CIHR Team in Frailty and Aging Research Projects RP #2

RP #2: Frailty: A longitudinal study of its expressions (FRÉLE: Fragilité, une étude longitudinale de ses expressions)

CO-INVESTIGATORS: H Payette, H Bergman, C Wolfson, J Morais, P Gaudreau, T Fulop, R Hébert, R Verreault, J Desrosiers, L Demers, MJ Kergoat, MV Zunzunegui, B Shatenstein.
RESEARCH STAFF: Claude Galand, John Fletcher.

INTRODUCTION: Frailty has been defined as a syndrome with an undefined set of components. The aim of this research project is to identify the components of frailty, determine their distribution and evolution in samples of elderly persons in Québec and study their predictors and consequences. We expect that taken together, the components of frailty syndrome will suggest evolving frailty profiles. One of the aims of this project is to identify these profiles and trace their trajectories in order to identify groups of elderly persons who are at high risk of deteriorating health status. Being able to identify these persons is one of the major public health challenges in Québec and throughout Canada.

MAIN OBJECTIVE: The main objective of this study is to contribute to the understanding of frailty among elderly persons in order to better plan social and health services for this population. More specifically, the study will:


Analyze frailty by identifying: 
- psychosocial, functional and biological markers of frailty; 
- frailty profiles; and 
- trajectories of frailty.


Determine, among men and women:
- the consequences of frailty on the prevalence of chronic diseases, disability and death; 
- the socio-economic and social network factors that promote the emergence of frailty and its
  trajectories; and
- the relationship between frailty and other dimensions of health.

SPECIFIC OBJECTIVES: Improving service planning for elderly persons can be accomplished through the following specific objectives:

1a & 1b: First, define the clinical frailty syndrome by its seven physiological and cognitive components. The first five are physiological (unintentional weight loss, low muscle strength, poor physical performance and feeling of exhaustion). Other physiological components that may be added include: sarcopenia and metabolic syndrome, which are not represented in this phenotype. The two other components are: cognition and depression.

The first objective (1a) of this study is to identify biological and psychosocial indicators that can potentially be added to the core frailty syndrome components. An assessment of sarcopenia will be considered. The second objective (2a) is to identify homogenous sub-groups of elderly persons or frailty profiles of people with similar frailty components.

1c: The interrelationship of frailty components varies with time. Frailty trajectories will therefore be identified, taking into consideration the biological, functional and psychosocial indicators previously defined in objectives 1a and 1b.

2a: The primary predictors of frailty trajectories considered in this study are: socio-economic status, structural aspects of social networks, the positive and negative aspects of social support, autonomy and gender. Smoking, insomnia, lifelong regular physical exercise and risks linked to energy or nutritional deficiencies will also be assessed.

2b: The impacts of frailty will be examined as a function of functional limitations, physical disabilities, chronic diseases and death.

METHODS: Two samples of elderly persons living at home will be used:

Sample 1: The NuAge longitudinal study cohort of 1793 elderly persons living in Sherbrooke and Montréal will be used to study the emergence of frailty and identify frailty profiles and trajectories as a function of biological, functional and psychosocial indicators. NuAge will allow us to use biological and performance tests as well as subjective measures. With NuAge data, we will be able to analyze the limits and biases associated with the identification of frailty profiles and trajectories by including only self-reported data and excluding biological indicators. It will also allow us to assess the relationship between frailty and its predictors and consequences.

Sample 2: Frailty and its predictors and consequences will be studied in the catchment areas of three CSSS to determine frailty profiles and trajectories among 1600 elderly persons living at home representing different environments: one metropolitan (CSSS Saint-Laurent-Bordeaux-Cartierville à Montréal), one urban (CSSS Institut universitaire de gériatrie de Sherbrooke [CSSS-IUGS]) and one semi-urban (CSSS Des Érables). Five phases of data collection are planned: three major face-to-face interviews spaced at one year intervals and two shorter telephone interviews to track and subjectively assess changes in health status.

This sample will be drawn from Régie de l’Assurance Maladie du Québec (RAMQ) lists.

Statistical procedures: A latent class model will be used to test the hypothesis that the biological, functional and psychosocial components of frailty be reduced to a limited number of frailty profiles in both the NuAge sample and the CSSS samples.

The second phase of our analysis will examine the effect of setting aside biological markers when identifying frailty profiles. Biological tests are costly, difficult and hard to manage in a large sample of elderly persons. Thus, the statistical procedure from the first phase will be applied only to measurements of performance and responses to a standardized questionnaire. The results of this analysis will be compared to those obtained from the NuAge sample, which includes biological measurements.

CONFIDENTIALITY: Information will be kept in locked filing cabinets inside a locked room. Data will also be modified to be completely anonymous.

TYPE OF RESEARCH: This population study involves the collection of five datasets through three face-to-face interviews spaced at one year intervals and two shorter follow-up telephone interviews.

EXPECTED RESULTS: Frailty among elderly persons has an impact on both the planning and the cost of public integrated healthcare services. Furthering the understanding of frailty predictors and trajectories will help identify frail elderly in the general population and make it possible to plan and implement policies and services adapted to their needs and that extend their functional disability-free states.

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