Cola intake and serum lipids in the Oslo Health Study
Bibliographic record
Abstract
Soft drinks can be a major source of sucrose, which may influence serum lipid concentration. We have examined the association between intake frequency of various types of soft drinks and the concentration of serum triglycerides (TG) and high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol in the cross-sectional Oslo Health Study. A total of 14 188 subjects of the altogether 18,770 participants of the study had data on intake frequency of colas and non-colas, with or without sugar. The population sample consisted of both sexes and 3 age groups: group 1 (30 years of age), group 2 (40 and 45 years of age), and group 3 (59-60 years of age). In both sexes, HDL decreased and TG increased significantly (p < 0.001) with increasing intake frequency of colas. In contrast, no consistent associations were found between the reported intake of non-cola soft drinks and the serum lipids. We found no significant differences related to the reported presence or absence of sugar in the soft drinks. In multiple linear regression analyses, the colas vs. serum lipid associations prevailed (p < 0.001) after including 13 possible confounders: sex; age group; time since last meal; physical activity; intake of alcohol, coffee, cheese, fruit and (or) berries, and fatty fish; smoking; length of education; use of cholesterol-lowering drugs; and intake of non-colas. Thus, the self-reported intake frequency of colas, but not other soft drinks, was negatively associated with serum HDL, and positively associated with TG and LDL.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".