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
Xlab, Center for Healthy Aging, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
Department of Physiotherapy and Occupational Therapy, University College Copenhagen, Copenhagen, Denmark.
JMIR Aging. 2022 May 10;5(2):e35696. doi: 10.2196/35696.
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.
This study aims to develop a model that estimates biological age based on physiological biomarkers of healthy aging.
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.
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.
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.
ClinicalTrials.gov NCT03680768; https://clinicaltrials.gov/ct2/show/NCT03680768.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19209.
衰老速度和疾病易感性的个体差异不能仅由实际年龄来解释。这些个体差异可以通过生物年龄更好地解释，生物年龄可通过生物标志物预测模型来估计。鉴于全球人口的老龄化趋势以及与生活方式相关的发病率增加，发明一种用于健康促进的新生物年龄模型很有意义。
本研究旨在开发一种基于健康衰老生理生物标志物来估计生物年龄的模型。
从100名男性和女性的健康研究人群中精心挑选生理变量作为生物标志物，以建立生物年龄估计值。对生物标志物应用主成分分析，并使用第一主成分来定义估计生物年龄的算法。
在结合平均动脉压、糖化血红蛋白、腰围、一秒用力呼气量、最大耗氧量、脂联素、高密度脂蛋白、总胆固醇和可溶性尿激酶型纤溶酶原激活剂受体的生物年龄模型中，第一主成分在女性中占总方差的31%，在男性中占25%。校正后的生物年龄与实际年龄之间的相关性，女性为r = 0.86（P <.001），男性为r = 0.81（P <.001），一致性高且无偏差。实际年龄均值与生物年龄均值之间未发现差异，两性回归线的斜率均接近1。
根据这9种衰老生物标志物估计生物年龄，可用于与健康衰老轨迹相比评估总体健康状况。这对于评估健康干预措施可能有用，并且有助于在偏离该轨迹时提高对个体健康风险和行为的认识。
ClinicalTrials.gov NCT03680768；https://clinicaltrials.gov/ct2/show/NCT03680768。
国际注册报告识别码（IRRID）：RR2-10.2196/19209。
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