Gut Microbiota Composition is Associated with Responses to Peanut Intervention in Multiple Parameters Among Adults with Metabolic Syndrome Risk
Bibliographic record
Abstract
INTRODUCTION: Peanuts are widely consumed as a meal ingredient and a snack, and are commonly considered as a healthy food based on their nutrient profile. Peanut consumption has been associated with a lower risk of metabolic syndrome (MetS) in epidemiological studies. This study aims to investigate whether consuming peanuts affects the gut microbiota in adults with risk of MetS and whether the intervention effect of peanuts is associated with gut microbiota composition. METHODS AND RESULTS: This study analyzes the gut microbiota of subjects from a 12-week randomized clinical trial comparing consumption of either peanuts or isocaloric carbohydrate bars. It is observed that there is high inter-individual variability on multiple clinical and anthropometrical parameters in response to peanut consumption. Meanwhile, the gut microbiota composition is also highly person-specific and have minor changes when compared laterally or longitudinally. This study employs a machine-learning algorithm and establishes prediction models using the microbiome data and the responsiveness data of different parameters in subjects with peanut intervention. As a result, it is found that the improvement of MetS risk and numerous parameters, including diastolic blood pressure, body weight, waist circumference, and fasting blood glucose level can be predicted for responsiveness with high accuracy that has a value of area under curve over 0.70 by receiver operating characteristic analysis. CONCLUSION: Together, the findings of this study suggest that individual gut microbiota configuration may modulate host metabolism and alter an individual's response to peanut intervention, thus highlighting the importance of personalized nutrition.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".