Addressing gaps in cardiometabolic health and nutrition in women of reproductive age
Notice bibliographique
Résumé
Most women do not meet dietary guidelines before or during pregnancy, and overweight or obesity are becoming the predominant presentation in antenatal services. Novel strategies to improve health before pregnancy are of interest, but research with women outside of pregnancy is impacted by issues with recruitment and retention. Clinical risk categorisation schema such as the Edmonton Obesity Staging System (EOSS) and the Cardiometabolic Disease Staging System (CMDS), show promise in guiding treatment prioritisation in the general population, but their use in pregnancy has not been adequately considered. The World Health Organisation recommends that all women receive nutrition and weight counselling during pregnancy. Current antenatal practices, however, do not address nutrition as standard. There is also no consensus on which outcomes are most important for pregnancy nutrition interventions, with little consideration for the ‘patient voice’ in what is evaluated. The aims of this thesis are to investigate the potential of pre-existing clinical practice tools to address cardiometabolic health and nutrition in women of reproductive age and to explore the priorities for nutrition research from the perspective of key stakeholders, including women before and during pregnancy. Outside of pregnancy, we found the EOSS characterised more women with obesity (81.3%) as metabolically unhealthy. This high prevalence potentially limits the clinical utility of the tool in delineating risk. Conversely, we found the CMDS characterised 46.9% of these women as metabolically unhealthy. We also found a relationship between inflammatory marker C3 Complement protein and cardiometabolic phenotype. In our mixed-methods study, we women of reproductive age reported altruistic motivations for taking part in preconception research, were recruited mostly by digital means and prioritised wellbeing over traditional health measures. In pregnancy, the limitations of the EOSS system were also highlighted, given the high prevalence of “at risk” categorisation, especially in late pregnancy (98.9%). We found an antenatal lifestyle intervention that of healthy eating and low glycaemic index (GI) dietary advice was successful in reducing the dietary inflammatory potential of women with overweight or obesity. This suggests that a low GI and healthy eating intervention may be useful in improving inflammation and cardiometabolic health. Our data suggests that the International Federation of Gynaecology and Obstetrics Nutrition (FIGO) Checklist is an acceptable and likely feasible resource to facilitate conversations on nutrition and weight during routine antenatal care. Finally, we identified 13 core outcomes for pregnancy nutrition research. These are pregnancy complications, gestational weight change; maternal vitamin and mineral status including anaemia; mental health; diet quality; nutritional intakes; need for treatments, interventions, medications, and supplements; pregnancy loss or perinatal death; birth defects or congenital anomalies, neonatal complications, new-born anthropometry and body composition; maternal wellbeing and delivery complications. Measurement of this core outcome set as a standard support will assist in the advancement of antenatal nutrition, by generating evidence for outcomes most important to stakeholders, including pregnant women. In conclusion, our data suggests a greater focus on well-being is needed in women’s health. C3 complement protein may hold potential as a novel risk marker in obesity and the FIGO Nutrition Checklist may support clinicians in appropriately addressing healthy eating and weight women of reproductive age. This may have benefits for reducing inflammation in women with overweight or obesity.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,000 | 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 ».