Aging at the Crossroads of Organ Interactions: Implications for the Heart
Bibliographic record
Abstract
Aging processes underlie common chronic cardiometabolic diseases such as heart failure and diabetes. Cross-organ/tissue interactions can accelerate aging through cellular senescence, tissue wasting, accelerated atherosclerosis, increased vascular stiffness, and reduction in blood flow, leading to organ remodeling and premature failure. This interorgan/tissue crosstalk can accelerate aging-related dysfunction through inflammation, senescence-associated secretome, and metabolic and mitochondrial changes resulting in increased oxidative stress, microvascular dysfunction, cellular reprogramming, and tissue fibrosis. This may also underscore the rising incidence and co-occurrence of multiorgan dysfunction in cardiometabolic aging in the population. Examples include interactions between the heart and the lungs, kidneys, liver, muscles, and brain, among others. However, this phenomenon can also present new translational opportunities for identifying diagnostic biomarkers to define early risks of multiorgan dysfunction, gain mechanistic insights, and help to design precision-directed therapeutic interventions. Indeed, this opens new opportunities for therapeutic development in targeting multiple organs simultaneously to disrupt the crosstalk-driven process of mutual disease acceleration. New therapeutic targets could provide synergistic benefits across multiple organ systems in the same at-risk patient. Ultimately, these approaches may together slow the aging process itself throughout the body. In the future, with patient-centered multisystem coordinated approaches, we can initiate a new paradigm of multiorgan early risk prediction and tailored intervention. With emerging tools including artificial intelligence-assisted risk profiling and novel preventive strategies (eg, RNA-based therapeutics), we may be able to mitigate multiorgan cardiometabolic dysfunction much earlier and, perhaps, even slow the aging process itself.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".