A decline in physical and/or cognitive function is a common feature of aging, and frailty has been shown to be associated with cognitive impairment and dementia. This study aimed to evaluate the association between dynapenia, sarcopenia, and cognitive impairment among community-dwelling older people in Taiwan. Data from the I-Lan Longitudinal Aging Study (ILAS) were retrieved for study. Global cognitive function was assessed by Mini-Mental State Examination (MMSE), whereas the Chinese Version Verbal Learning Test, Boston Naming Test, Verbal Fluency Test, Taylor Complex Figure Test, Digits Backward Test, and Clock Drawing Test were used to assess different domains of cognitive function. Association between sarcopenia and global cognitive function as well as all different dimensions of cognitive function were evaluated. Data from 731 elderly participants (mean age 73.4 ± 5.4 years, 53.8% males) were used for study analysis. The overall prevalence of sarcopenia was 6.8%, which was significantly higher in men (9.3% versus 4.1%, p < 0.05). The mean MMSE score was 23.4 ± 4.4 for all participants, and 10.3% of the study participants were cognitively impaired. Sarcopenia was not significantly associated with global cognitive function (odds ratio [OR] = 1.55, p = 0.317), but global cognitive impairment was significantly associated with low physical performance (OR = 2.31, p = 0.003) and low muscle strength (OR = 2.59, p = 0.011). Nonetheless, sarcopenia was significantly associated with impairment in the verbal fluency test (OR = 3.96, p = 0.006) after adjustment for potential confounders. Dynapenia was significantly associated with cognitive impairment in multiple dimensions and global cognitive function, but sarcopenia was only associated with an impaired verbal fluency test. Reduced muscle strength and/or physical performance related to non-muscle etiology were strongly associated with cognitive impairment. More longitudinal studies are needed.

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.

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.

Objectives Our aim was to explore the patterns of intrinsic capacity (IC) impairments among community-dwelling older adults and the associations of these different patterns with excessive polypharmacy, potentially inappropriate medications, and adverse drug reactions in a nationwide population-based study. Design A cross-sectional study included older adults from the Taiwan Integrated Care for Older People (ICOPE) program in 2020. Setting and Participants The study subjects comprised 38,308 adults aged 65 years and older who participated in the ICOPE Step 1 screening and assessed six domains of IC following the World Health Organization (WHO) ICOPE approach. Methods Latent class analysis was adopted to identify distinct subgroups with different IC impairments patterns. The associations between different IC impairments patterns and unfavorable medication utilization, including excess polypharmacy (EPP), potentially inappropriate medications (PIMs), and adverse drug reactions (ADRs), were assessed by multivariate logistic regression models. Results Latent class analysis identified five distinct subgroups with different IC impairment patterns: robust (latent class prevalence: 59.4%), visual impairment (17.7%), physio-cognitive decline (PCD) with sensory impairment (12.3%), depression with cognitive impairment (7.7%), and impairments in all domains (2.9%). Compared to the robust group, all other groups were at higher odds for unfavorable medication utilization. The “depression with cognitive impairment” group (EPP: aOR=4.35, 95% CI 3.52–5.39, p<0.01; PIMs: aOR=2.73, 95% CI 2.46–3.02, p<0.01) and the “impairment in all domains” group (EPP: aOR=9.02, 95% CI 7.16–11.37, p<0.01; PIMs: aOR=3.75, 95% CI 3.24–4.34, p<0.01) remained at higher odds for EPP and PIMs after adjustment. Conclusions We identified five distinct impairment patterns of IC, and each impairment pattern, particularly the “depression with cognitive impairment” and “impairment in all domains”, was associated with higher odds of EPP and PIMs. Further longitudinal and intervention studies are needed to explore long-term outcomes of different impairment pattern and their reversibility.