Molecular pathology endpoints useful for aging studies
Open accessAbstract
The first clinical trial aimed at targeting fundamental processes of aging will soon be launched (TAME: Targeting Aging with Metformin). In its wake is a robust pipeline of therapeutic interventions that have been demonstrated to extend lifespan or healthspan of preclinical models, including rapalogs, antioxidants, anti-inflammatory agents, and senolytics. This ensures that if the TAME trial is successful, numerous additional clinical trials are apt to follow. But a significant impediment to these trials remains the question of what endpoints should be measured? The design of the TAME trial very cleverly skirts around this based on the fact that there are decades of data on metformin in humans, providing unequaled clarity of what endpoints are most likely to yield a positive outcome. But for a new chemical entity, knowing what endpoints to measure remains a formidable challenge. For economy's sake, and to achieve results in a reasonable time frame, surrogate markers of lifespan and healthy aging are desperately needed. This review provides a comprehensive analysis of molecular endpoints that are currently being used as indices of age-related phenomena (e.g., morbidity, frailty, mortality) and proposes an approach for validating and prioritizing these endpoints.
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Limitations of current clinical trials include small recruitment sizes, few measurable clinically-relevant outcomes appropriate for older adults, and need for biomarkers tracking pharmacokinetic, pharmacodynamic, and mechanistic properties and serving as surrogate endpoints.
“Limitations of current clinical trials include small recruitment sizes, the paucity of measurable, clinically-relevant outcomes appropriate for older adults, as well as the need for biomarkers that follow pharmacokinetic, pharmacodynamic, and mechanistic properties of study drugs and could serve as surrogate endpoints [92–94].”
From Reducing Senescent Cell Burden in Aging and Disease (2020) · cites this paper as [94]
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‘Rejuvenating’ protein doubted
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Tempol intake improves inflammatory status in aged mice
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3 of the 95 references couldn’t be matched to a known paper.
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Measuring biological aging in humans: a quest
Ferrucci, L., Gonzalez‐Freire, M., Fabbri, E., Simonsick, E., Tanaka, T., Moore, Z., Salimi, S., Sierra, F., & de Cabo, R. (2020) · Aging Cell · doi:10.1111/acel.13080 · Cited by 781 · see citing works
Reducing Senescent Cell Burden in Aging and Disease
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Self-citationAnti-senescence compounds: A potential nutraceutical approach to healthy aging
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Yousefzadeh, M. J., Schafer, M. J., Hooten, N. N., Atkinson, E. J., Evans, M. K., Baker, D. J., Quarles, E. K., Robbins, P. D., Ladiges, W., LeBrasseur, N. K., et al. (2017) · Aging Cell · doi:10.1111/acel.12706 · Cited by 114 · see citing works
Self-citationCellular senescence in the lung across the age spectrum
Parikh, P., Wicher, S., Khandalavala, K., Pabelick, C. M., Britt, R. D., & Prakash, Y. S. (2019) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00424.2018 · Cited by 112 · see citing works
Strategies to Prevent or Remediate Cancer and Treatment-Related Aging
Guida, J., Agurs‐Collins, T., Ahles, T. A., Campisi, J., Dale, W., Demark‐Wahnefried, W., Dietrich, J., Fuldner, R., Gallicchio, L. M., Green, P. A., et al. (2020) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djaa060 · Cited by 108 · see citing works
Self-citationMurabito, J. M., Zhao, Q., Larson, M. G., Rong, J., Lin, H., Benjamin, E. J., Levy, D. A., & Lunetta, K. L. (2017) · The Journals of Gerontology Series A · doi:10.1093/gerona/glx144 · Cited by 84 · see citing works
Ranking Biomarkers of Aging by Citation Profiling and Effort Scoring
Hartmann, A., Hartmann, C., Secci, R., Hermann, A., Fuellen, G., & Walter, M. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.686320 · Cited by 81 · see citing works
Health and Aging: Unifying Concepts, Scores, Biomarkers and Pathways
Fuellen, G., Jansen, L., Cohen, A. A., Luyten, W. H. M. L., Gogol, M., Simm, A., Saul, N., Cirulli, F., Berry, A., Antal, P., et al. (2018) · Aging and Disease · doi:10.14336/ad.2018.1030 · Cited by 77 · see citing works
Huang, P., Zhou, Y., Tang, W., Ren, C., Jiang, A., Wang, X., Qian, X., Zhou, Z., & Gong, A. (2021) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2021.108911 · Cited by 74 · see citing works
Vaiserman, A. M., & Lushchak, O. V. (2017) · Journal of Translational Medicine · doi:10.1186/s12967-017-1259-8 · Cited by 71 · see citing works
Metformin prevents murine ovarian aging
Qin, X., Du, D., Chen, Q., Wu, M., Wu, T., Wen, J., Jin, Y., Zhang, J., & Wang, S. (2019) · Aging · doi:10.18632/aging.102016 · Cited by 70 · see citing works
Integrating Environment and Aging Research: Opportunities for Synergy and Acceleration
Malecki, K. M. C., Andersen, J. K., Geller, A. M., Harry, G. J., Jackson, C. L., James, K. A., Miller, G. W., & Ottinger, M. A. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.824921 · Cited by 61 · see citing works
Ayurveda and the science of aging
Rao, R. V. (2017) · Journal of Ayurveda and Integrative Medicine · doi:10.1016/j.jaim.2017.10.002 · Cited by 58 · see citing works
Comparison of naturally aging and D‑galactose induced aging model in beagle dogs
Ji, M., Su, X., Liu, J., Zhao, Y., Li, Z., Xu, X., Li, H., & Nashun, B. (2017) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2017.5327 · Cited by 51 · see citing works
Behavioral and Social Research to Accelerate the Geroscience Translation Agenda
Moffitt, T. E. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101146 · Cited by 47 · see citing works
Möller, P., Wils, R. S., Jensen, D. M., Andersen, M. H. G., & Roursgaard, M. (2018) · Critical Reviews in Toxicology · doi:10.1080/10408444.2018.1538201 · Cited by 42 · see citing works
Biological Age Predictors: The Status Quo and Future Trends
Erema, V. V., Yakovchik, A., Kashtanova, D. A., Bochkaeva, Z. V., Ivanov, M. V., Сосин, Д. В., Matkava, L. R., Yudin, V. S., Makarov, V. V., Keskinov, A. A., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232315103 · Cited by 41 · see citing works
MRI-based biomarkers of accelerated aging and dementia risk in midlife: how close are we?
Elliott, M. L. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101075 · Cited by 38 · see citing works
Definitions of healthspan: A systematic review
Masfiah, S., Kurnialandi, A., Meij, H. J., & Maier, A. B. (2025) · Ageing Research Reviews · doi:10.1016/j.arr.2025.102806 · Cited by 35 · see citing works
The potential use of physical resilience to predict healthy aging
Schorr, A., Carter, C. S., & Ladiges, W. (2017) · Pathobiology of Aging & Age-related Diseases · doi:10.1080/20010001.2017.1403844 · Cited by 32 · see citing works
Self-citationA New Preclinical Paradigm for Testing Anti-Aging Therapeutics
Ladiges, W., Snyder, J. M., Wilkinson, E., Imai, D. M., Snider, T. A., Ge, X., Ciol, M. A., Pettan-Brewer, C., Pillai, S. P. S., Morton, J., et al. (2017) · The Journals of Gerontology Series A · doi:10.1093/gerona/glx019 · Cited by 31 · see citing works
Self-citationOyebode, O. T., Abolaji, A. O., Oluwadare, J. O., Adedara, A. O., & Olorunsogo, O. O. (2020) · Journal of Functional Foods · doi:10.1016/j.jff.2020.103957 · Cited by 28 · see citing works
Lawrence, I., Bene, M. R., Nacarelli, T., Azar, A., Cohen, J., Torres, C. A., Johannes, G. J., & Sell, C. (2018) · GeroScience · doi:10.1007/s11357-018-0015-1 · Cited by 28 · see citing works
Whole Blood Gene Expression Associated With Clinical Biological Age
Lin, H., Lunetta, K. L., Zhao, Q., Mandaviya, P. R., Rong, J., Benjamin, E. J., Joehanes, R., Levy, D. A., van Meurs, J. B. J., Larson, M. G., et al. (2018) · The Journals of Gerontology Series A · doi:10.1093/gerona/gly164 · Cited by 24 · see citing works
Guo, D., Shen, Y., Li, W., Li, Q., Miao, Y., & Zhong, Y. (2020) · Aging · doi:10.18632/aging.102666 · Cited by 26 · see citing works
Proteomic assessment of serum biomarkers of longevity in older men
ORWOLL, E. S., Wiedrick, J. T., Nielson, C. M., Jacobs, J., Baker, E. S., Piehowski, P., Petyuk, V., Gao, Y., Shi, T., Smith, R. D., et al. (2020) · Aging Cell · doi:10.1111/acel.13253 · Cited by 21 · see citing works
Varela‐López, A., Romero‐Márquez, J. M., Navarro‐Hortal, M. D., Ramírez-Tortosa, C. L., Battino, M. A., Forbes-Hernández, T. Y., & Quiles, J. L. (2023) · Experimental Gerontology · doi:10.1016/j.exger.2023.112221 · Cited by 21 · see citing works
Guo, D., Shen, Y., Li, W., Li, Q., Zhao, Y., Pan, C., Chen, B., Zhong, Y., & Miao, Y. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00320 · Cited by 17 · see citing works
Senescence in the lung: is this getting old?
Morty, R. E., & Prakash, Y. S. (2019) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00081.2019 · Cited by 15 · see citing works
Geroscience and Alzheimer's Disease Drug Development
Cummings, J. L., Osse, A. M. L., & Kinney, J. (2023) · The Journal of Prevention of Alzheimer s Disease · doi:10.14283/jpad.2023.103 · Cited by 14 · see citing works
Kim, S., Lam, G. T., Yook, J. I., & Ahn, S. (2018) · Aging · doi:10.18632/aging.101549 · Cited by 13 · see citing works
Effect of Chronic Moderate Caloric Restriction on the Reproductive Function in Aged Male Wistar Rats
de Jesús, P. L., Arenas‐Ríos, E., Ruíz-Ramos, M., Flores-Alonso, J. C., Mendoza-Núñez, V. M., Arrieta‐Cruz, I., & Arteaga‐Silva, M. (2022) · Nutrients · doi:10.3390/nu14061256 · Cited by 11 · see citing works
Bock-Marquette, I., Maar, K., Maar, S., Lippai, B., Faskerti, G., Gallyas, F., Olson, E. N., & Srivastava, D. (2023) · International Immunopharmacology · doi:10.1016/j.intimp.2023.109741 · Cited by 11 · see citing works
Jiang, Z., He, Q., Wezeman, J., Darvas, M., & Ladiges, W. (2024) · GeroScience · doi:10.1007/s11357-024-01198-w · Cited by 10 · see citing works
Self-citationRobbins, P. D., & Niedernhofer, L. J. (2017) · Aging Cell · doi:10.1111/acel.12620 · Cited by 11 · see citing works
Self-citationTesting drug combinations to slow aging
Ladiges, W., & Liggitt, D. (2017) · Pathobiology of Aging & Age-related Diseases · doi:10.1080/20010001.2017.1407203 · Cited by 10 · see citing works
Self-citationMaar, K., Hetényi, R., Maar, S., Faskerti, G., Hanna, D., Lippai, B., Takátsy, A., & Bock-Marquette, I. (2021) · Cells · doi:10.3390/cells10061343 · Cited by 9 · see citing works
The emerging role of geropathology in preclinical aging studies
Ladiges, W. (2017) · Pathobiology of Aging & Age-related Diseases · doi:10.1080/20010001.2017.1304005 · Cited by 8 · see citing works
Self-citationMeasuring biological age in mice using differential mass spectrometry
Bell‐Temin, H., Yousefzadeh, M. J., Bondarenko, A. Y., Quarles, E. K., Jones-Laughner, J. M., Robbins, P. D., Ladiges, W., Niedernhofer, L. J., & Yates, N. A. (2019) · Aging · doi:10.18632/aging.101810 · Cited by 7 · see citing works
Self-citationFrailty and Cardiovascular Disease
Chainani, V., Riehl, R., Chainani, G., Abdo, A., Cohen, M. G., Alfonso, C., Martinez-Clark, P. O., Lavie, C. J., Reilly, J. P., & Abi‐Rafeh, N. (2017) · InTech eBooks · doi:10.5772/intechopen.69877 · Cited by 7 · see citing works
Pereira, G. C., Malik, S., Kis, Z., & Rocamonde, B. (2019) · Natural Bio-active Compounds · doi:10.1007/978-981-13-7205-6_6 · Cited by 2 · see citing works
Grp94 Inhibitor HCP1 Inhibits Human Dermal Fibroblast Senescence
Cui, X., Hao, X., Wen, J., Zhang, S., Zhao, B., & Miao, J. (2022) · Genes · doi:10.3390/genes13091651 · Cited by 6 · see citing works
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VIZITIU, E. L., Costea, A.-I., Silişteanu, S. C., & Constantinescu, M. A. (2023) · Balneo and PRM Research Journal · doi:10.12680/balneo.2023.624 · Cited by 5 · see citing works
Precision geromedicine: Science meets clinical practice
Chen, L. (2025) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2025.106057 · Cited by 4 · see citing works
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A SiQD-based hydrogel fiber sensor for portable, on-site β-galactosidase detection in serum
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Immune System Promotion and Antibacterial and Antivirus Effects of Anthocyanins
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Self-citationThe Role of Neuroimaging in the Ageing Brain
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Technical barriers to systemic interventions
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PathoClock and PhysioClock in mice recapitulate human multimorbidity and heterogeneous aging
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Self-citationEinfluss der Ernährung auf Alterungsprozesse und Erkrankungen sowie Anti-Aging-Empfehlungen
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The Landscape of AI in Computational Toxicology: A Survey of Current Practices and Open Issues
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- Authors
- Niedernhofer, L. J., Kirkland, J. L., & Ladiges, W.
- Affiliations
- Scripps Research Institute (US)
- Mayo Clinic (US)
- University of Washington (US)
- Corresponding author
- Niedernhofer, L.J.
- Venue
- Ageing Research Reviews
- Publisher
- Elsevier BV
- Published
- 2017-05
- Volume
- 35
- Pages
- 241-249
- ISSN
- 1568-1637 (ISSN-L)
- PMC
- PMC5357461
- OpenAlex
- W2530258438
- Cited by
- 59
- References
- 95
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 1 · 2023: 6 · 2022: 5 · 2021: 5 · 2020: 7 · 2019: 9 · 2018: 7 · 2017: 12
- FWCI
- 0.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Ageing Research Reviews (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH/NIA (P01 AG043376)
- Aldabra Biosciences
- NIH (AG13925, AG044396, DK50456, R24 AG047115)
- Connor Group
- the Noaber, Ellison, and Glenn Foundations
- American Federation for Aging Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolism, Diabetes, and Cancer
- Keywords
- molecular endpoints, aging, healthspan, lifespan extension, metformin, surrogate markers, senolytics, rapalogs, anti-inflammatory agents, antioxidants, frailty, mortality, morbidity, endpoint validation, anti-aging therapeutics
- MeSH terms
- Aging, Humans, Life Expectancy, Longevity, Biomarkers, Pathology, Molecular
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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