Leptin Predicts Bone and Fat Mass after Accounting for the Effects of Diet and Glucocorticoid Treatment in Piglets <sup>1</sup>
Bibliographic record
Abstract
The role of leptin in neonatal growth and bone metabolism has been investigated, but not simultaneously. The objectives of this study were to determine if leptin relates to bone mass during rapid growth; if consumption of maternal milk is related to elevated circulating concentrations of leptin resulting in higher fat mass; and if glucocorticoids result in higher fat mass and reduced bone mass due to elevated leptin. Thirty-two piglets were randomized to either a suckling or milk substitute plus either dexamethasone (DEX) or placebo injection for 15 days beginning at 5 days of age. Milk and blood samples were obtained at baseline, and after 15 days, blood was sampled again for measurement of leptin and bone biochemistry. Weight at baseline plus weight and length after 15 days were recorded, followed by measurement of whole body bone mineral content, bone area, and fat mass using dual energy x-ray absorptiometry. At baseline, plasma leptin was elevated in suckled piglets. Piglets that suckled had elevated fat mass as did those who received DEX. However, DEX resulted in suppressed weight and length, bone mass, and bone metabolism. Leptin was similar among groups after the 15 days. After accounting for body size and treatment effects, piglet plasma leptin was predictive of bone and fat mass. Leptin circulating early postnatally is linked to body composition, specifically fat and bone mass. Elevations in fat mass and reductions in bone mass observed after 15 days of DEX treatment are not related to leptin metabolism. Both human and porcine neonates share similar characteristics with respect to relationships of leptin with fat and bone mass.
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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 | 0.000 |
| 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 | 0.001 |
| 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".