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Consumption of 100% Fruit Juice and Body Weight in Children and Adults

2024· review· en· W4390918985 on OpenAlexafffund
Michelle Nguyen, Sarah Jarvis, Laura Chiavaroli, Sonia Blanco Mejía, Andreea Zurbau, Tauseef Khan, Deirdre K. Tobias, Walter C. Willett, Frank B. Hu, Anthony J. Hanley, Catherine S. Birken, John L. Sievenpiper, Vasanti Malik

Bibliographic record

VenueJAMA Pediatrics · 2024
Typereview
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsInstitute for Clinical Evaluative SciencesMount Sinai HospitalSt. Michael's HospitalUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesCanadian Institutes of Health Research
KeywordsMedicineCohort studyCohortBody mass indexWeight changeRandomized controlled trialProspective cohort studyObservational studyWeight gainPediatricsBody weightInternal medicineObesityWeight loss

Abstract

fetched live from OpenAlex

Importance: Concerns have been raised that frequent consumption of 100% fruit juice may promote weight gain. Current evidence on fruit juice and weight gain has yielded mixed findings from both observational studies and clinical trials. Objective: To synthesize the available evidence on 100% fruit juice consumption and body weight in children and adults. Data Sources: MEDLINE, Embase, and Cochrane databases were searched through May 18, 2023. Study Selection: Prospective cohort studies of at least 6 months and randomized clinical trials (RCTs) of at least 2 weeks assessing the association of 100% fruit juice with body weight change in children and adults were included. In the trials, fruit juices were compared with noncaloric controls. Data Extraction and Synthesis: Data were pooled using random-effects models and presented as β coefficients with 95% CIs for cohort studies and mean differences (MDs) with 95% CIs for RCTs. Main Outcomes and Measures: Change in body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) was assessed in children and change in body weight in adults. Results: A total of 42 eligible studies were included in this analysis, including 17 among children (17 cohorts; 0 RCTs; 45 851 children; median [IQR] age, 8 [1-15] years) and 25 among adults (6 cohorts; 19 RCTs; 268 095 adults; median [IQR] age among cohort studies, 48 [41-61] years; median [IQR] age among RCTs, 42 [25-59]). Among cohort studies in children, each additional serving per day of 100% fruit juice was associated with a 0.03 (95% CI, 0.01-0.05) higher BMI change. Among cohort studies in adults, studies that did not adjust for energy showed greater body weight gain (0.21 kg; 95% CI, 0.15-0.27 kg) than studies that did adjust for energy intake (-0.08 kg; 95% CI, -0.11 to -0.05 kg; P for meta-regression <.001). RCTs in adults found no significant association of assignment to 100% fruit juice with body weight but the CI was wide (MD, -0.53 kg; 95% CI, -1.55 to 0.48 kg). Conclusion and Relevance: Based on the available evidence from prospective cohort studies, in this systematic review and meta-analysis, 1 serving per day of 100% fruit juice was associated with BMI gain among children. Findings in adults found a significant association among studies unadjusted for total energy, suggesting potential mediation by calories. Further trials of 100% fruit juice and body weight are desirable. Our findings support guidance to limit consumption of fruit juice to prevent intake of excess calories and weight gain.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.046
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0040.006
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.019
GPT teacher head0.307
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations36
Published2024
Admission routes2
Has abstractyes

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