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We thank all the volunteers for their participation. We also thank the staff of the Center for Omics Sciences (COS) for generating the metabolomics data. The authors also appreciate the support of the Departament de Recerca i Universitats de la Generalitat de Catalunya al Grup de Recerca Nutrition and Metabolic Health (Codi: 2021 SGR 00213). The author's contributions were as follows: M.Bu. conceived and designed the study, obtained funds and supervised the analysis and drafting; C.P. contributed to the study design, supervised the analysis, and the manuscript drafting. L.G.-T. analyzed data and drafted the manuscript; M.R.-C., M.M., and L.P. contributed to the analysis; P.G.-G., A.R., J.L.C., M.M., M.Bo., and M.A. were involved in the study execution and acquired the data; H.M., N.N.-F., J.M.-F., D.P., and H.R. provided substantial intellectual contributions. All authors have approved the manuscript and agree with its publication.

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Metabolic Signature of Insulin Resistance and Risk of Alzheimer's Disease

Publicado en:Journals Of Gerontology Series A-Biological Sciences And Medical Sciences. 80 (3): glae283- - 2025-02-10 80(3), DOI: 10.1093/gerona/glae283

Autores: Gutierrez-Tordera, Laia; Panisello, Laura; Garcia-Gonzalez, Pablo; Ruiz, Agustin; Cantero, Jose Luis; Rojas-Criollo, Melina; Mursil, Muhammad; Atienza, Mercedes; Novau-Ferre, Nil; Mateu-Fabregat, Javier; Mostafa, Hamza; Puig, Domenec; Folch, Jaume; Rashwan, Hatem; Marquie, Marta; Boada, Merce; Papandreou, Christopher; Bullo, Monica

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Background Substantial evidence supports the relationship between peripheral insulin resistance (IR) and the development of Alzheimer's disease (AD)-dementia. However, the mechanisms explaining these associations are only partly understood. We aimed to identify a metabolic signature of IR associated with the progression from mild cognitive impairment (MCI) to AD-dementia. Methods This is a case-control study on 400 MCI subjects, free of type 2 diabetes, within the ACE cohort, including individuals ATN + and ATN-. After a median of 2.1 years of follow-up, 142 subjects converted to AD-dementia. IR was assessed using the homeostasis model assessment for insulin resistance (HOMA-IR). A targeted multiplatform approach profiled over 600 plasma metabolites. Elastic net penalized linear regression with 10-fold cross-validation was employed to select those metabolites associated with HOMA-IR. The prediction ability of the signature was assessed using support vector machine and performance metrics. The metabolic signature was associated with AD-dementia risk using a multivariable Cox regression model. Using counterfactual-based mediation analysis, we investigated the mediation role of the metabolic signature between HOMA-IR and AD-dementia. The metabolic pathways in which the metabolites were involved were identified using MetaboAnalyst. Results The metabolic signature comprised 18 metabolites correlated with HOMA-IR. After adjustments by confounders, the signature was associated with increased AD-dementia risk (HR = 1.234; 95% CI = 1.019-1.494; p < .05). The metabolic signature mediated 35% of the total effect of HOMA-IR on AD-dementia risk. Significant metabolic pathways were related to glycerophospholipid and tyrosine metabolism. Conclusions We have identified a blood-based metabolic signature that reflects IR and may enhance our understanding of the biological mechanisms through which IR affects AD-dementia.

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