High Prevalence and Common Forms of Maturity-onset Diabetes of the Young in Greenland
Bibliographic record
Abstract
OBJECTIVES: Population studies have identified common genetic variants contributing substantially to the burden of diabetes in Greenland. However, the handling of suspected monogenic diabetes in diabetes clinics in Greenland has not been described. In this study we aimed to describe the referral rate, prevalence, and genetic causes of clinically identified monogenic diabetes in Greenland. METHODS: All diabetes patients in Greenland referred for genetic testing due to suspected monogenic diabetes between 2014 and July 2022 were tallied. Targeted short-read sequencing and Sanger sequencing of probands and their family members were used to screen for potentially deleterious variants in the maturity-onset diabetes of the young (MODY) genes GCK, HNF1A, HNF1B, and HNF4A. Clinical data were extracted from the electronic medical records, and whole-genome sequencing was performed for families with potentially deleterious variants for genetic ancestry analysis. RESULTS: Between 2014 and July 2022, 58 probands were referred for genetic testing, equivalent to 0.1% of the population. Five variants were identified: GCK p.F133L, GCK p.D205E, HNF1A c.1108G>T, HNF1B p.Q182∗, and HNF4A -178A>G. These variants were found in 11 probands and 19 family members, equivalent to a population prevalence of monogenic diabetes of 0.05%. Local ancestry analysis revealed that all the variants were found exclusively in Inuit haplotypes, despite all individuals being admixed with both Inuit and European genetic ancestry. CONCLUSIONS: The rate of referral and prevalence of monogenic diabetes is substantially higher in Greenland than in other populations, and both rare and more common population-specific variants of Inuit genetic ancestry contribute to this high prevalence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".