152-OR: Changes in Consumption of Sugary Beverages and Artificially Sweetened Beverages and Risk of Type 2 Diabetes among U.S. Women and Men
Bibliographic record
Abstract
Background: Whether changes in consumption of sugary beverages or artificially sweetened beverages (ASBs) are associated with type 2 diabetes (T2D) risk has never been evaluated. Objective: We evaluated the association of changes in sugary beverage and ASB consumption over a 4-year period with subsequent 4-year risk of T2D among U.S. women and men. Methods: We followed-up 78,357 women in the Nurses’ Health Study (1986-2012), 82,937 women in the Nurses’ Health Study II (1991-2013) and 35,148 men in the Health Professionals Follow-up Study (1986-2012). Beverage intakes were assessed using validated food frequency questionnaires every 4 years. Cox proportional regression models were used to calculate hazard ratios (HRs) for T2D regarding 4-year changes in beverage consumption, adjusted for initial beverage intake and BMI. 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 cases of T2D. Increasing sugary beverage intake by >0.5 serving/day over a 4-year period was associated with a 14% (95% CI: 7%, 21%) higher T2D risk compared with maintaining a stable consumption. Both beverages with added sugars (e.g., soft drinks) and 100% fruit juices were associated with higher T2D risk. Increasing ASB consumption by >0.5 serving/day was associated with a 19% (95% CI: 12%, 27%) higher T2D risk. Substituting ASB for sugary beverages was not associated with subsequent T2D risk (HR: 0.99, 95% CI: 0.96, 1.02). However, decreasing sugary beverage consumption and increasing water, coffee or tea intake by 1 serving/day was associated with a 3% to 11% lower risk of T2D. Conclusion: Increasing consumption of sugary beverages or ASBs was associated with a higher risk of T2D compared with maintaining a stable consumption of these beverages. Substituting water, coffee or tea but not ASB for sugary beverages was associated with lower T2D risk. Disclosure J. Drouin-Chartier: Other Relationship; Self; Dairy Farmers of Canada. Y. Zheng: None. Y. Li: None. V. Malik: None. A. Pan: None. J.E. Manson: None. 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)
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".