Abstract Background Identifying individuals with intracranial injuries following mild traumatic brain injury (mTBI), i.e. complicated mTBI cases, is important for follow-up and prognostication. The aim of the current study was to identify the ability of single and multi-panel blood biomarkers of CNS injury and inflammation, from the acute to chronic phase after injury, to classify people with complicated mTBI on computer tomography (CT) and/or magnetic resonance imaging (MRI) acquired within 72 hours. Methods Patients with mTBI (n = 207, 16–60 years), i.e., Glasgow Coma Scale (GCS) score between 13 and 15, loss of consciousness (LOC) < 30 min and post-traumatic amnesia (PTA) < 24 hours, were included. Complicated mTBI was present in 8% (n = 16) based on CT (CT+) and 12% (n = 25) based on MRI (MRI+). Blood biomarkers were sampled at four timepoints following injury: admission (within 72 hours), 2 weeks (± 3 days), 3 months (± 2 weeks) and 12 months (± 1 month). CNS biomarkers included were GFAP, NFL and tau, along with a panel of 12 inflammation markers. Predictive models were generated with both single and multi-panel biomarkers and assessed using area under the curve analyses (AUCs). Results The most discriminative single biomarkers were GFAP at admission (CT+: AUC = 0.78; MRI+: AUC = 0.82) and NFL at 2 weeks (CT+: AUC = 0.81; MRI+: AUC = 0.89) and 3 months (MRI+: AUC = 0.86). MIP-1β and IP-10 concentrations were significantly lower at almost all timepoints in patients who were CT + and MRI+. Eotaxin and IL-9 were significantly lower in patients who were MRI + only. FGF-basic concentrations increased over time in patients who were MRI- and were significantly higher than patients MRI + at 3- and 12 months. Multi-biomarker panels improved discriminability at all timepoints (AUCs ≈ 0.90 of admission and 2-week models for CT + and AUC > 0.90 of admission, 2-week and 3-month models for MRI+). Conclusions The CNS biomarkers GFAP and NFL were useful diagnostic biomarkers of complicated mTBI in acute, subacute and chronic phases after mTBI. Several inflammation markers were significantly lower in patients with complicated mTBI, at all timepoints, and could discriminate between CT + and MRI + even after 12 months. Multi-biomarker panels improved diagnostic accuracy at all timepoints.
Introduction: Central ghrelin signaling is required for the rewarding effects of alcohol in mice. Because ghrelin is implied in other addictive behaviors such as eating disorders and smoking, and because there is co‐morbidity between these disorders and alcohol dependence, the ghrelin signaling system could be involved in mediating reward in general. Furthermore, in humans, single nucleotide polymorphisms (SNPs) and haplotypes of the pro‐ghrelin gene (GHRL) and the ghrelin receptor gene ( GHSR ) have previously been associated with increased alcohol consumption and increased body weight. Known gender differences in plasma ghrelin levels prompted us to investigate genetic variation of the ghrelin signaling system in females with severe alcohol dependence ( n = 113) and in a selected control sample of female low‐consumers of alcohol from a large cohort study in southwest Sweden ( n = 212). Methods: Six tag SNPs in the GHRL (rs696217, rs3491141, rs4684677, rs35680, rs42451, and rs26802) and four tag SNPs in the GHSR (rs495225, rs2232165, rs572169, and rs2948694) were genotyped in all individuals. Results: We found that one GHRL haplotype was associated with reports of paternal alcohol dependence as well as with reports of withdrawal symptoms in the female alcohol‐dependent group. Associations with 2 GHSR haplotypes and smoking were also shown. One of these haplotypes was also negatively associated with BMI in controls, while another haplotype was associated with having the early‐onset, more heredity‐driven, type 2 form of alcohol dependence in the patient group. Conclusion: Taken together, the genes encoding the ghrelin signaling system cannot be regarded as major susceptibility genes for female alcohol dependence, but is, however, involved in paternal heritability and may affect other reward‐ and energy‐related factors such as smoking and BMI.
Abstract Background Synapse damage and loss are fundamental to the pathophysiology of Alzheimer’s disease (AD) and lead to reduced cognitive function. The goal of this review is to address the challenges of forging new clinical development approaches for AD therapeutics that can demonstrate reduction of synapse damage or loss. The key points of this review include the following: Synapse loss is a downstream effect of amyloidosis, tauopathy, inflammation, and other mechanisms occurring in AD. Synapse loss correlates most strongly with cognitive decline in AD because synaptic function underlies cognitive performance. Compounds that halt or reduce synapse damage or loss have a strong rationale as treatments of AD. Biomarkers that measure synapse degeneration or loss in patients will facilitate clinical development of such drugs. The ability of methods to sensitively measure synapse density in the brain of a living patient through synaptic vesicle glycoprotein 2A (SV2A) positron emission tomography (PET) imaging, concentrations of synaptic proteins (e.g., neurogranin or synaptotagmin) in the cerebrospinal fluid (CSF), or functional imaging techniques such as quantitative electroencephalography (qEEG) provides a compelling case to use these types of measurements as biomarkers that quantify synapse damage or loss in clinical trials in AD. Conclusion A number of emerging biomarkers are able to measure synapse injury and loss in the brain and may correlate with cognitive function in AD. These biomarkers hold promise both for use in diagnostics and in the measurement of therapeutic successes.
Background: Neurofilament light (NFL) has been increasingly recognized for prognostic and therapeutic decisions. Objective: To validate the utility of cerebrospinal fluid NFL (cNFL) as a biomarker in clinical practice of relapsing-remitting multiple sclerosis (RRMS). Methods: RRMS patients ( n = 757) who had cNFL analyzed as part of the diagnostic work-up in a single academic multiple sclerosis (MS) center, 2001–2018, were retrospectively identified. cNFL concentrations were determined with two different immunoassays and the ratio of means between them was used for normalization. Results: RRMS with relapse had 4.4 times higher median cNFL concentration (1134 [interquartile range (IQR) 499–2744] ng/L) than those without relapse (264 [125–537] ng/L, p < 0.001) and patients with gadolinium-enhancing lesions had 3.3 times higher median NFL (1414 [606.8–3210] ng/L) than those without (426 [IQR 221–851] ng/L, p < 0.001). The sensitivity and specificity of cNFL to detect disease activity was 75% and 98.5%, respectively. High cNFL at MS onset predicted progression to Expanded Disability Status Scale (EDSS) ⩾ 3 ( p < 0.001, hazard ratios (HR) = 1.89, 95% CI = 1.44–2.65) and conversion to secondary progressive MS (SPMS, p = 0.001, HR = 2.5, 95% CI = 1.4–4.2). Conclusions: cNFL is a robust and reliable biomarker of disease activity, treatment response, and prediction of disability and conversion from RRMS to SPMS. Our data suggest that cNFL should be included in the assessment of patients at MS-onset.
The current development of immunotherapy for Alzheimer's disease is based on the assumption that human-derived amyloid beta protein (Abeta) can be targeted in a similar manner to animal cell-derived or synthetic Abeta. Because the structure of Abeta depends on its source and the presence of cofactors, it is of great interest to determine whether human-derived oligomeric Abeta species impair brain function and, if so, whether or not their disruptive effects can be prevented using antibodies. We report that untreated ex vivo human CSF that contains Abeta dimers rapidly inhibits hippocampal long-term potentiation in vivo and that acute systemic infusion of an anti-Abeta monoclonal antibody can prevent this disruption of synaptic plasticity. Abeta monomer isolated from human CSF did not affect long-term potentiation. These results strongly support a strategy of passive immunization against soluble Abeta oligomers in early Alzheimer's disease.
Importance Phosphorylated tau (p-tau) is a specific blood biomarker for Alzheimer disease (AD) pathology, with p-tau217 considered to have the most utility. However, availability of p-tau217 tests for research and clinical use has been limited. Expanding access to this highly accurate AD biomarker is crucial for wider evaluation and implementation of AD blood tests. Objective To determine the utility of a novel and commercially available immunoassay for plasma p-tau217 to detect AD pathology and evaluate reference ranges for abnormal amyloid β (Aβ) and longitudinal change across 3 selected cohorts. Design, Setting, and Participants This cohort study examined data from 3 single-center observational cohorts: cross-sectional and longitudinal data from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort (visits October 2017–August 2021) and Wisconsin Registry for Alzheimer’s Prevention (WRAP) cohort (visits February 2007–November 2020) and cross-sectional data from the Sant Pau Initiative on Neurodegeneration (SPIN) cohort (baseline visits March 2009–November 2021). Participants included individuals with and without cognitive impairment grouped by amyloid and tau (AT) status using PET or CSF biomarkers. Data were analyzed from February to June 2023. Exposures Magnetic resonance imaging, Aβ positron emission tomography (PET), tau PET, cerebrospinal fluid (CSF) biomarkers (Aβ42/40 and p-tau immunoassays), and plasma p-tau217 (ALZpath pTau217 assay). Main Outcomes and Measures Accuracy of plasma p-tau217 in detecting abnormal amyloid and tau pathology, longitudinal p-tau217 change according to baseline pathology status. Results The study included 786 participants (mean [SD] age, 66.3 [9.7] years; 504 females [64.1%] and 282 males [35.9%]). High accuracy was observed in identifying elevated Aβ (area under the curve [AUC], 0.92-0.96; 95% CI, 0.89-0.99) and tau pathology (AUC, 0.93-0.97; 95% CI, 0.84-0.99) across all cohorts. These accuracies were comparable with CSF biomarkers in determining abnormal PET signal. The detection of abnormal Aβ pathology using a 3-range reference yielded reproducible results and reduced confirmatory testing by approximately 80%. Longitudinally, plasma p-tau217 values showed an annual increase only in Aβ-positive individuals, with the highest increase observed in those with tau positivity. Conclusions and Relevance This study found that a commercially available plasma p-tau217 immunoassay accurately identified biological AD, comparable with results using CSF biomarkers, with reproducible cut-offs across cohorts. It detected longitudinal changes, including at the preclinical stage.
To investigate whether serum neurofilament light (NfL) concentration is increased in familial Alzheimer disease (FAD), both pre and post symptom onset, and whether it is associated with markers of disease stage and severity.
Methods:
We recruited 48 individuals from families with PSEN1 or APP mutations to a cross-sectional study: 18 had symptomatic Alzheimer disease (AD) and 30 were asymptomatic but at 50% risk of carrying a mutation. Serum NfL was measured using an ultrasensitive immunoassay on the single molecule array (Simoa) platform. Cognitive testing and MRI were performed; 33 participants had serial MRI, allowing calculation of atrophy rates. Genetic testing established mutation status. A generalized least squares regression model was used to compare serum NfL among symptomatic mutation carriers, presymptomatic carriers, and noncarriers, adjusting for age and sex. Spearman coefficients assessed associations between serum NfL and (1) estimated years to/from symptom onset (EYO), (2) cognitive measures, and (3) MRI measures of atrophy.
Results:
Nineteen of the asymptomatic participants were mutation carriers (mean EYO −9.6); 11 were noncarriers. Compared with noncarriers, serum NfL concentration was higher in both symptomatic (p < 0.0001) and presymptomatic mutation carriers (p = 0.007). Across all mutation carriers, serum NfL correlated with EYO (ρ = 0.81, p < 0.0001) and multiple cognitive and imaging measures, including Mini-Mental State Examination (ρ = −0.62, p = 0.0001), Clinical Dementia Rating Scale sum of boxes (ρ = 0.79, p < 0.0001), baseline brain volume (ρ = −0.62, p = 0.0002), and whole-brain atrophy rate (ρ = 0.53, p = 0.01).
Conclusions:
Serum NfL concentration is increased in FAD prior to symptom onset and correlates with measures of disease stage and severity. Serum NfL may thus be a feasible biomarker of early AD-related neurodegeneration.
Article11 November 2019Open Access Transparent process Cerebrospinal fluid and plasma biomarker trajectories with increasing amyloid deposition in Alzheimer's disease Sebastian Palmqvist Corresponding Author Sebastian Palmqvist [email protected] orcid.org/0000-0002-9267-1930 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden Department of Neurology, Skåne University Hospital, Lund, Sweden Search for more papers by this author Philip S Insel Philip S Insel Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA Search for more papers by this author Erik Stomrud Erik Stomrud Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden Memory Clinic, Skåne University Hospital, Malmö, Sweden Search for more papers by this author Shorena Janelidze Shorena Janelidze Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden Search for more papers by this author Henrik Zetterberg Henrik Zetterberg Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK UK Dementia Research Institute at UCL, London, UK Search for more papers by this author Britta Brix Britta Brix Euroimmun AG, Lübeck, Germany Search for more papers by this author Udo Eichenlaub Udo Eichenlaub Roche Diagnostics GmbH, Penzberg, Germany Search for more papers by this author Jeffrey L Dage Jeffrey L Dage Eli Lilly and Company, Indianapolis, IN, USA Search for more papers by this author Xiyun Chai Xiyun Chai Eli Lilly and Company, Indianapolis, IN, USA Search for more papers by this author Kaj Blennow Kaj Blennow Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden Search for more papers by this author Niklas Mattsson Niklas Mattsson orcid.org/0000-0002-8885-7724 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden Department of Neurology, Skåne University Hospital, Lund, Sweden Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden Search for more papers by this author Oskar Hansson Corresponding Author Oskar Hansson [email protected] orcid.org/0000-0001-8467-7286 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden Memory Clinic, Skåne University Hospital, Malmö, Sweden Search for more papers by this author Sebastian Palmqvist Corresponding Author Sebastian Palmqvist [email protected] orcid.org/0000-0002-9267-1930 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden Department of Neurology, Skåne University Hospital, Lund, Sweden Search for more papers by this author Philip S Insel Philip S Insel Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA Search for more papers by this author Erik Stomrud Erik Stomrud Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden Memory Clinic, Skåne University Hospital, Malmö, Sweden Search for more papers by this author Shorena Janelidze Shorena Janelidze Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden Search for more papers by this author Henrik Zetterberg Henrik Zetterberg Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK UK Dementia Research Institute at UCL, London, UK Search for more papers by this author Britta Brix Britta Brix Euroimmun AG, Lübeck, Germany Search for more papers by this author Udo Eichenlaub Udo Eichenlaub Roche Diagnostics GmbH, Penzberg, Germany Search for more papers by this author Jeffrey L Dage Jeffrey L Dage Eli Lilly and Company, Indianapolis, IN, USA Search for more papers by this author Xiyun Chai Xiyun Chai Eli Lilly and Company, Indianapolis, IN, USA Search for more papers by this author Kaj Blennow Kaj Blennow Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden Search for more papers by this author Niklas Mattsson Niklas Mattsson orcid.org/0000-0002-8885-7724 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden Department of Neurology, Skåne University Hospital, Lund, Sweden Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden Search for more papers by this author Oskar Hansson Corresponding Author Oskar Hansson [email protected] orcid.org/0000-0001-8467-7286 Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden Memory Clinic, Skåne University Hospital, Malmö, Sweden Search for more papers by this author Author Information Sebastian Palmqvist *,1,2, Philip S Insel1,3, Erik Stomrud1,4, Shorena Janelidze1, Henrik Zetterberg5,6,7,8, Britta Brix9, Udo Eichenlaub10, Jeffrey L Dage11, Xiyun Chai11, Kaj Blennow5,6, Niklas Mattsson1,2,12 and Oskar Hansson *,1,4 1Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden 2Department of Neurology, Skåne University Hospital, Lund, Sweden 3Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA 4Memory Clinic, Skåne University Hospital, Malmö, Sweden 5Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden 6Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden 7Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK 8UK Dementia Research Institute at UCL, London, UK 9Euroimmun AG, Lübeck, Germany 10Roche Diagnostics GmbH, Penzberg, Germany 11Eli Lilly and Company, Indianapolis, IN, USA 12Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden *Corresponding author. Tel: +46 46 177808; E-mail: [email protected] *Corresponding author. Tel: +46 40-335036; Fax: +46 40-335657; E-mail: [email protected] EMBO Mol Med (2019)11:e11170https://doi.org/10.15252/emmm.201911170 ELECSYS, COBAS, and COBAS E are registered trademarks of Roche. All trademarks mentioned enjoy legal protection PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Failures in Alzheimer's disease (AD) drug trials highlight the need to further explore disease mechanisms and alterations of biomarkers during the development of AD. Using cross-sectional data from 377 participants in the BioFINDER study, we examined seven cerebrospinal fluid (CSF) and six plasma biomarkers in relation to β-amyloid (Aβ) PET uptake to understand their evolution during AD. In CSF, Aβ42 changed first, closely followed by Aβ42/Aβ40, phosphorylated-tau (P-tau), and total-tau (T-tau). CSF neurogranin, YKL-40, and neurofilament light increased after the point of Aβ PET positivity. The findings were replicated using Aβ42, Aβ40, P-tau, and T-tau assays from five different manufacturers. Changes were seen approximately simultaneously for CSF and plasma biomarkers. Overall, plasma biomarkers had smaller dynamic ranges, except for CSF and plasma P-tau which were similar. In conclusion, using state-of-the-art biomarkers, we identified the first changes in Aβ, closely followed by soluble tau. Only after Aβ PET became abnormal, biomarkers of neuroinflammation, synaptic dysfunction, and neurodegeneration were altered. These findings lend in vivo support of the amyloid cascade hypotheses in humans. Synopsis Analysis of the evolution of 13 key cerebrospinal and plasma biomarkers in relation to increasing Aβ accumulation during Alzheimer's disease confirms the amyloid hypothesis, and highlight the presence of other disease mechanisms already prior to the threshold for amyloid positivity. Failures in Alzheimer's disease (AD) drug trials highlight the need to further explore disease mechanisms and alterations of biomarkers during the development of AD. The study examines seven cerebrospinal fluid (CSF) and six plasma biomarkers in relation to β-amyloid (Aβ) PET uptake to understand their evolution during AD. The first changes were seen in Aβ biomarkers, closely followed by soluble tau, and then approximately simultaneously in markers of neuroinflammation, synaptic dysfunction and neurodegeneration. The results were replicated using five different CSF assays for Aβ42, Aβ40, P-tau and T-tau. Introduction Continued failures in clinical trials for Alzheimer's disease (AD) against presumably the correct drug targets highlight the need for further research to understand all important mechanisms and at what stage they occur and become measurable (Honig et al, 2018; Jack et al, 2018; Egan et al, 2019; Knopman, 2019; Selkoe, 2019). The pathogenesis of AD is complex and involves many different mechanisms. According to the amyloid cascade hypothesis, the first hallmark pathology of AD is the abnormal accumulation of β-amyloid (Aβ) that can start decades before the dementia stage and continues throughout the course of the disease (Villemagne et al, 2013). Aβ is thought to trigger or drive tau pathology, which, possibly together with inflammatory mechanisms, may cause synaptic dysfunction and neurodegeneration that result in cognitive impairment and dementia (Jack et al, 2013; Sperling et al, 2014). There are now several cerebrospinal fluid (CSF) and plasma biomarkers that, to different extent, measure these different pathogenic mechanisms. Examining these, especially in the earlier stages of AD, would allow us to better understand the pathogenesis of the disease, which is essential for identifying potential drug targets, designing clinical trials, and improving diagnostics and the clinical work-up of AD (Blennow et al, 2010). By comparing CSF and plasma biomarkers, we can also understand which disease mechanisms we can identify by measuring biomarkers in blood instead of in CSF samples. In this study of 377 elderly, non-demented participants from the BioFINDER study, we examined CSF and plasma biomarkers for Aβ (Aβ42 and Aβ40), tau (P-tau), synaptic dysfunction (neurogranin), neurodegeneration [total-tau (T-tau) and neurofilament light chain (NfL)], and glial activation and neuroinflammation (YKL-40, measured only in CSF). The biomarker changes were modeled as functions of the Aβ positron emission tomography (PET) signal that measures the amount of accumulated fibrillar Aβ in the neocortex (used as a proxy for time in the disease). We identified at what Aβ load significant changes in the biomarkers occurred (change point of the trajectory). Differences between the change points of the biomarkers were examined to map the temporal evolution of the biomarkers. Finally, CSF Aβ42, Aβ40, T-tau, and P-tau assays from five different manufacturers were compared to assess the generalizability of the results. Results Demographic and clinical data for the study participants are shown in Table 1. Of the 377 included participants, 242 were cognitively unimpaired (CU) and 135 had mild cognitive impairment (MCI). According to the mixture modeling-derived Aβ PET cutoff of < 0.736 SUVR, 151 participants (40%) were Aβ-positive (Aβ+) and 226 (60%) Aβ-negative (Aβ−). All plasma and CSF biomarkers were significantly different between Aβ+ and Aβ− participants, except for CSF Aβ40, plasma Aβ40, plasma tau, and plasma neurogranin. Table 1. Demographic and clinical data stratified by Aβ positivity Variable Total population Aβ+ Aβ− P-value N 377 151 226 Age (years) 72.1 (5.4) 72.6 (5.0) 71.8 (5.6) 0.10 Sex (female) 50% 44% 54% 0.042 MMSE (0–30 points) 28.3 (1.6) 27.8 (1.6) 28.5 (1.5) <0.001 APOE ε4-positive 38% 66% 22% <0.001 Aβ PET (SUVR)a 0.782 (0.23) 1.023 (0.18) 0.622 (0.05) <0.001 Hippocampus volume/ICV 0.0045 (0.00069) 0.00425 (0.00062) 0.00468 (0.00068) <0.001 CSF biomarker (pg/ml) Aβ42 1,321 (650) 818 (319) 1,657 (596) <0.001 Aβ40 22,811 (82,293) 29,261 (129,856) 18,501 (5,362) 0.57 Aβ42/Aβ40 0.0717 (0.028) 0.0448 (0.0164) 0.0898 (0.0187) <0.001 T-tau 256 (116) 319 (139) 215 (73.0) <0.001 P-tau 22.8 (12.4) 30.0 (15.1) 17.9 (6.7) <0.001 NfL 1,192 (948) 1,399 (1,133) 1,053 (847) <0.001 Neurogranin 405 (213) 480 (253) 356 (164) <0.001 YKL-40 194,090 (63,108) 205,273 (64,958) 186,618 (60,847) 0.003 Plasma biomarker (pg/ml) Aβ42 31.6 (4.9) 29.9 (4.7) 32.7 (4.7) <0.001 Aβ40 484 (72) 483 (73) 485 (71) 0.66 Aβ42/Aβ40 0.0657 (0.0082) 0.0622 (0.0078) 0.0680 (0.0077) <0.001 T-tau 17.8 (5.3) 18.2 (5.0) 17.6 (5.5) 0.12 P-tau 2.7 (4.6) 3.4 (3.2) 2.1 (5.3) <0.001 NfL 22.9 (17.0) 23.9 (11.2) 22.2 (19.9) 0.003 Neurogranin 20,205 (10,655) 19,414 (10,961) 20,735 (10,437) 0.17 Values are in mean (SD) if not otherwise stated. Mann–Whitney was used to compare the Aβ+ and Aβ− groups. Bold P-values indicate statistical significance at P < 0.05. CU, cognitively unimpaired; ICV, intra cranial volume; MCI, mild cognitive impairment; MMSE, Mini-Mental State Examination; N, number of participants; NfL, neurofilament light chain; P-tau, phosphorylated-tau; SD, standard deviation, T-tau; total-tau. a Early Aβ accumulating ROI with a composite reference region (see Materials and Methods). CSF biomarker trajectories as a function of increasing Aβ accumulation The monotone spline models of the CSF biomarkers are shown in Fig 1A. Separate biomarker models with data points, significance level, and r2 value are shown in Appendix Fig S1. Note that all models were fitted using cross-sectional CSF, plasma, and PET data. The models were significantly fitted for all the CSF biomarkers. Initial declines were seen for both CSF Aβ42 and Aβ40, followed by a flat curve for Aβ40 (i.e., no association with further increases in SUVR) while Aβ42 continued to decrease after the level when Aβ positivity was reached. In concordance with this, the CSF Aβ42/Aβ40 ratio started with a plateau, followed by a later drop compared to Aβ42 (note that no increase in Aβ42/Aβ40 would be estimated given the a priori assumption of monotonicity of Aβ42/Aβ40 with respect to SUVR). Around 1.0 SUVR (after Aβ positivity was reached), both Aβ42 and Aβ42/40 flattened out and did not continue to decline as Aβ PET SUVR increased further. CSF T-tau and P-tau had very similar trajectories with the greatest increase after Aβ positivity was reached. CSF neurogranin showed a more modest increase and smaller dynamic range throughout the SUVR span, and the change was even more modest for CSF YKL-40 (< 0.5 z-score). CSF NfL exhibited a sigmoid trajectory with a subtle initial increase around the same point as most other CSF biomarkers, followed by a plateau, and then a marked increase at a later stage that continued to increase throughout the span of SUVRs (Fig 1A). Based on this appearance, two change points were established for CSF NfL (see below and Materials and Methods). As expected when using Aβ PET as the dependent variable, the best model fits were seen for CSF Aβ42 (r2 = 0.42) and CSF Aβ42/Aβ40 (r2 = 0.55), while poorer fits were seen for CSF P-tau (r2 = 0.30), T-tau (r2 = 0.25), neurogranin (r2 = 0.11), NfL (r2 = 0.11), YKL-40 (r2 = 0.02), and Aβ40 (r2 = 0.02). Figure 1. CSF and plasma biomarker trajectories as a function of increasing Aβ accumulation A, B. The biomarker data were fitted using monotone spline models where Aβ PET SUVR acted as a proxy for time in AD. CSF (A) and plasma (B) biomarkers are shown separately, but selected direct comparisons are shown in Fig 3. Individual spline models with actual data points are shown in Appendix Figs S1 and S2. The threshold for Aβ was established using mixture modeling statistics. Point of change on the trajectory (also referred to as significant biomarker change) is shown as vertical dashed lines. They were defined as a change in 2 SE (derived from 500 bootstrap samples) from the starting point of the modeled trajectory. Note that plasma P-tau data were missing in 34 cases. To facilitate comparisons between different CSF and plasma biomarkers, the levels have been transformed to z-scores based on the distribution in the present population (i.e., a z-score of 0 corresponds to the mean of the study cohort). Download figure Download PowerPoint CSF biomarker change points A significant biomarker change (or "change point") was defined as a 2 standard error (SE) change from the starting point of the spline based on 500 bootstrap samples. These change points are marked as vertical dashed lines in Fig 1 and shown with 95% CIs in Fig 2 and in Appendix Table S1. The first significant changes for CSF biomarkers were seen for Aβ40, followed by Aβ42 and then Aβ42/Aβ40. Later, increases in P-tau, T-tau, and NfL were seen, with no significant differences between them (i.e., overlapping 95% CIs). These latter biomarker changes occurred just before or at the time of Aβ positivity. Slightly later, changes in neurogranin, YKL-40, and hippocampal volume were seen. The second increase in NfL occurred last and significantly later than all other biomarker changes. Figure 2. Point of significant biomarker change with 95% CIsChange points (also referred to as a significant biomarker change) with 95% CIs of the modeled biomarker trajectories are shown in Fig 1. Hippocampus volume divided by total intracranial volume was added for reference. Download figure Download PowerPoint Plasma biomarker trajectories Plasma biomarker splines are shown in Fig 1B. As for the plasma biomarker models, all were significant except for neurogranin and Aβ40 (Appendix Fig S2). Similar to CSF Aβ42 and Aβ40, plasma Aβ42 and Aβ40 showed an initial, parallel decline followed by a flat line for Aβ40 resulting in a later drop for the Aβ42/40 ratio (Fig 3A). In contrast to CSF, plasma Aβ42 and Aβ42/40 showed more modest changes over the entire Aβ accumulation range (about 1 z-score vs. about 2 z-scores for CSF; Fig 3A) and had overall a lesser agreement with Aβ PET (plasma r2: 0.07–0.12; CSF r2: 0.42–0.55; Appendix Figs S1 and S2). This lesser agreement was true for all plasma biomarkers compared with the corresponding CSF biomarkers (Appendix Figs S1 and S2), except for plasma P-tau which was more similar to the corresponding CSF biomarker (Fig 3B). Figure 3. Comparison of selected CSF and plasma biomarker models A. Same models as in Fig 1A and B for CSF and plasma Aβ40, Aβ42, and Aβ42/40, but now in the same panel for easier comparison. B. Spline models from the same dataset where there were no missing data for plasma P-tau (n = 343); i.e., the plasma P-tau curve is the same as in Fig 1B, but CSF P-tau is slightly different compared Fig 1A. Data information: To facilitate comparisons between different CSF and plasma biomarkers, the levels have been transformed to z-scores based on the distribution in the present population (i.e., a z-score of 0 corresponds to the mean of the study cohort). Download figure Download PowerPoint Plasma biomarker change points The plasma biomarkers changed approximately at the same point as the corresponding CSF biomarkers (Fig 2, Appendix Table S1), except for plasma neurogranin and Aβ40, which had non-significant models. Compared to CSF, all plasma biomarkers had wider 95% CIs, indicating greater variability in the early Aβ phase and/or less rapid biomarker changes. Comparisons of CSF assays from five different manufacturers This comparative analysis was performed on a subset of the study population where complete data for all assays were available (n = 352 vs. n = 377 in the total population). Spline models for Aβ42 (Elecsys®, EUROIMMUN, INNOTEST, and MSD), Aβ42/40 (Elecsys®, EUROIMMUN, and MSD), P-tau (Elecsys®, EUROIMMUN, INNOTEST, Lilly P-tau181, and Lilly P-tau217), and T-tau (Elecsys®, EUROIMMUN, and MSD) are shown in Fig 4A–D. Overall, the different biomarkers had very similar trajectories between assays. No significant differences were seen in change points between biomarker results obtained using any of the assays (Fig 5). Figure 4. Comparison of CSF biomarker trajectories from five different vendors A–D. The biomarker data were fitted using monotone spline models for CSF Aβ42 (A), Aβ42/40 (B), P-tau (C), and T-tau (D) assays, where Aβ PET SUVR acted as a proxy for time in AD. Point of change on the trajectory (also referred to as significant biomarker change) is shown as vertical dashed lines. Significant changes were identified for all biomarkers, but some were almost identical and are therefore partially hidden (see Fig 5 for a better overview of change points). Note that this analysis was performed on a slightly smaller sample where all participants had a complete dataset of all assays (n = 352 vs. n = 377 in the whole population). Download figure Download PowerPoint Figure 5. Comparison of change points for the different Aβ42, Aβ42/40, T-tau, and P-tau assaysSignificant biomarker changes with 95% CIs of the modeled biomarker trajectories are shown in Fig 4. Note that this analysis was performed on a smaller sample (n = 352 vs. n = 377) where all participants had a complete dataset of all assays, which gave slightly different results for the Elecsys® assay compared to the full dataset. Download figure Download PowerPoint Discussion In this study of 377 individuals who were cognitively unimpaired (n = 242) or had MCI (n = 135), we examined 7 CSF and 6 plasma biomarkers in relation to fibrillar Aβ accumulation (measured using Aβ PET) to understand their evolution during the development of AD prior to the onset of dementia. In CSF, we found that the first significant changes were seen in Aβ42, followed closely by P-tau and T-tau (which all changed before Aβ PET positivity and concurrently with the Aβ42/Aβ40 ratio). Overt neurodegeneration (as measured by hippocampus volume and a second, more pronounced NfL increase) occurred later, after the threshold for Aβ positivity. In significantly modeled plasma biomarkers, we found no differences in change points compared with CSF (i.e., plasma biomarker changes were neither significantly earlier nor later than CSF; Fig 2). When comparing the trajectories of CSF and plasma biomarkers, we found that they were similar for Aβ42, Aβ40, Aβ42/40, and P-tau (Fig 3). Finally, we compared assays for CSF Aβ42, Aβ42/40, P-tau, and T-tau from different manufacturers and found that they were similar and could confirm the findings of the Elecsys® assays (Figs 4 and 5). This is the first study to conduct a direct comparison of the CSF biomarkers Aβ42, Aβ40, T-tau, P-tau, neurogranin, YKL-40, and NfL during increasing Aβ accumulation, and also the first study to include the corresponding plasma biomarker (except for YKL-40, which was not measured in plasma). Two recent studies have examined biomarker changes in the rare autosomal dominant variant of AD using estimated time to onset of cognitive symptoms as time variable in the Dominantly Inherited Alzheimer's Network (DIAN) disease study (McDade et al, 2018; Schindler et al, 2019). Despite using different samples and assays, they showed a similar sequence of change in biomarker levels with Aβ42 changing first, shortly after followed by P-tau, and then YKL-40 (neurogranin was also included in one of the studies but did not diverge significantly from healthy controls). A similar sequence of CSF Aβ42, T-tau, and P-tau changes has also recently been demonstrated in the Alzheimer's Disease Neuroimaging Initiative (ADNI) using longitudinal Aβ PET to estimate the time course of disease (Insel P et al, under review). Overall, these findings agree with the current AD models postulating that Aβ is an initiating factor in the pathogenesis and that we hitherto do not have a biomarker that changes before Aβ (Jack et al, 2013). The significant change in CSF Aβ42 before the Aβ PET threshold for Aβ positivity (Fig 2) is in agreement with previous studies showing that CSF Aβ42 becomes abnormal prior to Aβ PET (Palmqvist et al, 2016, 2017; Vlassenko et al, 2016). Further, the later change in Aβ42/40 compared to Aβ42 might be one of the explanations for the higher agreement between (dichotomous) Aβ PET and Aβ42/40 compared with Aβ PET and Aβ42 (Fig 2), which has been reported previously (Janelidze et al, 2016, 2017; Lewczuk et al, 2017; Doecke et al, 2018). Although this temporal difference in Aβ42 and Aβ42/40 is in agreement with the spline model showing an initial drop in Aβ40 followed by stable levels (Fig 3A), the earlier change in Aβ42 is not supported by a recent study on autosomal dominant AD (Schindler et al, 2019). The Aβ40 finding should also be interpreted cautiously since this initial decline was driven by few individuals and the Aβ40 model was barely significant for CSF (P = 0.02; Appendix Fig S1) and was not significant for plasma (P = 0.37; Appendix Fig S2). Even though there are previous studies supporting the use of CSF Aβ42 measured in isolation to detect early Aβ accumulation (Mattsson et al, 2015, 2019b; Palmqvist et al, 2016, 2017), the ratio probably provides a more reliable measure of accumulating Aβ fibrils and increases its specificity since Aβ40 acts as a reference peptide that can, for example, account for inter-individual differences in CSF concentrations and differences in pre-analytical handling of the samples which otherwise may lead to false-positive or false-negative results using just Aβ42 (Janelidze et al, 2016; Lewczuk et al, 2017). Although the biomarkers for tau phosphorylation state (P-tau), synaptic dysfunction (neurogranin), glial activation and inflammation (YKL-40), and neurodegeneration (NfL and T-tau) changed after CSF Aβ42, they still changed at a very early stage, just before or around the threshold for Aβ PET positivity. These results indicate that the known and measurable pathological mechanisms of AD all develop fairly simultaneously early on in the disease and that a long-lasting manifest Aβ stage ("Aβ positivity") is not required before alterations in other pathological mechanisms occur. This is well in accordance with earlier work, showing that both brain hypometabolism and cognitive decline start to accelerate before Aβ positivity is reached (Insel et al, 2016, 2017). However, to complicate matters, one must consider that we are examining biomarkers and not actual pathological mechanisms. The validity (i.e., does the biomarker measure what it is supposed to measure) may thus clearly affect these results. It is, for example, possible that the early increase in P-tau reflects a neuronal reaction to the amyloidosis, rather than actual neurofibrillary tau pathology (Mattsson et al, 2017b; Sato et al, 2018), or that increased neuronal activity and secretion of a variety of intra-neuronal proteins could be an early AD event (Cirrito et al, 2008; Li et al, 2013). The latter case would result in an increase in several peptides in the extracellular space without any actual new pathological mechanisms taking place (other than increased secretion). This could for, example, also cause the initial increase in CSF NfL that is too subtle to be clinically relevant (early change point; Figs 1A and 2), followed by a more marked change better corresponding to overt neurodegeneration (late change point; Figs 1A and 2). This hypothesis is also in agreement with the contradictory studies showing that subtle longitudinal changes in NfL can be detected 16 years before the onset of cognitive symptoms (Preische et al, 2019), while cross-sectional differences are only noticeable at the MCI and dementia stage (Mattsson et al, 2019a,b). In addition to the validity, the sensitivity of the biomarker (for detecting the underlying pathology) may also affect the results. For example, if CSF P-tau is much less sensitive to accumulating tau pathology in the brain than CSF Aβ42 to Aβ, we would find that CSF Aβ42 changed earlier even if tau was an earlier pathological mechanism in the brain. We therefore want to note that the identified order of biomarker changes (Fig 2) refers to the actual biomarkers, which may or may not translate to the order in which the underlying pathology appears in the brain. When comparing the 95% CIs of the change points for CSF and corresponding plasma biomarkers, they overlapped (Fig 2; Appendix Table S1). Nonetheless, there were still considerable differences between their trajectories. Overall, the plasma biomarker models had considerably lower r2 values than the corresponding CSF biomarkers and exhibited smaller dynamic ranges (Appendix Figs S1 and S2, and Figs 1A and B, and 3A).
To test whether plasma tau is altered in Alzheimer disease (AD) and whether it is related to changes in cognition, CSF biomarkers of AD pathology (including β-amyloid [Aβ] and tau), brain atrophy, and brain metabolism.This was a study of plasma tau in prospectively followed patients with AD (n = 179), patients with mild cognitive impairment (n = 195), and cognitive healthy controls (n = 189) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and cross-sectionally studied patients with AD (n = 61), mild cognitive impairment (n = 212), and subjective cognitive decline (n = 174) and controls (n = 274) from the Biomarkers for Identifying Neurodegenerative Disorders Early and Reliably (BioFINDER) study at Lund University, Sweden. A total of 1284 participants were studied. Associations were tested between plasma tau and diagnosis, CSF biomarkers, MRI measures, 18fluorodeoxyglucose-PET, and cognition.Higher plasma tau was associated with AD dementia, higher CSF tau, and lower CSF Aβ42, but the correlations were weak and differed between ADNI and BioFINDER. Longitudinal analysis in ADNI showed significant associations between plasma tau and worse cognition, more atrophy, and more hypometabolism during follow-up.Plasma tau partly reflects AD pathology, but the overlap between normal aging and AD is large, especially in patients without dementia. Despite group-level differences, these results do not support plasma tau as an AD biomarker in individual people. Future studies may test longitudinal plasma tau measurements in AD.
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