Serum Copper, Zinc and Selenium Levels in Subjects With and Without Metabolic Syndrome
Notice bibliographique
Résumé
Metabolic syndrome (MS) is defined as the clustering of cardiovascular risk factors and is associated with increased risk for cardiovascular morbidity and mortality [1, 2]. Oxidative stress has been hypothesized as one of the main mechanisms leading to MS [3]. Since copper, zinc and selenium are cofactors of antioxidant enzymes a lot of studies in different regions have been conducted in order to find possible differences in these trace element levels in subjects with or without MS [4-7]. However, their results were conflicting [4-7]. Therefore, the aim of the present study was to compare serum copper, zinc and selenium levels in subjects with or without MS. A total of 51 subjects (17 males/34 females, mean age ± standard deviation (SD): 69.0 ± 9.4 years, body mass index (BMI) ± SD: 33.8 ± 5.1 Kg/m2) with MS and 54 subjects without MS (22 males/32 females, mean age ± SD: 69.8 ± 9.7 years, BMI ± SD: 29.6 ± 3.7 Kg/m2), consecutively selected from the outpatient clinic of our hospital were enrolled into the study. Subjects having three or more of the criteria according to the NCEP ATP III report [8] were defined as having the MS. A thorough physical examination was performed and a detailed medical history was obtained for each participant. All measurements were performed in the morning, after 10 12 hours fast. Blood samples were drawn for measurement of serum copper, zinc and selenium levels. Serum levels of copper, zinc and selenium were determined by use of atomic mass spectrometry (ZEEnit 700, Analytical Jena, Germany). Trace element levels did not differ between subjects with or without MS: copper levels (120.7 ± 35.5 vs. 117.4 ± 39.2 μm/l, P = 0.65), zinc levels (91.5 ± 28.3 vs. 94.6 ± 23.4 μm/l, P = 0.54) and selenium levels (106.4 ± 33.8 vs. 102.8 ± 28.7 μm/l, P = 0.56). Univariate regression analysis showed that serum copper and selenium levels did not correlate with any of the MS components. Serum zinc levels correlated negatively only with glucose levels (beta = -0.50, P = 0.03) (Table 1). The present study showed that serum copper, zinc and selenium levels are not associated with the presence of MS. The Third National Health and Nutrition Examination Survey showed that serum selenium levels were similar in subjects with or without MS [5]. Furthermore, the Supplementation en Vitamines et Mineraux Antioxydants (SU.VI.MAX) trial [4], showed that serum selenium concentrations were not associated with MS. However, a recent study in Europe showed that only selenium was positively associated with a higher odd of MS in women but not in men. This association was not confirmed between copper or zinc and MS [9]. With regard to zinc the lack of association reported in our study is in accordance with the results of the SU.VI.MAX trial [4]. With regard to zinc and copper the lack of association is in accordance with the results of a study conducted in Iran [7]. In conclusion, the present study showed that serum copper, zinc and selenium levels are not associated with the components or the presence of MS. The only observed association was between serum zinc and glucose levels.
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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) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 ».