SynthesisAdvances in nutrition (Bethesda, Md.)2020
Dietary Meat Categories and Descriptions in Chronic Disease Research Are Substantively Different within and between Experimental and Observational Studies: A Systematic Review and Landscape Analysis.
Synthesis in Advances in nutrition (Bethesda, Md.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Seafood and Neurocognitive Development in Children: A Systematic Review.Advances in nutrition (Bethesda, Md.) · 2025Pooled it
- Meat Consumption and Gut Microbiota: a Scoping Review of Literature and Systematic Review of Randomized Controlled Trials in Adults.Advances in nutrition (Bethesda, Md.) · 2023Pooled it
- Intake of Unprocessed and Processed Meat and the Association with Cardiovascular Disease: An Overview of Systematic Reviews.Nutrients · 2021Pooled it
- Trial
- Effects of red meat taxes and warning labels on food groups selected in a randomized controlled trial.The international journal of behavioral nutrition and physical activity · 2024Trial
- Dietary consumption of beef and red meat: a scoping review and evidence map on cognitive outcomes across the lifespan.Public health nutrition · 2023Article
- Poultry Consumption and Human Cardiometabolic Health-Related Outcomes: A Narrative Review.Nutrients · 2023Review
- Trends in Beef Intake in the United States: Analysis of the National Health and Nutrition Examination Survey, 2001-2018.Nutrients · 2023Article
- Nutritional Effects of Removing a Serving of Meat or Poultry from Healthy Dietary Patterns-A Dietary Modeling Study.Nutrients · 2023Article
- Poultry Consumption and Human Health: How Much Is Really Known? A Systematically Searched Scoping Review and Research Perspective.Advances in nutrition (Bethesda, Md.) · 2022Article
- Healthy U.S.-style dietary patterns can be modified to provide increased energy from protein.Nutrition journal · 2022Article
- Plant- and Animal-Based Protein-Rich Foods and Cardiovascular Health.Current atherosclerosis reports · 2022Review
- A Standardized Assessment of Processed Red Meat and Processed Poultry Intake in the US Population Aged ≥2 Years Using NHANES.The Journal of nutrition · 2022Article
- Indicators and Recommendations for Assessing Sustainable Healthy Diets.Foods (Basel, Switzerland) · 2021Review
- The Molecular Effects of Dietary Acid Load on Metabolic Disease (The Cellular PasaDoble: The Fast-Paced Dance of pH Regulation).Frontiers in molecular medicine · 2021Review
- Heterogeneity in Meat Food Groups Can Meaningfully Alter Population-Level Intake Estimates of Red Meat and Poultry.Frontiers in nutrition · 2021Article
- Is There Scope for a Novel Mycelium Category of Proteins alongside Animals and Plants?Foods (Basel, Switzerland) · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This systematic review and landscape analysis describes patterns in dietary meat (skeletal muscle and associated tissues from mammalian, avian, and aquatic species; i.e., muscle foods) categories (CAT) and descriptions (DESCR) used throughout nutrition-related chronic disease literature, as there is anecdotally noted variation. A total of 1020 CAT and 776 DESCR were identified from 369 articles that assessed muscle food consumption and primary prevention of cardiovascular disease, obesity, type 2 diabetes, or cancer in adults ≥19 y from PubMed, Cochrane, and CINAHL up to March 2018. Specificity of CAT was analyzed on an empirical 1-7 ordinal scale as: 1) broad/undescriptive, "fish"; 2) muscle food type, "red meat"; 3) species, "poultry"; 4) broad + 1 descriptor, "processed meat"; 5) type/species + 1 descriptor, "fresh red meat"; 6) broad/type + 2 descriptors, "poached lean fish"; and 7) specific product, "luncheon meat." Median CAT specificity for randomized controlled trials (RCTs) and observational studies (OBSs) was 3 and 2 points out of 7, respectively, with no differences between chronic disease types. Specificity of OBS CAT was higher in recent articles but RCT CAT became less specific starting in the 2000s. RCT CAT were 400% more likely to include species, 500% more likely to include leanness, but 400% less likely to include processing degree compared with OBS CAT. A DESCR was included for 76% and 82% of OBS and RCT CAT, respectively. Researchers described processed meat, red meat, and total meat CAT more commonly than poultry or fish CAT. Among processed meat DESCR, 31% included a common term used in public regulatory definitions. In conclusion, muscle food categories and descriptions are substantively different within and between experimental and observational studies and do not match regulatory definitions. A practical muscle food classification system is warranted to improve interpretation of evidence regarding muscle food consumption and chronic disease.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.