Influence of inflammatory and non-inflammatory rheumatic disorders on the clinical and biological profile of type-2 diabetes
Bibliographic record
Abstract
OBJECTIVE: To study the profile of type-2 diabetes (T2D) in patients with RA or OA. METHODS: This observational, multicentre, cross-sectional study included, over a 24-month period, consecutive patients with adult-onset diabetes and RA or OA. We collected demographics, disease activity and severity indices, current treatments for RA and diabetes, history and complications of diabetes. A systematic blood test was performed, assessing inflammatory, immunological and metabolic parameters. The homoeostasis model assessment (HOMA)2-S was used to assess insulin resistance. RESULTS: We included 167 patients with T2D, 118 with RA and 49 with OA. RA and OA patients had severe T2D with suboptimal metabolic control and a biological profile of insulin resistance. Insulin resistance was significantly higher in RA than in OA patients after stratification on age, BMI and CS use [HOMA2-S: 63.5 (35.6) vs 98.4 (69.2), P < 0.001]. HOMA2-S was independently associated with DAS28 [odds ratio (OR): 4.46, 95% CI: 1.17, 17.08]. T2D metabolic control was not related to disease activity and functional impairment, but HbA1c levels were independently associated with bone erosions (OR: 4.43, 95% CI: 1.18, 16.61). Treatment with low-dose CSs was not associated with decreased insulin sensitivity or increased HbA1c levels. Treatment with TNF-α inhibitors was associated with increased insulin sensitivity compared with patients not receiving biologics [101.3 (58.71) vs 60.0 (32.5), P = 0.001]. CONCLUSION: RA patients display severe T2D with inflammation-associated insulin resistance. These findings may have therapeutic implications, with the potential targeting of insulin resistance through the treatment of joint and systemic inflammation.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".