Early recognition of a risk of Alzheimer's disease (AD) remains a global challenge, and blood proteomic markers are of particular interest for wide-scale diagnostic use. Quantitative multiple reaction Show more
Early recognition of a risk of Alzheimer's disease (AD) remains a global challenge, and blood proteomic markers are of particular interest for wide-scale diagnostic use. Quantitative multiple reaction monitoring (MRM) approach demonstrates good reproducibility in the characteristic changes in the levels of reported candidate biomarkers (CBs) in different cohorts in AD. Following up on our previous study, we performed a joint analysis of 331 blood plasma samples from two different clinical cohorts of participants, comprising a total of 95 samples from patients with AD, 136 samples from patients with mild cognitive impairment (MCI), and 100 samples from controls. The obtained results confirm the significance of 37 CBs. A logistic regression-based algorithm was used to build protein classifiers, and a total of 21 important proteins were selected, 13 of which (ORM1, APOA4, LBP, HP, FN1, BCHE, APOE, PZP, A1BG, TF, SERPINA7, TTR, and F12) formed a universal panel that demonstrated strong classification performance in distinguishing AD patients from controls (ROC-AUC = 0.90) and in separating stable and progressing patients with MCI (ROC-AUC = 0.81). Overall, the analysis confirms the high potential of the MRM method for validating CBs in independent cohorts. Show less
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