General characteristics of the eight studies included in the systematic review.

<p>General characteristics of the eight studies included in the systematic review.</p

Results of studies evaluating the effect of long-term exposure to HSCD <i>(≥ 2 years</i>) or the effect of discontinuation of a chronic treatment with HSCD on cardiovascular events.

<p>Results of studies evaluating the effect of long-term exposure to HSCD <i>(≥ 2 years</i>) or the effect of discontinuation of a chronic treatment with HSCD on cardiovascular events.</p

Results of studies evaluating the effect of short-term exposure to HSCD (≤ 7 days) on cardiovascular events.

<p>Results of studies evaluating the effect of short-term exposure to HSCD (≤ 7 days) on cardiovascular events.</p

Study quality.

<p>Assessment of the intra-study risk of bias using the EPHPP tool.</p

PRISMA flowchart summarizing the identification and selection process of the studies for inclusion in the systematic review.

<p>PRISMA flowchart summarizing the identification and selection process of the studies for inclusion in the systematic review.</p

Description of the drug-associated sodium intake.

<p>Description of the drug-associated sodium intake.</p

Characteristics of study populations associated with SSBP phenotype.

<p>Characteristics of study populations associated with SSBP phenotype.</p

Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data

<div><p>¹H Nuclear Magnetic Resonance (NMR)-based metabolic profiling is very promising for the diagnostic of the stages of chronic kidney disease (CKD). Because of the high dimension of NMR spectra datasets and the complex mixture of metabolites in biological samples, the identification of discriminant biomarkers of a disease is challenging. None of the widely used chemometric methods in NMR metabolomics performs a local exhaustive exploration of the data. We developed a descriptive and easily understandable approach that searches for discriminant local phenomena using an original exhaustive rule-mining algorithm in order to predict two groups of patients: 1) patients having low to mild CKD stages with no renal failure and 2) patients having moderate to established CKD stages with renal failure. Our predictive algorithm explores the m-dimensional variable space to capture the local overdensities of the two groups of patients under the form of easily interpretable rules. Afterwards, a L2-penalized logistic regression on the discriminant rules was used to build predictive models of the CKD stages. We explored a complex multi-source dataset that included the clinical, demographic, clinical chemistry, renal pathology and urine metabolomic data of a cohort of 110 patients. Given this multi-source dataset and the complex nature of metabolomic data, we analyzed 1- and 2-dimensional rules in order to integrate the information carried by the interactions between the variables. The results indicated that our local algorithm is a valuable analytical method for the precise characterization of multivariate CKD stage profiles and as efficient as the classical global model using chi2 variable section with an approximately 70% of good classification level. The resulting predictive models predominantly identify urinary metabolites (such as 3-hydroxyisovalerate, carnitine, citrate, dimethylsulfone, creatinine and N-methylnicotinamide) as relevant variables indicating that CKD significantly affects the urinary metabolome. In addition, the simple knowledge of the concentration of urinary metabolites classifies the CKD stage of the patients correctly.</p></div

Frequency of the cluster of buckets present in at least 50% of the local 1&2D models.

<p>This figure shows the frequency heatmap of the interactions (color scale) of each bucket pair of the cluster over the 100 local 1&2D models. The more robust the interaction is, the darker it will be. The interactions corresponding to the subgroup of patients with an eGFR < 60 ml/min/1.73m² are displayed in the lower triangle and conversely, the interactions corresponding to the subgroup of patients with an eGFR ≥ 60 ml/min/1.73m² are displayed in the upper triangle. The limit of the two triangles is represented by the red dashed-line.</p

Buckets’ frequency over the 100 metabolomic local 1D models.

<p>Each horizontal segment corresponds to a 1D rule characterized by its bucket’s name, covered values ranges (i.e., buckets bins which could be interpreted as relative concentration) and frequency (color scale). The more robust the rule is, the darker it will be. (A) shows the rules corresponding to the subgroup of patients with an eGFR <60 ml/min/1.73m² and conversely, (B) shows the rules corresponding to the subgroup of patients with an eGFR ≥ 60 ml/min/1.73m².</p