Ethnicity and obesity: why are some people more vulnerable?
Notice bibliographique
Résumé
Obesity is a global problem that affects all ethnic groups and managing it is a major challenge. In developing countries obesity coexists with underweight.BMI is the most widely used measure of obesity. World Health Organization cut-off values of BMI =25 or =30 kg/m2 for over weight and obesity, respectively, have been used worldwide for several years to assess the prevalence of obesity of varying degrees. The highest prevalence of overweight and obesity in the world is to be found in the Western Pacific Islands, especially among the populations of Nauru and Tonga, where it reaches 80–90%. Sub-Saharan Africa has the lowest prevalence of obesity. The greatest increase in obesity is occurring in countries with a diverse ethnic population, such as Mauritius and Brazil. An increased percentage of body fat is normally coupled to an increase in body weight. However, there is evidence to show that the association between BMI, percentage and distribution of body fat differs across populations, with Asians having the highest percentage of body fat compared with other populations. Asians also have a higher amount of visceral adipose tissue. The variation in percentage of body fat and body fat distribution relative to BMI across ethnic groups is reflected in ethnic differences in the health risks associated with obesity. For example, populations from the Asia-Pacific region have been found to have substantial risks of cardiovascular disease (CVD) below a BMI of 25 kg/m2. In all populations, cardiovascular risk increases with increasing waist circumference, even though it is influenced by ethnicity. For example, compared with white populations, Inuit and Polynesians have been found to have lower blood pressure, lipids, stimulated glucose and insulin levels for the same levels of waist circumference. The metabolic impact of different levels of obesity differs considerably across populations, especially with regard to diabetes and CVD. Therefore the ‘one-size-fits-all’ approach adopted internationally must be reconsidered and carefully analysed. BMI, waist circumference and waist-hip ratio all have their limitations when it comes to comparing obesity and its risk factors across ethnic groups and populations. The influence of genetics on the association between obesity and health risks remains unresolved. Data on obesity and metabolic risk factors including Inuit living in Greenland and Denmark showed that Inuit in Denmark followed the same patterns as an ethnic Danish reference population with regard to the association between obesity and cardiovascular risk factors. Lifestyle and environmental factors may therefore be more important than genetic factors regarding the influence of obesity on disease risk. Udgivelsesdato: 2008
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 | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,003 |
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 ».