Atherosclerosis has been considered as the main cause of morbidity, mortality, and disability worldwide. The first screening for antigen markers was conducted using the serological identification of antigens by recombinant cDNA expression cloning, which has identified adaptor-related protein complex 3 subunit delta 1 (AP3D1) as an antigen recognized by serum IgG antibodies of patients with atherosclerosis. Serum antibody levels were examined using the amplified luminescent proximity homogeneous assay-linked immunosorbent assay (AlphaLISA) using a recombinant protein as an antigen. It was determined that the serum antibody levels against AP3D1 were higher in patients with acute ischemic stroke, transient ischemic attack , diabetes mellitus (DM), cardiovascular disease , chronic kidney disease (CKD), esophageal squamous cell carcinoma (ESCC), and colorectal carcinoma than those in the healthy donors. The area under the curve values of DM, nephrosclerosis type of CKD, and ESCC calculated using receiver operating characteristic curve analysis were higher than that of other diseases. Correlation analysis showed that the anti-AP3D1 antibody levels were highly associated with maximum intima-media thickness, which indicates that this marker reflected the development of atherosclerosis. The results of the Japan Public Health Center-based Prospective Study indicated that this antibody marker is deemed useful as risk factors for AIS.

Background and Purpose: Ischemic stroke, such as Transient ischemic attack (TIA) and cerebral infarction (CI) , are the serious problems in the aging society. Therefore, development of biomarkers for TIA and CI is attempted.Methods: Candidate antigens recognized by IgG autoantibodies in the sera of nineteen TIA patients were screened by a human aortic endothelial cell cDNA library. Serum antibody levels against the antigens were examined by amplified luminescent proximity homogeneous assay-linked immunosorbent assay (AlphaLISA) in healthy donor (HD), TIA, and CI cohorts (n = 285, 92 and 529). A case-control study nested within the Japan Public Health Center-based Prospective Cohort Study (JPHC) was performed.Results: Aldolase A, fructose-bisphosphate (ALDOA) and fumarate hydratase (FH) were identified as the candidate antigens. AlphaLISA revealed that anti-ALDOA and anti-FH antibody levels were both higher in TIA or CI patients than in HDs ( P < 0.0001). The levels of anti-ALDOA [Odds ratio (OR): 2.46, P = 0.005] and anti-FH (OR: 2.49, P = 0.0037) were independent predictors of TIA by multivariate logistic regression analysis, similar results were found in CI. The case-control study showed the levels of anti-ALDOA (OR: 2.50, P < 0.01) and anti-FH (OR: 2.60, P < 0.01) were associated with risk of CI.Spearman's correlation analysis demonstrated an association between the anti-ALDOA and anti-FH levels and risk factors of ischemic stroke, such as age, smoking habit, coronary heart disease, and hypertension.Conclusions: Anti-ALDOA and anti-FH antibodies can serve as novel potential biomarkers for prediction of TIA and CI.

Background: Ischemic stroke, such as transient ischemic attack (TIA) and acute-phase cerebral infarction (aCI) , are the serious problems in the aging society. Therefore, development of biomarkers for TIA and aCI are attempted. Methods: Candidate antigens recognized by IgG autoantibodies in the serum of 19 TIA patients were screened by a human aortic endothelial cell cDNA library. Serum antibody levels against the antigens were examined by amplified luminescent proximity homogeneous assay-linked immunosorbent assay (AlphaLISA) in healthy donor (HD), TIA, and aCI cohorts ( n = 285, 92 and 529). The antibody levels in the sera of the Japan Public Health Center-based Prospective Cohort Study (JPHC) from 1991 to 1993 was also examined. Results: Aldolase A, fructose-bisphosphate (ALDOA) and fumarate hydratase (FH) were identified as the candidate antigens. AlphaLISA revealed that the levels of anti-ALDOA antibodies (ALDOA-Abs) and anti-FH antibodies (FH-Abs) were both higher in patients with TIA or aCI than those in HDs ( P < 0.05). The levels of ALDOA-Abs [odds ratio (OR): 2.46, P = 0.0050] and FH-Abs (OR: 2.49, P = 0.0037) were independent predictors of TIA by multivariate logistic regression analysis. The case-control study showed the levels of ALDOA-Abs (OR: 2.50, P < 0.01) and FH-abs (OR: 2.60, P < 0.01) were associated with risk of aCI. Correlation analysis demonstrated that both ALDOA-Abs and FH-Abs were well associated with hypertension, coronary heart disease and habitual smoking. These antibody levels were also correlated well with maximum intima-media thickness, which reflects atherosclerotic stenosis. Conclusions: ALDOA-Abs and FH-Abs can serve as novel potential biomarkers for prediction of atherosclerotic TIA and aCI.

High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions (LVOs). We hypothesized that prehospital diagnostic algorithms for strokes and their subcategories using machine learning could have high predictive value. Consecutive adult patients with suspected stroke as per emergency medical service personnel were enrolled in a prospective multicenter observational study in 12 hospitals in Japan. Five diagnostic algorithms using machine learning, including logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost), were evaluated for stroke and subcategories including acute ischemic stroke (AIS) with/without LVO, intracranial hemorrhage (ICH), and subarachnoid hemorrhage (SAH). Of the 1446 patients in the analysis, 1156 (80%) were randomly included in the training cohort and 290 (20%) were included in the test cohort. In the diagnostic algorithms for strokes using XGBoost had the highest diagnostic value (test data, area under the receiver operating curve [AUROC] 0.980, confidence interval [CI; 0.962–0.994]). In the diagnostic algorithms for the subcategories using XGBoost had a high predictive value (test data, AUROC [CI], AIS with LVO 0.898 [0.848–0.939], AIS without LVO 0.882 [0.836–0.923], ICH 0.866 [0.817–0.911], SAH 0.926 [0.874–0.971]). Prehospital diagnostic algorithms using machine learning had high predictive value for strokes and their subcategories.