Associations between internal exposure to acrylamide and sleep health: Evidence from NHANES 2013-2016

Associations between internal exposure to acrylamide and sleep health: Evidence from NHANES 2013-2016 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Associations between internal exposure to acrylamide and sleep health: Evidence from NHANES 2013-2016 Lin Gan, Jiaoyang Wang, Kang Qu, Wei Jiang, Zeshang Guo, Ming Dong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4625431/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Acrylamide (AA) is a ubiquitous neurotoxic contaminant. Our objectives were to evaluate associations of internal AA exposure with sleep health outcomes. Data from 2753 adults aged 20–79 years in the National Health and Nutrition Examination Survey (NHANES) was utilized. Internal AA exposure was assessed using hemoglobin adducts and urinary biomarkers. Short sleep duration (SSD) and self-reported trouble sleeping were employed as indicators of sleep health. Markers of systemic inflammation were calculated. Each one-unit increase in ln-transformed hemoglobin adducts of acrylamide (HbAA), hemoglobin adducts of glycidamide (HbGA) and HbAA + HbGA and creatinine-adjusted urinary N-Acetyl-S-(2-carbamoylethyl)-L-cysteine concentration was statistically significantly associated with 1.38-fold (95% confidence interval [CI]: 1.16, 1.64; p < 0.001), 1.29-fold (95%CI: 1.09, 1.53; p = 0.003), 1.38-fold (95%CI: 1.15, 1.64; p < 0.001), and 1.19-fold (95%CI: 1.06, 1.35; p = 0.004) risk in SSD, respectively. The significant associations were strengthened in smokers after stratification by smoking status. Higher AA hemoglobin biomarkers predicted increases in markers of systemic inflammation. In conclusion, internal AA exposure was associated with an increased risk of SSD and elevated systemic inflammation among United States adults. The findings shed light on the potential effects of AA’s health threat and future research is warranted to develop intervention strategies. Acrylamide Glycidamide N-Acetyl-S-(2-carbamoylethyl)-L-cysteine Short sleep duration Systemic inflammation Figures Figure 1 Figure 2 Introduction Sleep is an essential physiological process and disruption in sleep is a threat to human health. Short sleep duration (SSD, also known as insufficient sleep) and sleep disorders have been in associations with various adverse health outcomes, such as depression 1 , type-2 diabetes 2 , cardiovascular diseases 3 , chronic kidney disease 4 , and mortality 5 . Nevertheless, in 2020, 33.2% of adults in the United States (US) were self-reported to suffer from SSD (sleeping < 7 hours per 24-hour period) 6 . Apart from genetic predisposition and medical conditions, sleep outcomes are also affected by environmental determinants, such as noise and light exposure 7 . In recent years, a growing body of evidence indicates that environmental chemicals exposure may also contribute to the alterations in sleep quality 8 . Consequently, it is impressive to identify selected environmental hazards and develop interventional strategies to manage the putative risks. Acrylamide (AA) is a reactive molecule that is extensively utilized in the synthesis of polyacrylamide polymers, gels, and binding agents 9 . In the past decades, AA has attracted public attention because it could be developed during food processing at high temperatures via the Maillard reaction, such as frying and baking 10 . Meanwhile, AA is also detected in tobacco smoke from AA-containing cigarettes 11 . Thus, the general population could be primarily exposed to AA through ingestion from foods and drinking water and inhalation from tobacco smoke 11 , 12 . As estimated, diet contributes to an average of daily intake of 0.02–1.53 µg/kg body weight/day for the general population 13 . Once absorbed, AA is widely distributed to various organs and metabolized to a major metabolite, glycidamide (GA) in the liver 14 . Hemoglobin adducts of AA (HbAA) and GA (HbGA) and urinary N-Acetyl-S-(2-carbamoylethyl)-L-cysteine (AAMA) concentrations are validated biomarkers in human biomonitoring and frequently detected in the general population 15 , 16 . The ubiquitous presence of AA has raised worldwide health concerns, owing to its toxicological effects 9 , 10 . The neurotoxicity of AA has been well-documented via multiple mechanisms, including inflammation 17 . In vitro and in vivo studies indicated that AA treatment induced activations of the nuclear factor-κB (NF-κB) pathway and enhanced the releases of pro-inflammatory cytokines 18 , 19 . As a known neurotoxic chemical, AA exposure in relation to adverse sleep outcomes has been little elucidated in humans 20 . Therefore, using the National Health and Nutrition Examination Survey (NHANES) 2013–2016 cycles, our main analysis aimed to explore associations of AA exposure with risk of sleep outcomes in general adults aged 20–79 years and explore the potential impacts of systemic inflammation. Materials and Methods Study design and population The study data was extracted from the NHANES database 21 . This nationally representative survey included physical examinations, laboratory tests, dietary information, and health-related questionnaires. Written informed consent was obtained from each participant. The study protocol (Protocol #2011-17) was reviewed and approved by the NCHS Research Ethics Review Board (ERB). All methods of this study were performed in accordance with the principles outlined in the Declaration of Helsinki . A total of 20146 participants were enrolled in NHANES 2013–2016 cycles (supplemental materials, Figure S1 ). Adults aged 20–79 years were included (N = 10760). Women who were pregnant at the examination were excluded (N = 135). Furthermore, participants with missing sleep outcomes (N = 36), missing HbAA, HbGA, and AAMA measurements (N = 7824), and missing blood cell counts (N = 12) were excluded from the current analysis. Internal acrylamide exposure AA and its primary metabolite, GA in human whole blood or erythrocytes were measured 15 . The detailed laboratory methods were described previously 22 . Briefly, utilizing a modified Edman reaction, the adducts of AA and GA were cleaved. The Edman products were prepared by liquid-liquid extraction (LLE) and quantified by using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). The limits of detection (LODs) of AA and GA were 3.90 pmol/g hemoglobin (Hb) and 4.90 pmol/g Hb, respectively. As described previously 23 , AAMA concentration in urine samples was measured using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). The LOD for urinary AAMA level was 2.20 µg/L. Creatinine concentration in urine specimen was qualified by using an enzymatic method 21 . To correct for urine dilution, creatinine-adjusted AAMA concentration was used. Laboratory quality assurance and quality control can be found on the NHANES website 21 . Sleep health The primary endpoint of sleep outcomes in the current analysis was SSD. We defined a sleep duration of less than 7 hours per 24-hour period as SSD, according to the American Academy of Sleep Medicine and the Sleep Research Society 6 , 24 – 25 . Regarding the secondary endpoint, self-reported trouble sleeping was captured by asking the question: “Ever told doctor had trouble sleeping?”. Systemic inflammation Recently, two novel indicators derived from the counts of lymphocytes, neutrophils, monocytes, and platelets, the systemic immune-inflammation index (SII) and the system inflammation response index (SIRI) were introduced 26 . Initially, SII was applied in assessing the prognosis of patients with liver cancer and SIRI predicted survival after chemotherapy in patients with cancer 27 – 28 . Considering the easily obtained and biological significance, these indicators were widely used in evaluating systemic inflammation response in the general population (Wang et al., 2022b; Xia et al., 2023) 29 – 30 . The whole blood specimens were measured using automated hematology analyzing devices 24 . After analyzing in duplicate, the observed results were averaged to improve data quality. Utilizing the counts of peripheral blood cells (1000 cells/µL), we calculated SII using the formula 27 : $$\frac{platelets count\times neutrophils count}{lymphocytes count}$$ SIRI was also calculated as follows 28 : $$\frac{monocyte count\times neutrophils count}{lymphocytes count}$$ Covariates As reported in published studies 16 , 31 , socio-demographic characteristics, physical examination, dietary information, lifestyles, and medical conditions were considered as potential covariates in relation to AA exposure and sleep outcomes. The covariates were included if they changed the coefficient of AA internal biomarkers by greater than 10% in simple linear regression models. The following covariates were selected: gender, age, race/ethnicity, educational levels, family poverty-income ratio (PIR), body mass index (BMI), smoking status, alcohol consumption, physical activity, diabetes, and hypertension. Participants were categorized as smoker and non-smoker subgroups through the questionnaire of recent smoking status (“Smoking - Cigarette Use”). We further checked the consistency with a previous definition and found that a majority of the smokers (98.2%) had serum cotinine concentration higher than 3.08 µg/L 32 . Alcohol consumption was defined as participants who had at least 12 alcohol drinks per year 21 . Physical activity was categorized by vigorous and moderate recreational activity for at least 10 min continuously per week 33 . The diagnosis of diabetes was based on a glycohemoglobin (HbA1c) level of > 6.5%, a fasting blood glucose level of ≥ 7.0 mmol/L, a 2-hour plasma glucose level of ≥ 11.1 mmol/L, self-reported history of diagnosed diabetes, and self-reported taking a diabetes medicine 34 . Blood pressure was measured up to four consecutive times by qualified professionals and the systolic blood pressure (SBP) and diastolic blood pressure (DBP) were calculated by averaging at least consecutive three measures. Hypertension was defined as SBP ≥ 140 mmHg, DBP ≥ 90 mmHg, self-reported history of diagnosed hypertension, or taking a medicine for hypertension 35 . Statistical analysis The general characteristics of participants were summarized using median and interquartile range (IQR) for continuous variables and frequency and proportion for categorical variables, respectively. HbAA, HbGA, HbAA + HbGA, HbGA/HbAA, and urinary creatinine-adjusted AAMA levels were natural logarithm (ln) transformed owing to skew distribution. Correlation was assessed using Pearson r value. Multiple imputation with chained equations was applied for a small number of missing covariates, including family PIR, BMI, and alcohol consumption. Multivariable logistic regression models were used to estimate associations of adverse sleep outcomes with internal exposure to AA. Multivariable linear regression models were applied to explore associations of AA internal biomarkers with markers of inflammation. The collinearity of the linear regression models was assessed by a variance inflation factor (VIF) and no multi-collinearity was observed. Regarding covariates, two models were conducted. Model 1 was a crude model without any adjustment. Model 2 was adjusted for all the aforementioned covariates as a primary model. Generalized additive models (GAMs) with 3 degrees-of-freedom natural cubic splines were fitted to estimate the potential nonlinear associations of AA internal biomarkers with adverse sleep outcomes. As cigarette smoking is a major source of AA exposure 36 , we further introduced an interaction term between smoking and target biomarkers, and then performed stratified analysis if the interaction term was significant. A mediating role of systemic inflammation would be further examined if markers of systemic inflammation were associated with both sleep outcomes and AA internal biomarkers. The statistical analysis was performed using R version 4.2.1 ( https://www.r-project.org/ ). Statistical significance was considered as a two-sided p -value < 0.05 and p -value < 0.10 for interaction terms. Results A total of 2753 participants were included in the current analysis. The general characteristics are presented in Table 1 . The frequency and proportion of adults aged 20–59 years were 1991 and 72.3%. A majority of participants were Non-Hispanic White (36.9%) and had higher educational levels (56.4%) and alcohol consumption (72.6%). The proportions of SSD and self-reported trouble sleeping were 30.7% and 26.0%, respectively. Table 1 Descriptive statistics of general characteristics of 2753 participants from NHANES 2013–2016 Characteristics N (%) Variables Median (25th -75th percentile) Age (years) BMI (kg/m 2 ) 28.2 (24.4, 32.8) 20–59 1991 (72.3) PIR 2.12 (1.10, 4.17) 60–79 762 (27.7) SII 444 (315, 618) Gender SIRI 1.04 (0.72, 1.51) Male 1376 (50.0) Female 1377 (50.0) Race/Ethnicity Mexican American 448 (16.3) Other Hispanic 326 (11.8) Non-Hispanic White 1016 (36.9) Non-Hispanic Black 546 (19.8) Other race - including multi-racial 417 (15.2) Education Less than high school 599 (21.8) High school 601 (21.8) College and above 1553 (56.4) Smoking status Yes 583 (21.2) No 2170 (78.8) Alcohol consumption Yes 2000 (72.6) No 753 (27.4) Physical activity Vigorous or moderate recreational activities 1387 (50.4) No 1366 (49.6) Diabetes Yes 488 (17.7) No 2265 (82.3) Hypertension Yes 1386 (50.4) No 1367 (49.6) Abbreviations : N-frequency; %-proportion; BMI-body mass index; PIR-poverty income ratio; SII- systemic immune-inflammation index; SIRI- system inflammation response index; WBC-white blood cell counts; hs-CRP: high-sensitivity C-reactive protein HbAA and HbGA were detected in all the samples. The median values (25th percentile, 75th percentile) of HbAA, HbGA and HbAA + HbGA were 43.6 (33.4, 66.6) pmol/g Hb and 38.3 (28.0, 54.8) pmol/g Hb and 82.8 (62.4, 121) pmol/g Hb, respectively, and the median HbGA/HbAA was 0.824 (0.681, 0.985) (Supplemental materials, Table S1 ). AAMA was detected in almost all the urine samples (2751/2753). The median level of AAMA in urine was 52.5 (25.4, 106) µg/L. HbAA, HbGA, and urinary creatinine-adjusted AAMA concentration were highly correlated with Pearson r values ranging from 0.634 to 0.842 ( p < 0.001). The median (25th percentile, 75th percentile) calculated SII and SIRI were 444 (315, 618) and 1.04 (0.72, 1.51), respectively. A high correlation between SII and SIRI was observed (Pearson r value: 0.779, p < 0.001). Associations between AA internal biomarkers and adverse sleep outcomes are presented in Fig. 1 . After adjustment for potential confounders, HbAA, HbGA, HbAA + HbGA level, and creatinine-adjusted urinary AAMA concentration were statistically significantly associated with 1.38-fold (95% confidence interval [CI]: 1.16, 1.64; p < 0.001), 1.29-fold (95%CI: 1.09, 1.53; p = 0.003), 1.38-fold (95%CI: 1.15, 1.64; p < 0.001), and 1.19-fold (95%CI: 1.06, 1.35; p = 0.004) risk in SSD. No significant association between AA internal biomarkers and self-reported trouble sleeping was observed (Supplemental materials, Table S2). As shown in Fig. 2 , no evidence of statistically significant nonlinear associations between AA exposure and risk of SSD was found using GAM models ( p nonlinearity > 0.05). Associations between AA internal biomarkers and markers of systemic inflammation are displayed in Table 2 . After controlling for covariates, HbGA and HbGA/HbAA were significantly related to increased SII (regression coefficient [β] = 0.06, 95%CI: 0.02, 0.09; p = 0.005; β = 0.12, 95%CI: 0.05, 0.18; p < 0.001, respectively). HbGA and HbAA + HbGA were significantly associated with increased SIRI (β = 0.06, 95%CI: 0.01, 0.10; p = 0.012; β = 0.05, 95%CI: 0.00, 0.10; p = 0.032, respectively). Table 2 Estimated regression coefficients and 95% CI for markers of systemic inflammation and AA internal biomarkers SII (N = 2753) SIRI (N = 2753) β (95% CI) P β (95% CI) P Hemoglobin HbAA 0.01 (-0.02, 0.05) 0.473 0.04 (0.00, 0.08) 0.090 HbGA 0.06 (0.02, 0.09) 0.005 0.06 (0.01, 0.10) 0.012 HbAA + HbGA 0.03 (-0.01, 0.07) 0.099 0.05 (0.00, 0.10) 0.032 HbGA/HbAA 0.12 (0.05, 0.18) < 0.001 0.06 (-0.02, 0.13) 0.137 Urine AAMA 0.00 (-0.03, 0.02) 0.572 0.01 (-0.02, 0.04) 0.445 Abbreviations : β- regression coefficient; CI-confidence interval; SII-systemic immune-inflammation index; SIRI: system inflammation response index; HbAA-hemoglobin adducts of acrylamide; HbGA- hemoglobin adducts of glycidamide; AAMA- N-Acetyl-S-(2-carbamoylethyl)-L-cysteine Models were adjusted for age, gender, race/ethnicity, education, body mass index, family poverty-to-income ratio, smoking status, alcohol consumption, physical activity, hypertension, and diabetes Interaction terms between smoking status and AA hemoglobin biomarkers were partially statistically significant, thereby stratified analysis by smoking status was performed (Table 3 ). After stratification by smoking status, a statistically significant association between creatinine-adjusted urinary AMAA level and risk of SSD was only observed among smokers (odds ratio [OR] = 1.51, 95% CI: 1.14, 2.01; p = 0.004), but not among non-smokers (OR = 1.11, 95% CI: 0.97, 1.28; p = 0.130). HbAA, HbGA, and HbAA + HbGA were significantly positively related to the risk of SSD in both smokers (OR = 1.60, 95% CI: 1.16, 2.21; p = 0.004; OR = 1.38, 95% CI: 1.02, 1.88; p = 0.038; OR = 1.57, 95% CI: 1.13, 2.19; p = 0.007, respectively) and non-smokers (OR = 1.25, 95% CI: 1.01, 1.53; p = 0.040; OR = 1.23, 95% CI: 1.00, 1.52; p = 0.047; OR = 1.27, 95% CI: 1.02, 1.57; p = 0.032, respectively). No significant association between AA internal biomarkers and risk of self-reported trouble sleeping was found. Table 3 Stratified analysis by smoking status for associations of AA internal biomarkers with adverse sleep outcomes Smoker Non-smoker OR (95% CI) P OR (95% CI) P SSD HbAA 1.60 (1.16, 2.21) 0.004 1.25 (1.01, 1.53) 0.040 HbGA 1.38 (1.02, 1.88) 0.038 1.23 (1.00, 1.52) 0.047 HbAA + HbGA 1.57 (1.13, 2.19) 0.007 1.27 (1.02, 1.57) 0.032 HbGA/HbAA 0.73 (0.43, 1.24) 0.252 0.99 (0.70, 1.41) 0.968 AAMA 1.51 (1.14, 2.01) 0.004 1.11 (0.97, 1.28) 0.130 Self-reported trouble sleeping HbAA 1.12 (0.81, 1.56) 0.496 1.08 (0.86, 1.38) 0.496 HbGA 1.18 (0.86, 1.62) 0.294 1.12 (0.89, 1.41) 0.324 HbAA + HbGA 1.17 (0.83, 1.64) 0.362 1.11 (0.87, 1.42) 0.397 HbGA/HbAA 1.21 (0.70, 2.11) 0.488 1.12 (0.76, 1.64) 0.570 AAMA 1.00 (0.75, 1.33) 0.997 1.10 (0.95, 1.28) 0.204 Abbreviations : OR- odds ratio; CI-confidence interval; SSD-short sleep duration; HbAA-hemoglobin adducts of acrylamide; HbGA- hemoglobin adducts of glycidamide; AAMA- N-Acetyl-S-(2-carbamoylethyl)-L-cysteine Models were adjusted for age, gender, race/ethnicity, education, body mass index, family poverty-to-income ratio, smoking status, alcohol consumption, physical activity, hypertension, and diabetes No significant association of systemic inflammation markers with the risk of adverse sleep outcomes was observed (supplemental materials, Table S3). Thus, mediation analysis was not conducted. Discussion In this cross-sectional analysis, AA internal biomarkers were associated with an increased risk of SSD. In smoking-stratified analysis, the significant associations were more pronounced in smokers. We observed that internal exposure to AA in association with higher markers of systemic inflammation. Given no significant association between systemic inflammation and the risk of SSD, we did not examine the mediating effect of systemic inflammation on the association of internal AA exposure with the risk of SSD. AA was well-documented as a neurotoxic chemical that can lead to various neurological and psychiatric symptoms, such as depression, anxiety, neurodegenerative diseases, and cognitive impairment 37 – 40 . Epidemiological evidence on the impact of AA exposure on sleep outcomes was quite limited. In an occupational population, tunnel workers who were previously exposed to AA or N-methylolacrylamide had higher self-reported nervous system symptoms measured by Q-16 neuropsychiatric questionnaire, including sleep disturbances, compared with non-exposed tunnel workers 23 . Our findings comprehensively evaluated the risk of SSD in association with internal AA exposure by using hemoglobin adducts and urinary measurements. This significant finding may be attributed to the neurotoxicity of AA. Although the underlying biological mechanisms are still unknown, inflammation, oxidative stress, apoptosis and autophagy may play a critical role 17 . Similarly, several epidemiological studies have reported that adverse sleep outcomes were associated with other neurotoxic chemicals, including heavy metals, perfluoroalkyl and polyfluoroalkyl substances, and polycyclic aromatic hydrocarbons 31 , 41 , 42 . As demonstrated previously 9 , AA exposure could increase inflammation in various tissues, including neurons, the brain, the liver and the kidney. In vitro , AA exposure induced an inflammatory response via the nuclear factor-κB (NF-κB) pathway 19 . With NF-κB activations, the transcription of inflammatory genes was enhanced, and pro-inflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6), pro-IL-1β and pro-IL-18, were released. In rodent models, these findings were further verified. AA treatment induced the enhancement of serum cytokines, including TNF-α, pro-IL-1β, and IL-6 18,43 . Likewise, we observed increases in inflammation markers, relating to higher exposure to AA in general adults. In stratified analyses, we found more prominent associations between AA exposure and risk of SSD in smokers than those in non-smokers. This phenomenon has also been observed previously in associations of AA exposure with other health outcomes, including diabetes, cardiovascular diseases, depressive symptoms 37 , 44 – 46 . Cigarette smoking, a critical source of AA exposure, was associated with increases in local and systemic inflammation and SSD 47 , 48 . Compared with non-smokers, smokers had higher exposure levels of AA and other toxic chemicals, such as tar, formaldehyde, polycyclic aromatic hydrocarbons, and heavy metals 47 . We cannot exclude the possibility that AA in combination with a series of toxic chemicals in tobacco smoke contributes to the development of systemic inflammation, and increased risk of SSD. Consequently, additional epidemiological research is needed to assess exposure to a mixture of toxic chemicals in tobacco smoke associated with systemic inflammation and adverse sleep outcomes. As far as we know, this is the first epidemiological study to explore associations of internal AA exposure with adverse sleep outcomes and systemic inflammation in the general population. Meanwhile, AA exposure was assessed using hemoglobin adducts and a urinary measurement in human biomonitoring. Furthermore, SII and SIRI are novel markers of systemic inflammation that are easily obtained and have significant implications for the understanding of systemic inflammation associated with chemical exposure. However, several limitations in our study should be acknowledged. Firstly, owing to the cross-sectional study design, causality cannot be inferred between AA exposure and the risk of SSD. Secondly, although a broad set of covariates was adjusted in this study, we cannot exclude the possibility of residual confounders, such as occupational factors 49 , and other environmental contaminants 34 , 41 – 42 . Thirdly, AAMA was only measured in a spot urine sample and AA hemoglobin biomarkers can only reflect AA exposure in the last 120 days 49 . There is a possibility of exposure misclassification and repeated measurements would have provided a more accurate or long-term assessment of exposure levels. Conclusions AA exposure assessed using hemoglobin adducts and urinary biomarkers was associated with increased risk of SSD in general adults aged 20–79 years. This association was prominent in smokers. Moreover, internal exposure to AA was related to increases in systemic inflammation. The current findings provide suggestive evidence regarding the risk of SSD and the proinflammatory effects of AA exposure at environmentally relevant doses. Future research is warranted to confirm these findings and develop intervention strategies. Abbreviations AA: acrylamide HbAA: hemoglobin adducts of acrylamide HbGA: hemoglobin adducts of glycidamide AAMA: N-Acetyl-S-(2-carbamoylethyl)-L-cysteine SII: systemic immune-inflammation index SIRI: system inflammation response index SSD: short sleep duration NHANES: National Health and Nutrition Examination Survey LOD: limits of detection SII: systemic immune-inflammation index SIRI: system inflammation response index PIR: poverty-income ratio BMI: body mass index HbA1c: glycohemoglobin SBP: systolic blood pressure DBP: diastolic blood pressure GAM: Generalized additive model Declarations Data availability The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors. Acknowledgments We appreciated the adults for participation in the NHANES survey. We are also grateful for the United States Centers for Disease Control and Prevention (CDC) for providing the publicly available data. Funding This work was supported by Natural Science Foundation of Jilin Province of China (Grant No. 20200201606JC) and the National Natural Science Foundation of China (Grant No. 31872772). Author information Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, China Lin Gan, Jiaoyang Wang, Kang Qu, Wei Jiang, Ming Dong Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China Zeshang Guo Contributions L.G.: Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft. Writing – review & editing. J.W.: Data curation, Formal analysis, Methodology, Software, Supervision. K.Q.: Data curation, Formal analysis, Supervision, Writing – review & editing. W.J.: Conceptualization, Formal analysis, Supervision, Writing – review & editing. Z.G.: Conceptualization, Formal analysis, Writing – review & editing. M.D.: Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review & editing. All authors contributed to the article and approved the submitted version. Corresponding authors Correspondence to Ming Dong Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References Salo P, Sivertsen B, Oksanen T, Sjösten N, Pentti J, Virtanen M, et al. Insomnia symptoms as a predictor of incident treatment for depression: prospective cohort study of 40,791 men and women. Sleep Med , 13 :278-84. (2012). Shan Z, Ma H, Xie M, Yan P, Guo Y, Bao W, et al. Sleep duration and risk of type 2 diabetes: a meta-analysis of prospective studies. Diabetes Care . 38 :529-37. (2015) Cappuccio FP, Cooper D, D'Elia L, Strazzullo P, Miller MA. 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Cancer ; 122 : 2158-67. (2016) Wang P, Guo X, Zhou Y, Li Z, Yu S, Sun Y, et al. Monocyte-to-high-density lipoprotein ratio and systemic inflammation response index are associated with the risk of metabolic disorders and cardiovascular diseases in general rural population. Front Endocrinol (Lausanne). 13 : 944991. (2022) Xia Y, Xia C, Wu L, Li Z, Li H, Zhang J. Systemic Immune Inflammation Index (SII), System Inflammation Response Index (SIRI) and Risk of All-Cause Mortality and Cardiovascular Mortality: A 20-Year Follow-Up Cohort Study of 42,875 US Adults. J Clin Med, 12 :1128. (2023) Guo P, Jin Z, Bellia G, Luo J, Inoue K, Pollitt KJG, et al. Per- and polyfluoroalkyl substances and sleep health in U.S. adults, NHANES 2005-2014. Environ Res , 237 (Pt 2):117092. (2023) Benowitz NL, Bernert JT, Caraballo RS, Holiday DB, Wang J. Optimal serum cotinine levels for distinguishing cigarette smokers and nonsmokers within different racial/ethnic groups in the United States between 1999 and 2004. Am J Epidemiol . 169 :236-48. (2009) Chen H, Chen L, Hao G. Exercise attenuates the association between household pesticide exposure and depressive symptoms: Evidence from NHANES, 2005-2014. Environ Res, 188 : 109760. (2020) Xie X, Lu C, Wu M, Liang J, Ying Y, Liu K, et al. Association between triclocarban and triclosan exposures and the risks of type 2 diabetes mellitus and impaired glucose tolerance in the National Health and Nutrition Examination Survey (NHANES 2013-2014). Environ Int, 136 : 105445. (2020) Miao H, Liu Y, Tsai TC, Schwartz J, Ji JS. Association between blood lead level and uncontrolled hypertension in the US population (NHANES 1999-2016). J Am Heart Assoc , 9 : e015533. (2020) Kenwood BM, Zhu W, Zhang L, Bhandari D, Blount BC. Cigarette smoking is associated with acrylamide exposure among the U.S. population: NHANES 2011-2016. Environ Res . 209: 112774. (2022) Li Z, Sun J, Zhang D. Association between acrylamide hemoglobin adduct levels and depressive symptoms in US Adults: NHANES 2013-2016. J Agric Food Chem. 69 :13762-13771. (2021) Wang A, Wan X, Zhuang P, Jia W, Ao Y, Liu X et al. High fried food consumption impacts anxiety and depression due to lipid metabolism disturbance and neuroinflammation. Proc Natl Acad Sci U S A. 120 :e2221097120. (2023) Li J, Li D, Yang Y, Xu T, Li P, He D. Acrylamide induces locomotor defects and degeneration of dopamine neurons in Caenorhabditis elegans. J Appl Toxicol. 36 :60-7. (2016) Liu Y, Zhang X, Yan D, Wang Y, Wang N, Liu Y, et al. Chronic acrylamide exposure induced glia cell activation, NLRP3 infl-ammasome upregulation and cognitive impairment. Toxicol Appl Pharmacol . 393 :114949. (2020) Liu J, Ghastine L, Um P, Rovit E, Wu T. Environmental exposures and sleep outcomes: A review of evidence, potential mechanisms, and implications. Environ Res. 196 :110406. (2021) Zhao H, Fang L, Chen Y, Ni J, Chen X, Pan F. Independent and combined associations of polycyclic aromatic hydrocarbons exposure and sleep disorders among adults in the U.S. adult population. J Affect Disord . 350 :319-327. (2024) Zhang L, Wang E, Chen F, Yan H, Yuan Y. Potential protective effects of oral administration of allicin on acrylamide-induced toxicity in male mice. Food Funct, 4 : 1229-36. (2013) Yin G, Liao S, Gong D, Qiu H. Association of acrylamide and glycidamide haemoglobin adduct levels with diabetes mellitus in the general population. Environ Pollut, 277 : 116816. (2021) Wang B, Cheng M, Yang S, Qiu W, Li W, Zhou Y, et al. Exposure to acrylamide and reduced heart rate variability: The mediating role of transforming growth factor-β. J Hazard Mater. 395 :122677. (2020) Zhang Y, Huang M, Zhuang P, Jiao J, Chen X, Wang J, et al. Exposure to acrylamide and the risk of cardiovascular diseases in the National Health and Nutrition Examination Survey 2003-2006. Environ Int . 117 :154-163. (2018) Johannsen A, Susin C, Gustafsson A. Smoking and inflammation: evidence for a synergistic role in chronic disease. Periodontol, 64 ,111-26. (2000) Merianos AL, Mahabee-Gittens EM, Hill MJ, Olaniyan AC, Smith ML, Choi K. Electronic cigarette use and cigarette smoking associated with inadequate sleep duration among U.S. young adults. Prev Med . 175 :107712. (2023) Bergmark E, Calleman CJ, He F, Costa LG. Determination of hemoglobin adducts in humans occupationally exposed to acrylamide. Toxicol Appl Pharmacol, 120 : 45-54. (1993) Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4625431","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":327227304,"identity":"2ca13b4a-c006-4c6a-93dd-0e09ce746a7f","order_by":0,"name":"Lin Gan","email":"","orcid":"","institution":"Department of Neurology and Neuroscience Center, The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Gan","suffix":""},{"id":327227305,"identity":"eae1cf2f-a443-4f5c-9736-797b9bca20c4","order_by":1,"name":"Jiaoyang Wang","email":"","orcid":"","institution":"Department of Neurology and Neuroscience Center, The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jiaoyang","middleName":"","lastName":"Wang","suffix":""},{"id":327227306,"identity":"cb15031f-cb36-4cbf-9d2e-491c545e2dd0","order_by":2,"name":"Kang Qu","email":"","orcid":"","institution":"Department of Neurology and Neuroscience Center, The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Kang","middleName":"","lastName":"Qu","suffix":""},{"id":327227307,"identity":"4c241611-c2f1-405e-b7fd-8261900b8a64","order_by":3,"name":"Wei Jiang","email":"","orcid":"","institution":"Department of Neurology and Neuroscience Center, The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Jiang","suffix":""},{"id":327227308,"identity":"20749551-5626-4d1a-aff8-5d7d03fd46ec","order_by":4,"name":"Zeshang Guo","email":"","orcid":"","institution":"Department of Neurosurgery, The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Zeshang","middleName":"","lastName":"Guo","suffix":""},{"id":327227309,"identity":"483ab486-2cb0-4c45-b7b6-8b6f7649159b","order_by":5,"name":"Ming Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYDCCA2DShpkPRPGQoCWNmY1ULYcZiNfCd7z38MsvFefZ2SQSGB+8bWOQNyekRfLMuTRrmTO3mYFamA3ntjEY7mwgoMXgRo6ZsWQbWAubNG8bQ4LBAaK0/DsH0sL+m1gtxg8/NhwA28JMlBbJM2fMmBmOJTOz8TxslpxzTsJwAyEtfMd7jD/+qLFL5mdPPvjhTZmNPEFbgIBNGhgdyQwMjA1AjgRh9UDA/PEHA4MdUUpHwSgYBaNgZAIAm5c8cSSpj24AAAAASUVORK5CYII=","orcid":"","institution":"Department of Neurology and Neuroscience Center, The First Hospital of Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Ming","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2024-06-23 13:38:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4625431/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4625431/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60494015,"identity":"7b6b70bc-b4ac-4850-9131-acbf4a403e59","added_by":"auto","created_at":"2024-07-17 11:24:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":426093,"visible":true,"origin":"","legend":"\u003cp\u003eOdds ratios (ORs) and 95% confidence intervals (CIs) for the associations of AA internal biomarkers with risk of SSD\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: SSD-short sleep duration; HbAA-hemoglobin adducts of acrylamide; HbGA- hemoglobin adducts of glycidamide; AAMA- N-Acetyl-S-(2-carbamoylethyl)-L-cysteine\u003c/p\u003e\n\u003cp\u003eModel 1: crude model without any adjustment.\u003c/p\u003e\n\u003cp\u003eModel 2: adjusts for age, gender, race/ethnicity, education, body mass index, family poverty-to-income ratio, smoking status, alcohol consumption, physical activity, hypertension, and diabetes.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-4625431/v1/385696c7d96dd4f9c4fcd330.png"},{"id":60494016,"identity":"d59c2392-e828-48b8-9326-9100dc28ef59","added_by":"auto","created_at":"2024-07-17 11:24:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2446346,"visible":true,"origin":"","legend":"\u003cp\u003eThe exposure‐response relationships between AA internal biomarkers and risk of SSD\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e SSD-short sleep duration; HbAA-hemoglobin adducts of acrylamide; HbGA- hemoglobin adducts of glycidamide; AAMA- N-Acetyl-S-(2-carbamoylethyl)-L-cysteine\u003c/p\u003e\n\u003cp\u003eModels were adjusted for age, gender, race/ethnicity, education, body mass index, family poverty-to-income ratio, smoking status, alcohol consumption, physical activity, hypertension, and diabetes.\u003c/p\u003e","description":"","filename":"Figure21.png","url":"https://assets-eu.researchsquare.com/files/rs-4625431/v1/b0564af7e1dabd89e55368dd.png"},{"id":60981572,"identity":"845b243c-103a-4c7e-a8b6-7b50fe662fc1","added_by":"auto","created_at":"2024-07-24 09:15:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5503811,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4625431/v1/419acd7f-e9ca-49d4-929d-fbabc9de7380.pdf"},{"id":60493978,"identity":"4643b18b-f623-4c7d-81bb-a04db7abe941","added_by":"auto","created_at":"2024-07-17 11:24:10","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":93028,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4625431/v1/f4ccafe3610c4166068f0689.jpg"},{"id":60494013,"identity":"434f2a98-a341-4de4-a497-7d6244398429","added_by":"auto","created_at":"2024-07-17 11:24:12","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":135790,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4625431/v1/a1b5c9923fb1e85c1d57ac30.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between internal exposure to acrylamide and sleep health: Evidence from NHANES 2013-2016","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSleep is an essential physiological process and disruption in sleep is a threat to human health. Short sleep duration (SSD, also known as insufficient sleep) and sleep disorders have been in associations with various adverse health outcomes, such as depression\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, type-2 diabetes\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, chronic kidney disease\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, and mortality\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Nevertheless, in 2020, 33.2% of adults in the United States (US) were self-reported to suffer from SSD (sleeping\u0026thinsp;\u0026lt;\u0026thinsp;7 hours per 24-hour period) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Apart from genetic predisposition and medical conditions, sleep outcomes are also affected by environmental determinants, such as noise and light exposure\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In recent years, a growing body of evidence indicates that environmental chemicals exposure may also contribute to the alterations in sleep quality\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Consequently, it is impressive to identify selected environmental hazards and develop interventional strategies to manage the putative risks.\u003c/p\u003e \u003cp\u003eAcrylamide (AA) is a reactive molecule that is extensively utilized in the synthesis of polyacrylamide polymers, gels, and binding agents\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In the past decades, AA has attracted public attention because it could be developed during food processing at high temperatures via the Maillard reaction, such as frying and baking\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Meanwhile, AA is also detected in tobacco smoke from AA-containing cigarettes\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Thus, the general population could be primarily exposed to AA through ingestion from foods and drinking water and inhalation from tobacco smoke\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. As estimated, diet contributes to an average of daily intake of 0.02\u0026ndash;1.53 \u0026micro;g/kg body weight/day for the general population\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Once absorbed, AA is widely distributed to various organs and metabolized to a major metabolite, glycidamide (GA) in the liver\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Hemoglobin adducts of AA (HbAA) and GA (HbGA) and urinary N-Acetyl-S-(2-carbamoylethyl)-L-cysteine (AAMA) concentrations are validated biomarkers in human biomonitoring and frequently detected in the general population\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The ubiquitous presence of AA has raised worldwide health concerns, owing to its toxicological effects\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe neurotoxicity of AA has been well-documented via multiple mechanisms, including inflammation\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eIn vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies indicated that AA treatment induced activations of the nuclear factor-κB (NF-κB) pathway and enhanced the releases of pro-inflammatory cytokines\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. As a known neurotoxic chemical, AA exposure in relation to adverse sleep outcomes has been little elucidated in humans\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Therefore, using the National Health and Nutrition Examination Survey (NHANES) 2013\u0026ndash;2016 cycles, our main analysis aimed to explore associations of AA exposure with risk of sleep outcomes in general adults aged 20\u0026ndash;79 years and explore the potential impacts of systemic inflammation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThe study data was extracted from the NHANES database\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This nationally representative survey included physical examinations, laboratory tests, dietary information, and health-related questionnaires. Written informed consent was obtained from each participant. The study protocol (Protocol #2011-17) was reviewed and approved by the NCHS Research Ethics Review Board (ERB). All methods of this study were performed in accordance with the principles outlined in the Declaration of \u003cem\u003eHelsinki\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eA total of 20146 participants were enrolled in NHANES 2013\u0026ndash;2016 cycles (supplemental materials, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Adults aged 20\u0026ndash;79 years were included (N\u0026thinsp;=\u0026thinsp;10760). Women who were pregnant at the examination were excluded (N\u0026thinsp;=\u0026thinsp;135). Furthermore, participants with missing sleep outcomes (N\u0026thinsp;=\u0026thinsp;36), missing HbAA, HbGA, and AAMA measurements (N\u0026thinsp;=\u0026thinsp;7824), and missing blood cell counts (N\u0026thinsp;=\u0026thinsp;12) were excluded from the current analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInternal acrylamide exposure\u003c/h2\u003e \u003cp\u003eAA and its primary metabolite, GA in human whole blood or erythrocytes were measured\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The detailed laboratory methods were described previously\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Briefly, utilizing a modified Edman reaction, the adducts of AA and GA were cleaved. The Edman products were prepared by liquid-liquid extraction (LLE) and quantified by using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). The limits of detection (LODs) of AA and GA were 3.90 pmol/g hemoglobin (Hb) and 4.90 pmol/g Hb, respectively.\u003c/p\u003e \u003cp\u003eAs described previously\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, AAMA concentration in urine samples was measured using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). The LOD for urinary AAMA level was 2.20 \u0026micro;g/L. Creatinine concentration in urine specimen was qualified by using an enzymatic method\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. To correct for urine dilution, creatinine-adjusted AAMA concentration was used. Laboratory quality assurance and quality control can be found on the NHANES website\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSleep health\u003c/h2\u003e \u003cp\u003eThe primary endpoint of sleep outcomes in the current analysis was SSD. We defined a sleep duration of less than 7 hours per 24-hour period as SSD, according to the American Academy of Sleep Medicine and the Sleep Research Society\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Regarding the secondary endpoint, self-reported trouble sleeping was captured by asking the question: \u0026ldquo;Ever told doctor had trouble sleeping?\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSystemic inflammation\u003c/h2\u003e \u003cp\u003eRecently, two novel indicators derived from the counts of lymphocytes, neutrophils, monocytes, and platelets, the systemic immune-inflammation index (SII) and the system inflammation response index (SIRI) were introduced\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Initially, SII was applied in assessing the prognosis of patients with liver cancer and SIRI predicted survival after chemotherapy in patients with cancer\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Considering the easily obtained and biological significance, these indicators were widely used in evaluating systemic inflammation response in the general population (Wang et al., 2022b; Xia et al., 2023) \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe whole blood specimens were measured using automated hematology analyzing devices\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. After analyzing in duplicate, the observed results were averaged to improve data quality. Utilizing the counts of peripheral blood cells (1000 cells/\u0026micro;L), we calculated SII using the formula\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\frac{platelets count\\times neutrophils count}{lymphocytes count}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSIRI was also calculated as follows\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\frac{monocyte count\\times neutrophils count}{lymphocytes count}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eAs reported in published studies\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, socio-demographic characteristics, physical examination, dietary information, lifestyles, and medical conditions were considered as potential covariates in relation to AA exposure and sleep outcomes. The covariates were included if they changed the coefficient of AA internal biomarkers by greater than 10% in simple linear regression models. The following covariates were selected: gender, age, race/ethnicity, educational levels, family poverty-income ratio (PIR), body mass index (BMI), smoking status, alcohol consumption, physical activity, diabetes, and hypertension. Participants were categorized as smoker and non-smoker subgroups through the questionnaire of recent smoking status (\u0026ldquo;Smoking - Cigarette Use\u0026rdquo;). We further checked the consistency with a previous definition and found that a majority of the smokers (98.2%) had serum cotinine concentration higher than 3.08 \u0026micro;g/L\u003csup\u003e32\u003c/sup\u003e. Alcohol consumption was defined as participants who had at least 12 alcohol drinks per year\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Physical activity was categorized by vigorous and moderate recreational activity for at least 10 min continuously per week\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The diagnosis of diabetes was based on a glycohemoglobin (HbA1c) level of \u0026gt;\u0026thinsp;6.5%, a fasting blood glucose level of \u0026ge;\u0026thinsp;7.0 mmol/L, a 2-hour plasma glucose level of \u0026ge;\u0026thinsp;11.1 mmol/L, self-reported history of diagnosed diabetes, and self-reported taking a diabetes medicine\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Blood pressure was measured up to four consecutive times by qualified professionals and the systolic blood pressure (SBP) and diastolic blood pressure (DBP) were calculated by averaging at least consecutive three measures. Hypertension was defined as SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, self-reported history of diagnosed hypertension, or taking a medicine for hypertension\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe general characteristics of participants were summarized using median and interquartile range (IQR) for continuous variables and frequency and proportion for categorical variables, respectively. HbAA, HbGA, HbAA\u0026thinsp;+\u0026thinsp;HbGA, HbGA/HbAA, and urinary creatinine-adjusted AAMA levels were natural logarithm (ln) transformed owing to skew distribution. Correlation was assessed using Pearson r value. Multiple imputation with chained equations was applied for a small number of missing covariates, including family PIR, BMI, and alcohol consumption.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression models were used to estimate associations of adverse sleep outcomes with internal exposure to AA. Multivariable linear regression models were applied to explore associations of AA internal biomarkers with markers of inflammation. The collinearity of the linear regression models was assessed by a variance inflation factor (VIF) and no multi-collinearity was observed. Regarding covariates, two models were conducted. Model 1 was a crude model without any adjustment. Model 2 was adjusted for all the aforementioned covariates as a primary model. Generalized additive models (GAMs) with 3 degrees-of-freedom natural cubic splines were fitted to estimate the potential nonlinear associations of AA internal biomarkers with adverse sleep outcomes. As cigarette smoking is a major source of AA exposure\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, we further introduced an interaction term between smoking and target biomarkers, and then performed stratified analysis if the interaction term was significant. A mediating role of systemic inflammation would be further examined if markers of systemic inflammation were associated with both sleep outcomes and AA internal biomarkers.\u003c/p\u003e \u003cp\u003eThe statistical analysis was performed using R version 4.2.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Statistical significance was considered as a two-sided \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.10 for interaction terms.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 2753 participants were included in the current analysis. The general characteristics are presented in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. The frequency and proportion of adults aged 20\u0026ndash;59 years were 1991 and 72.3%. A majority of participants were Non-Hispanic White (36.9%) and had higher educational levels (56.4%) and alcohol consumption (72.6%). The proportions of SSD and self-reported trouble sleeping were 30.7% and 26.0%, respectively.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescriptive statistics of general characteristics of 2753 participants from NHANES 2013\u0026ndash;2016\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian (25th -75th percentile)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.2 (24.4, 32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1991 (72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.12 (1.10, 4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e762 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e444 (315, 618)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04 (0.72, 1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1376 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1377 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/Ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e448 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e326 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1016 (36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e546 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther race - including multi-racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e417 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e599 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e601 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1553 (56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e583 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2170 (78.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol consumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2000 (72.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e753 (27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous or moderate recreational activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1387 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1366 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e488 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2265 (82.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1386 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1367 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: N-frequency; %-proportion; BMI-body mass index; PIR-poverty income ratio; SII- systemic immune-inflammation index; SIRI- system inflammation response index; WBC-white blood cell counts; hs-CRP: high-sensitivity C-reactive protein\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eHbAA and HbGA were detected in all the samples. The median values (25th percentile, 75th percentile) of HbAA, HbGA and HbAA\u0026thinsp;+\u0026thinsp;HbGA were 43.6 (33.4, 66.6) pmol/g Hb and 38.3 (28.0, 54.8) pmol/g Hb and 82.8 (62.4, 121) pmol/g Hb, respectively, and the median HbGA/HbAA was 0.824 (0.681, 0.985) (Supplemental materials, Table \u003cspan\u003eS1\u003c/span\u003e). AAMA was detected in almost all the urine samples (2751/2753). The median level of AAMA in urine was 52.5 (25.4, 106) \u0026micro;g/L. HbAA, HbGA, and urinary creatinine-adjusted AAMA concentration were highly correlated with Pearson r values ranging from 0.634 to 0.842 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The median (25th percentile, 75th percentile) calculated SII and SIRI were 444 (315, 618) and 1.04 (0.72, 1.51), respectively. A high correlation between SII and SIRI was observed (Pearson r value: 0.779, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eAssociations between AA internal biomarkers and adverse sleep outcomes are presented in Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. After adjustment for potential confounders, HbAA, HbGA, HbAA\u0026thinsp;+\u0026thinsp;HbGA level, and creatinine-adjusted urinary AAMA concentration were statistically significantly associated with 1.38-fold (95% confidence interval [CI]: 1.16, 1.64; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 1.29-fold (95%CI: 1.09, 1.53; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), 1.38-fold (95%CI: 1.15, 1.64; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 1.19-fold (95%CI: 1.06, 1.35; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) risk in SSD. No significant association between AA internal biomarkers and self-reported trouble sleeping was observed (Supplemental materials, Table S2). As shown in Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e, no evidence of statistically significant nonlinear associations between AA exposure and risk of SSD was found using GAM models (\u003cem\u003ep\u003c/em\u003e \u003csub\u003enonlinearity\u003c/sub\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eAssociations between AA internal biomarkers and markers of systemic inflammation are displayed in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e. After controlling for covariates, HbGA and HbGA/HbAA were significantly related to increased SII (regression coefficient [\u0026beta;]\u0026thinsp;=\u0026thinsp;0.06, 95%CI: 0.02, 0.09; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; \u0026beta;\u0026thinsp;=\u0026thinsp;0.12, 95%CI: 0.05, 0.18; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). HbGA and HbAA\u0026thinsp;+\u0026thinsp;HbGA were significantly associated with increased SIRI (\u0026beta;\u0026thinsp;=\u0026thinsp;0.06, 95%CI: 0.01, 0.10; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012; \u0026beta;\u0026thinsp;=\u0026thinsp;0.05, 95%CI: 0.00, 0.10; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032, respectively).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eEstimated regression coefficients and 95% CI for markers of systemic inflammation and AA internal biomarkers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSII (N\u0026thinsp;=\u0026thinsp;2753)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSIRI (N\u0026thinsp;=\u0026thinsp;2753)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01 (-0.02, 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04 (0.00, 0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06 (0.02, 0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06 (0.01, 0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbAA\u0026thinsp;+\u0026thinsp;HbGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03 (-0.01, 0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05 (0.00, 0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbGA/HbAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12 (0.05, 0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06 (-0.02, 0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAAMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00 (-0.03, 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01 (-0.02, 0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: \u0026beta;- regression coefficient; CI-confidence interval; SII-systemic immune-inflammation index; SIRI: system inflammation response index; HbAA-hemoglobin adducts of acrylamide; HbGA- hemoglobin adducts of glycidamide; AAMA- N-Acetyl-S-(2-carbamoylethyl)-L-cysteine\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eModels were adjusted for age, gender, race/ethnicity, education, body mass index, family poverty-to-income ratio, smoking status, alcohol consumption, physical activity, hypertension, and diabetes\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eInteraction terms between smoking status and AA hemoglobin biomarkers were partially statistically significant, thereby stratified analysis by smoking status was performed (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e). After stratification by smoking status, a statistically significant association between creatinine-adjusted urinary AMAA level and risk of SSD was only observed among smokers (odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.51, 95% CI: 1.14, 2.01; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), but not among non-smokers (OR\u0026thinsp;=\u0026thinsp;1.11, 95% CI: 0.97, 1.28; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.130). HbAA, HbGA, and HbAA\u0026thinsp;+\u0026thinsp;HbGA were significantly positively related to the risk of SSD in both smokers (OR\u0026thinsp;=\u0026thinsp;1.60, 95% CI: 1.16, 2.21; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004; OR\u0026thinsp;=\u0026thinsp;1.38, 95% CI: 1.02, 1.88; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038; OR\u0026thinsp;=\u0026thinsp;1.57, 95% CI: 1.13, 2.19; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, respectively) and non-smokers (OR\u0026thinsp;=\u0026thinsp;1.25, 95% CI: 1.01, 1.53; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040; OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI: 1.00, 1.52; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047; OR\u0026thinsp;=\u0026thinsp;1.27, 95% CI: 1.02, 1.57; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032, respectively). No significant association between AA internal biomarkers and risk of self-reported trouble sleeping was found.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eStratified analysis by smoking status for associations of AA internal biomarkers with adverse sleep outcomes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60 (1.16, 2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 (1.01, 1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38 (1.02, 1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (1.00, 1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbAA\u0026thinsp;+\u0026thinsp;HbGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57 (1.13, 2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27 (1.02, 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbGA/HbAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73 (0.43, 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.70, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAAMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51 (1.14, 2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (0.97, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-reported trouble sleeping\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (0.81, 1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (0.86, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (0.86, 1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (0.89, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbAA\u0026thinsp;+\u0026thinsp;HbGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17 (0.83, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (0.87, 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbGA/HbAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21 (0.70, 2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (0.76, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAAMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.75, 1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (0.95, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: OR- odds ratio; CI-confidence interval; SSD-short sleep duration; HbAA-hemoglobin adducts of acrylamide; HbGA- hemoglobin adducts of glycidamide; AAMA- N-Acetyl-S-(2-carbamoylethyl)-L-cysteine\u003c/p\u003e\n\u003cp\u003eModels were adjusted for age, gender, race/ethnicity, education, body mass index, family poverty-to-income ratio, smoking status, alcohol consumption, physical activity, hypertension, and diabetes\u003c/p\u003e\n\u003cp\u003eNo significant association of systemic inflammation markers with the risk of adverse sleep outcomes was observed (supplemental materials, Table S3). Thus, mediation analysis was not conducted.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cross-sectional analysis, AA internal biomarkers were associated with an increased risk of SSD. In smoking-stratified analysis, the significant associations were more pronounced in smokers. We observed that internal exposure to AA in association with higher markers of systemic inflammation. Given no significant association between systemic inflammation and the risk of SSD, we did not examine the mediating effect of systemic inflammation on the association of internal AA exposure with the risk of SSD.\u003c/p\u003e \u003cp\u003eAA was well-documented as a neurotoxic chemical that can lead to various neurological and psychiatric symptoms, such as depression, anxiety, neurodegenerative diseases, and cognitive impairment\u003csup\u003e\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Epidemiological evidence on the impact of AA exposure on sleep outcomes was quite limited. In an occupational population, tunnel workers who were previously exposed to AA or N-methylolacrylamide had higher self-reported nervous system symptoms measured by Q-16 neuropsychiatric questionnaire, including sleep disturbances, compared with non-exposed tunnel workers\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Our findings comprehensively evaluated the risk of SSD in association with internal AA exposure by using hemoglobin adducts and urinary measurements. This significant finding may be attributed to the neurotoxicity of AA. Although the underlying biological mechanisms are still unknown, inflammation, oxidative stress, apoptosis and autophagy may play a critical role\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Similarly, several epidemiological studies have reported that adverse sleep outcomes were associated with other neurotoxic chemicals, including heavy metals, perfluoroalkyl and polyfluoroalkyl substances, and polycyclic aromatic hydrocarbons\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs demonstrated previously\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, AA exposure could increase inflammation in various tissues, including neurons, the brain, the liver and the kidney. \u003cem\u003eIn vitro\u003c/em\u003e, AA exposure induced an inflammatory response via the nuclear factor-κB (NF-κB) pathway\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. With NF-κB activations, the transcription of inflammatory genes was enhanced, and pro-inflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6), pro-IL-1β and pro-IL-18, were released. In rodent models, these findings were further verified. AA treatment induced the enhancement of serum cytokines, including TNF-α, pro-IL-1β, and IL-6\u003csup\u003e18,43\u003c/sup\u003e. Likewise, we observed increases in inflammation markers, relating to higher exposure to AA in general adults.\u003c/p\u003e \u003cp\u003eIn stratified analyses, we found more prominent associations between AA exposure and risk of SSD in smokers than those in non-smokers. This phenomenon has also been observed previously in associations of AA exposure with other health outcomes, including diabetes, cardiovascular diseases, depressive symptoms\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Cigarette smoking, a critical source of AA exposure, was associated with increases in local and systemic inflammation and SSD\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Compared with non-smokers, smokers had higher exposure levels of AA and other toxic chemicals, such as tar, formaldehyde, polycyclic aromatic hydrocarbons, and heavy metals\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. We cannot exclude the possibility that AA in combination with a series of toxic chemicals in tobacco smoke contributes to the development of systemic inflammation, and increased risk of SSD. Consequently, additional epidemiological research is needed to assess exposure to a mixture of toxic chemicals in tobacco smoke associated with systemic inflammation and adverse sleep outcomes.\u003c/p\u003e \u003cp\u003eAs far as we know, this is the first epidemiological study to explore associations of internal AA exposure with adverse sleep outcomes and systemic inflammation in the general population. Meanwhile, AA exposure was assessed using hemoglobin adducts and a urinary measurement in human biomonitoring. Furthermore, SII and SIRI are novel markers of systemic inflammation that are easily obtained and have significant implications for the understanding of systemic inflammation associated with chemical exposure. However, several limitations in our study should be acknowledged. Firstly, owing to the cross-sectional study design, causality cannot be inferred between AA exposure and the risk of SSD. Secondly, although a broad set of covariates was adjusted in this study, we cannot exclude the possibility of residual confounders, such as occupational factors\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, and other environmental contaminants\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Thirdly, AAMA was only measured in a spot urine sample and AA hemoglobin biomarkers can only reflect AA exposure in the last 120 days\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. There is a possibility of exposure misclassification and repeated measurements would have provided a more accurate or long-term assessment of exposure levels.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAA exposure assessed using hemoglobin adducts and urinary biomarkers was associated with increased risk of SSD in general adults aged 20–79 years. This association was prominent in smokers. Moreover, internal exposure to AA was related to increases in systemic inflammation. The current findings provide suggestive evidence regarding the risk of SSD and the proinflammatory effects of AA exposure at environmentally relevant doses. Future research is warranted to confirm these findings and develop intervention strategies.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAA:\u003c/em\u003e\u003c/strong\u003e acrylamide\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHbAA:\u003c/em\u003e\u003c/strong\u003e hemoglobin adducts of acrylamide\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHbGA:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ehemoglobin adducts of glycidamide\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAAMA:\u003c/em\u003e\u003c/strong\u003e N-Acetyl-S-(2-carbamoylethyl)-L-cysteine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSII:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003esystemic immune-inflammation index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSIRI:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003esystem inflammation response index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSSD:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eshort sleep duration\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNHANES:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLOD:\u003c/em\u003e\u003c/strong\u003e limits of detection\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSII:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003esystemic immune-inflammation index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSIRI:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003esystem inflammation response index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePIR:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003epoverty-income ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBMI:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ebody mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHbA1c:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eglycohemoglobin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSBP:\u003c/em\u003e\u003c/strong\u003e systolic blood pressure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDBP:\u003c/em\u003e\u003c/strong\u003e diastolic blood pressure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGAM:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eGeneralized additive model\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciated the adults for participation in the NHANES survey. We are also grateful for the\u0026nbsp;United States\u0026nbsp;Centers for Disease Control and Prevention (CDC) for providing the publicly available data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Natural Science Foundation of Jilin Province of China (Grant No. 20200201606JC) and the National Natural Science Foundation of China (Grant No. 31872772).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLin Gan, Jiaoyang Wang, Kang Qu,\u0026nbsp;Wei Jiang, Ming Dong\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Neurosurgery, The First Hospital of Jilin University, Changchun, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZeshang Guo\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.G.: Conceptualization, Data curation, Formal analysis, Methodology, Writing \u0026ndash; original draft. Writing \u0026ndash; review \u0026amp; editing. J.W.: Data curation, Formal analysis, Methodology, Software, Supervision. K.Q.: Data curation, Formal analysis, Supervision, Writing \u0026ndash; review \u0026amp; editing. W.J.: Conceptualization, Formal analysis, Supervision, Writing \u0026ndash; review \u0026amp; editing. Z.G.: Conceptualization, Formal analysis, Writing \u0026ndash; review \u0026amp; editing. M.D.: Conceptualization, Funding acquisition, Project administration, Supervision, Writing \u0026ndash; review \u0026amp; editing. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Ming Dong\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003ePublisher\u0026apos;s note\u003c/h3\u003e\n\u003cp\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSalo P, Sivertsen B, Oksanen T, Sj\u0026ouml;sten N, Pentti J, Virtanen M, et al. 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(1993)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acrylamide, Glycidamide, N-Acetyl-S-(2-carbamoylethyl)-L-cysteine, Short sleep duration, Systemic inflammation","lastPublishedDoi":"10.21203/rs.3.rs-4625431/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4625431/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAcrylamide (AA) is a ubiquitous neurotoxic contaminant. Our objectives were to evaluate associations of internal AA exposure with sleep health outcomes. Data from 2753 adults aged 20\u0026ndash;79 years in the National Health and Nutrition Examination Survey (NHANES) was utilized. Internal AA exposure was assessed using hemoglobin adducts and urinary biomarkers. Short sleep duration (SSD) and self-reported trouble sleeping were employed as indicators of sleep health. Markers of systemic inflammation were calculated. Each one-unit increase in ln-transformed hemoglobin adducts of acrylamide (HbAA), hemoglobin adducts of glycidamide (HbGA) and HbAA\u0026thinsp;+\u0026thinsp;HbGA and creatinine-adjusted urinary N-Acetyl-S-(2-carbamoylethyl)-L-cysteine concentration was statistically significantly associated with 1.38-fold (95% confidence interval [CI]: 1.16, 1.64; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 1.29-fold (95%CI: 1.09, 1.53; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), 1.38-fold (95%CI: 1.15, 1.64; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 1.19-fold (95%CI: 1.06, 1.35; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) risk in SSD, respectively. The significant associations were strengthened in smokers after stratification by smoking status. Higher AA hemoglobin biomarkers predicted increases in markers of systemic inflammation. In conclusion, internal AA exposure was associated with an increased risk of SSD and elevated systemic inflammation among United States adults. The findings shed light on the potential effects of AA\u0026rsquo;s health threat and future research is warranted to develop intervention strategies.\u003c/p\u003e","manuscriptTitle":"Associations between internal exposure to acrylamide and sleep health: Evidence from NHANES 2013-2016","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-17 11:24:04","doi":"10.21203/rs.3.rs-4625431/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad023471-d79a-4a10-9898-59349a465397","owner":[],"postedDate":"July 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-24T09:07:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-17 11:24:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4625431","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4625431","identity":"rs-4625431","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}