Mika Ala-Korpela, Terho Lehtimäki, Mika Kähönen, Jorma Viikari, Markus Perola, Veikko Salomaa, Johannes Kettunen, Olli T Raitakari, Ville-Petteri Mäkinen, Cross-sectionally Calculated Metabolic Aging Does Not Relate to Longitudinal Metabolic Changes—Support for Stratified Aging Models, The Journal of Clinical Endocrinology & Metabolism, Volume 108, Issue 8, August 2023, Pages 2099–2104, https://doi.org/10.1210/clinem/dgad032
Cross-sectionally calculated metabolic aging does not relate to longitudinal metabolic changes : support for stratified aging models
|Author:||Ala-Korpela, Mika1,2,3; Lehtimäki, Tero4; Kähönen, Mika5;|
1Systems Epidemiology, Faculty of Medicine, Center for Life Course Health Research, University of Oulu, Oulu 90014, Finland
2Biocenter Oulu, University of Oulu, Oulu 90014, Finland
3Faculty of Health Sciences, NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio 90014, Finland
4Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Fimlab Laboratories, and Finnish Cardiovascular Research Center Tampere, Tampere University, Tampere 33100, Finland
5Department of Clinical Physiology, Faculty of Medicine and Health Technology, Tampere University Hospital, and Finnish Cardiovascular Research Center Tampere, Tampere University, Tampere 33100, Finland
6Department of Medicine, University of Turku, Turku 20520, Finland
7Division of Medicine, Turku University Hospital, Turku 20520, Finland
8Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki 00271, Finland
9Estonian Genome Center, University of Tartu, Tartu 51010, Estonia
10Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku 20520, Finland
11Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20520, Finland
12Centre for Population Health Research, University of Turku and Turku University Hospital, Turku 20520, Finland
13Computational and Systems Biology Program, Precision Medicine Theme, South Australian Health and Medical Research Institute, Adelaide, SA 5000, Australia
14Australian Centre for Precision Health, University of South Australia, Adelaide, SA 5000, Australia
|Online Access:||PDF Full Text (PDF, 0.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20230922136193
|Publish Date:|| 2023-09-22
Context: Aging varies between individuals, with profound consequences for chronic diseases and longevity. One hypothesis to explain the diversity is a genetically regulated molecular clock that runs differently between individuals. Large human studies with long enough follow-up to test the hypothesis are rare due to practical challenges, but statistical models of aging are built as proxies for the molecular clock by comparing young and old individuals cross-sectionally. These models remain untested against longitudinal data.
Objective: We applied novel methodology to test if cross-sectional modeling can distinguish slow vs accelerated aging in a human population.
Methods: We trained a machine learning model to predict age from 153 clinical and cardiometabolic traits. The model was tested against longitudinal data from another cohort. The training data came from cross-sectional surveys of the Finnish population (n = 9708; ages 25–74 years). The validation data included 3 time points across 10 years in the Young Finns Study (YFS; n = 1009; ages 24–49 years). Predicted metabolic age in 2007 was compared against observed aging rate from the 2001 visit to the 2011 visit in the YFS dataset and correlation between predicted vs observed metabolic aging was determined.
Results: The cross-sectional proxy failed to predict longitudinal observations (R2 = 0.018%, P = 0.67).
Conclusion: The finding is unexpected under the clock hypothesis that would produce a positive correlation between predicted and observed aging. Our results are better explained by a stratified model where aging rates per se are similar in adulthood but differences in starting points explain diverging metabolic fates.
Journal of clinical endocrinology & metabolism
|Pages:||2099 - 2104|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3141 Health care science
112 Statistics and probability
This work was supported by the Academy of Finland, Novo Nordisk Foundation, and Sigrid Jusélius Foundation.
The datasets used in the current study are available from the cohorts through application process for researchers who meet the criteria for access to confidential data: https://thl.fi/ en/web/thl-biobank/for-researchers/apply (FINRISK cohorts) and http://youngfinnsstudy.utu.fi (YFS).
© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.