Is Your Biological Age a Better Surgical Risk Predictor Than Your Birthday? New Research From the ISSG Suggests It Might Be
An exploratory multicenter study finds that a DNA methylation-based measure of biological aging consistently outperforms chronological age in predicting perioperative complications in adult spinal deformity surgery — and calls for further investigation of this promising biomarker.
OPENING SUMMARY
Two patients may be the same age on their birth certificates while aging at fundamentally different rates biologically. One may have the cellular profile of someone a decade younger; the other, a decade older. In complex spine surgery — where the stakes of complications are high and the ability to predict them remains limited — knowing which type of patient you are operating on could meaningfully change how care is planned and delivered. This study takes a rigorous first look at whether a DNA-based measure of biological aging, called DNAm PhenoAge, can predict perioperative complications in adult spinal deformity patients better than chronological age. The results are exploratory and statistically preliminary — but consistently point in the same direction, and the implications for the field are significant enough to warrant serious attention.
STUDY SNAPSHOT
Study type – Multicenter ASD registry study; exploratory analysis
Number of patients – 200 (those with available laboratory data)
Biomarker studied – DNAm PhenoAge (calculated per Levine et al.)
Comparator – Chronological age
Primary analysis – Multivariable logistic regression; model discrimination (AUC), fit (AIC), and classification (NRI)
Mean DNAm PhenoAge – 53.7 ± 18.1 years
Mean chronological age – 61.1 ± 15.4 years
AUC range — DNAm PhenoAge models – 0.701–0.823
AUC range — chronological age models – 0.671–0.767
ΔAIC (model fit improvement) – 4.41–7.08 in favor of DNAm PhenoAge
NRI range – 0.198–0.500
Level of evidence – 3
Published in – GeroScience, online ahead of print, August 21, 2026
PMID – 42625100
DOI – 10.1007/s11357-026-02481-8
Lead author – Michael P. Kelly
WHY THIS MATTERS
Adult spinal deformity surgery is among the most complex and physiologically demanding elective procedures in orthopedic and neurological surgery. Patients are increasingly older, frequently carry multiple comorbidities, and face meaningful rates of perioperative complications — including cardiac events, pulmonary complications, wound infections, neurological deficits, and the need for reoperation. Accurately predicting which patients are at highest risk for these events is essential for surgical planning, informed consent, perioperative management, and patient selection.
Current risk stratification tools — including the Charlson Comorbidity Index, the American Society of Anesthesiologists classification, and various frailty indices — rely heavily on chronological age as a proxy for physiological vulnerability. But chronological age is an imperfect measure. Two 65-year-olds can have profoundly different physiological reserves, cellular aging profiles, and capacities to withstand and recover from major surgical stress. A biomarker that captures biological aging more accurately than calendar years could meaningfully improve the field's ability to predict who will and will not tolerate complex spine surgery.
DNAm PhenoAge is one of the most rigorously developed and validated epigenetic aging biomarkers available. In the broader geroscience literature, it has been shown to predict morbidity, mortality, and physical function more accurately than chronological age. This study asks whether that predictive advantage extends to the specific, high-stakes context of ASD surgery — a question that has not previously been examined.
BACKGROUND: WHAT IS DNAm PHENOAGE?
DNA methylation is a chemical modification to DNA in which methyl groups are added to specific sites on the genome. These modifications do not change the underlying DNA sequence, but they regulate gene expression — influencing which genes are turned on or off. DNA methylation patterns change systematically with age, and these changes can be measured from blood samples using standard laboratory techniques.
DNAm PhenoAge, developed by Morgan Levine and colleagues, is a composite measure calculated from DNA methylation patterns at specific genomic sites, calibrated against a panel of clinical biomarkers associated with aging-related disease and mortality. It produces an estimate of biological age that can diverge meaningfully from a person's actual age — reflecting factors including lifestyle, disease burden, stress exposure, and genetic variation in aging rate.
In the geroscience literature, DNAm PhenoAge has demonstrated stronger associations with mortality, chronic disease, physical function, and other aging-related outcomes than chronological age in multiple large population studies. This study is among the first to test its utility specifically in a surgical patient population.
KEY FINDINGS
1. Patients were biologically younger than their chronological age suggested. The mean DNAm PhenoAge of the 200 patients was 53.7 years — significantly lower than their mean chronological age of 61.1 years (p <0.001; 95% CI for the difference, 6.3–8.5 years). This is a meaningful finding in its own right: on average, the biological aging profile of these patients was substantially younger than their calendar years. This divergence between biological and chronological age is precisely what makes DNAm PhenoAge potentially informative — and it also raises questions about whether standard age-based risk tools are appropriately calibrated for ASD surgical populations.
2. DNAm PhenoAge models demonstrated consistently higher discrimination for all outcomes examined. Across all perioperative adverse events analyzed, models incorporating DNAm PhenoAge showed higher area under the receiver operating characteristic curve (AUC) than models using chronological age — with AUC values ranging from 0.701 to 0.823 for DNAm PhenoAge versus 0.671 to 0.767 for chronological age. The AUC is a measure of how well a model distinguishes between patients who will and will not experience a complication. Higher values consistently favored the biological age model.
3. Model fit consistently favored DNAm PhenoAge. The Akaike Information Criterion (AIC) — a measure of statistical model quality that penalizes unnecessary complexity — favored DNAm PhenoAge models across all outcomes, with improvements (ΔAIC) ranging from 4.41 to 7.08. In statistical modeling, an AIC difference of this magnitude is considered meaningful evidence that one model better fits the data than another.
4. Net reclassification improvement was meaningful across outcomes. The net reclassification improvement (NRI) — a measure of how many patients are more correctly classified as high or low risk when using the new model versus the old one — ranged from 0.198 to 0.500 across outcomes. These values indicate that substituting DNAm PhenoAge for chronological age resulted in meaningful improvements in how patients were categorized by risk level, with the degree of improvement varying by specific outcome.
5. DNAm PhenoAge showed odds ratios comparable to or greater than chronological age for most adverse events. When the authors compared the adjusted odds ratios for a 55-year-old versus a 75-year-old using each age metric, DNAm PhenoAge produced odds ratios that were comparable to or larger than those from chronological age for most adverse events. This suggests that biological age captures meaningful risk signal that is at least as strong as — and in some cases stronger than — what calendar age captures.
6. Differences did not reach statistical significance — but the pattern was consistent. No DeLong test comparisons between AUC values reached statistical significance (all p >0.05). The authors appropriately characterize this as an exploratory study and acknowledge that the sample size of 200 patients — limited by the availability of laboratory data for DNAm PhenoAge calculation — may have been insufficient to detect statistically significant differences even where real differences exist. Critically, the direction of improvement was consistent across all outcomes and across multiple complementary statistical measures — discrimination, model fit, and reclassification — which the authors describe as the basis for recommending further study.
PRACTICAL IMPLICATIONS
DNAm PhenoAge is not yet ready for routine clinical use in ASD surgery — but the evidence supports investing in its further development. The exploratory nature of this study means that DNAm PhenoAge cannot yet be recommended as a standard preoperative assessment tool. What this study establishes is a consistent, multi-measure signal that warrants validation in larger prospective cohorts with sufficient statistical power to detect whether the observed performance advantages reach significance. The authors' recommendation that DNAm PhenoAge "warrants further study as a candidate biomarker for perioperative risk stratification" is well-supported by the data presented.
The divergence between biological and chronological age has immediate implications for how we counsel patients. The finding that patients' mean biological age was approximately seven years younger than their chronological age is not merely a statistical curiosity — it suggests that age-based risk tools calibrated to chronological age may be systematically miscalibrated for this population. If ASD patients tend to be biologically younger than their calendar years, then risk models that use chronological age as a primary input may overestimate risk for many patients. This deserves attention independent of whether DNAm PhenoAge ultimately proves to be the superior metric.
Epigenetic biomarkers represent a broader opportunity for personalized risk stratification in spine surgery. DNAm PhenoAge is one of several epigenetic aging clocks that have been developed in the geroscience literature. The broader category of epigenetic biomarkers offers a fundamentally different approach to risk stratification — one that captures biological information that is simply not available from standard demographic and clinical variables. As these tools become more accessible and their clinical utility is validated, they may complement or eventually partially replace current risk stratification approaches in ASD and other complex surgical contexts.
The study opens a practical question: how would DNAm PhenoAge be obtained in clinical practice? DNAm PhenoAge is calculated from blood samples using laboratory assays that measure DNA methylation patterns. While not currently a standard clinical test, the assay is increasingly accessible in research and clinical research settings. If further validation confirms its utility in ASD surgery, the practical pathway to clinical implementation — including cost, turnaround time, and sample handling requirements — would need to be characterized. This is a question for future research and development rather than an obstacle to the current findings.
ABOUT THE RESEARCH: GEROSCIENCE AND SPINE SURGERY
This study is published in GeroScience — a journal dedicated to the biology of aging and its relationship to age-related disease. Its publication in this venue reflects an important and growing intersection between geroscience and surgical specialties: the recognition that understanding the biology of aging at a cellular and molecular level may have direct practical implications for how we select, prepare, and care for surgical patients. The ISSG's engagement with this research frontier positions the field of adult spinal deformity care at the leading edge of this emerging translational science.
CONCLUSION
This study does not establish DNAm PhenoAge as a ready-to-use clinical tool for ASD surgical risk stratification — and its authors are appropriately clear about that. What it does establish is a consistent and multi-dimensional signal that biological age, as measured through DNA methylation patterns, outperforms chronological age as a predictor of perioperative adverse events in this population, across every measure of model performance examined. The finding that patients are biologically younger than their calendar years adds an important additional dimension to the story. Together, these findings make a credible scientific case for larger, prospective validation studies — and suggest that the future of risk stratification in ASD surgery may ultimately be found not in a patient's birthdate, but in the molecular record of how their cells have aged.
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