A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study
Research output: Contribution to journal › Journal article › Research › peer-review
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
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.
|Number of pages||15|
|Publication status||Published - 2022|
- 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