Diagnostic Accuracy of Plasma Biomarkers for Mild Traumatic Brain Injury in Older Adults.

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Learn more: PMC Disclaimer | PMC Copyright Notice JAMA Netw Open . 2026 May 29;9(5):e2615678. doi: 10.1001/jamanetworkopen.2026.15678 Diagnostic Accuracy of Plasma Biomarkers for Mild Traumatic Brain Injury in Older Adults Gershon Spitz 1, 2, ✉ , Jake Mitchell 2 , Beatrice Duarte Martins 2 , Abigail Astridge 2 , Charlotte Copas 2 , William T O’Brien 2 , Georgia F Symons 2 , Meng Law 2, 3 , Terence J O’Brien 2, 4 , Matthew P Pase 2, 5 , Jennie L Ponsford 1 , Biswadev Mitra 6, 7 , Stuart J McDonald 2, 4 , Sandy R Shultz 2, 8, ✉ Author information Article notes Copyright and License information 1 Monash-Epworth Rehabilitation Research Centre, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia 2 Department of Neuroscience, School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia 3 Department of Radiology, Alfred Health, Melbourne, Victoria, Australia 4 Department of Neurology, The Alfred Hospital, Melbourne, Victoria, Australia 5 Turner Institute for Brain and Mental Health at the School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia 6 School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia 7 Emergency & Trauma Centre, The Alfred Hospital, Melbourne, Victoria, Australia 8 Institute on Aging & Lifelong Health, Faculty of Health, University of Victoria, Victoria, British Columbia, Canada Accepted for Publication: April 8, 2026. Published: May 29, 2026. doi: 10.1001/jamanetworkopen.2026.15678 Open Access: This is an open access article distributed under the terms of the CC-BY-NC-ND License , which does not permit alteration or commercial use, including those for text and data mining, AI training, and similar technologies. © 2026 Spitz G et al. JAMA Network Open . ✉ Corresponding Authors: Gershon Spitz, PhD, School of Psychological Sciences, Monash University, Wellington Road, Clayton, Melbourne, VIC 3800, Australia ( Gershon.Spitz@monash.edu ); Sandy R. Shultz, PhD, Department of Neuroscience, Monash University, The Alfred Centre, 99 Commercial Rd, Melbourne, VIC 3004, Australia ( sandy.shultz@monash.edu ). Author Contributions: Drs Spitz and Shultz had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Dr Spitz and Mr Mitchell contributed equally to this work and are considered co–first authors. Drs McDonald and Shultz contributed equally to this work and are considered co–senior authors. Concept and design: Spitz, Law, T. J. O’Brien, Pase, Mitra, McDonald, Shultz. Acquisition, analysis, or interpretation of data: Spitz, Mitchell, Martins, Astridge, Copas, W. T. O’Brien, Symons, Law, Pase, Ponsford, McDonald, Shultz. Drafting of the manuscript: Spitz, Mitchell, Law, T. J. O’Brien, Mitra, McDonald, Shultz. Critical review of the manuscript for important intellectual content: Spitz, Martins, Astridge, Copas, W. T. O’Brien, Symons, Law, Pase, Ponsford, McDonald, Shultz. Statistical analysis: Spitz, Shultz. Obtained funding: T. J. O’Brien, Ponsford, McDonald, Shultz. Administrative, technical, or material support: Spitz, Mitchell, Martins, Astridge, Copas, Symons, T. J. O’Brien, Pase, McDonald. Supervision: Spitz, Law, T. J. O’Brien, Mitra, McDonald, Shultz. Conflict of Interest Disclosures: Dr T. J. O’Brien reported receiving grants from the National Health and Medical Research Council (NHMRC) during the conduct of the study; receiving research and consulting contracts from UCB Pharma, Jazz Pharmaceuticals, LivaNova, Kinoxis Pharmaceutical, Eisai Pharmaceuticals, and ES Therapeutics; and receiving grants from the NHMRC, the Medical Research Future Fund, the Department of Defense, the National Institutes of Health (NIH), and the Australian Research Council outside the submitted work. Dr Pase reported receiving grants from the NHMRC during the conduct of the study; receiving personal fees from the Alzheimer’s Drug Discovery Foundation (ADDF); and receiving grants from the ADDF, Brain Foundation, Alzheimer Association, and NIH outside the submitted work. Dr Ponsford reported receiving grants from Abbott Pharmaceuticals outside the submitted work. Dr Mitra reported receiving grants from Abbott Diagnostics outside the submitted work. No other disclosures were reported. Funding/Support: This work was supported in part by support from Michael Smith Health Research BC to Dr Shultz. Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Data Sharing Statement: See Supplement 2 . Additional Information: The authors used Claude Opus 4.6 (Anthropic) from August 5, 2025, to April 20, 2026, to assist with code review for statistical analyses, edit for English language fluency, and format tables. The authors reviewed, edited, and take full responsibility for all final content. No AI tool was used in the study design, data collection, analysis, or interpretation of results. ✉ Corresponding author. Received 2026 Jan 29; Accepted 2026 Apr 8; Collection date 2026 May. Copyright 2026 Spitz G et al. JAMA Network Open . This is an open access article distributed under the terms of the CC-BY-NC-ND License, which does not permit alteration or commercial use, including those for text and data mining, AI training, and similar technologies. PMC Copyright notice PMCID: PMC13221694  PMID: 42213433 This cross-sectional study explores the diagnostic accuracy of 4 plasma biomarkers to identify mild traumatic brain injury and to improve diagnostic certainty in clinically ambiguous cases among adults older than 60 years. Key Points Question What is the diagnostic accuracy of plasma biomarkers for mild traumatic brain injury (mTBI) in older adults, and can these biomarkers improve diagnostic certainty in clinically ambiguous mTBI cases? Findings In this cross-sectional study of 89 participants at least 60 years of age, plasma glial fibrillary acidic protein (GFAP) concentrations demonstrated excellent accuracy for discriminating participants with mTBI from healthy controls within 72 hours of injury. In ambiguous suspected mTBI cases, most individuals had elevated GFAP and brain-derived tau concentrations exceeding their personalized age- and sex-adjusted diagnostic cutoffs. Meaning These findings suggest that plasma GFAP concentration, interpreted with age- and sex-specific thresholds, can serve as an objective tool for diagnosis of mTBI in older adults, particularly for uncertain presentations. Abstract Importance Mild traumatic brain injury (mTBI) is common among older adults but challenging to diagnose due to symptoms that overlap with age-related changes. Blood biomarkers are proposed to complement diagnostic criteria, but older adults are underrepresented in existing studies, leaving performance and thresholds uncertain. Objective To determine the diagnostic accuracy of 4 plasma biomarkers—glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), brain-derived tau (BD-tau), and neurofilament light (NfL)—for identifying mTBI and to explore the utility of these biomarkers for discriminating clinically ambiguous suspected mTBI in older adults. Design, Setting, and Participants This cross-sectional study was conducted between June 27, 2024, and August 5, 2025. Participants were individuals 60 years of age or older who visited a tertiary adult trauma center in Melbourne, Australia, within 72 hours of a suspected head injury or community-dwelling volunteers at least 60 years of age with no recent head injury or bodily trauma (controls). Exposure Clinical diagnosis of mTBI within 72 hours of injury based on 2023 American Congress of Rehabilitation Medicine criteria. Main Outcomes and Measures Glasgow Coma Scale score, ranging from 3 to 15, with higher values indicating milder injury, collected during enrollment. Diagnostic performance of each plasma biomarker quantified by age- and sex-adjusted area under the (receiver operating characteristic) curve (AAUC). Results Participants (n = 89) ranged in age from 60.0 to 84.0 years (mean [SD], 70.8 [6.6] years; 48 [54%] male). Among them, 35 were healthy controls, 45 were diagnosed with mTBI, and 9 had suspected mTBI. The majority of the group diagnosed with mTBI presented with a Glasgow Coma Scale score of 15 (n = 34 [76%]). All 4 biomarkers were significantly elevated in participants with diagnosed mTBI vs controls (B = 129.36 [95% CI, 86.66-172.06]; P < .001 for GFAP; B = 5.61 [95% CI, 3.99-7.24]; P < .001 for BD-tau; B = 4.63 [95% CI, 1.53-7.74]; P = .005 for NfL; and B = 16.52 [95% CI, 5.96-27.07]; P < .001 for UCH-L1). GFAP demonstrated excellent diagnostic accuracy (AAUC = 0.93 [95% CI, 0.86-0.98]), and BD-tau showed good accuracy (AAUC = 0.72 [95% CI, 0.54-0.95]). UCH-L1 and NfL showed fair accuracy (AAUC = 0.68 [95% CI, 0.487-0.93] and AAUC = 0.67 [95% CI, 0.54-0.79], respectively). Optimal diagnostic thresholds for biomarkers were age-dependent (eg for GFAP, 94-108 pg/mL at age 60 years to 194-208 pg/mL at age 84 years) and sex-dependent (eg for UCH-L1, approximately 9.6 pg/mL for females and approximately 2.3 pg/mL for males across the age range). The suspected mTBI group had elevated GFAP (B = −87.04 [95% CI, −151.38 to −22.70]; P = .01) and BD-tau (B = −5.59 [95% CI, −8.83 to −2.36]; P = .001) concentrations vs controls, with 6 participants (67%) to 8 participants (89%) exceeding their personalized diagnostic cutoffs, for GFAP and BD-tau, respectively. Conclusions and Relevance In this cross-sectional study of older adults presenting within 72 hours of injury, the plasma GFAP concentration demonstrated excellent diagnostic accuracy for mTBI. GFAP was elevated in the majority of clinically suspected mTBI cases using age- and sex-adjusted thresholds, supporting its potential to improve diagnostic certainty in mTBI, particularly in diagnostically ambiguous presentations, in older adults. Introduction Mild traumatic brain injury (mTBI) is a substantial and escalating public health problem, particularly among older adults, 1 , 2 , 3 who experience the highest incidence of TBI-related hospitalizations and deaths. 4 Following an mTBI, older adults can face disproportionately worse outcomes, including slower recovery trajectories and higher mortality, compared with their younger counterparts. 1 , 5 , 6 In older adults, the diagnostic process for mTBI in the emergency department is challenging. 7 Diagnostic criteria for mTBI typically involve a history of a plausible mechanism of injury coupled with the presence of clinical signs, symptoms, or abnormalities on clinical examination or neuroimaging. 8 The cardinal signs (eg, loss of consciousness, amnesia, and confusion) and symptoms (eg, headache, dizziness, feeling slowed down, vision problems, and mental fog) of mTBI are often subjective and nonspecific and overlap considerably with age-related changes and a host of neurological conditions common in this age group, such as syncope, delirium, or stroke. 9 This clinical ambiguity is further compounded by preexisting cognitive impairments, polypharmacy, and concurrent medical events, which complicate both diagnosis and management. 10 , 11 Consequently, there is an urgent need for objective measures to complement current diagnostic criteria. Blood-based biomarkers offer a promising solution by quantifying brain-specific proteins released into circulation following brain injury. 12 , 13 , 14 Glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase-L1 (UCH-L1), markers of astroglia and neuronal injury respectively, have already demonstrated clinical utility. When measured within 24 hours of injury, they are approved to help rule out the need for head computed tomography (CT) scans in adults with suspected mTBI. 15 , 16 , 17 , 18 Neurofilament light (NfL) is a promising biomarker for axonal injury following mTBI, particularly when measured subacutely. 19 , 20 Total tau can rise after TBI, although findings are inconsistent in mTBI; new assays for brain-derived tau (BD-tau) aim to improve specificity, but its temporal profile and utility in mTBI are unknown. 21 Under the latest American Congress of Rehabilitation Medicine (ACRM) mTBI diagnostic criteria, a plausible mechanism of mTBI combined with either clinical signs or neuroimaging abnormalities, or a plausible mechanism of mTBI with both acute symptoms and clinical examination or laboratory findings, can meet diagnostic criteria. Elevated biomarker concentrations are included under laboratory findings. Therefore, once validated and operationalized, they may facilitate diagnosis (eg, in suspected mTBI when clinical signs and neuroimaging abnormalities are absent or confounded but a plausible mechanism and symptoms are present). However, blood biomarker research has focused on younger patients with mTBI, and a critical knowledge gap persists regarding the diagnostic accuracy of biomarkers in older adults. 1 , 18 For example, baseline plasma concentrations of key biomarkers in healthy cohorts, most notably GFAP and NfL, are significantly higher and more variable in older individuals. 22 , 23 In addition, studies have largely focused on the day of injury utility. Given that many cases present later—and this may be more common in older adults due to delayed recognition or reporting 24 —an optimal biomarker would retain utility over a larger time frame. This study’s aim is to determine the diagnostic utility of 4 plasma biomarkers (GFAP, UCH-L1, NfL, and BD-tau) in older adults presenting to an emergency department within 72 hours of injury. We compared biomarker performance between diagnosed mTBI cases (meeting ACRM criteria) and healthy controls, established age- and sex-adjusted diagnostic thresholds, and explored whether multiple biomarker combinations improved diagnostic accuracy. As a principal second aim, we applied these thresholds to clinically ambiguous suspected mTBI cases, that is, cases involving individuals with plausible injury mechanisms and symptoms but lacking definitive clinical signs, to assess biomarker potential for improving diagnostic certainty. This objective was designed to directly address the diagnostic gray zone formalized by the 2023 ACRM criteria and to explore the clinical utility of biomarkers among patients whose condition is difficult to classify. Methods Study Design and Participants This was a single-center, cross-sectional study conducted at The Alfred Hospital Emergency Department, a tertiary adult trauma center in Melbourne, Australia. This study was approved by the Alfred Hospital Human Research Ethics Committee. All participants provided written informed consent prior to participation. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline for cross-sectional studies. Prospective recruitment of patients and community controls occurred between June 27, 2024, and August 5, 2025. The study was designed to capture the full spectrum of head injury presentations in older adults. All patients presenting to the emergency department with a suspected head injury were screened for eligibility. Following routine clinical evaluation and initial diagnosis by the treating medical team, enrolled patients were systematically classified according to the 2023 ACRM diagnostic criteria by trained research staff. This prespecified approach stratified patients into either a diagnosed mTBI group, defined by a plausible mechanism and at least 1 objective clinical sign of brain injury (eg, loss of consciousness, posttraumatic amnesia), or a suspected mTBI group, defined by a plausible mechanism and acute symptoms but a lack of definitive, observable clinical signs. A third group of healthy community control participants was also recruited. Controls were community-dwelling volunteers at least 60 years of age with no recent head injury or bodily trauma recruited from the local community via advertisement. Exclusion criteria for all groups included a history of moderate to severe TBI, major preexisting neurological disorders (eg, dementia, stroke, or multiple sclerosis), or major psychiatric disorders (eg, schizophrenia, bipolar disorder). Patients were also excluded if they required neurosurgical intervention, were under the influence of illicit substances, or were non-English speaking. Race and ethnicity data were collected to more fully characterize the cohort. Information was obtained by participant self-report at enrollment. Response categories comprised the following options: Arab (eg, Saudi, Egyptian); Black; East Asian (China, Hong Kong, Japan, Macau, Mongolia, North Korea, South Korea, and Taiwan); Indigenous or First Nations; Latin American; South Asian (Bangladesh, Bhutan, India, Pakistan, Nepal, and Sri Lanka); Southeast Asian (Brunei, Myanmar, Cambodia, Indonesia, Laos, Malaysia, the Philippines, Singapore, Thailand, and Vietnam); West Asian (eg, Afghan, Iranian); White; other (free-text specification); and prefer not to say. Participants were permitted to select a single category, and the 1 respondent classified as other self-described as Albanian. Procedures and Data Collection All assessments were performed within 72 hours of injury for the mTBI groups. During enrollment, demographic information, medical history, and details of the injury event (mechanism and Glasgow Coma Scale score) were collected. Glasgow Coma Scale scores range from 3 to 15, with higher values indicating milder injury. Postconcussive symptom burden was quantified using the Rivermead Post-Concussion Symptoms Questionnaire (RPQ), from which the number of positive symptoms (score ≥2) and the total severity score (range from 0 to 37, with higher values indicating greater number of symptoms) were derived. Venous blood samples were collected from all participants. Samples were processed to obtain plasma, which was stored at −80 °C until analysis. Plasma concentrations of 4 biomarkers (GFAP, BD-tau, NfL, and UCH-L1) were quantified using the Simoa Neurology 4-Plex D (N4PD) Advantage PLUS assay. Statistical Analysis All statistical analyses were performed using R, version 4.2 (R Project for Statistical Computing). A 2-sided P < .05 was considered statistically significant for all tests. Participant demographic and clinical characteristics were summarized for 3 groups: control, diagnosed mTBI, and suspected mTBI. To assess differences in plasma biomarker concentrations and RPQ scores between participant groups, robust linear models were fitted using the lmrob() function from the robustbase package. These models evaluated group status while controlling for age and biological sex as covariates. The diagnostic performance of each biomarker and RPQ score for discriminating between control participants and participants diagnosed with mTBI was evaluated using covariate-adjusted receiver operating characteristic (AROC) curve analysis. The semiparametric model (AROC.sp function) from the ROCnReg package was used, which adjusts the ROC curve for age and biological sex. The primary outcome was the adjusted area under the curve (AAUC), with 95% CIs calculated via bootstrapping. To determine age- and sex-specific diagnostic thresholds, the compute.threshold.AROC() function was applied to the fitted AROC models. The optimal cutoff for each biomarker was determined using the Youden index. To quantify the uncertainty around these thresholds, a robust, nonparametric bootstrap procedure with 1000 replications was implemented, with 95% CIs derived using the percentile method. These personalized thresholds were then applied to individuals in the suspected mTBI cohort. To assess whether combining multiple biomarkers could improve diagnostic accuracy beyond individual markers, we evaluated all pairwise and higher-order biomarker combinations using pooled standardized scores. Statistical comparisons between combination models and the best-performing individual biomarker were conducted using DeLong tests. Sensitivity analyses assessed biomarker performance stratified by timing of blood collection (<24 vs ≥24 hours after injury) and CT scan result (CT negative vs CT positive for mTBI). Results Participant Characteristics A total of 89 participants (mean [SD] age, 71.0 [6.5 years]; age range, 60.0-84.0 years; 41 [46%] female and 48 [54%] male; 82 [92%] White and 7 [8%] all other categories combined [as defined in the Methods]) were included: 35 healthy controls, 45 with diagnosed mTBI, and 9 with suspected mTBI ( Table ). Controls were slightly younger (mean [SD], 67.8 [5.2] years) than individuals in the diagnosed mTBI (mean [SD], 72.2 [6.8] years) and suspected mTBI (mean [SD], 75.6 [5.7] years) groups; age was controlled for in all analyses. In the injury groups, the most common injury mechanism was the head striking a surface, accounting for 42 (93%) of the diagnosed mTBI cases and 8 (89%) of the suspected mTBI cases. The majority of both injury groups presented with an initial Glasgow Coma Scale score of 15 (34 [76%] in the diagnosed mTBI group; 9 [100%] in the suspected mTBI group), with smaller percentages of the diagnosed mTBI group scoring 14 (8 [18%]) or 13 (3 [7%]). The mean (SD) time from injury to blood collection was 26.6 (15.8) hours for the diagnosed mTBI group and 17.1 (8.5) hours for the suspected mTBI group. Extracranial injuries were present in 25 individuals in the diagnosed mTBI group (56%) and 1 individual in the suspected mTBI group (11%). Table. Participant Characteristics. Characteristic Participants, No. (%) Control (n = 35) Diagnosed mTBI (n = 45) Suspected mTBI (n = 9) Age, mean (SD) [range], y 67.8 (5.2) [61.0-80.0] 72.2 (6.8) [60.0-84.0] 75.6 (5.7) [65.0-82.0] Sex Female 17 (49) 18 (40) 6 (67) Male 18 (51) 27 (60) 3 (33) Educational achievement <High school degree 0 7 (16) 2 (22) High school degree or equivalent 2 (11) a 8 (18) 1 (11) Some postsecondary but no degree 0 2 (4) 0 Apprenticeship or trade school 1 (6) a 3 (7) 0 TAFE or college certificate or diploma 2 (11) a 9 (20) 2 (22) Bachelor’s degree 6 (33) a 12 (27) 3 (33) Postgraduate degree 7 (39) a 4 (9) 1 (11) Missing 17 (49) 0 0 Mechanism of injury Acceleration or deceleration NA 3 (7) 1 (11) Head striking surface NA 42 (93) 8 (89) Initial GCS score b 13 NA 3 (7) 0 14 NA 8 (18) 0 15 NA 34 (76) 9 (100) Time to blood collection, mean (SD) [range], h 26.6 (15.8) [0.4-62.0] 17.1 (8.5) [4.8-26.0] History of concussion, yes 21 (60) 19 (42) 7 (78) Extracranial injury, yes 0 25 (56) 1 (11) Open in a new tab Abbreviations: GCS, Glasgow Coma Scale; mTBI, mild traumatic brain injury; NA, not applicable; TAFE, technical and further education. a Percentage derived based on nonmissing data. b GCS scores range from 3 to 15 with higher values indicating milder injury. Symptom Scores and Diagnostic Accuracy To investigate the utility of self-reported symptoms, 2 RPQ measures were analyzed ( Figure 1 ). After controlling for age and biological sex, individuals with diagnosed mTBI reported significantly more positive symptoms than controls (B = 2.48 [95% CI, 1.36-3.61]; P < .001). Similarly, the RPQ total severity score was also significantly higher in the diagnosed mTBI group (B = 4.90 [95% CI, 1.36-8.44]; P = .008). However, diagnostic accuracy was fair for both measures: positive symptoms AAUC = 0.69 (95% CI, 0.54-0.83); total severity score AAUC = 0.63 (95% CI, 0.46-0.77). Figure 1. Box Plots Showing the Association of RPQ Scores With mTBI and Its Diagnostic Performance, and Covariate-AROC Curves Illustrating Diagnostic Performance of Positive Symptom Counts and Total Severity Scores in Discriminating Diagnosed mTBI From Controls. Open in a new tab A and B, Individual scores shown as jittered dots overlaid on box plots. Black circle represents group mean; horizontal bar, median; and error bars, most extreme observations within 1.5 times the IQR. Statistics are from a robust linear model controlling for age and biological sex. C and D, Solid blue line represents AROC curve; dashed lines, 95% CI; and dotted black line, no discrimination. AAUC indicates adjusted area under the curve; AROC, adjusted receiver operating characteristic; FPF, false-positive fraction; mTBI, mild traumatic brain injury; RPQ, Rivermead Post-Concussion Symptoms Questionnaire; and TPF, true-positive fraction. Biomarker Concentrations in Diagnosed mTBI To assess differences in plasma biomarker concentrations between the diagnosed mTBI and control groups while accounting for age and biological sex, a series of robust linear models were performed ( Figure 2 ; eFigure 1 in Supplement 1 shows log-transformed concentrations between groups). After controlling for covariates, the concentrations of all 4 biomarkers were significantly elevated in individuals with mTBI compared with controls. Specifically, significant elevations were observed for GFAP (B = 129.36 [95% CI, 86.66-172.06]; P < .001), BD-tau (B = 5.61 [95% CI, 3.99-7.24]; P < .001), NfL (B = 4.63 [95% CI, 1.53-7.74]; P = .005), and UCH-L1 (B = 16.52 [95% CI, 5.96-27.07]; P < .001). To examine whether these differences were associated with other confounders, we explored the associations of biomarker concentrations with age and sex within the control group, and with CT positivity and extracranial injuries within the diagnosed mTBI group. These secondary analyses did not reveal any associations (eFigure 2 in Supplement 1 ). Figure 2. Box Plots Showing Plasma Biomarker Concentrations in Diagnosed Mild Traumatic Brain Injury (mTBI) and Control Groups. Open in a new tab Individual data points are shown as jittered dots overlaid on box plots. Solid black circles represent the group mean concentration; horizontal bar, median; and error bars, most extreme observations within 1.5 times the IQR. Statistical results from a robust linear model, controlling for age and biological sex, are displayed above each panel. BD-tau indicates brain-derived tau; GFAP, glial fibrillary acidic protein; NfL, neurofilament light; and UCH-L1, ubiquitin C-terminal hydrolase-L1. Diagnostic Accuracy of Individual and Combined Biomarkers To evaluate the diagnostic performance of each plasma biomarker after controlling for age and biological sex, AROC curve analyses were performed ( Figure 3 ). All 4 biomarkers demonstrated fair to excellent accuracy in discriminating individuals with diagnosed mTBI from healthy controls. GFAP was the top-performing marker with an AAUC of 0.93 (95% CI, 0.86-0.98). BD-tau also showed good performance (AAUC = 0.72 [95% CI, 0.54-0.95]). NfL and UCH-L1 demonstrated fair accuracy with an AAUC of 0.67 (95% CI, 0.54-0.79) and 0.68 (95% CI, 0.47-0.93), respectively. Figure 3. Receiver Operating Characteristic Curves Showing Diagnostic Performance of Plasma Biomarkers in Discriminating Diagnosed Mild Traumatic Brain Injury From Control. Open in a new tab Each curve plots the diagnostic sensitivity after adjusting for the effects of age and biological sex using a semiparametric model. Solid blue line represents the adjusted performance of each biomarker; dashed lines, 95% CI; and dotted black line, no discrimination. AAUC indicates adjusted area under the curve; BD-tau, brain-derived tau; FPF, false-positive fraction; GFAP, glial fibrillary acidic protein; NfL, neurofilament light; TPF, true-positive fraction; and UCH-L1, ubiquitin C-terminal hydrolase-L1. To further examine the robustness of these findings, sensitivity analyses were conducted (eFigures 3-5 in Supplement 1 ). The diagnostic accuracy of the biomarkers remained consistent when analyzing participants with blood collected within 24 hours of injury and at 24 hours or later. Specifically, GFAP maintained its excellent performance in both the less than 24 hours group (AAUC = 0.92 [95% CI, 0.79-0.99]) and the 24 hours or more group (AAUC = 0.94 [95% CI, 0.87-1.00]). Furthermore, in an analysis restricted to participants CT negative for mTBI, the accuracy of GFAP was similarly high (AAUC = 0.93 [95% CI, 0.85-0.98]), highlighting its utility in detecting brain injury even without radiographically evident abnormalities. To assess whether combining multiple biomarkers could improve diagnostic accuracy beyond individual markers, we evaluated all pairwise and higher-order biomarker combinations. The best-performing combination was GFAP and BD-tau (AAUC = 0.94 [95% CI, 0.86-0.99]), representing only a modest absolute improvement of 0.01 over GFAP alone. DeLong tests comparing each combination to GFAP alone revealed that none of the improvements reached statistical significance, with confidence intervals substantially overlapping (eFigure 6 in Supplement 1 ). Age- and Sex-Specific Diagnostic Thresholds To translate diagnostic accuracy into clinical application, we determined the optimal diagnostic thresholds for each biomarker as a continuous function of age and biological sex (eFigure 7 in Supplement 1 ). The analysis revealed that these cutoffs were not static, but the nature and magnitude of these dependencies varied considerably across biomarkers. Age was the dominant factor for GFAP and NfL, both showing substantial increases in optimal thresholds with advancing age. For GFAP, thresholds rose from approximately 94 to108 pg/mL at age 60 to 194 to208 pg/mL at age 84, representing a near doubling of the cutoff value. Similarly, NfL thresholds more than doubled, from approximately 10 to11 pg/mL to 24 to 25 pg/mL across the same age span. For both markers, sex differences were minimal. In contrast, BD-tau exhibited a more modest inverse relationship with age, with thresholds decreasing from approximately 6.5 to 8.3 pg/mL at age 60 to 4.4 to 6.3 pg/mL at age 84. The most pronounced sex-specific difference was observed for UCH-L1, where the optimal cutoff for females (approximately 9.6 pg/mL) was approximately 4 times higher than for males (approximately 2.3 pg/mL) across the entire age range, with relatively little age-related change for either sex. Biomarker Concentrations in Suspected mTBI To characterize the biomarker profile of individuals with suspected mTBI, their plasma biomarker concentrations were compared with both healthy control participants and participants with diagnosed mTBI using robust linear models, controlling for age and biological sex (eFigure 8 in Supplement 1 ). Concentrations of GFAP and BD-tau were significantly elevated in the suspected mTBI group compared with controls (GFAP: B = −87.04 [95% CI, −151.38 to −22.70]; P = .01; BD-tau: B = −5.59 [95% CI, −8.83 to −2.36]; P = .001). The difference between the suspected and diagnosed mTBI groups did not reach statistical significance for either marker (GFAP: B = 45.74 [95% CI, −16.56 to 108.04]; P = .15; BD-tau: B = −0.11 [95% CI, −3.19 to 2.98]; P = .95). For NfL and UCH-L1, concentrations in the suspected mTBI group showed an intermediate pattern between controls and diagnosed mTBI, but pairwise comparisons did not reach statistical significance for either marker (NfL: B = 3.29 [95% CI, −2.17 to 8.75]; P = .24; UCH-L1: B = 5.45 [95% CI, −0.90 to 11.80]; P = .10). Personalized Thresholds in Suspected mTBI To investigate whether individuals in the suspected mTBI group exhibited a biological profile consistent with confirmed injury, the age- and sex-specific optimal diagnostic cutoffs for all 4 biomarkers were applied to each of the 9 individuals in this cohort ( Figure 4 ). The analysis revealed that a majority of these individuals had biomarker concentrations indicative of injury. Of 9 individuals, 6 (67%) exceeded the calculated cutoff for GFAP, 8 (89%) exceeded their personalized threshold for BD-tau, 4 (44%) exceeded their cutoff for NfL, and 6 (67%) exceeded their threshold for UCH-L1. Notably, for the 2 biomarkers with the strongest individual performance, that is, GFAP and BD-tau, the consistent elevation above personalized thresholds in the majority of suspected cases provided objective evidence supporting the presence of brain injury in individuals who lacked definitive clinical signs at presentation. Figure 4. Line and Point Estimate Comparisons of Individuals With Suspected Mild Traumatic Brain Injury (mTBI) Cases to Personalized Diagnostic Thresholds. Open in a new tab Biomarker concentrations for each individual in the suspected mTBI group (n = 9) relative to their personalized age- and sex-specific diagnostic cutoff for glial fibrillary acidic protein (GFAP), brain-derived tau (BD-tau), neurofilament light (NfL), and ubiquitin C-terminal hydrolase-L1 (UCH-L1). Solid lines represent optimal diagnostic thresholds as a continuous function of age for females and males, derived from adjusted receiver operating characteristic curve analyses comparing controls and participants diagnosed with mTBI using the Youden index criterion. For each individual, horizontal black line indicates their personalized diagnostic threshold based on their age and sex. The vertical dashed line connects this threshold to the individual’s actual measured biomarker concentration, shown as a circle. Circles indicate whether an individual’s concentration exceeds their personalized threshold (brown) or falls below it (blue). Discussion This cross-sectional study was conducted to assess the diagnostic accuracy in identifying mTBI and clinical utility in discriminating suspected mTBI for 4 plasma biomarkers, GFAP, UCH-L1, BD-tau, and NfL, in older adults. Plasma GFAP demonstrated excellent diagnostic accuracy for discriminating participants with mTBI from healthy controls, even after adjusting for age and sex. This finding is important given older adults’ underrepresentation in biomarker validation studies. While baseline GFAP and NfL concentrations increase with aging, 22 , 23 the magnitude of the GFAP elevation following mTBI was sufficient to maintain excellent discriminatory power within 72 hours after injury. Sensitivity analyses showed that GFAP maintained robust performance across sampling time windows and in cases with negative findings for mTBI on CT scans, demonstrating reliability and utility when imaging failed to detect injury. The superior performance of GFAP aligns with known temporal dynamics. 17 , 20 GFAP is released rapidly from injured astrocytes, peaking within 24 hours, making it ideal for early assessment. 15 Conversely, NfL has a delayed release profile, peaking days to weeks after injury. 18 The fair accuracy of NfL in diagnostic accuracy for discriminating participants with mTBI likely reflects the acute sampling window rather than overall utility. The good performance of BD-tau and fair accuracy of UCH-L1 are consistent with the literature suggesting that GFAP is more sensitive than neuronal injury markers for detecting acute mTBI. 15 , 16 , 18 Our analysis underscores 2 critical points. First, optimal thresholds varied with age and sex in biomarker-specific patterns, requiring personalized interpretation. Second, applying personalized thresholds provided objective evidence that most individuals in the diagnostically ambiguous suspected mTBI group had biomarker profiles consistent with injury, illustrating the potential to improve diagnostic certainty. A key strength of this diagnostic strategy is comparing biomarker performance to self-reported symptoms. Although individuals in the diagnosed mTBI group reported significantly more symptoms than controls, diagnostic accuracy was modest. This finding underscores the limitations of relying solely on subjective reporting in cases in which baseline symptoms are common. The superior performance of GFPA highlights the value of objective biological measures. Age and sex dependencies varied considerably across biomarkers. Age dominated for GFAP and NfL, with thresholds nearly doubling (for GFAP) or more than doubling (for NfL) from age 60 to 84 years. BD-tau showed a modest inverse age association. UCH-L1 showed minimal age differences but pronounced sex differences. Notably, the thresholds for GFAP and NfL were substantially higher than those derived from younger populations. The US Food and Drug Administration–cleared GFAP cutoff of 22 pg/mL, developed to rule out positive findings of mTBI on CT scans in predominantly younger cohorts, is approximately 4 to 10 times lower than the optimal thresholds in our older adult sample (94-208 pg/mL). 16 Similarly, established NfL reference limits (approximately 10 pg/mL for adults aged 18-50 years) fall at the lower end of the thresholds calculated here (10-25 pg/mL). 25 , 26 However, direct comparison of absolute cutoff values across studies should be made cautiously, as differences in assay platform, sample matrix, and study design may influence reported thresholds. The clinical implications of our findings are important. Applying fixed thresholds for age-sensitive biomarkers would cause systematic misclassification. A cutoff appropriate for an individual 60 years of age would be too low for an individual 80 years of age (increasing false positives), while a cutoff from older cohorts would be too high for younger individuals (increasing false negatives). For UCH-L1, failing to account for sex would cause substantial error. Age- and sex-adjusted thresholds appeared to be essential, although required adjustments differed by biomarker. This study operationalizes the 2023 ACRM criteria that formally distinguish diagnosed from suspected mTBI, 8 acknowledging cases in which patients present with symptoms but lack objective signs. These cases represent a diagnostic gray zone, particularly for unwitnessed falls in older adults. The suspected mTBI group exhibited elevated GFAP and BD-tau compared with health controls in the present study. Importantly, when personalized thresholds were applied, clear majorities of participants exceeded their diagnostic cutoffs, providing compelling objective evidence of physiological brain injury when clinical signs alone were insufficient. This finding illustrates how biomarkers can convert suspected diagnoses into more certain ones, potentially altering clinical management and ensuring appropriate follow-up. Limitations This study has limitations. The suspected mTBI group was small, and while this limited statistical power, our findings provide valuable preliminary evidence for this novel biomarker application in diagnostically ambiguous cases. The cross-sectional design prevented assessment of prognostic value. While secondary analyses found no associations with confounders, other comorbidities common among older adults could influence baseline levels. Statistical uncertainty around threshold estimates, particularly for BD-tau and UCH-L1, indicated that these models require validation in larger datasets. This single-center study requires replication in multicenter cohorts to validate threshold models and establish clinical utility. In addition, the sample was predominantly White, limiting generalizability to other racial and ethnic groups. The use of healthy community controls rather than symptomatic emergency department patients without mTBI may overestimate diagnostic accuracy due to spectrum bias, and future studies should validate these thresholds against clinically relevant comparator groups. Future longitudinal studies should examine whether acute biomarker concentrations in older adults predict recovery trajectories, functional outcomes, and postconcussion symptom burden, which would further clarify the prognostic utility of these markers beyond initial diagnosis. Conclusions This cross-sectional study found that plasma biomarkers, particularly GFAP, retained high diagnostic accuracy for mTBI in older adults within 72 hours of injury, despite age-related challenges. Age- and sex-specific diagnostic thresholds were critical for accurate classification, although required adjustments differed by biomarker. Personalized biomarker thresholds may provide objective evidence of injury in clinically ambiguous suspected mTBI cases. These results support integrating plasma biomarkers into the diagnostic pathway for head injury in older adults to improve diagnostic certainty and guide clinical management in this population. Supplement 1. eFigure 1. Log-transformed plasma biomarker concentrations in control vs diagnosed mTBI groups eFigure 2. Association of covariates with plasma biomarker concentrations in controls and diagnosed mTBI eFigure 3. Covariate-adjusted ROC curves for discriminating mTBI <24hr after injury from healthy controls eFigure 4. Covariate-adjusted ROC curves for discriminating mTBI (≥24 hours post-injury) from healthy controls eFigure 5. Covariate-adjusted ROC curves for discriminating CT-negative mTBI from healthy controls eFigure 6. Multi-biomarker combination analysis for mTBI detection in older adults eFigure 7. Age- and sex-adjusted personalised diagnostic thresholds for plasma biomarkers in older adults eFigure 8. Plasma biomarker concentrations across control, suspected mTBI, and diagnosed mTBI groups jamanetwopen-e2615678-s001.pdf (1.6MB, pdf) Supplement 2. Data Sharing Statement jamanetwopen-e2615678-s002.pdf (14.7KB, pdf) References 1. Gardner RC, Dams-O’Connor K, Morrissey MR, Manley GT. Geriatric traumatic brain injury: epidemiology, outcomes, knowledge gaps, and future directions. J Neurotrauma. 2018;35(7):889-906. doi: 10.1089/neu.2017.5371 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Papa L, Mendes ME, Braga CF. Mild traumatic brain injury among the geriatric population. 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Establishment of reference values for plasma neurofilament light based on healthy individuals aged 5-90 years. Brain Commun. 2022;4(4):fcac174. doi: 10.1093/braincomms/fcac174 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplement 1. eFigure 1. Log-transformed plasma biomarker concentrations in control vs diagnosed mTBI groups eFigure 2. Association of covariates with plasma biomarker concentrations in controls and diagnosed mTBI eFigure 3. Covariate-adjusted ROC curves for discriminating mTBI <24hr after injury from healthy controls eFigure 4. Covariate-adjusted ROC curves for discriminating mTBI (≥24 hours post-injury) from healthy controls eFigure 5. Covariate-adjusted ROC curves for discriminating CT-negative mTBI from healthy controls eFigure 6. Multi-biomarker combination analysis for mTBI detection in older adults eFigure 7. Age- and sex-adjusted personalised diagnostic thresholds for plasma biomarkers in older adults eFigure 8. Plasma biomarker concentrations across control, suspected mTBI, and diagnosed mTBI groups jamanetwopen-e2615678-s001.pdf (1.6MB, pdf) Supplement 2. Data Sharing Statement jamanetwopen-e2615678-s002.pdf (14.7KB, pdf) Articles from JAMA Network Open are provided here courtesy of American Medical Association ACTIONS View on publisher site Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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