Plasma sDPP4 (Soluble Dipeptidyl Peptidase-4) and Cognitive Impairment After Noncardioembolic Acute Ischemic Stroke
Bibliographic record
Abstract
Background: DPP4 (dipeptidyl peptidase-4) inhibitors have been proven to promote neuronal regeneration, reverse the development of cognitive deficits. However, the association of circulating soluble form (sDPP4 [soluble DPP4]) with poststroke cognitive impairment (PSCI) is unclear. We aimed to investigate the association between plasma sDPP4 levels and PSCI in patients with ischemic stroke. Methods: A total of 600 noncardioembolic stroke patients were included based on a preplanned ancillary study from the CATIS (China Antihypertensive Trial in Acute Ischemic Stroke). We used the Montreal Cognitive Assessment to evaluate cognitive function at 3 months follow-up after ischemic stroke. Binary logistic regression analyses were performed to investigate the association of plasma sDPP4 levels with subsequent PSCI. We further calculated integrated discrimination improvement and category-free net reclassification improvement to investigate the incremental prognostic effect of plasma sDPP4 beyond the basic model with conventional risk factors. Results: Plasma sDPP4 was inversely associated with PSCI after ischemic stroke, and the adjusted odds ratio (95% CI) for the highest versus lowest quartile of sDPP4 was 0.49 (0.29–0.81; P for trend=0.011). Each 1-SD increase of logarithm-transformed plasma sDPP4 concentration was associated with 17% (odds ratio, 0.83 [95% CI, 0.70–0.99]) lower risk of PSCI. Adding plasma sDPP4 to the basic model notably improved risk reclassification for PSCI, as shown by a category-free net reclassification improvement of 19.10% (95% CI, 2.52%–35.68%; P =0.03) and integrated discrimination improvement of 0.79% (95% CI, 0.13%–1.46%; P =0.02). Conclusions: Higher plasma sDPP4 levels were associated with decreased risk of cognitive impairment after noncardioembolic ischemic stroke.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".