Effect of enforced physical inactivity induced by 60‐day of bed rest on hepatic markers of <scp>NAFLD</scp> in healthy normal‐weight women
Bibliographic record
Abstract
BACKGROUND & AIMS: Physical inactivity leads to a cluster of metabolic disorders that have been associated with non-alcoholic fatty liver diseases. We tested whether physical inactivity increases hepatic biomarkers of NAFLDs. METHODS: Sixteen normal-weight healthy women (body mass index = 21.2 ± 0.5 kg/m(2) ) were studied under controlled energy balance conditions during a previous 60-day bed rest with (n = 8) or without (n = 8) a combined aerobic/resistive exercise protocol. Stored samples were retrospectively used to measure plasma hepatic markers, i.e. steatosis-related alanine and aspartate transaminases, cytokeratin 18 and angiopoietin-like 3, at baseline, after 30 and 60 days of bed rest. Fasting insulin and triglycerides were measured at baseline and after 30 days of bed rest. Two indexes were calculated, one combining alanine and aspartate transaminase and cytokeratin 18 and another cytokeratin 18, homeostasis model assessment of insulin resistance and aspartate aminotransferase. RESULTS: Sixty days of bed rest increased all hepatic markers (P < 0.05 for all) and the two indexes (P < 0.01 for both). Exercise significantly reduced the elevation in aspartate transaminase, cytokeratin 18 and both indexes (P < 0.02 for all) but not the increase in alanine transaminase and angiopoietin-like 3. Changes between baseline and 30 days of bed rest in triglycerides were positively associated with changes in aspartate transaminase (R(2) = 0.28, P = 0.04) suggesting a role of hypertriglyceridaemia in the alteration of liver metabolism under inactive conditions. CONCLUSION: Physical inactivity increases, independent of fat mass, hepatic markers of steatosis and steatohepatitis. Regular exercise can limit these physical inactivity-induced metabolic alterations. Future studies need to elucidate the underlying mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".