Late onset depression (LOD) is considered to be one kind of the spectrum diseases of Alzheimer's disease (AD).The CARE index predictive model was constructed by our research group and the Medical College of Wisconsin in the United States based on the event risk model (EBP) fusion of behavioral biology, brain structure and function AD risk biomarkers. In previous studies,the CARE index has been proven to have good predictive performance in the Alzheimer's Disease Neuroimaging Initiative dataset .This study is to capitalize on LOD data from an independent Nanjing Aging and Dementia Study (NADS) cohort to verify the ability of CARE index for predicting LOD progression to AD across-datasets. A total of 48 LOD subjects were selected from the NADS dataset. Among them, 37 patients were in the cognitive stability group and 11 in the dementia conversion group. The CARE index was constructed using the EBP model to fuse AD risk biomarkers from behavioral, brain structure and brain function. Using a ROC curve, we applied the CARE index to identify those LOD individuals who progressed to AD-type dementia during the 32-month follow-up period. The CARE index achieves a fairly high predictive performance of the LOD-to-AD with 75.0% accuracy,72.7% sensitivity, 75.7% specificity, 74.2% balanced accuracy, 0.79 AUC on LOD subjects from the NADS dataset. Compared with the predictive power of individual biological markers, CARE index has a better predictability, as well as a higher balance accuracy. In addition, the individual CARE index scores at baseline were negatively correlated with MMSE scores at follow-up (R2=0.175, p<0.005).
Diagnosis of major depressive disorder (MDD) using resting-state functional connectivity (rs-FC) data faces many challenges, such as the high dimensionality, small samples, and individual difference. To assess the clinical value of rs-FC in MDD and identify the potential rs-FC machine learning (ML) model for the individualized diagnosis of MDD, based on the rs-FC data, a progressive three-step ML analysis was performed, including six different ML algorithms and two dimension reduction methods, to investigate the classification performance of ML model in a multicentral, large sample dataset [1021 MDD patients and 1100 normal controls (NCs)]. Furthermore, the linear least-squares fitted regression model was used to assess the relationships between rs-FC features and the severity of clinical symptoms in MDD patients. Among used ML methods, the rs-FC model constructed by the eXtreme Gradient Boosting (XGBoost) method showed the optimal classification performance for distinguishing MDD patients from NCs at the individual level (accuracy = 0.728, sensitivity = 0.720, specificity = 0.739, area under the curve = 0.831). Meanwhile, identified rs-FCs by the XGBoost model were primarily distributed within and between the default mode network, limbic network, and visual network. More importantly, the 17 item individual Hamilton Depression Scale scores of MDD patients can be accurately predicted using rs-FC features identified by the XGBoost model (adjusted R2 = 0.180, root mean squared error = 0.946). The XGBoost model using rs-FCs showed the optimal classification performance between MDD patients and HCs, with the good generalization and neuroscientifical interpretability.
Objectives: Inflammation plays a key role in the pathogenesis and progression of ischemic stroke (IS). The high mobility group box 1 (HMGB1) nucleoprotein is involved in the amplification of inflammatory responses during acute ischemic injury. HMGB1 levels in patients with active disease are higher than those in healthy controls. We performed a meta-analysis to assess currently published data pertaining to circulating blood HMGB1 levels in IS and the relationship with stroke severity.Methods: We systematically searched for studies investigating the circulating blood HMGB1 levels in patients with IS in PubMed/Medline, Embase, the Cochrane Library, Web of science and China National Knowledge Infrastructure (CNKI). Two independent researchers used the Cochrane Collaboration tools for data extraction and quality assessment. Extracted data were analyzed by Review Manager version 5.3.Results: A total of 28 studies were included with a total of 4497 participants, including 2671 IS patients and 1826 matched controls. The meta-analysis revealed that compared with control, IS patients had higher circulating blood HMGB1 levels (n = 4497, standardized mean difference (SMD) = 5.70, 95%confidence interval (CI) = 4.79 to 6.62, Z = 12.23, P < 0.00001), and the HMGB1 level was positively correlated with severity (n = 507, SMD = −2.12, 95%CI = −3.41 to −0.82, Z = 3.20, P < 0.00001) and infarct volume (n = 582, 95%CI = −4.06 to −1.70, Z = 4.79, P < 0.00001).Conclusions: This meta-analysis demonstrates that circulating blood HMGB1 levels elevate in IS and higher HMGB1 levels may indicate a more serious condition.
Author(s): Lu, Xiang | Advisor(s): Belin, Thomas R | Abstract: We developed an imputation model solving the missing-data problem in a high-dimensional longitudinal data set with mixed data types (continuous and ordinal) based on a factor-analysis and a linear mixed-effect model. Markov Chain Monte Carlo is used to fit the model, drawing parameters, latent variables and missing values iteratively. The imputation model is written in an R package. We tested the newly developed imputation model using simulated data sets under 32 scenarios and 2 hypothetical missing-data mechanisms. Two competitive models PAN (Multiple Imputation for Multivariate Panel or Clustered Data) and MICE (Multiple Imputation using Chained Equations) are also tested in the same way for comparison, to show the necessity of addressing the high-dimension and mixed continuous and ordinal data type issues. Part of the effort we made is to accelerate the simulation using C++ (a low-level language) and the parallel computing by the Hoffman 2 Cluster. Compared to running the simulation evaluation in an R program on one single computer, the program we use for the simulation evaluation runs approximately 600 times faster. We also tested the robustness of the newly developed imputation model in the cases of violation of assumptions. We found that assuming less than the true number of factors corresponds to invalid inferences, while assuming more than that corresponds to reasonable inferences. We also found that only omitting very strong underlying quadratic trends of the factor scores hurt the inferences based on the imputation. In the most unfavorable scenario we tested, when the underlying quadratic coefficient is as large as .8 of the linear coefficient, the actual coverage rates of 95% interval estimates start falling below 90%. An application to a dentistry data is shown, in comparison to the PAN, NORM and a fore runner of the newly developed method.
Late onset depression (LOD) is considered to be one kind of the spectrum diseases of Alzheimer's disease (AD).The CARE index predictive model was constructed by our research group and the Medical College of Wisconsin in the United States based on the event risk model (EBP) fusion of behavioral biology, brain structure and function AD risk biomarkers. In previous studies ， the CARE index has been proven to have good predictive performance in the Alzheimer's Disease Neuroimaging Initiative dataset .This study is to capitalize on LOD data from an independent Nanjing Aging and Dementia Study (NADS) cohort to verify the ability of CARE index for predicting LOD progression to AD across-datasets. A total of 48 LOD subjects were selected from the NADS dataset. Among them, 37 patients were in the cognitive stability group and 11 in the dementia conversion group. The CARE index was constructed using the EBP model to fuse AD risk biomarkers from behavioral, brain structure and brain function. Using a ROC curve, we applied the CARE index to identify those LOD individuals who progressed to AD-type dementia during the 32-month follow-up period. The CARE index achieves a fairly high predictive performance of the LOD-to-AD with 75.0% accuracy, 72.7% sensitivity, 75.7% specificity, 74.2% balanced accuracy, 0.79 AUC on LOD subjects from the NADS dataset. Compared with the predictive power of individual biological markers, CARE index has a better predictability, as well as a higher balance accuracy. In addition, the individual CARE index scores at baseline were negatively correlated with MMSE scores at follow-up (R2=0.175, p<0.005).
Abstract Aims Both amnestic mild cognitive impairment (aMCI) and remitted late‐onset depression (rLOD) confer a high risk of developing Alzheimer's disease (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 aMCI population or in an rLOD 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 aMCI population (27 stable subjects, 6 progressive subjects) and rLOD population (29 stable subjects, 10 progressive subjects) during the follow‐up period. Results AD diagnoses were predicted in the aMCI 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 rLOD 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 2 = .17, P < .001) at baseline 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 aMCI and rLOD populations effectively. Additionally, it can be used to monitor the disease severity of patients.
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. 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 an ultrasensitive quantitative method. Machine learning algorithms were performed for establishing an optimal model of identifying aMCI. Compared with healthy controls, Aβ40 and Aβ42 levels were lower and NFL levels were higher in plasma of aMCI patients with an exception of t-tau levels. In aMCI patients, the higher plasma Aβ40 levels were correlated with the impaired episodic memory and negative correlations were observed between plasma t-tau levels and global cognitive function and gray matter (GM) volume. In addition, the higher plasma NFL levels were correlated with reduced hippocampus volume and total GM volume of the left inferior and middle temporal gyrus. 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%). We demonstrated that plasma Aβ40, Aβ42, t-tau, 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.
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