Endocrinological and inflammatory markers in individuals with spinal cord injury: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Spinal cord injury (SCI) can lead to dramatic physiological changes which can be a factor in developing secondary health conditions and might be reflected in biomarker changes in this elevated risk group. We focused specifically on the endocrine and inflammation profile differences between SCI and able-bodied individuals (ABI). Our aim was to determine the differences in inflammatory markers and endocrine profiles between SCI and ABI. We systematically searched 4 electronic databases for relevant studies. Human observational (cross-sectional, cohort, case–control) studies that compared biomarkers of interest between SCI and ABI population were included. Weighted mean difference between SCI and ABI was calculated using random-effects models. Heterogeneity was computed using I2 statistic and chi-squared test. Study quality was evaluated through the Newcastle–Ottawa Scale. The search strategy yielded a total of 2,603 studies from which 256 articles were selected for full-text assessment. Sixty-two studies were included in the meta-analysis. SCI individuals had higher levels of pro-inflammatory C-reactive protein and IL-6 than ABI. Creatinine and 25-hydroxyvitamin D3 levels were lower in SCI than ABI. Total testosterone levels and IGF-1 were also found to be lower, while cortisol and leptin levels were higher in SCI when compared to ABI. Accordingly, meta-regression, subgroup analysis, and leave-one-out analysis were performed, however, they were only able to partially explain the high levels of heterogeneity. Individuals with SCI show higher levels of inflammatory markers and present significant endocrinological changes when compared to ABI. Moreover, higher incidence of obesity, diabetes, osteoporosis, and hypogonadism in SCI individuals, together with decreased creatinine levels reflect some of the readily measurable aspects of the phenotype changes in the SCI group. These findings need to be considered in anticipating medically related complications and personalizing SCI medical care.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".