A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study

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Standard

A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging : Cross-sectional Study. / Husted, Karina Louise Skov; Brink-Kjaer, Andreas; Fogelstrom, Mathilde; Hulst, Pernille; Bleibach, Akita; Henneberg, Kaj-Age; Sorensen, Helge Bjarup Dissing; Dela, Flemming; Jacobsen, Jens Christian Brings; Helge, Jorn Wulff.

I: JMIR Aging, Bind 5, Nr. 2, 35696, 2022.

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

Harvard

Husted, KLS, Brink-Kjaer, A, Fogelstrom, M, Hulst, P, Bleibach, A, Henneberg, K-A, Sorensen, HBD, Dela, F, Jacobsen, JCB & Helge, JW 2022, 'A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study', JMIR Aging, bind 5, nr. 2, 35696. https://doi.org/10.2196/35696

APA

Husted, K. L. S., Brink-Kjaer, A., Fogelstrom, M., Hulst, P., Bleibach, A., Henneberg, K-A., Sorensen, H. B. D., Dela, F., Jacobsen, J. C. B., & Helge, J. W. (2022). A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study. JMIR Aging, 5(2), [35696]. https://doi.org/10.2196/35696

Vancouver

Husted KLS, Brink-Kjaer A, Fogelstrom M, Hulst P, Bleibach A, Henneberg K-A o.a. A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study. JMIR Aging. 2022;5(2). 35696. https://doi.org/10.2196/35696

Author

Husted, Karina Louise Skov ; Brink-Kjaer, Andreas ; Fogelstrom, Mathilde ; Hulst, Pernille ; Bleibach, Akita ; Henneberg, Kaj-Age ; Sorensen, Helge Bjarup Dissing ; Dela, Flemming ; Jacobsen, Jens Christian Brings ; Helge, Jorn Wulff. / A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging : Cross-sectional Study. I: JMIR Aging. 2022 ; Bind 5, Nr. 2.

Bibtex

@article{a0a779a9df864defbec57b52aa5d2291,
title = "A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study",
abstract = "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 (PConclusions: 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.",
keywords = "biological age, model development, principal component analysis, healthy aging, biomarkers, aging, BODY-MASS INDEX, WAIST CIRCUMFERENCE, CARDIORESPIRATORY FITNESS, IDENTIFYING BIOMARKERS, CARDIOVASCULAR RISK, PHYSICAL-FITNESS, MORTALITY, MEN, PREDICTOR, SELECTION",
author = "Husted, {Karina Louise Skov} and Andreas Brink-Kjaer and Mathilde Fogelstrom and Pernille Hulst and Akita Bleibach and Kaj-Age Henneberg and Sorensen, {Helge Bjarup Dissing} and Flemming Dela and Jacobsen, {Jens Christian Brings} and Helge, {Jorn Wulff}",
note = "Correction: https://aging.jmir.org/2022/2/e40508",
year = "2022",
doi = "10.2196/35696",
language = "English",
volume = "5",
journal = "JMIR Aging",
issn = "2561-7605",
publisher = "JMIR Publications Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging

T2 - Cross-sectional Study

AU - Husted, Karina Louise Skov

AU - Brink-Kjaer, Andreas

AU - Fogelstrom, Mathilde

AU - Hulst, Pernille

AU - Bleibach, Akita

AU - Henneberg, Kaj-Age

AU - Sorensen, Helge Bjarup Dissing

AU - Dela, Flemming

AU - Jacobsen, Jens Christian Brings

AU - Helge, Jorn Wulff

N1 - Correction: https://aging.jmir.org/2022/2/e40508

PY - 2022

Y1 - 2022

N2 - 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 (PConclusions: 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.

AB - 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 (PConclusions: 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.

KW - biological age

KW - model development

KW - principal component analysis

KW - healthy aging

KW - biomarkers

KW - aging

KW - BODY-MASS INDEX

KW - WAIST CIRCUMFERENCE

KW - CARDIORESPIRATORY FITNESS

KW - IDENTIFYING BIOMARKERS

KW - CARDIOVASCULAR RISK

KW - PHYSICAL-FITNESS

KW - MORTALITY

KW - MEN

KW - PREDICTOR

KW - SELECTION

U2 - 10.2196/35696

DO - 10.2196/35696

M3 - Journal article

C2 - 35536617

VL - 5

JO - JMIR Aging

JF - JMIR Aging

SN - 2561-7605

IS - 2

M1 - 35696

ER -

ID: 314292126