Obstructive sleep apnea (OSA) is associated with 
increased risk of early-onset sarcopenia and sarcopenic obesity: Results from NHANES 2015-2018.

doi:10.21203/rs.3.rs-3635355/v1

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Obstructive sleep apnea (OSA) is associated with increased risk of early-onset sarcopenia and sarcopenic obesity: Results from NHANES 2015-2018.

https://doi.org/10.21203/rs.3.rs-3635355/v1

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Objective

This study aims to estimate the prevalence of early-onset sarcopenia and sarcopenic obesity in the United States and its relative risk due to obstructive sleep apnea (OSA).

Methods

Data in this cross-sectional study were extracted from the National Health and Nutritional Examination Survey (NHANES) 2015–2018(n = 4037). Individuals with missing information on the sleep disorder questionnaire, dual-energy x-ray absorptiometry examination, and other relevant variables were excluded. Early-onset sarcopenia and sarcopenic obesity were defined as those aged 18–39 according to FNIH (Foundation for the National Institutes of Health) criteria and previous studies. A weighted multistage stratified probability sampling design was considered to estimate the prevalence of early-onset sarcopenia and sarcopenic obesity. Weighted multivariable logistic regression analyses were performed to evaluate the association between OSA and early-onset sarcopenia. Weighted multivariable mediation models were applied to analyze the mediation effect of markers of chronic inflammation (serum chronic reaction protein, CRP), insulin resistance (homeostasis model assessment insulin resistance index, HOMA-IR), dietary quality (health eating index, HEI) and body mass (body mass index, BMI) on the association between OSA and early-onset sarcopenia.

Results

This observational study included 4037 participants (aged 18–59). Among them, 2162 participants aged 18–39 could represent 52.2 million noninstitutionalized residents of the same age in the United States. The prevalence of early-onset sarcopenia and early-onset sarcopenic obesity was estimated to be 5.6% and 4.6%, according to the multistage weighted survey design of NHANES. A higher prevalence of sarcopenia (12% V.S. 5.5%, P < 0.01) and sarcopenic obesity (10.3% V.S. 4.0%, P < 0.01) was observed among participants with OSA than those without OSA. Multivariable logistic regression models suggested that participants with OSA had higher odds ratios of suffering from early-onset sarcopenia [Odds Ratio (OR): 2.7, 95% confidence interval (CI):1.4–5.1] and early-onset sarcopenic obesity [OR: 3.0, 95% CI: 1.5-6.0] after adjusting for potential confounding variables including demographics, socioeconomic level, lifestyle, and comorbidities. Mediation analyses suggested CRP mediated 30.3% (P < 0.01), HOMA-IR mediated 10.3% (P < 0.01), BMI mediated 53.6% (P < 0.05), HEI mediated 8.6% (P < 0.01) of the potential effects of OSA on early-onset sarcopenia respectively.

Conclusion

Early-onset sarcopenia and sarcopenic obesity were prevalent among young adults in the US. OSA is a significant independent risk factor and may induce muscle loss by unhealthy diet habits, high BMI, inducing chronic inflammation, or insulin resistance. Given the progressive process of early-onset sarcopenia, it was essential for clinicians to arrange appropriate screening and interventions for patients with OSA to prevent muscle loss as early as possible.

Health sciences/Risk factors

Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity

Obstructive sleep apnea

Early-onset sarcopenia

Early-onset sarcopenic obesity

National Health and Nutrition Examination Survey (NHANES)

Sarcopenia is a rising but overlooked disease characterized by a progressive reduction in appendicular skeletal muscle mass and strength. Sarcopenia used to be considered a geriatric disease. According to a meta-analysis, it affects more than 10% of people aged over 60 around the world[1]. However, numerous cases of early-onset sarcopenia have recently been identified among young patients aged 18–39. Early-onset sarcopenia takes low muscle mass and progressive muscle loss as the primary phenotype[2]. Due to muscle mass gradually decreasing from 30 years old and accelerating as an individual ages, early-onset sarcopenia might bring more severe long-term clinical consequences for its longer duration, same as early-onset diabetes[3].

Besides, sarcopenia often interplays with obesity closely. Sarcopenia reduces physical activity, which leads to decreased energy expenditure and increases the risk of obesity. At the same, an increase in visceral fat will induce inflammation that contributes to the development of sarcopenia. Sarcopenia and obesity also share several pathophysiological mechanisms, such as increased proinflammatory cytokines, oxidative stress, insulin resistance, and hormonal changes[4]. Their coexistence was defined as sarcopenic obesity, where the synergistic effect may generate more adverse outcomes[5]. Both sarcopenia and sarcopenic obesity place a considerable economic burden. It was estimated that the US healthcare system spent $18.5 billion total on them in 2000[6]. And in 2014, only the estimated cost of hospitalizations in individuals with sarcopenia exceeded 40.4 billion dollars[7]. Nevertheless, there have been no specific drugs being approved for sarcopenia so far. An umbrella review identified ten common pharmacological interventions for sarcopenia but all of them had limited benefit[8]. Therefore, it is essential to determine the potential risk factors of early-onset sarcopenia and sarcopenic obesity to prevent them as early as possible.

Obstructive sleep apnea (OSA) is another highly prevalent health problem that confuses people of all ages. Patients with OSA suffer from breathing and sleeping disorders caused by repeated collapse of the upper airway during sleep. The prevalence of moderate-to-severe OSA is estimated to be around 23.4% in women and 49.7% in men[9]. OSA is prevalent among people with obesity and is tightly associated with chronic inflammation and insulin resistance. It is also known to be associated with many chronic diseases, such as hypertension, stroke, heart failure, diabetes and thyroid disease[10][11][12][13]. There were a limited number of studies that investigated the association between OSA and sarcopenia. Bekfani T et al. found a positive correlation between sleep-disordered breathing and low muscle strength in patients with heart failure[14]. However, Fernandes et al. indicated that OSA was not associated with sarcopenia in a population of patients with non-dialyzed chronic kidney disease[15]. Therefore, further studies based on a larger representative sample are needed.

The National Health and Nutrition Examination Survey (NHANES) provided nationally representative data of American people, which was conducted by the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS). It included a household interview and a standardized physical examination in a mobile examination center. All data in the NHANES were collected by well-designed protocols that could minimize any possible bias and be of high quality[16]. It employed a multistage stratified probability sampling design with oversampling of specified groups to make its estimates of the US population reliable and precise.

Above all, this study aimed to estimate the prevalence of early-onset sarcopenia and sarcopenic obesity in the US and to find out whether OSA was another risk factor by using data from the NHANES database.

2.1 Study Population in NHANES

NHANES is a large ongoing cross-sectional survey conducted and released every two years. It systematically gathers data from participants by household interviews, laboratory tests, examination in a mobile examination center (MEC), and questionnaires to evaluate the healthy conditions and health-related behaviors of residents in the US. The periodic cross-sectional surveys were conducted every two years using a stratified multistage clustered probability sampling approach by the NCHS. All procedures were approved by the NCHS Research Ethics Review Board, and all participants provided written informed consent. In this study, we involved participants from two consecutive NHANES cycles: 2015–2016 and 2017–2018, and a total of 19225 participants were included. We included variables on demographics, socioeconomic levels, lifestyle, and comorbidities as covariates. Among 5443 participants who participated in the DXA examination and sleep disorder questionnaire, 1406 participants with missing data were excluded. The detailed selection of participants is available in the Fig. 1.

2.1.1 Definitions of demographic variables and laboratory measure results

We obtained demographic information on age, sex/gender, race/ethnicity, education level, and income level of participants from the household interview. Age was considered a continuous variable and categorized into 18–39 and 40–59 groups. Gender was reported as male and female. Race/ethnicity was classified into non-Hispanic white, non-Hispanic black, Hispanic American, and other races. Body measure-related tables were obtained from the examination data file. Body weight was measured on an electronic digital scale calibrated in kilograms, and height was measured with a stadiometer. Body mass index(BMI) was categorized in to underweight(≤ 25kg/m²), overweight(25 < kg/m²&<30 kg/m²) and obesity (≥ 30kg/m²). Laboratory measure results were obtained from laboratory data files. C-reactive protein (CRP), an indicator of chronic inflammation, was measured by Beckman UniCel analyzers with the lowest limits of 0.011 mg/dl (2015–2016) and 0.015 mg/dl (2017–2018). Values below the lowest detection limit were replaced by the lowest detection limit divided by the square root of 2. Fasting glucose and insulin were measured in a fasting subsample of participants who were older than 12 years and fasting for at least 8 hours. Insulin resistance (IR) level was determined by the homeostasis model assessment (HOMA) method using the following formula: HOMA-IR = Fasting Glucose (mmol/L) * Fasting Insulin (mU/L)/22.5[17].

2.1.2 Definitions of socioeconomic levels and lifestyle variables

Education level was defined as high education attainment than high school if the participant reported having more than 12 years of schooling or equivalent and low education attainment for those having not. Income level was evaluated on the calculated poverty-to-income ratio (PIR) threshold after accounting for inflation and family size, with PIR values < 1.3 representing those below the poverty line. Smoking status was classified as non-smokers (not smoked at least 100 cigarettes in life), former smokers (smoked more than 100 cigarettes in life but did not smoke anymore), and current smokers (smoked more than 100 cigarettes in life and still smoked) by cigarette use questionnaire. Alcoholic drinking status was classified as non-drinkers and drinkers by whether they had at least 12 alcoholic drinks in the past year by alcohol use questionnaire. The overall dietary quality of participants was assessed using the 2015 version of the Health Eating Index (HEI-2015)[18]. Daily sedentary time(min) and caffeine intake (mg/d) were obtained from the household interview.

2.1.3 Definitions of early-onset sarcopenia, OSA, and other comorbidities

Appendicular lean mass (ALM) was measured by dual-energy X-ray absorptiometry (DXA) (Participants whose body weight was over 204 kg or height was over 1.98 were excluded). Pregnant females and subjects who were ≥ 192.5 centimeters (cm) (≥ 6 feet 5 inches) or weighed ≥ 204.1 kilograms (kg) (≥ 450 pounds) did not receive the DXA examination and thus were not included in the current study. The definitions of sarcopenia currently used by the scientific community are those proposed by the European Working Group on Sarcopenia in Older People (EWGSOP2) in 2018 and by the FNIH Sarcopenia Project[19][20]. Sarcopenia was defined by the FNIN criteria (ALM/BMI ≤ 0.789 for men and ≤ 0.512 for women) in this study for its high sensitivity to differentiate patients with sarcopenic obesity[21]. Sarcopenic obesity was defined as the coexistence of sarcopenia and obesity, where obesity was defined as body mass index (BMI) ≥ 30 kg/m²[22]. The age range of patients with early-onset sarcopenia and sarcopenic obesity was defined as 18–39 years old according to the definition of early-onset diabetes from previous studies[23]. Sleep-related data was obtained from questionnaires on sleep habits and disorders. Questions were asked by trained interviewers using the Computer Assisted Personal Interview (CAPI) system, and all participants aged 16 and older were eligible. OSA was defined as any of the following: snoring 3 or more nights per week, snorting, gasping or stopping breathing 3 or more nights per week or feeling excessively sleepy during the day more than 16–30 times per month despite sleeping around 7 or more hours per night on weekdays or work nights according to a previous study[24]. Diabetes was diagnosed as glycated hemoglobin (HbA1c) ≥ 6.5% or fasting glucose ≥ 7.0 mmol/L or self-reported diabetes on current American Diabetes Association (ADA) criteria. Blood pressure was obtained by a physician using a mercury sphygmomanometer. Hypertension was defined as systolic pressure ≥ 140 mmHg and diastolic pressure ≥ 90 mmHg. Information about thyroid and heart disease was obtained from the medical condition questionnaire. Heart diseases were defined as having a positive response to any of the following statements: “Ever told you had congestive heart failure?”, “Ever told you had coronary heart disease?”, “Ever told you had angina/angina pectoris?” or “Ever told you had a heart attack?” in the chronic disease questionnaire.

2.2 Statistical analysis methods

Given the complex probability cluster design of NHANES, all statistical analyses in the present study took sample weights into account. In this study, we combined two NHANES cycles and new sample weights were calculated by dividing the 2-year cycle sample weights by two according to the NHANES guidelines.

Characteristics of participants were presented as survey-weighted means with associated standard deviations (SDs) for continuous data and survey-weighted proportion for categorical data. Design-based students’ t-test and Chi-square test were separately used for continuous and categorical variables to analyze the characteristics of the study population. Analysis of variance (ANOVA) was applied to compare the characteristics of participants in different groups. Weighted multivariable logistic regression models were applied to calculate the odds ratios (OR) with 95% confidence intervals (CIs) for the sarcopenia and sarcopenic obesity risk caused by OSA. In the first model, we adjusted for demographic-related variables, such as age, gender, and race. In the second model, we further adjusted for socioeconomic status and lifestyle-related variables, such as education attainment level, poverty income ratio, cigarette use status, alcohol use status, caffeine intake status, sedentary time, and diet quality. In the third model, we further adjusted for comorbidities-related variables, such as diabetes, high blood pressure, and thyroid disease. In the mediation analysis, we included OSA as the independent variable, sarcopenia and sarcopenic obesity as dependent variables. We investigated the mediation effect of chronic inflammatory marker (CRP), insulin resistance marker (HOMA-IR), body mass marker (Body mass index) or dietary quality marker (health eating index) on the relationship between the exposure variable (OSA) and outcome (sarcopenia and sarcopenic obesity) separately. A thousand bootstraps were used in our analysis. The size of the indirect pathway effect, proportion of the mediating effect, and P-value for the mediating effect were all shown in Table 4.

Table 4

The mediation analysis of risk factors on the association between OSA and sarcopenia
	Early-onset sarcopenia
	Effect estimate	P value	Effect estimate	P value
Serum CRP
Total effect	0.06219	< 0.01	0.06336	< 0.01
Direct effect	0.04335	< 0.05	0.04639	< 0.01
Mediation effect	0.01884	< 0.01	0.01697	< 0.01
Proportion mediated	30.28%	< 0.01	26.78%	< 0.01
HOMA-IR
Total effect	0.03679	< 0.01	0.05674	< 0.01
Direct effect	0.03299	< 0.05	0.05282	< 0.01
Mediation effect	0.0038	< 0.01	0.00392	< 0.01
Proportion mediated	10.32%	< 0.01	6.91%	< 0.01
BMI
Total effect	0.0564	< 0.01	0.0593	< 0.01
Direct effect	0.0261	0.244	0.0273	0.153
Mediation effect	0.0303	< 0.01	0.032	< 0.01
Proportion mediated	53.64%	< 0.05	53.99%	< 0.01
Health eating index
Total effect	0.048927	< 0.01	0.049986	< 0.01
Direct effect	0.044698	< 2e-16	0.046901	< 0.01
Mediation effect	0.004229	< 0.05	0.003085	< 0.01
Proportion mediated	8.64%	< 0.01	6.17%	< 0.01

All statistical analyses were performed by R (version 4.2.0). Two-sided P-values < 0.05 was considered as statistically significant in this study.

4037 participants aged 18–59 were finally included in this observational study, which could represent 49.6 million noninstitutionalized residents of the United States. Of them, 49.8% were males and 50.2% were females, with a mean age of 38.5 ± 0.3 years. As for races, 61.1% were non-Hispanic white, 10.6% were non-Hispanic black, 11.1% were Mexican Americans, and 17.2% were other races. The prevalence of OSA was estimated to be 28.3%, and the prevalence of sarcopenia and sarcopenic obesity were estimated to be 7.4% and 5.8%, respectively.

Among 2162 participants aged 18–39, the prevalence of early-onset sarcopenia and sarcopenic obesity in the US was estimated to be 5.6% and 4.6% according to the multistage weighted survey design of NHANES 2015–2018 separately. Males had a higher prevalence of early-onset sarcopenia (6.8% V.S. 4.4%) and early-onset sarcopenic obesity (5.4% V.S. 3.8%) than females. While the prevalence of sarcopenia and sarcopenic obesity was estimated to be 9.4% and 7.0% for those aged 40–59.

3.1 Characteristics of participants stratified by OSA

Table 1 showed the characteristics of participants according to OSA. Participants with OSA symptoms were likely to be older, have lower poverty income, more caffeine intake and poorer HEI (P < 0.01), respectively. Regarding the health-related variables, diabetes, hypertension, heart disease, higher CPR, and HOMA-IR levels were significantly associated with OSA (P < 0.01). Besides, participants with OSA had higher BMI (P < 0.01) and ALM (P < 0.01) but lower ALM/BMI (P < 0.05). The prevalence of sarcopenia and sarcopenic obesity of participants with OSA were estimated to be 12.0% and 10.3%, respectively, while for those without OSA were 5.5% and 4.0%, respectively.

3.2 Characteristics of participants stratified by sarcopenia and obesity

Table 2 shows sleep-related differences of participants. Participants were divided into sarcopenia group, obesity group, sarcopenic obesity group, and healthy control group. For participants with sarcopenia or sarcopenic obesity, they had longer sleep duration(P < 0.05), more snore times (P < 0.01) and breath-stopping times (P < 0.01) than the healthy group. More participants with OSA were found in the sarcopenia group (31.1%, P < 0.01) and in the sarcopenic obesity group (49.7%, P < 0.01) than in the healthy group (19.5%).

Table 2

Descriptive statistics of sleep quality of study participants
	Sarcopenia only	Obesity only	Sarcopenic obesity	Healthy	P value
Number of participants	102(1.6%)	1263(32.0%)	262(5.8%)	2354(60.7%)	< 0.01
Age (years), mean (SD)	43.4(1.3) †	39.4(0.4) †	41.8(1.2) †	37.6(0.5)	< 0.01
Sex/Gender: Male, n (%)	66(64.3%) †	535(47.0%) †	127(52.2%) †	1213(50.8%)	< 0.01
BMI (kg/m2), mean (SD)	26.9(0.3) †	35.3(0.2) †	38.2(0.5) †	24.6(0.1)	< 0.01
ALM, mean (SD)	17.4(0.6) †	26.9(0.2) †	23.4(0.5) †	21.3(0.2)	< 0.01
Sleep duration(hours), mean (SD)	7.63(0.16) †	7.70(0.08) †	7.50(0.03) †	7.58(0.04)	< 0.01
Snore > 3 nights per week, n (%)	44(44.7%) †	709(59.6%) †	164(55.8%) †	778(31.5%)	< 0.01
Breath-stopping times > 3 per week, n (%)	11(7.0%) †	190(16.1%) †	45(18.1%) †	166(6.4%)	< 0.01
Sleep disorder, n (%)	23(19.6%) †	375(32.0%) †	87(36.6%) †	498(23.7%)	< 0.01
OSA, n (%)	33(31.1%) †	518(39.5%) †	126(49.7%) †	480(19.5%)	< 0.01
Diabetes, n (%)	14(14.2%) †	209(13.0%) †	64(21.9%) †	131(3.8%)	< 0.01
Hypertension, n (%)	26(24.0%) †	326(24.7%) †	74(29.9%) †	323(12.6%)	< 0.01
Thyroid disease, n (%)	8(10.6%) †	112(9.7%) †	22(10.3%) †	123(5.5%)	< 0.01
Heart disease, n (%)	5(4.3%) †	47(2.9%) †	16(6.0%) †	40(1.7%)	< 0.01

3.3 Results of weighted multivariable logistic regression analyses

The Multivariable logistic regression analysis was performed to explore the relation between OSA and early-onset sarcopenia or sarcopenic obesity in Table 3. In model 1, OSA was associated with both early-onset sarcopenia and early-onset sarcopenic obesity after adjusting for demographic-related variables, and their OR (95%) were 2.9 (1.6–5.1) and 3.5(1.8–6.5) separately. In model 2 and model 3, with further adjustments for socioeconomic status, lifestyle, comorbidities related variables, OSA was still associated with both (OR: 2.7, 95% CI: (1.5-5.0) for early-onset sarcopenia and OR: 3.5, 95% CI: (1.8–6.5) for early-onset sarcopenic obesity in model 2; 2.4(1.3–4.5) for early-onset sarcopenia and 2.8(1.4–5.5) for early-onset sarcopenic obesity in model 3, repectivel7). A subgroup analysis was applied among those aged 40–59 further. However, this association was not statistically significant anymore.

Table 3

Adjusted ORs with associated 95%CI of sarcopenia and sarcopenic obesity by OSA
		Sarcopenia			Sarcopenic obesity
		18 < = age < 40	40 < = age < 60	Total	18 < = age < 40	40 < = age < 60	Total
Model 1	OR (95%CI)	2.9(1.6–5.1)	1.7(0.9–3.1)	2.1(1.4–3.2)	3.5(1.8–6.5)	2.0(1.1–3.9)	2.5(1.6-4.0)
Model 1	P value	< 0.01	0.09	< 0.05	< 0.01	0.03	< 0.05
Model 2	OR (95%CI)	2.7(1.5-5.0)	1.5(0.8–2.8)	1.9(1.2–2.9)	3.5(1.8–6.7)	1.9(0.9–3.6)	2.4(1.5–3.8)
Model 2	P value	< 0.01	0.23	< 0.01	< 0.01	0.06	< 0.01
Model 3	OR (95%CI)	2.4(1.3–4.5)	1.4(0.7–2.4)	1.7(1.1–2.7)	2.8(1.4–5.5)	1.6(0.8–3.3)	2.0(1.2–3.3)
Model 3	P value	< 0.05	0.36	< 0.05	< 0.05	0.13	< 0.05
Note: All estimates accounted for complex survey designs. model1: adjusted for age, gender and races; model2: further adjusted for education level, poverty income ratio, cigarette use, alcohol use, caffeine intake, health eating index; model3: further adjusted for diabetes, high blood pressure and heart disease.
Results are presented as OR (95%CI) and P value.
Abbreviations: odds ratio (OR), confidence interval (CI).

3.4 Results of Weighted Mediation Analyses

In the mediation analyses, serum CRP significantly mediated the association between OSA and early-onset sarcopenia (proportion mediated [PM] = 30.28%, P < 0.01) as well as the association between OSA and sarcopenic obesity (PM = 26.78%, P < 0.01). Meanwhile, significant mediating effects were also found in the HOMA-IR index (10.32% for early-onset sarcopenia, P < 0.01; 6.91% for early-onset sarcopenic obesity, P < 0.01), HEI-2015 (8.64% for early-onset sarcopenia, P < 0.05; 6.17% for early-onset sarcopenic obesity, P < 0.01) and BMI (53.64% for early-onset sarcopenia, P < 0.01; 53.99% for early-onset sarcopenic obesity, P < 0.01).

In this study, we estimated the prevalence of early-onset sarcopenia and sarcopenic obesity in the US by the well-designed nationally representative database NHANES 2015–2018. We found that OSA was a significant independent risk factor for early-onset sarcopenia and early-onset sarcopenic obesity. Chronic inflammation, body mass, IR, and dietary quality had a significant mediating effect on this association.

Sarcopenia has become an urgent issue for healthcare systems all over the world. Loss of appendicular skeletal muscle not only obstacles old people’s activities of daily living (ADL) but is also associated with other complications such as osteoporosis, diabetes, and cardiovascular disease[25][26][27]. Early-onset sarcopenia, as an early term of sarcopenia, might bring more adverse outcomes but not get sufficient attention. In our study, we estimated that the prevalence of early-onset sarcopenia in the US has reached 5.6% and 4.6%, according to the multistage weighted survey design of NHANES. We found that patients with early-onset sarcopenia suffered from a higher prevalence of diabetes, hypertension, thyroid disease, heart disease than their healthy counterparts. And the adverse synergetic effect of sarcopenia and obesity also existed in those with early-onset sarcopenic obesity[28]. Besides, participants with early-onset sarcopenia or sarcopenic obesity were under poorer sleep quality. Compared to the healthy group, they had longer sleep duration(P < 0.05), more snore times (P < 0.01), and more breath-stopping times (P < 0.01). Meanwhile, the prevalence of OSA was estimated to be 31.1% in the sarcopenia group and 49.7% in the sarcopenic obesity group.

Existing studies have indicated some sleep-related problems are tightly associated with muscle mass and function. For example, Takuma Shibuki et al. found that long sleep was positively associated with sarcopenia. Moreover, they found poor sleep quality (quantified by times of insomnia per night) was associated with sarcopenia in normal sleepers but not in long sleepers[29]. Zhang et al. further indicated the U-shape nonlinear association between sleep duration and grip strength and pointed out that this association was meditated by BMI[30]. However, the association between OSA and sarcopenia has not reached a consensus yet. Some studies indicated that sleep deficiency and intermittent hypoxia status favored increased body mass, while others observed that OSA was only associated with obesity but not with sarcopenia[31]. Meanwhile, there was a relative scarcity of research on early-onset sarcopenia conducted in middle-aged adults compared to elderly adults previously. Our study supported and extended these previous studies. We found that participants with OSA had higher BMI but lower ALM/BMI than those without OSA. Our weighted multivariable logistic regression analyses further supported that OSA was an independent risk factor of early-onset sarcopenia and early-onset sarcopenic obesity after adjusting for many potential confounding variables. Our studies attached the importance to find the susceptible of early-onset sarcopenia or sarcopenic obesity among those with OSA because early-term interventions are of more use in the prevention and treatment of sarcopenia or sarcopenic obesity[32].

Several possible mechanisms may account for the relationship between OSA and early-onset sarcopenia. Interruption of breathing by OSA results in repeated intermittent hypoxia, hypercapnia and arousals from sleep. Such short and high-frequency bouts of blood O² desaturations activates hypoxia-inducible factor-1 (HIF-1) and HIF-2, which belong to the HIF family of transcriptional activators. And HIF-1α-dependent β cell dysfunction may induce hypersecretion and insulin resistance by increased generation of reactive oxygen species(ROS)[33]. On the other hand, insulin resistance obstacles peripheral glucose utilization and dysregulates protein turnover, especially in skeletal muscle and dietary nutrition quality[35]. We performed a mediation analysis to estimate the mediating effect of insulin resistance on the association between OSA and early-onset sarcopenia. HOMA-IR, as an indicator for insulin resistance, it mediated 10.32% of the potential effects of OSA on sarcopenia in our mediation models.

Intermittent hypoxia also induces the activation of the hypothalamic-pituitary-adrenal (HPA) axis and hypothalamic-pituitary-thyroid (HPT) axis, which may affect muscle catabolism through low-grade inflammation and oxidative stress[35]. And it was confirmed that clustered systemic and muscle oxidative stress and inflammation can accelerate the onset of sarcopenia in individuals with obesity[36]. CRP was an important chronic inflammation marker and mediated 30.28% of this association. Therefore, the inflammation hypothesis may be proposed as an important potential mechanism linking OSA and early-onset sarcopenia.

Given that early-onset sarcopenia was associated with obesity and poor dietary quality, we also estimated their mediating effect on this association. BMI was an indicator of obesity and mediated 53.64% of the potential effects after adjusting for confounders. This result indicated that a higher BMI score was related to a higher risk of early-onset sarcopenia. Because early-onset sarcopenia was often secondary to obesity and other chronic diseases, this may partly explain why the association between OSA and sarcopenia was not significant among the elderly in our study for there were a relatively small number of patients with obesity among those aged 40–60. Therefore, reducing excess adiposity remains the fundamental pathogenetic treatment for those with early-onset sarcopenic obesity. With respect to diet nutritional factors, HEI-2015 was an indicator of dietary quality and mediated 8.64% of the potential effects of OSA on sarcopenia. Although nutritional intervention was considered as therapy in priority to preserve muscle mass for sarcopenia and sarcopenic obesity, optimal dietary options and medical nutritional support strategies need to be confirmed for these young patients[37].

Our study has several strengths. First, data from NAHANES was collected cautiously and widely accepted because of its scientific design. Such a nationally representative dataset enabled us to minimize the sampling bias and take many confounders into consideration to get the precise estimate. Second, this study was the first to identify the prevalence of early-onset sarcopenia in the US and its odds ratio caused by OSA. And we performed a mediation analysis, which enables an insightful understanding of the inter-relationship of these factors with sarcopenia from a comprehensive perspective. We find out the mediating role of insulin resistance, poor dietary quality, chronic inflammation, body mass and obesity on the association between OSA and early-onset sarcopenia, which may provide new strategies for the prevention and treatment of early-onset sarcopenia.

There are also some limitations existing in our study as well. First, due to the cross-sectional nature of the study, we were unable to determine the causal relationship between the OSA and sarcopenia. And further longitudinal studies are necessary. Second, sleep-related problems and some chronic comorbidities were assessed by self-reported methods, which might cause recall bias although there is a good correlation between subjective perception of sleep quality and objective measures was reported[38]. Third, our study was also limited by the unmeasured variables. We selected a relatively simple method from previous studies to define early-onset sarcopenia which only focused on muscle mass and ignored muscle function. However, according to a previous study, these younger group with early-onset sarcopenia may still preserve muscle function. Including altered muscle functional parameters as a necessary component of the diagnostic process may even lead to potential borderline conditions[39]. Fourth, considering the complex and multifactorial etiology of early-onset sarcopenia, the association reported in this study could be biased by other unknown confounders although we had adjusted sociodemographic features, socioeconomic status, dietary quality, relevant comorbidity in our logistic models. And other biological mechanisms, such as epigenetic changes and neuroendocrine responses, may also play a role in the pathophysiology of sarcopenia. Therefore, future research is needed to incorporate other intermediate mechanisms and guide treatment decisions in the clinical practice.

Above all, early-onset sarcopenia is prevalent in the US, and young participants with OSA were more susceptible. Clinicians should pay more attention to screening those with early-onset sarcopenia to reduce long-term adverse outcomes of sarcopenia as early as possible. Weight control, healthy dietary habits, and other methods that could improve chronic inflammation and insulin resistance may be new strategies to control OSA and early-onset sarcopenia.

CDC, Centers for Disease Control and Preventions;

NCHS, National Center for Health Statistics;

NHANES, National Health and Nutrition Examination Survey;

FNIH, Foundation for the National Institutes of Health;

EWGSOP2, European Working Group on Sarcopenia in Older People;

ADA, American diabetes association;

RCT, Randomized controlled trial;

PIR, poverty-to-income ratio;

ADL, activities of daily living;

DXA, dual-energy X-ray absorptiometry;

ALM, appendicular lean muscle;

BMI, body mass index;

MEC, mobile examination center;

PSUs, primary sampling units;

SDs, standard deviations;

ANOVA, analysis of variance;

OSA, obstructive sleep apnea;

HPA, hypothalamic-pituitary-adrenal;

HPT, hypothalamic-pituitary-thyroid;

CRP, serum chronic reaction protein;

HOMA-IR, homeostasis model assessment insulin resistance index;

HEI, dietary quality index;

BMI, body mass index;

HIF-1, hypoxia-inducible factor-1.

Acknowledgments

We are grateful to all the subjects for their participation.

Author Contributions

The study concept and design were framed by XS and CL. XT conducted the statistical data analysis and drafted the manuscript. WL, RN, and WL contributed to the discussion. XS and CL contributed to the discussion and revision. All authors read and approved the final manuscript.

Funding Information

CL was funded by the Natural Science Foundation of China (NO. 82270928), the Fujian Provincial Health Science and Technology Project (No. 2022CXB016), and High-quality development Funds of The First Affiliated Hospital of Xiamen University (NO. YN81870611)

Competing interests

Authors do not have conflicts of interest to declare.

Data Availability Statement

No additional data are available.

Ethics statement

The proposed study does not involve the collection of primary data. Accordingly, no ethical approval is required.

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Table 1

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You are reading this latest preprint version

Obstructive sleep apnea (OSA) is associated with increased risk of early-onset sarcopenia and sarcopenic obesity: Results from NHANES 2015-2018.

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Version 1

Abstract

Objective

Methods

Results

Conclusion

Figures

1 Introduction

2 Study Design and Statistical Methods

2.1 Study Population in NHANES

2.1.1 Definitions of demographic variables and laboratory measure results

2.1.2 Definitions of socioeconomic levels and lifestyle variables

2.1.3 Definitions of early-onset sarcopenia, OSA, and other comorbidities

2.2 Statistical analysis methods

3. Results

3.1 Characteristics of participants stratified by OSA

3.2 Characteristics of participants stratified by sarcopenia and obesity

3.3 Results of weighted multivariable logistic regression analyses

3.4 Results of Weighted Mediation Analyses

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Tables

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Supplementary Files

Status:

Version 1