RESUMEN
BACKGROUND: Human health status can be measured on the basis of many different parameters. Statistical relationships among these different health parameters will enable several possible health care applications and an approximation of the current health status of individuals, which will allow for more personalized and preventive health care by informing the potential risks and developing personalized interventions. Furthermore, a better understanding of the modifiable risk factors related to lifestyle, diet, and physical activity will facilitate the design of optimal treatment approaches for individuals. OBJECTIVE: This study aims to provide a high-dimensional, cross-sectional data set of comprehensive health care information to construct a combined statistical model as a single joint probability distribution and enable further studies on individual relationships among the multidimensional data obtained. METHODS: In this cross-sectional observational study, data were collected from a population of 1000 adult men and women (aged ≥20 years) matching the age ratio of the typical adult Japanese population. Data include biochemical and metabolic profiles from blood, urine, saliva, and oral glucose tolerance tests; bacterial profiles from feces, facial skin, scalp skin, and saliva; messenger RNA, proteome, and metabolite analyses of facial and scalp skin surface lipids; lifestyle surveys and questionnaires; physical, motor, cognitive, and vascular function analyses; alopecia analysis; and comprehensive analyses of body odor components. Statistical analyses will be performed in 2 modes: one to train a joint probability distribution by combining a commercially available health care data set containing large amounts of relatively low-dimensional data with the cross-sectional data set described in this paper and another to individually investigate the relationships among the variables obtained in this study. RESULTS: Recruitment for this study started in October 2021 and ended in February 2022, with a total of 997 participants enrolled. The collected data will be used to build a joint probability distribution called a Virtual Human Generative Model. Both the model and the collected data are expected to provide information on the relationships between various health statuses. CONCLUSIONS: As different degrees of health status correlations are expected to differentially affect individual health status, this study will contribute to the development of empirically justified interventions based on the population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47024.
RESUMEN
BACKGROUND: Perineuronal nets (PNNs) are insoluble aggregates of extracellular matrix molecules in the brain that consist of hyaluronan (HA) and chondroitin sulfate proteoglycans (CSPGs). PNNs promote the acquisition and storage of memories by stabilizing the formation of synapses in the adult brain. Although the deterioration of PNNs has been suggested to contribute to the age-dependent decline in brain function, the molecular mechanisms underlying age-related changes in PNNs remain unclear. METHODS: The amount and solubility of PNN components were investigated by sequential extraction followed by a disaccharide analysis and immunoblotting. We examined the interaction between HA and aggrecan, a major HA-binding CSPG, by combining mass spectrometry and pull-down assays. RESULTS: The solubility and amount of HA increased in the brain with age. Among several CSPGs, the solubility of aggrecan was selectively elevated during aging. In contrast to alternations in biochemical properties, the expression of PNN components at the transcript level was not markedly changed by aging. The increased solubility of aggrecan was not due to the loss of HA-binding properties. Our results indicated that the degradation of high-molecular-mass HA induced the release of the HA-aggrecan complex from PNNs in the aged brain. CONCLUSION: The present study revealed a novel mechanism underlying the age-related deterioration of PNNs in the brain.
