Salvatore Corrao, Dario Tuccinardi, Roberto Baratta
BACKGROUND: Obesity is commonly viewed as a reversible condition primarily driven by excess body weight. Increasing evidence, however, suggests that adipose tissue may undergo persistent immunometabolic and structural alterations that do not fully revert after weight loss, raising the hypothesis of a durable biological imprint that could hinder long-term remission. Whether such persistence is consistently reflected across the biomedical literature remains uncertain. METHODS: We performed an AI-driven semantic analysis of a construct-anchored corpus of obesity-related publications retrieved from PubMed and Scopus using transformer-based biomedical embeddings (PubMedBERT). Unsupervised density-based clustering (HDBSCAN) identified coherent semantic regions, and Uniform Manifold Approximation and Projection (UMAP) enabled visualization. Core macro-domains were selected using predefined quantitative criteria (cluster stability, temporal persistence, and semantic coherence) and independently evaluated by two blinded experts. Interpretability was assessed through stratified human validation, quantifying inter-rater agreement (Cohen's κ) and AI label acceptance rates. An exportable curated core corpus of mapped publications was generated to support downstream focused screening and structured synthesis. RESULTS: Three mutually exclusive yet highly coherent macro-domains emerged: (1) inflammatory adipose biology, (2) adipose remodeling and chronic dysfunction, and (3) stress-triggered immune persistence. Despite document-level exclusivity, these domains showed exceptionally high semantic similarity (pairwise cosine similarity > 0.97), indicating a shared conceptual core. CONCLUSIONS: The semantic architecture of the corpus is consistent with obesogenic memory conceptualized as biological hysteresis in adipose tissue, although not constituting mechanistic proof. The curated corpus provides a structured foundation for subsequent conventional evidence synthesis.