Editorial: Pre-natal and post-natal environmental impacts on metabolic control
Notice bibliographique
Résumé
Diverse environmental and maternal behavioural factors influence the future metabolic health of the offspring, including the pre-pregnancy health of the mother, the in utero environment, and early childhood development. Environmental risk factors are diverse and include maternal stress, nutrition, environmental pollutants, and lifestyle choices such as recreational drugs and alcohol. Such variables can impact diverse fetal tissues including the pancreas, liver, kidneys, skeletal muscle, adipose tissue, cardiovascular system, and brain. The placenta is a key mediator between maternal metabolic stress and altered fetal growth and development. Substantial gaps exist in our understanding of how individual organs and tissues are modified in both form and function by early life stressors, how limitations in tissue plasticity combine to build risk for adult disease, and where potential opportunities exist for metabolic rescue before or after birth.The intent of this Special Issue was to illustrate key structural and functional developmental changes to organs and tissues that arise in response to sub-optimal environmental conditions during pregnancy and neonatal life, and how these can mutually build risk for adult metabolic diseases. Mechanistic changes include altered tissue stem cell populations, altered hormone and growth factor presence, epigenetic modifications in key genes regulating metabolism, and cellular stress can alter maternal and newborn health. GDM is often associated with large-forgestational age birth size and an increased risk of adult chronic diseases in the offspring and developing Type 2 diabetes mellitus in the mother. A retrospective cohort study compared perioperative management options during caesarean section. An 'Enhanced recovery after surgery' protocol (ERAS) was compared to a normally managed group of women with GDM. ERAS includes a pre-operative low carbohydrate drink which induces anabolism through an increase in insulin release. This can stabilize perioperative glycemia and prevent post-operative complications. However, the impact on metabolism in women with GDM was unclear. Results showed that the differences in preoperative glucose levels between ERAS and control group were minimal, but the ERAS group experienced a substantial decrease in composite adverse outcome, including perioperative maternal and neonatal hypoglycemia and hypertension. This could be anticipated to result in a lower referral to neonatal intensive care with reduced associated costs.The papers included in this special issue highlight the range of environmental stressors during pregnancy that can impact the development of the fetus and the health of the offspring, from macronutrition to micronutrients and the host microbiome. The importance of early intervention and prevention strategies aimed at optimizing maternal health and the in utero environment is emphasized. While lifestyle and therapeutic interventions can improve maternal metabolic control and reduce the risk of abnormal neonatal outcomes, reversing the adverse metabolic programming remains a major challenge. Further, care protocols surrounding birth can also help to ensure neonatal health.
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 | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,008 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,009 |
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 ».