B cell-intrinsic insulin resistance as a mechanism of obesity-linked immune dysfunction 3949
Bibliographic record
Abstract
Abstract Description Obesity predisposes to insulin resistance, a pathophysiological state that is accompanied by a multifaceted dysregulation of the immune system. In obese individuals, aberrant nutritional, endocrine and gut microbial signals promote a state of meta-inflammation that disrupts insulin signaling and metabolic homeostasis. As demonstrated in past pandemic such as H1N1 influenza (2019), these metabolic and immune alterations culminated in detrimental impairments to both vaccine-induced and natural antiviral immunity. Despite these observations, if and how metabolic inflammation impacts protective immunity against foreign pathogens remains largely understudied. We observed that obesity altered B cell differentiation, function, and insulin receptor. Thus, we set out to test whether B cell-intrinsic insulin resistance impairs B cell-mediated immunity, using an animal model of B cell-targeted genetic deletion of the insulin receptor. We found that insulin signalling converges with BCR signaling to modulate antibody class switch recombination and affinity maturation, and that loss of insulin receptor on B cells dampened antibody responses to vaccination and H1N1 influenza infection. These findings suggest potential points of modulation in the Insulin receptor signaling pathway to boost B cell-mediated protective immunity. Funding Sources CIHR461888 Topic Categories Immune Response Regulation: Molecular Mechanisms (IRM)
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".