Chronic inflammation and the hallmarks of aging
Bibliographic record
Abstract
BACKGROUND: Recently, the hallmarks of aging were updated to include dysbiosis, disabled macroautophagy, and chronic inflammation. In particular, the low-grade chronic inflammation during aging, without overt infection, is defined as "inflammaging," which is associated with increased morbidity and mortality in the aging population. Emerging evidence suggests a bidirectional and cyclical relationship between chronic inflammation and the development of age-related conditions, such as cardiovascular diseases, neurodegeneration, cancer, and frailty. How the crosstalk between chronic inflammation and other hallmarks of aging underlies biological mechanisms of aging and age-related disease is thus of particular interest to the current geroscience research. SCOPE OF REVIEW: This review integrates the cellular and molecular mechanisms of age-associated chronic inflammation with the other eleven hallmarks of aging. Extra discussion is dedicated to the hallmark of "altered nutrient sensing," given the scope of Molecular Metabolism. The deregulation of hallmark processes during aging disrupts the delicate balance between pro-inflammatory and anti-inflammatory signaling, leading to a persistent inflammatory state. The resultant chronic inflammation, in turn, further aggravates the dysfunction of each hallmark, thereby driving the progression of aging and age-related diseases. MAIN CONCLUSIONS: The crosstalk between chronic inflammation and other hallmarks of aging results in a vicious cycle that exacerbates the decline in cellular functions and promotes aging. Understanding this complex interplay will provide new insights into the mechanisms of aging and the development of potential anti-aging interventions. Given their interconnectedness and ability to accentuate the primary elements of aging, drivers of chronic inflammation may be an ideal target with high translational potential to address the pathological conditions associated with aging.
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.
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 | 0.001 |
| 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) | 0.003 | 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".