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SummaryThis study investigated the effect of a single oral ingestion of alpha-linolenic acid-enriched diacylglycerol (ALA-DAG) on postprandial serum triglyceride (TG) levels. A randomized, double-blind, controlled, crossover study was performed in subjects with normal or moderately high fasting serum TG levels. Subjects ingested 0.00 g [control: triacylglycerol; TAG (rapeseed oil)], 1.25 g (1.25-g: mixture of 1.25 g ALA-DAG and 1.25 g TAG), or 2.50 g (2.50 g) of ALA-DAG in random order with a 6-d washout period. Serum TG levels were evaluated in the fasting state, and at 2, 3, 4, and 6 h after the test meal. Thirty-eight subjects completed the study and were defined as the per protocol set. As the primary outcome, postprandial serum TG levels were significantly lower in the 2.50-g treatment compared with the control. The TG level did not differ significantly between the 1.25-g and control. The suppressive effect of ALA-DAG on the serum TG level correlated significantly with the body mass index and fasting insulin level. ALA-DAG at a dose of 2.50 g had greater effects on serum TG and apolipoprotein B levels in subjects with a higher body mass index ($25 kg/m 2 ) and higher fasting serum insulin levels (.10 mU/mL). Our findings suggest that ingesting 2.50 g ALA-DAG suppresses the postprandial serum TG level in people with normal and moderately high fasting serum TG levels, presumably as a result of poor reesterification of dietary fat into TG in the intestinal mucosa.

BACKGROUND Human health status can be measured in several different ways and statistical relationships among various measurements can be represented as a joint probability distribution. Approximation of the current health status of individuals will allow for more personalized and preventive healthcare by informing the potential risks and developing personalized interventions. Understanding 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 dataset of comprehensive healthcare information to construct a virtual human generative model (VHGM) based on a joint probability distribution. METHODS In this cross-sectional observational study, data will be collected from a population of 1000 adult men and women (aged ≥20 years) matching the age ratio of the typical adult Japanese population. Data will include biochemical and metabolic profiles from blood, urine, saliva, and oral glucose tolerance tests; bacterial profiles from feces, facial skin, scalp skin, and saliva; mRNA, proteome, and metabolite analyses from facial and scalp skin surface lipids; lifestyle survey and questionnaire; physical, motor, cognitive, and vascular function analyses; alopecia; and comprehensive analyses of body odor components. Statistical analyses will examine multiple health-related items using a joint probability distribution model. We will train a joint probability distribution, the VHGM, by combining a commercially available healthcare dataset containing large amounts of relatively low-dimensional data with a high-dimensional, cross-sectional dataset. The trained VHGM is expected to enable various healthcare applications through application program interface calls. RESULTS Written informed consent will be required to participate in the study. The study has been approved by the Institutional Review Boards of the Kao Corporation (Approval # K0023-2108) and the Preferred Network, Inc. (Approval # ET22110047). CONCLUSIONS The collected data are expected to provide information on the relationships between various health statuses. Because different degrees of health status correlations are expected to have different effects on individual health status, this study will contribute to developing empirically justified interventions based on the population. CLINICALTRIAL The trial is registered with the University Hospital Medical Information Network (Registration No. UMIN000045746).

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