Trajectories of blood-based protein biomarkers in chronic traumatic brain injury

Trajectories of blood-based protein biomarkers in chronic traumatic brain injury | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Trajectories of blood-based protein biomarkers in chronic traumatic brain injury Amelia J. Hicks , Jay Plourde , Enna Selmanovic , Nicola L. de Souza , Kaj Blennow , Henrik Zetterberg , Kristen Dams-O’Connor doi: https://doi.org/10.1101/2025.02.16.25322303 Amelia J. Hicks 1 Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: amelia.hicks{at}mountsinai.org Jay Plourde 1 Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Enna Selmanovic 1 Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nicola L. de Souza 1 Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai , New York, NY, USA 2 Department of Neurology, University of Utah School of Medicine , Salt Lake City, UT, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kaj Blennow 3 Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg , Mölndal, Sweden 4 Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital , Mölndal, Sweden 5 Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University , Paris, France 6 Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, and Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC , Hefei, P.R. China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Henrik Zetterberg 3 Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg , Mölndal, Sweden 4 Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital , Mölndal, Sweden 7 Department of Neurodegenerative Disease, UCL Institute of Neurology , Queen Square, London, UK 8 UK Dementia Research Institute at UCL , London, UK 9 Hong Kong Center for Neurodegenerative Diseases, InnoHK , Hong Kong, China 10 Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison , Madison, WI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kristen Dams-O’Connor 1 Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai , New York, NY, USA 11 Department of Neurology, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Blood-based protein biomarkers may provide important insights into the long-term neuropathology of traumatic brain injury (TBI). This is urgently required to identify mechanistic processes underlying post-traumatic neurodegeneration (PTND); a progressive post-recovery clinical decline experienced by a portion of TBI survivors. The aim of this study was to examine change over time in protein levels in a chronic TBI cohort. We selected six markers (Aβ 42 /Aβ 40 , GFAP, NfL, BD-tau, p-tau231, and p-tau181) with known importance in acute TBI and/or other neurodegenerative conditions. We used a longitudinal design with two time points approximately 3.5 years apart on average (SD 1.34). Proteins were measured in plasma using the ultrasensitive Single molecule array technology for 63 participants with mild to severe chronic TBI (sustained ≥ 1 year ago; M 28 years; SD 16.3 since their first blow to the head) from the Late Effects of TBI study (48% female; current age M 52 years; SD 13.4). Multivariate linear mixed effect models with adjustments for multiple comparisons were performed to examine trajectories in proteins over time with age and age squared as covariates. A series of sensitivity analyses were conducted to account for outliers and to explore effects of key covariates: sex, APOE ε4 carrier status, medical comorbidities, age at first blow to the head, time since first blow to the head, and injury severity. Over an average of 3.5 years, there were significant reductions in plasma Aβ 42 /Aβ 40 (β = −0.004, SE = 0.001, t = −3.75, q = .001) and significant increases in plasma GFAP (β = 12.96, SE = 4.41, t = 2.94, q = .01). There were no significant changes in NFL, BD-tau, p-tau231, or p-tau181. Both plasma Aβ 42 /Aβ 40 and GFAP have been associated with brain amyloidosis, suggesting a role for Aβ mis-metabolism and aggregation in the long-term neuropathological consequences of TBI. These findings are hypothesis generating for future studies exploring the diverse biological mechanisms of PTND. Traumatic brain injury (TBI) is now recognized as a chronic and dynamic health condition. 1 , 2 Following a period of recovery, many individuals 3 experience ongoing but stable functional disability, 4 – 7 cognitive impairment, 5 , 8 – 13 poor emotional health, 3 , 5 , 7 , 10 , 14 – 17 and neurobehavioral dysregulation 3 , 18 with lifelong implications on quality of life. Approximately one third of individuals, however, experience post-recovery decline that is not accounted for by age. 19 – 21 The term ‘post-traumatic neurodegeneration’ (PTND) has been employed to distinguish chronic but stable post-TBI impairments from the progressive decline experienced by some. 22 – 27 The notion that a subset of TBI survivors may experience a decline in clinical function years after injury has been recognized for decades through work highlighting the elevated rates of dementia following TBI. 28 – 30 Efforts to classify post-TBI dementia within existing neurodegenerative diseases, mainly Alzheimer’s disease (AD), 31 – 58 have yielded mixed and inconclusive results (Parkinson’s disease, 32 , 34 , 59 – 61 Frontotemporal dementia, 32 , 62 Amyotrophic Lateral Scleorsis, 63 , 64 and Vascular Dementia). 65 PTND is now considered a multiple etiology dementia (MED) in the AD and Alzheimer’s disease-related dementia (AD/ADRD) framework, 66 that appears to have distinct clinical presentations. 40 , 65 , 67 – 69 The pathologies driving PTND remain unknown, 25 , 26 and the long-term neuropathological consequences of TBI more broadly have been understudied. Knowledge advancement has been impeded by a dearth of post-mortem tissue from long-term survivors of TBI, which was historically been available for individuals who had died acutely after injury. 70 – 82 In recent years, there has been significant investment in blood biomarkers research, 83 , 84 which unlike PET imaging with radiotracers and cerebrospinal fluid analyses requiring lumbar puncture, presents a cost-effective and minimally invasive method to capture pathology during life. To date, TBI blood biomarkers have almost exclusively focused on acute TBI diagnosis and prognostication, 85 – 87 with several promising markers identified: S100 calcium binding protein B (S100B), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), glial fibrillary acid protein (GFAP), and neurofilament light (NfL). 86 , 88 A recent systematic review found only 30 studies investigating blood biomarkers in the chronic stages (≥1 year post-injury). 89 The most robust evidence from studies with TBI sample sizes ranging from n=22-298, identified elevations in select proteins compared with controls, even in those with mild TBI. 90 , 91 This includes inflammatory markers (12 months: eotaxin-1, IFN-y, IL-8, IL-9, IL-17A, MCP-1, MIP-1 β, FGF-basic, and TNF-α 91 ; 9 years: IL-6 90 ), GFAP (12 months 92 – 94 and M 7 years 95 ), UCHL-1 (M 22 years 58 ), p-tau181 (M 22 years 58 ), and neurodegenerative-disease associated protein variants of amyloid-β (Aβ), TAR DNA-binding protein 43 (TDP-43), α-synuclein, and tau (M 29 years 57 ). Notably, most TBI blood biomarker studies have been cross-sectional 96 precluding characterization of trajectories over time. This is most notable for chronic TBI cohorts, for which there is limited examination of trajectories beyond 12 months. Within the first 12 months post-injury, most evidence suggests trajectories of decrease over time for brain derived tau (BD-tau; 0 days – 12months; 97 7 days – 12 months 97 ), p-tau231 (0 days – 12months; 97 7 days – 12 months 97 ), NfL (7 days – 12 months; 97 3 – 12 months; 98 ), and total tau (0 days – 12months; 97 7 days – 12 months; 97 3 – 12 months 98 ). Important questions remain about whether changes in these blood biomarkers can be detected years after injury and their potential role in long-term clinical dysfunction. Evidence of ongoing changes in protein levels in the chronic TBI period would be extremely informative for elucidating pathological processes contributing to PTND. To draw accurate conclusions about biomarker trajectories in chronic TBI, it is essential that we account for known associations with key demographic variables. Failure to consider contributions from these variables may lead to incorrect interpretations about the role of TBI in protein change over time. Increasing age has been consistently associated with several commonly examined blood-based protein biomarkers (e.g. Aβ42, 99 Aβ40, 99 Aβ42/40, 99 NfL, 99 , 100 T-tau, 99 , 101 p-tau, 102 inflammatory markers 103 ). Sex may also impact levels of some proteins, with supporting evidence in non-TBI 104 and TBI cohorts 91 , 98 , 105 – 108 . Likewise, APOE ε4 genotype has been robustly associated with levels of several proteins measured in blood, Aβ, 109 GFAP, 110 NfL, 111 and the progression of pathological burden over time. 112 Medical health conditions may also impact some protein levels. This is particularly important in TBI cohorts, in which comorbidity of multiple medical conditions is common. 113 – 115 Several conditions that are commonly comorbid with TBI 116 , 117 have been independently associated with increased levels of select blood-based proteins including diabetes, 118 cardiovascular conditions, 118 and cerebrovascular conditions. 119 The aim of the current study was to examine change over time in protein levels in a longitudinal cohort of individuals living with TBI. We selected six markers with known importance in acute TBI and/or other neurodegenerative conditions to test the hypotheses that: (1) there will be a reduction in plasma Aβ 42 /Aβ 40 levels over time, and (2) an increase in plasma levels of GFAP, NfL, BD-tau, p-tau231, and p-tau181. Methods Participants Participants were enrolled in the Late Effects of Traumatic Brain Injury Study (LETBI). 120 The LETBI Study is an ongoing multi-center prospective longitudinal brain donor program. The aims of the LETBI study are to identify the clinical characteristics, in-vivo biomarkers, and postmortem neuropathology of PTND. Participants complete longitudinal study visits for clinical assessment, biosample collection, and neuroimaging, and make known their wishes for brain donation. Study visits are scheduled approximately every 2-3 years. LETBI participants are recruited from TBI research registries at the Icahn School of Medicine at Mount Sinai (ISMMS), The University of Washington, Massachusetts General Hospital, Indiana University, Kessler Institute for Rehabilitation, and New York University, as well as from community-based settings. This study reports on data collected from the ISMMS site only. Eligible participants were: (1) aged 18 years or older, (2) has sustained at least two mild TBIs, or at least one complicated mild, moderate, or severe TBI (defined as a blow to the head resulting in a period of altered mental status or unconsciousness) 121 (3) at least 1 year post first head trauma exposure, and (4) English speaking. Participants are not removed from the LETBI study if they sustain a subsequent head trauma during study participation, as recurrent head trauma exposure is common 122 – 124 and has important implications for long-term outcomes. 125 , 126 All participants (or their proxies) provided written informed consent before any study procedures as per protocols approved by the ISMMS program for the protection of human subjects. Head trauma exposure ascertainment The Brain Injury Screening Questionnaire (BISQ) 127 , 128 was used to characterize lifetime history of TBI and RHI exposure. The BISQ is a semi-structured questionnaire that queries blows to the head by providing 20 contextual recall cues (e.g., in a vehicular accident, falling from a height). For each reported exposure, subsequent items record the presence and duration of unconsciousness and/or altered mental status, and date of injury. Injury severity classification is made for each exposure per standard criteria. 121 Head trauma exposure history was verified through review of medical and neuroimaging records whenever available. Procedures Participants attended study visits at ISMMS. Each study visit included a clinical assessment, comprehensive neuropsychological evaluation, blood draw, and MRI scan. This study reports on longitudinal data from the subset of individuals from ISMMS who completed two study visits with blood draw. Demographic, health, and BISQ data were provided by participant self-report at LETBI visit 1. APOE genotype was determined from blood samples provided at LETBI visit 1. Blood-based protein data are examined at both visits. All visits for these analyses were conducted between 5/21/2014 to 12/10/2021. Blood analysis Blood samples are collected in 7mL and 10mL EDTA tubes. Within 30 minutes of blood draw, samples are centrifuged at 1500g for 15 minutes. 1.5mL of plasma is then aliquoted into each cryovial. Aliquoted plasma samples are placed upright in −80°C freezers within 2 hours of collection and stored there until use. Plasma analysis was conducted at the Sahlgrenska Academy at the University of Gothenburg using ultrasensitive Single molecule array (Simoa) technology on the HD-X platform (Quanterix, Billerica, MA, USA). All samples were analyzed using the same batch of reagents by certified laboratory technicians who were blinded to clinical information. The proteins analyzed were: Aβ 42 , Aβ 40, GFAP, NfL, and p-tau181 (commercial kits from Quanterix), and BD-tau and p-tau231 using in-house assays as previously described. 129 , 130 For some participants, data was not available for one or more proteins(s) as their samples had insufficient volume or were below the limit of quantification (i.e., the lowest concentration of a biomarker that can be reliably measured with acceptable accuracy and precision in each assay). Statistical Analysis Multivariate linear mixed effect models with adjustments for multiple comparisons were performed using the lmerTest Package 131 in Rstudio 132 to identify the change in level for 6 unique proteins: Aβ 42 /Aβ 40, GFAP, NFL, BD-tau, p-tau231, and p-tau181 between two visits in the same participant. In each model, visit was a fixed effect, participant was a random effect, age and age squared were covariates, and the level of protein in blood was the outcome. Protein data met criteria for Missing Completely at Random (due to insufficient volume/below the limit of quantification), so pairwise deletion was used when data were missing. To correct for multiple comparisons, p -values were corrected using false discovery rate, producing q -values that were considered significant when q < .05. Spaghetti plots were generated for each protein to visualize heterogeneity and outliers in the data. In addition to observing the spaghetti plots, leverage and sensitivity analyses were completed to explore the extent to which outliers had an undue effect on results. The first sensitivity analysis excluded only the most extreme of outliers to preserve robustness and allow for expected heterogeneity in the data; i.e., outliers defined as 400% of interquartile range beyond the first and third quartile. A more conservative sensitivity analysis was also conducted that removed all outliers defined as 150% of interquartile range beyond the first and third quartile. An additional sensitivity analysis was conducted that removed all participants who had TBIs within a year before their second visit to remove potential bias from pathological response associated with acute injury. A final series of sensitivity analyses with additional covariates were also run to determine whether the relationship over time was still significant over and above the covariate. In addition to using each of these models as sensitivity analyses, chi-square tests with false discovery rate multiple comparison adjustments were used to compare each model against the model with only age and age squared covariates to determine if the model could be improved by addition of the covariate. Demographic covariates were selected based on their known associations with protein levels in TBI and/or non-TBI cohorts (see above): sex, APOE 4 carrier status, and comorbidities (defined as sum of diabetes, hypertension, heart failure, and stroke; coded as 0 or 1+). Based on emerging evidence about the importance of injury-related factors for blood-based protein levels 133 we also included the following covariates: age at first blow to the head, years since first blow to the head, and injury severity. Results A total of 63 participants provided blood samples for analysis ( Table 1 ). Participants were 48% female (Age M 52 years; SD 13.41) and were on average 28.04 years (SD 16.3) since their first blow to the head. The most severe injury was classified as severe for 62% of the sample. Between visits there was on average of 3.52 years (SD 1.34; Min 2.04; Max 7.48). View this table: View inline View popup Download powerpoint Table 1 Demographic characteristics of the sample (n=63) Change over time in proteins After adjustment for age and age squared and correction for multiple comparisons, two markers exhibited significant change between visits: Aβ 42 /Aβ 40 and GFAP ( Table 2 , Figure 1 ). Aβ 42 /Aβ 40 ratio tended to decrease by .00383 pg/mL between visits (β = −-0.004, SE = 0.001, t = −3.75, q = .001). GFAP tended to increase by 12.962 pg/mL between visits (β = 12.96, SE = 4.41, t = 2.94, q = .01). There were no significant changes for NfL, BD-tau, p-tau231, or p-tau181. View this table: View inline View popup Download powerpoint Table 2 General linear mixed-effect models for proteins across 2 visits Download figure Open in new tab Figure 1. Estimated marginal means, confidence intervals, and change in proteins between visits. Aβ 42 /Aβ 40 , q = .001; GFAP q = .01; NfL q = .29; p-tau231 q = .78; p-tau181 q = .43 Sensitivity Analyses We conducted a series of sensitivity analyses for the two markers showing significant change over time (Aβ 42 /Aβ 40 and GFAP). Both markers retained statistically significant change in all sensitivity analyses. Specifically, significance did not change when we excluded (1) extreme outliers only, (2) all outliers, or (3) those who sustained a TBI within 1 year of their second visit (Supplemental Table 1). We next ran a series of models incorporating additional covariates to determine whether changes in protein levels were accounted for by key covariates: sex, APOE ε4 carrier status, medical comorbidities, age at first blow to the head, years since first blow to the head, and injury severity. The models with covariates did not impact the significance for either Aβ 42 /Aβ 40 and GFAP and did not significantly improve model fit compared to the model with age and age squared only (Supplemental Tables 2-13). Discussion We examined trajectories of blood-based protein biomarker levels over an average of 3.5 years in a cohort of chronic TBI survivors who had sustained their first blow to the head an average of 28 years prior. Consistent with our hypotheses, there were significant reductions in Aβ 42 /Aβ 40 and significant increases in GFAP over time, implicating a possible role for brain amyloidosis in long-term TBI neuropathology. These findings were robust to key covariates (current age, sex, APOE ε4 carrier status, medical comorbidities, age at first blow to the head, years since first blow to the head, injury severity). There were no significant changes in NfL, BD-tau, p-tau231, or p-tau181. To our knowledge, this is the first study to examine change in Aβ 42 /Aβ 40 and GFAP over time in a large sample with TBI survivors in the very chronic period. Lower Aβ 42 /Aβ 40 in blood is an established marker of high Aβ deposition in the brain. 134 – 140 This inverse association occurs when Aβ 42 peptides, which are more prone to aggregation than Aβ 40 , 141 accumulates into amyloid plaques, 142 sequestering Aβ 42 in the brain tissue, leading to decreased levels of soluble Aβ 42 in blood and lowering the Aβ 42 /Aβ 40 ratio. Lower Aβ 42 /Aβ 40 ratio in blood is associated with higher risk of later developing MCI, 143 , 144 dementia, 145 – 147 AD, 143 , 144 , 146 and greater cognitive decline in healthy control subjects 148 and community dwelling older adults. 149 Cross-sectional studies confirm lower Aβ 42 /Aβ 40 ratio in blood is observed in MCI 136 and AD 135 patients compared to controls, and is associated with poorer cognitive performance in these cohorts. 139 There is preliminary evidence of dysregulated Aβ levels in blood from TBI cohorts. In the acute post-injury period, one study reported increases in Aβ 42 over the first months post injury in 13 TBI survivors, with elevations remaining for a subset of participants to 3 months post-injury. 150 A cross-sectional analyses of Aβ in the chronic period showed significant elevations in two neurodegenerative disease-associated Aβ protein variants (A4, C6T) relative to controls in 25 TBI survivors at an average of 30 years post-injury. 57 In contrast, however, a larger study (n=138) did not identify elevated levels of Aβ 42 an average of 8.3 years post-injury compared controls using similar covariates to our analyses (covariates: age, body mass index APOE e4 status, gender). 151 Taken together, it remains unclear whether later dysregulations in Aβ result from a continued acute-phase response or from new trigger(s) in the chronic period after the acute response subsides. Acute elevations in Aβ are well established in TBI and attributed to axonal injury, 36 , 81 , 152 – 154 with a contribution from increased APP gene expression also implicated. 74 , 153 , 155 Maintenance of Aβ elevations in to the chronic post-injury period may be facilitated by persistent axonal injury, 36 , 45 self-propagation, 156 and/or neuroinflammation, 157 – 159 which can persist for decades following injury. 160 Self-propagation may be achieved directly by Aβ stimulating transcription factors that up-regulate Aβ cleavage enzymes, 156 or indirectly by generating various cellular dysfunctions such as oxidative stress, which, through various feedback loops, have the net effect of up-regulating Aβ. 161 Consistent with our hypothesis, we found that GFAP levels increased over time in this chronic TBI cohort. GFAP is the signature intermediate filament of astrocytes. 162 Astrocytes are essential for many functions, including regulating blood flow, supporting synaptic activity, and maintaining extracellular fluid, ion and neurotransmitter homeostasis. 162 Astrocytes also form an integral physiological and functional part of the blood-brain barrier (BBB). 163 In response to insults to the central nervous system, astrocytes can become reactive and perform essential beneficial functions, including BBB repair, protection of neurons, and regulation of inflammation. 162 Elevated GFAP is considered an indicator of astrocytic activation or damage, 162 , 164 which are prominent features of TBI. 162 , 165 Trajectories of GFAP levels have been previously examined in the post-acute period. A significant increase over time was identified in a small sample (n=12) from approximately 8 months to M 8.3 years post-injury. 166 However, several other studies have not identified significant changes in GFAP over time (pre-injury to 12 months post-injury; 167 3-12 months 98 ; 30 days to 5 years 94 ). Notably, one of these studies found that GFAP was elevated in the moderate and severe TBI groups compared to controls at each time point (12 months, 2 years, 3 years, 4 years, 5 years), 94 which may suggest an ongoing but stable elevation. Further cross-sectional studies in the chronic period also support an ongoing elevation, with significantly higher levels in TBI cohorts across severity levels compared to controls (12 months, 92 , 93 6 to 13 years, 168 M ∼7 years 95 ). However, without longitudinal analysis these studies cannot discern whether these cross-sectional snapshots reflect an ongoing but stable elevation or an increase over time. Elevated GFAP levels have also been identified in other neurodegenerative conditions including frontotemporal lobar degeneration, 169 , 170 AD, 171 , 172 Dementia with Lewy Bodies, 171 Parkinson’s disease, 173 Parkinson’s Disease Dementia, 171 and cognitively normal older adults at risk of dementia 174 and AD. 138 In ADRDs, higher GFAP in the blood has been associated with poorer performance on cognitive tests, 139 , 170 , 171 a steeper rate of cognitive decline, 174 , 175 worse functional status, 170 and higher rates of eventual conversion to dementia. 175 , 176 When compared with MRI data, higher GFAP has also been associated with faster decline in hippocampal volume compared to those with lower GFAP. 175 Ultimately, understanding the complex pathologies of long-term TBI and PTND will require an appreciation of the inter-relationships between pathologies. High GFAP in blood has been robustly related with high Aβ burden. 138 – 140 , 176 – 178 Up-regulated GFAP may reflect a neuroprotective response whereby reactive astrogliosis occurs as a mechanism of Aβ peptide degradation. 162 Indeed, astrocytes internalize and degrade Aβ via lysosomal, autophagic, and enzymatic pathways, including the production and expression of Aβ-degrading proteases such as neprilysin. 179 In contrast, up-regulated GFAP has also been associated with Aβ-burdened reactive astrocytes and astrocyte-derived amyloid plaques that occur when astrocytes internalize but fail to fully degrade Aβ. 180 Such functionally impaired astrocytes are common in AD. 181 Complex statistical modelling with multiple data points is required to explore the evolving relationship between Aβ and GFAP in chronic TBI. Contrary to our hypotheses, changes in NfL, BD-tau, p-tau231, and p-tau181 were not observed. This expands upon prior work in acute TBI samples reporting decreases within the first 12 months for BD-tau 97 and p-tau231, 97 suggesting that levels may plateau beyond 12 months post injury. Longitudinal studies report decreases in NfL from 30 days to 5 years 94 and ∼8 months to M 8.3 years post-injury. 166 Early decreases captured in these studies may obscure long-term stability, with evidence supporting decline in NfL over the first 12 months post-injury. 97 , 98 It is also notable that these studies had smaller sample sizes, with only 29 94 and 12 166 TBI survivors included at the final follow-up. Null findings may also reflect the heterogeneity expected in chronic TBI. Firstly, we know that only a sub-set of individuals will experience decline in the post-injury period. 19 – 21 For these individuals, whom we would expect proliferating pathology, the constellation of pathologies driving this change may differ inter-individually given preliminary evidence that PTND may be best conceptualized as a poly-pathology. 37 , 38 As such, for this study any significant increases in NfL, BD-tau, p-tau231, or p-tau181 may have been obscured by those with (1) other PTND pathologies and/or (2) those without PTND. Indeed, there remain strong theoretical grounds to continue exploring these markers given robust associations with other neurodegenerative diseases and dementia (NfL, 182 BD-tau, 183 p-tau231, 184 and p-tau181 185 ). Limitations This study is not without limitations. First, the lack of a matched control group precludes investigation into whether the presence and rate of protein changes differ in relation to those without TBI. Our results were, however, robust to several key covariates (current age, sex, APOE ε4 carrier status, medical comorbidities) that may impact proteins levels in the blood. Examining associations with clinical outcomes is a critical next step to identify pathological processes that may contribute to clinical decline observed in some TBI survivors years after injury. 19 – 21 It will be of great interest to understand how changes in proteins over time are coupled with clinical manifestations of decline, including the domain(s) affected (e.g. cognitive, neurobehavioral, motor, psychiatric, sleep), the onset, and progression of decline. Although the sample size was small, our study is one of the largest conducted with a cohort of chronic TBI survoivrs. 89 Larger samples will be required to facilitate analyses of heterogeneity in trajectories that may help to identify sub-groups who have or at risk for PTND. There are also several considerations inherent to all studies using blood-based biomarkers to make inferences about protein expression in the brain. It is possible the plasma levels of a protein also partly reflects changes in the BBB permeability and/or glymphatic system disruption; which are both thought to occur in TBI and may persist into the chronic period. 186 , 187 Further, both GFAP 162 and Aβ 188 are generated outside of the central nervous system, with peripheral tissue expression undoubtedly contributing to the overall plasma levels obtained. Conclusions This study provides preliminary evidence for ongoing changes in Aβ 42 /Aβ 40 and GFAP between study visits conducted an average of 3.5 years apart in individuals living with chronic TBI. Consistent with our hypotheses, we observed significant reductions in Aβ 42 /Aβ 40 and significant increases in GFAP that were robust to adjustment for key demographic and injury covariates. Hypothesized changes in NfL, BD-tau, p-tau231, or p-tau181 were not observed over this same time interval. Changes in Aβ 42 /Aβ 40 and GFAP have been previously associated with cognitive decline and risk of dementia, and both are used as biomarkers for brain amyloidosis. Blood-based biomarker analyses have the potential to accelerate our understanding of chronic TBI neuropathology and identify potential pathological process driving PTND. Data Availability Data are available upon reasonable request to the authors. Funding Information This study was supported by grants #1RF1NS115268 and #RF1NS128961 (Dams-O’Connor) from the National Institutes of Health/National Institute of Neurological Disorders and Stroke, grant #U01NS086625 (Dams-O’Connor) from the National Institutes of Health/National Institute of Neurological Disorders and Stroke and National Institute of Child Health and Development, a postdoctoral training grant (Kristen Dams-O’Connor) from the National Institute on Disability Independent Living & Rehabilitation Research (#90ARHF0008), and grant #UL1TR001433 from the National Center for Advancing Translational Sciences, National Institutes of Health. HZ is a Wallenberg Scholar and a Distinguished Professor at the Swedish Research Council supported by grants from the Swedish Research Council (#2023-00356, #2022-01018 and #2019-02397), the European Union’s Horizon Europe research and innovation programme under grant agreement #101053962, and Swedish State Support for Clinical Research (#ALFGBG-71320). KB is supported by the Swedish Research Council (#2017-00915 and #2022-00732), the Swedish Alzheimer Foundation (#AF-930351, #AF-939721, #AF-968270, and #AF-994551), Hjärnfonden, Sweden (#ALZ2022-0006, #FO2024-0048-TK-130 and FO2024-0048-HK-24), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-965240 and #ALFGBG-1006418), the European Union Joint Program for Neurodegenerative Disorders (JPND2019-466-236), the Alzheimer’s Association 2021 Zenith Award (ZEN-21-848495), the Alzheimer’s Association 2022-2025 Grant (SG-23-1038904 QC), La Fondation Recherche Alzheimer (FRA), Paris, France, the Kirsten and Freddy Johansen Foundation, Copenhagen, Denmark, Familjen Rönströms Stiftelse, Stockholm, Sweden, and an anonymous philanthropist and donor. Author Disclosure Statement KB has served as a consultant and at advisory boards for Abbvie, AC Immune, ALZPath, AriBio, Beckman-Coulter, BioArctic, Biogen, Eisai, Lilly, Moleac Pte. Ltd, Neurimmune, Novartis, Ono Pharma, Prothena, Quanterix, Roche Diagnostics, Sanofi and Siemens Healthineers; has served at data monitoring committees for Julius Clinical and Novartis; has given lectures, produced educational materials and participated in educational programs for AC Immune, Biogen, Celdara Medical, Eisai and Roche Diagnostics; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper. HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZpath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, Enigma, LabCorp, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Quanterix, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures sponsored by Alzecure, BioArctic, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, Roche, and WebMD, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). The other authors have no competing interests to disclose. Appendix A. Supplementary data Authorship Contribution Statement Amelia J Hicks: Conceptualization, Data Curation, Methodology, Project Administration, Supervision, Writing – Original Draft, Writing – Review & Editing. Jay Plourde: Data Curation, Formal Analysis, Methodology, Software, Validation, Visualization, Writing – Review & Editing Enna Selmanovic: Investigation, Project Administration, Writing – Review & Editing. Nicola de Souza: Data Curation, Software, Writing – Review & Editing. Kaj Blennow: Investigation, Resources, Writing – Review & Editing. Henrik Zetterberg: Investigation, Resources, Writing – Review & Editing. Kristen Dams-O’Connor: Conceptualization, Funding Acquisition, Project Administration, Supervision, Writing – Review & Editing. Acknowledgements We acknowledge with thanks to the participants who contribute so generously to the LETBI study. We also acknowledge the LETBI study team and thank them for their continued efforts. References 1. ↵ Corrigan JD , Hammond FM , Sander A , Kroenke K . Recognition of traumatic brain injury as a chronic condition: A commentary . J Neurotrauma . 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Hicks , Jay Plourde , Enna Selmanovic , Nicola L. de Souza , Kaj Blennow , Henrik Zetterberg , Kristen Dams-O’Connor medRxiv 2025.02.16.25322303; doi: https://doi.org/10.1101/2025.02.16.25322303 Share This Article: Copy Citation Tools Trajectories of blood-based protein biomarkers in chronic traumatic brain injury Amelia J. 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