Glycerol Kinase Gene Variant as a Cause of Pseudohypertriglyceridemia and Apparent Poor Response to Plozasiran
Bibliographic record
Abstract
Abstract Severe hypertriglyceridemia (HTG) is characterized by plasma triglyceride (TG) levels >500 mg/dL (SI: 5.7 mmol/L) (reference range, <150 mg/dL [SI: <1.7 mmol/L]) and is linked to cardiovascular disease and pancreatitis risk. Treatment typically involves dietary restrictions and lipid-lowering medications. Glycerol kinase deficiency (GKD) is a rare genetic disorder that causes pseudo-HTG. In SHASTA-2, a study of patients with severe HTG, most subjects (>90%) treated with plozasiran, an apolipoprotein C-III (APOC3) small interfering RNA (siRNA), achieved TG levels <500 mg/dL (SI: 5.7 mmol/L), below the risk threshold for acute pancreatitis. We report herein a case study of a 65-year-old male apparently not responding to plozasiran. The patient was shown to carry a loss-of-function variant in the GK gene resulting in GKD, with high free glycerol (40.24 mg/dL or 4.37 mmol/L) (reference range, 0.03-0.13 mmol/L) that contributed to an overestimation of TG concentration. After correcting for free glycerol, the patient was noted to have had mild HTG, with plozasiran treatment decreasing real TG values by up to 71%. This case report suggests that in the absence of response to APOC3 inhibition, measuring free glycerol could be clinically relevant. It also highlights that APOC3 inhibition has no effect on free glycerol concentration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".