Serum Resistin (FIZZ3) Protein Is Increased in Obese Humans
Notice bibliographique
Résumé
Degawa-Yamauchi et al.(1) demonstrate an increase in resistin in obese compared with lean adult subjects. They observe significant positive correlation between resistin and body mass index, however serum resistin was not a significant predictor of insulin resistance in their study. Serious flaws in the study design have led us to question their interpretation of the presented data. The authors compared lean and obese individuals of profoundly different ages and gender distribution (1). The lean group had a mean age of 33 ± 2 yr compared with 47 ± 1 yr in the obese group. Age-related changes in insulin resistance are well described. Similar changes in resistin with age may also occur, however this issue was not addressed by the authors. The sexual dimorphism in circulating levels of adipokines, such as leptin and adiponectin, is well described (2, 3). Based on the review of results published to date, sexual dimorphism in resistin levels has been observed in humans by several authors (2–4). Although the lean group had an equal number of male and female subjects, subjects in the obese group studied by Degawa-Yamauchi et al. were predominantly women (∼75%). Hence, higher resistin levels occurred in the obese group. While interpreting data on resistin levels, caution should be exercised in regard to gonadal hormone status in the studied subjects. It is obvious from the average age of the obese group (47 yr) that a significant number of postmenopausal women must have been included in this study. However, the gonadal hormone status and its relation to resistin levels or the presence of hormone replacement therapy is not discussed. This is an additional confounding variable that should have been addressed by the authors. Inclusion of diabetic subjects in the obese group could also have confounded the results. These diabetic subjects were treated with sulfonylurea, metformin, or insulin therapy. Because fasting insulin and glucose levels are affected by pharmacotherapy, homeostasis model assessment of insulin resistance is a poor measure of insulin resistance in this situation. Furthermore, hyperglycemia and glucotoxicity itself may have an effect on resistin expression independent of obesity (5). Indeed, the average glucose level (6.76 mmol/liter) in the obese group suggests the direct effect of glucotoxicity on insulin resistance in the obese group irrespective of resistin levels. The authors did not exclude the possibility of additional undiagnosed diabetic individuals in either group. An oral glucose tolerance test would have been necessary to ensure that diabetic individuals were not misclassified. These circumstances render rather difficult the authors’ effort to describe the relation between resistin, obesity, and insulin resistance. Statistical analysis and its description are insufficient to engender confidence in the authors’ interpretation of the data. Because the data for male and female subgroups were not presented separately, it is unclear whether the difference in resistin between lean and obese groups would remain significant after adjustment for gender and/or age. In terms of multiple linear regression analysis, it would be of interest to know what would be the power for studied parameters after exclusion of a patient with missing age, five diabetic subjects on pharmacotherapy, and subjects with “two outlying data points” for an unspecified variable. Although it remains to be determined by larger studies whether the physiological concentration of resistin in circulation plays a role in regulating insulin resistance of obesity in humans, clarification of the authors’ results might be useful to the readership of the JCEM.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 ».