Relevant Serum Endoplasmic Reticulum Stress Biomarkers in Type 2 Diabetes and Its Complications: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Endoplasmic reticulum stress (ERS) is activated in all cells by stressors such as hyperglycemia. However, it remains unclear which specific serum biomarkers of ERS are consistently altered in type 2 diabetes (T2D). We aimed to identify serum ERS biomarkers that are consistently altered in T2D and its complications, and their correlation with metabolic and anthropometric variables. We performed a systematic review and meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-Analyses and Systematic Reviews of Observational Studies (MOOSE). The risk of bias was assessed using the Newcastle–Ottawa scale. Random-effects models weighted by the inverse variance were employed to estimate the standardized mean difference and correlations as effect size measures. Indicators of heterogeneity and meta-regressions were evaluated. Of the 1206 identified studies, 22 were finally included, representing 11,953 subjects (2224 with T2D and 9992 non-diabetic controls). Most studies were of high quality. Compared with controls, subjects with T2D had higher circulating levels of heat shock protein 70 (HSP70; SMD: 2.30, 95% CI 1.13–3.46; p < 0.001) and secretagogin (SMD: 0.60, 95%CI 0.19–1.01; p < 0.001). They also had higher serum levels of peroxiredoxin-1, -2, -4, and -6. Secretagogin inversely correlated with HOMA-IR, yet positively correlated with HOMA-B, HbA1c, and FPG. PRX4 negatively correlated with HbA1c and FPG, while HSP70 positively correlated with HbA1c. In conclusion, six ERS biomarkers are consistently elevated in human T2D and correlate with glycemic control, insulin resistance, and β-cell function. Emerging evidence links serum ERS biomarkers to diabetes complications, but further research should evaluate their prognostic implications.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".