Clinical Aging Clock Based on Principal Components Reveals Characteristics of Healthy Aging and Targets for Clinical Interventions

Background of the Study

As the population ages, promoting healthy aging and slowing down biological aging has become an important issue. To accurately predict all-cause mortality and obtain feasible measures to promote healthy aging, researchers have designed biological clocks to measure biological age. However, existing biological clocks lack predictive power for specific clinical interventions.

Source of the Study

This study was led by renowned scientists Sheng Fong, Kamil Pabis, Djakim Latumalea, among others, from institutions such as the National University of Singapore, Duke-NUS Medical School, and the Yale-NUS College. It was published in Nature Aging on May 8, 2024, with the online publication date to be determined.

Study Details

a) Overview of the Research Process

The study comprised multiple independent steps, using principal component analysis (PCA) to reduce the dimensions of clinical data to generate a clinical aging clock (PCAge). The study subjects included adult male and female samples from the National Health and Nutrition Examination Survey (NHANES). The study also created a simplified aging clock (LinAge), which achieves similar predictive capabilities using fewer features.

b) Analysis of Main Research Results

PCAge successfully identified trajectories of healthy and unhealthy aging, metabolic disorders, cardiac and renal dysfunction, and inflammatory markers, revealing that these processes could be impacted by existing drug interventions. Furthermore, LinAge demonstrated similar predictive capability to PCAge while relying on fewer features. Retraining with long-term data from the CALERIE study showed that mild caloric restriction significantly reduces biological age.

c) Conclusion and Research Significance

By integrating PCA and various biomarkers, this study provides targets for preventive medicine and promoting healthy aging. The research indicates that caloric restriction may significantly impact slowing down aging, providing scientific support for the future development of aging intervention measures.

d) Highlights of the Study

The development of PCAge and LinAge offers a unique perspective, not only predicting the risk of age-related diseases but also providing real-time monitoring of intervention effects. The application of PCA in this study highlights the potential of extracting aging patterns from high-dimensional biomedical data.

e) Additional Notable Information

The study validated the adaptability of LinAge to an independent dataset, NHANES III, demonstrating the model’s robustness even in the face of data scarcity.

Conclusion

As a concluding point, this study reinforces the value of biological aging clocks in analyzing individual aging trajectories, screening potential healthy agers, and providing recommendations for clinical interventions. By combining clinical data and predictive models, we have obtained a powerful tool to address the global challenge of aging. Additionally, the study’s finding that mild caloric restriction can significantly reduce biological age opens a new chapter in future anti-aging strategies.