Observational research suggests higher red and processed meat intakes predict greater risks of developing or dying from cardiovascular diseases (CVD) and type 2 diabetes mellitus (T2DM), but this research limits causal inference. This systematic review of reviews utilizes both observational and experimental research findings to infer causality of these relations. Reviews from four databases were screened by two researchers. Reviews included unprocessed red meat (URM), processed meat (PM), or mixed URM + PM intake, and reported CVD or T2DM outcomes. Twenty-nine reviews were included, and causality was inferred using Bradford Hill’s Criteria. Observational assessments of CVD outcomes and all meat types consistently reported weak associations while, T2DM outcomes and PM and Mixed URM + PM assessments consistently reported strong associations. Experimental assessments of Mixed URM + PM on CVD and T2DM risk factors were predominately not significant which lacked coherence with observational findings. For all meat types and outcomes, temporality and plausible mechanisms were established, but specificity and analogous relationships do not support causality. Evidence was insufficient for URM and T2DM. More experimental research is needed to strengthen these inferences. These results suggest that red and processed meat intakes are not likely causally related to CVD but there is potential for a causal relationship with T2DM.

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.

Background: Ultra-processed food (UPF) accounts for a majority of calories consumed in the United States, but the impact on human health remains unclear. We aimed to identify poly-metabolite scores in blood and urine that are predictive of UPF intake. Methods and findings: Of the 1,082 Interactive Diet and Activity Tracking in AARP (IDATA) Study (clinicaltrials.gov ID NCT03268577) participants, aged 50−74 years, who provided biospecimen consent, n = 718 with serially collected blood and urine and one to six 24-h dietary recalls (ASA-24s), collected over 12-months, met eligibility criteria and were included in the metabolomics analysis. Ultra-high performance liquid chromatography with tandem mass spectrometry was used to measure >1,000 serum and urine metabolites. Average daily UPF intake was estimated as percentage energy according to the Nova system. Partial Spearman correlations and Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to estimate UPF-metabolite correlations and build poly-metabolite scores of UPF intake, respectively. Scores were tested in a post-hoc analysis of a previously conducted randomized, controlled, crossover-feeding trial (clinicaltrials.gov ID NCT03407053) of 20 subjects who were admitted to the NIH Clinical Center and randomized to consume ad libitum diets that were 80% or 0% energy from UPF for 2 weeks immediately followed by the alternate diet for 2 weeks; eligible subjects were between 18−50 years old with a body mass index of >18.5 kg/m² and weight-stable. IDATA participants were 51% female, and 97% completed ≥4 ASA-24s. Mean intake was 50% energy from UPF. UPF intake was correlated with 191 (of 952) serum and 293 (of 1,044) 24-h urine metabolites (FDR-corrected P-value < 0.01), including lipid (n = 56 serum, n = 22 24-h urine), amino acid (n = 33, 61), carbohydrate (n = 4, 8), xenobiotic (n = 33, 70), cofactor and vitamin (n = 9, 12), peptide (n = 7, 6), and nucleotide (n = 7, 10) metabolites. Using LASSO regression, 28 serum and 33 24-h urine metabolites were selected as predictors of UPF intake; biospecimen-specific scores were calculated as a linear combination of selected metabolites. Overlapping metabolites included (S)C(S)S-S-Methylcysteine sulfoxide (r_s = −0.23, −0.19), N2,N5-diacetylornithine (r_s = −0.27 for serum, −0.26 for 24-h urine), pentoic acid (r_s = −0.30, −0.32), and N6-carboxymethyllysine (r_s = 0.15, 0.20). Within the cross-over feeding trial, the poly-metabolite scores differed, within individual, between UPF diet phases (P-value for paired t test < 0.001). IDATA Study participants were older US adults whose diets may not be reflective of other populations. Conclusions: Poly-metabolite scores, developed in IDATA participants with varying diets, are predictive of UPF intake and could advance epidemiological research on UPF and health. Poly-metabolite scores should be evaluated and iteratively improved in populations with a wide range of UPF intake.

Standardized methods are needed to investigate intake patterns of processed meat subtypes, considering health concerns surrounding processed meat intake. The objectives of this study were to create a standardized method of disaggregating processed meat into processed red meat and processed poultry and describe intake patterns of the US population aged ≥2 y. Two researchers independently manually disaggregated processed meat from the Food Patterns Equivalents Database into processed red meat and processed poultry based on available information from the Foods and Nutrient Database for Dietary Studies. We created an SAS program (called Processed Meat Categories) to mimic the manual coding. We used the program to describe intake patterns and trends over time of processed red meat and processed poultry using 24-h recalls from 2007–2008 through 2017–2018 NHANES data with SAS survey-weighted procedures for complex surveys. The SAS program had high agreement with the manual code (Pearson concordance correlation ≥0.95). Of the US population aged ≥2 y, 46.8% (95% CI: 45.3, 48.2%) reported consuming any processed meat, 42.5% (95% CI: 41.0, 43.9%) reported consuming processed red meat, and 11.3% (95% CI: 10.2, 12.4%) reported consuming processed poultry. Most [74.1 ± 0.13% (SEM)] processed meat reported was red meat compared with poultry, and 32.1 ± 0.01% of total red meat and 13.7 ± 0.01% of total poultry reported were processed. Prevalence of processed poultry intake increased from 9.5% (95% CI: 8.9, 10.1%) in 2007–2010 to 11.3% (95% CI: 10.2, 12.4%) in 2015–2018 (P < 0.0001), but mean intake amount did not change. Prevalence of processed red meat intake did not change over time, but mean intake decreased from 0.8 ± 0.03 ounce-equivalents in 2007–2010 to 0.7 ± 0.02 ounce-equivalents (P = 0.0058) in 2015–2018. The Processed Meat Categories SAS program is a tool available for researchers to standardize estimates of processed meat subtypes for future dietary patterns research. Intake of total processed meat did not change in the United States, but intake amount of processed red meat decreased and the prevalence of processed poultry consumers increased.