Dyslipidemia is common among patients on hemodialysis, but its etiology is not fully understood. Although changes in cholesterol homeostasis and fatty acid metabolism play an important role during dialysis, the interaction of these metabolic pathways has yet to be studied in sufficient detail. In this study, we enrolled 26 patients on maintenance hemodialysis treatment (high-volume hemodiafiltration, HV HDF) without statin therapy (17 men/9 women) and an age/gender-matched group of 26 individuals without signs of nephropathy. The HV-HDF group exhibited more frequent signs of cardiovascular disease, disturbed saccharide metabolism, and altered lipoprotein profiles, manifesting in lower HDL-C, and raised concentrations of IDL-C and apoB-48 (all p < 0.01). HV-HDF patients had higher levels of campesterol (p < 0.01) and β-sitosterol (p = 0.06), both surrogate markers of cholesterol absorption and unchanged lathosterol concentrations. Fatty acid (FA) profiles were changed mostly in cholesteryl esters, with a higher content of saturated and n-3 polyunsaturated fatty acids (PUFA) in the HV-HDF group. However, n-6 PUFA in cholesteryl esters were less abundant (p < 0.001) in the HV-HDF group. Hemodialysis during end-stage kidney disease induces changes associated with higher absorption of cholesterol and disturbed lipoprotein metabolism. Changes in fatty acid metabolism reflect the combined effect of renal insufficiency and its comorbidities, mostly insulin resistance.

Long-chain polyunsaturated fatty acids (LC-PUFAs) play important roles in human health, from controlling inflammation to lipid and glucose homeostasis. In our previous study, which employed a cluster analysis of a plasma fatty acid (FA) pattern, we identified two clusters of metabolic syndrome (MetS) independent of clinical and biochemical parameters within the whole study group (controls together with metabolic syndrome (MetS) patients). FA desaturase (FADS) genes are the key regulators of LC-PUFA metabolism. The aim of this study was to analyze associations between FADS polymorphisms and clusters of MetS. The study group consisted of 188 controls and 166 patients with MetS. The first cluster contained 71 controls (CON1) and 109 MetS patients (MetS1). The second cluster consisted of 117 controls (CON2) and 57 MetS patients (MetS2). In comparison with MetS2, cluster MetS1 displayed a more adverse risk profile. Cluster CON1 had, in comparison with CON2, higher body weight and increased triacylglycerol levels (p < 0.05). We found that the FADS rs174537 (p < 0.001), rs174570 (p < 0.01), and rs174602 (p < 0.05) polymorphisms along with two inferred haplotypes had statistically significant genotype associations with the splitting of MetS into MetS1 and MetS2. Conversely, we observed no significant differences in the distribution of FADS polymorphisms between MetS and CON subjects, or between CON1 and CON2. These associations between FADS polymorphisms and two clusters of MetS (differing in waist circumference, HOMA-IR, lipolysis, and oxidative stress) implicate the important influence of genetic factors on the phenotypic manifestation of MetS.