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Associations between nocturnal bedtime and asthma among adults in the United States

BMC Pulmonary Medicine volume 24, Article number: 419 (2024) Cite this article

Abstract

Background

Sleep disorders have a significant impact on asthma. The aim of this study was to explore the association between nocturnal bedtime and asthma among adults in the United States.

Methods

This study was a cross-sectional analysis involving 11,475 participants from the National Health and Nutrition Examination Survey (NHANES) during the period of 2015–2018. Nocturnal bedtime was categorized into three distinct groups: 2100 h or earlier, between 2100 h and 2300 h, and 2300 h or later. The association between night bedtime and asthma was detected using multivariable logistic regression analyses. Additionally, subgroup analyses were conducted to assess the impact of subgroups.

Results

After adjustment for confounders, a positive association was revealed between later bedtime (after 2300 h) and the prevalence of asthma (OR = 1.20, 95%CI: 1.01–1.43). In the subgroup analysis, the following factors were associated with increased risk: 18–39 years (OR = 1.23, 95%CI: 1.02–1.48); female sex (OR = 1.30, 95%CI: 1.01–1.68); Hispanic patients (OR = 1.66, 95%CI: 1.17–2.37); heavy drinkers (OR = 1.52, 95%CI: 1.17–1.96); Body Mass Index (BMI) (< 25 kg/m2) (OR = 1.45, 95%CI: 1.13–1.87); vigorous physical activity (OR = 1.32, 95%CI: 1.05–1.65);Significant interactions were found between nocturnal bedtime and asthma based on age, sex, eosinophils (EOS) percent and depression (P Interaction < 0.05).

Conclusion

Our results confirmed a moderately increased risk of asthma attributed to later bedtime, especially in 18–39 years, women and patients of Hispanic ethnicity. Future studies should investigate the underlying mechanisms of this association and explore the clinical implications for asthma management.

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Introduction

Asthma is a prevalent and chronic respiratory disease affecting approximately 339 million people with at least 250,000 deaths per year, which results in substantial morbidity and increased annual healthcare burden [1,2,3]. The prevalence of asthma is 10–18% in different countries, with a dramatic rise over the past decade [4, 5]. It is necessary to take appropriate measures to prevent and treat asthma. Previous studies found that obesity, metabolic syndrome, diabetes, cigarette smoking, and allergic rhinitis are associated with an increased risk of asthma [6,7,8,9]. Sleep disorders such as chronic insomnia symptoms [10] and insufficient sleep [11] have been found to have a significant impact on the development of asthma [12].

Bedtime at night is a behavioral habit affected by biological and sociological factors [13]. The influence of bedtime on circadian rhythms has been proposed, with any disturbance to these rhythms potentially increasing the susceptibility to health-related complications [14]. Numerous observational studies have demonstrated a significant association between bedtime and various health outcomes including obesity [15] and depression [16] in children and adolescents, and non-alcoholic fatty liver disease [17], angina pectoris [18], and type 2 diabetes [19] in adults. Only two studies [20, 21] have been conducted to identify the associations of late bedtime with asthma, but these studies are limited to children under the age of 12 years.

The relationship between night bedtime and asthma has been rarely reported among adult individuals. To address this research gap, we aimed to explore the correlation between nocturnal bedtime and asthma by utilizing a considerably extensive sample cohort of adults in the United States.

Methods

Study population

NHANES [22] is a cross-sectional survey that aims to acquire nationally representative estimates of the health and nutritional status of the non-institutionalized civilian population.

A total of 19,225 participants completed all surveys from 2015 to 2018, which included questions on demographics, body measures, daily alcohol drinking, physical activity, medical conditions, sleep disorders, smoking, high-sensitivity C-reactive protein, complete blood count, depression, health insurance and urine pregnancy test data. Finally, 11,475 participants were included for the final analysis after excluding 7,750 individuals. The exclusion criteria were as follows: (a) age < 18 years (n = 7,377); (b) pregnant women (n = 120); (c) missing bedtime data or bedtime between 0800 h and 1800 h (n = 243); and (d) incomplete asthma survey (n = 10) (Fig. 1).

Fig. 1
figure 1

Flow chart of included population

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All related data came from the open NHANES database, and no asthmatics were required to be recruited. NHANES study was approved by the IRB the of the National Center for Health Statistics of the U.S. Centers for Disease Control and Prevention. The Institutional Review Board at Nanjing University of Chinese Medicine exempted the study from review.

Study variables

Primary exposure

The determination of nocturnal bedtimes was based on the following question (NHANES questionnaire variables SLQ300): “Usual sleep time on weekdays or workdays” and were categorized into three distinct groups: 2100 h or earlier, between 2100 h and 2300 h, and 2300 h or later [23].

Outcomes

Bronchial asthma is a heterogeneous disease characterized by chronic inflammation of the airways, which is associated with airway hyperresponsiveness. It typically presents with widespread and variable reversible expiratory airflow restriction, leading to recurrent symptoms such as wheezing, shortness of breath, chest tightness, and/or cough, with varying intensity over time. The diagnosis of asthma typically requires a comprehensive assessment of various clinical features and test results, but there is no single “gold standard” for a definitive diagnosis. To determine asthma, Self-reported asthma was determined using the NHANES questionnaire variables MCQ010 “Ever been told you have asthma: yes/no” or MCQ035 “Still have asthma: yes/no.”

Covariates

After conducting a covariate check and screening using Empowerstats software, covariates were selected based on the criterion that their inclusion or exclusion in the complete model resulted in a change of the regression coefficient of X by more than 10%. We also collected sociodemographic characteristics and other covariates as potential confounding factors; age, sex, and race were the first factors to be considered. The current study classified age into three groups, namely 18–39, 40–59, and ≥ 60 years. Race/ethnicity was classified as Hispanic, Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Asian, and other races (American Indian or Alaska Native, Native Hawaiian or other Pacific Islander, and multiracial persons). BMI (kg/m2) was classified as < 25.0 kg/m2, 25.0–30.0 kg/m2, 30.0–35.0 kg/m2, and ≥ 35.0 kg/m2.

In the current study, covariates were ascertained based on known confounders from previously described methods and clinical practice. Smoking was classified as never, former (quit smoking), or current (smoking now and smoked at least 100 cigarettes per year). Based on the criteria of the National Institute on Alcohol Abuse and Alcoholism (NIAAA), alcohol consumption was categorized as moderate (1–2 drinks/day for men or 1 drink/day for women), or heavy (≥ 3 drinks/day for men or ≥ 2 drinks/day for women). Furthermore, in accordance with the Physical Activity Guidelines recommendation, physical activity was categorized into three distinct levels: active (≥ 75 min/week of vigorous or ≥ 150 min/week of moderate physical activity), less active (< 75 min/week of vigorous or < 150 min/week of moderate physical activity), and inactive (no physical activity). Moreover, the percentage of eosinophils (EOS) was classified into three categories: <4%, 4–15%, and 15–50%. The Patient Health Questionnaire-9 (PHQ-9) [24] was utilized as a screening tool for identifying depressive symptoms. Comprising nine items aligned with the diagnostic criteria for depressive disorders, the PHQ-9 is recognized for its reliability and validity in assessing the severity of depression. Scores falling within the range of 0 to 4 typically suggest the absence of a depressive disorder. Participants were classified into two groups: those without depression (no depressive spectrum disorder; PHQ-9 score 0–4) and those with depression (indicating a high likelihood of a depressive spectrum disorder diagnosis; PHQ-9 score ≥ 5). Participants reported their health insurance coverage (“yes” or “no”) from any source (e.g., private individual insurance, employer provided, Medicare, Medicaid, and Veteran’s Administration). Include health insurance obtained through employment or purchased directly as well as government programs like Medicare and Medicaid that provide medical care or help pay medical bills.

Statistical analysis

The data were classified as either categorical or continuous variables. The variables were represented as weighted mean ± SD or weighted % (95% confidence interval). To assess differences in clinical characteristics among various groups, the weighted chi-square test was employed for categorical variables, while the weighted linear regression model was utilized for continuous variables.

Univariable and multivariable logistic regression models were employed to calculate the odds ratio (OR) and 95% confidence interval (CI) to elucidate the risk factors associated with the occurrence of asthma. In accordance with the guidelines outlined in the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement [25], an independent association was conducted by constructing three logical regression models: (1) unadjusted; (2) adjusted for age, sex, and race; and (3) adjusted for age, sex, race, alcohol, body mass index, physical activity, smoking, sleep duration, HSCRP, EOS percent, depression and health insurance. Additionally, subgroup analysis was performed to further explore the relationship between bedtime and asthma. Interaction analyses were also conducted between nocturnal bedtime and asthma risk factors (including age, sex, race, alcohol, body mass index, smoking, physical activity, EOS percent, depression and health insurance.) in the final multivariable logistic regression model.

All statistical analyses were conducted using R version 3.4.3, Empowerstats software, and STATA 17.0. The complex survey design was accounted for by applying appropriate examination weights, with a p-value of < 0.05 indicating statistically significant.

Results

Study population characteristics

A cumulative count of 11,475 individuals were deemed suitable in the ultimate analysis, of whom 48.5% were male and 51.5% were female, with an average age of 47.5 years. Among these eligible participants, 1,729 (15.4%) individuals from the general population reported a history of asthma.

Table 1 provides a comprehensive summary of the sociodemographic characteristics and descriptive clinical baseline characteristics within the three groups. Significant variations were observed in many outcomes across quartiles of nocturnal bedtime among all participants, with the exception of EOS percent (p > 0.05). Besides, individuals with later bedtime (2300 h or later) tended to be younger (18–39 years), male, Non-Hispanic Asian, had the lowest BMI (< 25 kg/m2), engaged in less physical activity, and had shorter sleep duration (7.1 ± 1.6). In contrast, participants with earlier bedtime (2100 h or earlier) were middle-aged and older (> 40 years), education level less than 9th grade, overweight status (BMI between 30 and 35 kg/m2), former smoker, and longer sleep duration (8.5 ± 1.5) (Table 1).

Table 1 General characteristics of participants (n = 11475) stratified by weekdays bedtime in the NHANES 2015–2018
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Association of nocturnal bedtime and asthma

Over the study period, univariable logistic regression showed that having nocturnal bedtimes > 2300 h (OR: 1.26; 95%CI: 1.06–1.50; P = 0.011) exhibited a positive association with the prevalence of asthma compared to the reference group (2100–2300 h). After adjusting for age, sex, and race, individuals with a bedtime of 2300 h or later were found to have a 24% higher risk of asthma (OR: 1.24, 95%CI: 1.04–1.49, P = 0.021). Additionally, after adjusting for age, sex, race, poverty index, alcohol consumption, smoking, physical activity, body mass index, diabetes, sleep duration (total sleeping time), high-sensitivity C-reactive protein, and EOS percent, individuals with nocturnal bedtimes after 2300 h still had a 20% increased risk of asthma (OR: 1.20; 95%CI: 1.02–1.42) (Table 2).

Table 2 Associations between weekdays bedtime and asthma (n = 11475), NHANES 2015–2018
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Subgroup analysis

We further investigated the impact of covariates using subgroup analyses and interaction analyses (Fig. 2). Subgroup analyses stratified by age, sex, race, poverty index, alcohol consumption, smoking, physical activity, body mass index, diabetes, and EOS percent were conducted to address the potential heterogeneous population. Compared to the reference group (between 2100 h and 2300 h), stratified analysis indicated that the correlation of a later bedtime (2300 or later) and asthma remained significant in 18–39 years (OR = 1.23, 95%CI: 1.02–1.48); woman (OR = 1.30, 95%CI: 1.01–1.68); Hispanic (OR = 1.66, 95%CI: 1.17–2.37); heavy drinkers (OR = 1.52, 95%CI: 1.17–1.96); BMI < 25 kg/m2 (OR = 1.45, 95%CI: 1.13–1.87); vigorous physical activity (OR = 1.32, 95%CI: 1.05–1.65); Statistically significant interactions were observed between nocturnal bedtime and asthma in relation to age, sex, eosinophils (EOS) percent and depression (P Interaction < 0.05).

Fig. 2
figure 2

Subgroup and interaction analyses for the impact of covariates on the association of nocturnal bedtime and Asthma. adjusted for Age, Gender, Race, Daily alcohol drinking status, BMI, EOS percent, Physical activity level, Smoking status, HSCRP, Sleep duration, Depression, Health Insurance. BMI: body mass index; HSCRP: High Sensitivity C-Reactive Protein (mg/L); EOS: Eosinophils; NHANES: National Health and Nutrition Examination Survey

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Discussion

Our findings showed a statistically significant relationship between bedtime at night and the prevalence of asthma. Notably, our research findings indicate a significant association between a bedtime of 2300 h or later and asthma, as determined through the construction of multivariable logistic models. Importantly, this association remained significant even after controlling for all potential confounding factors. Later sleep may affect the circadian rhythm and cause a decrease in melatonin secretion [26]. This reduction in melatonin secretion has been shown to effectively inhibit the chronic inflammatory response associated with asthma [27]. In addition, a delayed bedtime was observed, along with an atypical elevation in bronchial eosinophil levels, and the release of their associated mediators, including histamine. This phenomenon subsequently may lead to nocturnal airway hyperreactivity, thereby contributing to the development or exacerbation of asthma [28]. Moreover, pulmonary function exhibits a diurnal rhythm even in healthy adults, with a small circadian variation in FEV1; FEV1/FEVC; and PEF (forced expiratory volume in the first second [FEV1], forced vital capacity [FVC], FEV1/FVC, forced expiratory flow rate, and mid-exhalation [FEF]). Disordered circadian rhythms can correspondingly affect changes in lung function, such as significantly lower FEV1, FEV1%, PEF, and PEF% [29], which are commonly used to confirm the diagnosis of asthma [30].

Thus far, only two studies have focused on the association between nocturnal bedtime and asthma [20, 21], which is limited to subjects < 12 years old. Contrary to our study, a study on 4,876 preschool children in China [20] and 2,529 children in The Netherlands [21] suggested no significant associations between late bedtime and asthma. The observed disparities among the existing studies could potentially be attributed to dissimilarities in the variables and sample sizes employed in this investigation compared to those utilized in previous studies. Furthermore, the questionnaire content could also be a contributing factor.

Subsequently, we performed a stratified analysis of the effects of bedtime on asthma in several categories, and the findings revealed that the effects of nocturnal bedtime on asthma differed across groups. In the later bedtime cohort, 18-39years, womam, BMI < 25 kg/m2, Hispanic ethnicity, heavy alcohol drinkers, those engaged in vigorous physical activities were more prone to developing asthma than others.

First, Asthma is more common in males during childhood, but becomes more prevalent in females during adolescence and young adulthood due to the impact of female hormones on the immune response, including the modulation of inflammatory response by estrogens and the activity of various cells and proteins [31]. Another study revealed that having a persistently low BMI at an early age was associated with higher risk of asthma [32]. Moreover, this may involve a plethora of factors, including well-established anatomical, hormonal, or smoking-related distinctions within the respiratory system [33]. Second, in comparison to other racial groups, Hispanics demonstrated a higher prevalence of asthma. Epigenetic mechanisms such as distinct patterns of DNA methylation (DNAm) in whole blood, have been found to validate associations between asthma DNAm and Hispanic groups [34]. This finding is consistent with previous reports [35]. Third, heavy drinkers were more likely to develop asthma than other groups, which is consistent with the UK Biobank cohort study [36]. One potential explanation is the heightened presence of acetaldehyde in the bloodstream, which triggers the degranulation of mast cells or basophils, subsequently leading to the release of histamine and potentially exacerbating asthma symptoms [37].

Therefore, we conducted interaction analyses and found that age, sex, EOS percent and depression modified the association between nocturnal bedtime and asthma risk. Recent studies suggested that blood EOS count was a potential candidate biomarker for both asthma and nocturnal bedtime. There is increasing evidence that EOS levels are linked to disease outcomes and treatment response. In asthma, exacerbations are more frequent in patients with high counts (> 400 cells·µL-1) than those with counts below this threshold [38]. Depression is a common comorbidity in asthmatic outpatients and is linked to uncontrolled asthma and lower ACT scores [39]. Major depression can cause dysregulation of the hypothalamic-pituitary-adrenal axis and autonomic nervous system, leading to chronic secretion of cortisol and catecholamines, decreased expression of glucocorticoid and β2-adrenergic receptors, and ultimately aberrant immune responses, heightened airway inflammation, and diminished efficacy of short-acting β2-agonists in individuals with asthma [40].

Limitations

Our study has some limitations, especially because we used the NHANES data. First, certain variables such as asthma, sleep factors and lifestyle factors were assessed through questionnaire surveys, which may have introduced a recall bias. Second, the data obtained for this study exclusively focused on the bedtimes during weekdays; no information regarding variables associated with weekend bedtimes was gathered. In addition, people have different thresholds for consulting a general practitioner, which in turn influences the probability of getting the diagnosis of both insomnia disorder and asthma. We cannot rule out the possibility that such information bias influences our results.

Conclusion

This study evaluated the impact of nocturnal bedtime on the occurrence of asthma, revealing a modestly heightened risk of asthma associated with later bedtime, particularly among women and individuals of Hispanic ethnicity. This finding contributes to the expansion and reinforcement of the consensus and endorsement surrounding asthma. Subsequent research should delve into the underlying mechanisms of this correlation and explore the potential clinical ramifications for the management of asthma.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://www.cdc.gov/nchs/nhanes/index.htm. These datasets were derived from the following public domain resources: https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/DEMO_I.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/BMX_I.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/CBC_I.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/HSCRP_I.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/UCPREG_I.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/ALQ_I.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/INQ_I.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/MCQ_I.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/PAQ_I.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/SLQ_I.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/SMQ_I.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/DPQ_I.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/HIQ_I.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/DEMO_J.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/BMX_J.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/CBC_J.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/HSCRP_J.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/UCPREG_J.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/ALQ_J.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/DIQ_J.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/INQ_J.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/MCQ_J.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/PAQ_J.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/SLQ_J.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/SMQ_J.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/DPQ_J.htm. https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/HIQ_J.htm.

Abbreviations

BMI:

Body Mass Index

CI:

Confidence interval

NHANES:

National Health and Nutrition Examination Survey

NIAAA:

National Institute on Alcohol Abuse and Alcoholism

OR:

Odds ratio

PIR:

Poverty-Income Ratio

HSCRP:

High Sensitivity C-Reactive Protein (mg/L)

EOS:

Eosinophil

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Acknowledgements

We express our gratitude for the invaluable support and insightful discussions provided by our colleagues in the respiratory department.

Funding

This work was supported by the National Natural Science Foundation of China (82004271) and Natural Science Foundation of Jiangsu Province (BK20200260).

Author information

Author notes

  1. Huawei Zhuang and Xin Huang contributed equally to this work.

Authors and Affiliations

  1. The First Clinical Medical College, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing, 210023, Jiangsu, China

    Huawei Zhuang, Xin Huang & Lizhong Guo

  2. Department of Respiratory, Kunshan Hospital of Traditional Chinese Medicine, Kunshan, Jiangsu, China

    Hui Huang

Authors
  1. Huawei Zhuang
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  2. Xin Huang
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  3. Hui Huang
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  4. Lizhong Guo
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Contributions

H-WZ, XH, HH and L-ZG contributed to the conception and design, acquisition, analysis, interpretation of the data, drafting of the article, and critical revision for important intellectual content. H-WZ wrote the first draft of the manuscript. All authors contributed to the study conception and design and commented on the previous versions of the manuscript and read and approved the final manuscript.

Corresponding author

Correspondence to Lizhong Guo.

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Zhuang, H., Huang, X., Huang, H. et al. Associations between nocturnal bedtime and asthma among adults in the United States. BMC Pulm Med 24, 419 (2024). https://doi.org/10.1186/s12890-024-03245-w

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  • DOI: https://doi.org/10.1186/s12890-024-03245-w

Keywords

BMC Pulmonary Medicine

ISSN: 1471-2466

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