To define relationships between lipidomics, inflammasome, and exhaled volatile organic compounds (VOCs) in ischemic heart disease (IHD) and develop a VOC-based diagnostic machine learning model for no Show more
To define relationships between lipidomics, inflammasome, and exhaled volatile organic compounds (VOCs) in ischemic heart disease (IHD) and develop a VOC-based diagnostic machine learning model for non-invasive diagnosis. A single-center prospective study involved 80 participants between 27 Oct 2023 and 11 Jun 2024: 31 with stress-computed tomography (CT) myocardial-perfusion-confirmed IHD and 49 perfusion-negative controls. All underwent stress CT perfusion, bicycle-ergometry, and breath collection at rest, peak exercise, and 3-minute recovery into a PTR-TOF-MS-1000. Lipid measurements were made (total, high-density lipoprotein [HDL]-, low-density lipoprotein [LDL]-, very LDL-cholesterol, triglycerides, apolipoprotein B [ApoB], lipoprotein-a) and inflammatory biomarkers (interleukin-6, C-reactive protein). LASSO regression mapped VOC-biomarker associations. An XGBoost classifier integrating VOCs, lipidome, inflammasome, and lipid-lowering therapy status was evaluated with cross-validated Youden index. Controls showed minimal biomarker-VOC relationships. Patients exhibited significant lipid-VOC correlations, including HDL-C with m/z 49.995 (r=0.31) and an inverse correlation between total cholesterol and m/z 94.053 (r=-0.35). Key discriminative VOCs were 2-ethyl-2,5-dihydro-4,5-dimethylthiazole, HO3PS2, CH8N3P, and m/z 49.995. Exercise revealed dynamic ApoB and LDL interactions exclusive to IHD. Inflammasome had limited direct VOC links; IL-6 inversely correlated with total cholesterol in IHD, while CRP aligned with HDL in controls. The final model achieved: AUC 0.931 (95% confidence interval [CI], 0.869-0.978), sensitivity 0.613 (95% CI, 0.435-0.793), specificity 1.000 (95% CI, 1.000-1.000), NPV 0.803 (95% CI, 0.692-0.903), PPV 1.000 (95% CI, 1.000-1.000). Exhaled VOC patterns reflect lipid dysregulation in IHD. Combined with lipid and inflammatory data, VOCs enable high-accuracy, non-invasive IHD discrimination, supporting breathomics as a promising diagnostic adjunct. ClinicalTrials.gov Identifier: NCT06181799. Show less
Kidney diseases pose a serious healthcare problem because of their high prevalence, worsening of patients' quality of life, and high mortality. Patients with kidney diseases are often asymptomatic unt Show more
Kidney diseases pose a serious healthcare problem because of their high prevalence, worsening of patients' quality of life, and high mortality. Patients with kidney diseases are often asymptomatic until disease progression starts. Expensive renal replacement therapy options, such as dialysis or kidney transplant, are required for end-stage kidney disease. Early diagnosis of kidney pathology is crucial for slowing down or curbing further damage. This study aimed to analyze the features of the protein composition of blood plasma in patients with the most common kidney pathologies: kidney calculus, kidney cyst, and kidney cancer. The study involved 75 subjects. Proteins associated with kidney pathologies (CFB, SERPINA3, HPX, HRG, SERPING1, HBB, ORM2, and CP) were proposed. These proteins are important participants of complement and coagulation cascade activation and lipid metabolism. The revealed phosphorylated proteoforms (CFB, C4A/C4B, F2, APOB, TTR, and NRAP) were identified. For them, modification sites were mapped on 3D protein models, and the potential role in formation of complexes with native partner proteins was assessed. The study demonstrates that the selected kidney pathologies have a similar proteomic profile, and patients can be classified into kidney pathology groups with an accuracy of (70-80)%. Show less