A systematic review and meta-analysis on the regulation of programmed cell death-1 on T-cells in type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: To assess T-cell exhaustion mediated by programmed cell death 1 (PD-1) pathway in patients living with type 2 diabetes (T2D). METHODS: MEDLINE and ProQuest electronic databases were searched for eligible studies from inception up to February 2020. The risk of bias and the quality of evidence were independently assessed by two reviewers using the modified Newcastle-Ottawa Scale adapted for cross-sectional studies and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool, respectively. The random effects model was used to calculate effect estimates. RESULTS: We identified 5 studies involving 380 participants which met the inclusion criteria. The pooled estimates showed elevated T helper cell exhaustion in patients with T2D in comparison to controls (mean difference [MD]: 2.57% [95% confidence interval [CI]: -3.84, 8.97]; I2 = 100%, P < .00001). Likewise, T2D patients had increased levels of cytotoxic T-cells exhaustion (MD: 3.09% [95% CI: -12.96, 19.14]; I2 = 100%, P < .00001). Although the upregulation of PD-1 on T-cells did not affect glucose metabolism-related profiles, it was associated with inflammation and the development of cardiovascular disease. CONCLUSION: In patients living with T2D, immune dysfunction is at least in part due to T-cell exhaustion mediated by the upregulation of PD-1 expression. Therefore, the use of immune checkpoint inhibitors as a therapeutic strategy may be beneficial in restoring immune function in patients with T2D.
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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".