Sensory network dysregulation in type 2 diabetes: Linking olfactory, visual, and cognitive function
Bibliographic record
Abstract
AIMS: This study investigates the relationship between multisensory (visual, somatosensory, and olfactory) dysfunction and cognitive decline in Type 2 diabetes (T2D), with a particular focus on the mediating role of olfactory dysfunction. METHODS: We used resting-state fMRI to assess seed-based functional connectivity from the primary sensory cortices (visual, somatosensory, and olfactory) and whole-brain regional activity metrics in 152 patients with T2D and 50 controls. A Multisensory Dysfunction Index (MSDI) was constructed to quantify integrated sensory dysfunction, and moderated mediation analysis was performed to examine the impact of sensory complications on cognitive function. RESULTS: The MSDI was correlated with sensory complication burden and associated with worse global cognitive performance (Montreal Cognitive Assessment, MoCA). Mediation analysis showed that odour identification mediated the relationship between MSDI and MoCA in T2D. This indirect effect was absent in diabetic peripheral neuropathy (DPN)+ individuals but remained significant in DPN- individuals. Additionally, olfactory dysfunction had both direct and indirect effects on cognition in DPN- patients. CONCLUSIONS: Our findings highlight the central role of olfactory dysfunction in linking multisensory impairment to cognitive decline in T2D. The results emphasize the need for personalized management strategies based on sensory complications and suggest that preserving sensory network integrity may help maintain olfactory and cognitive health in T2D.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".