Relationship between obstructive sleep apnea and obesity in sarcopenia and presarcopenia among elderly people

doi:10.21203/rs.3.rs-4370108/v1

Background

Obstructive sleep apnea (OSA), obesity and sarcopenia are issues have been attracting increasing attention. The associations between OSA, obesity and sarcopenia have rarely been investigated in previous studies.

Methods

We invited healthy adults aged 60 years or older who lived in Chang Gung Health and Culture Village in Taoyuan or Songshan District in Taipei city between September 2019 and October 2020 to participate in this study. Demographics were collected from the electronic medical records of our hospital or statements of the participants. Full-channel home polysomnography (PSG), handgrip strength, the 4-meter walk test, and bioelectrical impedance analysis (BIA) were used to evaluate OSA and sarcopenia.

Results

A total of 96 participants were included. Considering the small number of sarcopenia participants and the pathophysiology of OSA, we included presarcopenia participants in the sarcopenia group. The severity of OSA and BMI increased with age, and the peak values were observed between 70 and 80 years of age. A delayed increase in the severity of OSA in females was observed. Males were more likely to be obese than females. The prevalence of sarcopenia increased with age and was greater in females. In those without sarcopenia, a positive correlation was observed between the severity of OSA and obesity (p = 0.043).

Conclusions

Sarcopenia and low muscle mass may be confounding factors when evaluating the relationship between OSA and obesity in elderly individuals.

obstructive sleep apnea

obesity

sarcopenia

muscle mass

elderly

Obstructive sleep apnea (OSA) is a disorder of repeated upper airway collapse during sleep that causes apnea or hypopnea to varying degrees. Intermittent oxygen desaturation, hypercapnia and sleep fragmentation contribute to excessive daytime sleepiness, which leads to reduced quality of life, impaired work performance, and increased risk of traffic accidents(1–3). OSA is also associated with the development of several clinical comorbidities, including hypertension, cardiovascular disease, cardiac arrhythmia, pulmonary hypertension, stroke, cognitive impairment, and metabolic syndrome(4–9).

Advanced age, male sex, obesity, and smoking have been proposed as risk factors for OSA(1, 2, 10). The prevalence of OSA increases with age. Although a greater proportion of OSA was found in people aged 65 years and older, the trend of OSA incidence was not only a simple positive correlation with age in elderly people(11). The etiology of OSA in the aged population might be different from that in the middle-aged population(11).

Sarcopenia is a skeletal muscle disorder characterized by low muscle strength, low muscle mass and low physical performance, which contribute to recurrent falls, fractures and mortality(12–14). Muscle weakness may lead to increased hospitalization and health care costs(15, 16). Changes in lifestyle, physical activity, nutritional conditions, hormonal status and chronic metabolic disease with age lead to the development of sarcopenia(17).

The prevalence and severity of both OSA and sarcopenia increase with increasing age(12, 18). One of the pathogenic mechanisms associated with OSA, sarcopenia and sarcopenia is poor upper airway muscle function, which may induce upper airway collapse(19, 20). However, few studies have evaluated the interaction between age, OSA and sarcopenia. Our study aimed to investigate the relationships among OSA, obesity and sarcopenia in an aged population.

Study population

We invited healthy adults aged more than or equal to 60 years old to join the Integrated Systematic Data of Geriatric Medicine to Explore the Solution for Healthy Aging (ISD-HA) study between September 2019 and October 2020 during their general medical examination at Chang Gung Health and Culture Village in Taoyuan city or Songshan District in Taipei city. The inclusion criteria were as follows: (1) aged ≥ 60 years; (2) visited Chang Gung Memorial Hospital at least once within 1 year before recruitment; and (3) stayed in Taiwan for more than 180 days within 1 year before recruitment. The exclusion criteria were as follows: (1) had clinical evidence of severe abnormalities in any major organ system; (2) had a medical history of severe autoimmune disease; (3) were receiving cancer therapy at the time of recruitment; (4) had active infection with antibiotic treatment within one month of recruitment; (5) were diagnosed with dementia or major depressive disorder; and (6) were too frail to stand or walk.

Data collection

Demographics were collected from the electronic medical records of our hospital or statements of the participants. We obtained demographic information, including age, sex, body height, body weight, body mass index (BMI), and medical history (including hypertension, hyperlipidemia, diabetes mellitus, osteoporosis, osteoarthritis, chronic obstructive pulmonary disease, chronic kidney disease and malignancy). The study was approved by the Institutional Review Board of Chang Gung Medical Foundation, Taiwan (IRB approval number: 201900702A3). The study procedures followed the Declaration of Helsinki. All participants provided written informed consent. All personal information was encrypted in a database and anonymized, so there was no breach of privacy.

Sleep assessments

Objective sleep assessment was performed with a single night of standardized in-home polysomnography (PSG) using a portable monitoring system (Philips Alice 6 lDe; Philips Health care, Andover, MD, USA). Sleep stages were scored by a certificated sleep technologist and an experienced sleep specialist according to the established criteria(21). The time spent in each sleep stage, total sleep time, total awake time, sleep efficiency and sleep onset latency were recorded. OSA severity is quantified according to the apnea–hypopnea index (AHI), the number of apnea or hypopnea events recorded per hour of sleep(22). An AHI score of 5 to 15 events per hour was defined as mild OSA, 15 to 30 events per hour was defined as moderate OSA, and ≥ 30 events per hour was defined as severe OSA(22).

Assessment of sarcopenia

Handgrip strength (kg) was measured by a hand dynamometer (Jamar® Plus + Digital Hand Dynamometer) to evaluate muscle strength. Gait speed was measured by the 4-meter walk test and is presented as the distance (m) per second. Appendicular skeletal muscle mass (ASM) was measured by bioelectrical impedance analysis (BIA), and the ASM index was calculated as the ASM divided by the square of the height. The Asian Working Group for Sarcopenia (AWGS) proposed that the cutoff value for low muscle strength was defined as a handgrip strength < 28 kg for men and < 18 kg for women. Low physical performance was defined as a gait speed < 1.0 m/s. Loss of muscle mass was defined as an ASM index < 7.0 kg/m² in men and < 5.7 kg/m² in women(23). The European Working Group on Sarcopenia in Older People (EWGSOP) and the AWGS both proposed that sarcopenia is characterized by a loss of skeletal muscle mass plus a loss of muscle strength and/or reduced physical performance(23, 24). The EWGSOP also proposed the concept of presarcopenia, which is characterized by a loss of skeletal muscle mass without an impact on muscle strength or physical performance(24).

Statistical analysis

Our study aimed to evaluate whether there is a positive correlation between OSA and sarcopenia in people aged ≥ 60 years. We also investigated the associations between OSA, age, sex and obesity. All the statistical analyses were performed using the statistics program IBM SPSS version 21.0 (SPSS, Inc., Chicago, IL, USA). A t test was used to compare the means between two groups. Linear regression was used to test the relationship between BMI and the AHI to determine the changes in BMI with the severity of OSA. A p value of 0.05 or less was considered to indicate statistical significance, and all the data are expressed as the mean ± SEM.

A total of 112 participants were initially included, 16 of whom were excluded because they were too frail to complete the assessment of sarcopenia. The demographic data of the 96 final participants are summarized in Table 1. Considering the small number of sarcopenia participants and the pathophysiology of OSA, we included presarcopenia participants in the sarcopenia group. Body weight, BMI, incidence of OSA, ASM index and handgrip strength were significantly different between the participants with and without sarcopenia (p < 0.001, p < 0.001, p = 0.018, p < 0.001, p = 0.004, respectively; Table 1).

The relationship between the severity of OSA and age in different sex groups.

The relationship between the AHI and age in elderly individuals is shown in Fig. 1. The mean AHI increased with age, and the peak AHI was observed among the participants aged between 70 and 80 years. However, it decreased among those aged above 80 years (Fig. 1A). The trend of correlation between the AHI and age in the female participants was similar to that in the whole participants; nonetheless, the AHI decreased with age in the male participants. Among those aged between 60 and 70 years, the male participants had a greater AHI than did the female participants (p = 0.049, Fig. 1B).

The relationship between obesity and age in different sex groups

The relationship between BMI and age is presented in Fig. 2. BMI increased with age among the participants aged between 60 and 70 years and between 70 and 80 years but decreased among those aged older than 80 years (Fig. 2A). Among those aged between 60 and 70 years, male participants had a higher BMI (p = 0.046, Fig. 2B).

The relationship between the incidence of sarcopenia and age in elderly individuals.

The proportion of patients with sarcopenia increased with age (p = 0.036, Fig. 3A). Among those aged between 60 and 70 years and between 70 and 80 years, the proportion of patients with sarcopenia was greater among the female participants (p = 0.029 and p = 0.048, respectively; Fig. 3B). Among those aged above 80 years, the proportion of females with sarcopenia was close to that of males.

Correlations between the AHI and BMI in elderly individuals with or without sarcopenia

The AHI was not correlated with BMI in any of the participants (p = 0.63, Fig. 4A). Taking sarcopenia into account, the correlation between the AHI and BMI was also unremarkable (Fig. 4B). Nevertheless, in those without sarcopenia, a positive correlation was observed between the AHI and BMI (Fig. 4C, p = 0.043). Therefore, sarcopenia is a confounding factor when exploring the relationship between OSA and obesity.

In the present study, both the severity of OSA and obesity increased with age; moreover, the peak values were both in the group aged 70–80 years. The AHI increased later in the female participants. The prevalence of sarcopenia increased with increasing age in both males and females. Obesity is not positively correlated with the severity of OSA. However, in the group without sarcopenia, the severity of OSA increased with increasing obesity.

In our study, there was no significant correlation between the severity of OSA and BMI. This result is different from previous studies suggesting that obesity is an important risk factor for OSA(25, 26). However, in the group without sarcopenia, there was a positive correlation between the severity of OSA and BMI. This could be explained by the likelihood of sarcopenia being inversely associated with BMI in elderly individuals(27). A British cohort study demonstrated that greater gains in BMI are associated with greater muscle mass(28). The phenomenon of increased lean muscle along with fat mass probably contributed to this result(29). Another cross-sectional study revealed that in nonobese individuals, upper body obesity significantly increased the frequency of OSA(26). In our study, we did not investigate the impact of upper body obesity on OSA, which may influence the relationship between OSA and obesity. One study in Brazil revealed that a high risk of OSA was associated with low muscle mass and low muscle strength among obese participants(30). In our study, the number of individuals with low muscle strength was too low to investigate the correlation with OSA.

Some previous studies have demonstrated that the prevalence of sarcopenia increases with age(31), while others have shown the opposite results(32). The prevalence of sarcopenia varies when different definitions and cutoff values are used(33). In Japan, a study with 1882 participants revealed a positive correlation between sarcopenia and advanced age(34). Another study in China also reported similar results(27, 35). Considering the small number of sarcopenia participants who met the criteria of the AWGS and the pathophysiology of OSA, which is correlated with changes in the muscle and fat proportions of the upper airway and muscle tone, we included presarcopenia participants in the sarcopenia group(1, 2, 23, 24). In our study, the proportion of sarcopenia increased with age; moreover, the prevalence in women was greater than that in men. These results are consistent with other studies in Japan and China(27, 34, 35). The close living environment and similar lifestyles likely contributed to this result.

A previous study showed that the severity of OSA increased with increasing age(36, 37). Fietze et al. demonstrated that the AHI is significantly greater for participants aged 60 years or older than for participants under 60 years old(18). These findings are consistent with our study. Nonetheless, in our study, the severity of OSA did not follow this trend in participants aged above 80 years. Gabbay et al. conducted a retrospective study with 23806 participants between 2000 and 2009(38). A plateau in the AHI was found for participants aged between 70 and 75 years, especially males. This demonstration was similar to our results. Moderate-to-severe OSA is associated with an increased risk of all-cause mortality with increasing age, which may lead to a healthy survival effect(39). According to our study design, we excluded people with severe major organ dysfunction and poor performance status. The above factors may contribute to selection bias. Moreover, few studies have investigated adults aged above 80 years independently(18, 36–38, 40). However, further studies are needed to confirm our results.

The severity of OSA is greater in males than in females; moreover, Fietze et al. and Gabbay et al. both demonstrated that the AHI increases significantly in females aged greater than 50–59 years(18, 38). In our study, the AHI was also greater in the male group than in the female group and increased significantly with increasing age between the females aged 60 and 70 years and between the females aged 70 and 80 years. The phenomenon of a delayed increase in the severity of OSA in the female group is consistent with the findings of a previous study, which might be due to the diminished protective effect of gonadal hormones on OSA(41). Changes in serum gonadal hormone levels may contribute to the redistribution of body fat to central regions and the loss of lean muscle mass with a proportional increase in fat mass(1).

Globally, the incidence of obesity increases with increasing age from 20 years of age, reaches a peak between the ages of 50 and 65 years of age, and then declines thereafter(42). The prevalence of obesity in females was greater than that in males in all age groups. Haslam et al. investigated the prevalence of obesity by age and sex in subregions of the world and demonstrated that the proportion of obese individuals increases after 15 to 29 years of age and reaches a peak between 60 and 69 years of age in China and Vietnam(43). The incidence of obesity is slightly greater in females than in males; however, the trend of the curve is similar in both males and females(43). In our study, the prevalence of obesity was greater in males than in females, which is in contrast with the findings of previous studies(42, 43). However, a cross-sectional study demonstrated a slightly greater BMI in males than in females in southern China(44). This phenomenon is likely related to their similar lifestyles and ethnicities.

There were several inherent limitations in our study. First, the number of participants with sarcopenia was small. Therefore, we included participants with presarcopenia in the sarcopenia group. The different definitions led to different results compared with those of previous studies. Second, we did not record data on upper body obesity, which likely influenced the severity of OSA. Third, the exclusion criteria of our study may have contributed to selection bias. Considering that people with severe major organ dysfunction and poor performance status may have different results when investigating the relationships among age, OSA, obesity and sarcopenia.

In conclusion, the severity of OSA and obesity and the prevalence of sarcopenia increased with increasing age. The severity of OSA increased later in females, which is likely correlated with postmenopause. Sarcopenia and low muscle mass are confounding factors when evaluating the relationship between OSA and obesity.

OSA

obstructive sleep apnea

BMI

body mass index

PSG

polysomnography

AHI

apnea-hypopnea index

ASM

appendicular skeletal muscle mass

BIA

bioelectrical impedance analysis

AWGS

Asian Working Group for Sarcopenia

EWGSOP

European Working Group on Sarcopenia in Older People

Ethics approval and consent to participate

This study was carried out in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Review Committee of Chang Gung Medical Foundation (approval number: 201900702A3). Informed consent was obtained from all participants.

Consent for publication

Not applicable.

Availability of data and materials

The data sets analysed during the current study are available from the corresponding author upon reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was supported by grant CORPG3J0371 & CMRPG3J1931 from Chang Gung Memorial Hospital. The sponsors only provided financial support.

Author’s contributions

Li-Pang Chuang, Ning-Hung Chen and Ji-Tseng Fang contributed to the conception and design of the study. Shih-Wei Huang, Wen-Jui Chang, and Pi-Hung Tung analysed and interpreted the data. Shih-Wei Huang and Li-Pang Chuang drafted the manuscript. Ting-Wei Liao and Geng-Hao Liu provided the study materials and selected patients. Shih-Wei Lin and Han-Chung Hu collected and assembled the data. All authors read and approved the final manuscript and have agreed to be personally accountable for the author's own contributions, and to ensure that questions related to the accuracy or integrity of any part of the work are addressed.

Acknowledgements

We thank all the investigators and members of the Integrating Systematic Data

of Geriatric Medicine to Explore the Solution for Health Aging Study from Formosa Biomedical Technology Corporation and Chang Gung Medical Foundation.

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Table 1. Demographics of the participants
Demographics	Total, n = 96	Sarcopenia, n = 25	Nonsarcopenia, n = 71	p value
Gender
Male, n(%)	42(43.8%)	7(28%)	35(49.3%)
Female, n(%)	54(56.2%)	18(72%)	36(50.7%)	0.065
Age, years, mean ± SD	73.3 ± 7.0	75.3 ± 7.99	72.7 ± 6.6	0.103
Height, cm, mean ± SD	160.2 ± 7.6	158 ± 7.7	160.1 ± 7.5	0.097
Weight, kg, mean ± SD	60.9 ± 10.9	51.9 ± 7.6	64.1 ± 10.1	<0.001
BMI, kg/m², mean ± SD	23.6 ± 3.4	20.7 ± 2.07	24.7 ± 3.19	<0.001
Medical history
Hypertension, n(%)	33(34.4%)	7(28%)	26(36.6%)	0.435
Hyperlipidemia, n(%)	33(34.4%)	11(44%)	22(31%)	0.239
Diabetes mellitus, n(%)	15(15.6%)	2(8%)	13(18.3%)	0.222
Osteoporosis, n(%)	7(7.3%)	4(16%)	3(4.2%)	0.051
Osteoarthritis, n(%)	8(8.3%)	1(4%)	7(9.9%)	0.362
Chronic obstructive pulmonary disease, n(%)	6(6.3%)	3(12%)	3(4.2%)	0.167
Chronic kidney disease, n(%)	2(2.1%)	0(0%)	2(2.8%)	0.396
Malignancy, n(%)	5(5.2%)	1(4%)	4(5.6%)	0.752
Obstructive sleep apnea, n(%)	77(80.2%)	16(64%)	61(85.9%)	0.018
AHI, events/hour, mean ± SD	20.1 ± 14.4	16.4 ± 11.9	21.2 ± 15.0	0.186
Mild, n(%)	28(29.2%)	5(20%)	23(32.4%)	0.241
Moderate, n(%)	25(26%)	7(28%)	18(25.4%)	0.795
Severe, n(%)	24(25%)	4(16%)	20(28.2%)	0.227
ASM index, kg/m², mean ± SD	6.8 ± 1.2	5.8 ± 0.6	7.2 ± 1.2	<0.001
Handgrip strength, kg, mean ± SD	28.2 ± 8.2	24.1 ± 7.5	29.6 ± 8.0	0.004
Gait speed, m/s, mean ± SD	1.1 ± 0.3	1.1 ± 0.3	1.2 ± 0.3	0.353
BMI, Body Mass Index; AHI, Apnea-Hypopnea Index; ASM index, appendicular skeletal muscle mass index

No competing interests reported.

Relationship between obstructive sleep apnea and obesity in sarcopenia and presarcopenia among elderly people

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Background

Methods

Study population

Data collection

Sleep assessments

Assessment of sarcopenia

Statistical analysis

Results

The relationship between obesity and age in different sex groups

Correlations between the AHI and BMI in elderly individuals with or without sarcopenia

Discussion

Conclusions

Abbreviations

Declarations

References

Tables

Additional Declarations

Status:

Version 1