Inflamm-ageing: the role of inflammation in age-dependent cardiovascular disease
Bibliographic record
Abstract
The ongoing worldwide increase in life expectancy portends a rising prevalence of age-related cardiovascular (CV) diseases in the coming decades that demands a deeper understanding of their molecular mechanisms. Inflammation has recently emerged as an important contributor for CV disease development. Indeed, a state of chronic sterile low-grade inflammation characterizes older organisms (also known as inflamm-ageing) and participates pivotally in the development of frailty, disability, and most chronic degenerative diseases including age-related CV and cerebrovascular afflictions. Due to chronic activation of inflammasomes and to reduced endogenous anti-inflammatory mechanisms, inflamm-ageing contributes to the activation of leucocytes, endothelial, and vascular smooth muscle cells, thus accelerating vascular ageing and atherosclerosis. Furthermore, inflamm-ageing promotes the development of catastrophic athero-thrombotic complications by enhancing platelet reactivity and predisposing to plaque rupture and erosion. Thus, inflamm-ageing and its contributors or molecular mediators might furnish targets for novel therapeutic strategies that could promote healthy ageing and conserve resources for health care systems worldwide. Here, we discuss recent findings in the pathophysiology of inflamm-ageing, the impact of these processes on the development of age-related CV diseases, results from clinical trials targeting its components and the potential implementation of these advances into daily clinical practice.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".