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. 2024 May 31;43:103626. doi: 10.1016/j.nicl.2024.103626

Translocator protein (TSPO) genotype does not change cerebrospinal fluid levels of glial activation, axonal and synaptic damage markers in early Alzheimer’s disease

Dominique Gouilly a,1,⁎, Agathe Vrillon b,c,1, Elsa Bertrand d, Marie Goubeaud d, Hélène Catala d, Johanne Germain d, Nadéra Ainaoui d, Marie Rafiq a,e, Leonor Nogueira f, François Mouton-Liger c, Mélanie Planton a,e, Anne-Sophie Salabert e,g, Anne Hitzel g, Déborah Méligne e, Laurence Jasse a, Benjamine Sarton e,h, Stein Silva e,h, Béatrice Lemesle a, Patrice Péran e, Pierre Payoux e,g, Claire Thalamas d, Claire Paquet b,c,2, Jérémie Pariente a,d,e,2
PMCID: PMC11201347  PMID: 38850834

Highlights

  • •

    Glial activation, synaptic and axonal damage are not affected by TSPO genotype in AD.

  • •

    Studies can combine TSPO PET and CSF biomarkers for a selected TSPO phenotype in AD.

  • •

    Results from one TSPO phenotype might be generalizable to an entire AD population.

Keywords: TSPO, Neuro-inflammation, Rs6971, Cerebrospinal fluid, Alzheimer’s disease

Abstract

Background

PET imaging of the translocator protein (TSPO) is used to assess in vivo brain inflammation. One of the main methodological issues with this method is the allelic dependence of the radiotracer affinity. In Alzheimer’s disease (AD), previous studies have shown similar clinical and patho-biological profiles between TSPO genetic subgroups. However, there is no evidence regarding the effect of the TSPO genotype on cerebrospinal-fluid biomarkers of glial activation, and synaptic and axonal damage.

Method

We performed a trans-sectional study in early AD to compare cerebrospinal-fluid levels of GFAP, YKL-40, sTREM2, IL-6, IL-10, NfL and neurogranin between TSPO genetic subgroups.

Results

We recruited 33 patients with early AD including 16 (48%) high affinity binders, 13 (39%) mixed affinity binders, and 4/33 (12%) low affinity binders. No difference was observed in terms of demographics, and cerebrospinal fluid levels of each biomarker for the different subgroups.

Conclusion

TSPO genotype is not associated with a change in glial activation, synaptic and axonal damage in early AD. Further studies with larger numbers of participants will be needed to confirm that the inclusion of specific TSPO genetic subgroups does not introduce selection bias in studies and trials of AD that combine TSPO imaging with cerebrospinal fluid biomarkers.

1. Introduction

Imaging of the translocator protein (TSPO) with positron emission tomography (PET) enables in vivo measurement of neuro-inflammation (Kreisl et al., 2020). However, one of the main issues with the use of TSPO biomarkers in PET is the allelic dependence of the radiotracer affinity (Turkheimer et al., 2015). A single-nucleotide polymorphism in the TSPO gene (rs6971) causes an alanine-to-threonine substitution in position 147 which is associated with different binding phenotypes defined as high (HAB), mixed (MAB) and low affinity binders (LAB) (Kreisl et al., 2013a, Owen et al., 2012). In healthy individuals, this leads to a 30–50 % uptake difference between HAB and MAB depending on the tracer used (Kreisl et al., 2013a, Lavisse et al., 2015, Yoder et al., 2013). However, the low specific binding observed among LAB is negligible and this leads to the constraint of studies being performed solely on HAB and MAB individuals, or even just on HAB individuals (Turkheimer et al., 2015).

In Alzheimer’s disease (AD), investigations have been performed to ascertain whether or not there is a clinical and patho-biological difference between TSPO genetic subgroups, and therefore, whether there is any selection bias in TSPO PET studies. Edison and colleagues showed that HAB and MAB AD patients were similar in terms of APOE4 carriership, cognition and amyloid load at baseline and cognitive decline (Fan et al., 2015). Another neuropathological study highlighted that TSPO messenger RNA and TSPO levels were not influenced by TSPO rs6971 in gray and white matter and in the cerebellum (Gui et al., 2020). Interestingly, they also demonstrated that TSPO rs6971 does not influence amyloid and tau burden, cortical thickness and neuro-inflammation (Gui et al., 2020). Together, these two studies suggest that results from one TSPO genetic subgroup can be applied to an entire population of AD patients.

Furthermore, measurement of cerebrospinal fluid (CSF) biomarkers enables to measure in vivo multiple distinct molecular processes related to glial activation, axonal, and synaptic damage. CSF levels of glial fibrillary acidic protein (GFAP) and chitinase-3-like protein 1 (YKL-40) reflect astrocyte reactivity (Bellaver et al., 2021), and are known to correlate with AD pathology and predict cognitive decline even in cognitively unimpaired individuals (Bellaver et al., 2023, Benedet et al., 2021, Pelkmans et al., 2023). The soluble variant of the soluble triggering receptor expressed on myeloid cell 2 (sTREM-2) is a reflection of TREM-2 expression and signaling in the CSF, which is involved in several microglial mechanistic changes in AD (Morenas-Rodríguez et al., 2022, Ulland and Colonna, 2018). CSF levels of interleukin (IL)-6 and IL-10 measurements are pro and anti inflammatory cytokines, respectively, which are also involved in neuro-inflammation in AD (Ogunmokun et al., 2021). In addition, CSF levels of neurofilament light chain (NfL) and neurogranin reflect axonal and synaptic damage, respectively, which are correlated with AD pathology and longitudinal cognitive decline and brain structural changes (Mattsson et al., 2016, Öhrfelt et al., 2020).

However, the effect of TSPO rs6971 on these biomarkers remains unknown. It appears of interest to assess how these biomarkers are related to different TSPO genetic subgroups in early AD, especially for studies and trials in which TSPO PET imaging will be combined with CSF biomarkers (Bieger et al., 2023). This is also of interest due to the evidence that TSPO genetic subgroups are differently associated with some psychiatric conditions (Colasanti et al., 2013, Costa et al., 2009b, Nakamura et al., 2006), and with the alteration of specific biological pathways (Costa et al., 2009a, Prossin et al., 2018). We addressed these issues in a cross-sectional study designed to assess the effect of TSPO rs6971 on a panel of CSF biomarkers in early AD.

2. Method

2.1. Participants

This study was ancillary to a phase II trial (NCT03435861) on the effect of a non-steroidal anti-inflammatory drug in early AD (neflamapimod, EIP Pharma, Boston, MA, USA). This trial was approved by the French Ethics Comity “Comité de Protection des Personnes Sud-Est 1” (reference number: 2017–78), and by the French Drug Safety and Health Products Agency (reference number: MEDAECNAT-2018–01-0034). Only pre-treatment data were analyzed in this study. All the patients were willing and able to give informed consent.

Patients were recruited at the Neurology Department Memory Clinic of Toulouse University Hospital (France). The inclusion criteria were: (1) age ranging from 50 to 90 years, (2) amnestic or mixed mild cognitive impairment (MCI), (3) mini-mental state examination > 20/30, and (4) CSF biomarker evidence of AD (Dubois et al., 2014). Exclusion criteria were (1) use of medications with potential effects on cognition and neuroinflammation, (2) history of alcohol or illicit drug abuse, and (3) evidence of significant comorbidity (e.g., other neurodegenerative disease, psychiatric disorder, cancer, significant infectious disease, metabolic or immune disorder). In addition, blood samples were obtained to characterize APOE and TSPO genotypes.

A detailed clinical and neuropsychological presentation of the included patients can be found elsewhere (Gouilly et al., 2023).

2.2. Cerebrospinal fluid biomarkers

CSF was collected in polypropylene tubes and centrifuged immediately after being obtained (3500g, 10mn, 4 °C), aliquoted, and frozen at −20 °C until assayed (<2 weeks) for the measurement of AD biomarkers. For quantification, we used either ELISA (INNOTEST) or the Lumipulse G1200 system (Fujeribo, Ghent, Belgium) in line with the manufacturer’s procedures. All the participants had CSF evidence of AD, as detailed previously (Gouilly et al., 2021, Gouilly et al., 2023). These biomarkers were used to confirm AD diagnosis although they were not used in the statistical analyses due to the two distinct quantification methods used.

CSF samples was also collected in polypropylene tubes, homogenized and stored at −80 °C and then transferred to Lariboisière Hospital, Paris (France) for measurement of CSF GFAP, YKL-40, sTREM2, IL-6, IL-10, NfL and neurogranin. Samples were measured in duplicates over two analytical runs using commercial kits, according to the manufacturer’s instruction for all markers. Two control samples were run at the beginning and at the end of the plate. CSF NfL and GFAP were measured using the SIMOA technology with the Neurology 2-plexB #103520 kit from Quanterix® on a HD-X analyzer (Quanterix, Lexington, MA) (Rissin et al., 2010). CSF neurogranin was measured using a commercial Elisa from Euroimmun (#EQ 6551–9601-L). CSF YKL-40 and sTREM2 levels were measured using commercial Elisa assay from R&D system (sTREM-2: # DY1828-05, YKL-40 # DY2599). IL-6 and IL10 levels were measured using the Human Cytokine Multiplex Panel A from R&D System (#QC11). All the samples were detected and displayed values above the lower limit of quantification reported for the assay.

2.3. Statistical analyses

The distribution of biomarker values in the HAB patients was used as a reference to estimate the number of patients required to observe a pairwise mean difference of 30 % in CSF values compared to HAB, using statistical cutoffs of α = 0.05 and β = 0.80 (Devanarayan et al., 2024). These calculations were performed using the “pwr” package on R software (v4.1.2). For IL-10 and sTREM2, we determined that a population of at least 9 individuals was required. As we recruited 16 HAB and 13 MAB patients in our study (table 1), these estimates indicate that a 30 % difference in biomarker values was detectable for these biomarkers when comparing HAB to MAB individuals. However, for GFAP, YKL-40, IL-6, NfL and neurogranin, we determined that a population of ≥ 14 individuals was required (GFAP: n = 14; YKL-40: n = 17; IL-6: n = 23.6; NfL: n = 58; neurogranin: n = 27.4). For these biomarkers, we acknowledge that our results should be interpreted with caution as they may reflect sampling variability and remain exploratory.

Demographics of HAB, MAB and LAB AD patients were compared using Chi2 (qualitative) or non-parametric Kruskal-Wallis test, and post-hoc Dunn test (quantitative). Significance was set at p < 0.05, two-tailed. For qualitative variables (sex, family history of AD and APOE4 carriership), comparisons were only made between HAB and MAB AD patients due to the low number of LAB AD patients. In addition, CSF biomarker values of neuro-inflammation, synaptic, and axonal damage were compared between HAB, MAB and LAB AD patients using the non-parametric Kruskal-Wallis test, and post-hoc Dunn test. Significance was set at p < 0.05, two-tailed, and corrected for multiple testing (Benjamini-Hochberg false discovery rate). All the figures and statistical analyses were performed with the R software v.4.1.2.

3. Results

Thirty-three patients diagnosed with MCI due to AD were recruited in this study including 16 who were HABs (48 %), 13 MABs (39 %) and 4 LABs (12 %). The clinical characteristics of these patients are shown in Table 1. There was no difference between TSPO genetic subgroups in terms of demographics and MMSE scores (P > 0.05) with the exception of the number of patients with a family history of AD, which was significantly lower among the MAB group compared to the HAB group (P = 0.005, Table 1).

Table 1.

Demographics and cerebrospinal fluid biomarker values.

High affinity binders Mixed affinity binders Low affinity binders
Demographics
n 16 13 4
Sex, female, n (%) 6 (38) 6 (46) 3 (75)
Age, mean (SD) 68.2 (7.1) 69.2 (7.7) 63.2 (8.3)
Education, years, mean (SD) 14.4 (3.3) 13 (2.1) 11 (2.4)
Family history of AD, n (%) 12 (75) 2 (15) 3 (75)
Anti-cholinesterase inhibitor, n (%) 10 (63) 4 (31) 0
APOE4 carriership, n (%) 10 (63) 9 (69) 3 (75)
MMSE score, median [IQR] 23.5 [21 – 28.2] 24 [22 – 26] 21.5 [21 – 23.5]



CSF biomarker values, median [IQR]
GFAP (pg/ml) 2029.8 [1793.6 – 2612.2] 2414.1 [1961.2 – 3177.4] 2107.7 [1732.7 – 2424]
YKL-40 (ng/ml) 295.3 [233.3 – 364.5] 331.4 [300.8 – 389.2] 317 [227.7 – 415.7]
sTREM2 (pg/ml) 3640.5 [3166.6 – 4293.9] 3577.8 [3080.1 – 4508.6] 3246.9 [2820.3 – 3687.4]
IL-6 (pg/ml) 1.4 [1 – 1.8] 1.9 [1.5 – 2.2] 1.2 [1.1 – 1.7]
IL-10 (pg/ml) 0.6 [0.5 – 0.6] 0.6 [0.6 – 0.6] 0.6 [0.5 – 0.6]
NfL (pg/ml) 220.5 [181.2 – 290.2] 250.3 [220.9 – 379.1] 311.4 [217.7 – 392.8]
Neurogranin (pg/ml) 445.8 [399.4 – 539.2] 608.8 [432.2 – 821.8] 395.1 [302.7 – 493.6]
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Demographics of HAB, MAB and LAB AD patients were compared using Chi2 (qualitative) or non-parametric Kruskal-Wallis test, and post-hoc Dunn test (quantitative). Significance was set at p < 0.05, two-tailed. For qualitative variables (sex, family history of AD and APOE4 carriership), comparisons were only made between HAB and MAB patients due to the small number of LAB individuals. No significant difference between the populations was observed with the exception that a higher proportion of family history of AD was observed in the HAB group compared to the MAB group (p = 0.005). In addition, no difference was observed between CSF biomarker values in the HAB, MAB and LAB groups (p > 0.05, corrected for multiple testing using Benjamini-Hochberg false discovery rate correction). Statistical analyses were performed on R v.4.1.2.

Abbreviations: CSF, cerebrospinal fluid; GFAP, glial fibrillary acidic protein; HAB, high affinity binder; IL-6, interleukin 6; IL-10, interleukin 10; IQR, inter-quartile range; LAB, low affinity binder; MAB; mixed affinity binder; MMSE, mini-mental state examination; NfL, neuro-filament light chain; SD, standard deviation; sTREM2, soluble triggering receptor expressed on myeloid cell 2.

There was no difference in the CSF values of GFAP, YKL-40, sTREM2, IL-6, IL-10, NfL and neurogranin between the HAB, MAB and LAB groups (P > 0.05, uncorrected, Fig. 1). Pairwise group comparisons were also performed using Dunn’s post-hoc test, and no difference in CSF values was observed (P > 0.05, uncorrected). Some outliers were observed for each biomarker. We defined these outliers as values of ± 2 standard deviations around the mean of our population (i.e., a null z-value of one HAB patient corresponds to the mean of HAB patients). Outliers included: one MAB patient for GFAP (5322 pg/ml), one MAB patient for YKL-40 (846 ng/ml), two MAB patients for sTREM2 (5591 and 1943 pg/ml), one MAB patient for IL-6 (18 pg/ml), one MAB and one HAB patient for IL-10 (0.96 and 0,86 pg/ml, respectively), one MAB patients for NfL (1464 pg/ml) and two MAB patients for neurogranin (1419 and 1154 pg/ml). These outliers were excluded from our biomarker value comparisons, and we still found no significant difference between the groups (P > 0.05, uncorrected).

Fig. 1.

Fig. 1

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Cerebrospinal fluid biomarker values in TSPO genotype subgroups. Concentrations of GFAP (A), sTREM2 (C), IL-6 (D), IL-10 (E), NfL (F), neurogranin (G) are shown in pg/ml. YKL-40 (B) is shown in ng/ml. Each point represents one patient. No significant difference was observed between HAB, MAB and LAB AD patients using the non-parametric Kruskal-Wallis test, and the post-hoc Dunn test. Significance was set at p < 0.05, two-tailed, and corrected for multiple testing (Benjamini-Hochberg false discovery rate). One outlier was removed from panel D (concentration: 18 pg/ml). Statistical analyses and plots were computed using the R software (v.4.1.2). Abbreviations: CSF, cerebrospinal fluid; GFAP, glial fibrillary acidic protein; HAB, high affinity binder; IL-6, interleukin 6; IL-10, interleukin 10; LAB, low affinity binder; MAB; mixed affinity binder; NfL, neuro-filament light chain; ns, not significant; sTREM2, soluble triggering receptor expressed on myeloid cell 2; YKL-40, chitinase-3-like protein 1.

4. Discussion

In this study, we used a panel of emerging CSF biomarkers to demonstrate that TSPO rs6971 was not associated with a change in glial activation in early AD. We also showed that synaptic and axonal damage does not appear to be influenced by TSPO rs6971. This is consistent with neuropathological evidence that these patients exhibit a similar burden of AD pathology, neurodegeneration and neuro-inflammation, as measured in the temporal cortex through stereology-based counts of GFAP-positive astrocytes and CD68-positive microglia (Gui et al., 2020). This is also in line with the observation that HAB, MAB and LAB AD patients showed a similar burden of amyloid on PET as well as comparable cognitive trajectories (Fan et al., 2015).

Fluidic and TSPO PET biomarkers of neuroinflammation have already been combined in AD (Cisbani et al., 2020, Parbo et al., 2020, Pascoal et al., 2021, Yasuno et al., 2022), corticobasal syndrome (Palleis et al., 2024), and multiple sclerosis (Saraste et al., 2023). However, TSPO rs6971 is a major limitation for the studies based on second-generation TSPO tracers, including [11C]-PBR-28 and [18F]-DPA-714 (Turkheimer et al., 2015). Only one TSPO genetic subgroup is usually considered in these studies (usually HAB), and the generalization of the results from fluidic biomarkers to the whole population has remained uncertain. Although the number of participants in our study is small, our results support the notion that TSPO rs6971 only affects the affinity of tracer binding on PET and has no pathophysiological impact on AD, especially on neuro-inflammation. Therefore, our results also confirm that no selection bias may exist when including specific TSPO genetic subgroups in studies and trials of AD that combine CSF biomarkers with TSPO PET.

Furthermore, some pathophysiological processes related to TSPO functions were shown to be altered by this genetic polymorphism. This includes the regulation of the hypothalamic–pituitary–adrenal axis (Colasanti et al., 2013), the regulation of diurnal cortisol rhythm (Prossin et al., 2018), and the production of neuro-steroids as shown for pregnenolone (Costa, Pini, Gabelloni, et al., 2009). These associations are probably in line with the fact that TSPO rs6971 influences the susceptibility to some psychiatric conditions (Colasanti et al., 2013, Costa et al., 2009b, Da Pozzo et al., 2012, Nakamura et al., 2006, Prossin et al., 2018) and with the response to anxiolytic treatment (D. R. J. Owen et al., 2011). The hypothesis that TSPO is involved in neuro-steroid formation through cholesterol binding provides an explanation for these findings. In addition, several cellular functions have been associated with TSPO in the brain without any alteration by rs6971 (Nutma et al., 2021). The hypothesis that TSPO contributes to immune processes and mitochondrial bioenergetics is of major concern in AD, as neuroinflammation and mitochondrial dysfunction are part of the pathophysiological process (Ashleigh et al., 2022, Betlazar et al., 2020, Leng and Edison, 2021). Our results confirm that the rs6971 genotype is not associated with the molecular pathways of GFAP, YKL-40, sTREM2, IL-6, IL-10, NfL and neurogranin in AD. However, in our study, the presence of a significant psychiatric comorbidity was an exclusion criterion. Therefore, our results should be interpreted with caution especially as some pathophysiological processes related to TSPO functions may be altered by rs6971 in psychiatric disorders.

Previous studies found that age, sex, APOE4 carriership and cognitive performances correlated to GFAP, YKL-40, sTREM2, NfL and neurogranin biomarker values in the plasma and CSF of AD patients (Bridel et al., 2019, Gonzales et al., 2022, Prins et al., 2022, Vergallo et al., 2020, Wallin et al., 1996, Wang and Alzheimer’s Disease Neuroimaging Initiative, 2019, Yang et al., 2023). One strength of our study is the exclusion of participants with a confounding medication or significant comorbidity with a potential impact on cognition and neuro-inflammation. In addition, we observed that no significant difference existed between HAB, MAB and LAB patients in terms of age, sex ratio, APOE4 carriership, and MMSE score. Therefore, it seems unlikely that these parameters have influenced our results. Furthermore, we found that the presence of a family history of AD was higher in HAB compared to MAB patients. This result should be interpreted with caution as TSPO rs6971 is not a risk factor for AD, and probably reflects sampling variability in our study.

We found similar proportions of HAB, MAB and LAB patients to that reported in previous European studies of AD (Femminella et al., 2019, Hamelin et al., 2016). Furthermore, the prevalence of these genetic subgroups varies according to their geographical origin. The prevalence of HAB individuals seems to be higher in African and American countries (∼60–70 %) (Kreisl, Lyoo, et al., 2013), and even higher in Asian countries (∼95 %) (Lee et al., 2022). However, to our knowledge, no TSPO PET studies have shown that the different geographical prevalence of TSPO genetic subgroups would be associated with different AD pathophysiological processes. In addition, the study of biological differences between groups of different origins is not permitted by the French law and jurisprudence. Therefore, we were unable to describe the ethnicity of the population included in our study. Although we cannot exclude the influence of different ethnicities in our results, the evidence remains limited regarding fluidic biomarkers of neuro-inflammation (Gonzales et al., 2023, Honig et al., 2023, Ramanan et al., 2023, Windon et al., 2022).

One limitation of our study lies in the use of two different quantification methods for CSF AD biomarkers which precludes ascertaining whether or not there is a difference in the levels of these biomarkers for HAB, MAB and LAB patients. However, there is convincing biomarker and neuropathological evidence that AD pathology is similar between these subgroups (Fan et al., 2015, Gui et al., 2020). In our study, the HAB, MAB and LAB groups had a comparable level of cognitive impairment. It seems unlikely that these groups exhibited different levels of AD pathology. In addition, this study is cross-sectional and the absence of follow-up precludes ascertaining the effect of the TSPO genetic subgroup on the longitudinal variation of glial activation, and axonal and synaptic damage markers. Finally, non-TSPO PET tracers for neuro-inflammation are gaining interest in AD (Calsolaro et al., 2021), as well as for other neuro-psychiatric disorders (Meyer et al., 2020). Therefore, our focus on the second-generation of TSPO tracers is another limitation of this study.

5. Conclusion

In conclusion, we have shown that TSPO rs6971 is not associated with changes in GFAP, YKL-40, sTREM2, IL-6, IL-10, NfL and neurogranin biomarker in early AD.

CRediT authorship contribution statement

Dominique Gouilly: Writing – review & editing, Writing – original draft, Funding acquisition, Formal analysis, Data curation, Conceptualization. Agathe Vrillon: Writing – review & editing, Visualization, Validation, Project administration, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Elsa Bertrand: Writing – review & editing, Visualization, Validation, Project administration, Investigation. Marie Goubeaud: Writing – review & editing, Visualization, Validation, Project administration, Investigation. Hélène Catala: Writing – review & editing, Visualization, Validation, Project administration, Investigation. Johanne Germain: Writing – review & editing, Visualization, Validation, Project administration, Investigation. Nadéra Ainaoui: Writing – review & editing, Validation, Supervision, Project administration, Investigation. Marie Rafiq: Writing – review & editing, Visualization, Validation, Project administration, Investigation. Leonor Nogueira: Writing – review & editing, Validation, Supervision, Investigation. François Mouton-Liger: Writing – review & editing, Visualization, Validation, Investigation. Mélanie Planton: Writing – review & editing, Visualization, Validation, Investigation. Anne-Sophie Salabert: Writing – review & editing, Visualization, Validation, Project administration, Investigation. Anne Hitzel: Writing – review & editing, Visualization, Validation, Project administration, Investigation. Déborah Méligne: Writing – review & editing, Visualization, Validation, Investigation, Funding acquisition, Conceptualization. Laurence Jasse: Writing – review & editing, Visualization, Validation, Investigation, Funding acquisition, Conceptualization. Benjamine Sarton: Writing – review & editing, Visualization, Validation, Project administration, Investigation. Stein Silva: Writing – review & editing, Visualization, Validation, Project administration, Investigation. Béatrice Lemesle: Writing – review & editing, Visualization, Validation, Investigation, Conceptualization. Patrice Péran: Writing – review & editing, Visualization, Validation, Project administration, Investigation. Pierre Payoux: Writing – review & editing, Visualization, Validation, Project administration, Investigation. Claire Thalamas: Writing – review & editing, Visualization, Validation, Supervision, Project administration, Funding acquisition, Conceptualization. Claire Paquet: Writing – review & editing, Visualization, Validation, Supervision, Project administration, Funding acquisition, Conceptualization. Jérémie Pariente: Writing – review & editing, Visualization, Validation, Supervision, Project administration, Investigation, Funding acquisition, Data curation, Conceptualization.

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dominique Gouilly received research funding from the Fondation de l’Avenir (Paris, France) for this study. Other authors have no relevant financial or non-financial interests to disclose.

Acknowledgments

We would like to thank the Fondation Alzheimer, and the Fondation de l’Avenir for the financial backing of this study, the Toulouse Purpan University Hospital Center, especially Jasmine Carlier, Marie Benaiteau, Camille Tisserand, Delphine Vernet and Benjamin Cottin. We also would like to the French National Agency for Research called “Investissements d’Avenir” IRON Labex (number ANR-11-LABX-0018-01) for their support. We are grateful to the INSERM/UPS Tonic PET and MRI platforms for their technical assistance especially to Laura Guerrier, Hélène Gros-Dagnac, Mélissa Villatte and Maëva Fisher for help advancing the project. For their technical assistance, support for data acquisition and caring for patients, we also would like to thank the Center of Clinical Investigation (CIC 1436) especially Stéphanie Bras, Fabienne Calvas, Monique Galitzky, Laurent Marquine, Célie Laplace, Béatrice Lagarde, Sandrine Rolland, Sandrine Bonnet, Edith Carneiro, Laurent Cales, and Pascale Gauteul. Finally, we would like to thank all the study participants for their contribution.

Funding

This study was co-funded by Fondation Alzheimer (Paris, France), and Fondation de l’Avenir (Paris, France; 2022; reference number: AP-RM-21-042).

Data availability

Anonymized data not published in this article will be made available on request from any qualified investigator.

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