Frailty Index Predicts All-Cause Mortality for Middle-Aged and Older Taiwanese: Implications for Active-Aging Programs

Unveiling the multifaceted nexus of subjective aging, biological aging, and chronological age: Findings from a nationally representative cohort study

OBJECTIVES

This study aims to investigate how subjective aging influences the psychological and behavioral responses of older individuals, specifically focusing on the associations between subjective aging and longitudinal changes in biological age.

METHODS

This is a retrospective cohort study retrieving data from the Taiwan Longitudinal Study on Aging (TLSA), over a 4-year follow-up period. Subjective aging is assessed by asking participants if they perceive themselves as old, while frailty is measured using a frailty index comprising 34 deficits from various domains. Participants are categorized into three groups based on their chronological age. The association between subjective aging and transition of biological age (as indicated by an increased frailty index) from 2011 to 2015 is examined using logistic regression models.

RESULTS

The study consisted of 2412 participants, who were categorized into middle-age (n = 1,082), young-old (n = 779), and old-old (n = 551) groups. Among them, individuals exhibiting subjective aging at baseline were more likely to be older in chronological age, female, illiterate, and unemployed, compared to those without subjective aging. The adjusted odds ratios (aORs) for the association between subjective aging and an increased biological age were 1.72 [95% CI: 0.88-3.34], 1.61 [0.77-3.37], and 1.08 [0.65-1.80], in the middle-age, young-old, and old-old groups, respectively.

DISCUSSIONS

No significant associations were found between changes in biological age and subjective aging across various chronological age groups. Notably, within the younger age group, a discernible trend towards an association was observed, indicating the potential age-related nuances in the complex interrelation between subjective age, biological aging, and chronological aging.

Data-driven health deficit assessment improves a frailty index's prediction of current cognitive status and future conversion to dementia: results from ADNI

Frailty is a dementia risk factor commonly measured by a frailty index (FI). The standard procedure for creating an FI requires manually selecting health deficit items and lacks criteria for selection optimization. We hypothesized that refining the item selection using data-driven assessment improves sensitivity to cognitive status and future dementia conversion, and compared the predictive value of three FIs: a standard 93-item FI was created after selecting health deficit items according to standard criteria (FIs) from the ADNI database. A refined FI (FIr) was calculated by using a subset of items, identified using factor analysis of mixed data (FAMD)-based cluster analysis. We developed both FIs for the ADNI1 cohort (n = 819). We also calculated another standard FI (FIc) developed by Canevelli and coworkers. Results were validated in an external sample by pooling ADNI2 and ADNI-GO cohorts (n = 815). Cluster analysis yielded two clusters of subjects, which significantly (pFDR < .05) differed on 26 health items, which were used to compute FIr. The data-driven subset of items included in FIr covered a range of systems and included well-known frailty components, e.g., gait alterations and low energy. In prediction analyses, FIr outperformed FIs and FIc in terms of baseline cognition and future dementia conversion in the training and validation cohorts. In conclusion, the data show that data-driven health deficit assessment improves an FI's prediction of current cognitive status and future dementia, and suggest that the standard FI procedure needs to be refined when used for dementia risk assessment purposes.

Frailty and risks of all-cause and cause-specific death in community-dwelling adults: a systematic review and meta-analysis

BACKGROUND

The associations of frailty with all-cause and cause-specific mortality remain unclear. Therefore, we performed this meta-analysis to fill this gap.

METHODS

We searched the PubMed and Embase databases through June 2022. Prospective cohort studies or clinical trials examining frailty were evaluated, and the multiple adjusted risk estimates of all-cause and cause-specific mortality, such as death from cardiovascular disease (CVD), cancer, respiratory illness, dementia, infection, and coronavirus disease 2019 (COVID-19), were included. A random effects model was used to calculate the summary hazard ratio (HR).

RESULTS

Fifty-eight studies were included for the qualitative systematic review, of which fifty-six studies were eligible for the quantitative meta-analysis, and the studies included a total of 1,852,951 individuals and more than 145,276 deaths. Compared with healthy adults, frail adults had a significantly higher risk of mortality from all causes (HR 2.40; 95% CI 2.17-2.65), CVD (HR 2.64; 95% CI 2.20-3.17), respiratory illness (HR 4.91; 95% CI 2.97-8.12), and cancer (HR 1.97; 95% CI 1.50-2.57). Similar results were found for the association between prefrail adults and mortality risk. In addition, based on the studies that have reported the HRs of the mortality risk per 0.1 and per 0.01 increase in the frailty index, we obtained consistent results.

CONCLUSIONS

The present study demonstrated that frailty was not only significantly related to an increased risk of all-cause mortality but was also a strong predictor of cause-specific mortality from CVD, cancer, and respiratory illness in community-dwelling adults. More studies are warranted to clarify the relationship between frailty and cause-specific mortality from dementia, infection, and COVID-19.

TRIAL REGISTRATION

PROSPERO (CRD42021276021).

Self-rated health trajectory and frailty among community-dwelling older adults: evidence from the Taiwan Longitudinal Study on Aging (TLSA)

OBJECTIVES

Self-rated health (SRH) is an assessment and predictor of health based on an individual's general condition; however, evidence of the value of SRH for predicting frailty remains scarce for older Asian adults. This study aimed to evaluate the relationship between SRH score trajectory and frailty among older individuals in Taiwan.

DESIGN

An 8-year retrospective cohort study.

SETTING

Data were retrieved from the Taiwan Longitudinal Study on Aging from 1999 to 2007.

PARTICIPANTS

Respondents aged 53-69 years old who were not frail or disabled in 1999 (n=1956).

PRIMARY AND SECONDARY OUTCOME MEASURES

Frailty was defined using the Fried criteria. The group-based trajectory modelling technique was used to estimate SRH trajectories. Logistic regression analysis was used to examine the associations between changes in SRH and frailty.

RESULTS

Four SRH trajectory classes were identified across the 8-year follow-up: 232 participants (11.9%) were classified into the constantly poor SRH group, 1123 (57.4%) into the constantly fair SRH group, 335 (17.1%) into the constantly good SRH group and 266 (13.6%) into the good-to-fair SRH group. After adjusting for gender, age, level of education, income, social participation, health behaviours and major comorbidities, it was found that age, poor income satisfaction, without job and constantly poor SRH were associated with increased risk of frailty, while constantly good SRH (OR 0.04, 95% CI (0.01 to 0.32)) and good-to-fair SRH (OR 0.19, 95% CI (0.06 to 0.63)) were associated with reduced risks of frailty.

CONCLUSIONS

Constantly poor SRH was associated with an increased risk of frailty in older age. SRH in older adults should be recognised as a predictive tool for future frailty. Diet and exercise interventions may help to prevent frailty among high-risk older individuals with constantly low SRH.

Derivation and validation of 10-year all-cause and cardiovascular disease mortality prediction model for middle-aged and elderly community-dwelling adults in Taiwan

Prediction model mainly focused on specific diseases, such as diabetes, hypertension, cardiovascular disease, or patients with cancer, or populations of Europe and America, thereby limiting its generalization. This study aimed to develop and validate a 10-year mortality risk score by using data from a population-representative sample of adults. Data were collected from 2,221 Taichung Community Health study participants aged ≥40 years. The baseline period of the study was 2004, and all participants were followed up until death or in 2016. Cox’s proportional hazards regression analyses were used to develop the prediction model. A total of 262 deaths were ascertained during the 10-year follow-up. The all-cause mortality prediction model calculated the significant risk factors, namely, age, sex, marital status, physical activity, tobacco use, estimated glomerular filtration rate, and albumin-to-creatinine ratio, among the baseline risk factors. The expanded cardiovascular disease (CVD) mortality prediction model consisted of six variables: age, sex, body mass index, heart disease plus heart disease medication use, stroke plus medication use, and ankle–brachial index. The areas under receiver operating curves of the 3-, 5- and 10-year predictive models varied between 0.97, 0.96, and 0.88 for all-cause mortality, and between 0.97, 0.98, and 0.84 for expanded CVD mortality. These mortality prediction models are valid and can be used as tools to identify the increased risk for mortality.

PM2.5 air pollution contributes to the burden of frailty

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.

Comparisons Between Hypothesis- and Data-Driven Approaches for Multimorbidity Frailty Index: A Machine Learning Approach

BACKGROUND

Using big data and the theory of cumulative deficits to develop the multimorbidity frailty index (mFI) has become a widely accepted approach in public health and health care services. However, constructing the mFI using the most critical determinants and stratifying different risk groups with dose-response relationships remain major challenges in clinical practice.

OBJECTIVE

This study aimed to develop the mFI by using machine learning methods that select variables based on the optimal fitness of the model. In addition, we aimed to further establish 4 entities of risk using a machine learning approach that would achieve the best distinction between groups and demonstrate the dose-response relationship.

METHODS

In this study, we used Taiwan's National Health Insurance Research Database to develop a machine learning multimorbidity frailty index (ML-mFI) using the theory of cumulative diseases/deficits of an individual older person. Compared to the conventional mFI, in which the selection of diseases/deficits is based on expert opinion, we adopted the random forest method to select the most influential diseases/deficits that predict adverse outcomes for older people. To ensure that the survival curves showed a dose-response relationship with overlap during the follow-up, we developed the distance index and coverage index, which can be used at any time point to classify the ML-mFI of all subjects into the categories of fit, mild frailty, moderate frailty, and severe frailty. Survival analysis was conducted to evaluate the ability of the ML-mFI to predict adverse outcomes, such as unplanned hospitalizations, intensive care unit (ICU) admissions, and mortality.

RESULTS

The final ML-mFI model contained 38 diseases/deficits. Compared with conventional mFI, both indices had similar distribution patterns by age and sex; however, among people aged 65 to 69 years, the mean mFI and ML-mFI were 0.037 (SD 0.048) and 0.0070 (SD 0.0254), respectively. The difference may result from discrepancies in the diseases/deficits selected in the mFI and the ML-mFI. A total of 86,133 subjects aged 65 to 100 years were included in this study and were categorized into 4 groups according to the ML-mFI. Both the Kaplan-Meier survival curves and Cox models showed that the ML-mFI significantly predicted all outcomes of interest, including all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions at 1, 5, and 8 years of follow-up (P<.01). In particular, a dose-response relationship was revealed between the 4 ML-mFI groups and adverse outcomes.

CONCLUSIONS

The ML-mFI consists of 38 diseases/deficits that can successfully stratify risk groups associated with all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions in older people, which indicates that precise, patient-centered medical care can be a reality in an aging society.

Efficacy of multidomain interventions to improve physical frailty, depression and cognition: data from cluster-randomized controlled trials

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.

Using a genetic algorithm to derive a highly predictive and context-specific frailty index

The frailty index (FI) is one of the most widespread tools used to predict poor, health-related outcomes in older persons. The selection of clinical and functional deficits to include in a FI is mostly based on the users’ clinical experience. However, this approach may not be sufficiently accurate to predict health outcomes in particular subgroups of individuals. In this study, we implemented an optimization algorithm, the genetic algorithm, to create a highly performant (FI) based on our prediction goals, rather than on a predetermined clinical selection of deficits, using data from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K) and 109 potential deficits identified in the dataset. The algorithm was personalized to obtain a FI with high discrimination ability in the prediction of mortality. The resulting FI included 40 deficits and showed areas under the curve consistently higher than 0.80 (range 0.81-0.90) in the prediction of 3-year and 6-year mortality in the whole sample and in sex and age subgroups. This methodology represents a promising opportunity to optimize the exploitation of medical and administrative databases in the construction of clinically relevant frailty indices.

Determinants and indicators of successful ageing associated with mortality: a 4-year population-based study

Successful aging may be a solution to the major challenges that population aging poses to healthcare systems, financial security, and labor force supply. Hence, we studied the value of factors discovered by exploratory factor analysis in predicting four main indicators of successful aging, and their association with mortality. We followed-up a nationally representative sample of 1284 older adults for a median of 50 months. Successful aging was defined by fast walking, independence, emotional vitality, and self-rated health. Exploratory factor analysis revealed five determinants: physical activity, life satisfaction and financial status, health status, stress, and cognitive function. Physical activity and health status were significant factors in living independently. Life satisfaction and financial status were associated with walking speed. Stress was solely associated with emotional vitality. Life satisfaction and financial status, and health status, were important predictors of self-rated health. Compared to people without any successful aging indicators, those with one, two, three, or four showed dose-dependent lessening of mortality risk, with respective hazard ratios of 0.39 (95% CI 0.25-0.59), 0.29 (95% CI 0.17-0.50), 0.23 (95% CI 0.11-0.51), and 0.09 (95% CI 0.01-0.66). These associations were stronger in males, older adults, smokers, and drinkers, than in their counterparts.

Metabolic dysregulation in vitamin E and carnitine shuttle energy mechanisms associate with human frailty

Global ageing poses a substantial economic burden on health and social care costs. Enabling a greater proportion of older people to stay healthy for longer is key to the future sustainability of health, social and economic policy. Frailty and associated decrease in resilience plays a central role in poor health in later life. In this study, we present a population level assessment of the metabolic phenotype associated with frailty. Analysis of serum from 1191 older individuals (aged between 56 and 84 years old) and subsequent longitudinal validation (on 786 subjects) was carried out using liquid and gas chromatography-mass spectrometry metabolomics and stratified across a frailty index designed to quantitatively summarize vulnerability. Through multivariate regression and network modelling and mROC modeling we identified 12 significant metabolites (including three tocotrienols and six carnitines) that differentiate frail and non-frail phenotypes. Our study provides evidence that the dysregulation of carnitine shuttle and vitamin E pathways play a role in the risk of frailty.

Risk of age-related chronic disorders and decrease in resilience is associated with ageing. Here the authors analyse the human blood metabolome and identify metabolites associated with frailty.

Frailty, transition in frailty status and all-cause mortality in older adults of a Taichung community-based population

BACKGROUND

Previous studies have reported the associations of frailty phenotype or its components with mortality. However, studies that explored the effects of transition in frailty status on mortality were far less in Asian or Chinese. The aim of this study was to evaluate baseline frailty status and one-year change of frailty status in relation to all-cause mortality in Taiwanese community-dwelling older adults who participated in the Taichung Community Health Study for Elders.

METHODS

We conducted a community-based prospective cohort study. A total of 921 community-dwelling elderly men and women aged 65-99 years in Taichung City were enrolled in 2009-2010 and were followed up through 2016. We adopted the definition of frailty proposed by Fried et al., including five components: shrinking, weakness, poor endurance and energy, slowness, and low physical activity. Cox proportional hazards models were used to determine adjusted hazard ratios (HRs) of mortality with 95% confidence intervals (CIs) for frailty at baseline and one-year change in frailty status.

RESULTS

There were 160 deaths during the follow-up period. The mortality rates in groups of robust and frail were 20.26 and 84.66 per 1000 person-years respectively. After multivariate adjustment, the HR (CIs) for baseline frailty was 2.67 (1.73-4.12). Poor endurance and energy [1.88 (1.03-3.42)], slowness [2.60 (1.76-3.83)] and weakness [1.65 (1.16-2.33)] were found to be predictors of mortality. Increased risks in mortality for subgroups of robust-to-frail [2.76 (1.22-6.27)], frail-to-robust [3.87 (1.63, 9.19)], and frail-to-frail [4.08 (1.92-8.66)] over one-year period were observed compared with those remaining robust.

CONCLUSION

Baseline frailty status and one-year change in frailty status are associated with 6-year all-cause mortality among Taiwanese elderly adults. Frailty may be useful for identifying older adults at high risks for mortality prevention.

Impact of Health Literacy on Frailty among Community-Dwelling Seniors

Health literacy has been reported to have effects on health behavior change and health-related outcomes, but few studies have explored the association between health literacy and frailty. The aim of our study is to investigate the relationships between health literacy and frailty among community-dwelling seniors. This cross-sectional study enrolled 603 community-dwelling older adults (307 women) in residential areas, with a mean age of 70.9 ± 5.82 years. Health literacy was assessed using the Mandarin version of the European Health Literacy Survey Questionnaire. Physical frailty was defined by Fried frailty phenotype. Logistic regression was carried out to determine potential risk factors of frailty. In the multivariate logistic regression model, physical activity (Odds Ratio [OR] 1.47, 95% Confidence Interval [CI] 1.06–2.03) and health literacy (sufficient vs. excellent: OR 2.51, 95% CI 1.32–4.77) were associated with prefrailty and frailty. In subgroup analysis, pre-frailty and frailty were also negatively associated with health literacy in individuals with ‘insufficiently active’ (inadequate vs. excellent: OR 5.44, 95% CI 1.6–18.45) and ‘sufficiently/highly active’ physical activity levels (sufficient vs. excellent: OR 2.41, 95% CI 1.07–5.42). Therefore, in these community-dwelling elderly adults, health literacy was associated with pre-frailty and frailty regardless of age, gender, socio-economic status, and education level.

The synergic effects of frailty on disability associated with urbanization, multimorbidity, and mental health: implications for public health and medical care

Frailty is garnering increasing interest as a potential target in disability prevention. Since it is uncertain how frailty interacts with multimorbidity, urbanization, and mental health to affect disability, we investigated the epidemiology of frailty and its synergies with these factors. The study enrolled 20,898 participants aged 65 and older living in New Taipei city. All participants received face to face interview to assess frailty, multimorbidity, urban or rural residence, and mental health. Individual versus combined effects of risk factors were evaluated using the Rothman synergy index. Prevalence of frailty was 5.2% overall, 7.2% in multimorbid participants, 9.6% in rural residents, and 20.8% in those with mental disorders. Logistic regression, adjusted for age and sex, showed significant associations between disability and frailty (OR 8.5, 95% CI 6.4–11.2), multimorbidity (OR 1.3, 95% CI 1.0–1.6), urbanization (OR 1.3, 95% CI 1.0–1.7), and mental disorders (OR 7.3, 95% CI 5.6–9.5); these factors had a significant synergic effect on disability. Frailty is common in older adults and associated with disability, and was synergetic with multimorbidity, mental disorders, and residing rurally. Targeting frailty prevention and intervention needs a special attention on those vulnerable groups.

Biological age as a health index for mortality and major age-related disease incidence in Koreans: National Health Insurance Service - Health screening 11-year follow-up study

Purpose

A comprehensive health index is needed to measure an individual's overall health and aging status and predict the risk of death and age-related disease incidence, and evaluate the effect of a health management program. The purpose of this study is to demonstrate the validity of estimated biological age (BA) in relation to all-cause mortality and age-related disease incidence based on National Sample Cohort database.

Patients and methods

This study was based on National Sample Cohort database of the National Health Insurance Service - Eligibility database and the National Health Insurance Service - Medical and Health Examination database of the year 2002 through 2013. BA model was developed based on the National Health Insurance Service - National Sample Cohort (NHIS - NSC) database and Cox proportional hazard analysis was done for mortality and major age-related disease incidence.

Results

For every 1 year increase of the calculated BA and chronological age difference, the hazard ratio for mortality significantly increased by 1.6% (1.5% in men and 2.0% in women) and also for hypertension, diabetes mellitus, heart disease, stroke, and cancer incidence by 2.5%, 4.2%, 1.3%, 1.6%, and 0.4%, respectively (p<0.001).

Conclusion

Estimated BA by the developed BA model based on NHIS - NSC database is expected to be used not only as an index for assessing health and aging status and predicting mortality and major age-related disease incidence, but can also be applied to various health care fields.

Gender-associated factors for frailty and their impact on hospitalization and mortality among community-dwelling older adults: a cross-sectional population-based study

Background

Frailty associated with aging increases the risk of falls, disability, and death. We investigated gender-associated factors for frailty.

Methods

Data of 3,079 geriatric subjects were retrieved from the National Health and Nutrition Examination Survey (NHANES) 2007–2010 database. After excluding 1,126 subjects with missing data on frailty, medical history and survival, data of 1,953 patients were analyzed. Main endpoints were frailty prevalence, mortality rates and causes of death.

Results

Frailty prevalence was 5.4% in males, 8.8% in females. Significant risk factors for geriatric frailty in males were being widowed/divorced/separated, low daily total calorie intake, physical inactivity, sleeping >9 h, smoking and hospitalization history; and in females were obesity, physical inactivity, sleeping <6 h, family history of diabetes and heart attack, and hospitalization history. Frail subjects had higher mortality rates (22.5% male; 8.5% female) than pre-frail (8.7% male; 6.4% female) and non-frail (5.4% male; 2.5% female). Main causes of death were heart diseases (41%) and chronic lower respiratory diseases (23.0%) in males and nephritis/nephrosis (32.3%) and chronic lower respiratory diseases (17.6%) in females.

Discussion

Factors associated with frailty differ by gender, with higher frailty prevalence in females and higher mortality in males. Gender-associated factors for frailty identified in this study may be useful in evaluating frailty and guiding development of public health measures for prevention.

Key Message

Common predictive factors for frailty among older adults of both genders, including more frequent previous hospitalizations, physical inactivity, and certain gender-associated factors for frailty, are consistent with results of other NHANES studies in which self-reported higher levels of illness and sedentary behavior were directly associated with frailty.

What factors mediate the inter-relationship between frailty and pain in cognitively and functionally sound older adults? A prospective longitudinal ageing cohort study in Taiwan

OBJECTIVES

The main aim was to investigate the complex inter-relationship between frailty and pain, and the mediating roles of cognitive function, morbidity and mood in this nexus.

DESIGN

A cross-sectional analysis.

SETTING

A prospective community-dwelling population-based cohort.

PARTICIPANTS

1682 adults age ≥50 years without evident cognitive or functional impairment, or history of cancer.

PRIMARY AND SECONDARY OUTCOME MEASURES

The mediating effect of depression, cognitive function and comorbidity on the nexus between pain and frailty among older and middle-aged adults.

RESULTS

The pain score among older subjects (≥65 years), increased with the degree of frailty (robust=0.96±0.82; pre-frail=1.13±0.86; frail=1.63±1.02; P<0.001); multivariate analysis gave the same result, while moderate pain was associated with frailty in older subjects (OR=3.00, 95% CI 1.30 to 6.60). Conversely, pain and frailty among middle-aged subjects (aged 50-64 years) did not appear to be significantly related; in mediation analysis, pain exerted an indirect effect on frailty via depression (indirect effect=0.03, 95% CI 0.01 to 0.07), while neither cognitive function nor comorbidity had any significant effect in mediating the relationship between pain and frailty.

CONCLUSION

In cognitively and functionally sound community-dwelling adults aged ≥50 years, moderate pain was related to frailty in those older than 65 years, but not younger ones. Besides the direct influence of pain on frailty, depression partially mediated the pain-frailty nexus. The mechanism by which depression influences pain and frailty requires further investigation.

Predicting mortality and hospitalization of older adults by the multimorbidity frailty index

Background

Existing operational definitions of frailty are personnel-costly and time-consuming, resulting in estimates with a small sample size that cannot be generalized to the population level. The objectives were to develop a multimorbidity frailty index using Taiwan’s claim database, and to understand its ability to predict adverse event.

Methods

This is a retrospective cohort study. Subjects aged 65 to 100 years who have full National Health Insurance coverage in 2005 were included. We constructed the multimorbidity frailty index using cumulative deficit approach and categorized study population according to the multimorbidity frailty index quartiles: fit, mild frailty, moderate frailty and severe frailty. The multimorbidity frailty index included deficits from outpatient and inpatient diagnosis. Associations with all-cause mortality, unplanned hospitalization and intensive care unit admission were assessed using Kaplan-Meier curves and Cox regression analyses.

Results

The multimorbidity frailty index incorporated 32 deficits, with mean multimorbidity frailty index score of 0.052 (standard deviation = 0.060) among 86,133 subjects included. Compared to subjects in fit category, subjects with severe frailty were associated with a 5.0-fold (adjusted hazard ratio, aHR 4.97; 95% confidence interval, 95% CI 4.49–5.50) increased risk of death at 1 year after adjusting for age and gender. Subjects with moderate frailty or mild frailty was associated with 3.1- (adjusted HR 3.08; 95% CI 2.80–3.39) or 1.9- (adjusted HR 1.86; 95% CI 1.71–2.01) folds increased risk, respectively.4.49–5.50). The risk trend of unplanned hospitalization and intensive care unit admission is similar among the study population. Besides, the association between the frailty categories and all three outcomes was slightly stronger among women.

Conclusion

The multimorbidity frailty index was highly associated with all-cause mortality, unplanned hospitalization and ICU admission. It could serve as an efficient tool for stratifying older adults into different risk groups for planning care management programs.