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A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study
BACKGROUND: Individual differences in the rate of aging and susceptibility to disease are not accounted for by chronological age alone. These individual differences are better explained by biological age, which may be estimated by biomarker prediction models. In the light of the aging demographics o...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9131142/ https://www.ncbi.nlm.nih.gov/pubmed/35536617 http://dx.doi.org/10.2196/35696 |
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author | Husted, Karina Louise Skov Brink-Kjær, Andreas Fogelstrøm, Mathilde Hulst, Pernille Bleibach, Akita Henneberg, Kaj-Åge Sørensen, Helge Bjarup Dissing Dela, Flemming Jacobsen, Jens Christian Brings Helge, Jørn Wulff |
author_facet | Husted, Karina Louise Skov Brink-Kjær, Andreas Fogelstrøm, Mathilde Hulst, Pernille Bleibach, Akita Henneberg, Kaj-Åge Sørensen, Helge Bjarup Dissing Dela, Flemming Jacobsen, Jens Christian Brings Helge, Jørn Wulff |
author_sort | Husted, Karina Louise Skov |
collection | PubMed |
description | BACKGROUND: Individual differences in the rate of aging and susceptibility to disease are not accounted for by chronological age alone. These individual differences are better explained by biological age, which may be estimated by biomarker prediction models. In the light of the aging demographics of the global population and the increase in lifestyle-related morbidities, it is interesting to invent a new biological age model to be used for health promotion. OBJECTIVE: This study aims to develop a model that estimates biological age based on physiological biomarkers of healthy aging. METHODS: Carefully selected physiological variables from a healthy study population of 100 women and men were used as biomarkers to establish an estimate of biological age. Principal component analysis was applied to the biomarkers and the first principal component was used to define the algorithm estimating biological age. RESULTS: The first principal component accounted for 31% in women and 25% in men of the total variance in the biological age model combining mean arterial pressure, glycated hemoglobin, waist circumference, forced expiratory volume in 1 second, maximal oxygen consumption, adiponectin, high-density lipoprotein, total cholesterol, and soluble urokinase-type plasminogen activator receptor. The correlation between the corrected biological age and chronological age was r=0.86 (P<.001) and r=0.81 (P<.001) for women and men, respectively, and the agreement was high and unbiased. No difference was found between mean chronological age and mean biological age, and the slope of the regression line was near 1 for both sexes. CONCLUSIONS: Estimating biological age from these 9 biomarkers of aging can be used to assess general health compared with the healthy aging trajectory. This may be useful to evaluate health interventions and as an aid to enhance awareness of individual health risks and behavior when deviating from this trajectory. TRIAL REGISTRATION: ClinicalTrials.gov NCT03680768; https://clinicaltrials.gov/ct2/show/NCT03680768 INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19209 |
format | Online Article Text |
id | pubmed-9131142 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-91311422022-05-26 A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study Husted, Karina Louise Skov Brink-Kjær, Andreas Fogelstrøm, Mathilde Hulst, Pernille Bleibach, Akita Henneberg, Kaj-Åge Sørensen, Helge Bjarup Dissing Dela, Flemming Jacobsen, Jens Christian Brings Helge, Jørn Wulff JMIR Aging Original Paper BACKGROUND: Individual differences in the rate of aging and susceptibility to disease are not accounted for by chronological age alone. These individual differences are better explained by biological age, which may be estimated by biomarker prediction models. In the light of the aging demographics of the global population and the increase in lifestyle-related morbidities, it is interesting to invent a new biological age model to be used for health promotion. OBJECTIVE: This study aims to develop a model that estimates biological age based on physiological biomarkers of healthy aging. METHODS: Carefully selected physiological variables from a healthy study population of 100 women and men were used as biomarkers to establish an estimate of biological age. Principal component analysis was applied to the biomarkers and the first principal component was used to define the algorithm estimating biological age. RESULTS: The first principal component accounted for 31% in women and 25% in men of the total variance in the biological age model combining mean arterial pressure, glycated hemoglobin, waist circumference, forced expiratory volume in 1 second, maximal oxygen consumption, adiponectin, high-density lipoprotein, total cholesterol, and soluble urokinase-type plasminogen activator receptor. The correlation between the corrected biological age and chronological age was r=0.86 (P<.001) and r=0.81 (P<.001) for women and men, respectively, and the agreement was high and unbiased. No difference was found between mean chronological age and mean biological age, and the slope of the regression line was near 1 for both sexes. CONCLUSIONS: Estimating biological age from these 9 biomarkers of aging can be used to assess general health compared with the healthy aging trajectory. This may be useful to evaluate health interventions and as an aid to enhance awareness of individual health risks and behavior when deviating from this trajectory. TRIAL REGISTRATION: ClinicalTrials.gov NCT03680768; https://clinicaltrials.gov/ct2/show/NCT03680768 INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19209 JMIR Publications 2022-05-10 /pmc/articles/PMC9131142/ /pubmed/35536617 http://dx.doi.org/10.2196/35696 Text en ©Karina Louise Skov Husted, Andreas Brink-Kjær, Mathilde Fogelstrøm, Pernille Hulst, Akita Bleibach, Kaj-Åge Henneberg, Helge Bjarup Dissing Sørensen, Flemming Dela, Jens Christian Brings Jacobsen, Jørn Wulff Helge. Originally published in JMIR Aging (https://aging.jmir.org), 10.05.2022. https://creativecommons.org/licenses/by/4.0/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 use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Aging, is properly cited. The complete bibliographic information, a link to the original publication on https://aging.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Husted, Karina Louise Skov Brink-Kjær, Andreas Fogelstrøm, Mathilde Hulst, Pernille Bleibach, Akita Henneberg, Kaj-Åge Sørensen, Helge Bjarup Dissing Dela, Flemming Jacobsen, Jens Christian Brings Helge, Jørn Wulff A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study |
title | A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study |
title_full | A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study |
title_fullStr | A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study |
title_full_unstemmed | A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study |
title_short | A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study |
title_sort | model for estimating biological age from physiological biomarkers of healthy aging: cross-sectional study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9131142/ https://www.ncbi.nlm.nih.gov/pubmed/35536617 http://dx.doi.org/10.2196/35696 |
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