Introduction
Lymphoid neoplasms (LN) represent a heterogeneous collection of malignancies originating from B-lymphocytes, T-lymphocytes, or natural killer (NK) cells at various stages of differentiation [ 1 , 2 ]. Their incidence, particularly for subtypes like diffuse large B-cell lymphoma (DLBCL), has been increasing, and they predominantly affect older adults [ 1 , 3 ]. However, certain lymphoid neoplasms demonstrate unique age distributions. Hodgkin lymphoma (HL), for example, exhibits a bimodal age distribution with peaks in young adults (ages 15–40) and older adults (after age 55), suggesting that aging processes may influence lymphomagenesis differently across the age spectrum [ 4 , 5 ]. The strong association between chronological age and LN incidence points towards underlying biological aging processes playing a critical role in lymphomagenesis [ 6 ].
However, chronological age is an imperfect proxy for an individual’s physiological state. Biological age refers to the functional state of an individual’s body systems and their susceptibility to age-related diseases, which can differ substantially from chronological age [ 7 , 8 ]. Biological age reflects functional capacity and vulnerability to age-related decline and disease, often diverges from chronological age, and may provide a more accurate assessment of aging [ 7 , 9 ]. Over the past decade, significant advances have been made in developing various “aging clocks”: biomarkers or algorithms designed to quantify biological age [ 10 , 11 ]. Epigenetic clocks are computational models that use DNA methylation patterns at specific cytosine-guanine dinucleotide (CpG) sites to estimate biological age [ 12 , 13 ]. Among these, epigenetic clocks, based on age-related DNA methylation (DNAm) patterns, have emerged as particularly robust predictors [ 9 , 12 , 14 - 16 ].
Accelerated biological aging, where the biological age surpasses the chronological age, is associated with an increased risk for numerous age-related diseases and all-cause mortality [ 17 , 18 ]. Growing evidence now links accelerated biological aging, often measured by these clocks, specifically to the risk and progression of LN [ 19 - 21 ]. Recent work has shown that B cell lymphoma development is associated with age-associated clonal B cell populations driven by epigenetic changes and accelerated biological aging [ 22 ]. This review explores the complex interplay between LN development, the fundamental processes of aging (particularly immunosenescence), and the insights provided by both biological age and aging clocks. While our focus primarily centers on LN subtypes that predominantly affect older adults, we also consider the role of biological aging in lymphomas with unique age distributions, such as Hodgkin lymphoma. We discuss the underlying mechanisms, along with the clinical implications for diagnosis, prognosis, and treatment of LN, and outline future research directions needed to harness this knowledge for improved patient outcomes.
Lymphoid Neoplasms and the Impact of Chronological Aging
Lymphoid neoplasms exhibit distinct age-related incidence patterns. While precursor T- and B-cell malignancies peak in children and young adults, most LN derived from mature lymphocytes, particularly B-cells, show a steady increase in incidence with age, typically peaking between 75 and 99 years [ 6 ]. However, Hodgkin lymphoma represents a notable exception with its characteristic bimodal age distribution. This bimodal pattern may reflect “the varying proportions of cell populations across the age range, with an immune system rich in precursor cells in young people and a predominance of germinal centre and memory B-cells in older adults.” This pattern holds across various geographical regions, although the absolute rates differ [ 6 ]. This age-related distribution likely reflects changes in the composition and function of the immune system over the lifespan. Interestingly, some studies suggest a potential decline or plateau in incidence and mortality in the very oldest cohorts (e.g., >80–85 years), possibly linked to factors like decreased lymphopoiesis or impaired angiogenesis limiting tumor growth in extreme old age [ 23 , 24 ]. However, the overwhelming trend is a significantly higher burden of LN in the elderly population [ 25 ].
Immunosenescence: A Key Mechanistic Link
The age-related increase in LN is strongly linked to immunosenescence: the multifaceted deterioration of immune function with age [ 26 , 27 ]. Like an aging security system that becomes less effective at detecting threats, immunosenescence leaves the body vulnerable to both infections and malignant transformations. This process impacts both innate and adaptive immunity, leading to reduced responses to new antigens and vaccines, increased susceptibility to infections, diminished tumor immunosurveillance, and a chronic, low-grade pro-inflammatory state known as “inflammaging” [ 28 - 31 ].
Several key features characterize immunosenescence and are relevant to LN pathogenesis. Direct pathways linking aging to lymphomagenesis include DNA damage accumulation, telomere attrition, and impaired DNA repair mechanisms [ 32 ]. These processes are particularly relevant in the context of viral-associated lymphomas. For instance, Epstein–Barr virus (EBV), which is detectable in approximately 40% of Hodgkin lymphoma cases in Western countries, exploits age-related immune dysfunction [ 33 ]. EBV prevalence in HL follows the bimodal age distribution, with higher rates in children and older adults compared to young adults, suggesting age-specific interactions between viral persistence and immune surveillance [ 34 ]. Changes occur at the level of hematopoietic stem cells (HSCs), where aging leads to a bias towards myeloid differentiation (myeloid skewing) at the expense of lymphopoiesis, particularly affecting B-cell development [ 28 , 35 - 37 ]. This shift is partly driven by epigenetic alterations and reduced responsiveness to crucial cytokines like IL-7 [ 28 , 38 ]. Furthermore, the progressive involution of the thymus gland, starting early in life, results in an exponential decline in naive T-cell output [ 39 ]. This thymic decline restricts T-cell receptor (TCR) repertoire diversity, thereby impairing the capacity to respond effectively to novel antigens, including tumor antigens [ 39 , 40 ]. Studies show that this decline in TCR diversity occurs later in females, which may partially explain their lower overall cancer incidence [ 39 ]. Concurrently, the composition of peripheral lymphocyte populations shifts; older individuals typically show fewer naive T and B cells but a relative increase in memory T cells, often exhibiting features of exhaustion such as the loss of CD28/CD27 co-stimulatory molecules, and certain memory B cell subsets like IgD-CD27-double-negative B cells [ 41 - 44 ]. Functionally, aging lymphocytes display a reduced proliferative capacity, impaired B-cell class-switching and antibody affinity, decreased NK cell cytotoxicity, and altered macrophage polarization that can favor pro-tumorigenic M2 phenotypes [ 45 - 47 ]. Finally, inflammaging (chronic low-grade inflammation driven by senescent cells producing the senescence-associated secretory phenotype (SASP), persistent infections like CMV, and metabolic changes) creates an environment that promotes cancer development [ 48 - 50 ].
Emerging evidence suggests that immunosenescence, particularly the decline driven by thymic involution, may be a more potent driver of age-related cancer incidence, including LN, than the traditionally emphasized accumulation of somatic mutations. The paradigmatic example is Epstein–Barr virus-positive DLBCL (EBV + DLBCL), often termed “DLBCL of the elderly,” where declining immune surveillance against EBV-infected B cells in the context of immunosenescence is thought to be central to its pathogenesis [ 51 , 52 ]. Recent evidence suggests that EBV-positive large B-cell lymphoma in young patients (<45 years) frequently shows PD-L1 overexpression (77%) and preferential nodal involvement with an excellent prognosis, contrasting with the more aggressive clinical features in elderly patients [ 53 ]. This age-dependent difference in tumor behavior highlights how immunosenescence influences lymphomagenesis through distinct mechanisms across the lifespan.
Quantifying Aging: Biological Age and Aging Clocks
While chronological age is associated with immunosenescence and LN risk, significant inter-individual variability exists. Biological age aims to capture this variability, providing a measure of an individual’s physiological state and risk for age-related outcomes [ 7 ]. Recent transcriptomic studies have identified universal and specific mechanisms of aging across tissues, providing molecular hallmarks that can quantify both aging and mortality risk [ 54 ].
Measures of Biological Age
Biological age can be estimated using various approaches. These include clinical and functional measures like frailty indices, gait speed, grip strength, and cognitive function assessments [ 7 , 55 ]. Molecular biomarkers such as telomere length, inflammatory markers (e.g., CRP, IL-6), and metabolic markers also provide insights [ 19 , 56 , 57 ]. Furthermore, “omics”-based predictors derived from transcriptomics, proteomics, metabolomics, and notably, epigenomics, offer high-dimensional data for estimating biological age [ 54 , 58 ].
Aging Clocks: Robust Tools for Biological Age Estimation
Aging clocks use molecular data to predict and quantify biological aging. Epigenetic clocks, which track age-related changes in DNA methylation at specific CpG sites, are now the gold standard for measuring biological age [ 12 , 14 , 59 ]. First-generation clocks, including Horvath’s pan-tissue clock (353 CpGs) and Hannum’s blood-based clock (71 CpGs), primarily predict chronological age [ 12 , 14 ]. Second-generation clocks, such as PhenoAge (513 CpGs linked to phenotypic aging and mortality) and GrimAge (1030 CpGs predicting time-to-death, and incorporating smoking history and plasma protein surrogates), demonstrate stronger associations with healthspan, disease risk, and mortality [ 15 , 16 ]. The discrepancy between the clock’s predicted biological age (e.g., DNAm Age) and an individual’s chronological age is termed “age acceleration,” and is a critical metric linked to adverse health outcomes [ 17 , 19 ]. A 5-year increase in epigenetic age acceleration, for instance, has been associated with approximately a 20% increase in mortality risk [ 18 ]. Recent epigenome-wide Mendelian randomization studies have identified causal CpG sites that contribute to aging, distinguishing between damaging and adaptive methylation changes [ 9 ].
Beyond epigenetics, other biological clocks have been developed based on diverse inputs (summarized in Table 1 ). These include the immunity clock, utilizing immune function variables like neutrophil and lymphocyte activity [ 60 ]; the inflammatory clock (iAge), based on circulating inflammatory proteins such as CXCL9 [ 61 ]; the glycan clock, which uses age-related changes in IgG glycosylation patterns [ 62 ]; and hematological clocks derived from parameters in standard blood counts, like the lymphocyte-to-monocyte ratio or white blood cell counts [ 63 , 64 ]. Recent advances have also led to plasma protein-based organ-specific aging models that reveal diseases as accelerated aging of specific organ systems [ 57 ]. These various clocks provide quantitative measures that can potentially disentangle the effects of biological aging from chronological age in the context of LN.
Epigenetic Age Acceleration and LN Risk
Emerging research directly links accelerated biological aging, measured by various clocks, to LN risk and behavior.
Several studies have demonstrated that accelerated epigenetic aging is associated with an increased risk of developing LN. Cohort studies utilizing clocks like HorvathAge, HannumAge, PhenoAge, and GrimAge have found significant associations between epigenetic age acceleration and the risk of incident mature B-cell neoplasms, including DLBCL and other NHL subtypes [ 17 , 19 , 20 ]. Notably, the second-generation clocks, PhenoAge and GrimAge, often exhibit stronger associations with cancer risk compared to first-generation clocks [ 19 ]. These findings suggest that accelerated biological aging processes, as captured by DNAm patterns, may precede and actively contribute to the process of lymphomagenesis. Integrative analyses have uncovered specific mechanisms by which aging drives B cell lymphoma, including promoter hypermethylation and age-associated clonal expansions [ 22 , 65 ]. The nature of epigenetic aging involves both co-regulated changes and stochastic components, as revealed by single-cell analyses [ 66 ].
Beyond initial risk, biological age metrics are also pertinent to the clinical course and prognosis of LN. In Chronic Lymphocytic Leukemia (CLL), studies indicate that proliferating tumor cell fractions exhibit a higher estimated DNAm Age compared to resting fractions, suggesting an acquisition of longevity-associated methylation changes as the disease progresses [ 21 ]. Furthermore, epigenetic classification systems derived from DNAm profiles offer robust prognostic information in CLL, potentially outperforming traditional markers like IGHV mutation status [ 67 , 68 ]. Age also significantly impacts outcomes in marginal zone lymphoma (MZL), with a recent multicenter study demonstrating that patients >70 years had inferior progression-free survival and overall survival despite the development of new therapies [ 69 ]. Older MZL patients presented with worse performance status, advanced stage disease, and bone marrow involvement, yet were more likely to receive single-agent rituximab rather than chemoimmunotherapy, highlighting the complex interplay between biological age, treatment tolerance, and outcomes. Researchers have proposed that measuring epigenetic age acceleration during remission could predict relapse risk, though this requires specific investigation in LN subtypes [ 70 ].
The relationship between other aging biomarkers and LN is also under investigation. Telomere length, for instance, shows complex associations; some studies report positive correlations between longer leukocyte telomere length and the risk for lymphoid leukemia, multiple myeloma, and NHL, which contrasts with findings for many solid tumors [ 56 , 71 ]. This underscores the necessity for context-specific interpretation of aging biomarkers in different cancer types. While less studied specifically for LN risk compared to epigenetic clocks, measures derived from immunity clocks [ 60 ] and inflammatory clocks (iAge) [ 61 ] are mechanistically linked to immunosenescence and inflammaging, suggesting their potential relevance for LN development and prognosis. Additionally, the recent identification of lymphoid clonal hematopoiesis of indeterminate potential (L-CHIP) in healthy individuals provides a potential precursor state. Characterized by clonal expansions carrying mutations typically found in LN, L-CHIP is specifically associated with future LN development and may represent an early manifestation of age-related processes culminating in overt malignancy [ 72 ]. The relationship between L-CHIP and immunosenescence remains an active area of investigation, with emerging evidence suggesting that age-related immune dysfunction may permit the expansion of these precursor clones [ 32 ]. This is supported by findings that age-associated clonal B cells (ACBCs) drive B cell lymphoma development through epigenetic alterations and accelerated biological aging [ 22 ].
Clinical Implications and Therapeutic Opportunities
The understanding that biological age, distinct from chronological age, influences LN development and outcomes has significant clinical implications.
Diagnosing LN in older adults is often complicated by the presence of frailty, multiple comorbidities, and atypical clinical presentations [ 1 ]. Biological age assessments could significantly complement chronological age and standard prognostic factors. While not a direct aging clock, Comprehensive Geriatric Assessment (CGA) incorporates functional aspects of aging and is crucial for identifying frail patients who may struggle to tolerate standard therapies [ 29 , 73 ]. Sarcopenia, the age- and cancer-related decline in muscle mass and function, is another critical factor. It serves as a powerful predictor of treatment-related toxicity, such as febrile neutropenia, and poorer survival outcomes in LN patients, particularly those with DLBCL [ 29 , 74 - 76 ]. Objective measurement via CT imaging (e.g., L3 skeletal muscle index, LSMI) facilitates its assessment in clinical practice [ 74 ]. Sarcopenia likely reflects aspects of accelerated biological aging and strongly correlates with frailty [ 77 ]. Studies have shown that mitochondrial dysfunction, a hallmark of aging, contributes to muscle wasting and may be targeted by interventions such as acarbose that modulate aging pathways [ 78 , 79 ]. Integrating quantitative aging clock data (e.g., DNAm age acceleration) with CGA and sarcopenia measures could potentially lead to more precise risk stratification and better prediction of treatment tolerance in the future. However, practical challenges including the cost and accessibility of DNAm testing remain significant barriers to clinical implementation. While costs have decreased substantially, comprehensive epigenetic profiling still requires specialized equipment and expertise that may not be readily available in all clinical settings [ 12 ]. Recent innovations such as TIME-seq have reduced both the time and cost of DNA methylation measurement for epigenetic clock construction [ 80 ].
Insights derived from biological age could guide more individualized therapeutic approaches for LN patients. Identifying biologically older or frail individuals, irrespective of their chronological age, could support decisions regarding the use of dose-reduced regimens or alternative therapies, aiming to mitigate toxicity while preserving efficacy [ 29 , 73 ]. As observed in CLL, epigenetic profiles associated with biological aging might predict both treatment response and relapse risk, potentially informing therapy selection [ 67 , 70 ]. Furthermore, for cancer survivors or patients exhibiting signs of accelerated aging, interventions targeting fundamental aging processes (such as senolytics designed to clear senescent cells, or drugs like metformin and resveratrol) are being explored, although rigorous clinical validation in the context of LN is still required [ 7 ]. Currently, there are no dedicated clinical trials specifically investigating senolytics or metformin for lymphoid malignancies, representing a significant research gap. These anti-aging agents have the theoretical potential to “reverse aging or at least slow down the accelerated aging process and improve quality of life in cancer survivors during the active treatment and after the treatment ends,” but their specific application to LN requires investigation [ 7 ]. Successfully reversing or slowing biological age acceleration could potentially enhance long-term outcomes and improve the quality of life for these patients.
The escalating costs associated with novel LN therapies underscore the need for costeffective management strategies [ 40 , 67 , 81 ]. Biological age assessment could potentially aid in optimizing resource allocation by helping to identify patients who are most likely to derive significant benefit from intensive or high-cost treatments, versus those for whom supportive or palliative approaches may be more appropriate. The development of standardized platforms for biological age profiling, such as ClockBase, facilitates the clinical implementation of these tools [ 10 ].
Future Directions
Despite significant progress, several areas demand further investigation to fully harness the potential of biological aging insights in LN. A primary need is the refinement and validation of aging clocks specifically tailored to lymphoid tissues or LN subtypes, alongside improving the clinical applicability and accessibility of current clock technologies [ 8 , 11 ]. Recent developments in foundation models for the DNA methylome, such as MethylGPT, offer new opportunities for more accurate and interpretable aging biomarkers [ 59 ]. Establishing clear causal links between accelerated biological aging, its drivers like immunosenescence, and LN development is crucial, moving beyond correlative findings through longitudinal studies tracking biological age changes prior to LN diagnosis. Recent advances in causal inference for epigenetic aging, including epigenome-wide Mendelian randomization approaches, provide tools to identify causal methylation sites [ 9 , 13 ].
Further research should delve deeper into the mechanisms by which specific aging pathways, such as the senescence-associated secretory phenotype (SASP), T-cell mitochondrial dysfunction [ 82 , 83 ], or altered HSC function [ 84 ], contribute to lymphomagenesis. Studies have shown that protective genetic backgrounds with a reduced burden of damaging variants contribute to exceptional longevity [ 85 ], suggesting potential targets for intervention. Prospective clinical trials are essential to evaluate the utility of biological age assessments (integrating clocks, CGA, and sarcopenia) in guiding LN treatment decisions and to test the efficacy of “geroprotective” or anti-aging interventions as adjuncts to standard LN therapy or in high-risk individuals (e.g., those with L-CHIP or significant age acceleration). Integrating multi-omics data (combining epigenetic, transcriptomic, proteomic, and metabolomic clocks with clinical information) will likely provide a more holistic view of biological aging in LN [ 58 , 86 ]. High-dimensional representations of biological aging across functional modules, such as the Ageome framework, enable the comprehensive assessment of aging dynamics within individuals and populations [ 58 ]. Specific interventions for thymic rejuvenation warrant investigation, including interleukin-7 (IL-7) recombination therapy, which plays a crucial role in T cell development and homeostasis [ 87 ]. Studies have shown that thymic hyperplasia occurs in 28.4% of lymphoma patients after chemotherapy, with a younger age being the strongest predictor of thymic regrowth. Other promising approaches include mTOR inhibition and NAD+ activation to overcome T-cell terminal differentiation, which could enhance adoptive cellular immunotherapies in LN patients. Finally, developing effective strategies to counteract the detrimental aspects of immunosenescence, potentially through restoring TCR diversity, rejuvenating thymic function [ 88 ], or modulating inflammaging, represents a significant opportunity for LN prevention and therapy.
Conclusions
The intersection of lymphoid neoplasms, biological aging, and aging clocks represents a rapidly evolving field with profound clinical implications. Chronological age remains a major risk factor for LN, and is largely mediated through immunosenescence. However, biological age, quantified by increasingly sophisticated aging clocks, provides a more precise measure of an individual’s aging trajectory and vulnerability. Accelerated biological aging is demonstrably linked to increased LN risk and impacts disease progression and potentially treatment outcomes. The complex relationship between aging and immune dysfunction has been further elucidated by studies showing how genetic factors influence COVID-19 risk through aging-related pathways [ 89 ], highlighting the broader implications of immunosenescence in disease susceptibility. Integrating assessments of biological age, including aging clocks and clinical measures like frailty and sarcopenia, into oncological practice holds immense promise for personalizing LN management, particularly in the growing elderly population. Future research focused on mechanistic understanding, refining predictive tools, and testing targeted interventions aimed at biological aging pathways will be critical to translate these insights into tangible benefits for patients with lymphoid neoplasms. Open scientific competitions, such as the Biomarkers of Aging Challenge, are driving innovation in aging biomarker development and validation [ 90 ].
Funding:
This research was funded by the National Institutes of Health and National Institute on Aging, grant number 1F99AG088431-01.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Vovelle J ; Row C ; Larosa F ; Guy J ; Mihai A-M ; Maynadié M ; Barben J ; Manckoundia P Prescription of blood lymphocyte immunophenotyping in the diagnosis of lymphoid neoplasms in older adults . J. Clin. Med 2022 , 11 , 1748 . 35330073 10.3390/jcm11061748 PMC8949070
2. Catovsky D . Proceedings: Methods for the study and cell differentiation in acute leukaemia . Br. J. Cancer 1975 , 32 , 282 – 283 . 1212406 10.1038/bjc.1975.220 PMC2024830
3. Swerdlow SH ; Campo E ; Pileri SA ; Harris NL ; Stein H ; Siebert R ; Advani R ; Ghielmini M ; Salles GA ; Zelenetz AD ; The 2016 revision of the World Health Organization classification of lymphoid neoplasms . Blood 2016 , 127 , 2375 – 2390 . 26980727 10.1182/blood-2016-01-643569 PMC4874220
4. Vadakara J ; Andrick B Current advances in Hodgkin’s lymphoma . Chronic Dis. Transl. Med 2019 , 5 , 15 – 24 . 30993260 10.1016/j.cdtm.2019.02.003 PMC6450804
5. Drechsel KCE ; Pilon MCF ; Stoutjesdijk F ; Meivis S ; Schoonmade LJ ; Wallace WHB ; van Dulmen-den Broeder E ; Beishuizen A ; Kaspers GJL ; Broer SL ; Reproductive ability in survivors of childhood, adolescent, and young adult Hodgkin lymphoma: A review . Hum. Reprod. Update 2023 , 29 , 486 – 517 . 36779325 10.1093/humupd/dmad002 PMC10320502
6. Sant M ; Allemani C ; Tereanu C ; De Angelis R ; Capocaccia R ; Visser O ; Marcos-Gragera R ; Maynadié M ; Simonetti A ; Lutz J-M ; Incidence of hematologic malignancies in Europe by morphologic subtype: Results of the HAEMACARE project . Blood 2010 , 116 , 3724 – 3734 . 20664057 10.1182/blood-2010-05-282632
7. Wang T ; Shao W ; Huang Z ; Tang H ; Zhang J ; Ding Z ; Huang K MOGONET Integrates Multi-Omics Data Using Graph Convolutional Networks Allowing Patient Classification and Biomarker Identification . Nat. Commun 2021 , 12 , 3445 . 34103512 10.1038/s41467-021-23774-w PMC8187432
8. Moqri M ; Herzog C ; Poganik JR ; Ying K ; Justice JN ; Belsky DW ; Higgins-Chen AT ; Chen BH ; Cohen AA ; Fuellen G ; Validation of biomarkers of aging . Nat. Med 2024 , 30 , 360 – 372 . 38355974 10.1038/s41591-023-02784-9 PMC11090477
9. Ying K ; Liu H ; Tarkhov AE ; Sadler MC ; Lu AT ; Moqri M ; Horvath S ; Kutalik Z ; Shen X ; Gladyshev VN Causality-enriched epigenetic age uncouples damage and adaptation . Nat. Aging 2024 , 4 , 231 – 246 . 38243142 10.1038/s43587-023-00557-0 PMC11070280
10. Ying K ; Tyshkovskiy A ; Trapp A ; Liu H ; Moqri M ; Kerepesi C ; Gladyshev VN ClockBase: A comprehensive platform for biological age profiling in human and mouse . bioRxiv 2023 .
11. Ying K ; Paulson S ; Eames A ; Tyshkovskiy A ; Li S ; Perez-Guevara M ; Emamifar M ; Martínez MC ; Kwon D ; Kosheleva A ; A Unified Framework for Systematic Curation and Evaluation of Aging Biomarkers . bioRxiv 2024 .
12. Horvath S . DNA methylation age of human tissues and cell types . Genome Biol . 2013 , 14 , 3156 . 10.1186/gb-2013-14-10-r115 PMC4015143 24138928
13. Ying K . Causal inference for epigenetic ageing . Nat. Rev. Genet 2024 , 26 , 3 . 10.1038/s41576-024-00799-7 39472743
14. Hannum G ; Guinney J ; Zhao L ; Zhang L ; Hughes G ; Sadda S ; Klotzle B ; Bibikova M ; Fan J-B ; Gao Y ; Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates . Mol. Cell 2013 , 49 , 359 – 367 . 23177740 10.1016/j.molcel.2012.10.016 PMC3780611
15. Levine ME An epigenetic biomarker of aging for lifespan and healthspan . Aging 2018 , 10 , 573 – 591 . 29676998 10.18632/aging.101414 PMC5940111
16. Lu AT ; Seeboth A ; Tsai P-C ; Sun D ; Quach A ; Reiner AP ; Kooperberg C ; Ferrucci L ; Hou L ; Baccarelli AA ; DNA methylation-based estimator of telomere length . Aging 2019 , 11 , 5895 – 5923 . 31422385 10.18632/aging.102173 PMC6738410
17. Dugué P ; Bassett JK ; Wong EM ; Joo JE ; Li S ; Yu C ; Schmidt DF ; Makalic E ; Doo NW ; Buchanan DD ; Biological aging measures based on blood DNA methylation and risk of cancer: A prospective study . JNCI Cancer Spectr . 2020 , 5 , pkaa109 . 33442664 10.1093/jncics/pkaa109 PMC7791618
18. Strickland M ; Yacoubi-Loueslati B ; Bouhaouala-Zahar B ; Pender SLF ; Larbi A Relationships Between Ion Channels, Mitochondrial Functions and Inflammation in Human Aging . Front. Physiol 2019 , 10 , 158 . 30881309 10.3389/fphys.2019.00158 PMC6405477
19. Deng Y ; Tsai C-W ; Chang W-S ; Xu Y ; Huang M ; Bau D-T ; Gu J The significant associations between epigenetic clocks and bladder cancer risks . Cancers 2024 , 16 , 2357 . 39001419 10.3390/cancers16132357 PMC11240392
20. Dugué P ; Bassett JK ; Joo JE ; Jung C ; Ming Wong E ; Moreno-Betancur M ; Schmidt D ; Makalic E ; Li S ; Severi G ; DNA methylation-based biological aging and cancer risk and survival: Pooled analysis of seven prospective studies . Int. J. Cancer 2018 , 142 , 1611 – 1619 . 29197076 10.1002/ijc.31189
21. Bartholdy BA ; Wang X ; Yan X-J ; Pascual M ; Fan M ; Barrientos J ; Allen SL ; Martinez-Climent JA ; Rai KR ; Chiorazzi N ; CLL intraclonal fractions exhibit established and recently acquired patterns of DNA methylation . Blood Adv . 2020 , 4 , 893 – 905 . 32150608 10.1182/bloodadvances.2019000817 PMC7065474
22. Castro JP ; Shindyapina AV ; Barbieri A ; Ying K ; Strelkova OS ; Paulo JA ; Tyshkovskiy A ; Meinl R ; Kerepesi C ; Petrashen AP ; Age-associated clonal B cells drive B cell lymphoma in mice . Nat. Aging 2024 , 4 , 1403 – 1417 . 39117982 10.1038/s43587-024-00671-7
23. Shoutko AN Overview of hematopoietic stem cells in systemic cancer treatment, aging, pregnancy, and radiation hormesis . Adv. Mol. Imaging 2019 , 09 , 19 – 42 .
24. Rivard A ; Fabre J-E ; Silver M ; Chen D ; Murohara T ; Kearney M ; Magner M ; Asahara T ; Isner JM Age-dependent impairment of angiogenesis . Circulation 1999 , 99 , 111 – 120 . 9884387 10.1161/01.cir.99.1.111
25. Alves ASB ; Bataglia FB ; Conterno L ; Segato R ; Payão S Epidemiological and cytogenetic profiles of patients with hematological malignancies and their relationship with aging . Hematol. Transfus. Cell Ther 2018 , 40 , 200 – 206 . 30128427 10.1016/j.htct.2017.10.001 PMC6098168
26. Mancuso S ; Carlisi M ; Santoro M ; Napolitano M ; Raso S ; Siragusa S Immunosenescence and lymphomagenesis . Immun. Ageing 2018 , 15 , 22 . 30258468 10.1186/s12979-018-0130-y PMC6151062
27. Lutshumba J ; Nikolajczyk B ; Bachstetter A Dysregulation of systemic immunity in aging and dementia . Front. Cell. Neurosci 2021 , 15 , 652111 . 34239415 10.3389/fncel.2021.652111 PMC8258160
28. Bosco N ; Noti M The aging gut microbiome and its impact on host immunity . Genes Immun . 2021 , 22 , 289 – 303 . 33875817 10.1038/s41435-021-00126-8 PMC8054695
29. Zilioli V ; Muzi C ; Pagani C ; Ravano E ; Meli E ; Daffini R ; Ravelli E ; Cairoli R ; Re A Current treatment options and the role of functional status assessment in classical hodgkin lymphoma in older adults: A review . Cancers 2023 , 15 , 1515 . 36900306 10.3390/cancers15051515 PMC10000520
30. Tchkonia T ; Zhu Y ; van Deursen JV ; Campisi J ; Kirkland J Cellular senescence and the senescent secretory phenotype: Therapeutic opportunities . J. Clin. Investig 2013 , 123 , 966 – 972 . 23454759 10.1172/JCI64098 PMC3582125
31. Li X ; Li C ; Zhang W ; Wang Y ; Qian P ; Huang H Inflammation and aging: Signaling pathways and intervention therapies . Signal Transduct. Target. Ther 2023 , 8 , 239 . 37291105 10.1038/s41392-023-01502-8 PMC10248351
32. Lian J ; Yue Y ; Yu W ; Zhang Y Immunosenescence: A key player in cancer development . J. Hematol. Oncol 2020 , 13 , 151 . 33168037 10.1186/s13045-020-00986-z PMC7653700
33. Shannon-Lowe C ; Rickinson AB ; Bell AI Epstein–Barr virus-associated lymphomas . Philos. Trans. R. Soc. B Biol. Sci 2017 , 372 , 20160271 . 10.1098/rstb.2016.0271 PMC5597738 28893938
34. Jarrett RF Viruses and Hodgkin’s lymphoma . Ann. Oncol 2002 , 13 , 23 – 29 . 10.1093/annonc/13.s1.23 12078898
35. Inoue S ; Saito M ; Kotani J Immunosenescence in neurocritical care . J. Intensive Care 2018 , 6 , 65 . 30349725 10.1186/s40560-018-0333-5 PMC6186132
36. Knight AK ; Craig JM ; Theda C ; Bækvad-Hansen M ; Bybjerg-Grauholm J ; Hansen CS ; Hollegaard MV ; Hougaard DM ; Mortensen PB ; Weinsheimer SM ; An epigenetic clock for gestational age at birth based on blood methylation data . Genome Biol . 2016 , 17 , 206 . 27717399 10.1186/s13059-016-1068-z PMC5054584
37. Signer R ; Montecino-Rodriguez E ; Witte O ; McLaughlin J ; Dorshkind K Age-related defects in B lymphopoiesis underlie the myeloid dominance of adult leukemia . Blood 2007 , 110 , 1831 – 1839 . 17554060 10.1182/blood-2007-01-069401 PMC1976345
38. Passtoors W ; van den Akker EB ; Deelen J ; Maier AB ; van der Breggen R ; Jansen R ; Trompet S ; van Heemst D ; Derhovanessian E ; Pawelec G ; IL7R gene expression network associates with human healthy ageing . Immun. Ageing 2015 , 12 , 21 . 26566388 10.1186/s12979-015-0048-6 PMC4642670
39. Zahid HJ ; Taniguchi R ; Noceda MG ; Robins HS ; Greissl J T cell receptor diversity, cancer and sex: Insights from 30,000 tcr β repertoires . bioRxiv 2025 .
40. Chen Q ; Jain N ; Ayer T ; Wierda WG ; Flowers CR ; O’Brien SM ; Keating MJ ; Kantarjian HM Economic burden of chronic lymphocytic leukemia in the era of oral targeted therapies in the united states . J. Clin. Oncol 2017 , 35 , 166 – 174 . 27870563 10.1200/JCO.2016.68.2856 PMC5559889
41. Kulesh V ; Peskov K ; Helmlinger G ; Bocharov G Systematic review and quantitative meta-analysis of age-dependent human T-lymphocyte homeostasis . Front. Immunol 2025 , 16 , 1475871 . 39931065 10.3389/fimmu.2025.1475871 PMC11808020
42. Morel D ; Robert C ; Paragios N ; Grégoire V ; Deutsch E Translational frontiers and clinical opportunities of immunologically fitted radiotherapy . Clin. Cancer Res 2024 , 30 , 2317 – 2332 . 38477824 10.1158/1078-0432.CCR-23-3632 PMC11145173
43. Colonna-Romano G ; Buffa S ; Bulati M ; Candore G ; Lio D ; Pellicano M ; Vasto S B cells compartment in centenarian offspring and old people . Curr. Pharm. Des 2010 , 16 , 604 – 608 . 20388070 10.2174/138161210790883750
44. Vicente R ; Mausset-Bonnefont A ; Jorgensen C ; Louis-Plence P ; Brondello J Cellular senescence impact on immune cell fate and function . Aging Cell 2016 , 15 , 400 – 406 . 26910559 10.1111/acel.12455 PMC4854915
45. Frasca D ; Landin AM ; Lechner SC ; Ryan JG ; Schwartz R ; Riley RL ; Blomberg BB Aging Down-Regulates the Transcription Factor E2A, Activation-Induced Cytidine Deaminase, and Ig Class Switch in Human B Cells . J. Immunol 2008 , 180 , 5283 – 5290 . 18390709 10.4049/jimmunol.180.8.5283
46. Ayroldi E ; Cannarile L ; Adorisio S ; Delfino D ; Riccardi C Role of endogenous glucocorticoids in cancer in the elderly . Int. J. Mol. Sci 2018 , 19 , 3774 . 30486460 10.3390/ijms19123774 PMC6321146
47. Shen X ; Wu B ; Jiang W ; Li Y ; Zhang Y ; Zhao K ; Nie N ; Gong L ; Liu Y ; Zou X ; Scale Bar of Aging Trajectories for Screening Personal Rejuvenation Treatments . bioRxiv 2022 . 10.1016/j.csbj.2022.10.021 PMC9619353 36382193
48. Sousa-Pimenta M ; Estevinho M ; Dias MS ; Martins  ; Estevinho L Oxidative stress and inflammation in b-cell lymphomas . Antioxidants 2023 , 12 , 936 . 37107311 10.3390/antiox12040936 PMC10135850
49. Prattichizzo F ; De Nigris V ; La Sala L ; Procopio AD ; Olivieri F ; Ceriello A ‘Inflammaging’ as a druggable target: A senescence-associated secretory phenotype—Centered view of type 2 diabetes . Oxidative Med. Cell. Longev 2016 , 2016 , 1810327 . 10.1155/2016/1810327 PMC4908264 27340505
50. Ukraintseva S ; Yashin A Individual aging and cancer risk: How are they related? Demogr. Res 2003 , 9 , 163 – 196 .
51. Oyama T ; Ichimura K ; Suzuki R ; Suzumiya J ; Ohshima K ; Yatabe Y ; Yokoi T ; Kojima M ; Kamiya Y ; Taji H ; Senile EBV+ b-cell lymphoproliferative disorders: A clinicopathologic study of 22 patients . Am. J. Surg. Pathol 2003 , 27 , 16 – 26 . 12502924 10.1097/00000478-200301000-00003
52. Oyama T ; Yamamoto K ; Asano N ; Oshiro A ; Suzuki R ; Kagami Y ; Morishima Y ; Takeuchi K ; Izumo T ; Mori S ; Age-related EBV-associated b-cell lymphoproliferative disorders constitute a distinct clinicopathologic group: A study of 96 patients . Clin. Cancer Res 2007 , 13 , 5124 – 5132 . 17785567 10.1158/1078-0432.CCR-06-2823
53. Nicolae A ; Pittaluga S ; Abdullah S ; Steinberg SM ; Pham TA ; Davies-Hill T ; Xi L ; Raffeld M ; Jaffe ES EBV-positive large B-cell lymphomas in young patients: A nodal lymphoma with evidence for a tolerogenic immune environment . Blood 2015 , 126 , 863 – 872 . 25999451 10.1182/blood-2015-02-630632 PMC4536540
54. Tyshkovskiy A ; Kholdina D ; Ying K ; Davitadze M ; Molière A ; Tongu Y ; Kasahara T ; Kats LM ; Vladimirova A ; Moldakozhayev A ; Transcriptomic Hallmarks of Mortality Reveal Universal and Specific Mechanisms of Aging, Chronic Disease, and Rejuvenation . bioRxiv 2024 .
55. Fried LP ; Tangen CM ; Walston J ; Newman AB ; Hirsch C ; Gottdiener J ; Seeman T ; Tracy R ; Kop WJ ; Burke G ; Frailty in older adults: Evidence for a phenotype . J. Gerontol. Ser. A Biol. Sci. Med. Sci 2001 , 56 , M146 – M156 . 11253156 10.1093/gerona/56.3.m146
56. Bian L ; Ma Z ; Fu X ; Ji C ; Wang T ; Yan C ; Dai J ; Ma H ; Hu Z ; Shen H ; Associations of combined phenotypic aging and genetic risk with incident cancer: A prospective cohort study . medRxiv 2023 . 10.7554/eLife.91101 PMC11060710 38687190
57. Goeminne LJE ; Vladimirova A ; Eames A ; Tyshkovskiy A ; Argentieri MA ; Ying K ; Moqri M ; Gladyshev V N Plasma protein-based organ-specific aging and mortality models unveil diseases as accelerated aging of organismal systems . Cell Metab . 2025 , 37 , 205 – 222.e6 . 39488213 10.1016/j.cmet.2024.10.005
58. Ying K ; Tyshkovskiy A ; Chen Q ; Latorre-Crespo E ; Zhang B ; Liu H ; Matei-Dedu B ; Poganik JR ; Moqri M ; Kirschner K ; High-dimensional Ageome Representations of Biological Aging across Functional Modules . bioRxiv 2024 .
59. Ying K ; Song J ; Cui H ; Zhang Y ; Li S ; Chen X ; Liu H ; Eames A ; McCartney DL ; Marioni RE ; MethylGPT: A foundation model for the DNA methylome . bioRxiv 2024 .
60. Martínez de Toda I ; Ceprián N ; Díaz-Del Cerro E ; De la Fuente M The Role of Immune Cells in Oxi-Inflamm-Aging . Cells 2021 , 10 , 2974 . 34831197 10.3390/cells10112974 PMC8616159
61. Singh S ; Giron LB ; Shaikh MW ; Shankaran S ; Engen PA ; Bogin ZR ; Bambi SA ; Goldman AR ; Azevedo JLLC ; Orgaz L ; Distinct intestinal microbial signatures linked to accelerated systemic and intestinal biological aging . Microbiome 2024 , 12 , 31 . 38383483 10.1186/s40168-024-01758-4 PMC10882811
62. Mijakovac A ; Frkatović A ; Hanić M ; Ivok J ; Martinić Kavur M ; Pučić-Baković M ; Spector T ; Zoldoš V ; Mangino M ; Lauc G Heritability of the glycan clock of biological age . Front. Cell Dev. Biol 2022 , 10 , 982609 . 36619858 10.3389/fcell.2022.982609 PMC9815111
63. Kállai A ; Ungvari Z ; Fekete M ; Maier AB ; Mikala G ; Andrikovics H ; Lehoczki A Genomic instability and genetic heterogeneity in aging: Insights from clonal hematopoiesis (CHIP), monoclonal gammopathy (MGUS), and monoclonal B-cell lymphocytosis (MBL) . GeroScience 2024 , 47 , 703 – 720 . 39405013 10.1007/s11357-024-01374-y PMC11872960
64. Parker D ; Bartlett BN ; Cohen HJ ; Fillenbaum G ; Huebner JL ; Kraus VB ; Pieper C ; Belsky DW Association of blood chemistry quantifications of biological aging with disability and mortality in older adults . J. Gerontol. Ser. A Biol. Sci. Med. Sci 2019 , 75 , 1671 – 1679 . 10.1093/gerona/glz219 PMC7494046 31693736
65. Castro JP ; Shindyapina AV ; Barbieri A ; Ying K ; Strelkova OS ; Paulo JA ; Tyshkovskiy A ; Meinl R ; Kerepesi C ; Petrashen AP ; Integrative analyses uncover mechanisms by which aging drives B cell lymphoma . bioRxiv 2021 .
66. Tarkhov AE ; Lindstrom-Vautrin T ; Zhang S ; Ying K ; Moqri M ; Zhang B ; Tyshkovskiy A ; Levy O ; Gladyshev VN Nature of epigenetic aging from a single-cell perspective . Nat. Aging 2024 , 4 , 854 – 870 . 38724733 10.1038/s43587-024-00616-0
67. Nannini DR ; Cortese R ; Egwom P ; Palaniyandi S ; Hildebrandt GC Time to relapse in chronic lymphocytic leukemia and DNA-methylation-based biological age . Clin. Epigenetics 2023 , 15 , 81 . 37165442 10.1186/s13148-023-01496-8 PMC10170738
68. Grimm C ; Herling CD ; Komnidi A ; Hussong M ; Kreuzer K-A ; Hallek M ; Schweiger MR Evaluation of a Prognostic Epigenetic Classification System in Chronic Lymphocytic Leukemia Patients . Biomark. Insights 2022 , 17 , 11772719211067972. 10.1177/11772719211067972 PMC8793417 35095271
69. Torka P ; Grover NS ; Voorhees TJ ; Karmali R ; Annunzio K ; Watkins MP ; Anampa-Guzmán A ; Reves H ; Tavakkoli M ; Christian B ; Impact of Age on Biology, Presentation and Outcomes in Marginal Zone Lymphoma: Results from a Multicenter Cohort Study . Hematol. Oncol 2025 , 43 , e70087 . 40260947 10.1002/hon.70087 PMC12013245
70. Segura A ; Prohens L ; Mezquida G ; Amoretti S ; Bioque M ; Ribeiro M ; Gurriarán-Bas X ; Rementería L ; Berge D ; Rodriguez-Jimenez R ; Epigenetic clocks in relapse after a first episode of schizophrenia . Schizophrenia 2022 , 8 , 61 . 35869075 10.1038/s41537-022-00268-2 PMC9307769
71. Schneider CV ; Schneider KM ; Teumer A ; Rudolph KL ; Hartmann D ; Rader DJ ; Strnad P Association of Telomere Length with Risk of Disease and Mortality . JAMA Intern. Med 2022 , 182 , 291 – 300 . 35040871 10.1001/jamainternmed.2021.7804 PMC8767489
72. Stuckey R ; Bilbao-Sieyro C ; Segura-Díaz A ; Gómez-Casares M Molecular studies for the early detection of philadelphianegative myeloproliferative neoplasms . Int. J. Mol. Sci 2023 , 24 , 12700 . 37628880 10.3390/ijms241612700 PMC10454334
73. Tucci A ; Ferrari S ; Bottelli C ; Borlenghi E ; Drera M ; Rossi G A comprehensive geriatric assessment is more effective than clinical judgment to identify elderly diffuse large cell lymphoma patients who benefit from aggressive therapy . Cancer 2009 , 115 , 4547 – 4553 . 19562776 10.1002/cncr.24490
74. Lanic H ; Kraut-Tauzia J ; Modzelewski R ; Clatot F ; Mareschal S ; Picquenot JM ; Stamatoullas A ; Leprêtre S ; Tilly H ; Jardin F Sarcopenia is an independent prognostic factor in elderly patients with diffuse large B-cell lymphoma treated with immunochemotherapy . Leuk. Lymphoma 2014 , 55 , 817 – 823 . 23781925 10.3109/10428194.2013.816421
75. Xiao DY ; Luo S ; O’Brian K ; Ganti A ; Riedell P ; Sanfilippo KM ; Lynch RC ; Liu W ; Carson KR Impact of sarcopenia on treatment tolerance in United States veterans with diffuse large B-cell lymphoma treated with CHOP-based chemotherapy . Am. J. Hematol./Oncol 2016 , 91 , 1002 – 1007 . 10.1002/ajh.24465 PMC5324973 27356783
76. Chu M ; Lieffers J ; Ghosh S ; Belch A ; Chua NS ; Fontaine A ; Sangha R ; Turner RA ; Baracos VE ; Sawyer MB Skeletal muscle density is an independent predictor of diffuse large B-cell lymphoma outcomes treated with rituximab-based chemoimmunotherapy . J. Cachexia Sarcopenia Muscle 2016 , 8 , 298 – 304 . 27897411 10.1002/jcsm.12161 PMC5377388
77. Jäger J ; Callensee L ; Albayrak-Rena S ; Esser S ; Witzke O ; Schönfeld A Prevalence of sarcopenia among people living with HIV defined by the revised European working group on sarcopenia in older people . Int. J. STD AIDS 2024 , 35 , 902 – 909 . 39033398 10.1177/09564624241262549
78. Bitto A ; Tung H ; Ying K ; Smith DL ; Kayser E-B ; Morgan PG ; Sedensky MM ; Kaeberlein M Aging and Mitochondrial Disease: Shared Mechanisms and Therapies? Innov. Aging 2019 , 3 , S395 .
79. Bitto A ; Grillo AS ; Ito TK ; Stanaway IB ; Nguyen BMG ; Ying K ; Tung H ; Smith K ; Tran N ; Velikanje G ; Acarbose suppresses symptoms of mitochondrial disease in a mouse model of Leigh syndrome . Nat. Metab 2023 , 5 , 955 – 967 . 37365290 10.1038/s42255-023-00815-w PMC13112404
80. Griffin PT ; Kane AE ; Trapp A ; Li J ; Arnold M ; Poganik JR ; Conway RJ ; McNamara MS ; Meer MV ; Hoffman N ; TIME-seq reduces time and cost of DNA methylation measurement for epigenetic clock construction . Nat. Aging 2024 , 4 , 261 – 274 . 38200273 10.1038/s43587-023-00555-2 PMC11332592
81. Shanafelt TD ; Borah BJ ; Finnes HD ; Chaffee KG ; Ding W ; Leis JF ; Chanan-Khan AA ; Parikh SA ; Slager SL ; Kay NE ; Impact of Ibrutinib and Idelalisib on the Pharmaceutical Cost of Treating Chronic Lymphocytic Leukemia at the Individual and Societal Levels . J. Oncol. Pract 2015 , 11 , 252 – 258 . 25804983 10.1200/JOP.2014.002469
82. Zhang Y ; Chu J ; Hou Q ; Qian S ; Wang Z ; Yang Q ; Song W ; Dong L ; Shi Z ; Gao Y ; Ageing microenvironment mediates lymphocyte carcinogenesis and lymphoma drug resistance: From mechanisms to clinical therapy (Review) . Int. J. Oncol 2024 , 64 , 65 . 38757347 10.3892/ijo.2024.5653 PMC11095602
83. Desdín-Micó G ; Soto-Heredero G ; Aranda JF ; Oller J ; Carrasco E ; Gabandé-Rodríguez E ; Blanco EM ; Alfranca A ; Cussó L ; Desco M ; T cells with dysfunctional mitochondria induce multimorbidity and premature senescence . Science 2020 , 368 , 1371 – 1376 . 32439659 10.1126/science.aax0860 PMC7616968
84. Abukanna A ; AlAnazi FM ; AlAnazi ZM ; AlAnazi FAL ; AlAnaz AHO ; AlAnazi RML Aging and changes in white blood cells count and immunity: A systematic review . Clin. Cancer Investig. J 2022 , 11 , 25 – 30 .
85. Ying K ; Castro JP ; Shindyapina AV ; Tyshkovskiy A ; Moqri M ; Goeminne LJE ; Milman S ; Zhang ZD ; Barzilai N ; Gladyshev VN Depletion of loss-of-function germline mutations in centenarians reveals longevity genes . Nat. Commun 2024 , 15 , 9030 . 39424787 10.1038/s41467-024-52967-2 PMC11489729
86. Moqri M ; Poganik J ; Herzog C ; Ying K ; Chen Q ; Emamifar M ; Tyshkovskiy A ; Eames A ; Mur J ; Matei-Dediu B ; Integrative epigenetics and transcriptomics identify aging genes in human blood . bioRxiv 2024 . 10.1038/s41467-025-67369-1 PMC12820199 41554691
87. Sun D-P ; Wang L ; Ding C-Y ; Liang J-H ; Zhu H-Y ; Wu Y-J ; Fan L ; Li J-Y ; Xu W Investigating Factors Associated with Thymic Regeneration after Chemotherapy in Patients with Lymphoma . Front. Immunol 2016 , 7 , 654 . 28082988 10.3389/fimmu.2016.00654 PMC5186774
88. Henry CJ ; Marusyk A ; DeGregori J Aging-associated changes in hematopoiesis and leukemogenesis: What’s the connection? Stress Aging Brain 2011 , 1 , 35 . 10.18632/aging.100351 PMC3164372 21765201
89. Ying K ; Zhai R ; Pyrkov TV ; Shindyapina AV ; Mariotti M ; Fedichev PO ; Shen X ; Gladyshev VN Genetic and phenotypic analysis of the causal relationship between aging and COVID-19 . Commun. Med 2021 , 1 , 35 . 35602207 10.1038/s43856-021-00033-z PMC9053191
90. Ying K ; Paulson S ; Reinhard J ; de Lima Camillo LP ; Träuble J ; Jokiel S ; Biomarkers of Aging Consortium ; Gobel D ; Herzog C ; Poganik JR ; An Open Competition for Biomarkers of Aging . bioRxiv 2024 . 10.1038/s43587-026-01139-6 PMC13476092 42277290