Handgrip Strength and Timed Up-and-Go (TUG) Test are Predictors of Short-Term Mortality among Elderly in a Population-Based Cohort in Singapore

Chua, Kevin Yiqiang; Lim, Wee Shiong; Lin, Xinyi; Yuan, Jian‐Min; Koh, Woon‐Puay

doi:10.1007/s12603-020-1337-0

Cited by 46 publications

(36 citation statements)

References 33 publications

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“…In a most recent study, longer TUG with or without low HGS was associated with increased short-term mortality. 35 Similar to other studies, our MetS group had significantly lower MMSE scores and a higher prevalence of cognitive impairment. 36 Persons with MetS are more likely to have vascular dementia related to hypertension, elevated fasting glucose, and hypertriglyceridemia.…”

Section: Discussionsupporting

confidence: 90%

Prevalence of Metabolic Syndrome and Association with Grip Strength in Older Adults: Findings from the HOPE Study

Merchant

Chan

Lim

et al. 2020

DMSO

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Objective: To determine the prevalence of metabolic syndrome (MetS) in older adults and assess the association of MetS and adverse outcomes with handgrip strength (HGS), HGS/ body weight (BWT), and HGS/body mass index (BMI). Methods: A cross-sectional population study in Singapore. Data were collected on demographics, HGS, Timed-Up and Go (TUG), fasting glucose, lipid profile, blood pressure, waist circumference, frailty status, and cognition in 722 older adults ≥65 years old. MetS was defined using the Modified ATP III for Asians where at least three of the following conditions must be fulfilled, central obesity, high blood glucose (or diagnosed diabetes mellitus), high blood pressure (or diagnosed hypertension), low high-density lipoprotein, and high triglycerides. The waist circumference in the Modified ATP III for Asians is ≥90 cm for males or ≥80 cm for females. HGS and HGS normalized by BWT or BMI were used for the association. Results: The prevalence of MetS in older adults was 41.0%, and those ≥85 years old 50.0%. The prevalence was higher in females ≥70 years old, with 8 in 10 females ≥85 years having MetS. After adjusting for age, years of education, physical exercise, as well as history of smoking and alcohol consumption, higher HGS normalized by BWT or BMI was significantly associated with lower odds of having MetS (OR: 0.51,95% CI 0.43-0.61, p<0.01) and (OR: 0.13, 95% CI 0.07-0.24, p<0.01). Conclusion: Almost 1 in 2 older adults had MetS, with the prevalence in females much higher than that in males over 70 years old. Our findings suggest that both HGS/BWT and HGS/BMI had a significant negative association with MetS, its components, and adverse effects. Further studies are needed to validate the association and to determine optimal cutoffs of HGS/BWT and HGS/BMI for MetS, and the effectiveness of interventions in averting the risk.

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Section: Discussionsupporting

confidence: 90%

Prevalence of Metabolic Syndrome and Association with Grip Strength in Older Adults: Findings from the HOPE Study

Merchant

Chan

Lim

et al. 2020

DMSO

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“…Moreover, the TUG test can be considered, in a sense, a global measure of body function. Poor performance has been associated with increased cardiovascular disease and mortality as well as all-cause mortality in older adults [ 53 - 55 ] and in patients with chronic kidney disease [ 56 ]. Additional predictors beyond the TUG score selected in the decision trees as informative are also linked with poor cognitive performance, including a measure of kidney function, GFR.…”

Section: Discussionmentioning

confidence: 99%

Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study

Rankin¹,

Black²,

Flanagan³

et al. 2020

JMIR Med Inform

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Background Machine learning techniques, specifically classification algorithms, may be effective to help understand key health, nutritional, and environmental factors associated with cognitive function in aging populations. Objective This study aims to use classification techniques to identify the key patient predictors that are considered most important in the classification of poorer cognitive performance, which is an early risk factor for dementia. Methods Data were used from the Trinity-Ulster and Department of Agriculture study, which included detailed information on sociodemographic, clinical, biochemical, nutritional, and lifestyle factors in 5186 older adults recruited from the Republic of Ireland and Northern Ireland, a proportion of whom (987/5186, 19.03%) were followed up 5-7 years later for reassessment. Cognitive function at both time points was assessed using a battery of tests, including the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), with a score <70 classed as poorer cognitive performance. This study trained 3 classifiers—decision trees, Naïve Bayes, and random forests—to classify the RBANS score and to identify key health, nutritional, and environmental predictors of cognitive performance and cognitive decline over the follow-up period. It assessed their performance, taking note of the variables that were deemed important for the optimized classifiers for their computational diagnostics. Results In the classification of a low RBANS score (<70), our models performed well (F1 score range 0.73-0.93), all highlighting the individual’s score from the Timed Up and Go (TUG) test, the age at which the participant stopped education, and whether or not the participant’s family reported memory concerns to be of key importance. The classification models performed well in classifying a greater rate of decline in the RBANS score (F1 score range 0.66-0.85), also indicating the TUG score to be of key importance, followed by blood indicators: plasma homocysteine, vitamin B6 biomarker (plasma pyridoxal-5-phosphate), and glycated hemoglobin. Conclusions The results suggest that it may be possible for a health care professional to make an initial evaluation, with a high level of confidence, of the potential for cognitive dysfunction using only a few short, noninvasive questions, thus providing a quick, efficient, and noninvasive way to help them decide whether or not a patient requires a full cognitive evaluation. This approach has the potential benefits of making time and cost savings for health service providers and avoiding stress created through unnecessary cognitive assessments in low-risk patients.

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“…Moreover, the TUG test can be considered, in a sense, a global measure of body function. Poor performance has been associated with increased cardiovascular disease and mortality as well as all-cause mortality in older adults [53][54][55] and in patients with chronic kidney disease [56]. Additional predictors beyond the TUG score selected in the decision trees as informative are also linked with poor cognitive performance, including a measure of kidney function, GFR.…”

Section: Principal Findingsmentioning

confidence: 99%

Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study (Preprint)

Rankin¹,

Black²,

Flanagan³

et al. 2020

Preprint

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BACKGROUND Machine learning techniques, specifically classification algorithms, may be effective to help understand key health, nutritional, and environmental factors associated with cognitive function in aging populations. OBJECTIVE This study aims to use classification techniques to identify the key patient predictors that are considered most important in the classification of poorer cognitive performance, which is an early risk factor for dementia. METHODS Data were used from the Trinity-Ulster and Department of Agriculture study, which included detailed information on sociodemographic, clinical, biochemical, nutritional, and lifestyle factors in 5186 older adults recruited from the Republic of Ireland and Northern Ireland, a proportion of whom (987/5186, 19.03%) were followed up 5-7 years later for reassessment. Cognitive function at both time points was assessed using a battery of tests, including the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), with a score <70 classed as poorer cognitive performance. This study trained 3 classifiers—decision trees, Naïve Bayes, and random forests—to classify the RBANS score and to identify key health, nutritional, and environmental predictors of cognitive performance and cognitive decline over the follow-up period. It assessed their performance, taking note of the variables that were deemed important for the optimized classifiers for their computational diagnostics. RESULTS In the classification of a low RBANS score (<70), our models performed well (F1 score range 0.73-0.93), all highlighting the individual’s score from the Timed Up and Go (TUG) test, the age at which the participant stopped education, and whether or not the participant’s family reported memory concerns to be of key importance. The classification models performed well in classifying a greater rate of decline in the RBANS score (F1 score range 0.66-0.85), also indicating the TUG score to be of key importance, followed by blood indicators: plasma homocysteine, vitamin B6 biomarker (plasma pyridoxal-5-phosphate), and glycated hemoglobin. CONCLUSIONS The results suggest that it may be possible for a health care professional to make an initial evaluation, with a high level of confidence, of the potential for cognitive dysfunction using only a few short, noninvasive questions, thus providing a quick, efficient, and noninvasive way to help them decide whether or not a patient requires a full cognitive evaluation. This approach has the potential benefits of making time and cost savings for health service providers and avoiding stress created through unnecessary cognitive assessments in low-risk patients.

show abstract

Handgrip Strength and Timed Up-and-Go (TUG) Test are Predictors of Short-Term Mortality among Elderly in a Population-Based Cohort in Singapore

Cited by 46 publications

References 33 publications

<p>Prevalence of Metabolic Syndrome and Association with Grip Strength in Older Adults: Findings from the HOPE Study</p>

<p>Prevalence of Metabolic Syndrome and Association with Grip Strength in Older Adults: Findings from the HOPE Study</p>

Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study

Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study (Preprint)

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