Awareness of cognitive decline trajectories in asymptomatic individuals at risk for AD

doi:10.21203/rs.3.rs-49468/v1

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Research

Awareness of cognitive decline trajectories in asymptomatic individuals at risk for AD

https://doi.org/10.21203/rs.3.rs-49468/v1

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Journal Publication

published 14 Oct, 2020

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Background

Lack of awareness of cognitive decline (ACD) is common in late-stage Alzheimer's disease (AD). Recent studies showed that ACD can also be reduced in the early stages.

Methods

We performed a Latent Class Analysis to identify longitudinal changes of ACD over 3 years in 314 elderly memory-complainers and its association to amyloid burden and brain metabolism. We also analysed the impact of ACD at baseline on the cognitive scores’ evolution.

Results

76.8% of subjects constantly had an accurate ACD (reference class). 18.95% showed a persistent heightened ACD (“worried-well” individuals). 4.25% constantly showed low ACD. They had higher amyloid burden than the reference class, and were mostly men. We found no overall effect of baseline ACD on cognitive scores’ evolution.

Conclusions

ACD begins to decrease during the preclinical phase in a certain group of individuals, who are of great interest because more at risk of being affected by AD.

Trial registration

The present study was conducted as part of the INSIGHT-PreAD study. The identification number of INSIGHT-PreAD study (ID-RCB) is 2012-A01731-42

Cognitive Neuroscience

preclinical Alzheimer’s disease

awareness

cognitive decline

brain

amyloid

Patients with Alzheimer's dementia often exhibit anosognosia, showing little or no awareness of their progressive cognitive decline (e.g. [1]). According to longitudinal and cross-sectional studies, the awareness of cognitive decline (ACD) is not only impaired in patients with dementia, but also in about 50% of individuals with mild cognitive impairment (MCI) [2,3]. Anosognosic MCI patients seem to have a greater risk of future progression to dementia than non-anosognosic ones (e.g. [4-6]).

Recently, few studies have suggested the possibility of a very early reduction of the ACD in Alzheimer’s disease (AD), that is in its preclinical phase, without this constituting a clear anosognosia. In this initial phase of the disease, insidious neuropathological processes have started, which however have no or very slight impact on cognition [7-9]. In the stage II of preclinical AD according to [9], individuals have a subtle cognitive decline, which corresponds to slight differences in cognitive efficiency and occasional memory lapses. Cognitive scores remain within age and education norms, but probably close to the pathological threshold of the test. An altered subjective perception of these initial cognitive changes could indicate an underlying early-stage AD pathology, and allow an early diagnosis and patient management.

To date, very few studies have attempted to clarify the degree of ACD in individuals at the preclinical stage. These studies selected samples of cognitively-unimpaired subjects at risk for AD. To measure the ACD, they used questionnaires asking the patient or his/her study-partner to evaluate the severity of the patient's cognitive decline. The indices of ACD were calculated as follows: (a) as the subject-informant discrepancy [e.g. 10,11]; (b) as the discrepancy between the patient’s rating and the objective cognitive performance [e.g. 12,13]; (c) a combination of both [14]. These studies lead to discordant results. For instance, we found greater amyloid burden and brain hypometabolism in cognitively-unimpaired participants with lower ACD [10]. On the contrary, others found a positive correlation between the degree of ACD and amyloid burden, meaning that the participants with heightened ACD (here called “hypernosognosia”, otherwise known as “subjective cognitive decline” [15]) have a greater risk of being affected by AD [16]. Interestingly, this latter study suggested that these two conditions (hypernosognosia and low ACD) may occur in succession along the preclinical phase of AD. Consistently, a recent longitudinal study [13] found that (i) a subset of individuals with preclinical AD who progressed to MCI showed a ‘hypernosognosia’ up to 1.6 years before MCI diagnosis; (ii) the participants with MCI who progressed to dementia showed low ACD, eventually constituting a clear anosognosia 3.2 years before the diagnosis of Alzheimer’s dementia.

In the present article, we analysed longitudinal data from the INSIGHT-PreAD study, in order to capture the temporality of the changes in the ACD in the preclinical phase of AD. Our main objectives were:

1- to identify groups of participants who share a similar longitudinal trajectory in the ACD, and to characterise each group according to their demographical data and AD neuroimaging markers at baseline (i.e. amyloid burden and brain metabolism),

2- to study the impact of ACD at baseline on the evolution of cognitive scores across the 3 years of study.

1. Participants

The INSIGHT-PreAD cohort has been previously described [17]. In summary, 318 participants were included in the study, and just as many study-partners. They were French individuals between 70 and 85 years of age, with normal scores on Mini Mental State Examination (MMSE ≥ 27), Clinical Dementia Rating scale (CDR=0) and Free and Cued Selective Rating Test (FCSRT total recall score ≥ 41), who reported cognitive complaints at baseline. They had no evidence of monogenic AD mutation and neurological disorder. The study-partner must be a person close to the subject, and informed about potential recent changes in health and cognition. Each participant signed an informed consent, and Paris VI ethical committee approved the study protocol. INSIGHT-PreAD is a prospective ongoing cohort study, currently collecting data at the 60th month of follow-up. To date, a total of 14 participants were excluded from the study as they no longer met the inclusion criteria (i.e. "decliners"). In particular, we excluded subjects with a CDR increasing from 0 to 0.5 and/or an MMSE below 26 and/or a FCSRT total score below 40 (at least two of them), over two consecutive evaluations 6 months apart. In this article, we analysed data from the first 36 months of follow-up.

2. Cognitive measures

All INSIGHT-PreAD participants performed the MMSE and the CDR every 6 months, and underwent a comprehensive neuropsychological evaluation every 12 months.

In the present study, we only analysed those cognitive measures that are supposed to be more relevant for the study of ACD in AD [18-21].

We included (1) the MMSE as measure of global cognitive functioning; (2) the free recall and total recall scores from the FCSRT as measures of episodic memory; (3) as measures of executive functioning, we included the Trail Making Test (TMT) B-A score, the Lexical and Semantic Fluency and the Frontal Assessment Battery (FAB).

3. Determination of the Awareness of Cognitive Decline Index (ACDI)

The procedure for identifying the ACDI is reported in our previous publication [10]. In summary, both the subject and the informant filled two parallel versions of the Healthy Aging Brain Care Monitor (HABC-M [22,23]). This is a questionnaire asking how often, during the last two weeks, the subject has encountered target everyday-life difficulties (for example, “Repeating the same stories or questions”). Answers range from 0 (never) to 3 (very often). Since we aimed at studying awareness of cognitive decline, we only considered the HABC-M Cognitive score, which is the sum of 6 items and ranges from 0 to 18. The ACDI was determined by subtracting the HABC-M Cognitive score obtained by the informant from that obtained by the subject. The ACDI ranged from –18 to 18, where higher scores indicated higher cognitive complaints, and lower scores, low ACD. The ACDI was computed at each visit (every 6 months) and treated as a continuous variable.

4. Brain imaging

In the present study, we included baseline neuroimaging markers of AD progression.

All participants performed amyloid-PET imaging using ¹⁸F-AV-45 (¹⁸F-florbetapir; Amyvid^TM, Avid Radiopharmaceuticals) as a tracer. The standardized uptake value (¹⁸F-AV-45-SUV) was calculated with the CATI pipeline (Centre d’Acquisition et de Traitement d’Images, https://cati-neuroimaging.com), and normalized to the cerebellum and pons, resulting in a SUV ratio (SUVr). In the present work, we have included the ¹⁸F-AV-45 SUVr as a continuous measure of amyloid load (no positivity threshold used).

We also examined cortical glucose metabolism using fluorodeoxyglucose (FDG) PET. A metabolism index was calculated by averaging the FDG-SUVs of four bilateral regions of interest, whose metabolic changes are considered to be a "signature" of AD [24]: posterior cingulate cortex, inferior parietal lobule, precuneus, and inferior temporal gyrus. The pons was used as a reference region. The FDG-SUV has been included as a continuous variable.

More details about imaging data acquisition are available in previous works [17] .

5. Statistical analysis

To study the longitudinal evolution of the ACDI, we performed a Latent Class Linear Mixed Model (LCLMM [25]) to investigate heterogeneous trajectories, since they are expected in a cohort of memory-complainers. The LCLMM first identifies G classes of subjects who share a similar trend of evolution of the ACDI and then compares the classes. In order to find the adequate and clinically relevant number of classes G, we computed the model from one to three classes and selected the one which minimised the Bayesian Information Criterion (BIC). Mean of posterior probabilities, as well as percentage of posterior probabilities higher than 0.7 were computed. The evolution of the ACDI was modelled by the interaction between classes and visits. Using a multinomial logistic model, the baseline characteristics of each class (i.e. amyloid load, glucose metabolism, age, gender and educational level) were compared to the class with the largest number of subjects. Normality of residuals and random effects as well as heteroskedasticity were checked visually. For this analysis, subjects with at least two time points of ACDI and with no missing baseline data (amyloid load, metabolism, age, gender and education) were included.

We also performed generalized linear mixed effects models (GLMM) to evaluate the effect of the ACDI at baseline on the evolution of the cognitive scores. Link function was chosen for the underlying data generation mechanism with logit for binomial data, and identity for continuous data. One GLMM was performed for each score, then Benjamini-Hochberg method was used to correct for multiple comparisons. We entered the following baseline variables as fixed effects: ACDI, age, gender and educational level, visit, as well as the interaction between visit and ACDI, to test the impact of the ACDI at baseline on cognitive scores' evolution. All two-way interactions between these effects were tested independently and were added in the final model if significant. The participant was added as a random effect. Type II likelihood ratio tests were used to test each fixed effect and interaction. Cohen's f2 were calculated, using the marginal R2 [26], for each effect to estimate their size. For this analysis, we only included subjects with at least two time-points for one cognitive score, and with no missing data for ACDI at baseline, age at baseline, gender and education. Baseline characteristics were compared between subjects included and excluded in the analysis using the χ2 test for categorical variables and the Student’s t-test for continuous variables.

A p-value <.05 was considered significant.

Statistical analyses were performed using R3.6.1. The packages lme4 (1.1-21) and LCMM (1.8.1) were used to perform LMM and LCLMM respectively.

The longitudinal evolution of the ACDI was studied in 306 out of 318 subjects. Indeed, we excluded 12 subjects who had only one ACDI available (n=6) or none (n=6). The baseline characteristics of the included subjects are presented in Table 1. Mean age was 76.0 (SD=3.4), subjects were mostly women (62.8%) and had high educational level (69% of the sample had at least a high school diploma).

The three-class LCLMM provided the best fit with a BIC=8287.2, compared to 8384.0 for the two-class LCLMM and 8626.7 for the one-class LCLMM. The three classes were well distinct, with Class 1 mean posterior probabilities of 0.91 for belonging to Class 1; Class 2 mean posterior probabilities of 0.96 for belonging to Class 2; and Class 3 mean posterior probabilities of 0.94 for belonging to Class 3. Moreover, more than 90% of the subjects in each class had a posterior probability higher than 0.7.

Table 1. Baseline characteristics of the participants included in the analysis

	All subjects^¥ N=306 (96.23%)
Age [years; M ± SD]	75.95 ± 3.43
Gender [female; n (%)]	192 (62.75%)
Education [high^§; n (%)]	211 (68.95%)
ACDI [M ± SD]	1.44 ± 2.92
HABC-M Cognitive score (subject) [M ± SD]	3.32 ± 2.79
HABC-M Cognitive score (informant) [M ± SD]	1.84 ± 2.21
MMSE [M ± SD]	28.67 ± 0.95
CDR [=0; n (%)]	306 (100%)
FSCRT Free Recall [M ± SD]	30.15 ± 5.35
FSCRT Total Recall [M ± SD]	46.10 ± 1.97
FAB [M ± SD]	16.44 ± 1.66
TMT B-A [seconds; M ± SD]	47.81 ± 35.40
Lexical fluency [M ± SD]	22.46 ± 5.91
Semantic fluency [M ± SD]	31.44 ± 7.16
APOE [presence of ε4; n (%)]	60 (19.61%)
Amyloid load [¹⁸F-AV-45 SUVr; M ± SD]	0.78 ± 0.19
Brain glucose metabolism [¹⁸F-FDG SUV; M ± SD]	2.45 ± 0.25

Note. ACDI: Awareness of Cognitive Decline Index; HABC-M: Healthy Aging Brain Care – Monitor; MMSE: Mini Mental State Examination; CDR: Clinical Dementia Rating; FCSRT: Free and Cued Selective Reminding Test; FAB: Frontal Assessment Battery; TMT: Trail Making Test. ¹⁸F-AV-45 SUVr: Florbetapir standardized uptake value ratio (index of amyloid deposition); ¹⁸F-FDG SUV: Fluorodeoxyglucose standardized uptake value (metabolic index).

^¥Subjects included in the analysis were those with age, gender, educational level, ¹⁸F-AV-45 SUVr and ¹⁸F-FDG SUV available at baseline, and at least two time-points for the ACDI.

^§Equal to or higher than high-school diploma (≥ 12 years).

The three trajectories identified by the LCLMM are presented in Figure 1. Class 1 included 58 subjects (18.95% of the sample) having a relatively higher positive ACDI, meaning that these participants reported more cognitive difficulties than their study-partner did, and it remained stable over time. We refer to these participants as belonging to the “worried-well” class. Class 2 included 235 subjects (76.8% of the sample) with an ACDI of around 0, indicating a good match between the subject's and the informant's assessments (“accurate ACD” class). The ACDI of this class remained unchanged over time. Class 3 included 13 subjects (4.25% of the sample) with a relatively lower negative ACDI, which means that these participants expressed less difficulties compared to their informant. In this class, the ACDI slightly increased at 6 months, and then tended to decline. We refer to this group as the “Low ACD” class.

We compared the characteristics of Classes 1 and 3 with those of Class 2 (Table 2), which has been chosen as the reference. Indeed, this was the numerically largest class, and the average ACDI was constantly around 0 in this class, indicating that the subject and his/her study-partner have similarly assessed the subject's cognitive functioning.

Compared to Class 2, individuals in Class 3, who had a slight decline and lower values of the ACDI, had higher amyloid burden (OR ± se: 57.2 ± 69.9, p= 0.0009) and were mostly men (OR ± se: 4.7 ± 3.3, p= 0.0307). No statistical difference was found in terms of age, educational level and brain metabolism between these two classes. Class 3 includes 3 decliners (excluded from the study after 24, 42 and 54 months of follow-up respectively), which represents the 23.1% of this class

No statistically significant difference was found when comparing Classes 1 and 2 in terms of age, gender, educational level, amyloid burden and brain metabolism. Class 1 included 1 decliner (excluded from INSIGHT-PreAD study at the 36-month visit) representing the 1.72% of this class. Class 2 included 10 converters (n=2 after 24 months of follow-up; n=8 after 60 months), which represents the 4.25% of this class.

Table 2. Comparison between Class 2 (reference) and both Class 1 and 3.

	Class 1 (“Worried-well” class)		Class 3 (“Low ACD” class)
	OR ± se	p-value	OR ± se	p-value
Intercept	0.03 ± 0.12	0.3566	1867.11 ± 14807.31	0.3422
Age at baseline	1.02 ± 0.04	0.6175	0.87 ± 0.08	0.1325
Gender [female]	1.47 ± 0.46	0.2123	4.66 ± 3.32	0.0307*
Education [high^§]	1.73 ± 0.53	0.0783	0.28 ± 0.30	0.2389
Amyloid load [¹⁸F-AV-45 SUVr]	0.99 ± 0.85	0.9950	57.18 ± 69.93	0.0009*
Brain metabolism [¹⁸F-FDG SUV]	1.03 ± 0.62	0.9560	0.18 ± 0.26	0.2289

Note. Class 2, with stable evolution and matching subject’s and informant’s ratings of decline, was the reference class. For categorical variables, category in brackets was the reference.

¹⁸F-AV-45 SUVr: Florbetapir standardized uptake value ratio (index of amyloid deposition); ¹⁸F-FDG SUV: Fluorodeoxyglucose standardized uptake value (metabolic index).

^§Equal to or higher than high-school diploma (≥ 12 years).

OR: odd ratio; se: standard errors

The second objective of this study was to investigate if the ACDI at baseline had an impact on the evolution of the cognitive scores of interest across the 3-year follow-up. To do so, we only analysed the participants who had the ACDI available at baseline, and at least two time-points for one or more cognitive score(s) (n=270). No significant difference was found when comparing subjects included and excluded from this analysis, in terms of demographic variables, cognitive scores and biomarkers (all p>0.156). Mean age was 75.9 (SD=3.4) and subjects included were mostly women (61.9%) and had high educational level (68.5%).

Concerning the GLMM, the final models also included interactions between gender and visit; educational level and age; ACDI and age; in addition to the effects stated in the statistical analysis paragraph. Marginal R2s, based on the GLMM fixed part, were very low from 0.05 (MMSE model) to 0.12 (Lexical fluency model). There was a significant overall evolution on all cognitive scores, except for the TMT B-A (p= 0.1449). However, this effect was very small (Cohen’s ƒ² ranging from 0.004 to 0.034 for the different scores).

The main effect of the ACDI at baseline on the evolution of the scores was not significant for any test (all p>0.6236).

The interaction between age and ACDI had a significant effect on the average evolution of Semantic Fluency score (p_{non-corrected=}0.0443) and TMT B-A score (p_{non-corrected}=0.0166), but the effect size was small (Cohen’s ƒ²=0.009 for Semantic Fluency score; Cohen’s ƒ²=0.015 for TMT B-A score), and the two effects did not survive the correction for multiple comparisons (p= 0.1160 for TMT B-A score; p= 0.1550 for Semantic Fluency score).

The effect of the interaction between visit and ACDI on the scores was not significant (all p>0.1649).

It has recently been proposed that low ACD (patient’s complaint < informant’s complaint) may serve as a marker of early-stage phase of AD. This is a recent field of research and the studies available are few, especially the longitudinal ones. Thus, this is one of the first studies to appreciate the longitudinal evolution of the ACD in a population of asymptomatic individuals at risk for AD, due to their age, cognitive complaints and amyloid burden (where appropriate).

In our sample, we identified three patterns of evolution of the ACD across the 3 years of study.

Most subjects (around 77%) consistently expressed an accurate assessment of their own cognitive functioning, that is, the cognitive difficulties reported by the subject were comparable to those reported by his/her study-partner (ACDI consistently around 0). This represents an accurate ACD.

The other two classes represented two forms of inaccurate ACD (accordingly with the model proposed by Dalla Barba et al. [27]).

On the one hand, around 19% of participants consistently belonged to the “worried-well” class.

They were comparable to those with accurate ACD in terms of demographical characteristics and AD biomarkers, meaning that belonging to this group does not increase the risk of having positive AD markers. Moreover, only one of these participants (1.72%) was tagged as decliner during follow-up. This is consistent with previous studies, such as [10,11]. Indeed, subjective cognitive decline is potentially due to various aetiologies, including AD [28], but also normal aging [29], personality traits [30], depression [31], anxiety and nosophobia [32], use of medications [33], sleep disorders [34], among others.

On the other hand, approximately 4% of subjects consistently had a low ACD, as they reported that they encounter fewer cognitive difficulties than estimated by their study-partner. This means that the ACD could already be reduced in asymptomatic elderly individuals at risk for AD, and is particularly interesting if we considered that we studied a population of memory complainers. Thus, it seems that these two phenomena (low ACD and cognitive complaints) can coexist in the same individual, despite being apparently opposed. Indeed, individuals from the “low ACD” group were all complaining of a certain degree of cognitive difficulties, still underestimating them (when compared to an informant).

These subjects showed higher amyloid burden compared to those with normal ACD and, as a consequence, a higher risk of developing AD [35], consistently with what has been found by [10,11] . Around 1/4 of them (n=3) were tagged as decliners during follow-up, a fraction which is qualitatively larger than in the other classes (although no statistics were performed due to the low number of decliners).

Interestingly, we found no difference between individuals with low and normal ACD in terms of brain metabolism. This suggests that the reduction of the ACD would be associated with amyloid accumulation prior to neurodegeneration, of which the brain hypometabolism is a marker [36]. This would be consistent with previous evidence about the temporal sequence of imaging markers reflecting the pathologic cascade of AD [37,38], and underlines how this symptom could occur very early in the course of the disease.

It should be noted, however, that we examined the cerebral glucose metabolism through a global index, which was an average of the regional indices from the AD “signature” regions [24]. This could mask regional differences in glucose metabolism. Indeed, we believe that the reduction of the ACD could be associated with a reduction of the metabolism in certain areas of the brain (in particular, in the frontal lobe) and not in others. Additional studies should be conducted to explore these regional differences, both in the glucose metabolism and in the amyloid accumulation.

Another key result of this study is that individuals with low ACD were mostly men, probably due to a socio-cultural issue. Research findings have been strikingly consistent in showing that men and women have different health-related behaviours (e.g. [39]). For instance, men, as a group, are more likely to avoid seeking help for physical and mental health problems, due to a gender-role conflict (see [40] for a review). When men do seek medical help, their behaviour may be different compared to women. For instance, they may ask fewer questions than women do [41]. We believe that these socio-cultural factors could explain why the group of participants who underestimated their cognitive difficulties included mostly men.

The longitudinal trajectory of the ACDI showed an increase at 6 months and then a tendency to decline, in those with consistently low ACD. We believe that the ACD is likely to be amplified in the presence of the earliest subtle cognitive difficulties (i.e. SCD), and later in the preclinical phase it would begin to decrease, and become a clear anosognosia in AD dementia.

In the present study, we also explored whether an early reduction in ACD could be an indicator of a future major neurocognitive disorder due to AD. We found that the evolution of cognitive scores over 3 years did not depend on the degree of ACD at baseline. Indeed, this is a cohort of cognitively-intact individuals, with a fraction of them likely being in a very early (preclinical) stage of AD. Their cognitive scores remained on average within normal ranges over the three years of follow-up. Therefore a lack of association between baseline ACD and the evolution of cognitive scores was somewhat expected, consistently with what we found in [10] in the same cohort.

We believe that if the follow-up had been longer, we would have identified an association between baseline ACD and cognitive scores' evolution (in the sense that those who have a lower baseline ACD would experience a more marked cognitive decline) .

Of note, however, is the effect of the interaction between age and baseline ACDI on the evolution of TMT B minus A and verbal fluences, both markers of frontal function, before correcting for multiple comparisons. This may indicate that low ACD may reflect an early subtle frontal dysfunction, mostly in older participants. Whether these subtle frontal signs are followed by a characteristic amnestic syndrome suggestive of AD will have to be determined in further studies with longer follow-up.

Limitations

This study was conducted on a single-center cohort, with highly standardized clinical assessment, neuropsychological testing, and imaging acquisition procedures. However, it should be noticed that our results may possibly be biased by the high average education level, and by the fact that women were over-represented. This could limit the generalization of our findings.

To conclude, ACD may start to decrease in the very early stages of AD, especially in a certain group of individuals who need to be further characterised through additional studies. This group is of great interest because it is more at risk of being affected by AD than other individuals. Indeed, a progressive decline of ACD, but not the persistent presence of cognitive complaints, was shown to be associated with greater amyloid deposition. The presence of an informant is therefore strongly recommended both in clinical practice and in research trials. Indeed, this can be useful to orient the clinician in the scope of a timely diagnosis of AD. Inclusion criteria of studies targeting preclinical AD should also take into consideration this evidence.

ACD - Awareness of cognitive decline

MCI - Mild Cognitive Impairment

MMSE - Mini Mental State Examination

CDR - Clinical Dementia Rating

FCSRT - Free and Cued Selective Reminding Test

TMT - Trail Making Test

FAB - Frontal Assessment Battery

ACDI - Awareness of Cognitive Decline Index

HABC-M - Healthy Aging Brain Care Monitor

¹⁸F-AV-45 - ¹⁸F-florbetapir; Amyvid^TM, Avid Radiopharmaceuticals

¹⁸F-AV-45-SUV - ¹⁸F-florbetapir standardized uptake value

CATI - Centre d’Acquisition et de Traitement d’Images

FDG-PET - fluorodeoxyglucose positron emission tomography

LCLMM - Latent Class Linear Mixed Model

BIC - Bayesian Information Criterion

GLMM - generalized linear mixed effects models

Ethics approval and consent to participate:

Each participant of the INSIGHT-PreAD cohort signed an informed consent, and Paris VI ethical committee approved the study protocol.

Consent for publication

Not applicable

Availability of Data and Materials

Data may be made available upon reasonable request from qualified investigators.

Competing interests

Federica Cacciamani reports no disclosures.

Luisa Sambati reports no disclosures.

Marion Houot reports no disclosures.

Marie-Odile Habert has received speaker honoraria from Lilly, GE Healthcare and Piramal.

Bruno Dubois has received consultancy fees from Biogen, Boehringer Ingelheim, Eli Lilly, and MedAvante and grants for his institution from Merck, Pfizer, and Roche.

Stéphane Epelbaum reports receiving fees from Roche, Eli Lilly, Biogen and GE Healthcare as a consultant or advisory board member.

Funding:

Supported by NIH (OR 76675882).

The study was promoted by INSERM in collaboration with ICM, and Pfizer and has received support within the "Investissement d'Avenir" (ANR-10-AIHU-06) program. The study was promoted in collaboration with the "CHU de Bordeaux" (coordination CIC EC7), the promoter of Memento cohort, funded by the Foundation Plan-Alzheimer. The study was further supported by AVID/Lilly. This project/research has received funding from the European Union’s Horizon 2020 Framework Program for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2). The funding sources had no role in the study design, data collection, data analysis, or data interpretation.

Authors’ contributions:

Federica Cacciamani	Study design; Data acquisition; Interpretation of results; Drafting of manuscripts
Luisa Sambati	Study design; Data acquisition; Interpretation of results; Drafting of manuscripts
Marion Houot	Statistical analysis
Marie-Odile Habert	Imaging data acquisition and processing
Bruno Dubois	Study design; Interpretation of results
Stéphane Epelbaum	Study design; Interpretation of results; Drafting of manuscripts

Acknowledgements

INSIGHT Pre-AD study group: Audrain C, Auf- fret A, Bakardjian H, Baldacci F, Batrancourt B, Benakki I, Benali H, Bertin H, Bertrand A, Boukadida L, Cacciamani F, Causse V, Cavedo E, Cherif Touil S, Chiesa P A, Colliot O, Dalla Barba G, Depaulis M, Dos Santos A, Dubois B, Dubois M, Epelbaum S, Fontaine B, Francisque H, Gagliardi G, Genin A, Genthon R, Glasman P, Gombert F, Habert M O, Hampel H, Hewa H, Houot M, Jungalee N, Kas A, Kilani M, La Corte V, Le Roy F, Lehericy S, Leton- dor C, Levy M, Lista S, Lowrey M, Ly J, Makiese O, Masetti I, Mendes A, Metzinger C, Michon A, Mochel F, Nait Arab R, Nyasse F, Perrin C, Poirier F, Poisson C, Potier M C, Ratovohery S, Revillon M, Rojkova K, Santos-Andrade K, Schindler R, Servera M C, Seux L, Simon V, Skovronsky D, Thiebaut M, Uspenskaya O, Vlaincu M.

Authors' information

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Awareness of cognitive decline trajectories in asymptomatic individuals at risk for AD

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