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Advancing Diabetes Reclassification Using the NexDi AI System With Multi-Omic Insights
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The NexDi AI framework reconceptualizes diabetes as a fluid spectrum shaped by genetic, nutritional, and environmental pressures — including exposure to endocrine-disrupting agents and microplastics — which disturb metabolic and microbiome stability. This work highlights microplastics as a contributing environmental stressor in the growing global diabetes burden, supporting research into their systemic metabolic effects.
NexDi is introduced as an advanced framework that reconceptualizes diabetes as a fluid continuum of β-cell vulnerability shaped by genetic, nutritional, and environmental pressures. Type 1 diabetes is a state where autoimmune activity increasingly overlaps with insulin resistance pathways, whereas type 5 diabetes emerges from maternal anemia, hidden hunger, and early-life undernutrition. This spectrum reflects the dual burden observed in Egypt, where rising childhood diabetes coexists with persistent nutritional deficits. Environmental stressors, including exposure to endocrine-disrupting agents and microplastic, further disturb metabolic and polybiome stability. By integrating nutrigenomics, AI-enabled prediction, and polybiome analytics, NexDi provides a scalable and culturally grounded pathway toward predictive, preventive, and personalized diabetes care.
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Can the Human Plastisphere Account for the Rise of Type 1 Diabetes: A Computational Molecular Mimicry Study
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Researchers used AI-assisted structural modeling to test whether fragments of six common microplastics can mimic the shape of insulin and other Type 1 diabetes epitopes that bind high-risk HLA molecules, finding that several plastic polymer segments dock into HLA binding grooves with affinities comparable to known T1DM antigens—raising a computational hypothesis for molecular mimicry as a mechanism linking microplastic exposure to autoimmune diabetes.
Can the Human Plastisphere Account for the Rise of Type 1 Diabetes: A Computational Molecular Mimicry Study
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Researchers used AI-based structural modeling to test whether common microplastic polymer fragments could mimic the shape of insulin-related antigens that bind to HLA molecules associated with Type 1 diabetes risk, finding that fragments of HDPE, LDPE, PET, PP, PS, and PVC all fit within HLA binding regions with configurations resembling known T1DM epitopes — raising the hypothesis that the human 'plastisphere' may contribute to autoimmune triggering.
Emerging Contaminants: An Emerging Risk Factor for Diabetes Mellitus
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This review examines how emerging environmental contaminants, including microplastics and nanoplastics, may contribute to the development and progression of diabetes. These contaminants can disrupt glucose metabolism through oxidative stress, inflammation, and interference with hormone signaling. The findings suggest that chronic exposure to microplastics and other pollutants in food and water could be an overlooked risk factor for the growing global diabetes epidemic.
Precision Nutrition Redefined: Integrative Molecular Frameworks for Personalized Dietary Interventions
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This review explored the biochemical and molecular foundations of personalized nutrition, integrating nutrigenetics, epigenomics, microbiomics, and metabolomics to tailor dietary strategies to individual biology. The authors identified environmental contaminants, including microplastics, as factors that may interact with genetic and metabolic profiles to influence nutritional outcomes.
Polybiome Systems Medicine: Conceptual Architecture, Methodological Foundations, and Translational Applications — Volume I: Vision and Foundational Methodology
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This foundational document presents the Polybiome Systems Medicine framework, proposing that the human organism is a co-constructed biological consortium shaped by host genomics, multi-kingdom microbiomes, environmental exposures, and immune networks. The framework incorporates environmental factors including microplastics and endocrine disruptors as modulators of human health. The authors outline a five-layer architecture designed to integrate diverse biological data for a more holistic approach to medicine.
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