Editing Senescence (section)

==Biomarkers of aging==
{{Main|Biomarkers of aging}}

If different individuals age at different rates, then fecundity, mortality, and functional capacity might be better predicted by [[biomarker]]s than by chronological age.<ref name="Gasmi">{{cite journal | vauthors = Gasmi A, Chirumbolo S, Peana M, Mujawdiya PK, Dadar M, Menzel A, Bjørklund G | title = Biomarkers of Senescence during Aging as Possible Warnings to Use Preventive Measures | journal = Current Medicinal Chemistry | volume = 28 | issue = 8 | pages = 1471–88 | date = 2020-09-17 | pmid = 32942969 | doi = 10.2174/0929867327999200917150652 | s2cid = 221789280 }}</ref><ref name="BakerGT">{{cite journal | vauthors = Baker GT, Sprott RL | title = Biomarkers of aging | journal = Experimental Gerontology | volume = 23 | issue = 4–5 | pages = 223–39 | year = 1988 | pmid = 3058488 | doi = 10.1016/0531-5565(88)90025-3 | s2cid = 31039588 | url = https://zenodo.org/record/1258547 | access-date = 12 July 2019 | archive-date = 24 October 2021 | archive-url = https://web.archive.org/web/20211024064308/https://zenodo.org/record/1258547 | url-status = live }}</ref> However, [[Human hair color#Gray and white hair|graying of hair]],<ref>{{cite journal | vauthors = Van Neste D, Tobin DJ | title = Hair cycle and hair pigmentation: dynamic interactions and changes associated with aging | journal = Micron | volume = 35 | issue = 3 | pages = 193–200 | year = 2004 | pmid = 15036274 | doi = 10.1016/j.micron.2003.11.006 }}</ref> [[Face#Other characteristics|face aging]], [[wrinkle|skin wrinkles]] and other common changes seen with aging are not better indicators of future functionality than chronological age. [[Gerontology#Biogerontology|Biogerontologists]] have continued efforts to find and validate biomarkers of aging, but success thus far has been limited.

Levels of [[CD4]] and [[CD8]] [[memory T cell]]s and [[naive T cell]]s have been used to give good predictions of the expected lifespan of middle-aged mice.<ref>{{cite journal | vauthors = Miller RA | title = Biomarkers of aging: prediction of longevity by using age-sensitive T-cell subset determinations in a middle-aged, genetically heterogeneous mouse population | journal = The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences | volume = 56 | issue = 4 | pages = B180-6 | date = April 2001 | pmid = 11283189 | pmc = 7537444 | doi = 10.1093/gerona/56.4.b180 | doi-access = free }}</ref>

=== Aging clocks ===
There is interest in an [[epigenetic clock]] as a biomarker of aging, based on its ability to predict human chronological age.<ref>{{cite journal |last1=Naue |first1=Jana |title=Getting the chronological age out of DNA: using insights of age-dependent DNA methylation for forensic DNA applications |journal=Genes & Genomics |date=October 2023 |volume=45 |issue=10 |pages=1239–1261 |doi=10.1007/s13258-023-01392-8|pmid=37253906 |pmc=10504122 }}</ref> Basic blood [[biochemistry]] and cell counts can also be used to accurately predict the chronological age.<ref name="pmid27191382">{{cite journal | vauthors = Putin E, Mamoshina P, Aliper A, Korzinkin M, Moskalev A, Kolosov A, Ostrovskiy A, Cantor C, Vijg J, Zhavoronkov A | title = Deep biomarkers of human aging: Application of deep neural networks to biomarker development | journal = Aging | volume = 8 | issue = 5 | pages = 1021–33 | date = May 2016 | pmid = 27191382 | pmc = 4931851 | doi = 10.18632/aging.100968 }}</ref> It is also possible to predict the human chronological age using transcriptomic aging clocks.<ref>{{cite journal | vauthors = Peters MJ, Joehanes R, Pilling LC, Schurmann C, Conneely KN, Powell J, Reinmaa E, Sutphin GL, Zhernakova A, Schramm K, Wilson YA, Kobes S, Tukiainen T, Ramos YF, Göring HH, Fornage M, Liu Y, Gharib SA, Stranger BE, De Jager PL, Aviv A, Levy D, Murabito JM, Munson PJ, Huan T, Hofman A, Uitterlinden AG, Rivadeneira F, van Rooij J, Stolk L, Broer L, Verbiest MM, Jhamai M, Arp P, Metspalu A, Tserel L, Milani L, Samani NJ, Peterson P, Kasela S, Codd V, Peters A, Ward-Caviness CK, Herder C, Waldenberger M, Roden M, Singmann P, Zeilinger S, Illig T, Homuth G, Grabe HJ, Völzke H, Steil L, Kocher T, Murray A, Melzer D, Yaghootkar H, Bandinelli S, Moses EK, Kent JW, Curran JE, Johnson MP, Williams-Blangero S, Westra HJ, McRae AF, Smith JA, Kardia SL, Hovatta I, Perola M, Ripatti S, Salomaa V, Henders AK, Martin NG, Smith AK, Mehta D, Binder EB, Nylocks KM, Kennedy EM, Klengel T, Ding J, Suchy-Dicey AM, Enquobahrie DA, Brody J, Rotter JI, Chen YD, Houwing-Duistermaat J, Kloppenburg M, Slagboom PE, Helmer Q, den Hollander W, Bean S, Raj T, Bakhshi N, Wang QP, Oyston LJ, Psaty BM, Tracy RP, Montgomery GW, Turner ST, Blangero J, Meulenbelt I, Ressler KJ, Yang J, Franke L, Kettunen J, Visscher PM, Neely GG, Korstanje R, Hanson RL, Prokisch H, Ferrucci L, Esko T, Teumer A, van Meurs JB, Johnson AD | title = The transcriptional landscape of age in human peripheral blood | journal = Nature Communications | volume = 6 | pages = 8570 | date = October 2015 | pmid = 26490707 | pmc = 4639797 | doi = 10.1038/ncomms9570 | bibcode = 2015NatCo...6.8570. }}</ref>

There is research and development of further biomarkers, detection systems and software systems to measure biological age of different tissues or systems or overall. For example, a [[deep learning]] (DL) software using anatomic [[magnetic resonance image]]s estimated [[brain aging|brain age]] with relatively high accuracy, including detecting early signs of Alzheimer's disease and varying [[neuroanatomical]] patterns of neurological aging,<ref>{{cite journal |last1=Yin |first1=Chenzhong |last2=Imms |first2=Phoebe |last3=Cheng |first3=Mingxi |display-authors=et al. |title=Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment |journal=Proceedings of the National Academy of Sciences |date=10 January 2023 |volume=120 |issue=2 |pages=e2214634120 |doi=10.1073/pnas.2214634120 |pmid=36595679 |pmc=9926270 |bibcode=2023PNAS..12014634Y }}
* University press release: {{cite news |title=How old is your brain, really? AI-powered analysis accurately reflects risk of cognitive decline and Alzheimer's disease |url=https://medicalxpress.com/news/2023-01-brain-ai-powered-analysis-accurately-cognitive.html |access-date=17 February 2023 |work=University of Southern California via medicalxpress.com |archive-date=17 February 2023 |archive-url=https://web.archive.org/web/20230217232042/https://medicalxpress.com/news/2023-01-brain-ai-powered-analysis-accurately-cognitive.html |url-status=live }}
* News article about the study: {{cite news |title=KI kann wahres Alter des Hirns bestimmen |url=https://www.deutschlandfunknova.de/nachrichten/alterungsprozess-ki-kann-wahres-alter-des-hirns-bestimmen |access-date=17 February 2023 |work=[[Deutschlandfunk Nova]] |language=de |archive-date=17 February 2023 |archive-url=https://web.archive.org/web/20230217232037/https://www.deutschlandfunknova.de/nachrichten/alterungsprozess-ki-kann-wahres-alter-des-hirns-bestimmen |url-status=live }}</ref> and a DL tool was reported as to calculate a person's [[Inflammaging|inflammatory age]] based on patterns of systemic age-related inflammation.<ref>{{cite journal |last1=Sayed |first1=Nazish |last2=Huang |first2=Yingxiang |last3=Nguyen |first3=Khiem |last4=Krejciova-Rajaniemi |first4=Zuzana |last5=Grawe |first5=Anissa P. |last6=Gao |first6=Tianxiang |last7=Tibshirani |first7=Robert |last8=Hastie |first8=Trevor |last9=Alpert |first9=Ayelet |last10=Cui |first10=Lu |last11=Kuznetsova |first11=Tatiana |last12=Rosenberg-Hasson |first12=Yael |last13=Ostan |first13=Rita |last14=Monti |first14=Daniela |last15=Lehallier |first15=Benoit |last16=Shen-Orr |first16=Shai S. |last17=Maecker |first17=Holden T. |last18=Dekker |first18=Cornelia L. |last19=Wyss-Coray |first19=Tony |last20=Franceschi |first20=Claudio |last21=Jojic |first21=Vladimir |last22=Haddad |first22=François |last23=Montoya |first23=José G. |last24=Wu |first24=Joseph C. |last25=Davis |first25=Mark M. |last26=Furman |first26=David |title=An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging |journal=Nature Aging |date=July 2021 |volume=1 |issue=7 |pages=598–615 |doi=10.1038/s43587-021-00082-y |pmid=34888528 |pmc=8654267 |doi-access=free }}
* News article about the study: {{cite news |title=Tool that calculates immune system age could predict frailty and disease |url=https://newatlas.com/science/stanford-immune-system-age-biomarker-blood-test/ |access-date=26 July 2021 |work=New Atlas |date=13 July 2021 |archive-date=26 July 2021 |archive-url=https://web.archive.org/web/20210726110032/https://newatlas.com/science/stanford-immune-system-age-biomarker-blood-test/ |url-status=live }}</ref>

Aging clocks have been used to evaluate impacts of interventions on humans, including [[combination therapy|combination therapies]].<ref>{{cite journal |title=Potential reversal of epigenetic age using a diet and lifestyle intervention: a pilot randomized clinical trial |journal=Aging |year=2021 |pmid=33844651 |url=https://www.aging-us.com/article/202913/text |access-date=28 June 2021 |last1=Fitzgerald |first1=K. N. |last2=Hodges |first2=R. |last3=Hanes |first3=D. |last4=Stack |first4=E. |last5=Cheishvili |first5=D. |last6=Szyf |first6=M. |last7=Henkel |first7=J. |last8=Twedt |first8=M. W. |last9=Giannopoulou |first9=D. |last10=Herdell |first10=J. |last11=Logan |first11=S. |last12=Bradley |first12=R. |volume=13 |issue=7 |pages=9419–32 |doi=10.18632/aging.202913 |pmc=8064200 |archive-date=2 June 2021 |archive-url=https://web.archive.org/web/20210602114006/https://www.aging-us.com/article/202913/text |url-status=live }}</ref>{{additional citation needed|date=March 2023}} Employing aging clocks to identify and evaluate longevity interventions represents a fundamental goal in aging biology research. However, achieving this goal requires overcoming numerous challenges and implementing additional validation steps.<ref>{{Cite journal |last1=Moqri |first1=Mahdi |last2=Herzog |first2=Chiara |last3=Poganik |first3=Jesse R. |last4=Justice |first4=Jamie |last5=Belsky |first5=Daniel W. |last6=Higgins-Chen |first6=Albert |last7=Moskalev |first7=Alexey |last8=Fuellen |first8=Georg |last9=Cohen |first9=Alan A. |last10=Bautmans |first10=Ivan |last11=Widschwendter |first11=Martin |last12=Ding |first12=Jingzhong |last13=Fleming |first13=Alexander |last14=Mannick |first14=Joan |last15=Han |first15=Jing-Dong Jackie |date=August 2023 |title=Biomarkers of aging for the identification and evaluation of longevity interventions |url=https://doi.org/10.1016/j.cell.2023.08.003 |journal=Cell |volume=186 |issue=18 |pages=3758–75 |doi=10.1016/j.cell.2023.08.003 |pmid=37657418 |pmc=11088934 }}</ref><ref>{{Cite journal |last1=Moqri |first1=Mahdi |last2=Herzog |first2=Chiara |last3=Poganik |first3=Jesse R. |last4=Ying |first4=Kejun |last5=Justice |first5=Jamie N. |last6=Belsky |first6=Daniel W. |last7=Higgins-Chen |first7=Albert T. |last8=Chen |first8=Brian H. |last9=Cohen |first9=Alan A. |last10=Fuellen |first10=Georg |last11=Hägg |first11=Sara |last12=Marioni |first12=Riccardo E. |last13=Widschwendter |first13=Martin |last14=Fortney |first14=Kristen |last15=Fedichev |first15=Peter O. |date=February 2024 |title=Validation of biomarkers of aging |journal=Nature Medicine |volume=30 |issue=2 |pages=360–372 |doi=10.1038/s41591-023-02784-9 |issn=1546-170X |pmid=38355974|pmc=11090477 }}</ref>