Frailty trajectories in three longitudinal studies of aging: Is the level or the rate of change more predictive of mortality?

Interplay of body mass index and metabolic syndrome: association with physiological age from midlife to late-life

Obesity and metabolic syndrome (MetS) share common pathophysiological characteristics with aging. To better understand their interplay, we examined how body mass index (BMI) and MetS jointly associate with physiological age, and if the associations changed from midlife to late-life. We used longitudinal data from 1,825 Swedish twins. Physiological age was measured as frailty index (FI) and functional aging index (FAI) and modeled independently in linear mixed-effects models adjusted for chronological age, sex, education, and smoking. We assessed curvilinear associations of BMI and chronological age with physiological age, and interactions between BMI, MetS, and chronological age. We found a significant three-way interaction between BMI, MetS, and chronological age on FI (p-interaction = 0·006), not FAI. Consequently, we stratified FI analyses by age: < 65, 65-85, and ≥ 85 years, and modeled FAI across ages. Except for FI at ages ≥ 85, BMI had U-shaped associations with FI and FAI, where BMI around 26-28 kg/m2 was associated with the lowest physiological age. MetS was associated with higher FI and FAI, except for FI at ages < 65, and modified the BMI-FI association at ages 65-85 (p-interaction = 0·02), whereby the association between higher BMI levels and FI was stronger in individuals with MetS. Age modified the MetS-FI association in ages ≥ 85, such that it was stronger at higher ages (p-interaction = 0·01). Low BMI, high BMI, and metabolic syndrome were associated with higher physiological age, contributing to overall health status among older individuals and potentially accelerating aging.

Effects from medications on functional biomarkers of aging in three longitudinal studies of aging in Sweden

Antihypertensive, lipid-lowering, and blood glucose-lowering drugs have slowed down the aging process in animal models. In humans, studies are limited, have short follow-up times, and show mixed results. Therefore, this study aimed to estimate the effects of commonly used medications on functional aging, cognitive function, and frailty. We included information on individuals from three Swedish longitudinal population-based studies collected between 1986 and 2014. Our exposures were the 21 most used groups of medications among individuals aged 65 years and older in the Swedish population in 2022. Functional aging index (n = 1191), cognitive function (n = 1094), and frailty index (n = 1361) were the outcomes of interest. To estimate the medication effects, we used a self-controlled analysis, where each individual is his/her own control, thereby adjusting for all time-stable confounders. The analysis was additionally adjusted for time-varying confounders (chronological age, Charlson Comorbidity Index, smoking, body mass index, and the number of drugs). The participants were 65.5-82.8 years at the first in-person assessment. Adrenergics/inhalants (effect size = 0.089) and lipid-modifying agents/plain (effect size = 0.082) were associated with higher values of cognitive function (improvement), and selective calcium channel blockers with mainly vascular effects (effect size = -0.129) were associated with lower values of the functional aging index (improvement). No beneficial effects were found on the frailty index. Adrenergics/inhalants, lipid-modifying agents/plain, and selective calcium channel blockers with mainly vascular effects may benefit functional biomarkers of aging. More research is needed to investigate their clinical value in preventing adverse aging outcomes.

Advanced biological ageing predicts future risk for neurological diagnoses and clinical examination findings

Age is a dominant risk factor for some of the most common neurological diseases. Biological ageing encompasses interindividual variation in the rate of ageing and can be calculated from clinical biomarkers or DNA methylation data amongst other approaches. Here, we tested the hypothesis that a biological age greater than one's chronological age affects the risk of future neurological diagnosis and the development of abnormal signs on clinical examination. We analysed data from the Swedish Adoption/Twin Study of Aging (SATSA): a cohort with 3175 assessments of 802 individuals followed-up over several decades. Six measures of biological ageing were generated: two physiological ages (created from bedside clinical measurements and standard blood tests) and four blood methylation age measures. Their effects on future stroke, dementia or Parkinson's disease diagnosis, or development of abnormal clinical signs, were determined using survival analysis, with and without stratification by twin pairs. Older physiological ages were associated with ischaemic stroke risk; for example one standard deviation advancement in baseline PhenoAgePhys or KDMAgePhys residual increased future ischaemic stroke risk by 29.2% [hazard ratio (HR): 1.29, 95% confidence interval (CI) 1.06-1.58, P = 0.012] and 42.9% (HR 1.43, CI 1.18-1.73, P = 3.1 × 10-4), respectively. In contrast, older methylation ages were more predictive of future dementia risk, which was increased by 29.7% (HR 1.30, CI 1.07-1.57, P = 0.007) per standard deviation advancement in HorvathAgeMeth. Older physiological ages were also positively associated with future development of abnormal patellar or pupillary reflexes, and the loss of normal gait. Measures of biological ageing can predict clinically relevant pathology of the nervous system independent of chronological age. This may help to explain variability in disease risk between individuals of the same age and strengthens the case for trials of geroprotective interventions for people with neurological disorders.

Recent developments in frailty identification, management, risk factors and prevention: A narrative review of leading journals in geriatrics and gerontology

Frailty is an age-related clinical condition characterised by an increased susceptibility to stressors and an elevated risk of adverse outcomes such as mortality. In the light of global population ageing, the prevalence of frailty is expected to soar in coming decades. This narrative review provides critical insights into recent developments and emerging practices in frailty research regarding identification, management, risk factors, and prevention. We searched journals in the top two quartiles of geriatrics and gerontology (from Clarivate Journal Citation Reports) for articles published between 01 January 2018 and 20 December 2022. Several recent developments were identified, including new biomarkers and biomarker panels for frailty screening and diagnosis, using artificial intelligence to identify frailty, and investigating the altered response to medications by older adults with frailty. Other areas with novel developments included exercise (including technology-based exercise), multidimensional interventions, person-centred and integrated care, assistive technologies, analysis of frailty transitions, risk-factors, clinical guidelines, COVID-19, and potential future treatments. This review identified a strong need for the implementation and evaluation of cost-effective, community-based interventions to manage and prevent frailty. Our findings highlight the need to better identify and support older adults with frailty and involve those with frailty in shared decision-making regarding their care.

Longitudinal Changes of Cognition and Frailty With All-Cause and Cause-Specific Mortality in Chinese Older Adults: An 11-Year Cohort Study

Background and Objectives

Physical function deterioration is always accompanied by a cognitive decline in older adults. However, evidence is lacking for the long-term simultaneous changing patterns of cognition and physical frailty and their associations with mortality among older adults.

Research Design and Methods

This study included 8,231 adults aged ≥65 with a baseline and at least one follow-up assessment of both cognition and physical frailty from the 2007-2018 Chinese Longitudinal Healthy Longevity Survey. Physical frailty (FRAIL phenotype) and cognition (Mini-Mental State Examination) were applied. Group-based joint trajectory modeling was used to fit the joint trajectories of cognition and physical frailty. Cox proportional hazards model was used to evaluate the trajectory-mortality associations.

Results

Three distinct joint trajectories were identified: no joint progression (34.4%), moderate joint progression (47.0%), and rapid joint progression (18.6%). During a median follow-up of 8.3 years, the rapid joint progression group, compared to the no joint progression, had the highest risk for all-cause mortality (hazard ratio (HR), 3.37 [95% CI: 2.99-3.81]), cardiovascular (CVD) mortality (3.21 [2.08-4.96]) and non-CVD mortality (2.99 [2.28-3.92]), respectively. Joint trajectory was found to be more predictive of mortality as compared to baseline measures of cognition and/or frailty (C-statistic ranged from 0.774 to 0.798). Higher changing rates of cognition and frailty were observed among all-cause decedents compared to CVD and non-CVD decedents over a 45-year span (aged 65-110) before death.

Discussion and Implications

Our study suggested that subjects with the worst cognitive decline and severest physical frailty progression were at the highest risk for all-cause and cause-specific mortality. Our findings expand the limited prior knowledge on the dynamic course of cognition and frailty.

Correlation of frailty assessment metrics in one-year follow-up of aged care residents: a sub-study of a randomised controlled trial

INTRODUCTION

Frailty is increasingly recognised as a dynamic syndrome, with multiple causes, dimensions and consequences. There is little understanding of how those frailty assessment metrics interact over time. The aim of this study was to describe the longitudinal correlation between five frailty metrics, namely multimorbidity, muscular strength, mood alterations, cognitive capacity, and functional capacity in a cohort study of aged care (nursing home) residents.

METHODS

248 aged care residents with Frailty Index at baseline of < 0.4 and no dementia were followed for 12 months. A multimorbidity score and an activity of daily living limitation score were created using individual items of the Frailty Index. Muscular strength was measured by grip strength. Cognitive capacity was measured using the Montreal Cognitive Assessment (MoCA) test. Mood alterations were measured using the anxiety/depression screening question from EQ-5D. We analysed the inter-individual correlation at baseline, association between baseline and future change, and within-individual correlation at baseline, 6 and 12 months.

RESULTS

Population analysis shows that metrics were not associated at baseline. All of the studied metrics at baseline were associated with change in 12 months, with the exception of anxiety/depression scores. Pairwise within-individual correlation was strong between MoCA and grip strength (0.13, p = 0.02) and activity of daily living (- 0.48, p < 0.001), and between activities of daily living and multimorbidity index (0.28, p < 0.001). No within-individual correlation was found between anxiety depression score and other metrics.

CONCLUSION

The results suggest an interdependence between comorbidities, physical capacity, cognition and activities of daily living in aged care residents. Comprehensive measurement of frailty-related metrics may provide improved understanding of frailty progression at later life stages.

Is Frailty Different in Younger Adults Compared to Old? Prevalence, Characteristics, and Risk Factors of Early-Life and Late-Life Frailty in Samples from Sweden and UK

INTRODUCTION

Although frailty is commonly considered as a syndrome of old individuals, recent studies show that it can affect younger adults, too. Whether and how frailty differs in younger adults compared to old is however unknown. To this end, we analyzed the prevalence, characteristics, and risk factors of early-life (aged <65) and late-life (aged ≥65) frailty.

METHODS

We analyzed individuals in the UK Biobank (N = 405,123) and Swedish Screening Across the Lifespan Twin (SALT; N = 43,641) study. Frailty index (FI) scores ≥0.21 were used to demarcate frailty. Characteristics of early-life versus late-life frailty were analyzed by collating the FI items (deficits) into domains and comparing the domain scores between younger and older frail individuals. Logistic regression was used to assess the risk factors of frailty.

RESULTS

The pooled prevalence rates of frailty were 10.3% (95% confidence interval [CI]: 2.7-32.7), 14.4% (95% CI: 4.5-37.2), 19.2% (95% CI: 2.5-68.5) in individuals aged ≤55, 55-64, 65-74, respectively. Younger frail adults (aged <65) had higher scores in immunological, mental wellbeing, and pain-related domains, whereas older frail adults (aged ≥65) had higher scores in cardiometabolic, cancer, musculoskeletal, and sensory-related domains. Higher age, female sex, smoking, lower alcohol consumption, lower education, obesity, overweight, low income, and maternal smoking were similarly associated with the risk of early-life and late-life frailty.

CONCLUSION

Frailty is prevalent also in younger age groups (aged <65) but differs in some of its characteristics from the old. The risk factors of frailty are nevertheless largely similar for early-life and late-life frailty.

Genetic and Environmental Influences on Longitudinal Frailty Trajectories From Adulthood into Old Age

BACKGROUND

Frailty is a complex, dynamic geriatric condition, but limited evidence has shown how genes and environment may contribute to its longitudinal changes. We sought to investigate sources of individual differences in the longitudinal trajectories of frailty, considering potential selection bias when including a sample of oldest-old twins.

METHODS

Data were from 2 Swedish twin cohort studies: a younger cohort comprising 1 842 adults aged 29-96 years followed up to 15 waves, and an older cohort comprising 654 adults aged ≥79 years followed up to 5 waves. Frailty was measured using the frailty index (FI). Age-based latent growth curve models were used to examine longitudinal trajectories, and extended to a biometric analysis to decompose variability into genetic and environmental etiologies.

RESULTS

A bilinear model with an inflection point at age 75 best described the data, indicating a fourfold to fivefold faster FI increase after 75 years. Twins from the older cohort had significantly higher mean FI at baseline but slower rate of increase afterward. FI level at age 75 was moderately heritable in both men (42%) and women (55%). Genetic influences were relatively stable across age for men and increasing for women, although the most salient amplification in FI variability after age 75 was due to individual-specific environmental influences for both men and women; conclusions were largely consistent when excluding the older cohort.

CONCLUSION

Increased heterogeneity of frailty in late life is mainly attributable to environmental influences, highlighting the importance of targeting environmental risk factors to mitigate frailty in older adults.

Critical Appraisal of the Concept Frailty: Rating of Frailty in Elderly People has Weak Scientific Basis and should not be Used for Managing Individual Patients

The concept frail elderly has been used to highlight the biological, rather than chronological, age. International and national bodies recommend that individuals over age 70 who visit healthcare facilities should be screened for frailty. There are important objections to the concept. Diagnostics: 'Frailty' is used for several completely different types of health problems. There are no useful biomarkers, but more than 60 different published rating methods for frailty, where different methods provide very different prevalence of frailty and also do not identify the same groups of elderly people. There is significant overlap between Clinical Frailty Scale- scores and activity of daily living (ADL)-scores. There is no gold standard method against which published frailty rating scales can be validated. It is unclear when, where and how often screening for frailty should occur in healthcare. Treatment: The evidence for treatment of frailty is very weak. A recent systematic overview found that the 21 included randomised, controlled studies (RCTs) were very heterogeneous as regards inclusion/exclusion criteria, how the condition of frailty was defined, what treatment was given and what health outcomes were assessed. In addition, there are often problems with the quality of the studies. The lack of a clear definition and evidence-based treatment of frailty means that it is inappropriate to introduce assessments of frailty in individual elderly patients in health care.

Trajectories, Transitions, and Trends in Frailty among Older Adults: A Review

Frailty is an age-related clinical state associated with deterioration across multiple physiological systems and a leading cause of morbidity and mortality later in life. To understand how frailty develops and what causes its progression, longitudinal data with repeated frailty measurements are required. This review summarizes evidence from longitudinal studies on frailty trajectories, transitions, and trends. We identified several consistent findings: frailty increases with aging and is a dynamic condition, and more recent generations of older adults have higher frailty levels. These findings have both clinical and public health relevance, including the provision of healthcare and aged care services in the coming years. Further studies are required, particularly those conducted in low- and middle-income countries and those investigating factors associated with changes in frailty. The latter may help develop better-targeted interventions to reverse or slow the progression of frailty.

Transitions between degrees of multidimensional frailty among older people admitted to intermediate care: a multicentre prospective study

Background

Frailty is a dynamic condition that is clinically expected to change in older individuals during and around admission to an intermediate care (IC) facility. We aimed to characterize transitions between degrees of frailty before, during, and after admission to IC and assess the impact of these transitions on health outcomes.

Methods

Multicentre observational prospective study in IC facilities in Catalonia (North-east Spain). The analysis included all individuals aged ≥ 75 years (or younger with chronic complex or advanced diseases) admitted to an IC facility. The primary outcome was frailty, measured by the Frail-VIG index and categorized into four degrees: no frailty, and mild, moderate, and advanced frailty. The Frail-VIG index was measured at baseline (i.e., 30 days before IC admission) (Frail-VIG₀), on IC admission (Frail-VIG₁), at discharge (Frail-VIG₂), and 30 days post-discharge (Frail-VIG₃).

Results

The study included 483 patients with a mean (SD) age of 81.3 (10.2) years. At the time of admission, 27 (5.6%) had no frailty, and 116 (24%), 161 (33.3%), and 179 (37.1%) mild, moderate, and severe frailty, respectively. Most frailty transitions occurred within the 30 days following admission to IC, particularly among patients with moderate frailty on admission. Most patients maintained their frailty status after discharge. Overall, 135 (28%) patients died during IC stay. Frailty, measured either at baseline or admission, was significantly associated with mortality, although it showed a stronger contribution when measured on admission (HR 1.16; 95%CI 1.10–1.22; p < 0.001) compared to baseline (HR 1.10; 1.05–1.15; p < 0.001). When including frailty measurements at the two time points (i.e., baseline and IC admission) in a multivariate model, frailty measured on IC admission but not at baseline significantly contributed to explaining mortality during IC stay.

Conclusions

Frailty status varied before and during admission to IC. Of the serial frailty measures we collected, frailty on IC admission was the strongest predictor of mortality. Results from this observational study suggest that routine frailty measurement on IC admission could aid clinical management decisions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03378-9.

Association Between Changes in Frailty Index and Clinical Outcomes: An Observational Cohort Study

Purpose

Although the association between a single assessment of frailty index (FI) and clinical outcomes has been revealed in prior studies, there is a lack of knowledge about the prognostic value of FI at different time points and the changes in repeated measurements of FI. Hence, we sought to determine the clinically meaningful changes in FI and reveal the association with the changes and a composite outcome of mortality and institutionalization.

Participants and Methods

This study was based on a longitudinal study of the Pyeongchang Rural Area cohort that included people aged 65 years or older, ambulatory and living at home. Individuals were divided into the worsened group (changes in FI ≥ 0.03 during 2 years) and the stable group (changes in FI < 0.03 during 2 years). The incidence of a composite outcome was compared between the two groups and the relationship was adjusted for age, sex, baseline FI, and follow-up FI.

Results

Of the 953 participants, 403 (42.3%) and 550 (57.7%) were included in the worsened group and the stable group, respectively. The worsened group had a significantly higher risk of the composite outcome than the stable group (HR, 2.37 [95% CI, 1.54–3.67]; p < 0.001). Although the higher risk remained significant after adjusting for age, sex, and baseline FI, the statistical significance disappeared after adjusting for follow-up FI (p = 0.614). The aggravation of FI in the worsened group was predominantly due to aggravation of FI domains, such as activities in daily living, cognitive function and mood, and mobility rather than comorbidity burden.

Conclusion

Aggravation of FI was associated with a composite outcome regardless of baseline FI, and the association was significantly reflected in the follow-up measurement of FI. The worsening FI was mainly attributable to functional geriatric domains.

Frailty in the oldest old: is the current level or the rate of change more predictive of mortality?

BACKGROUND

It is unclear whether frailty index (FI) change captures mortality risk better than and independently of the current FI level, i.e. whether a regular FI assessment among older adults provides additional insights for mortality risk stratification or not.

METHODS

We used data from the LASA 75-PLUS-study, which monitored health among 508 older adults (75+) between 2016 and 2019 every 9 months. Joint models for longitudinal and time-to-event data were used to assess the impact of both current FI and within-person FI change during the last year on mortality risk.

RESULTS

Twenty percent of the participants died during 4.5 years of follow-up. Adding within-person FI change to the current FI model improved model fit and it showed that FI increases during the last year were associated with an increase in mortality risk. Consequently, the effect of the current FI decreased considerably and became statistically non-significant.

CONCLUSIONS

The rate of FI change was more important than the current FI level for short-term mortality prediction among the oldest old, which highlights the benefits of regular frailty assessments.

The dynamics of frailty development and progression in older adults in primary care in England (2006-2017): a retrospective cohort profile

BACKGROUND

Frailty is a common condition in older adults and has a major impact on patient outcomes and service use. Information on the prevalence in middle-aged adults and the patterns of progression of frailty at an individual and population level is scarce. To address this, a cohort was defined from a large primary care database in England to describe the epidemiology of frailty and understand the dynamics of frailty within individuals and across the population. This article describes the structure of the dataset, cohort characteristics and planned analyses.

METHODS

Retrospective cohort study using electronic health records. Participants were aged ≥50 years registered in practices contributing to the Oxford Royal College of General Practitioners Research and Surveillance Centre between 2006 to 2017. Data include GP practice details, patient sociodemographic and clinical characteristics, twice-yearly electronic Frailty Index (eFI), deaths, medication use and primary and secondary care health service use. Participants in each cohort year by age group, GP and patient characteristics at cohort entry are described.

RESULTS

The cohort includes 2,177,656 patients, contributing 15,552,946 person-years, registered at 419 primary care practices in England. The mean age was 61 years, 52.1% of the cohort was female, and 77.6% lived in urban environments. Frailty increased with age, affecting 10% of adults aged 50-64 and 43.7% of adults aged ≥65. The prevalence of long-term conditions and specific frailty deficits increased with age, as did the eFI and the severity of frailty categories.

CONCLUSION

A comprehensive understanding of frailty dynamics will inform predictions of current and future care needs to facilitate timely planning of appropriate interventions, service configurations and workforce requirements. Analysis of this large, nationally representative cohort including participants aged ≥50 will capture earlier transitions to frailty and enable a detailed understanding of progression and impact. These results will inform novel simulation models which predict future health and service needs of older people living with frailty.

STUDY REGISTRATION

Registered on www.clinicaltrials.gov October 25th 2019, NCT04139278 .