Non-Hodgkin Lymphoma and Circulating Markers of Inflammation and Adiposity in a Nested Case–Control Study: The Multiethnic Cohort
Bibliographic record
Abstract
BACKGROUND: Because immune dysfunction is thought to underlie the development of non-Hodgkin lymphoma (NHL), obesity and chronic inflammation may be involved in its etiology. We examined the association of prediagnostic inflammatory markers and adipokines with NHL risk. METHODS: We conducted a nested case-control analysis (272 cases and 541 matched controls) within the Multiethnic Cohort. Luminex technology was used to measure a 10-plex panel of cytokines, ELISA assays for adipokines, and an autoanalyzer for C-reactive protein (CRP). ORs and 95% confidence intervals (CI) for tertiles of analytes were estimated by conditional logistic regression. RESULTS: After a median time of 2.7 years from phlebotomy to diagnosis, interleukin (IL)-10 was significantly related to NHL risk (ORT3 vs. T1 = 3.07; 95%CI, 2.02-4.66; Ptrend < 0.001). TNF-α and IL-8 showed borderline elevated risks, whereas IFN-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, and CRP were not associated with NHL. Leptin but not adiponectin was related to NHL risk (ORT3 vs. T1 = 0.48; 95%CI, 0.30-0.76; Ptrend < 0.001). Adjustment for body mass index did not substantially affect the risk estimates. Stratification by subtype indicated significant associations with IL-10 and leptin for follicular but not for diffuse large B-cell lymphoma. Excluding cases diagnosed less than 1 year after phlebotomy attenuated all associations. CONCLUSIONS: IL-10 was the only cytokine and leptin the only adipokine associated with NHL, but due to the short follow-up time, preclinical effects cannot be excluded. IMPACT: Although markers of inflammation and adiposity may provide new insights into the etiology of NHL, they need to be assessed many years before clinical diagnosis.
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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 | 0.005 | 0.001 |
| 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 | 0.000 | 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".