Browsing by Subject "Blood biomarkers"

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    Bifactor Model of the Sport Concussion Assessment Tool Symptom Checklist: Replication and Invariance Across Time in the CARE Consortium Sample
    (Sage, 2020-09) Brett, Benjamin L.; Kramer, Mark D.; McCrea, Michael A.; Broglio, Steven P.; McAllister, Thomas; Nelson, Lindsay D.; Hazzard, Joseph B., Jr.; Kelly, Louise A.; Ortega, Justus; Port, Nicholas; Pasquina, Paul F.; Jackson, Jonathan; Cameron, Kenneth L.; Houston, Megan N.; Goldman, Joshua T.; Giza, Christopher; Buckley, Thomas; Clugston, James R.; Schmidt, Julianne D.; Feigenbaum, Luis A.; Eckner, James T.; Master, Christina L.; Collins, Michael W.; Kontos, Anthony P.; Chrisman, Sara P.D.; Duma, Stefan M.; Miles, Christopher M.; Susmarski, Adam; Psychiatry, School of Medicine
    Background: Identifying separate dimensions of concussion symptoms may inform a precision medicine approach to treatment. It was previously reported that a bifactor model identified distinct acute postconcussion symptom dimensions. Purpose: To replicate previous findings of a bifactor structure of concussion symptoms in the Concussion Assessment Research and Education (CARE) Consortium sample, examine measurement invariance from pre- to postinjury, and evaluate whether factors are associated with other clinical and biomarker measures. Study design: Cohort study (Diagnosis); Level of evidence, 2. Methods: Collegiate athletes were prospectively evaluated using the Sport Concussion Assessment Tool-3 (SCAT-3) during preseason (N = 31,557); 2789 were followed at <6 hours and 24 to 48 hours after concussion. Item-level SCAT-3 ratings were analyzed using exploratory and confirmatory factor analyses. Bifactor and higher-order models were compared for their fit and interpretability. Measurement invariance tested the stability of the identified factor structure across time. The association between factors and criterion measures (clinical and blood-based markers of concussion severity, symptom duration) was evaluated. Results: The optimal structure for each time point was a 7-factor bifactor model: a General factor, on which all items loaded, and 6 specific factors-Vestibulo-ocular, Headache, Sensory, Fatigue, Cognitive, and Emotional. The model manifested strict invariance across the 2 postinjury time points but only configural invariance from baseline to postinjury. From <6 to 24-48 hours, some dimensions increased in severity (Sensory, Fatigue, Emotional), while others decreased (General, Headache, Vestibulo-ocular). The factors correlated with differing clinical and biomarker criterion measures and showed differing patterns of association with symptom duration at different time points. Conclusion: Bifactor modeling supported the predominant unidimensionality of concussion symptoms while revealing multidimensional properties, including a large dominant General factor and 6 independent factors: Headache, Vestibulo-ocular, Sensory, Cognitive, Fatigue, and Emotional. Unlike the widely used SCAT-3 symptom severity score, which declines gradually after injury, the bifactor model revealed separable symptom dimensions that have distinct trajectories in the acute postinjury period and different patterns of association with other markers of injury severity and outcome. Clinical relevance: The SCAT-3 total score remains a valuable, robust index of overall concussion symptom severity, and the specific factors identified may inform management strategies. Because some symptom dimensions continue to worsen in the first 24 to 48 hours after injury (ie, Sensory, Fatigue, Emotional), routine follow-up in this time frame may be valuable to ensure that symptoms are managed effectively.
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    Blood Biomarkers for Detection of Brain Injury in COVID-19 Patients
    (Mary Ann Liebert, 2021) DeKosky, Steven T.; Kochanek, Patrick M.; Valadka, Alex B.; Clark, Robert S. B.; Chou, Sherry H. Y.; Au, Alicia K.; Horvat, Christopher; Jha, Ruchira M.; Mannix, Rebekah; Wisniewski, Stephen R.; Wintermark, Max; Rowell, Susan E.; Welch, Robert D.; Lewis, Lawrence; House, Stacey; Tanzi, Rudolph E.; Smith, Darci R.; Vittor, Amy Y.; Denslow, Nancy D.; Davis, Michael D.; Glushakova, Olena Y.; Hayes, Ronald L.; Pediatrics, School of Medicine
    The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus attacks multiple organs of coronavirus disease 2019 (COVID-19) patients, including the brain. There are worldwide descriptions of neurological deficits in COVID-19 patients. Central nervous system (CNS) symptoms can be present early in the course of the disease. As many as 55% of hospitalized COVID-19 patients have been reported to have neurological disturbances three months after infection by SARS-CoV-2. The mutability of the SARS-COV-2 virus and its potential to directly affect the CNS highlight the urgency of developing technology to diagnose, manage, and treat brain injury in COVID-19 patients. The pathobiology of CNS infection by SARS-CoV-2 and the associated neurological sequelae of this infection remain poorly understood. In this review, we outline the rationale for the use of blood biomarkers (BBs) for diagnosis of brain injury in COVID-19 patients, the research needed to incorporate their use into clinical practice, and the improvements in patient management and outcomes that can result. BBs of brain injury could potentially provide tools for detection of brain injury in COVID-19 patients. Elevations of BBs have been reported in cerebrospinal fluid (CSF) and blood of COVID-19 patients. BB proteins have been analyzed in CSF to detect CNS involvement in patients with infectious diseases, including human immunodeficiency virus and tuberculous meningitis. BBs are approved by the U.S. Food and Drug Administration for diagnosis of mild versus moderate traumatic brain injury and have identified brain injury after stroke, cardiac arrest, hypoxia, and epilepsy. BBs, integrated with other diagnostic tools, could enhance understanding of viral mechanisms of brain injury, predict severity of neurological deficits, guide triage of patients and assignment to appropriate medical pathways, and assess efficacy of therapeutic interventions in COVID-19 patients.
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    Blood Biomarkers from Research Use to Clinical Practice: What Must Be Done? A Report from the EU/US CTAD Task Force
    (Springer, 2022) Angioni, D.; Delrieu, J.; Hansson, O.; Fillit, H.; Aisen, P.; Cummings, J.; Sim, J. R.; Braunstein, J. B.; Sabbagh, M.; Bittner, T.; Pontecorvo, M.; Bozeat, S.; Dage, J. L.; Largent, E.; Mattke, S.; Correa, O.; Gutierrez Robledo, L. M.; Baldivieso, V.; Willis, D. R.; Atri, A.; Bateman, R. J.; Ousset, P-J.; Vellas, B.; Weiner, M.; Neurology, School of Medicine
    Timely and accurate diagnosis of Alzheimer’s disease (AD) in clinical practice remains challenging. PET and CSF biomarkers are the most widely used biomarkers to aid diagnosis in clinical research but present limitations for clinical practice (i.e., cost, accessibility). Emerging blood-based markers have the potential to be accurate, cost-effective, and easily accessible for widespread clinical use, and could facilitate timely diagnosis. The EU/US CTAD Task Force met in May 2022 in a virtual meeting to discuss pathways to implementation of blood-based markers in clinical practice. Specifically, the CTAD Task Force assessed: the state-of-art for blood-based markers, the current use of blood-based markers in clinical trials, the potential use of blood-based markers in clinical practice, the current challenges with blood-based markers, and the next steps needed for broader adoption in clinical practice.
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    Clinical and analytical comparison of six Simoa assays for plasma P-tau isoforms P-tau181, P-tau217, and P-tau231
    (BMC, 2021-12-04) Bayoumy, Sherif; Verberk, Inge M.W.; den Dulk, Ben; Hussainali, Zulaiga; Zwan, Marissa; van der Flier, Wiesje M.; Ashton, Nicholas J.; Zetterberg, Henrik; Blennow, Kaj; Vanbrabant, Jeroen; Stoops, Erik; Vanmechelen, Eugeen; Dage, Jeffrey L.; Teunissen, Charlotte E.; Neurology, School of Medicine
    Introduction: Studies using different assays and technologies showed highly promising diagnostic value of plasma phosphorylated (P-)tau levels for Alzheimer's disease (AD). We aimed to compare six P-tau Simoa assays, including three P-tau181 (Eli Lilly, ADx, Quanterix), one P-tau217 (Eli Lilly), and two P-tau231 (ADx, Gothenburg). Methods: We studied the analytical (sensitivity, precision, parallelism, dilution linearity, and recovery) and clinical (40 AD dementia patients, age 66±8years, 50%F; 40 age- and sex-matched controls) performance of the assays. Results: All assays showed robust analytical performance, and particularly P-tau217 Eli Lilly; P-tau231 Gothenburg and all P-tau181 assays showed robust clinical performance to differentiate AD from controls, with AUCs 0.936-0.995 (P-tau231 ADx: AUC = 0.719). Results obtained with all P-tau181 assays, P-tau217 Eli Lilly assay, and P-tau231 Gothenburg assay strongly correlated (Spearman's rho > 0.86), while correlations with P-tau231 ADx results were moderate (rho < 0.65). Discussion: P-tau isoforms can be measured robustly by several novel high-sensitive Simoa assays.
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    Cross-Sectional Exploration of Plasma Biomarkers of Alzheimer's Disease in Down Syndrome: Early Data from the Longitudinal Investigation for Enhancing Down Syndrome Research (LIFE-DSR) Study
    (MDPI, 2021-04-28) Hendrix, James A.; Airey, David C.; Britton, Angela; Burke, Anna D.; Capone, George T.; Chavez, Ronelyn; Chen, Jacqueline; Chicoine, Brian; Costa, Alberto C.S.; Dage, Jeffrey L.; Doran, Eric; Esbensen, Anna; Evans, Casey L.; Faber, Kelley M.; Foroud, Tatiana M.; Hart, Sarah; Haugen, Kelsey; Head, Elizabeth; Hendrix, Suzanne; Hillerstrom, Hampus; Kishnani, Priya S.; Krell, Kavita; Ledesma, Duvia Lara; Lai, Florence; Lott, Ira; Ochoa-Lubinoff, Cesar; Mason, Jennifer; Nicodemus-Johnson, Jessie; Proctor, Nicholas Kyle; Pulsifer, Margaret B.; Revta, Carolyn; Rosas, H. Diana; Rosser, Tracie C.; Santoro, Stephanie; Schafer, Kim; Scheidemantel, Thomas; Schmitt, Frederick; Skotko, Brian G.; Stasko, Melissa R.; Talboy, Amy; Torres, Amy; Wilmes, Kristi; Woodward, Jason; Zimmer, Jennifer A.; Feldman, Howard H.; Mobley, William; Medical and Molecular Genetics, School of Medicine
    With improved healthcare, the Down syndrome (DS) population is both growing and aging rapidly. However, with longevity comes a very high risk of Alzheimer's disease (AD). The LIFE-DSR study (NCT04149197) is a longitudinal natural history study recruiting 270 adults with DS over the age of 25. The study is designed to characterize trajectories of change in DS-associated AD (DS-AD). The current study reports its cross-sectional analysis of the first 90 subjects enrolled. Plasma biomarkers phosphorylated tau protein (p-tau), neurofilament light chain (NfL), amyloid β peptides (Aβ1-40, Aβ1-42), and glial fibrillary acidic protein (GFAP) were undertaken with previously published methods. The clinical data from the baseline visit include demographics as well as the cognitive measures under the Severe Impairment Battery (SIB) and Down Syndrome Mental Status Examination (DS-MSE). Biomarker distributions are described with strong statistical associations observed with participant age. The biomarker data contributes to understanding DS-AD across the spectrum of disease. Collectively, the biomarker data show evidence of DS-AD progression beginning at approximately 40 years of age. Exploring these data across the full LIFE-DSR longitudinal study population will be an important resource in understanding the onset, progression, and clinical profiles of DS-AD pathophysiology.
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    Detecting amyloid positivity in early Alzheimer’s disease using combinations of plasma Aβ42/Aβ40 and p-tau
    (Wiley, 2022-02) Janelidze, Shorena; Palmqvist, Sebastian; Leuzy, Antoine; Stomrud, Erik; Verberk, Inge M.W.; Zetterberg, Henrik; Ashton, Nicholas J.; Pesini, Pedro; Sarasa, Leticia; Allué, José Antonio; Teunissen, Charlotte E.; Dage, Jeffrey L.; Blennow, Kaj; Mattsson-Carlgren, Niklas; Hansson, Oskar; Neurology, School of Medicine
    Introduction: We studied usefulness of combining blood amyloid beta (Aβ)42/Aβ40, phosphorylated tau (p-tau)217, and neurofilament light (NfL) to detect abnormal brain Aβ deposition in different stages of early Alzheimer's disease (AD). Methods: Plasma biomarkers were measured using mass spectrometry (Aβ42/Aβ40) and immunoassays (p-tau217 and NfL) in cognitively unimpaired individuals (CU, N = 591) and patients with mild cognitive impairment (MCI, N = 304) from two independent cohorts (BioFINDER-1, BioFINDER-2). Results: In CU, a combination of plasma Aβ42/Aβ40 and p-tau217 detected abnormal brain Aβ status with area under the curve (AUC) of 0.83 to 0.86. In MCI, the models including p-tau217 alone or Aβ42/Aβ40 and p-tau217 had similar AUCs (0.86-0.88); however, the latter showed improved model fit. The models were implemented in an online application providing individualized risk assessments (https://brainapps.shinyapps.io/PredictABplasma/). Discussion: A combination of plasma Aβ42/Aβ40 and p-tau217 discriminated Aβ status with relatively high accuracy, whereas p-tau217 showed strongest associations with Aβ pathology in MCI but not in CU.
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    miRNA, transcriptional regulation and cognitive decline
    (Wiley, 2025-01-03) Fischer, Andre; Sananbenesi, Farahnaz; Nho, Kwangsik; Manfred Krüger, Dennis; Shaw, Leslie M.; Saykin, Andrew J.; Delalle, Ivana; Radiology and Imaging Sciences, School of Medicine
    Background: Despite significant advancements in the development of blood biomarkers for AD, challenges persist due to the complex interplay of genetic and environmental risk factors in AD pathogenesis. Epigenetic processes, including non‐coding RNAs and especially microRNAs (miRs), have emerged as important players in the molecular mechanisms underlying neurodegenerative diseases. MiRs have the ability to fine‐tune gene expression and proteostasis, and microRNAome profiling in liquid biopsies is gaining increasing interest since changes in miR levels can indicate the presence of multiple pathologies. We have profiled blood samples via smallRNA sequencing for 1056 individuals of the DELCODE and 847 individuals of the ANDI cohort. Methods: We profiled blood samples via smallRNA sequencing for 1056 individuals of the DELCODE (German Longitudinal Cognitive Impairment and Dementia Study) and 847 individuals of the ANDI (Aging and Dementia in the Community) cohort, consisting of individuals diagnosed with SCD, MCI, AD, or control. Results: By applying differential expression, WGCNA, as well as linear and non‐linear machine learning approaches, we identify microRNA signatures that can help identify patients at distinct stages of disease progression, as well as signatures that can predict the course of the disease. These data are compared with phenotyping data, such as cognitive function and ATN biomarkers. We will also discuss the role of other non‐coding RNAs besides microRNAs and provide a framework for developing RNA‐based point‐of‐care assays.
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    Neuro-ophthalmologic and blood biomarker responses in ADHD following subconcussive head impacts: a case–control trial
    (Frontiers Media, 2023-11-22) Nowak, Madeleine K.; Kronenberger, William G.; Rettke, Devin; Ogbeide, Osamudiamen; Klemsz, Lillian M.; Quinn, Patrick D.; Mickleborough, Timothy D.; Newman, Sharlene D.; Kawata, Keisuke; Psychiatry, School of Medicine
    Introduction: This clinical trial aimed to determine the influence of attention-deficit/hyperactivity disorder (ADHD) on neuro-ophthalmologic function and brain-derived blood biomarkers following acute subconcussive head impacts. Methods: The present trial consisted of age- and sex-matched samples with a ratio of 1:1 between two groups with a total sample size of 60 adults (age ± SD; 20.0 ± 1.8 years). Soccer players diagnosed with and medicated daily for ADHD were assigned into an ADHD group (n = 30). Soccer players without ADHD were assigned into a non-ADHD group (n = 30). Participants performed 10 soccer headers with a soccer ball projected at a velocity of 25mph. King-Devick test (KDT), near point of convergence (NPC), and serum levels of NF-L, tau, GFAP, and UCH-L1 were assessed at baseline (pre-heading) and at 2 h and 24 h post-heading. Results: There were no statistically significant group-by-time interactions in outcome measures. However, at baseline, the ADHD group exhibited lower neuro-ophthalmologic functions compared to the non-ADHD group (NPC: p = 0.019; KDT: p = 0.018), and persisted at 2 h-post (NPC: p = 0.007; KDT: p = 0.014) and 24 h-post heading (NPC: p = 0.001). NPC significantly worsened over time in both groups compared to baseline [ADHD: 2 h-post, 1.23 cm, 95%CI:(0.77, 1.69), p < 0.001; 24 h-post, 1.68 cm, 95%CI:(1.22, 2.13), p = 0.001; Non-ADHD: 2 h-post, 0.96 cm, 95%CI:(0.50, 1.42), p < 0.001; 24 h-post, 1.09 cm, 95%CI:(0.63, 1.55), p < 0.001]. Conversely, improvements in KDT time compared to baseline occurred at 2 h-post in the non-ADHD group [-1.32 s, 95%CI:(-2.55, -0.09), p = 0.04] and at 24 h-post in both groups [ADHD: -4.66 s, 95%CI:(-5.89, -3.43), p < 0.001; Non-ADHD: -3.46 s, 95%CI:(-4.69, -2.23), p < 0.001)]. There were no group-by-time interactions for GFAP as both groups exhibited increased levels at 2 h-post [ADHD: 7.75 pg./mL, 95%CI:(1.41, 14.10), p = 0.019; Non-ADHD: 7.91 pg./mL, 95%CI:(1.71, 14.14), p = 0.015)] that returned to baseline at 24 h-post. NF-L levels increased at 2 h-post heading in the ADHD group [0.45 pg./mL, 95%CI:(0.05, 0.86), p = 0.032], but no significant NF-L changes were observed in the non-ADHD group over time. Discussion: Ten soccer headers elevated GFAP levels and NPC impairment in both groups. However, persisting group difference in NPC, blunted KDT performance, and increased NF-L levels in the ADHD group suggest that ADHD may reduce neuro-ophthalmologic function and heighten axonal response to soccer headers.
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    Plasma amyloid beta levels are associated with cerebral amyloid and tau deposition
    (Elsevier, 2019-07-26) Risacher, Shannon L.; Fandos, Noelia; Romero, Judith; Sherriff, Ian; Pesini, Pedro; Saykin, Andrew J.; Apostolova, Liana G.; Radiology and Imaging Sciences, School of Medicine
    Introduction: We investigated the relationship of plasma amyloid beta (Aβ) with cerebral deposition of Aβ and tau on positron emission tomography (PET). Methods: Forty-four participants (18 cognitively normal older adults [CN], 10 mild cognitive impairment, 16 Alzheimer's disease [AD]) underwent amyloid PET and a blood draw. Free and total plasma Aβ40 and Aβ42 were assessed using a validated assay. Thirty-seven participants (17 CN, 8 mild cognitive impairment, 12 AD) also underwent a [18F]flortaucipir scan. Scans were preprocessed by standard techniques, and mean global and regional amyloid and tau values were extracted. Free Aβ42/Aβ40 (Aβ F42:F40) and total Aβ42/Aβ40 (Aβ T42:T40) were evaluated for differences by diagnosis and relation to PET Aβ positivity. Relationships between these measures and cerebral Aβ and tau on both regional and voxel-wise basis were also evaluated. Results: Lower Aβ T42:T40 was associated with diagnosis and PET Aβ positivity. Lower plasma Aβ T42:T40 ratios predicted cerebral Aβ positivity, both across the full sample and in CN only. Finally, lower plasma Aβ T42:T40 ratios were associated with increased cortical Aβ and tau in AD-related regions on both regional and voxel-wise analyses. Discussion: Plasma Aβ measures may be useful biomarkers for predicting cerebral Aβ and tau. Additional studies in larger samples are warranted.
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    Plasma biomarkers combinations for prescreening rapid amyloid accumulation in cognitively unimpaired individuals at‐risk of Alzheimer’s disease
    (Wiley, 2025-01-09) Contador, José; Milà-Alomà, Marta; Escalante, Armand González; Ashton, Nicholas J.; Shekari, Mahnaz; Ortiz-Romero, Paula; Karikari, Thomas K.; Vanmechelen, Eugeen; Day, Theresa A.; Dage, Jeffrey L.; Zetterberg, Henrik; Gispert, Juan Domingo; Blennow, Kaj; Suarez-Calvet, Marc; Neurology, School of Medicine
    Background: Alzheimer’s disease (AD) blood biomarkers alone can detect amyloid‐β (Aβ) pathology in cognitively unimpaired (CU) individuals. We assessed whether combining different plasma biomarkers improves the detection of Aβ‐positivity and identifies rapid amyloid deposition in CU individuals. Method: CU participants from the ALFA+ cohort were included. Among them, 361 had CSF Aβ42/40 and 328 amyloid PET‐scans [194 with two longitudinal scans; mean interval=3.35 (0.56) years]. Plasma Aβ42/40, p‐tau181, p‐tau231, GFAP, NfL (Simoa‐based) and p‐tau217 and t‐tau (MSD‐based) were measured at baseline (Table 1). We used simple and multiple logistic models to estimate Aβ‐positivity (defined as CSF Aβ42/40<0.071 or amyloid‐PET>12 Centiloids) or Aβ accumulation rate (“Fast accumulators” defined as >3 Centiloids/year). The model contained plasma biomarkers and demographics (age and sex) as covariates. We selected as "best model" (BM) that with lowest AIC. We defined parsimonious models as those with an AUC not significantly different (DeLong test) from BM or from each other yet outperforming single biomarkers and/or demographics models (FDR corrected). For the positive agreement closest to 90%, we calculated savings in lumbar punctures and amyloid PET‐scans. Result: For CSF Aβ‐positive detection, BM included plasma Aβ42/40, p‐tau181, p‐tau217, p‐tau231, GFAP and t‐tau (AUC=0.84). All simpler biomarkers combinations included plasma Ab42/40 and p‐tau231 (Table 2A). For PET Ab‐positive detection, BM included plasma Aβ42/40, p‐tau181, p‐tau217, GFAP, NFL and age (AUC=0.88). All simpler biomarkers combinations included plasma Ab42/40 and p‐tau217 (Table 2B). Regarding fast accumulators’ detection, plasma p‐tau217 was the single biomarker with the highest performance (AUC=0.70). BM included plasma Aβ42/40, p‐tau217, p‐tau231 and GFAP (AUC= 0.76). BM and the plasma Aβ42/40, p‐tau217 and GFAP (AUC=0.75) combination were the only models that outperformed the age and sex combination and single biomarkers, except for plasma p‐tau217, Aβ42/40 (AUC=0.69) or GFAP (AUC=0.68) alone (Table 2C). The combination of biomarkers could save up to 11% of lumbar punctures or 44% of amyloid‐PET to detect Ab‐positive CU individuals and 16% amyloid‐PETs to detect fast Aβ‐accumulation compared to the best single plasma biomarker (Table 2). Conclusion: In CU individuals, diverse combinations of plasma biomarkers detect Aβ‐positivity and future Aβ‐accumulation with high accuracy and can lead to substantial cost savings in AD detection.