Ludger Jan Elzue Goeminne

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.

While aging manifests differently across organs and individuals, existing approaches to measure it lack the spatial resolution to capture this complexity. Here, we develop an approach that applies multi-modal imaging, segmentation algorithms, and deep-learning to assess organ-specific aging across 39 anatomical regions in a total of 134K individuals in the UK Biobank. Our analysis reveals significant organ aging heterogeneity across and within individuals and a remarkable prevalence of organ-specific extreme aging. We validate that our imaging measures capture pathophysiologically meaningful aging through correlation with organ-specific biomarkers, revealing biologically coherent patterns. We find that accelerated organ aging is robustly predictive of corresponding organ disease. We identify the cerebrum as one of the strongest predictors of organismal aging. We investigate organ aging patterns underlying disease risk and find that each disease is linked to aging of highly distinct subsets of organs. Exploring lifestyle factors and interventions reveals a range of divergent organ-specific effects. Our work establishes a powerful paradigm for noninvasively evaluating human aging at anatomical resolution and population scale.

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.

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.

Xenogenic biomaterial durability, including bioprosthetic heart valves (BPVs), is compromised by pathological extracellular matrix (ECM) remodeling, resulting in progressive structural degeneration. Mass spectrometry-based proteomics can help reveal BPV degeneration mechanisms; however, peptide sequence similarity between donor and host species complicates protein-level analysis. We present a cross-species proteomic analytical strategy for xenogenic biomaterials and cross-species proteomic datasets. In silico tryptic digestion of human and bovine protein databases identified over 400 overlapping proteins with a high protein percent identity. Explanted human BPV tissue was divided into macroscopically distinct regions of degeneration and analyzed by mass spectrometry. A peptide-level strategy quantified protein abundances in a species-delineated analysis. We highlighted degeneration region-specific depositions of key human ECM proteins and bovine ECM proteins whose abundance is time dependent. We demonstrated that single-species analysis of a cross-species proteome results in inaccurate quantification. This study highlights the importance of distinguishing between donor and host species proteomes for accurate protein quantification. While focused on clinically explanted biomaterials, our approach is broadly applicable to all forms of xenotransplantation and the use of xenogenic matrices.

Cellular metabolism is highly dynamic during hematopoiesis, yet the regulatory networks that maintain metabolic homeostasis during differentiation are incompletely understood. Herein, we have studied the grave immunodeficiency syndrome reticular dysgenesis caused by loss of mitochondrial adenylate kinase 2 (AK2) function. By coupling single-cell transcriptomics in samples from patients with reticular dysgenesis with a CRISPR model of this disorder in primary human hematopoietic stem cells, we found that the consequences of AK2 deficiency for the hematopoietic system are contingent on the effective engagement of metabolic checkpoints. In hematopoietic stem and progenitor cells, including early granulocyte precursors, AK2 deficiency reduced mechanistic target of rapamycin (mTOR) signaling and anabolic pathway activation. This conserved nutrient homeostasis and maintained cell survival and proliferation. In contrast, during late-stage granulopoiesis, metabolic checkpoints were ineffective, leading to a paradoxical upregulation of mTOR activity and energy-consuming anabolic pathways such as ribonucleoprotein synthesis in AK2-deficient cells. This caused nucleotide imbalance, including highly elevated adenosine monophosphate and inosine monophosphate levels, the depletion of essential substrates such as NAD+ and aspartate, and ultimately resulted in proliferation arrest and demise of the granulocyte lineage. Our findings suggest that even severe metabolic defects can be tolerated with the help of metabolic checkpoints but that the failure of such checkpoints in differentiated cells results in a catastrophic loss of homeostasis.

Recent advances in plasma proteomics have led to a surge of computational models that accurately predict chronological age, mortality, and diseases from a simple blood draw. We leverage the data of ∼50,000 participants in the UK Biobank to investigate the predictive power of such models compared to individual proteins and metabolites by assessing disease risk and organ aging. We find that, with the exception of brain-related diseases, individual protein levels often match or surpass the predictive power of elaborate clocks trained on chronological age or mortality risk. Certain proteins effectively predict multiple diseases affecting specific organs. We show that in most cases, proteins predict diseases better than polygenic risk scores, and identify novel associations between human plasma protein levels and diseases, including LAMP3 and COPD, CHHR2 and liver disease, FAMC3 and kidney disease, and TMED1 and gout. We present a focused panel of 21 protein biomarkers that reveals the health state of the six organs associated with major age-related diseases. Our panel predicts common age-related diseases, including liver cirrhosis and fibrosis, dementia, kidney failure, and type II diabetes better than established blood panels and aging models. Through its vast coverage of age-related diseases, our compact panel offers a cost-effective alternative to full-scale proteomic analyses, making it a prime candidate for the non-invasive clinical detection and management of numerous age-related diseases simultaneously.

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.

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.

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.