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Frailty, fitness and late-life mortality in relation to chronological and biological age.

Mitnitski AB, Graham JE, Mogilner AJ, Rockwood K - BMC Geriatr (2002)

Bottom Line: From the frailty index, relative (to CA) fitness and frailty were estimated, as was an individual's biological age.The average value of the frailty index increased with age in a log-linear relationship (r = 0.91; p < 0.001).The frailty index is a sensitive predictor of survival.

View Article: PubMed Central - HTML - PubMed

Affiliation: Ecole Polytechnique, Montreal QB, Canada. arnold@grbb.polymtl.ca

ABSTRACT

Background: People age at remarkably different rates, but how to estimate trajectories of senescence is controversial.

Methods: In a secondary analysis of a representative cohort of Canadians aged 65 and over (n = 2914) we estimated a frailty index based on the proportion of 20 deficits observed in a structured clinical examination. The construct validity of the index was examined through its relationship to chronological age (CA). The criterion validity was examined in its ability to predict mortality, and in relation to other predictions about aging. From the frailty index, relative (to CA) fitness and frailty were estimated, as was an individual's biological age.

Results: The average value of the frailty index increased with age in a log-linear relationship (r = 0.91; p < 0.001). In a Cox regression analysis, biological age was significantly more highly associated with death than chronological age. The average increase in the frailty index (i.e. the average accumulation of deficits) amongst those with no cognitive impairment was 3 per cent per year.

Conclusions: The frailty index is a sensitive predictor of survival. As the index includes items not traditionally related to adverse health outcomes, the finding is compatible with a view of frailty as the failure to integrate the complex responses required to maintain function.

No MeSH data available.


Related in: MedlinePlus

Inter signs synergy graph. Nodes indicate the deficits (codes correspond to the deficits from Methods and [29]) and edges indicate the statistically significant relationships between deficits, i.e. when the conditional probability of one deficit, given another is statistically different (p < 0.05; t-test) from the unconditional probability of the first deficit.
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Figure 7: Inter signs synergy graph. Nodes indicate the deficits (codes correspond to the deficits from Methods and [29]) and edges indicate the statistically significant relationships between deficits, i.e. when the conditional probability of one deficit, given another is statistically different (p < 0.05; t-test) from the unconditional probability of the first deficit.

Mentions: In addition, we were limited in our databases to individuals aged 65 years and older, and drew from the screened clinical sample, so that we cannot make a claim about the representativeness of the data. The incorporation of data on middle-aged and representative samples should allow more general claims about PBA to be examined. Third, to simplify the calculations we have suggested, as a first approximation, that a state variable can be estimated as the proportion of deficits. This may seem naive, as if has the effect of equalizing all the deficits. Evidently, at an individual level, not all deficits are equally important: heart problems or diabetes likely may cause death sooner than for example, difficulties in getting dressed or skin problems per se. The finding that the proportion of deficits in a given individual can include seemingly arbitrary or even trivial ones requires further investigation. For now, we understand this finding to mean that accumulating several deficits results overall in impaired adaptive ability. This is likely to be the case if the signs are redundant, i.e. if a given deficit represents a set of others, and if the items of the index are related. The latter appears to be the case, as illustrated in Figure 7, which shows that the deficits are not independent. The nodes of the graph correspond to the deficits (numbered in the Materials and methods) and the edges represent statistically significant relationships between the deficits (defined as the difference between the unconditional probability of the occurrence of deficit X and conditional probability of deficit X given deficit Y) [11]. This is not surprising when we consider that synergetic relationships are typical for age-associated deficits. In other words, roughly speaking, everything is dependent on everything else in complex organisms so that changes in one subsystem affects many others. For example, vision impairment may be caused by the numerous reasons. Since vision loss, by itself, is not readily regarded as a life-threatening factor, it may indicate a more serious problem (e.g. diabetes, stroke). The more deficits that are used in deriving the frailty index, the greater the chance that such secondary signs are linked to serious illnesses. As argued elsewhere [1,26,28] this is a central aspect of many characterizations of frailty. Moreover, whether this holds for any combination of deficits (and not just age-associated ones) additionally requires further study, although we recognize that such summarization does not allow the influence of individual disease states to be tested. [22,29]


Frailty, fitness and late-life mortality in relation to chronological and biological age.

Mitnitski AB, Graham JE, Mogilner AJ, Rockwood K - BMC Geriatr (2002)

Inter signs synergy graph. Nodes indicate the deficits (codes correspond to the deficits from Methods and [29]) and edges indicate the statistically significant relationships between deficits, i.e. when the conditional probability of one deficit, given another is statistically different (p < 0.05; t-test) from the unconditional probability of the first deficit.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC88955&req=5

Figure 7: Inter signs synergy graph. Nodes indicate the deficits (codes correspond to the deficits from Methods and [29]) and edges indicate the statistically significant relationships between deficits, i.e. when the conditional probability of one deficit, given another is statistically different (p < 0.05; t-test) from the unconditional probability of the first deficit.
Mentions: In addition, we were limited in our databases to individuals aged 65 years and older, and drew from the screened clinical sample, so that we cannot make a claim about the representativeness of the data. The incorporation of data on middle-aged and representative samples should allow more general claims about PBA to be examined. Third, to simplify the calculations we have suggested, as a first approximation, that a state variable can be estimated as the proportion of deficits. This may seem naive, as if has the effect of equalizing all the deficits. Evidently, at an individual level, not all deficits are equally important: heart problems or diabetes likely may cause death sooner than for example, difficulties in getting dressed or skin problems per se. The finding that the proportion of deficits in a given individual can include seemingly arbitrary or even trivial ones requires further investigation. For now, we understand this finding to mean that accumulating several deficits results overall in impaired adaptive ability. This is likely to be the case if the signs are redundant, i.e. if a given deficit represents a set of others, and if the items of the index are related. The latter appears to be the case, as illustrated in Figure 7, which shows that the deficits are not independent. The nodes of the graph correspond to the deficits (numbered in the Materials and methods) and the edges represent statistically significant relationships between the deficits (defined as the difference between the unconditional probability of the occurrence of deficit X and conditional probability of deficit X given deficit Y) [11]. This is not surprising when we consider that synergetic relationships are typical for age-associated deficits. In other words, roughly speaking, everything is dependent on everything else in complex organisms so that changes in one subsystem affects many others. For example, vision impairment may be caused by the numerous reasons. Since vision loss, by itself, is not readily regarded as a life-threatening factor, it may indicate a more serious problem (e.g. diabetes, stroke). The more deficits that are used in deriving the frailty index, the greater the chance that such secondary signs are linked to serious illnesses. As argued elsewhere [1,26,28] this is a central aspect of many characterizations of frailty. Moreover, whether this holds for any combination of deficits (and not just age-associated ones) additionally requires further study, although we recognize that such summarization does not allow the influence of individual disease states to be tested. [22,29]

Bottom Line: From the frailty index, relative (to CA) fitness and frailty were estimated, as was an individual's biological age.The average value of the frailty index increased with age in a log-linear relationship (r = 0.91; p < 0.001).The frailty index is a sensitive predictor of survival.

View Article: PubMed Central - HTML - PubMed

Affiliation: Ecole Polytechnique, Montreal QB, Canada. arnold@grbb.polymtl.ca

ABSTRACT

Background: People age at remarkably different rates, but how to estimate trajectories of senescence is controversial.

Methods: In a secondary analysis of a representative cohort of Canadians aged 65 and over (n = 2914) we estimated a frailty index based on the proportion of 20 deficits observed in a structured clinical examination. The construct validity of the index was examined through its relationship to chronological age (CA). The criterion validity was examined in its ability to predict mortality, and in relation to other predictions about aging. From the frailty index, relative (to CA) fitness and frailty were estimated, as was an individual's biological age.

Results: The average value of the frailty index increased with age in a log-linear relationship (r = 0.91; p < 0.001). In a Cox regression analysis, biological age was significantly more highly associated with death than chronological age. The average increase in the frailty index (i.e. the average accumulation of deficits) amongst those with no cognitive impairment was 3 per cent per year.

Conclusions: The frailty index is a sensitive predictor of survival. As the index includes items not traditionally related to adverse health outcomes, the finding is compatible with a view of frailty as the failure to integrate the complex responses required to maintain function.

No MeSH data available.


Related in: MedlinePlus