Influence of Fasting Status and Sample Preparation on Metabolic Biomarker Measurements in Postmenopausal Women
Bibliographic record
Abstract
BACKGROUND: Epidemiologic data linking metabolic markers-such as insulin, insulin-like growth factors (IGFs)-and adipose tissue-derived factors with cancer are inconsistent. Between-study differences in blood collection protocols, in particular participant's fasting status, may influence measurements. METHODS: We investigated the impact of fasting status and blood sample processing time on components of the insulin/IGF axis and in adipokines in a controlled feeding study of 45 healthy postmenopausal-women aged 50-75 years. Fasting blood samples were drawn (T0), after which subjects ate a standardized breakfast; subsequent blood draws were made at 1 hour (T1), 3 hours (T3), and 6 hours (T6) after breakfast. Serum samples were assayed for insulin, C-peptide, total- and free-IGF-I, IGF-binding protein [BP]-1 and -3, total and high molecular weight (HMW)-adiponectin, retinol binding protein-4, plasminogen activator inhibitor (PAI)-1, and resistin. RESULTS: Insulin and C-peptide levels followed similar postprandial trajectories; intra-class correlation coefficients [ICC] for insulin = 0.75, (95%CI:0.64-0.97) and C-peptide (ICC = 0.66, 95%CI:0.54-0.77) were similarly correlated in fasting (Spearman correlation, r = 0.78, 95%CI:0.64-0.88) and postprandial states (T1, r = 0.77 (95%CI: 0.62-0.87); T3,r = 0.78 (95%CI: 0.63-0.87); T6,r = 0.77 (95%CI: 0.61-0.87)). Free-IGF-I and IGFBP-1 levels were also affected by fasting status, whereas total-IGF-I and IGFBP-3 levels remained unchanged. Levels of adipokines were largely insensitive to fasting status and blood sample processing delays. CONCLUSION: Several components of the insulin/IGF axis were significantly impacted by fasting state and in particular, C-peptide levels were substantially altered postprandially and in a similar manner to insulin.
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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 | 0.001 |
| 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".