Re: “Associations of Body Mass Index, Smoking, and Alcohol Consumption With Prostate Cancer Mortality in the Asia Cohort Consortium”
Bibliographic record
Abstract
Fowke et al. (1) recently reported null associations between several risk factors for prostate cancer (body mass index (BMI; weight (kg)/height (m)2), smoking, and alcohol consumption) and prostate cancer mortality across 6 countries in southern and eastern Asia. The authors concluded that the lack of association they found casts doubt on the validity of these risk factors and that differences in prostate cancer mortality between Asian and Western populations may reflect variation in prostate cancer screening practices. The accompanying commentary (2) focused on the impact of screening on risk factors for cancer and suggested that understanding the etiology of cancers may be best accomplished through the study of populations without widespread screening. We agree with both sets of authors about the importance of assessing the impact of screening on cancer outcomes. However, concluding that the previously identified risk factors have limited utility is premature in the absence of high-quality data on these risk factors and exposures. We suggest that inadequate assessment of potential risk factors is an alternative explanation for the observed null associations between BMI, smoking, and alcohol consumption and prostate cancer in southern and eastern Asia. One significant limitation of the study by Fowke et al. is that data on risk factors in the Asia Cohort Consortium were collected only at baseline, whereas cancer surveillance occurred over decades in some of the cohorts. Regarding tobacco, smoking status was limited to never smoking versus ever smoking at baseline. These available data could not identify how prostate cancer risk may have changed with changes in smoking behavior such as cessation. It is well-known that the risk of developing lung and other types of cancer decreases with smoking cessation and continues to decrease with more tobacco-free years (3). The BMI analysis presents an additional challenge. Although current World Health Organization BMI cutoff points are used for international classification of underweight, overweight, and obesity, there is considerable debate over interpretation of BMI cutoffs in Asian populations (4), with many authors suggesting that determination of overweight and obesity should be made at lower BMI levels in Asian populations (5, 6). Thus, the “healthy” reference BMI range of 22.5–24.9 in this study may have included persons with BMI-associated health risks and may have obscured associations between overweight/obesity and prostate cancer. Furthermore, as Fowke et al. mentioned in the Discussion section of their paper (1), the most consistent relationships between alcohol consumption and prostate cancer have been shown at higher levels of consumption than were present in the Asia Cohort Consortium (5). Thus, their analysis did not provide a basis for drawing conclusions about this potential risk factor. Lastly, it is important to consider the endpoint when assessing the impact of risk factors. For diseases with long latency periods and high survival rates such as prostate cancer, incidence rather than mortality may be a more appropriate endpoint for identifying etiological indicators (6). Prostate cancer mortality reflects the severity of the cancer, therapies received, and additional factors that may be independent of those that are linked with disease incidence. In summary, given the limitations of their data set, it is not surprising Fowke et al. found null associations (1). We suggest that this study demonstrates the need for better measurement of potential etiological variables to advance our understanding of the roles of both modifiable lifestyle risk factors and screening in the prevention and early detection of prostate cancer. R.A.M. was an employee of DSM Nutritional Products (Parsippany, New Jersey) from 2014 to 2015. DSM Nutritional Products was not involved with any aspect of this publication, and R.A.M. does not have existing relationships with DSM.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.044 | 0.044 |
| Insufficient payload (model declined to judge) | 0.008 | 0.013 |
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".