Journal of Frailty & Aging最新文献

Frailty, a biologic syndrome of decreased reserve and resistance to stressors, affects 5 % to 17 % of older adults and is linked to factors like low BMI, female sex, and low exercise levels. With India's older population expected to double by 2050, frailty presents major public health and economic challenges. This study summarizes the prevalence of frailty among community-dwelling Indians. This systematic review and meta-analysis followed PRISMA guidelines to determine the prevalence of frailty among adults in India. We conducted a comprehensive search across multiple databases, including PubMed, Scopus, EMBASE, and Web of Science, up to January 16, 2024, excluding hospital-based studies and reviews. Data were analyzed using STATA software with a random-effects model, and quality was assessed using the JBI Critical Appraisal Checklist. The meta-analysis revealed a pooled frailty prevalence of 36 % (95 % CI: 29 % to 44 %) among 330,007 community-dwelling adults in India, with significant heterogeneity across studies (I² = 99.95 %). Frailty prevalence varied by assessment method, with 48 % using the frailty index and 31 % using the Fried phenotype. Subgroup analyses indicated significant variability in frailty prevalence by gender, data source, and assessment tool, with no significant publication bias detected. This meta-analysis found a pooled frailty prevalence of 36 % and pre-frailty prevalence of 48 % among adults in India, with higher frailty in women (45 %) than men (35 %) and variation across assessment tools. Future research should focus on longitudinal studies and developing tailored frailty assessment tools.

The objective of this review was to identify evidence-based, community-based physical activity (PA) and nutrition-based programs aimed at facilitating health behaviour change among low-income older adults and/or those living in rural/remote areas. This review followed the scoping review methodology proposed by Arksey & O'Malley. The Michie behaviour change wheel was used to categorize intervention types. Of the 2954 retrieved citations, 25 articles met the inclusion criteria. All study interventions demonstrated positive outcomes, including improvements in fruit and vegetable consumption, PA levels, physical function and nutrition knowledge. Study findings highlight that PA and nutrition-based interventions can be effective to facilitate behavior change in low-income and/or rural older adults. Limited research exists looking specifically at older adults living in rural communities, with only two of the 25 included articles including rural study populations.

Background: Intrinsic capacity (IC), representing an individual's full range of physical and mental abilities, is influenced by objective socioeconomic status (SES); however, the impact of subjective SES remains unclear.

Objectives: This study aims to assess IC and investigate the relationship between SES and IC deficits, with a particular focus on the role of subjective SES.

Design: Cross-sectional study SETTING: 45 communities in two provinces in China PARTICIPANTS: Community-dwelling middle-aged and older adults aged 50 and above MEASUREMENTS: IC was assessed following the Integrated Care for Older People guideline. SES was measured through objective SES (education and occupation) and subjective SES (measured by MacArthur Scale). Ordinal logistic regression models were performed to estimate the association between SES and IC.

Results: Among 3,058 participants (61.3 ± 8.05 years, 54.8 % women), 2,333 (76.3 %) showed deficits in at least one IC subdomain, particularly sensory (63.5 %), vitality (25.8 %) and cognition (18.4 %). A dose-response association was observed between SES and IC deficits. Individuals with high subjective SES (OR: 0.72, 0.60-0.87), high education (OR: 0.54, 0.38-0.75), and high occupation (OR: 0.64 0.50-0.81) exhibited lower IC deficits risk compared with counterparts. Individuals with high education and middle subjective SES or high occupation and middle subjective SES had 67 % (OR: 0.33, 0.18-0.60) and 49 % (OR: 0.51, 0.35-0.74) lower risk than those with low SES.

Conclusions: These findings suggest that individuals with low SES may be more vulnerable to IC deficits. Addressing social inequalities in the early assessment of IC is crucial for reducing health disparities and promoting healthy ageing.

Background: The concept of 'cognitive frailty' (CF) was first developed by an international consensus group in 2013 and defined as evidence of both physical frailty and cognitive impairment without a clinical diagnosis of AD or another dementia. CF has been associated with adverse health outcomes and early identification is vital. Difficulty in the assessment of CF however is the lack of a diagnostic gold standard.

Objectives: This review aimed to identify assessment tools used to diagnose cognitive impairment in the diagnosis of cognitive frailty, their psychometric qualities and clinical utility.

Research design and methods: Six databases were searched between 2013-2024. Studies were eligible if they reported a method of defining cognitive frailty, named the assessment tools, and stated cutoff values used to define cognitive impairment.

Results: In the 116 included studies, large heterogeneity was found in the tools utilised, and cutoff scores applied, to diagnose cognitive impairment in the diagnosis of cognitive frailty. This review has demonstrated that diagnosis of CF relies predominantly on the use of three cognitive assessment tools (Mini Mental State Examination, Montreal Cognitive Assessment, Clinical Dementia Rating) from a total of 22 different tools identified in the literature. For assessment of physical frailty, 11 different tools were identified, with the Fried Frailty Index and FRAIL Scale predominantly utilised.

Discussion and implications: The variation in the tools used to identify the diagnosis of CF means there is inconsistency in reporting, potentially impacting both the understanding of the prevalence, and the appropriate direction of intervention strategies.

Background: As the World Trade Center (WTC) General Responder Cohort ages, it is imperative to study their aging process and identify factors that can be targeted for interventions.

Objectives: Our goal was to utilize a previously developed WTC Clinical Frailty Index (CFI) to identify distinct frailty trajectories and associated factors in this cohort.

Design: A latent class mixed model evaluated frailty trajectories using WTC CFIs. Multinomial regression models were used to assess associations between frailty trajectory and sociodemographic and WTC characteristics.

Setting: We utilized data collected during routine WTC monitoring visits from 2004 until 2021.

Participants: The participants were WTC general responders.

Measurements: Relative risk ratios (RRR) assessed associations with a 95 % confidence interval (CI).

Results: Three distinct linear frailty trajectories were identified: high CFI (indicating higher frailty), low CFI, and progressively increasing CFI. Compared with the low CFI group, females were more likely to be in the high CFI and progressively increasing CFI groups (RRR 1.66, 95 %CI 1.46, 1.90; RRR 1.32, 95 %CI 1.15, 1.53, respectively). Education beyond high school and elevated income were protective against high CFI and progressively increasing CFI groups. Individuals that self-identified as Hispanic had an elevated RRR for the high CFI group (RRR 1.17, 95 %CI 1.04, 1.31). Occupation on 9/11, such as construction and maintenance and repair, as well as high/very high WTC exposure were significantly associated with both the high CFI and progressively increasing CFI groups.

Conclusions: Several sociodemographic and WTC variables were associated with more hazardous frailty trajectories in WTC general responders. This work is beneficial to informing and directing future interventions for those at higher-risk for more hazardous frailty progression.

Background: Electronic health record (EHR) has been in place in many parts of the world. This fits in very well to the frailty index calculation proposed by Rockwood and thus a frailty index can potentially be generated automatically from an EHR database. Therefore, the Hong Kong Hospital Authority (HA) attempted to develop an electronic frailty index (HK eFI), by employing thirty-eight health variables from her own EHR database.

Methods: Five cohorts of patients aged 60 years or above ever attended any services provided by the Hong Kong HA in the year 2015, 2016, 2017, 2018 and 2019, were included. The HK eFI trajectory with ageing, generated by the five cohorts, were compared to the one described by Rockwood's group. Following the UK eFI method, 4 levels of frailty were categorized, and they were examined whether they were related to mortality, readmission rate and hospitalization patient days.

Results: Each successive cohort consisted of increasing number of patients aged 60 years or above. (2015, 1.14 million; 2016, 1.19 million; 2017,1.25 million; 2018, 1.31 million; 2019, 1.38 million). The gradients of the five trajectories ranged from 0.035 to 0.037, representing an increase in FI approximately 3.6 % annually. The intercept of the curves converged at 0.1, representing the FI at age 60 years was 0.1. Compared to the fit group, the adjusted hazard ratios of mortality of the mild, moderate and severe frail group were 1.77, 3.31 and 6.65 respectively and they were all statistically higher than the fit group. (p < 0.005) Likewise, there was a stepwise increase in readmission rate and hospital patient days utilization with increasing frailty levels.

Conclusion: It is feasible to develop an eFI and a biological age trajectory from a large EHR database with local adaptation.

Background: The older population is increasingly reliant on social care, especially those who are frail. However, an estimated 1.5 million people over 65 in England have unmet care needs. The relationship between receiving care, or receiving insufficient care, and changes in frailty status remains unclear.

Objectives: To investigate the associations between care receipt (paid or unpaid), unmet care needs, frailty status, and mortality.

Design: We used multistate models to estimate the risk of increasing or decreasing levels of frailty, using English Longitudinal Study of Ageing (ELSA) data. Covariates included age, gender, wealth, area deprivation, education, and marital status. Care status was assessed through received care and self-reported unmet care needs, while frailty status was determined using a frailty index.

Participants: 15,003 individuals aged 50+, using data collected over 18 years (2002-2019).

Results: Individuals who receive care are more susceptible to frailty and are less likely to recover from frailty to a less frail state. The hazard ratio of males receiving care transitioning from prefrailty to frailty was 2.1 [95 % CI: 1.7-2.6] and for females 1.8 [1.5-2.0]. Wealth is an equally influential predictor of changes in frailty status: individuals in the lowest wealth quintile who do not receive care are as likely to become frail as those in the highest wealth quintile who do receive care. As individuals receiving care (including unpaid care) are likely to be in poorer health than those who do not receive care, this highlights stark inequalities in the risk of frailty between the richest and poorest individuals. Unmet care needs were associated with transitioning from prefrailty to frailty for males (hazard ratio: 1.7 [1.2-2.4]) but not for females.

Conclusions: Individuals starting to receive care (paid or unpaid) and people in the poorest wealth quintile are target groups for interventions aimed at delaying the onset of frailty.

Background: Age-related morbidity, including frailty and cardiometabolic disease has become increasingly prevalent among people living with HIV (PWH), and each frailty characteristic may, independently and synergistically, play a role in cardiometabolic disease.

Objective: To evaluate the prevalence of unique frailty clusters and the prevalence ratios of cardiometabolic diseases within frailty clusters among a large diverse cohort of PWH in clinical care.

Design: Cross-sectional analyses within longitudinal clinical cohort.

Setting: The Center for AIDS Research Network of Integrated Clinical Systems (CNICS) from 8 Clinics PARTICIPANTS: 4,856 PWH, mean age 61 years. 16 % frail, 45 % pre-frail, 40 % robust.

Measurements: The validated, modified Fried Phenotype from patient-reported outcomes and clustering (15 clusters) of the frailty characteristics and cardiometabolic diseases (7 diseases and multimorbidity) within each cluster.

Results: Among 4856 PWH (age: 61 ± 6 years), the prevalence of frail, pre-frail, and robust was 16 %, 45 %, and 40 %, respectively. The most prevalent cardiometabolic disease among frail PWH was hypertension (62.6 %), followed by dyslipidemia (58.8 %) and diabetes (31.4 %). Among pre-frail PWH, the most prevalent cardiometabolic diseases were dyslipidemia (65.8 %), hypertension (61.8 %), and obesity (30.5 %). The prevalence of cardiometabolic disease among frailty clusters varied. For example, PWH in the "fatigue + poor mobility" cluster had a greater prevalence of cerebrovascular disease (PR: 2.23; 95 % CI: 1.01-4.91), diabetes (1.76; 95 % CI: 1.41-2.21), and obesity (1.66; 95 % CI: 1.35-2.05) when compared with robust PWH. Individuals in the "poor mobility" cluster had a higher prevalence of diabetes (1.37; 95 % CI: 1.15-1.64), hypertension (1.12; 95 % CI: 1.04 - 1.22), and obesity (1.38; 95 % CI: 1.17-1.61) compared with robust PWH.

Conclusions: The frailty components, independently and synergistically, were associated with an increased prevalence of cardiometabolic disease. This study identified distinct frailty clusters that may be associated with increased prevalence of cardiometabolic disease among PWH.

Background: Muscle mass is associated with physical and functional performance across adulthood. Its reduction plays a crucial role in the development of age-related conditions such as frailty and sarcopenia. Genetic variations potentially impact muscle health, particularly in an aged population.

Objectives: For this reason, we aimed to evaluate the association between genetic biomarkers and appendicular lean mass index (ALMI), a marker of muscle health, to identify possible risk factors for age-related sarcopenia in a population-based study.

Materials and methods: We cross-sectionally analyzed data collected in 2015 from the São Paulo Epidemiologic Sleep Study (EPISONO). Participants underwent bioelectrical impedance and genetic evaluations.

Results: After adjusting the data for age and sex, 12 single nucleotide polymorphisms (SNP) were significantly associated with ALMI. Among them, rs9928094 (beta = -0.031 p = 0.029) and rs9930333 (beta = -0.030 p = 0.035) are located in the FTO gene, which is related to obesity and fat gain and, rs16839632 (beta = 0.038 p = 0.029) located in the FMN2 gene, responsible for actin cytoskeleton and cell polarity.

Conclusions: Poor muscle health is a multifactorial condition and genetic biomarkers can support the stratification of the risk for adverse body composition states affecting muscle and physical performance across adulthood.