Serum antinuclear autoantibodies are associated with measures of oxidative stress and lifestyle factors: analysis of LIPIDOGRAM2015 and LIPIDOGEN2015 studies
Bibliographic record
Abstract
Introduction Oxidative stress is one of many factors suspected to promote antinuclear autoantibody (ANA) formation. Reactive oxygen species can induce changes in the antigenic structure of macromolecules, causing the immune system to treat them as “neo-antigens” and start production of autoantibodies. This study was designed to evaluate the relationship between oxidative stress markers, lifestyle factors and the detection of ANA. Material and methods We examined measures of oxidative stress indices of free-radical damage to lipids and proteins, such as total oxidant status (TOS), concentration of protein thiol groups (PSH), and malondialdehyde (MDA), activity of superoxide dismutase (SOD) in 1731 serum samples. The parameters of the non-enzymatic antioxidant system, such as total antioxidant status (TAS) and uric acid (UA) concentration, were also measured and the oxidative stress index (OSI-index) was calculated. All samples were tested for the presence of ANA using an indirect immunofluorescence assay (IIFA). Results The presence of ANA in women was associated with lower physical activity (p = 0.036), less frequent smoking (p = 0.007) and drinking of alcohol (p = 0.024) accompanied by significant changes in SOD isoenzymes activity (p < 0.001) and a higher uric acid (UA) concentration (p < 0.001). In ANA positive males we observed lower concentrations of PSH (p = 0.046) and increased concentrations of MDA (p = 0.047). Conclusions The results indicate that local oxidative stress may be associated with increased probability of ANA formation in a sex-specific manner.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".