Abnormal glucose homeostasis in patients of HbEβ-thalassemia: Prevalence and possible pathogenesis using the Oxford HOMA model
Bibliographic record
Abstract
Objectives: Eβ-thalassemia, the most serious form of HbE syndromes, may develop pre-diabetes (PD) and diabetes mellitus (DM), together constituting abnormal glucose homeostasis (AGH) as an endocrinopathy. This study aims to assess AGH prevalence and pathogenesis in this thalassemia subtype. Material and Methods: A cross-sectional study was conducted at a tertiary care hospital from February 2017 to December 2018 (1.9 years). One hundred and four HbEβ-thalassemia patients were randomly selected aged ≥5 years, irrespective of transfusion requirement. AGH was diagnosed by the American Diabetes Association criteria. The patient’s history, relevant examination details, and parameters related to glucose homeostasis were studied. The homeostasis assessment (HOMA) model of Oxford University was used, and formulae were applied to calculate HOMA-insulin resistance (IR) or HOMA-β (β-cell function). Results: The status of glucose homeostasis was as follows: Normal glucose homeostasis tolerance 83/104(79.8%), PD 20/104(19.2%), and DM one(1%). The patient’s age, age of starting transfusions, and HOMA-IR were significantly related to AGH. AGH was inversely associated with the age of starting chelation, though not significant (P = 0.07). There was no statistical significance of AGH development, with transfusion dependence (P = 0.63), family history of DM (P = 0.42), hepatitis C (P = 0.36), and higher ferritin levels (800/1000/1500/1700 ng/ml) (P > 0.5)/HOMA-β (P > 0.5). Conclusion: HbEβ-thalassemia patients are prone to develop AGH including overt diabetes. It is related to the patient’s age, age of initiation, and duration of transfusion therapy. The likely mechanism of pathogenesis is IR, though pancreatic β-cell destruction may also be contributory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".