Risk of Diabetes Mellitus among Patients Diagnosed with Giant Cell Arteritis or Granulomatosis with Polyangiitis: Comparison with the General Population
Bibliographic record
Abstract
OBJECTIVE: Patients with organ- or life-threatening vasculitis receive high cumulative glucocorticoid (GC) doses during their disease course. GC have diabetogenic effects, but the risk of diabetes mellitus (DM) related to vasculitis therapy is not well characterized. We assessed the DM risk among patients diagnosed with giant cell arteritis (GCA) or granulomatosis with polyangiitis (GPA), i.e., patients with relatively common forms of systemic vasculitis. METHODS: We used Danish healthcare registries to identify 1682 patients diagnosed with GCA and 342 patients diagnosed with GPA from 1997 to 2015 and to obtain information regarding medication exposures. Each patient with vasculitis was matched with 9 population controls. Date of new-onset DM was defined as date of first claimed prescription for an antidiabetic drug. We used Cox regression analyses to calculate incidence rate ratios (IRR) for DM as a measure of the DM risk among patients relative to population controls. Logistic regression was used to study the association between prednisolone/prednisone (PRED) dose and DM. RESULTS: Median duration of followup was 6.5 years [interquartile range (IQR) 2.6-10.4] in the GCA cohort and 5.8 years (IQR 1.7-10.6) in the GPA cohort. During the first year after diagnosis of vasculitis, the IRR for DM was 7.0 (95% CI 5.2-9.3) among patients with GCA and 10.4 (95% CI 4.4-24) among patients with GPA. IRR for DM were not significantly increased in either cohort during later followup periods. Within the first year, treatment with high cumulative prednisolone/PRED doses was associated with new-onset DM among the patients with vasculitis. CONCLUSION: Patients diagnosed with GCA or GPA have a markedly increased risk of new-onset DM during early treatment phases.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".