Epigenetic age biomarkers and risk assessment in adult spinal deformity: a novel association of biological age with frailty and disability

Michael M. Safaee Department of Neurological Surgery, University of Southern California, Los Angeles, California;

Search for other papers by Michael M. Safaee in
jns
Google Scholar
PubMed
Close
 MD
,
Varun B. Dwaraka TruDiagnostic, Lexington, Kentucky;

Search for other papers by Varun B. Dwaraka in
jns
Google Scholar
PubMed
Close
 PhD
,
Justin M. Lee Departments of Neurological Surgery,

Search for other papers by Justin M. Lee in
jns
Google Scholar
PubMed
Close
 BA
,
Marissa Fury Departments of Neurological Surgery,

Search for other papers by Marissa Fury in
jns
Google Scholar
PubMed
Close
 BS
,
Tavis L. Mendez TruDiagnostic, Lexington, Kentucky;

Search for other papers by Tavis L. Mendez in
jns
Google Scholar
PubMed
Close
 PhD
,
Ryan Smith TruDiagnostic, Lexington, Kentucky;

Search for other papers by Ryan Smith in
jns
Google Scholar
PubMed
Close
 BS
,
Jue Lin Biochemistry and Biophysics, and

Search for other papers by Jue Lin in
jns
Google Scholar
PubMed
Close
 PhD
,
Dana L. Smith Biochemistry and Biophysics, and

Search for other papers by Dana L. Smith in
jns
Google Scholar
PubMed
Close
 MS
,
John F. Burke Departments of Neurological Surgery,

Search for other papers by John F. Burke in
jns
Google Scholar
PubMed
Close
 MD
,
Justin K. Scheer Departments of Neurological Surgery,

Search for other papers by Justin K. Scheer in
jns
Google Scholar
PubMed
Close
 MD
,
Hannah Went TruDiagnostic, Lexington, Kentucky;

Search for other papers by Hannah Went in
jns
Google Scholar
PubMed
Close
 BS
, and
Christopher P. Ames Departments of Neurological Surgery,
Orthopedic Surgery, University of California, San Francisco, California

Search for other papers by Christopher P. Ames in
jns
Google Scholar
PubMed
Close
 MD
Restricted access

Purchase Now

USD  $45.00

Spine - 1 year subscription bundle (Individuals Only)

USD  $392.00

JNS + Pediatrics + Spine - 1 year subscription bundle (Individuals Only)

USD  $636.00
USD  $45.00
USD  $392.00
USD  $636.00
Print or Print + Online Sign in

OBJECTIVE

Surgery for spinal deformity has the potential to improve pain, disability, function, self-image, and mental health. These surgical procedures carry significant risk and require careful selection, optimization, and risk assessment. Epigenetic clocks are age estimation tools derived by measuring the methylation patterns of specific DNA regions. The study of biological age in the adult deformity population has the potential to shed insight onto the molecular basis of frailty and to improve current risk assessment tools.

METHODS

Adult patients who underwent deformity surgery were prospectively enrolled. Preoperative whole blood samples were used to assess epigenetic age and telomere length. DNA methylation patterns were quantified and processed to extract 4 principal component (PC)–based epigenetic age clocks (PC Horvath, PC Hannum, PC PhenoAge, and PC GrimAge) and the instantaneous pace of aging (DunedinPACE). Telomere length was assessed using both quantitative polymerase chain reaction (telomere to single gene [T/S] ratio) and a methylation-based telomere estimator (PC DNAmTL). Patient demographic and surgical data included age, BMI, American Society of Anesthesiologists Physical Status Classification System class, and scores on the Charlson Comorbidity Index, adult spinal deformity frailty index (ASD-FI), Edmonton Frail Scale (EFS), Oswestry Disability Index, and Scoliosis Research Society-22r questionnaire (SRS-22r). Medical or surgical complications within 90 days of surgery were collected. Spearman correlations and beta coefficients (β) from linear regression, adjusted for BMI and sex, were calculated.

RESULTS

Eighty-three patients were enrolled with a mean age of 65 years, and 45 were women (54%). All patients underwent posterior fusion with a mean of 11 levels fused and 33 (40%) 3-column osteotomies were performed. Among the epigenetic clocks adjusted for BMI and sex, DunedinPACE showed a significant association with ASD-FI (β = 0.041, p = 0.002), EFS (β = 0.696, p = 0.026), and SRS-22r (β = 0.174, p = 0.013) scores. PC PhenoAge showed associations with ASD-FI (β = 0.029, p = 0.028) and SRS-22r (β = 0.159, p = 0.018) scores. PC GrimAge showed associations with ASD-FI (β = 0.029, p = 0.037) and SRS-22r (β = 0.161, p = 0.025) scores. Patients with postoperative complications were noted to have shorter telomere length (T/S 0.790 vs 0.858, p = 0.049), even when the analysis controlled for BMI and sex (OR = 1.71, 95% CI 1.07–2.87, p = 0.031).

CONCLUSIONS

Epigenetic clocks showed significant associations with markers of frailty and disability, while patients with postoperative complications had shorter telomere length. These data suggest a potential role for aging biomarkers as components of surgical risk assessment. Integrating biological age into current risk calculators may improve their accuracy and provide valuable information for patients, surgeons, and payers.

ABBREVIATIONS

ASA = American Society of Anesthesiologists Physical Status Classification System; ASD-FI = adult spinal deformity frailty index; bp = base pairs; CCI = Charlson Comorbidity Index; EBL = estimated blood loss; EFS = Edmonton Frail Scale; ODI = Oswestry Disability Index; PI-LL = pelvic incidence–lumbar lordosis; PC = principal component; qPCR = quantitative polymerase chain reaction; SRS-22r = Scoliosis Research Society-22r questionnaire; T/S = telomere to single gene; 3CO = 3-column osteotomy.

Supplementary Materials

    • Supplemental Table 1 (PDF 376 KB)
  • Collapse
  • Expand
Images from Özer and Demirtaş (pp 351–358).
  • 1

    Safaee MM, Ames CP, Smith JS. Epidemiology and socioeconomic trends in adult spinal deformity care. Neurosurgery. 2020;87(1):2532.

  • 2

    Weinstein JN, Lurie JD, Olson PR, Bronner KK, Fisher ES. United States’ trends and regional variations in lumbar spine surgery: 1992-2003. Spine (Phila Pa 1976). 2006;31(23):27072714.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3

    Deyo RA, Mirza SK, Martin BI, Kreuter W, Goodman DC, Jarvik JG. Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults. JAMA. 2010;303(13):12591265.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4

    Whitmore RG, Stephen J, Stein SC, et al. Patient comorbidities and complications after spinal surgery: a societal-based cost analysis. Spine (Phila Pa 1976). 2012;37(12):10651071.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Saklad M. Grading of patients for surgical procedures. Anesthesiology. 1941;2:281284.

  • 6

    Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373383.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7

    Scheer JK, Oh T, Smith JS, et al. Development of a validated computer-based preoperative predictive model for pseudarthrosis with 91% accuracy in 336 adult spinal deformity patients. Neurosurg Focus. 2018;45(5):E11.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8

    Scheer JK, Osorio JA, Smith JS, et al. Development of a preoperative predictive model for reaching the Oswestry Disability Index minimal clinically important difference for adult spinal deformity patients. Spine Deform. 2018;6(5):593599.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    Scheer JK, Osorio JA, Smith JS, et al. Development of validated computer-based preoperative predictive model for proximal junction failure (PJF) or clinically significant PJK with 86% accuracy based on 510 ASD patients with 2-year follow-up. Spine (Phila Pa 1976). 2016;41(22):E1328E1335.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Scheer JK, Smith JS, Schwab F, et al. Development of a preoperative predictive model for major complications following adult spinal deformity surgery. J Neurosurg Spine. 2017;26(6):736743.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11

    Sciubba D, Jain A, Kebaish KM, et al. Development of a preoperative adult spinal deformity comorbidity score that correlates with common quality and value metrics: length of stay, major complications, and patient-reported outcomes. Global Spine J. 2021;11(2):146153.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12

    Miller EK, Neuman BJ, Jain A, et al. An assessment of frailty as a tool for risk stratification in adult spinal deformity surgery. Neurosurg Focus. 2017;43(6):E3.

  • 13

    Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008;8:24.

  • 14

    Jylhävä J, Pedersen NL, Hägg S. Biological Age Predictors. EBioMedicine. 2017;21:2936.

  • 15

    Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018;19(6):371384.

  • 16

    Belsky DW, Caspi A, Corcoran DL, et al. DunedinPACE, a DNA methylation biomarker of the pace of aging. eLife. 2022;11:e73420.

  • 17

    Levine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018;10(4):573-591.

  • 18

    Somani S, Capua JD, Kim JS, et al. ASA Classification as a risk stratification tool in adult spinal deformity surgery: a study of 5805 patients. Global Spine J. 2017;7(8):719726.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19

    Dindo D, Demartines N, Clavien PA. Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg. 2004;240(2):205213.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20

    Higgins-Chen AT, Thrush KL, Wang Y, et al. A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking. Nat Aging. 2022;2(7):644661.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21

    Hannum G, Guinney J, Zhao L, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359367.

  • 22

    Belsky DW, Caspi A, Arseneault L, et al. Quantification of the pace of biological aging in humans through a blood test, the DunedinPoAm DNA methylation algorithm. eLife. 2020;9:e54870.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23

    Belsky DW, Caspi A, Houts R, et al. Quantification of biological aging in young adults. Proc Natl Acad Sci U S A. 2015;112(30):E4104E4110.

  • 24

    Lu AT, Seeboth A, Tsai PC, et al. DNA methylation-based estimator of telomere length. Aging (Albany NY). 2019;11(16):58955923.

  • 25

    Cawthon RM. Telomere measurement by quantitative PCR. Nucleic Acids Res. 2002;30(10):e47.

  • 26

    Lin J, Epel E, Cheon J, et al. Analyses and comparisons of telomerase activity and telomere length in human T and B cells: insights for epidemiology of telomere maintenance. J Immunol Methods. 2010;352(1-2):7180.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27

    Farzaneh-Far R, Lin J, Epel E, Lapham K, Blackburn E, Whooley MA. Telomere length trajectory and its determinants in persons with coronary artery disease: longitudinal findings from the heart and soul study. PLoS One. 2010;5(1):e8612.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28

    Müezzinler A, Zaineddin AK, Brenner H. A systematic review of leukocyte telomere length and age in adults. Ageing Res Rev. 2013;12(2):509519.

  • 29

    McCarthy I, O’Brien M, Ames C, et al. Incremental cost-effectiveness of adult spinal deformity surgery: observed quality-adjusted life years with surgery compared with predicted quality-adjusted life years without surgery. Neurosurg Focus. 2014;36(5):E3.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30

    Bonano J, Cummins DD, Burch S, et al. Economic impact of revision operations for adjacent segment disease of the subaxial cervical spine. J Am Acad Orthop Surg Glob Res Rev. 2022;6(4):e22.00058.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31

    Froehner M, Koch R, Litz R, Heller A, Oehlschlaeger S, Wirth MP. Comparison of the American Society of Anesthesiologists Physical Status classification with the Charlson score as predictors of survival after radical prostatectomy. Urology. 2003;62(4):698701.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32

    Bilimoria KY, Liu Y, Paruch JL, et al. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg. 2013;217(5):833-842.e13.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33

    Lee MJ, Cizik AM, Hamilton D, Chapman JR. Predicting medical complications after spine surgery: a validated model using a prospective surgical registry. Spine J. 2014;14(2):291299.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34

    Veeravagu A, Li A, Swinney C, et al. Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool. J Neurosurg Spine. 2017;27(1):8191.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35

    Pellisé F, Serra-Burriel M, Smith JS, et al. Development and validation of risk stratification models for adult spinal deformity surgery. J Neurosurg Spine. 2019;31(4):587599.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36

    Khan SS, Singer BD, Vaughan DE. Molecular and physiological manifestations and measurement of aging in humans. Aging Cell. 2017;16(4):624633.

  • 37

    Schaum N, Lehallier B, Hahn O, et al. Ageing hallmarks exhibit organ-specific temporal signatures. Nature. 2020;583(7817):596602.

  • 38

    Miller EK, Vila-Casademunt A, Neuman BJ, et al. External validation of the adult spinal deformity (ASD) frailty index (ASD-FI). Eur Spine J. 2018;27(9):23312338.

  • 39

    Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115.

  • 40

    Teschendorff AE, Menon U, Gentry-Maharaj A, et al. Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res. 2010;20(4):440446.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41

    Jongbloed F, Meijers RWJ, IJzermans JNM, et al. Effects of bariatric surgery on telomere length and T-cell aging. Int J Obes (Lond). 2019;43(11):21892199.

  • 42

    Morton JM, Garg T, Leva N. Association of laparoscopic gastric bypass surgery with telomere length in patients with obesity. JAMA Surg. 2019;154(3):266268.

  • 43

    Safaee MM, Lin J, Smith DL, et al. Association of telomere length with risk of complications in adult spinal deformity surgery: a pilot study of 43 patients. J Neurosurg Spine. 2022;38(3):331339.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44

    Waziry R, Ryan CP, Corcoran DL, et al. Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial. Nat Aging. 2023;3(3):248257.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45

    Verschoor CP, Lin DTS, Kobor MS, et al. Epigenetic age is associated with baseline and 3-year change in frailty in the Canadian Longitudinal Study on Aging. Clin Epigenetics. 2021;13(1):163.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46

    Pang APS, Higgins-Chen AT, Comite F, et al. Longitudinal study of DNA methylation and epigenetic clocks prior to and following test-confirmed COVID-19 and mRNA vaccination. Front Genet. 2022;13 819749.

    • PubMed
    • Search Google Scholar
    • Export Citation

Metrics

All Time Past Year Past 30 Days
Abstract Views 2029 2029 219
Full Text Views 292 292 13
PDF Downloads 373 373 15
EPUB Downloads 0 0 0