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The impact of comprehensive healthy lifestyles on obstructive sleep apnea and the mediating role of BMI: insights from NHANES 2005–2008 and 2015–2018

BMC Pulmonary Medicine volume 24, Article number: 601 (2024) Cite this article

Abstract

Objective

In this study, the associations between healthy lifestyles and obstructive sleep apnea (OSA) in middle-aged and elderly adults were investigated via data from the National Health and Nutrition Examination Survey (NHANES) for the periods of 2005–2008 and 2015–2018.

Methods

A total of 6,406 participants aged 40 years and older were included in the analysis. Healthy lifestyle behaviors were assessed through diet quality, physical activity, sleep duration, alcohol consumption, smoking status, and body mass index (BMI). A composite healthy lifestyle score (ranging from 0 to 6) was created and categorized into insufficient (0–2), intermediate (3–4), and optimal (5–6) health groups. Weighted logistic regression models were used to examine the association between these lifestyle scores and OSA, adjusting for some demographic, socioeconomic, and clinical covariates. Additionally, mediation analysis was conducted to evaluate the role of BMI as a mediator in the relationship between the composite healthy lifestyle score and OSA, determining the proportion of the total effect mediated by BMI.

Results

Participants were classified into insufficient (17.81%), intermediate (56.82%), and optimal (25.37%) lifestyle groups. Higher dietary quality (OR: 0.81, 95% CI: 0.66–0.99) and adequate weight (OR: 0.09, 95% CI: 0.07–0.11) were statistically associated with reduced OSA odds after adjustments, whereas the variables were not. Each one-point increase in the healthy lifestyle score was linked to a 33% reduction in OSA odds (OR: 0.67, 95% CI: 0.63–0.71). A significant linear trend was observed, with better adherence to healthy lifestyle correlating with lower odds of OSA (p for trend < 0.001). Compared with insufficient lifestyle, intermediate lifestyle was linked to a 27% reduction in OSA (OR: 0.73, 95% CI: 0.58–0.91), whereas optimal lifestyle was associated with a 74% reduction (OR: 0.26, 95% CI: 0.21–0.33). Mediation analysis revealed that BMI significantly mediated the relationship between healthy lifestyle score and OSA, accounting for approximately 59.2% of the total effect (P < 0.001). The direct effect of the healthy lifestyle score on OSA remained significant even when controlling for BMI (P < 0.001). Subgroup analyses confirmed consistent benefits across different demographic groups.

Conclusions

This study revealed that adherence to healthy lifestyles significantly reduces the odds of OSA, with optimal lifestyles leading to a marked decrease in the odds of OSA. Notably, BMI plays a critical mediating role in this relationship. These findings emphasize the importance of promoting healthy lifestyle interventions as a key strategy for the prevention and management of OSA.

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Introduction

Obstructive sleep apnea (OSA) is a prevalent and serious sleep disorder characterized by repeated episodes of partial or complete upper airway obstruction during sleep, leading to intermittent hypoxia and sleep fragmentation [1]. OSA affects more than 20% of the global population, and is a major public health issue with significant implications for cardiovascular health, metabolic function, and cognitive performance [2,3,4]. In the United States, approximately 54 million individuals are affected by mild to severe OSA, with 24 million experiencing moderate to severe OSA [5]. It is associated with a range of adverse health outcomes, including cardiovascular diseases, metabolic disorders, and impaired cognitive function [6,7,8,9,10,11]. The prevalence of OSA is notably high among adults aged over 60 years, highlighting the urgent need for targeted prevention and management strategies [12, 13].

Healthy lifestyle behaviors, including diet, physical activity, sleep quality, smoking status, alcohol consumption, and weight management, have been consistently linked to the occurrence of OSA [1, 14,15,16]. Many studies [17,18,19] have explored the impact of individual lifestyle on OSA. Zeng X [17] reported a strong association between smoking and OSA, with individuals who had a smoking history of more than 20 pack-years facing a significantly greater probability of developing OSA. Choi Y [18] showed that both regular breakfast skipping and late-night eating were linked to an increased likelihood of OSA. Bonsignore MR [19] discussed the connection between obesity and OSA, highlighting that body weight was a key factor contributing to the development of OSA. However, interest in understanding how a combination of these behaviors influences OSA outcomes is increasing. Compared with isolated behavioral changes, comprehensive lifestyle interventions may offer a more effective approach for preventing OSA [20].

Given the aging global population and the increasing prevalence of chronic conditions, it is essential to explore how lifestyle behaviors influence diseases like OSA. Adults aged 30 years and above are particularly vulnerable to OSA-related risk factors, including obesity, hypertension, and diabetes, all of which contribute significantly to its development [1, 21]. This issue is critical, as OSA not only affects a large segment of the global workforce but also associated with significant economic challenges through increased healthcare costs and lost productivity [22, 23]. Addressing OSA through comprehensive lifestyle changes could significantly increase quality of life and reduce the economic burden on healthcare systems.

This study investigated the associations between comprehensive healthy lifestyles and the odds of OSA in middle-aged and elderly adults. We utilized data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2008 and 2015–2018, which provides a representative sample of the U.S. population. We hypothesized that a greater number of healthy lifestyles would be associated with a lower odds of OSA. By comprehensively examining the influence of multiple healthy lifestyles on OSA, our study contributes to a better understanding of preventive strategies for OSA, which could have significant public health implications.

Methods

Study participants

This study utilized data from the National Health and Nutrition Examination Survey (NHANES), a program administered by the Centers for Disease Control and Prevention (CDC) and conducted by the National Center for Health Statistics (NCHS) to provide nationally representative estimates of the U.S. population. Given the limited availability of OSA data, we analyzed publicly accessible NHANES datasets from the 2005–2008 and 2015–2018 cycles [24], and all analyses followed the NHANES data analysis guidelines [25]. The study protocols were approved by the NCHS Research Ethics Review Board, and informed consent was obtained from all participants.

In accordance with previous studies [26, 27], participants with missing data on healthy lifestyle behaviors, OSA status, or other relevant covariates were excluded from this study. Finally, we included 6406 participants aged 40 years and older in the analysis. Figure 1 illustrates the process of participant selection.

Fig. 1
figure 1

Flow chart of the study participants selection

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Ascertainment of healthy lifestyle behaviors

The Healthy Eating Index (HEI) measures adherence to the Dietary Guidelines for Americans, with scores ranging from 0 to 100 [28]. Higher scores indicate better dietary quality [29]. Specifically, scores in the upper 40% represent high diet quality, whereas scores below the 60th percentile indicate low dietary quality [30].

The physical activity (PA) questionnaire collected data on participants’ physical activities via a Computer-Assisted Personal Interviewing (CAPI) system conducted at their homes. We standardized physical activity measures by calculating the total Metabolic Equivalent of Task (MET) days of activity per week, where the MET is defined as the ratio of the activity rate to the resting metabolic rate. PA was classified into two levels: active and inactive. Active individuals engage in 600 or more MET minutes per week, a level associated with significant health benefits. Inactive individuals engage in less than 600 MET minutes per week [31,32,33].

Sleep duration was self-reported by the question ‘How many hours of sleep do you typically get at night on weekdays or workdays?’ Responses were classified into two categories: optimal sleep (7–9 h per night) and nonoptimal sleep (< 7 or > 9 h per night), following established sleep duration guidelines [34,35,36].

Alcohol consumption was classified into five primary levels: never (had < 12 drinks in their lifetime); former (had ≥ 12 drinks in 1 year and did not drink in the last year but drank ≥ 12 drinks in their lifetime); mild (≤ 1 drink per day for women or ≤ 2 drinks per day for men on average over the past year); moderate (≥ 2 drinks per day for females, ≥ 3 drinks per day for males, or binge drinking ≥ 2 days per month); and heavy (≥ 3 drinks per day for females, ≥ 4 drinks per day for males, or binge drinking [≥ 4 drinks on the same occasion for females, ≥ 5 drinks on the same occasion for males] on 5 or more days per month) [37, 38]. For analytical purposes, these categories are dichotomized into ‘heavy alcohol drinking’ (heavy) and ‘moderate or less alcohol drinking’ (never, former, mild, and moderate).

Participants were categorized into two groups on the basis of the World Health Organization’s body mass index (BMI) [39, 40]: individuals with a BMI of less than 25 kg/m² were classified as having adequate weight, whereas those with a BMI of 25 kg/m² or greater were classified as having overweight/obesity.

On the basis of previous similar study [41], healthy lifestyle behaviors were defined via six key factors: high-quality diet, active physical activity, optimal sleep, never smoking, moderate or low drinking and normal weight. Each participant was assigned a score for each factor they met, yielding a total healthy lifestyle score that ranged from 0 (representing the lowest health score and highest risk) to 6 (indicating the highest health score and lowest risk). These scores were then categorized into three levels of health: optimal (5–6 points), intermediate (3–4 points), and insufficient (0–2 points).

Assessment of covariates

To comprehensively investigate the association between healthy lifestyles and OSA, our study intentionally selected covariates informed by previous research [42, 43], with participants categorized on the basis of well-defined criteria to ensure robust analysis. The family income to poverty ratio (PIR) was used to classify participants into low (< 1.3) and mild-high (≥ 1.3) PIR categories [44]. Hypertension was defined as a systolic blood pressure (SBP) of ≥ 140 mmHg and/or a diastolic blood pressure (DBP) of ≥ 90 mmHg, or a self-reported history of diagnosed hypertension, or the use of antihypertensive medications [45]. Participants were defined as having hyperlipidemia if they met one or more of the following criteria: cholesterol levels (total cholesterol ≥ 200 mg/dL [5.18 mmol/L]), triglyceride levels (≥ 150 mg/dL), low-density lipoprotein (LDL) ≥ 130 mg/dL (3.37 mmol/L), high-density lipoprotein (HDL) < 40 mg/dL (1.04 mmol/L) in men and < 50 mg/dl (1.29 mmol/L) in women, or the use of cholesterol-lowering medications [46]. Asthma was identified through the following question: “Have you ever been told you have asthma, or do you use anti-asthmatic drugs?”

Cardiovascular disease (CVD) [47] was determined by a combination of self-reported physician diagnoses and standardized medical status questionnaires completed during individual interviews. The participants were asked if they had been diagnosed with conditions such as congestive heart failure, coronary heart disease, stroke, heart attack, or angina. If an individual answered “yes” to any of these questions, they were classified as having CVD.

Diabetes was determined as follows: self-reported diabetes diagnosis by a doctor, HbA1c ≥ 6.5%, fasting glucose ≥ 7.0 mmol/L, random blood glucose ≥ 11.1 mmol/L, two-hour oral glucose tolerance test blood glucose ≥ 11.1 mmol/L, or the use of diabetes medication or insulin [48].

Chronic obstructive pulmonary disease (COPD) was diagnosed by any of the following criteria [49]: forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) ratio < 0.7 after a β2-adrenergic bronchodilator medication was inhaled; individuals aged 40 years or older with a history of smoking or chronic bronchitis who were currently undergoing COPD treatment, including inhaled corticosteroids, mast cell stabilizers, leukotriene modifiers, and selective phosphodiesterase-4 inhibitors; reported by a doctor or health professional with emphysema or bronchitis; and previously used related medications.

Statistical analyses

The data were recorded in a database via Microsoft Excel and analyzed via the survey package in R software. Categorical variables were expressed as proportions and frequencies and were analyzed via the chi-squared test. Sample weights were calculated via the following formula: weight = 1/4 * wtmec2 year. To assess the associations between healthy lifestyles and OSA, weighted logistic regression was used to examine the associations between these lifestyle scores and OSA, adjusting for some confounding covariates. Three models were constructed to ensure the stability of the results: the crude model, with no covariates adjusted; model one, which was adjusted for age, sex, ethnicity, education, marital status, and the family income to poverty ratio; and model two, which was adjusted for all variables from model one plus clinical factors, including diabetes, hypertension, hyperlipidemia, cardiovascular diseases (CVD), chronic obstructive pulmonary disease, and asthma.

Additionally, mediation analysis was conducted to explore the indirect effects of healthy lifestyles on OSA through BMI, estimating the average causal mediation effects (ACME) and average direct effects (ADE). Subgroup analyses focused on potential modifiers such as age, sex, ethnicity, education, and the poverty income ratio. Interaction effects were assessed by including product terms for the stratifying variables and healthy lifestyle behaviors in the regression model. Additionally, we analyzed the potential linear relationships between lifestyle behaviors and OSA. Statistical significance was defined by a two-sided p-value of ≤ 0.05.

Results

Baseline characteristics of the participants

Table 1 presents the baseline characteristics of 6406 participants, categorized by healthy lifestyles into three groups: insufficient (1141), intermediate (3640), and optimal (1625). The participants primarily ranged in age from 40 to 59 years (65.05%), with a mean age of 58.00 ± 11.77 years. Significant differences observed across categories in terms of age, sex, and ethnicity (P < 0.0001).

Educational level, family size and income, and marital status also significantly varied by lifestyles (P < 0.0001). Health conditions such as depression, diabetes, hypertension, hyperlipidemia, CVD, COPD, and asthma differed significantly across lifestyle categories (P < 0.0001).

Lifestyle-related factors, including dietary quality, physical activity, sleep quality, smoking, and drinking habits, were significantly associated with varying health outcomes (P < 0.0001). The BMI profiles further highlighted disparities, with a greater percentage of participants in the insufficient lifestyle group having a higher BMI than those in the optimal lifestyle group (P < 0.0001).

Table 1 Baseline characteristics of NHANES participants (2005–2008, 2015–2018) stratified by healthy lifestyles (unweighted)
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Relationships between individual lifestyles and OSA

Table 2 presents the results of separate analyses examining the associations between individual healthy lifestyles and OSA. Higher dietary quality was statistically associated with a reduced odds of OSA (Model I OR: 0.80, 95% CI: 0.65–0.98, p = 0.03; Model II OR: 0.81, 95% CI: 0.66–0.99, p = 0.05).

The association between active physical activity and OSA was not statistically significant, either before (unadjusted OR: 1.11, 95% CI: 0.97–1.26, p = 0.13) or after adjustments (Model II OR: 0.95, 95% CI: 0.79–1.13, p = 0.53).

Optimal sleep initially appeared protective against OSA (unadjusted OR: 0.81, 95% CI: 0.71–0.93, p < 0.01), but this effect was not significant after adjustment (Model II OR: 0.89, 95% CI: 0.72–1.09, p = 0.26).

Never smoking was significantly associated with a reduced likelihood of OSA in the unadjusted analysis (OR: 0.68, 95% CI: 0.59–0.78, p < 0.0001). However, this association was no longer significant after adjusting for confounders (Model II OR: 0.91, 95% CI: 0.72–1.16, p = 0.45).

Moderate or less alcohol consumption tended toward a lower likelihood of OSA in the unadjusted model (OR: 0.84, 95% CI: 0.70–1.01, p = 0.06), but this association was not statistically significant and remained nonsignificant after adjustments (Model II OR: 0.84, 95% CI: 0.64–1.09, p = 0.18).

Adequate weight was associated with a significantly reduced odds of OSA, with odds ratios of 0.11, 0.08, and 0.09 across non-adjusted, Model I, and Model II analyses, respectively, all with p-values < 0.001.

A multicollinearity analysis showed no significant multicollinearity among lifestyle factors (Appendix Table 1).

Table 2 Separate analyses of the associations between healthy lifestyles and OSA
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Relationship between comprehensive healthy lifestyle score and OSA

The associations between comprehensive healthy lifestyle score and the odds of OSA were analyzed and are summarized in Table 3. A higher healthy lifestyle score was significantly associated with a decreased likelihood of OSA. In the unadjusted model, each one-point increase in the healthy lifestyle score was linked to 33% lower odds of having OSA (OR: 0.67, 95% CI: 0.64–0.70). This inverse relationship remained significant after adjusting for age, sex, ethnicity, education, family size, marital status, and PIR (OR: 0.64, 95% CI: 0.60–0.68) and continued to hold after further adjustments for comorbid conditions such as depression, diabetes, hypertension, hyperlipidemia, CVD, COPD and asthma (OR: 0.67, 95% CI: 0.63–0.71).

Individuals with intermediate lifestyles had significantly lower odds of OSA than did those with insufficient lifestyles (OR: 0.83, 95% CI: 0.69–0.99). This reduction remained statistically significant in Model I (OR: 0.69, 95% CI: 0.55–0.85) and Model II (OR: 0.73, 95% CI: 0.58–0.91).

Individuals with optimal lifestyle behaviors had significantly lower odds of OSA in the unadjusted model (OR: 0.27, 95% CI: 0.23–0.32). After adjustments, the odds remained consistently lower in Model I (OR: 0.22, 95% CI: 0.18–0.28) and Model II (OR: 0.26, 95% CI: 0.21–0.33), with all adjusted models showing statistically significant associations between optimal lifestyle and reduced odds of OSA (P < 0.001).

A significant trend across all the models revealed that better adherence to healthy lifestyle behaviors was consistently correlated with lower odds of OSA (p for trend < 0.001). Figure 2 further illustrates this linear relationship, reflecting how higher healthy lifestyle scores are associated with a reduced development of OSA, highlighting the gradient of decreasing likelihood as lifestyle scores improve.

Table 3 Association between healthy lifestyle score and OSA
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Fig. 2
figure 2

Linear relationship between healthy lifestyle score and OSA

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Mediating role of BMI in the relationship between healthy lifestyle score and OSA

Table 4 presents the results of mediation analysis, revealing the mediating effect of BMI on the associations between healthy lifestyle score, including diet quality, physical activity, sleep duration, alcohol consumption, smoking status, and OSA. The indirect effect of BMI on development of OSA is -0.032 (95% CI: -0.035 to -0.030, p < 0.001), indicating that BMI significantly mediated this relationship. The direct effect of healthy lifestyle score on OSA, controlling for BMI, was − 0.022 (95% CI: -0.026 to -0.020, p < 0.001), indicating a direct impact independent of BMI. The total effect of healthy lifestyle score on development of OSA was − 0.054 (95% CI: -0.059 to -0.050, p < 0.001), reflecting both direct and indirect pathways through BMI. Approximately 59.2% of this total effect was mediated by BMI (95% CI: 0.543 to 0.650, p < 0.001), emphasizing the significant role of BMI in this relationship. Figure 3 visually illustrates these effects, depicting the indirect pathway from healthy lifestyle score to OSA via BMI, as well as the direct pathway from healthy lifestyle score to OSA. Subgroup analyses indicated that BMI significantly mediated this relationship, with variations based on age, sex, ethnicity, education, and PIR (Appendix Table 2).

Table 4 Mediating role of BMI in the relationship between healthy lifestyle score and OSA
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Fig. 3
figure 3

Mediating Effect of BMI on the relationship between healthy lifestyle and OSA

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Subgroup analysis of healthy lifestyle score and OSA

Figure 4 illustrates the associations between healthy lifestyle score and OSA across different subgroups including age, sex, ethnicity, education, PIR, marital status, and family size, with all variables adjusted except when serving as the stratification variable.

Overall, adopting an optimal healthy lifestyle was associated with significantly lower odds of OSA in all the examined groups. Notably, individuals aged 40–59 years experienced the greatest reduction in OSA odds (OR: 0.20, 95% CI: 0.15–0.28), followed by those aged 60–69 years (OR: 0.20, 95% CI: 0.12–0.33) and those aged ≥ 70 years (OR: 0.33, 95% CI: 0.20–0.56). Females showed a slightly stronger protective effect from optimal lifestyle behaviors (OR: 0.21, 95% CI: 0.15–0.29) compared to males (OR: 0.22, 95% CI: 0.17–0.29). Ethnic disparities were also evident, with the largest reductions in odds of OSA observed in the White group (OR: 0.19, 95% CI: 0.15–0.25) and the Other Race group (OR: 0.23, 95% CI: 0.09–0.57). Significant reductions were also found in the Black (OR: 0.34, 95% CI: 0.21–0.53) and Mexican American groups (OR: 0.38, 95% CI: 0.19–0.76). All education levels were linked to reduced OSA odds, with individuals beyond high school education showing an OR of 0.23 (95% CI: 0.16–0.35). In terms of family income, the mid-high income group presented greater reduction in OSA odds (OR: 0.21, 95% CI: 0.17–0.27) compared with the low-income group (OR: 0.29, 95% CI: 0.17–0.51). Interaction analyses did not reveal statistically significant differences across subgroups (P interaction > 0.05), suggesting a uniform benefit of healthy lifestyles in reducing the odds of OSA.

Fig. 4
figure 4

Association between healthy lifestyle score and OSA, stratified by subgroup

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Discussion

Our study revealed that adopting a combination of healthy lifestyle behaviors-high dietary quality, active physical activity, optimal sleep, never smoking, moderate or less drinking, and maintaining a healthy weight–was adversely associated with a lower odd of OSA. The subgroup analyses yielded consistent results. These findings offer important evidence for adoption of a healthy lifestyle to prevent OSA among the general population.

While many studies have examined the links between specific lifestyle factors and OSA [14, 15, 50], research focusing on the combined effects of multiple lifestyles remains limited. Our study developed a variable reflecting the cumulative effect of six modifiable lifestyle factors. The results indicated that each one-point increase in the healthy lifestyle score corresponded to a 33% decrease in the odds of having OSA. Compared to those with insufficient habits, participants with intermediate and optimal lifestyle habits presented 37% and 74% lower likelihoods of OSA, respectively. The inverse trend underscores comprehensive lifestyle management. These results are consistent with the literature on other populations, including studies of Chinese adults [20], and research on sleep-related disturbances [51] and disruptions [52]. This consistency emphasized the cumulative benefits of multiple healthy lifestyles in reducing the likelihood of OSA.

Lifestyle factors may play an important role in modulating OSA through metabolic and inflammatory pathways. For example, obesity, particularly upper body fat, can lead to airway obstruction [19, 53]. Diets high in refined carbohydrates and unhealthy fats are linked to insulin resistance and metabolic syndrome, increasing the potential for OSA [54]. Regular physical activity can enhance metabolic function, reduce fat accumulation, and mitigate systemic inflammation associated with OSA [55]. Furthermore, smoking and excessive alcohol consumption may cause airway edema and increased collapse susceptibility [17, 56]. Suboptimal sleep hygiene can increase cortisol levels, contributing to fat deposition and inflammation [57]. Similarly, the cumulative influence of these factors may be substantial. The adoption of a range of healthy lifestyles can provide synergistic benefits, improving metabolic function and reducing inflammation, ultimately lowering the probability of OSA.

Among individual lifestyle behaviors, maintaining a lower weight and higher dietary quality were found as protective factors against OSA. This finding aligns with prior studies linking both weight [58] and diet [18, 59] to OSA. Our mediation analysis revealed that BMI plays a significant role in mediating the relationship between healthy lifestyle behaviors and OSA. An increase in BMI, especially due to fat deposition around the neck and upper airway, could lead to structural changes that heighten the likelihood of airway collapse and obstruction during sleep [60, 61]. By reducing BMI, healthy lifestyles may indirectly lower the odds of OSA, serving as a crucial intermediary between behaviors (e.g. diet, exercise, et al.) and physiological improvements such as reduced airway obstruction. Efforts to reduce BMI could be particularly effective in preventing OSA, even among those who already maintain healthy lifestyle practices. Several studies [62,63,64] have emphasized the need to address obesity, as it is closely linked to OSA severity and its associated metabolic complications. The mediating effect of BMI highlights the importance of weight management in OSA prevention strategies, which is consistent with current clinical guidelines prioritizing weight reduction as a primary intervention for OSA [15, 65, 66].

This study has important clinical and public health implications, underscoring the need for comprehensive lifestyle interventions for OSA prevention. Healthcare providers should focus on the synergistic effects of multiple factors, such as high dietary quality [67], regular physical activity [68], good sleep hygiene [69], and so on. Given the increasing prevalence of OSA and its comorbidities, implementing these evidence-based strategies at the population level could potentially reduce OSA incidence and improve overall health outcomes. Future public health policies and clinical guidelines should prioritize integrated approaches that encourage individuals to adopt and sustain multiple healthy lifestyles for optimal health outcomes.

This study has several limitations. First, the cross-sectional design limits the ability to establish causality between healthy lifestyles and OSA, necessitating longitudinal studies for confirmation. Second, some information bias may have existed in this study because some data were obtained from self-reported. Third, some variables, such as family function, may not have been considered in the analysis. Fourth, generalizability may be limited, as the NHANES sample may not reflect other populations or age groups.

Conclusions

This study shows that adherence to comprehensive healthy lifestyles are strongly associated with a reduced odds of OSA in adults aged 40 years and older, based on NHANES data from 2005 to 2008 and 2015–2018. Higher dietary quality and lower BMI were associated with a protective effect against OSA. Specifically, optimal lifestyles were associated with a 74% reduction in OSA odds, whereas BMI mediated approximately 59.2% of the impact of these lifestyle behaviors on OSA. These findings emphasize the importance of promoting healthy lifestyle changes for OSA prevention and control, suggesting that public health strategies should prioritize these modifiable factors. Future research should focus on elucidating the underlying mechanisms driving these associations, as well as assessing the effectiveness of targeted lifestyle interventions in diverse populations. Specific investigations into the long-term benefits of integrated lifestyle modifications on OSA outcomes will be essential for informing public health policies and clinical guidelines aimed at mitigating the increasing prevalence of OSA and its associated comorbidities.

Data availability

All NHANES data and information are publicly available at https://www.cdc.gov/nchs/nhanes/index.htm.

Abbreviations

OSA:

Obstructive sleep apnea

OR:

Odds ratio

CI:

Confidence interval

PA:

Physical activity

HEI:

Healthy eating index

BMI:

Body mass index

PIR:

Family income to poverty ratio

LDL:

Low-density lipoprotein

HDL:

High-density lipoprotein

CVD:

Cardiovascular disease

COPD:

Chronic obstructive pulmonary disease

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Acknowledgements

We thank the participants for their cooperation in the NHANES project and the staff members for their contribution of data collection and making the data publicly available.

Funding

The study was supported by the Pingshan District Maternal and Child Healthcare Hospital of Shenzhen.

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  1. Pingshan District Maternal & Child Healthcare Hospital of Shenzhen, Shenzhen, 518118, China

    Jinsong Mou, Haishan Zhou, Shiya Huang & Zhangui Feng

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Contributions

Jinsong Mou conceived and designed the study. Haishan Zhou and Zhangui Feng implemented the study and analyzed data. Shiya Huang assisted in the manuscript preparation. Jinsong Mou had primary responsibility for final content, and all authors have read and approved the final manuscript.

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Correspondence to Jinsong Mou.

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The NHANES program was approved by the National Center for Health Statistics Ethics Review Board, and all participants provided written informed consent.

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Mou, J., Zhou, H., Huang, S. et al. The impact of comprehensive healthy lifestyles on obstructive sleep apnea and the mediating role of BMI: insights from NHANES 2005–2008 and 2015–2018. BMC Pulm Med 24, 601 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12890-024-03404-z

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Keywords

BMC Pulmonary Medicine

ISSN: 1471-2466

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