Integrating the Care of Metabolic Dysfunction–associated Steatotic Liver Disease Into Cardiac Rehabilitation: A Multisystem Approach
Bibliographic record
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent chronic liver disease worldwide, affecting 1 in 3 adults and driven by the rising incidence of obesity and type 2 diabetes. MASLD is also a significant contributor to cardiovascular morbidity and mortality. Cardiovascular disease remains the leading cause of death in people with MASLD, often preceding liver complications. In this review, we outline the epidemiology and shared pathophysiologic mechanisms of MASLD with cardiovascular disease. Furthermore, we present a compelling case for integrating cardiac rehabilitation (CR) into MASLD care to address the shared metabolic and inflammatory drivers of both conditions. Evidence from recent clinical trials and guidelines supports the need for holistic, multidisciplinary strategies, including exercise, diet, and pharmacotherapy. CR is no longer solely a post-cardiac event intervention, but an opportunity for proactive cardiometabolic risk reduction in MASLD patients, especially those with fibrosis or type 2 diabetes. Incorporating MASLD into CR programs can facilitate early identification of high-risk individuals and deliver integrated care targeting both liver and cardiovascular outcomes. Cardiologists, hepatologists, and primary care providers must recognize MASLD as a cardiometabolic risk enhancer and consider CR referral, even before overt cardiovascular events occur. CR may be a critical yet underutilized opportunity to modify cardiovascular- and liver-related outcomes in people with MASLD.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".