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Aging is accompanied by changes in the cardiovascular physiology that promote the development of age-related diseases. This paper presents a modern approach to quantify the physiological effects of age on the cardiovascular system by applying modern machine learning techniques to several indicators of autonomic cardiovascular function. In 885 healthy subjects, 33 different indices were calculated on resting state electrocardiogram and continuous blood pressure recordings. Based on those parameters, five different approaches were applied in order to reconstruct the calendar age of healthy subjects, i.e., linear regression (LR), neural network (NN), Gaussian process regression (GPR), support vector regression (SVR), and relevance vector regression (RVR). Hyper parameters of machine learning methods were optimized via grid search. After 20 repetitions of a five-fold cross-validation, the mean absolute error (MAE) was computed between the calendar and estimated age to assess the accuracy of each method. The results show that the lowest error for age estimation was achieved using SVR with a MAE of 5.49 years. GPR performed comparably well to SVR with a MAE of 5.55 years, while NN led to a MAE of 5.72 years. RVR and LR revealed MAE of more than six years (6.21 and 6.34 years). The error of age estimation could be further reduced by applying outlier correction to the input data leading to a minimum MAE of 4.53 years with GPR. In conclusion, our results suggest that machine learning can be used to quantify the effects of healthy aging on cardio-vascular function.

Regular participation in sports results in a series of physiological adaptations. However, little is known about the brain adaptations to physical activity. Here we aimed to investigate whether young endurance athletes and non‐athletes differ in the gray and white matter of the brain and whether cardiorespiratory fitness (CRF) is associated with these differences. We assessed the CRF, volumes of the gray and white matter of the brain using structural magnetic resonance imaging (sMRI), and brain white matter connections using diffusion magnetic resonance imaging (dMRI) in 20 young male endurance athletes and 21 healthy non‐athletes. While total brain volume was similar in both groups, the white matter volume was larger and the gray matter volume was smaller in the athletes compared to non‐athletes. The reduction of gray matter was located in the association areas of the brain that are specialized in processing of sensory stimuli. In the microstructure analysis, significant group differences were found only in the association tracts, for example, the inferior occipito‐frontal fascicle (IOFF) showing higher fractional anisotropy and lower radial diffusivity, indicating stronger myelination in this tract. Additionally, gray and white matter brain volumes, as well as association tracts correlated with CRF. No changes were observed in other brain areas or tracts. In summary, the brain signature of the endurance athlete is characterized by changes in the integration of sensory and motor information in the association areas. This study unveils the intriguing relationship between regular endurance sports participation, brain structure, and cardiorespiratory fitness. The findings shed light on the intricate interplay between physical activity and brain physiology, contributing valuable insights to our understanding of the brain's response to exercise. [ABSTRACT FROM AUTHOR]

The baroreflex is a powerful physiological mechanism for rapidly adjusting heart rate in response to changes in blood pressure. Spontaneous baroreflex sensitivity (BRS) has been shown to decrease with age. However, studies of sex differences in these age-related changes are rare. Here we investigated several markers of spontaneous baroreflex function in a large sample of healthy individuals. Cardiovascular signals were recorded in the supine position under carefully controlled resting conditions. After quality control, n = 980 subjects were divided into five age groups [age < 30 yr (n = 612), 30-39 yr (n = 140), 40-49 yr (n = 95), 50-59 yr (n = 61), and >60 yr (n = 72)]. Spontaneous baroreflex function was assessed in the time domain (bradycardic and tachycardic slope) and in the frequency domain in the low- and high-frequency band (LF-a, HF-a) applying the transfer function. General linear models showed a significant effect of factor age (P < 0.001) and an age - sex interaction effect (P < 0.05) on each indicator of the baroreflex function. Simple main effects showed a significantly higher BRS as indicated by tachycardic slope, LF-a and HF-a in middle-aged women compared with men (30-39 yr) and higher LF-a, bradycardic and tachycardic slope in men compared with women of the oldest age group (>60 yr). Changes in BRS over the lifespan suggest that baroreflex function declines more slowly but earlier in life in men than in women. Our findings could be linked to age-related changes in major sex hormone levels, suggesting significant implications for diverse cardiovascular outcomes and the implementation of targeted preventive strategies. [ABSTRACT FROM AUTHOR]

Decreased vagal modulation, which has consistently been observed in schizophrenic patients, might contribute to increased cardiac mortality in schizophrenia. Previously, associations between CHRM2 (Cholinergic Receptor Muscarinic 2) and cardiac autonomic features have been reported. Here, we tested for possible associations between these polymorphisms and heart rate variability in patients with schizophrenia. A total of three single nucleotide polymorphisms (SNPs) in CHRM2 (rs73158705 A>G, rs8191992 T>A and rs2350782 T>C) that achieved significance (p < 5 * 10−8) in genome-wide association studies for cardiac autonomic features were genotyped in 88 drug-naïve patients, 61 patients receiving antipsychotic medication and 144 healthy controls. Genotypes were analysed for associations with parameters of heart rate variability and complexity, in each diagnostic group. We observed a significantly altered heart rate variability in unmedicated patients with identified genetic risk status in rs73158705 A>G, rs8191992 T>A and rs2350782 T>C as compared to genotype non-risk status. In patients receiving antipsychotic medication and healthy controls, these associations were not observed. We report novel candidate genetic associations with cardiac autonomic dysfunction in schizophrenia, but larger cohorts are required for replication. [ABSTRACT FROM AUTHOR]