Abstract
To evaluate the predictive validity of sarcopenia defined by the Foundation for the National Institutes of Health (FNIH) Sarcopenia Project among Asian older adults.
Data of the I-Lan Longitudinal Aging Study were obtained for analysis. Overall, 1,839 community-dwelling people aged 50 years and older, capable of completing a 6-m walk, with life expectancy of more than 6 months, and not institutionalized at time of data collection were enrolled for study. Data for subjects aged 65 years and older were obtained for study. The outcome measures were all-cause mortality and a composite adverse outcome which includes hospitalizations, emergency department visits, institutionalization, and falls.
Data of 728 eligible elderly participants (73.4 ± 5.4 years; 52.9% males) were analyzed. The prevalence of FNIH-diagnosed sarcopenia was 9.5%: 11.9% males; 6.7% females. Participants having FNIH-defined sarcopenia were considerably older, frailer, more obese, with poorer physical performance than nonsarcopenic subjects (All p < .001); during mean follow-up of 32.9 ± 8.8 months, they also had 3.8 times higher risk of dying, independent of age, sex, multimorbidity, cognitive function, and nutritional status (hazard ratio = 3.8; 95% confidence interval = 1.26–11.45; p = .018). Moreover, sarcopenia defined by grip strength-BMI ratio (WeakBMI) showed stronger association with composite adverse outcomes than traditional handgrip strength (hazard ratio = 1.99; 95% confidence interval = 1.01–3.93; p = .047 vs hazard ratio = 1.80; 95% confidence interval = 0.89–3.62; p = .102 in fully-adjusted model).
Among community-dwelling older people in Taiwan, participants with FNIH-defined sarcopenia had a significantly greater risk of all-cause mortality and composite falls, emergency department visits, institutionalization, and hospitalization.
Since Rossenberg and Roubenoff coined the term sarcopenia to denote age related loss of skeletal muscle mass and function (1), this condition has garnered avid interest among geriatricians, particularly given its relation to physical limitation and the potential for reversibility. Nevertheless, as with any newly described disease entity, formulating an operational definition of sarcopenia has proven challenging. One of the biggest problems was defining the meaning of low appendicular lean muscle mass (ALM). Baumgartner first proposed height-standardized lean mass in reference to body mass index (BMI) (2), an approach since endorsed globally by the European Working Group on Sarcopenia in Older People (EWGSOP) (3), the International Working Group on Sarcopenia (IWGS) (4), and the Asian Working Group for Sarcopenia (AWGS) (5). However, accruing evidence suggests that adiposity may significantly affect functional performance, independent of ALM (6–11); this association is more evident in women who generally have greater adiposity than men (6,7,10,12,13). Another major challenge was lack of evidence affirming the prognostic value of diagnosing sarcopenia. As population norms for older individuals were not fully established to begin with, the cutoff point for low ALM in older people is commonly regarded to be equivalent to the lowest quintile of young, healthy adults (4,5,11); alternatively, some studies use two standard deviations below the mean ALM of a young reference group (2,5,10).
The significant ethnic differences of cutoff points increase the diagnostic challenge. Based on 2 standard deviation (SD) below the mean of younger population (skeletal muscle mass) or sex-specific lowest quintile (skeletal muscle mass)/quartile (handgrip strength) of the study population (3,5), it is conceivable that the cutoff points of low skeletal muscle mass and low grip strength would be diverse among ethnic groups. For instance, AWGS defined low skeletal muscle mass as less than 7.0 kg/m2 for men and 5.4 kg/m2 for women by dual x-ray absorptiometry(DXA), and 7.0 kg/m2 for men and 5.7 kg/m2 for women by bioimpedance analysis (BIA); while low handgrip strength was defined as less than 26 kg for men and 18 kg for women (5). Comparing to the cutoff points proposed by EWGSOP, these values were 5%–15% lower (3). The difference of body composition and muscle performance between Asian and Western population is also noteworthy. In general, Asian population, especially women, tend to have greater age-related increase in fat mass with more central distribution, but the age-related skeletal muscle decline is slower than Western population (14,15). The findings implicated the importance of considering adiposity when diagnosing sarcopenia in Asian people.
The Foundation for the National Institutes of Health (FNIH) Sarcopenia Project, launched in 2009, has attempted to define sarcopenia through an outcome-based approach; its goals included: (a) define a clinically-relevant degree of muscle weakness, (b) define a clinically-relevant degree of low ALM with muscle weakness, (c) among subjects without current mobility limitations, determine whether weakness and low ALM criteria predict future mobility limitations, and (d) compare criteria for weakness and low ALM with other proposed criteria. The results of the FNIH Sarcopenia Project may influence treatment decisions and development, and help to identify groups of older people at risk for poor outcomes and in whom interventions should be tested; the final reports were published in 2014 (12,13,16–18).
The FNIH study pooled and analyzed data from nine major cohorts comprising 26,625 community-dwelling older people:(16) Age, Gene/Environment Susceptibility-Reykjavik Study (19); Boston Puerto Rican Health Study (20); Framingham Heart Study, original and offspring cohorts (21,22); Health, Aging and Body Composition Study (23,24); Osteoporotic Fractures in Men Study, and ancillary sleep study (24,25); Study of Osteoporotic Fractures, original and African American cohorts (26–28); Rancho Bernardo Study (29); and inCHIANTI study (30); nevertheless, the FNIH population was ethnically homogenous, being 90% of Europid ancestry (16). Therefore, this study aimed to investigate the predictive validity of FNIH-defined sarcopenia in an Asian population.
Methods
Design and Participants
The I-Lan Longitudinal Ageing Study is an ongoing aging research cohort of community dwellers aged 50 years and above, who were randomly selected from household registration data of I-Lan (Yilan) County, in Northeast Taiwan, as described elsewhere (11,31,32). ILAS was conducted to evaluate the interrelation between aging and common age-related maladies such as frailty, sarcopenia, and cognitive decline (11,31). The inclusion criteria were: (i) inhabitants of Yilan County with no intentions of relocating; and (ii) aged 50 years or older. All participants provided prior written consent. ILAS excluded subjects: (i) unable to communicate with the interviewer or to give consent, (ii) unable to complete a 6-m walk assessment, (iii) with major illness, such as cancer, with life expectancy of less than 6 months, (iv) unable to undergo magnetic resonance imaging, or (v) institutionalized at the time of data collection.
Baseline data included subjects’ demographic information, physical examination and performance results, muscle strength, and body composition. Research nurses conducted follow-up interviews with all participants every 3 months and documented any changes of health status, timed to the nearest month. The National Yang Ming University Institutional Review Board approved this study protocol (IRB № YM103008F).
Demographics and Physical Examinations
Trained interviewers administered a questionnaire to elicit demographic information, medical history, and burden of chronic diseases, according to Charlson’s Comorbidity Index (CCI) (33) and the Functional Autonomy Measurement System (SMAF) (34). They also performed comprehensive functional assessments including: the Taiwanese Geriatric Depression Scale (T-GDS) (35) for mood condition, the Mini-Nutrition Assessment-Short Form (MNA-SF) for nutritional status, and the Mini-Mental State Examination (MMSE) for cognitive function. Research nurses recorded anthropometric measurements, which included height (to the nearest 0.1 cm) and body weight (to the nearest 0.1 kg)—body mass index (BMI) was calculated as body weight (kg), divided by height (m) squared (kg/m2).
Grip Strength and Physical Performance
Handgrip strength to the nearest 0.5 kg was measured with a digital dynamometer (Smedley’s Dynamo Meter; TTM, Tokyo, Japan); participants stood upright with their arms by the sides and held the device with their dominant hand, they were encouraged to exert the greatest possible force without squeezing their arms against their body. Statistical analysis used the maximum measured from three attempts. A timed 6-m walk was used to evaluate physical performance; slow walking speed was defined as less than 0.8 m/s (3,36).
Skeletal Muscle Mass Measurement
Whole-body dual-energy x-ray absorptiometry (DXA) was used to measure total body-fat mass and fat-free lean body mass. ALM was defined as the total lean soft-tissue mass of four limbs. In this study, height-adjusted muscle index, or relative appendicular skeletal muscle (RASM) was calculated by ALM, divided by height (m) squared (kg/m2). BMI-adjusted muscle index (ALM-BMI) was calculated by ALM, divided by BMI (m2).
Outcome Measures
Based on AWGS consensus (5), a composite of several adverse outcomes: (i) fall, (ii) required urgent medical attention entailing an emergency department visit, (iii) institutionalization due to functional decline or change of social status, or (iv) hospitalization was selected. All-cause mortality was considered as a separated measurement. The timing of these events was recorded to the nearest month from the day of the first interview. If multiple composite outcomes occurred during the study period, survival analysis included only the first.
Diagnosis of Sarcopenia by the FNIH Sarcopenia Project
FNIH criteria define sarcopenia as low grip strength (weakness) plus low muscle mass (low ALM-BMI) (16,18), with proposed cutoffs of < 26.0 kg in men and <16.0 kg in women for grip strength, and < 0.789 m2 in men and <0.512 m2 in women for low ALM (18). The FNIH suggested grip-BMI ratio (WeakBMI), calculated as handgrip strength, divided by BMI (m2), as an alternative measure of muscle strength, with cutoffs of < 1.00 m2 in men and < 0.56 m2 in women (13).
Statistical Analysis
Continuous variables were expressed as the mean ± SD and categorical data as percentages. Between-group comparisons of continuous data were made by Student’s T test, and comparisons of categorical data by Chi squared test, as appropriate. Fisher’s exact test was applied when expected values were ≤ 5 (37). Survival analysis by the Cox proportional hazard model was used to estimate the association of FNIH-defined sarcopenia with composite adverse outcomes and all-cause mortality, and to obtain the hazard ratios and their 95% confidence intervals (38,39). Final multivariable Cox regression models were conducted by adjusting covariates that were statistically or clinically significantly correlated with sarcopenia status; these included age, sex, comorbidities (CCI), nutrition status (MNA-SF), and cognitive function (MMSE). All analyses were done using SPSS statistics software (SPSS 22.0; APSS, Chicago, IL). A P value of less than 0.05 (two-tailed) was considered statistically significant.
Results
Participant Characteristics
Data of 1,839 I-LAN participants aged ≥50 years from August 2011 to August 2014 were excerpted for study, among whom 756 were older than 65; having excluded 25 due to incomplete data and another three lost in follow-up, data from 728 older community-dwelling people were retrieved for analysis.
The final study cohort comprised 52.9% males, mean age 73.4 ± 5.4 years, with mean follow-up of 32.9 ± 8.8 months; 69 participants fit the FNIH diagnostic criteria for sarcopenia, a prevalence of 9.5%: 11.9% males; 6.7% females. Table 1 summarizes the participants’ demographic characteristics. Those with FNIH-defined sarcopenia were considerably older and predominantly males. BMI among men in the sarcopenic and nonsarcopenic groups was marginally different; however, males with FNIH-defined sarcopenia had significantly less muscle mass, adjusted either by height or by BMI. Women with FNIH-defined sarcopenia had higher BMI and BMI-standardized low ALM than those without, whereas there was no between-group difference in height-adjusted low ALM among women.
Demographic Characteristics of Participants With and Without Sarcopenia by FNIH Criteria (Weak + ALM-BMI)
| Participant Characteristics: Data Values Show Mean ± SD or Number (%) | With Sarcopenia (n = 69) | Without Sarcopenia (n = 659) | p Value |
|---|---|---|---|
| Age (years) | 76.5 ± 5.7 | 73.1 ± 5.3 | <.001 |
| Male | 46 (66.7) | 339 (46.6) | .014 |
| BMI (kg/m2) | |||
| Men | 25.3 ± 3.2 | 24.3 ± 3.3 | .047 |
| Women | 28.5 ± 3.0 | 24.9 ± 3.7 | <.001 |
| Relative appendicular skeletal muscle (kg/m2) | |||
| Men | 7.1 ± 1.0 | 7.7 ± 0.7 | |
| Women | 6.3 ± 0.6 | 6.3 ± 0.7 | |
| Appendicular lean muscle mass-BMI (m2) | |||
| Men | 0.71 ± 0.07 | 0.85 ± 0.10 | |
| Women | 0.48 ± 0.03 | 0.60 ± 0.08 | |
| Handgrip strength (kg) | |||
| Men | 21.5 ± 2.8 | 31.4 ± 6.6 | |
| Women | 12.8 ± 2.5 | 19.5 ± 4.9 | |
| 6-m walk speed (m/s) | 1.1 ± 0.4 | 1.4 ± 0.4 | |
| Charlson Comorbidity Index | 2.0 ± 1.4 | 1.6 ± 1.4 | .045 |
| Mini-Mental State Examination | 21.4 ± 5.0 | 23.6 ± 4.3 | <.001 |
| Taiwan Geriatric Depression Scale | 0.1 ± 0.2 | 0.1 ± 0.5 | .272 |
| Mini-Nutrition Assessment-Short Form | 13.6 ± 0.8 | 13.3 ± 1.1 | .008 |
| Functional Autonomy Measurement System (SMAF) | −0.6 ± 2.1 | −0.4 ± 2.5 | .374 |
| All-cause mortality | 5 (7.2) | 12 (1.8) | .017 |
| Composite adverse outcomes | 10 (14.5) | 52 (7.9) | .064 |
| Participant Characteristics: Data Values Show Mean ± SD or Number (%) | With Sarcopenia (n = 69) | Without Sarcopenia (n = 659) | p Value |
|---|---|---|---|
| Age (years) | 76.5 ± 5.7 | 73.1 ± 5.3 | <.001 |
| Male | 46 (66.7) | 339 (46.6) | .014 |
| BMI (kg/m2) | |||
| Men | 25.3 ± 3.2 | 24.3 ± 3.3 | .047 |
| Women | 28.5 ± 3.0 | 24.9 ± 3.7 | <.001 |
| Relative appendicular skeletal muscle (kg/m2) | |||
| Men | 7.1 ± 1.0 | 7.7 ± 0.7 | |
| Women | 6.3 ± 0.6 | 6.3 ± 0.7 | |
| Appendicular lean muscle mass-BMI (m2) | |||
| Men | 0.71 ± 0.07 | 0.85 ± 0.10 | |
| Women | 0.48 ± 0.03 | 0.60 ± 0.08 | |
| Handgrip strength (kg) | |||
| Men | 21.5 ± 2.8 | 31.4 ± 6.6 | |
| Women | 12.8 ± 2.5 | 19.5 ± 4.9 | |
| 6-m walk speed (m/s) | 1.1 ± 0.4 | 1.4 ± 0.4 | |
| Charlson Comorbidity Index | 2.0 ± 1.4 | 1.6 ± 1.4 | .045 |
| Mini-Mental State Examination | 21.4 ± 5.0 | 23.6 ± 4.3 | <.001 |
| Taiwan Geriatric Depression Scale | 0.1 ± 0.2 | 0.1 ± 0.5 | .272 |
| Mini-Nutrition Assessment-Short Form | 13.6 ± 0.8 | 13.3 ± 1.1 | .008 |
| Functional Autonomy Measurement System (SMAF) | −0.6 ± 2.1 | −0.4 ± 2.5 | .374 |
| All-cause mortality | 5 (7.2) | 12 (1.8) | .017 |
| Composite adverse outcomes | 10 (14.5) | 52 (7.9) | .064 |
Note: ALM = Appendicular lean muscle mass; ALM-BMI = ALM to BMI ratio; BMI = Body mass index; FNIH = Foundation for the National Institutes of Health (USA).
Demographic Characteristics of Participants With and Without Sarcopenia by FNIH Criteria (Weak + ALM-BMI)
| Participant Characteristics: Data Values Show Mean ± SD or Number (%) | With Sarcopenia (n = 69) | Without Sarcopenia (n = 659) | p Value |
|---|---|---|---|
| Age (years) | 76.5 ± 5.7 | 73.1 ± 5.3 | <.001 |
| Male | 46 (66.7) | 339 (46.6) | .014 |
| BMI (kg/m2) | |||
| Men | 25.3 ± 3.2 | 24.3 ± 3.3 | .047 |
| Women | 28.5 ± 3.0 | 24.9 ± 3.7 | <.001 |
| Relative appendicular skeletal muscle (kg/m2) | |||
| Men | 7.1 ± 1.0 | 7.7 ± 0.7 | |
| Women | 6.3 ± 0.6 | 6.3 ± 0.7 | |
| Appendicular lean muscle mass-BMI (m2) | |||
| Men | 0.71 ± 0.07 | 0.85 ± 0.10 | |
| Women | 0.48 ± 0.03 | 0.60 ± 0.08 | |
| Handgrip strength (kg) | |||
| Men | 21.5 ± 2.8 | 31.4 ± 6.6 | |
| Women | 12.8 ± 2.5 | 19.5 ± 4.9 | |
| 6-m walk speed (m/s) | 1.1 ± 0.4 | 1.4 ± 0.4 | |
| Charlson Comorbidity Index | 2.0 ± 1.4 | 1.6 ± 1.4 | .045 |
| Mini-Mental State Examination | 21.4 ± 5.0 | 23.6 ± 4.3 | <.001 |
| Taiwan Geriatric Depression Scale | 0.1 ± 0.2 | 0.1 ± 0.5 | .272 |
| Mini-Nutrition Assessment-Short Form | 13.6 ± 0.8 | 13.3 ± 1.1 | .008 |
| Functional Autonomy Measurement System (SMAF) | −0.6 ± 2.1 | −0.4 ± 2.5 | .374 |
| All-cause mortality | 5 (7.2) | 12 (1.8) | .017 |
| Composite adverse outcomes | 10 (14.5) | 52 (7.9) | .064 |
| Participant Characteristics: Data Values Show Mean ± SD or Number (%) | With Sarcopenia (n = 69) | Without Sarcopenia (n = 659) | p Value |
|---|---|---|---|
| Age (years) | 76.5 ± 5.7 | 73.1 ± 5.3 | <.001 |
| Male | 46 (66.7) | 339 (46.6) | .014 |
| BMI (kg/m2) | |||
| Men | 25.3 ± 3.2 | 24.3 ± 3.3 | .047 |
| Women | 28.5 ± 3.0 | 24.9 ± 3.7 | <.001 |
| Relative appendicular skeletal muscle (kg/m2) | |||
| Men | 7.1 ± 1.0 | 7.7 ± 0.7 | |
| Women | 6.3 ± 0.6 | 6.3 ± 0.7 | |
| Appendicular lean muscle mass-BMI (m2) | |||
| Men | 0.71 ± 0.07 | 0.85 ± 0.10 | |
| Women | 0.48 ± 0.03 | 0.60 ± 0.08 | |
| Handgrip strength (kg) | |||
| Men | 21.5 ± 2.8 | 31.4 ± 6.6 | |
| Women | 12.8 ± 2.5 | 19.5 ± 4.9 | |
| 6-m walk speed (m/s) | 1.1 ± 0.4 | 1.4 ± 0.4 | |
| Charlson Comorbidity Index | 2.0 ± 1.4 | 1.6 ± 1.4 | .045 |
| Mini-Mental State Examination | 21.4 ± 5.0 | 23.6 ± 4.3 | <.001 |
| Taiwan Geriatric Depression Scale | 0.1 ± 0.2 | 0.1 ± 0.5 | .272 |
| Mini-Nutrition Assessment-Short Form | 13.6 ± 0.8 | 13.3 ± 1.1 | .008 |
| Functional Autonomy Measurement System (SMAF) | −0.6 ± 2.1 | −0.4 ± 2.5 | .374 |
| All-cause mortality | 5 (7.2) | 12 (1.8) | .017 |
| Composite adverse outcomes | 10 (14.5) | 52 (7.9) | .064 |
Note: ALM = Appendicular lean muscle mass; ALM-BMI = ALM to BMI ratio; BMI = Body mass index; FNIH = Foundation for the National Institutes of Health (USA).
Individuals with FNIH-diagnosed sarcopenia walked slower and had less muscle strength than nonsarcopenic participants. They also had more comorbidities, with higher CCI, and lower MNA-SF and MMSE scores indicating poorer nutritional status and cognitive function.
Participant with FNIH-defined sarcopenia had a significant greater risk of all-cause mortality, while the between-group difference in composite adverse outcomes tended toward statistical significance (p = .064).
Weak + Low ALM-BMI
Table 2 and Figures 1 and 2 showed how sarcopenia associated with all-cause mortality and composite adverse outcomes. Having low handgrip strength (Weak) and low ALM-BMI consistently conferred the highest likelihood of all-cause mortality, even after adjusting for demographic factors. The hazard ratio(HR) was 3.44 (95% CI 1.17–10.10, P value = .025) (Model 2). This group’s risk of mortality became more significant after further adjustment for comorbidities, nutrition status, and cognitive function, with HR = 3.80 (95% CI 1.26–11.45, p value = .018) (Model 3); widening divergence in decremental survival was already evident during follow-up (Figure 1a), with a sharp drop-off after 1 year. A positive association between FNIH-defined sarcopenia and composite adverse outcomes in the crude model (Model 1) attenuated after adjusting for confounding factors. The HR in fully-adjusted model was 1.80 (95% CI 0.89–3.62, p value = .102) (Table 2, Model 3; Figure 2a).
All-cause Mortality and Composite Adverse Outcomes by Cox Proportional Hazard Models
| Model 1* | Model 2† | Model 3‡ | ||||
|---|---|---|---|---|---|---|
| HR (95% CI) | p Value | HR (95% CI) | p Value | HR (95% CI) | p Value | |
| Weak + low ALM-BMI | ||||||
| All-cause mortality | 4.40 (1.55–12.49) | .005 | 3.44 (1.17–10.10) | .025 | 3.80 (1.26–11.45) | .018 |
| Composite adverse outcomes | 2.00 (1.02–3.94) | .044 | 1.68 (0.84–3.35) | .140 | 1.80 (0.89–3.62) | .102 |
| WeakBMI+ low ALM-BMI | ||||||
| All-cause mortality | 4.24 (1.49–12.04) | .007 | 3.18 (1.08–9.39) | .036 | 3.85 (1.25–11.85) | .019 |
| Composite adverse outcomes | 2.22 (1.16–4.26) | .017 | 1.85 (0.95–3.59) | .071 | 1.99 (1.01–3.93) | .047 |
| Model 1* | Model 2† | Model 3‡ | ||||
|---|---|---|---|---|---|---|
| HR (95% CI) | p Value | HR (95% CI) | p Value | HR (95% CI) | p Value | |
| Weak + low ALM-BMI | ||||||
| All-cause mortality | 4.40 (1.55–12.49) | .005 | 3.44 (1.17–10.10) | .025 | 3.80 (1.26–11.45) | .018 |
| Composite adverse outcomes | 2.00 (1.02–3.94) | .044 | 1.68 (0.84–3.35) | .140 | 1.80 (0.89–3.62) | .102 |
| WeakBMI+ low ALM-BMI | ||||||
| All-cause mortality | 4.24 (1.49–12.04) | .007 | 3.18 (1.08–9.39) | .036 | 3.85 (1.25–11.85) | .019 |
| Composite adverse outcomes | 2.22 (1.16–4.26) | .017 | 1.85 (0.95–3.59) | .071 | 1.99 (1.01–3.93) | .047 |
Note: ALM = Appendicular lean muscle mass; ALM-BMI = ALM to BMI ratio; BMI = Body mass index; CI = Confidence interval; HR = Hazard ratio; WeakBMI = Handgrip-BMI ratio.
*Crude. †Adjusted for age and sex. ‡Adjusted for age, sex, Charlson Comorbidity Index, Mini-Mental State Examination and Mini-Nutrition Assessment-Short Form.
All-cause Mortality and Composite Adverse Outcomes by Cox Proportional Hazard Models
| Model 1* | Model 2† | Model 3‡ | ||||
|---|---|---|---|---|---|---|
| HR (95% CI) | p Value | HR (95% CI) | p Value | HR (95% CI) | p Value | |
| Weak + low ALM-BMI | ||||||
| All-cause mortality | 4.40 (1.55–12.49) | .005 | 3.44 (1.17–10.10) | .025 | 3.80 (1.26–11.45) | .018 |
| Composite adverse outcomes | 2.00 (1.02–3.94) | .044 | 1.68 (0.84–3.35) | .140 | 1.80 (0.89–3.62) | .102 |
| WeakBMI+ low ALM-BMI | ||||||
| All-cause mortality | 4.24 (1.49–12.04) | .007 | 3.18 (1.08–9.39) | .036 | 3.85 (1.25–11.85) | .019 |
| Composite adverse outcomes | 2.22 (1.16–4.26) | .017 | 1.85 (0.95–3.59) | .071 | 1.99 (1.01–3.93) | .047 |
| Model 1* | Model 2† | Model 3‡ | ||||
|---|---|---|---|---|---|---|
| HR (95% CI) | p Value | HR (95% CI) | p Value | HR (95% CI) | p Value | |
| Weak + low ALM-BMI | ||||||
| All-cause mortality | 4.40 (1.55–12.49) | .005 | 3.44 (1.17–10.10) | .025 | 3.80 (1.26–11.45) | .018 |
| Composite adverse outcomes | 2.00 (1.02–3.94) | .044 | 1.68 (0.84–3.35) | .140 | 1.80 (0.89–3.62) | .102 |
| WeakBMI+ low ALM-BMI | ||||||
| All-cause mortality | 4.24 (1.49–12.04) | .007 | 3.18 (1.08–9.39) | .036 | 3.85 (1.25–11.85) | .019 |
| Composite adverse outcomes | 2.22 (1.16–4.26) | .017 | 1.85 (0.95–3.59) | .071 | 1.99 (1.01–3.93) | .047 |
Note: ALM = Appendicular lean muscle mass; ALM-BMI = ALM to BMI ratio; BMI = Body mass index; CI = Confidence interval; HR = Hazard ratio; WeakBMI = Handgrip-BMI ratio.
*Crude. †Adjusted for age and sex. ‡Adjusted for age, sex, Charlson Comorbidity Index, Mini-Mental State Examination and Mini-Nutrition Assessment-Short Form.
Survival curves for all-cause mortality according to sarcopenia status, adjusted for age, sex, Charlson Comorbidity Index, Mini-Mental State Examination and Mini-Nutrition Assessment-Short Form. (a) Weak + low ALM-BMI. (b) WeakBMI + low ALM-BMI. ALM = Appendicular lean muscle mass; ALM-BMI = ALM to BMI ratio; BMI = Body mass index; HR = Hazard ratio; WeakBMI = Handgrip-BMI ratio.
Survival curves for composite adverse outcomes according to sarcopenia status, adjusted for age, sex, Charlson Comorbidity Index, Mini-Mental State Examination and Mini-Nutrition Assessment-Short Form. (a) Weak + low ALM-BMI. (b) WeakBMI + low ALM-BMI. ALM = Appendicular lean muscle mass; ALM-BMI = ALM to BMI ratio; BMI = Body mass index; HR = Hazard ratio; WeakBMI = Handgrip-BMI ratio.
WeakBMI + Low ALM-BMI
Likewise, there was a highly positive association between sarcopenia and all-cause mortality in all models that used WeakBMI as an alternative muscle strength metric, sarcopenia being defined as WeakBMI plus low ALM-BMI; again with a higher death rate than the nonsarcopenic population (Figure 1b) with HR = 3.85 (95% CI 1.25–11.85, p value = .019) in the final model (Table 2). Compared to defining sarcopenia by Weak + ALM-BMI, WeakBMI + low ALM-BMI was more strongly associated with composite adverse outcomes after adjusting for demographic factors, CCI, MMSE and MNA-SF. The HR was 1.99 (95% CI 1.01–3.93, p value = .047) (Table 2, Model 3; Figure 2b).
Discussion
In this study, FNIH-defined sarcopenia conferred nearly fourfold higher risk of dying, independent of age, sex, multimorbidity, cognitive function, or nutritional status. The significant predictive validly in mortality risk of FNIH-defined sarcopenia was observed in other non-Caucasian ethnic group such as Korean (40).
The prevalence of sarcopenia varies greatly, according to its diagnostic criteria (4,16,41,42); previous reports using AWGS criteria estimated the prevalence in Asians to range from 8.6% to 22.0% (5,11,17,41–44). The prevalence of 9.5% for FNIH-diagnosed sarcopenia in the ILAS community-dwelling older cohort is slightly below reported values, corroborating the FNIH surmise that prevalence of FNIH-diagnosed sarcopenia is likely to be lower than that estimated by the AWGS or EWGSOP algorithms, due to applying stricter diagnostic criteria (18).
In this study, with average follow-up of 2.0–3.5 years, the respective hazard ratios for mortality and important clinical outcomes such as fall, emergency department visit, institutionalization and hospitalization were 3.8 and 1.8. In another study of mortality in FNIH-defined sarcopenia over 9.8 ± 3.0 years, the likelihood ratio for mortality was 2.03 (13). However, mortality risk patterns over 10 years in FNIH reports (17) were inconsistent among the six cohorts studied (22,24,26–28,30).
Distinguishable populations of sarcopenia in our study were defined by different lean mass measurements. Less than half of sarcopenic subjects diagnosed by FNIH criteria would be considered sarcopenic according to the AWGS algorithm (a prevalence of 45.1%: 45.7% males; 8.7% females). One possibility for the considerable difference between genders may be caused by small positive sample size in the female cohort in this study. Only two subjects were considered sarcopenic by both FNIH and AWGS definition; they contributed to the female sarcopenic population 8.7% and 14.3%, respectively. Another possibility for the difference was the consideration of adiposity in FNIH-defined sarcopenia. Wu and colleagues suggested Asian women has significant greater age-related increase in fat mass (15) In this study and our previous work (11), among individuals with different obesity status, BMI-standardized ALM and weight-adjusted ALM both decreased with BMI, while height-adjusted ALM increased with BMI. In other words, high BMI was associated with a greater risk of low ALM-BMI and therefore higher risk of FNIH-defined sarcopenia.
Similarly, Brazilian authors surmised that using height-standardized ALM may underestimate the prevalence of sarcopenia in overweight and obese people, while more than 95% with sarcopenia diagnosed by height-standardized ALM were lean (45). The results of this study echoed the importance and prognostic value of body weight/adiposity in sarcopenia diagnosis for Asian population and may provide further evidence to facilitate AWGS for modification of diagnostic cutoff points.
Diagnosing sarcopenia with FNIH criteria has several advantages: First, it is more efficient than established definitions, without compromising validity. For disabled older people, walking 6 m may be demanding; the FNIH algorithm, which only enrolls subjects with low grip strength and low muscle mass, avoids the inconvenience of measuring walking speed without compromising predictive power compared with EWGSOP and AWGS (18,43,46). Moreover, walking is a complex motor function that is significantly influenced by factors besides muscle mass and muscle function, such as cognition and coordination (16); these correspondences can lead sarcopenia and frailty to be confused. Differentiating sarcopenia from frailty is essential to devising interventions aimed at preserving or improving muscle mass and strength in older adults.
Second, our results suggest that BMI-standardized handgrip strength (WeakBMI) is more strongly associated with composite adverse outcomes than is absolute handgrip strength (p = .047 vs 0.102). However, there is some controversy about the evidence supporting this assertion (13). The FNIH concluded that both low absolute handgrip strength (Weak) and low WeakBMI strongly predict incident mobility impairment after 3 years among nonimpaired older men and women (13). Nevertheless, there was evidence of heterogeneity for the association with WeakBMI among females. Furthermore, the same investigators concluded that WeakBMI was more strongly associated with incident mobility impairment than with muscle weakness. Before including WeakBMI in the working definition of sarcopenia, further studies are required to assess its validity as a measure of muscle strength, and to establish the cutoff points.
Third, previous studies indicate that each population needs its own cutoff values for low lean mass, due to ethnic and geographical variations (3,42). We ascertained that the FNIH cutoffs for low lean mass (0.789 m2 for men and 0.512 m2 for women) were similar to the lowest twentieth percentile in our study group, especially in women (0.748 m2 for men and 0.513 m2 for women). This implies that BMI-standardized ALM, in which ALM is adjusted for both height and weight, can potentially minimize genetic variation in body composition and yield more universal and comparable lean mass data. Congruently, others have suggested that sarcopenic obesity, where sarcopenia was defined by height-standardized ALM, was associated with poorer functional status and worse clinical outcomes than was sarcopenia alone (47–50). By defining low ALM by BMI, which takes obesity into account, the FNIH algorithm theoretically provides more comprehensive adjustment for lean mass.
Our study has several strengths. It was a population-based study on a large cohort of community-dwelling older people and investigated the association of FNIH-defined sarcopenia with mortality and important clinical outcomes. This prospective study established a consistent relationship between sarcopenia and adverse events. Besides, there was sufficient follow-up time to capture enough events and thereby ensure sufficient power to perform appropriate statistical tests; only three participants were lost to follow up, minimizing bias likely to result from applying statistical methods based on noninformative censoring, such as Cox proportional hazard test (38,39). As this study excluded people with severe functional impairment, overall physical performance might compare better to other studies. This allowed us to illustrate that sarcopenia, as a progressive condition, occurs before mortality and adverse outcomes; however, further research is essential to establish how long it takes for sarcopenic individuals to develop major clinical events. Last, ILAS is based on a homogenous population of older people, who were born and live in a well-defined geographical area; this minimizes the differences in access to medical resources or exposure to environmental hazards between the study groups that may affect all-cause mortality and adverse clinical outcomes independent of sarcopenia.
Nonetheless, there are noteworthy limitations. As in all cohort studies, selective survival before cohort entry and health selection bias have to be considered in interpreting these findings. Also, since ILAS was an observational study, there may be unmeasured confounding factors; further elucidation of confounding factors in sarcopenia is required. Although all hospital admissions were recorded as composite adverse outcomes, even when terminated by the death of study subjects, this is a nondifferential bias; the results need careful interpretation to avoid overestimating the incidence of composite adverse outcomes in sarcopenic subjects. Furthermore, the limited number of participants with sarcopenia may have reduced statistical power in multivariable analysis, which increases the likelihood of Type II error and made it impossible to perform a stratified analysis by sex. Because ILAS is a longitudinal study, this effect could be mitigated as sample size increases.
In conclusion, sarcopenia strongly predicts all-cause mortality and composite adverse outcomes among older Taiwanese adults. A large-scale study in Asian subjects, using classification and regression tree analysis similar to the FNIH Sarcopenia Project, is essential to establishing evidence-based cutoff values for lean mass and muscle strength.
Funding
This study was supported by the Aging and Health Research Center, National Yang Ming University; Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, as well as the Ministry of Science and Technology of Taiwan (MOST 104-2633-B-400-001; MOST 105-3011-B-010-001, and MOST 101-2314-B-010-008); however, these funding sources had no role in designing the study, collecting, analyzing, or interpreting data, writing the report, or the decision to submit the article for publication.
Conflict of Interest
None reported.
Acknowledgments
The authors express our gratitude to staff at the Taipei Veterans General Hospital (TVGH) Center for Geriatrics and Gerontology and at TVGH Yuanshan Branch Department of Family Medicine, and to the study participants for their assistance. Dr David Neil (PhD), of Content Ed Net (Taiwan), provided medical writing services on behalf of Taipei Veterans General Hospital.
