The role of sugar-sweetened beverages in the global epidemics of obesity and chronic diseases
Bibliographic record
Abstract
Sugar-sweetened beverages (SSBs) are a major source of added sugars in the diet. A robust body of evidence has linked habitual intake of SSBs with weight gain and a higher risk (compared with infrequent SSB consumption) of type 2 diabetes mellitus, cardiovascular diseases and some cancers, which makes these beverages a clear target for policy and regulatory actions. This Review provides an update on the evidence linking SSBs to obesity, cardiometabolic outcomes and related cancers, as well as methods to grade the strength of nutritional research. We discuss potential biological mechanisms by which constituent sugars can contribute to these outcomes. We also consider global trends in intake, alternative beverages (including artificially-sweetened beverages) and policy strategies targeting SSBs that have been implemented in different settings. Strong evidence from cohort studies on clinical outcomes and clinical trials assessing cardiometabolic risk factors supports an aetiological role of SSBs in relation to weight gain and cardiometabolic diseases. Many populations show high levels of SSB consumption and in low-income and middle-income countries, increased consumption patterns are associated with urbanization and economic growth. As such, more intensified policy efforts are needed to reduce intake of SSBs and the global burden of obesity and chronic diseases. Evidence from cohort studies and clinical trials supports an aetiological role of sugar-sweetened beverage (SSB) intake in the development of obesity and related chronic diseases. This Review provides an up-to-date view, considering the evidence, potential mechanisms and policy actions to reduce the global intake of SSBs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".