Higher CSF sTREM2 and microglia activation are associated with slower rates of beta‐amyloid accumulation
2020; Springer Nature; Volume: 12; Issue: 9 Linguagem: Inglês
10.15252/emmm.202012308
ISSN1757-4684
AutoresMichael Ewers, Gloria Biechele, Marc Suárez‐Calvet, Christian Sacher, Tanja Blume, Estrella Morenas‐Rodríguez, Yuetiva Deming, Laura Piccio, Carlos Cruchaga, Gernot Kleinberger, Leslie M. Shaw, John Q. Trojanowski, Jochen Herms, Martin Dichgans, Matthias Brendel, Christian Haass, Nicolai Franzmeier,
Tópico(s)Tryptophan and brain disorders
ResumoArticle10 August 2020Open Access Source DataTransparent process Higher CSF sTREM2 and microglia activation are associated with slower rates of beta-amyloid accumulation Michael Ewers Corresponding Author Michael Ewers [email protected] orcid.org/0000-0001-5231-1714 Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University (LMU), Munich, Germany German Center for Neurodegenerative Diseases (DZNE), Munich, Germany Search for more papers by this author Gloria Biechele Gloria Biechele Department of Nuclear Medicine, University Hospital Munich, Ludwig Maximilian University Munich, Munich, Germany Search for more papers by this author Marc Suárez-Calvet Marc Suárez-Calvet orcid.org/0000-0002-2993-569X Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain Servei de Neurologia, Hospital del Mar, Barcelona, Spain Search for more papers by this author Christian Sacher Christian Sacher Department of Nuclear Medicine, University Hospital Munich, Ludwig Maximilian University Munich, Munich, Germany Search for more papers by this author Tanja Blume Tanja Blume German Center for Neurodegenerative Diseases (DZNE), Munich, Germany Search for more papers by this author Estrella Morenas-Rodriguez Estrella Morenas-Rodriguez Chair of Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany Search for more papers by this author Yuetiva Deming Yuetiva Deming orcid.org/0000-0001-7512-5703 Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Laura Piccio Laura Piccio Department of Neurology, Washington University School of Medicine, St Louis, MO, USA Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia Search for more papers by this author Carlos Cruchaga Carlos Cruchaga Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA Search for more papers by this author Gernot Kleinberger Gernot Kleinberger orcid.org/0000-0002-5811-8226 Chair of Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany ISAR Bioscience GmbH, Planegg, Germany Search for more papers by this author Leslie Shaw Leslie Shaw Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Search for more papers by this author John Q Trojanowski John Q Trojanowski Center for Neurodegenerative Disease Research, Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Search for more papers by this author Jochen Herms Jochen Herms orcid.org/0000-0002-6201-1042 German Center for Neurodegenerative Diseases (DZNE), Munich, Germany Search for more papers by this author Martin Dichgans Martin Dichgans Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University (LMU), Munich, Germany German Center for Neurodegenerative Diseases (DZNE), Munich, Germany Munich Cluster for Systems Neurology (SyNergy), Munich, Germany Search for more papers by this author the Alzheimer's Disease Neuroimaging Initiative (ADNI) the Alzheimer's Disease Neuroimaging Initiative (ADNI)Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found in the appendix (“ADNI_coinvestigators.docx”). Search for more papers by this author Matthias Brendel Matthias Brendel Department of Nuclear Medicine, University Hospital Munich, Ludwig Maximilian University Munich, Munich, Germany Search for more papers by this author Christian Haass Christian Haass orcid.org/0000-0002-4869-1627 German Center for Neurodegenerative Diseases (DZNE), Munich, Germany Chair of Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany Munich Cluster for Systems Neurology (SyNergy), Munich, Germany Search for more papers by this author Nicolai Franzmeier Nicolai Franzmeier orcid.org/0000-0001-9736-2283 Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University (LMU), Munich, Germany Search for more papers by this author Michael Ewers Corresponding Author Michael Ewers [email protected] orcid.org/0000-0001-5231-1714 Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University (LMU), Munich, Germany German Center for Neurodegenerative Diseases (DZNE), Munich, Germany Search for more papers by this author Gloria Biechele Gloria Biechele Department of Nuclear Medicine, University Hospital Munich, Ludwig Maximilian University Munich, Munich, Germany Search for more papers by this author Marc Suárez-Calvet Marc Suárez-Calvet orcid.org/0000-0002-2993-569X Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain Servei de Neurologia, Hospital del Mar, Barcelona, Spain Search for more papers by this author Christian Sacher Christian Sacher Department of Nuclear Medicine, University Hospital Munich, Ludwig Maximilian University Munich, Munich, Germany Search for more papers by this author Tanja Blume Tanja Blume German Center for Neurodegenerative Diseases (DZNE), Munich, Germany Search for more papers by this author Estrella Morenas-Rodriguez Estrella Morenas-Rodriguez Chair of Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany Search for more papers by this author Yuetiva Deming Yuetiva Deming orcid.org/0000-0001-7512-5703 Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Laura Piccio Laura Piccio Department of Neurology, Washington University School of Medicine, St Louis, MO, USA Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia Search for more papers by this author Carlos Cruchaga Carlos Cruchaga Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA Search for more papers by this author Gernot Kleinberger Gernot Kleinberger orcid.org/0000-0002-5811-8226 Chair of Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany ISAR Bioscience GmbH, Planegg, Germany Search for more papers by this author Leslie Shaw Leslie Shaw Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Search for more papers by this author John Q Trojanowski John Q Trojanowski Center for Neurodegenerative Disease Research, Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Search for more papers by this author Jochen Herms Jochen Herms orcid.org/0000-0002-6201-1042 German Center for Neurodegenerative Diseases (DZNE), Munich, Germany Search for more papers by this author Martin Dichgans Martin Dichgans Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University (LMU), Munich, Germany German Center for Neurodegenerative Diseases (DZNE), Munich, Germany Munich Cluster for Systems Neurology (SyNergy), Munich, Germany Search for more papers by this author the Alzheimer's Disease Neuroimaging Initiative (ADNI) the Alzheimer's Disease Neuroimaging Initiative (ADNI)Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found in the appendix (“ADNI_coinvestigators.docx”). Search for more papers by this author Matthias Brendel Matthias Brendel Department of Nuclear Medicine, University Hospital Munich, Ludwig Maximilian University Munich, Munich, Germany Search for more papers by this author Christian Haass Christian Haass orcid.org/0000-0002-4869-1627 German Center for Neurodegenerative Diseases (DZNE), Munich, Germany Chair of Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany Munich Cluster for Systems Neurology (SyNergy), Munich, Germany Search for more papers by this author Nicolai Franzmeier Nicolai Franzmeier orcid.org/0000-0001-9736-2283 Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University (LMU), Munich, Germany Search for more papers by this author Author Information Michael Ewers *,1,2, Gloria Biechele3, Marc Suárez-Calvet4,5,6, Christian Sacher3, Tanja Blume2, Estrella Morenas-Rodriguez7, Yuetiva Deming8, Laura Piccio9,10,11, Carlos Cruchaga10,12, Gernot Kleinberger7,13, Leslie Shaw14, John Q Trojanowski15, Jochen Herms2, Martin Dichgans1,2,16, , Matthias Brendel3, Christian Haass2,7,16 and Nicolai Franzmeier1 1Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University (LMU), Munich, Germany 2German Center for Neurodegenerative Diseases (DZNE), Munich, Germany 3Department of Nuclear Medicine, University Hospital Munich, Ludwig Maximilian University Munich, Munich, Germany 4Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain 5IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain 6Servei de Neurologia, Hospital del Mar, Barcelona, Spain 7Chair of Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany 8Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA 9Department of Neurology, Washington University School of Medicine, St Louis, MO, USA 10Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA 11Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia 12Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA 13ISAR Bioscience GmbH, Planegg, Germany 14Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA 15Center for Neurodegenerative Disease Research, Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA 16Munich Cluster for Systems Neurology (SyNergy), Munich, Germany *Corresponding author. Tel: +49 89 4400 46221; E-mail: [email protected] EMBO Mol Med (2020)12:e12308https://doi.org/10.15252/emmm.202012308 [Correction added on 25th September, after first online publication: Projekt Deal funding statement has been added.] 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 Microglia activation is the brain's major immune response to amyloid plaques in Alzheimer's disease (AD). Both cerebrospinal fluid (CSF) levels of soluble TREM2 (sTREM2), a biomarker of microglia activation, and microglia PET are increased in AD; however, whether an increase in these biomarkers is associated with reduced amyloid-beta (Aβ) accumulation remains unclear. To address this question, we pursued a two-pronged translational approach. Firstly, in non-demented and demented individuals, we tested CSF sTREM2 at baseline to predict (i) amyloid PET changes over ∼2 years and (ii) tau PET cross-sectionally assessed in a subset of patients. We found higher CSF sTREM2 associated with attenuated amyloid PET increase and lower tau PET. Secondly, in the AppNL-G-F mouse model of amyloidosis, we studied baseline 18F-GE180 microglia PET and longitudinal amyloid PET to test the microglia vs. Aβ association, without any confounding co-pathologies often present in AD patients. Higher microglia PET at age 5 months was associated with a slower amyloid PET increase between ages 5-to-10 months. In conclusion, higher microglia activation as determined by CSF sTREM2 or microglia PET shows protective effects on subsequent amyloid accumulation. Synopsis TREM2 is a protein almost exclusively expressed by microglia in the brain. This study investigates the association between soluble TREM2 (sTREM2) levels in cerebrospinal fluid and the longitudinal Aβ accumulation in human and mouse. In patients with Aβ pathology, higher cerebrospinal fluid (CSF) levels of sTREM2 are associated with lower rates of Aβ accumulation. Higher CSF sTREM2 levels are associated with lower neurofibrillary tangles. In the Aβ mouse model, higher microglia activation at baseline is associated with lower rates of Aβ accumulation between 5 and 10 months of age, when Aβ deposition primarily takes place. The paper explained Problem Microglia activation forms the brain's innate immune response to cerebral pathologies including fibrillar beta-amyloid (Aβ), i.e., a primary pathology in Alzheimer's disease. TREM2 is a receptor protein expressed in the brain by microglia, where signaling through TREM2 triggers the phagocytosis of pathogens such as Aβ. Since microglia activation such as that triggered by TREM2 activation constitutes a potential drug target, understanding the role of increased TREM2 and microglia activation in AD progression is pivotal. Here, we tested the role of microglia activation in a two-pronged approach. First, we assessed cerebrospinal fluid (CSF) biomarker levels of the soluble fraction of TREM2 (sTREM2) as a predictor of longitudinal increase in Aβ as assessed by amyloid PET imaging over 2 years on average in non-demented and demented participants. Second, in order to confirm a link between higher microglia activation and changes in Aβ accumulation, we tested microglia activation (assessed by microglia PET imaging) as a predictor of rates of amyloid PET increase in a mouse model of genetically caused Aβ. Results We found that higher baseline CSF sTREM2 levels were associated with a slower rate of amyloid PET accumulation, in particular during phases of intermediate baseline Aβ levels in elderly subjects. In the transgenic mouse model of Aβ, we found that higher microglia PET at 5 months of age, i.e., when Aβ accumulation starts to emerge globally in the brain, to be associated with reduced rates of amyloid PET during a phase until 10 months of age, i.e., a period when Aβ is strongly increasing. Impact We conclude that higher microglia activation and in particular TREM2 during a period of intermediate baseline levels of Aβ are associated with reduced rates of Aβ accumulation. These results suggest that enhancing TREM2-related microglia activation may exert protective effects on Aβ. Introduction The deposition of amyloid-β (Aβ) peptide is a key primary alteration that defines Alzheimer's disease (AD), involving a cascade of pathological brain changes resulting in the occurrence of dementia symptoms (Hardy & Higgins, 1992; Selkoe & Hardy, 2016). Recent evidence from genome-wide association studies suggests that alterations in the innate immune system are associated with enhanced risk of AD and thus modulate the development of AD (Lambert et al, 2009; Guerreiro et al, 2013; Jonsson et al, 2013; Reitz et al, 2013; Efthymiou & Goate, 2017; Sims et al, 2017; Jansen et al, 2019). Microglia activation is the brain's major innate immune response to pathogens, and reactive microglia surrounding the amyloid plaques are a hallmark of AD (Itagaki et al, 1989). Microglia activation involves the phagocytosis of amyloid plaques as well as a reduction in Aβ-neurotoxicity and thus plays a key role to restore brain homeostasis (Sarlus & Heneka, 2017; Butovsky & Weiner, 2018). However, microglia activation can also be detrimental by inducing neurotoxic inflammation (Meda et al, 1995; Hong et al, 2016). Hence, understanding the role of microglia activation in response to core AD pathology (including Aβ) is pivotal for both tracking disease progression and identifying potential therapeutic interventions. The overall aim of our study was to assess whether microglial function is associated with lower rates of Aβ accumulation throughout the Alzheimer's continuum. We addressed this question using two different approaches. First, in humans, we tested whether cerebrospinal fluid levels of triggering receptor expressed on myeloid cell type 2 (TREM2) as a marker of TREM2-mediated microglia response predict a change in accumulation of Aβ pathology. Second, we tested in a mouse model of amyloidosis whether changes in GE 180 (i.e., a PET tracer of microglial activation) predict longitudinal changes in Aβ deposition. Our study on TREM2 was motivated by previous findings in both humans and animal models of Aβ, suggesting that TREM2-related microglia activation may have a protective role in AD (for review, see Butovsky & Weiner, 2018). TREM2 is a transmembrane protein of the immunoglobulin family that is expressed by immune cells throughout the body, but in the brain almost exclusively by microglia (Hickman et al, 2013). TREM2 forms a signaling complex with the DNAX activation protein 12 (DAP12) that triggers microglial activation, associated with increased microglial proliferation, chemotaxis, and phagocytosis of Aβ fibrils, as well as reduction of excessive pro-inflammatory responses (Takahashi et al, 2005; Wang et al, 2015; Zhong et al, 2015; Keren-Shaul et al, 2017; Mazaheri et al, 2017; Zhao et al, 2018). Genetic studies in humans showed that rare variants in the TREM2 gene are associated with an exceptionally increased risk of AD, comparable to increase in risk by the APOE ε4 genotype (Guerreiro & Hardy, 2013; Jonsson et al, 2013), suggesting that TREM2-related microglia activation may reduce the risk of AD dementia. The soluble TREM2 (sTREM2) fraction is detectable in the cerebrospinal fluid (CSF) (Piccio et al, 2008), possibly through a mechanism of proteolytic cleavage of TREM2 and release into interstitial fluid (Kleinberger et al, 2014; Schlepckow et al, 2017). CSF sTREM2 is increased in humans with a biomarker profile of AD years before the onset of dementia symptoms (Heslegrave et al, 2016; Piccio et al, 2016; Suarez-Calvet et al, 2016a,b, 2019), where higher CSF sTREM2 at a given biomarker level of Aβ and pathologic tau was associated with larger gray matter volume (Gispert et al, 2016b) and slower subsequent cognitive and clinical decline in symptomatic elderly participants (Ewers et al, 2019). Together, these findings support a protective role of elevated sTREM2 in AD in the symptomatic phase of AD. However, whether increased CSF sTREM2 is associated with reduced rates of longitudinal increase of fibrillar Aβ is unclear. Therefore, in the first part of our study we set out to determine whether in elderly participants throughout the AD continuum, CSF sTREM2 levels at baseline are associated with longitudinal changes in amyloid PET. In patients with AD, the rate of amyloid PET accumulation does not increase in a linear fashion, but shows a peak at about intermediate levels of global amyloid deposition before leveling off again, thus resembling a quadratic curve across the range of Aβ brain levels (Villemagne et al, 2013). Here, we hypothesized that higher CSF sTREM2 levels modulate this dynamic change in the rate of amyloid PET increase, i.e., higher CSF sTREM2 is associated with slower amyloid PET increase during the peak phase of amyloid deposition. A secondary goal in the current human PET study was to assess whether higher CSF sTREM2 levels are associated with lower tau PET independent of Aβ levels. Recent studies on mouse models of tau pathology suggest that that TREM2 deficiency is associated with higher tau pathology (Jiang et al, 2015, 2016), although inconsistent findings have been reported as well (Leyns et al, 2017). However, no study in humans has yet tested whether higher CSF sTREM2 is associated with lower tau PET in elderly subjects with biomarker evidence of Aβ. In the last part of our study, we assessed microglia PET as a predictor of amyloid PET accumulation in a mouse model of genetically caused Aβ pathology. The use of mouse models of Aβ has the major advantage to assess any association between higher microglia activation and lower Aβ accumulation in a more controlled setting, i.e., in the absence of potentially confounding co-pathologies such as vascular changes that may drive microglia activation in patients with AD, where mixed pathologies are frequent (Kapasi et al, 2017). Furthermore, although CSF sTREM2 levels may reflect the amount of signaling-competent TREM2 on the surface of microglia (Kleinberger et al, 2014; Schlepckow et al, 2017) and thus provide a marker of microglia activation, the link between CSF sTREM2 and microglia activation is not firmly established. Therefore, we tested in mice the translocator protein-18 kDa (TSPO) PET as a marker of microglia activation in our second part of the study, reasoning that neuroimaging approaches including TSPO-PET allow for a more direct in vivo assessment of activated microglia activation in a longitudinal fashion (Edison et al, 2018). In longitudinal dual tracer studies, we previously showed that both microglia PET and amyloid PET are increased in an age-dependent manner in mouse models of amyloidosis (Brendel et al, 2017; Blume et al, 2018). Levels of sTREM2 in brain homogenate were associated with higher microglia PET in mouse models for amyloid pathology, which was positively correlated with higher immunohistochemically determined microglia numbers (Sacher et al, 2019), suggesting that higher microglia PET in mice with amyloidosis reflects higher microglia activation that is strongly correlated with TREM2 levels. We used this dual tracer paradigm in transgenic mice to test whether sporadically occurring interindividual variation in microglia activation at baseline is predictive of the rate of subsequent Aβ deposition. Specifically, we obtained both 18F GE-180 TSPO microglia PET and 18F-florbetaben amyloid PET in the AppNL-G-F mice, which show global Aβ deposition at 4 months of age (Saito et al, 2014), reaching a plateau by 9 months of age (Mehla et al, 2019). We hypothesized that higher baseline microglia PET levels assessed at 5 months of age are associated with slower rates of amyloid PET increase during the subsequent follow-up period until 10 months of age. Results Patient characteristics The baseline characteristics of all study participants are displayed in Table 1. The overall sample included 300 participants (136 female) with a mean (SD) age of 72.54 (6.4). The total sample included 94 CN Aβ− vs. 206 participants covering the Alzheimer's continuum (i.e., 55 CN Aβ+, 136 MCI Aβ+, 15 AD dementia). The sample size of subject in the AD continuum was sufficient to detect an effect size of R2 = 0.038 for our hypothesized effect of the interaction CSF sTREM2 × AV45 PET on the rate of AV45 PET change at a power of 0.8 and alpha = 0.05. Compared to the CN Aβ− group, the Alzheimer's continuum group showed abnormal AD biomarker levels (i.e., AV45 global SUVR, CSF total tau, CSF p-tau181) and cognition (ADNI-MEM, MMSE), and these abnormalities increased in symptomatic stages (i.e., MCI Aβ+, AD dementia). All group differences were assessed at the pre-defined alpha level of 0.05 (two-tailed). Table 1. Baseline sample characteristics Controls AD spectrum P-value CN Aβ− N = 94 CN Aβ+ N = 55 MCI Aβ+ N = 136 AD dementia N = 15 Age 72.9 (5.88)d 72.9 (6.00)d 71.6 (6.54)d 77.9 (7.22)a,b,c 0.003 Gender (f/m) 41/53 34/21c 56/80b 5/10 0.047 Education 16.9 (2.37) 16.2 (2.67) 16.2 (2.61) 15.5 (2.70) 0.099 ApoE4 (pos/neg) 81/13b,c,d 30/25a,d 51/85a 2/13a,b < 0.001 ADNI-MEM 1.18 (0.56)c,d 1.17 (0.68)c,d 0.18 (0.61)a,b,d −0.83 (0.33)a,b,c < 0.001 MMSE 29.2 (1.07)c,d 29.1 (0.88)c,d 27.8 (1.81)a,b,d 22.5 (1.81)a,b,c < 0.001 AV45 global SUVR 0.74 (0.03) 0.91 (0.10) 0.97 (0.11) 1.05 (0.07) < 0.001 Time between AV45 visits 2.16 (0.59)b,c,d 2.33 (0.85)a,c,d 2.12 (0.50)a,b,d 2.00 (0.03)a,b,c 0.098 CSF sTREM2 4,163 (2,086) 3,701 (2,007) 4,317 (2,212) 4,640 (2,599) 0.270 CSF total tau 220 (78.3)c,d 268 (107)c,d 333 (132)a,b 381 (118)a,b < 0.001 CSF p-tau181 19.3 (7.11)b,c,d 25.8 (11.4)a,c,d 32.9 (14.4)a,b 39.1 (12.9)a,b < 0.001 Number of participants with tau PETe 14 14 26 0 a Sig. (P < 0.05) different from CN AB−. b Sig. (P < 0.05) different from CN AB+. c Sig. (P < 0.05) different from MCI AB+. d Sig. (P < 0.05) different from AD dementia. e Tau PET had to be obtained maximum 3 years after the last joint AV45 PET/CSF sTREM2 assessment. Inverse U-shaped association between baseline amyloid PET levels and annual amyloid accumulation Previous studies in autosomal dominant and sporadic AD have shown that the rate of amyloid accumulation across the Alzheimer's continuum shows an inverse u-shape, where amyloid PET accelerates until a plateau is reached, after which amyloid PET accumulation decelerates (Villemagne et al, 2013; Gordon et al, 2018; Leal et al, 2018). To confirm this inversely u-shaped amyloid PET accumulation in the current sample, we assessed association between baseline AV45 PET levels and annual AV45 PET changes across the pooled sample (i.e., CN Aβ− & Alzheimer's continuum). Using a linear mixed effects model, we confirmed in our sample the hypothesized inversely u-shaped (i.e., quadratic) effect of baseline AV45 PET on annual AV45 PET change (t(396) = −4.605, b/SE = −0.195/0.042, 95% CI = [−0.277; −0.113], P < 0.001, Cohen's d = −0.463, Fig 1A). Specifically, very low and very high baseline AV45 PET levels were associated with little annual AV45 PET changes, whereas intermediate AV45 PET levels were associated with relatively strong annual AV45 PET increases (controlling for baseline age, gender, education, diagnosis, CSF p-tau181, APOE ε4, time between AV45 PET visits, and random intercept). The quadratic model of baseline AV45 PET on annual AV45 PET showed a better fit than the lineal model (χ2(1) = 21.23, P < 0.001). Testing the above described model in the Alzheimer's continuum group only (t(271) = −3.354, b/SE = −0.253/0.076, 95% CI = [−0.399; −0.011], P < 0.001, Cohen's d = −0.407) and in symptomatic AD only (i.e., MCI and AD dementia, t(226) = −2.240, b/SE = −0.185/0.083, 95% CI = [−0.344; −0.026], P = 0.026, Cohen's d = −0.298) yielded consistent results. This finding confirms previous evidence that annual AV45 PET increase (reflecting amyloid accumulation) accelerates initially but saturates at high AV45 PET levels. Results are summarized in Table 2, and all reported P-values are two-tailed. Figure 1. Association between baseline amyloid and amyloid PET change A, B. Regression plot showing the association between baseline AV45 PET (x-axis) and annual AV45 PET change (y-axis) across the entire sample (N = 300). The vertical dashed line represents the Aβ-positivity threshold of AV45 PET SUVR > 0.079 (A). Regression plot for the association between baseline AV45 PET and annual AV45 PET change stratified by baseline CSF sTREM2 levels (B). Source data are available online for this figure. Source Data for Figure 1 [emmm202012308-sup-0002-SDataFig1.xlsx] Download figure Download PowerPoint Table 2. Linear mixed models testing the effects of baseline AV45 and CSF sTREM2 on annual AV45 PET change Dependent variable: annual AV45 change b/SE 95% CI t-value P-value Cohen's d Partial R2 Whole group Model 1a: Main effect—Baseline AV45 SUVR2 −0.195/0.042 −0.277; −0.113 −4.605 < 0.001 −0.463 0.050 Model 2b: Main effect—Baseline CSF sTREM2 −0.023/0.009 −0.0041; −0.0006 −2.584 0.010 −0.260 0.016 Model 3b: Interaction—Baseline AV45 SUVR2 × CSF sTREM2 0.211/0.086 0.045; 0.376 2.460 0.014 0.266 0.015 AD continuum Model 1a: Main effect—Baseline AV45 SUVR2 −0.253/0.076 −0.399; −0.011 −3.354 < 0.001 −0.407 0.039 Model 2b: Main effect—Baseline CSF sTREM2 −0.003/0.001 −0.0053; −0.0006 −2.400 0.017 −0.292 0.020 Model 3b: Interaction—Baseline AV45 SUVR2 × CSF sTREM2 0.636/0.165 0.320; 0.952 3.861 < 0.001 0.472 0.051 AD clinical (MCI Aβ+ & AD Dementia)a Model 1a: Main effect—Baseline AV45 SUVR2 −0.185/0.083 −0.344; −0.026 −2.240 0.026 −0.298 0.021 Model 2b: Main effect—Baseline CSF sTREM2 −0.004/0.001 −0.0062; 0.0009 2.563 0.011 −0.342 0.027 Model 3b: Interaction—Baseline AV45 SUVR
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