
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39256472
72215
10.1038/s41598-024-72215-3
Article
Serum cotinine levels and adolescents’ sleep health outcomes from NHANES 2005 to 2018
Du Xuanjin 2
Peng Ting 3
Ma Ling 4
Cheng Guoqiang gqcheng_cm@fudan.edu.cn

1
1 https://ror.org/05wg75z42 grid.507065.1 Fujian Key Laboratory of Neonatal Diseases, Xiamen Children’s Hospital, Xiamen, 361000 China
2 https://ror.org/05n13be63 grid.411333.7 0000 0004 0407 2968 Department of Nephrology, Shanghai Kidney Development and Pediatric Kidney Disease Research Center, Children’s Hospital of Fudan University, Shanghai, 201102 China
3 https://ror.org/05n13be63 grid.411333.7 0000 0004 0407 2968 Department of Neonatology, Children’s Hospital of Fudan University, Shanghai, 201102 China
4 grid.16821.3c 0000 0004 0368 8293 Department of Child Health Care, School of Medicine, Shanghai Children’s Hospital, Shanghai Jiao Tong University, Shanghai, 200127 China
10 9 2024
10 9 2024
2024
14 2107611 3 2024
4 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The association between tobacco smoke exposure and sleep has been widely discussed, but the correlation between serum cotinine levels and sleep health outcomes in adolescents has not been well described. This study aimed to further evaluate the association between serum cotinine levels and sleep health outcomes in adolescents using data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. This cross-sectional study included participants aged 16–19 years from the NHANES 2005–2018. A weighted multivariate logistic regression model was used for the primary analysis. A restricted cubic spline (RCS) model was employed to investigate the non-linear association between serum cotinine levels and trouble sleeping. Subgroup analyses based on population characteristics were also conducted. In total, 2630 participants were included, which are representative of the 11.5 million US adolescents. Higher serum cotinine levels (≥ 3 ng/ml) were significantly associated with trouble sleeping in the fully adjusted model (odds ratio [OR] 1.817). The RCS model revealed a non-linear relationship between serum cotinine levels and trouble sleeping. Subgroup analyses indicated that this relationship was consistent and stable across various population characteristics. Serum cotinine levels are associated with sleep health outcomes in adolescents, with high serum cotinine levels being linked to increased trouble sleeping and longer or shorter sleep duration.

Subject terms

Computational biology and bioinformatics
Neuroscience
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pmcIntroduction

The importance of sleep for adolescent health is widely recognized, as this period represents a critical stage of growth and development1,2. Sleep disorders have become increasingly prominent among adolescents in recent years. For example, a study focusing on depression and insomnia in adolescents showed that nearly a quarter of respondents had symptoms of insomnia3. Another study found that 60.5% of adolescents had difficulty falling asleep, 73.6% had difficulty maintaining sleep, and 30.7% of adolescents were asleep for less than 8 h4. Sleep problems in adolescents pose a range of important and complex health risks, including inattentiveness, mood disturbances, increased risk of obesity, and cardio-metabolic dysfunction5,6. Moreover, developing good sleep habits in adolescence are crucial for sleep health in adulthood7.

Many studies have confirmed the strong relationship between smoking and sleep disorders8,9. Nicotine, the primary alkaloid in tobacco, is the main cause of tobacco addiction10. Cotinine, with a biological half-life of 19–24 h, is a reliable biomarker for estimating nicotine exposure due to its specificity and ease of detection11–13.

These effects of cotinine have been extensively described, including cardiovascular and pulmonary diseases, asthma, cancer, and Alzheimer’s disease14–20. Previous research has also shown that smokers are more prone to sleep disorders, such as shorter sleep duration, increased sleep latency, daytime sleepiness, and trouble maintaining sleep, compared to non-smokers21–23. Another study suggested a correlation between high urinary cotinine levels and increased sleep disorders in adults24. However, few studies have investigated the relationship between adolescent sleep health and serum cotinine25.

Given the limited research on the relationship between serum cotinine levels and sleep health in adolescents, this study aims to fill this gap using National Health and Nutrition Examination Survey (NHANES) data from 2005 to 2018. Specifically, this study aims to investigate the relationship between serum cotinine levels and sleep disorders in adolescents aged 16–19 years. We hope that this study can provide valuable insights for the prevention and reduction of sleep disorders in adolescents.

Materials and methods

Data and sample sources

Data were downloaded from the NHANES, a nationally representative cross-sectional survey designed and conducted by the National Center for Health Statistics (NCHS). The NCHS Research Ethics Review Board authorized the survey, ensuring that all participants provided informed consent. Detailed statistics and methodology are available at https://www.cdc.gov/nchs/nhanes/.

Study population

The NHANES is a large, biennial, nutrition-related cross-sectional survey of United States (US) citizens, employing a multi-stage sampling design to ensure national representativeness. Publicly available data from seven NHANES cycles (2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018) were included in this study.

A total of 70,190 data points were initially screened. As our focus was on adolescents aged 16–19 years, we excluded 25,489 participants under 16 years and 39,749 participants over 19 years. Additionally, we excluded 601 participants without serum cotinine data, 10 participants without sleep data (trouble sleeping and sleep duration), and 1711 participants without data on covariates (age, sex, education, poverty, race, body mass index (BMI), physical activity, total energy intake, caffeine intake, and asthma). This resulted in a final sample size of 2630 participants (Fig. 1).Fig. 1 The flow chart of study population selection.

NHANES cotinine laboratory analysis

Serum cotinine levels were categorized to reflect tobacco exposure as follows: (1) < 0.05 ng/ml, unexposed and nonsmokers; (2) 0.05–3 ng/ml, exposed to second-hand smoke; and (3) ≥ 3 ng/ml, active smokers26. Serum specimens were processed, stored, and shipped to the Division of Laboratory Sciences, National Center for Environmental Health, and Centers for Disease Control and Prevention (CDC) for analysis. Samples were stored under appropriate frozen conditions (− 20 °C) until testing. Detailed quality assurance and control procedures were described in the NHANES Laboratory Procedures Manual (https://wwwn.cdc.gov/nchs/data/nhanes/2013-2014/manuals/2013_MEC_Laboratory_Procedures_Manual.pdf).

Serum samples were analyzed using isotope dilution high-performance liquid chromatography/atmospheric-pressure chemical ionization tandem mass spectrometry. The lower detection limit for cotinine was 0.015 ng/ml27. Values below this threshold were reported as 0.011 ng/ml. In total, 813 individuals with cotinine levels below the detection limit were included in this study (https://www.cdc.gov/nchs/nhanes/about_nhanes.htm). Additional information on sample collection is available from the CDC28.

Sleep disorder and sleep factors assessment

Trouble sleeping

Data from the NHANES “Sleep Disorders” datasets (2005–2014, 2015–2016, and 2017–2018) were used for this analysis. All participants aged 16 and older were eligible. Trained interviewers administered these questions using a computer-assisted personal interview system at participants’ homes. The presence of sleep disorders was determined based on responses to the question (SLQ050): “Have you told a doctor or other health professional that you had sleep problems?”.

Sleep duration

Sleep duration was assessed based on responses to questions regarding sleep hours: “How much sleep do you get (hours)?” (2005–2014/SIDO10H), “Sleep hours” (2015–2016/SLD012), and “Sleep hours-weekdays or workdays” (2017–2018/SLD012).

Sleep duration was categorized into three subgroups based on recommended sleep times, which vary among different age groups29. For teenagers aged 16–17 years: “less than recommended” = sleep duration < 8 h, “recommended” = 8–10 h, and “more than recommended” ≥ 10 h. For teenagers aged 18–19 years: “less than recommended” = sleep duration < 7 h, “recommended” = 7–9 h, and “more than recommended” ≥ 9 h25.

Covariates

The following social and demographic factors were considered as potential covariates: age, sex, race, education, poverty income ratio (PIR), BMI, physical activity, total energy intake, caffeine intake, and asthma30–34. A hierarchical approach was used to stratify the data based on various covariates. Age was categorized into two groups: 16–17 years and 18–19 years. Participants were divided into male and female groups. Racial information was used to create four distinct groups: non-Hispanic white, non-Hispanic black, Mexican American, and other ethnic groups. Based on education level, participants were categorized into high school, higher than high school, and lower than high school. The PIR, representing the ratio of income to the poverty threshold, was divided into three groups: < 1, 1–3, and ≥ 3. According to the American BMI standards, participants were grouped into three categories: < 25, 25–30, and ≥ 30 kg/m235,36. Total energy intake was calculated using two indicators (total nutrient intakes and total dietary supplements) from 24-h dietary recalls in NHANES. All NHANES participants were eligible to participate in two 24-h dietary recall interviews. Both total energy intake and caffeine intake were expressed as the average intake from the two 24-h recalls. Physical activity was assessed using the Physical Activity Questionnaire (PAQ) in NHANES and was expressed as the metabolic equivalent (MET). Energy expenditure (MET·min) was calculated as the recommended MET value × exercise time for each corresponding activity (min)34,37. Asthma status was determined based on responses to the question: “Has a doctor or other health professional ever told you that you have asthma?” (MCQ010).

Statistical analysis

Both digital analysis and image generation were performed using R software (version 4.0.5). Population data were analyzed using a rank sum test, and two categorical variables (age and sex) were tested using the Wilcoxon Mann–Whitney test. Multicenter categorical variables (race, education, PIR, and BMI) were tested with the Kruskal–Wallis test, and these data were weighted according to the NHANES multi-stage design. Appropriate weighting procedures were applied in accordance with NHANES analysis guidelines. After 2002, NHANES weights were calculated every 2 years, and the data for 2005–2018 covered two 2-year sampling cycles. The recalculated weights were represented as (1/7) × WTDR2D05–06 + (1/7) × WTDR2D07–08 + (1/7) × WTDR2D09–10 + (1/7) × WTDR2D11–12 + (1/7) × WTDR2D13–14 + (1/7) × WTDR2D15–16 + (1/7) × WTDR2D17–18, where WTDR2Ds were variables from NHANES 2005–2018. Serum cotinine was analyzed both as a categorical variable and a continuous variable. Clear indications for categorical (serum cotinine categories) and continuous (serum cotinine continuous) variables were provided in the analysis. Additionally, three models were used to estimate the weighted logistic regression results for serum cotinine levels in relation to sleep disorders: Crude model: model without adjusted covariates. Model 1: adjusted for age, sex, race, education, PIR, and BMI. Model 2: adjusted for age, sex, race, education, PIR, BMI, physical activity, total energy intake, caffeine intake, and asthma. The choice of covariates was based on previous studies34,38,39.

When exploring the association of log-transformed serum cotinine with trouble sleeping through population stratification, all factors (age, sex, race, education, PIR, BMI, physical activity, total energy intake, caffeine intake, and asthma) were adjusted in each stratification, except for the stratification factor itself. Stratification analysis was used to explore whether population stratification factors had an interactive effect between serum cotinine and trouble sleeping. Similarly, all factors were adjusted in the subgroup analysis. Restricted cubic spline (RCS) regression was used to describe the non-linear relationship40. Statistical significance was set at P < 0.05.

Results

Population characteristics

The 2630 NHANES participants with valid sleep data represented 11.5 million US adolescents aged 16–19 years. The demographics and characteristics of the study population are shown in Table 1. There were no significant differences in education, PIR, BMI, physical activity, total energy intake, caffeine intake, asthma, or race distribution among the subjects. Notably, 10.4% of the participants reported trouble sleeping. Adolescents were divided into two subgroups (16–17 years and 18–19 years), and the incidence of trouble sleeping was significantly higher in the 18–19 years group compared to the 16–17 years group (P = 0.015). Additionally, the incidence of trouble sleeping was higher in female than in male (P = 0.004). Based on the data from 2005 to 2018 in Table 1, when serum cotinine levels were ≥ 3 ng/ml, the occurrence of trouble sleeping was significantly higher compared to the other two groups (16.09% vs. 8.36%/9.67%, P = 0.007).Table 1 Weighted characteristics of study population by trouble sleeping in NHANES 2005–2018.

Variable	All (n=2630)	Non-trouble sleeping (n=2356)	Trouble sleeping (n=274)	P value	
Age, n (%)	0.015	
 16–17	1320 (50.190)	1212 (50.441)	108 (35.624)		
 18–19	1310 (49.810)	1144 (49.559)	166 (64.376)		
Sex, n (%)				0.004	
 Male	1395 (53.042)	1279 (54.519)	116 (39.439)		
 Female	1235 (46.958)	1077 (45.481)	158 (60.561)		
Race, n (%)	0.057	
 Non-Hispanic White	793 (30.152)	683 (60.231)	110 (59.905)		
 Non-Hispanic Black	691 (26.274)	636 (13.506)	55 (10.658)		
 Mexican American	636 (24.183)	591 (14.327)	45 (10.562)		
 Others	510 (19.392)	446 (11.936)	64 (18.875)		
Education, n (%)	0.133	
 Below high school	1739 (66.122)	1576 (64.553)	163 (54.199)		
 High school	510 (19.392)	449 (19.621)	61 (27.723)		
 Above high school	381 (14.487)	331 (15.826)	50 (18.078)		
Poverty income ratio, n (%)	0.119	
 ＜ 1	828 (31.483)	733 (22.621)	95 (27.939)		
 [1,3)	1072 (40.760)	957 (37.938)	115 (42.318)		
 ≥ 3	730 (27.757)	666 (39.441)	64 (29.743)		
Body mass index, n (%)	0.268	
 ＜ 25	1521 (57.833)	1375 (61.492)	146 (54.372)		
 [25, 30)	593 (22.548)	533 (20.642)	60 (21.958)		
 ≥ 30	516 (19.620)	448 (17.866)	68 (23.670)		
Total energy intake, median (IQR), Kcal	2012.000 (1570.500, 2604.000)	2042.500 (1588.000, 2604.000)	1861.500 (1415.000, 2595.000)	0.058	
Caffeine intake, median (IQR), mg	37.000 ( 4.000, 84.500)	37.000 (4.000, 83.500)	38.500 (3.500, 93.000)	0.953	
Serum cotinine continuous, median (IQR), ng/ml	0.062 (0.015, 1.710)	0.057 (0.015, 1.190)	0.146 (0.016, 20.100)	0.063	
Serum cotinine categories, n (%)	0.007	
 ＜ 0.05	1208 (45.932)	1107 (47.985)	101 (39.892)		
 [0.05, 3)	869 (33.042)	785 (31.592)	84 (26.269)		
 ≥ 3	553 (21.027)	464 (20.423)	89 (33.838)		
Sleep duration, n (%)	0.787	
 Recommended	1524 (57.947)	1385 (61.251)	139 (58.850)		
 Less than recommended	968 (36.806)	850 (32.527)	118 (35.460)		
 More than recommended	138 (5.247)	121 (6.222)	17 (5.690)		
Physical activity, MET min/weeks, n (%)	0.612	
 ＜ 450	623 (23.688)	567 (15.243)	56 (16.823)		
 ≥ 450	2007 (76.312)	1789 (84.757)	218 (83.177)		
Asthma, n (%)	0.136	
 No	2108 (80.152)	1908 (80.280)	200 (74.682)		
 Yes	522 (19.848)	448 (19.720)	74 (25.318)		
Variables are presented as median (IQR) or numbers (percentages).

NHANES National Health and Nutrition Examination Survey, IQR interquartile range.

P values derived from Wilcoxon test for continuous variable without normal distribution and chi-square tests for categorical variables.

Detailed information on the weighted characteristics of the study population for each of the 2-year cycles from 2005 to 2018 is presented in Table S1. Information about the weighted characteristics of the study population by sleep duration in NHANES 2005–2018 can be found in Table S2.

Serum cotinine and trouble sleeping

As shown in Table 2, higher serum cotinine levels (≥ 3 ng/ml) were significantly associated with trouble sleeping across all three models. Using serum cotinine < 0.05 ng/ml as a reference, higher serum cotinine (≥ 3 ng/ml) was positively correlated with trouble sleeping. In the crude model (odds ratio [OR] 1.993, 95% confidence interval [CI] 1.216–3.267, P for trend = 0.015); in model 1 (OR 1.880, 95% CI 1.123–3.149, P for trend = 0.034); and in model 2 (OR 1.817, 95% CI 1.093–3.023, P for trend = 0.045). Additionally, the association remained significant for log-transformed serum cotinine continuous across all three models.Table 2 Weighted logistic regression results of serum cotinine level with sleep disorders.

	Crude model
OR (95% CI)	Model 1
OR (95% CI)	Model 2
OR (95% CI)	
Trouble sleeping	
Serum cotinine categories	
  < 0.05	1.000 [Reference]	1.000 [Reference]	1.000 [Reference]	
  [0.05, 3)	1.000 (0.591, 1.692)	0.936 (0.566, 1.548)	0.888 (0.533, 1.479)	
  ≥ 3	1.993 (1.216, 3.267)**	1.880 (1.123, 3.149)*	1.817 (1.093, 3.023)*	
 P for trend	0.015	0.034	0.045	
 Log-transformed serum cotinine continuous	1.092 (1.027, 1.161)**	1.085 (1.017, 1.158)*	1.082 (1.015, 1.153)*	
Less than recommended vs recommended sleep duration	
 Serum cotinine categories	
  < 0.05	1.000 [Reference]	1.000 [Reference]	1.000 [Reference]	
  [0.05, 3)	1.480 (1.080, 2.028)*	1.697 (1.230, 2.343)**	1.652 (1.215, 2.247)**	
  ≥ 3	1.124 (0.765, 1.652)	1.600 (1.037, 2.468)*	1.507 (0.958, 2.368)	
 P for trend	0.254	0.009	0.023	
 Log-transformed serum cotinine continuous	1.030 (0.986, 1.075)	1.075 (1.022, 1.130)**	1.067 (1.013, 1.124)*	
More than recommended vs recommended sleep duration	
 Serum cotinine categories	
  < 0.05	1.000 [Reference]	1.000 [Reference]	1.000 [Reference]	
  [0.05, 3)	1.604 (0.754, 3.413)	1.390 (0.623, 3.101)	1.357 (0.625, 2.946)	
  ≥ 3	2.556 (1.338, 4.883)**	2.056 (1.015, 4.163)*	2.164 (1.050, 4.459)*	
 P for trend	0.005	0.047	0.040	
 Log-transformed serum cotinine continuous	1.140 (1.058, 1.229)***	1.117 (1.026, 1.216)*	1.127 (1.033, 1.230)**	
Crude model: no covariate was adjusted. Model 1: age, sex, race, education, poverty income ratio, and body mass index were adjusted. Model 2: age, sex, race, education, poverty income ratio, body mass index, physical activity, total energy intake, caffeine intake, and asthma were adjusted.

OR odds ratio, CI confidence intervals.

*Denotes P < 0.05, **denotes P < 0.01, ***denotes P < 0.001.

The association between log-transformed serum cotinine levels and trouble sleeping, stratified by population characteristics, is shown in Fig. 2. There was no interaction between these covariates (except education level), serum cotinine, and trouble sleeping. Specifically, the association of log-transformed serum cotinine with trouble sleeping was interacted with education level (high school, P < 0.001, P for interaction = 0.014). In subgroup analyses, age showed an interactive effect on the relationships between serum cotinine categories and the OR of trouble sleeping (Table S3).Fig. 2 Association of log-transformed serum cotinine continuous with trouble sleeping stratified by population characteristics.

RCS analyses were performed to explore the dose–response relationship between serum cotinine and the risk of trouble sleeping. The results, presented in Fig. 3, indicated that log-transformed serum cotinine levels were non-linearly associated with adjusted trouble sleeping. When serum cotinine levels exceeded 0.1306 ng/ml, the risk of trouble sleeping gradually increased.Fig. 3 The dose–response relationship between log-transformed serum cotinine continuous and trouble sleeping.

Serum cotinine and sleep duration

When exploring the correlation between sleep duration and serum cotinine, both model 1 and 2 supported the correlation between higher serum cotinine levels (≥ 3 ng/ml) and longer sleep duration, as well as the correlation between high serum cotinine levels (0.05–3 ng/ml) and shorter sleep duration (Table 2).

The association between log-transformed serum cotinine levels and sleep duration, stratified by population characteristics, is shown in Fig. S1. The stratification analysis indicated that age and BMI interacted with the relationship between sleep duration and serum cotinine levels.

Discussion

This study found that serum cotinine levels were associated with self-reported sleep disorders, such as trouble sleeping and sleep duration. The relationship remained significant after adjusting for other covariates, including age, sex, race, BMI, PIR, physical activity, total energy intake, caffeine intake, and asthma. Higher serum cotinine levels (≥ 3 ng/ml) were associated with increased trouble sleeping, and subgroup analyses excluded possible interactions among trouble sleeping, serum cotinine, and other covariates. The dose–response analysis also showed a non-linear correlation between higher serum cotinine levels and trouble sleeping. Additionally, there was a correlation between sleep duration and serum cotinine levels, with higher levels (≥ 3 ng/ml) related to sleep duration beyond the recommended amount, and this trend was statistically significant. This trend was remained significant in both model 1 and 2 when using log-transformed serum cotinine.

The correlation between tobacco exposure and sleep has been well documented, with researchers initially assessing differences in sleep quality between smokers and nonsmokers41,42. After the discovery of nicotine and its metabolic pathways, serum/urinary cotinine became an important biological indicator of tobacco smoke exposure. High serum or urinary cotinine levels have been associated with an increased risk of sleep disorders. A 2016–2018 cross-sectional study reported an association between high urinary cotinine levels and poor sleep quality, with a more pronounced trend among women and self-reported never-smokers24. To reduce potential exposure misclassification and associated bias from self-reported results, binary logistic regression analysis was performed using national sample data from the Canadian Survey of Health Measures (2007–2013) to confirm the association between urinary cotinine and sleep quality. This study used a range of quantified sleep quality measures, including sleep duration, sleep continuity or efficiency, sleep satisfaction, and alertness during normal waking hours. The results supported the association between tobacco smoke exposure and poor sleep quality, with a stronger association observed in women compared to men43. A recent study on the association of adolescent sleep disorders with tobacco smoke exposure also suggested that adolescents exposed to secondhand smoke reported sleep deprivation and hypersomnia44.

The results of this study confirmed that higher serum cotinine levels were associated with trouble sleeping and suggested that females were more susceptible than males (Table 1). Additionally, there was an interaction between education level and this correlation in the stratified analysis (Fig. 2). However, such interactions were not demonstrated in subgroup analyses (Table S3).

The RCS analysis revealed that higher serum cotinine levels were related to a higher risk of trouble sleeping, further illustrating the consistency of our findings. Another cohort study reported the same trend in children aged 3–9 years45. Secondhand smoke exposure shortens sleep duration during childhood45. A cross-sectional study from NHANES 2017–2018 suggested that short sleep duration was significantly associated with high serum cotinine levels46. Furthermore, data from 2005–2006 supported this conclusion42. Elevated urinary cotinine levels were associated with significantly higher odds of shorter or longer sleep duration43. The 2013–2018 NHANES included 914 non-tobacco-using adolescents aged 16–19 years, indicating that adolescents exposed to tobacco have a higher chance of sleep deprivation25. These results are consistent with our findings; however, our study covered a longer period.

Exposure to smoke during pregnancy and lactation increases the likelihood of sleep problems in infants. Tobacco metabolites in breast milk may affect the neurodevelopment of the fetus and infant, leading to a range of neurobehavioral problems47,48. In addition to studies in humans, animal experiments have confirmed that early life exposure to nicotine/cotinine produces long-lasting sleep alterations and downregulates hippocampal corticosteroid receptors in adult mice49. Prenatal nicotine exposure in rodents is associated with behavioral changes, partly controlled by the pontine laterodorsal tegmentum50. Thus, studies on tobacco smoke exposure, sleep disturbance, and other neurological impairments are recommended to understand how tobacco exposure affects sleep in humans. Beyond affecting brain development, smoke exposure can also lead to sleep disorders by affecting neurophysiological brain functions. As the main active substance in tobacco, nicotine can affect sleep by influencing the release of neurotransmitters such as dopamine51. In addition, it may stimulate cholinergic neurons in the basal forebrain and cause physiological arousal, leading to the occurrence of sleep disorders such as insomnia52.

In addition to the tobacco exposure discussed above, several other potential covariates that influence sleep health were not included in this study due to severe missing data. These covariates include parental education level, family structure, academic and social pressures, alcohol consumption, mental health issues, drug use, and experiences of neglect. Previous research has well documented the relationships between these factors and sleep. Higher parental education levels and stable family structures are associated with better adolescent sleep quality, while family conflicts and single-parent households correlate with poorer sleep53. Academic pressure and social stressors, including peer relationships and bullying, are linked to reduced sleep duration and increased insomnia risk54,55. Cigarette smoking and secondhand smoke exposure reduce sleep quality, and there is a significant correlation between smoking and alcohol consumption in adolescents, compounding sleep issues56,57. Alcohol consumption disrupts sleep patterns by altering the sleep cycle, reducing REM sleep, and causing frequent awakenings58. Mental health issues, such as depression and anxiety, significantly impact sleep, with depression leading to changes in sleep architecture and being a predictor of smartphone addiction, and anxiety disorders causing over 50% of affected individuals to experience sleep disturbances59–61. Illicit drug use also disrupts sleep, leading to difficulty falling asleep and frequent awakenings62. Experiencing neglect profoundly impacts sleep, causing disturbances due to a lack of consistent routines and emotional security, and leading to heightened arousal and disrupted sleep architecture63. These factors highlight the complex interplay between various covariates and adolescent sleep, suggesting the need for a comprehensive approach in future research to address these interconnected influences on sleep quality and overall well-being.

The accuracy and reliability of our results were enhanced by quantifiable serum cotinine levels and a large representative sample of adolescents aged 16–19 years in the US. Several prior studies have indicated a correlation between higher serum cotinine levels and trouble sleeping45,46. However, our study stands out for its innovation, incorporating data from multiple yearly cycles and specifically targeting adolescents aged 16 to 19 years. Despite the strengths, this study had some limitations. First, as a cross-sectional study, it was impossible to infer causal relationships or exclude bidirectional relationships. Additionally, a single measurement of serum cotinine concentrations inevitably leads to misclassification of exposure. In our study, SLQ050 (Have you told a doctor or other health professional that you had sleep problems?) was used to determine the population with sleep disorders, but this result did not represent a medical diagnosis. Furthermore, bias and errors were inevitable because of self-reported data and the lack of objective data. As the main subjects were adolescents aged 16–19 years, some covariates were incomplete (e.g., drinking, alcohol consumption, mental health issues), thus it was impossible to do related analysis and to confirm whether these covariates played interactive roles in this study. Despite these limitations, this analysis provided meaningful results that can be used as a basis for further research. As this was a cross-sectional study, causation determination could not be established, and the causative relationship between serum cotinine levels and sleep disorders warrants further investigation.

The samples included in our study were from the NHANES 2005 to 2018. In US adolescents aged 16–19 years, high serum cotinine levels were associated with increased trouble sleeping and longer or shorter sleep duration. This finding suggested that higher serum cotinine levels are non-linearly associated with trouble sleeping.

Supplementary Information

Supplementary Figure S1.

Supplementary Table S1.

Supplementary Table S2.

Supplementary Table S3.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-72215-3.

Acknowledgements

Thanks to Zhang Jing (Second Department of Infectious Disease, Shanghai Fifth People’s Hospital, Fudan University) for his work on the NHANES database. His outstanding work, nhanesR package and webpage, makes it easier for us to explore NHANES database. This study was supported by the Medical Science Data Center of Fudan University.

Author contributions

TP and XD participated in the investigation, visualization, writing original manuscript, reviewing and editing the study. LM contributed in the methodology. GC contributed to critical supervision.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Ethical approval

The NCHS Research Ethics Review Board authorized the survey, verifying that all participants provided informed consent, Detailed statistics are accessible at https://www.cdc.gov/nchs/nhanes/.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Xuanjin Du and Ting Peng.
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