Advances in the care of lipodystrophies
Bibliographic record
Abstract
PURPOSE OF REVIEW: Lipodystrophies are a group of rare, heterogeneous disorders characterized by a lack or maldistribution of adipose tissue. Treatment focusses on the management of complications, including hypertriglyceridemia, which can be severe. Patients are predisposed to early atherosclerotic cardiovascular disease and acute pancreatitis. This review summarizes the recent advances in the treatment of lipodystrophies, with a particular focus on the treatment of hypertriglyceridemia in familial partial lipodystrophy (FPLD). RECENT FINDINGS: Treatment of dyslipidemia in FPLD requires management of secondary exacerbating factors, particularly insulin resistance and diabetes, together with modification of atherosclerotic cardiovascular disease risk factors. In addition, specific lipid-lowering therapies are usually needed, starting with statins and fibrates. Leptin therapy improves triglycerides. Several emerging treatments for hypertriglyceridemia include apo C-III antagonists (volanesorsen, AKCEA-APOCIII-LRx and ARO-APOC3) and angiopoietin-like 3 antagonists (evinacumab, vupanorsen and ARO-ANG3); efficacy observed in clinical trials of these agents in nonlipodystrophic patients with severe hypertriglyceridemia suggests that they may also be helpful in lipodystrophy. SUMMARY: Emerging therapies for dyslipidemia show promise in advancing the care of patients with lipodystrophy. However, these treatments are not yet approved for use in lipodystrophy. Further study of their efficacy and safety in this patient population is needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".