α‐1 acid glycoprotein but not C‐reactive protein is associated with significantly lower serum zinc concentrations among Congolese children aged 6–59 months and has a substantial impact on prevalence estimates of zinc deficiency
Notice bibliographique
Résumé
Background Serum zinc (Zn) is a negative acute phase reactant; hence, concentrations decrease in the presence of inflammation and infection. There is no current consensus on how to adjust serum Zn concentrations for inflammation. Objectives The aim was to determine associations between serum Zn concentration and inflammation biomarkers (C‐reactive protein [CRP] and α‐1 acid glycoprotein [AGP]) and to compare means and the prevalence of Zn deficiency using unadjusted serum Zn concentrations and serum Zn concentrations adjusted for inflammation using study generated correction factors (CFs) among children in the Democratic Republic of the Congo. Methods Non‐fasting blood was collected in trace‐element free vacutainers from 744 children (6–59 mo) recruited from South Kivu and Bas Congo provinces in 2014 using a probability proportionate to size sampling method. Serum was analyzed for Zn (n=691), CRP and AGP concentrations (n=687). Linear regression was used to estimate associations between serum Zn and AGP and CRP concentrations and to generate CFs (calculated as 1 divided by the geometric mean ratio) for Zn based on three stages of inflammation: (incubation [CRP >5 mg/L], early convalescence [CRP >5 mg/L and AGP >1 g/L] and late convalescence [AGP >1 g/L]), relative to children with no inflammation. Results Overall, the prevalence of acute (CRP >5 mg/L) and chronic (AGP >1 g/L) inflammation was 29% (n=197) and 66% (n=455), respectively. Unadjusted mean (95% CI) serum Zn concentration was 9.4 (9.3, 9.6) μmol/L. Unadjusted mean ± SD serum Zn concentration was 10.0 ± 1.9 μmol/L among children with no inflammation (n=213; 35%), 9.8 ± 1.1 μmol/L among children in the incubation stage (n=11; 2%), 8.7 ± 2.1 μmol/L among children in early convalescence (n=117; 19%), and 9.4 ± 2.1 μmol/L among children in late convalescence (n=260; 43%). A 1 g/L increase in AGP was associated with a 0.5 (95% CI: 0.3, 0.7) μmol/L lower serum Zn concentration (P<0.001). CRP was not significantly associated with serum Zn concentration (P=0.11). Study generated CFs (95% CI) for Zn were 1.01 (0.88, 1.15), 1.16 (1.11, 1.21) and 1.07 (1.03, 1.11) for incubation, early and late convalescence stages, respectively. After applying the CFs, adjusted mean (95% CI) serum Zn concentration was 10.1 (9.8, 10.2) μmol/L. Overall, the prevalence of Zn deficiency (<8.7 μmol/L) decreased from 35% (n=244) using unadjusted serum Zn concentrations to 24% (n=160) using serum Zn concentrations adjusted for inflammation. Conclusion AGP is associated with significantly lower serum Zn concentrations and the application of study generated CFs had a substantial impact on Zn deficiency prevalence rates. AGP appears more useful than CRP to measure the effects of inflammation on serum Zn in our study population; however, we acknowledge that the use of more than one inflammation biomarker is ideal. Adjustment for inflammation appears to be warranted for accurate estimates of population‐level Zn status. Support or Funding Information Funding for this research was provided by HarvestPlus. C.D.K. received a doctoral research award from the International Development Research Centre (Canada) and a Vanier scholarship from the Canadian Institutes of Health Research (CIHR).
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) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,000 | 0,001 |
| 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 ».