Understanding the Role of the Complement System in Insulin Resistance and Metabolic Syndrome in Patients With Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: The complement system has been associated with the etiopathogenesis of rheumatoid arthritis (RA). Insulin resistance (IR) and metabolic syndrome (MetS) are prevalent among patients with RA. The aim of this study was to explore the relationship between a comprehensive evaluation of the complement system and IR, as well as MetS, in patients with RA. METHODS: A total of 339 nondiabetic patients with RA were recruited. Functional assays of the 3 complement pathways were assessed. Additionally, serum levels of the following individual components of the complement system were measured: C1q (classical); lectin (lectin); C2, C4, and C4b (classical lectin); factor D and properdin (alternative); C3 and C3a (common); C5, C5a, and C9 (terminal); as well as the factor I and C1 inhibitor regulators. IR and β cell function indices were calculated using the homeostatic model assessment. Criteria for MetS were applied. Multivariable linear regression analysis was performed to investigate the association between the complement system and IR in patients with RA. RESULTS: Many elements of the upstream and common complement pathways, but not the functional tests of the 3 routes, correlated positively with higher levels of IR and β cell function. However, after multivariable adjustment for factors associated with IR, these relationships were lost. Conversely, the presence of MetS in patients with RA maintained a relationship with higher levels of C1q, C4, C3, properdin, and factor I after adjusting for confounders. CONCLUSION: There is a positive correlation between the complement system and MetS among nondiabetic patients with RA. This association is independent of traditional IR factors.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".