Can genetic testing help in the management of dyslipidaemias?
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this study was to evaluate the potential role of genetic testing, particularly next-generation DNA sequencing, in diagnosing and managing dyslipidaemias, particularly monogenic dyslipidaemias. RECENT FINDINGS: Targeted DNA sequencing of the genes causing monogenic dyslipidaemias is becoming more accessible. Some societies' position statements advise selective utilization of DNA testing in combination with clinical and biochemical assessment. However, high-quality peer-reviewed evidence showing that a DNA-based diagnosis impacts upon long-term patient outcomes is currently lacking. Nonetheless, we show anecdotal examples of tangible clinical actions following from a genetic diagnosis. In any event, care must be taken when interpreting genetic reports. We strongly feel that expertise in both genetics and dyslipidaemias is required to adequately interpret and report results to patients, as well as to make informed treatment decisions that can have a potential lifelong impact. SUMMARY: There are some examples of monogenic dyslipidaemias for which having a molecular diagnosis might beneficially affect patient outcomes, for example certain cases of suspected familial hypercholesterolemia, familial chylomicronemia syndrome, sitosterolemia or lysosomal acid lipase deficiency. In general, we recommend limiting genetic testing to selected cases of monogenic dyslipidaemias. Finally, we advise that there is currently no proven clinical benefit in testing for polygenic dyslipidaemias.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".