The role of autoantibodies in bridging obesity, aging, and immunosenescence
Bibliographic record
Abstract
Antibodies are essential to immune homeostasis due to their roles in neutralizing pathogenic agents. However, failures in central and peripheral checkpoints that eliminate autoreactive B cells can undermine self-tolerance and generate autoantibodies that mistakenly target self-antigens, leading to inflammation and autoimmune diseases. While autoantibodies are well-studied in autoimmune and in some communicable diseases, their roles in chronic conditions, such as obesity and aging, are less understood. Obesity and aging share similar aspects of immune dysfunction, such as diminished humoral responses and heightened chronic inflammation, which can disrupt immune tolerance and foster autoantigen production, thus giving rise to autoreactive B cells and autoantibodies. In return, these events may also contribute to the pathophysiology of obesity and aging, to the associated autoimmune disorders linked to these conditions, and to the development of immunosenescence, an age-related decline in immune function that heightens vulnerability to infections, chronic diseases, and loss of self-tolerance. Furthermore, the cumulative exposure to antigens and cellular debris during obesity and aging perpetuates pro-inflammatory pathways, linking immunosenescence with other aging hallmarks, such as proteostasis loss and mitochondrial dysfunction. This review examines the mechanisms driving autoantibody generation during obesity and aging and discusses key putative antigenic targets across these conditions. We also explore the therapeutic potential of emerging approaches, such as CAR-T/CAAR-T therapies, vaccines, and BiTEs, to tackle autoimmune-related conditions in aging and obesity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".