Diet, nutrients, and obesity : an investigation of relationships in the Australian context
Bibliographic record
Abstract
Overweight and obesity are global public health problems affecting millions of adults, and Australia is no exception. In the population-level rise of overweight and obesity, dietary risk factors have been implicated. However, evidence is lacking on the key issues of how dietary patterns are associated with obesity outcomes: whether unhealthy diet mediates the association between socio-economic disadvantage and obesity; whether unhealthy diet is associated with weight-related complications, defined using the Edmonton Obesity Staging System (EOSS); and whether nutrient deficiencies are associated with obesity. Thus, this thesis addresses this lack, examining the relationships between diet, nutrients and obesity, and weight-related complications. In this thesis, I conducted four studies: one umbrella review and three cross-sectional studies. In the umbrella review (Study 1), I summarized and graded evidence from 16 systematic reviews of observational studies on dietary patterns and overweight/obesity outcomes in the adult population. The focus of Study 1 was the association between unhealthy versus healthy dietary patterns and overweight/obesity incidence or weight gain. For Study 2, data from 7,044 adults were analysed using log-binary regression since the prevalence of obesity in the sample was greater than 10%. To estimate the extent to which the association between socio-economic disadvantage and obesity could be explained by unhealthy diet, I also conducted mediation analysis. For Study 3, I analysed the data from 5,055 adults living with overweight or obesity and used logistic regression to explore the association between unhealthy diet and EOSS. Also, I conducted stratified analysis and propensity score matching to further examine the association in a sensitivity analysis. For Study 4, I analysed data from 3,539 adults who had biomarkers for micronutrients, applying weights to correct for the complex sampling. Results: The findings in this thesis highlight the following three points: first, diet-related efforts in obesity prevention strategies could benefit from including Mediterranean-type dietary patterns, particularly for socially disadvantaged groups. Second, since the results did not support the usefulness of the Mediterranean diet for weight-related complications, more work exploring its relationship with specific EOSS components is required. Third, the findings show the co-existence of severe obesity and vitamin D deficiency, but longitudinal studies are needed to confirm the direction of the association.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".