Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».