Effects of Dietary Beef and Chicken With and Without High Amylose Maize Starch on Blood Malondialdehyde, Interleukins, IGF-I, Insulin, Leptin, MMP-2, and TIMP-2 Concentrations in Rats
Bibliographic record
Abstract
Dietary red and processed meats may increase risk of colorectal cancer (CRC), whereas fiber may be protective. Recently, we demonstrated that dietary beef causes greater colonic DNA strand breakage than equivalent levels of chicken in rats and that resistant starch (RS) as 20% high amylose maize starch (HAMS) attenuated the damage. From that study, we now report measures of circulating factors that may influence CRC initiation or progression including malondialdehyde (MDA), leptin, insulin-like growth factor-I (IGF-I), insulin, matrix metalloproteinase-2 (MMP-2), tissue inhibitor of MMP-2 (TIMP-2), interleukins (IL), and short chain fatty acids. MDA levels were increased by beef diets relative to the chicken diets. Leptin concentrations, which were lower for chicken than beef at the 35% level in the absence of HAMS, were lowered by HAMS. Higher dietary chicken (but not beef) increased IGF-I irrespective of HAMS feeding. Higher levels of chicken resulted in greater insulin concentrations than for beef in rats fed HAMS. Without dietary HAMS, TIMP-2 concentration increased in response to both meats but was highest for chicken. MMP-2 and TIMP-2 concentrations were higher for HAMS diets. IL-1beta and IL-12 concentrations were lowered by HAMS feeding. Colonic DNA strand breakage was positively associated with circulating leptin and MDA concentrations as well as tissue MDA concentrations and negatively associated with plasma TIMP-2 concentration. MMP-2 and TIMP-2 positively correlated with hepatic portal butyrate levels but leptin concentrations correlated negatively. These results suggest diets high in meat or RS could influence cancer initiation or progression by changes in circulating levels of hormones and other factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".