Bibliographic record
Abstract
Chapter 1 -16 -investigating the effects of dietary factors in treating obesity and any low calorie diet seems to work.105 But, dietary factors which could induce a quick weight loss among obese subjects may not be appropriate for a long-term use in order to prevent weight gain, such as the well-known very-low-carbohydrate diet.[106][107][108] In addition, they may not equally effective in obesity prevention.For example, in the Women's Health Initiative, one of the longer-term intervention studies on weight loss, the low-fat group initially lost weight but then gained weight at least as fast as the control group.109 Investigating the effects of dietary factors on the prevention of subsequent weight gain in the general population is of important public health relevance.The DiOGenes project was set up with the primary goal as to determine the efficacy of dietary macronutrient components for the prevention of weight gain and regain, due to the difficulties of conducting large intervention studies on primary weight gain.110 Within the research line of the DiOGenes population-based cohort study, this thesis was initiated to investigate the associations of dietary factors having impacts on satiation or satiety, including dietary GI, GL, ED, and fiber intake, and the genetic variants involved in energy intake regulation with subsequent weight and waist circumference change.For this purpose, we analyzed data of 89,432 participants from five European countries that are involved in the existing EPIC study (European Prospective Investigation into Cancer and Nutrition).First, to better understand the GI concept, a narrative review about the physiological mechanisms underlying the potential associations between GI and chronic diseases including diabetes, cardiovascular diseases, obesity and certain cancers was conducted and this part of work is presented in Chapter 2. Given that the food frequency questionnaires (FFQs) used in this study are not specially designed to assess GI and GL and the validities of the measurements were unknown, two studies were conducted to obtain insights into the relative validities of GI and GL measured by the FFQ used in the Dutch part of the EPIC study.Chapter 3 describes the cross-sectional associations between GI, GL and food intake as well as metabolic risk factors including blood glucose, insulin, lipids and the inflammatory marker C-reactive protein.In Chapter 4, the reproducibility and relative validity of GI and GL, as compared to the measurements from multiple 24-hour recalls, is described.Chapter 5, 6 and 7 present the associations of GI and GL, dietary energy density, and the intake of fiber (in total and from different food sources) with subsequent changes of weight and waist circumference respectively.Given the critical role of the hypothalamic signaling network in regulating energy intake hence body weight, the effects of SNPs in or near genes involved in this network have been investigated.The interactions of these SNPs with dietary GI were also investigated (Chapter 8).This thesis ends with Chapter 9, in which the research findings are discussed in a broader context and implications for future research and developments are explored.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".