Influence of Hepatic Steatosis (Fatty Liver) on Severity and Composition of Dyslipidemia in Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to examine the associations between the severity of hepatic steatosis and dyslipidemia in type 2 diabetes, including circulating apolipoprotein B100 (apoB) concentrations and lipoprotein particle size and numbers. RESEARCH DESIGN AND METHODS: Computed tomography imaging was used to assess hepatic fat content and adipose tissue distribution in 67 men and women with type 2 diabetes, withdrawn from antidiabetic medications preceding the study. Fasting serum lipoprotein number and size was determined by nuclear magnetic resonance. Insulin sensitivity was measured with a glucose clamp and a [6,6-(2)H(2)]glucose isotope infusion. RESULTS: Two-thirds of the cohort had fatty liver. Hepatic steatosis correlated with serum triglycerides (r = 0.40, P < 0.01) and lower HDL cholesterol (r = -0.31, P < 0.05). ApoB and LDL cholesterol did not, being virtually identical in those with or without steatosis. The association between serum triglycerides and hepatic steatosis was largely accounted for by greater triglyceride enrichment in VLDL particles, which were larger. Severe steatosis was also associated with 70% higher small, dense LDL concentrations. Visceral obesity did not fully explain these associations, and hepatic steatosis was better correlated with triglycerides than with hyperglycemia or hepatic insulin resistance (P > 0.05). CONCLUSIONS: The presence of hepatic steatosis in type 2 diabetes does not appear to affect apoB levels, but potentially increases atherogenesis by increasing triglycerides, reducing HDL levels, and increasing small, dense LDL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 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".