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Record W4324140416 · doi:10.1161/circ.147.suppl_1.mp56

Abstract MP56: Intake of Ultra-Processed Foods is Associated With an Increase in Risk for Type Two Diabetes: Results From Three U.S. Cohort Studies and a Meta-Analysis

2023· article· en· W4324140416 on OpenAlexaff
Zhangling Chen, Neha Khandpur, Clémence Desjardins, Carlos Augusto Monteiro, Sinara Laurini Rossato, Teresa T. Fung, JoAnn E. Manson, Walter C. Willett, Eric B. Rimm, Frank B. Hu, Qi Sun, Jean‐Philippe Drouin‐Chartier

Bibliographic record

VenueCirculation · 2023
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversité LavalDesjardins
Fundersnot available
KeywordsMedicineHazard ratioType 2 diabetesMeta-analysisCohort studyProspective cohort studyProportional hazards modelPopulationDemographyEnvironmental healthCohortRelative riskLower riskDiabetes mellitusInternal medicineConfidence intervalEndocrinology

Abstract

fetched live from OpenAlex

Introduction: There is limited evidence on the association between long-term consumption of ultra-processed foods (UPF) and the risk of type 2 diabetes (T2D), among the U.S population. The overall strength of this association has also not been established. Hypothesis: Higher intake of UPF is associated with a higher risk of T2D in U.S. adults. The pooled risk estimates from published literature reinforce the positive relationship between the UPF intakes and T2D. Methods: We first assessed this relationship among 71,871 women from the Nurses’ Health Study (NHS, 1984-2016), 87,918 women from NHSII (1991-2017), and 38,847 men from the Health Professionals Follow-up Study (HPFS, 1986-2016) who were all free of T2D at baseline. Diet was assessed using food frequency questionnaires, every 2-4 years. UPF were categorized according to the Nova classification. Information on incident cases of T2D was obtained through follow-up questionnaires every 2 years. The association between UPF intake and incident T2D was examined using Cox proportional hazards models. Second, after conducting a systematic review of prospective cohort studies, risk estimates from all included cohorts were pooled in a random-effects, dose-response, meta-analysis to assess nonlinearity of the association between total UPF intake and T2D risk. Finally, the strength of the meta-evidence was assessed using NutriGrade. Results: During 5,187,678 person-years of follow-up across the three cohorts, 19,503 T2D cases were documented. The pooled multivariable-adjusted hazard ratios (HRs) for T2D between the extreme quintiles of total UPF intake (% of grams/day), was 1.36 (95% confidence interval (CI): 1.29, 1.44; P trend <0.0001). This relationship was driven by intakes of ultra-processed animal-based products, ready-to-eat mixed dishes and artificially- and sugar-sweetened beverages. Ultra-processed cereals and ultra-processed dark breads and whole-grain breads were inversely associated with T2D risk. In the meta-analysis (7 risk estimates, 415,554 participants and 21,932 T2D cases), a significant positive dose-response association between total UPF intake and T2D was observed (P=0.90 for non-linearity): a 10% increase in total UPF intake (% grams from UPF/day) was associated with a 10% higher risk of T2D (95%CI: 8%, 12%; I 2 =23.1%; P heterogeneity =0.25). Per NutriGrade, the evidence supporting the positive relationship between total UPF intake and T2D was of high quality. Conclusions: High quality evidence shows that total UPF consumption is associated with higher risk of T2D, although not all individual foods classified as ultra-processed were associated with a higher risk in these U.S. cohorts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.033
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0100.053
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.115
GPT teacher head0.349
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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