Half Dose Once-Daily Pemafibrate Effectively Improved Hypertriglyceridemia in Real Practice
Bibliographic record
Abstract
BACKGROUND: Hyperlipidemia is a worldwide problem related to cardiovascular disease (CVD) and sudden death. Low-density lipoprotein cholesterol (LDL-C) has been treated well by the use of statins, but hypertriglyceridemia was not the case. Previous fibrates have been shown a certain effect of preventing CVD events, but some remain not enough or even could cause adverse events. Pemafibrate is a selective peroxisome proliferator-activated receptor α modulator (SPPARMα) with the potential to reduce high triglycerides. To evaluate the clinical effectiveness and safety profile of Pemafibrate, we have started with half dose once-daily administration. METHODS: Thirty-three patients with hypertriglyceridemia, triglyceride (TG) levels > 150 mg/dL, were treated with Pemafibrate (0.1 mg, once daily) from July 2018 to February 2019. Changes in TG (non-fasting) and LDL-C, high-density lipoprotein cholesterol (HDL-C), aspartate aminotransferase (AST), alanine aminotransferase (ALT), creatine kinase (CK), creatinine (Cre), blood glucose (PBG) (postprandial), hemoglobin A1c (HbA1c), and body weight (BW) levels were investigated, compared to the baseline levels of the previous visit. RESULTS: Of the 33 patients, 11 were using other fibrates before. Nine were given statins along with. Baseline TG was 285.0 (210.5 - 423.0) mg/dL, LDL-C 116.4 ± 33.4 mg/dL, and HDL-C 46.5 ± 12.5 mg/dL. TG changes were statistically significant (-20.8 ± 47.6%; P < 0.01). Patients with TG > 200 mg/dL, who used fibrates for the first time, experienced the most significant changes in TG levels (-34.5 ± 37.2%; P < 0.01). In patients using statins already, TG reduction was relatively less, compared to those not using statins (-25.4 ± 36.1%; P < 0.01). HDL-C increased by 3.9 ± 10.2 mg/dL (P < 0.05). LDL-C increased by 16.6 ± 23.7 mg/dL (P < 0.001) in patients not using statins, while patients using statins did not show such significant change. AST, ALT, CK, Cre, PBG, HbA1c and BW did not significantly change. CONCLUSIONS: A selective PPARα modulator, Pemafibrate, effectively improved hypertriglyceridemia without major adverse events in real practice, with half dose once-daily administration. Combined use of statins might be a potent therapeutic maneuver for dyslipidemia.
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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 | 0.001 |
| 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".