Non-Alcoholic Fatty Liver in Patients with Chylomicronemia
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is frequent in patients with features of the metabolic syndrome (MetS), obesity, or type 2 diabetes. Lipoprotein lipase (LPL) is the main driver of triglyceride (TG) hydrolysis in chylomicrons and very-low density lipoproteins (VLDL). In some patients with MetS, dysfunction of this pathway can lead to plasma TG values > 10 mmol/L (multifactorial chylomicronemia or MCS). Chylomicronemia also characterizes LPL deficiency (LPLD), a rare autosomal recessive disease called familial chylomicronemia syndrome (FCS), which is associated with an increased risk of recurrent pancreatitis. This study aims to investigate the expression of NAFLD, as assessed by transient elastography, in MCS and FCS subjects. Data were obtained from 38 subjects with chylomicronemia; 19 genetically confirmed FCS and 19 sex- and age-matched MCS. All participants underwent liver ultrasonography and stiffness measurement after a 4-h fast using transient elastography (FibroScan®, Echosens, Waltham, MA, USA). NAFLD (controlled attenuation parameter (CAP) > 280 dB/m) was observed in 42.1% of FCS and 73.7% of MCS subjects (p = 0.05). FCS subjects had lower body mass index (BMI) than MCS. Only 25% of FCS subjects with NAFLD had a BMI ≥ 30 compared to 64.3% in MCS (p = 0.004). In FCS, NAFLD occurred even in the presence of very low (≤18 kg/m2) BMI. In both FCS and MCS, CAP was negatively associated with acute pancreatitis risk. In this study, NAFLD was commonly observed in both FCS and MCS subjects and occurred independently of the BMI and fasting glucose values in FCS; NAFLD was associated with a lower occurrence of acute pancreatitis episodes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".