Faster but less accurate: An explorative study on the effects of three weeks of ketogenic diet on cognitive functions in undergraduate students

by Gianluigi Serio, Consiglia Pacelli, Claudia Piccoli, Nazzareno Capitanio, Giuseppe Cibelli, Anna Antonia Valenzano, Francesca Landini, Leonardo Carlucci, Paola Palladino

The ketogenic diet (KD) is a low-carbohydrate diet that induces and sustains a ketosis state and minimizes somatic glucose levels. Several psychological studies have described the positive effects of ketosis on cognitive functions for a wide range of neuropsychiatric conditions (e.g., Alzheimer’s disease; epilepsy), leading to greater interest in the KD today. However, the psychological and cognitive effects of inducing ketosis via diet remain unclear, especially in healthy people. From an initial pool of thirty participants, eight undergraduate students performed a cognitive assessment before (baseline) and after three weeks (follow-up) of an isocaloric ketogenic diet. Several neuropsychological measures and psychometric tests have been administered to investigate psychological chronotype, sleep quality, eating habits, anxiety and cognitive components of attention, inhibition, and memory. Non-parametric Bayesian analysis showed that the ketogenic diet affected cognitive functions. Participants performed cognitive tests faster at follow-up than at baseline, showing improvements in visual-motor cognitive and processing speed components. However, they were less accurate on working memory tasks, suggesting a decreasing performance of higher cognitive functions. Finally, no differences in anxiety levels were found between baseline and follow-up. The results could have significant implications for identifying specific cognitive models of students based on specific lifestyle habits and nutritional patterns, allowing the implementation of targeted interventions to improve university learning conditions.

Identifying risk patterns for sudden cardiac death in athletes: A clustering and principal component analysis approach

by Giacinto Angelo Sgarro, Paride Vasco, Domenico Santoro, Luca Grilli, Marco Giglio, Natale Daniele Brunetti, Luigi Traetta, Giuseppe Cibelli, Anna Antonia Valenzano

Sudden Cardiac Death (SCD) is a critical and unexpected condition that occurs due to cardiac causes within one hour of the onset of acute cardiovascular symptoms or twenty-four hours in unwitnessed cases. Despite advancements in cardiovascular medicine, practical methods for predicting SCD are still lacking, and there are no standardized systems to identify individuals at risk, especially in seemingly healthy populations such as athletes. In this study, we employed hierarchical clustering and principal component analysis (PCA) on data from 711 competitive athletes, revealing distinct patterns and cluster distributions in PCA space. Specifically, Clustering revealed characteristic feature combinations associated with increased SCD risk in athletes. Notably, certain clusters shared traits, including participation in Class C sports, sinus tachycardia, ventricular pre-excitation, personal or family history of heart disease, T-wave inversions, and prolonged QTc intervals. PCA helped visualize these patterns in distinct spatial regions, highlighting underlying structures and aiding intuitive risk interpretation. These results enable scientists to derive cluster metrics that serve as reference points for classifying new individuals and visually representing risk patterns in a clear graphical format. These findings establish a foundation for predictive tools that, with additional clinical validation, could aid in the prevention of SCD. The dataset used in this study, along with the clustering and PCA results, is available to the scientific community in an open format, together with the necessary tools and scripts to enable independent experimentation and further analysis.