Association between inflammatory biomarkers and all-cause, cardiovascular and cancer-related mortality
Bibliographic record
Abstract
BACKGROUND: -acid glycoprotein (AGP) was found to have the strongest association with 5-year mortality in a recent study of 106 biomarkers. We examined whether AGP is a better biomarker of mortality risk than the more widely used inflammatory biomarkers interleukin-6 (IL-6) and C-reactive protein (CRP). METHODS: We analyzed data for 6545 men and women aged 45-69 (mean 55.7) years from the Whitehall II cohort study. We assayed AGP, IL-6 and CRP levels from fasting serum samples collected in 1997-1999. Mortality followup was until June 2015. Cox regression analysis was used to model associations of inflammatory biomarkers with all-cause, cardiovascular and cancer-related mortality. RESULTS: Over the mean follow-up of 16.7 years, 736 deaths occurred, of which 181 were from cardiovascular disease and 347 from cancer. In the model adjusted for all covariates (age, sex, socioeconomic status, body mass index, health behaviours and chronic disease), AGP did not predict mortality beyond the first 5 years of follow-up; over this period, IL-6 and CRP had stronger associations with mortality. When we considered all covariates and biomarkers simultaneously, AGP no longer predicted all-cause mortality over the entire follow-up period (adjusted hazard ratio [HR] 0.99, 95% confidence interval [CI] 0.90-1.08). Only IL-6 predicted all-cause mortality (adjusted HR 1.22, 95% CI 1.12-1.33) and cancer-related mortality (adjusted HR 1.13, 95% CI 1.00-1.29) over the entire follow-up period, whereas CRP predicted only cardiovascular mortality (adjusted HR 1.30, 95% CI 1.06-1.61). INTERPRETATION: Our findings suggest that AGP is not a better marker of short-or long-term mortality risk than the more commonly used biomarkers IL-6 and CRP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".