Low density lipoprotein (LDL) and intermediate density lipoprotein (IDL) classes have been shown to be composed of discrete metabolic entities or subclasses. Present ultracentrifugal methods are unable to precisely determine these subclasses. A new analytical micro-ultracentrifugal method was developed that facilitates the determination of IDL and LDL subclasses and their F1.21 flotation coefficient from ultracentrifugal scans. The method is based on the modification of a published equation (Fujita, H. 1956. J. Chem. Phys. 24: 1084-1090) adapted to calculate concentration gradient boundary curves for IDL and LDL that are approximately Gaussian in form. Using an extension of this modified equation, theoretical distributions of the gradient curves were calculated. By applying the theoretical distributions, IDL and LDL subclasses were resolved from absorbance scans as Gaussian concentration gradient boundary curves. Both theoretically calculated and experimentally determined boundary curves for IDL and LDL lipoproteins were plotted and found to be in excellent agreement. Three subclasses of LDL and four subclasses of IDL were determined. The mean flotation rates of the LDL subclasses were: LDL1 = 37.2 +/- 0.6, LDL2 = 31.1 +/- 0.9, and LDL3 = 26.7 +/- 0.7. The mean flotation rates of the IDL subclasses were: IDL1 = 61.6 +/- 0.9, IDL2 = 53.9 +/- 1.0, IDL3 = 50.1 +/- 0.6, and IDL4 = 45.6 +/- 1.1.
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Front Cell Infect Microbiol
January 2025
Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Background: Prior studies have established correlations between gut microbiota (GM) dysbiosis, circulating metabolite alterations, and gastric cancer (GC) risk. However, the causal nature of these associations remains uncertain.
Methods: We utilized summary data from genome-wide association studies (GWAS) on GM (European, n=8,956), blood metabolites (European, n=120,241; East Asian, n=4,435), and GC (European, n=476,116; East Asian, n=167,122) to perform a bidirectional Mendelian randomization (MR) analysis, investigating the causal effects of GM and metabolites on GC risk.
Diabetes Obes Metab
January 2025
Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
Background: Medications targeting the leptin and Apolipoprotein CIII (APOC3) pathways are currently under development for the treatment of hypertriglyceridaemia. Given that both pathways are implicated in triglyceride regulation, it is unknown whether they function independently or interact under physiological conditions and under acute or long-term energy deficiency.
Methods: APOC3 levels and their association with circulating lipids and lipoproteins were evaluated in the context of two randomised controlled studies.
Nutrients
December 2024
Department of Nutrition and Movement Sciences, NUTRIM Institute of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands.
Background: Recently, we reported that longer-term mixed nut intake significantly reduced serum total and low-density lipoprotein (LDL)-cholesterol, but these markers may not fully capture lipoprotein-related cardiovascular disease (CVD) risk.
Objectives: This randomized, controlled, single-blinded, crossover trial in older adults with overweight or obesity examined the effects of longer-term mixed nut consumption on lipoprotein particle size, number, and lipid distribution.
Methods: Twenty-eight participants (aged 65 ± 3 years; BMI 27.
PLoS Genet
December 2024
Institute of Translational Genomics, Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany.
Circulating metabolite levels have been associated with type 2 diabetes (T2D), but the extent to which T2D affects metabolite levels and their genetic regulation remains to be elucidated. In this study, we investigate the interplay between genetics, metabolomics, and T2D risk in the UK Biobank dataset using the Nightingale panel composed of 249 metabolites, 92% of which correspond to lipids (HDL, IDL, LDL, VLDL) and lipoproteins. By integrating these data with large-scale T2D GWAS from the DIAMANTE meta-analysis through Mendelian randomization analyses, we find 79 metabolites with a causal association to T2D, all spanning lipid-related classes except for Glucose and Tyrosine.
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