Patients with type 1 diabetes show signs of vascular dysfunction in response to multiple high-fat meals
Bibliographic record
Abstract
BACKGROUND: A high-fat diet promotes postprandial systemic inflammation and metabolic endotoxemia. We investigated the effects of three consecutive high-fat meals on endotoxemia, inflammation, vascular function, and postprandial lipid metabolism in patients with type 1 diabetes. METHODS: Non-diabetic controls (n = 34) and patients with type 1 diabetes (n = 37) were given three high-caloric, fat-containing meals during one day. Blood samples were drawn at fasting (8:00) and every two hours thereafter until 18:00. Applanation tonometry was used to assess changes in the augmentation index during the investigation day. RESULTS: Three consecutive high-fat meals had only a modest effect on serum LPS-activity levels and inflammatory markers throughout the day in both groups. Of note, patients with type 1 diabetes were unable to decrease the augmentation index in response to the high-fat meals. The most profound effects of the consecutive fat loads were seen in chylomicron and HDL-metabolism. The triglyceride-rich lipoprotein remnant marker, apoB-48, was elevated in patients compared to controls both at fasting (p = 0.014) and postprandially (p = 0.035). The activities of the HDL-associated enzymes PLTP (p < 0.001), and CETP (p = 0.007) were higher and paraoxonase (PON-1) activity, an anti-oxidative enzyme bound to HDL, decreased in patients with type 1 diabetes (p = 0.027). CONCLUSIONS: In response to high-fat meals, early signs of vascular dysfunction alongside accumulation of chylomicron remnants, higher augmentation index, and decreased PON-1 activity were observed in patients with type 1 diabetes. The high-fat meals had no significant impact on postprandial LPS-activity in non-diabetic subjects or patients with type 1 diabetes suggesting that metabolic endotoxemia may be more central in patients with chronic metabolic disturbances such as obesity, type 2 diabetes, or diabetic kidney disease.
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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) | 0.000 | 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 | 0.001 |
| 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".