Strategy for identifying biomarkers of aging in long-lived species

Ingram, Donald K.; Nakamura, Eitaro; Smucny, Darlene; Roth, George S.; Lane, Mark A.

doi:10.1016/s0531-5565(01)00110-3

Cited by 81 publications

(57 citation statements)

References 19 publications

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“…This challenge demands a logical strategy for identifying candidate biomarkers of aging. From the previous analyses of rhesus monkeys (Ingram et al, 2001;Nakamura et al, 1994Nakamura et al, , 1998, we identified important criteria for evaluation of a candidate's biomarkers: 1) significant crosssectional correlation with age; 2) significant longitudinal change with age consistent with the cross-sectional correlation; 3) no obvious redundancy with other selected variables; and 4) rate of age-related change proportional to differences in lifespan among related species. In the present study, we applied these criteria to human data to identify a candidate's 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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“…There has been a longstanding debate in the field whether biomarkers of ageing should (and could) specifically measure basic underlying processes independent of age-related disease (1)(2)(3)(4)(5)(6)(7) . It has often been proposed that ageing, being a basic process underlying the development of disease and frailty, should be researched (and biomarkers of ageing should be validated) in disease-free subjects (for review see (9) ).…”

Section: Ageing and Disease Or What Is A Biomarker Of Healthy Ageing?mentioning

confidence: 99%

“…For instance, TL as a biomarker of ageing was only introduced in 2000 (33) . In this case, the best validation strategy follows the biomarker criteria derived by Nakamura et al (3,5) by combining longitudinal analyses in multiple birth cohorts, in which the longitudinal change with age is expected to be consistent with the cross-sectional differences between the cohorts. However, in human subjects life history is strongly dependent on year of birth, and life expectation increases with time (57) .…”

Section: Use Of Birth Cohorts For Biomarker Validationmentioning

confidence: 99%

“…'better predict functional capability at some late age, than will chronological age', although the impact of age-related disease as originally excluded by Baker and Sprott is still a matter of debate (1)(2)(3)(4)(5)(6)(7) . Extensive programmes to validate marker candidates for intervention testing in mice (7) and non-human primates (5) have been run, however, with limited success so far (6) . This is to a large extent due to our still insufficient mechanistic understanding of the ageing process.…”

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confidence: 99%

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Biomarkers of healthy ageing: expectations and validation

Martin-Ruiz

Zglinicki

2014

Proc. Nutr. Soc.

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The challenge of devising a set of biomarkers capable of measuring the ageing rate in human subjects was articulated long ago. In recent years, progress in the basic biology of ageing suggests the realistic possibility of preventive or restaurative interventions that may extend healthy lifespan in mammals including human subjects. Specifically, frailty is being increasingly recognised as a clinically relevant syndrome that may be therapeutically addressed. This greatly enhances the need for sensitive and specific biomarkers of healthy ageing that are validated in both experimental animals and, importantly, in human subjects over the whole age range. Here, we will discuss the present challenges and requirements for biomarker validation in human subjects. We propose the central requirements for a validated biomarker of healthy ageing as: (i) better predictive power than chronological age for multiple dimensions of ageing; (ii) identification of the age range in which the marker is informative; (iii) establishment of sensitivity/specificity as indicators of its predictive power at the level of the individual; (iv) minimisation of methodological variation between laboratories.

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“…Kimura et al [6] [7]. The theoretical based methods can assess the discrepancy between chronological aging and physical fitness aging within individuals.…”

Section: Introductionmentioning

confidence: 99%

Fitness Age Score and the Risk of Long-Term Care Insurance Certification—The Kyoto-Kameoka Longitudinal Study

Yoshida¹,

Kimura²,

Yamada³

et al. 2017

OJEpi

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Long-Term Care Insurance System has been introduced to provide appropriate care for the elderly who needs nursing care in Japan. Physical function is one of most important function for the elderly to live independently. A previous study had established a Fitness Age Score (FAS) based on a theory for biomarker of aging. This study clarified whether the FAS could be used to predict the need for certification of long-term care (CLTC) in independent elderly people. We included 939 independent, community-dwelling-elderly (average age, 74.4 years) whose physical function was measured and for whom the CLTC was tracked for 24 months. The FAS comprised five physical fitness items: Walking time, grip strength, one-leg standing, vertical jump, and functional reach test. Based on the FAS, we calculated the odds ratio (OR) for the CLTC. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) was used for further analysis. We also used the Youden Index (YI), minimum distance method (MD), and two-graph ROC method (TG) to calculate cutoff values (CVs) for screening. We divided subjects into the low and high physical fitness group with CVs. We developed cumulative CLTC curves using the Kaplan-Meier method. Sixteen people could not be tracked for the full 24 months. The measurement of FAS was completed for 798 people, of whom 28 became CLTC. The measurement of FAS was not completed for 125 people, of whom 21 became CLTC. The CLTC OR for people 191with incomplete FAS was 5.553. The AUC was 0.72 (p < 0.001). The CVs for the FAS were −0.29 for YI (sensitivity, 92.9% and specificity, 40.4%), −1.23 for MD (57.1% and 73.4%), and −0.81 for TG (60.7% and 60.9%). The cumulative CLTC curves according to CVs all showed significant differences (p = 0.000 -0.020). In conclusion, the FAS can be used to predict CLTC.

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Strategy for identifying biomarkers of aging in long-lived species

Cited by 81 publications

References 19 publications

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

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

Biomarkers of healthy ageing: expectations and validation

Fitness Age Score and the Risk of Long-Term Care Insurance Certification—The Kyoto-Kameoka Longitudinal Study

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