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Enregistrement W2080335611 · doi:10.1038/oby.2006.305

The Neurobiology of Obesity

2006· article· en· W2080335611 sur OpenAlexaffabout
Denis Richard, P. Boisvert

Notice bibliographique

RevueObesity · 2006
Typearticle
Langueen
DomaineNeuroscience
ThématiqueRegulation of Appetite and Obesity
Établissements canadiensUniversité Laval
Organismes subventionnairesnon disponible
Mots-clésObesityMedicineNeuroscienceMEDLINEPsychologyBiologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

Obesity is an energy balance regulation dysfunction that leads to an excessive fat deposition, which increases the risk for cardiovascular, respiratory, and metabolic diseases, in particular when such deposition occurs within the abdominal cavity. The high prevalence of obesity and its costly co-morbidities has significantly contributed to intensify efforts to understand obesity, which is currently at the forefront of biochemical, biomolecular, and neurobiological research. We now confront the tremendous challenge of preventing obesity and developing effective treatments. In the last 15 years, research has generated many important discoveries, including those of adipose tissue-derived hormones such as leptin and adiponectin, those of the gastrointestinal tract hormones such as ghrelin, and the determination of numerous central and peripheral circuitries involved in energy balance regulation. The neurobiology of obesity represents a rapidly growing research field that has proved to be essential in the understanding of the etiology of obesity. The brain plays a major role in energy balance regulation as it exerts controls on both food intake and energy expenditure (Figure 1). Three brain entities are particularly important in these controls, namely the hypothalamus, the dorsal vagal complex, and the reward system, which are inter-related structures capable of controlling energy intake as well as thermogenesis. Brain systems controlling energy intake and energy expenditure have been divided into anabolic and catabolic systems, each system comprising different types of neurons capable of controlling energy intake as well as energy expenditure. These neurons release various molecules that include neuropeptide Y, agouti-related peptide, melanin-concentrating hormone, the endocannabinoids, α-melanocyte-stimulating hormone, cocaine- and amphetamine-regulated transcript, corticotropin-releasing factor, thyrotropin-releasing hormone, and serotonin. These neurosystems are modulated by short- and long-term signals that report on the status of the energy stores and energy fluxes. Whereas leptin and insulin are recognized as the main long-term signals, the gastrointestinal hormones ghrelin, peptide tyrosine-tyrosine, cholecystokinin, and glucagon-like peptide 1 are known as short-term signals that inform about the nutritional status. The way an organism regulates energy balance is in large part a function of its genes and the environment. One energy expenditure component, which is perhaps not so important in human energetics but which is undoubtedly relevant in laboratory rodents used by most neurobiologists of obesity, is brown adipose tissue thermogenesis, the activity of which depends on factors activating the sympathetic nervous system. The extraordinary thermogenic power of brown adipose tissue is attributable to uncoupling protein 1, a mitochondrial protein uniquely found in brown adipose tissue. The regulation of energy stores. NPY, neuropeptide Y; AgRP, agouti-related peptide; MCH, melanin-concentrating hormone; DVC, dorsal vagal complex; αMSH, alpha-melanocyte-stimulating hormone; CART, cocaine- and amphetamine-regulated transcript; CRF, corticotropin-releasing factor; TRH, thyrotropin-releasing hormone; SNS, sympathetic nervous system; GI, gastrointestinal; BAT, brown adipose tissue; UCP1, uncoupling protein 1. It is within the mission of the Merck Frosst/CIHR Research Chair in Obesity to provide continuing education about the best possible knowledge on obesity to scientists, physicians, health professionals, as well as to the public at large, regarding the causes, complications, treatment, and prevention of obesity. In this regard, the purpose of the annual symposium is to summarize evidence regarding important topics related to obesity and to propose novel obesity research directions. Since its creation in 1997, the Merck Frosst/CIHR Research Chair in Obesity has held six international symposia devoted entirely to obesity research. The first symposium of the series, entitled “The Uncoupling Proteins and Obesity,” was held in 1998 (1). The second symposium, which was organized the following year, was oriented toward “The New Biology of Adipose Tissue” (2). In 2000, the third symposium was entitled “The Ponderostat: From Behavior to Neural Substrates” (3) and focused on the mechanisms whereby energy balance is regulated. The fourth symposium, organized in November 2002 and entitled “Complications of Obesity: The Inflammatory Link,” summarized the latest most important findings on the mechanisms underlying the link between obesity and its complications such as diabetes and cardiovascular diseases (4). In 2003, the fifth symposium brought a comprehensive view on the “Role of Gene Regulation in Obesity and its Complications” (5) and, specifically, on the regulation of peroxisome proliferator-activated receptors, prostaglandin C-1, Forkhead transcription factor-1, and silent information regulator 2. In 2004, the sixth symposium focused on the understanding of “The Endocannabinoid System and its Role in Energy Homeostasis and Abdominal Obesity Management” (6). Given the originality and importance of the research currently in progress on the neurobiology system aimed at unraveling the mechanisms whereby energy homeostasis is achieved, better understanding of obesity, and determining therapeutic targets, an advisory committee on the symposium series recommended to the Chair to organize the present symposium on that important topic. The Scientific Program Committee for this symposium, which was chaired by D. Richard (Canada), included K. Sharkey (Canada), W. Colmers (Canada), and A. Ferguson (Canada). P. Boisvert was in charge of the symposium logistics. The present proceedings recapitulate the highlights of the seventh symposium of the Merck Frosst/CIHR Research Chair in Obesity, which was entitled “The Neurobiology of Obesity.” The symposium was held November 4–5, 2005, at Auberge Saint-Antoine in Québec City, Canada. The first of the three one-half-day sessions, chaired by W. Colmers, was aimed at presenting overviews of “The Regulation of Energy Balance.” The second session was chaired by A. Ferguson and was dedicated to the “Circuitries Involved in Energy Balance Regulation.” Finally, K. Sharkey chaired the third session, entitled “Targets for Drugs,” on the peripheral and central mechanisms controlling food intake and energy balance. In closing, our gratitude goes to all of the invited experts, who not only presented authoritative and stimulating presentations but also participated with enthusiasm and competence in the lively discussion periods of the meeting. We thank K. Sharkey, A. Ferguson, and W. Colmers, who very skillfully chaired the meeting sessions and provided direction during the invigorating discussion periods. K. Sharkey, A. Ferguson, W. Colmers, and D. Richard hold a New Emerging Team research grant from the Canadian Institutes of Health Research. This supplement of Obesity was edited by D. Richard and P. Boisvert. We also acknowledge the contribution of the Centre de Recherche de l'Hôpital Laval affiliated to Université Laval, as well as Desjardins Sécurité Financière for their support. This symposium was supported by an unrestricted grant from Merck Frosst Canada Limited.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,163
Score d'incertitude au seuil0,260

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,013
Tête enseignante GPT0,230
Écart entre enseignants0,217 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations14
Publié2006
Routes d'admission2
Résumé présentoui

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