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-Kjær, Andreas
AU - Fogelstrøm, Mathilde
AU - Hulst, Pernille
AU - Bleibach, Akita
AU - Henneberg, Kaj Åge
AU - Sørensen, Helge Bjarup Dissing
AU - Dela, Flemming
AU - Jacobsen, Jens Christian Brings
AU - Helge, Jørn Wulff
N1 - Publisher Copyright:
©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.
PY - 2022/4/1
Y1 - 2022/4/1
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 (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.
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 (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.
KW - aging
KW - biological age
KW - biomarkers
KW - healthy aging
KW - model development
KW - principal component analysis
UR - http://www.scopus.com/inward/record.url?scp=85130587047&partnerID=8YFLogxK
U2 - 10.2196/35696
DO - 10.2196/35696
M3 - Article
AN - SCOPUS:85130587047
SN - 2561-7605
VL - 5
JO - JMIR Aging
JF - JMIR Aging
IS - 2
M1 - e35696
ER -
