Lindsay Wallace

Modifications to the frailty phenotype criteria: Systematic review of the current literature and investigation of 262 frailty phenotypes in the Survey of Health, Ageing, and Retirement in Europe

We conducted a systematic review to determine variability in how the criteria of the frailty phenotype (grip strength, weight loss, exhaustion, walking speed, physical activity) were assessed. We then evaluated the impact on estimating prevalence and mortality of modifying the criteria, using the Survey of Health, Ageing, & Retirement in Europe (SHARE). Five databases were searched for original research articles published after 2000, which evaluated frailty using the phenotypic criteria. Among the 264 included studies, 24 studies provided enough information to demonstrate that all criteria were assessed as proposed in the original frailty phenotype study by Fried et al. (2001). Physical inactivity and weight loss were the criteria most often modified. We then created 262 phenotypes from SHARE based on common modifications found in the review. Among these phenotypes, frailty prevalence ranged from 12.7% to 28.2%. Agreement with the primary frailty phenotype ranged from 0.662 to 0.967 and internal consistency ranged from 0.430 to 0.649. Women had 2.1-16.3% higher frailty prevalence than men. Areas under receiver operating characteristic curves for discriminating five-year mortality ranged from 0.607 (95% CI: 0.583-0.630) to 0.668 (0.645-0.691). The frailty phenotype often has been modified, and these modifications have important impact on its classification and predictive ability.

Comparison of alternate scoring of variables on the performance of the frailty index

BackgroundThe frailty index (FI) is used to measure the health status of ageing individuals. An FI is constructed as the proportion of deficits present in an individual out of the total number of age-related health variables considered. The purpose of this study was to systematically assess whether dichotomizing deficits included in an FI affects the information value of the whole index.MethodsSecondary analysis of three population-based longitudinal studies of community dwelling individuals: Nova Scotia Health Survey (NSHS, nu2009=u20093227 aged 18+), Survey of Health, Ageing and Retirement in Europe (SHARE, nu2009=u200937546 aged 50+), and Yale Precipitating Events Project (Yale-PEP, nu2009=u2009754 aged 70+). For each dataset, we constructed two FIs from baseline data using the deficit accumulation approach. In each dataset, both FIs included the same variables (23 in NSHS, 70 in SHARE, 33 in Yale-PEP). One FI was constructed with only dichotomous values (marking presence or absence of a deficit); in the other FI, as many variables as possible were coded as ordinal (graded severity of a deficit). Participants in each study were followed for different durations (NSHS: 10xa0years, SHARE: 5xa0years, Yale PEP: 12xa0years).ResultsWithin each dataset, the difference in mean scores between the ordinal and dichotomous-only FIs ranged from 0 to 1.5 deficits. Their ability to predict mortality was identical; their absolute difference in area under the ROC curve ranged from 0.00 to 0.02, and their absolute difference between Cox Hazard Ratios ranged from 0.001 to 0.009.ConclusionsAnalyses from three diverse datasets suggest that variables included in an FI can be coded either as dichotomous or ordinal, with negligible impact on the performance of the index in predicting mortality.

Accumulation of Non-Traditional Risk Factors for Coronary Heart Disease Is Associated with Incident Coronary Heart Disease Hospitalization and Death

Background Assessing multiple traditional risk factors improves prediction for late-life diseases, including coronary heart disease (CHD). It appears that non-traditional risk factors can also predict risk. The objective was to investigate contributions of non-traditional risk factors to coronary heart disease risk using a deficit accumulation approach. Methods Community-dwelling adults with no known history of CHD (nu200a=u200a2195, mean age 46.9±18.7 years, 51.8% women) participated in the 1995 Nova Scotia Health Survey. Three risk factor indices were constructed to quantify the proportion of deficits present in individuals: 1) a 17-item Non-Traditional Risk Factor Index (e.g. sinusitis, arthritis); 2) a 9-item Traditional Risk Factor Index (e.g. hypertension, diabetes); and 3) a frailty index (25 items combined from the other two index measures). Ten-year risks of CHD events (defined as CHD-related hospitalization and CHD-related mortality) were evaluated. Results The Non-Traditional Risk Factor Index, made up of health deficits unrelated to CHD, was independently associated with incident CHD events over 10 years after controlling for age, sex, and the Traditional Risk Factor Index [adjusted {adj.} Hazard Ratio {HR}u200a=u200a1.31; Confidence Interval {CI} 1.14–1.51]. When all health deficits, both those related and unrelated to CHD, were included in a frailty index the corresponding adjusted hazard ratio was 1.61; CI 1.40–1.85. Conclusion Both traditional and non-traditional risk factor indices are independently associated with incident CHD events. CHD risk assessment may benefit from consideration of general health information as well as from traditional risk factors.

Social vulnerability as a predictor of mortality and disability: cross-country differences in the survey of health, aging, and retirement in Europe (SHARE)

BackgroundSocial factors are important for health; the concept of social vulnerability considers them holistically and can be quantified using a social vulnerability index (SVI).AimsInvestigate the SVI in relation to mortality and disability, independent of frailty, in middle-aged and older European adults, and examine how this relationship differs across countries.Methods18,289 community-dwelling participants 50xa0years and older from SHARE wave 1 (2004) were included in our sample. A 32-item SVI and a 57-item frailty index were calculated for individuals as the proportion of deficits present out of the total number considered. Countries were grouped based on their social model: Nordic (Denmark, Netherlands, Sweden), Continental (France, Austria, Belgium, Germany) and Mediterranean (Greece, Italy, Spain). Outcome measures were 5-year mortality and disability (≥1 dependency with activities of daily living) at wave 4 (2011–2012).ResultsHigh social vulnerability (highest quartile) predicted mortality (HRxa0=xa01.25, 95xa0% CI 1.07–1.45), and disability (ORxa0=xa01.36, 95xa0% CI 1.15–1.62) after controlling for age, sex, baseline disability and frailty level. When analyses were split by social model, social vulnerability remained a significant predictor of mortality for Continental (HRxa0=xa01.36, CI 1.05–1.77) and Mediterranean (HRxa0=xa01.33, CI 1.03–1.72) countries, but not the Nordic (HRxa0=xa01.02, CI 0.76–1.37) countries; the same pattern was observed for disability (Nordic ORxa0=xa01.06, CI 0.72–1.55; Continental ORxa0=xa01.53, CI 1.20–1.96; Mediterranean ORxa0=xa01.58, CI 1.13–2.23).Discussion/ConclusionsSocial vulnerability was a significant predictor of mortality and disability, though when controlling for frailty, this relationship varied by the social model of the country.

Commentary: Age-related neurodegenerative disease research needs aging models

In his recent article, Ian Johnson identifies a need for research into neurodegenerative diseases that makes use of animal models more closely resembling the complex, age-related reality of these diseases in people (Johnson, 2015). The purpose of this commentary is to build upon this idea using insights from animal and human models of frailty. n nJohnson has highlighted the growing importance of finding suitable treatments for neurodegenerative disorders as our population ages and these diseases become more common. Current animal models of neurodegenerative disease largely ignore the context in which many of these diseases arise; they are inextricably linked to aging. We argue that the process of aging represents more than just the passage of time. As the author notes, “age at the time of neuronal injury affects neuronal survival, so it is a small step to suggest that age-related differences in neuronal survival requirements could explain the disappointing translation of basic research to clinical situations” (Johnson, 2015). We would like to take the reader one step further to consider the differences between young and old individuals. Specifically, we draw attention to the concept that aging represents the accumulation of deficits in a complex system, such as a living organism. It is hypothesized that subcellular deficits accumulate to eventually give rise to tissue level deficits and continue to “scale up” to organ and system level deficits that are clinically recognizable (Howlett and Rockwood, 2013; Rockwood et al., 2015). n nThe accumulation of health deficits has been used to measure a persons level of frailty, which can be conceptualized as a state of increased vulnerability to poor health outcomes that differs among people of the same chronological age (Mitnitski et al., 2001; Rockwood and Mitnitski, 2007; Clegg et al., 2013). In this way, among people who are the same age, different individuals have differing levels of health and risk of death (Mitnitski et al., 2013). This difference in health state can be characterized as the number of health deficits present in an individual; someone with 3 health problems is less likely to suffer adverse health events than someone with 10 health problems (Mitnitski et al., 2001). Thus, frailty can be measured as the accumulation of deficits, and this quantification has been shown to be valid and reliable across both human and, more recently, rodent populations (Mitnitski et al., 2002, 2004; Goggins et al., 2005; Parks et al., 2012; Wallace et al., 2014; Whitehead et al., 2014; Feridooni et al., 2015; Kane et al., 2015). n nTo truly understand the origins of (and thus, appropriately treat) age-associated neurodegenerative disorders, we must consider them in the context in which they arise—in aging organisms as Johnson has suggested. Identifying and modeling frailty may be useful in this endeavor. One of us (SH) has developed an approach to modeling frailty in mice, allowing researchers to quantify health deficits (Parks et al., 2012; Feridooni et al., 2015; Huizer-Pajkos et al., 2015; Kane et al., 2015). Interestingly, mice exhibit an exponential increase in frailty with age, with a pattern that is remarkably similar to that seen in human populations (Whitehead et al., 2014). Further, increased frailty is associated with other adverse health effects, including poor heart cell function in mice (Parks et al., 2012). Studies in mice have also shown that frailty is attenuated by known longevity interventions including caloric restriction and treatment with the anti-oxidant, resveratrol (Kane et al., 2015). n nJohnson presents some barriers to modeling aging in mice, particularly that mice tend to acquire age-related health problems, which may pose a threat to the quality and accuracy of research. If we consider (1) that very few people with neurodegenerative disease suffer from no other health problems, and (2) the presence of multiple age-related conditions that may be complex and inter-related create the exact conditions we describe as characteristic of the aging process, this may not serve as a barrier. To date, research on neurodegenerative disease has largely ignored the fact that age-related conditions come as a package in a complex system, the human body. Thus, research conducted on young animals with few health problems in attempts to isolate mechanisms may not be useful for this particular research problem and could contribute to the lack of translation we see in clinical trials. Given the research efforts and funding devoted to understanding the mechanisms and treatment of neurodegenerative diseases without major gain, waiting 2–3 years for animals to age to acquire quality results seems like a better alternative than doing research that does not translate well to humans. n nAnother point we argue in considering how best to translate research from animal models to humans, is to encourage the inclusion of female animals in models simulating age-related disease. Females are frequently excluded from studies because effects of hormone cycles are considered to be a confounder, although the evidence suggests that results obtained in females are not inherently more variable than results in males (Clayton and Collins, 2014). Further, the reality is that women live longer than men and tend to suffer from age-related diseases at a disproportionate rate. Including female animals may be another way to improve the translational ability of animal models in studies of aging. n nBased on research to date, a single mechanism responsible for each age-related disease is unlikely. Instead, it may be useful to consider these problems as arising in complex, interacting systems, and embrace the possibility that these age-related diseases are of multiple origins. Aging animal research can play a significant role in realizing this aim, particularly in modeling disease among frail older individuals. We applaud the author of the original editorial for bringing this issue to light and hope that readers will consider frailty in how we can bring about meaningful change in animal modeling of age-related neurodegenerative disease.

Lower Frailty Is Associated with Successful Cognitive Aging Among Older Adults with HIV

Aging with HIV poses unique and complex challenges, including avoidance of neurocognitive disorder. Our objective here is to identify the prevalence and predictors of successful cognitive aging (SCA) in a sample of older adults with HIV. One hundred three HIV-infected individuals aged 50 and older were recruited from the Modena HIV Metabolic Clinic in Italy. Participants were treated with combination antiretroviral therapy for at least 1 year and had suppressed plasma HIV viral load. SCA was defined as the absence of neurocognitive impairment (as defined by deficits in tasks of episodic learning, information processing speed, executive function, and motor skills) depression, and functional impairment (instrumental activities of daily living). In cross-sectional analyses, odds of SCA were assessed in relation to HIV-related clinical data, HIV-Associated Non-AIDS (HANA) conditions, multimorbidity (≥2HANA conditions), and frailty. A frailty index was calculated as the number of deficits present out of 37 health variables. SCA was identified in 38.8% of participants. Despite no differences in average chronologic age between groups, SCA participants had significantly fewer HANA conditions, a lower frailty index, and were less likely to have hypertension. In addition, hypertension (odds ratio [OR]u2009=u20090.40, pu2009=u2009.04), multimorbidity (ORu2009=u20090.35, pu2009=u2009.05), and frailty (ORu2009=u20090.64, pu2009=u2009.04) were significantly associated with odds of SCA. Frailty is associated with the likelihood of SCA in people living with HIV. This defines an opportunity to apply knowledge from geriatric population research to people aging with HIV to better appreciate the complexity of their health status.

Predictors of transitions in frailty severity and mortality among people aging with HIV

Background People aging with HIV show variable health trajectories. Our objective was to identify longitudinal predictors of frailty severity and mortality among a group aging with HIV. Methods Exploratory analyses employing a multistate transition model, with data from the prospective Modena HIV Metabolic Clinic Cohort Study, based in Northern Italy, begun in 2004. Participants were followed over four years from their first available visit. We included all 963 participants (mean age 46.8±7.1; 29% female; 89% undetectable HIV viral load; median current CD4 count 549, IQR 405–720; nadir CD4 count 180, 81–280) with four-year data. Frailty was quantified using a 31-item frailty index. Outcomes were frailty index score or mortality at four-year follow-up. Candidate predictor variables were baseline frailty index score, demographic (age, sex), HIV-disease related (undetectable HIV viral load, current CD4+ T-cell count, nadir CD4 count, duration of HIV infection, and duration of antiretroviral therapy [ARV] exposure), and behavioral factors (smoking, injection drug use (IDU), and hepatitis C virus co-infection). Results Four-year mortality was 3.0% (n = 29). In multivariable analyses, independent predictors of frailty index at follow-up were baseline frailty index (RR 1.06, 95% CI 1.05–1.07), female sex (RR 0.93, 95% CI 0.87–0.98), nadir CD4 cell count (RR 0.96, 95% CI 0.93–0.99), duration of HIV infection (RR 1.06, 95% CI 1.01–1.12), duration of ARV exposure (RR 1.08, 95% CI 1.02–1.14), and smoking pack-years (1.03, 1.01–1.05). Independent predictors of mortality were baseline frailty index (OR 1.19, 1.02–1.38), current CD4 count (0.34, 0.20–0.60), and IDU (2.89, 1.30–6.42). Conclusions Demographic, HIV-disease related, and social and behavioral factors appear to confer risk for changes in frailty severity and mortality among people aging with HIV.

Is it better to be happy or not depressed? Depression mediates the effect of psychological well-being on adverse health outcomes in older adults

To examine the relationship between psychological well‐being and depression in older adults and the relative contribution these psychological factors have on risk of functional disability, frailty, and mortality.

THE IMPACT OF FRAILTY AND COGNITIVE DECLINE ON QUALITY OF LIFE: HOW DOES SOCIAL CONTEXT MATTER?

status and baseline factors were determined. Results: At the 6year follow-up, 319 participants had normal cognition, 210 had MCI, and 67 had dementia; 163 withdrew from the study and 114 were deceased. When compared to normal cognition at followup, the factor most strongly associated with the poorer cognition outcomes (MCI and dementia) and attrition (withdrawn and deceased) was baseline MCI (Table 1). Older age, slower walking speed, and poorer smelling ability were associated with at least one poorer cognition outcome and attrition. Atrial fibrillation and an apolipoprotein E ε4 allele were uniquely associated with poorer cognition outcomes (MCI and both MCI and dementia, respectively), and being male, depressed or on antidepressants and having lower physical activity were uniquely associated with attrition. All baseline factors significantly associated with MCI or dementia remained so in sensitivity analyses that excluded participants lost to attrition. However, significant associations emerged for additional baseline factors: slower walking speed [odds ratio (95% confidence interval)1⁄41.11 (1.00-1.22), p1⁄40.042] and poorer smelling ability [0.89 (0.79-1.00), p1⁄40.040] for MCI, and diabetes [0.11 (0.01-0.94), p1⁄40.043], hypercholesterolaemia [0.48 (0.24-0.96), p1⁄40.039] and being on antidepressants [3.71 (1.22-11.30), p1⁄40.021] for dementia. Conclusions:MCI, older age, poorer smelling ability, slower walking speed, and an apolipoprotein E ε4 allele all predicted MCI or dementia 6 years later. This has implications for the early identification of individuals most at risk of developing dementia, and the implementation of strategies to minimise future cognitive decline.

Construct validation of a Frailty Index, an HIV Index and a Protective Index from a clinical HIV database

Background Standard care for HIV clinical practice has started focusing on age-related problems, but despite this recent change physicians involved in HIV care do not often screen HIV patients for frailty. Our aim was to construct three indexes from an HIV clinical database (i.e. Frailty Index, (FI), HIV Index, (HIVI), and Protective Index (PI)) and to assess levels of frailty, HIV severity and demographic and protective lifestyle factors among HIV patients. Methods and findings We included data from 1612 patients who attended an Italian HIV clinic between September 2016 and December2017 (mean±SD age: 53.1±8 years, 73.9% men).We used 92 routine variables collected by physicians and other health care professionals to construct three indexes: a 72-item FI (biometric, psychiatric, blood test, daily life activities, geriatric syndromes and nutrition data), a 10-item HIVI (immunological, viral and therapeutics) and a 10-item PI (income, education, social engagement, and lifestyle habits data)(the lower the FI and HIVI scores, and the higher the PI scores, the lower the risk for participants).The FI, HIVI and PI scores were 0.19±0.08, 0.48±0.17 and 0.62±0.13, respectively. Men had higher FI (0.19±0.08 vs 0.18±0.08; p = 0.010) and lower HIVI (0.47±0.18 vs 0.50±0.15; p = 0.038) scores than women. FI and HIVI scores both increased 1.9% per year of age (p < 0.001), whereas the PI decreased 0.2% per year (p<0.050). In addition, the FI score increased 1.6% and the PI score decreased 0.5% per year of HIV infection (p < 0.001). Conclusion It is feasible to assess levels of frailty, HIV severity and protective lifestyle factors in HIV patients using data from a clinical database. Frailty levels are high among HIV patients and even higher among older patients and those with a long duration of HIV. Future studies need to examine the ability of the three indices to predict adverse health outcomes such as hospitalization and mortality.