Indirect Calorimetry to Measure Metabolic Rate and Energy Expenditure in Psychiatric Populations: A Systematic Review
Bibliographic record
Abstract
Psychiatric and metabolic disorders are highly comorbid and the relationship between these disorders is bidirectional. The mechanisms underlying the association between psychiatric and metabolic disorders are presently unclear, which warrants investigation into the dynamics of the interplay between metabolism, substrate utilization, and energy expenditure in psychiatric populations, and how these constructs compare to those in healthy controls. Indirect calorimetry (IC) methods are a reliable, minimally invasive means for assessing metabolic rate and substrate utilization in humans. This review synthesizes the extant literature on the use of IC on resting metabolism in psychiatric populations to investigate the interaction between psychiatric and metabolic functioning. Consistently, resting energy expenditures and/or substrate utilization values were significantly different between psychiatric and healthy populations in the studies contained in this review. Furthermore, resting energy expenditure values were systematically overestimated when derived from predictive equations, compared to when measured by IC, in psychiatric populations. High heterogeneity between study populations (e.g., differing diagnoses and drug regimens) and methodologies (e.g., differing posture, time of day, and fasting status at measurement) impeded the synthesis of results. Standardized IC protocols would benefit this line of research by enabling meta-analyses, revealing trends within and between different psychiatric disorders.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".