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Diabetes-linked metabolic dysfunction relates with distinct tau phosphorylation patterns, neuroinflammation and cognitive impairment in mouse models of Alzheimer's disease

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URI: http://hdl.handle.net/10498/38842

DOI: 10.1016/j.bbi.2025.106204

ISSN: 1090-2139

ISSN: 0889-1591

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Vargas-Soria et al_BBI_2025.pdf (4.176Mb)
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Author/s
Vargas Soria, MaríaAuthority UCA; Corraliza Gómez, MiriamAuthority UCA; Infante-Garcia, Carmen; Stitt, Alan W.; Simó, Rafael; García Alloza, MónicaAuthority UCA
Date
2025-11-30
Department
Biomedicina, Biotecnología y Salud Pública
Source
Brain, Behavior, and Immunity, Vol. 132, 2026
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-β plaques and tau neurofibrillary tangles, with tau pathology being closely linked to cognitive decline. Growing evidence suggests that metabolic dysfunction including type 1 diabetes (T1D) and type 2 diabetes (T2D), as well as prediabetes (PreDM), exacerbate AD by promoting different degrees of insulinopenia, insulin resistance and hyperglycemia which can drive chronic inflammation and oxidative stress across multiple organs. Precisely how these metabolic disturbances influence tau phosphorylation remains unclear. To address this, we studied mouse models of AD, T1D, PreDM, T2D and the combination of AD with all three metabolic alterations, at 26 weeks of age, when pathologies are well established. The fact that we are including models of insulin resistance and insulin deficiency allows us to further explore the specific role of insulin as observed in the clinic. We assessed metabolic status, tau phosphorylation and cytokine levels in the brain cortex and cognitive function using the Morris water maze (MWM) and novel object discrimination (NOD) tests. Our results revealed that AD mice with metabolic disorders exhibited tau hyperphosphorylation, particularly at Ser199, Ser202/Thr205 and Ser404, correlating with metabolic dysfunction, cognitive impairment and inflammatory markers. Notably, AD-T2D mice showed the most severe deficits in MWM and NOD performance, indicating a synergistic cognitive decline. Machine learning analysis by random forest effectively classified AD-metabolic phenotypes, identifying key molecular and metabolic markers of neurodegeneration, mainly blood glucose and plasma insulin. These findings highlight the critical role of metabolic dysfunction in exacerbating tau pathology and accelerating cognitive decline in AD. Targeting metabolic pathways may provide concomitant therapeutic opportunities for AD patients with diabetes. Future research should explore interventions that restore insulin signaling and glucose metabolism to mitigate AD progression, probably by repurposing antidiabetic drugs.
Subjects
Alzheimer’s disease; Cognitive impairment; Machine learning; Prediabetes; Tau phosphorylation; Type 1 diabetes; Type 2 diabetes
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  • Artículos Científicos [12009]
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
This work is under a Creative Commons License Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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