Estrogens regulate glycosylation of IgG in women and men
Bibliographic record
Abstract
The immunologic potency of IgG is modulated by glycosylation, but mechanisms regulating this process are undefined. A role for sex hormones is suggested by differences in IgG glycans between women and men, most prominently with respect to galactose. We therefore assessed IgG galactosylation in 713 healthy adults from 2 cohorts as well as in 159 subjects from 4 randomized controlled studies of endocrine manipulation: postmenopausal women receiving conjugated estrogens, raloxifene, or placebo; premenopausal women deprived of gonadal hormones with leuprolide and treated with estradiol or placebo; men deprived of gonadal hormones with goserelin and given testosterone or placebo; and men deprived of gonadal hormones with goserelin and given testosterone or placebo together with anastrozole to block conversion of testosterone to estradiol. Menopause was associated with an increase in agalactosylated IgG glycans, particularly in the most abundant fucosylated nonbisected (G0F) glycoform. Conjugated estrogens and raloxifene reduced G0F glycans in postmenopausal women, while in premenopausal women leuprolide increased G0F glycans in a manner reversed by estradiol. Among men, goserelin increased G0F glycans, an effect blocked by testosterone through conversion to estradiol. These results establish estrogens as an in vivo modulator of IgG galactosylation in both women and men, defining a pathway by which sex modulates immunity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".