All Publications

  • Validation of biomarkers of aging. Nature medicine Moqri, M., Herzog, C., Poganik, J. R., Ying, K., Justice, J. N., Belsky, D. W., Higgins-Chen, A. T., Chen, B. H., Cohen, A. A., Fuellen, G., Hägg, S., Marioni, R. E., Widschwendter, M., Fortney, K., Fedichev, P. O., Zhavoronkov, A., Barzilai, N., Lasky-Su, J., Kiel, D. P., Kennedy, B. K., Cummings, S., Slagboom, P. E., Verdin, E., Maier, A. B., Sebastiano, V., Snyder, M. P., Gladyshev, V. N., Horvath, S., Ferrucci, L. 2024


    The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.

    View details for DOI 10.1038/s41591-023-02784-9

    View details for PubMedID 38355974

    View details for PubMedCentralID 9792204

  • Causality-enriched epigenetic age uncouples damage and adaptation. Nature aging Ying, K., Liu, H., Tarkhov, A. E., Sadler, M. C., Lu, A. T., Moqri, M., Horvath, S., Kutalik, Z., Shen, X., Gladyshev, V. N. 2024


    Machine learning models based on DNA methylation data can predict biological age but often lack causal insights. By harnessing large-scale genetic data through epigenome-wide Mendelian randomization, we identified CpG sites potentially causal for aging-related traits. Neither the existing epigenetic clocks nor age-related differential DNA methylation are enriched in these sites. These CpGs include sites that contribute to aging and protect against it, yet their combined contribution negatively affects age-related traits. We established a new framework to introduce causal information into epigenetic clocks, resulting in DamAge and AdaptAge-clocks that track detrimental and adaptive methylation changes, respectively. DamAge correlates with adverse outcomes, including mortality, while AdaptAge is associated with beneficial adaptations. These causality-enriched clocks exhibit sensitivity to short-term interventions. Our findings provide a detailed landscape of CpG sites with putative causal links to lifespan and healthspan, facilitating the development of aging biomarkers, assessing interventions, and studying reversibility of age-associated changes.

    View details for DOI 10.1038/s43587-023-00557-0

    View details for PubMedID 38243142

    View details for PubMedCentralID 9957516

  • Mechanisms, pathways and strategies for rejuvenation through epigenetic reprogramming. Nature aging Cipriano, A., Moqri, M., Maybury-Lewis, S. Y., Rogers-Hammond, R., de Jong, T. A., Parker, A., Rasouli, S., Schöler, H. R., Sinclair, D. A., Sebastiano, V. 2023


    Over the past decade, there has been a dramatic increase in efforts to ameliorate aging and the diseases it causes, with transient expression of nuclear reprogramming factors recently emerging as an intriguing approach. Expression of these factors, either systemically or in a tissue-specific manner, has been shown to combat age-related deterioration in mouse and human model systems at the cellular, tissue and organismal level. Here we discuss the current state of epigenetic rejuvenation strategies via partial reprogramming in both mouse and human models. For each classical reprogramming factor, we provide a brief description of its contribution to reprogramming and discuss additional factors or chemical strategies. We discuss what is known regarding chromatin remodeling and the molecular dynamics underlying rejuvenation, and, finally, we consider strategies to improve the practical uses of epigenetic reprogramming to treat aging and age-related diseases, focusing on the open questions and remaining challenges in this emerging field.

    View details for DOI 10.1038/s43587-023-00539-2

    View details for PubMedID 38102454

    View details for PubMedCentralID 4917370

  • OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records. bioRxiv : the preprint server for biology Chen, Q., Dwaraka, V. B., Carreras-Gallo, N., Mendez, K., Chen, Y., Begum, S., Kachroo, P., Prince, N., Went, H., Mendez, T., Lin, A., Turner, L., Moqri, M., Chu, S. H., Kelly, R. S., Weiss, S. T., Rattray, N. J., Gladyshev, V. N., Karlson, E., Wheelock, C., Mathé, E. A., Dahlin, A., McGeachie, M. J., Smith, R., Lasky-Su, J. A. 2023


    Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process.

    View details for DOI 10.1101/2023.10.16.562114

    View details for PubMedID 37904959

    View details for PubMedCentralID PMC10614756

  • Dynamic lipidome alterations associated with human health, disease and ageing. Nature metabolism Hornburg, D., Wu, S., Moqri, M., Zhou, X., Contrepois, K., Bararpour, N., Traber, G. M., Su, B., Metwally, A. A., Avina, M., Zhou, W., Ubellacker, J. M., Mishra, T., Schüssler-Fiorenza Rose, S. M., Kavathas, P. B., Williams, K. J., Snyder, M. P. 2023


    Lipids can be of endogenous or exogenous origin and affect diverse biological functions, including cell membrane maintenance, energy management and cellular signalling. Here, we report >800 lipid species, many of which are associated with health-to-disease transitions in diabetes, ageing and inflammation, as well as cytokine-lipidome networks. We performed comprehensive longitudinal lipidomic profiling and analysed >1,500 plasma samples from 112 participants followed for up to 9 years (average 3.2 years) to define the distinct physiological roles of complex lipid subclasses, including large and small triacylglycerols, ester- and ether-linked phosphatidylethanolamines, lysophosphatidylcholines, lysophosphatidylethanolamines, cholesterol esters and ceramides. Our findings reveal dynamic changes in the plasma lipidome during respiratory viral infection, insulin resistance and ageing, suggesting that lipids may have roles in immune homoeostasis and inflammation regulation. Individuals with insulin resistance exhibit disturbed immune homoeostasis, altered associations between lipids and clinical markers, and accelerated changes in specific lipid subclasses during ageing. Our dataset based on longitudinal deep lipidome profiling offers insights into personalized ageing, metabolic health and inflammation, potentially guiding future monitoring and intervention strategies.

    View details for DOI 10.1038/s42255-023-00880-1

    View details for PubMedID 37697054

    View details for PubMedCentralID 7736650

  • Biomarkers of aging for the identification and evaluation of longevity interventions. Cell Moqri, M., Herzog, C., Poganik, J. R., Biomarkers of Aging Consortium, Justice, J., Belsky, D. W., Higgins-Chen, A., Moskalev, A., Fuellen, G., Cohen, A. A., Bautmans, I., Widschwendter, M., Ding, J., Fleming, A., Mannick, J., Han, J. J., Zhavoronkov, A., Barzilai, N., Kaeberlein, M., Cummings, S., Kennedy, B. K., Ferrucci, L., Horvath, S., Verdin, E., Maier, A. B., Snyder, M. P., Sebastiano, V., Gladyshev, V. N. 2023; 186 (18): 3758-3775


    With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice.

    View details for DOI 10.1016/j.cell.2023.08.003

    View details for PubMedID 37657418

  • Organ-specific aging and the risk of chronic diseases. Nature medicine Moqri, M., Snyder, M. 2023

    View details for DOI 10.1038/s41591-023-02338-z

    View details for PubMedID 37161069

  • The Blood Plasma Lipidome: Distinct Molecular signatures delineate metabolic health in a cross-sectional human cohort Hornburg, D. under review. 0000
  • AgeIndex, a whole-genome epigenetic aging and rejuvenation index Moqri, M. under review. 0000