The increasing prevalence of diet-related diseases calls for an improvement in nutritional advice. Personalized nutrition aims to solve this problem by adapting dietary and lifestyle guidelines to the unique circumstances of each individual. With the latest advances in technology and data science, researchers can now automatically collect and analyze large amounts of data from a variety of sources, including wearable and smart devices. By combining these diverse data, more comprehensive insights of the human body and its diseases can be achieved. However, there are still major challenges to overcome, including the need for more robust data and standardization of methodologies for better subject monitoring and assessment. Here, we present the AI4Food database (AI4FoodDB), which gathers data from a nutritional weight loss intervention monitoring 100 overweight and obese participants during 1 month. Data acquisition involved manual traditional approaches, novel digital methods and the collection of biological samples, obtaining: (i) biological samples at the beginning and the end of the intervention, (ii) anthropometric measurements every 2 weeks, (iii) lifestyle and nutritional questionnaires at two different time points and (iv) continuous digital measurements for 2 weeks. To the best of our knowledge, AI4FoodDB is the first public database that centralizes food images, wearable sensors, validated questionnaires and biological samples from the same intervention. AI4FoodDB thus has immense potential for fostering the advancement of automatic and novel artificial intelligence techniques in the field of personalized care. Moreover, the collected information will yield valuable insights into the relationships between different variables and health outcomes, allowing researchers to generate and test new hypotheses, identify novel biomarkers and digital endpoints, and explore how different lifestyle, biological and digital factors impact health. The aim of this article is to describe the datasets included in AI4FoodDB and to outline the potential that they hold for precision health research. Database URL https://github.com/AI4Food/AI4FoodDB

The development of functional foods with health-promoting properties is a priority in addressing chronic diseases. Studying the effect of these foods on gut microbiota provides critical insights into the interplay between microorganisms, health, and disease. This study aimed to evaluate the effects of fiber-enriched kombucha tea on biochemical parameters and gut microbiota composition in a healthy population. A randomized, double-blind, parallel nutritional intervention trial was conducted with 60 participants (58 completed: 42 women and 16 men; mean ± SD age: 39.79 ± 14.58 years). Participants were randomized into two groups consuming either 250 mL/day of a control beverage (unfermented tea) or the fiber-enriched kombucha tea for six weeks. Lifestyle and biochemical data were collected, and gut microbiota composition and diversity were assessed using 16S rDNA sequencing. Significant biochemical and microbiota-related improvements were observed in the study group compared to the control. The treatment significantly affected triglyceride levels (p-value = 0.031). In particular, the study group exhibited a reduction in triglyceride levels very close to significance (baseline mean ± SEM: 69.59 ± 6.98 mg/dL; post-intervention: 62.80 ± 5.14 mg/dL; p = 0.053), while the control group did not experience a significant variation. Additionally, fiber-enriched kombucha consumption led to a notable increase in Bifidobacterium abundance, recognized for its intestinal health benefits and immunomodulatory effects. A reduction in Ruminococcus torques, linked to inflammatory bowel diseases, was also observed. The studied fiber-enriched kombucha drink demonstrated potential health benefits, including triglyceride modulation and positive alterations in gut microbiota composition. These findings suggest its promise as a functional beverage for improving metabolic and gut health in healthy individuals. However, further randomized controlled trials are warranted to confirm these benefits. This trial was registered at clinicaltrials.gov as NCT06626997.

Cancer cells commonly display metabolic fluctuations. Together with the Warburg effect and the increased glutaminolysis, alterations in lipid metabolism homeostasis have been recognized as a hallmark of cancer. Highly proliferative cancer cells upregulate de novo synthesis of fatty acids (FAs) which are required to support tumor progression by exerting multiple roles including structural cell membrane composition, regulators of the intracellular redox homeostasis, ATP synthesis, intracellular cell signaling molecules, and extracellular mediators of the tumor microenvironment. Epigenetic modifications have been shown to play a crucial role in human development, but also in the initiation and progression of complex diseases. The study of epigenetic processes could help to design new integral strategies for the prevention and treatment of metabolic disorders including cancer. Herein, we first describe the main altered intracellular fatty acid processes to support cancer initiation and progression. Next, we focus on the most important regulatory and non-coding RNAs (small noncoding RNA—sncRNAs—long non-coding RNAs—lncRNAs—and other regulatory RNAs) which may target the altered fatty acids pathway in cancer.

Precision medicine might be the response to the recent questioning of the use of metformin as an anticancer drug in colorectal cancer (CRC). Thus, in order to establish properly its benefits, its application need to be assayed on the different progression stages of CRC. In this way, organoids imply a more physiological tool, representing a new therapeutic opportunity for CRC personalized treatment to assay tumor stage-dependent drugs effects. Since the lipid metabolism-related axis, ACSL/SCD, stimulates colon cancer progression and Metformin is able to rescuing the invasive and migratory phenotype conferred to cancer cells upon this axis overexpression; we checked ACSL/SCD status, its regulatory miRNAs and the effect of Metformin treatment in organoids as a model for specific and personalized treatment. Despite ACSL4 expression is upregulated in CRC-like organoids, Metformin is able to downregulate it, especially in the first stages. Besides, organoids are clearly more sensitive in this first stage (Apc mutated) to Metformin than current chemotherapeutic drugs such as fluorouracil (5-FU). Metformin performs an independent “Warburg effect” blockade to cancer progression and is able to reduce crypt stem cell markers expression such as Lgr5+. These results suggest a putative increased efficiency of the use of Metformin in the first stages of CRC than in advanced disease.