Marian Beekman, Hae-Won Uh, Diana van Heemst, Manfred Wuhrer, L. Renee Ruhaak, Vanessa Gonzalez-Covarrubias, Thomas Hankemeier, Jeanine J. Houwing-Duistermaat, P. Eline Slagboom
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.
Pharmacology, Toxicology and Pharmaceutical Science
3
9%
Chemistry
2
6%
Biochemistry, Genetics and Molecular Biology
2
6%
Other
9
28%
Unknown
6
19%
Attention Score in Context
Attention Score in Context
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 November 2019.
All research outputs
#6,762,795
of 22,896,955 outputs
Outputs from Frontiers in Public Health
#2,162
of 10,048 outputs
Outputs of similar age
#102,165
of 313,742 outputs
Outputs of similar age from Frontiers in Public Health
#21
of 74 outputs
Altmetric has tracked 22,896,955 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 10,048 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.0. This one has done well, scoring higher than 78% of its peers.
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We're also able to compare this research output to 74 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
