Biomarkers of Prevalent and Incident Cognitive Dysfunction in People with Dysglycemia: Data from the ORIGIN Trial
Bibliographic record
Abstract
BACKGROUND: Diabetes and cardiovascular disease increase the risk of incident cognitive dysfunction. Identification of novel biochemical markers for cognitive dysfunction may identify people at the highest risk while yielding insights regarding the pathophysiology of cognitive dysfunction. OBJECTIVE: To identify cardiovascular biomarkers in serum that are independent predictors of cognitive dysfunction in individuals with dysglycemia. METHODS: This analysis was conducted in 8,365 participants in the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial whose stored serum was analyzed for 238 cardio-metabolic biomarkers and completed a baseline Mini-Mental State Examination (MMSE). Fine and Gray sub distribution hazard models accounting for the competing risk of death accounting for clinical risk factors and the baseline MMSE were used to identify biomarkers that predicted incident cognitive dysfunction (MMSE < 24 or dementia) using forward selection with an inclusion p-value < 0.0002 to account for multiplicity. RESULTS: During a median follow-up period of 6.2 years, 939 individuals developed cognitive dysfunction. After accounting for 17 clinical risk factors, glargine allocation, and the baseline MMSE, three biomarkers (α-2 Macroglobulin, HR 1.19; 95% CI 1.12, 1.27; Macrophage Inflammatory Protein 1α, HR 1.11; 95% CI 1.06, 1.16; and Growth Hormone, HR 0.91; 95% CI 0.87, 0.96) independently predicted incident cognitive dysfunction (p < 0.0002). Addition of these biomarkers to a model that included clinical risk factors, however, did not improve the ability to predict cognitive dysfunction. CONCLUSION: Addition of independent biomarkers to clinical risk factors for cognitive dysfunction in people with dysglycemia did not predict incident cognitive dysfunction better than clinical risk factors alone.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".