RESUMO
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.
RESUMO
The incidence of dementia, a clinical symptom characterized by severe cognitive decline, is increasing worldwide. Predictive biomarkers are therefore required for early identification and management. D-amino acids in the brain contribute to cognitive function and are suggested as useful biomarkers for diagnosing dementia risk. To clarify their relationship with human cognitive decline, we developed an identification method of chiral metabolomics for detecting slight differences in chiral amino acid amounts. Chiral tandem liquid chromatography-tandem mass spectrometry systems were applied for sensitive and selective amino acid species along with chiral species determination based on anion and zwitterion exchange mechanisms. In a comprehensive health cohort (cross-sectional study), we measured blood chiral amino acid levels from 305 women (65-80 years old) classified into Control, Mild-cognitive-Impairment (MCI), and Dementia groups using the Mini-Mental State Examination. MCI exhibited higher D-Pro (D-Pro/(D-Pro + L-Pro)) proportion vs the Control group, suggesting this proportion as a useful biomarker for MCI. Biomarker accuracy was improved in combination with D-Ser proportion. Receiver operating characteristics analysis of the Control vs. MCI proportion obtained area under the curve (0.80) with 70% sensitivity and 84% specificity at the optimal cutoff value (0.30). Thus, dementia monitoring can be improved by including trace D-amino acids measurements.
