Assessment of Epicardial Fat Thickness in Infants of Diabetic Mothers and its Relation to Glycated Albumin Level
Bibliographic record
Abstract
Background: Infants of diabetic mothers (IDMs) are at increased risk for developing cardiometabolic disorders. Traditional markers like HbA1c have limitations in certain clinical scenarios, highlighting the need for alternative indicators. Epicardial fat thickness (EFT), due to its anatomical and functional proximity to the heart, and glycated albumin, a short-term glycemic marker, may provide early risk detection. Objective: To assess epicardial fat thickness and umbilical cord glycated albumin levels in neonates of diabetic mothers and determine their association, exploring their potential as early predictors of short-term glycemic control and cardiometabolic risk. Methods: This cross-sectional, case-control study involved 35 IDMs and 30 neonates born to non-diabetic mothers (NIDMs). Echocardiography measured EFT in the first week of life, while glycated albumin was measured via ELISA from umbilical cord blood. Statistical analysis included ROC curves and logistic regression to evaluate diagnostic performance. Results: EFT and glycated albumin were significantly elevated in IDMs compared to NIDMs (p < 0.001). A cutoff value of EFT >16.3 mm had a specificity of 93.1% and a sensitivity of 54.29%. Glycated albumin>434 g/dl showed a sensitivity of 91.43% and specificity of 83.33%. Multivariate analysis identified glycated albumin as a strong independent predictor of pregestational diabetes mellitus, while both biomarkers were associated with gestational diabetes. Conclusions: EFT and glycated albumin serve as effective, non-invasive markers for early detection of cardiometabolic risk in neonates born to diabetic mothers. Their incorporation into routine assessments may enhance early diagnosis and targeted intervention strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".