Periodontitis (PO), a chronic microbially-induced inflammation of the supporting tissues of the tooth, is linked to various systemic diseases. We analyze its bidirectional relationship to chronic kidney disease (CKD), a major health-care problem with impressive excess mortality. Overwhelming associative relationship between CKD and PO are analyzed. Major pathophysiologic mechanisms that link CKD to PO are then presented: systemic inflammation, endothelial dysfunction, and imbalance of oxidative stress characteristic of CKD have a role in PO development and might influence escape mechanisms of oral microbiota. Subclinical local and systemic inflammation induced by PO might influence in turn CKD outcomes. Homeostatic changes induced by CKD such as mineral bone disorders, acidosis, uremic milieu, or poor salivary flow are also relevant for the occurrence of PO. There is insufficient evidence to recommend a standardized diagnostic and therapeutic approach regarding association of PO to CKD.

The mechanism underlying podocyte dysfunction in minimal change disease (MCD) remains unknown. This study aimed to shed light on the potential pathophysiology of MCD using glomerular proteomic analysis. Shotgun proteomics using label-free quantitative mass spectrometry was performed on formalin-fixed, paraffin-embedded (FFPE) renal biopsies from two groups of samples: control (CTR) and MCD. Glomeruli were excised from FFPE renal biopsies using laser capture microdissection (LCM), and a single-pot solid-phase-enhanced sample preparation (SP3) digestion method was used to improve yield and protein identifications. Principal component analysis (PCA) revealed a distinct separation between the CTR and MCD groups. Forty-eight proteins with different abundance between the two groups (p-value ≤ 0.05 and |FC| ≥ 1.5) were identified. These may represent differences in podocyte structure, as well as changes in endothelial or mesangial cells and extracellular matrix, and some were indeed found in several of these structures. However, most differentially expressed proteins were linked to the podocyte cytoskeleton and its dynamics. Some of these proteins are known to be involved in focal adhesion (NID1 and ITGA3) or slit diaphragm signaling (ANXA2, TJP1 and MYO1C), while others are structural components of the actin and microtubule cytoskeleton of podocytes (ACTR3 and NES). This study suggests the potential of mass spectrometry-based shotgun proteomic analysis with LCM glomeruli to yield valuable insights into the pathogenesis of podocytopathies like MCD. The most significantly dysregulated proteins in MCD could be attributable to cytoskeleton dysfunction or may be a compensatory response to cytoskeleton malfunction caused by various triggers.

Tacrolimus has a narrow therapeutic window; a whole-blood trough target concentration of between 5 and 8 ng/mL is considered a safe level for stable kidney transplant recipients. Tacrolimus serum levels must be closely monitored to obtain a balance between maximizing efficacy and minimizing dose-related toxic effects. Currently, there is no specific tacrolimus toxicity biomarker except a graft biopsy. Our study aimed to identify specific serum metabolites correlated with tacrolinemia levels using serum high-precision liquid chromatography–mass spectrometry and standard laboratory evaluation. Three machine learning algorithms were used (Naïve Bayes, logistic regression, and Random Forest) in 19 patients with high tacrolinemia (8 ng/mL) and 23 patients with low tacrolinemia (5 ng/mL). Using a selected panel of five lipid metabolites (phosphatidylserine, phosphatidylglycerol, phosphatidylethanolamine, arachidyl palmitoleate, and ceramide), Mg²⁺, and uric acid, all three machine learning algorithms yielded excellent classification accuracies between the two groups. The highest classification accuracy was obtained by Naïve Bayes, with an area under the curve of 0.799 and a classification accuracy of 0.756. Our results show that using our identified five lipid metabolites combined with Mg²⁺ and uric acid serum levels may provide a novel tool for diagnosing tacrolimus toxicity in kidney transplant recipients. Further validation with targeted MS and biopsy-proven TAC toxicity is needed.

Background/Objectives: In hemodialysis (HD), inflammatory biomarkers are discussed as prognostic markers for survival and cardiovascular events (CVEs). The results of the studies are not uniform and there are particularities related to population groups and comorbidities. In addition, it is known that inflammation determines protein malnutrition and less about the effect of adipose tissue on inflammation in HD. This study investigates the relationship between inflammatory molecules and nutritional biomarkers, and CVE and survival in HD patients. Methods: We included, in an observational, longitudinal study, 65 patients with chronic HD (53 without diabetes and 22 smokers), with a mean age of 60.1 ± 12.4 years. High-sensitivity C-reactive protein (hs-CRP), interleukin 1 beta, tumor necrosis factor alpha (TNF-alpha), interleukin 6, soluble tumor necrosis factor-like weak inducer of apoptosis (sTWEAK), soluble CD163 (sCD163), and fibroblast growth factor 21 were determined. We recorded survival and cardiovascular events for 60 months. Univariate and multivariate analyses were performed. Results: Hs-CRP was significantly associated with survival (p = 0.014) in the total group. In smokers and former smokers, TNF-α lower than 368.34 pg/mL was associated with better survival. In multivariate analysis, hs-CRP was correlated with adipose tissue biomarkers (p = 0.006), and sCD163 was correlated with total and LDL cholesterol (p = 0.002). In addition, in univariate analysis, sTWEAK was correlated with serum albumin (p = 0.026, r = −0.30). In conclusion, in HD patients, hs-CRP was significantly associated with survival, and low TNF-alpha values in smokers and former smokers were linked to better survival. Hs-CRP was also correlated with adipose tissue biomarkers, CD163 was correlated with total and LDL cholesterol, and albumin was inversely associated with sTWEAK. The relation between inflammatory molecules and adipose tissue biomarkers was less identified in HD patients until now.