The measurement of biological age

Ludwig, Frédéric C.; Smoke, Mary E.

doi:10.1080/03610738008258384

Cited by 77 publications

(31 citation statements)

References 57 publications

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“…Many efforts have been made to define the best means of measurement of biomarkers of aging, but considerable disagreement exists concerning the logical strategy for criteria of selection as well as its concept (Adelman, 1987;Baker and Sprott, 1988;Ingram, 1988;Ludwig and Smoke, 1980;Masoro, 1988). Some problems are associated with crosssectional criteria of selection of biomarkers of aging.…”

Section: Discussionmentioning

confidence: 99%

Biomarkers of Aging in Women and the Rate of Longitudinal Changes.

Ueno

Yamashita

Moritani

et al. 2003

J. Physiol. Anthropol.

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The purposes of this study were 1) to estimate biological age score (BAS) in Japanese healthy women based on the 4-7 years longitudinal data for physiological, hematological and biochemical examinations and 2) to examine the rate of aging changes in adult women based on the estimated BAS. The samples consisted of cross-sectional (nϭ981) and longitudinal (nϭ110) groups. Out of 31 variables examined, five variables (forced expiratory volume in 1.0 s, systolic blood pressure, mean corpuscular hemoglobin, glucose, albumin/globulin ratio) that met the following criteria: 1) significant cross-sectional correlation with age; 2) significant longitudinal change in the same direction as the cross-sectional correlation; and 3) assessment of redundancy, were selected as candidate biomarkers of aging. This variable set was then submitted into a principal component analysis, and the first principal component obtained from this analysis was used as an equation for assessing one's BAS. Individual BAS showed a high longitudinal stability of age-related changes, suggesting high predictive validity of our newly developed aging measurement equation. However, changes in the aging rate based on the estimated BAS were not constant. The mean slopes of the regression lines of BAS for the three age groups (ageϽ45, 45ϹageϽ65 yrs, 65Ϲage) were 0.095, 0.065, 0.138, respectively. One-way analysis of variance detected a significant difference (Fϭ5.14, pϽ0.01) among the three age groups. These results suggest that the rate of aging in adult women is relatively slower until 65 years of age, but after 65, the rate of aging shows a rapid increase. We concluded that the longitudinal method used for selection of variables to compute the BAS was useful and theoretically valid compared to those obtained from cross-sectional data analysis.

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Section: Discussionmentioning

confidence: 99%

Biomarkers of Aging in Women and the Rate of Longitudinal Changes.

Ueno

Yamashita

Moritani

et al. 2003

J. Physiol. Anthropol.

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“…Moreover, the simple, noninvasive and highly reliable nature of the selected biomarkers will make the BA prediction models practical diagnostic tools in assessing functional decline and vulnerability. Putative biomarkers of BA should represent function across several different physiological systems [28]. The selected biomarkers represent various functional capacities and physiological systems.…”

Section: Discussionmentioning

confidence: 99%

Development and Application of Biological Age Prediction Models with Physical Fitness and Physiological Components in Korean Adults

et al. 2012

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Background: Several biological age (BA) prediction models have been suggested with a variety of biomarkers. Valid models should be able to measure BA in a relatively short time period and predict subsequent physiological capability. Physiological and physical fitness variables have been shown to be distinctive markers for predicting BA and morbidity. The practical and noninvasive nature of such variables makes them useful as clinical assessment tools in estimating BA for in-depth diagnosis and corresponding intervention. Objective: To identify, develop and evaluate biomarkers and BA prediction models and validate their clinical usefulness for the practical diagnosis of functional aging. Methods: Fourteen variables were measured in 3,112 male and 1,233 female participants aged 30 and older between the years 2004 and 2007. Through a series of parsimonious stepwise elimination processes, two sets of 8 gender-specific variables were selected as candidate biomarkers for 1,604 men and 760 women. Principal component analysis, linear regression analysis and adjustment methods were further applied to obtain two sets of true BA (TBA) prediction models. The TBA models were examined for validity by comparing TBA to the corresponding chronological age (CA) with clinical risk factors. Results: TBA prediction models with r² values of 0.638 and 0.672 were developed, each unique to men and women, respectively. The overall mean TBA and CA of the participants were 53.9 and 51.8 years, respectively, with a marginal difference of –2.1 and –1.3 years. The regression slopes or rates of TBA as a function of CA were 1.00 and 1.28 for men and women with r values of 0.799 and 0.820 (p < 0.001), respectively. In comparing TBA to CA rates between healthy and clinical risk groups, both sarcopenic and obese groups showed significant increases in TBA. Conclusions: The selected biomarkers encompass various complex physiopathological factors related to intrinsic and extrinsic physiological and functional aging. The BA prediction models based on the selected biomarkers could be practical in assessing BA for Korean adults.

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“…Driven by these goals, researchers in the 1970s and 1980s proposed a large number of candidate biomarkers of aging (Baker, 1975; Ludwig & Smoke, 1980; Ingram & Reynolds, 1986). Although these studies identified several putative biomarkers, many of the published multivariable composite scores were overfit—that is, overly tuned to a very specific dataset.…”

Section: Introductionmentioning

confidence: 99%

Predicting all‐cause mortality from basic physiology in the Framingham Heart Study

Zhang

Pincus

2015

Aging Cell

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SummaryUsing longitudinal data from a cohort of 1349 participants in the Framingham Heart Study, we show that as early as 28–38 years of age, almost 10% of variation in future lifespan can be predicted from simple clinical parameters. Specifically, we found diastolic and systolic blood pressure, blood glucose, weight, and body mass index (BMI) to be relevant to lifespan. These and similar parameters have been well‐characterized as risk factors in the relatively narrow context of cardiovascular disease and mortality in middle to old age. In contrast, we demonstrate here that such measures can be used to predict all‐cause mortality from mid‐adulthood onward. Further, we find that different clinical measurements are predictive of lifespan in different age regimes. Specifically, blood pressure and BMI are predictive of all‐cause mortality from ages 35 to 60, while blood glucose is predictive from ages 57 to 73. Moreover, we find that several of these parameters are best considered as measures of a rate of ‘damage accrual’, such that total historical exposure, rather than current measurement values, is the most relevant risk factor (as with pack‐years of cigarette smoking). In short, we show that simple physiological measurements have broader lifespan‐predictive value than indicated by previous work and that incorporating information from multiple time points can significantly increase that predictive capacity. In general, our results apply equally to both men and women, although some differences exist.

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The measurement of biological age

Cited by 77 publications

References 57 publications

Biomarkers of Aging in Women and the Rate of Longitudinal Changes.

Biomarkers of Aging in Women and the Rate of Longitudinal Changes.

Development and Application of Biological Age Prediction Models with Physical Fitness and Physiological Components in Korean Adults

Predicting all‐cause mortality from basic physiology in the Framingham Heart Study

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