Inborn errors of cytoplasmic triglyceride metabolism
Bibliographic record
Abstract
Triglyceride (TG) synthesis, storage, and degradation together constitute cytoplasmic TG metabolism (CTGM). CTGM is mostly studied in adipocytes, where starting from glycerol-3-phosphate and fatty acyl (FA)-coenzyme A (CoA), TGs are synthesized then stored in cytoplasmic lipid droplets. TG hydrolysis proceeds sequentially, producing FAs and glycerol. Several reactions of CTGM can be catalyzed by more than one enzyme, creating great potential for complex tissue-specific physiology. In adipose tissue, CTGM provides FA as a systemic energy source during fasting and is related to obesity. Inborn errors and mouse models have demonstrated the importance of CTGM for non-adipose tissues, including skeletal muscle, myocardium and liver, because steatosis and dysfunction can occur. We discuss known inborn errors of CTGM, including deficiencies of: AGPAT2 (a form of generalized lipodystrophy), LPIN1 (childhood rhabdomyolysis), LPIN2 (an inflammatory condition, Majeed syndrome, described elsewhere in this issue), DGAT1 (protein loosing enteropathy), perilipin 1 (partial lipodystrophy), CGI-58 (gene ABHD5, neutral lipid storage disease (NLSD) with ichthyosis and "Jordan's anomaly" of vacuolated polymorphonuclear leukocytes), adipose triglyceride lipase (ATGL, gene PNPLA2, NLSD with myopathy, cardiomyopathy and Jordan's anomaly), hormone-sensitive lipase (HSL, gene LIPE, hypertriglyceridemia, and insulin resistance). Two inborn errors of glycerol metabolism are known: glycerol kinase (GK, causing pseudohypertriglyceridemia) and glycerol-3-phosphate dehydrogenase (GPD1, childhood hepatic steatosis). Mouse models often resemble human phenotypes but may diverge markedly. Inborn errors have been described for less than one-third of CTGM enzymes, and new phenotypes may yet be identified.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".