Chronological and biological age

Ries, W; Pöthig, D

doi:10.1016/0531-5565(84)90041-x

Cited by 41 publications

(12 citation statements)

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“…Many theories have been proposed but none satisfactorily explain ageing. In any individual it may result from a complex combination of genetic, environmental and social factors [Trosko and Chang, 1983;Ries and Pothig, 1984]. It no longer seems credi ble to look at ageing in toto in an attempt to find a single causal event, or a single rate determining step.…”

Section: Introductionmentioning

confidence: 99%

Does Progeria Provide the Best Model of Accelerated Ageing in Humans?

Mills

Weiss²

1990

Gerontology

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Studies of the causes of ageing are often obscured by the complexity of this phenomenon, hindering investigations in this area. In particular, the variable characteristics of ageing complicate analysis at a molecular level. It is proposed that to gain insight into ageing, the problem must first be simplified by restricting studies to an aspect of the ageing processes. It is suggested that progeria, which presents a less complicated etiology and phenotype than other accelerated ageing diseases, allows research to focus on a regulation site involved in development and ageing.

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Section: Introductionmentioning

confidence: 99%

Does Progeria Provide the Best Model of Accelerated Ageing in Humans?

Mills

Weiss²

1990

Gerontology

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“…Thus, the SHARE-FI75+ is more in keeping with frailty being an age-independent marker of risk, 73 closer to the concept of biological (as opposed to chronological) age. 74 In addition, an age-adjusted method for frailty screening may be more sensitive in younger ages and more specific in older ages, benefiting the purpose of population screening. 75 The continuous, age-adjusted SHARE-FI75+ score values range from 0 to 1, making it a much more intuitive score than the age-unadjusted one given by SHARE-FI (−4 to 6).…”

Section: Discussionmentioning

confidence: 99%

A Frailty Instrument for primary care for those aged 75 years or more: findings from the Survey of Health, Ageing and Retirement in Europe, a longitudinal population-based cohort study (SHARE-FI75+)

Romero-Ortuño

Soraghan

2014

BMJ Open

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ObjectiveTo create and validate a frailty assessment tool for community-dwelling adults aged ≥75 years.DesignLongitudinal, population-based study.SettingThe Survey of Health, Ageing and Retirement in Europe (SHARE).Participants4001 women and 3057 men aged ≥75 years from the second wave of SHARE. 3325 women and 2587 men had complete information for the frailty indicators: fatigue, low appetite, weakness, observed gait (walking without help, walking with help, chairbound/bedbound, unobserved) and low physical activity.Main outcome measuresThe internal validity of the frailty indicators was tested with latent class analysis, by modelling an underlying variable with three ordered categories. The predictive validity of the frailty classification was tested against 2-year mortality and 4-year disability. The mortality prediction of SHARE-FI75+ was compared with that of previously operationalised frailty scales in SHARE (SHARE-FI, 70-item index, phenotype, FRAIL).ResultsIn both genders, all frailty indicators significantly aggregated into a three-category ordinal latent variable. After adjusting for baseline age, comorbidity and basic activities of daily living (BADL) disability, the frail had an OR for 2-year mortality of 2.2 (95% CI 1.2 to 3.8) in women and 4.2 (2.6 to 6.8) in men. The mortality prediction of SHARE-FI75+ was similar to that of the other SHARE frailty scales. By wave 4, 49% of frail women (78 of 159) had at least one more limitation with BADL (compared with 18% of non-frail, 125 of 684; p<0.001); in men, these proportions were 39% (26 of 66) and 18% (110 of 621), respectively (p<0.001). A calculator is supplied for point-of-care use, which automatically replicates the frailty classification for any given measurements.ConclusionsSHARE-FI75+ could help frailty case finding in primary care and provide a focus for personalised community interventions. Further validation in trials and clinical programmes is needed.

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“…Thus, individuals with negative residual values are considered biologically older relative to their chronological ages, whereas individuals with positive residual values are biologically younger compared to their chronological ages. A number of population-based physiological studies have used this approach [e.g., Furukawa et al, 1975;Durbina et al, 1984;Ries and Po¨thig, 1984;Uttley and Crawford, 2000]. However, it is not known whether such a measure of biological age has any genetic basis in human populations.…”

Section: Introductionmentioning

confidence: 97%

Genetic determination of biological age in the mennonites of the midwestern United States

Duggirala

Uttley

Williams

et al. 2002

Genetic Epidemiology

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Numerous studies have shown that longevity is moderately heritable in human populations. Longevity, however, contains limited information on functional status, since individuals may exhibit differential survival patterns. In this study, we employed a stepwise multiple regression approach to estimate biological aging in a Mennonite population, using chronological age as a dependent variable and various predictors of chronological age including subphenotypes related to diabetes, coronary heart disease, hypertension, renal function, and markers of functional ability. The residual (the difference between chronological and predicted ages) is considered a marker of biological age. In fact, two different data sets were used to obtain residuals due to the availability of data. In each analysis, chronological age was regressed on predictor variables in a stepwise manner, retaining the variables significant at the 5% level. The first analysis (N=729) included 6 significant predictors (R(2)=44.3%): glucose, blood urea nitrogen (BUN), cholesterol, albumin, systolic blood pressure (SBP), and ln potassium, and the second analysis (N=232) included 9 significant predictors (R(2)=71.5%): BUN, albumin, SBP, low-density lipoprotein cholesterol, forced expiratory volume in 1 sec (FEV1), grip strength, trunk flexibility, reaction time, and FEV1xsex. Using a variance components approach, we found that the data set-specific residuals were significantly heritable (h(2)+/-SE): first analysis=0.265+/-0.106, and second analysis=0.469+/-0.180. The residuals from the second data set appear to be more informative for biological aging, perhaps due to the inclusion of functional ability-related phenotypes in addition to the blood chemistry variables. In summary, we have shown that markers of biological aging in Mennonites are under substantial additive genetic influences.

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Chronological and biological age

Cited by 41 publications

References 1 publication

Does Progeria Provide the Best Model of Accelerated Ageing in Humans?

Does Progeria Provide the Best Model of Accelerated Ageing in Humans?

A Frailty Instrument for primary care for those aged 75 years or more: findings from the Survey of Health, Ageing and Retirement in Europe, a longitudinal population-based cohort study (SHARE-FI75+)

Genetic determination of biological age in the mennonites of the midwestern United States

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