Plasma ferritin concentration is positively associated with <i>in vivo</i> fatty acid mobilization and insulin resistance in obese women
Bibliographic record
Abstract
NEW FINDINGS: What is the central question of this study? Do obese women with relatively high whole-body iron stores exhibit elevated in vivo rates of fatty acid (FA) release from adipose tissue compared with a well-matched cohort of obese women with relatively low iron stores? What is the main finding and its importance? Obese women with high plasma [ferritin] (a marker of whole-body iron stores) had greater FA mobilization, lipolytic activation in adipose tissue and insulin resistance (IR) compared with obese women with lower plasma [ferritin]. Given that elevated FA mobilization is intimately linked with the development of IR, these findings suggest that elevated iron stores might contribute to IR in obesity by increasing systemic FA availability. ABSTRACT: High rates of fatty acid (FA) mobilization from adipose tissue are associated with insulin resistance (IR) in obesity. In vitro evidence suggests that iron stimulates lipolysis in adipocytes, but whether iron is related to in vivo FA mobilization is unknown. We hypothesized that plasma ferritin concentration ([ferritin]), a marker of body iron stores, would be positively associated with FA mobilization. We measured [ferritin], the rate of appearance of FA in the systemic circulation (FA Ra; stable isotope dilution), key adipose tissue lipolytic proteins and IR (hyperinsulinaemic-euglycaemic clamp) in 20 obese, premenopausal women. [Ferritin] was correlated with FA Ra (r = 0.65; P = 0.002) and IR (r = 0.57; P = 0.008); these relationships remained significant after controlling for body mass index and plasma [C-reactive protein] (a marker of systemic inflammation) in multiple regression analyses. We then stratified subjects into tertiles based on [ferritin] to compare subjects with 'High-ferritin' versus 'Low-ferritin'. Plasma [hepcidin] was more than fivefold greater (P < 0.05) in the High-ferritin versus Low-ferritin group, but there was no difference in plasma [C-reactive protein] between groups, indicating that the large difference in plasma [ferritin] reflects a difference in iron stores, not systemic inflammation. We found that FA Ra, adipose protein abundance of hormone-sensitive lipase and adipose triglyceride lipase, and IR were significantly greater in subjects with High-ferritin versus Low-ferritin (all P < 0.05). These data provide the first evidence linking iron and in vivo FA mobilization and suggest that elevated iron stores might contribute to IR in obesity by increasing systemic FA availability.
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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".