Assessing the Effect of Tree Nut and Peanut Consumption on Adiposity: Is a Calorie a Calorie?
Bibliographic record
Abstract
Nuts have been shown to have diabetes and cardiovascular related health benefits, yet there remains concern that nuts may contribute to weight gain due to their high energy density. To address this concern, the research carried out in this thesis includes a systematic review and meta-analysis (SRMA) of prospective cohorts and randomized controlled trials (RCTs) to investigate the role of nuts on measures of adiposity, and an exploratory analysis of a randomized crossover trial to assess the bioaccessibility of energy and macronutrients of almonds. The SRMA identified 7 prospective cohort studies and 86 RCTs involving 569,910 and 12,092 participants, respectively. Nut consumption was inversely associated with the primary outcome overweight/obesity, as well secondary outcomes (body weight, risk of ≥5 kg weight gain, waist circumference) in prospective cohort studies. Similarly, there was no adverse effect of nuts on the primary outcome of body weight or any of the secondary outcomes (BMI, body fat, waist circumference, waist-to-hip ratio or visceral adipose tissue) in RCTs. The 3-phase randomized crossover trial including 22 participants with hyperlipidemia indicated fat from almonds was only 78.5±3.1% bioaccessible with an average overall energy loss of 21.2±3.1% (40.6 kcal/d). When considering the diet as a whole, there was significantly less bioaccessibility of energy and fat with almond consumption, but not carbohydrate or protein, compared to the control. Inclusion of ~73 g of almonds each day decreased the energy bioaccessibility of the diet by approximately 2%. Thus, the energy content of almonds may not be as bioaccessible as predicted by Atwater factors. Current evidence demonstrates that nuts are not associated with increased overweight/obesity incidence or weight gain, possibly due to the reduced bioaccessibility of energy and fat content of nuts. The research conducted in this thesis suggest nuts may be recommended without the concern that they contribute to weight gain.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".