There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Huimin Cai |
| Institution: | Xuanwu Hospital Capital Medical University |
| Department: | Department of Neurology |
| Country: | |
| Proposed Analysis: | Introduction: This data analysis protocol aims to investigate the association between peripheral inflammatory biomarkers in blood and Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) using data from the ADNI database. The analysis will include data preparation, statistical analysis, and interpretation of results, as well as additional analyses for diagnostic and predictive capabilities, mediation analysis, and restricted cubic splines (RCS) for modeling non-linear relationships. 2. Data Source and Variables: The data will be obtained from the ADNI database, including peripheral inflammatory biomarker levels, demographic information, cognitive test scores, AD/MCI diagnosis, and core AD biomarkers. 3. Data Preprocessing: Before conducting any analysis, perform necessary data preprocessing steps, which may involve data cleaning, data transformation, and handling missing data. 4. Descriptive Statistics: Calculate descriptive statistics for the variables of interest, such as peripheral inflammatory biomarker levels, cognitive test scores, and demographic information. Utilize visualizations like histograms and box plots to gain insights into the dataset's characteristics. 5. Group Comparison: Compare peripheral inflammatory biomarker levels between different groups (AD patients, MCI patients, normal cognitive controls). Use appropriate statistical tests (e.g., t-tests, ANOVA) to determine significant differences between groups. 6. Correlation Analysis: Assess the correlation between peripheral inflammatory biomarker levels and cognitive test scores within each group. Additionally, investigate the associations between inflammatory biomarkers and disease severity (e.g., MMSE scores) within AD and MCI groups. 7. Diagnostic and Predictive Analysis: a. Diagnostic Analysis: - Utilize receiver operating characteristic (ROC) curve analysis to assess the discriminatory power of peripheral inflammatory biomarkers for differentiating AD/MCI patients from normal controls. - Calculate area under the curve (AUC) values and 95% confidence intervals to quantify diagnostic accuracy. - Determine the optimal cutoff points for biomarker levels that maximize sensitivity and specificity. b. Predictive Analysis: - Perform longitudinal analysis using data at baseline to predict the conversion of MCI to AD, normal control to MCI/AD. - Apply logistic regression or other appropriate predictive modeling techniques to identify predictive biomarkers for conversion. - Assess model performance using metrics such as accuracy, sensitivity, specificity, and AUC. 8. Mediation Analysis: a. Use Core AD Biomarkers as a Mediator: - Identify core AD biomarkers (e.g., Aβ42, tau, p-tau, cortical thickness) available in the ADNI database. - Perform mediation analysis to investigate whether the association between peripheral inflammatory biomarkers and AD/MCI is partially mediated by core AD biomarkers. - Use appropriate statistical methods for mediation analysis (e.g., bootstrapping) to estimate indirect effects and their significance. 9. Restricted Cubic Splines (RCS): Investigate potential non-linear relationships between peripheral inflammatory biomarkers and AD/MCI risk using RCS. Fit regression models with RCS terms to allow for more flexible modeling of the biomarker-disease relationship. |
| Additional Investigators | |
| Investigator's Name: | Longfei Jia |
| Proposed Analysis: | Introduction: This data analysis protocol aims to investigate the association between peripheral inflammatory biomarkers in blood and Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) using data from the ADNI database. The analysis will include data preparation, statistical analysis, and interpretation of results, as well as additional analyses for diagnostic and predictive capabilities, mediation analysis, and restricted cubic splines (RCS) for modeling non-linear relationships. 2. Data Source and Variables: The data will be obtained from the ADNI database, including peripheral inflammatory biomarker levels, demographic information, cognitive test scores, AD/MCI diagnosis, and core AD biomarkers. 3. Data Preprocessing: Before conducting any analysis, perform necessary data preprocessing steps, which may involve data cleaning, data transformation, and handling missing data. 4. Descriptive Statistics: Calculate descriptive statistics for the variables of interest, such as peripheral inflammatory biomarker levels, cognitive test scores, and demographic information. Utilize visualizations like histograms and box plots to gain insights into the dataset's characteristics. 5. Group Comparison: Compare peripheral inflammatory biomarker levels between different groups (AD patients, MCI patients, normal cognitive controls). Use appropriate statistical tests (e.g., t-tests, ANOVA) to determine significant differences between groups. 6. Correlation Analysis: Assess the correlation between peripheral inflammatory biomarker levels and cognitive test scores within each group. Additionally, investigate the associations between inflammatory biomarkers and disease severity (e.g., MMSE scores) within AD and MCI groups. 7. Diagnostic and Predictive Analysis: a. Diagnostic Analysis: - Utilize receiver operating characteristic (ROC) curve analysis to assess the discriminatory power of peripheral inflammatory biomarkers for differentiating AD/MCI patients from normal controls. - Calculate area under the curve (AUC) values and 95% confidence intervals to quantify diagnostic accuracy. - Determine the optimal cutoff points for biomarker levels that maximize sensitivity and specificity. b. Predictive Analysis: - Perform longitudinal analysis using data at baseline to predict the conversion of MCI to AD, normal control to MCI/AD. - Apply logistic regression or other appropriate predictive modeling techniques to identify predictive biomarkers for conversion. - Assess model performance using metrics such as accuracy, sensitivity, specificity, and AUC. 8. Mediation Analysis: a. Use Core AD Biomarkers as a Mediator: - Identify core AD biomarkers (e.g., Aβ42, tau, p-tau, cortical thickness) available in the ADNI database. - Perform mediation analysis to investigate whether the association between peripheral inflammatory biomarkers and AD/MCI is partially mediated by core AD biomarkers. - Use appropriate statistical methods for mediation analysis (e.g., bootstrapping) to estimate indirect effects and their significance. 9. Restricted Cubic Splines (RCS): Investigate potential non-linear relationships between peripheral inflammatory biomarkers and AD/MCI risk using RCS. Fit regression models with RCS terms to allow for more flexible modeling of the biomarker-disease relationship. |
