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* disabled [[macroautophagy]]
* [[chronic inflammation]]
* [[dysbiosis]]<ref name="10.1016/j.cell.2022.11.001">{{cite journal |last1=López-Otín |first1=Carlos |last2=Blasco |first2=Maria A. |last3=Partridge |first3=Linda |last4=Serrano |first4=Manuel |last5=Kroemer |first5=Guido |title=Hallmarks of aging: An expanding universe |journal=Cell |date=19 January 2023 |volume=186 |issue=2 |pages=243–278 |doi=10.1016/j.cell.2022.11.001 |pmid=36599349 |s2cid=255394876
The environment induces damage at various levels, e.g. [[DNA damage theory of aging|damage to DNA]], and damage to tissues and cells by oxygen [[radical (chemistry)|radicals]] (widely known as [[Free-radical theory|free radicals]]), and some of this damage is not repaired and thus accumulates with time.<ref name="pmid1383772">{{cite journal |vauthors=Holmes GE, Bernstein C, Bernstein H |title=Oxidative and other DNA damages as the basis of aging: a review |journal=Mutat. Res. |volume=275 |issue=3–6 |pages=305–15 |date=September 1992 |pmid=1383772 |doi= 10.1016/0921-8734(92)90034-m}}</ref> [[Cloning]] from [[somatic cell]]s rather than germ cells may begin life with a higher initial load of damage. [[Dolly the sheep]] died young from a contagious lung disease, but data on an entire population of cloned individuals would be necessary to measure mortality rates and quantify aging.{{citation needed|date=December 2019}}
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=== Aging clocks ===
{{Expand section|date=March 2023}}
There is interest in an [[epigenetic clock]] as a biomarker of aging, based on its ability to predict human chronological age.<ref name="Horvath2013">{{cite journal | vauthors = Horvath S | title = DNA methylation age of human tissues and cell types | journal = Genome Biology | volume = 14 | issue = 10 | pages = R115 | year = 2013 | pmid = 24138928 | pmc = 4015143 | doi = 10.1186/gb-2013-14-10-r115 | doi-access = free }}</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 | display-authors = 6 | 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 | display-authors = 6 | 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><ref>{{Cite journal |
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 |language=en |issn=0027-8424 }}
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* 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=9419–9432 |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}} Exmploying 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 |
==Genetic determinants of aging==
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