Metabolites associated with type 2 diabetes and Alzheimer’s disease trigger differential intracellular signaling responses in mouse primary neurons

preprint OA: closed CC-BY-4.0
📄 Open PDF Full text JSON View at publisher
AI-generated deep summary by claude@2026-07, 2026-07-03 · read from full text

The study examined nine blood-brain barrier–permeable metabolites previously linked in literature to protective or harmful effects in type 2 diabetes (T2D) and Alzheimer’s disease (AD), using mouse primary cortical neuron monocultures treated with each metabolite. Intracellular signaling responses were quantified via Luminex, and univariate plus multivariate analyses identified pathways differing between AD/T2D–associated versus protective-associated metabolites, with partial least squares discriminant analysis separating disease- and protective-associated groups. The authors found Akt and STAT5 up-regulated by AD- and T2D-associated metabolites, while c-Jun and MEK1 were up-regulated by protective-associated metabolites, and canonical correlation analysis related intracellular signaling to previously collected neuronal cytokine data, correlating detrimental and protective protein patterns with IL-9 and MCP-1 respectively. The paper’s main limitation is that responses were measured in primary neuron monocultures stimulated with individual metabolites rather than in a more complete in vivo or cell-interaction context. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

ABSTRACT Alzheimer’s disease (AD) is a progressive neurodegenerative disease that is accelerated by the pathological features of type 2 diabetes (T2D). Neuroinflammation is an extensively studied component shared by T2D and AD that remains poorly understood. In this work, we studied nine blood-brain barrier permeable metabolites associated with protective or harmful effects of AD and T2D in literature (aminoadipic acid, arachidonic acid, asparagine, D-sorbitol, fructose-6-phosphate, lauric acid, L-tryptophan, niacinamide, and retinol) and quantified intracellular signaling responses in primary cortical neuron monocultures. After stimulation of neuronal cultures with each metabolite, we quantified signaling analytes with a Luminex assay. Using univariate and multivariate analysis approaches, we identified potential intracellular signaling pathways linked to AD and T2D pathology. With partial least squares discriminant analysis, we identified the separation between the disease and protective-associated metabolites. We identified Akt and STAT5 up-regulation by AD- and T2D-associated metabolites, whereas c-Jun and MEK1 were up-regulated by disease-protective metabolites. Finally, we performed a canonical correlation analysis to link neuronal cytokine data we previously collected from these cultures to our new intracellular signaling data, to which we found intracellular proteins associated with detrimental and protective properties that correlated with IL-9 and MCP-1, respectively. Our experimental and computational approach identified potential associations between intracellular and cytokine signaling molecules in the context of AD and T2D pathology. Nevertheless, primary neuron responses to metabolites associated with T2D and AD may contribute to neuroinflammation and progressive cognitive decline.
Full text 2,570 characters · extracted from oa-doi-fallback · click to expand
ABSTRACT Alzheimer’s disease (AD) is a progressive neurodegenerative disease that is accelerated by the pathological features of type 2 diabetes (T2D). Neuroinflammation is an extensively studied component shared by T2D and AD that remains poorly understood. In this work, we studied nine blood-brain barrier permeable metabolites associated with protective or harmful effects of AD and T2D in literature (aminoadipic acid, arachidonic acid, asparagine, D-sorbitol, fructose-6-phosphate, lauric acid, L-tryptophan, niacinamide, and retinol) and quantified intracellular signaling responses in primary cortical neuron monocultures. After stimulation of neuronal cultures with each metabolite, we quantified signaling analytes with a Luminex assay. Using univariate and multivariate analysis approaches, we identified potential intracellular signaling pathways linked to AD and T2D pathology. With partial least squares discriminant analysis, we identified the separation between the disease and protective-associated metabolites. We identified Akt and STAT5 up-regulation by AD- and T2D-associated metabolites, whereas c-Jun and MEK1 were up-regulated by disease-protective metabolites. Finally, we performed a canonical correlation analysis to link neuronal cytokine data we previously collected from these cultures to our new intracellular signaling data, to which we found intracellular proteins associated with detrimental and protective properties that correlated with IL-9 and MCP-1, respectively. Our experimental and computational approach identified potential associations between intracellular and cytokine signaling molecules in the context of AD and T2D pathology. Nevertheless, primary neuron responses to metabolites associated with T2D and AD may contribute to neuroinflammation and progressive cognitive decline. Competing Interest Statement The authors have declared no competing interest. - ABBREVIATIONS - AD - Alzheimer’s disease - Akt - Protein kinase B - BBB - Blood-brain barrier - BCA - Bicinchoninic acid - CCA - Canonical correlation analysis - CV - Canonical variable - FDR - False discovery rate - IL-9 - Interleukin-9 - JNK - c-Jun N-terminal kinase - LV - Latent variable - MEK1 - mitogen-activated protein kinase 1 - MCP-1 - Monocyte chemoattractant protein-1 - PLS-DA - Partial least squares discriminant analysis - STAT1 - Signal transducer and activator transcription 1 - STAT5 - Signal transducer and activator transcription 5 - T2D - Type 2 diabetes - VEGF - Vascular endothelial growth factor - VIP - Variable importance in projection

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00
unpaywall
last seen: 2026-05-24T02:00:01.246996+00:00
License: CC-BY-4.0