Study On Objective Biomarkers For Early Identification Of Individuals At High Risk Of Alzheimer’s Disease

Part I Combining Neuroimaging and Machine Learning Techniques to Explore Plasma Objective Biomarkers for Early Identification of Individuals at High Risk of Alzheimer’s DiseaseChapter 1:Potential Value of Plasma Alzheimer’s Disease Candidate Biomarkers for Early Identification of Individuals at High Risk of Alzheimer’s DiseaseBackground:Non-invasive,convenient,and effective biomarkers are very important to the early diagnosis of Alzheimer’s disease(AD).The objective of the study was to explore the potential value of plasma indicators for identifying amnesic mild cognitive impairment(aMCI)and determine whether levels of plasma indicators are related to the performance of cognitive function and brain tissue volumes.Methods:In total,155 participants(68 aMCI patients and 87 health controls)were recruited in the present cross-sectional study.The levels of plasma amyloid-β(Aβ)40,Aβ42,total tau(T-tau),and neurofilament light(NFL)were measured using Single Molecular Array(Simoa).Various machine learning algorithms,combining demographic information,hippocampal structure,and plasma indicators,were performed to establish an optimal model of identifying aMCI.Results:Compared with healthy subjects,Aβ40 and Aβ42 levels were significantly lower and NFL levels were significantly higher in plasma of aMCI patients.There was no difference in except for T-tau levels between the two groups.Plasma Aβ40 was negatively correlated with episodic memory(r =-0.229,p < 0.01)and executive function scores(r =-0.234,p < 0.01),and Aβ42 was negatively correlated with MMSE scores(r =-0.168,p < 0.05)and executive function scores(r =-0.198,p < 0.05).Plasma T-tau levels were also negatively correlated with MMSE scores(r =-0.237,p < 0.01).Plasma NFL levels were negatively correlated with episodic memory(r =-0.229,p < 0.01).In addition,plasma T-tau levels were inversely correlated with total gray matter volume(GM)(r =-0.188,p < 0.05),NFL levels were inversely correlated with hippocampal volume(r =-0.163,p < 0.05).GM volumes in the left middle temporal gyrus/inferior temporal gyrus were inversely correlated with plasma NFL levels in patients with aMCI(r =-0.515,p < 0.001).An integrated model included clinical features,hippocampus volume,and plasma Aβ42 and NFL and had the highest accuracy for detecting aMCI patients(Accuracy,74.2%).Conclusions:We demonstrated that plasma Aβ40,Aβ42 and NFL may be useful to identify aMCI and correlate with cognitive decline and brain atrophy.Among these plasma indicators,Aβ42 and NFL are more valuable as key members of a peripheral biomarker panel to detect aMCI.Chapter2:Correlation between Plasma AD Candidate Biomarkers and Hippocampal-related Memory Networks in Patients with aMCIBackground:Plasma Aβ42/40 and NFL were considered as potential biomarkers for AD.Episodic memory(EM)impairment is the core symptom of aMCI.However,the neural mechanism by which these peripheral blood biomarkers promote episodic memory impairment in aMCI is still unclear.Methods:Based on the study in Chapter 1,65 aMCI and 81 HCs were included in the study,with the quality of imaging data as an inclusion/exclusion criterion.We first evaluated whether the level of plasma Aβ42/40 and NFL were different between the two groups and were associated with EM,respectively.The effect of EM,plasma Aβ42/40 and plasma NFL on the hippocampal functional connectivity(HFC)in aMCI group was studied by whole-brain regression analysis,and their interaction was examined by conjunction analysis.We further explored whether the intensity of HFC mediated the association between plasma candidate biomarker and EM through mediation analysis.In addition,we used a support vector classifier(SVC)model to build a model to identify patients with aMCI.Results:Significant differences were showed in plasma Aβ42/40 and NFL between patients with aMCI and HC,and plasma NFL levels were inversely correlated with EM.The overlapping effects of plasma Aβ42/40 level,plasma NFL level and hippocampal-related EM network were mainly located in prefrontal limbic system and right parietal lobule in aMCI group.Besides,at the nervous system level,HFC in these brain regions mediated the effect of plasma biomarkers on EM.We also constructed a composite model combining the plasma candidate protein markers,hippocampal structures,and specific HFC to effectively identify patients with aMCI(Accuracy 85.6%).Conclusions:Plasma Aβ42/40 and plasma NFL promote EM impairment in aMCI by affecting HFC in the prefrontal-limbic system and parietal lobule.A composite model combining plasma AD candidate biomarkers,hippocampal structure,and hippocampal-specific functional connectivity can effectively identify patients with aMCI and may become an important tool for identification of aMCI.Part Ⅱ Validation of the CARE index in Independent Samples and Study on Its Correlation with Plasma Candidate BiomarkersChapter 3:Predicting Conversion to Alzheimer’s Disease among Individual High-risk Patients Using the CARE index ModelBackground:Both aMCI and remitted late-onset depression(rLOD)confer a high risk of developing AD.This study aims to determine whether the Characterizing AD Risk Events(CARE)index model can effectively predict conversion in individuals at high risk for AD development either in an independent a MCI population or in an r LOD population.Methods:The CARE index model was constructed based on the event-based probabilistic framework fusion of AD biomarkers to differentiate individuals progressing to AD from cognitively stable individuals in the a MCI population(27 stable subjects,6 progressive subjects)and r LOD population(29 stable subjects,10 progressive subjects)during the follow-up period.Results:AD diagnoses were predicted in the a MCI population with a balanced accuracy of 80.6%,a sensitivity of 83.3%,and a specificity of 77.8%.They were also predicted in the r LOD population with a balanced accuracy of 74.5%,a sensitivity of 80.0%,and a specificity of 69.0%.In addition,the CARE index scores were observed to be negatively correlated with the composite Z-scores for episodic memory(r =-0.415,p < 0.001)at baseline and with the MMSE score at follow-up(r =-0.391,p < 0.001)in the combined high-risk population(N = 72).Conclusions:The CARE index model can be used for the prediction of conversion to AD in both a MCI and r LOD populations effectively.Additionally,it can be used to monitor severity of illness in patients.Chapter 4.Correlation between Plasma AD Candidate Markers and CARE indexBackground:Based on our previous research,the CARE index system has been shown to be a risk assessment system for AD with high sensitivity,high specificity,especially to characterize the progression stage of AD at an individual level.We will verify its classification performance of identifying a MCI patients from HC in the dataset used in Section 2,and further evaluate the diagnostic value of plasma AD candidate biomarkers using the CARE index system.Methods:A total of 65 patients with a MCI and 81 HCs were enrolled.Based on the EBP framework,biomarkers from multiple dimensions were fused into individual CARE index.Receiver operating characteristic curve(ROC)was used to evaluate their ability to identify a MCI.The correlation between CARE index and plasma AD candidate biomarkers was investigated using partial correlation analysis.Results:The CARE index system could effectively identify a MCI from HC.The sensitivity,specificity,balance accuracy and AUC were 72.3%,75.3%,73.8% and 0.774,respectively.The higher the CARE index,the higher the severity of cognitive impairment in patients with a MCI.In all participants,higher CARE index at the individual level indicated lower plasma Aβ42 level(r =-0.205,p = 0.015),higher plasma T-tau level(r = 0.387,p < 0.001)and higher plasma NFL level(r = 0.437,p < 0.001).Conclusion:The results showed that the CARE index system is a good prediction model for AD with balanced classification performance,strong robustness and generalization ability.Among the many plasma protein candidate markers,plasma Aβ42,T-tau,especially NFL,were considered to be better candidates for monitoring the progression of AD.