Fructose: a highly lipogenic nutrient implicated in insulin resistance, hepatic steatosis, and the metabolic syndrome
Bibliographic record
Abstract
As dietary exposure to fructose has increased over the past 40 years, there is growing concern that high fructose consumption in humans may be in part responsible for the rising incidence of obesity worldwide. Obesity is associated with a host of metabolic challenges, collectively termed the metabolic syndrome. Fructose is a highly lipogenic sugar that has profound metabolic effects in the liver and has been associated with many of the components of the metabolic syndrome (insulin resistance, elevated waist circumference, dyslipidemia, and hypertension). Recent evidence has also uncovered effects of fructose in other tissues, including adipose tissue, the brain, and the gastrointestinal system, that may provide new insight into the metabolic consequences of high-fructose diets. Fructose feeding has now been shown to alter gene expression patterns (such as peroxisome proliferator-activated receptor-γ coactivator-1α/β in the liver), alter satiety factors in the brain, increase inflammation, reactive oxygen species, and portal endotoxin concentrations via Toll-like receptors, and induce leptin resistance. This review highlights recent findings in fructose feeding studies in both human and animal models with a focus on the molecular and biochemical mechanisms that underlie the development of insulin resistance, hepatic steatosis, and the metabolic syndrome.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".