Associations Between Levels of Peripheral NCAPH2 Promoter Methylation and Different Stages of Alzheimer's Disease: A Cross-Sectional Study.

BACKGROUND: Several studies have examined NCAPH2 methylation in amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD), but little is known of NCAPH2 methylation in subjective cognitive decline (SCD). OBJECTIVE: To examine whether methylation of peripheral NCAPH2 are differentially changed at various phases of AD, and whether it could serve as a diagnostic biomarker for SCD. METHODS: A total of 40 AD patients, 52 aMCI patients, 148 SCD patients, and 193 cognitively normal controls (NCs) were recruited in the current case-control study. Besides, 54 cognitively normal individuals have received amyloid positron emission tomography (amyloid PET) scans. Using bisulfite pyrosequencing method, we measured blood DNA methylation in the NCAPH2 gene promoter. RESULTS: The main outcomes were: 1) For SCD, there was no significant difference between SCD and NC regarding NCAPH2 methylation; 2) For aMCI, NCAPH2 methylation at CpG2 were significantly lower in aMCI compared with NC and SCD in the entire population and male subgroup; 3) For AD, NCAPH2 methylation at CpG1 were significantly lower in AD compared with NC among females; 4) A relationship with apolipoprotein E (APOE) ɛ4 status was shown. Receiver operating characteristic (ROC) analysis by combining NCAPH2 methylation, age, education, and APOEɛ4 status could distinguish between patients with aMCI (area under the curve (AUC): 0.742) and AD (AUC: 0.873) from NCs. CONCLUSION: NCAPH2 methylation levels were altered at the aMCI and AD stage and may be convenient and cost-effective biomarkers of AD and aMCI.

Prediction model based on machine learning for short- and long-term adverse events in left atrial appendage closure.

Background: At present, the prediction of adverse events (AE) had practical significance in clinic and the accuracy of AE prediction model after left atrial appendage closure (LAAC) needed to be improved. To identify a good prediction model based on machine learning for short- and long-term AE after LAAC. Methods: In this study, 869 patients were included from the Department of Cardiovascular Medicine of Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital during 2017 and 2021. Univariate and multivariate analyses were conducted for short-term AE after LAAC to determine possible risk factors related with AE. We compared 8 machine learning algorithms for prediction short-term AE, and XGBoost was found to have the best performance. In addition, Cox-regression was used for long-term AE to find out the risk factors and establish a prediction model. Results: In univariate and multivariate analysis, body mass index (BMI) [odds ratio (OR) =0.91], congestive heart failure, hypertension, age ≥75 years, diabetes, stroke2 attack (CHADS2) score (OR =0.49) and bleeding history or predisposition, labile international normalized ratio (INR), elderly, drug/alcohol usage (BLED) score (OR =1.71) were shown to be significant risk factors for short-term AE. The XGbosst algorithm was used to predict short-term AE based on 15 possible risk factors. For long-term AE, Cox regression was used for the prediction. The CHADS2 score [hazard ratio (HR) =1.43], hypertension (HR =2.18), age more than 75 (HR =0.49), diabetes (HR =0.57), BLED score (HR=0.28), stroke (HR =19.8), hepatopathy (HR =3.97), nephropathy (HR =2.93), INR instability (HR =4.18), drinking (HR =2.67), and drugs (HR =2.36) were significant risk factors for long-term AE. The XGBoost had a good receiver operating characteristic (ROC) curve and area under the curve (AUC) was 0.85. The accuracy of the XGBoost model stayed at nearly 0.95. Conclusions: In short- and long-term AE, CHADS2 score and BLED score were the most obvious risk factors. Several other risk factors also played roles in AE of LAAC. The incidence of long-term AE is under 15% and LAAC is effective and safe. The XGBoost model had good prediction accuracy and ROC curve.

Blood NCAPH2 Methylation Is Associated With Hippocampal Volume in Subjective Cognitive Decline With Apolipoprotein E ε4 Non-carriers.

Objective: This study assessed the methylation of peripheral NCAPH2 in individuals with subjective cognitive decline (SCD), identified its correlation with the hippocampal volume, and explored whether the correlation is influenced by apolipoprotein E ε4 (APOE ε4) status. Methods: Cognitively normal controls (NCs, n = 56), individuals with SCD (n = 81), and patients with objective cognitive impairment (OCI, n = 51) were included from the Sino Longitudinal Study on Cognitive Decline (NCT03370744). All participants completed neuropsychological assessments, blood tests, and structural MRI. NCAPH2 methylation was compared according to the diagnostic and APOE ε4 status. Partial correlation analysis was conducted to assess the correlations between the hippocampal volume, cognitive tests, and the NCAPH2 methylation levels. Results: Individuals with SCD and patients with OCI showed significantly lower levels of NCAPH2 methylation than NCs, which were independent of the APOE ε4 status. The NCAPH2 methylation levels and the hippocampal volumes were positively correlated in the SCD APOE ε4 non-carriers but not in the OCI group. No association was found between the NCAPH2 methylation levels and the cognitive function. Conclusion: Abnormal changes in blood NCAPH2 methylation were found to occur in SCD, indicating its potential to be used as a useful peripheral biomarker in the early stage of Alzheimer's disease screening.