Understanding CNS diseases through organ specific aging biomarkers
- PI
- Zhou, Wenyu
- DAR ID
- 11404
- Initial Approval Date
- February 27, 2025
- Current DAR Status
- Expired
- PI Institution
- Teal Omics. Inc
- PI Institution Country
- United States
- PI Institution Type
- For-Profit
Requested Datasets (5)
NG00113Metabolomic and lipidomic signatures in Alzheimer disease brainsNG00127A longitudinal study of Alzheimer's Disease and other dementing illnesses – KnightADRC GWASNG00135UAB ADRC - University of Alabama at Birmingham Alzheimer’s Disease Research CenterNG00102Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disordersNG00147Amyloid Neuroimaging and Genetics Initiative (ANGI) GWAS
Research Use Statement
Can blood-based protein biomarkers of aging predict functional decline in organ health and in systemic health? We have developed organ-specific aging models derived from plasma proteins, and previously found that the blood proteome can be used to monitor organ health and aging in smaller cohorts (Oh, et., al., Nature 624, 164–172 (2023)). We aim to test this hypothesis at scale with diversified datasets.
Aim 1. Validate our approach to modeling aging and brain health with NIAGADS cohorts. Approach: we will use protein biomarker data from cohorts to train machine learning models of organ aging. We will employ machine learning best practice for data curation, data augmentation, data normalization, and training/testing, to evaluate whether we can reproducibly estimate brain aging in diversified healthy cohorts and disease cohorts.
Aim 2. Test the effects of brain aging on future disease risk and functional decline. We will focus particularly on the aging brain to investigate the relationship between organ aging and cognitive decline. Approach: we will evaluate the relationships between our model aging predictions and aging, diseases, and clinical phenotypes, such as mortality and lab and physical assessments. This is primarily done by first calculating the difference between a sample’s chronological age and its predicted organ age. This organ age residual is then tested for association with phenotypes of interest, such as Alzheimer’s Disease status or future risk of cognitive decline.
Aim 1. Validate our approach to modeling aging and brain health with NIAGADS cohorts. Approach: we will use protein biomarker data from cohorts to train machine learning models of organ aging. We will employ machine learning best practice for data curation, data augmentation, data normalization, and training/testing, to evaluate whether we can reproducibly estimate brain aging in diversified healthy cohorts and disease cohorts.
Aim 2. Test the effects of brain aging on future disease risk and functional decline. We will focus particularly on the aging brain to investigate the relationship between organ aging and cognitive decline. Approach: we will evaluate the relationships between our model aging predictions and aging, diseases, and clinical phenotypes, such as mortality and lab and physical assessments. This is primarily done by first calculating the difference between a sample’s chronological age and its predicted organ age. This organ age residual is then tested for association with phenotypes of interest, such as Alzheimer’s Disease status or future risk of cognitive decline.
Non-Technical Summary
As we age, our risk of getting sick increases, but did you know that everyone ages differently? Some people's bodies deteriorate faster than others, and we don't fully understand why. By studying how our bodies change with age, we aim to identify which organs are aging the most and find ways to improve quality of life tailored to each individual's needs.
We want to understand how people age differently, both as individuals and as a population on organ specific levels. By analyzing molecules in blood, we aim to:
- Identify how aging affects people uniquely
- Link aging patterns to diseases and lifespan
- Use advanced computer models to connect blood changes to organ function
Our research can lead to:
- Personalized patient care
- New discoveries for medicines
- Improved understanding of aging and age-related diseases
We'll use a large, diverse dataset to validate our approach and gain valuable insights into the aging process. By unlocking these secrets, we can work towards a healthier future for everyone.
We want to understand how people age differently, both as individuals and as a population on organ specific levels. By analyzing molecules in blood, we aim to:
- Identify how aging affects people uniquely
- Link aging patterns to diseases and lifespan
- Use advanced computer models to connect blood changes to organ function
Our research can lead to:
- Personalized patient care
- New discoveries for medicines
- Improved understanding of aging and age-related diseases
We'll use a large, diverse dataset to validate our approach and gain valuable insights into the aging process. By unlocking these secrets, we can work towards a healthier future for everyone.
Reported Publications
Publications shown are those reported by investigators through NIAGADS DAR reporting and renewal processes and may not represent a complete list of publications associated with the project.
No publications have been reported for this DAR.