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Record W3021203066 · doi:10.1210/jendso/bvaa046.103

SUN-593 Variants in Known Monogenic Causal Genes of Hypertriglyceridemia Are Not Major Contributors for Hypertriglyceridemia in Lipodystrophy Due to a LMNA Mutation

2020· article· en· W3021203066 on OpenAlexaff
Masako Ueda, Adam D. McIntyre, Alan T. Remaley, Robert A. Hegele, Daniel J. Rader

Bibliographic record

VenueJournal of the Endocrine Society · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsLMNALipodystrophyHypertriglyceridemiaProbandGeneticsEndocrinologyInternal medicineSanger sequencingSingle-nucleotide polymorphismMedicineBiologyMutationGenotypeGeneTriglycerideCholesterol

Abstract

fetched live from OpenAlex

Abstract Background: Lipodystrophy is a heterogeneous disorder of adiposity, and one common lipid manifestation is hypertriglyceridemia (HTG). The LMNA gene, which encodes for nuclear envelope proteins, is a known causal gene for heritable lipodystrophy. At present, underlying mechanisms for each clinical manifestation of lipodystrophy due to a LMNA mutation are unknown. Hypothesis: A likely explanation for HTG in lipodystrophy is the paucity of adipose tissue where excess triglycerides (TGs) are normally stored, thus it may not be due to a specific defect in lipoprotein metabolism. Consequently, rare variants in HTG-associated genes would not be expected to be major contributors for HTG in lipodystrophy with LMNA mutations. Method: A proband and her father with a clinical diagnosis of lipodystrophy were recruited into an IRB-approved study investigating molecular etiologies of dyslipidemia at the University of Pennsylvania. Next-generation sequencing (NGS) with the LipidSeq panel, targeting causal genes for lipodystrophy, and monogenic HTG was performed, and confirmed by Sanger sequencing. Also, unweighted TG-polygenic scores by summing the number of TG-raising alleles from 14 single nucleotide polymorphisms (SNPs) associated with TG levels were assessed. Results: The proband and her father were diagnosed with lipodystrophy of two different subtypes, generalized in the daughter and partial in the father. The proband reported a gradual loss of subcutaneous fat starting around age 10. A highest reported TG in the proband was19,000 mg/dL with eruptive xanthomas, whereas TG in the father was never >500 mg/dL. Their BMI’s and DEXA body fat% were 12.9 kg/m2 and 7% in the proband, and 25.7 kg/m2 and 25% in the father, corresponding to their fat storage capacities. The molecular analyses revealed only a lipodystrophy causal mutation in LMNA, c.29C>T, T10I with no other significant findings in18 other lipodystrophy-related genes. No deletion or duplication was identified by a targeted array CGH of LMNA. As predicted, no rare monogenic variants in HTG-causal genes (LPL, GPIHBP1, APOA5, APOC2, LMF1, GPD1) were identified in either subject. However, TG-polygenic scores were 17/28 (95th %ile) in the proband, and 13/28 (50th %ile) in the father, the same trend as the level of HTG levels seen in them. Apolipoprotein E genotypes were non-contributory, (3/3) in the proband, and (3/4) in the father. Conclusion: Our findings support that the pathophysiology of HTG in lipodystrophy is likely to be due to lack of TG-storage space (adipose tissues), and is unlikely due to a defect in lipoprotein metabolism seen in patients with rare monogenic HTG-variants. Although the HTG-polygenic score was higher in the proband, and the accumulative effects of the at-risk alleles may be contributor to the HTG phenotype, it is unlikely to be the leading cause of severe HTG seen in the proband.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.244
Teacher spread0.232 · 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 designBench or experimental
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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