Aim: Although lipoproteins are well-established risk factors for cardiovascular disease (CVD) mortality, conventional measurements failed to identify lipoprotein particle sizes. This study aimed to investigate associations of lipoprotein subclasses categorized by particle sizes with risk of all-cause and CVD mortality in individuals with type 2 diabetes.
Methods: This study included 6575 individuals with type 2 diabetes from the UK Biobank. Concentrations of very low-, low-, intermediate- and high-density lipoprotein [very-low-density lipoprotein (VLDL), low-density lipoprotein (LDL), intermediate-density lipoprotein and high-density lipoprotein (HDL)] particles in 14 subclasses and lipid constituents within each subclass were measured by quantitative nuclear magnetic resonance. Multivariable-adjusted Cox proportional-hazard regression models were used to estimate the hazard ratio (HR) for per standard deviation increment of log-transformed lipoprotein subclasses with risk of mortality. All p-values were adjusted by the false discovery rate method.
Results: During a median follow-up of 11.4 years, 943 deaths were documented, including 310 CVD deaths. Small HDL particles were inversely associated with CVD mortality, with HR (95% CI) of 0.78 (0.69, 0.87), whereas very large and large HDL particles were positively associated with CVD mortality with HR (95% CI) of 1.28 (1.12, 1.45) and 1.19 (1.05, 1.35), respectively. A similar pattern was observed for all-cause mortality [small HDL particle (HR, 95% CI): 0.79, 0.74-0.85; large HDL particle: 1.15, 1.07-1.24; very large HDL particle: 1.26, 1.17-1.36]. For VLDL and LDL, very small VLDL particle was positively, while medium LDL particle was inversely associated with all-cause mortality, but not associated with CVD mortality. The pattern of association with all-cause and CVD mortality for cholesterol and triglyceride within lipoprotein particles was similar to those for lipoprotein particles themselves.
Conclusions: The associations between lipoprotein particles, particularly HDL particles, with all-cause and CVD mortality among patients with type 2 diabetes were significantly varied by particle sizes, highlighting the importance of particle size as a lipoprotein metric in mortality risk discrimination.
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http://dx.doi.org/10.1111/dom.15224 | DOI Listing |
Front Biosci (Landmark Ed)
January 2025
School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, SE5 9NU London, UK.
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School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
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Division of Epidemiology, Vanderbilt Epidemiology Center, Department of Medicine, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA.
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Department of Nutrition and Dietetics, School of Physical Education, Sports and Dietetics, University of Thessaly, 42132 Trikala, Greece.
Cardiovascular diseases (CVDs) remain a leading cause of global mortality and morbidity. Traditional risk prediction models, while foundational, often fail to capture the multifaceted nature of risk factors or leverage the expanding pool of healthcare data. Machine learning (ML) and artificial intelligence (AI) approaches represent a paradigm shift in risk prediction, offering dynamic, scalable solutions that integrate diverse data types.
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Department of Medical Surgical Nursing, College of Nursing, King Saud University, Riyadh 11451, Saudi Arabia.
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality among older adults. Lifestyle modifications, including diet, physical activity, and smoking cessation, are key to reducing cardiovascular risk. This study examines the combined effects of these behaviors on cardiovascular outcomes and their mediating mechanisms.
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