MétaCan
Menu
Retour à la cohorte
Enregistrement W2969433328 · doi:10.1111/apa.14982

Human milk analyser underestimated protein content of unfortified and fortified samples compared to elemental analysis

2019· article· en· W2969433328 sur OpenAlexaffabout
Alexandra Thajer, Gerhard Fusch, Christoph Binder, Angelika Berger, Christoph Fusch

Notice bibliographique

RevueActa Paediatrica · 2019
Typearticle
Langueen
DomaineNursing
ThématiqueInfant Nutrition and Health
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésPasteurizationMedicineAnalyserFood scienceChemistryChromatography

Résumé

récupéré en direct d'OpenAlex

When premature infants are given human milk it needs to be fortified with bovine protein to provide all the essential nutrients they need, including protein.1 The MIRIS human milk analyser (MIRIS AB, Uppsala, Sweden) analyses human milk at the bedside, but concerns have been raised about its accuracy.2 This prospective study was conducted from January 2012 to August 2012 at the Medical University of Vienna, Austria, and McMaster University, Hamilton, Canada. It showed how accurately MIRIS analysed fortified human milk by comparing it with elemental analysis, a new validated micro-method that precisely measures the protein content.3 The effect that pasteurisation had on the protein content was also determined. We used two frozen 24-hour pooled samples from each of five mothers. After thawing, one sample underwent pasteurisation and the other did not. All 10 samples were measured with MIRIS and elemental analysis. The study was approved by the local ethics committee in Vienna (EC Nr: 1121/2012), but informed consent was not required as the samples were surplus to requirements. The samples were frozen at −21 to −27°C until needed, then thawed at +4 to +6°C in the refrigerator over 12 hours. One sample from each mother was pasteurised at 63°C for 30 minutes with the Barkey clinitherm pasteur XPT (Barkey GmbH, Leopoldshoehe, Germany). We measured the unfortified human milk, and then, we added 4.3 g/100 mL of Milupa Aptamil FMS (Danone GmbH, Friedrichsdorf, Germany) followed by Milupa Aptamil Protein+ (Danone GmbH, Friedrichsdorf, Germany) in eight 0.5 g steps, to a maximum of 4.0 g/100 mL. Both products are based on casein and whey protein hydrolysates. All the samples were divided into two aliquots and frozen again at −80°C. The first set was thawed and measured using the MIRIS human milk analyser in Austria. The second set was sent to Canada on dry ice and measured with elemental analysis. MIRIS uses mid-infrared transmission spectroscopy.2 All human milk samples were heated to 40°C and vortexed to homogenise all the components properly before each measurement. The elemental analyser was the Vario PYRO cube (Elementar Americas Inc, New Jersey, USA).3 It is based on the Dumas combustion principle, and its precision is comparable to the Kjeldahl method. Both devices were calibrated, and quality control checks were carried out before measurements. The outcomes were expressed as medians and ranges. We compared the protein contents of pasteurised and unpasteurised samples using Kruskal-Wallis analysis of variance then the Mann-Whitney U Test. Analysis of variance with repeated measurements was used to identify differences in protein between the two methods. The analysis used SPSS version 20 (IBM Corporation, New York, USA). Figure 1 shows that the protein content of the unfortified milk was different when measured by MIRIS (median 1.05 g/100 mL, range 0.90-1.40 g) and elemental analysis (median 1.22 g/100 mL, range 0.92-1.40 g). There was an impressive deviation of 19% between MIRIS and elemental analysis at the maximum fortification level. Repeated measurement analysis of variance provided significant results for the increase in protein concentration (P < .01) and elemental analysis (P < .01). Fortifying the human milk had a significant impact on group comparisons. Standard fortified human milk showed significantly lower protein values (median 1.85 g/100 mL, range 1.20-2.10 g/100 mL) with MIRIS than elemental analysis (median 1.98 g/100 mL, range 1.80-2.28 g/100 mL). As increasing protein was added, the difference between the two methods increased. For example, the protein difference was 0.98 g/100 mL when the maximum of 4.0 g protein per 100 mL was reached (P < .01). There were no significant differences between the pasteurised and unpasteurised samples in both groups (P > .05). Fusch et al indicated that MIRIS underestimated the protein content in human milk by −0.2 g/dL and showed that device-specific correction factors and reconfiguration would provide reliable results.2 One reason for the MIRIS gap could be the protein powder, which affects the human milk matrix. As a result, the fortified human milk was outside the confidence limits of the MIRIS calibration and could not be adequately measured. Another reason might be that the human milk became too viscous after fortification and, therefore, could not be measured correctly. Manufacturers have launched human milk fortifier and protein powders that can be mixed with human milk without any data on compatibility checks or the bioavailability of single components. We showed that pasteurisation had no impact on the protein content of human milk, in line with previously published data.4, 5 It appears to preserve the biological activity of proteins and the amino acids remain stable. Nevertheless, a number of factors might affect the outcome, like heating time, temperature, heating method and milk volume, as well as the type of human milk analyser. Our study was limited by the small sample size and further studies are needed to confirm our findings. The strength of the study was that we compared MIRIS to elemental analysis. We showed that MIRIS significantly underestimated the protein content of human milk compared with elemental analysis and was more inaccurate at higher protein fortification levels. Both methods revealed that pasteurisation had no effect on protein content. None.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,339
Score d'incertitude au seuil0,710

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,063
Tête enseignante GPT0,320
Écart entre enseignants0,257 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2019
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueActa PaediatricaMême sujetInfant Nutrition and HealthTravaux en français237 207