Biomarkers of aging and disease: Introduction and definitions

doi:10.1016/j.exger.2009.07.008

Experimental Gerontology

Volume 45, Issue 1, January 2010, Pages 2-4

https://doi.org/10.1016/j.exger.2009.07.008 Get rights and content

Abstract

The concept of biomarkers of aging and age-related disease dates to the early 1980s as scientists engaged in aging research worked to clearly define aging and separate processes from disease with better prediction of both as an objective. The concept of basic aging processes, separate from disease, was then, and still is, not universally accepted. While the search for biomarkers of aging has a relatively long and difficult history, the search for biomarkers of disease is conceptually more straightforward. The biomarkers discussed in this issue are generally easier to understand, can be related to relevant functions, and often are the product of concentrated research interest. Further, these biomarkers could lead to earlier and more successful treatments and thus are often of very immediate interest. After a long period of concentration on lifespan extension and longevity by basic biologists, there is now a growing interest in healthspan research. The reviews in this issue provide a strong rationale for increased interchange between basic biologists and their clinical colleagues.

Section snippets

NIA biomarkers of aging program

In 1988 the National Institute on Aging began a 10 year initiative to develop biomarkers of aging (Sprott and Austad, 2006). Over the 10 year period this initiative funded 18 laboratories to conduct the search for biomarkers of aging in an array of physiological and behavioral systems. All of the animals (mice) used in these studies were raised in a single facility, fed the same diets (ad lib or calorically restricted) and then sent to the investigators laboratories in order to eliminate the

References (13)

L.K. Heilbronn et al.
Calorie restriction and aging: review of the literature and implications for studies in humans
Am. J. Clin. Nutr.
(2003)
R. Sprott et al.
Historical development of animal models of aging
S.N. Austad
Why We Age
(1997)
Baker III, G.T., Sprott, R.L., (Eds.), 1988. Biomarkers of aging. Exp. Gerontol. 23, 223-239 (special...
R.N. Butler et al.
Biomarkers of aging: from primitive organisms to man
J. Gerontol. A Biol. Sci. Med. Sci.
(2004)
P.L. Larsen et al.
Genes that regulate both development and longevity in Caenorhabditis elegans
Genetics
(1995)

There are more references available in the full text version of this article.

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Citation Excerpt :
A reliable cross-sectional metric that can quantify this variability—a measure of biological age—would be valuable both for clinical practice and research into longevity and ageing health. This metric could facilitate the monitoring of age-related changes beyond that captured by disease specific risk factors—ie, by incorporating mechanisms of decline due to both disease and typical ageing.1 Likewise, the metric could help to detect people who are ageing more rapidly than expected, before the onset of clinical manifestations,2 alongside being able to detect traits related to delayed ageing, cognitive maintenance, and longevity.
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Experimental Gerontology

Abstract

Section snippets

NIA biomarkers of aging program

References (13)

Calorie restriction and aging: review of the literature and implications for studies in humans

Am. J. Clin. Nutr.

Historical development of animal models of aging

Why We Age

Biomarkers of aging: from primitive organisms to man

J. Gerontol. A Biol. Sci. Med. Sci.

Genes that regulate both development and longevity in Caenorhabditis elegans

Genetics

Cited by (130)

Recent advances in electrochemical biosensors for the detection of Aβ42, a biomarker for Alzheimer disease diagnosis

Life course, genetic, and neuropathological associations with brain age in the 1946 British Birth Cohort: a population-based study

Concomitant use of relative telomere length, biological health score and physical/social statuses in the biological aging evaluation of mustard-chemical veterans

Associations of serum IL-6 with muscle, bone, and adipose tissue in women

Changes in plasma phospholipids and sphingomyelins with aging in men and women: A comprehensive systematic review of longitudinal cohort studies

Longevity as a complex genetic trait

ReviewBiomarkers of aging and disease: Introduction and definitions

Abstract

Section snippets

NIA biomarkers of aging program

Am. J. Clin. Nutr.

Why We Age

Biomarkers of aging: from primitive organisms to man

J. Gerontol. A Biol. Sci. Med. Sci.

Genes that regulate both development and longevity in Caenorhabditis elegans

Genetics

Review
Biomarkers of aging and disease: Introduction and definitions