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Muscle strength may play an important role in cardiovascular health. The study was intended to evaluate the association between cardiometabolic risk, risk of coronary artery disease and handgrip strength by using the relative handgrip strength. Data of 927 Taiwanese aged 53 years and older (510 men and 417 women) were retrieved from a nationwide representative population-based cohort cross-sectional study in 2006. All participants were interviewed face-to-face and received measures of anthropometry, dominant handgrip strength, relative handgrip strength (summation of both handgrip strength divided by body mass index) and serum biomarkers. Multivariate linear regression analysis showed the significant association between relative handgrip strength and favorable cardiometabolic risk factors including blood pressure, triglyceride, total cholesterol to high density cholesterol(HDL-C) ratio, glycohemoglobin (HbA1c), uric acid, Framingham risk score in men, and HDL-C, fasting glucose, HbA1c, log hsCRP in women. Dominant hand grip strength was only associated with log hsCRP in women. (p<0.05 for all), but was not significant associated with all cardiovascular biomarkers and FRS in both sex. Joint with handgrip strength and body size, as relative handgrip strength, may be a better tool to capture conceptual concomitant health, which may be a simple, inexpensive, and easy-to-use tool when targeting cardiovascular health in public health level.

Cognitive frailty (CF) featured as frailty plus cognitive impairment was deemed to be a novel target for dementia and disable prevention. The study was intended to investigate the epidemiology of CF and the association between CF and all-cause mortality. The national representative cohort study was comprised of 1,103 community-living middle-aged and older adults. CF was defined as the co-existence of dynapenia (weakness and/or slowness) and cognitive impairment (1.5 standard deviations below the age-, sex- and education-matched norms in cognitive tests) without known neurodegenerative diseases. Dynapenia was defined by the Asian Working Group for Sarcopenia and cognitive function was assessed by the Short Portable Mental Status Questionnaire. The prevalence of CF was 8.6% in this study. Subjects with CF were older, more likely to be women, having less regular exercise, fewer educational years, more depressive symptoms and greater multimorbidity. Compared to robust individuals, CF was significantly associated with all-cause mortality (HR: 3.1, 95% CI:1.3-7.7, p = 0.012). Dynapenia and cognitive impairment synergistically contribute to the mortality risk for the participants in this study. Further study is needed to explore the underlying pathophysiology and the reversibility of CF.

Background Frailty is the pre‐eminent exigency of aging. Although frailty‐related impairments are preventable, and multidomain interventions appear more effective than unimodal ones, the optimal components remain uncertain. Methods We devised multidomain interventions against physical and cognitive decline among prefrail/frail community‐dwelling ≥65‐year‐olds and evaluated these in complementary cluster‐randomized trials of efficacy and participant empowerment. The Efficacy Study compared ~3‐monthly telephone consultations vs. 16, 2 h sessions/year comprising communally partaken physical and cognitive training plus nutrition and disease education; the Empowerment Study compared the standard Efficacy Study multidomain intervention (Sessions 1–10) vs. an enhanced version redesigned to empower and motivate individual participants. Changes from baseline in physical, functional, and cognitive performance were measured after 6 and 12 months in the Efficacy Study and after 6 months in the Empowerment Study, with post‐intervention follow‐up at 9 months. Primary outcomes are as follows: Cardiovascular Health Study frailty score; gait speed; handgrip strength; and Montreal Cognitive Assessment (MoCA). Secondary outcomes are as follows: instrumental activities of daily living; metabolic equivalent of task (MET); depressed mood (Geriatric Depression Scale‐5 ≥2); and malnutrition (Mini‐Nutritional Assessment short‐form ≤11). Intervention effects were analyzed using a generalized linear mixed model. Results Efficacy Study participants (n = 1082, 40 clusters) were 75.1 ± 6.3 years old, 68.7% women, and 64.7% prefrail/frail; analytic clusters: 19 intervention (410/549 completed) vs. 21 control (375/533 completed). Empowerment Study participants (n = 440, 14 clusters) were 75.9 ± 7.1 years old, 83.6% women, and 56.7% prefrail/frail; analytic clusters: seven intervention (209/230 completed) vs. seven control (189/210 completed). The standard and enhanced multidomain interventions both reduced frailty and significantly improved aspects of physical, functional, and cognitive performance, especially among ≥75‐year‐olds. Standard multidomain intervention decreased depression [odds ratio 0.56, 95% confidence interval (CI) 0.32, 0.99] and malnutrition (odds ratio 0.45, 95% CI 0.26, 0.78) by 12 months and improved concentration at Months 6 (0.23, 95% CI 0.04, 0.42) and 12 (0.46, 95% CI 0.22, 0.70). Participant empowerment augmented activity (4.67 MET/h, 95% CI 1.64, 7.69) and gait speed (0.06 m/s, 95% CI 0.00, 0.11) at 6 months, with sustained improvements in delayed recall (0.63, 95% CI 0.20, 1.06) and MoCA performance (1.29, 95% CI 0.54, 2.03), and less prevalent malnutrition (odds ratio 0.39, 95% CI 0.18, 0.84), 3 months after the intervention ceased. Conclusions Pragmatic multidomain intervention can diminish physical frailty, malnutrition, and depression and enhance cognitive performance among community‐dwelling elders, especially ≥75‐year‐olds; this might supplement healthy aging policies, probably more effectively if participants are empowered.

Background Sarcopenic obesity aims to capture the risk of functional decline and cardiometabolic diseases, but its operational definition and associated clinical outcomes remain unclear. Using data from the Longitudinal Aging Study of Taipei, this study explored the roles of the muscle‐to‐fat ratio (MFR) with different definitions and its associations with clinical characteristics, functional performance, cardiometabolic risk and outcomes. Methods (1) Appendicular muscle mass divided by total body fat mass (aMFR), (2) total body muscle mass divided by total body fat mass (tMFR) and (3) relative appendicular skeletal muscle mass (RASM) were measured. Each measurement was categorized by the sex‐specific lowest quintiles for all study participants. Clinical outcomes included all‐cause mortality and fracture. Results Data from 1060 community‐dwelling older adults (mean age: 71.0 ± 4.8 years) were retrieved for the study. Overall, 196 (34.2% male participants) participants had low RASM, but none was sarcopenic. Compared with those with high aMFR, participants with low aMFR were older (72 ± 5.6 vs. 70.7 ± 4.6 years, P = 0.005); used more medications (2.9 ± 3.3 vs. 2.1 ± 2.5, P = 0.002); had a higher body fat percentage (38 ± 4.8% vs. 28 ± 6.4%, P < 0.001), RASM (6.7 ± 1.0 vs. 6.5 ± 1.1 kg/m2, P = 0.001), and cardiometabolic risk [fasting glucose: 105 ± 27.5 vs. 96.8 ± 18.7 mg/dL, P < 0.001; glycated haemoglobin (HbA1c): 6.0 ± 0.8 vs. 5.8 ± 0.6%, P < 0.001; triglyceride: 122.5 ± 56.9 vs. 108.6 ± 67.5 mg/dL, P < 0.001; high‐density lipoprotein cholesterol (HDL‐C): 56.2 ± 14.6 vs. 59.8 ± 16 mg/dL, P = 0.010]; and had worse functional performance [Montreal Cognitive Assessment (MoCA): 25.7 ± 4.2 vs. 26.4 ± 3.0, P = 0.143, handgrip strength: 24.7 ± 6.7 vs. 26.1 ± 7.9 kg, P = 0.047; gait speed: 1.8 ± 0.6 vs. 1.9 ± 0.6 m/s, P < 0.001]. Multivariate linear regression showed that age (β = 0.093, P = 0.001), body mass index (β = 0.151, P = 0.046), total percentage of body fat (β = 0.579, P < 0001) and RASM (β = 0.181, P = 0.016) were associated with low aMFR. Compared with those with high tMFR, participants with low tMFR were older (71.7 ± 5.5 vs. 70.8 ± 4.7 years, P = 0.075); used more medications (2.8 ± 3.3 vs. 2.1 ± 2.5, P = 0.006); had a higher body fat percentage (38.1 ± 4.7 vs. 28 ± 6.3%, P < 0.001), RASM (6.8 ± 1.0 vs. 6.5 ± 1.1 kg/m2, P < 0.001), and cardiometabolic risk (fasting glucose: 104.8 ± 27.6 vs. 96.9 ± 18.7 mg/dL, P < 0.001; HbA1c: 6.1 ± 0.9 vs. 5.8 ± 0.6%, P < 0.001; triglyceride: 121.4 ± 55.5 vs. 108.8 ± 67.8 mg/dL, P < 0.001; HDL‐C: 56.4 ± 14.9 vs. 59.7 ± 15.9 mg/dL, P = 0.021); and had worse functional performance (MoCA: 25.6 ± 4.2 vs. 26.5 ± 3.0, P = 0.056; handgrip strength: 24.6 ± 6.7 vs. 26.2 ± 7.9 kg, P = 0.017; gait speed: 1.8 ± 0.6 vs. 1.9 ± 0.6 m/s, P < 0.001). Low tMFR was associated with body fat percentage (β = 0.766, P < 0.001), RASM (β = 0.476, P < 0.001) and Mini‐Nutritional Assessment (β = −0.119, P < 0.001). Gait speed, MoCA score, fasting glucose, HbA1c and tMFR were significantly associated with adverse outcomes, and the effects of aMFR were marginal (P = 0.074). Conclusions Older adults identified with low MFR had unfavourable body composition, poor functional performance, high cardiometabolic risk and a high risk for the clinical outcome.

Association of frailty with adverse clinical outcomes has been reported in Western countries, but data from the Asian population are scarce. This study aimed to evaluate the epidemiology of frailty among community-dwelling middle-aged and elderly population and to explore its association with musculoskeletal health in Taiwan. I-Lan Longitudinal Aging Study (ILAS) data were retrieved for this study. Frailty was defined by the Fried's criteria; a comparison of demographic characteristics, physical performance, and body composition, including skeletal muscle mass and bone mineral density (BMD), as well as recent falls, history of hip fractures and the functional status of subjects with different frailty statuses were accomplished. Overall, the data of 1,839 participants (mean age: 63.9±9.3 years, male 47.5%) were obtained for analysis. The prevalence of pre-frailty was 42.3% in men and 38.8% in women, whereas the prevalence of frailty was 6.9% and 6.7% in men and women, respectively. Frailty was significantly associated with older age, the male gender, larger waist circumference, lower skeletal muscle index, lower hip BMD, poorer physical function, poorer nutritional status, and poorer cognitive function. Also, frailty was significantly associated with osteoporosis (OR: 7.73, 95% CI: 5.01-11.90, p<0.001), history of hip fractures (OR: 8.66, 95% CI: 2.47-30.40, p = 0.001), and recent falls (O.R: 2.53, 95% CI: 1.35-4.76, p = 0.004). Frailty and pre-frailty, in Taiwan, was closely associated with recent falls, history of hip fractures and osteoporosis among community-dwelling people 50 years of age and older. Furthermore, frailty intervention programs should take an integrated approach towards strengthening both and muscle mass, as well as prevention of falls.

The utilization of benzodiazepines (BZDs) and z-hypnotics has substantially increased with the aging of the population, but the risk of BZDs and z-hypnotics in the development of dementia remains a strong concern. This cohort study aimed to evaluate the risk of BZDs and z-hypnotics for subsequent dementia development with a special consideration of their half-lives and the concomitant use of these medications. People aged 65 years and older who were newly prescribed oral BZDs or z-hypnotics between 2003 and 2012 were identified from Taiwan's National Health Insurance Research Database. All BZDs were categorized as long-acting drugs (≥ 20 h) or short-acting drugs (< 20 h) for further comparisons, and data were collected on a quarterly basis, starting on the first date of drug prescription and ending on the date of death, occurrence of dementia, or end of the follow-up period (December 31, 2012), whichever came first. All dementia events except vascular dementia occurring during the follow-up period were identified. Among 260,502 eligible subjects, short-acting BZDs and z-hypnotics users were at greater risk of dementia than long-acting users [adjusted odds ratio (95% confidence interval) in short-acting BZD users, 1.98 (1.89–2.07); z-hypnotic users, 1.79 (1.68–1.91); and long-acting BZD users, 1.47 (1.37–1.58)]. In addition, subjects concomitantly using 2 or more BZDs or z-hypnotics had a higher risk of dementia than those who used 1 of these drugs (4.79 (3.95–5.81)). The use of BZDs and z-hypnotics was strongly associated with the risk of dementia development, especially the short-acting BZDs, z-hypnotics, and concomitant use of multiple agents. These findings deserve further interventional studies for clarification.

Frailty is common among older people and results in adverse health outcomes. We investigated whether exposure to PM 2.5 is associated with frailty. This cross-sectional study involved 20,606 community-dwelling participants aged ≥ 65 years, residing in New Taipei City, Taiwan. Analytic data included phenotypic frailty, disease burden by Charlson Comorbidity Index (CCI), urban or rural residence, and household income. PM 2.5 exposure was calculated from air quality monitoring records, with low exposure defined as the lowest quartile of the study population. 1,080 frail participants (5.2%) were older, predominantly female, had more comorbidities, lived rurally, and had low PM 2.5 exposure (all p  < 0.001). In multinomial logistic regression analyses, the likelihood of high PM 2.5 exposure was higher in prefrail (OR 1.4, 95% CI 1.3–1.5) and frail adults (OR 1.5, 95% CI 1.2–1.9) than in robust individuals, with stronger associations in those who were male (frail: OR 2.1, 95% CI 1.5–3.1; prefrail: OR 2.2, 95% CI 1.9–2.6), ≥ 75 years old (frail: OR 1.8, 95% CI 1.3–2.4; prefrail: OR 1.5, 95% CI 1.3–1.8), non-smokers (frail: OR 1.6, 95% CI 1.3–2.0; prefrail: OR 1.4, 95% CI 1.2–1.5), had CCI ≥ 2 (frail: OR 5.1, 95% CI 2.1–12.6; prefrail: OR 2.1, 95% CI 1.2–3.8), and with low household income (frail: OR 4.0, 95% CI 2.8–5.8; prefrail: OR 2.7, 95% CI 2.2–3.3). This study revealed a significant association between PM 2.5 exposure and frailty, with a stronger effect in vulnerable groups.

Background Myostatin is a negative regulator of muscle growth but the relationship between serum myostatin levels and muscle mass is unclear. This study investigated the association between serum myostatin levels and skeletal muscle mass among healthy older community residents in Taiwan, to evaluate the potential of serum myostatin as a biomarker for diagnosing sarcopenia and/or evaluating the effect of its treatment. Methods Study data were excerpted from a random subsample of the I‐Lan Longitudinal Aging Study population. Serum myostatin levels were determined and categorized into tertiles (low, medium, high). Relative appendicular skeletal muscle mass (RASM) was calculated as appendicular lean body mass by dual‐energy X‐ray absorptiometry divided by height squared (kg/m2). Low muscle mass was defined as recommended by the Asian Working Group for Sarcopenia. Results The analytic study sample comprised 463 adults (mean age: 69.1 years; 49.5% men). Compared with subjects with normal RASM, those with lower RASM were older and frailer, with significantly higher prevalence of malnutrition, lower serum dehydroepiandrosterone (DHEA) levels, and were more likely to have low serum myostatin status. Multivariable logistic regression analysis showed that male sex (OR 3.60, 95% CI 1.30–9.92), malnutrition (OR 4.39, 95% CI 1.56–12.36), DHEA (OR 0.99, 95% CI 0.99–1.00), and low myostatin (OR 3.23, 95% CI 1.49–7.01) were all independent risk factors for low RASM (all P < 0.05). In men, DHEA (OR 0.99, 95% CI 0.98–1.00) and low myostatin (OR 4.89, 95% CI 1.79–13.37) were significantly associated with low RASM (both P < 0.05); however, only malnutrition was associated with low RASM in women (OR 13.59, 95% CI 2.22–83.25, P < 0.05). Conclusions Among healthy community‐living older adults, low serum myostatin levels were associated with low skeletal muscle mass in men, but not in women. Our results do not support using serum myostatin levels to diagnose sarcopenia, or to monitor how it responds to treatments. Further research is needed to understand why men apparently differ from women in the interrelationship between their myostatin levels and muscle mass.

Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia. This study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident dementia using a novel approach incorporating machine learning methods. Using Taiwan's National Health Insurance Research Database, data from 15,700 older people with dementia and 15,700 nondementia controls matched on age, sex, and index year (n=10,466, 67% for the training data set and n=5234, 33% for the testing data set) were retrieved for analysis. Using machine learning methods to capture specific hierarchical disease triplet clusters prior to dementia, we designed a study algorithm with four steps: (1) data preprocessing, (2) disease or symptom pathway selection, (3) model construction and optimization, and (4) data visualization. Among 15,700 identified older people with dementia, 10,466 and 5234 subjects were randomly assigned to the training and testing data sets, and 6215 hierarchical disease triplet clusters with positive correlations with dementia onset were identified. We subsequently generated 19,438 features to construct prediction models, and the model with the best performance was support vector machine (SVM) with the by-group LASSO (least absolute shrinkage and selection operator) regression method (total corresponding features=2513; accuracy=0.615; sensitivity=0.607; specificity=0.622; positive predictive value=0.612; negative predictive value=0.619; area under the curve=0.639). In total, this study captured 49 hierarchical disease triplet clusters related to dementia development, and the most characteristic patterns leading to incident dementia started with cardiovascular conditions (mainly hypertension), cerebrovascular disease, mobility disorders, or infections, followed by neuropsychiatric conditions. Dementia development in the real world is an intricate process involving various diseases or conditions, their co-occurrence, and sequential relationships. Using a machine learning approach, we identified 49 hierarchical disease triplet clusters with leading roles (cardio- or cerebrovascular disease) and supporting roles (mental conditions, locomotion difficulties, infections, and nonspecific neurological conditions) in dementia development. Further studies using data from other countries are needed to validate the prediction algorithms for dementia development, allowing the development of comprehensive strategies to prevent or care for dementia in the real world.

Background Components of physical frailty cluster into subtypes, but it remains unknown how these might be associated with age‐related functional declines and multimorbidities. This study aims to investigated associations of physical frailty subtypes with functional declines and multimorbidity in a 10 year longitudinal cohort survey. Methods Complementary longitudinal cohort study used group‐based multitrajectory modelling to verify whether frailty subtypes discovered in Taiwan are presented in another aging cohort, then investigated associations of these subtypes with cognitive decline and multimorbidity. Participants aged ≥50 years were recruited from the third to sixth waves (May 2002 to July 2010) of the National Institute for Longevity Sciences‐Longitudinal Study of Aging, in Japan. People with incomplete data, pre‐frail/frail status before their index wave, and those with incomplete data or who died during follow‐up, were excluded. Group‐based trajectory analysis denoted five established physical frailty criteria as time‐varying binary variables in each wave during follow‐up. Incident frailty was classified as mobility subtype (weakness/slowness), non‐mobility subtype (weight loss/exhaustion), or low physical activity subtype. General linear modelling investigated associations of these frailty subtypes with activities of daily living, digit symbol substitution test (DSST) and Charlson Comorbidity Index (CCI) at 2 year follow‐up. Results We identified four longitudinal trajectories of physical frailty, which corroborated the distinct subtypes we discovered previously. Among 940 eligible participants, 38.0% were robust, 18.4% had mobility subtype frailty, 20.7% non‐mobility subtype, and 20.1% low physical activity subtype. People with mobility subtype frailty were older than those with other frailty subtypes or robust status and had higher prevalence of hypertension, diabetes, and heart failure. In the multivariable‐adjusted general linear models, mobility‐subtype frailty was associated with a significantly lower DSST score (point estimate −2.28, P = 0.03) and higher CCI (point estimate 0.82, P < 0.01) than the other groups. Conclusions Mobility‐subtype frailty was associated with functional declines and progression of multimorbidity; the long‐term effects of physical frailty subtypes deserve further investigation.