Nutritional predictors of depression in a cohort of community-dwelling elderly Canadians: NuAge cohort
Notice bibliographique
Résumé
Depression is associated with decreased quality of life and high mortality risk at all ages. Diet is hypothesized to be a factor associated with depression. Despite some longitudinal evidence from studies with middle-aged adults, it is still largely unknown how depression affects nutrient intake in the elderly population. The three objectives of this thesis were to explore the longitudinal associations of (i) dietary patterns and macronutrient intake and of (ii) B6, B12, and folate intakes with the incidence of depression in late life; and (iii) to investigate whether depression significantly affects short-term nutrient intakes (reverse causality effect) in comparison with intakes among nondepressed seniors. The Quebec Longitudinal Study on Nutrition and Aging (NuAge) collected social, health, and biological data of 1793 community-dwelling men and women aged 68 to 82 years at recruitment for 4 years. Incidence of depression was defined by scores in the 30-item Geriatric Depression Scale ≥11 or antidepressant medication use over the 3 years of follow-up, and those deemed “depressed” at baseline were excluded. Dietary patterns were created through principal component analysis (PCA) on amount (grams) of food items consumed in each of the 32 predefined food categories. Tertiles of B-vitamin intake were created from the mean of three nonconsecutive 24-h recalls. Multiple logistic regression models were adjusted for several demographic, health, and social confounders. For the study of the reverse causality hypothesis, we conducted a nested case-control study. Participants free of depression at baseline who developed depression at some point of follow-up were matched by age group and sex with nondepressed participants. The intakes of energy, protein, saturated fat, dietary fibre, B6, B12, and folate at the time point of depression were compared with intakes the year prior between depressed and nondepressed groups using mixed-model repeated measures ANCOVA. Incidence of depression was 12.5% (n = 170, 63% women). PCA revealed three dietary patterns: varied diet, traditional diet, and convenience diet. Only varied diet was protective of depression incidence before adjustment for confounders. None of the three patterns were associated with the outcome in fully adjusted models. Tertiles of total energy intake were, however, inversely and independently associated with depression incidence. Men in the highest tertile of B12 intake from food had lower risk of depression compared with those in the lowest tertile. Higher B6 intake from food was protective among women, but the effect was dependent on total energy intake. Neither intakes from food + supplements (total) nor folate intake showed detectable benefits. The study of depression leading to changes in intake showed no evidence of reverse causality effect in the short term, as seniors deemed depressed at some time point of follow-up did not decrease their intakes significantly over time compared with nondepressed seniors, except for small declines in B12. These findings suggest that differences in quantity of food (energy intake) are more strongly associated with the likelihood of developing depression in the following years among generally healthy seniors living in the community than small differences in quality (dietary patterns). Nutrients may, in fact, predict depression better than food items, particularly nonenergy-adjusted B6 in women and energy-adjusted B12 in men, whose lower intakes were associated with increased risk for depression. These vitamins could be a proxy for other behavioural or biological risk factors, or they could have direct, but different, roles in the mental health of men and women. An examination of how newly depressed seniors fared on dietary change indicated that they were resilient to change despite their depression.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».