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BackgroundActions 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. ObjectiveThis interdisciplinary study intends to develop a biological age model and explore its usefulness. MethodsThe 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. ResultsWe 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. ConclusionsWe 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 RegistrationClinicalTrials.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

BackgroundMany people in Western countries do not follow public health physical activity (PA) recommendations. Web-based interventions provide cost- and time-efficient means of delivering individually targeted lifestyle modification at a population level. ObjectiveTo examine whether access to a website with individually tailored feedback and suggestions on how to increase PA led to improved PA, anthropometrics, and health measurements. MethodsPhysically inactive adults (n = 12,287) participating in a nationwide eHealth survey and health examination in Denmark were randomly assigned to either an intervention (website) (n = 6055) or a no-intervention control group (n = 6232) in 2008. The intervention website was founded on the theories of stages of change and of planned behavior and, apart from a forum page where a physiotherapist answered questions about PA and training, was fully automated. After 3 and again after 6 months we emailed participants invitations to answer a Web-based follow-up questionnaire, which included the long version of the International Physical Activity Questionnaire. A subgroup of participants (n = 1190) were invited to a follow-up health examination at 3 months. ResultsLess than 22.0% (694/3156) of the participants logged on to the website once and only 7.0% (222/3159) logged on frequently. We found no difference in PA level between the website and control groups at 3- and 6-month follow-ups. By dividing participants into three groups according to use of the intervention website, we found a significant difference in total and leisure-time PA in the website group. The follow-up health examination showed no significant reductions in body mass index, waist circumference, body fat percentage, and blood pressure, or improvements in arm strength and aerobic fitness in the website group. ConclusionsBased on our findings, we suggest that active users of a Web-based PA intervention can improve their level of PA. However, for unmotivated users, single-tailored feedback may be too brief. Future research should focus on developing more sophisticated interventions with the potential to reach both motivated and unmotivated sedentary individuals. Trial RegistrationClinicaltrials.gov NCT01295203; http://clinicaltrials.gov/ct2/show/NCT01295203 (Archived by WebCite at http://www.webcitation.org/6B7HDMqiQ)

A non‐exercise method equation using seismocardiography for estimating V̇O2peak (SCG V̇O2peak) has previously been validated in healthy subjects. However, the performance of the SCG V̇O2peak within a trained population is unknown, and the ability of the model to detect changes over time is not well elucidated. Forty‐seven sub‐elite football players were tested at the start of pre‐season (SPS) and 36 players completed a test after eight weeks at the end of the pre‐season (EPS). Testing included an SCG V̇O2peak estimation at rest and a graded cardiopulmonary exercise test (CPET) on a treadmill for determination of V̇O2peak. Agreement between SCG V̇O2peak and CPET V̇O2peak showed a large underestimation at SPS (bias ± 95% CI: −9.9 ± 1.8, 95% Limits of Agreement: 2.2 to −22.0 mL·min−1 kg−1). At EPS no interaction (p = 0.3590) but a main effect of time (p < 0.0001) and methods (p < 0.0001) was observed between SCG and CPET V̇O2peak. No correlation in V̇O2peak changes was observed between SCG and CPET (r = −20.0, p = 0.2484) but a fair agreement in classifying the correct directional change in V̇O2peak with the SCG method was found (Cohen's κ coefficient = 0.28 ± 0.25). Overall, the SCG V̇O2peak method lacks accuracy and despite being able to estimate group changes, it was incapable of detecting individual changes in V̇O2peak following a pre‐season period in sub‐elite football players. The SCG algorithm needs to be further adjusted and the accuracy and precision improved for the method to be applicable for use within a trained population. Highlights: This is the first time a non‐exercise method using seismocardiography for the estimation of V̇O2peak has been validated in sub‐elite football players.The seismocardiography V̇O2peak method lacks accuracy within a trained population as it underestimates V̇O2peak compared to the gold standard measurement in sub‐elite football players.The seismocardiography V̇O2peak method can estimate the group changes in V̇O2peak following the pre‐season training period in sub‐elite football players. However, it is incapable of detecting individual changes.Adjustment of the seismocardiography V̇O2peak algorithm is needed and the accuracy and precision improved for the method to be applicable for use within a trained population. [ABSTRACT FROM AUTHOR]

Background The aim of this study was to investigate the effect of prolonged endurance exercise on adipose tissue inflammation markers and mitochondrial respiration in younger and older men. Methods "Young" (aged 30 years, n = 7) and "old" (aged 65 years, n = 7) trained men were exposed to an exercise intervention of 15 consecutive days biking 7 to 9 hours/day at 63% and 65% of maximal heart rate (young and old, respectively), going from Copenhagen, Denmark to Palermo, Italy. Adipose tissue was sampled from both the gluteal and abdominal depot before and after the intervention. Mitochondrial respiration was measured by high-resolution respirometry, and adipose inflammation was assessed by immunohistochemical staining of paraffin embedded sections. Results An increased number of CD163+ macrophages was observed in both the gluteal and abdominal depot (P <.01). In addition, an increased mitochondrial respiration was observed in the abdominal adipose tissue from men in the young group with complex I (CI p) stimulated respiration, complex I + II (CI+II p) stimulated respiration and the capacity of the electron transport system (ETS) (P <.05), and in the older group an increase in CIp and CI+II p stimulated respiration (P <.05) was found. Conclusion Overall, we found a positive effect of prolonged endurance exercise on adipose tissue inflammation markers and mitochondrial respiration in both young and old trained men, and no sign of attenuated function in adipose tissue with age. [ABSTRACT FROM AUTHOR]

Growth differentiation factor 15 (GDF15) is a stress-induced cytokine. Although the exact physiological function of GDF15 is not yet fully comprehended, the significant elevation of circulating GDF15 levels during gestation suggests a potential role for this hormone in pregnancy. This is corroborated by genetic association studies in which GDF15 and the GDF15 receptor, GDNF family receptor alpha like (GFRAL) have been linked to morning sickness and hyperemesis gravidarum (HG) in humans. Here, we studied GDF15 biology during pregnancy in mice, rats, macaques, and humans. In contrast to macaques and humans, mice and rats exhibited an underwhelming induction in plasma GDF15 levels in response to pregnancy (-75-fold increase in macaques vs. -2-fold increase in rodents). The changes in circulating GDF15 levels were corroborated by the magnitude of Gdf15 mRNA and GDF15 protein expression in placentae from mice, rats, and macaques. These species-specific findings may help guide future studies focusing on GDF15 in pregnancy and on the evaluation of pharmacological strategies to interfere with GDF15-GFRAL signaling to treat severe nausea and HG. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]