Lathosterol and Noncholesterol Sterols in Routine Use for the Differentiation and Monitoring of Dietary and Drug Induced Treatment of Hypercholesterolemias in Children and Adolescents
Bibliographic record
Abstract
Aims: The authors discuss their 15 years of experience with use of noncholesterol sterols (NCS) when diagnosing heterozygous familial hypercholesterolemia (HFH) and the dietary and drug treatment of children and adolescents when lathosterol (Lat) and desmosterol (Des) as cholesterol synthesis precursors, and campesterol (Cam) and sitosterol (Sit) as cholesterol absorption precursors are included. Patients and Methods: 38 children and adolescents (6-18 yrs) with HFH proven by molecular genetic testing of LDL-cholesterol receptor deficit; 107 children patients with clinical and laboratory symptoms of other hypercholesterolemias; 84 healthy school-age children as a control group. Routine lipid spectrum scan—total cholesterol (TCh), LDL-Ch, HDL-Ch, TAG, with additional apo A1, apo B, Lp (a), LDL-receptors, apo E polymorphism; Lat, Des, Cam and Sit in the plasma—was established by means of GC/MS. Results: The HFH patients on a low cholesterol diet (LCHD) who come to our lipid outpatient clinic have elevated levels of Lat and Des, unlike patients with alimentary hypercholesterolemia (p<0, 001). Lat and Des levels are high following interruption of medical treatment during long vacations or when drug treatment is neglected. Administration of statins only in sufficiently high therapeutic doses reduces Lat and Des (p<0, 001). Compensatory elevation of Cam and Sit occurs only in few pediatric patients. Ezetimibe decreases Cam and Sit more efficiently than Lat or Des. Combination of statin with ezetimibe is most efficient in decrease of not only TCh but also Lat and Des, as well as Cam and Sit. Conclusions: Extending the laboratory spectrum by precursors of cholesterol synthesis and absorption improves the differential diagnosis of HFH and makes monitoring and/or treatment of children and adolescents more precise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".