Wanted: Driving Force of Neuroinflammation

We are pleased to present the newly published article "Individual regional associations between Aβ-, tau- and neurodegeneration (ATN) with microglial activation in patients with primary and secondary tauopathies". A study within the framework of ActiGliA.
Published in Neuroscience
Wanted: Driving Force of Neuroinflammation
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The aging population calls for more intensive research in the field of neurodegenerative diseases. Currently, the spectrum of dementias is a widely studied topic, as profound knowledge about pathophysiology, diagnosis, and therapy is still needed. The interdisciplinary study ActiGliA - "Activity of Cerebral Networks, Amyloid and Microglia in Alzheimer’s Disease" - examines the correlation between neuroinflammation, structural and functional networks, and the deposition of specific proteins (Aβ, tau) in the brain. By combining modern investigation tools such as positron-emission-tomography (PET), magnetic-resonance-imaging (MRI), cerebrospinal fluid samples (CSF), and clinical tests, new possibilities open up for gaining deeper insights. Our research group presents an article that provides a deeper understanding of the ATN-sequence within primary and secondary tauopathies.

But what is ATN all about?

ATN suggests that β-amyloidosis [A] precedes and accelerates tau pathology [T], ultimately leading to neurodegeneration [N] (1). This concept is now evolving into an ATX(N) system, with X representing promising new candidate biomarkers shedding light on additional pathophysiological mechanisms (2). Neuroinflammation is a mediator in protein aggregation, spreading, and disease progression, and holds the potential to serve as a valuable "X" parameter in the ATX(N) system, subsequently referred to as ATI(N). The focus of our work is the effect of ATN on microglial activation [I] in primary and secondary tauopathies.

What makes the study so novel?

Until now, AT(I)N studies have predominantly focused on patient cohorts with secondary tauopathies, like Alzheimer's disease. However, primary tauopathies also display Aβ-pathology (3), which is why we particularly included this cohort. Furthermore, a novel methodological approach has been adopted to analyze ATN-associated microglial activation not only at the group level but also at the individual patient level. Our interdisciplinary expert team harnessed cutting-edge investigation tools, novel PET-tracers, and advanced analysis pipelines.

What have we found out?

As a brief glimpse of our results, we present our single-patient analysis that identifies tau pathology dominating the regional associations of ATN biomarkers with microglial activation (Figure 2). Furthermore, tau- and Aβ- associated microglial activation indices demonstrate distinct associations with cognitive performance and sTrem2 levels (CSF), potentially serving for in vivo assessment of neuroinflammation.

The article is located here: https://www.nature.com/articles/s41380-023-02188-8

Multiregional regression analysis to determine the association of ATN biomarkers with microglial activation at the individual patient level. A Methodological workflow: PET and MRI data were spatial normalized to tracer specific templates acquired in previous in house studies (Montreal Neurology Institute (MNI) space) and parcellated into 210 cortical and 36 subcortical brain regions. PET-tracer uptake and gray-matter-volumes (GMV, from MRI) were transformed into z-scores with age- and sex-matched controls. A multiple linear regression model was performed (I = β0+ βAxA + βTxT + βN1xN1 + βN2xN2 + ε) at the single-patient-level and standardized coefficients (β) were derived for each ATN biomarker. B β-coefficients were visualized as a combined violin-box-plot and show the association of ATN biomarkers with microglia activation, each dot representing a single subject. Within each diagnosis group, the β-coefficients of the four independent variables were tested for group differences using ANOVA with age and sex as covariates. Multiple posthoc tests (dichotomous ANOVA, age, and sex corrected) were performed if there was a significant group difference. Combined false discovery rate (FDR, six p-values) and Bonferroni (five p-values) correction were applied to decrease the risk of α-error-accumulation. *p < 0.05, **p < 0.01, ***p < 0.001. AD Alzheimer’s disease, AD-CBS corticobasal syndrome with AD-pathology, 4RT four-repeat tauopathies, LAB low affinity binder, CTRL healthy controls, TSPO 18-kDa translocator protein, Aβ β-amyloid, GMV gray-matter-volume, PET positron-emissions-tomography, MRI magnetic-resonance-imaging, MNI Montreal Neurology Institute, ANOVA analysis of variance, FDR false discovery rate.
Figure 2 Multiregional regression analysis to determine the association of ATN biomarkers with microglial activation at the individual patient level. A Methodological workflow: PET and MRI data were spatial normalized to tracer specific templates acquired in previous in house studies (Montreal Neurology Institute (MNI) space) and parcellated into 210 cortical and 36 subcortical brain regions. PET-tracer uptake and gray-matter-volumes (GMV, from MRI) were transformed into z-scores with age- and sex-matched controls. A multiple linear regression model was performed (I = β0+ βAxA + βTxT + βN1xN1 + βN2xN2 + ε) at the single-patient-level and standardized coefficients (β) were derived for each ATN biomarker. B β-coefficients were visualized as a combined violin-box-plot and show the association of ATN biomarkers with microglia activation, each dot representing a single subject. Within each diagnosis group, the β-coefficients of the four independent variables were tested for group differences using ANOVA with age and sex as covariates. Multiple posthoc tests (dichotomous ANOVA, age, and sex corrected) were performed if there was a significant group difference. Combined false discovery rate (FDR, six p-values) and Bonferroni (five p-values) correction were applied to decrease the risk of α-error-accumulation. *p < 0.05, **p < 0.01, ***p < 0.001. AD Alzheimer’s disease, AD-CBS corticobasal syndrome with AD-pathology, 4RT four-repeat tauopathies, LAB low affinity binder, CTRL healthy controls, TSPO 18-kDa translocator protein, Aβ β-amyloid, GMV gray-matter-volume, PET positron-emissions-tomography, MRI magnetic-resonance-imaging, MNI Montreal Neurology Institute, ANOVA analysis of variance, FDR false discovery rate.

 

  1. Tan MS, Ji X, Li JQ, Xu W, Wang HF, Tan CC, et al. Longitudinal trajectories of Alzheimer's ATN biomarkers in elderly persons without dementia. Alzheimers Res Ther. 2020;12(1):55.
  2. Hampel H, Cummings J, Blennow K, Gao P, Jack CR, Jr., Vergallo A. Developing the ATX(N) classification for use across the Alzheimer disease continuum. Nat Rev Neurol. 2021;17(9):580-9.
  3. Jecmenica Lukic M, Kurz C, Respondek G, Grau-Rivera O, Compta Y, Gelpi E, et al. Copathology in Progressive Supranuclear Palsy: Does It Matter? Mov Disord. 2020;35(6):984-93.

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