Abstract
BackgroundFrailty is an aging condition that reflects multisystem decline and an increased risk for adverse outcomes, including differential cognitive decline and impairment. Two prominent approaches for measuring frailty are the frailty phenotype and the frailty index. We explored a complementary data-driven approach for frailty assessment that could detect early frailty profiles (or subtypes) in relatively healthy older adults. Specifically, we tested whether (1) modalities of early frailty profiles could be empirically determined, (2) the extracted profiles were differentially related to longitudinal cognitive decline, and (3) the profile and prediction patterns were robust for males and females.MethodsParticipants (n = 649; M age = 70.61, range 53–95) were community-dwelling older adults from the Victoria Longitudinal Study who contributed data for baseline multi-morbidity assessment and longitudinal cognitive trajectory analyses. An exploratory factor analysis on 50 multi-morbidity items produced 7 separable health domains. The proportion of deficits in each domain was calculated and used as continuous indicators in a data-driven latent profile analysis (LPA). We subsequently examined how frailty profiles related to the level and rate of change in a latent neurocognitive speed variable.ResultsLPA results distinguished three profiles: not-clinically-frail (NCF; characterized by limited impairment across indicators; 84%), mobility-type frailty (MTF; characterized by impaired mobility function; 9%), and respiratory-type frailty (RTF; characterized by impaired respiratory function; 7%). These profiles showed differential neurocognitive slowing, such that MTF was associated with the steepest decline, followed by RTF, and then NCF. The baseline frailty index scores were the highest for MTF and RTF and increased over time. All observations were robust across sex.ConclusionsA data-driven approach to early frailty assessment detected differentiable profiles that may be characterized as morbidity-intensive portals into broader and chronic frailty. Early inventions targeting mobility or respiratory deficits may have positive downstream effects on frailty progression and cognitive decline.
Highlights
Frailty is an aging condition that reflects multisystem decline and an increased risk for adverse outcomes, including differential cognitive decline and impairment
Values on the index reflect the number of deficits that an individual has accumulated—and in pre-clinical aging, the global frailty index may be relatively low while specific morbidity sources or domains of impairment are emerging
RG3: Generalizability of profile membership and prediction patterns across sex We found that profile membership was similar across sex such that male sex was related to the likelihood of being classified into mobility-type frailty (MTF) (OR = .62, ns) or respiratory-type frailty (RTF) (OR = 0.61, ns) as compared to NCF
Summary
Frailty is an aging condition that reflects multisystem decline and an increased risk for adverse outcomes, including differential cognitive decline and impairment. The phenotypes are ordered on the basis of the number of deficits, such that an individual with no deficits is classified as robust, one to two deficits is pre-frail, and three or more deficits is frail [6]. Because this approach incorporates a restricted number of physical characteristics, it may be limited in early detection of frailty risk. Values on the index reflect the number of deficits that an individual has accumulated—and in pre-clinical aging, the global frailty index may be relatively low while specific morbidity sources or domains of impairment are emerging. New data-driven analytic technologies may be useful in early detection of frailty profiles that serve as portals to the emergence of global frailty in aging—and as harbingers of a host of adverse aging outcomes
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