Glycated haemoglobin (HbA<sub>1c</sub>) and fasting plasma glucose relationships in sea‐level and high‐altitude settings
Bibliographic record
Abstract
Abstract Aim Higher haemoglobin levels and differences in glucose metabolism have been reported among high‐altitude residents, which may influence the diagnostic performance of HbA 1c . This study explores the relationship between HbA 1c and fasting plasma glucose ( FPG ) in populations living at sea level and at an altitude of > 3000 m. Methods Data from 3613 Peruvian adults without a known diagnosis of diabetes from sea‐level and high‐altitude settings were evaluated. Linear, quadratic and cubic regression models were performed adjusting for potential confounders. Receiver operating characteristic ( ROC ) curves were constructed and concordance between HbA 1c and FPG was assessed using a Kappa index. Results At sea level and high altitude, means were 13.5 and 16.7 g/dl ( P > 0.05) for haemoglobin level; 41 and 40 mmol/mol (5.9% and 5.8%; P < 0.01) for HbA 1c ; and 5.8 and 5.1 mmol/l (105 and 91.3 mg/dl; P < 0.001) for FPG , respectively. The adjusted relationship between HbA 1c and FPG was quadratic at sea level and linear at high altitude. Adjusted models showed that, to predict an HbA 1c value of 48 mmol/mol (6.5%), the corresponding mean FPG values at sea level and high altitude were 6.6 and 14.8 mmol/l (120 and 266 mg/dl), respectively. An HbA 1c cut‐off of 48 mmol/mol (6.5%) had a sensitivity for high FPG of 87.3% (95% confidence interval (95% CI ) 76.5 to 94.4) at sea level and 40.9% (95% CI 20.7 to 63.6) at high altitude. Conclusion The relationship between HbA 1c and FPG is less clear at high altitude than at sea level. Caution is warranted when using HbA 1c to diagnose diabetes mellitus in this setting.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".