Cardiometabolic risk management: insights from a European Society of Cardiology Cardiovascular Round Table
Bibliographic record
Abstract
Metabolic comorbidities are common in patients with cardiorenal disease; they can cause atherosclerotic cardiovascular disease (ASCVD), speed progression, and adversely affect prognosis. Common comorbidities are Type 2 diabetes mellitus (T2DM), obesity/overweight, chronic kidney disease (CKD), and chronic liver disease. The cardiovascular system, kidneys, and liver are linked to many of the same risk factors (e.g. dyslipidaemia, hypertension, tobacco use, diabetes, and central/truncal obesity), and shared metabolic and functional abnormalities lead to damage throughout these organs via overlapping pathophysiological pathways. The COVID-19 pandemic has further complicated the management of cardiometabolic diseases. Obesity, T2DM, CKD, and liver disease are associated with increased risk of poor outcomes of COVID-19 infection, and conversely, COVID-19 can lead to worsening of pre-existing ASCVD. The high rates of these comorbidities highlight the need to improve recognition and treatment of ASCVD in patients with obesity, insulin resistance or T2DM, chronic liver diseases, and CKD and equally, to improve recognition and treatment of these diseases in patients with ASCVD. Strategies to prevent and manage cardiometabolic diseases include lifestyle modification, pharmacotherapy, and surgery. There is a need for more programmes at the societal level to encourage a healthy diet and physical activity. Many pharmacotherapies offer mechanism-based approaches that can target multiple pathophysiological pathways across diseases. These include sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists, selective mineralocorticoid receptor antagonists, and combined glucose-dependent insulinotropic peptide/glucagon-like peptide-1 receptor agonist. Non-surgical and surgical weight loss strategies can improve cardiometabolic disorders in individuals living with obesity. New biomarkers under investigation may help in the early identification of individuals at risk and reveal new treatment targets.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".