The Macrophage Activation Marker Soluble CD163 is Longitudinally Associated With Insulin Sensitivity and β-cell Function
Bibliographic record
Abstract
CONTEXT: Chronic inflammation arising from adipose tissue macrophage (ATM) activation may be central in type 2 diabetes etiology. Our objective was to assess the longitudinal associations of soluble CD163 (sCD163), a novel biomarker of ATM activation, with insulin sensitivity, β-cell function, and dysglycemia in high-risk subjects. METHODS: Adults at risk for type 2 diabetes in the Prospective Metabolism and Islet Cell Evaluation (PROMISE) study had 3 assessments over 6 years (n = 408). Levels of sCD163 were measured using fasting serum. Insulin sensitivity was assessed by HOMA2-%S and the Matsuda index (ISI). β-cell function was determined by insulinogenic index (IGI) over HOMA-IR and insulin secretion-sensitivity index-2 (ISSI-2). Incident dysglycemia was defined as the onset of impaired fasting glucose, impaired glucose tolerance, or type 2 diabetes. Generalized estimating equations (GEE) evaluated longitudinal associations of sCD163 with insulin sensitivity, β-cell function, and incident dysglycemia adjusting for demographic and lifestyle covariates. Areas under receiver-operating-characteristic curve (AROC) tested whether sCD163 improved dysglycemia prediction in a clinical model. RESULTS: Longitudinal analyses showed significant inverse associations between sCD163 and insulin sensitivity (% difference per standard deviation increase of sCD163 for HOMA2-%S (β = -7.01; 95% CI, -12.26 to -1.44) and ISI (β = -7.60; 95% CI, -11.09 to -3.97) and β-cell function (ISSI-2 (β = -4.67; 95 %CI, -8.59 to -0.58) and IGI/HOMA-IR (β = -8.75; 95% CI, -15.42 to -1.56)). Increased sCD163 was associated with greater risk for incident dysglycemia (odds ratio = 1.04; 95% CI, 1.02-1.06; P < 0.001). Adding sCD163 data to a model with clinical variables improved prediction of incident dysglycemia (AROC=0.6731 vs 0.638; P < 0.05). CONCLUSIONS: sCD163 was longitudinally associated with core disorders that precede the onset of type 2 diabetes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".