Genetic syndromes with diabetes: A systematic review
Bibliographic record
Abstract
Previous reviews and clinical guidelines have identified 10-20 genetic syndromes associated with diabetes, but no systematic review has been conducted to date. We provide the first comprehensive catalog for syndromes with diabetes mellitus. We conducted a systematic review of MEDLINE, Embase, CENTRAL, PubMed, OMIM, and Orphanet databases for case reports, case series, and observational studies published between 1946 and January 15, 2020, that described diabetes mellitus in adults and children with monogenic or chromosomal syndromes. Our literature search identified 7,122 studies, of which 160 fulfilled inclusion criteria. Our analysis of these studies found 69 distinct diabetes syndromes. Thirty (43.5%) syndromes included diabetes mellitus as a cardinal clinical feature, and 56 (81.2%) were fully genetically elucidated. Sixty-three syndromes (91.3%) were described more than once in independent case reports, of which 59 (93.7%) demonstrated clinical heterogeneity. Syndromes associated with diabetes mellitus are more numerous and diverse than previously anticipated. While knowledge of the syndromes is limited by their low prevalence, future reviews will be needed as more cases are identified. The genetic etiologies of these syndromes are well elucidated and provide potential avenues for future gene identification efforts, aid in diagnosis and management, gene therapy research, and developing personalized medicine treatments.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".