Fibrillin-1 and fibrillin-1-derived asprosin in adipose tissue function and metabolic disorders
Bibliographic record
Abstract
The extracellular matrix microenvironment of adipose tissue is of critical importance for the differentiation, remodeling and function of adipocytes. Fibrillin-1 is one of the main components of microfibrils and a key player in this process. Furin processing of profibrillin-1 results in mature fibrillin-1 and releases the C-terminal propeptide as a circulating hunger hormone, asprosin. Mutations in the fibrillin-1 gene lead to adipose tissue dysfunction and causes Marfan syndrome, marfanoid progeroid lipodystrophy syndrome, and neonatal progeroid syndrome. Increased TGF-β signaling, altered mechanical properties and impaired adipogenesis are potential causes of adipose tissue dysfunction, mediated through deficient microfibrils. Circulating asprosin on the other hand is secreted primarily by white adipose tissue under fasting conditions and in obesity. It increases hepatic glucose production and drives insulin secretion and appetite stimulation through inter-organ cross talk. This review discusses the metabolic consequences of fibrillin-1 and fibrillin-1-derived asprosin in pathological conditions. Understanding the dynamic role of fibrillin-1 in the adipose tissue milieu and of circulating asprosin in the body can provide novel mechanistic insights into how fibrillin-1 may contribute to metabolic syndrome. This could lead to new management regimens of patients with metabolic disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".