Response to comment on “Relation of fruit juice with adiposity and diabetes depends on how fruit juice is defined: a re-analysis of the EFSA draft scientific opinion on the tolerable upper intake level for dietary sugars” by Chen et al. 2023
Bibliographic record
Abstract
We would like to thank Martínez et al. for their insightful comments [ 1 ] and for bringing to our attention the publication of the final version of the European Food Safety Authority (EFSA)’s Scientific Opinion on the Tolerable Upper Intake Level for dietary sugars [ 2 ]. We appreciate that the authors have included the EPIC-InterAct study [ 3 ] in the final version to reflect the totality of evidence and have concluded on fruit juice in general, rather than specifically on 100% fruit juice, to address the issue of misclassification. However, we note that an analysis separated by fruit juice type (100% fruit juice vs. non-specified fruit juice) is still missing. Although EFSA combined the analyses for 100% fruit juice and total fruit juice (including sugar-sweetened fruit juice) due to the similarity in their content of free sugars [ 2 ], this approach may not provide an accurate picture of the risk associated with each juice type. Large evidence syntheses of randomized controlled trials and prospective cohort studies have shown that the impact of fructose-containing sugars on cardiometabolic outcomes may depend on the food source [ 4 , 5 ]. For example, harm is observed for sugar-sweetened beverages while benefit is observed for fruit. The beneficial nutrients and bioactive compounds found in natural fruit are often retained in 100% fruit juice but are either absent or present in only small amounts in fruit drinks. These nutrients and bioactive compounds may counteract any effect of free sugars in 100% fruit juice for cardiometabolic outcomes. For example, a recent systematic review and meta-analysis of controlled trials demonstrated that 100% fruit juice when providing less than 10% of calories decreased body weight and BMI, while fruit drinks increased body weight, BMI and body fat [ 6 ]. Similarly, systematic reviews and meta-analyses of prospective cohort studies have also demonstrated a benefit at low to moderate doses, showing a U-shaped association between 100% fruit juice intake and various cardiometabolic outcomes including hypertension [ 7 ], metabolic syndrome [ 4 ] and cardiovascular event risk [ 8 ]; however, this was not the case for non-specified fruit juice. Therefore, we emphasize the importance of conducting a stratified analysis by fruit juice type [ 9 ]. Martínez et al. also identified the low number and heterogeneity of the included studies as barriers to conducting a quantitative analysis. While we agree that more studies are needed to improve the certainty of the evidence, two studies are considered sufficient to perform a quantitative meta-analysis [ 10 ]. We addressed some of the heterogeneity by conducting separate analyses for children and adults, pooling only data that assessed the same outcomes (e.g., incident abdominal obesity was reported separately from change in body weight) and adjusting for the study period (e.g., studies including data on change in BMI over a study period different than 1-year were adjusted to per 1-year). We also provided separate conclusions based upon these populations and endpoints. Although EFSA’s final version of the scientific opinion on fruit juice has greatly improved from the draft version, our comprehensive and granular analysis based on fruit juice type provides additional information that is not present in EFSA’s final version. Therefore, our perspective piece should be seen as complementing EFSA’s scientific opinion and not detracting from it. Our study stands as a more comprehensive analysis of the work done by EFSA when relating to fruit juice type and adiposity and diabetes outcomes.
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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 | 0.008 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.027 | 0.028 |
| Insufficient payload (model declined to judge) | 0.029 | 0.026 |
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".