The role of animal and plant protein foods in Canadian sustainable diets
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".