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Record W4412178985 · doi:10.1210/jcemcr/luaf146

Glycerol Kinase Gene Variant as a Cause of Pseudohypertriglyceridemia and Apparent Poor Response to Plozasiran

2025· article· en· W4412178985 on OpenAlexaff
Miriam Larouche, Christie M. Ballantyne, Josiane Dufour, Diane Brisson, Bruce D. Given, Daniel Gaudet

Bibliographic record

VenueJCEM Case Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicLipid metabolism and disorders
Canadian institutionsUniversité de MontréalUniversité du Québec à Chicoutimi
FundersArrowhead Pharmaceuticals
KeywordsGlycerol kinaseGlycerolKinaseGeneBiologyGeneticsChemistryBiochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.295
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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