The association of soluble CD163, a novel biomarker of macrophage activation, with type 2 diabetes mellitus and its underlying physiological disorders: A systematic review
Bibliographic record
Abstract
This systematic review investigates the association of sCD163, a novel biomarker of macrophage activation, with type 2 diabetes mellitus (T2DM), insulin resistance, and beta-cell dysfunction. Sixteen studies (seven cross-sectional, two case-control, one nested case-control, three prospective cohort, and three experimental) were identified. Most studies demonstrated that elevated sCD163 concentrations were associated with increased insulin resistance. Cross-sectional, case-control, and nested case-control studies showed higher sCD163 in subjects with T2DM compared with healthy individuals. An 18-year follow-up prospective cohort study showed that elevated baseline sCD163 was a strong predictor of T2DM incidence. Prospective cohort studies demonstrated that baseline measures and longitudinal changes in sCD163 were positively associated with insulin resistance; however, associations with beta-cell function were inconsistent. Two experimental studies evaluated the relationship of sCD163 with T2DM and HOMA-IR after weight-reducing interventions. After very low-calorie diet treatments, sCD163 concentration declined significantly in patients with T2DM but was not associated with insulin resistance. Bariatric surgery did not significantly impact sCD163 levels. In a double-blind randomized controlled trial, resveratrol supplementation significantly reduced circulating sCD163 in T2DM patients. Current studies demonstrate the potential utility of sCD163 as an early biomarker of T2DM risk and highlight a potential mechanism linking obesity with T2DM onset.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".