Mahdi Moqri, PhD

All Publications

• Autonomous AI Agents Discover Aging Interventions from Millions of Molecular Profiles. bioRxiv : the preprint server for biology Ying, K., Tyshkovskiy, A., Moldakozhayev, A., Wang, H., De Magalhães, C. G., Iqbal, S., Garza, A. E., Tskhay, A., Poganik, J. R., Huang, K., Qu, Y., Glubokov, D., Jin, C., Lee, D., Liu, H., Leote, C., Trapp, A., de Lima Camillo, L. P., Kerepesi, C., Moqri, M., Zhang, O., Jiang, K., Galkin, F., Zhavoronkov, A., Van Raamsdonk, J. M., Wang, M., Cong, L., Regev, A., Leskovec, J., Wyss-Coray, T., Gladyshev, V. N. 2025

  Abstract

  Decades of publicly available molecular studies have generated millions of samples testing diverse interventions, yet these datasets were rarely analyzed for their effects on aging. Aging clocks now enable biological age estimation and life outcome prediction from molecular data, creating an opportunity to systematically mine this untapped resource. We developed ClockBase Agent, a publicly accessible platform that reanalyzes millions of human and mouse methylation and RNA-seq samples by integrating them with over 40 aging clock predictions. ClockBase Agent employs specialized AI agents that autonomously generate aging-focused hypotheses, evaluate intervention effects on biological age, conduct literature reviews, and produce scientific reports across all datasets. Reanalyzing 43,602 intervention-control comparisons through multiple aging biomarkers revealed thousands of age-modifying effects missed by original investigators, including over 500 interventions that significantly reduce biological age (e.g., ouabain, KMO inhibitor, fenofibrate, and NF1 knockout). Large-scale systematic analysis reveals fundamental patterns: significantly more interventions accelerate rather than decelerate aging, disease states predominantly accelerate biological age, and loss-of-function genetic approaches systematically outperform gain-of-function strategies in decelerating aging. As validation, we show that identified interventions converge on canonical longevity pathways and with strong concordance to independent lifespan databases. We further experimentally validated ouabain, a top-scoring AI-identified candidate, demonstrating reduced frailty progression, decreased neuroinflammation, and improved cardiac function in aged mice. ClockBase Agent establishes a paradigm where specialized AI agents systematically reanalyze all prior research to identify age-modifying interventions autonomously, transforming how we extract biological insights from existing data to advance human healthspan and longevity.

  View details for DOI 10.1101/2023.02.28.530532

  View details for PubMedID 41332661

  View details for PubMedCentralID PMC12667862

• A unified framework for systematic curation and evaluation of aging biomarkers. Nature aging Ying, K., Paulson, S., Eames, A., Tyshkovskiy, A., Li, S., Eynon, N., Jacques, M., Grolaux, R., Seale, K., Jacques, E., Goeminne, L. J., Cipriano, A., Perez-Guevara, M., Emamifar, M., Casas Martínez, M., Kwon, D., Kosheleva, A., Snyder, M., Gobel, D., Herzog, C., McCartney, D. L., Marioni, R. E., Lasky-Su, J., Poganik, J. R., Moqri, M., Gladyshev, V. N. 2025

  Abstract

  Aging biomarkers are essential tools for quantifying biological aging, but systematic validation has been hindered by methodological inconsistencies and fragmented datasets. Here we show that the ability of traditional aging clocks to predict chronological age does not correlate with mortality prediction capacity (R = 0.12, P = 0.67), suggesting that these metrics capture distinct biological processes. We developed Biolearn, an open-source framework enabling standardized evaluation of 39 biomarkers across over 20,000 individuals from diverse cohorts. The Horvath skin and blood clock achieved the highest chronological age accuracy (R2 = 0.88), while GrimAge2 demonstrated the strongest mortality association (hazard ratio = 2.57) and healthspan prediction (hazard ratio = 2.00). Our systematic evaluation reveals considerable heterogeneity in biomarker performance across different clinical outcomes, with optimal biomarkers varying according to specific application. Biolearn provides unified data processing pipelines with quality control and cell-type deconvolution capabilities, establishing a foundation for reproducible aging research and facilitating development of robust aging biomarkers.

  View details for DOI 10.1038/s43587-025-00987-y

  View details for PubMedID 41188602

  View details for PubMedCentralID 11088934

• Digital biomarkers of ageing for monitoring physiological systems in community-dwelling adults. The lancet. Healthy longevity Lu, J. K., Wang, W., Mahadzir, M. D., Poganik, J. R., Moqri, M., Herzog, C., Verdin, E., Sebastiano, V., Gladyshev, V. N., Maier, A. B. 2025: 100725

  Abstract

  Digital health technologies are transforming health care and personal health management by providing quantifiable data on physiological, behavioural, and environmental health parameters using digital biomarkers. This narrative review classified, characterised, and evaluated digital biomarkers of ageing across ten physiological systems to explore the applications of these biomarkers in research and clinical practice. The systematic search identified minimally invasive or non-invasively measured digital biomarkers suitable for longitudinal studies and practical use by community-dwelling adults. The digital biomarkers were classified according to their physiological system, characterised by their capture methods, and evaluated based on the following criteria: validity (age-associated, function-associated, and mortality-associated), generalisability, responsiveness to interventions, associations with clinical outcomes, and cost-effectiveness in large-scale settings. Digital biomarkers of ageing were found across eight physiological systems. Registered clinical trials that used these digital biomarkers as outcomes were also identified. Continued research and technological advancements are crucial for maximising the potential of digital biomarkers in promoting healthy ageing and longevity.

  View details for DOI 10.1016/j.lanhl.2025.100725

  View details for PubMedID 40517785

• Invigorating discovery and clinical translation of aging biomarkers. Nature aging Jacques, E., Herzog, C., Ying, K., Tomusiak, A., Kasamoto, J., Sehgal, R., Paulson, S., Reinhard, J., Träuble, J., Hastings, W. J., Tyshkovskiy, A., Hägg, S., Earls, J. C., Behrens, C. E., Lasky-Su, J., Zhou, G., Morgen, E., Tsang, J. S., Marioni, R. E., Ma, X. J., Stolzing, A., Glorioso, C., Gootenberg, J. S., Abudayyeh, O. O., Argentieri, M. A., Mak, R. H., Cox, L. S., Brack, A. S., Lauc, G., Furman, D., Buenrostro, J. D., Schumacher, B., Justice, J. N., Woods, T., Gobel, D., Perez, V. I., Sinclair, D. A., Maier, A. B., Barzilai, N., Snyder, M. P., Wyss-Coray, T., Horvath, S., Ferrucci, L., Poganik, J. R., Moqri, M., Gladyshev, V. N. 2025

  View details for DOI 10.1038/s43587-025-00838-w

  View details for PubMedID 40164770

  View details for PubMedCentralID 11088934

• What makes biological age epigenetic clocks tick NATURE AGING Moqri, M., Poganik, J. R., Horvath, S., Gladyshev, V. N. 2025

  View details for DOI 10.1038/s43587-025-00833-1

  View details for Web of Science ID 001429185700001

  View details for PubMedID 39994479

  View details for PubMedCentralID 11090477

• Balancing the promise and risks of geroscience interventions NATURE AGING Cohen, A. A., Beard, J. R., Ferrucci, L., Fulop, T., Gladyshev, V. N., Moqri, M., Rikkert, M., Picard, M. 2025; 5 (1): 4-8

  View details for DOI 10.1038/s43587-024-00788-9

  View details for Web of Science ID 001404799000003

  View details for PubMedID 39753893

  View details for PubMedCentralID 10439920

• Disagreement on foundational principles of biological aging. PNAS nexus Gladyshev, V. N., Anderson, B., Barlit, H., Barré, B., Beck, S., Behrouz, B., Belsky, D. W., Chaix, A., Chamoli, M., Chen, B. H., Cheng, K., Chuprin, J., Churchill, G. A., Cipriano, A., Colville, A., Deelen, J., Deigin, Y., Edmonds, K. K., English, B. W., Fang, R., Florea, M., Gershteyn, I. M., Gill, D., Goetz, L. H., Gorbunova, V., Griffin, P. T., Horvath, S., Borch Jensen, M., Jin, X., Jovanovska, S., Kajderowicz, K. M., Kasahara, T., Kerepesi, C., Kulkarni, S., Labunskyy, V. M., Levine, M. E., Libert, S., Lu, J. Y., Lu, Y. R., Marioni, R. E., McCoy, B. M., Mitchell, W., Moqri, M., Nasirian, F., Niimi, P., Oh, H. S., Okundaye, B., Parkhitko, A. A., Peshkin, L., Petljak, M., Poganik, J. R., Pridham, G., Promislow, D. E., Prusisz, W., Quiniou, M., Raj, K., Richard, D., Ricon, J. L., Rutledge, J., Scheibye-Knudsen, M., Schork, N. J., Seluanov, A., Shadpour, M., Shindyapina, A. V., Shuken, S. R., Sivakumar, S., Stoeger, T., Sugiura, A., Sutton, N. R., Suvorov, A., Tarkhov, A. E., Teeling, E. C., Trapp, A., Tyshkovskiy, A., Unfried, M., Ward-Caviness, C. K., Yim, S. H., Ying, K., Yunes, J., Zhang, B., Zhavoronkov, A. 2024; 3 (12): pgae499

  Abstract

  To gain insight into how researchers of aging perceive the process they study, we conducted a survey among experts in the field. While highlighting some common features of aging, the survey exposed broad disagreement on the foundational issues. What is aging? What causes it? When does it begin? What constitutes rejuvenation? Not only was there no consensus on these and other core questions, but none of the questions received a majority opinion-even regarding the need for consensus itself. Despite many researchers believing they understand aging, their understanding diverges considerably. Importantly, as different processes are labeled as "aging" by researchers, different experimental approaches are prioritized. The survey shed light on the need to better define which aging processes this field should target and what its goals are. It also allowed us to categorize contemporary views on aging and rejuvenation, revealing critical, yet largely unanswered, questions that appear disconnected from the current research focus. Finally, we discuss ways to address the disagreement, which we hope will ultimately aid progress in the field.

  View details for DOI 10.1093/pnasnexus/pgae499

  View details for PubMedID 39660064

  View details for PubMedCentralID PMC11630784

• An Open Competition for Biomarkers of Aging. bioRxiv : the preprint server for biology Ying, K., Paulson, S., Reinhard, J., de Lima Camillo, L. P., Träuble, J., Jokiel, S., Gobel, D., Herzog, C., Poganik, J. R., Moqri, M., Gladyshev, V. N. 2024

  Abstract

  Open scientific competitions have successfully driven biomedical advances but remain underutilized in aging research, where biological complexity and heterogeneity require methodological innovations. Here, we present the results from Phase I of the Biomarkers of Aging Challenge, an open competition designed to drive innovation in aging biomarker development and validation. The challenge leverages a unique DNA methylation dataset and aging outcomes from 500 individuals, aged 18 to 99. Participants are asked to develop novel models to predict chronological age, mortality, and multi-morbidity. Results from the chronological age prediction phase show important advances in biomarker accuracy and innovation compared to existing models. The winning models feature improved predictive power and employ advanced machine learning techniques, innovative data preprocessing, and the integration of biological knowledge. These approaches have led to the identification of novel age-associated methylation sites and patterns. This challenge establishes a paradigm for collaborative aging biomarker development, potentially accelerating the discovery of clinically relevant predictors of aging-related outcomes. This supports personalized medicine, clinical trial design, and the broader field of geroscience, paving the way for more targeted and effective longevity interventions.

  View details for DOI 10.1101/2024.10.29.620782

  View details for PubMedID 39554132

  View details for PubMedCentralID PMC11565782

• High-dimensional Ageome Representations of Biological Aging across Functional Modules. bioRxiv : the preprint server for biology Ying, K., Tyshkovskiy, A., Chen, Q., Latorre-Crespo, E., Zhang, B., Liu, H., Matei-Dediu, B., Poganik, J. R., Moqri, M., Kirschne, K., Lasky-Su, J., Gladyshev, V. N. 2024

  Abstract

  The aging process involves numerous molecular changes that lead to functional decline and increased disease and mortality risk. While epigenetic aging clocks have shown accuracy in predicting biological age, they typically provide single estimates for the samples and lack mechanistic insights. In this study, we challenge the paradigm that aging can be sufficiently described with a single biological age estimate. We describe Ageome, a computational framework for measuring the epigenetic age of thousands of molecular pathways simultaneously in mice and humans. Ageome is based on the premise that an organism's overall biological age can be approximated by the collective ages of its functional modules, which may age at different rates and have different biological ages. We show that, unlike conventional clocks, Ageome provides a high-dimensional representation of biological aging across cellular functions, enabling comprehensive assessment of aging dynamics within an individual, in a population, and across species. Application of Ageome to longevity intervention models revealed distinct patterns of pathway-specific age deceleration. Notably, cell reprogramming, while rejuvenating cells, also accelerated aging of some functional modules. When applied to human cohorts, Ageome demonstrated heterogeneity in predictive power for mortality risk, and some modules showed better performance in predicting the onset of age-related diseases, especially cancer, compared to existing clocks. Together, the Ageome framework offers a comprehensive and interpretable approach for assessing aging, providing insights into mechanisms and targets for intervention.

  View details for DOI 10.1101/2024.09.17.613599

  View details for PubMedID 39345525

• Challenges and recommendations for the translation of biomarkers of aging. Nature aging Herzog, C. M., Goeminne, L. J., Poganik, J. R., Barzilai, N., Belsky, D. W., Betts-LaCroix, J., Chen, B. H., Chen, M., Cohen, A. A., Cummings, S. R., Fedichev, P. O., Ferrucci, L., Fleming, A., Fortney, K., Furman, D., Gorbunova, V., Higgins-Chen, A., Hood, L., Horvath, S., Justice, J. N., Kiel, D. P., Kuchel, G. A., Lasky-Su, J., LeBrasseur, N. K., Maier, A. B., Schilling, B., Sebastiano, V., Slagboom, P. E., Snyder, M. P., Verdin, E., Widschwendter, M., Zhavoronkov, A., Moqri, M., Gladyshev, V. N. 2024

  Abstract

  Biomarkers of aging (BOA) are quantitative parameters that predict biological age and ideally its changes in response to interventions. In recent years, many promising molecular and omic BOA have emerged with an enormous potential for translational geroscience and improving healthspan. However, clinical translation remains limited, in part due to the gap between preclinical research and the application of BOA in clinical research and other translational settings. We surveyed experts in these areas to better understand current challenges for the translation of aging biomarkers. We identified six key barriers to clinical translation and developed guidance for the field to overcome them. Core recommendations include linking BOA to clinically actionable insights, improving affordability and availability to broad populations and validation of biomarkers that are robust and responsive at the level of individuals. Our work provides key insights and practical recommendations to overcome barriers impeding clinical translation of BOA.

  View details for DOI 10.1038/s43587-024-00683-3

  View details for PubMedID 39285015

  View details for PubMedCentralID 4852871

• Transcriptional and epigenetic characterization of a new in vitro platform to model the formation of human pharyngeal endoderm. Genome biology Cipriano, A., Colantoni, A., Calicchio, A., Fiorentino, J., Gomes, D., Moqri, M., Parker, A., Rasouli, S., Caldwell, M., Briganti, F., Roncarolo, M. G., Baldini, A., Weinacht, K. G., Tartaglia, G. G., Sebastiano, V. 2024; 25 (1): 211

  Abstract

  The Pharyngeal Endoderm (PE) is an extremely relevant developmental tissue, serving as the progenitor for the esophagus, parathyroids, thyroids, lungs, and thymus. While several studies have highlighted the importance of PE cells, a detailed transcriptional and epigenetic characterization of this important developmental stage is still missing, especially in humans, due to technical and ethical constraints pertaining to its early formation.Here we fill this knowledge gap by developing an in vitro protocol for the derivation of PE-like cells from human Embryonic Stem Cells (hESCs) and by providing an integrated multi-omics characterization. Our PE-like cells robustly express PE markers and are transcriptionally homogenous and similar to in vivo mouse PE cells. In addition, we define their epigenetic landscape and dynamic changes in response to Retinoic Acid by combining ATAC-Seq and ChIP-Seq of histone modifications. The integration of multiple high-throughput datasets leads to the identification of new putative regulatory regions and to the inference of a Retinoic Acid-centered transcription factor network orchestrating the development of PE-like cells.By combining hESCs differentiation with computational genomics, our work reveals the epigenetic dynamics that occur during human PE differentiation, providing a solid resource and foundation for research focused on the development of PE derivatives and the modeling of their developmental defects in genetic syndromes.

  View details for DOI 10.1186/s13059-024-03354-z

  View details for PubMedID 39118163

  View details for PubMedCentralID 5241818

• PRC2-AgeIndex as a universal biomarker of aging and rejuvenation. Nature communications Moqri, M., Cipriano, A., Simpson, D. J., Rasouli, S., Murty, T., de Jong, T. A., Nachun, D., de Sena Brandine, G., Ying, K., Tarkhov, A., Aberg, K. A., van den Oord, E., Zhou, W., Smith, A., Mackall, C., Gladyshev, V. N., Horvath, S., Snyder, M. P., Sebastiano, V. 2024; 15 (1): 5956

  Abstract

  DNA methylation (DNAm) is one of the most reliable biomarkers of aging across mammalian tissues. While the age-dependent global loss of DNAm has been well characterized, DNAm gain is less characterized. Studies have demonstrated that CpGs which gain methylation with age are enriched in Polycomb Repressive Complex 2 (PRC2) targets. However, whole-genome examination of all PRC2 targets as well as determination of the pan-tissue or tissue-specific nature of these associations is lacking. Here, we show that low-methylated regions (LMRs) which are highly bound by PRC2 in embryonic stem cells (PRC2 LMRs) gain methylation with age in all examined somatic mitotic cells. We estimated that this epigenetic change represents around 90% of the age-dependent DNAm gain genome-wide. Therefore, we propose the "PRC2-AgeIndex," defined as the average DNAm in PRC2 LMRs, as a universal biomarker of cellular aging in somatic cells which can distinguish the effect of different anti-aging interventions.

  View details for DOI 10.1038/s41467-024-50098-2

  View details for PubMedID 39009581

  View details for PubMedCentralID PMC11250797

• Nature of epigenetic aging from a single-cell perspective. Nature aging Tarkhov, A. E., Lindstrom-Vautrin, T., Zhang, S., Ying, K., Moqri, M., Zhang, B., Tyshkovskiy, A., Levy, O., Gladyshev, V. N. 2024

  Abstract

  Age-related changes in DNA methylation (DNAm) form the basis of the most robust predictors of age-epigenetic clocks-but a clear mechanistic understanding of exactly which aspects of aging are quantified by these clocks is lacking. Here, to clarify the nature of epigenetic aging, we juxtapose the dynamics of tissue and single-cell DNAm in mice. We compare these changes during early development with those observed during adult aging in mice, and corroborate our analyses with a single-cell RNA sequencing analysis within the same multiomics dataset. We show that epigenetic aging involves co-regulated changes as well as a major stochastic component, and this is consistent with transcriptional patterns. We further support the finding of stochastic epigenetic aging by direct tissue and single-cell DNAm analyses and modeling of aging DNAm trajectories with a stochastic process akin to radiocarbon decay. Finally, we describe a single-cell algorithm for the identification of co-regulated and stochastic CpG clusters showing consistent transcriptomic coordination patterns. Together, our analyses increase our understanding of the basis of epigenetic clocks and highlight potential opportunities for targeting aging and evaluating longevity interventions.

  View details for DOI 10.1038/s43587-024-00616-0

  View details for PubMedID 38724733

  View details for PubMedCentralID 4015143

• 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

  Abstract

  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

  Abstract

  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

  Abstract

  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

  Abstract

  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

  Abstract

  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

  Abstract

  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