Predictors Linking Obesity and the Gut Microbiome (the PROMISE Study): Protocol and Recruitment Strategy for a Cross-Sectional Study on Pathways That Affect the Gut Microbiome and Its Impact on Obesity
Bibliographic record
Abstract
BACKGROUND: The prevalence of obesity has increased substantially over recent decades and is associated with considerable health inequalities. Although the causes of obesity are complex, key drivers include overconsumption of highly palatable, energy-dense, and nutrient-poor foods, which have a profound impact on the composition and function of the gut microbiome. Alterations to the microbiome may play a critical role in obesity by affecting energy extraction from food and subsequent energy metabolism and fat storage. OBJECTIVE: We report the study protocol and recruitment strategy of the PRedictors linking Obesity and the gut MIcrobiomE (PROMISE) study, which characterizes the gut microbiome in 2 populations with different metabolic disease risk (Pacific and European women) and different body fat profiles (normal and obese). It investigates (1) the role of gut microbiome composition and functionality in obesity and (2) the interactions between dietary intake; eating behavior; sweet, fat, and bitter taste perception; and sleep and physical activity; and their impact on the gut microbiome, metabolic and endocrine regulation, and body fat profiles. METHODS: ). In addition to anthropometric measurements and assessment of the body fat content using dual-energy x-ray absorptiometry, participants completed sweet, fat, and bitter taste perception tests; food records; and sleep diaries; and they wore accelerometers to assess physical activity and sleep. Fasting blood samples were analyzed for metabolic and endocrine biomarkers and DNA extracted from fecal samples was analyzed by shotgun sequencing. Participants completed questionnaires on dietary intake, eating behavior, sleep, and physical activity. Data were analyzed using descriptive and multivariate regression methods to assess the associations between dietary intake, taste perception, sleep, physical activity, gut microbiome complexity and functionality, and host metabolic and body fat profiles. RESULTS: Of the initial 351 women enrolled, 142 Pacific women and 162 NZ European women completed the study protocol. A partnership with a Pacific primary health and social services provider facilitated the recruitment of Pacific women, involving direct contact methods and networking within the Pacific communities. NZ European women were primarily recruited through Web-based methods and special interest Facebook pages. CONCLUSIONS: This cross-sectional study will provide a wealth of data enabling the identification of distinct roles for diet, taste perception, sleep, and physical activity in women with different body fat profiles in modifying the gut microbiome and its impact on obesity and metabolic health. It will advance our understanding of the etiology of obesity and guide future intervention studies involving specific dietary approaches and microbiota-based therapies. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618000432213; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370874. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/14529.
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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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".