Intake of Sugar-Sweetened and Low-Calorie Sweetened Beverages and Risk of Cardiovascular Disease: A Meta-Analysis and Systematic Review
Bibliographic record
Abstract
The long-term associations between the consumption of sugar-sweetened beverages (SSBs) and low-calorie sweetened beverages (LCSBs) with cardiovascular diseases (CVDs) remains inconsistent. To synthesize the evidence, we conducted a meta-analysis of prospective cohort studies published up to 1 December, 2019 on the associations between SSB and LCSB intake and the risk of CVD incidence and mortality. Out of 5301 articles retrieved from our literature search, 11 articles evaluating the consumption of SSBs (16,915 incident CVD cases, 18,042 CVD deaths) and 8 articles evaluating the consumption of LCSBs (18,077 incident CVD cases, 14,114 CVD deaths) were included in the meta-analysis. A 1 serving/d increment of SSBs was associated with an 8% (RR: 1.08; 95% CI: 1.02, 1.14, I2 = 43.0%) and 8% (RR: 1.08; 95% CI: 1.04, 1.13, I2 = 40.6%) higher risk of CVD incidence and CVD mortality, respectively. A 1 serving/d increment of LCSBs was associated with a 7% (RR: 1.07; 95% CI: 1.05, 1.10, I2 = 0.0%) higher risk of CVD incidence. The association between LCSBs and CVD mortality appeared to be nonlinear (P = 0.003 for nonlinearity) with significant associations observed at high intake levels (>2 servings/d). Under an assumption of causality, the consumption of SSBs may be linked to 9.3% (95% CI: 6.6%, 11.9%) of predicted CVD incidence in the USA from 2015 to 2025, among men and nonpregnant women, who were aged 40-79 y in 2015-2016. The habitual consumption of SSBs was associated with a higher risk of CVD morbidity and mortality in a dose-response manner. LCSBs were also associated with a higher risk of these outcomes, however, the interpretation of these findings may be complicated by reverse causation and residual confounding.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".