Hypertriglyceridemia: a too long unfairly neglected major cardiovascular risk factor
Bibliographic record
Abstract
The existence of an independent association between elevated triglyceride (TG) levels, cardiovascular (CV) risk and mortality has been largely controversial. The main difficulty in isolating the effect of hypertriglyceridemia on CV risk is the fact that elevated triglyceride levels are commonly associated with concomitant changes in high density lipoprotein (HDL), low density lipoprotein (LDL) and other lipoproteins. As a result of this problem and in disregard of the real biological role of TG, its significance as a plausible therapeutic target was unfoundedly underestimated for many years. However, taking epidemiological data together, both moderate and severe hypertriglyceridaemia are associated with a substantially increased long term total mortality and CV risk. Plasma TG levels partially reflect the concentration of the triglyceride-carrying lipoproteins (TRL): very low density lipoprotein (VLDL), chylomicrons and their remnants. Furthermore, hypertriglyceridemia commonly leads to reduction in HDL and increase in atherogenic small dense LDL levels. TG may also stimulate atherogenesis by mechanisms, such excessive free fatty acids (FFA) release, production of proinflammatory cytokines, fibrinogen, coagulation factors and impairment of fibrinolysis. Genetic studies strongly support hypertriglyceridemia and high concentrations of TRL as causal risk factors for CV disease. The most common forms of hypertriglyceridemia are related to overweight and sedentary life style, which in turn lead to insulin resistance, metabolic syndrome (MS) and type 2 diabetes mellitus (T2DM). Intensive lifestyle therapy is the main initial treatment of hypertriglyceridemia. Statins are a cornerstone of the modern lipids-modifying therapy. If the primary goal is to lower TG levels, fibrates (bezafibrate and fenofibrate for monotherapy, and in combination with statin; gemfibrozil only for monotherapy) could be the preferable drugs. Also ezetimibe has mild positive effects in lowering TG. Initial experience with en ezetimibe/fibrates combination seems promising. The recently released IMPROVE-IT Trial is the first to prove that adding a non-statin drug (ezetimibe) to a statin lowers the risk of future CV events. In conclusion, the classical clinical paradigm of lipids-modifying treatment should be changed and high TG should be recognized as an important target for therapy in their own right. Hypertriglyceridemia should be treated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".