LDL, LDL receptors, and PCSK9 as modulators of the risk for type 2 diabetes: a focus on white adipose tissue
Bibliographic record
Abstract
Type 2 diabetes (T2D) and cardiovascular disease (CVD) share many risk factors such as obesity, unhealthy lifestyle, and metabolic syndrome, whose accumulation over years leads to disease onset. However, while lowering plasma low-density lipoprotein cholesterol (LDLC) is cardio-protective, novel evidence have recognised a role for common LDLC-lowering variants (e.g. in HMGCR, PCSK9, and LDLR) and widely used hypocholesterolemic drugs that mimic the effects of some of these variants (statins) in higher risk for T2D. As these conditions decrease plasma LDLC by increasing tissue-uptake of LDL, a role for LDL receptor (LDLR) pathway was proposed. While underlying mechanisms remain to be fully elucidated, work from our lab reported that native LDL directly provoke the dysfunction of human white adipose tissue (WAT) and the activation of WAT NLRP3 (Nucleotide-binding domain and Leucine-rich repeat Receptor, containing a Pyrin domain 3) inflammasome, which play a major role in the etiology of T2D. However, while elevated plasma numbers of apolipoprotein B (apoB)-containing lipoproteins (measured as apoB, mostly as LDL) is associated with WAT dysfunction and related risk factors for T2D in our cohort, this relation was strengthened in regression analysis by lower plasma proprotein convertase subtilisin/kexin type 9 (PCSK9). This supports a central role for upregulated pathway of LDLR and/or other receptors regulated by PCSK9 such as cluster of differentiation 36 (CD36) in LDL-induced anomalies. Targeting receptor-mediated uptake of LDL into WAT may reduce WAT inflammation, WAT dysfunction, and related risk for T2D without increasing the risk for CVD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".