O-GlcNAc modification is associated with insulin sensitivity in the whole blood of healthy young adult males
Bibliographic record
Abstract
BACKGROUND: Hemoglobin A1c (HbA1c) is the predominant diagnostic tool for diabetes diagnosis and progression. However, it has proven to be insensitive at pre-diabetic threshold values. O-linked-β-N-acetylglucosamine (O-GlcNAc) modification has emerged as a sensitive biomarker. The purpose of this study was to explore the sensitivity of O-GlcNAc expression as a potential marker of early metabolic dysfunction in a young adult population. Healthy, young males (18-35 y) from the Assessing Inherited Metabolic syndrome Markers in the Young study (AIMMY), were divided into low (LH,0.60) or high (HH,1.61) homeostatic model assessment of insulin resistance (HOMA-IR) cohorts. FINDINGS: The relationships between a panel of anthropometric, metabolic measures and whole blood global protein O-GlcNAc was examined. O-GlcNAc and O-GlcNAc transferase (OGT) levels were quantified by immunoblotting and compared to anthropometric measures: body mass index (BMI), percentage body fat, aerobic fitness, blood glucose, triglycerides, HDL, insulin, and HbA1c. HOMA-IR cohorts showed no differences in BMI, blood glucose or HbA1c, but differed in percent body fat, plasma triglycerides, and circulating insulin. Greater O-GlcNAc expression was observed in the whole blood of HH compared to LH. Moreover, a positive association between HOMA-IR and O-GlcNAc emerged, while no relationship was found between HbA1c and HOMA-IR. This effect was not related to OGT expression. CONCLUSIONS: Results indicate that O-GlcNAc has a greater sensitivity to metabolic status compared to HbA1c in this population. O-GlcNAc has the potential to serve as a screening tool for predicting future metabolic disturbances in a young healthy adult population free of any clinically relevant pathologies.
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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 | 0.001 |
| 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) | 0.001 | 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".