| Title: | Development and validation of the predict model of healthy aging and longevity based on the lifestyle factors across the life course, genetic factors, and circulating plasma proteins |
| Lead Institution: | Sun Yat-Sen University |
| Principal investigator: |
Application 406475
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About
Exploring the mechanisms that lead to death and the sources of increased biological heterogeneity in old age are key questions in aging research. We will use traditional and machine learning methods to develop a prediction model for successful aging and longevity. A wide range of variables including lifestyle factors across the life course (diet, sleep, physical activity smoking, alcohol and childhood adverse events etc.), genetic factors, and circulating plasma proteins will be included. The outcomes will involve the dynamic phenotypic age the fundamental measure of population health, and longevity. The traditional COX regression and ensemble ML algorithms will be used to develop and validate the prediction model of aging and longevity. Our study will help to promote the understanding of aging and longevity, and the model could be used in identifying groups of individuals who are at high risk of short successful aging and are more likely to benefit from interventions.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 16366 | Integrated analysis identifies key genes underlying the bidirectional association between depression and renal failure | Zhengqi Qiu (+5) | 2025 | Scientific Reports |
Enabling scientific discoveries that improve human health