Decreased Adiponectin Levels Are a Risk Factor for Cognitive Decline in Spinal Cord Injury
Bibliographic record
Abstract
Objective. Spinal cord injury (SCI) has become popular in recent years, and cognitive decline is a common complication. Adiponectin is a common protein hormone involved in the course of many diseases, but its relationship with SCI has not yet been elucidated. The purpose of our prospective study is to explore whether adiponectin can be used as a biomarker of cognitive decline in SCI. Methods. A total of 64 healthy volunteers and 92 patients with acute SCI were recruited by us. Serum adiponectin levels, demographic data (age and gender), lifestyle (smoking and drinking), medical history (diabetes and hypertension), and clinical baseline data (low-density lipoprotein, high-density lipoprotein, and fasting blood glucose) were recorded. Three months after enrollment, we used the Montreal Cognitive Assessment (MoCA) to evaluate cognitive function. Based on a quarter of the serum adiponectin levels, SCI patients were divided into 4 groups, and the differences in their MoCA scores were compared. In addition, we used multivariate linear regression to predict the risk factors of the MoCA score. Results. The serum adiponectin level ( 6.1 ± 1.1 μ g / ml ) of SCI patients was significantly lower than that of the healthy control group ( 6.7 ± 0.9 μ g / ml ), and there was a significant difference between the two ( p < 0.001 ). The group with higher serum adiponectin levels after 3 months of spinal cord injury had higher MoCA scores. Multivariate regression analysis showed that serum adiponectin level is a protective factor for cognitive function after SCI ( β = 0.210 , p = 0.043 ). Conclusions. Serum adiponectin levels can be used as an independent predictor of cognitive function in patients with acute SCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".