Biomedical subjects

Can Chen

Publications and source records attributed to Can Chen.

2 recordsLinked to original sources

PubMed2026

Early life sugar rationing and ageing related diseases, biological ageing and mortality.

Early-life nutrition may influence lifelong ageing, yet human evidence is scarce. Using Britain's postwar sugar rationing as a natural experiment, we examine its long-term effects in 64,809 United Kingdom Biobank participants. Exposure to sugar rationing during the first 1,000 days of life is associated with a 9% lower incidence of hallmark-related disease, with a hazard ratio of 0.91 and a 95% confidence interval of 0.88-0.94, and a 19% lower risk of all-cause mortality, with a hazard ratio of 0.81 and a 95% confidence interval of 0.69-0.93. Mediation analysis indicates that the survival association is statistically mediated, by approximately 60%, through differences in incident hallmark-related disease. Rationed individuals show 1.0-1.2-year younger biological ages across multiple clocks and lower organ ages, particularly in the lung, heart, and liver. Proteomic profiling identifies 47 altered proteins, with enrichment of adenosine monophosphate-activated protein kinase and longevity pathways and suppression of mechanistic target of rapamycin signaling. These findings are consistent with international recommendations to limit free or added sugars from the World Health Organization, United States Dietary Guidelines, and American Heart Association, and may inform policy discussions related to sugar taxation and infant food and marketing policies under the United Nations 2030 Agenda.

Humans↗

PubMed2026

PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.

MOTIVATION: Integrating multi-omics data provides valuable insights into biological processes by capturing information across multiple molecular layers, enabling a comprehensive understanding of complex diseases and driving advancements in precision medicine. However, existing computational methods for multi-omics integration face significant challenges, such as low reliability and poor generalizability, due to the high dimensionality and low sample size nature of omics data. RESULTS: To address these challenges, we present PEARL (Pearson-Enhanced spectrAl gRaph convoLutional networks), a novel deep graph learning method for biomedical classification and functional important omics features identification. PEARL leverages a simple yet effective learning architecture to achieve superior and robust performance in high-dimensional, low-sample-size multi-omics settings. Our results demonstrate that PEARL significantly outperforms existing state-of-the-art methods on both synthetic and real biomedical datasets. Furthermore, applied to Alzheimer's disease (AD) brain multi-omics data, features prioritized by PEARL lead to functionally important genes that demonstrate significant enrichment in AD-related pathways. These findings highlight PEARL's practical utility in biomedical research and its potential to enhance biological interpretability in multi-omics studies. AVAILABILITY AND IMPLEMENTATION: The source code of our computational framework is available at https://github.com/zqq121017/PEARL.

Multiomics↗