The Insulin Resistance: Analysing Divergent Physiological Dynamics and Remission Pathways in Prediabetes versus Overt Type 2 Diabetes using Large Language Model
DOI:
https://doi.org/10.22399/ijcesen.5499Keywords:
Generative AI(Gen-AI), LLMs, Insulin Resistance, Beta-Cell RemissionAbstract
Insulin resistance (IR) is a metabolic pandemic affecting South Asia, where both genetic predisposition and high carbohydrate exposure combined with protein-deficient nutrition are key drivers. Standard laboratory markers measure static disease thresholds, but often do not teach patients about the systems-level physiological changes and daily fluctuations that drive metabolic decline. This study illustrates how Generative AI and Large Language Model (LLM) can create a novel paradigm to bridge the gap between complex biochemical diagnostics and individualized patient education by combining LLM with multi-system biomarker analysis to assess divergent physiological dynamics between two different metabolic phenotypes (early subclinical prediabetes [34-year-old female] vs. severe, decompensated overt Type 2 Diabetes Mellitus [49-year-old male]). The LLM pipeline synthesizes cell-state analyses of multi-system biomarker axes (e.g., glycemic dynamics, purine stress, atherogenic dyslipidemia, hematological parameters, micronutrients, and inflammation) that highlight the 1-to-3-year golden window of beta-cell reversibility in prediabetes, while establishing targeted non-dietary clinical imaging and cardiovascular risk stratification protocols for overt T2DM. Within the context of deterministic medical states, LLM frameworks transition endocrinological care from glucocentric symptom management to proactive, patient-engaged metabolic remission.
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