Skip to main content
NCBI home page
Brain logoLink to Brain
. 2017 Apr 27;140(6):1784–1791. doi: 10.1093/brain/awx103

Cognitive reserve and TMEM106B genotype modulate brain damage in presymptomatic frontotemporal dementia: a GENFI study

Enrico Premi 1, Mario Grassi 2, John van Swieten 3, Daniela Galimberti 4, Caroline Graff 5,6, Mario Masellis 7, Carmela Tartaglia 8, Fabrizio Tagliavini 9, James B Rowe 10, Robert Laforce Jr 11, Elizabeth Finger 12, Giovanni B Frisoni 13,14, Alexandre de Mendonça 15, Sandro Sorbi 16,17, Stefano Gazzina 1, Maura Cosseddu 1, Silvana Archetti 18, Roberto Gasparotti 19, Marta Manes 1, Antonella Alberici 1, Manuel J Cardoso 20,21, Martina Bocchetta 20, David M Cash 20,21, Sebastian Ourselin 21, Alessandro Padovani 1, Jonathan D Rohrer 20,*, Barbara Borroni 1,*,✉; on behalf of the Genetic FTD Initiative (GENFI)*
PMCID: PMC5445253  PMID: 28460069

Frontotemporal dementia (FTD) shows substantial phenotypic variability. In a multicentre study, Premi et al. explore the effect of cognitive reserve and TMEM106B genotype in modulating grey matter volume in presymptomatic FTD. Environmental as well as genetic factors affect rates of brain atrophy, suggesting a possible strategy for delaying disease onset.

Keywords: frontotemporal dementia, genetics, cognitive reserve, TMEM106b, structural MRI

Abstract

Frontotemporal dementia is a heterogeneous neurodegenerative disorder with around a third of cases having autosomal dominant inheritance. There is wide variability in phenotype even within affected families, raising questions about the determinants of the progression of disease and age at onset. It has been recently demonstrated that cognitive reserve, as measured by years of formal schooling, can counteract the ongoing pathological process. The TMEM106B genotype has also been found to be a modifier of the age at disease onset in frontotemporal dementia patients with TDP-43 pathology. This study therefore aimed to elucidate the modulating effect of environment (i.e. cognitive reserve as measured by educational attainment) and genetic background (i.e. TMEM106B polymorphism, rs1990622 T/C) on grey matter volume in a large cohort of presymptomatic subjects bearing frontotemporal dementia-related pathogenic mutations. Two hundred and thirty-one participants from the GENFI study were included: 108 presymptomatic MAPT, GRN, and C9orf72 mutation carriers and 123 non-carriers. For each subject, cortical and subcortical grey matter volumes were generated using a parcellation of the volumetric T1-weighted magnetic resonance imaging brain scan. TMEM106B genotyping was carried out, and years of education recorded. First, we obtained a composite measure of grey matter volume by graph-Laplacian principal component analysis, and then fitted a linear mixed-effect interaction model, considering the role of (i) genetic status; (ii) educational attainment; and (iii) TMEM106B genotype on grey matter volume. The presence of a mutation was associated with a lower grey matter volume (P = 0.002), even in presymptomatic subjects. Education directly affected grey matter volume in all the samples (P = 0.02) with lower education attainment being associated with lower volumes. TMEM106B genotype did not influence grey matter volume directly on its own but in mutation carriers it modulated the slope of the correlation between education and grey matter volume (P = 0.007). Together, these results indicate that brain atrophy in presymptomatic carriers of common frontotemporal dementia mutations is affected by both genetic and environmental factors such that TMEM106B enhances the benefit of cognitive reserve on brain structure. These findings should be considered in evaluating outcomes in future disease-modifying trials, and support the search for protective mechanisms in people at risk of dementia that might facilitate new therapeutic strategies.

Introduction

Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by neuronal loss in the frontal and temporal lobes (Hodges et al., 2004; Rohrer et al., 2011; Warren et al., 2013). It presents clinically with behavioural symptoms, deficits of executive functions and language impairment, and in some cases, with motor neuron disease, progressive supranuclear palsy or corticobasal syndrome (Seelaar et al., 2011). Up to 40% of cases have a family history of dementia, with an autosomal dominant inheritance in around a third of patients (Stevens et al., 1998). Mutations within microtubule-associated protein tau (MAPT) (Hutton et al., 1998), granulin (GRN) (Baker et al., 2006; Cruts et al., 2006), and chromosome 9 open reading frame 72 (C9orf72) (DeJesus-Hernandez et al., 2011; Renton et al., 2011) are proven major causes of genetic FTD, accounting for 10–20% of all FTD cases. MAPT mutations lead to FTD with neuronal tau inclusions, while GRN and C9orf72 are associated with intraneuronal TAR DNA-binding protein 43 (TDP-43) inclusions (Baborie et al., 2011).

Recently, it has been demonstrated in the Genetic Frontotemporal Dementia Initiative (GENFI) study that grey matter and cognitive changes can be identified 5–10 years before the expected onset of symptoms in adults at risk of genetic FTD (Rohrer et al., 2015), and even earlier for those with C9orf72 expansions. However, there is wide variation in the age at onset within families, and possible modifiers of disease progression (including genetic and environmental factors) have yet to be investigated. Such modifiers will be important for several reasons: to properly define biomarkers that can stage presymptomatic disease and track disease progression, to correctly identify individuals most suitable for clinical trials, and to reduce heterogeneity and increase the statistical power of analyses of such trials.

Cognitive reserve and genetic factors have both been proposed as moderators of the onset of disease. Cognitive reserve is a theoretical concept proposing that certain lifetime experiences, including education, individual intelligence quotient, degree of literacy, and occupational attainment, increase the flexibility, efficiency, and capacity of brain networks, thereby allowing individuals with higher cognitive reserve to sustain greater levels of brain pathology before showing clinical impairment (for a review, see Stern, 2009). In healthy individuals, higher educational attainment (Arenaza-Urquijo et al., 2013) as well as cognitive enrichment (Sun et al., 2016) have been related to greater volume and greater metabolism in frontotemporal regions, thus likely enhancing brain performance (Barulli and Stern, 2013).

Genetic modifiers of disease expression also exist, affecting the phenotype and prognosis. TMEM106B has been identified as a genetic modifier in FTD, modulating the age at disease onset in frontotemporal lobar degeneration–TDP-43 disease (Cruchaga et al., 2011; Gallagher et al., 2014; van Blitterswijk et al., 2014). The TMEM106B rs1990622 TT genotype is detrimental and associated with earlier age at disease onset (Cruchaga et al., 2011) and greater functional impairment in frontal regions in presymptomatic GRN mutation carriers (Premi et al., 2014). Conversely, the role of this polymorphism in C9orf72 mutation carriers is still unclear, as it has been suggested a detrimental effect of TMEM106B rs1990622 CC genotype on disease onset and death (Deming and Cruchaga, 2014; Gallagher et al., 2014; van Blitterswijk et al., 2014).

In this study, we aimed to evaluate modifiers of structural brain changes in presymptomatic mutation carriers from a large international cohort of subjects at risk for genetic FTD, investigating the effect of (i) pathogenetic mutation, i.e. MAPT, GRN and C9orf72 carriers versus non-carriers; (ii) cognitive reserve, as measured by years of formal schooling; and (iii) TMEM106B rs1990622 genotype, and their interaction, on grey matter volume.

We hypothesized that individually and together, these three factors will modulate the degree of structural atrophy.

Materials and methods

Participants

Data for this study were drawn from the GENFI multicentre cohort study (Rohrer et al., 2015), which consists of 13 research centres in the UK, Italy, The Netherlands, Sweden, and Canada. Inclusion and exclusion criteria have been previously described (Rohrer et al., 2015). Local ethics committees approved the study at each site and all participants provided written informed consent according to the Declaration of Helsinki. For the aim of the present work, we considered participants at 50% risk of carrying a GRN, C9orf72 or MAPT mutation based on having a first-degree relative who was a known symptomatic mutation carrier. Between January 2012 and April 2015, 365 participants were recruited into GENFI, of which 294 were at risk and 71 symptomatic. Of the 294 at-risk participants, 22 did not have a T1-weighted MRI scan suitable for volumetric analysis. Included at-risk subjects underwent a careful recording of demographic data, including years of formal schooling (education), past medical history, and a standardized clinical and neuropsychological assessment, as previously published (Rohrer et al., 2015). Genotyping was then performed for the TMEM106B rs1990622 (C/T) single nucleotide polymorphism according to standard procedures (Premi et al., 2014) (at the individual sites in 70.6% of cases, and at the University of Brescia, Italy in the remaining 29.4%). Genotype was not available for 41 participants, and so the final analysis was performed on 231 participants: 108 presymptomatic mutation carriers [genetic status (GS) = 1], 61 with GRN, 33 with C9orf72 and 14 with MAPT mutations) and 123 non-carriers (GS = 0)]. Participants (GS = 0 and GS = 1) came from 77 families (15 with MAPT, 33 with GRN, and 29 with C9orf72 mutations). TMEM106B genotype distribution was comparable between groups (GS = 0 versus GS = 1, Pearson χ2 test, P = 0.958), as well as among GS = 1 subgroups (i.e. GRN, C9orf72 and MAPT mutation carriers, P = 0.419). Demographic characteristics of GS = 1, subgrouped on the basis of mutation type, and GS = 0 are reported in Table 1. Mean age of GS = 1 was 45.9 years (range 20.5–70.5 years) and of GS = 0 was 48.3 years (range 19.4–85.7 years). No significant differences were found in age, gender, years of education, and neuropsychological tests between the groups.

Table 1.

Demographic and clinical characteristics and TMEM106B genotype in the studied groups

GS = 0 (n = 123) GS = 1 (n = 108) C9orf72 (n = 33) GRN (n = 61) MAPT (n = 14) P-value
Age, years 48.3 ± 14.4 45.9 ± 11.3 43.6 ± 10.5 49.4 ± 10.6 36.4 ± 9.5 0.164*
Gender, % female (n) 63.4 (78) 64.8 (70) 57.6 (19) 65.6 (40) 78.6 (11) 0.891†
Education, years 13.7 ± 3.3 14.0 ± 3.1 13.8 ± 3.1 14.0 ± 3.2 14.3 ± 2.6 0.596*
TMEM rs1990622 0.958†
    C/C (%) 11.4 (14) 10.1 (11) 9.1 (3) 9.8 (6) 14.3 (2)
    C/T (%) 51.2 (63) 51.9 (56) 51.5 (17) 57.4 (35) 28.6 (4)
    T/T (%) 37.4% (46) 38.0 (41) 39.4 (13) 32.8 (20) 57.1 (8)
Neuropsychological evaluation
    CBI-Revised 3.28 ± 5.13 3.78 ± 7.00 5.14 ± 6.54 3.26 ± 7.70 3.07 ± 4.71 0.402#
    MMSE 29.22 ± 1.25 29.14 ± 1.33 29.12 ± 1.32 29.00 ± 1.41 29.77 ± 0.83 0.211#
    Logical Memory-Immediate Recall 0.54 ± 1.48 0.68 ± 1.45 0.76 ± 1.57 0.50 ± 1.35 1.25 ± 1.51 0.608#
    Logical Memory-Delayed Recall 0.22 ± 1.10 0.33 ± 1.13 0.44 ± 1.23 0.19 ± 1.09 0.66 ± 1.03 0.597#
    Digit Span forwards 0.11 ± 1.02 −0.02 ± 1.03 −0.12 ± 1.12 −0.03 ± 1.02 0.28 ± 0.84 0.225#
    Digit Span backwards −0.03 ± 0.95 −0.15 ± 0.92 −0.06 ± 1.01 −0.24 ± 0.86 0.05 ± 0.99 0.311#
    Trail Making Test Part A 0.29 ± 0.75 0.28 ± 0.63 0.22 ± 0.62 0.22 ± 0.63 0.71 ± 0.52 0.352#
    Trail Making Test Part B 0.29 ± 0.76 0.30 ± 0.83 0.21 ± 1.09 0.28 ± 0.66 0.62 ± 0.72 0.565#
    Digit Symbol Task 0.34 ± 1.05 0.38 ± 1.00 0.34 ± 0.96 0.22 ± 0.96 1.14 ± 0.98 0.575#
    Boston Naming Test 0.10 ± 0.88 0.11 ± 0.97 −0.13 ± 1.15 0.29 ± 0.68 −0.08 ± 1.41 0.933#
    Letter Fluency 0.22 ± 1.02 0.21 ± 0.98 0.38 ± 0.94 0.09 ± 0.92 0.38 ± 1.27 0.593#
    Category Fluency −0.02 ± 1.07 −0.15 ± 1.32 −0.89 ± 1.12 0.23 ± 1.32 −0.02 ± 1.01 0.281#
    Block Design 0.04 ± 0.99 0.15 ± 1.07 −0.24 ± 1.24 0.21 ± 0.94 0.78 ± 0.83 0.799#
Open in a new tab

GS = 0: mutation non-carriers; GS = 1: mutation carriers.

CBI = Cambridge Behavioural Inventory; MMSE = Mini-Mental State Examination.

Results are expressed as mean ± standard deviation or otherwise indicated. P-values, GS = 1 versus GS = 0 comparison: *Student t-test; †χ2 Chi-Square test; #one-way ANCOVA (expressed as Z-scores).

Imaging analysis

T1-weighted volumetric MRI scans were parcellated into cortical and subcortical regions as previously described (Rohrer et al., 2015), using an atlas propagation and label fusion strategy (Cardoso et al., 2015), combining regions of interest to calculate grey matter cortical volumes (separated into the frontal, temporal, parietal, occipital, cingulate, and insular cortices), subcortical volumes (hippocampus, amygdala, caudate, putamen, accumbens, pallidum, and thalamus), cerebellum volume (http://www.neuromorphometrics.org:808/seg/) (Diedrichsen et al., 2009). Whole-brain volumes were measured using a semi-automated segmentation method (Freeborough et al., 1997). All measures were expressed as a percentage of total intracranial volume (measured with SPM12 with a combination of grey matter, white matter, and CSF segmentations).

Statistical analysis

We fitted a linear mixed effect interaction model (Gałecki and Burzykowski, 2013). We assessed the main effect of three factors on grey matter: (i) the presence of pathogenetic mutation (GS, coded as GS = 1 for GRN, MAPT or C9orf72 mutation carriers and GS = 0 for mutation non-carriers); (ii) the role of cognitive reserve as measured by years of formal education; and (iii) the TMEM106B rs1990622 C/T genotype (coded as CC, CT or TT). The relationship between each factor and grey matter volume was labelled as β1, β2, and β3, respectively (Fig. 1, dark blue lines). Furthermore, we considered the two-way interaction effect of each factor (i.e. GS and education, labelled as β4 (red line), GS and TMEM106B genotype, labelled as β5 (orange line), and education and TMEM106B genotype, labelled as β6 (green line) (Fig. 1). Finally, we considered the three-way interaction effect, i.e. GS, education, and TMEM106B genotype, on grey matter (β7) (Fig. 1). These main and interaction effects were adjusted by fixed covariates, namely age and gender. Moreover, we considered two random effect factors, study site and pedigree, which permitted analysis of the correlations of subjects in the same cluster (centres of subjects’ enrolment or individual families).

Figure 1.

Figure 1

Open in a new tab

Model design and results of interaction model on grey matter volume. β1, β2, β3: main effect relationship of each factors (dark blue lines); β1: pathogenetic mutation (GRN, MAPT or C9orf72); β2: cognitive reserve as measured by years of formal education, and β3: TMEM106B rs1990622 polymorphism (coded as TT, TC and CC). β4, β5, β6: two-way interaction effect of each factor [β4: genetic status and education (red line), β5: genetic status and TMEM106B (orange line), and β6 (green line): education and TMEM106B]. β7: three-way interaction effect (genetic status, education, and TMEM106B) on grey matter volume (purple line). ROI = region of interest.

To overcome the complexity of multiple comparison corrections, we first carried out data reduction of grey matter parcellation data. We proposed a graph-Laplacian Principal Component Analysis (gLPCA) to obtain a low dimensional representation of grey matter parcellation, which incorporated graph structure (Jiang et al., 2013). We did not apply principal component analysis (PCA), widely used to obtain a low-dimensional representation, as the imaging data presented spatial distribution and high left-right correlation (Belkin and Niyogi, 2001). Graph-Laplacian PCA (gLPCA) has several advantages: (i) it is modelled on the representation of the data; (ii) it can be easily calculated, presenting a compact closed-form solution; and (iii) it allows noise removal. Once we obtained data reduction, bivariate correlations between principal component (PC) scores and each grey matter measure were computed. Finally, we fitted the mixed-effect interaction models with grey matter, summarized by the first PC scores, as outcome variable. Statistical analysis was performed via R packages (www.r-project.org) and in-house R scripts.

Results

By gLPCA using the skeleton graph between grey matter measures (Supplementary Fig. 1), the first PC (PC1) was selected to summarize the grey matter volume data. Frontal, parietal and temporal regions were the areas that contributed most to graph construction and PC1 scores, based on correlations between PC1 scores and grey matter measures (Supplementary Table 1).

Fitting the linear mixed-interaction model with fixed covariates (age and gender) and random effects (study site and pedigree), a significant direct effect of GS and years of education on grey matter outcome (PC1 scores) was observed (P = 0.002 and P = 0.02, respectively), while no effect of TMEM106B genotype on grey matter was detected. We did not find any significant two-way interaction between the considered variables, but did find a three-way interaction on grey matter (P = 0.007) (Table 2).

Table 2.

Output from the linear mixed effect interaction model

Fixed effects β Estimate SE Z-value P-value
GS β 1 −0.262 0.084 −3.120 0.002
Education β 2 0.035 0.015 2.350 0.020
TMEM106B β 3 −0.035 0.066 −0.522 0.600
GS*Education β 4 −0.046 0.027 −1.729 0.080
GS × TMEM106b β 5 0.055 0.133 0.413 0.680
Education × TMEM106b β 6 −0.0005 0.020 −0.025 0.980
GS × Education × TMEM106b β 7 0.110 0.041 2.700 0.007
Age − −0.052 0.003 −14.58 <0.001
Gender − −0.527 0.088 −6.008 <0.001
Random effects Variance SE
Pedigree (no. groups = 77) − 0.085 0.290
Site (no. groups = 13) − 0.004 0.063
Open in a new tab

TMEM106B = TMEM106B rs1990622 polymorphism; SE = standard error; random effects = variance of the random intercept between groups. Bold values represent significant values of the studied effect (β1, β2, β7), as reported by P values column.

The data are summarized in Fig. 2. On the x-axis, years of education (i.e. cognitive reserve) are reported, and on the y-axis grey matter volume (PC1). Years of education had a significant direct effect on grey matter volume, independently of GS. We found that the greater the years of education, the greater the grey matter volume (in both GS = 0 and GS = 1), suggesting that cognitive reserve was able to exert an effect in presymptomatic at-risk participants carrying pathogenetic mutations as well as in non-carriers, by increasing grey matter volume.

Figure 2.

Figure 2

Open in a new tab

Summary of the results from the fitted interaction model.x-axis, education attainment (years) y-axis, grey matter volume as obtained by considering principal component (PC) 1, GS = 1: mutation carriers; GS = 0: mutation non-carriers; TMEM = TMEM106B. See ‘Results’ section for details.

In comparison to non-carriers (GS = 0, red line), mutation carriers (GS = 1, blue line) showed a significant decrease of grey matter volume, confirming the effect of pathogenetic mutations in shaping progressive atrophy before the onset of symptoms (Rohrer et al., 2015).

The TMEM106B genotype did not exert a direct effect on grey matter volume, and it did not affect grey matter (Fig. 2, red line, GS = 0 and TMEM106B CC or CT or TT). However, in those individuals carrying pathogenetic mutations (GS = 1), TMEM106B polymorphism modulated the slope of the relationship between education and grey matter volume (GS*Education*TMEM106B): a steeper slope was found in TMEM106B TT carriers compared with CT carriers, which in turn was greater than that of CC carriers, with a dose-dependent effect (Fig. 2, green and purple lines). Considering the contribution of each mutation separately, the effect of TMEM106B genotype (GS*Education*TMEM106B) was mainly driven by C9orf72 mutation carriers, the subgroup of patients with the greatest atrophy (Supplementary Table 2 and Supplementary Fig. 2).

Discussion

Autosomal dominant FTD presents with significant inter- and intra-familial variability among individuals bearing the same pathogenetic mutation. This suggests the presence of environmental, genetic and/or epigenetic modifiers, influencing the age at disease onset and clinical phenotype (Borroni and Padovani, 2013). The effect of genetic modifiers and environmental factors that might trigger the onset of neurodegeneration in carriers of the pathogenetic mutation (which is present at birth but only manifests symptoms in mid-late adulthood) are of extreme interest. In this view, the pathogenesis of inherited FTD may be a model of ‘Latent Early-Life Associated Regulation’ (LEARn), in which latent expression of associated genes is triggered by environmental and non-environmental factors (Maloney et al., 2012; Maloney and Lahiri, 2016), with neurodegeneration being modulated by lifetime exposure to one or more environmental factors as well as genetic background.

In the present study, we aimed at identifying modulating factors of neuronal loss in presymptomatic subjects bearing pathogenetic mutations within GRN, MAPT and C9orf72 genes through the surrogate marker of volumetric MRI.

We analysed the effect of (i) pathogenetic mutations; (ii) cognitive reserve as measured by years of schooling; and (iii) TMEM106B genotype on grey matter volume in the large GENFI cohort. Indeed, we chose grey matter volume as an endpoint measure as it correlates well with indexes of disease severity (Premi et al., 2016) and progression (Brambati et al., 2007; Lam et al., 2014).

First, we confirmed, as previously reported (Rohrer et al., 2015), that pathogenetic mutations are detrimental to grey matter volume years before expected age at disease onset, being associated with smaller volumes i.e. greater atrophy as compared to siblings who did not inherit the mutation. C9orf72 repeat expansion carriers had a greater degree of atrophy (Rohrer et al., 2015), as compared to GRN mutation carriers and MAPT mutation carriers, with the latter being the smallest group in our sample.

Furthermore, we demonstrated that cognitive reserve is associated with brain atrophy and also modulate neuronal loss years before the onset of symptoms. TMEM106B polymorphism, on the other hand, only modulated grey matter volume in those with an autosomal dominant mutation and with the lowest education.

The duration of formal schooling, as a proxy of cognitive reserve, was associated with greater grey matter volume in both non-carriers and in mutation carriers. This might suggest that those subjects with higher educational attainment were able to better counteract the detrimental effect of a pathogenetic mutation than their counterparts with lower education. However, this effect was also found in the group that did not carry mutations: those with higher education attainment had greater grey matter volume then those with low education. Hence the finding is not specific to mutation carriers suggesting a broader effect of cognitive reserve on maintaining and ameliorating brain functioning. Interestingly, even if presymptomatic mutation carriers already had mild structural changes (Rohrer et al., 2015), the relationship between years of education and structural changes was comparable to that observed in non-carriers (direct correlation, the higher the education the greater the grey matter volume) (Sole-Padulles et al., 2009; Rzezak et al., 2015), rather than that reported in symptomatic FTD (inverse correlation, the higher the education the lower grey matter volume) (Borroni et al., 2009). The concept of cognitive reserve was originally proposed to explain the lack of a direct relationship between the degree of brain pathology and the severity of the clinical manifestations that should supposedly result from such damage in neurodegenerative disorders such as Alzheimer’s disease (Stern et al., 1992, 1994) and FTD (Borroni et al., 2009; Premi et al., 2012, 2013). Cognitive reserve represents the hypothesized capacity of the adult brain to compensate for the effects of a disease or injury that would be sufficient to cause clinical dementia in an individual with less cognitive reserve (Stern, 2002). Herein, we propose that high education might postpone the onset of dementia in those subjects at risk of developing FTD (Akbaraly et al., 2009; Craik et al., 2010; Pettigrew et al., 2013). These findings extend previous results obtained in healthy subjects (Sole-Padulles et al., 2009; Rzezak et al., 2015), and might represent a possible strategy to delay onset of inherited FTD.

Conversely to education attainment, TMEM106B genotype did not have any effect on mutation free individuals, but this genetic trait might represent an additional non-modifiable risk factor in mutation carriers. Literature data have widely proven that TMEM106B variants are genetically associated to frontotemporal lobar degeneration–TDP-43 pathology and are considered a major risk factor for this disease (Chen-Plotkin et al., 2012; Busch et al., 2013; Nicholson and Rademakers, 2016). It has been suggested that the TMEM106B polymorphism might modulate progranulin plasma levels, thus affecting age at onset of symptoms in GRN mutation carriers and explaining in part the reported variability (Cruchaga et al., 2011). Furthermore, presymptomatic GRN mutation carriers bearing the TMEM106B TT genotype showed greater functional brain damage than those with CT/CC TMEM106B genotypes (Premi et al., 2014). In frontotemporal lobar degeneration–TDP-43 due to C9orf72 mutations, the relationship is less clear, and it has been suggested that TMEM106B might be able to affect disease pathology, but with an opposite association (Gallagher et al., 2014; van Blitterswijk et al., 2014): two independent groups analysed the association of TMEM106B variants with disease risk, age at onset, and age at death in C9orf72 expansion carriers with the CC genotype (protective in GRN carriers) found to be associated with earlier onset and earlier death in C9orf72 expansion carriers (Deming and Cruchaga, 2014; Gallagher et al., 2014; van Blitterswijk et al., 2014). This effect may be an example of the general phenomenon of epistasis, in which a genetic variant is beneficial on some genetic backgrounds but deleterious in others (Gallagher et al., 2014; Busch et al., 2016). In particular, as hypothesized, if in GRN-related TDP-43 pathology TMEM106B is related to endosomal-lysosomal dysfunction and to the perturbation of the progranulin pathway, in C9orf72 knockdown mice TMEM106B over-expression may produce a phenotypic rescue effect (Busch et al., 2016). However, further studies are needed to elucidate its mechanism of action. Another possibility is that TMEM106B is simply in linkage disequilibrium with the actual associated variant and when different populations are examined, the allele associated with disease modulation is different.

In the present work, a moderating, dose dependent effect of TMEM106B rs1990622 genotype together with education attainment was observed on grey matter volume in presymptomatic subjects carrying pathogenetic mutations. This finding supports the idea that epigenetic modifications in TMEM106B might occur. Epigenetic mechanisms, mostly mediated by DNA methylation, have been shown to be important in other neurodegenerative disorders (Piaceri et al., 2015) and to be influenced by socioeconomic status, which is strongly associated with cognitive reserve (Tehranifar et al., 2013). Thus, it could be hypothesized that TMEM106B, a gene containing a number of methylation sites (http://genome.ucsc.edu), might exert its effect on structural changes in at-risk subjects via cognitive reserve. Future studies, however, need to be performed to confirm this hypothesis.

We indeed found a detrimental effect of the CC genotype, but the present results were mainly driven by subjects carrying C9orf72 mutations, in which CC is the risk genotype (Supplementary Table 2).

We acknowledge that there are limitations with this work. First, the correlation between the factors herein considered and age at disease onset would benefit from longitudinal follow-up and independent studies to confirm the present results. The lack of assessment of leisure activities prevents us from characterizing the entire spectrum of cognitive reserve proxies (Nucci et al., 2012), and we are aware of possible biases determined by different school systems across the involved countries. Finally, the role of TMEM106b genotype should be further evaluated in the different genetic groups alone.

In conclusion, our findings indicate that even several years before the onset of symptoms, brain changes in inherited FTD may be modulated by environmental and genetic factors. In the absence of effective pharmacotherapeutic treatments for counteracting the onset of symptoms in pathogenetic mutation carriers, high education may represent a large-scale strategy to be considered by national health system policies.

TMEM106b genotype needs to be considered as an extra non-modifiable trait affecting brain pathology, and each FTD mutation should be analysed individually. Future clinical trials in genetic FTD should take into account both education level and TMEM106b genotype to define subjects with greater brain damage, thus representing those at higher risk of developing FTD at an earlier age.

Funding

This work was supported in part by operating grants from the Canadian Institutes of Health Research (CIHR) to M.M. (Centres of Excellence in Neurodegeneration/CIHR: INE117893 and CIHR: MOP137116) and from Italian Ministry of Health to F.T.

Supplementary material

Supplementary material is available at Brain online.

Supplementary Material

Supplementary Data
Click here for additional data file. (3.2MB, zip)

Glossary

Abbreviations

FTD

frontotemporal dementia

GS

genetic status

Appendix 1

GENFI consortium members

See Supplementary material for consortium member affiliations.

Christin Andersson, Andrea Arighi, Luisa Benussi, Giuliano Binetti, Sandra Black, Katrina Dick, Marie Fallström, Carlos Ferreira, Chiara Fenoglio, Nick Fox, Morris Freedman, Giorgio Fumagalli, Roberta Ghidoni, Marina Grisoli, Vesna Jelic, Lize Jiskoot, Ron Keren, Gemma Lombardi, Carolina Maruta, Lieke Meeter, Gabriel Miltenberger-Miltényi, Benedetta Nacmias, Linn Öijerstedt, Jessica Panman, Michela Pievani, Cristina Polito, Sara Prioni, Rosa Rademakers, Veronica Redaelli, Ekaterina Rogaeva, Giacomina Rossi, Martin Rossor, Elio Scarpini, David Tang-Wai, David Thomas, Håkan Thonberg, Pietro Tiraboschi, Rick van Minkelen, Ana Verdelho, Jason Warren.

References

  1. Akbaraly TN, Portet F, Fustinoni S, Dartigues JF, Artero S, Rouaud O et al. Leisure activities and the risk of dementia in the elderly: results from the three-city study. Neurology 2009; 73: 854–61. [DOI] [PubMed] [Google Scholar]
  2. Arenaza-Urquijo EM, Landeau B, La Joie R, Mevel K, Mezenge F, Perrotin A et al. Relationships between years of education and gray matter volume, metabolism and functional connectivity in healthy elders. Neuroimage 2013; 83: 450–7. [DOI] [PubMed] [Google Scholar]
  3. Baborie A, Griffiths TD, Jaros E, McKeith IG, Burn DJ, Richardson A et al. Pathological correlates of frontotemporal lobar degeneration in the elderly. Acta Neuropathol 2011; 121: 365–71. [DOI] [PubMed] [Google Scholar]
  4. Baker M, Mackenzie IR, Pickering-Brown SM, Gass J, Rademakers R, Lindholm C et al. Mutations in progranulin cause tau-negative frontotemporal dementia linked to chromosome 17. Nature 2006; 442: 916–9. [DOI] [PubMed] [Google Scholar]
  5. Barulli D, Stern Y. Efficiency, capacity, compensation, maintenance, plasticity: emerging concepts in cognitive reserve. Trends Cogn Sci 2013; 17: 502–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv Neural Inf Process Syst 2001; 14585–91. [Google Scholar]
  7. Borroni B, Padovani A. Dementia: a new algorithm for molecular diagnostics in FTLD. Nat Rev Neurol 2013; 9: 241–2. [DOI] [PubMed] [Google Scholar]
  8. Borroni B, Premi E, Agosti C, Alberici A, Garibotto V, Bellelli G et al. Revisiting brain reserve hypothesis in frontotemporal dementia: evidence from a brain perfusion study. Dement Geriatr Cogn Disord 2009; 28: 130–5. [DOI] [PubMed] [Google Scholar]
  9. Brambati SM, Renda NC, Rankin KP, Rosen HJ, Seeley WW, Ashburner J et al. A tensor based morphometry study of longitudinal gray matter contraction in FTD. Neuroimage 2007; 35: 998–1003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Busch JI, Martinez-Lage M, Ashbridge E, Grossman M, Van Deerlin VM, Hu F et al. Expression of TMEM106B, the frontotemporal lobar degeneration-associated protein, in normal and diseased human brain. Acta Neuropathol Commun 2013; 1: 36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Busch JI, Unger TL, Jain N, Tyler Skrinak R, Charan RA, Chen-Plotkin AS. Increased expression of the frontotemporal dementia risk factor TMEM106B causes C9orf72-dependent alterations in lysosomes. Hum Mol Genet 2016; 25: 2681–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cardoso MJ, Modat M, Wolz R, Melbourne A, Cash D, Rueckert D et al. Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans Med Imaging 2015; 34: 1976–88. [DOI] [PubMed] [Google Scholar]
  13. Chen-Plotkin AS, Unger TL, Gallagher MD, Bill E, Kwong LK, Volpicelli-Daley L et al. TMEM106B, the risk gene for frontotemporal dementia, is regulated by the microRNA-132/212 cluster and affects progranulin pathways. J Neurosci 2012; 32: 11213–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Craik FI, Bialystok E, Freedman M. Delaying the onset of Alzheimer disease: bilingualism as a form of cognitive reserve. Neurology 2010; 75: 1726–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cruchaga C, Graff C, Chiang HH, Wang J, Hinrichs AL, Spiegel N et al. Association of TMEM106B gene polymorphism with age at onset in granulin mutation carriers and plasma granulin protein levels. Arch Neurol 2011; 68: 581–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Cruts M, Gijselinck I, van der Zee J, Engelborghs S, Wils H, Pirici D et al. Null mutations in progranulin cause ubiquitin-positive frontotemporal dementia linked to chromosome 17q21. Nature 2006; 442: 920–4. [DOI] [PubMed] [Google Scholar]
  17. DeJesus-Hernandez M, Mackenzie IR, Boeve BF, Boxer AL, Baker M, Rutherford NJ et al. Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron 2011; 72: 245–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Deming Y, Cruchaga C. TMEM106B: a strong FTLD disease modifier. Acta Neuropathol 2014; 127: 419–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Diedrichsen J, Balsters JH, Flavell J, Cussans E, Ramnani N. A probabilistic MR atlas of the human cerebellum. Neuroimage 2009; 46: 39–46. [DOI] [PubMed] [Google Scholar]
  20. Freeborough PA, Fox NC, Kitney RI. Interactive algorithms for the segmentation and quantitation of 3-D MRI brain scans. Comput Methods Programs Biomed 1997; 53: 15–25. [DOI] [PubMed] [Google Scholar]
  21. Gałecki A, Burzykowski T. Linear mixed-effects models using R. A step-by-step approach. 1st ednNew York, NY: Springer-Verlag; 2013. [Google Scholar]
  22. Gallagher MD, Suh E, Grossman M, Elman L, McCluskey L, Van Swieten JC et al. TMEM106B is a genetic modifier of frontotemporal lobar degeneration with C9orf72 hexanucleotide repeat expansions. Acta Neuropathol 2014; 127: 407–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hodges JR, Davies RR, Xuereb JH, Casey B, Broe M, Bak TH et al. Clinicopathological correlates in frontotemporal dementia. Ann Neurol 2004; 56: 399–406. [DOI] [PubMed] [Google Scholar]
  24. Hutton M, Lendon CL, Rizzu P, Baker M, Froelich S, Houlden H et al. Association of missense and 5’-splice-site mutations in tau with the inherited dementia FTDP-17. Nature 1998; 393: 702–5. [DOI] [PubMed] [Google Scholar]
  25. Jiang B, Ding C, Luo B, Tang J. Graph-laplacian PCA: closed-form solution and robustness. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 23–28 June 2013; Portland, OR; 2013, p. 3492–8. [Google Scholar]
  26. Lam BY, Halliday GM, Irish M, Hodges JR, Piguet O. Longitudinal white matter changes in frontotemporal dementia subtypes. Hum Brain Mapp 2014; 35: 3547–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Maloney B, Lahiri DK. Epigenetics of dementia: understanding the disease as a transformation rather than a state. Lancet Neurol 2016; 15: 760–74. [DOI] [PubMed] [Google Scholar]
  28. Maloney B, Sambamurti K, Zawia N, Lahiri DK. Applying epigenetics to Alzheimer’s disease via the latent early-life associated regulation (LEARn) model. Curr Alzheimer Res 2012; 9: 589–99. [DOI] [PubMed] [Google Scholar]
  29. Nicholson AM, Rademakers R. What we know about TMEM106B in neurodegeneration. Acta Neuropathol 2016; 132: 639–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Nucci M, Mapelli D, Mondini S. Cognitive Reserve Index questionnaire (CRIq): a new instrument for measuring cognitive reserve. Aging Clin Exp Res 2012; 24: 218–26. [DOI] [PubMed] [Google Scholar]
  31. Pettigrew C, Soldan A, Li S, Lu Y, Wang MC, Selnes OA et al. Relationship of cognitive reserve and APOE status to the emergence of clinical symptoms in preclinical Alzheimer’s disease. Cogn Neurosci 2013; 4: 136–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Piaceri I, Raspanti B, Tedde A, Bagnoli S, Sorbi S, Nacmias B. Epigenetic modifications in Alzheimer’s disease: cause or effect? J Alzheimers Dis 2015; 43: 1169–73. [DOI] [PubMed] [Google Scholar]
  33. Premi E, Formenti A, Gazzina S, Archetti S, Gasparotti R, Padovani A et al. Effect of TMEM106B polymorphism on functional network connectivity in asymptomatic GRN mutation carriers. JAMA Neurol 2014; 71: 216–21. [DOI] [PubMed] [Google Scholar]
  34. Premi E, Garibotto V, Alberici A, Paghera B, Giubbini R, Padovani A et al. Nature versus nurture in frontotemporal lobar degeneration: the interaction of genetic background and education on brain damage. Dement Geriatr Cogn Disord 2012; 33: 372–8. [DOI] [PubMed] [Google Scholar]
  35. Premi E, Gazzina S, Bozzali M, Archetti S, Alberici A, Cercignani M et al. Cognitive reserve in granulin-related frontotemporal dementia: from preclinical to clinical stages. PLoS One 2013; 8: e74762. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Premi E, Gualeni V, Costa P, Cosseddu M, Gasparotti R, Padovani A et al. Looking for measures of disease severity in the frontotemporal dementia continuum. J Alzheimers Dis 2016; 52: 1227–35. [DOI] [PubMed] [Google Scholar]
  37. Renton AE, Majounie E, Waite A, Simon-Sanchez J, Rollinson S, Gibbs JR et al. A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD. Neuron 2011; 72: 257–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Rohrer JD, Lashley T, Schott JM, Warren JE, Mead S, Isaacs AM et al. Clinical and neuroanatomical signatures of tissue pathology in frontotemporal lobar degeneration. Brain 2011; 134(Pt 9): 2565–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Rohrer JD, Nicholas JM, Cash DM, van Swieten J, Dopper E, Jiskoot L et al. Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis. Lancet Neurol 2015; 14: 253–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Rzezak P, Squarzoni P, Duran FL, de Toledo Ferraz Alves T, Tamashiro-Duran J, Bottino CM et al. Relationship between brain age-related reduction in gray matter and educational attainment. PLoS One 2015; 10: e0140945. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Seelaar H, Rohrer JD, Pijnenburg YA, Fox NC, van Swieten JC. Clinical, genetic and pathological heterogeneity of frontotemporal dementia: a review. J Neurol Neurosurg Psychiatry 2011; 82: 476–86. [DOI] [PubMed] [Google Scholar]
  42. Sole-Padulles C, Bartres-Faz D, Junque C, Vendrell P, Rami L, Clemente IC et al. Brain structure and function related to cognitive reserve variables in normal aging, mild cognitive impairment and Alzheimer’s disease. Neurobiol Aging 2009; 30: 1114–24. [DOI] [PubMed] [Google Scholar]
  43. Stern Y. What is cognitive reserve? Theory and research application of the reserve concept. J Int Neuropsychol Soc 2002; 8: 448–60. [PubMed] [Google Scholar]
  44. Stern Y. Cognitive reserve. Neuropsychologia 2009; 47: 2015–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Stern Y, Alexander GE, Prohovnik I, Mayeux R. Inverse relationship between education and parietotemporal perfusion deficit in Alzheimer’s disease. Ann Neurol 1992; 32: 371–5. [DOI] [PubMed] [Google Scholar]
  46. Stern Y, Gurland B, Tatemichi TK, Tang MX, Wilder D, Mayeux R. Influence of education and occupation on the incidence of Alzheimer’s disease. JAMA 1994; 271: 1004–10. [PubMed] [Google Scholar]
  47. Stevens M, van Duijn CM, Kamphorst W, de Knijff P, Heutink P, van Gool WA et al. Familial aggregation in frontotemporal dementia. Neurology 1998; 50: 1541–5. [DOI] [PubMed] [Google Scholar]
  48. Sun J, Chen Q, Zhang Q, Li Y, Li H, Wei D et al. Training your brain to be more creative: brain functional and structural changes induced by divergent thinking training. Hum Brain Mapp 2016; 37; 3375–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Tehranifar P, Wu HC, Fan X, Flom JD, Ferris JS, Cho YH et al. Early life socioeconomic factors and genomic DNA methylation in mid-life. Epigenetics 2013; 8: 23–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. van Blitterswijk M, Mullen B, Nicholson AM, Bieniek KF, Heckman MG, Baker MC et al. TMEM106B protects C9ORF72 expansion carriers against frontotemporal dementia. Acta Neuropathol 2014; 127: 397–406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Warren JD, Rohrer JD, Rossor MN. Clinical review. Frontotemporal dementia. BMJ 2013; 347: f4827. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Data
Click here for additional data file. (3.2MB, zip)

Articles from Brain are provided here courtesy of Oxford University Press

RESOURCES