Hyperlipidemia due to Nephrotic Syndrome: Its Effects and Effects of Interventions on Atherogenesis, Cardiovascular and Renal Outcomes
Bibliographic record
Abstract
Nephrotic syndrome (NS) is one of the most important causes of secondary hyperlipidemia. Here, I describe characteristics and mechanisms for hyperlipidemia due to NS, and systematically reviewed the association of such hyperlipidemia with atherosclerotic progression and the development of cardiovascular diseases (CVD) by Pubmed. Further, I searched literatures on the effects of interventions including diet, statin, fibrates, low-density lipoprotein (LDL)-apheresis and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors on hyperlipidemia and cardiovascular and renal outcomes in NS patients. Although dyslipidemia may be associated with atherosclerosis in NS, other factors such as age, duration of disease, number of relapses and blood pressure are also crucial determinants of atherosclerosis. The disease-specific risk of thromboembolism was different across the histological groups. One cohort study suggested that persons with NS are at increased risk of coronary heart disease (CHD). Among various interventions for NS, statin is relatively safe and effective for hyperlipidemia due to NS, but, it does not show sufficient evidence for cardiovascular and renal outcomes. Although PCSK9 inhibitors are promising therapeutic options for NS, large-scale trials are needed to elucidate such effect. J Endocrinol Metab. 2020;10(3-4):63-73 doi: https://doi.org/10.14740/jem663
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".