Type 2 Diabetes Mellitus, Cognitive Performance, and Incident Dementia; Identifying Mediating Pathways and Biomarkers From the Plasma Proteome
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM) is associated with poorer cognitive performance and increased dementia risk. Pathophysiological mechanisms are not fully understood. In this prospective study of UK Biobank participants, (n = 9943 without T2DM, age = 56.3 ± 8.2 years, 55% female, n = 3752 with T2DM, age = 59.1 ± 7.7 years, 41% female), T2DM was associated with poorer attention (Hedges' g = -0.15[-0.17, -0.10]), processing speed (Hedges' g = -0.14[-0.17, -0.09]), and a higher risk of incident dementia over 15 years (HR = 2.13[1.74, 2.61]). Among 2923 proteins measured by Olink proteomics, 1739 were differentially expressed in T2DM. Four-way decomposition models of proteomic markers, and KEGG pathway analyses, were used to identify potential mediating and moderating effects of biological pathways on the association between T2DM and cognitive or dementia outcomes. For dementia, 230 protein mediators implicated inflammatory pathways (complement/coagulation cascades, cytokine-cytokine receptor interactions, and the janus kinase-signal transducer and activator of transcription signaling pathway), and 11 proteins implicated cholesterol/lipid metabolism as moderators (including apolipoprotein E, low-density lipoprotein receptor and prostaglandin reductase 1). Mediators with the highest accuracy to predict incident dementia in T2DM were glial fibrillary acidic protein (AUC = 0.71[0.67, 0.76]) and neurofilament light polypeptide (AUC = 0.71 [0.67, 0.75]). Multivariate proteomic/clinical models (AUC = 0.78 [0.75, 0.81]) improved accuracy beyond clinical risk factors alone (AUC = 0.74 [0.69, 0.78]). Subgroup analyses by sex, apolipoprotein E ε4 carrier status and age showed some features unique within strata. This study suggests potential targets within inflammatory, oxidative, angiogenesis-related, and metabolic pathways to mitigate cognitive decline and dementia risk in T2DM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".