Recent studies have shown a growing interest in the complex relationship between the human gut microbiota, metabolism, and overall health. This review aims to explore the gut microbiota–host association, focusing on its implications for precision nutrition and personalized medicine. The objective is to highlight how gut microbiota modulate metabolic and immune functions, contributing to disease susceptibility and wellbeing. The review synthesizes recent research findings, analyzing key studies on the influence of gut microbiota on lipid and carbohydrate metabolism, intestinal health, neurobehavioral regulation, and endocrine signaling. Data were drawn from both experimental and clinical trials examining microbiota–host interactions relevant to precision nutrition. Our findings highlight the essential role of gut microbiota-derived metabolites in regulating host metabolism, including lipid and glucose pathways. These metabolites have been found to influence immune responses and gut barrier integrity. Additionally, the microbiota impacts broader physiological processes, including neuroendocrine regulation, which could be crucial for dietary interventions. Therefore, understanding the molecular mechanisms of dietary–microbiota–host interactions is pivotal for advancing personalized nutrition strategies. Tailored dietary recommendations based on individual gut microbiota compositions hold promise for improving health outcomes, potentially revolutionizing future healthcare approaches across diverse populations.

The human gut microbiome, an intricate ecosystem housing trillions of microorganisms within the gastrointestinal tract, holds significant importance in human health and the development of diseases. Recent advances in technology have allowed for an in-depth exploration of the gut microbiome, shedding light on its composition and functions. Of particular interest is the role of diet in shaping the gut microbiome, influencing its diversity, population size, and metabolic functions. Precision nutrition, a personalized approach based on individual characteristics, has shown promise in directly impacting the composition of the gut microbiome. However, to fully understand the long-term effects of specific diets and food components on the gut microbiome and to identify the variations between individuals, longitudinal studies are crucial. Additionally, precise methods for collecting dietary data, alongside the application of machine learning techniques, hold immense potential in comprehending the gut microbiome’s response to diet and providing tailored lifestyle recommendations. In this study, we investigated the complex mechanisms that govern the diverse impacts of nutrients and specific foods on the equilibrium and functioning of the individual gut microbiome of seven volunteers (four females and three males) with an average age of 40.9 ± 10.3 years, aiming at identifying potential therapeutic targets, thus making valuable contributions to the field of personalized nutrition. These findings have the potential to revolutionize the development of highly effective strategies that are tailored to individual requirements for the management and treatment of various diseases.

Nutrition is a cross-cutting sector in medicine, with a huge impact on health, from cardiovascular disease to cancer. Employment of digital medicine in nutrition relies on digital twins: digital replicas of human physiology representing an emergent solution for prevention and treatment of many diseases. In this context, we have already developed a data-driven model of metabolism, called a “Personalized Metabolic Avatar” (PMA), using gated recurrent unit (GRU) neural networks for weight forecasting. However, putting a digital twin into production to make it available for users is a difficult task that as important as model building. Among the principal issues, changes to data sources, models and hyperparameters introduce room for error and overfitting and can lead to abrupt variations in computational time. In this study, we selected the best strategy for deployment in terms of predictive performance and computational time. Several models, such as the Transformer model, recursive neural networks (GRUs and long short-term memory networks) and the statistical SARIMAX model were tested on ten users. PMAs based on GRUs and LSTM showed optimal and stable predictive performances, with the lowest root mean squared errors (0.38 ± 0.16–0.39 ± 0.18) and acceptable computational times of the retraining phase (12.7 ± 1.42 s–13.5 ± 3.60 s) for a production environment. While the Transformer model did not bring a substantial improvement over RNNs in term of predictive performance, it increased the computational time for both forecasting and retraining by 40%. The SARIMAX model showed the worst performance in term of predictive performance, though it had the best computational time. For all the models considered, the extent of the data source was a negligible factor, and a threshold was established for the number of time points needed for a successful prediction.

Chewing is essential in regulating metabolism and initiating digestion. Various methods have been used to examine chewing, including analyzing chewing sounds and using piezoelectric sensors to detect muscle contractions. However, these methods struggle to distinguish chewing from other movements. Electromyography (EMG) has proven to be an accurate solution, although it requires sensors attached to the skin. Existing EMG devices focus on detecting the act of chewing or classifying foods and do not provide self-awareness of chewing habits. We developed a non-invasive device that evaluates a personalized chewing style by analyzing various aspects, like chewing time, cycle time, work rate, number of chews and work. It was tested in a case study comparing the chewing pattern of smokers and non-smokers, as smoking can alter chewing habits. Previous studies have shown that smokers exhibit reduced chewing speed, but other aspects of chewing were overlooked. The goal of this study is to present the device and provide additional insights into the effects of smoking on chewing patterns by considering multiple chewing features. Statistical analysis revealed significant differences, as non-smokers had more chews and higher work values, indicating more efficient chewing. The device provides valuable insights into personalized chewing profiles and could modify unhealthy chewing habits.

UVA radiation induces oxidative stress, mitochondrial dysfunction, and cell death in human dermal fibroblasts, contributing to skin aging and damage. In this study, we investigated the protective effects of polydatin, a natural polyphenol, against UVA-induced cell damage. Our results show that polydatin preserves cell viability and reduces intracellular reactive oxygen species (ROS) levels after UVA exposure. In addition, polydatin maintains mitochondrial integrity by preserving mitochondrial membrane potential and improving mitochondrial respiration. From a molecular perspective, polydatin regulates the expression of Nrf2, a key regulator of the cellular antioxidant response, thereby promoting cellular defense mechanisms. Additionally, polydatin attenuates UVA-induced mitochondrial fission, supporting a balanced mitochondrial dynamic profile. These results suggest that polydatin exerts a protective effect on UVA-irradiated fibroblasts, highlighting its potential for cosmetic and dermatological applications aimed at preventing photoaging and oxidative skin damage.

The rising obesity epidemic requires effective and sustainable weight loss intervention strategies that take into account both of individual preferences and environmental impact. This study aims to develop and evaluate the effectiveness of an innovative digital biohacking approach for dietary modifications in promoting sustainable weight loss and reducing carbon footprint impact. A pilot study was conducted involving four participants who monitored their weight, diet, and activities over the course of a year. Data on food consumption, carbon footprint impact, calorie intake, macronutrient composition, weight, and energy expenditure were collected. A digital replica of the metabolism based on nutritional information, the Personalized Metabolic Avatar (PMA), was used to simulate weight changes, plan, and execute the digital biohacking approach to dietary interventions. The dietary modifications suggested by the digital biohacking approach resulted in an average daily calorie reduction of 236.78 kcal (14.24%) and a 15.12% reduction in carbon footprint impact (−736.48 gCO₂eq) per participant. Digital biohacking simulations using PMA showed significant differences in weight change compared to actual recorded data, indicating effective weight reduction with the digital biohacking diet. Additionally, linear regression analysis on real data revealed a significant correlation between adherence to the suggested diet and weight loss. In conclusion, the digital biohacking recommendations provide a personalized and sustainable approach to weight loss, simultaneously reducing calorie intake and minimizing the carbon footprint impact. This approach shows promise in combating obesity while considering both individual preferences and environmental sustainability.