Non‐Hodgkin lymphoma and obesity: A pooled analysis from the InterLymph Consortium
Bibliographic record
Abstract
Nutritional status is known to alter immune function, a suspected risk factor for non-Hodgkin lymphoma (NHL). To investigate whether long-term over, or under, nutrition is associated with NHL, self-reported anthropometric data on weight and height from over 10,000 cases of NHL and 16,000 controls were pooled across 18 case-control studies identified through the International Lymphoma Epidemiology Consortium. Study-specific odds ratios (OR) were estimated using logistic regression and combined using a random-effects model. Severe obesity, defined as BMI of 40 kg m(-2) or more, was not associated with NHL overall (pooled OR = 1.00, 95% confidence interval (CI) 0.70-1.41) or the majority of NHL subtypes. An excess was however observed for diffuse large B-cell lymphoma (pooled OR = 1.80, 95% CI 1.24-2.62), although not all study-specific ORs were raised. Among the overweight (BMI 25-29.9 kg m(-2)) and obese (BMI 30-39.9 kg m(-2)), associations were elevated in some studies and decreased in others, while no association was observed among the underweight (BMI < 18.5 kg m(-2)). There was little suggestion of increasing ORs for NHL or its subtypes with every 5 kg m(-2) rise in BMI above 18.5 kg m(-2). BMI components height and weight were also examined, and the tallest men, but not women, were at marginally increased risk (pooled OR = 1.19, 95% CI 1.06-1.34). In summary, whilst we conclude that there is no evidence to support the hypothesis that obesity is a determinant of all types of NHL combined, the association between severe obesity and diffuse large B-cell lymphoma may warrant further investigation.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".