Recent studies indicate that intermittent fasting (IF) can paradoxically increase serum total and LDL cholesterol levels in healthy, nonobese individuals. While IF offers numerous health benefits, this adverse effect on lipid profiles is concerning. To address this, researchers are leveraging Natural Language Processing (NLP) to analyze extensive biomedical literature for potential nutritional interventions that could mitigate these negative outcomes while maintaining the benefits of fasting. Using a custom NLP pipeline, relevant PubMed publications were analyzed. This pipeline included Named Entity Recognition for identifying nutrients, fasting, and lipid-related terms, and relationship extraction via dependency parsing. Feature engineering created numerical representations of nutrient-fasting-cholesterol interactions, and text embedding through BioBERT enhanced contextual understanding. The resulting machine learning classification predicted the efficacy of various nutrients. Key findings from the analysis revealed omega-3 fatty acids, soluble fiber, and plant sterols as top candidates for reducing LDL cholesterol when combined with IF. The NLP-driven approach not only identified nutrients that counteract the LDL-raising effect of fasting but also demonstrated significant LDL reduction. However, clinical validation is essential to confirm the efficacy and safety of these findings. Rigorous clinical trials and peer-reviewed studies will be necessary to establish standardized protocols for broader application. Such validation will enhance the credibility of this methodology and facilitate its integration into clinical practice, ultimately improving patient outcomes and advancing nutritional science.

Shula Shazman has completed her PhD from the Technion Israel and postdoctoral studies from the department of biochemistry & molecular biophysics, Columbia University, New-York, USA. She has published more than 12 papers in reputed journals. She is currently working at the Open University as a researcher and as a lecturer. Her current projects are using machine learning and deep learning approaches to reveal mechanisms of diseases such as autism spectrum disorder and Type 2 Diabetes.