Artificial intelligence-assisted reinterpretation of preclinical progeria research suggests a hierarchical...

Preclinical research traditionally advances through hypothesis-driven experimentation that establishes mechanistic pathways to support translational development. While this approach has generated major biological insights, it may underemphasize alternative organizational patterns embedded within complex datasets, particularly in rare diseases where opportunities for experimental reiteration are limited. Recent advances in conversational artificial intelligence (AI) provide an opportunity to support structured analytical dialogue as a complementary approach for re-examining validated experimental observations. Here, we evaluated the feasibility and informative value of an investigator-led structured analytical dialogue to reinterpret a previously published preclinical study of Hutchinson-Gilford Progeria Syndrome (HGPS), a rare disorder characterized by accelerated cardiovascular aging. Investigators defined the analytical questions, established interpretative boundaries, and critically evaluated successive AI-generated outputs, while the AI platform functioned exclusively as an analytical support tool for exploring complementary conceptual organization of experimentally validated findings. The original study showed that delivery of the longevity-associated LAV-BPIFB4 gene preserved left ventricular diastolic function, reduced perivascular fibrosis, increased coronary arteriole density, and attenuated cellular senescence without modifying progerin accumulation. Structured analytical dialogue generated complementary hierarchical interpretations of these observations. By integrating graphical dispersion with individual-level numerical data, the investigator-led dialogue identified heterogeneous response trajectories and suggested that cardiovascular protection may be viewed as emerging from coordinated interactions between nuclear stress adaptation and vascular remodelling within a broader resilience framework. These interpretations are presented as hypothesis-generating conceptual extensions rather than new experimental findings. This study demonstrates the feasibility of structured investigator-led analytical dialogue as a complementary methodological approach for broadening interpretation of existing preclinical datasets while preserving the original experimental evidence. By making analytical reasoning more transparent and explicitly distinguishing validated observations from conceptual reinterpretation, this framework may assist prioritization of future mechanistic investigations, particularly in rare cardiovascular diseases where maximizing insight from existing datasets is especially important.