The interplay between lipoprotein phenotypes, adiponectin, and alcohol consumption.

CONTEXT AND OBJECTIVE: Lipoproteins are involved in the pathophysiology of several metabolic diseases. Here we focus on the interplay between lipoprotein metabolism and adiponectin with the extension of alcohol intake. DESIGN AND SUBJECTS: Eighty-three low-to-moderate and 80 heavy alcohol drinkers were studied. Plasma adiponectin, other biochemical and extensive lipoprotein data were measured. Self-organizing maps were applied to characterize lipoprotein phenotypes and their interrelationships with biochemical measures and alcohol consumption. RESULTS: Alcohol consumption and plasma adiponectin had a strong positive association. Heavy alcohol consumption was associated with decreased low-density lipoprotein cholesterol (LDL-C). Nevertheless, two distinct lipoprotein phenotypes were identified, one with elevated high-density lipoprotein cholesterol (HDL-C) and decreased very-low-density lipoprotein triglycerides (VLDL-TG) together with low prevalence of metabolic syndrome, and the other vice versa. The HDL particles were enlarged in both phenotypes related to the heavy drinkers. The low-to-moderate alcohol drinkers were characterized with high LDL-C and C-enriched LDL particles. CONCLUSIONS: The analyses per se illustrated the multi-faceted and non-linear nature of lipoprotein metabolism. The heavy alcohol drinkers were characterized either by an anti-atherogenic lipoprotein phenotype (with also the highest adiponectin concentrations) or by a phenotype with pro-atherogenic and metabolic syndrome-like features. Clinically this underlines the need to distinguish the differing individual risk for lipid-related metabolic disturbances also in heavy alcohol drinkers.

High serum adiponectin is associated with favorable lipoprotein subclass profile in 6.4-year follow-up.

OBJECTIVE: Adiponectin is linked to a favorable lipoprotein profile, but potential longitudinal associations are not known. DESIGN: A population-based follow-up study of all inhabitants born in 1942, 1947, 1952, and 1957 (n=1294) in Pieksämäki, a town in Finland. Of the 690 subjects participating in both the check-ups, 228 subjects with diabetes or any medication for dyslipidemia, high blood pressure, or diabetes were excluded. The final study population consisted of 462 (182 men and 280 women) apparently healthy subjects. METHODS: Main outcome measures were lipoprotein particle sizes and concentrations, apolipoprotein A-1 (APOA1) and APOB levels at baseline and follow-up across baseline adiponectin tertiles. Serum adiponectin concentrations were determined using an enzyme immunoassay, and lipoprotein subclasses using proton nuclear magnetic resonance spectroscopy. RESULTS: At the second health check-up 6.4 years later, the very low-density lipoprotein particle concentration decreased across the baseline adiponectin tertiles in men from 1.04 (0.28) to 0.91 (0.29) nmol/l (P for linearity=0.011) and in women from 0.92 (0.32) to 0.80 (0.24) nmol/l (P=0.002). Correspondingly, the mean high-density lipoprotein particle size increased from 9.78 to 9.90 nm in men (P<0.006) and from 10.00 to 10.14 nm in women (P<0.001). CONCLUSION: The favorable links between adiponectin and lipoproteins are detectable 6.4 years later.

Metabonomic, transcriptomic, and genomic variation of a population cohort.

Comprehensive characterization of human tissues promises novel insights into the biological architecture of human diseases and traits. We assessed metabonomic, transcriptomic, and genomic variation for a large population-based cohort from the capital region of Finland. Network analyses identified a set of highly correlated genes, the lipid-leukocyte (LL) module, as having a prominent role in over 80 serum metabolites (of 134 measures quantified), including lipoprotein subclasses, lipids, and amino acids. Concurrent association with immune response markers suggested the LL module as a possible link between inflammation, metabolism, and adiposity. Further, genomic variation was used to generate a directed network and infer LL module's largely reactive nature to metabolites. Finally, gene co-expression in circulating leukocytes was shown to be dependent on serum metabolite concentrations, providing evidence for the hypothesis that the coherence of molecular networks themselves is conditional on environmental factors. These findings show the importance and opportunity of systematic molecular investigation of human population samples. To facilitate and encourage this investigation, the metabonomic, transcriptomic, and genomic data used in this study have been made available as a resource for the research community.

Characterization of metabolic interrelationships and in silico phenotyping of lipoprotein particles using self-organizing maps.

Plasma lipid concentrations cannot properly account for the complex interactions prevailing in lipoprotein (patho)physiology. Sequential ultracentrifugation (UCF) is the gold standard for physical lipoprotein isolations allowing for subsequent analyses of the molecular composition of the particles. Due to labor and cost issues, however, the UCF-based isolations are usually done only for VLDL, LDL, and HDL fractions; sometimes with the addition of intermediate density lipoprotein (IDL) particles and the fractionation of HDL into HDL(2) and HDL(3) (as done here; n = 302). We demonstrate via these data, with the lipoprotein lipid concentration and composition information combined, that the self-organizing map (SOM) analysis reveals a novel data-driven in silico phenotyping of lipoprotein metabolism beyond the experimentally available classifications. The SOM-based findings are biologically consistent with several well-known metabolic characteristics and also explain some apparent contradictions. The novelty is the inherent emergence of complex lipoprotein associations; e.g., the metabolic subgrouping of the associations between plasma LDL cholesterol concentrations and the structural subtypes of LDL particles. Importantly, lipoprotein concentrations cannot pinpoint lipoprotein phenotypes. It would generally be beneficial to computationally enhance the UCF-based lipoprotein data as illustrated here. Particularly, the compositional variations within the lipoprotein particles appear to be a fundamental issue with metabolic and clinical corollaries.

Reconsideration of hydrophobic lipid distributions in lipoprotein particles.

Lipoprotein particles are commonly known as micellar aggregates with hydrophobic lipids located within the core and amphipathic molecules in the surface. Using a new structural model for optimizing the distribution of hydrophobic lipids, namely triglyceride (TG) and cholesterol ester (CE) molecules, we reveal that particle size-dependent proportion of these 'core lipids' may locate in the surface of lipoprotein particles. The composition of the particles also strongly influences the actual molecular content of the surface. For example, in high-density lipoprotein (HDL) particles the percentage of CEs of all surface lipids is between 13% and 27% due to the high tendency of CEs to locate in the surface and the high concentration of CEs in the particles. Conversely, although the percentage of TG molecules in the surface of HDL particles is also high, approximately 60% as for CE, the percentage of TGs of all surface lipids is low, only up to 5%, because HDL particles have a low-TG concentration. These structural models provide an intuitive and coherent structural rationale for various metabolic cascades in lipoprotein metabolism with the catalytic enzyme action and molecular binding for transport proteins taking place at the surface of the particles.