Impact of body composition on very-low-density lipoprotein-triglycerides kinetics
Bibliographic record
Abstract
Upper body obese (UBO) subjects have greater cardiovascular disease risk than lower body obese (LBO) or lean subjects. Obesity is also associated with hypertriglyceridemia that may involve greater production and impaired removal of very-low-density lipoprotein (VLDL)-triglycerides (TG). In these studies, we assessed the impact of body composition on basal VLDL-TG production, VLDL-TG oxidation, and VLDL-TG storage. VLDL-TG kinetics were assessed in 10 UBO, 10 LBO, and 10 lean women using a bolus injection of [1-(14)C]VLDL-TG. VLDL-TG oxidation was measured by (14)CO(2) production (hyamine trapping) and VLDL-TG adipose tissue storage by fat biopsies. Insulin sensititvity was assessed by the hyperinsulinemic-euglycemic clamp technique and body composition by dual X-ray absorptiometry in combination with computed tomography. Hepatic VLDL-TG production was significantly greater in UBO than in lean women [(mumol/min) UBO: 64.8 (SD 40.0) vs. LBO: 42.5 (SD 25.6) vs. lean: 31.8 (SD 13.3), P = 0.04], whereas VLDL-TG oxidation was similar in the three groups and averaged 20% of resting energy expenditure [(mumol/min) UBO: 38.3 (SD 26.5) vs. LBO: 23.5 (SD 13.5) vs. lean: 21.1 (SD 9.7), P = 0.09]. In UBO women, more VLDL-TG was deposited in upper body subcutaneous fat [VLDL-TG redeposition in abdominal adipose tissue (mumol/min): UBO: 5.0 (SD 2.9) vs. LBO: 4.0 (SD 3.2) vs. lean: 1.3 (SD 1.0), ANOVA P = 0.01]; in LBO women, more VLDL-TG was deposited in femoral fat [VLDL-TG redeposition in femoral adipose tissue (mumol/min): UBO: 5.1 (SD 3.1) vs. LBO: 5.8 (SD 4.3) vs. lean: 2.3 (SD 1.5), ANOVA P = 0.04]. Only a small proportion of VLDL-TG (8-16%) was partitioned into redeposition in either group. We found that elevated VLDL-TG production without concomitant increased clearance via oxidation and adipose tissue redeposition contributes to hypertriglyceridemia in UBO 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 | 0.000 | 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) | 0.001 | 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".