Cardiovascular Risk in Patients With Glomerular Disease: A Narrative Review of the Epidemiology, Mechanisms, Management, and Patient Priorities
Bibliographic record
Abstract
Purpose of review: Cardiovascular (CV) disease is a major cause of morbidity and mortality for patients with glomerular disease. Despite the fact that mechanisms underpinning CV disease risk in this population are likely distinct from other forms of kidney disease, treatment and preventive strategies tend to be extrapolated from studies of patients with undifferentiated chronic kidney disease (CKD). There is an unmet need to delineate the pathophysiology of CV disease in patients with glomerular disease, establish unique risk factors, and identify novel therapeutic targets for disease prevention. The aims of this narrative review are to summarize the existing knowledge regarding the epidemiology, molecular mechanisms, and management of CV disease in patients with common glomerular disease, highlight the patient perspective, and propose specific areas for future study. Sources of information: The literature for this narrative review was accessed using common research search engines, including PubMed, PubMed Central, Medline, and Google Scholar. Information for the patient perspective section was collected through iterative discussions with a patient partner. Methods: We reviewed the epidemiology, molecular mechanisms of disease, management approaches, and the patient perspective in relation to CV disease in patients with glomerulopathies. Throughout, we have highlighted the current knowledge and have discussed future research approaches, both clinical and translational, while integrating the patient perspective. Key findings: Patients with glomerular disease have significant CV disease risk driven by multifactorial, molecular mechanisms originating from their glomerular disease but complicated by existing comorbidities, kidney disease, and medication side effects. The current approach to risk stratification and treatment relies heavily on existing data from CKD patients, but this may not always be appropriate given the unique pathophysiology and mechanisms associated with CV disease risk in patients with glomerular disease. We highlight the need for ongoing glomerular disease-focused studies aimed to better delineate CV disease risk, while integrating the patient perspective. Limitations: This is a narrative review and does not represent a comprehensive and systematic review of the literature.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".