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Senolytic therapy in mild Alzheimer’s disease: a phase 1 feasibility trial

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Abstract

Cellular senescence contributes to Alzheimer’s disease (AD) pathogenesis. An open-label, proof-of-concept, phase I clinical trial of orally delivered senolytic therapy, dasatinib (D) and quercetin (Q), was conducted in early-stage symptomatic patients with AD to assess central nervous system (CNS) penetrance, safety, feasibility and efficacy. Five participants (mean age = 76 + 5 years; 40% female) completed the 12-week pilot study. D and Q levels in blood increased in all participants (12.7–73.5 ng ml−1 for D and 3.29–26.3 ng ml−1 for Q). In cerebrospinal fluid (CSF), D levels were detected in four participants (80%) ranging from 0.281 to 0.536 ml−1 with a CSF to plasma ratio of 0.422–0.919%; Q was not detected. The treatment was well-tolerated, with no early discontinuation. Secondary cognitive and neuroimaging endpoints did not significantly differ from baseline to post-treatment further supporting a favorable safety profile. CSF levels of interleukin-6 (IL-6) and glial fibrillary acidic protein (GFAP) increased (t(4) = 3.913, P = 0.008 and t(4) = 3.354, P = 0.028, respectively) with trending decreases in senescence-related cytokines and chemokines, and a trend toward higher Aβ42 levels (t(4) = −2.338, P = 0.079). In summary, CNS penetrance of D was observed with outcomes supporting safety, tolerability and feasibility in patients with AD. Biomarker data provided mechanistic insights of senolytic effects that need to be confirmed in fully powered, placebo-controlled studies. ClinicalTrials.gov identifier: NCT04063124.

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Fig. 1: Study design and timeline.
Fig. 2: CONSORT flow diagram.
Fig. 3: Concentration of D and Q in blood and cerebrospinal fluid before and after oral administration of senolytics.
Fig. 4: Baseline and post-treatment ADRD cerebrospinal fluid biomarkers assessed using Lumipulse.

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Data availability

The minimum datasets necessary to interpret, verify and extend the research in the article are available within the paper and its Supplementary Information. The trial was registered on ClinicalTrials.gov: NCT04063124 and the full study protocol was published29.

Change history

  • 14 December 2023

    In the version of the article initially published, part of Extended Data Table 3 was missing but has now been added.

References

  1. Prince, M. J. et al. World Alzheimer Report 2015—The Global Impact of Dementia: An Analysis of Prevalence, Incidence, Cost and Trends (Alzheimer’s Disease International, 2015).

  2. Cummings, J., Ritter, A. & Zhong, K. Clinical trials for disease-modifying therapies in Alzheimer’s disease: a primer, lessons learned, and a blueprint for the future. J. Alzheimers Dis. 64, S3–S22 (2018).

    PubMed  PubMed Central  Google Scholar 

  3. Aisen, P. S. et al. The future of anti-amyloid trials. J. Prev. Alzheimers Dis. 7, 146–151 (2020).

    CAS  PubMed  Google Scholar 

  4. Haass, C. & Selkoe, D. If amyloid drives Alzheimer disease, why have anti-amyloid therapies not yet slowed cognitive decline? PLoS Biol. 20, e3001694 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Korczyn, A. D. Mixed dementia—the most common cause of dementia. Ann. N. Y. Acad. Sci. 977, 129–134 (2002).

    PubMed  Google Scholar 

  6. Musi, N. et al. Tau protein aggregation is associated with cellular senescence in the brain. Aging Cell 17, e12840 (2018).

    PubMed  PubMed Central  Google Scholar 

  7. Dehkordi, S. K. et al. Profiling senescent cells in human brains reveals neurons with CDKN2D/p19 and tau neuropathology. Nat. Aging 1, 1107–1116 (2021).

    PubMed  PubMed Central  Google Scholar 

  8. Bussian, T. J. et al. Clearance of senescent glial cells prevents tau-dependent pathology and cognitive decline. Nature 562, 578–582 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Kirkland, J. L. & Tchkonia, T. Cellular senescence: a translational perspective. EBioMedicine 21, 21–28 (2017).

    PubMed  PubMed Central  Google Scholar 

  10. Zhu, Y. I. et al. The Achilles’ heel of senescent cells: from transcriptome to senolytic drugs. Aging Cell 14, 644–658 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Kritsilis, M. et al. Ageing, cellular senescence and neurodegenerative disease. Int. J. Mol. Sci. 19, 2937 (2018).

    PubMed  PubMed Central  Google Scholar 

  12. Sharma, V., Gilhotra, R., Dhingra, D. & Gilhotra, N. Possible underlying influence of p38MAPK and NF-κB in the diminished anti-anxiety effect of diazepam in stressed mice. J. Pharmacol. Sci. 116, 257–263 (2011).

    CAS  PubMed  Google Scholar 

  13. Acosta, J. C. et al. A complex secretory program orchestrated by the inflammasome controls paracrine senescence. Nat. Cell Biol. 15, 978–990 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Jurk, D. et al. Postmitotic neurons develop a p21-dependent senescence-like phenotype driven by a DNA damage response. Aging Cell 11, 996–1004 (2012).

    CAS  PubMed  Google Scholar 

  15. Riessland, M. et al. Loss of SATB1 induces p21-dependent cellular senescence in post-mitotic dopaminergic neurons. Cell Stem Cell 25, 514–530 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Bhat, R. et al. Astrocyte senescence as a component of Alzheimer’s disease. PLoS ONE 7, e45069 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Chinta, S. J. et al. Cellular senescence is induced by the environmental neurotoxin paraquat and contributes to neuropathology linked to Parkinson’s disease. Cell Rep. 22, 930–940 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Streit, W. J. & Xue, Q.-S. Human CNS immune senescence and neurodegeneration. Curr. Opin. Immunol. 29, 93–96 (2014).

    CAS  PubMed  Google Scholar 

  19. Zhang, P. et al. Senolytic therapy alleviates Aβ-associated oligodendrocyte progenitor cell senescence and cognitive deficits in an Alzheimer’s disease model. Nat. Neurosci. 22, 719–728 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Bryant, A. G. et al. Cerebrovascular senescence is associated with tau pathology in Alzheimer’s disease. Front. Neurol. 11, 575953 (2020).

    PubMed  PubMed Central  Google Scholar 

  21. Tchkonia, T. & Kirkland, J. L. Aging, cell senescence, and chronic disease: emerging therapeutic strategies. JAMA 320, 1319–1320 (2018).

    PubMed  Google Scholar 

  22. Lindauer, M. & Hochhaus, A. Dasatinib. Recent Results Cancer Res. 184, 83–102 (2010).

    CAS  PubMed  Google Scholar 

  23. Boots, A. W., Haenen, G. R. M. M. & Bast, A. Health effects of quercetin: from antioxidant to nutraceutical. Eur. J. Pharmacol. 585, 325–337 (2008).

    CAS  PubMed  Google Scholar 

  24. Vafadar, A. et al. Quercetin and cancer: new insights into its therapeutic effects on ovarian cancer cells. Cell Biosci. 10, 32 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Ogrodnik, M. et al. Cellular senescence drives age-dependent hepatic steatosis. Nat. Commun. 8, 15691 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Kirkland, J. L. & Tchkonia, T. Senolytic drugs: from discovery to translation. J. Intern. Med. 288, 518–536 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Justice, J. N. et al. Senolytics in idiopathic pulmonary fibrosis: results from a first-in-human, open-label, pilot study. EBioMedicine 40, 554–563 (2019).

    PubMed  PubMed Central  Google Scholar 

  28. Hickson, L. J. et al. Senolytics decrease senescent cells in humans: preliminary report from a clinical trial of Dasatinib plus Quercetin in individuals with diabetic kidney disease. EBioMedicine 47, 446–456 (2019).

    PubMed  PubMed Central  Google Scholar 

  29. Gonzales, M. M. et al. Senolytic therapy to modulate the progression of Alzheimer’s disease (SToMP-AD): a pilot clinical trial. J. Prev. Alzheimers Dis. 9, 22–29 (2022).

    CAS  PubMed  Google Scholar 

  30. Morris, J. C. Clinical dementia rating: a reliable and valid diagnostic and staging measure for dementia of the Alzheimer type. Int. Psychogeriatr. 9, 173–176 (1997).

    PubMed  Google Scholar 

  31. Nasreddine, Z. S. et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 53, 695–699 (2005).

    PubMed  Google Scholar 

  32. Jack, C. R. Jr et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 257–262 (2011).

    PubMed  PubMed Central  Google Scholar 

  33. Alcolea, D. et al. Agreement of amyloid PET and CSF biomarkers for Alzheimer’s disease on Lumipulse. Ann. Clin. Transl. Neurol. 6, 1815–1824 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Scalbert, A. & Williamson, G. Dietary intake and bioavailability of polyphenols. J. Nutr. 130, 2073S–2085S (2000).

    CAS  PubMed  Google Scholar 

  35. Iwashina, T. Flavonoid properties of five families newly incorporated into the order Caryophyllales. Bull. Natl Mus. Nat. Sci. B 39, 25–51 (2013).

    Google Scholar 

  36. Porkka, K. et al. Dasatinib crosses the blood-brain barrier and is an efficient therapy for central nervous system Philadelphia chromosome-positive leukemia. Blood 112, 1005–1012 (2008).

    CAS  PubMed  Google Scholar 

  37. Gong, X. et al. A higher dose of dasatinib may increase the possibility of crossing the blood–brain barrier in the treatment of patients with Philadelphia chromosome-positive acute lymphoblastic leukemia. Clin. Ther. 43, 1265–1271 (2021).

    CAS  PubMed  Google Scholar 

  38. Erickson, M. A. & Banks, W. A. Blood–brain barrier dysfunction as a cause and consequence of Alzheimer’s disease. J. Cereb. Blood Flow Metab. 33, 1500–1513 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. O’Hare, T. et al. In vitro activity of Bcr–Abl inhibitors AMN107 and BMS-354825 against clinically relevant imatinib-resistant Abl kinase domain mutants. Cancer Res. 65, 4500–4505 (2005).

    PubMed  Google Scholar 

  40. Wróbel-Biedrawa, D. et al. A flavonoid on the brain: Quercetin as a potential therapeutic agent in central nervous system disorders. Life (Basel) 12, 591 (2022).

    PubMed  Google Scholar 

  41. Sun, S. W. et al. Quercetin attenuates spontaneous behavior and spatial memory impairment in d-galactose-treated mice by increasing brain antioxidant capacity. Nutr. Res. 27, 169–175 (2007).

    CAS  Google Scholar 

  42. Ishisaka, A. et al. Accumulation of orally administered quercetin in brain tissue and its antioxidative effects in rats. Free Radic. Biol. Med. 51, 1329–1336 (2011).

    CAS  PubMed  Google Scholar 

  43. Ren, S. C. et al. Quercetin permeability across blood–brain barrier and its effect on the viability of U251 cells. Sichuan Da Xue Xue Bao Yi Xue Ban 41, 751–754 (2010).

    CAS  PubMed  Google Scholar 

  44. Wiczkowski, W. et al. Quercetin and isorhamnetin aglycones are the main metabolites of dietary quercetin in cerebrospinal fluid. Mol. Nutr. Food Res. 59, 1088–1094 (2015).

    CAS  PubMed  Google Scholar 

  45. Lundholm, M. D. & Charnogursky, G. A. Dasatinib-induced hypoglycemia in a patient with acute lymphoblastic leukemia. Clin. Case Rep. 8, 1238–1240 (2020).

    PubMed  PubMed Central  Google Scholar 

  46. Yu, L., Liu, J., Huang, X. & Jiang, Q. Adverse effects of dasatinib on glucose–lipid metabolism in patients with chronic myeloid leukaemia in the chronic phase. Sci. Rep. 9, 17601 (2019).

    PubMed  PubMed Central  Google Scholar 

  47. Banyer, J. L., Hamilton, N. H., Ramshaw, I. A. & Ramsay, A. J. Cytokines in innate and adaptive immunity. Rev. Immunogenet. 2, 359–373 (2000).

    CAS  PubMed  Google Scholar 

  48. Lamers, K. J. B. et al. Protein S-100B, neuron-specific enolase (NSE), myelin basic protein (MBP) and glial fibrillary acidic protein (GFAP) in cerebrospinal fluid (CSF) and blood of neurological patients. Brain Res. Bull. 61, 261–264 (2003).

    CAS  PubMed  Google Scholar 

  49. Benedet, A. L. et al. Differences between plasma and cerebrospinal fluid glial fibrillary acidic protein levels across the Alzheimer disease continuum. JAMA Neurol. 78, 1471–1483 (2021).

    PubMed  Google Scholar 

  50. Zhang, X. et al. Rejuvenation of the aged brain immune cell landscape in mice through p16-positive senescent cell clearance. Nat. Commun. 13, 5671 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Andreasen, N. et al. Cerebrospinal fluid β-amyloid(1-42) in Alzheimer disease: differences between early- and late-onset Alzheimer disease and stability during the course of disease. Arch. Neurol. 56, 673–680 (1999).

    CAS  PubMed  Google Scholar 

  52. Ito, K. et al. Understanding placebo responses in Alzheimer’s disease clinical trials from the literature meta-data and CAMD database. J. Alzheimers Dis. 37, 173–183 (2013).

    PubMed  Google Scholar 

  53. Tuttle, C. S. L. et al. Cellular senescence and chronological age in various human tissues: a systematic review and meta-analysis. Aging Cell 19, e13083 (2020).

    CAS  PubMed  Google Scholar 

  54. Wiley, C. D. et al. Analysis of individual cells identifies cell-to-cell variability following induction of cellular senescence. Aging Cell 16, 1043–1050 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Yousefzadeh, M. J. et al. An aged immune system drives senescence and ageing of solid organs. Nature 594, 100–105 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Psychological Corporation. WMS-IV: Wechsler Memory Scale 4th Edition: Administration and Scoring Manual (Harcourt, Brace, & Company, 2009).

  57. Weintraub, S. et al. The Alzheimer’s Disease Centers’ Uniform Data Set (UDS): the neuropsychologic test battery. Alzheimer Dis. Assoc. Disord. 23, 91–101 (2009).

    PubMed  PubMed Central  Google Scholar 

  58. Tombaugh, T. N., Kozak, J. & Rees, L. Normative data stratified by age and education for two measures of verbal fluency: FAS and animal naming. Arch. Clin. Neuropsychol. 14, 167–177 (1999).

    CAS  PubMed  Google Scholar 

  59. Kaplan, E., Goodglass, H. & Weintraub, S. Boston Naming Test (2nd (BNT-2), Second Edition (Pro-Ed, 2001).

  60. Benedict, R. H. B., Schretlen, D., Groninger, L. & Brandt, J. Hopkins Verbal Learning Test-revised: normative data and analysis of inter-form and test-retest reliability. Clin. Neuropsychol. 12, 43–55 (1998).

    Google Scholar 

  61. Graf, C. The Lawton Instrumental Activities of Daily Living (IADL) Scale. Medsurg Nurs. 18, 315–316 (2009).

    PubMed  Google Scholar 

  62. Doshi, J. et al. MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection. Neuroimage 127, 186–195 (2016).

    PubMed  Google Scholar 

  63. Srinivasan, D. et al. A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: findings about size and age bias, and inter-scanner stability in multi-site aging studies. Neuroimage 223, 117248 (2020).

    PubMed  Google Scholar 

  64. Habes, M. et al. The brain chart of aging: machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans. Alzheimers Dement. 17, 89–102 (2021).

    CAS  PubMed  Google Scholar 

  65. Wilcock, D. et al. MarkVCID cerebral small vessel consortium: I. Enrollment, clinical, fluid protocols. Alzheimers Dement. 17, 704–715 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank the volunteers, study participants and the Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases at UT Health San Antonio and the South Texas Alzheimer’s Disease Research Center (P30AG066546 to S.S.) research staff who conducted the study recruitment and assessments. This work was made possible by pilot funding from the Institute for Integration of Medicine & Science and the Center for Biomedical Neurosciences at UT Health Science Center in San Antonio (to M.M.G., N.M. and M.E.O.); the Alzheimer’s Drug Discovery Foundation (GC-201908-2019443 to M.E.O.), pilot funding from the Coordinating Center for Claude D. Pepper Older Americans Independence Centers (U24AG059624 to M.E.O. and M.M.G.); the Translational Geroscience Network (R33AG061456 to J.L.K.). We also acknowledge philanthropic support from the JMR Barker Foundation, Bill Reed Endowment for Precision Medicine, the Kleberg/McGill Foundation and UT STARS award. M.M.G., S.S., V.R.G., T.F.K., J.J.M., H.Z., C.F., M.H. and A.S. are supported by the South Texas Alzheimer’s Disease Research Center (P30AG066546). Additionally, M.M.G. was supported as an RL5 Scholar in the San Antonio Claude D. Pepper Older Americans Independence Center (P30AG044271) and the National Institute on Aging (R01AG077472 and P30AG066546). V.R.G. was supported by a National Institute on Aging Training Grant on the Biology of Aging (T32AG021890) and a National Center for Advancing Translational Sciences NRSA Training Core (TR002647). M.L.C. was supported by the San Antonio Claude D. Pepper Older Americans Independence Center (PG30AG044271) and by the National Institute on Aging (P30AG013319 and U01AG22307). H.Z. and S.K.D. were supported by the National Institute on Aging (R01AG057896, 1RF1AG063507, R01AG068293, 1R01AG0665241A, 1R01AG065301 and P30AG066546) and the National Institute of Neurological Disorders and Stroke (RF1NS112391 and U19NS115388). J.P.P. was supported by the San Antonio Claude D. Pepper Older Americans Independence Center (RL5 Scholar, P30AG044271), the American Federation of Aging Research and the Cure Alzheimer’s Fund. B.Z. was supported by the National Institute on Aging (U01AG046170 and R01AG068030). M.H. was supported by the National Institute on Aging (R01AG080821). S.C. was supported by the National Institute on Aging (P30AG072947). R.C.P. was supported by the National Institute on Aging (P30 AG062677, U01 AG006786, U24 AG057437 and U19 AG024904), National Institute of Neurological Disorders and Stroke (UF1 NS125417) and the GHR Foundation. T.T. and J.L.K. were supported by the National Institute on Aging (R37AG13925 and P01AG062413), the Alzheimer’s Association (PTC REG-20-651687), the Connor Fund, Robert J. and Theresa W. Ryan, and the Noaber Foundation. S.S. was supported by the National Institute on Aging (R01AG066524, R01AG054076, R01AG033193 and RF1AG059421), National Institute of Neurological Disorders and Stroke (R01NS017950) and the National Heart, Lung and Blood Institute (R01HL105756). N.M. was supported by the National Institute on Aging (P30AG044271, P30AG013319, U54AG07594, R01AG069690 and R01AG075684). M.E.O. was supported by the US Department of Veterans Affairs (I01BX005717), National Institute on Aging (R01AG068293), National Institute of Neurological Disorders and Stroke (R21NS125171), Cure Alzheimer’s Fund and Hevolution Foundation/American Federation of Aging Research. The sponsors had no role in the design and conduct of the study; in the collection, analysis and interpretation of data; in the preparation of the manuscript or in the review or approval of the manuscript.

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Authors

Contributions

M.M.G. and M.E.O. conceived the project, acquired funding, analyzed and interpreted data, drafted and submitted the manscript. M.M.G. provided study oversight and supervision. N.M. and A.S. provided medical oversight of the trial. V.R.G. recruited study participants, collected data, and drafted the manuscript. T.F.K. and J.J.M. performed Simoa®, Lumipulse and MSD assays, as well as analyses. J.P.P., S.K.D., H.Z., P.X., and B.Z. conducted statistical analyses and interpreted biofluid data. T.T. contributed to biofluid analyses and interpretation. M.L.C. performed HPLC–MS/MS study design, oversight, and analyses. C.F. and M.H. conducted MRI image acquisition and analyses. S.S., S.C., R.C.P. and J.K.L contributed to data interpretation. All authors edited and approved the final manuscript.

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Correspondence to Mitzi M. Gonzales or Miranda E. Orr.

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Competing interests

M.M.G. reports personal stock in Abbvie. R.C.P. reports personal fees from Roche, no personal fees from Eisai, and personal fees from Genentech, personal fees from Eli Lilly and personal fees from Nestle, outside the submitted work. R.C.P. receives royalties from Oxford University Press and UpToDate and receives fees from Medscape for educational activities. J.L.K. and T.T. have a patent for Killing Senescent Cells and Treating Senescence-Associated Conditions Using an SRC Inhibitor and a Flavonoid with royalties paid to Mayo Clinic by Unity Biotechnologies and a patent for Treating Cognitive Decline and Other Neurodegenerative Conditions by Selectively Removing Senescent Cells from Neurological Tissue with royalties paid to Mayo Clinic by Unity Biotechnologies. S.C. reports Scientific Advisory Board membership for T3D Therapeutics and the Neurodegenerative Consortium, outside the submitted work. M.E.O., H.Z. and S.K.D. have a patent for Biosignature and therapeutic approach for neuronal senescence, which is pending. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Baseline and Post-Treatment ADRD Plasma and Cerebrospinal Fluid Biomarkers Assessed Using the Simoa® Quanterix HD-X Analyzer.

Baseline and post-treatment values for each analyte color-coded by participant as listed in Table 2. Mean [95% CI]: CSF (a) pTau-181, −12.04 [−34.01 to 9.930]; (b) pTau-231, −1.304 [−25.19 to 22.58]; (c) NfL,−23.20 [−695.6 to 649.2]; (d) GFAP, 2560 [440.9 to 4679]; (e) Aß40, 148.9 [−1359 to 1656]; (f) Aß42, −3.110 [−96.96 to 90.74]. Plasma, mean [95% CI]: (g) pTau-181, −0.0042 [−0.8779 to 0.8695]; (h) NfL, −1.774 [−9.567 to 6.019]; (i) GFAP, 24.29 [−42.74 to 91.32]; (j) Aß40, −0.6714 [−13.04 to 11.69]; (k) Aß42, −0.1080 [−0.6495 to 0.4335]. Baseline to post-treatment values were assessed using two-sided paired sample t-tests, p < 0.05. 95% CI: 95 percent confidence interval for the post vs baseline mean difference. No correction for multiple comparisons was made due to small sample size (N = 5). CSF: Cerebrospinal fluid.

Extended Data Table 1 Baseline and post-treatment cognitive and functional status assessments
Extended Data Table 2 Baseline and post-treatment neuroimaging outcomes
Extended Data Table 3 Significantly differentially expressed proteins in plasma and cerebrospinal fluid from baseline to post-treatment

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Gonzales, M.M., Garbarino, V.R., Kautz, T.F. et al. Senolytic therapy in mild Alzheimer’s disease: a phase 1 feasibility trial. Nat Med 29, 2481–2488 (2023). https://doi.org/10.1038/s41591-023-02543-w

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