Abstract
Objectives
Asian studies on how physical tests predict short-term mortality in elderly are scarce. We assessed handgrip strength and timed-up-and-go (TUG) as such predictors among elderly Chinese in Singapore.
Design
Prospective cohort study.
Setting
Community-dwelling Chinese elderly in Singapore.
Participants
We used data from 13,789 subjects in the prospective, population-based Singapore Chinese Health Study, who had a mean age of 74 (range 63 to 97) years at time of measurements.
Measurements
Subjects underwent assessment for handgrip strength and TUG. They were followed for mortality via linkage with nationwide death registry through 2018.
Results
In multivariable analyses, handgrip strength was inversely associated with risk of mortality in a dose-dependent manner: the hazard ratio (HR) [95% confidence interval (CI)] comparing extreme quartiles was 2.05 (1.44–2.90) (Ptrend<0.001). TUG was positively associated with mortality in a stepwise manner: the HR (95% CI) comparing extreme quartiles was 3.08 (2.17–4.38) (Ptrend<0.001). Compared to those with stronger handgrip and faster TUG, participants who either had weaker handgrip or slower TUG had a significant 1.59 to 2.11 fold increase in risk of mortality; while the HR (95% CI) for those who had both weaker handgrip and slower TUG was 3.93 (3.06–5.05). In time-dependent receiver operating characteristic curves, adding handgrip strength and TUG time to a Cox model containing sociodemographic and lifestyle factors, comorbidities, and body measurements significantly improved the area under the curve for the prediction of mortality from 0.5 to 2 years (P≤0.001).
Conclusion
Among elderly in a Chinese population, handgrip strength and TUG test were strong and independent predictors of short-term mortality.
Key words: Chinese, handgrip strength, mortality, sarcopenia, gait speed
Introduction
Numerous studies have shown that objective tests of physical strength, such as handgrip strength (1, 2, 3, 4, 5), and physical performance, such as the timed-up-and-go (TUG) test (6, 7, 8, 9, 10, 11), are associated with risk of all-cause mortality among older adults in community-dwelling populations. However, with follow-up periods ranging from three years (12, 13) to 33 years (14) for studies on handgrip strength, and from three years (15) to 15 years (7, 8) for studies on the TUG test, most of these studies have examined the association between these tests and risk of mortality over relatively long periods. Furthermore, few of these studies have assessed the prognostic value of these tests as predictors of mortality; and to the best of our knowledge, none of them have done so for short-term mortality within two years. In addition, few of these studies have been conducted in Asian populations, which when compared to Western populations, have been shown to have a unique combination of relatively weaker handgrip strength (16), but not necessarily slower gait speed (17).
In this study, we aimed to assess the association between handgrip strength and the TUG test with risk of mortality in a population-based cohort of Chinese elderly living in Singapore over a relatively short follow-up period of about two years. We investigated the association of each physical test separately, as well as their combined effects, on the risk of all-cause mortality and major causes of death. We also aimed to evaluate the clinical utility of handgrip strength and the TUG test for the prediction of mortality over two years of follow-up time.
Methods
Study population
This study was nested within the prospective, population-based cohort known as the Singapore Chinese Health Study (SCHS) (18). Briefly, 63,257 subjects (27,959 men and 35,298 women) of the two major Chinese dialect groups in Singapore (the Hokkien and Cantonese), and who were aged between 45 and 74 (mean 53) years, were recruited from April 1993 to December 1998. Among consenting survivors of this cohort, follow-up interviews were conducted every 5 to 6 years, and the third follow-up interviews and physical measurements were conducted in-person among 17,048 surviving participants from 2014 to 2017. Among these participants, 13,789 consented to undergoing assessment for handgrip strength and TUG time. This study was approved by the Institutional Review Board at the National University of Singapore and written informed consent was obtained from all study participants.
Assessment of handgrip, TUG and other covariates
During the third follow-up interviews and physical measurements conducted from 2014 to 2017, trained staff measured handgrip strength from each participant using the Takei hand grip dynamometer (Model TKK5401 Grip D) following standard protocols. Participants were instructed to stand upright and hold the handgrip dynamometer with their arms let down naturally, and then to subsequently clasp the grip with full force. Measurements obtained from this procedure were recorded to the nearest 0.1 kg. Two such trials were performed for each hand, and only the highest value obtained from each hand was used. Overall handgrip strength was calculated as the mean of the measurements from the left and right hands.
During the third follow-up interviews, a TUG test (19) was also administered. In the TUG test, each participant was instructed to stand up from a sitting position on a chair of approximately 46 cm in height, walk at a usual pace towards a marker on the floor 3 m away, turn around, walk back to the chair, and then sit on it. The time which the participant took to complete this test was timed with a stopwatch to the nearest one second. Participants wore their regular footwear and were allowed to use any walking aids which they normally required. Two such trials were performed, and only the faster time was used.
During the third follow-up interviews, we also asked the participants for their sociodemographic characteristics, history of smoking, as well as history of physician-diagnosed comorbidities: hypertension, coronary artery disease, stroke, diabetes, and cancer. At the same visit, weight and waist circumference were measured, while height was self-reported or measured at previous interviews. This weight divided by height squared (kg/m2) was used to calculate body mass index (BMI).
Mortality assessment
Study participants were followed up for the date and primary cause of death through linkage with the nation-wide Singapore Birth and Death Registry through 31 December 2018. These causes of deaths were coded using the International Statistical Classification of Diseases and Related Health Problems 9th Revision (ICD-9, up to 31 December 2011) or 10th Revision (ICD-10) (years 2012–2018). All-cause mortality, as well as cause-specific mortality from cardiovascular diseases (CVD) (ICD-9 codes 390–459, ICD-10 codes I00–I99), cancer (ICD-9 codes 140–239, ICD-10 codes C00–C97) and other causes (all other causes of death) were the primary endpoints for this study.
Statistical analysis
We calculated person-years for each participant from the date of physical tests until the date of death or 31 December 2018. We used Cox proportional hazards models to examine associations between handgrip strength and TUG time with the risk of all-cause and cause-specific mortality. When the physical tests were modelled as continuous variables, 1,080 participants had outliers in handgrip strength and/or TUG time (defined as values exceeding two times the interquartile range from the median). These participants were retained in the analysis, but had their measurements were set to the respective boundary values. In Model 1 of our analyses, we adjusted for age (at date of physical tests) and sex only. In Model 2, we additionally adjusted for sociodemographic and lifestyle factors (dialect group, level of education, and smoking status), comorbidities (hypertension, coronary artery disease, stroke, diabetes, and cancer), and body measurements (BMI group and waist circumference). Finally, in Model 3, we additionally adjusted for handgrip strength and/or TUG test time.
We defined quartile categories for men and women separately using sex-specific cut-offs. In our analyses, we used the highest quartile (strongest) as the reference category for handgrip strength; and we used the lowest quartile (fastest) as the reference category for TUG. Within the handgrip and TUG quartiles, linear trend was assessed for using orthogonal polynomial contrast coding. These quartile results then guided us in defining sex-specific cutoffs to create a higher and a lower category for each test. Men and women were categorized as ‘weak' if their handgrip strength fell within the lowest (weakest) quartile; they were categorized as ‘strong' if it fell within any of the other higher quartiles. Similarly, they were categorized as ‘slow' if their TUG time fell within the highest (slowest) quartile; they were categorized as ‘fast' if it fell within any other lower quartiles. Based on the combination of their handgrip strength and TUG time categories, participants were divided into four mutually exclusive groups.
To evaluate the clinical utility of handgrip strength and TUG time for the prediction of short-term mortality, time-dependent receiver operating characteristic (ROC) curves were computed at 0.5 years for two Cox models: (1) a model containing sociodemographic and lifestyle factors, comorbidities, and body measurements (Model 2); (2) Model 2 with the addition of handgrip strength and TUG time (i.e., Model 3). In both these models, handgrip strength and TUG time were modelled as continuous variables. To evaluate the predictive accuracy of the two Cox models across different time periods, we also computed the area under these time-dependent receiver operating characteristic curves [AUC(t)] over 2 years of follow-up and plotted them against follow-up time (t).
Statistical analyses were conducted using Stata/SE 14.2 software (20), and R version 3.5.3 software (21) with the survival (22) and timeROC (23) packages. All P-values presented were two-sided, and P < 0.05 was considered statistically significant.
Results
Participants had a mean age of 74 [standard deviation (SD), (6) years at the time of the physical tests. Among the 13,789 participants included in the present analysis, 8,133 (59.0%) were women. Men had higher handgrip strength, and slightly faster TUG test times across all age groups compared to women (Supplementary Figure S1). In both sexes, factors associated with weaker handgrip strength and slower TUG time were older age, lower levels of education, smoking, and presence of comorbidities (except cancer) (Table 1). Interestingly, weaker handgrip strength was associated with lower BMI and lower waist circumference (Table 1). Conversely, slower TUG time was associated with higher BMI and higher waist circumference (Table 1).
Figure 1.
Time-dependent receiver operator characteristic (ROC) curves at 0.5 years comparing two Cox proportional hazards models: (1) a model containing sociodemographic and lifestyle factors, comorbidities, and body measurements (Model 2); (2) Model 2 with the addition of handgrip strength and TUG time. Model 2: adjusted for age, sex, dialect group, level of education, smoking status, hypertension, coronary artery disease, stroke, diabetes, cancer, BMI group, waist circumference. AUC: area under the ROC curve
Table 1.
Characteristics among the sex-specific quartiles of handgrip strength and TUG test time. Continuous variables were presented as mean (SD), while categorical variables were presented as N (%)
| Sex-specific quartiles of handgrip strength | P-value | ||||
|---|---|---|---|---|---|
| Q4 (Strongest) | Q3 | Q2 | Q1 (Weakest) | ||
| (N=3,435) | (N=3,459) | (N=3,416) | (N=3,479) | ||
| Mean handgrip strength (SD)/kg | 27.39 (6.57) | 22.59 (5.25) | 19.37 (4.58) | 14.44 (4.27) | <0.001 |
| Mean TUG test time (SD)/s | 9.74 (3.50) | 10.90 (4.70) | 12.29 (6.56) | 16.08 (11.94) | <0.001 |
| Mean age at test (SD)/years | 71.00 (4.73) | 72.99 (5.62) | 74.96 (6.05) | 77.17 (6.59) | <0.001 |
| Sex | 0.87 | ||||
| Men | 1,406 (40.9%) | 1,410 (40.8%) | 1,422 (41.6%) | 1,418 (40.8%) | |
| Dialect group | <0.001 | ||||
| Hokkien | 1,469 (42.8%) | 1,727 (49.9%) | 1,750 (51.2%) | 1,958 (56.3%) | |
| Cantonese | 1,966 (57.2%) | 1,732 (50.1%) | 1,666 (48.8%) | 1,521 (43.7%) | |
| Level of education | <0.001 | ||||
| None | 465 (13.5%) | 546 (15.8%) | 648 (19.0%) | 778 (22.4%) | |
| Primary | 1,398 (40.7%) | 1,518 (43.9%) | 1,566 (45.8%) | 1,652 (47.5%) | |
| Secondary and above | 1,572 (45.8%) | 1,395 (40.3%) | 1,202 (35.2%) | 1,049 (30.2%) | |
| Smoking status | 0.02 | ||||
| Never smoker | 2,689 (78.3%) | 2,639 (76.3%) | 2,593 (75.9%) | 2,596 (74.6%) | |
| Former smoker | 502 (14.6%) | 525 (15.2%) | 547 (16.0%) | 583 (16.8%) | |
| Current smoker | 244 (7.1%) | 294 (8.5%) | 275 (8.1%) | 300 (8.6%) | |
| Hypertension | 1,979 (57.6%) | 2,110 (61.0%) | 2,167 (63.4%) | 2,285 (65.7%) | <0.001 |
| Coronary artery disease | 268 (7.8%) | 358 (10.3%) | 385 (11.3%) | 495 (14.2%) | <0.001 |
| Stroke | 89 (2.6%) | 148 (4.3%) | 179 (5.2%) | 250 (7.2%) | <0.001 |
| Diabetes | 552 (16.1%) | 726 (21.0%) | 806 (23.6%) | 923 (26.5%) | <0.001 |
| Cancer | 201 (5.9%) | 207 (6.0%) | 234 (6.9%) | 240 (6.9%) | 0.15 |
| Mean BMI (SD)/kg/m2 | 23.72 (3.44) | 23.14 (3.57) | 22.70 (3.68) | 22.20 (3.85) | <0.001 |
| Mean waist circumference (SD)/cm | 88.31 (10.33) | 87.27 (10.60) | 86.58 (11.42) | 85.41 (11.48) | <0.001 |
| Sex-specific quartiles of TUG test time | P-value | ||||
| Q1 (Fastest) | Q2 | Q3 | Q4 (Slowest) | ||
| (N=4,012) | (N=3,727) | (N=3,314) | (N=2,736) | ||
| Mean TUG test time (SD)/s | 7.81 (0.97) | 10.02 (0.69) | 12.35 (0.94) | 21.74 (13.36) | <0.001 |
| Mean handgrip strength (SD)/kg | 22.65 (6.88) | 22.23 (7.07) | 20.55 (6.73) | 17.10 (6.08) | <0.001 |
| Mean age at test (SD)/years | 70.44 (4.54) | 72.88 (5.27) | 75.52 (5.76) | 79.09 (6.20) | <0.001 |
| Sex | <0.001 | ||||
| Men | 1,414 (35.2%) | 1,694 (45.5%) | 1,520 (45.9%) | 1,028 (37.6%) | |
| Dialect group | 0.031 | ||||
| Hokkien | 1,930 (48.1%) | 1,901 (51.0%) | 1,677 (50.6%) | 1,396 (51.0%) | |
| Cantonese | 2,082 (51.9%) | 1,826 (49.0%) | 1,637 (49.4%) | 1,340 (49.0%) | |
| Level of education | <0.001 | ||||
| None | 338 (8.4%) | 548 (14.7%) | 682 (20.6%) | 869 (31.8%) | |
| Primary | 1,496 (37.3%) | 1,678 (45.0%) | 1,600 (48.3%) | 1,360 (49.7%) | |
| Secondary and above | 2,178 (54.3%) | 1,501 (40.3%) | 1,032 (31.1%) | 507 (18.5%) | |
| Smoking status | <0.001 | ||||
| Never smoker | 3,340 (83.3%) | 2,795 (75.0%) | 2,338 (70.5%) | 2,044 (74.8%) | |
| Former smoker | 439 (10.9%) | 622 (16.7%) | 635 (19.2%) | 461 (16.9%) | |
| Current smoker | 233 (5.8%) | 310 (8.3%) | 341 (10.3%) | 229 (8.4%) | |
| Hypertension | 2,046 (51.0%) | 2,248 (60.3%) | 2,230 (67.3%) | 2,017 (73.7%) | <0.001 |
| Coronary artery disease | 271 (6.8%) | 335 (9.0%) | 430 (13.0%) | 470 (17.2%) | <0.001 |
| Stroke | 69 (1.7%) | 133 (3.6%) | 159 (4.8%) | 305 (11.1%) | <0.001 |
| Diabetes | 614 (15.3%) | 736 (19.7%) | 813 (24.5%) | 844 (30.8%) | <0.001 |
| Cancer | 240 (6.0%) | 242 (6.5%) | 220 (6.6%) | 180 (6.6%) | 0.64 |
| Mean BMI (SD)/kg/m2 | 22.58 (3.29) | 22.92 (3.55) | 23.14 (3.84) | 23.35 (4.17) | <0.001 |
| Mean waist circumference (SD)/cm | 83.72 (9.86) | 86.87 (10.41) | 88.04 (11.30) | 90.16 (11.83) | <0.001 |
SD: standard deviation; TUG: timed up-and-go; BMI: body mass index.
Over the mean 2.3 year follow-up period, there were 533 deaths (3.9%) in the study population. Among these deaths, there were 158 (29.6%) CVD deaths, 217 (40.7%) cancer deaths and 158 (29.6%) deaths from other causes. We found that handgrip strength was inversely associated with risk of all-cause mortality in a dose-dependent manner (Table 2). In the overall adjusted model with TUG time (Model 3), compared to subjects in the highest quartile (strongest), subjects in the lowest quartile (weakest) had a hazard ratio (HR) [95% confidence interval (CI)] of 2.05 (1.44–2.90) (Ptrend < 0.001). There was no interaction between sex and handgrip strength, or between age group (<75 years and ≥75 years) and handgrip strength on risk of mortality (Pinteraction = 0.21). When handgrip strength was modelled as a continuous variable, the HR (95% CI) for each 5kg decrease was 1.27 (1.16–1.40) (Model 3) (Table 2). We also found that TUG test time was positively related to the risk of all-cause mortality in a stepwise manner. Compared with subjects in the lowest quartile (fastest), subjects in the highest quartile (slowest) had a HR (95% CI) of 3.08 (2.17–4.38) (Ptrend < 0.001) (Model 3) (Table 2). Similarly, there was no interaction between sex or age group with TUG test time in association with risk of mortality (Pinteraction = 0.24). When TUG time was modelled continuously, the HR (95% CI) for each SD (3.50s) increase was 1.58 (1.42–1.75) (Model 3) (Table 2).
Table 2.
Associations between sex-specific quartiles and continuous measures of handgrip strength and TUG time, with risk of all-cause mortality. Presented as hazard ratio (HR) [95% confidence interval (CI)]
| Sex-specific quartiles of handgrip strength | Ptrend | Per 5kg decrease in handgrip strength | ||||
|---|---|---|---|---|---|---|
| Q4 (Strongest) | Q3 | Q2 | Q1 (Weakest) | |||
| Person-years | 7829 | 7910 | 7809 | 7800 | 31347 | |
| All deaths | 46 | 94 | 116 | 277 | 533 | |
| Model 1† | Ref. | 1.65 (1.16–2.36) | 1.71 (1.21–2.42) | 3.40 (2.45–4.72) | <0.001 | 1.56 (1.43–1.69) |
| Model 2‡ | Ref. | 1.49 (1.03–2.13) | 1.48 (1.03–2.11) | 2.80 (1.99–3.92) | <0.001 | 1.47 (1.35–1.62) |
| Model 3§ | Ref. | 1.35 (0.94–1.94) | 1.25 (0.87–1.80) | 2.05 (1.44–2.90) | <0.001 | 1.27 (1.16–1.40) |
| Sex-specific quartiles of TUG test time | Ptrend | Per SD increase in TUG test time (SD=3.50s) | ||||
| Q1 (Fastest) | Q2 | Q3 | Q4 (Slowest) | |||
| Person-years | 9107 | 8583 | 7593 | 6065 | 31347 | |
| All deaths | 51 | 71 | 139 | 272 | 533 | |
| Model 1† | Ref. | 1.15 (0.80–1.66) | 2.12 (1.52–2.95) | 4.40 (3.18–6.09) | <0.001 | 1.77 (1.62–1.94) |
| Model 2‡ | Ref. | 1.14 (0.79–1.64) | 1.84 (1.31–2.59) | 3.79 (2.69–5.33) | <0.001 | 1.74 (1.57–1.91) |
| Model 3§ | Ref. | 1.08 (0.75–1.56) | 1.64 (1.16–2.31) | 3.08 (2.17–4.38) | <0.001 | 1.58 (1.42–1.75) |
† Model 1: adjusted for age, sex; ‡ Model 2: adjusted for Model 1 and dialect group, level of education, smoking status, hypertension, coronary artery disease, stroke, diabetes, cancer, BMI group, waist circumference; § Model 3: adjusted for Model 2 and handgrip strength, TUG test time.
These associations remained similar when analyzed by specific causes of death (Supplementary Tables S1 to S3). For CVD deaths, the HR comparing extreme quartiles of handgrip strength was 2.20 (1.15–4.19) (Ptrend = 0.035); while the HR comparing extreme quartiles of TUG time was 2.17 (1.16–4.05) (Ptrend = 0.008) (Supplementary Table S1). For cancer deaths, the HR comparing extreme quartiles of handgrip strength was 1.77 (1.06–2.95) (Ptrend = 0.047); while the HR comparing extreme quartiles of TUG time was 3.09 (1.81–5.25) (Ptrend < 0.001) (Supplementary Table S2). For all other causes of the death, the HR comparing extreme quartiles of handgrip strength was 2.44 (1.16–5.12) (Ptrend = 0.021); while the HR comparing extreme quartiles of TUG time was 4.43 (2.11–9.31) (Ptrend < 0.001) (Supplementary Table S3).
Table 3.
Joint association between handgrip strength/TUG time, with risk of short-term all-cause or specific-cause mortality. Presented as hazard ratio (HR) [95% confidence interval (CI)]
| All deaths† | TUG - Fast | TUG - Slow | Pinteraction |
|---|---|---|---|
| Grip - Strong | (N=171) | (N=85) | 0.42 |
| Ref. | 2.11 (1.57–2.83) | ||
| Grip - Weak | (N=90) | (N=187) | |
| 1.59 (1.21–2.09) | 3.93 (3.06–5.05) | ||
| CVD deaths† | TUG - Fast | TUG - Slow | Pinteraction |
| Grip - Strong | (N=45) | (N=25) | 0.62 |
| Ref. | 2.05 (1.18–3.56) | ||
| Grip - Weak | (N=27) | (N=61) | |
| 1.77 (1.07–2.94) | 4.34 (2.75–6.86) | ||
| Cancer deaths† | TUG - Fast | TUG - Slow | Pinteraction |
| Grip - Strong | (N=88) | (N=37) | 0.54 |
| Ref. | 1.93 (1.23–3.02) | ||
| Grip - Weak | (N=33) | (N=59) | |
| 1.32 (0.87–2.01) | 3.07 (2.08–4.54) | ||
| Deaths from all other causes† | TUG - Fast | TUG - Slow | Pinteraction |
| Grip - Strong | (N=38) | (N=23) | 0.90 |
| Ref. | 2.65 (1.49–4.68) | ||
| Grip - Weak | (N=30) | (N=67) | |
| 1.98 (1.19–3.30) | 4.99 (3.08–8.10) | ||
† Model 3: adjusted for age, sex, dialect group, level of education, smoking status, hypertension, coronary artery disease, stroke, diabetes, cancer, BMI group, waist circumference, handgrip strength, TUG test time.
Based on their handgrip strength and TUG time categories, participants were then categorized into four mutually exclusive groups. Compared to those who were both strong (higher handgrip) and fast (shorter TUG), those who were either weak or slow had a significant 1.59 to 2.11 fold increase in risk of all-cause mortality; while the HR in those who were both weak and slow was 3.93 (3.06–5.05) (Table 3). When analyzed by specific causes of death, those who were both weak and slow retained this significantly increased risk for: CVD deaths, with a HR of 4.34 (2.75–6.86); cancer deaths, with a HR of 3.07 (2.08–4.54); and for deaths from all other causes, with a HR of 4.99 (3.08–8.10) (Table 3). We did not find any statistically significant effect interactions between the two physical tests for all-cause or specific causes of mortality (Pinteraction = 0.42) (Table 3).
When evaluated at 0.5 years of follow-up time, the model containing sociodemographic and lifestyle factors, comorbidities, and body measurements (Model 2) had an area under the ROC curve (AUC) (95% CI) of 0.76 (0.70–0.83) for the prediction of mortality (Figure 1). The addition of handgrip strength and TUG test time to Model 2 significantly increased this predictive value (P = 0.001), to an AUC of 0.83 (0.78–0.88) (Figure 1). We further compared the AUC of these two models over two years of follow-up time (Figure 2). When compared to Model 2, the model with the physical tests maintained a significantly increased predictive value from 0.5 to two years of follow-up time (P ≤ 0.001) (Figure 2).
Figure 2.
Plot of area under the time-dependent receiver operator characteristic curve [AUC(t)] against time (t), comparing two Cox proportional hazards models over 2 years of follow-up time: (1) a model containing sociodemographic and lifestyle factors, comorbidities, and body measurements (Model 2); (2) Model 2 with the addition of handgrip strength and TUG time. Model 2: adjusted for age, sex, dialect group, level of education, smoking status, hypertension, coronary artery disease, stroke, diabetes, cancer, BMI group, waist circumference
Discussion
In this study, we showed that even after accounting for sociodemographic and lifestyle factors, comorbidities, and body measurements, both handgrip strength and TUG time remained strong and independent predictors of short-term mortality in an elderly Chinese community-dwelling population in Singapore. We showed that adding handgrip strength and TUG time to a Cox model containing comorbidities, body measurements, sociodemographic and lifestyle factors significantly improved its ability to predict short-term mortality from 0.5 to 2 years.
In our study, the risk estimates for mortality associated with handgrip strength and TUG test, whether studied as a continuous variable or as quartile categories, were stronger than most of the estimates reported by other studies with longer follow-up time (1, 2, 3, 4, 6, 8, 10, 11). These studies had follow-up periods which ranged from three years (12, 13) to 33 years (14) for handgrip strength; and they had follow-up periods which ranged from three years (15) to 15 years (7, 8) for the TUG test. In contrast, our study measured the associations over a shorter mean follow-up period of 2.3 years. The stronger risk estimates in our study with shorter follow-up compared to those in other studies with longer follow-up period suggested that the increased predictive value of the model with the physical tests could diminish over time. This is expected given that the ability to perform these physical tests could change with time. This could in turn lead to non-differential misclassification and attenuation of the risk estimates towards null. The stronger associations in our study might also have been due in part to the unique characteristics of our study population. A previous study conducted among 2,565 elderly in Singapore, with mean age of 68.8 (ranging from 60 to 105) years, found that older adults in Singapore had a relatively weak handgrip strength compared to their counterparts in the US, UK, Japan, Hong Kong, and Malaysia (16).
Studies in elderly Asian or Western populations of similar ages that evaluated handgrip strength or TUG test time together with other tests of physical performance have also found that they were independently associated with mortality. A study among a population-based cohort of 1,085 initially nondisabled older Japanese aged 65 to 89 years in Tokyo found that grip strength, walking speed, and standing balance were all independently associated with all-cause mortality (24). A cohort study of 2,529 elderly women (aged 72.6 ± 4.8 years) from the Norwegian Healthy survey also showed that grip strength and chair-rise performance were both independently associated with all-cause mortality (25).
Few studies have evaluated the prognostic value of handgrip strength or the TUG test for the prediction of mortality; and to the best of our knowledge, none have done so for short-term mortality. In the aforementioned Tokyo study, grip strength and standing balance improved the ability of a basic Cox model (containing only walking speed) to predict all-cause mortality at 10.5 years (24). In the British Birth Cohort study, 1,355 men and 1,411 women with data on physical capability at age 53 were followed up for death over 13 years, and the investigators also found that grip strength, chair rise speed and standing balance time all improved the ability of a basic Cox model (containing only sex) to predict mortality (26).
Our finding that these physical tests are strongly associated with mortality are not unexpected. Handgrip strength (27, 28), gait speed (27, 28, 29) and ability to independently rise from a chair (29), the latter two being essential components of the TUG test, are tests of muscle strength and function that have been recommended by the European Working Group on Sarcopenia in Older People (EWGSOP) (27), the Asian Working Group for Sarcopenia (AWGS)[28], and the International Working Group on Sarcopenia (IWGS)[29] for the screening and diagnosis of sarcopenia. Sarcopenia is a syndrome defined by the age-associated decline of skeletal muscle mass and function (28, 29); and all working groups for the criteria and diagnosis of sarcopenia have mandated that the definition should include low muscle function as well as low muscle mass (27, 28, 29). The development of sarcopenia in the elderly is a key precursor (29) and component (30) of frailty; and it has been shown to increase the risk of adverse health outcomes such as falls, physical disability, hospital admission, long-term care placement, poor quality of life and death (27, 28, 29, 31).
We showed that weaker handgrip strength was associated with lower BMI and lower waist circumference; while slower TUG times were associated with higher BMI and higher waist circumference. Prior studies have also shown that higher BMI (32), greater height (33), and greater weight (33) were associated with stronger grip strength; while higher BMI was associated with poorer chair rise performance, slower walking speed, and poorer standing balance (32). This is likely explained by the fact that handgrip strength is an indicator of absolute muscle strength; while the TUG test is an indicator of relative muscle strength (muscle strength per kg of body weight) (34). While a larger or obese individual might have a higher absolute strength than a smaller or non-obese individual, the larger or obese individual's relative strength might actually be lower (35). This highlights the importance of adjusting for body measurements in the analysis of physical tests like handgrip strength and TUG.
One major strength of our study, which enabled us to derive statistically significant results over a relatively short mean follow-up period of 2.3 year, was its relatively large sample size and substantial number of deaths. Linkage with the nationwide Singapore Birth and Death registry also allowed complete ascertainment of deaths in this cohort. Other strengths of this study included its prospective design, use of community-dwelling study participants and use of objective tests of physical performance. One potential limitation might be the method which we used to measure handgrip strength. A systematic review by Roberts et al. found that wide variability in the measurement and reporting of handgrip strength could affect recorded results in clinical and epidemiological studies (36), and make comparisons of handgrip strength between studies difficult (36). Nevertheless, the protocol we utilized in our study of using only the mean of the maximum values from both dominant and non-dominant hands to calculate overall handgrip strength, mostly matched that of the large Prospective Urban-Rural Epidemiology (PURE) study, a large, longitudinal population study done in 17 countries of varying incomes and sociocultural settings (3).
In conclusion, our study showed that even after accounting for sociodemographic and lifestyle factors, comorbidities, and body measurements, both handgrip strength and TUG time remained strong and independent predictors of short-term mortality in an elderly Chinese community-dwelling population in Singapore. Among elderly in the community, these simple physical tests have the potential to be strong and useful clinical markers for the prediction for short-term mortality.
Acknowledgments
We thank Siew-Hong Low of the National University of Singapore for supervising the fieldwork in the Singapore Chinese Health Study.
Author Contributions
W-P Koh designed and conducted the study, W-P Koh, X Lin and KY Chua analyzed data, W-S Lim and J-M Yuan assisted in interpreting the data, KY Chua wrote the first draft and all authors critically edited the manuscript; W-P Koh had primary responsibility for final content. All authors read and approved the final manuscript.
Funding Sources
This work was supported by the Singapore National Medical Research Council (NMRC/CSA/0055/2013) and the United States National Cancer Institute, National Institutes of Health (UM1 CA182876 and R01 CA144034), and the Saw Swee Hock School of Public Health, National University of Singapore. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Disclosure Statement
The authors declare no conflict of interest.
Statement of Ethics
The study was approved by the Institutional Review Board at the National University of Singapore and written informed consent was obtained from all study participants.
Electronic supplementary material
Supplementary material is available for this article at https://doi.org/10.1007/s12603-020-1337-0 and is accessible for authorized users.
Handgrip Strength and Timed Up-and-Go (TUG) Test are Predictors of Short-Term Mortality Among Elderly in a Population-based Cohort in Singapore
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Supplementary Materials
Handgrip Strength and Timed Up-and-Go (TUG) Test are Predictors of Short-Term Mortality Among Elderly in a Population-based Cohort in Singapore