BMI and deep brain stimulation: A comprehensive review and future directions with AI integration.

Abstract

Deep brain stimulation (DBS) has revolutionized the treatment of movement disorders, including Parkinson's disease (PD), essential tremors, dystonia, and treatment-refractory obsessive-compulsive disorder (OCD). This systematic review and meta-analysis aimed to assess the impact of DBS on Body Mass Index (BMI). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, data from 49 studies were reviewed, with 46 studies specifically focusing on BMI and DBS. These studies involved 1,478 participants, predominantly PD patients, with an average age of 58.82 years. The primary DBS implantation site was the subthalamic nucleus (STN). Over six months, the mean BMI increased from 25.69 to 27.41, despite a reduction in daily energy intake from 1992 to 1873 kJ. While the findings suggest a correlation between DBS and weight gain, the study has limitations. The sample largely comprised PD patients (91%), preventing analysis of other subtypes. Additionally, most studies focused on the STN, limiting comparisons with other targets like the globus pallidus internus (GPi). Inconsistencies in assessing daily energy intake and food consumption further complicate the results. Integrating artificial intelligence (AI) in future research could address these gaps. For example, machine learning algorithms, such as those used by Oliveira et al., can predict post-DBS weight changes based on pre-surgical BMI and demographic factors. Similarly, AI-driven models like CLOVER-DBS can optimize DBS settings for improved motor control in PD patients. In conclusion, DBS affects BMI, and AI has the potential to enhance the precision of future studies.