Mahdi Moqri

Drugs for aging-related diseases may modulate aging itself, but standard clinical trial designs cannot detect such effects. Aging clocks could close this gap, but epigenetic models often yield inconsistent, hard-to-interpret results. In contrast, proteomic clocks, by tracking the immediate effectors of biological change, may excel in providing aging biomarkers or mechanistic insight. Here we compare six proteomic clocks (ProtAge, OrganAge _mortality , OrganAge _chrono , PAC, ipfP3GPT and PAOPAC) on serum proteomes from a published 12-week phase 2a trial of the candidate anti-fibrotic drug rentosertib in idiopathic pulmonary fibrosis. We measure the variance between the clocks and find that all six clocks consistently predicted lower biological age in treated arms. However, proteomic clocks alone cannot deconvolute aging- and disease-specific effects. We addressed this issue indirectly through pathway analyses that identified potential anti-aging shifts in senescence and metabolic processes alongside the anti-fibrotic activity of rentosertib. This work supports the goal of dual-purpose clinical trial designs that integrate aging endpoints into studies for specific disease indications.

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.

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.

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.

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.

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.

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.

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.

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.