Lecithin Cholesterol Acyltransferase Null Mice Are Protected from Diet-induced Obesity and Insulin Resistance in a Gender-specific Manner through Multiple Pathways
Bibliographic record
Abstract
Complete lecithin cholesterol acyltransferase (LCAT) deficiency uniformly results in a profound HDL deficiency. We recently reported unexpected enhanced insulin sensitivity in LCAT knock-out mice in the LDL receptor knock-out background (Ldlr(-/-)×Lcat(-/-); double knock-out (DKO)), when compared with their Ldlr(-/-)×Lcat(+/+) (single knock-out (SKO)) controls. Here, we report that LCAT-deficient mice (DKO and Lcat(-/-)) are protected against high fat high sucrose (HFHS) diet-induced obesity without hypophagia in a gender-specific manner compared with their respective (SKO and WT) controls. The metabolic phenotypes are more pronounced in the females. Changes in endoplasmic reticulum stress were examined as a possible mechanism for the metabolic protection. The female DKO mice developed attenuated HFHS-induced endoplasmic reticulum stress as evidenced by a lack of increase in mRNA levels of the hepatic unfolded protein response (UPR) markers Grp78 and CHOP compared with SKO controls. The DKO female mice were also protected against diet-induced insulin resistance. In white adipose tissue of chow-fed DKO mice, we also observed a reduction in UPR, gene markers for adipogenesis, and markers for activation of Wnt signaling. In skeletal muscles of female DKO mice, we observed an unexpected increase in UCP1 in association with increase in phospho-AMPKα, PGC1α, and UCP3 expressions. This increase in UCP1 was associated with ectopic islands of brown adipocytes between skeletal muscle fibers. Our findings suggest that LCAT deficiency confers gender-specific protection against diet-induced obesity and insulin resistance at least in part through regulation in UPR, white adipose tissue adipogenesis, and brown adipocyte partitioning.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".