Does the aging process significantly modify the Mean Heart Rate?

A metabolomic signature of decelerated physiological aging in human plasma

The degenerative processes that occur during aging increase the risk of disease and impaired health. Meanwhile, interventions that target aging to promote healthy longevity are gaining interest, both academically and in the public. While nutritional and physical interventions exist, efficacy is often difficult to determine. It is therefore imperative that an aging score measuring the biological aging process is available to the wider public. However, simple, interpret, and accessible biological aging scores are lacking. Here, we developed PhysiAge, a physiological aging score based on five accessible parameters that have influence on or reflect the aging process: (1) average daily step count, (2) blood glucose, (3) systolic blood pressure, (4) sex, and (5) age. Here, we found that compared to calendar age alone, PhysiAge better predicts mortality, as well as established muscle aging markers such as decrease in NAD+ levels, increase in oxidative stress, and decline in physical functioning. In order to demonstrate the usefulness of PhysiAge in identifying relevant factors associated with decelerated aging, we calculated PhysiAges for a cohort of aged individuals and obtained mass spectrometry-based blood plasma metabolomic profiles for each individual. Here, we identified a metabolic signature of decelerated aging, which included components of the TCA cycle, including malate, citrate, and isocitrate. Higher abundance of these metabolites was associated with decelerated aging, in line with supplementation studies in model organisms. PhysiAge represents an accessible way for people to track and intervene in their aging trajectories, and identifies a metabolic signature of decelerated aging in human blood plasma, which can be further studied for its causal involvement in human aging.

Behavior and Task Classification Using Wearable Sensor Data: A Study across Different Ages

In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community.

Differences in Physiological Signals Due to Age and Exercise Habits of Subjects during Cycling Exercise

Numerous studies indicated the physical benefits of regular exercise, but the neurophysiological mechanisms of regular exercise in elders were less investigated. We aimed to compare changes in brain activity during exercise in elderly people and in young adults with and without regular exercise habits. A total of 36 healthy young adults (M/F:18/18) and 35 healthy elderly adults (M/F:20/15) participated in this study. According to exercise habits, each age group were classified into regular and occasional exerciser groups. ECG, EEG, and EMG signals were recorded using V-AMP with a 1-kHz sampling rate. The participants were instructed to perform three 5-min bicycle rides with different exercise loads. The EEG spectral power of elders who exercised regularly revealed the strongest positive correlation with their exercise intensity by using Pearson correlation analysis. The results demonstrate that exercise-induced significant cortical activation in the elderly participants who exercised regularly, and most of the p-values are less than 0.001. No significant correlation was observed between spectral power and exercise intensity in the elders who exercised occasionally. The young participants who exercised regularly had greater cardiac and neurobiological efficiency. Our results may provide a new exercise therapy reference for adult groups with different exercise habits, especially for the elders.

Liver-specific NG37 overexpression leads to diet-dependent fatty liver disease accompanied by cardiac dysfunction

BACKGROUND

Environmental factors are well-known causes of diseases. However, aside from a handful of risk indicators, genes' encoding susceptibility to chronic illnesses and their associated environmental triggers are largely unknown. In this era of increasingly rich diets, such genetic predispositions would be immensely helpful from a public health perspective. The novel transgenic mouse model with liver-specific NG37 overexpression characterized in this article identifies the diet-dependent function of NG37 in the pathogenesis of fatty liver disease and cardiac arrhythmia.

RESULTS

The liver-specific NG37 overexpression transgenic mouse model described here was generated using the Alb-SV40 polyA expression plasmid backbone. NG37 cDNA under control of the albumin promoter for liver-specific expression was fused with a 5' terminal M2 FLAG sequence and a SV40 early region transcription terminator/polyadenylation site attached at the 3'-UTR. These NG37 transgenic mice developed normally and were physiologically normal on a standard diet. However, in comparison to non-transgenic (nTG) litter mates, these mice develop dramatic phenotypes within 12-18 days of starting a high-fat diet: (i) increased body weight (28.5 ± 12.3 g), (ii) increased liver weight (87.4 ± 35.7 mg), (iii) increased heart weight (140 ± 38.4 mg), and (iv) cardiac arrhythmia. The enlarged livers of high-fat diet NG37 transgenic mice was histologically similar to human fatty liver disease and contained Maltese cross birefringent active depositions in hepatocytes that are indicative of fatty liver disease. We also confirmed via X-ray diffraction the steatotic vesicles in the diseased hepatocytes of our high-fat diet NG37 mice was composed of cholesteryl derivatives also found in human fatty liver disease. In addition to cardiac enlargement, NG37 transgenic mice on high-fat diet also exhibited highly irregular bradycardia not present in either high-fat diet nTG littermates or normal-diet transgenic litter mates.

CONCLUSIONS

The dramatic high-fat diet-dependent symptoms (increased body weight, cardiac enlargement, fatty liver, and cardiac arrhythmias) characterized in our liver-specific NG37 overexpression mouse model identifies NG37 as a gene encoding latent lipid metabolism pathology induced only in the presence of an environmental factor relevant to human health: high-fat diet.