Influence of Emulsion Lipid Droplet Crystallinity on Postprandial Endotoxin Transporters and Atherogenic And Inflammatory Profiles in Healthy Men – A Randomized Double‐Blind Crossover Acute Meal Study
Bibliographic record
Abstract
Scope Consumption of high‐fat meals is associated with increased endotoxemia, inflammation, and atherogenic profiles, with repeated postprandial responses suggested as contributors to chronically elevated risk factors. However, effects of lipid solid versus liquid state specifically have not been investigated. Methods and results This exploratory randomized crossover study tests the impact of lipid crystallinity on plasma levels of endotoxin transporters (lipopolysaccharide [LPS] binding protein [LBP] and soluble cluster of differentiation 14 [sCD14]) and select proinflammatory and atherogenic markers (tumor necrosis factor‐alpha [TNF‐α], C‐reactive protein [CRP], interleukin‐1‐beta [IL‐1β], interferon‐gamma [IFN‐γ], interleukin‐6 [IL‐6], soluble intercellular adhesion molecule [sICAM], soluble vascular cell adhesion molecule [sVCAM], monocyte chemoattractant protein‐1 [MCP‐1/CCL2], plasminogen activator inhibitor‐1 [PAI‐1], and fibrinogen). Fasted healthy men (n = 14, 28 ± 5.5 years, 24.1 ± 2.6 kg m−2) consumed two 50 g palm stearin oil‐in‐water emulsions tempered to contain either liquid or crystalline lipid droplets at 37 °C on separate occasions with blood sampling at 0, 2‐, 4‐, and 6‐h post‐meal. Timepoint data, area under the curve, and peak concentration values are compared. Overall, no treatment effects are seen (p > 0.05). There are significant effects of time, with values decreasing from baseline, for TNF‐α, MCP‐1/CCL2, PAI‐1, and fibrinogen (p < 0.05). Conclusion Responder analysis pointed to differential treatment effects associated with some participant baseline characteristics but, overall, palm‐stearin emulsion droplet crystallinity does not acutely affect plasma endotoxin transporters nor select inflammatory and atherogenic markers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".