The mixed-meal tolerance test as an appetite assay: methodological and practical considerations
Bibliographic record
Abstract
Appetite control is a topic which attracts widespread interest given its importance to energy balance and obesity. In this research area, the mixed-meal tolerance test (MM-TT) has emerged as an 'appetite regulation assay', facilitating the dynamic assessment of appetite parameters (e.g. subjective appetite perceptions, appetite-related hormones, food reward) in response to an individual meal. The MM-TT is commonly employed in observational and experimental studies to examine population differences and intervention effects. Problematically, no practice standard exists for the MM-TT and protocols vary widely. This presents a challenge for researchers designing new MM-TTs and hampers the comparability of findings. Therefore, within this narrative review we sought to identify and discuss key methodological considerations inherent within a MM-TT. The scope of our review extends to evaluating participant familiarisation and methodological standardisation practices, test meal characteristics, appetite perception assessment, blood sampling techniques, measurement of appetite-related hormones and data handling/analysis. A checklist has been devised to summarise relevant methodological issues identified within this review. This checklist can be used as a tool by researchers to facilitate MM-TT design and promote greater standardisation/comparability between studies. This review highlights the need for broader standardisation of MM-TT procedures to support consistency across future research. Additional research is needed to strengthen the evidence base on which various recommendations are made, particularly relating to participant familiarisation and methodological standardisation practices. Additional scrutiny of less common outcomes employed in MM-TTs (not addressed here), such as diet-induced thermogenesis, gastric emptying and ad libitum energy intake, is also needed.
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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 | 0.336 | 0.484 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".