Genetics and reverse epidemiology among patients on chronic hemodialysis

Abstract

A reversal in the association between traditional and nontraditional risk factors and clinical outcomes is often encountered in patients with chronic illness, including among those with advanced chronic kidney disease (CKD) on maintenance hemodialysis (MHD). The effects of the malnutrition-inflammation complex syndrome (MICS) may play a significant role in the reversal of this risk factor-outcomes association. the MICS, this syndrome complex is not universal in its prevalence among MHD patients. The significant inter- and intra-individual differences in the prevalence of inflammation, oxidative stress, and malnutrition, indicates the influence of genetic factors in this variability. In recent years, enormous advancement in the field of molecular genetics, genomics and bioinformatics, have revolutionized studies of the genetic epidemiology of several diseases. However, genetic association studies are at a preliminary stage in the population with advanced CKD (Table 1). Preliminary studies of the impact of polyphisms in inflammation and oxidative stress-related genes and genes affecting body composition and metabolism suggest that genetic variation may indeed affect the phenotype of the MHD population. Further, some of these gene polymorphisms may also contribute to a reversal of the association between traditional risk factors, such as BMI, blood pressure, and cholesterol and clinical outcomes in this vulnerable patient population. Genetic studies in patients with advanced CKD pose enormous challenges, including recruitment of sufficient numbers of patients to achieve adequate statistical power, resolution of immense genotypic and phenotypic heterogeneity, and gene-environment and gene-gene interactions. However, well-designed adequately powered studies with carefully defined phenotypes may potentially allow definition of risk profiles characterized by combinations of relevant Single nucleotide polymorphisms in the setting of given environmental factors. Accurate risk stratification that takes into account genetic information would allow more informed targeting of pharmacologic intervention and better refined clinical trial methodologies.