Frailty and neuropathology in relation to dementia status: the Cambridge City over-75s Cohort study

Sustained frailty remission and dementia risk in older adults: A longitudinal study

INTRODUCTION

Physical frailty is reversible, but little is known about the sustainability of frailty remission and its impact on dementia.

METHODS

Data were derived from the National Health and Aging Trends Study (NHATS) (2011 to 2021). Physical frailty was assessed using the Fried frailty phenotype, and frailty transition patterns across three waves were defined. The relationship of sustained frailty remission with incident dementia was examined using Cox proportional regression, stratified by age and gender.

RESULTS

Among 1931 participants, 348 (18.0%) were capable of sustained frailty remission. During the 8-year follow-up, 279 participants developed dementia. In a fully adjusted model, sustained remission was associated with a lower risk of dementia (hazard ratio = 0.66, 95% confidence interval = 0.47 to 0.93). The association was more pronounced among younger-old and male participants but not observed among their counterparts.

DISCUSSION

Sustained frailty remission was associated with a reduced risk of developing dementia. Physical frailty could be an essential forewarning of dementia and a target for interventions.

HIGHLIGHTS

We provided new insights into the natural progression of frailty and its impact on dementia risk using a nationally representative sample Sustained frailty remission reduced risk of incident dementia. Age and gender played a role in the frailty-dementia link, and thus individualized dementia risk screening is necessary. Physical frailty could be an essential forewarning of cognitive decline and an ideal target for interventions to prevent dementia.

Population attributable fractions of modifiable risk factors for dementia: a systematic review and meta-analysis

BACKGROUND

More than 57 million people have dementia worldwide. Evidence indicates a change in dementia prevalence and incidence in high-income countries, which is likely to be due to improved life-course population health. Identifying key modifiable risk factors for dementia is essential for informing risk reduction and prevention strategies. We therefore aimed to estimate the population attributable fraction (PAF) for dementia associated with modifiable risk factors.

METHODS

In this systematic review and meta-analysis, we searched Embase, MEDLINE, and PsycINFO, via Ovid, from database inception up to June 29, 2023, for population-derived or community-based studies and reviews reporting a PAF value for one or more modifiable risk factor for later-life dementia (prevalent or incident dementia in people aged ≥60 years), with no restrictions on dementia subtype, the sex or baseline age of participants, or the period of study. Articles were independently screened for inclusion by four authors, with disagreements resolved through consensus. Data including unweighted and weighted PAF values (weighted to account for communality or overlap in risk) were independently extracted into a predefined template by two authors and checked by two other authors. When five or more unique studies investigated a given risk factor or combination of the same factors, random-effects meta-analyses were used to calculate a pooled PAF percentage estimate for the factor or combination of factors. The review protocol was registered on PROSPERO, CRD42022323429.

FINDINGS

4024 articles were identified, and 74 were included in our narrative synthesis. Overall, PAFs were reported for 61 modifiable risk factors, with sufficient data available for meta-analysis of 12 factors (n=48 studies). In meta-analyses, the highest pooled unweighted PAF values were estimated for low education (17·2% [95% CI 14·4-20·0], p<0·0001), hypertension (15·8% [14·7-17·1], p<0·0001), hearing loss (15·6% [10·3-20·9], p<0·0001), physical inactivity (15·2% [12·8-17·7], p<0·0001), and obesity (9·4% [7·3-11·7], p<0·0001). According to weighted PAF values, low education (9·3% [6·9-11·7], p<0·0001), physical inactivity (7·3% [3·9-11·2], p=0·0021), hearing loss (7·2% [5·2-9·7], p<0·0001), hypertension (7·1% [5·4-8·8], p<0·0001), and obesity (5·3% [3·2-7·4], p=0·0001) had the highest pooled estimates. When low education, midlife hypertension, midlife obesity, smoking, physical inactivity, depression, and diabetes were combined (Barnes and Yaffe seven-factor model; n=9 studies), the pooled unweighted and weighted PAF values were 55·0% (46·5-63·5; p<0·0001) and 32·0% (26·6-37·5; p<0·0001), respectively. The pooled PAF values for most individual risk factors were higher in low-income and middle-income countries (LMICs) versus high-income countries.

INTERPRETATION

Governments need to invest in a life-course approach to dementia prevention, including policies that enable quality education, health-promoting environments, and improved health. This investment is particularly important in LMICs, where the potential for prevention is high, but resources, infrastructure, budgets, and research focused on ageing and dementia are limited.

FUNDING

UK Research and Innovation (Medical Research Council).

Associations of Frailty with Neuropsychiatric Symptoms of Alzheimer's Disease: A Longitudinal Study

Background

Frailty is a vulnerability state increasing the risk of many adverse health outcomes, but little is known about the effects of frailty on neuropsychiatric health.

Objective

To explore the associations between frailty and the risk of neuropsychiatric symptoms (NPSs) in Alzheimer's disease (AD), especially in its different clinical stages.

Methods

We included 2,155 individuals assessed using modified frailty index-11 (mFI-11), Neuropsychiatric Inventory (NPI) and Neuropsychiatric Inventory Questionnaire (NPI-Q) in the Alzheimer's Disease Neuroimaging Initiative (ADNI). The relationships between frailty and NPSs were explored with logistic regression models and Cox proportional hazard regression models. Causal mediation analyses were conducted to explore the mediation factors between frailty and NPSs.

Results

Among mild cognitive impairment (MCI) participants, frailty was cross-sectionally associated with an increased risk of apathy, and longitudinally associated with increased risk of depression and apathy. Among AD participants, frailty was cross-sectionally associated with increased risk of depression and anxiety, and longitudinally associated with an increased risk of apathy. Among participants with cognitive progression, frailty was associated with increased risk of depression and apathy. In MCI participants, the influence of frailty on NPSs was partially mediated by hippocampus volume, whole brain volume, and monocytes, with mediating proportions ranging from 8.40% to 9.29%.

Conclusions

Frailty was associated with NPSs such as depression, anxiety, and apathy among MCI, AD, and cognitive progression participants. Atrophy of the hippocampus and whole brain, as well as peripheral immunity may be involved in the potential mechanisms underlying the above associations.

Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer's disease spectrum: a COMPASS-ND study

BACKGROUND

Frailty indicators can operate in dynamic amalgamations of disease conditions, clinical symptoms, biomarkers, medical signals, cognitive characteristics, and even health beliefs and practices. This study is the first to evaluate which, among these multiple frailty-related indicators, are important and differential predictors of clinical cohorts that represent progression along an Alzheimer's disease (AD) spectrum. We applied machine-learning technology to such indicators in order to identify the leading predictors of three AD spectrum cohorts; viz., subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD. The common benchmark was a cohort of cognitively unimpaired (CU) older adults.

METHODS

The four cohorts were from the cross-sectional Comprehensive Assessment of Neurodegeneration and Dementia dataset. We used random forest analysis (Python 3.7) to simultaneously test the relative importance of 83 multi-modal frailty indicators in discriminating the cohorts. We performed an explainable artificial intelligence method (Tree Shapley Additive exPlanation values) for deep interpretation of prediction effects.

RESULTS

We observed strong concurrent prediction results, with clusters varying across cohorts. The SCI model demonstrated excellent prediction accuracy (AUC = 0.89). Three leading predictors were poorer quality of life ([QoL]; memory), abnormal lymphocyte count, and abnormal neutrophil count. The MCI model demonstrated a similarly high AUC (0.88). Five leading predictors were poorer QoL (memory, leisure), male sex, abnormal lymphocyte count, and poorer self-rated eyesight. The AD model demonstrated outstanding prediction accuracy (AUC = 0.98). Ten leading predictors were poorer QoL (memory), reduced olfaction, male sex, increased dependence in activities of daily living (n = 6), and poorer visual contrast.

CONCLUSIONS

Both convergent and cohort-specific frailty factors discriminated the AD spectrum cohorts. Convergence was observed as all cohorts were marked by lower quality of life (memory), supporting recent research and clinical attention to subjective experiences of memory aging and their potentially broad ramifications. Diversity was displayed in that, of the 14 leading predictors extracted across models, 11 were selectively sensitive to one cohort. A morbidity intensity trend was indicated by an increasing number and diversity of predictors corresponding to clinical severity, especially in AD. Knowledge of differential deficit predictors across AD clinical cohorts may promote precision interventions.

Physical and brain frailty in ischaemic stroke or TIA: Shared occurrence and outcomes. A cohort study

BACKGROUND

There is increasing interest in the concept of frailty in stroke, including both physical frailty and imaging-evidence of brain frailty. We aimed to establish the prevalence of brain frailty in stroke survivors as well as the concurrent and predictive validity of various frailty measures against long-term cognitive outcomes.

METHODS

We included consecutively admitted stroke or transient ischaemic attack (TIA) survivors from participating stroke centres. Baseline CT scans were used to generate an overall brain frailty score for each participant. We measured frailty via the Rockwood frailty index, and a Fried frailty screening tool. Presence of major or minor neurocognitive disorder at 18-months following stroke or TIA was established via a multicomponent assessment. Prevalence of brain frailty was established based upon observed percentages within groups defined by frailty status (robust, pre-frail, frail). We assessed the concurrent validity of brain frailty and frailty scales via Spearman's rank correlation. We conducted multivariable logistic regression analyses, controlling for age, sex, baseline education and stroke severity, to evaluate association between each frailty measure and 18-month cognitive impairment.

RESULTS

Three-hundred-forty-one stroke survivors participated. Three-quarters of people who were frail had moderate-severe brain frailty and prevalence increased according to frailty status. Brain frailty was weakly correlated with Rockwood frailty (Rho: 0.336; p < 0.001) and with Fried frailty (Rho: 0.230; p < 0.001). Brain frailty (OR: 1.64, 95% CI = 1.17-2.32), Rockwood frailty (OR: 1.05, 95% CI = 1.02-1.08) and Fried frailty (OR: 1.93, 95% CI = 1.39-2.67) were each independently associated with cognitive impairment at 18 months following stroke.

CONCLUSIONS

There appears to be value in the assessment of both physical and brain frailty in patients with ischaemic stroke and TIA. Both are associated with adverse cognitive outcomes and physical frailty remains important when assessing cognitive outcomes.

Higher frailty levels are associated with lower cognitive test scores in a multi-country study: evidence from the study on global ageing and adult health

Background

Frailty has been recognized as a growing issue in older adults, with recent evidence showing that this condition heralds several health-related problems, including cognitive decline. The objective of this work is to determine if frailty is associated with cognitive decline among older adults from different countries.

Methods

We analyzed the baseline the Study on Global Ageing and Adult Health (SAGE), that includes six countries (Ghana, South Africa, Mexico, China, Russia, and India). A cross-section analysis was used to assess how Frailty was related with the Clinical Frailty Scale decision tree, while cognitive decline was evaluated using standardized scores of tests used in SAGE.

Results

A total of 30,674 participants aged 50 years or older were included. There was an association between frailty levels and cognitive performance. For example, women had an inverse relationship between frailty levels and cognitive scores, even when comparing robust category with frailty level 2 (RRR = 0.85; p = 0.41), although the relative risks decrease significantly at level 3 (RRR = 0.66; p = 0.03). When controlling for age, the relative risks between frailty levels 4 to 7 significantly decreased as cognitive performance increased (RRR = 0.46, RRR = 0.52, RRR = 0.44, RRR = 0.32; p < 0.001).

Conclusion

Our results show an association between frailty levels measured in a novel way, and cognitive decline across different cultural settings.

Frailty and functional brain connectivity (FBC) in older adults with mild cognitive impairment (MCI): baseline results from the SYNERGIC Trial

Functional brain connectivity (FBC), or areas that are anatomically separate but temporally synchronized in their activation, represent a sensitive biomarker for monitoring dementia progression. It is unclear whether frailty is associated with FBC in those at higher risk of progression to dementia (e.g., mild cognitive impairment -MCI-) and if sex plays a role. We used baseline data from the SYNERGIC trial, including participants with MCI that received brain MRI. In this cross-sectional analyses (n = 100), we measured frailty using a deficit accumulation frailty index. Using the CONN toolbox, we assessed FBC of networks and regions of interest across the entire connectome. We used Pearson's correlation to investigate the relationship between FBC and frailty index in the full sample and by sex. We also divided the full sample and each sex into tertiles based upon their frailty index score and then assessed between-tertile differences in FBC. The full sample (cluster: size = 291 p-FDR < 0.05) and males (cluster: size = 993 and 451 p-FDR < 0.01) demonstrated that increasing (stronger) connectivity between the right hippocampus and clusters in the temporal gyrus was positively correlated with increasing (worse) frailty. Males also demonstrated between-tertile differences in right hippocampus connectivity to clusters in the lateral occipital cortex (cluster: size = 289 p-FDR < 0.05). Regardless of frailty status, females demonstrated stronger within-network connectivity of the Default-Mode (p = 0.024). Our results suggest that increasing (worse) frailty was associated with increasing (stronger) connectivity between regions not typically linked, which may reflect a compensation tactic by the plastic brain. Furthermore, the relationship between the two variables appears to differ by sex. Our results may help elucidate why specific individuals progress to a dementia syndrome. NCT02808676. https://www.clinicaltrials.gov/ct2/show/NCT02808676.

Frailty and Associated Environmental Factors Only Have Small Effects on Age of Onset in Huntington's Disease

BACKGROUND

Over one third of age of onset variation in Huntington's disease is unexplained by CAG repeat length. In Alzheimer's disease, frailty partly modulates the relationship between neuropathology and dementia.

OBJECTIVE

We investigated whether a multi-domain frailty index, reflecting non-genetic factors in Huntington's disease, similarly modulates the relationship between CAG repeat length and age of onset.

METHODS

We created a frailty index assessing comorbidities, substance abuse, polypharmacy, and education. We applied multiple linear regression models to 2,741 subjects with manifest Huntington's disease from the Enroll-HD cohort study, including 729 subjects with late-onset (post-60 years) disease, using frailty index or constituent item scores and CAG repeat length as independent variables. We used actual and "residual" ages of onset (difference between actual and CAG-based predicted onset) as dependent variables, the latter offsetting the increased time available to accumulate comorbidities in older subjects.

RESULTS

Higher frailty index scores were associated with significantly lower residual ages of onset in the late-onset subgroup (p = 0.03), though the effect was small (R2 = 0.27 with frailty as a predictor vs. 0.26 without). Number of comorbidities was also associated with significantly lower residual ages of onset in the late-onset subgroup (p = 0.04). Drug abuse and smoking were associated with significantly earlier ages of onset in the whole cohort (p < 0.01, p = 0.02) and late-onset subgroup (p < 0.01, p = 0.03).

CONCLUSIONS

The impact of non-genetic factors on age of onset, assessed using a frailty index or separately, in Huntington's disease is limited.

Multi-Concept Frailty Predicts the Late-Life Occurrence of Cognitive Decline or Dementia: An Updated Systematic Review and Meta-Analysis of Longitudinal Studies

Background

Frailty is a multidimensional syndrome that increases an individual’s vulnerability for developing adverse health outcomes, which include dementia. It might serve as a promising target for dementia prevention. However, there are currently no studies summarizing the association between multi-concept frailty and the risk of cognitive disorders. This study aims to summarize the evidence of associations between multi-concept frailty and cognitive disorders based on longitudinal studies.

Methods

Scopus, The Cochrane Library, PsycINFO, CINAHL, PubMed, and EMBASE databases were searched from inception to January 2, 2022. Longitudinal studies, which explored the association of frailty with incident risk of cognitive decline or dementia, were included. The multivariable-adjusted effect estimates were pooled by random-effects models. The evidence credibility was depicted according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method.

Results

A total of 30 longitudinal studies were included. Four types of frailty concepts were involved, including physical, cognitive, social, and biopsychosocial frailty. The meta-analysis comprised 20 studies of 252,571 older adults (mean age: 64.1–80.4 years), among whom 7,388 participants developed cognitive decline or dementia. Physical frailty was associated with higher risk of developing cognitive disorders [pooled relative risk (pRR) = 1.52, 95% confidence interval (CI): 1.28–1.80, I2 = 21.2%, pRR = 1.62 for cognitive decline, 95% CI: 1.07–2.45, I2 = 40.2%, pRR = 1.37 for all-cause dementia (ACD), 95% CI: 1.13–1.66, I2 = 0.0%]. Cognitive frailty (pRR = 2.90, 95% CI: 1.28–6.55, I2 = 78.1%) and pre-frailty (pRR = 4.24, 95% CI: 2.74–6.56, I2 = 30.2%) were linked to higher risk of ACD. Biopsychosocial frailty could predict a 41% (pRR = 1.41, 95% CI: 1.17–1.71) elevated risk of cognitive decline or dementia [pRR = 1.53 (95% CI: 1.19–1.96) for ACD and 1.11 (95% CI: 1.05–1.17) for Alzheimer’s disease (AD)]. In the systematic review, social frailty was associated with a 53% higher risk of AD. Preventing frailty could avoid a maximum of 9.9% cognitive disorders globally. The overall evidence strength is rated as low-to-moderate. Inconsistency and imprecision are major sources of bias.

Conclusion

Frailty in late life is a promising risk factor for cognitive disorders. Frail elderly should be monitored for their cognitive dynamics and initiate early prevention of dementia.

Systematic Review Registration

www.ClinicalTrials.gov, identifier CRD4202127 3434.

Characterizing the long-term cognitive impairment following delirium episodes: A call to action

The possible link between delirium and a long-term cognitive impairment is significant for patients, carers, and clinicians. To make progress, we need urgently novel advances in understanding the neural substrates underlying this cognitive impairment and any accompanying behavioral symptoms.