Age estimation based on different molecular clocks in several tissues and a multivariate approach: an explorative study

  • Julia Becker
  • Nina Sophia MahlkeEmail author
  • A. Reckert
  • S. B. Eickhoff
  • S. Ritz-Timme
Original Article


Several molecular modifications accumulate in the human organism with increasing age. Some of these “molecular clocks” in DNA and in proteins open up promising approaches for the development of methods for forensic age estimation. A natural limitation of these methods arises from the fact that the chronological age is determined only indirectly by analyzing defined molecular changes that occur during aging. These changes are not linked exclusively to the expired life span but may be influenced significantly by intrinsic and extrinsic factors in the complex process of individual aging. We tested the hypothesis that a combined use of different molecular clocks in different tissues results in more precise age estimates because this approach addresses the complex aging processes in a more comprehensive way. Two molecular clocks (accumulation of d-aspartic acid (d-Asp), accumulation of pentosidine (PEN)) in two different tissues (annulus fibrosus of intervertebral discs and elastic cartilage of the epiglottis) were analyzed in 95 cases, and uni- and multivariate models for age estimation were generated. The more parameters were included in the models for age estimation, the smaller the mean absolute errors (MAE) became. While the MAEs were 7.5–11.0 years in univariate models, a multivariate model based on the two protein clocks in the two tissues resulted in a MAE of 4.0 years. These results support our hypothesis. The tested approach of a combined analysis of different molecular clocks analyzed in different tissues opens up new possibilities in postmortem age estimation. In a next step, we will add the epigenetic clock (DNA methylation) to our protein clocks (PEN, d-Asp) and expand our set of tissues.


Age estimation Pentosidine d-Aspartic acid Machine learning Age prediction model Molecular clocks 


Compliance with ethical standards

Ethical approval

All procedures performed in studies involving human tissue were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards (approved by Ethics Committee at the Medical Faculty of Heinrich-Heine University: 6191R, 3667). This article does not contain any studies with animals performed by any of the authors.

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Ritz-Timme S, Cattaneo C, Collins MJ, Waite ER, Schütz HW, Kaatsch HJ, Borrman HI (2000) Age estimation: the state of the art in relation to the specific demands of forensic practise. Int J Legal Med 113(3):129–136Google Scholar
  2. 2.
    Meissner C, Ritz-Timme S (2010) Molecular pathology and age estimation. Forensic Sci Int 203(1–3):34–43. Google Scholar
  3. 3.
    Ritz-Timme S, Collins MJ (2002) Racemization of aspartic acid in human proteins. Ageing Res Rev 1(1):43–59Google Scholar
  4. 4.
    Zapico SC, Ubelaker DH (2013) Applications of physiological bases of ageing to forensic sciences. Estimation of age-at-death. Ageing Res Rev 12(2):605–617. Google Scholar
  5. 5.
    Ritz-Timme S (1999) Lebensaltersbestimmung aufgrund des Razemisierungsgrades von Asparaginsäure: Grundlagen, Methodik, Möglichkeiten, Grenzen, Anwendungsbereiche ; mit 6 Tabellen. Arbeitsmethoden der medizinischen und naturwissenschaftlichen Kriminalistik, Bd 23. Schmidt-Römhild, LübeckGoogle Scholar
  6. 6.
    Freire-Aradas A, Phillips C, Lareu MV (2017) Forensic individual age estimation with DNA: from initial approaches to methylation tests. Forensic Sci Rev 29(2):121–144Google Scholar
  7. 7.
    Ritz-Timme S, Schneider PM, Mahlke NS, Koop BE, Eickhoff SB (2018) Altersschätzung auf Basis der DNA-Methylierung. Rechtsmedizin 28(3):202–207. Google Scholar
  8. 8.
    Lee HY, Lee SD, Shin K-J (2016) Forensic DNA methylation profiling from evidence material for investigative leads. BMB Rep 49(7):359–369Google Scholar
  9. 9.
    Goel N, Karir P, Garg VK (2017) Role of DNA methylation in human age prediction. Mech Ageing Dev 166:33–41. Google Scholar
  10. 10.
    Vidaki A, Kayser M (2018) Recent progress, methods and perspectives in forensic epigenetics. Forensic Sci Int Genet 37:180–195. Google Scholar
  11. 11.
    Parson W (2018) Age estimation with DNA: from forensic DNA fingerprinting to forensic (epi)genomics: a mini-review. Gerontology 64(4):326–332. Google Scholar
  12. 12.
    Zapico SC (2017) Mechanisms linking aging, diseases and biological age estimation. 23-Epigenetics. CRC Press, PortlandGoogle Scholar
  13. 13.
    Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14(10):R115. Google Scholar
  14. 14.
    Jung S-E, Shin K-J, Lee HY (2017) DNA methylation-based age prediction from various tissues and body fluids. BMB Rep 50(11):546–553Google Scholar
  15. 15.
    Jones MJ, Goodman SJ, Kobor MS (2015) DNA methylation and healthy human aging. Aging Cell 14(6):924–932. Google Scholar
  16. 16.
    Spólnicka M, Pośpiech E, Adamczyk JG, Freire-Aradas A, Pepłońska B, Zbieć-Piekarska R, Makowska Ż, Pięta A, Lareu MV, Phillips C, Płoski R, Żekanowski C, Branicki W (2018) Modified aging of elite athletes revealed by analysis of epigenetic age markers. Aging 10(2):241–252. Google Scholar
  17. 17.
    Gao X, Zhang Y, Breitling LP, Brenner H (2016) Relationship of tobacco smoking and smoking-related DNA methylation with epigenetic age acceleration. Oncotarget 7(30):46878–46889. Google Scholar
  18. 18.
    Kandi V, Vadakedath S (2015) Effect of DNA methylation in various diseases and the probable protective role of nutrition: a mini-review. Cureus 7(8):e309. Google Scholar
  19. 19.
    Stephenson RC, Clarke S (1989) Succinimide formation from aspartyl and asparaginyl peptides as a model for the spontaneous degradation of proteins. J Biol Chem 264(11):6164–6170Google Scholar
  20. 20.
    Geiger T, Clarke S (1987) Deamidation, isomerization, and racemization at asparaginyl and aspartyl residues in peptides. Succinimide-linked reactions that contribute to protein degradation. J Biol Chem 262(2):785–794Google Scholar
  21. 21.
    Dobberstein RC, Tung S-M, Ritz-Timme S (2010) Aspartic acid racemisation in purified elastin from arteries as basis for age estimation. Int J Legal Med 124(4):269–275. Google Scholar
  22. 22.
    Klumb K, Matzenauer C, Reckert A, Lehmann K, Ritz-Timme S (2016) Age estimation based on aspartic acid racemization in human sclera. Int J Legal Med 130(1):207–211. Google Scholar
  23. 23.
    Matzenauer C, Reckert A, Ritz-Timme S (2014) Estimation of age at death based on aspartic acid racemization in elastic cartilage of the epiglottis. Int J Legal Med 128(6):995–1000. Google Scholar
  24. 24.
    Ohtani S, Yamamoto K (1991) Age estimation using the racemization of amino acid in human dentin. J Forensic Sci 36(3):792–800Google Scholar
  25. 25.
    Ritz S, Schütz HW, Schwarzer B (1990) The extent of aspartic acid racemization in dentin: a possible method for a more accurate determination of age at death? Zeitschrift fur Rechtsmedizin. J Legal Med 103(6):457–462Google Scholar
  26. 26.
    Ritz S, Schütz HW, Peper C (1993) Postmortem estimation of age at death based on aspartic acid racemization in dentin: its applicability for root dentin. Int J Legal Med 105(5):289–293Google Scholar
  27. 27.
    Ohtani S, Yamamoto T (2010) Age estimation by amino acid racemization in human teeth. J Forensic Sci 55(6):1630–1633. Google Scholar
  28. 28.
    Chen S, Lv Y, Wang D, Yu X (2016) Aspartic acid racemization in dentin of the third molar for age estimation of the Chaoshan population in South China. Forensic Sci Int 266:234–238. Google Scholar
  29. 29.
    Elfawal MA, Alqattan SI, Ghallab NA (2015) Racemization of aspartic acid in root dentin as a tool for age estimation in a Kuwaiti population. Med Sci Law 55(1):22–29. Google Scholar
  30. 30.
    Wochna K, Bonikowski R, Śmigielski J, Berent J (2018) Aspartic acid racemization of root dentin used for dental age estimation in a Polish population sample. Forensic Sci Med Pathol 14(3):285–294. Google Scholar
  31. 31.
    Ritz-Timme S, Rochholz G, Stammert R, Ritz H-J (2002) Biochemische Altersschätzung Zur Frage genetischer und soziokultureller (ethnischer) Einflüsse auf die Razemisierung von Asparaginsäure in Dentin. Rechtsmedizin 12(4):203–206. Google Scholar
  32. 32.
    Ulrich P, Cerami A (2001) Protein glycation, diabetes, and aging. Recent Prog Horm Res 56:1–21Google Scholar
  33. 33.
    Singh R, Barden A, Mori T, Beilin L (2001) Advanced glycation end-products: a review. Diabetologia 44(2):129–146. Google Scholar
  34. 34.
    Goldberg T, Cai W, Peppa M, Dardaine V, Baliga BS, Uribarri J, Vlassara H (2004) Advanced glycoxidation end products in commonly consumed foods. J Am Diet Assoc 104(8):1287–1291. Google Scholar
  35. 35.
    Nass N, Bartling B, Navarrete Santos A, Scheubel RJ, Börgermann J, Silber RE, Simm A (2007) Advanced glycation end products, diabetes and ageing. Z Gerontol Geriatr 40(5):349–356. Google Scholar
  36. 36.
    Greis F, Reckert A, Fischer K, Ritz-Timme S (2018) Analysis of advanced glycation end products (AGEs) in dentine: useful for age estimation? Int J Legal Med 132(3):799–805. Google Scholar
  37. 37.
    Odetti P, Rossi S, Monacelli F, Poggi A, Cirnigliaro M, Federici M, Federici A (2005) Advanced glycation end products and bone loss during aging. Ann N Y Acad Sci 1043:710–717. Google Scholar
  38. 38.
    Pokharna HK, Phillips FM (1998) Collagen crosslinks in human lumbar intervertebral disc aging. Spine 23(15):1645–1648Google Scholar
  39. 39.
    Verzijl N, DeGroot J, Oldehinkel E, Bank RA, Thorpe SR, Baynes JW, Bayliss MT, Bijlsma JW, Lafeber FP, Tekoppele JM (2000) Age-related accumulation of Maillard reaction products in human articular cartilage collagen. Biochem J 350(Pt 2):381–387Google Scholar
  40. 40.
    Ramalho JS, Marques C, Pereira PC, Mota MC (1996) Role of glycation in human lens protein structure change. Eur J Ophthalmol 6(2):155–161Google Scholar
  41. 41.
    Pillin A, Pudil F, Bencko V, Bezdícková D (2007) Contents of pentosidine in the tissue of the intervertebral disc as an indicator of the human age. Soud Lek 52(4):60–64Google Scholar
  42. 42.
    Dyer DG, Dunn JA, Thorpe SR, Bailie KE, Lyons TJ, McCance DR, Baynes JW (1993) Accumulation of Maillard reaction products in skin collagen in diabetes and aging. J Clin Invest 91(6):2463–2469. Google Scholar
  43. 43.
    Valenzuela A, Guerra-Hernández E, Rufián-Henares JÁ, Márquez-Ruiz AB, Hougen HP, García-Villanova B (2018) Differences in non-enzymatic glycation products in human dentine and clavicle: changes with aging. Int J Legal Med 132(6):1749–1758. Google Scholar
  44. 44.
    Li H, Yu S-J (2018) Review of pentosidine and pyrraline in food and chemical models: formation, potential risks and determination. J Sci Food Agric 98(9):3225–3233. Google Scholar
  45. 45.
    Stitt AW, Jenkins AJ, Cooper ME (2002) Advanced glycation end products and diabetic complications. Expert Opin Investig Drugs 11(9):1205–1223. Google Scholar
  46. 46.
    Ritz S, Turzynski A, Schütz HW (1994) Estimation of age at death based on aspartic acid racemization in noncollagenous bone proteins. Forensic Sci Int 69(2):149–159Google Scholar
  47. 47.
    Ritz S, Turzynski A, Schütz HW, Hollmann A, Rochholz G (1996) Identification of osteocalcin as a permanent aging constituent of the bone matrix: basis for an accurate age at death determination. Forensic Sci Int 77(1–2):13–26Google Scholar
  48. 48.
    Ritz-Timme S, Laumeier I, Collins M (2003) Age estimation based on aspartic acid racemization in elastin from the yellow ligaments. Int J Legal Med 117(2):96–101. Google Scholar
  49. 49.
    Monum T, Jaikang C, Sinthubua A, Prasitwattanaseree S, Mahakkanukrauh P (2017) Age estimation using aspartic amino acid racemization from a femur. Aust J Forensic Sci 116:1–9. Google Scholar
  50. 50.
    Ohtani S, Matsushima Y, Kobayashi Y, Kishi K (1998) Evaluation of aspartic acid racemization ratios in the human femur for age estimation. J Forensic Sci 43(5):949–953Google Scholar
  51. 51.
    Ohtani S, Yamamoto T, Abe I, Kinoshita Y (2007) Age-dependent changes in the racemisation ratio of aspartic acid in human alveolar bone. Arch Oral Biol 52(3):233–236. Google Scholar
  52. 52.
    Tiplamaz S, Gören MZ, Yurtsever NT (2018) Estimation of chronological age from postmortem tissues based on amino acid racemization. J Forensic Sci 63(5):1533–1538. Google Scholar
  53. 53.
    Sivan SS, Tsitron E, Wachtel E, Roughley P, Sakkee N, van der Ham F, Degroot J, Maroudas A (2006) Age-related accumulation of pentosidine in aggrecan and collagen from normal and degenerate human intervertebral discs. Biochem J 399(1):29–35. Google Scholar
  54. 54.
    Schmidt MB, Mow VC, Chun LE, Eyre DR (1990) Effects of proteoglycan extraction on the tensile behavior of articular cartilage. J Orthop Res 8(3):353–363. Google Scholar
  55. 55.
    Heems D, Luck G, Fraudeau C, Vérette E (1998) Fully automated precolumn derivatization, on-line dialysis and high-performance liquid chromatographic analysis of amino acids in food, beverages and feedstuff. J Chromatogr A 798(1–2):9–17. Google Scholar
  56. 56.
    Kaufman DS, Manley WF (1998) A new procedure for determining dl amino acid ratios in fossils using reverse phase liquid chromatography. Quat Sci Rev 17(11):987–1000. Google Scholar
  57. 57.
    Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33(1–2):1–39. Google Scholar
  58. 58.
    Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. Adaptive computation and machine learning. MIT Press, CambridgeGoogle Scholar
  59. 59.
    Cho S, Jung S-E, Hong SR, Lee EH, Lee JH, Lee SD, Lee HY (2017) Independent validation of DNA-based approaches for age prediction in blood. Forensic Sci Int Genet 29:250–256. Google Scholar
  60. 60.
    Ritz S, Schütz HW (1993) Aspartic acid racemization in intervertebral discs as an aid to postmortem estimation of age at death. J Forensic Sci 38(3):633–640Google Scholar
  61. 61.
    Sell DR, Nagaraj RH, Grandhee SK, Odetti P, Lapolla A, Fogarty J, Monnier VM (1991) Pentosidine: a molecular marker for the cumulative damage to proteins in diabetes, aging, and uremia. Diabetes Metab Rev 7(4):239–251Google Scholar
  62. 62.
    Brownlee M (1995) Advanced protein glycosylation in diabetes and aging. Annu Rev Med 46:223–234. Google Scholar
  63. 63.
    Semba RD, Nicklett EJ, Ferrucci L (2010) Does accumulation of advanced glycation end products contribute to the aging phenotype? J Gerontol A Biol Sci Med Sci 65A(9):963–975. Google Scholar
  64. 64.
    Aliferi A, Ballard D, Gallidabino MD, Thurtle H, Barron L, Syndercombe Court D (2018) DNA methylation-based age prediction using massively parallel sequencing data and multiple machine learning models. Forensic Sci Int Genet 37:215–226. Google Scholar
  65. 65.
    Jung S-E, Lim SM, Hong SR, Lee EH, Shin K-J, Lee HY (2019) DNA methylation of the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes for age prediction from blood, saliva, and buccal swab samples. Forensic Sci Int Genet 38:1–8. Google Scholar
  66. 66.
    Naue J, Hoefsloot HCJ, Mook ORF, Rijlaarsdam-Hoekstra L, van der Zwalm MCH, Henneman P, Kloosterman AD, Verschure PJ (2017) Chronological age prediction based on DNA methylation: massive parallel sequencing and random forest regression. Forensic Sci Int Genet 31:19–28. Google Scholar
  67. 67.
    Rhein M, Hagemeier L, Klintschar M, Muschler M, Bleich S, Frieling H (2015) DNA methylation results depend on DNA integrity—role of post mortem interval. Front Genet 6.
  68. 68.
    Jarmasz JS, Stirton H, Davie JR, Del Bigio MR (2019) DNA methylation and histone post-translational modification stability in post-mortem brain tissue. Clin Epigenetics 11(1):5. Google Scholar
  69. 69.
    Vidaki A, Ballard D, Aliferi A, Miller TH, Barron LP, Syndercombe Court D (2017) DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing. Forensic Sci Int Genet 28:225–236. Google Scholar
  70. 70.
    Shi L, Jiang F, Ouyang F, Zhang J, Wang Z, Shen X (2018) DNA methylation markers in combination with skeletal and dental ages to improve age estimation in children. Forensic Sci Int Genet 33:1–9. Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Julia Becker
    • 1
  • Nina Sophia Mahlke
    • 1
    Email author
  • A. Reckert
    • 1
  • S. B. Eickhoff
    • 2
    • 3
  • S. Ritz-Timme
    • 1
  1. 1.Institute of Legal MedicineUniversity Hospital DüsseldorfDusseldorfGermany
  2. 2.Institute for Systems NeuroscienceUniversity Hospital DüsseldorfDusseldorfGermany
  3. 3.Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7)Research Centre JülichJulichGermany

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