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Complexity and Non-Linearity of Cardiovascular Risk Factors in Older Patients With Multimorbidity and Reduced Renal Function

The aim of this study was to reveal the complex pathophysiology network that may underly associations between chronic kidney disease (CKD), defined with mildly to moderately decreased renal function, and increased CV risk. For this purpose, we used a set of parameters indicating biochemical disorders, known to be associated with CKD. This set of parameters was taken from the larger dataset, where clinical characteristics of older patients with multimorbidity (the existence of two or more chronic diseases at the same person) and mildly to moderately decreased renal function, have been described with multiple parameters. On the selected set of parameters, we applied Machine Learning (ML) methods, to demonstrate relationships between the parameters. We used SMOreg algorithm for developing regression model. At first, we applied the SMOreg algorithm on the dataset to predict C-reactive protein (CRP) and then we used same algorithm to discover pairwise nonlinear relationships between variables such as Age-fglu, Chol-HTC and HB-FE, Age-Homcis, Clear-Homcis, Homcis-TG, CRP-TG. The assessment of non-linear relationships among multiple parameters indicating confounding factors of renal function decline, in older people with multimorbidity, has revealed the close associations between insulin resistance and serum albumin and homocystein levels. Several hypotheses are arising from these study with the potential to facilitate research on the concept of the reverse epidemiology. Although this analytical approach is far from being sufficient to provide the full understanding of the relationships between cardiovascular risk factors and decreased renal function, in older population, by means of the reverse epidemiology, this study emphasizes a need for more integrated and dynamical approaches, when assessing these factors.

Cardiovascular disease (CKD) SMOReg SVR nonlinear relationship

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