ReviewJournal of diabetes and metabolic disorders2026
Managing metabolic risks in coronary artery disease: A scoping review of recent clinical and therapeutic evidence.
Review in Journal of diabetes and metabolic disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Article
- Clinical and Genetic Factors Associated with Multivessel Coronary Artery Disease: A Cross-Sectional Study of CYP2C19 Polymorphisms and Metabolic Comorbidities.International journal of general medicine · 2026Article
- Pemafibrate is associated with greater reductions in MDA-LDL and small dense LDL fractions with exploratory PBMC gene-expression changes: a prespecified subanalysis of the PARADISE in Kagoshima study.Frontiers in clinical diabetes and healthcare · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Coronary artery disease (CAD) remains the leading global cause of mortality, with metabolic abnormalities such as diabetes, dyslipidemia, hypertension, and obesity significantly driving its progression. Recent studies have focused on the integration of metabolic risk management with cardiovascular care to improve outcomes. Objective: This scoping review aimed to synthesize the latest evidence (2024-2025) on the management of metabolic risk factors in CAD, highlighting emerging biomarkers, therapeutic strategies, and research gaps. Methods: A systematic literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar. Twenty-one studies meeting predefined eligibility criteria were included. Data were extracted and thematically analyzed according to the PRISMA-ScR framework. Results: Included studies emphasized the synergistic impact of diabetes, dyslipidemia, and hypertension on CAD outcomes. Pharmacologic interventions such as SGLT2 inhibitors, GLP-1 receptor agonists, pemafibrate, and Rho-kinase inhibitors showed consistent improvements in metabolic and cardiovascular parameters. Lifestyle interventions, including Mediterranean and DASH diets, and telemedicine-based programs, demonstrated adjunctive benefits, though long-term adherence remained challenging. Emerging biomarkers like lipoprotein(a) and microRNAs, along with imaging modalities such as SPECT and PET/CT, enhanced precision risk stratification. Conclusion: Recent evidence underscores the importance of integrating metabolic management into CAD prevention and therapy. A shift toward precision cardiometabolic care-combining biomarker-driven diagnostics, sustained lifestyle modification, and individualized pharmacotherapy is essential. Future multicentric trials and digital health innovations should address persistent evidence and practice gaps, especially within high-risk Asian and Indian populations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.