Hypercholesterolemia During Pregnancy is Caused by Increased Endogenous Cholesterol Synthesis: Let’s Use it for Screening of Familial Hypercholesterolemias Too!
Bibliographic record
Abstract
Aims: To demonstrate the origin and the diagnostic significance of non-cholesterol sterols (NCSs) in healthy pregnant women with gestational hypercholesterolemia. Patients and Methods: Based on a total of 21,000 clinical biochemistry tests of healthy pregnant women with hypercholesterolemia observed during pregnancy, a group of 84 women with TC (total cholesterol) >7.0 mmol/L was recruited to analyze their NCSs using Gas Chromatography–Mass Spectrometry. The NCSs under examination comprised lathosterol (Lat) and desmosterol (Des) as markers of endogenous cholesterol synthesis, and campesterol (Cam) and sitosterol (Sit) as markers for intestinal absorption. Results: In the total of 21,000 pregnant women, the median values were: TC 6.8 mol/l, LDL-C 4.6 mmol/L, and HDL-C 2.2 mmol/L. In the testing group of 84 women, the average values were: Lat 7.8+/-1.7 μmol/L, Des 4.7+/-0.9 μmol/L, Cam 9.8+/-2.6 μmol/L, and Sit 9.6 +/-3.8 μmol/L. Lat was found to correlate with TC (r = 0.53), LDL-C (r = 0.36), and non-HDL-C (r = 0.35). No such correlations were observed for Sit (r = 0.162) or Cam (r = 0.153). Conclusion: Our findings show that the high incidence of hypercholesterolemia during pregnancy is caused by increased endogenous cholesterol synthesis via lathosterol. The enormous rise of TC levels during pregnancy can be effectively used to detect familial hypercholesterolemia in women.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".