Modeling the Rate of Senescence: Can Estimated Biological Age Predict Mortality More Accurately Than Chronological Age?

Clicks: 1
ID: 305666
2012
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #88 of 129 articles by views in the journals of gerontology series a, biological sciences and medical sciences

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 129 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Biological age (BA) is useful for examining differences in aging rates.Nevertheless, little consensus exists regarding optimal methods for calculating BA.The aim of this study is to compare the predictive ability of five BA algorithms.The sample included 9,389 persons, aged 30-75 years, from National Health and Nutrition Examination Survey III.During the 18-year follow-up, 1,843 deaths were counted.Each BA algorithm was compared with chronological age on the basis of predictive sensitivity and strength of association with mortality.Results found that the Klemera and Doubal method was the most reliable predictor of mortality and performed significantly better than chronological age.Furthermore, when included with chronological age in a model, Klemera and Doubal method had more robust predictive ability and caused chronological age to no longer be significantly associated with mortality.Given the potential of BA to highlight heterogeneity, the Klemera and Doubal method algorithm may be useful for studying a number of questions regarding the biology of aging.
Reference Key
openalex_W2168228291 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Morgan E. Levine
Journal the journals of gerontology series a, biological sciences and medical sciences
Year 2012
DOI
10.1093/gerona/gls233
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.