Background: Middle-aged adults have the highest obesity rates, leading to significant health complications in later years. Obesity triggers the release of altered molecules, including extracellular vesicles (EVs) from excess adipose tissue (AT), contributing to various health complications. In this study, we assessed the effects of age and a high-fat diet on AT-derived EV miRNA profiles to understand their potential roles in aging and obesity. Method: C57BL/6 male mice were subjected to a normal chow diet (NCD) or a high-fat diet (HFD) for either 10–12 weeks (young mice, n = 10) or 50–61 weeks (middle-aged mice, n = 12). After evaluating metabolic characteristics, peri-gonadal white AT was isolated and cultured to obtain EVs. AT-derived EV miRNAs were profiled using a NanoString miRNA panel (n = 599). Results: Middle-aged mice exhibited obesity regardless of diet. Young mice fed an HFD showed similar metabolic traits to middle-aged mice. In the NCD group, 131 differentially expressed miRNAs (DE-miRNAs) emerged in middle-aged mice compared to young mice, including miR-21, miR-148a, and miR-29a, associated with cancer, neuro/psychological disorders, and reproductive diseases. In the HFD group, 55 DE-miRNAs were revealed in middle-aged mice compared to young mice. These miRNAs were associated with significantly suppressed IGF1R activity. Conclusion: This study demonstrates the potential significant impact of miRNAs of AT EVs on aging- and obesity-related diseases.

A digital twin is a virtual representation that serves as the real-time digital counterpart of a physical object or system. Recent advancements have extended the concept of digital twins to humans, incorporating complex biological data such as DNA (Predictiv Care, Inc.) and immune system profiles. These sophisticated models go beyond mere pictorial representations, offering a more holistic digital reflection. However, a significant gap remains. The current human digital twin models are not capable of learning one’s preferences. In this pilot study, we introduce Diginality, a chatbot powered by HyperCLOVA X, a Large Language Model (LLM) developed by NAVER, Inc. Diginality, learns one’s preferences with custom training from data collected by interview-style questions on the user’s topic of interest. Our findings demonstrate that Diginality successfully answers one’s preferences, thereby adding a new dimension to the concept of human digital twins. This work represents a pioneering step towards creating a more comprehensive and psychologically nuanced human digital twin.