Bibliographic record
Abstract
We thank Dr. Kapoor for his interest in our report of non-Hodgkin lymphoma (NHL) and obesity.1 In our article, we examined the risk of NHL associated with obesity across 18 case–control studies which were conducted in USA, Canada, Europe, and Japan. Studies were identified through the InterLymph Consortium and constitute the majority of contemporary case–control studies of lymphoma with anthropometric data. We found increased risks of NHL among overweight and obese persons in some studies whereas in others there was no positive association. Overall, this led us to conclude that there was no consistent effect of obesity on the risk of NHL. We did however, like others, find that the risk of diffuse large B-cell lymphoma was elevated among persons considered morbidly obese (body mass index ≥ 40 kg m−2). Kapoor considers other evidence in support of the hypothesis that obesity confers an increased risk of NHL. Polymorphisms involved in energy homeostasis, and which may also have a role in immune regulation, have been investigated with respect to NHL. As well as the cited article by Skibola et al.,2 we too have examined the role of the genetic variants in the leptin (LEP 19G>A, LEP -2548G>A) and leptin receptor (LEPR 223Q>R) genes,3 and we found no association between these polymorphisms and diffuse large B-cell lymphoma in a large UK case–control study. Moreover, there seemed to be little relationship between obesity, the leptin polymorphisms and the risk of diffuse large B-cell lymphoma. Another proposed explanation in support of a relationship between NHL and obesity involved lipoproteins. In particular, Kapoor refers to levels of high-density lipoprotein cholesterol (HDL-C), which are low in obese persons but can also be altered by chronic inflammation. Following observations by others that NHL patients have low circulating levels of HDL-C, Lim and colleagues4 conducted a prospective cohort study to examine the lipoprotein's role in lymphomagenesis. A protective association with high levels of HDL-C was reported within the first 10 years of follow-up, but no association was found with thereafter. As such, the authors suggested that the relationship between NHL and HDL-C may not be causal, and could be a consequence of underlying chronic immune stimulation arising from, for instance, chronic inflammatory conditions or autoimmune diseases. Although the associations between obesity and other illnesses and some cancers are well-established, the relationship with NHL is unclear. As stated in our article,1 many studies show an effect but several have not. Our meta-analysis was based on individual data from—to the best of our knowledge—all but one5 of the current case–control studies of lymphoma with anthropometric information. We found that our study-specific risk estimates demonstrated some quite marked heterogeneity. Heterogeneous risks are found too across recently published prospective cohort studies (Fig. 1). Our self-reported anthropometric data were collected retrospectively and could be biased by participation and reporting but in contrast to the data presented in Figure 1, are unaffected by publication bias. Taken as a whole, the interpretation of the findings on this topic are not straightforward. Given that associations between NHL and obesity are typically not strong, it seems reasonable to conclude that studying other factors (some of which may be related to anthropometry) may prove more informative. Published risks of non-Hodgkin lymphoma associated with obesity from prospective cohort studies. Obesity was defined as a body mass index of 30 kg m−2 in most studies except: the cut point for the most obese persons was 27 kg m−2 in Oh et al. (2005); 28 kg m−2 and 26 kg m−2 for men and women respectively in Chiu et al. (2006); 35 kg m−2 in Lim et al. (2007) and Calle et al. (2003); and 40 kg m−2 for women only in Engeland et al. (2006). Risk estimates were published for men (M), women (F) or both (M&F). [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.] Yours sincerely, Eleanor V. Willett. Eleanor V. Willett*, * Epidemiology and Genetics Unit, Department of Health Sciences, University of York, YO10 5DD, United Kingdom.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| 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".