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Background Patients with chronic kidney disease stage 5 (CKD5) are predisposed to vascular calcification (VC), but the combined effect of factors associated with VC was sparsely investigated. We applied the relaxed linear separability (RLS) feature selection model to identify features that concomitantly associate with VC in CKD5 patients. Methods Epigastric arteries collected during surgery from living donor kidney transplant recipients were examined to score the histological extent of medial VC. Sixty‐two phenotypic features in 152 patients were entered into RLS model to differentiate between no–minimal VC (n = 93; score 0‐1) and moderate–extensive VC (n = 59; score 2‐3). The subset of features associated with VC was selected on the basis of cross‐validation procedure. The strength of association of the selected features with VC was expressed by the absolute value of ‘RLS factor’. Results Among 62 features, a subset of 17 features provided optimal prediction of VC with 89% of patients correctly classified into their groups. The 17 features included traditional risk factors (diabetes, age, cholesterol, BMI and male sex) and markers of bone metabolism, endothelial function, metabolites, serum antibodies and mitochondrial‐derived peptide. Positive RLS factors range from 1.26 to 4.05 indicating features associated with increased risk of VC, and negative RLS factors range from −0.95 to −1.83 indicating features associated with reduced risk of VC. Conclusion The RLS model identified 17 features including novel biomarkers and traditional risk factors that together concomitantly associated with medial VC. These results may inform further investigations of factors promoting VC in CKD5 patients.

These results indicate a larger involvement of hereditary factors in inflammation than might have been expected and suggest that inclusion of genotype features in risk assessment studies is critical. The RLS model demonstrates that inflammation in CKD is determined by an extensive panel of factors and may prove to be a suitable tool that could enable a much-needed multifactorial approach as opposed to the commonly utilized single-factor analysis.

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Identification of risk factors in patients with a particular disease can be analyzed in clinical data sets by using feature selection procedures of pattern recognition and data mining methods. The applicability of the relaxed linear separability (RLS) method of feature subset selection was checked for high-dimensional and mixed type (genetic and phenotypic) clinical data of patients with end-stage renal disease. The RLS method allowed for substantial reduction of the dimensionality through omitting redundant features while maintaining the linear separability of data sets of patients with high and low levels of an inflammatory biomarker. The synergy between genetic and phenotypic features in differentiation between these two subgroups was demonstrated.