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Principal Investigator  
Principal Investigator's Name: Wing Fung Chau
Institution: Cardiff University
Department: Medical School
Country:
Proposed Analysis: Title: Systematic analysis of lipids and metabolites in Alzheimer’s Disease Background: Alzheimer’s Disease (AD) is the most common neurodegenerative disease that increasingly challenges our aging population. AD is traditionally characterised by beta-amyloid plaques and Tau-protein neurofibrillary tangle. However, lipids are known to involve in function and structure of neural membranes in the brain and are hugely important in brain function. It would be foolish to ignore such a critical component when looking at AD, therefore new research hypothesised correlation between dysmetabolism of lipids and AD progression. To help understand the multifacet of AD, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) http://adni.loni.usc.edu/ and the Alzheimer’s Disease Metabolomics Consortium (ADMC) https://sites.duke.edu/adnimetab/ have collated information on: • Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scans • Genetics • Cognitive test scores of Mini Mental State Exam (MMSE) and Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog) • Cerebral Spinal Fluid (CSF) and Blood biomarkers (including 341 known lipids) from 1650 individuals over 13-year period. Hence, these 2 databases serves as an excellent source of reliable and up-to-date information on AD. Recent advancement in using Big Data approach can help reduce or eliminate confounding factors such as aging, medication and gender when investigating link between AD and lipids. Question: The null hypothesis for this project is ‘There is no statistically significant difference in lipids/metabolites between 4 categories of individuals: Severe Cognitive Impairment patients, Moderate Cognitive Impairment patients, Mild Cognitive Impairment patients and Normal Subjects.’ I will categorise individuals base on MMSE score which data is available in the 2 databases. Score cut off explained in image below: Methods: I will use Big Data approaches to analyse data and do statistical calculations. The name of the programming software is ‘R’. I will import data from ADNI and ADMC and run ‘dry’ experiments and calculation to find, if any, significant findings. Image of overview below: Anticipated outcome: Make observations and comment on, if any, significant changes in lipids and metabolites between these 4 categories of individuals. I will discuss reasons behind these lipid dysmetabolism. Future outcome: Use data generated by the experiment to suggest clinical application such as making improvement to screening test by incorporating, if any, significant lipids. Publish a report on my findings.
Additional Investigators  
Investigator's Name: You Zhou
Proposed Analysis: Title: Systematic analysis of lipids and metabolites in Alzheimer’s Disease Background: Alzheimer’s Disease (AD) is the most common neurodegenerative disease that increasingly challenges our aging population. AD is traditionally characterised by beta-amyloid plaques and Tau-protein neurofibrillary tangle. However, lipids are known to involve in function and structure of neural membranes in the brain and are hugely important in brain function. It would be foolish to ignore such a critical component when looking at AD, therefore new research hypothesised correlation between dysmetabolism of lipids and AD progression. To help understand the multifacet of AD, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) http://adni.loni.usc.edu/ and the Alzheimer’s Disease Metabolomics Consortium (ADMC) https://sites.duke.edu/adnimetab/ have collated information on: • Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scans • Genetics • Cognitive test scores of Mini Mental State Exam (MMSE) and Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog) • Cerebral Spinal Fluid (CSF) and Blood biomarkers (including 341 known lipids) from 1650 individuals over 13-year period. Hence, these 2 databases serves as an excellent source of reliable and up-to-date information on AD. Recent advancement in using Big Data approach can help reduce or eliminate confounding factors such as aging, medication and gender when investigating link between AD and lipids. Question: The null hypothesis for this project is ‘There is no statistically significant difference in lipids/metabolites between 4 categories of individuals: Severe Cognitive Impairment patients, Moderate Cognitive Impairment patients, Mild Cognitive Impairment patients and Normal Subjects.’ I will categorise individuals base on MMSE score which data is available in the 2 databases. Score cut off explained in image below: Methods: I will use Big Data approaches to analyse data and do statistical calculations. The name of the programming software is ‘R’. I will import data from ADNI and ADMC and run ‘dry’ experiments and calculation to find, if any, significant findings. Image of overview below: Anticipated outcome: Make observations and comment on, if any, significant changes in lipids and metabolites between these 4 categories of individuals. I will discuss reasons behind these lipid dysmetabolism. Future outcome: Use data generated by the experiment to suggest clinical application such as making improvement to screening test by incorporating, if any, significant lipids. Publish a report on my findings.
Investigator's Name: XianFang Sun
Proposed Analysis: Background: Alzheimer’s Disease (AD) is the most common neurodegenerative disease that increasingly challenges our aging population. AD is traditionally characterised by beta-amyloid plaques and Tau-protein neurofibrillary tangle. However, lipids are known to involve in function and structure of neural membranes in the brain and are hugely important in brain function. It would be foolish to ignore such a critical component when looking at AD, therefore new research hypothesised correlation between dysmetabolism of lipids and AD progression. To help understand the multifacet of AD, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) http://adni.loni.usc.edu/ and the Alzheimer’s Disease Metabolomics Consortium (ADMC) https://sites.duke.edu/adnimetab/ have collated information on: • Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scans • Genetics • Cognitive test scores of Mini Mental State Exam (MMSE) and Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog) • Cerebral Spinal Fluid (CSF) and Blood biomarkers (including 341 known lipids) from 1650 individuals over 13-year period. Hence, these 2 databases serves as an excellent source of reliable and up-to-date information on AD. Recent advancement in using Big Data approach can help reduce or eliminate confounding factors such as aging, medication and gender when investigating link between AD and lipids. Question: The null hypothesis for this project is ‘There is no statistically significant difference in lipids/metabolites between 4 categories of individuals: Severe Cognitive Impairment patients, Moderate Cognitive Impairment patients, Mild Cognitive Impairment patients and Normal Subjects.’ I will categorise individuals base on MMSE score which data is available in the 2 databases. Methods: I will use Big Data approaches to analyse data and do statistical calculations. The name of the programming software is ‘R’. I will import data from ADNI and ADMC and run ‘dry’ experiments and calculation to find, if any, significant findings Anticipated outcome: Make observations and comment on, if any, significant changes in lipids and metabolites between these 4 categories of individuals. I will discuss reasons behind these lipid dysmetabolism. Future outcome: Use data generated by the experiment to suggest clinical application such as making improvement to screening test by incorporating, if any, significant lipids. Publish a report on my findings
Investigator's Name: Shenglei Fang
Proposed Analysis: Title: Systematic analysis of lipids and metabolites in Alzheimer’s Disease Background: Alzheimer’s Disease (AD) is the most common neurodegenerative disease that increasingly challenges our aging population. AD is traditionally characterised by beta-amyloid plaques and Tau-protein neurofibrillary tangle. However, lipids are known to involve in function and structure of neural membranes in the brain and are hugely important in brain function. It would be foolish to ignore such a critical component when looking at AD, therefore new research hypothesised correlation between dysmetabolism of lipids and AD progression. To help understand the multifacet of AD, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) http://adni.loni.usc.edu/ and the Alzheimer’s Disease Metabolomics Consortium (ADMC) https://sites.duke.edu/adnimetab/ have collated information on: • Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scans • Genetics • Cognitive test scores of Mini Mental State Exam (MMSE) and Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog) • Cerebral Spinal Fluid (CSF) and Blood biomarkers (including 341 known lipids) from 1650 individuals over 13-year period. Hence, these 2 databases serves as an excellent source of reliable and up-to-date information on AD. Recent advancement in using Big Data approach can help reduce or eliminate confounding factors such as aging, medication and gender when investigating link between AD and lipids. Question: The null hypothesis for this project is ‘There is no statistically significant difference in lipids/metabolites between 4 categories of individuals: Severe Cognitive Impairment patients, Moderate Cognitive Impairment patients, Mild Cognitive Impairment patients and Normal Subjects.’ I will categorise individuals base on MMSE score which data is available in the 2 databases. Score cut off explained in image below: Methods: I will use Big Data approaches to analyse data and do statistical calculations. The name of the programming software is ‘R’. I will import data from ADNI and ADMC and run ‘dry’ experiments and calculation to find, if any, significant findings. Image of overview below: Anticipated outcome: Make observations and comment on, if any, significant changes in lipids and metabolites between these 4 categories of individuals. I will discuss reasons behind these lipid dysmetabolism. Future outcome: Use data generated by the experiment to suggest clinical application such as making improvement to screening test by incorporating, if any, significant lipids. Publish a report on my findings.