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    University - Affiliate Department: Genetics

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  • Immune aging biomarkers for clinical trials. Nature medicine Cipriano, A., Justice, J., Poganik, J. R., Moqri, M., Abudayyeh, O. O., Gootenberg, J., Haynes, L., Levin, Y., Martin-Herranz, D. E., Akbar, A. N., Mannick, J., Pawelec, G., Shen-Orr, S. S., Tsang, J., Wagner, W., Merad, M., Gnjatic, S., Gellhorn, A., Corley, M. J., Kuchel, G. A., Ferrucci, L., Furman, D., Herzog, C. M. 2026

    Abstract

    Aging profoundly remodels the immune system, impairing defense, repair and homeostatic function across tissues. Because the immune system operates in every organ, its deterioration has been proposed to drive or exacerbate systemic dysfunction and accelerate overall biological aging, making it an attractive biomarker and target for geroscience-guided trials. Despite this central role, there is no consensus on how to quantify immune aging, especially in clinical trials. Here, we establish a translational framework to identify immune aging biomarkers for this purpose. We define five evaluation criteria for immune aging biomarkers and apply these to candidate biomarkers, discussing their utility in the context of a major international healthspan competition, XPRIZE Healthspan. Metrics encapsulating multidimensional aspects of immune function, inflammaging scores and functional assays performed best against our selection criteria. Finally, we identify promising emerging measures, together with critical gaps that must be addressed to develop reliable, predictive biomarkers of human immune competence. Our framework provides a coherent path toward actionable and clinically meaningful immune aging biomarkers capable of quantifying immune fitness and resilience, and accelerating the clinical translation of geroscience-guided interventions.

    View details for DOI 10.1038/s41591-026-04493-5

    View details for PubMedID 42399672

    View details for PubMedCentralID 4852871

  • Meta-analysis of DNA methylation aging signatures in 17 human tissues NATURE AGING Jacques, M., Seale, K., Voisin, S., Lysenko, A., Grolaux, R., Jones-Freeman, B., Lamon, S., Abeysooriya, M., Levinger, I., Bauer, C., Sharples, A. P., Heikkinen, A., Sillanpaa, E., Ollikainen, M., Smith, C., Broatch, J. R., Zarekookandeh, N., Gillberg, L., Blom, I., Poganik, J. R., Moqri, M., Gladyshev, V. N., Taper, M., Malecki, C., Lal, S., Saurat, N., Horvath, S., Teschendorff, A., Eynon, N. 2026

    Abstract

    Epigenetic changes, in particular DNA methylation, accumulate with age across different tissues, but whether these changes follow consistent patterns across different organs remains poorly understood. Here we show, through a meta-analysis of more than 15,000 human methylation profiles spanning 17 tissues, that aging produces both conserved and tissue-specific epigenetic signatures. We identify systemic shifts in methylation levels, increases in methylation variability, and growing molecular disorder across tissues. Network analysis revealed tightly connected gene clusters that are not modified by beneficial interventions, alongside a more modifiable cluster linked to NAD+ metabolism, supporting NAD+ as a potential therapeutic target in aging. A gene encoding a cell-adhesion protein, PCDHGA1, emerged as a conserved hub across tissues, implicating cell-to-cell communication pathways in aging across multiple organs. Our methylation atlas therefore provides a resource for dissecting the molecular basis of human aging and for identifying potential biomarkers and translational therapies.

    View details for DOI 10.1038/s43587-026-01164-5

    View details for Web of Science ID 001804622600001

    View details for PubMedID 42362890

    View details for PubMedCentralID 3836174

  • An open competition for biomarkers of aging. Nature aging Ying, K., Paulson, S., Reinhard, J., Träuble, J., Jokiel, S., Lermer, R., de Lima Camillo, L. P., Traeuble, K., Gobel, D., Herzog, C., Heinig, M., Schunkert, H., Kaminski Schierle, G. S., Poganik, J. R., Moqri, M., Gladyshev, V. N. 2026

    View details for DOI 10.1038/s43587-026-01139-6

    View details for PubMedID 42277290

    View details for PubMedCentralID 4852871

  • Universal transcriptomic hallmarks of mammalian ageing and mortality. Nature Tyshkovskiy, A., Kholdina, D., Davitadze, M., Molière, A., Moldakozhayev, A., Tongu, Y., Kasahara, T., Glubokov, D., Eames, A., Kats, L. M., Vladimirova, A., Ying, K., Liu, H., Zhang, B., Khasanova, U., Moqri, M., Van Raamsdonk, J. M., Harrison, D. E., Strong, R., Abe, T., Dmitriev, S. E., Gladyshev, V. N. 2026

    Abstract

    Ageing and interventions modulate health and mortality1, yet the underlying molecular mechanisms of this modulation remain unclear. Here we integrate more than 11,000 transcriptomes from more than 25 tissues across 4 mammals (mouse, rat, macaque and human) to develop accurate, interpretable rodent and multi-species biomarkers of chronological age and expected mortality, predicting lifespan-modulating interventions, time to death, chronic diseases and rejuvenation. Ageing-related changes were conserved across species and cell types, revealing universal transcriptomic signatures of mammalian ageing and mortality, including CDKN1A and LGALS3, whose protein levels were also associated with mortality and multimorbidity in UK Biobank. Mortality-associated features were recapitulated across in vivo and in vitro damage-accumulation models, including inflammation, replicative senescence, metabolic inhibition and γ-irradiation, and were attenuated or reversed by cell immortalization, reprogramming, heterochronic parabiosis and early embryogenesis. Network analysis uncovered a modular architecture of ageing- and mortality-associated hallmarks, encompassing inflammation, interferon signalling, mitochondrial function, chromatin modification and extracellular matrix organization. To quantify ageing of individual cellular components, we developed module-specific clocks, which revealed pathway-specific effects of interventions: chronic diseases primarily accelerated inflammatory-module ageing, whereas caloric restriction and Klotho (also known as Kl) deficiency targeted mitochondrial and metabolic modules. Transcriptomic and DNA methylation clocks showed correlated age acceleration in human blood, which was strongest for the chromatin-associated module clock, highlighting mechanistic links between molecular ageing modalities. This study reveals conserved signatures and a modular architecture of mortality regulation, providing a framework for quantifying and targeting ageing of cellular subsystems across species and tissues.

    View details for DOI 10.1038/s41586-026-10542-3

    View details for PubMedID 42203874

    View details for PubMedCentralID 4933668

  • OpenScientist: evaluating an open agentic AI co-scientist to accelerate biomedical discovery. medRxiv : the preprint server for health sciences Roberts, K. F., Abrams, Z. B., Cappelletti, L., Moqri, M., Heugel, N., Caufield, J. H., Bourdenx, M., Li, Y., Banerjee, J., Foschini, L., Galeano, D., Harris, N. L., Li, M., Ying, K., Melendez, J. A., Barthélemy, N. R., Bollinger, J. G., He, Y., Ovod, V., Benzinger, T. L., Flores, S., Gordon, B. A., Ojewole, A. A., Phatak, M., Elbert, D. L., Biber, S., Landsness, E. C., Mungall, C. J., Bateman, R. J., Reese, J. T. 2026

    Abstract

    Advances in medicine depend on analyzing large and complex data sources, but discovery is partly constrained by the limited time and domain expertise of human researchers. Agentic artificial intelligence (agentic AI) can accelerate discovery by automating components of the scientific workflow, including information retrieval, data analysis, and knowledge synthesis.OpenScientist, an open-source agentic AI co-scientist, aims to accelerate biomedical discovery by semi-autonomously investigating scientist-defined queries and generating clinically relevant, verifiable scientific insights.Domain experts evaluated OpenScientist for novel discoveries in four clinical case studies: (1) a prespecified analysis in a community-based Alzheimer's disease biomarker cohort, (2) unsupervised modeling for plasma proteomic survival prediction, (3) hypothesis investigation in single-cell transcriptomic data from neurons with neurofibrillary tangles, and (4) hypothesis generation with validation in a multiple myeloma dataset with a randomized negative control.OpenScientist completed analyses in minutes that otherwise would take weeks to months of human time and expertise. It identified %ptau217 as the best predictor of amyloid PET status, generated a plasma proteomic survival model with performance comparable to published models, proposed a mechanism linking tau pathology to altered lysosomal acidification, and generated multiple myeloma hypotheses that were validated in an external cohort while distinguishing true signal from randomized controls.OpenScientist demonstrates that open, auditable, agentic AI can support real-world clinical research by generating hypotheses, executing analyses, and discovering insights from complex datasets.

    View details for DOI 10.64898/2026.03.15.26348338

    View details for PubMedID 41891004

    View details for PubMedCentralID PMC13015679

  • OMICmAge quantifies biological age by integrating multi-omics with electronic medical records. Nature aging Chen, Q., Dwaraka, V. B., Carreras-Gallo, N., Armstrong, J. F., Sehgal, R., Argentieri, M. A., Richmond, A., Aparicio, A., Mendez, K., Chen, Y., Begum, S., Kachroo, P., Prince, N., Guo, T., 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. E., Mathé, E. A., Dahlin, A., McGeachie, M. J., Marioni, R. E., Higgins-Chen, A. T., Smith, R., Lasky-Su, J. 2026

    Abstract

    Biological aging reflects complex cellular and biochemical processes that can be measured across multiple omic layers. Using routine clinical laboratory data from ~31,000 participants in the Mass General Brigham Biobank, we developed EMRAge, a biomarker of mortality risk that can be broadly recapitulated across electronic medical records. Here we show that EMRAge can be modeled using elastic net regression with DNA methylation and multi-omics to generate DNAmEMRAge and OMICmAge, respectively. Both biomarkers are strongly associated with incident and prevalent chronic diseases and mortality, performing comparably or better than current biomarkers across discovery (Massachusetts General Brigham Aging Biobank Cohort, n = 3,451) and validation cohorts (TruDiagnostic, n = 14,213; Generation Scotland, n = 18,672). Importantly, OMICmAge leverages epigenetic biomarker proxies to integrate proteomic, metabolomic and clinical domains while remaining quantifiable from DNA methylation alone. This framework establishes an accessible, scalable measure of biological aging with potential to reveal molecular interconnections that shape healthspan and disease risk.

    View details for DOI 10.1038/s43587-026-01073-7

    View details for PubMedID 41741793

    View details for PubMedCentralID 9221786

  • APOE-stratified Proteomic and Metabolomic Analysis Reveals Mitochondrial Dysfunction Inflammation and Lipid Dysregulation in Alzheimer's Disease. Advanced science (Weinheim, Baden-Wurttemberg, Germany) Li, F., Chen, Y., Western, D., Ali, M., Liu, M., Gong, K., Xu, Y., Lowery, J., Holtzman, D. M., Robins, C., Eicher, J. D., Huang, Y. N., Liu, S., Park, T., Saykin, A. J., Nho, K., Moqri, M., Mohs, R. C., Farinas, A., Moran-Losada, P., Oh, H. S., Wyss-Coray, T., Cruchaga, C. 2026: e13872

    Abstract

    Apolipoprotein E (APOE) ε4 is the strongest genetic risk factor for Alzheimer's disease (AD). However, it is known that other pathways independent of APOE also play a role in AD. Disentangling APOE-dependent and independent effects is instrumental for understanding the biology of AD. We conducted an APOE-stratified multi-omic analysis in multiple large datasets to identify AD-associated plasma proteins and metabolites. More than 64% of the identified proteins were not found in non-APOE stratified studies, and 17% of the proteins showed APOE-specific trends. Mitochondrial dysfunction was associated in AD independently of APOE and was accompanied by disruptions in glucose and lipid metabolism and cell death and increased in inflammatory signaling activation. Lipid upregulation was found in AD cases when compared with controls with the same APOE genotype, indicating that additional factors beyond APOE affect lipid regulation and AD risk. These findings may be informative in guiding the development of effective medications for AD.

    View details for DOI 10.1002/advs.202513872

    View details for PubMedID 41642643

  • Integrative epigenetics and transcriptomics identify aging genes in human blood. Nature communications Moqri, M., Ying, K., Poganik, J. R., Herzog, C., Chen, Q., Emamifar, M., Tyshkovskiy, A., Eames, A., Mur, J., Glubokov, D., Matei-Dediu, B., Goeminne, L., Mitchell, W., McCartney, D. L., Salas, L. A., Marioni, R. E., Lasky-Su, J. A., Snyder, M. P., Gladyshev, V. N. 2026; 17 (1): 725

    Abstract

    Recent epigenome-wide studies have identified a large number of genomic regions that consistently exhibit changes in their methylation status with aging across diverse populations, but the functional consequences of these changes are largely unknown. On the other hand, transcriptomic changes are more easily interpreted than epigenetic alterations, but previously identified age-related gene expression changes have shown limited replicability across populations. Here, we develop an approach that leverages high-resolution multi-omic data for an integrative analysis of epigenetic and transcriptomic age-related changes and identify genomic regions associated with both epigenetic and transcriptomic age-dependent changes in blood. Our results show that these multi-omic aging genes in blood are enriched for adaptive immune functions, replicate more robustly across diverse populations and are more strongly associated with aging-related outcomes compared to the genes identified using epigenetic or transcriptomic data alone. These multi-omic aging genes may serve as targets for epigenetic editing to facilitate cellular rejuvenation.

    View details for DOI 10.1038/s41467-025-67369-1

    View details for PubMedID 41554691

    View details for PubMedCentralID 10501909

  • The Right Person, the Right Treatment, at the Right Time in Alzheimer's Disease: Insights From the 2025 Brain Aging Symposium AGING CELL De Magalhaes, C. G., Moldakozhayev, A., Lopez, M., Bowman, G. L., Chhatwal, J. P., Kellis, M., Mohs, R., Nisenbaum, L., Quiroz, Y. T., Raju, R. M., Sperling, R. A., Moqri, M., Gladyshev, V. N. 2026; 25 (1): e70351

    Abstract

    On October 22nd, 2025, Brain Aging Symposium took place at Harvard Medical School bringing together leading researchers from academia and partner organizations to discuss recent advances in measuring and monitoring human brain aging trajectories, with a particular focus on Alzheimer's disease (AD). A central theme emerged: achieving "the right treatment for the right person and the right time" through precision medicine approaches. Key advances included the unprecedented validation of plasma-based biomarkers, particularly brain-derived p-Tau217 that can identify seeding AD pathology with remarkable specificity, making large-scale screening newly feasible. Integrating multi-level "omic" modalities, spanning genetic information, molecular biomarkers of nutrition, lipid and protein signatures, neuroimaging measures, cognitive assessments, and lifestyle factors, enhances disease risk modeling and trajectory prediction beyond the capacity of any single marker. Early findings highlight critical roles for nutritional and lipid metabolism, and myelin integrity in brain aging, with cell and sex-specific vulnerabilities identified in response to nutrition, social isolation, and metabolic stress. Computational approaches that combine single-cell genomics, epigenomics, and artificial intelligence have been shown to accelerate causal discovery and therapeutic development. However, significant challenges remain: current biomarkers explain only half the variance in cognitive decline, racial and ethnic differences in biomarker levels lack mechanistic understanding, and scalable tools for comprehensive brain aging assessment are needed. The symposium underscored that preventing AD will require intervening during the preclinical asymptomatic phase. These multimodal screening platforms, coupled with mechanistically driven therapeutics, reduction in modifiable risk factors, including nutrition, vascular health, and social determinants of health, could profoundly impact the field.

    View details for DOI 10.1111/acel.70351

    View details for Web of Science ID 001658604800013

    View details for PubMedID 41466363

    View details for PubMedCentralID PMC12748508

  • Recommendations for biomarker data collection in clinical trials by longevity biotechnology companies. npj aging Herzog, C. M., Poganik, J. R., Boekstein, N., Fortney, K., Peyer, J. G., Mellon, J., Starr, R., Barzilai, N., Moqri, M. 2025

    Abstract

    Biomarkers of aging have the potential to transform geroscience clinical trials because of their broad applications in stratifying participants, prioritizing interventions, and monitoring responses to geroprotectors. As longevity biotechnology companies (LBCs) continue to plan and launch innovative clinical trials, standard practices in collecting data and applying biomarkers of aging will allow the field to support parallel and ongoing validation and benchmarking efforts for aging biomarkers. Moreover, defining best practices will ensure future reuse of valuable clinical data through pre-competitive alignment on shared tools. Here, we propose recommendations for such collections. We believe that wide adoption of these recommendations will allow LBCs to produce and leverage the highest quality data from their clinical trials, while also benefiting the geroscience field more broadly with minimal additional effort.

    View details for DOI 10.1038/s41514-025-00313-1

    View details for PubMedID 41436473

  • Epigenetic age predictors for non-invasive assessment of human skin NPJ AGING Menendez Vazquez, A., Katsanos, D., Vasile, M., Graham, A., Dyster, V., Kaveh, S., Moqri, M., Banila, C. 2025; 12 (1): 11

    Abstract

    Skin is both the most visible and most environmentally exposed organ, with apparent aging phenotypes. DNA methylation clocks faithfully capture the progression of aging, but so far have been limited to training on abundant in vitro material or invasively collected samples to generate narrow methylomes using microarray platforms. Here, we demonstrate that skin biological age can be measured directly from a person's face with superior accuracy, using non-invasive tape-stripping. We developed two clocks, MitraSolo, based on single CpGs, and MitraCluster, on regions, trained on the largest enzymatic methyl-sequencing dataset of human epidermis (n = 462). Our models were validated on independent, longitudinal, and external datasets and were compared against established clocks. They predict age accurately, with an error of approximately 4 years, outperforming others on epidermal samples. They maintain high accuracy at low sequencing depths, enabling cost-effective scalability and show intra-individual prediction variation <2 years, highlighting their reproducibility. Their predictive capacity generalised across anatomical sites, conversion and sampling methodologies and on in vitro material. They also successfully captured the rejuvenating effects of Yamanaka factor treatment. MitraSolo and MitraCluster represent a new class of epigenetic clocks optimised for human skin with characteristics that support their use in clinical research, intervention monitoring, and skincare innovation.

    View details for DOI 10.1038/s41514-025-00314-0

    View details for Web of Science ID 001666732900001

    View details for PubMedID 41387742

    View details for PubMedCentralID PMC12820032

  • BIOMARKERS OF AGING - A CONSORTIUM STORY Moqri, M. OXFORD UNIV PRESS. 2025

    View details for DOI 10.1093/geroni/igaf122.1668

    View details for Web of Science ID 001653213000001

  • An unbiased comparison of 14 epigenetic clocks in relation to 174 incident disease outcomes NATURE COMMUNICATIONS Mavrommatis, C., Belsky, D. W., Ying, K., Moqri, M., Campbell, A., Richmond, A., Gladyshev, V. N., Chandra, T., McCartney, D. L., Marioni, R. E. 2025; 16 (1): 11164

    Abstract

    Epigenetic Clocks have been trained to predict chronological age, healthspan and lifespan. Such clocks are often analysed in relation to disease outcomes - typically using small datasets and a limited number of clocks. Here, we present a large-scale (n = 18,859), unbiased comparison of 14 widely used clocks as predictors of 174 incident disease outcomes and all-cause mortality over 10-years of follow up. Second- and third-generation clocks significantly outperform first-generation clocks, which have limited applications in disease settings. Of the 176 Bonferroni significant (P < 0.05/174) associations from fully-adjusted Cox regression models controlling for lifestyle and socioeconomic measures, there are 27 diseases (including primary lung cancer and diabetes) where the hazard ratio for the clock exceeds the clock's association with all-cause mortality. Furthermore, for 32 of the 176 findings, adding the clock to a null classification model with traditional risk factors significantly increases the classification accuracy by >1%. However, there is minimal evidence for interactions between the clocks and sex or smoking (ever/never) status. Second- and third-generation epigenetic clocks show promise for disease risk prediction, particularly in relation to respiratory and liver-based conditions.

    View details for DOI 10.1038/s41467-025-66106-y

    View details for Web of Science ID 001641320300010

    View details for PubMedID 41402269

    View details for PubMedCentralID PMC12708718

  • 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

  • Profiling Epigenetic Aging at Cell-Type Resolution Through Long-Read Sequencing AGING CELL Eames, A., Moqri, M., Poganik, J. R., Gladyshev, V. N. 2025; 24 (8): e70084

    Abstract

    DNA methylation can give rise to robust biomarkers of aging, yet most studies profile it at the bulk tissue level, which masks cell type-specific alterations that may follow distinct aging trajectories. Long-read sequencing technology enables methylation profiling of extended DNA fragments, enabling mapping to their cell type of origin. In this study, we introduce a framework for evaluating cell type-specific aging using long-read sequencing data, without the need for cell sorting. Leveraging cell type-specific methylation patterns, we map long-read fragments to individual cell types and generate cell type-specific methylation profiles, which are used as input to a newly developed probabilistic aging model, LongReadAge, capable of predicting epigenetic age at the cell type level. We use LongReadAge to track aging of myeloid cells and lymphocytes from bulk leukocyte data as well as circulating cell-free DNA, demonstrating robust performance in predicting age despite limited shared features across samples. This approach provides a novel method for profiling the dynamics of epigenetic aging at cell type resolution.

    View details for DOI 10.1111/acel.70084

    View details for Web of Science ID 001520957700001

    View details for PubMedID 40599132

    View details for PubMedCentralID PMC12341782

  • Biomarkers of Aging-NIA Joint Symposium 2024: New Insights Into Aging Biomarkers AGING CELL Herzog, C., Poganik, J. R., Barzilai, N., Basisty, N., Beerman, I., Belsky, D. W., de Cabo, R., Candia, J., Faghri, F., Horvath, S., Maier, A. B., Perez, V., Sen, P., Moqri, M., Gladyshev, V. N., Ferrucci, L. 2025; 24 (7): e70124

    Abstract

    The second Biomarkers of Aging Symposium, jointly hosted by the National Institute on Aging (NIA) Intramural Research Program and the Biomarkers of Aging Consortium (BAC) on September 12, 2024, in Baltimore, MD, convened leading researchers, clinicians, and stakeholders in the aging field to share new developments and discuss roadmaps to advance biomarkers of aging. This meeting report summarizes the highlights of this symposium and underscores the urgent need to understand longitudinal, complex, and heterogeneous processes of aging to unlock the full potential of aging biomarkers.

    View details for DOI 10.1111/acel.70124

    View details for Web of Science ID 001509876000001

    View details for PubMedID 40525821

    View details for PubMedCentralID PMC12266742

  • 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

  • Plasma protein-based organ-specific aging and mortality models unveil diseases as accelerated aging of organismal systems CELL METABOLISM Goeminne, L. J. E., Vladimirova, A., Eames, A., Tyshkovskiy, A., Argentieri, M., Ying, K., Moqri, M., Gladyshev, V. N. 2025; 37 (1): 205-222

    Abstract

    Aging is a complex process manifesting at molecular, cellular, organ, and organismal levels. It leads to functional decline, disease, and ultimately death, but the relationship between these fundamental biomedical features remains elusive. By applying elastic net regularization to plasma proteome data of over 50,000 human subjects in the UK Biobank and other cohorts, we report interpretable organ-specific and conventional aging models trained on chronological age, mortality, and longitudinal proteome data. These models predict organ/system-specific disease and indicate that men age faster than women in most organs. Accelerated organ aging leads to diseases in these organs, and specific diets, lifestyles, professions, and medications influence organ aging rates. We then identify proteins driving these associations with organ-specific aging. Our analyses reveal that age-related chronic diseases epitomize accelerated organ- and system-specific aging, modifiable through environmental factors, advocating for both universal whole-organism and personalized organ/system-specific anti-aging interventions.

    View details for DOI 10.1016/j.cmet.2024.10.005

    View details for Web of Science ID 001399317700001

    View details for PubMedID 39488213

  • 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

  • Depletion of loss-of-function germline mutations in centenarians reveals longevity genes NATURE COMMUNICATIONS Ying, K., Castro, J. P., Shindyapina, A. V., Tyshkovskiy, A., Moqri, M., Goeminne, L. J. E., Milman, S., Zhang, Z. D., Barzilai, N., Gladyshev, V. N. 2024; 15 (1): 9030

    Abstract

    While previous studies identified common genetic variants associated with longevity in centenarians, the role of the rare loss-of-function (LOF) mutation burden remains largely unexplored. Here, we investigated the burden of rare LOF mutations in Ashkenazi Jewish individuals from the Longevity Genes Project and LonGenity study cohorts using whole-exome sequencing data. We found that centenarians had a significantly lower burden (11-22%) of LOF mutations compared to controls. Similar effects were also observed in their offspring. Gene-level burden analysis identified 35 genes with depleted LOF mutations in centenarians, with 14 of these validated in the UK Biobank. Mendelian randomization and multi-omic analyses on these genes identified RGP1, PCNX2, and ANO9 as longevity genes with consistent causal effects on multiple aging-related traits and altered expression during aging. Our findings suggest that a protective genetic background, characterized by a reduced burden of damaging variants, contributes to exceptional longevity, likely acting in concert with specific protective variants to promote healthy aging.

    View details for DOI 10.1038/s41467-024-52967-2

    View details for Web of Science ID 001337874800002

    View details for PubMedID 39424787

    View details for PubMedCentralID PMC11489729

  • 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

  • E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database PLOS ONE Safaei, N., Safaei, B., Seyedekrami, S., Talafidaryani, M., Masoud, A., Wang, S., Li, Q., Moqri, M. 2022; 17 (5): e0262895

    Abstract

    Improving the Intensive Care Unit (ICU) management network and building cost-effective and well-managed healthcare systems are high priorities for healthcare units. Creating accurate and explainable mortality prediction models helps identify the most critical risk factors in the patients' survival/death status and early detect the most in-need patients. This study proposes a highly accurate and efficient machine learning model for predicting ICU mortality status upon discharge using the information available during the first 24 hours of admission. The most important features in mortality prediction are identified, and the effects of changing each feature on the prediction are studied. We used supervised machine learning models and illness severity scoring systems to benchmark the mortality prediction. We also implemented a combination of SHAP, LIME, partial dependence, and individual conditional expectation plots to explain the predictions made by the best-performing model (CatBoost). We proposed E-CatBoost, an optimized and efficient patient mortality prediction model, which can accurately predict the patients' discharge status using only ten input features. We used eICU-CRD v2.0 to train and validate the models; the dataset contains information on over 200,000 ICU admissions. The patients were divided into twelve disease groups, and models were fitted and tuned for each group. The models' predictive performance was evaluated using the area under a receiver operating curve (AUROC). The AUROC scores were 0.86 [std:0.02] to 0.92 [std:0.02] for CatBoost and 0.83 [std:0.02] to 0.91 [std:0.03] for E-CatBoost models across the defined disease groups; if measured over the entire patient population, their AUROC scores were 7 to 18 and 2 to 12 percent higher than the baseline models, respectively. Based on SHAP explanations, we found age, heart rate, respiratory rate, blood urine nitrogen, and creatinine level as the most critical cross-disease features in mortality predictions.

    View details for DOI 10.1371/journal.pone.0262895

    View details for Web of Science ID 000892294900002

    View details for PubMedID 35511882

    View details for PubMedCentralID PMC9070907

  • Effect of "Following" on Contributions to Open Source Communities JOURNAL OF MANAGEMENT INFORMATION SYSTEMS Moqri, M., Mei, X., Qiu, L., Bandyopadhyay, S. 2018; 35 (4): 1188-1217

    View details for DOI 10.1080/07421222.2018.1523605

    View details for Web of Science ID 000453555300008

  • 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
https://orcid.org/0000-0001-6675-0566