Clinical and biochemical features of different molecular etiologies of familial chylomicronemia
Bibliographic record
Abstract
BACKGROUND: Familial chylomicronemia syndrome (FCS) is an ultra-rare phenotype that is usually caused by biallelic mutations in the LPL gene encoding lipoprotein lipase, or less often in APOC2, APOA5, LMF1, or GPIHBP1 genes encoding cofactors or interacting proteins. OBJECTIVES: We evaluated baseline phenotypes among FCS participants in a phase 3 randomized placebo-controlled trial of volanesorsen (NCT02211209). METHODS: Baseline clinical, fasting, and postfat load metabolic markers were assessed. Targeted next-generation DNA sequencing plus custom bioinformatics was used to genotype subjects. RESULTS: Among 52 FCS individuals, 41 had biallelic LPL gene mutations (LPL-FCS patients): 82%, 7%, and 11% were missense, nonsense, and splicing variants, respectively. Eleven individuals had non-LPL-FCS; 2 had mutations in APOA5, 5 in GPIHBP1, and 1 each in LMF1 and APOC2 genes, respectively. Two other individuals were double heterozygotes, each with 1 normal LPL allele. All subjects had extremely high triglycerides (TGs) and chylomicrons, but very low levels of other lipoproteins. Compared with LPL-FCS individuals, non-LPL-FCS individuals were very similar for most traits, but had significantly higher postheparin LPL activity, higher 4-hour postprandial insulin and C-peptide levels; and higher low-density lipoprotein cholesterol levels. In non-LPL-FCS individuals compared to those with LPL-FCS, there were also nonsignificant trends toward lower levels of total and chylomicron TGs, lower 4-hour postprandial chylomicron TG levels, and higher very-low-density lipoprotein TG levels. CONCLUSION: Thus, LPL FCS and non-LPL FCS are largely phenotypically similar. However, LPL FCS patients have lower postheparin LPL activity and a trend toward higher TGs, whereas low-density lipoprotein cholesterol was higher in non-LPL-FCS patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.003 | 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".