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Record W2948695913 · doi:10.2337/db19-159-or

159-OR: Changes in Dairy Product Consumption and Risk of Type 2 Diabetes among U.S. Men and Women

2019· article· en· W2948695913 on OpenAlexaboutno aff
Jean‐Philippe Drouin‐Chartier, Yanping Li, Andres V Ardisson Korat, Ming Ding, Benoı̂t Lamarche, JoAnn E. Manson, Eric B. Rimm, Walter C. Willett, Frank B. Hu

Bibliographic record

VenueDiabetes · 2019
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineType 2 diabetesHazard ratioLower riskConsumption (sociology)Diabetes mellitusProportional hazards modelEnvironmental healthDemographyConfidence intervalAnimal scienceInternal medicineEndocrinologyBiology

Abstract

fetched live from OpenAlex

Background: The relationship between changes in dairy product consumption and risk of type 2 diabetes (T2D) has not been evaluated. Objective: We evaluated the association of 4-year changes in dairy product consumption with subsequent 4-year risk of T2D among U.S. men and women. Methods: We followed-up 35,148 men in the Health Professionals Follow-up Study (1986-2012), 78,357 women in the Nurses’ Health Study (1986-2012), and 82,937 women in the Nurses’ Health Study II (1991-2013). Diet was assessed using validated food frequency questionnaires every 4 years. Cox proportional regression models were used to calculate hazard ratios (HRs) for T2D associated with 4-year changes in dairy product consumption, with adjustment for initial dairy intake and multiple T2D risk factors including BMI and diet quality. Results of the 3 cohorts were pooled using an inverse variance-weighted, fixed-effect meta-analysis. Results: During 2,849,389 person-years of follow-up, we documented 12,007 incident T2D cases. Changes in milk consumption during a 4-year period were not associated with T2D risk in the following 4 years. Increasing yogurt consumption by >0.5 serving/day was associated with a 13% (95% CI: 6%, 19%) lower risk of T2D compared with maintaining a stable consumption. Increasing cheese consumption by >0.5 serving/day was associated with an 8% (95% CI: 2%, 16%) higher risk of T2D. Substituting reduced-fat milk for whole milk or low-fat cheese for high-fat cheese was not associated with subsequent T2D risk. However, increasing intake of yogurt or reduced-fat milk by 1 serving/day and concomitantly decreasing cheese intake by 1 serving/day was associated with a 16% (95% CI: 10%, 22%) and 11% (95% CI: 7%, 15%) lower risk of T2D, respectively. Conclusion: Increasing yogurt consumption was associated with a moderately lower risk of T2D, while increasing cheese consumption was associated with a moderately higher risk. Substituting yogurt or reduced-fat milk for cheese was associated with lower risk of T2D. Disclosure J. Drouin-Chartier: Other Relationship; Self; Dairy Farmers of Canada. Y. Li: None. A.V. Ardisson Korat: None. M. Ding: None. B. Lamarche: Advisory Panel; Self; Dairy Farmers of Canada. J.E. Manson: None. E. Rimm: Advisory Panel; Self; Take C/O, US Highbush Blueberry Council/USDA. W.C. Willett: None. F. Hu: None. Funding National Institutes of Health (UM1CA186107, UM1CA176726, UM1CA167552, DK112940, HL60712, HL118264); Canadian Institutes of Health Research (BPF-156628)

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.007
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.013
GPT teacher head0.240
Teacher spread0.226 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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