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Cited by 127Statistical Methods and ApplicationsPsychometric Methodologies and TestingFrailty in Older Adults

Assessment of biological age by principal component analysis

Nakamura, E., Miyao, K., Ozeki, T.·1988·Mechanisms of Ageing and Development
DOI 10.1016/0047-6374(88)90109-1
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Abstract

A method of assessing biological age by the application of principal component analysis is reported. Healthy individuals (462) randomly selected from about 6000 men who had taken a 2-day health examination were studied. Out of the 30 physiological variables examined in routine check-ups, 11 variables were selected as suitable for the assessment of biological age based on the results of factor analysis and the physiological meaning of each test. This variable set was then submitted to principal component analysis, and the 1st principal component obtained from this analysis was used as an equation for assessing one's biological age. However, the biological age calculated from this equation is expressed as a score, so the estimated score was transformed to years (biological age) using the T-score idea. The biological age estimated by this method is practically useful and theoretically valid in contrast with the multiple regression model, because this approach eliminates and overcomes the following 2 big problems of the multiple regression model: (1) the distortion of the individual biological age at the regression edges; and (2) a theoretical contradiction in that a perfect model will merely be predicting the subject's chronological age, not his biological age.

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  • Principal component analysis-based biological age estimators, which involve unsupervised dimensionality reduction to unbiasedly identify ageing signals, seemingly perform even better than clocks that train on chronological age.

    “This concept has been demonstrated by principal component analysis-based biological age estimators, which involve unsupervised dimensionality reduction to unbiasedly identify ageing signals, seemingly even better than clocks that train on chronological age173–175.”

    From Measuring biological age using omics data (2022) · cites this paper as [173]

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Authors
Nakamura, E., Miyao, K., & Ozeki, T.
Affiliations
Kyoto University (JP)
Corresponding author
Nakamura, E.
Venue
Mechanisms of Ageing and Development
Publisher
Elsevier BV
Published
1988-01
Volume
46
Issue
1-3
Pages
1-18
ISSN
0047-6374 (ISSN-L)
DOI
10.1016/0047-6374(88)90109-1
OpenAlex
W2044024309
Cited by
127
References
26
Citations by year
2026: 6 · 2025: 8 · 2024: 11 · 2023: 8 · 2022: 12 · 2021: 7 · 2020: 6 · 2019: 7 · 2018: 1 · 2017: 7 · 2016: 1 · 2015: 3 · 2014: 1 · 2013: 1 · 2012: 3
FWCI
3.39
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Mechanisms of Ageing and Development (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Health Sciences › Medicine › Geriatrics and Gerontology › Frailty in Older Adults
Keywords
biological age, principal component analysis, physiological parameters, medical examination, aging assessment, healthy men
MeSH terms
Adult, Aged, Aged, 80 and over, Aging, Data Interpretation, Statistical, Diabetes Mellitus, Humans, Hypertension, Male, Middle Aged, Reference Values, Regression Analysis

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