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  • Tong et al. construct simulations using DNA methylation data to quantify what proportion of the predictive accuracy of epigenetic clocks could be explained by stochastic methylation changes, suggesting that stochasticity contributes more toward the accuracy of chronological rather than biological age predictions.

    • Huige Tong
    • Varun B. Dwaraka
    • Andrew E. Teschendorff
    AnalysisOpen Access
  • In a longitudinal population-based cohort, Liu et al. demonstrate that integrating polygenic risk scores and the gut microbiome improved prediction, over traditional risk factors, for heart disease, diabetes, Alzheimer disease and prostate cancer.

    • Yang Liu
    • Scott C. Ritchie
    • Michael Inouye
    AnalysisOpen Access
  • Identifying individuals at risk of developing Alzheimer’s disease is an important task. Here Tang et al. leverage electronic health records to predict Alzheimer’s disease onset, and utilize knowledge networks to prioritize shared genes behind the clinical data as well as facilitate contextualization based on sex.

    • Alice S. Tang
    • Katherine P. Rankin
    • Marina Sirota
    AnalysisOpen Access
  • Early type 2 diabetes (T2D) risk assessment could help slow or prevent disease onset. Here the authors used blood-based DNA methylation data to develop 10-year risk prediction models for incident T2D. The results show an improvement in performance beyond standard risk factors typically used to predict the risk of T2D onset.

    • Yipeng Cheng
    • Danni A. Gadd
    • Riccardo E. Marioni
  • The authors found that, across tissues and in multiple datasets, aging is accompanied by a length-associated transcriptome imbalance. In most cases, a decrease in the relative abundance of long transcripts was observed and could be reversed by interventions targeting aging.

    • Thomas Stoeger
    • Rogan A. Grant
    • Luis A. Nunes Amaral
    AnalysisOpen Access
  • The authors analyze microbiome profiles from several public repositories to identify the higher-level indices that best reflect the abundance and ranking of disease-associated and health-associated gut microbes and that may help identify targets for therapeutic modulation.

    • Tarini Shankar Ghosh
    • Fergus Shanahan
    • Paul W. O’Toole
    AnalysisOpen Access
  • Transcription factors control cell identity and function in health, disease and aging. Here the authors identify age-associated changes of transcriptional regulatory networks in single cells, revealing cell-type and tissue-type-independent patterns in key pathways, including circadian rhythm, antigen processing, collagen processing and inflammation.

    • Alok K. Maity
    • Xue Hu
    • Andrew E. Teschendorff
  • An economic analysis suggests that targeting aging offers potentially larger economic gains than eradicating individual diseases. Slowing aging to increase life expectancy by 1 year is worth US$38 trillion, and by 10 years, US$367 trillion.

    • Andrew J. Scott
    • Martin Ellison
    • David A. Sinclair
    AnalysisOpen Access
  • Using genetic and demographic data from the UK Biobank, the authors clustered 116 common diseases based on their age-of-onset profiles and found increased genetic similarity within clusters, suggesting common etiologies. Two of the four disease clusters were associated with aging-related genes but differed in functional enrichment and evolutionary profiles.

    • Handan Melike Dönertaş
    • Daniel K. Fabian
    • Janet M. Thornton