| Title: | Integrating modifiable, phenotypic, and multi-omics data to enhance the understanding of chronic non-communicable diseases |
| Lead Institution: | Beijing Hospital |
| Principal investigator: |
Application 902799
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
About
With non-communicable diseases accounting for nearly two-thirds of deaths worldwide, their emergence as the predominant challenge to global health is undisputed. These diseases also cause prolonged suffering, diminished quality of life, and immense economic burdens. To meet this challenge, the United Nations aims to reduce premature mortality from chronic non-communicable diseases by one-third by 2030. In this project, we aim to detangle noncommunicable chronic diseases such as metabolic diseases and related complications, cardiovascular disease, cerebrovascular disease, Alzheimer's disease, dementia, and cancers by analyzing multidimensional data such as environmental factors, lifestyle information, anthropometric measurements, clinical parameters, genomics, metabolomics, proteomics, and imaging. Our team could perform cohort analyses, multi-omics analytical methods, machine learning, and other advanced statistical techniques. We focus on elucidating risk factors, biomarkers, molecular drivers, physiological mechanisms, and novel therapeutic targets for chronic diseases. Additionally, we hope to construct prediction models for chronic diseases to offer guidance on their prediction, diagnosis, and prognosis. The findings of this project will benefit risk stratification and clinical management of chronic diseases and have the potential to reduce their global burden. In addition, this project may offer insights into the underlying mechanisms of chronic diseases and identify potential therapeutic targets for therapy.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 17370 | A U-Shaped Association Between Blood mtDNA Copy Number and Risk of Type 2 Diabetes. | Xinhao Huang (+11) | 2026 | Diabetes Care |
Enabling scientific discoveries that improve human health