The role of autoantibodies in bridging obesity, aging, and immunosenescence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it