Integrating New Technologies in Lipidology: A Comprehensive Review

Area of Science:

• Cardiovascular Medicine
• Biotechnology
• Data Science

Background:

• Cardiovascular disease is the leading cause of death globally.
• Residual risk persists in patients achieving low-density lipoprotein cholesterol targets.
• Emerging technologies offer new avenues for assessing and treating dyslipidemia.

Purpose of the Study:

• To review how advanced lipidomics and diagnostics redefine dyslipidemia understanding.
• To explore the potential of new therapies for personalized cardiovascular risk reduction.
• To identify scientific, regulatory, and ethical barriers to innovation implementation.

Main Methods:

• Narrative review of current literature on lipidomics, diagnostics, and therapeutics.
• Analysis of machine learning applications in cardiovascular risk prediction.
• Examination of technological advancements and their clinical implications.

Main Results:

• High-resolution lipidomics and point-of-care diagnostics expand lipid assessment beyond traditional panels.
• RNA/gene-based therapies offer novel lipid-modifying treatment strategies.
• Machine learning integration of multimodal data can enhance cardiovascular risk stratification.

Conclusions:

• Emerging technologies hold significant promise for earlier, personalized, and durable cardiovascular risk reduction.
• Overcoming scientific, regulatory, and ethical hurdles is crucial for equitable benefit.
• A multidisciplinary approach is needed to translate innovations into clinical practice.