Abstract
In older people, chronological age may not be the best predictor of residual lifespan and mortality, because with age the heterogeneity in health is increasing. Biomarkers for biological age and residual lifespan are being developed to predict disease and mortality better at an individual level than chronological age. In the current paper, we aim to classify a group of older people into those with longevity potential or controls. In the Leiden Longevity Study participated 1671 offspring of nonagenarian siblings, as the group with longevity potential, and 744 similarly aged controls. Using known risk factors for cardiovascular disease, previously reported markers for human longevity and other physiological measures as predictors, classification models for longevity potential were constructed with multiple logistic regression of the offspring-control status. The Framingham Risk Score (FRS) is predictive for longevity potential [area under the receiver operating characteristic curve (AUC) = 64.7]. Physiological parameters involved in immune responses and glucose, lipid and energy metabolism further improve the prediction performance for longevity potential (AUCmale = 71.4, AUCfemale = 68.7). Using the FRS, the classification of older people in groups with longevity potential and controls is moderate, but can be improved to a reasonably good classification in combination with markers of immune response, glucose, lipid, and energy metabolism. We show that individual classification of older people for longevity potential may be feasible using biomarkers from a wide variety of different biological processes.
Highlights
The aging process is underlying the physiological and functional decline of the body with time
We considered the components of the Framingham Risk Score (FRS) as the “cardiovascular risk factors” [7], which are current smoking habits, prevalence of hypertension, type 2 diabetes, plasma levels of total cholesterol, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)
As “Longevity markers” we considered molecular measures that have been reported to be different between offspring of longlived individuals and controls, which are plasma levels of glucose [14], insulin [14], free triiodothyronine [15], triglycerides [14], adiponectin [16], the ratio of total cholesterol over HDL-C (Total/HDL ratio) [17], and LDL particle size [18, 19]
Summary
The aging process is underlying the physiological and functional decline of the body with time. The heterogeneity of bodily decline and the ability to cope with exposure and resiliency is increasing and chronological age may not be the best predictor of disease risk and mortality. Biomarker studies for the bodily functional decline aim to develop a measure for biological age as a better marker for the residual lifespan and mortality. The challenge in biomarker development lays in the prediction of residual lifespan or mortality risk for an individual. Chronological age may not be the best predictor of residual lifespan and mortality, because with age the heterogeneity in health is increasing. Biomarkers for biological age and residual lifespan are being developed to predict disease and mortality better at an individual level than chronological age. We aim to classify a group of older people into those with longevity potential or controls
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