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: | Jinqiu Yuan |
| Institution: | Sun Yat-sen University |
| Department: | The Seventh Affiliated Hospital |
| Country: | |
| Proposed Analysis: | INTRODUCTION Cognitive impairment in the elderly is one of the greatest health challenge owing to aging population. Early identification of high risk population plays an important role in the prevention of cognitive impairment. As peripheral blood sample is easily accessible, blood markers for cognitive impairment show great value in clinical practice, although current research is still limited. It has been shown that metabolic disorders including central obesity, hyperglycemia, dyslipidemia, and hypertension, are closely related to cognitive impairment. In this proposed analysis, we will evaluated associations between metabolic markers with cognitive functions based on the ANDI data sets. In addition, we will test marker of oxidative stress, and evaluated the potential pathway of ‘metabolism disorders - oxidative stress - cognitive impairment’ with structural equation model. This study will establish valid molecular markers for predicting cognitive impairment and provide insights into the underlying mechanisms. Our findings will contribute to the prevention of cognitive impairment. METHOD Study design: Longitudinal study Participants: Older subjects (aged 65 or over) diagnosed with mild cognitive impairment, Alzheimer’s Disease, and cognitively normal individuals from ANDI. Exposure: Blood metabolic markers, such as HbA1c, total cholesterol, triglyceride, HDL, LDL, and other metabolomic and lipidomic biomarkers. Outcome: Cognitive assessment by the Preclinical Alzheimer Cognitive Composite, Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, Logical Memory Delayed Recall, and other cognitive testing tools. Covariates: Demographics, comorbidities, important genetic factors, medications, and other potential confounding factors. Statistical Methods: The characteristics of the participants will be summarized, and differences will be tested by analysis of variance or Pearsonχ2 test. The associations between exposures and outcomes will be analyzed by mixed effect model adjusting for potential confounding factors. We will under subgroup analyses to evaluate potential interaction effect (age, sex, baseline cognitive function). Reporting: We will report the results in two or three manuscripts based on the individual vascular factors. The final manuscript will be drafted according to the STROBE Statement. |
| Additional Investigators | |
| Investigator's Name: | Wenjing Li |
| Proposed Analysis: | INTRODUCTION Cognitive impairment in the elderly is one of the greatest health challenge owing to aging population. Early identification of high risk population plays an important role in the prevention of cognitive impairment. As peripheral blood sample is easily accessible, blood markers for cognitive impairment show great value in clinical practice, although current research is still limited. It has been shown that metabolic disorders including central obesity, hyperglycemia, dyslipidemia, and hypertension, are closely related to cognitive impairment. In this proposed analysis, we will evaluated associations between metabolic markers with cognitive functions based on the ANDI data sets. In addition, we will test marker of oxidative stress, and evaluated the potential pathway of ‘metabolism disorders - oxidative stress - cognitive impairment’ with structural equation model. This study will establish valid molecular markers for predicting cognitive impairment and provide insights into the underlying mechanisms. Our findings will contribute to the prevention of cognitive impairment. METHOD Study design: Longitudinal study Participants: Older subjects (aged 65 or over) diagnosed with mild cognitive impairment, Alzheimer’s Disease, and cognitively normal individuals from ANDI. Exposure: Blood metabolic markers, such as HbA1c, total cholesterol, triglyceride, HDL, LDL, and other metabolomic and lipidomic biomarkers. Outcome: Cognitive assessment by the Preclinical Alzheimer Cognitive Composite, Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, Logical Memory Delayed Recall, and other cognitive testing tools. Covariates: Demographics, comorbidities, important genetic factors, medications, and other potential confounding factors. Statistical Methods: The characteristics of the participants will be summarized, and differences will be tested by analysis of variance or Pearsonχ2 test. The associations between exposures and outcomes will be analyzed by mixed effect model adjusting for potential confounding factors. We will under subgroup analyses to evaluate potential interaction effect (age, sex, baseline cognitive function). Reporting: We will report the results in two or three manuscripts based on the individual vascular factors. The final manuscript will be drafted according to the STROBE Statement. |
| Investigator's Name: | Qiangsheng He |
| Proposed Analysis: | INTRODUCTION Cognitive impairment in the elderly is one of the greatest health challenge owing to aging population. Early identification of high risk population plays an important role in the prevention of cognitive impairment. As peripheral blood sample is easily accessible, blood markers for cognitive impairment show great value in clinical practice, although current research is still limited. It has been shown that metabolic disorders including central obesity, hyperglycemia, dyslipidemia, and hypertension, are closely related to cognitive impairment. In this proposed analysis, we will evaluated associations between metabolic markers with cognitive functions based on the ANDI data sets. In addition, we will test marker of oxidative stress, and evaluated the potential pathway of ‘metabolism disorders - oxidative stress - cognitive impairment’ with structural equation model. This study will establish valid molecular markers for predicting cognitive impairment and provide insights into the underlying mechanisms. Our findings will contribute to the prevention of cognitive impairment. METHOD Study design: Longitudinal study Participants: Older subjects (aged 65 or over) diagnosed with mild cognitive impairment, Alzheimer’s Disease, and cognitively normal individuals from ANDI. Exposure: Blood metabolic markers, such as HbA1c, total cholesterol, triglyceride, HDL, LDL, and other metabolomic and lipidomic biomarkers. Outcome: Cognitive assessment by the Preclinical Alzheimer Cognitive Composite, Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, Logical Memory Delayed Recall, and other cognitive testing tools. Covariates: Demographics, comorbidities, important genetic factors, medications, and other potential confounding factors. Statistical Methods: The characteristics of the participants will be summarized, and differences will be tested by analysis of variance or Pearsonχ2 test. The associations between exposures and outcomes will be analyzed by mixed effect model adjusting for potential confounding factors. We will under subgroup analyses to evaluate potential interaction effect (age, sex, baseline cognitive function). Reporting: We will report the results in two or three manuscripts based on the individual vascular factors. The final manuscript will be drafted according to the STROBE Statement. |
