Differential Effects of Chronic Ingestion of Refined Sugars versus Natural Sweeteners on Insulin Resistance and Hepatic Steatosis in a Rat Model of Diet-Induced Obesity
Bibliographic record
Abstract
While the detrimental effect of refined sugars on health has been the subject of many investigations, little is known about the long-term impact of natural sweeteners on metabolic disorders. In this study we compared the metabolic responses to chronic ingestion of refined sugars compared to various natural sweeteners in diet-induced obese rats. Wistar rats were fed a high-fat high-sucrose diet (HFHS) for 8 weeks and daily gavaged with a solution containing 1 g of total carbohydrates from refined sugar (sucrose or fructose) or six different natural sugar sources, followed by assessment of glucose homeostasis, hepatic lipid accumulation, and inflammation. While glucose tolerance was similar following treatments with refined and natural sugars, lowered glucose-induced hyperinsulinemia was observed with fructose. Consumption of fructose and all-natural sweeteners but not corn syrup were associated with lower insulin resistance as revealed by reduced fasting insulin and homeostatic model assessment of insulin resistance (HOMA-IR) compared to sucrose treatment of HFHS-fed rats. All-natural sweeteners and fructose induced similar liver lipid accumulation as sucrose. Nevertheless, maple syrup, molasses, agave syrup, and corn syrup as well as fructose further reduced hepatic IL-1β levels compared to sucrose treatment. We conclude that natural sweeteners and especially maple syrup, molasses, and agave syrup attenuate the development of insulin resistance and hepatic inflammation compared to sucrose in diet-induced obese rats, suggesting that consumption of those natural sweeteners is a less harmful alternative to sucrose in the context of obesity.
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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 | 0.001 | 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 | 0.001 |
| 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".