality Index; EDF, estimated degree of freedom.

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Associations between urinary TCAA concentrations and PSQI component-specific scores among healthy young men. The concentrations of urinary TCAA were divided by urinary creatinine concentrations and then ln-transformed. Models were adjusted for age (continuous, years), BMI (continuous, kg/m2), WHR (continuous), education levels (less than undergraduate versus at least undergraduate), living areas (urban versus rural), marital status (married versus other), ever fathered a child (yes versus no), household income (≤ 4000, 4001–8000, or ≥ 8001 CNY/month), smoking status (current versus former/never), alcohol consumption (current versus former/never), tea drinking (yes versus no), and occupation status (employed, student, or unemployed). Abbreviations: HAAs, haloacetic acids; TCAA, trichloroacetic acid; Cr, creatinine; BMI, body mass index; WHR, waist-to-hip ratio; PSQI, Pittsburgh Sleep Quality Index; OR, odds ratio.

DBP Exposure, Oxidative Stress, and Sleep Quality

The concentrations of TCAA were positively associated with 8-OHdG, HNE-MA, and 8-isoPGF2α in urine samples (percentage changes = 17.9 (95% CI: 11.5, 24.7), 45.9 (95% CI: 30.1, 63.7), and 21.6 (95% CI: 7.5, 37.5), respectively, per 2.7-fold increase in TCAA concentrations; Table S2). However, only urinary HNE-MA concentrations, in turn, were positively associated with PSQI scores (regression coefficients = 0.21 (95% CI: 0.08, 0.35), per 2.7-fold increase in HNE-MA concentrations; Table ). Further analyses showed that HNE-MA mediated 22.24% (95% CI: 4.44%, 80.00%) of the positive association between TCAA and PSQI scores (Figure C).

There was no interaction between TCAA exposure and BMI, physical activity, alcohol consumption, electronic device use, short sleep duration, staying up late, night shift, or depression on sleep quality. However, the positive association of urinary TCAA concentrations with PSQI scores appeared to be slightly stronger among current smokers (P for interaction = 0.11; Figure ). The associations of DBP exposure with PSQI scores were substantially unchanged when we performed prospective analyses, ensuring that the HAA exposure was determined before sleep quality assessment (Table S3), when we additionally adjusted for recent water-use activities (Table S4), and when we modeled creatinine as a covariate (Table S5).

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Association between urinary TCAA concentrations and PSQI score in subgroup analyses. The concentrations of urinary TCAA were divided by urinary creatinine concentrations and then ln-transformed. Models were adjusted for age (continuous, years), BMI (continuous, kg/m2), WHR (continuous), education levels (less than undergraduate versus at least undergraduate), living areas (urban versus rural), marital status (married versus other), ever fathered a child (yes versus no), household income (≤ 4000, 4001–8000, or ≥ 8001 CNY/month), smoking status (current versus former/never), alcohol consumption (current versus former/never), tea drinking (yes versus no), and occupation status (employed, student, or unemployed). Abbreviations: HAAs, haloacetic acids; TCAA, trichloroacetic acid; Cr, creatinine; BMI, body mass index; WHR, waist-to-hip ratio; PSQI, Pittsburgh Sleep Quality Index; β, standard regression coefficient.

Discussion

Among healthy Chinese men screened as potential sperm donors, we found a positive dose–response relationship between urinary TCAA concentrations and PSQI scores. In the analyses of individual PSQI components, TCAA was associated with a greater risk of difficulty falling asleep, a short sleep duration, and daytime dysfunction. Mediation analyses suggested that urinary HNE-MA mediated 22% of the positive association between the TCAA and PSQI scores.

Toxicological and population studies support that HAA exposure may lead to neurological and psychiatric problems, which have been linked to poor sleep quality. , For instance, Singh and colleagues reported that high-dose TCAA gavage administration in rats caused hydrocephalus, neuropil vacuolation, alterations in choroid plexus architecture, and neuronal cell apoptosis in offspring, indicating that the central nervous system, which regulates the sleep-wake cycle, is susceptible to TCAA exposure. In a more recent study, Celik and colleagues continuously treated rats with TCAA for 52 days and found reduced activity of neurological enzymes such as butyrylcholinesterase. Other species of HAAs, including dibromoacetic acid, monochloroacetic acid, and chloroacetic acid, have been reported to exhibit neurotoxic properties, as manifested by reduced Purkinje cells in the cerebellum in rats, inhibited differentiation of human neural stem cells, and disrupted melatonin rhythm in rats. In our previous population study of 438 mother-infant pairs, we found that maternal urinary TCAA was inversely associated with Neonatal Behavioral Neurological Assessment scores, suggesting potential adverse effects on neurodevelopment. Moreover, DBP exposure has been associated with hypertension, reproductive health, osteoarthritis, and asthma, which may also partly explain the associations of TCAA exposure with a greater risk of difficulty falling asleep, shorter sleep duration, and daytime dysfunction.

Previous studies have well-documented that BMI, physical activity, alcohol consumption, electronic device use, staying up late, night shifts, and depression are associated with sleep quality. We did not find any interactions between TCAA exposure and these lifestyle or physiological factors, suggesting that TCAA’s effect on sleep quality is independent of these risk factors. However, we observed that the association between TCAA concentrations and PSQI scores was slightly stronger among current smokers, suggesting that smoking may amplify the increased risk of poor sleep quality in relation to TCAA exposure. This aligns with previous studies indicating that smoking can disrupt sleep architecture and exacerbate sleep disorders (e.g., insomnia, parasomnias, bruxism, and restless legs). In the present study, blood THM concentrations were unrelated to sleep quality, which may be partly attributed to the weaker toxicological potency of THMs compared to HAAs. Prior toxicological studies indicate that HAAs exhibit stronger cellular and genotoxic toxicity effects than THMs via proximal mechanisms such as GAPDH inhibition followed by ROS-mediated DNA damage, whereas THMs act largely through a threshold-dependent cytotoxicity–regenerative mode of action. Erythrocyte sequestration further reduces THMs’ target-tissue bioavailability, collectively supporting that THMs exhibit weaker toxicological potency than HAAs. The inconsistency may also be partly related to the differences in exposure levels and sampling strategy. Blood concentrations of THMs are generally lower than urinary HAA concentrations. More importantly, urinary HAA concentrations were assessed in repeated urine samples during the 3-month follow-up period, whereas THMs were measured in a single blood sample, which may have generated exposure misclassification.

Our present study suggests that oxidative stress may have played an important role. We observed positive associations between urinary TCAA and oxidative stress marker concentrations, which were consistent with our previous findings showing positive dose–response relationships between urinary HAA and 8-OHdG, HNE-MA, and 8-isoPGF2α concentrations among 1748 Chinese pregnant women. Meanwhile, toxicological studies have demonstrated that DBP exposure, including TCAA, can induce oxidative stress. , Our further analyses showed that urinary HNE-MA concentrations were associated with higher PSQI scores, which is not surprising given that the suprachiasmatic nucleus (SCN), the “circadian clock” of mammals, is susceptible to elevated levels of reactive oxygen species. , Mediation analyses showed that about a quarter of the positive association between TCAA exposure and PSQI scores was explained by urinary HNE-MA concentrations. However, there was no mediation effect of 8-OHdG and 8-isoPGF2α, which could be largely related to their involvement in distinct cellular processes. For instance, 8-OHdG concentrations are specifically indicative of oxidative DNA damage and can be alleviated by repair mechanisms. HNE-MA and 8-isoPGF2α are both well-documented markers for lipid peroxidation. Nevertheless, HNE-MA derives from multiple polyunsaturated fatty acids (PUFAs), while 8-isoPGF2α exclusively originates from arachidonic acid. Besides oxidative stress, in vitro and human studies show that HAA exposure is associated with neuroinflammation, which plays an important role in regulating circadian rhythms of the SCN through glutamatergic signaling. − HAA exposure is also reported to trigger apoptosis in neuronal cells via reactive oxygen species-induced endoplasmic reticulum stress signaling pathway.

The major strength of this study is that we measured THMs in blood samples and HAAs and oxidative stress markers in repeated urine samples to estimate DBP exposure and oxidative stress levels. Additionally, most participants completed the PSQI questionnaire twice over 90 days to improve the accuracy of sleep quality assessments. However, our study also has several limitations. First, sleep quality was self-reported, which may have led to measurement errors. Second, despite the adjustment for various covariates, unmeasured or residual confounding cannot be fully excluded. Third, because we recruited healthy volunteers from a sperm bank, the proportion of men with poor sleep quality is relatively low, which may restrict the generalization of our findings.

Conclusion

In this prospective cohort, we found that a higher urinary TCAA was associated with reduced sleep quality, including difficulties in falling asleep, short sleep duration, and daytime dysfunction among healthy Chinese men. The association was independent of most lifestyle and psychological factors, including BMI, physical activity, alcohol consumption, electronic device use, staying up late, night shift, and depression. Mediation analysis showed that about a quarter of the positive association between TCAA exposure and PSQI scores was mediated by the oxidative stress marker HNE-MA. Our results suggest that exposure to higher TCAA in drinking water may be associated with reduced sleep quality, highlighting the necessity of maintaining disinfection byproduct concentrations in tap water within safe limits to mitigate potential health risks.

Supplementary Material

eh5c00286_si_001.pdf (739.8KB, pdf)

Acknowledgments

This study was supported by the National Natural Science Foundation of China [No. 82473581].

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/envhealth.5c00286.

  • Additional information on the characteristics of the study population, results for mediation and sensitivity analyses, and directed acyclic graph between DBP exposure and PSQI scores (PDF)

S.X.: investigation, conceptualization methodology, data curation, formal analysis, visualization, software, and writing-original draft preparation. G.L.: Supervision, investigation, data curation. V.M.: conceptualization, investigation, validation, writing – review and editing. Y.Z.: writing – review and editing. C.M.: writing – review and editing. A.J.G.: writing – review and editing. C.X.: methodology. T.M.: data curation, supervision. S.P.: supervision, writing – review and editing. Y.- X.W.: investigation, supervision, data curation, project administration, funding acquisition, writing – original draft, writing – review and editing.

The authors declare no competing financial interest.

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