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. 2026 Jan;22(1):e70959.
doi: 10.1002/alz.70959.

Brain-peripheral proteome crosstalk in Alzheimer's disease with and without diabetes mellitus

Affiliations

Brain-peripheral proteome crosstalk in Alzheimer's disease with and without diabetes mellitus

Anat Yaskolka Meir et al. Alzheimers Dement. 2026 Jan.

Abstract

Background: Although considerable research has investigated diabetes mellitus (DM) as a risk factor for Alzheimer's disease (AD) dementia, the mechanistic understanding of the associations between peripheral and central biological processes in AD and AD within DM remains limited.

Methods: We performed tandem mass tag-based phosphoproteome profiling on postmortem prefrontal cortex (n = 191), deltoid muscle (n = 191), and antemortem serum (n = 96) from older adults with/without pathologic AD and with/without DM (DM/NDM).

Results: We observed significant brain-muscle and brain-serum correlations in phosphorylated and unphosphorylated peptides. Among individuals with DM, 59 were with AD and 36 were without. Among NDM, 63 were with AD and 33 were without. In a differential expression analysis, muscle phosphorylated seryl-tRNA synthetase 2 (SARS2)-S126 was significantly expressed in pathologic AD, whereas relative abundance of serum alpha-2-HS-glycoprotein (AHSG)-S346 and insulin-like growth factor binding protein 2 (IGFBP2)-S142 showed marginal expression for AD within the DM strata.

Conclusions: Elucidating central and peripheral proteome crosstalk is valuable for uncovering potential AD biomarkers in accessible (peripheral) biospecimens.

Highlights: We profiled peptides in brain, muscle, and serum biosamples. The study design allowed discovery of diabetes-associated peptides in Alzheimer's disease (AD). Strong brain-muscle, but weaker brain-serum peptide correlations were identified. Muscle seryl-tRNA synthetase 2-S126 was linked to AD pathology. Serum insulin-like growth factor binding protein 2-S142 and alpha-2-HS-glycoprotein-S346 were linked to AD in persons with diabetes.

Keywords: Alzheimer's disease; diabetes mellitus; multi‐tissue omics; neurodegeneration; peptides.

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Conflict of interest statement

There are no conflicts of interest pertaining to this work. The primary source of funding for this work was obtained from the National Institutes of Health. Zoe Arvanitakis wishes to report receipt of funds for educational activities such as presentations at international and national conferences or grand rounds at academic institutions, and for continuing medical education (CME) activities (Spire Learning). She also conducts consulting for medico‐legal cases, for governmental entities (e.g., state and national grant awarding bodies, domestically and internationally), for industry (Amylyx; Eisai Strategic Council; Novo Nordisk), and the lay public (Summus). She serves as the Specialty Chief Editor for “Aging and Risk Factors for Dementia” for the journal Frontiers in Dementia. Steven E. Arnold reported receiving institutional grant or sponsored research support from the Alzheimer's Association, Alzheimer Drug Discovery Foundation, Challenger Foundation, John Sperling Foundation, National Institutes of Health, Prion Alliance, AbbVie Inc, ACImmune SA, Amylyx Inc, Athira Pharma Inc, Chromadex Inc, Cyclerion Terapeutics Inc, EIP Pharma Inc, Janssen Pharmaceutical/Johnson & Johnson, Ionis Pharmaceuticals, Novartis AG, Seer Bioscience Inc, vTv Therapeutics; honoraria for lectures from AbbVie Inc, Biogen Inc, and Eisai Co Ltd; payments for participation on scientific advisory boards of Allyx Therapeutics Inc, Bob's Last Marathon, Quince Therapeutics/Cortexyme Inc, Jocasta Neuroscience, and Sage Therapeutics Inc; and consulting fees from Cognito Therapeutics Inc, Cassava Sciences, EIP Pharma Inc, M3 Biotechnology Inc, Orthogonal Neuroscience Inc, Risen Pharmaceutical Technology. Other authors have no specific disclosures. Any author disclosures are available in the Supporting Information.

Figures

FIGURE 1
FIGURE 1
(A−D) Study design, background characteristics, and number of peptides across biosamples. (A) Study design; n = 192 men and women began follow‐up with a median age difference from baseline to death of 7.9 years, followed by brain and muscle harvesting. Half of the sample (n = 96) was selected for serum analysis. Peptide identification and quantification was carried out by LC‐MS/MS. (B) Sex, AD, AD by DM status, and age across annual clinical evaluations for the n = 191 included in the analysis. (C) Venn diagram showing the number of unique and overlapping phosphorylated peptides across biosamples. (D) Venn diagram showing the number of unique and overlapping unphosphorylated peptides across biosamples. AD, Alzheimer's disease; ADH, pathologic AD; ADL, no pathologic AD; DM, diabetes mellitus; NDM, no diabetes mellitus; PMI, postmortem interval; QC, quality control.
FIGURE 2
FIGURE 2
Correlations between overlapping phosphorylated peptides in central (brain) versus peripheral (muscle and serum) biosamples. Top positive and negative brain–muscle and brain–serum phosphorylated peptides correlations. Red represents positive correlations and blue represents negative correlations.
FIGURE 3
FIGURE 3
Brain hits for pathologic AD and muscle phosphorylated peptide correlations: enriched pathways. Gene‐set enrichment analysis and network diagram; n = 253 unique genes of 799 muscle phosphorylated peptides significantly correlated with brain AD hits. GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.
FIGURE 4
FIGURE 4
(A−D) Differentially expressed muscle phosphorylated peptides in a subset of previously uncovered AD‐related peptides, in pathologic AD and AD with/without DM. Volcano plots demonstrating the effect size and histograms for p‐values distribution. (A) Differentially expressed phosphorylated peptides in AD status (ref.: ADL). (B) Differentially expressed phosphorylated peptides in AD status (ref.: ADL) within DM. (C) Differentially expressed phosphorylated peptides in AD status (ref.: ADL) within no diabetes mellitus (NDM). (D) Differentially expressed phosphorylated peptides: AD×DM interaction. Muscle models include the following covariates: AD, DM, AD×DM, age at death, sex, and PMI. A two‐degree‐of‐freedom LRT was used to identify significant expression for AD or DM, and a one‐degree‐of‐freedom LRT was used to identify significant expression for AD×DM. AD, Alzheimer's disease; ADL, no pathologic AD; DM, diabetes mellitus; LRT, likelihood ratio test; NDM, no DM.

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