The selective peroxisome proliferator-activated receptor alpha modulator (SPPARMα) paradigm: conceptual framework and therapeutic potential
Bibliographic record
Abstract
In the era of precision medicine, treatments that target specific modifiable characteristics of high-risk patients have the potential to lower further the residual risk of atherosclerotic cardiovascular events. Correction of atherogenic dyslipidemia, however, remains a major unmet clinical need. Elevated plasma triglycerides, with or without low levels of high-density lipoprotein cholesterol (HDL-C), offer a key modifiable component of this common dyslipidemia, especially in insulin resistant conditions such as type 2 diabetes mellitus. The development of selective peroxisome proliferator-activated receptor alpha modulators (SPPARMα) offers an approach to address this treatment gap. This Joint Consensus Panel appraised evidence for the first SPPARMα agonist and concluded that this agent represents a novel therapeutic class, distinct from fibrates, based on pharmacological activity, and, importantly, a safe hepatic and renal profile. The ongoing PROMINENT cardiovascular outcomes trial is testing in 10,000 patients with type 2 diabetes mellitus, elevated triglycerides, and low levels of HDL-C whether treatment with this SPPARMα agonist safely reduces residual cardiovascular risk.
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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 | 0.010 | 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 | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".