The anti‐adipogenic effect of peripheral blood mononuclear cells is absent with <scp>PCSK</scp>9 loss‐of‐function variants
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of (1) an oral fat load and (2) pro-protein convertase subtilisin/kexin type (PCSK) 9 loss-of-function (LOF) variant status on the ability of peripheral blood mononuclear cells (PBMC) to inhibit human adipogenesis. METHODS: PBMC from subjects with one or more PCSK9 LOF variants versus non-variant controls were compared in the fasting state and after an oral fat load. RESULTS: Fasting triglyceride (TG) levels were lower in the LOF variant versus non-variant group but rose to the same level after the oral fat load. Conditioned medium from PBMC was obtained in fasting (PBMC-CM-F) and 4-h postprandial (PBMC-CM-PP) states. PBMC-CM-PP from non-variant controls inhibited adipogenesis of human preadipocytes more than did PBMC-CM-F. In contrast, PBMC-CM-F or -PP from PCSK9 LOF variant subjects had no effect on adipogenesis. After the oral fat load, PBMC from PCSK9 LOF variant subjects showed significant increases in mRNA levels of interleukin-1β, tumor necrosis factor-α, sterol regulatory element binding protein-1c, CD36, and monocyte chemoattractant protein-1 (MCP-1), only MCP-1 mRNA levels increased in PBMC from non-variant controls. CONCLUSIONS: The absence of anti-adipogenic action of PBMC from PCSK9 LOF variant subjects points to a novel role for PCSK9 in PBMC-adipose cell interactions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".