ReviewJournal of clinical medicine2025
Integrating New Technologies in Lipidology: A Comprehensive Review.
Review in Journal of clinical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Beyond Traditional Risk Scores: Artificial Intelligence in Coronary Plaque Characterization and Personalized Atherosclerosis Management.Journal of cardiovascular development and disease · 2026Review
- Precision Lipid Management in the Era of Biotechnology and Artificial Intelligence: From Gene Editing to Smart Drug Delivery.Therapeutic innovation & regulatory science · 2026Review
- Artificial Intelligence in Cardiovascular Risk Prediction: An Up-to-Date Narrative Review on the Emerging Role of Lipid Profile-Based Models.Journal of clinical medicine · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular disease remains the world's leading cause of death, and even when patients reach guideline low-density lipoprotein cholesterol targets, a substantial "residual risk" persists, underscoring the need for more nuanced assessment and intervention. At the same time, rapid advances in high-resolution lipidomics, connected point-of-care diagnostics, and RNA- or gene-based lipid-modifying therapies are transforming what clinicians can measure, monitor, and treat. Integrating multimodal data through machine learning algorithms capable of handling high-dimensional datasets has the potential to improve cardiovascular risk prediction and re-stratification compared to traditional models. This narrative review therefore sets out to (i) trace how these emerging technologies expand our understanding of dyslipidemia beyond the traditional lipid panel, (ii) examine their potential to enable earlier, more personalized and durable cardiovascular risk reduction, and (iii) highlight the scientific, regulatory and ethical hurdles that must be cleared before such innovations can deliver widespread, equitable benefit.
Indexed as
Identifiers
What Socratic 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 Socratic graph.