Adverse effects of fructose on cardiometabolic risk factors and hepatic lipid metabolism in subjects with abdominal obesity
Bibliographic record
Abstract
BACKGROUND: Overconsumption of dietary sugars, fructose in particular, is linked to cardiovascular risk factors such as type 2 diabetes, obesity, dyslipidemia and nonalcoholic fatty liver disease. However, clinical studies have to date not clarified whether these adverse cardiometabolic effects are induced directly by dietary sugars, or whether they are secondary to weight gain. OBJECTIVES: ), served with their habitual diet over 12 weeks, on liver fat content and other cardiometabolic risk factors in a large cohort (n = 71) of abdominally obese men. METHODS: We analysed changes in body composition, dietary intake, an extensive panel of cardiometabolic risk markers, hepatic de novo lipogenesis (DNL), liver fat content and postprandial lipid responses after a standardized oral fat tolerance test (OFTT). RESULTS: Fructose consumption had modest adverse effects on cardiometabolic risk factors. However, fructose consumption significantly increased liver fat content and hepatic DNL and decreased β-hydroxybutyrate (a measure of β-oxidation). The individual changes in liver fat were highly variable in subjects matched for the same level of weight change. The increase in liver fat content was significantly more pronounced than the weight gain. The increase in DNL correlated positively with triglyceride area under the curve responses after an OFTT. CONCLUSION: Our data demonstrated adverse effects of moderate fructose consumption for 12 weeks on multiple cardiometabolic risk factors in particular on liver fat content despite only relative low increases in weight and waist circumference. Our study also indicates that there are remarkable individual differences in susceptibility to visceral adiposity/liver fat after real-world daily consumption of fructose-sweetened beverages over 12 weeks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".