Frailty in Relation to the Risk of Alzheimer's Disease, Dementia, and Death in Older Chinese Adults: A Seven-Year Prospective Study

The Association Between Urbanization and Frailty Status in China

Background: A frailty index (FI) can identify individuals with frailty in a population of interest. Previous literature suggests a need for frailty assessment methods for older adults in China and that urbanization may impact frailty status. We used a FI to examine the association between frailty and urbanization as living in a less urbanized area may put older adults at a higher risk frailty and poor healthcare outcomes. Methods: We included adults aged 55 years and older (n = 7695) from the China Health and Nutrition Survey (2018). The FI was based on health outcomes correlating with a deficit score divided by number of health items: robust (<0.08), pre-frail (0.08-0.24), and frail (≥0.25). We used multinomial logistic regression models to examine associations between urbanization tertile (low, medium, and high) and frailty, using our novel FI. We also conducted sub-analyses examining how urbanization level modifies the relationship between frailty status and region of residence, and education and income levels. Results: Living in an area of low versus high urbanization was associated with higher odds of frail versus robust (1.5; 1.2-2.0), and pre-frail versus robust (1.6; 1.4-2.0) status in the fully adjusted model. Generally, higher odds of worse frailty status (e.g., pre-frail or frail) was associated with lower tertiles of urbanization for region, income, and education when compared to the highest urbanization tertile. Conclusions: A FI can help identify specific characteristics that may benefit from individualized interventions to counteract frailty. Living in less urbanized areas was associated with higher odds of pre-frailty and frailty. Inclusion of urbanization level, geographic residence, and social determinants of health in FI development can help identify older adults most at risk of frailty and contribute to individual and policy-level frailty prevention interventions.

Frailty in motion: Amnestic mild cognitive impairment and Alzheimer's disease cohorts display heterogeneity in multimorbidity classification and longitudinal transitions

BackgroundData-driven examination of multiple morbidities and deficits are informative for clinical and research applications in aging and dementia. Resulting profiles may change longitudinally according to dynamic alterations in extent, duration, and pattern of risk accumulation. Do such frailty-related changes include not only progression but also stability and reversion?ObjectiveWith cognitively impaired and dementia cohorts, we employed data-driven analytics to (a) detect the extent of heterogeneity in frailty-related multimorbidity and deficit burden subgroups and (b) identify key person characteristics predicting differential transition patterns.MethodsWe assembled baseline and 2-year follow-up data from the National Alzheimer's Coordinating Center for amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) cohorts. We applied factor analyses to 43 multimorbidity and deficit indicators. Latent Transition Analysis (LTA) was applied to the resulting domains in order to detect subgroups differing in transition patterns for multimorbidity and deficit burden. We characterized heterogeneity in change patterns by evaluating key person characteristics as differential predictors.ResultsFactor analyses revealed five domains at two time points. LTA showed that two latent burden subgroups at Time 1 (Low, Moderate) differentiated into an additional two subgroups at Time 2 (adding Mild, Severe). Transition analyses detected heterogeneous changes, including progression, stability, and reversion. Baseline classifications and transitions varied according to clinical cohort, global cognition, sex, age, and education.ConclusionsHeterogeneous frailty-related subgroup transitions can be (a) detected in aging adults living with aMCI and AD, (b) characterized as not only progression but also stability and reversion, and (c) predicted by precision characteristics.

Integrated care for older people improved intrinsic capacity in elderly patients: a case control study

OBJECTIVES

Observe the effect of integrated care for older people on intrinsic capacity in elderly patients.

METHODS

Sixty elderly patients from department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology between June 2021 and December 2021 were selected and divided into control group and intervention group with 30 patients in each group using random sampling method. Two groups implement routine care, and the intervention group implement integrated care by a "hospital-community-family" multidisciplinary team. After 12 weeks of intervention, the differences in frailty, cognition, depression scores, comprehension social support scores, and World Quality of Life Scale scores were compared between the two groups.

RESULTS

Before intervention, there was no significant difference in the grip strength, step speed, frailty scores, cognition, depression scores, social support and quality of survival between the control group and the intervention group. After intervention, grip strength and depression scores improved both in the control group and the intervention group, but there was no difference between the two groups. Cognitive scores, comprehension social support and quality of survival scores improved only in the intervention group, while only the cognitive ability showed significant differences between the intervention group and the control group after 12 weeks of intervention (P<0.05).

CONCLUSIONS

Integrated care for older people intervention can help to improve the intrinsic capacity of the patients, especially cognitive abilities.

Dementia risk prediction modelling in low- and middle-income countries: current state of evidence

Dementia is a leading cause of death and disability with over 60% of cases residing in low- and middle-income countries (LMICs). Therefore, new strategies to mitigate risk are urgently needed. However, despite the high burden of disease associated with dementia in LMICs, research into dementia risk profiling and risk prediction modelling is limited. Further, dementia risk prediction models developed in high income countries generally do not transport well to LMICs suggesting that context-specific models are instead needed. New prediction models have been developed, in China and Mexico only, with varying predictive accuracy. However, none has been externally validated or incorporated variables that may be important for predicting dementia risk in LMIC settings such as socio-economic status, literacy, healthcare access, nutrition, stress, pollutants, and occupational hazards. Since there is not yet any curative treatment for dementia, developing a context-specific dementia prediction model is urgently needed for planning early interventions for vulnerable groups, particularly for resource constrained LMIC settings.

To Be Frail or Not to Be Frail: This Is the Question-A Critical Narrative Review of Frailty

Many factors have contributed to rendering frailty an emerging, relevant, and very popular concept. First, many pandemics that have affected humanity in history, including COVID-19, most recently, have had more severe effects on frail people compared to non-frail ones. Second, the increase in human life expectancy observed in many developed countries, including Italy has led to a rise in the percentage of the older population that is more likely to be frail, which is why frailty is much a more common concern among geriatricians compared to other the various health-care professionals. Third, the stratification of people according to the occurrence and the degree of frailty allows healthcare decision makers to adequately plan for the allocation of available human professional and economic resources. Since frailty is considered to be fully preventable, there are relevant consequences in terms of potential benefits both in terms of the clinical outcome and healthcare costs. Frailty is becoming a popular, pervasive, and almost omnipresent concept in many different contexts, including clinical medicine, physical health, lifestyle behavior, mental health, health policy, and socio-economic planning sciences. The emergence of the new "science of frailty" has been recently acknowledged. However, there is still debate on the exact definition of frailty, the pathogenic mechanisms involved, the most appropriate method to assess frailty, and consequently, who should be considered frail. This narrative review aims to analyze frailty from many different aspects and points of view, with a special focus on the proposed pathogenic mechanisms, the various factors that have been considered in the assessment of frailty, and the emerging role of biomarkers in the early recognition of frailty, particularly on the role of mitochondria. According to the extensive literature on this topic, it is clear that frailty is a very complex syndrome, involving many different domains and affecting multiple physiological systems. Therefore, its management should be directed towards a comprehensive and multifaceted holistic approach and a personalized intervention strategy to slow down its progression or even to completely reverse the course of this condition.

Frailty in relation to the risk of carotid atherosclerosis and cardiovascular events in Chinese community-dwelling older adults: A five-year prospective cohort study

OBJECTIVE

To investigate the frailty, as estimated by accumulated health deficits, in association with the symptomatic carotid atherosclerosis and in relation to five-year cardiovascular (CVD) outcomes.

METHODS

This is a five-year prospective cohort study. Secondary analysis of data from the Beijing Longitudinal Study on Aging. Community-dwelling people aged 55+ years (n = 1257) have been followed between 2009 and 2014, and having carotid ultrasonography examinations with no CVD events at baseline. Frailty was quantified using the deficit accumulation-based frailty index (FI), constructed from 37 health deficits assessed at baseline. The association between the degree of frailty and carotid atherosclerosis was examined using odds ratios (OR) with multivariate logistic regression analyses. Effects of frailty on the probability of five-year cardiovascular events and mortality were evaluated using Cox proportional hazard ratios (HR). The analyses were adjusted for demographics, baseline carotid atherosclerosis status, and CVD risk factors.

RESULTS

The FI showed characteristic properties and was independently associated with the major carotid atherosclerosis symptoms, including carotid artery intima-media thickening (the most frail vs. the least frail: OR = 4.39: 1.98-7.82), carotid plaque (OR = 3.41: 1.28-6.54), and carotid plaque stability (OR = 1.19, 95 % CI: 1.01-3.59). Compared with the least frail, the most frail individuals were more likely to develop a cardiovascular event in five years, including myocardial infarction (HR = 3.38, 95 % CI = 1.84-6.19), stroke (HR = 1.26, 95 % CI = 1.00-5.87), CVD death (HR = 6.33, 95 % CI = 1.69-11.02), and all-cause death (HR = 5.95, 95 % CI = 2.74-8.95).

CONCLUSION

Deficit accumulation was closely associated with carotid atherosclerosis risks and strongly predicted five-year CVD events. The frailty index can be used to help identify older adults at high risks of CVD for improved preventive healthcare.

Multi-domain prognostic models used in middle-aged adults without known cognitive impairment for predicting subsequent dementia

BACKGROUND

Dementia, a global health priority, has no current cure. Around 50 million people worldwide currently live with dementia, and this number is expected to treble by 2050. Some health conditions and lifestyle behaviours can increase or decrease the risk of dementia and are known as 'predictors'. Prognostic models combine such predictors to measure the risk of future dementia. Models that can accurately predict future dementia would help clinicians select high-risk adults in middle age and implement targeted risk reduction.

OBJECTIVES

Our primary objective was to identify multi-domain prognostic models used in middle-aged adults (aged 45 to 65 years) for predicting dementia or cognitive impairment. Eligible multi-domain prognostic models involved two or more of the modifiable dementia predictors identified in a 2020 Lancet Commission report and a 2019 World Health Organization (WHO) report (less education, hearing loss, traumatic brain injury, hypertension, excessive alcohol intake, obesity, smoking, depression, social isolation, physical inactivity, diabetes mellitus, air pollution, poor diet, and cognitive inactivity). Our secondary objectives were to summarise the prognostic models, to appraise their predictive accuracy (discrimination and calibration) as reported in the development and validation studies, and to identify the implications of using dementia prognostic models for the management of people at a higher risk for future dementia.

SEARCH METHODS

We searched MEDLINE, Embase, PsycINFO, CINAHL, and ISI Web of Science Core Collection from inception until 6 June 2022. We performed forwards and backwards citation tracking of included studies using the Web of Science platform.  SELECTION CRITERIA: We included development and validation studies of multi-domain prognostic models. The minimum eligible follow-up was five years. Our primary outcome was an incident clinical diagnosis of dementia based on validated diagnostic criteria, and our secondary outcome was dementia or cognitive impairment determined by any other method.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened the references, extracted data using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), and assessed risk of bias and applicability of included studies using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We synthesised the C-statistics of models that had been externally validated in at least three comparable studies.  MAIN RESULTS: We identified 20 eligible studies; eight were development studies and 12 were validation studies. There were 14 unique prognostic models: seven models with validation studies and seven models with development-only studies. The models included a median of nine predictors (range 6 to 34); the median number of modifiable predictors was five (range 2 to 11). The most common modifiable predictors in externally validated models were diabetes, hypertension, smoking, physical activity, and obesity. In development-only models, the most common modifiable predictors were obesity, diabetes, hypertension, and smoking. No models included hearing loss or air pollution as predictors. Nineteen studies had a high risk of bias according to the PROBAST assessment, mainly because of inappropriate analysis methods, particularly lack of reported calibration measures. Applicability concerns were low for 12 studies, as their population, predictors, and outcomes were consistent with those of interest for this review. Applicability concerns were high for nine studies, as they lacked baseline cognitive screening or excluded an age group within the range of 45 to 65 years. Only one model, Cardiovascular Risk Factors, Ageing, and Dementia (CAIDE), had been externally validated in multiple studies, allowing for meta-analysis. The CAIDE model included eight predictors (four modifiable predictors): age, education, sex, systolic blood pressure, body mass index (BMI), total cholesterol, physical activity and APOEƐ4 status. Overall, our confidence in the prediction accuracy of CAIDE was very low; our main reasons for downgrading the certainty of the evidence were high risk of bias across all the studies, high concern of applicability, non-overlapping confidence intervals (CIs), and a high degree of heterogeneity. The summary C-statistic was 0.71 (95% CI 0.66 to 0.76; 3 studies; very low-certainty evidence) for the incident clinical diagnosis of dementia, and 0.67 (95% CI 0.61 to 0.73; 3 studies; very low-certainty evidence) for dementia or cognitive impairment based on cognitive scores. Meta-analysis of calibration measures was not possible, as few studies provided these data.

AUTHORS' CONCLUSIONS

We identified 14 unique multi-domain prognostic models used in middle-aged adults for predicting subsequent dementia. Diabetes, hypertension, obesity, and smoking were the most common modifiable risk factors used as predictors in the models. We performed meta-analyses of C-statistics for one model (CAIDE), but the summary values were unreliable. Owing to lack of data, we were unable to meta-analyse the calibration measures of CAIDE. This review highlights the need for further robust external validations of multi-domain prognostic models for predicting future risk of dementia in middle-aged adults.

Frailty and risk of adverse outcomes among community-dwelling older adults in China: a comparison of four different frailty scales

Background

Data on which frailty scales are most suitable for estimating risk in Chinese community populations remain limited. Herein we examined and compared four commonly used frailty scales in predicting adverse outcomes in a large population-based cohort of Chinese older adults.

Methods

A total of 5402 subjects (mean age 66.3 ± 9.6 years, 46.6% male) from the WHO Study on global AGEing and adult health (SAGE) in Shanghai were studied. Frailty was measured using a 35-item frailty index (FI), the frailty phenotype (FP), FRAIL, and Tilburg Frailty Indicator (TFI). Multivariate logistic regression models were performed to evaluate the independent association between frailty and outcomes including 4-year disability, hospitalization, and 4- and 7-year all-cause mortality. The accuracy for predicting these outcomes was determined by evaluating the area under the curve (AUC). The prevalence of frailty, sensitivity, and specificity were calculated using our proposed cut-off points and other different values.

Results

Prevalence of frailty ranged from 4.2% (FRAIL) to 16.9% (FI). FI, FRAIL and TFI were comparably associated with 4-year hospitalization, and 4- and 7-year mortality (adjusted odds ratios [aORs] 1.44-1.69, 1.91-2.22 and 1.85-2.88, respectively). FRAIL conferred the greatest risk of 4-year disability, followed by FI and TFI (aOR 5.55, 3.50, and 1.91, respectively). FP only independently predicted 4- and 7-year mortality (aOR 1.57 and 2.21, respectively). AUC comparisons showed that FI, followed by TFI and FRAIL, exhibited acceptable predictive accuracy for 4-year disability, 4- and 7-year mortality (AUCs 0.76-0.78, 0.71-0.71, 0.65-0.72, respectively), whereas all scales poorly predicted 4-year hospitalization (AUCs 0.53-0.57). For each scale, while specificity estimates (85.3-97.3%) were high and similar across all outcomes, their sensitivity estimates (6.3-56.8%) were not sufficient yet. Prevalence of frailty, sensitivity, and specificity varied considerably when different cut-off points were used.

Conclusion

Frailty defined using any of the four scales was associated with an increased risk of adverse outcomes. Although FI, FRAIL and TFI exhibited fair-to-moderate predictive accuracy and high specificity estimates, their sensitivity estimates were not sufficient yet. Overall, FI performed best in estimating risk, while TFI and FRAIL were additionally useful, the latter perhaps being more applicable to Chinese community-dwelling older adults.

A frailty index based on routine laboratory data predicts increased risk of mortality in Chinese community-dwelling adults aged over 55 years: a five-year prospective study

Background

Frailty can be operationalized based on the accumulation of deficits using a frailty index (FI) and is associated with an increased risk of adverse health outcomes. Here, we aim to compare validity of a FI from laboratory data with that of the common clinical FI for prediction of mortality in adults aged 55 + years, also examine whether combined FI could improve identification of adults aged 55 + years at increased risk of death.

Methods

Data for this analysis were obtained from the Beijing Longitudinal Study of Aging that involved 1,257 community-dwelling Chinese people, aged 55 + years at baseline. The main outcome measure was 5-year mortality. An FI-self-report based on 30 self-reported health-related data was constructed. An FI-lab was developed using laboratory data, in addition to pulse, systolic and diastolic blood pressure, pulse pressure, body mass index (BMI) and waist. A combined FI comprised all items from each FI. Kaplan–Meier survival curve and Cox proportional hazards models were performed to evaluate the risk of each FI on death. The area under receiver operating characteristic(ROC) curves were used to compare the discriminative performance of each FI.

Results

Of 1257 participants, 155 died and 156 lost at the end of the 5-year follow-up. The mean FI-self-report score was 0.11 ± 0.10, the FI-lab score was 0.33 ± 0.14 and FI-combined score was 0.19 ± 0.09. Higher frailty level defined by each FI was associated with higher risk of death. After adjustment for age and sex, Cox proportional hazards models showed that the higher scores of frailty were associated with a higher risk of mortality for each FI, the hazard ratios for the FI-self-report and FI-lab and FI-combined were 1.04 (1.03 to 1.05) and 1.02 (1.01 to 1.03) and 1.05 (1.04 to 1.07), respectively. The areas under the ROC curve were 0.79 (0.77–0.82) for the FI-self-report, 0.77(0.75–0.80) for the FI-lab and 0.81(0.78–0.82) for FI-combined.

Conclusions

A FI from laboratory data can stratify older adults at increased risk of death alone and in combination with FI based on self-report data. Assessment in clinical settings of creating an FI using routine collected laboratory data needs to be further developed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03374-z.

Genetically predicted frailty index and risk of stroke and Alzheimer's disease

BACKGROUND AND PURPOSE

Previous studies have reported the association between frailty and stroke or Alzheimer's disease (AD). However, the causality remains unclear. The aim of the present study was to evaluate whether genetically predicted frailty is associated with the risk of stroke or AD by a Mendelian randomization (MR) study.

METHODS

Genetic variants associated with the frailty index (FI) were obtained from a large genome-wide association study (GWAS). Summary-level data for stroke and AD were adopted from the corresponding large GWAS of individuals of European ancestry. The inverse variance weighted method was used for estimating causal effects. Multivariable analysis was performed for further adjustment.

RESULTS

The present MR study indicated a suggestive association between genetically predicted FI and a higher risk of any stroke (odds ratio 1.360, 95% confidence interval 1.006-1.838, p = 0.046). Regarding the subtypes of stroke, genetically predicted FI was associated with a higher risk of large artery atherosclerosis stroke (LAS) (odds ratio 2.487, 95% confidence interval 1.282-4.826, p = 0.007). No causal links were identified between genetically predicted FI and any ischaemic stroke, intracranial haemorrhage, cardioembolic stroke, small artery stroke, AD or AD-by-proxy. Multivariable MR analysis indicated that the association of genetically predicted FI with LAS was attenuated after adjustment for inflammatory bowel disease (p = 0.114).

CONCLUSIONS

The MR study suggested that genetically predicted FI may be associated with an increased risk of any stroke. Subgroup analysis indicated a suggestive association between genetically predicted FI and the risk of LAS. The underlying mechanisms need further investigation.

A Longitudinal Study on the Association of Interrelated Factors Among Frailty Dimensions, Cognitive Domains, Cognitive Frailty, and All-Cause Mortality

BACKGROUND

Cognitive frailty integrating impaired cognitive domains and frailty dimensions has not been explored.

OBJECTIVE

This study aimed to explore 1) associations among frailty dimensions and cognitive domains over time and 2) the extended definitions of cognitive frailty for predicting all-cause mortality.

METHODS

This four-year cohort study recruited 521 older adults at baseline (2011-2013). We utilized 1) generalized linear mixed models exploring associations of frailty dimensions (physical dimension: modified from Fried et al.; psychosocial dimension: integrating self-rated health, mood, and social relationship and support; global frailty: combining physical and psychosocial frailty) with cognition (global and domain-specific) over time and 2) time-dependent Cox proportional hazard models assessing associations between extended definitions of cognitive frailty (cognitive domains-frailty dimensions) and all-cause mortality.

RESULTS

At baseline, the prevalence was 3.0%for physical frailty and 37.6%for psychosocial frailty. Greater physical frailty was associated with poor global cognition (adjusted odds ratio = 1.43-3.29, β: -1.07), logical memory (β: -0.14 to -0.10), and executive function (β: -0.51 to -0.12). Greater psychosocial frailty was associated with poor global cognition (β: -0.44) and attention (β: -0.15 to -0.13). Three newly proposed definitions of cognitive frailty, "mild cognitive impairment (MCI)-psychosocial frailty," "MCI-global frailty," and "impaired verbal fluency-global frailty," outperformed traditional cognitive frailty for predicting all-cause mortality (adjusted hazard ratio = 3.49, 6.83, 3.29 versus 4.87; AIC = 224.3, 221.8, 226.1 versus 228.1).

CONCLUSION

Notably, extended definitions of cognitive frailty proposed by this study better predict all-cause mortality in older adults than the traditional definition of cognitive frailty, highlighting the importance of psychosocial frailty to reduce mortality in older adults.

Olfactory Phenotypes Differentiate Cognitively Unimpaired Seniors from Alzheimer's Disease and Mild Cognitive Impairment: A Combined Machine Learning and Traditional Statistical Approach

BACKGROUND

Olfactory dysfunction (OD) is an early symptom of Alzheimer's disease (AD). However, olfactory testing is not commonly performed to test OD in the setting of AD.

OBJECTIVE

This work investigates objective OD as a non-invasive biomarker for accurately classifying subjects as cognitively unimpaired (CU), mild cognitive impairment (MCI), and AD.

METHODS

Patients with MCI (n = 24) and AD (n = 24), and CU (n = 33) controls completed two objective tests of olfaction (Affordable, Rapid, Olfactory Measurement Array -AROMA; Sniffin' Sticks Screening 12 Test -SST12). Demographic and subjective sinonasal and olfaction symptom information was also obtained. Analyses utilized traditional statistics and machine learning to determine olfactory variables, and combinations of variables, of importance for differentiating normal and disease states.

RESULTS

Inability to correctly identify a scent after detection was a hallmark of MCI/AD. AROMA was superior to SST12 for differentiating MCI from AD. Performance on the clove scent was significantly different between all three groups. AROMA regression modeling yielded six scents with AUC of the ROC of 0.890 (p < 0.001). Random forest model machine learning algorithms considering AROMA olfactory data successfully predicted MCI versus AD disease state. Considering only AROMA data, machine learning algorithms were 87.5%accurate (95%CI 0.4735, 0.9968). Sensitivity and specificity were 100%and 75%, respectively with ROC of 0.875. When considering AROMA and subject demographic and subjective data, the AUC of the ROC increased to 0.9375.

CONCLUSION

OD differentiates CUs from those with MCI and AD and can accurately predict MCI versus AD. Leveraging OD data may meaningfully guide management and research decisions.

Frailty in Rheumatic Diseases

Frailty is a syndrome characterized by the decline in the physiologic reserve and function of several systems, leading to increased vulnerability and adverse health outcomes. While common in the elderly, recent studies have underlined the higher prevalence of frailty in chronic diseases, independent of age. The pathophysiological mechanisms that contribute to frailty have not been completely understood, although significant progresses have recently been made. In this context, chronic inflammation is likely to play a pivotal role, both directly and indirectly through other systems, such as the musculoskeletal, endocrine, and neurological systems. Rheumatic diseases are characterized by chronic inflammation and accumulation of deficits during time. Therefore, studies have recently started to explore the link between frailty and rheumatic diseases, and in this review, we report what has been described so far. Frailty is dynamic and potentially reversible with 8.3%-17.9% of older adults spontaneously improving their frailty status over time. Muscle strength is likely the most significant influencing factor which could be improved with training thus pointing at the need to maintain physical activity. Not surprisingly, frailty is more prevalent in patients affected by rheumatic diseases than in healthy controls, regardless of age and is associated with high disease activity to affect the clinical outcomes, largely due to chronic inflammation. More importantly, the treatment of the underlying condition may prevent frailty. Scales to assess frailty in patients affected by rheumatic diseases have been proposed, but larger casuistries are needed to validate disease-specific indexes, which could allow more accurate prognostic estimates than demographic and disease-related variables alone. Frail patients can be more vulnerable and more difficult to treat, due to the risk of side effects, therefore frailty should be taken into account in clinical decisions. Clinical trials addressing frailty could identify patients who are less likely to tolerate potentially toxic medications and might benefit from more conservative regimens. In conclusion, the implementation of the concept of frailty in rheumatology will allow a better understanding of the patient global health, a finest risk stratification and a more individualized management strategy.

CCCDTD5 recommendations on early non cognitive markers of dementia: A Canadian consensus

Introduction

Cognitive impairment is the hallmark of Alzheimer's disease (AD) and related dementias. However, motor decline has been recently described as a prodromal state that can help to detect at‐risk individuals. Similarly, sensory changes, sleep and behavior disturbances, and frailty have been associated with higher risk of developing dementia. These clinical findings, together with the recognition that AD pathology precedes the diagnosis by many years, raises the possibility that non‐cognitive changes may be early and non‐invasive markers for AD or, even more provocatively, that treating non‐cognitive aspects may help to prevent or treat AD and related dementias.

Methods

A subcommittee of the Canadian Consensus Conference on Diagnosis and Treatment of Dementia reviewed areas of emerging evidence for non‐cognitive markers of dementia. We examined the literature for five non‐cognitive domains associated with future dementia: motor, sensory (hearing, vision, olfaction), neuro‐behavioral, frailty, and sleep. The Grading of Recommendations Assessment, Development, and Evaluation system was used to assign the strength of the evidence and quality of the recommendations. We provide recommendations to primary care clinics and to specialized memory clinics, answering the following main questions: (1) What are the non‐cognitive and functional changes associated with risk of developing dementia? and (2) What is the evidence that sensory, motor, behavioral, sleep, and frailty markers can serve as potential predictors of dementia?

Results

Evidence supported that gait speed, dual‐task gait speed, grip strength, frailty, neuropsychiatric symptoms, sleep measures, and hearing loss are predictors of dementia. There was insufficient evidence for recommending assessing olfactory and vision impairments as a predictor of dementia.

Conclusions

Non‐cognitive markers can assist in identifying people at risk for cognitive decline or dementia. These non‐cognitive markers may represent prodromal symptoms and several of them are potentially amenable to treatment that might delay the onset of cognitive decline.

Developing and Validating a Primary Care EMR-based Frailty Definition using Machine Learning

Introduction

Individuals who have been identified as frail have an increased state of vulnerability, often leading to adverse health events, increased health spending, and potentially detrimental outcomes.

Objective

The objective of this work is to develop and validate a case definition for frailty that can be used in a primary care electronic medical record database.

Methods

This is a cross-sectional validation study using data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) in Southern Alberta. 52 CPCSSN sentinels assessed a random sample of their own patients using the Rockwood Clinical Frailty scale, resulting in a total of 875 patients to be used as reference standard. Patients must be over the age of 65 and have had a clinic visit within the last 24 months. The case definition for frailty was developed using machine learning methods using CPCSSN records for the 875 patients.

Results

Of the 875 patients, 155 (17.7%) were frail and 720 (84.2%) were not frail. Validation metrics of the case definition were: sensitivity and specificity of 0.28, 95% CI (0.21 to 0.36) and 0.94, 95% CI (0.93 to 0.96), respectively; PPV and NPV of 0.53, 95% CI (0.42 to 0.64) and 0.86, 95% CI (0.83 to 0.88), respectively.

Conclusions

The low sensitivity and specificity results could be because frailty as a construct remains under-developed and relatively poorly understood due to its complex nature. These results contribute to the literature by demonstrating that case definitions for frailty require expert consensus and potentially more sophisticated algorithms to be successful.

Association Between Brain β-Amyloid and Frailty in Older Adults

BACKGROUND

We sought to determine whether cortical and regional β-amyloid (Aβ) were cross-sectionally and prospectively associated with change in frailty status in older adults.

METHODS

We used data from 269 community-dwelling participants from the Multidomain Alzheimer's Preventive Trial (MAPT) who were assessed for brain Aβ using positron-emission tomography scan. Regional and cortical-to-cerebellar standardized uptake value ratios were obtained. Frailty was assessed by a frailty index composed of 19 items not directly linked to cognition and Alzheimer's disease.

RESULTS

A significant and positive cross-sectional and prospective relationship was found for Aβ in the anterior putamen (cross-sectional: β = 0.11 [0.02-0.20], p = .02; prospective: β = 0.11 [0.03-0.19], p = .007), posterior putamen (cross-sectional: β = 0.12 [0.009-0.23], p = .03; prospective: β = 0.11 [0.02-0.21], p = .02), and precuneus regions (cross-sectional: β = 0.07 [0.01-0.12], p = .01; prospective: β = 0.07 [0.01-0.12], p = .01) with increasing frailty.

CONCLUSIONS

This study has found new information regarding cross-sectional and prospective positive associations between region-specific brain Aβ deposits and worsening frailty. The potential mechanisms involved require further investigation.

Alzheimer's associated amyloid and tau deposition co-localizes with a homeostatic myelin repair pathway in two mouse models of post-stroke mixed dementia

The goal of this study was to determine the chronic impact of stroke on the manifestation of Alzheimer’s disease (AD) related pathology and behavioral impairments in mice. To accomplish this goal, we used two distinct models. First, we experimentally induced ischemic stroke in aged wildtype (wt) C57BL/6 mice to determine if stroke leads to the manifestation of AD-associated pathological β-amyloid (Aβ) and tau in aged versus young adult wt mice. Second, we utilized a transgenic (Tg) mouse model of AD (hAPP-SL) to determine if stroke leads to the worsening of pre-existing AD pathology, as well as the development of pathology in brain regions not typically expressed in AD Tg mice. In the wt mice, there was delayed motor recovery and an accelerated development of cognitive deficits in aged mice compared to young adult mice following stroke. This corresponded with increased brain atrophy, increased cholinergic degeneration, and a focal increase of Aβ in areas of axonal degeneration in the ipsilateral hemisphere of the aged animals. By contrast, in the hAPP-SL mice, we found that ischemia induced aggravated behavioral deficits in conjunction with a global increase in Aβ_, tau, and cholinergic pathology compared to hAPP-SL mice that underwent a sham stroke procedure. With regard to a potential mechanism, in both models, we found that the stroke-induced Aβ and tau deposits co-localized with increased levels of β-secretase 1 (BACE1), along with its substrate, neuregulin 1 (NGR1) type III, both of which are proteins integral for myelin repair. Based on these findings, we propose that the chronic sequelae of stroke may be ratcheting-up a myelin repair pathway, and that the consequent increase in BACE1 could be causing an inadvertent cleavage of its alternative substrate, AβPP, resulting in greater Aβ seeding and pathogenesis.