ArticleFrontiers in public health2016
Classification for Longevity Potential: The Use of Novel Biomarkers.
Article in Frontiers in public health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- N-of-1 health optimization: Digital monitoring of biomarker dynamics to gamify adherence to metabolic switching.PNAS nexus · 2024Article
- Premature aging in childhood cancer survivors.Oncology letters · 2023Review
- Prediction of vascular aging based on smartphone acquired PPG signals.Scientific reports · 2020Article
- The risks of biomarker-based epidemiology: Associations of circulating calcium levels with age, mortality, and frailty vary substantially across populations.Experimental gerontology · 2018Review
- Cholesterol efflux capacity does not associate with coronary calcium, plaque vulnerability, and telomere length in healthy octogenarians.Journal of lipid research · 2018Article
- Subclass-specific IgG glycosylation is associated with markers of inflammation and metabolic health.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn 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.
methodsIn 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.
resultsThe 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).
conclusionUsing 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.
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