Clinical and molecular characterization of a severe form of partial lipodystrophy expanding the phenotype of PPARγ deficiency
Bibliographic record
Abstract
Familial partial lipodystrophy (FPLD) is characterized by abnormal fat distribution and a metabolic syndrome with hypertriglyceridemia. We identified a family with a severe form of FPLD3 with never-reported clinical features and a novel mutation affecting the DNA binding domain of PPARγ (E157D). Apart from the lipodystrophy and severe metabolic syndrome, individuals presented musculoskeletal and hematological issues. E157D heterozygotes had a muscular habitus yet displayed muscle weakness and myopathy. Also, E157D heterozygotes presented multiple cytopenias and a susceptibility to autoimmune disease. In vitro studies showed that the E157D mutation does not decrease the receptor's affinity to classical PPAR response elements or its responsiveness to a PPARγ agonist, yet it severely reduces its target gene transcription. Microarray experiments demonstrated a decreased activation of a wide array of genes, including genes involved in the PPAR response, the immune response, hematopoiesis, and metabolism in muscle. In addition, a subset of genes with cryptic PPAR response elements was activated. In summary, we describe a large family with a novel PPARγ mutation, which extends the clinical phenotype of FPLD3 to include muscular, immune, and hematological features. Together, our results support the role of PPARγ in controlling homeostasis of multiple systems beyond lipid metabolism.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".