Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2016 Jun:156:42-54.
doi: 10.1016/j.mad.2016.04.010. Epub 2016 Apr 29.

Dynamics of biomarkers in relation to aging and mortality

Konstantin G Arbeev  1 , Svetlana V Ukraintseva  2 , Anatoliy I Yashin  2
Affiliations
Review

Dynamics of biomarkers in relation to aging and mortality

Konstantin G Arbeev et al. Mech Ageing Dev. 2016 Jun.

Abstract

Contemporary longitudinal studies collect repeated measurements of biomarkers allowing one to analyze their dynamics in relation to mortality, morbidity, or other health-related outcomes. Rich and diverse data collected in such studies provide opportunities to investigate how various socio-economic, demographic, behavioral and other variables can interact with biological and genetic factors to produce differential rates of aging in individuals. In this paper, we review some recent publications investigating dynamics of biomarkers in relation to mortality, which use single biomarkers as well as cumulative measures combining information from multiple biomarkers. We also discuss the analytical approach, the stochastic process models, which conceptualizes several aging-related mechanisms in the structure of the model and allows evaluating "hidden" characteristics of aging-related changes indirectly from available longitudinal data on biomarkers and follow-up on mortality or onset of diseases taking into account other relevant factors (both genetic and non-genetic). We also discuss an extension of the approach, which considers ranges of "optimal values" of biomarkers rather than a single optimal value as in the original model. We discuss practical applications of the approach to single biomarkers and cumulative measures highlighting that the potential of applications to cumulative measures is still largely underused.

Keywords: Aging; Biomarker; Dynamics; Longitudinal data; Mortality; Risk factor; Stochastic process model.

PubMed Disclaimer

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Aalen OO, Borgan O, Gjessing HK. Survival and event history analysis: a process point of view. New York, USA: Springer; 2008.
    1. Aalen OO, Gjessing HK. Survival models based on the Ornstein-Uhlenbeck process. Lifetime Data Anal. 2004;10:407–423. - PubMed
    1. Allison DB, Faith MS, Heo M, Kotler DP. Hypothesis concerning the U-shaped relation between body mass index and mortality. Am. J. Epidemiol. 1997;146:339–349. - PubMed
    1. Allison DB, Zannolli R, Faith MS, Heo M, Pietrobelli A, VanItallie TB, Pi-Sunyer FX, Heymsfield SB. Weight loss increases and fat loss decreases all-cause mortality rate: results from two independent cohort studies. Int. J. Obesity. 1999;23:603–611. - PubMed
    1. Arbeev KG, Akushevich I, Kulminski AM, Arbeeva LS, Akushevich L, Ukraintseva SV, Culminskaya IV, Yashin AI. Genetic model for longitudinal studies of aging, health, and longevity and its potential application to incomplete data. J. Theor. Biol. 2009;258:103–111. - PMC - PubMed

Publication types