Is diabetic neuropathy associated with increased risk of developing mental disorders?
Bibliographic record
Abstract
Objective: It is largely unknown whether individuals with diabetic neuropathy face an increased risk of developing mental illness. Therefore, in a population-based cohort study, we aimed to examine whether individuals with diabetic neuropathy are at elevated risk of being diagnosed with a mental disorder compared to diabetes-duration-matched individuals without diabetic neuropathy. Methods: We used the nationwide Danish registers to identify all individuals diagnosed with diabetic neuropathy between January 1, 1996, and January 1, 2019. For each of these individuals, we identified up to five individuals with diabetes, matched on the duration of illness, who were not diagnosed with diabetic neuropathy. We then compared incidence rates of mental disorders between individuals with diabetic neuropathy and the diabetes-duration-matched individuals using a Cox proportional-hazards model. Restults: Individuals with diabetic neuropathy had a substantial and statistically significant increased risk of being diagnosed with any mental disorder (age- and sex-adjusted hazard rate ratio: 1.40, 95% CI: 1.31-1.48) as well as all specific mental disorders (psychotic disorder, bipolar disorder, unipolar depression, and/or anxiety disorder) compared with diabetes-duration-matched individuals without diabetic neuropathy. Conclusions: Diabetic neuropathy appears to be associated with a substantially increased risk of developing a mental disorder. Knowledge of the potential mechanisms underlying this association could inform prevention and treatment and should therefore be pursued further.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".