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.

Cardiovascular (CV) complications are the leading cause of death in patients with chronic kidney disease (CKD). Endothelin-1 (ET-1), a potent vasoconstrictor involved in both renal and vascular dysfunction, may represent a promising biomarker for the disease. ET-1 plasma levels were quantified in 692 Spanish CKD patients (stages 1-5) and used to stratify individuals into three clusters (cluster 3 meaning highest concentrations). Associations with CKD progression, CVE, and all-cause mortality were assessed over a mean follow-up of 48.6 ± 27.4 months using linear mixed-effects models and Cox regression analyses adjusted for conventional risk factors. ET-1 levels increased with CKD severity (mean±SD: 1.65 ± 0.71 pg/mL for stages 1-2; 1.82 ± 0.71 pg/mL for stage 3; 2.39 ± 1.08 pg/mL for stages 4-5; p < 0.001). Higher ET-1 levels were independently associated with accelerated eGFR decline over 3 years (β = -12.64, p < 0.001 for cluster 2; and β = -11.71, p = 0.034 for cluster 3). Sixty-nine CVE (10.1 %) were recorded. Participants with higher ET-1 levels had significantly lower CV event-free survival [HR = 2.24 (1.12-4.45), p = 0.022, and HR = 2.50 (1.09-5.73), p = 0.03] for clusters 2 and 3, respectively. ET-1 also predicted all-cause mortality (p < 0.001) although the association lost significance after adjusting for age. Random forest models for CV risk and all-cause mortality including the ET-1 cluster produced C-indices of 0.835 and 0.837, respectively. Elevated ET-1 levels are independently associated with both CKD progression and CV complications. ET-1 may serve as a dual biomarker for renal deterioration and CV risk, potentially improving clinical stratification in CKD management.

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.

Background: Nutritional biomarkers are essential for assessing the impact of dietary interventions on health. Amino Acids (AA) serve as potential biomarkers due to their role in metabolic pathways, although the influence of combining macronutrients on AA metabolism remains unclear. Objective: This study aims to evaluate the effects of combining macronutrients (fat, protein, and fiber) on AA metabolism. Methods: A dietary intervention was conducted with 41 participants who were assigned to two groups: the Protein Fat (PF) group and the Protein Fat and Fiber (PFF) group. AA concentration was measured using LC-MS/MS. Results: Our findings indicated that combining macronutrients reduced plasma levels of AA with statistically significant differences between the two diets (p < 0.05 for EAA and BCAA; p < 0,01 for NEAA and AAA). Metabolic profile analysis showed differences between the groups, especially at 55 days (55 D) (R2X: 0.749; R2Y: 0.589; Q2: 0.582, p < 0.001) and 98 days (98 D) (R2X: 0.886; R2Y: 0.7; Q2: 0.605, p < 0.001). Five metabolites (leucine, asparagine, alanine, cysteine, and creatine) were the most influential factors in group differentiation (VIP score), with values ranging between 1.04 and 1.75. Conclusions: The combination of macronutrients led to a significant reduction in plasma amino acids and amines in the PFF group, with recovery post-intervention. These findings highlight the possibility that these metabolites are related to different health statuses.

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. In 579 CKD patients, plasma FGF19 and β-Klotho con-centrations were quantified, and 64 genetic variants across FGF19, KLB, FGFR1 and FGFR4 genes were analyzed. 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 associa-tions: 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 discrimina-tive ability (C-index = 0.80), with the FGF19/β-Klotho cluster showing predictive im-portance comparable to diabetes and previous CV history. These findings suggest that in-tegrating FGF19-Klotho biomarkers with genetic information may enhance CV risk pre-diction in CKD.