Study population
The China Health and Retirement Longitudinal Study(CHARLS) started in between June 2011 and March 2012 and involved 17708 individuals aged more than 45 years, which represented a nationally population-based health, social and economic status with covering 450 urban or rural areas in 28 provinces of China. CHARLS uses a face-to-face computer-assisted personal interview(CAPI) with physical measurements, blood sample collection, and depression assessment. Follow-up was conducted every 2 years with the participation of new individuals in CHARLS. The Biomedical Ethics Review Committee of Peking University approved CHARLS. Written informed consents were collected in National School of Development of Peking University. More detailed description about CHARLS has been reported elsewhere [11].
Study on global AGEing and adult health(SAGE) is designed by the World Health Organization(WHO) and plans to assess and compare health status and socio-economic consequences of adult populations and the ageing process in six countries(China, Ghana, India, Mexico, Russian Federation and South Africa) worldwide. SAGE in China wave 1 was created between 2007 and 2010, involved 15050 individuals and covered the eight provinces. In 2012, the date involving 19 districts in Shanghai and 9524 individuals were included in SAGE. WHO Ethical Review Committee and local ethics research review boards approved ethical and obtained written informed consent. All information and data were found in elsewhere12 and the following link: https://apps.who.int/healthinfo/systems/survey data/index.php/ catalog/sage/about(accessed on 30 May 2021) [12].
Definition of asthma, sarcopenia and depression
The diagnosis of asthma was based on a positive answer of the following question: Have you ever been diagnosed with asthma by a doctor. In WHO SAGE, we collected the relevant data about asthmatic medications treatment and symptoms (attacks, awakening, and severe shortness of breath) in the past 12 months. Severe shortness of breath was based on a positive answer of the following question: Have you had an attack of shortness of breath that came on without obvious cause when you were not exercising or doing some physical activity?.
According to the recommend of AWGS 2019 [6], muscle strength and physical performance in the diagnosis of sarcopenia were measured by using handgrip strength(<28.0 kg for men and <18.0 kg for women) and gait speed(<1.0 m/s) in CHARLS and WHO SAGE. In addition, CHARLS also provided 5-time chair stand test to measure low physical performance(≥12 s). The following anthropometric equation for the height-adjusted muscle mass (ASM/Ht2) can be used to determine whether low ASM exists in Chinese population[6, 13]: ASM/Ht2=(0.193*body weight + 0.107* height- 4.157* gender - 0.037*age-2.631)/height2. Similar to previous studies
[14–16], the cut-off for defining low muscle mass was based on the ASM/Ht2 of the lowest 20th% percentile of the study population. Therefore, ASM/Ht2 < 6.92 for men and < 5.14 for female are regarded as low ASM. The age cutoffs of sarcopenina are set at 60 years old [6]. Low ASM with low muscle strength or physical performance were defined as sarcopenia, meanwhile patients with severe sarcopenia were associated with low ASM, muscle strength and physical performance [6].
CHARLS assessed whether depression exists by using the 10-item Center for Epidemiological Studies–Depression Scale (CES-D10). The 10 items with 4 four answers were used to estimate the depressive feelings and behaviors of individuals over one week, and value of 0-3 is assigned to each answer. Previous studies identified that CES-D10 harbors adequate reliability and validity in the assessment of depression for the community-dwelling older Chinese population [17]. CES-D10≥ 12 scores with total scores of 0 to 30 scores was considered depression [17, 18].
Variables
In CHARLS, this study included the following demographic characteristics as adjusted confounding factors: sex, age, region, urban/rural, married status, alcohol, smoking, body mass index(BMI), night sleep duration and thirteen physician- diagnosed comorbidities (hypertension, dyslipidemia, hyperglycemia, cancers, chronic lung diseases, liver diseases, heart diseases, stroke, kidney diseases, digestive diseases, emotional or psychiatric problems, memory-related diseases, and arthritis or rheumatism). Lung function was measured through peak expiratory flow(PEF) in CHARLS.
In WHO SAGE, the following variables were used to adjusted the associations between asthma and sarcopenia: sex, age, region, urban/rural, married status, alcohol, smoking, vigorous-intensity activity, moderate-intensity activity, BMI, night sleep duration, hypertension, diabetes, angina, stroke, chronic lung diseases and arthritis. This study evaluated lung function in individuals with asthma through the following variables: forced expiratory volume in the first second (FEV1), FEV1/forced vital capacity(FVC), PEF, forced expiratory flow rate, mid-exhalation (FEF25–75%). FEV1/FVC<0.7 was regarded as airway obstruction. Individuals with chronic lung diseases and FEV1/FVC<0.7 were diagnosed as having chronic obstructive pulmonary disease (COPD).
For Chinese adults, BMI was divided into four groups: underweight (< 18.5 kg/m2), normal (18.5 to < 24.0 kg/m2), overweight (24.0 to < 28.0 kg/m2), and obesity (≥ 28.0 kg/m2) [19]. Age was divide into three groups: 60-69, 70-79, and 80 years. More detailed groups of all variables were shown in Table 1 and Table 2.
Table 1
The characteristics of study population in the China Health and Retirement Longitudinal Study
| No sarcopenia | Sarcopenia | Severe sarcopenia | P |
N | 12159 | 2350 | 895 | |
Age | 66.7 ± 5.6 | 69.9 ± 6.7 | 73.9 ± 7.2 | <0.01 |
Year | | | | <0.01 |
2011 | 3604 (29.6%) | 877 (37.3%) | 310 (34.6%) | |
2013 | 3846 (31.6%) | 715 (30.4%) | 259 (28.9%) | |
2015 | 4709 (38.7%) | 758 (32.3%) | 326 (36.4%) | |
Sex | | | | 0.09 |
Male | 6303 (51.8%) | 1199 (51.0%) | 495 (55.3%) | |
Female | 5856 (48.2%) | 1151 (49.0%) | 400 (44.7%) | |
Region | | | | <0.01 |
Southwest | 3271 (26.9%) | 924 (39.3%) | 322 (36.0%) | |
South and central | 6344 (52.2%) | 1163 (49.5%) | 447 (49.9%) | |
North | 2544 (20.9%) | 263 (11.2%) | 126 (14.1%) | |
Urban/Rural | | | | <0.01 |
Urban | 7232 (59.5%) | 1746 (74.3%) | 671 (75.0%) | |
Rural | 4927 (40.5%) | 604 (25.7%) | 224 (25.0%) | |
Married status | | | | <0.01 |
Current unmarried | 2021 (16.6%) | 551 (23.4%) | 302 (33.7%) | |
Current married | 10138 (83.4%) | 1799 (76.6%) | 593 (66.3%) | |
Alcohol | | | | <0.01 |
More than once a month | 3170 (26.1%) | 605 (25.7%) | 220 (24.6%) | |
Less than once a month | 927 (7.6%) | 144 (6.1%) | 48 (5.4%) | |
Never | 8062 (66.3%) | 1601 (68.1%) | 627 (70.1%) | |
Smoking | | | | <0.01 |
Never | 6806 (56.0%) | 1175 (50.0%) | 456 (50.9%) | |
Ever | 1556 (12.8%) | 239 (10.2%) | 119 (13.3%) | |
Current | 3797 (31.2%) | 936 (39.8%) | 320 (35.8%) | |
Body mass index category | | | | <0.01 |
Underweight | 76 (0.6%) | 834 (35.5%) | 344 (38.4%) | |
Normal | 5996 (49.3%) | 1512 (64.3%) | 549 (61.3%) | |
Overweight | 4460 (36.7%) | 4 (0.2%) | 2 (0.2%) | |
Obesity | 1627 (13.4%) | 0 (0.0%) | 0 (0.0%) | |
Night sleep duration | | | | <0.01 |
<360 mins | 4000 (32.9%) | 931 (39.6%) | 370 (41.3%) | |
360-419 mins | 2641 (21.7%) | 459 (19.5%) | 148 (16.5%) | |
420-479 mins | 2150 (17.7%) | 308 (13.1%) | 107 (12.0%) | |
480-539 mins | 2352 (19.3%) | 427 (18.2%) | 160 (17.9%) | |
≥540 mins | 1016 (8.4%) | 225 (9.6%) | 110 (12.3%) | |
Hypertension | | | | <0.01 |
No | 8392 (69.0%) | 1854 (78.9%) | 675 (75.4%) | |
Yes | 3767 (31.0%) | 496 (21.1%) | 220 (24.6%) | |
Dyslipidemia | | | | <0.01 |
No | 10637 (87.5%) | 2185 (93.0%) | 828 (92.5%) | |
Yes | 1522 (12.5%) | 165 (7.0%) | 67 (7.5%) | |
Hyperglycemia | | | | <0.01 |
No | 11167 (91.8%) | 2243 (95.4%) | 858 (95.9%) | |
Yes | 992 (8.2%) | 107 (4.6%) | 37 (4.1%) | |
Cancer | | | | 0.69 |
No | 12054 (99.1%) | 2331 (99.2%) | 885 (98.9%) | |
Yes | 105 (0.9%) | 19 (0.8%) | 10 (1.1%) | |
Chronic lung diseases | | | | <0.01 |
No | 10846 (89.2%) | 1999 (85.1%) | 773 (86.4%) | |
Yes | 1313 (10.8%) | 351 (14.9%) | 122 (13.6%) | |
Liver diseases | | | | 0.002 |
No | 11608 (95.5%) | 2271 (96.6%) | 871 (97.3%) | |
Yes | 551 (4.5%) | 79 (3.4%) | 24 (2.7%) | |
Heart diseases | | | | <0.01 |
No | 10316 (84.8%) | 2069 (88.0%) | 786 (87.8%) | |
Yes | 1843 (15.2%) | 281 (12.0%) | 109 (12.2%) | |
Stroke | | | | 0.02 |
No | 11821 (97.2%) | 2308 (98.2%) | 867 (96.9%) | |
Yes | 338 (2.8%) | 42 (1.8%) | 28 (3.1%) | |
Kidney diseases | | | | 0.48 |
No | 11329 (93.2%) | 2199 (93.6%) | 842 (94.1%) | |
Yes | 830 (6.8%) | 151 (6.4%) | 53 (5.9%) | |
Digestive diseases | | | | 0.01 |
No | 9527 (78.4%) | 1785 (76.0%) | 680 (76.0%) | |
Yes | 2632 (21.6%) | 565 (24.0%) | 215 (24.0%) | |
Emotional, nervous, or psychiatric problems | | | 0.10 |
No | 11997 (98.7%) | 2327 (99.0%) | 889 (99.3%) | |
Yes | 162 (1.3%) | 23 (1.0%) | 6 (0.7%) | |
Memory-related diseases | | | | 0.76 |
No | 11907 (97.9%) | 2306 (98.1%) | 875 (97.8%) | |
Yes | 252 (2.1%) | 44 (1.9%) | 20 (2.2%) | |
Arthritis or rheumatism | | | | 0.34 |
No | 8044 (66.2%) | 1584 (67.4%) | 607 (67.8%) | |
Yes | 4115 (33.8%) | 766 (32.6%) | 288 (32.2%) | |
Asthma | | | | <0.01 |
No | 11671 (96.0%) | 2228 (94.8%) | 846 (94.5%) | |
Yes | 488 (4.0%) | 122 (5.2%) | 49 (5.5%) | |
Depression | | | | <0.01 |
No | 8847 (72.8%) | 1551 (66.0%) | 588 (65.7%) | |
Yes | 3312 (27.2%) | 799 (34.0%) | 307 (34.3%) | |
PEF | 289.5 ± 119.5 | 237.5 ± 108.7 | 195.9 ± 105.3 | <0.01 |
Table 2
The characteristics of study population in the Study on global AGEing and adult health from China
| No sarcopenia | Sarcopenia | Severe sarcopenia | P |
N | 8998 | 753 | 512 | |
Age | 68.8 ± 6.9 | 73.0 ± 7.2 | 76.1 ± 7.3 | <0.01 |
Sex | | | | <0.01 |
Male | 4506 (50.1%) | 312 (41.4%) | 199 (38.9%) | |
Female | 4492 (49.9%) | 441 (58.6%) | 313 (61.1%) | |
Region | | | | 0.01 |
North | 2754 (30.6%) | 195 (25.9%) | 141 (27.5%) | |
South | 6244 (69.4%) | 558 (74.1%) | 371 (72.5%) | |
Urban/Rural | | | | <0.01 |
Urban | 4985 (55.4%) | 297 (39.4%) | 151 (29.5%) | |
Rural | 4013 (44.6%) | 456 (60.6%) | 361 (70.5%) | |
Married status | | | | <0.01 |
Current unmarried | 1790 (19.9%) | 253 (33.6%) | 205 (40.0%) | |
Current married | 7208 (80.1%) | 500 (66.4%) | 307 (60.0%) | |
Alochol | | | | 0.05 |
Ever | 2349 (26.1%) | 175 (23.2%) | 115 (22.5%) | |
Never | 6649 (73.9%) | 578 (76.8%) | 397 (77.5%) | |
Smoking | | | | 0.73 |
Never | 6302 (70.0%) | 522 (69.3%) | 371 (72.5%) | |
Ever | 888 (9.9%) | 72 (9.6%) | 44 (8.6%) | |
Current | 1808 (20.1%) | 159 (21.1%) | 97 (18.9%) | |
Body mass index | | | | <0.01 |
Underweight | 131 (1.5%) | 231 (30.7%) | 146 (28.5%) | |
Normal | 3992 (44.4%) | 521 (69.2%) | 364 (71.1%) | |
Overweight | 3525 (39.2%) | 1 (0.1%) | 2 (0.4%) | |
Obesity | 1350 (15.0%) | 0 (0.0%) | 0 (0.0%) | |
Moderate-intensity activity | | | <0.01 |
Yes | 2840 (31.6%) | 304 (40.4%) | 188 (36.7%) | |
No | 6158 (68.4%) | 449 (59.6%) | 324 (63.3%) | |
Vigorous-intensity activity | | | | 0.61 |
Yes | 748 (8.3%) | 66 (8.8%) | 37 (7.2%) | |
No | 8250 (91.7%) | 687 (91.2%) | 475 (92.8%) | |
Night sleep duration | | | | <0.01 |
<360mins | 751 (8.3%) | 79 (10.5%) | 62 (12.1%) | |
360-419 mins | 1210 (13.4%) | 92 (12.2%) | 53 (10.4%) | |
420-479mins | 1958 (21.8%) | 126 (16.7%) | 58 (11.3%) | |
480-539mins | 2890 (32.1%) | 224 (29.7%) | 122 (23.8%) | |
≥540min | 2189 (24.3%) | 232 (30.8%) | 217 (42.4%) | |
Hypertension | | | | <0.01 |
No | 5359 (59.6%) | 586 (77.8%) | 403 (78.7%) | |
Yes | 3639 (40.4%) | 167 (22.2%) | 109 (21.3%) | |
Diabetes | | | | <0.01 |
No | 8082 (89.8%) | 718 (95.4%) | 489 (95.5%) | |
Yes | 916 (10.2%) | 35 (4.6%) | 23 (4.5%) | |
Stroke | | | | 0.22 |
No | 8051 (89.5%) | 685 (91.0%) | 467 (91.2%) | |
Yes | 947 (10.5%) | 68 (9.0%) | 45 (8.8%) | |
Angina | | | | 0.32 |
No | 8486 (94.3%) | 720 (95.6%) | 483 (94.3%) | |
Yes | 512 (5.7%) | 33 (4.4%) | 29 (5.7%) | |
Chronic lung diseases | | | | <0.01 |
No | 8101 (90.0%) | 654 (86.9%) | 434 (84.8%) | |
Yes | 897 (10.0%) | 99 (13.1%) | 78 (15.2%) | |
Arthritis | | | | 0.01 |
No | 6879 (76.5%) | 615 (81.7%) | 376 (73.4%) | |
Yes | 2119 (23.5%) | 138 (18.3%) | 136 (26.6%) | |
Asthma | | | | 0.1 |
No | 8733 (97.1%) | 724 (96.1%) | 490 (95.7%) | |
Yes | 265 (2.9%) | 29 (3.9%) | 22 (4.3%) | |
FEV1 | 1.7 ± 0.7 | 1.5 ± 0.7 | 1.3 ± 0.7 | <0.01 |
FEV1/FVC | | | | <0.01 |
≥0.7 | 7252 (80.6%) | 555 (73.7%) | 352 (68.8%) | |
<0.7 | 1746 (19.4%) | 198 (26.3%) | 160 (31.2%) | |
PEF | 78.6 ± 19.9 | 76.6 ± 19.5 | 73.8 ± 21.0 | <0.01 |
FEF25-75 | 2.0 ± 1.1 | 1.6 ± 1.1 | 1.3 ± 1.0 | <0.01 |
Statistical analysis
Study populations were categorized into three groups: no sarcopenia, non-severe sarcopenia and severe sarcopenia. SPSS described categorical variables by counts and percentages (%), subsequently compared the difference between three groups via a chi-square test. Continuous variables were presented as means and standard deviations with Mann-Whitney U test for skewed continuous variables and Student’s t test or one-way ANOVA for normally distributed continuous variables. Chi-square goodness-of-fit method tested the normality of distribution of the data. The first component of the study assessed the associations in the prevalence between sarcopenia and asthma via three generalized additive models with binomial regression.The adjusted variables of each model were shown in each table. Model 1 included patient demographic characteristics, model 2 added physical/behavioral factors, and model 3 added physical/behavioral factors and comorbidities. In CHARLS, the least absolute shrinkage and selection operator (LASSO) [20] and multivariate logistic analyses with binomial regression were used to screen the independent risk factors of sarcopenia in asthmatics. We also evaluated the relationships between sarcopenia with asthmatic medications and symptoms in the second component. Model 4 also included lung function on the basis of model 3 in Table 3. The third component was that three generalized additive models with Possion regression were used to compare the differences of lung function among three sarcopenia groups in asthmatics. In WHO SAGE, model 4 added the adjustments of asthmatic medications and symptoms. Finally, three generalized additive models with binomial regression were used to evaluate the associations between sarcopenia with depression and COPD in asthmatics. All statistical analyses were done in SPSS, Empower(R) (www. empowerstats.com; X&Y solutions, Inc., Boston MA). Odd ratios (ORs) for binomial regression and analysis and rate ratio(RR) for Poisson regression analysis with 95% confidence intervals (CIs) represented the strength of association, meanwhile a two tailed P < 0.05 was considered statistically significant.
Table 3
The associations between sarcopenia and asthma-related symptoms in the Study on global AGEing and adult health from China
| Model 1 | Model 2 | Model 3 | Model 4 |
Asthma-related attacks | | | |
Sarcopenia(no vs yes) | 0.97(0.40, 2.38) | 0.82(0.32, 2.06) | 0.72(0.28, 1.86) | 0.53(0.19, 1.51) |
Asthma-related awakening | | | |
Sarcopenia(no vs yes) | 1.72(0.69, 4.31) | 1.97(0.74, 5.26) | 2.02(0.73, 5.59) | 2.66(0.90, 7.82) |
Asthma-related severe shortness of breath | | |
Sarcopenia(no vs yes) | 3.51(1.50, 8.20)# | 3.63(1.50, 8.79)# | 3.41(1.38, 8.42)# | 3.71(1.43, 9.60)# |
Model 1 adjusted the following variables: sex, age, region, urban/rural, married status and body mass index, Model 2 adjusted the following variables: sex, age, region, urban/rural, married status, body mass index, alcohol, smoking, vigorous-intensity activity, moderate- intensity activity and night sleep duration. Model 3 adjusted the following variables: sex, age, region, urban/rural, married status,body mass index, alcohol, smoking, vigorous-intensity activity, moderate- intensity activity, night sleep duration, hypertension, diabetes, angina, stroke, chronic lung diseases, and arthritis. Model 4 adjusted the following variables: sex, age, region, urban/rural, married status, alcohol, smoking, vigorous-intensity activity, moderate-intensity activity, body mass index, night sleep duration, hypertension, diabetes, angina, stroke, chronic lung diseases, arthritis, FEV1, airway obstruction, PEF, and FEF 25%-75%). # P < 0.01 |