Incidence, reversibility, risk factors and the protective effect of high body mass index against sarcopenia in community-dwelling older Chinese adults

Authors

  • Ruby Yu,

    Corresponding author
    1. Department of Medicine and Therapeutics, The Chinese University of Hong Kong, New Territories, Hong Kong
    • Correspondence: Dr Ruby Yu PhD, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, New Territories, Hong Kong. Email: rubyyu@cuhk.edu.hk

    Search for more papers by this author
  • Moses Wong,

    1. Department of Medicine and Therapeutics, The Chinese University of Hong Kong, New Territories, Hong Kong
    Search for more papers by this author
  • Jason Leung,

    1. Jockey Club Center for Osteoporosis Care and Control, The Chinese University of Hong Kong, New Territories, Hong Kong
    Search for more papers by this author
  • Jenny Lee,

    1. The S. H. Ho Center for Gerontology and Geriatrics, The Chinese University of Hong Kong, New Territories, Hong Kong
    2. Department of Medicine and Geriatrics, Shatin Hospital, New Territories, Hong Kong
    Search for more papers by this author
  • Tung Wai Auyeung,

    1. The S. H. Ho Center for Gerontology and Geriatrics, The Chinese University of Hong Kong, New Territories, Hong Kong
    2. Department of Medicine and Geriatrics, Pok Oi Hospital, New Territories, Hong Kong
    Search for more papers by this author
  • Jean Woo

    1. Department of Medicine and Therapeutics, The Chinese University of Hong Kong, New Territories, Hong Kong
    Search for more papers by this author

Errata

This article is corrected by:

  1. Errata: Corrigendum Volume 14, Issue 3, 730, Article first published online: 25 July 2014

Abstract

Aim

We examined the incidence and the reversibility of sarcopenia and their associated factors over a 4-year period using the European Working Group on Sarcopenia in Older People (EWGSOP) criteria.

Methods

A total of 4000 community-dwelling older adults aged ≥65 years were evaluated for which detailed information regarding demographics, socioeconomic, medical history, lifestyle, and clinical factors were documented at baseline, 2 years, and 4 years later. Sarcopenia was defined according to the EWGSOP algorithm. Incident sarcopenia and its reversibility were documented at each follow-up year, and related to possible factors.

Results

At baseline, of the 4000 participants, 361 (9.0%) had sarcopenia. Between baseline and 2-year follow-up, 6.0% of the participants without sarcopenia at baseline had developed sarcopenia, and 18.8% of the initially sarcopenic participants had reverted to normal. Between baseline and 4-year follow-up, the corresponding figures were 6.3% and 14.1%, respectively. The average annual incidence over 4 years was 3.1%. After multivariate adjustments, older age, female sex, presence of chronic obstructive pulmonary disease, presence of stroke, higher physical activity levels, presence of instrumental activities of daily living impairments, and lower body mass index were associated with incident sarcopenia, whereas younger age, female sex, higher body mass index and absence of instrumental activities of daily living impairments, but not physical activity, were associated with its reversibility. Protein and vitamin D intake were not significantly associated with sarcopenia incidence or its reversibility.

Conclusion

Sarcopenia incidence increases with age, but is potentially reversible in a Chinese elderly population. High body mass index is protective against sarcopenia incidence and its reversibility. Increasing physical activity and maintaining a healthy weight could be beneficial in the prevention of sarcopenia. Geriatr Gerontol Int 2014; 14 (Suppl. 1): 15–28.

Introduction

Muscle mass decreases as part of the physiological changes with age, and contributes to physical function decline.[1] However, the rate of decrease, and possible reversibility, might be amendable to intervention. The term “sarcopenia” was first coined to describe this condition[2, 3] in order to raise awareness, and place it in a similar category as osteoporosis and osteopenia. By developing a universal definition, research into prevalence, risk factors and intervention might be facilitated, as was the case for osteoporosis. Although this condition is now accepted as one of the geriatric syndromes, a universal consensus in definition is still lacking. Initially, the definition consists of the measurement of appendicular mass divided by height in meter squared.[4] However, some use body weight as the denominator.[5] Another indicator used is percentage skeletal muscle index, calculated as the total skeletal mass divided by weight × 100.[6] More recently, there is a gathering consensus worldwide that the definition should include a measure of muscle power and/or physical performance measure.[7, 8] At the same time, the concept of dynapenia has emerged,[9] which describes the age-related loss in muscle power and seeks to differentiate muscle mass from power. Many definitions include absolute cut-off values for these measurements, and it is uncertain whether these can be translated to populations with different ethnicity with different body size and shape. For example, values of appendicular skeletal muscle mass (ASM)/height2 related to incident physical limitation are lower in Chinese older people than Caucasians.[10] Recently, the Asian Working Group for Sarcopenia Research adopted an algorithm of sarcopenia that is similar to the European Working Group on Sarcopenia in Older People (EWGSOP), which avoided the use of absolute cut-off values by using the lowest 20th percentile of population values (unpublished data).

In reality, an internationally agreed method of definition might not need to include absolute values, unless a rigorous multinational prevalence study is to be carried out. Such a study might be less meaningful than those measuring changes, such as documenting the rate of decline or reversibility, risk factors affecting the decline, the inclusion of sarcopenia in community assessments and interventional studies where sarcopenia is the primary outcome measure. From the public health perspective, it would be important to document not only the prevalence, but incidence of sarcopenia in aging populations, as it predisposes the older individual to adverse outcomes, such as falls and fractures, dependency, use of health services, and mortality.[10-13] Furthermore, the identification of risk factors could allow preventive efforts to reduce the incidence. In a study of 4000 Chinese men and women aged 65 years and older living in the community, we addressed the question of the incidence of sarcopenia over a 4-year period using the EWGSOP criteria, and examined the risk factors predisposing to the onset of sarcopenia. The reversibility of sarcopenia and its predictors were also examined.

Methods

Participants

A total of 4000 community-dwelling Chinese men and women aged 65 years and older were recruited for a cohort study on osteoporosis and general health (Mr. Os) in Hong Kong between August 2001 and February 2003 by placing recruitment notices in community centers for older adults and housing estates. Several talks were also given at these centers explaining the purpose, procedures and investigations to be carried out. Participants were volunteers, and the aim was to recruit a stratified sample so that approximately 33% would each be aged 65–69 years, 70–74 years, and 75 years and older. Those who were unable to walk independently, had had a bilateral hip replacement or were not competent to give informed consent were excluded. Eligible participants were invited to attend a health check at the School of Public Health, The Chinese University of Hong Kong. A team of trained research assistants admonished the study questionnaire and took physical measurements for each participant on the same day. The cohort was invited to re-attend for repeat questionnaire interviews and physical measurements after 2 and 4 years. Details of the survey population have been reported elsewhere.[14] All participants gave written consent, and the study was approved by the Clinical Research Ethics Committee of the Chinese University of Hong Kong.

Questionnaire

The information from the questionnaire used in the present study included demographics, socioeconomic status, self-reported history of chronic diseases (chronic obstructive pulmonary disease [COPD], diabetes, hypertension, stroke and cancer), smoking, physical activity, dietary intake, cognitive function and instrumental activities of daily living (IADL). Socioeconomic status was measured on the basis of the education level, the community ladder and the Hong Kong ladder.[15] Physical activity levels were assessed using the Physical Activity Scale of the Elderly (PASE). This is a 12-item scale measuring the average number of hours per day spent in leisure, household and occupational physical activities over the previous 7 days. Activity weights for each item were determined based on the amount of energy expended, and each item score was calculated by multiplying the activity weight by activity daily frequency. A summary score of all the items reflected the daily physical activity level.[16] Dietary intake was assessed at baseline using a validated semi-quantitative food frequency questionnaire.[17] Cognitive function was assessed using the cognitive score of the Chinese version of the Community Screening Instrument of Dementia (CSI-D), the validity of which has been examined elsewhere.[18] The cut-off point for probable dementia is <28.4.[18, 19] IADL impairments were assessed by noting any impairment in walking two to three blocks outside on level ground, climbing up 10 steps without resting, preparing own meals, doing heavy housework such as scrubbing floors or washing windows, and doing own shopping for groceries or clothes, a concept originally developed by Lawton and Brody.[20]

Physical measurements

Body weight was measured, with participants wearing a light dressing gown, by the Physician Balance Beam Scale (Health-O-Meter, Arlington Heights, IL, USA). Height was measured by the Holtain Harpenden stadiometer (Holtain, Crosswell, UK). Body mass index (BMI) was calculated by dividing the weight in kilogram by height in meter squared. Body composition was measured by dual energy X-ray absorptiometry (DXA) using a Hologic Delphi W4500 densitometer (Hologic Delphi, auto whole body version 12.4; Hologic, Bedford, MA, USA). Total appendicular skeletal muscle mass (ASM) was calculated as the sum of appendicular lean mass minus bone mineral content of both arms and legs. Grip strength was measured using a dynamometer (JAMAR Hand Dynamometer 5030JO; Sammons Preston, Bolingbrook, IL, USA). Two readings were taken from each side, and the average value between right and left was used for analysis. Gait speed was measured using the best time in seconds to complete a walk along a straight line 6-m long. A warm up period of <5 min was followed by two walks, and the best time recorded.

Diagnosis of sarcopenia

Sarcopenia was defined according to the EWGSOP algorithm,[8] in which a person who has low muscle mass, low muscle strength and/or low physical performance was categorized as having sarcopenia. With reference to the lowest quintile value of the distribution of the study population, low muscle mass was defined as ASM index (ASM/height2) <6.52 kg/m2 for males and <5.44 kg/m2 for females; low muscle strength was defined as grip strength ≤28 kg for males and ≤18 kg for females; and low physical performance as gait speed ≤ 0.8 m/s for both males and females.

Statistical analyses

Characteristics of individuals at baseline were presented using means and standard deviations (SD) for continuous variables, and frequencies and percentages for categorical variables. The changes of sarcopenia categories from baseline to 2-year, 2- to 4-year, and baseline to 4-year follow-up were presented by age group and sex. Incidence proportions of sarcopenia at each follow-up year were calculated by the number of new sarcopenic cases within a specified time period divided by the size of the population initially at risk. The average annual incidence over 4 years was calculated by weighted average of the number of new sarcopenic cases per population initially at risk per 2 years from baseline to 2-year and 2- to 4-year follow-up. Risk factors for incident sarcopenia at each follow-up year were first analyzed individually using logistic regressions. Subsequently, multiple logistic regression models were constructed by stepwise and backward elimination algorithms. In these models, age, sex, education levels, socioeconomic ladders, medical history, lifestyle and nutritional factors, cognitive function, IADL impairments, and BMI were included. Analyses were repeated for the reversibility of sarcopenia at each follow-up year. As a rule of thumb for carrying out logistic regression analysis, at least 10 events per variable (EPV) are required in most instances;[21] variables with a EPV value of less than 10 were excluded in the multiple logistic regression models of the incidence and reversibility of sarcopenia. All analyses were carried out using the Windows-based SPSS Statistical Package (version 17.0; SPSS, Chicago, IL, USA), and P-values less than 0.05 were considered statistically significant.

Results

Baseline characteristics

By February 2003, 2000 men and 2000 women aged 65 years or older with a mean age of 72.5 ± 5.2 years were participating in the study (Table 1). Of these participants, 45.6% had primary level of education or above, 6.9% were current smokers and the mean BMI was 23.7 kg/m2. Hypertension was the most frequent self-reported chronic disease (42.7%). There were 606 (15.2%) participants with probable dementia and 25.8% had IADL impairments.

Table 1. Baseline characteristics of study population
 All (n = 4,000)
  1. aFigures are based on valid cases (n) observed at baseline, for socioeconomic status ladder – Community (n = 3835), socioeconomic status ladder – Hong Kong (n = 3760), protein intake (g/day; n = 3995), vitamin D intake (IU/day; n = 3995) and number of instrumental activities of daily living (IADL) impairments (n = 3996). Data are presented as mean ± SD or number (percentage). Percentages might not sum up to 100% due to rounding. CSI-D, Community Screening Instrument of Dementia; PASE, Physical Activity Scale of the Elderly.
Demographics 
Age (years)72.5 ± 5.2
Socioeconomic status 
Education level 
Have not received any education856 (21.4)
Some primary school1324 (33.1)
Primary school683 (17.1)
Secondary school / Matriculation747 (18.7)
University / College390 (9.8)
Socioeconomic status ladder – Communitya 
1–3244 (6.4)
4–51030 (26.9)
6–102561 (66.8)
Socioeconomic status ladder – Hong Konga 
1–31120 (29.8)
4–51715 (45.6)
6–10925 (24.6)
Medical history 
Chronic obstructive pulmonary disease333 (8.3)
Diabetes579 (14.5)
Hypertension1707 (42.7)
Stroke175 (4.4)
Cancer177 (4.4)
Lifestyle factors and dietary intake 
Current smoker275 (6.9)
Physical activity (PASE total score)91.3 ± 43.0
Protein intake (g/day)a76.5 ± 33.5
Vitamin D intake (IU/day)a13.3 ± 21.0
Energy intake (Kcal/day)1841.4 ± 587.5
Cognitive function 
CSI-D level 
Normal (CSI-D score ≥29.5)2931 (73.3)
Borderline (28.4 ≤ CSI-D score < 29.5)463 (11.6)
Probable dementia (CSI-D score < 28.4)606 (15.2)
Instrumental activities of daily living impairments 
No. IADL impairmenta 
02967 (74.2)
1–2875 (21.9)
3–5154 (3.9)
Body measurements 
Body mass index (kg/m2)23.7 ± 3.3

Changes in sarcopenia categories and incidence proportions

Figure 1 and Table 2 show the onset of disease over the follow-up period. Of the 4000 participants at baseline, 361 (9.0%) had sarcopenia. Between baseline and 2-year follow-up, 217 (6.0%) of the participants without sarcopenia at baseline had developed sarcopenia, and 68 (18.8%) of the initially sarcopenic participants had reverted to normal. Between baseline and 4-year follow-up, the corresponding figures were 6.3% and 14.1%, respectively. The incidence proportions of sarcopenia from baseline to 2-year, 2- to 4-year, and baseline to 4-year follow-up were 6.9, 5.4, and 7.8%, respectively. The average annual incidence over 4 years was 3.1% (2.9% for male and 3.3% for female). Overall, the incidence of sarcopenia increased with age for both sexes, and males aged 85 years and older tended to have a substantially higher incidence than their female counterparts.

Figure 1.

Dynamic flow of sarcopenic participants by time of observation. *Missing observations include those who died during follow-up, default cases and returning participants who failed to complete measurement.

Table 2. Sarcopenia status and age- and sex-specific incidence proportion by follow-up periods
 Both sexesMaleFemale
All ages65–74 years75–84 years≥85 yearsAll ages65–74 years75–84 years≥85 yearsAll ages
n%n%n%n%n%n%n%n%n%
  1. Individual cells might not sum up to total due to rounding. A total of 583 missing cases at 2-year follow-up were excluded among 4000 participants from baseline. Disease-free observations at the beginning of each follow-up period were used as denominator. A total of 436 missing cases at 4-year follow-up were excluded among 3417 participants who attended 2-year follow-up. §A total of 583 missing cases at 2-year follow-up were excluded among 4000 participants from baseline for error adjustment, regardless of the subsequent disease status at 4-year follow-up. Another 436 missing cases at 4-year follow-up were excluded among 3417 participants who attended 2-year follow-up. The average annual incidence over 4 years was calculated by weighted average of the number of new sarcopenic cases per population initially at risk per 2 years from baseline to 2-year and 2- to 4-year follow-up. For example, average annual incidence over 4 years for all ages, both sexes = (3142 × 6.9% / 2 + 2605 × 5.4% / 2) / (3142 + 2605) = 3.1%.
Baseline to 2-year                  
Remained normal292585.6112891.336476.21346.4150586.4102788.836775.52678.8142084.8
Normal to sarcopenic2176.4463.7418.6414.3915.2736.35010.339.11267.5
Sarcopenic to normal682.0211.7112.327.1342.0181.6163.300.0342.0
Remained sarcopenic2076.1413.36213.0932.11126.4383.35310.9412.1955.7
Total3417100.01236100.0478100.028100.01742100.01156100.0486100.033100.01675100.0
Incidence proportion (%)2176.9463.94110.1423.5915.7736.65012.0310.31268.2
2- to 4-year                  
Remained normal251684.4100989.927072.8950.0128885.291381.429973.81672.7122883.5
Normal to sarcopenic1434.8454.0318.4316.7795.2363.2276.714.5644.4
Sarcopenic to normal913.1201.861.600.0261.7413.7245.900.0654.4
Remained sarcopenic2317.7484.36417.3633.31187.8534.75513.6522.71137.7
Total2981100.01122100.0371100.018100.01511100.01043100.0405100.022100.01470100.0
Incidence proportion (%)1435.4454.33110.3325.0795.8363.8278.315.9645.0
Baseline to 4-year§                  
Remained normal255985.8101490.427273.3950.0129585.793889.931076.51672.7126486.0
Normal to sarcopenic2167.2565.04812.9527.81097.2605.84410.9313.61077.3
Sarcopenic to normal481.6151.341.100.0191.3161.5133.200.0292.0
Remained sarcopenic1585.3373.34712.7422.2885.8292.8389.4313.6704.8
Total2981100.01122100.0371100.018100.01511100.01043100.0405100.022100.01470100.0
Incidence proportion (%)2167.8565.24815.0535.71097.8606.04412.4315.81077.8
Average annual incidence over 4 years 3.1 2.0 5.1 12.1 2.9 2.7 5.2 4.3 3.3

Risk factors of incident sarcopenia

Factors associated with incident sarcopenia from baseline to 2-year, 2- to 4-year, and baseline to 4-year follow-up are shown in Table 3. After adjustments for demographics, socioeconomic status, medical history, lifestyle and nutritional factors, cognitive function, IADL impairments, and BMI, age (adjusted OR 1.11, 95% CI 1.07–1.14), presence of stroke (adjusted OR 2.56, 95% CI 1.32–4.95), physical activity (adjusted OR 0.995, 95% CI 0.991–0.999), IADL impairments (adjusted OR 2.12, 95% CI 1.49–3.02) and BMI (adjusted OR 0.66, 95% CI 0.62–0.70) were associated with the development of sarcopenia from baseline to 4-year follow-up. Female sex (adjusted OR 1.58; 95% CI 1.15–2.16) and presence of COPD (adjusted OR 1.84, 95% CI 1.02–3.31) were associated with incident sarcopenia from baseline to 2-year and from 2- to 4-year follow-up, respectively. Protein and vitamin D intake were not significantly associated with incident sarcopenia.

Table 3. Binary logistic regression on incidence of sarcopenia during different periods of observation
 Baseline to 2-year2- to 4-yearBaseline to 4-year
TotalIncident casesOR (95% CI)TotalIncident casesOR (95% CI)TotalIncident casesOR (95% CI)
UnivariateMultivariate§UnivariateMultivariate§UnivariateMultivariate§
  1. Extra number of missing observations (n) being removed from the regression model – socioeconomic status ladder – Community (n = 105), socioeconomic status ladder – Hong Kong (n = 168), protein intake (g/day) (n = 4), vitamin D intake (IU/day; n = 4), energy intake (Kcal/day; n = 4), and number of instrumental activities of daily living (IADL) impairment (n = 3) in baseline to 2-year model; socioeconomic status ladder – Community (n = 81), socioeconomic status ladder – Hong Kong (n = 129), protein intake (g/day; n = 3), vitamin D intake (IU/day; n = 3), energy intake (Kcal/day; n = 3) and number of IADL impairments (n = 3) in 2- to 4-year model; and socioeconomic ladder – Community (n = 84), socioeconomic ladder – Hong Kong (n = 136), protein (g/day; n = 3), vitamin D (IU/day; n = 3), energy intake (Kcal/day; n = 3) and number of IADL impairments (n = 3) in baseline to 4-year model. The logistic regression model for baseline to 2-year observation was based on disease-free observations at baseline. Participants lost to 2-year follow-up were excluded. The 2- to 4-year model was based on disease-free observations at 2-year follow-up. Participants lost to 4-year follow-up were excluded. Baseline to 4-year model was based on disease-free observations at baseline. Participants lost to 2-year and 4-year follow-up were excluded. §The backward stepwise (Wald) procedure was used in multivariate binary logistic regression with all variables entered at the beginning, except those variables with events per variable value less than 10. Socioeconomic status ladder – Community, and disease status of stroke were excluded in baseline to 2-year model; socioeconomic status ladder – Community, and disease status of cancer were excluded in 2- to 4-year model; and disease status of cancer was excluded in baseline to 4-year model accordingly. Odds ratios and confidence intervals were presented only for significant variables with P < 0.05. BMI, body mass index; COPD, Chronic Obstructive Pulmonary Disease; CSI-D, Community Screening Instrument of Dementia; PASE, Physical Activity Scale of the Elderly; SES, socioeconomic status.
Demographics            
Age (years)3142217

1.097

(1.069–1.126)

1.095

(1.064–1.127)

2659143

1.097

(1.062–1.134)

1.091

(1.053–1.130)

2775216

1.109

(1.080–1.140)

1.106

(1.073–1.139)

Sex            
Male159691ReferentReferent136779Referent 1404109Referent 
Female1546126

1.467

(1.110–1.940)

1.577

(1.152–2.157)

129264

0.850

(0.606–1.192)

 1371107

1.006

(0.762–1.328)

 
Socioeconomic status            
Education level            
Primary and below2201166Referent 184396Referent 1934153Referent 
Secondary and above94151

0.702

(0.508–0.971)

 81647

1.112

(0.777–1.593)

 84163

0.943

(0.695–1.279)

 
SES ladder-community           
1–31768Referent 1438Referent 14510Referent 
4–581447

1.287

(0.597–2.774)

 69841

1.053

(0.483–2.297)

 71751

1.034

(0.512–2.087)

 
6–102047154

1.708

(0.825–3.538)

 173787

0.890

(0.422–1.874)

 1829148

1.189

(0.612–2.309)

 
SES ladder-Hong Kong           
1–385563Referent 71535Referent 74757Referent 
4–5138390

0.875

(0.627–1.222)

 118362

1.075

(0.702–1.644)

 123493

0.987

(0.700–1.390)

 
6–1073647

0.858

(0.580–1.268)

 63238

1.243

(0.775–1.993)

 65857

1.148

(0.783–1.684)

 
Medical history            
COPD            
No2906193Referent 2479125ReferentReferent2583193Referent 
Yes23624

1.591

(1.018–2.487)

 18018

2.092

(1.245–3.516)

1.838

(1.020–3.309)

19223

1.685

(1.064–2.669)

 
Diabetes            
No2693185Referent 2292123Referent 2386181Referent 
Yes44932

1.040

(0.705–1.535)

 36720

1.016

(0.625–1.652)

 38935

1.204

(0.825–1.759)

 
Hypertension            
No1798134Referent 151179Referent 1589124Referent 
Yes134483

0.817

(0.616–1.085)

 114864

1.070

(0.763–1.502)

 118692

0.994

(0.750–1.316)

 
Stroke            
No3026209Referent 2563131ReferentReferent2676202ReferentReferent
Yes1168

0.998

(0.480–2.075)

 9612

2.652

(1.413–4.979)

3.142

(1.555–6.350)

9914

2.017

(1.126–3.615)

2.557

(1.321–4.952)

Cancer            
No3013204Referent 2555140Referent 2663209Referent 
Yes12913

1.543

(0.855–2.785)

 1043

0.512

(0.160–1.636)

 1127

0.783

(0.360–1.704)

 
Lifestyle factors            
Current smoker            
No2940197Referent 2501129Referent 2608195Referent 
Yes20220

1.530

(0.943–2.482)

 15814

1.788

(1.004–3.182)

 16721

1.780

(1.101–2.877)

 
PASE total score3142217

0.992

(0.989–0.996)

0.995

(0.991–0.999)

2659143

0.992

(0.988–0.997)

0.995

(0.990–0.999)

2775216

0.992

(0.989–0.996)

0.995

(0.991–0.999)

Dietary intake            
Protein (g/day)3138217

0.996

(0.992–1.001)

 2656143

0.999

(0.994–1.004)

 2772216

1.000

(0.996–1.004)

 
Vitamin D (IU/day)3138217

1.002

(0.997–1.008)

 2656143

1.003

(0.997–1.009)

 2772216

1.003

(0.998–1.008)

 
Energy (1000 Kcal/day)3138217

0.784

(0.614–1.002)

 2656143

0.938

(0.702–1.252)

 2772216

0.933

(0.735–1.185)

 
Cognitive function            
CSI-D level            
Normal to borderline (CSI-D score ≥28.4)2720175Referent 2330127Referent 2423189Referent 
Probable dementia (CSI-D score <28.4)42242

1.607

(1.129–2.289)

 32916

0.887

(0.520–1.511)

 35227

0.982

(0.645–1.494)

 
IADL impairments            
No. IADL impairments           
02420154Referent 208594ReferentReferent2174145ReferentReferent
1–571963

1.413

(1.041–1.918)

 57149

1.988

(1.389–2.846)

2.045

(1.351–3.097)

59871

1.885

(1.397–2.544)

2.122

(1.491–3.019)

Body measurements            
BMI (kg/m2)3142217

0.669

(0.632–0.709)

0.646

(0.606–0.688)

2659143

0.698

(0.652–0.747)

0.680

(0.631–0.733)

2775216

0.691

(0.653–0.731)

0.656

(0.615–0.699)

Risk factors of the reversibility of sarcopenia

Factors associated with the reversibility of sarcopenia from baseline to 2-year, 2- to 4-year, and baseline to 4-year follow-up are shown in Table 4. After adjustments for demographics, socioeconomic status, medical history, lifestyle factors, cognitive function, IADL impairments and BMI, age (adjusted OR 0.90, 95% CI 0.84–0.96) was the only significant predictor consistently associated with the reversibility of sarcopenia across the different study periods. BMI was associated with the reversibility of sarcopenia (adjusted OR 1.16, 95% CI 1.02–1.31) from baseline to 2-year follow-up, whereas females (adjusted OR 2.65, 95% CI 1.50–4.68) and those without IADL impairments at baseline (adjusted OR 0.35, 95% CI 0.18–0.71) were more likely to return to non-sarcopenic from 2- to 4-year follow-up. However, physical activity, protein or vitamin D intakes were not significantly associated with the reversibility of sarcopenia.

Table 4. Binary logistic regression on participants who had returned to non-sarcopenic during different periods of observation
 Baseline to 2-year2- to 4-yearBaseline to 4-year
TotalRevert casesOR (95% CI)TotalRevert casesOR (95% CI)TotalRevert casesOR (95% CI)
UnivariateMultivariate§UnivariateMultivariate§UnivariateMultivariate§
  1. Extra number of missing observations (n) being removed from the regression model – socioeconomic status ladder – Community (n = 11), socioeconomic status ladder – Hong Kong (n = 16), protein intake (g/day); n = 1), vitamin D intake (IU/day; n = 1), and energy intake (Kcal/day; n = 1) in baseline to 2-year model; socioeconomic ladder – Community (n = 10), socioeconomic ladder – Hong Kong (n = 18) in 2- to 4-year model; and socioeconomic status ladder – Community (n = 7) and socioeconomic status ladder – Hong Kong (n = 11) in baseline to 4-year model. Logistic regression model for baseline to 2-year observation was based on disease observations at baseline. Participants lost to 2-year follow-up were excluded. The 2- to 4-year model was based on disease observations at 2 years. Participants lost to 4-year follow-up were excluded. The baseline to 4-year model was based on disease observations at baseline. Participants lost to 2-year and 4-year follow-up were excluded. §The backward stepwise (Wald) procedure was used in multivariate binary logistic regression with all variables entered at the beginning, except those variables with events per variable value less than 10. Socioeconomic status ladder – Community, disease status of chronic obstructive pulmonary disease, diabetes, stroke, cancer, and smoking status – were excluded in baseline to 2-year model; socioeconomic status ladder – Community, disease status of chronic obstructive pulmonary disease, stroke, cancer, and smoking status were excluded in 2- to 4-year model; socioeconomic status ladder – Community, disease status of chronic obstructive pulmonary disease, diabetes, stroke, cancer, smoking status and Community Screening Instrument of Dementia (CSI-D) level were excluded in baseline to 4-year model accordingly. Odds ratios and confidence intervals are presented only for significant variables with P < 0.05. BMI, body mass index; COPD, Chronic Obstructive Pulmonary Disease; IADL, instrumental activities of daily living; PASE, Physical Activity Scale of the Elderly, SES socioeconomic status.
Demographics            
Age (years)27568

0.938

(0.892–0.986)

0.935

(0.886–0.986)

32291

0.905

(0.861–0.951)

0.923

(0.875–0.975)

20648

0.906

(0.851–0.966)

0.897

(0.839–0.959)

Sex            
Male14634Referent 14426ReferentReferent10719Referent 
Female12934

1.179

(0.681–2.040)

 17865

2.611

(1.548–4.404)

2.651

(1.501–4.681)

9929

1.919

(0.994–3.706)

 
Socioeconomic status           
Education level            
Primary and below19952Referent 24070Referent 14936Referent 
Secondary and above7616

0.754

(0.399–1.424)

 8221

0.836

(0.473–1.476)

 5712

0.837

(0.400–1.753)

 
SES ladder-community           
1–3284Referent 203Referent 183Referent 
4–57715

1.452

(0.437–4.816)

 7720

1.988

(0.526–7.509)

 5811

1.170

(0.288–4.758)

 
6–1015944

2.296

(0.753–6.994)

 21563

2.349

(0.665–8.297)

 12332

1.758

(0.478–6.473)

 
SES ladder-Hong Kong           
1–310125Referent 10630Referent 7420Referent 
4–59625

1.070

(0.563–2.034)

 12436

1.036

(0.584–1.839)

 7316

0.758

(0.356–1.613)

 
6–106213

0.807

(0.377–1.725)

 7417

0.756

(0.380–1.502)

 4810

0.711

(0.299–1.688)

 
Medical history            
COPD            
No24064Referent 28284Referent 17848Referent 
Yes354

0.355

(0.121–1.045)

 407

0.500

(0.213–1.175)

 280 
Diabetes            
No24562Referent 27976Referent 18540Referent 
Yes306

0.738

(0.288–1.889)

 4315

1.431

(0.725–2.825)

 218

2.231

(0.865–5.755)

 
Hypertension            
No17640Referent 20861Referent 13028Referent 
Yes9928

1.341

(0.765–2.352)

 11430

0.861

(0.515–1.437)

 7620

1.301

(0.672–2.517)

 
Stroke            
No25764Referent 30687Referent 19345Referent 
Yes184

0.862

(0.274–2.712)

 164

0.839

(0.263–2.673)

 133

0.987

(0.260–3.741)

 
Cancer            
No25867Referent 30286Referent 19447Referent 
Yes171

0.178

(0.023–1.369)

 205

0.837

(0.295–2.375)

 121

0.284

(0.036–2.261)

 
Lifestyle factors            
Current smoker            
No25163Referent 29685Referent 18944Referent 
Yes245

0.785

(0.282–2.190)

 266

0.745

(0.289–1.919)

 174

1.014

(0.315–3.268)

 
PASE total score27568

1.002

(0.994–1.009)

 32291

1.003

(0.996–1.010)

 20648

1.005

(0.996–1.014)

 
Dietary intake            
Protein (g/day)27468

1.000

(0.992–1.008)

 32291

0.995

(0.988–1.002)

 20648

0.998

(0.988–1.007)

 
Vitamin D (IU/day)27468

1.002

(0.986–1.019)

 32291

0.997

(0.986–1.008)

 20648

0.995

(0.976–1.014)

 
Energy (1000Kcal/day)27468

0.963

(0.594–1.560)

 32291

0.732

(0.470–1.140)

 20648

0.776

(0.430–1.400)

 
Cognitive function            
CSI-D level            
Normal to borderline (CSI-D score ≥28.4)22755Referent 26571Referent 17240Referent 
Probable dementia (CSI-D score <28.4)4813

1.162

(0.574–2.351)

 5720

1.477

(0.804–2.713)

 348

1.015

(0.426–2.418)

 
IADL impairments            
No. IADL impairments           
017847Referent 22876ReferentReferent13938Referent 
1–59721

0.770

(0.428–1.385)

 9415

0.380

(0.205–0.704)

0.354

(0.177–0.707)

6710

0.466

(0.216–1.006)

 
Body measurements            
BMI (kg/m2)27568

1.145

(1.023–1.281)

1.155

(1.021–1.307)

32291

1.105

(1.002–1.219)

 20648

1.119

(0.976–1.283)

 

Discussion

Understanding the causes, prevention and treatment of sarcopenia is increasingly important in geriatric medicine. Although much research effort has been directed toward development of a universal definition of sarcopenia and determining its prevalence, risk factors, and consequences, there have been few studies aimed at examining the incidence and the reversibility of sarcopenia and their risk factors. To our knowledge, this is the first prospective study of community-dwelling Chinese examining the factors predisposing to the development of sarcopenia and its reversibility using updated criteria. The average annual incidence of sarcopenia over 4 years was 3.1%. The present findings also confirmed the incidence of sarcopenia increased with age. This is compatible with the extensive literature documenting the loss of muscle mass and function that occurs with aging. Annual loss of muscle mass has been reported as 1–2% at the age of 50 years onwards,[22] with the rates to be higher in men than in women.[23] However, women, on average, have a longer life expectancy than men, which implies that sarcopenia for women is a greater public health concern. Nevertheless, we did not find a female preponderance after the 2-year follow-up in the present study. It is possibly a result of the natural bias, as those who are more sarcopenic or frail might default from follow-up, resulting in an underestimation of incidence.

Higher physical activity levels, as measured with the PASE, were associated with lower sarcopenic risk, as noted in our previous study and others.[24-26] However, these studies were cross-sectional, and could not establish a causal relationship between physical activity and sarcopenia. Being longitudinal, our results of the protective effect of physical activity indicate a need for intervention. Past studies have shown that resistance strength training, such as weight lifting, has particularly strong beneficial effects on increasing muscle protein synthesis, muscle mass and strength in the elderly, including the oldest old,[27] possibly by evoking muscle hypertrophy along with neuromuscular adaptations.[28] However, strength training was usually intensive, which might not be practical to many untrained or sedentary older adults with various stages of functional decline. Aerobic exercise training could be an alternative in maintaining or increasing lean muscle mass. Such regimens have been shown to result in stimulating muscle protein synthesis,[29] and improving muscle fibers size and function.[30] Previously, we have also shown that heavy housework is associated with reduced mortality and cancer deaths over a 9-year period in the same study population;[31] further studies on the role of non-leisure time physical activity on sarcopenia, especially housework participation, are warranted.

Although obesity is believed to be a risk factor for many adverse outcomes, in elderly populations, being slightly overweight might be beneficial. Previously, we have shown that older men were resistive to hazards of overweight and adiposity; and mild-grade overweight, obesity, and even central obesity could favor survival.[32] Similarly, a study in hospitalized elderly individuals found that fat mass was associated with a lower risk of death or complications.[33] The findings from the present study give further support to the inverse relationship between BMI and sarcopenia. Furthermore, BMI was positively associated with ASM (aged-adjusted Pearson's correlation coefficient [r] = 0.578, P < 0.001) and grip strength (age-adjusted r = 0.033, P < 0.05; data not shown), components of sarcopenia. This is in line with a previous study of Caucasian women, which found that participants who were overweight had a significantly reduced risk in developing sarcopenia when compared with their normal weight counterparts.[34] Despite possible favorable effects of BMI on muscle mass and strength, those with BMI <18.5 kg/m2 or ≥25 kg/m2 had slower walking speeds compared with their counterparts (walking speeds of 0.97, 1.03, 1.03, 0.99 m/s for BMI groups of <18.5, 18.5 to <23.0, 23.0 to <25.0 and ≥25 kg/m2, respectively, data not shown), as has been reported in our previous study[35] as well as one other.[36] Other than muscle mass, BMI was also positively associated with fat mass (age-adjusted r = 0.843, P < 0.001, data not shown), which is thought to be an energy reserve in older adults that helps the individual survive illnesses and chronic conditions.[37] It has been pointed out that fat mass can have several age-rated effects on lean mass, whereas individuals with higher fat mass might have a higher protein intake, which is a protective factor against sarcopenia.[38] Given this, we postulated that high BMI might serve as a protective buffer in countering losses in muscle performance in the elderly population. Therefore, maintaining a healthy weight is important for older adults in order to preserve muscle mass and strength.

A number of studies have shown that protein intake is a key factor for optimal muscle and bone health in older adults.[39, 40] However, of greater practical importance, is the determination of the optimal quantity and quality of protein intake to preserve muscle mass and maintain physical functions in older adults. Although the current recommended dietary allowance of protein intake for male aged 50 years and older is 56 g protein/day, and for their female counterparts is 46 g/day,[41] a higher protein intake might be required for optimizing muscle health, particularly in older adults.[42] Insufficient or ineffectual protein intake in elderly individuals might facilitate the loss of muscle by blunting muscle protein synthesis and thus promoting net muscle protein catabolism.[43, 44] However, the present findings showed no association between protein intake and incident sarcopenia. It is possible that associations are only apparent with a wide variation in protein intake in the study population, in that our participants could have a fairly high or adequate mean protein intake (76.5 g/day),[45] compared with other population-based studies.[46, 47] Furthermore, 82% of our male participants and 76% of our female participants had protein intake at or above the recommended dietary allowance levels. Alternatively, the protein intake per mealtime of our participants might not be high enough to achieve significant protective effects; whereas an intake of 25–30 g at each mealtime could be beneficial in increasing protein synthesis and conserving muscle mass in older adults.[48] Nevertheless, data regarding protein intake per mealtime are not available in the present study, its association with sarcopenia remains to be explored.

The role of vitamin D for sarcopenia in aging populations remains controversial. Although a number of studies have shown an independent association between low serum 25-hydroxyvitamin D (25OHD) and muscle mass or physical function;[49, 50] others have found no association.[51] Previously, we have shown no association of serum 25OHD levels with baseline or 4-year change in muscle mass and physical performance measures in the same study population of older Chinese men.[52] Similar results were obtained in the present study by using dietary vitamin D intake, suggesting that vitamin D does not appear to be important in this elderly cohort. As noted, the prevalence of vitamin D deficiency was lower than in other published studies, which could explain the absence of such an association.[52]

The present study also showed that stroke was an independent risk factor of incident sarcopenia. This observation is not unexpected, given the close association of stroke-associated disability with muscle atrophy and neuromuscular changes.[53] COPD, associated with inflammation and muscle wasting,[54] was also associated with a higher risk. IADL impairment, denoting a critical physical limitation and a level of dependency, also seems to play an important role in the development of sarcopenia. Several prospective studies have shown the relationship between sarcopenia and functional decline in older adults.[55, 56] However, no significant associations were found between cognitive impairment and incident sarcopenia; although IADL impairments have been associated with cognitive decline.[57] Perhaps IADL impairments imply how poor the functional health really is, and therefore it was more predictive of sarcopenia than cognitive function. Despite cognitive function not being a significant predictor in the present study, those who were cognitively impaired were associated with a higher prevalence of IADL impairments (IADL score ≥3; 8.9%) compared with their counterparts (2.9%), χ2 = 49.3, P < 0.001 (data not shown); therefore, it might be possible that the role of IADL impairments in the prediction of sarcopenia could be partly attributed to cognitive impairments, thus suggesting that improvement in cognitive function might improve muscle mass and delay progression to sarcopenia in the elderly population. Nevertheless, stroke, COPD, IADL impairments and cognitive function might not be easy to modify. Efforts should be made to increase participation in physical exercise for preserving muscle mass and prevention of sarcopenia.

The present study also showed the reversibility of sarcopenia during follow-up, and that age was consistently the independent predictor across the different study periods. Perhaps older age denoted a higher risk of persistent sarcopenia. Those with IADL impairments at baseline were also less likely to return to non-sarcopenic during follow-up. On the contrary, sarcopenia might be partly reversible with increasing body weight. We also found that females were also more likely to return to non-sarcopenic compared with their male counterparts. The mechanism underlying such sex-related differences with aging remains to be elucidated. However, physical activity could play a role, and that women might be more health conscious than men. Among the 322 sarcopenic participants at 2-year follow-up, there was a significant increase in physical activity levels (baseline 80.02 to 4-year 92.94, P < 0.001) for women, whereas for men the levels tended to decline (baseline 89.59 to 4-year 85.29, P = 0.359; data not shown). Nevertheless, physical activity was not significantly associated with the reversibility of sarcopenia, although it was significantly associated with a lower risk of incident sarcopenia. This observation is not unexpected, given the small number of reverted cases during the follow-up years, which could decrease the power to detect an association. In addition, the difference between the characteristics of participants included in the analyses for the development of sarcopenia and its reversibility exists. For example, participants with sarcopenia were older, had a higher prevalence of COPD and stroke, and had lower mean values on their MMSE score, PASE score, and BMI than those without sarcopenia; therefore factors associated with incident sarcopenia might be different from its reversibility.

The present study had some limitations. Our cohort was more educated and more physically active than the general elderly population in Hong Kong; therefore, findings should not be generalized to those who are institutionalized or frailer, or with lower education levels. Nutrient quantitation might not be exact. The use of a food frequency questionnaire rather than 24-h recall might have overestimated the intake. Serum 25OHD levels were accurate; however, data were only available in a subsample of men. Furthermore, those defaulting from follow-up were older, had more disabling diseases including sarcopenia and could have deteriorated more, which probably will result in an underestimation of incidence, particularly with longer duration of follow-up. In the present study, the proportion of missing cases differed between those with and without sarcopenia (2-year follow-up, 23.8% vs 13.7% and 4-year follow-up, 39.1% vs 20.4%); which could underestimate the true rate of muscle and functional loss in older adults over time if the rate was different between the two groups. For accurate data, we need to visit all participants at home if they do not come back. Finally, the number of participants who had returned to non-sarcopenic during follow-up was small (n = 68 at 2-year, and n = 48 at 4-year follow-up, respectively), thus findings warrant confirmation in larger studies with longer follow-up.

In conclusion, the present study confirmed that sarcopenia incidence increased with age, but is potentially reversible, with several modifiable lifestyle-related factors as predictors. High BMI was protective against incident sarcopenia and its reversibility. Increasing physical activity and maintaining a healthy weight could be beneficial in the prevention of sarcopenia. Further studies with longer duration of follow-up are required to confirm these associations, and to examine other potential lifestyle behaviors that might contribute to sarcopenia and its reversibility.

Acknowledgements

We thank Dr Edith Lau who set up the elderly cohort, and the support from the SH Ho Centre for Gerontology and Geriatrics, Faculty of Medicine, The Chinese University of Hong Kong.

Disclosure statement

The authors declare no conflict of interest.

Ancillary