Triglyceride reduction in secondary atherosclerotic cardiovascular disease prevention: core concepts in contemporary therapeutic targeting
Bibliographic record
Abstract
This commentary refers to ‘Real-world risk of cardiovascular outcomes associated with hypertriglyceridaemia among individuals with atherosclerotic cardiovascular disease and potential eligibility for emerging therapies’, by P.R. Lawler et al., 2020;41:86–94. Elevated plasma triglyceride (TG), including in optimally treated patients, is a marker of future cardiovascular event risk in patients with prevalent atherosclerotic cardiovascular disease (ASCVD), occurring in as many as one in four patients.1 Triglyceride may be associated with residual ASCVD risk in at least several ways (Figure 1). First, TG reflects the concentration of circulating atherogenic remnant cholesterol. Second, higher TG is associated with higher atherogenic small, dense low density lipoproteins (LDL).2 (apoB—a clinically measurable marker found in a 1:1 ratio on all atherogenic lipoproteins—may quantify these effects in aggregate, reflecting both more TG-rich lipoproteins as well as an increased number of small, dense LDL.3) Beyond lipid and lipoprotein risk pathways, however, elevated TG may also herald the presence of insulin resistance. Insulin resistance is characterized by a complex, integral interplay between dyslipidaemia, adiposity, hypertension, and inflammation. Insulin resistance promotes increased fatty acid secretion from adipose tissues, driving up plasma TG. Glucose levels rise due to impaired stimulation of glucose transport, as does blood pressure due to deficient nitric oxide release. Hence, plasma TG is linked to several inter-related ASCVD risk pathways, and thus may identify at-risk patients for targeted therapy. Overview of pathways associated with triglyceride-related atherosclerotic cardiovascular disease risk. Prior and ongoing trials of TG-reducing pharmacotherapies have focused primarily on (i) peroxisome proliferator-activated receptor (PPAR) modulation (e.g. fibrates) and (ii) reducing hepatic generation of TG-rich lipoproteins (e.g. omega-3 fatty acids such as icosapent ethyl; as well as statins).2 , 4 Neutral fibrate trials (FIELD, ACCORD, and BIP), juxtaposed against the large benefit associated with statins, muted enthusiasm for fibrates for ASCVD event reduction (in the absence of very high TG), and these therapies are infrequently used in clinical practice. In contrast, building on the positive JELIS trial (NCT00231738), the REDUCE-IT trial (NCT01492361) observed large reductions in ASCVD events in high-risk primary and secondary prevention settings with icosapent ethyl among statin-treated patients with residually elevated TG. However, interest in fibrates has been renewed in part through the ongoing PROMINENT trial (NCT03071692) of pemafibrate, a potent selective PPAR-α modulator, in high-risk primary and secondary prevention patients with hypertriglyceridaemia. As Dr Spence points out, further evidence for PPAR modulation of TG-associated cardiometabolic risk is evolving.5 While we share Dr Spence’s renewed enthusiasm for targeting PPAR in ASCVD risk reduction,5 further trials are awaited before broadly embracing fibrate therapy in contemporary secondary prevention, outside of patients with very high TG.4 We hence did not focus on these therapies in our study. The authors thank freelance medical illustrator Gail Rudakevich for generation of the figure. Conflict of interest: R.S.R. receives research support from Amgen, Novartis, Regeneron, serves on advisory boards for Amgen, C5, CVS Caremark, Corvidia; receives honoraria for non-promotional speaking from Amgen, Kowa, Pfizer and Regneron; has stock holdings in MediMergent LLC; and receives royalties from UpToDate, Inc. Other authors declared no conflicts of interest to disclose.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 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".