CCL2 in the Circulation Predicts Long‐Term Progression of Interstitial Lung Disease in Patients With Early Systemic Sclerosis: Data From Two Independent Cohorts
Bibliographic record
Abstract
OBJECTIVE: There are few clinical predictors of the progression of systemic sclerosis (SSc)-related interstitial lung disease (ILD). The purpose of this study was to examine the predictive significance of key cytokines for long-term progression of ILD and survival in 2 independent cohorts of patients with early SSc. METHODS: Plasma levels of 11 Th1/Th2 cytokines (interleukin-1β [IL-1β], IL-5, IL-6, IL-8, IL-10, IL-12, IL-13, tumor necrosis factor, CCL2, interferon-inducible T cell α chemoattractant, and interferon-γ-inducible 10-kd protein) were measured in 266 patients with early SSc in the Genetics versus Environment in Scleroderma Outcome Study (GENISOS) discovery cohort. Levels of CCL2, IL-10, and IL-6 were measured in 171 patients with early SSc in the Canadian Scleroderma Research Group (CSRG) replication cohort. The primary outcome measure was a decline in the forced vital capacity percent predicted (FVC%) value over time. A joint analysis of longitudinal FVC% values and survival was performed. RESULTS: After adjustment for age, sex, and ethnicity, CCL2 and IL-10 were found to be significant predictors of ILD progression in the discovery cohort. Higher CCL2 levels predicted a faster decline in FVC% values (b = -0.57, P = 0.032), while higher IL-10 levels predicted a slower decline (b = 0.26, P = 0.01). A higher CCL2 value was also predictive of poorer survival (hazard ratio 1.76, P = 0.030). In the CSRG replication cohort, higher CCL2 levels predicted a faster decline in FVC% values (b = -0.58, P = 0.038), but neither IL-10 nor IL-6 had predictive significance. A higher CCL2 level also predicted poorer survival (hazard ratio 3.89, P = 0.037). CONCLUSION: Higher CCL2 levels in the circulation were predictive of ILD progression and poorer survival in patients with early SSc, findings that support the notion that CCL2 has a role as a biomarker and potential therapeutic target.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".