The Determinants of Nutritional Risk in Paediatric Cancer
Notice bibliographique
Résumé
The five-year survival rates of paediatric cancer patients have improved considerably in the last 40 years with the implementation of more intensive and progressive treatments. Consequently attention is shifting to the reduction of treatment-related sequelae during and after the completion of therapy. Malnutrition and vitamin D inadequacy are a major concern as they are thought to increase the risk of short- and long-term complications in this population. Furthermore, emerging evidence has found a protective role of antioxidants and docosahexanoic acid (DHA) and eicosapentaenoic acid (EPA) against chronic conditions, including cardiovascular disease and cancer, which are common long-term complications in survivors of paediatric cancer. Therefore, this thesis aimed to investigate the nutritional status (NS), vitamin D, antioxidants and oxidative stress levels, as well as DHA and EPA levels of paediatric cancer patients. Potential factors that may contribute to the development of malnutrition in this population were also investigated. \nA prospective cohort-study of SE Scottish children aged <18 years, diagnosed with and treated for cancer between Aug 2010-Jan 2014 was performed. Clinical and nutritional data were collected at defined periods up to 36 months. NS was assessed using anthropometry, bioelectrical impedance analyisis (BIA), plasma micronutrients and dietary intake. DHA, EPA, antioxidant capacity and oxidative stress were measured at baseline and 6 months between April 2013-Jan 2014. Paediatric cancer was stratified by treatment risk (high, medium and low) and by diagnostic criteria. The primary outcome was malnutrition defined as body mass index (BMI) according to UK growth chart centiles; underweight (<2.3rd), overweight (85-95th) and obese (>95th). Vitamin D status was defined by the Endocrine Society Clinical Practice Guidelines (2011); inadequacy (<50nmol/L). \nEighty-two patients [median(IQR) age 3.9(1.9-8.8) years; 56% males)] were recruited. At diagnosis, the prevalence of undernutrition was 13%, overweight 7% and obesity 15%. TSF identified the highest prevalence of undernutrition (15%) and the lowest of obesity (1%). BMI [p<0.001; 95% CI (1.31-3.47)] and FM (BIA) [p<0.05; 95% CI (0.006-0.08)] significantly increased after 3 months of treatment, whilst FFM (BIA) [p<0.05; 95% CI (-0.78-(-0.01)] significantly decreased during the first three months and these patterns remained until the end of the study. High-treatment risk significantly contributed to undernutrition during the first three months of treatment [p=0.04; 95% CI (-16.8-(-0.4)] and solid tumours had the highest prevalence of undernutrition [BMI (17%)]. Vitamin D inadequacy was highly prevalent (64%; 42/65) at both baseline and during treatment (33-50%) and those children who were not supplemented had the lowest vitamin D levels at every stage with median(IQR) levels ranging from 32.0(21.0-46.5)nmol/L to 45.0(28.0-64.5)nmol/L. Paediatric cancer patients had high levels of oxidative stress and low levels of DHA and EPA, especially at baseline. Antioxidant status remained steady at 6 months, however antioxidant capacity increased slightly. Finally, antioxidant levels, antioxidant capacity, oxidative stress and EPA and DHA did not statistically differ between children receiving nutritional support and those who were not. \nArm anthropometry (or BIA) alongside appropriate nutritional supplementation should be implemented in clinical practice due to the high risk of malnutrition (undernutrition and obesity), the changes in body composition (increase in fat mass and reduction in lean mass) and vitamin D inadequacy, as well as the low levels of EPA and DHA seen in this paediatric cancer cohort.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 tête enseignante, 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 ».