Changes in Consumption of Sugary Beverages and Artificially Sweetened Beverages and Subsequent Risk of Type 2 Diabetes: Results From Three Large Prospective U.S. Cohorts of Women and Men
Bibliographic record
Abstract
OBJECTIVE: We evaluated the associations of long-term changes in consumption of sugary beverages (including sugar-sweetened beverages and 100% fruit juices) and artificially sweetened beverages (ASBs) with subsequent risk of type 2 diabetes. RESEARCH DESIGN AND METHODS: We followed up 76,531 women in the Nurses' Health Study (1986-2012), 81,597 women in the Nurses' Health Study II (1991-2013), and 34,224 men in the Health Professionals' Follow-up Study (1986-2012). Changes in beverage consumption (in 8-ounce servings/day) were calculated from food frequency questionnaires administered every 4 years. Multivariable Cox proportional regression models were used to calculate hazard ratios for diabetes associated with changes in beverage consumption. Results of the three cohorts were pooled using an inverse variance-weighted, fixed-effect meta-analysis. RESULTS: During 2,783,210 person-years of follow-up, we documented 11,906 incident cases of type 2 diabetes. After adjustment for BMI and initial and changes in diet and lifestyle covariates, increasing total sugary beverage intake (including both sugar-sweetened beverages and 100% fruit juices) by >0.50 serving/day over a 4-year period was associated with a 16% (95% CI 1%, 34%) higher diabetes risk in the subsequent 4 years. Increasing ASB consumption by >0.50 serving/day was associated with 18% (2%, 36%) higher diabetes risk. Replacing one daily serving of sugary beverage with water, coffee, or tea, but not ASB, was associated with a 2-10% lower diabetes risk. CONCLUSIONS: Increasing consumption of sugary beverages or ASBs was associated with a higher risk of type 2 diabetes, albeit the latter association may be affected by reverse causation and surveillance bias.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".