There is a pressing need for more precise biomarkers of chronic kidney disease (CKD). Plasma samples from 820 subjects [231 with CKD, 325 with end-stage kidney disease (ESKD) and 264 controls] were analyzed by liquid chromatography with tandem mass spectrometry (LC-MS/MS) to determine a metabolic profile of 28 amino acids (AAs) and biogenic amines to test their value as markers of CKD risk and progression. The kynurenine/tryptophan ratio showed the strongest correlation with estimated glomerular filtration rate values (coefficient = −0.731, p < 0.0001). Models created with orthogonal partial least squares-discriminant analysis (OPLS-DA) containing the metabolic signature showed a high goodness of fit and predictability for controls/CKD (R2X:0.73:R2Y:0.92:Q2:0.92, p < 0.0001) and lower values for CKD/ESKD (R2X:0.56:R2Y:0.59:Q2:0.55, p < 0.0001). Based on generated VIP scores, the most relevant markers for segregating samples into control/CKD and CKD/ESKD groups were citrulline (1.63) and tryptophan (1.47), respectively. ROC analysis showed that the addition of the metabolic profile to a model including CKD classic risk factors improved the AUC from 86.7% (83.6–89.9) to 100% (100–100) for CKD risk (p < 0.0001) and from 63.0% (58.2–67.8) to 96.5% (95.3–97.8) for the risk of progression from CKD to ESKD (p < 0.0001). Plasma concentrations of AAs and related amines may be useful as diagnostic biomarkers of kidney disease, both for CKD risk and for progression of CKD patients to ESKD.

Genes in the epoxygenase pathway of arachidonic acid metabolism leading to vasoactive eicosanoids, mainly 20-hydroxyeicosatetraenoic (20-HETE) and epoxyeicosatrienoic (EETs) acids, have been related to glucose-induced renal damage in preclinical reports. We genotyped 1088 diabetic kidney disease (DKD) patients and controls for seven polymorphisms in five genes (CYP2C8, CYP2J2, CYP4F2, CYP4A11, and EPHX2) along this metabolic route and evaluated their effect on DKD risk, clinical outcomes, and the plasma/urine levels of eicosanoids measured by LC/MS/MS and immunoenzymatic assays. The CYP4F2 433M variant allele was associated with lower incidence of DKD (OR = 0.65 (0.48–0.90), p = 0.008), whilst the CYP2C8*3/*3 genotype was related to increased risk (OR = 3.21 (1.05–9.87), p = 0.036). Patients carrying the 433M allele also showed lower eGFR [median and interquartile range vs. wildtype carriers: 30.8 (19.8) and 33.0 (23.2) mL/min/1.73 m², p = 0.037). Finally, the 433VM/MM variant genotypes were associated with lower urinary levels of 20-HETE compared with 433VV (3.14 (0.86) vs. 8.45 (3.69) ng/mg Creatinine, p = 0.024). Our results indicate that the CYP4F2 V433M polymorphism, by decreasing 20-HETE levels, may play an important role in DKD.

Diabetic kidney disease (DKD) has been pointed out as a prominent cause of chronic and end-stage renal disease (ESRD). There is a genetic predisposition to DKD, although clinically relevant loci are yet to be identified. We utilized a custom target next-generation sequencing 70-gene panel to screen a discovery cohort of 150 controls, DKD and DKD-ESRD patients. Relevant SNPs for the susceptibility and clinical evolution of DKD were replicated in an independent validation cohort of 824 controls and patients. A network analysis aiming to assess the impact of variability along specific pathways was also conducted. Forty-eight SNPs displayed significantly different frequencies in the study groups. Of these, 28 with p-values lower than 0.01 were selected for replication. MYH9 rs710181 was inversely associated with the risk of DKD (OR = 0.52 (0.28–0.97), p = 0.033), whilst SOWAHB rs13140552 and CNDP1 rs4891564 were not carried by cases or controls, respectively (p = 0.044 and 0.023). In addition, the RGMA rs1969589 CC genotype was significantly correlated with lower albumin-to-creatinine ratios in the DKD patients (711.8 ± 113.0 vs. 1375.9 ± 474.1 mg/g for TC/TT; mean difference = 823.5 (84.46–1563.0); p = 0.030). No biological pathway stood out as more significantly affected by genetic variability. Our findings reveal new variants that could be useful as biomarkers of DKD onset and/or evolution.

Prostaglandin E2 (PGE2) is a major actor mediating renal injury. We aimed to determine genetic variability in the genes coding for its receptors (PTGER1-4) and study associations with nephrosclerosis risk and clinical outcomes. We identified 96 tag-SNPs capturing global variability in PTGER1-4 and screened 1209 nephrosclerosis patients and controls. The effect of these variants was evaluated by multivariate regression analyses. Two PTGER3 SNPs, rs11209730 and rs10399704, remained significant in a backward elimination regression model with other non-genetic variables (OR = 1.45 (1.07–1.95), p = 0.016 and OR = 0.71 (0.51–0.99), p = 0.041, respectively). In the nephrosclerosis patients, a proximal region of PTGER3 was tagged as relevant for eGFR (p values for identified SNPs ranged from 0.0003 to 0.038). Two consecutive PTGER3 SNPs, rs2284362 and rs2284363, significantly decreased systolic (p = 0.005 and p = 0.0005), diastolic (p = 0.039 and p = 0.005), and pulse pressure values (p = 0.038 and 0.014). Patients were followed for a median of 47 months (7–54) to evaluate cardiovascular (CV) risk. Cox regression analysis showed that carriers of the PTGER1rs2241360 T variant had better CV event-free survival than wild-type individuals (p = 0.029). In addition, PTGER3rs7533733 GG carriers had lower event-free survival than AA/AG patients (p = 0.011). Our results indicate that genetic variability in PGE2 receptors, particularly EP3, may be clinically relevant for nephrosclerosis and its associated CV risk.

There is a pressing need for more precise biomarkers of chronic kidney disease (CKD). Plasma samples from 820 subjects [231 with CKD, 325 with end-stage kidney disease (ESKD) and 264 controls] were analyzed by LC-MS/MS to determine a metabolic profile of 28 aminoacids (AA) and biogenic amines to test their value as markers of CKD risk and progression. The Kynurenine/Tryptophan ratio showed the strongest correlation with estimated glomerular filtration rate values (coefficient=-0.731, P<0.0001). Models created with Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) containing the metabolic signature showed high goodness of fit and predictability for controls/CKD (R2X:0.73;R2Y:0.92;Q2:0.92) and lower for CDK/ESKD (R2X:0.56;R2Y:0.59;Q2:0.55). Based on generated VIP scores, the most relevant markers for segregating samples into control/CKD or CKD/ESKD groups were citrulline (1.67) and tryptophan (1.59), respectively. ROC analysis showed that the addition of the metabolic profile to a model including CKD classic risk factors improved AUC from 86.7% (83.6-89.9) to 100% (100-100) for CKD risk (P<0.0001), and from 63.0% (58.2-67.8) to 96.5% (95.3-97.8) for the risk of progression from CKD to ESKD (P<0.0001). Plasma concentrations of AA and related amines may be useful as diagnostic biomarkers of kidney disease, both for CKD risk and for progression of CKD patients to ESKD.

Background: Chronic kidney disease (CKD) markedly increases the risk of cardiovascular events (CVE), yet conventional biomarkers often fail to capture this excess risk. We evaluated whether circulating levels and genetic variability within the FGF19/β-Klotho/FGFR axis contribute to CV risk stratification in CKD. Methods: In 579 CKD patients, plasma FGF19 and β-Klotho concentrations were quantified, and 64 genetic variants across FGF19, KLB, FGFR1, and FGFR4 genes were analyzed. Results: Cluster analysis identified three distinct biomarker profiles, with one cluster—characterized by low/intermediate FGF19 and markedly elevated β-Klotho—showing significantly reduced CV event-free survival. After adjustment for clinical covariates, this cluster was independently associated with higher CV risk [HR = 2.97 (1.12–7.92), p = 0.029]. Two genetic variants also showed independent associations: FGFR1 rs2288696 (protective) [HR = 0.51 (0.27–0.95), p = 0.029] and KLB rs2687971 (risk-increasing) [HR = 2.03 (0.97–4.27), p = 0.046]. A combined CV risk model incorporating biomarker clusters, relevant SNPs, and traditional risk factors achieved good discriminative ability (C-index = 0.80), with the FGF19/β-Klotho cluster showing predictive importance comparable to diabetes and previous CV history. Conclusions: These results indicate that integrating FGF19-Klotho biomarkers with genetic information may improve CV risk prediction in CKD.