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Enregistrement W7162038315 · doi:10.82308/33866

The role of animal and plant protein foods in Canadian sustainable diets

2024· dissertation· en· W7162038315 sur OpenAlexaboutno aff
Olivia Auclair

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueAgriculture Sustainability and Environmental Impact
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésContext (archaeology)Plant proteinNutrientPopulationAnimal foodProtein qualityFood groupCarbon footprint

Résumé

récupéré en direct d'OpenAlex

Background: Greenhouse gas emissions (GHGE) from the food system are projected to exceed global scientific targets for climate change. However, the impact of animal and plant protein foods on a combination of nutrition, health, and climate outcomes in the context of Canadian self-selected diets is not known. The objectives of this dissertation were four-fold: 1) to assess usual protein intake, inadequacy, and the contribution of animal and plant-based sources to nutrient intakes in Canadian diets; 2) to quantify the carbon footprint of Canadian diets and to compare intake of food groups, nutrients, and diet quality between low- and high-GHGE diets; 3) to conduct a systematic review of studies that modeled replacements of animal with plant protein foods in self-selected diets on diet-related GHGE, nutrition, and health outcomes; and 4) to model the impact of partial substitutions of red and processed meat or dairy with plant protein foods in Canadian diets on nutrient inadequacy, health, and diet-related GHGE.Methodology: In Manuscripts 1, 2, and 4, we utilized the dietary data of non-pregnant and non-lactating adults ≥19 y with a 24-h recall from the 2015 Canadian Community Health Survey (CCHS) – Nutrition. In Manuscript 1, we estimated usual protein intakes and inadequacy among Canadian adults and used population ratios to determine the contribution of animal and plant-based foods to intakes of protein, nutrients, and energy. In Manuscript 2, we linked GHGE estimates for food commodities from the database of Food Impacts on the Environment for Linking to Diets and food loss estimates from Statistics Canada to foods and beverages reported in the CCHS to quantify the carbon footprint of Canadian self-selected diets. Low- and high-GHGE diet respondents were compared in terms of their consumption of animal and plant-based foods, intake of nutrients of concern (calcium, vitamin D, iron, potassium) and to limit (sodium, saturated fat, sugars), and diet quality (Alternative Healthy Eating Index-2010). In Manuscript 3, we systematically searched PubMed, Scopus, and EMBASE for nutrition surveys or cohorts that modeled substitutions of animal with plant protein foods in self-selected diets and that reported data for diet-related GHGE and, optionally, the percentage of the population meeting nutrient recommendations or changes to life expectancy. In Manuscript 4, we used individuals’ dietary intake from the CCHS to model graded replacements (25% and 50%) of either red and processed meat or dairy with plant protein foods. Health outcomes (i.e., changes to life expectancy and life years) were estimated using life table models. Changes to nutrient inadequacy, health outcomes, and diet-related GHGE were compared between observed and modeled diets. Results: Most Canadian adults had adequate protein intakes (Manuscript 1). Red and processed meat contributed the most to total protein intakes (21.6±0.55%), followed by poultry and eggs (20.1±0.81%), cereals, grains, and breads (19.5±0.31%), and dairy (16.7±0.38%). Dairy contributed most to intakes of calcium (53.4±0.61%) and vitamin D (38.7±1.01%), but also saturated fat (40.6±0.69%). Animal-based foods contributed three-quarters of Canadians’ total diet-related GHGE, with red and processed meat alone accounting for 47.05±0.82% (Manuscript 2). Respondents with high-GHGE diets consumed more animal-based foods. They had higher intakes of nutrients of concern, but also saturated fat and sodium, and a lower diet quality score compared to low-GHGE diet respondents (47.27±0.46 vs. 55.31±0.49 points). Six of the 1,188 studies retrieved were included in the systematic review (Manuscript 3), and whereas all reported on diet-related GHGE, two reported on nutrition outcomes and none on health outcomes. Replacing meat led to the greatest reductions in diet-related GHGE (3-55%), most of which was attributed to beef alone (10-40%), and increased the percentage of the population meeting requirements for fibre, calcium, potassium, and iron by 1-5%. Replacing meat and dairy also increased the percentage of the population meeting requirements for iron (5-15%) and vitamin D (2-7%) and decreased the percentage above recommendations for saturated fat (10-76%), but increased the percentage below requirements for calcium (9-33%) and vitamin A (8-48%). Modeling partial substitutions of red and processed meat with plant protein foods in Canadian self-selected diets induced minor changes to nutrient inadequacy, while replacing dairy increased calcium inadequacy by up to 14% (Manuscript 4). Replacing red and processed meat or dairy increased life expectancy by up to 8.7 or 7.6 months, respectively, but gains in the dairy scenarios were attenuated due to reductions in life expectancy with lower milk intakes. Diet-related GHGE decreased by up to 25% when red and processed meat was substituted and by up to 5% when dairy was replaced. The magnitude of health and environmental impacts was greater for males than for females.Conclusion: Despite the prominence of animal protein foods in Canadian self-selected diets, consuming more plant protein foods can lead to beneficial synergistic effects with diet-related GHGE, nutrient adequacy, and health outcomes, especially when partially replacing red and processed meat. These findings are relevant for future dietary guidance and food policy in facilitating the shift towards healthy and sustainable diets in Canada and other high-income countries

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,018
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,050
Score d'incertitude au seuil0,360

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,018
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0040,009
Études des sciences et des technologies0,0020,002
Communication savante0,0030,001
Science ouverte0,0020,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,002
Tête enseignante GPT0,192
Écart entre enseignants0,190 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

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

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

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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