Accuracy of a Clinical Applicable Method for Prediction of VO2max Using Seismocardiography.

Cardiorespiratory fitness measured as ˙VO2max is considered an important variable in the risk prediction of cardiovascular disease and all-cause mortality. Non-exercise ˙VO2max prediction models are applicable, but lack accuracy. Here a model for the prediction of ˙VO2max using seismocardiography (SCG) was investigated. 97 healthy participants (18-65 yrs., 51 females) underwent measurement of SCG at rest in the supine position combined with demographic data to predict ˙VO2max before performing a graded exercise test (GET) on a cycle ergometer for determination of ˙VO2max using pulmonary gas exchange measurements for comparison. Accuracy assessment revealed no significant difference between SCG and GET ˙VO2max (mean±95% CI; 38.3±1.6 and 39.3±1.6 ml·min-1·kg-1, respectively. P=0.075). Further, a Pearson correlation of r=0.73, a standard error of estimate (SEE) of 5.9 ml·min-1·kg-1, and a coefficient of variation (CV) of 8±1% were found. The SCG ˙VO2max showed higher accuracy, than the non-exercise model based on the FRIENDS study, when this was applied to the present population (bias=-3.7±1.3 ml·min-1·kg-1, p<0.0001. r=0.70. SEE=7.4 ml·min-1·kg-1, and CV=12±2%). The SCG ˙VO2max prediction model is an accurate method for the determination of ˙VO2max in a healthy adult population. However, further investigation on the validity and reliability of the SCG ˙VO2max prediction model in different populations is needed for consideration of clinical applicability.

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

[This corrects the article DOI: 10.2196/35696.].

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 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.

A Biological Age Model Designed for Health Promotion Interventions: Protocol for an Interdisciplinary Study for Model Development.

BACKGROUND: Actions to improve healthy aging and delay morbidity are crucial, given the global aging population. We believe that biological age estimation can help promote the health of the general population. Biological age reflects the heterogeneity in functional status and vulnerability to disease that chronological age cannot. Thus, biological age assessment is a tool that provides an intuitively meaningful outcome for the general population, and as such, facilitates our understanding of the extent to which lifestyle can increase health span. OBJECTIVE: This interdisciplinary study intends to develop a biological age model and explore its usefulness. METHODS: The model development comprised three consecutive phases: (1) conducting a cross-sectional study to gather candidate biomarkers from 100 individuals representing normal healthy aging people (the derivation cohort); (2) estimating the biological age using principal component analysis; and (3) testing the clinical use of the model in a validation cohort of overweight adults attending a lifestyle intervention course. RESULTS: We completed the data collection and analysis of the cross-sectional study, and the initial results of the principal component analysis are ready. Interpretation and refinement of the model is ongoing. Recruitment to the validation cohort is forthcoming. We expect the results to be published by December 2021. CONCLUSIONS: We expect the biological age model to be a useful indicator of disease risk and metabolic risk, and further research should focus on validating the model on a larger scale. TRIAL REGISTRATION: ClinicalTrials.gov NCT03680768, https://clinicaltrials.gov/ct2/show/NCT03680768 (Phase 1 study); NCT04279366 https://clinicaltrials.gov/ct2/show/NCT04279366 (Phase 3 study). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19209.