Underlying Biological Processes in Mild Cognitive Impairment: Amyloidosis Versus Neurodegeneration

Abstract

The amyloid cascade hypothesis proposes amyloid-β (Aβ) as the earliest and key pathological hallmark of Alzheimer’s disease (AD), but this mandatory “amyloid-first pathway” has been contested. Longitudinal studies of mild cognitive impairment (MCI) patients represent an opportunity to investigate the intensity of underlying biological processes (amyloidosis versus neurodegeneration) and their relevance for progression to AD. We re-examined our cohort of amnestic MCI, grouped according to cerebrospinal fluid (CSF) biomarkers, aiming at establishing their prognostic value for Alzheimer-type dementia and testing the hypothetical model of biomarkers sequence, based on the amyloid cascade. Our baseline population consisted of 217 MCI patients, 63% with neurodegeneration markers and 47% with amyloidosis. Within the longitudinal study-group (n = 165), 85 progressed to AD and 80 remained cognitively stable. Age, CSF Aβ₄₂, and t-Tau were identified as the best single predictors of conversion to AD. Regarding MCI classification according to the NIA-AA criteria, the high-AD-likelihood group (HL-both amyloid and neurodegeneration markers) was the most frequent (42%); followed by the Suspected Non-Alzheimer Pathophysiology group (SNAP-26%), the low-AD-likelihood group (LL-negative biomarkers-22%), and the Isolated Amyloid Pathology group (IAP-10%). Risk of progression to AD was higher in HL in relation to the LL group (HR = 6.1, 95% CI = 2.1–18.0, p = 0.001). SNAP and IAP groups were equivalent in terms of risk of progression to AD (IAP: HR = 2.6, 95% CI = 0.7–9.3, p = 0.141; SNAP: HR = 3.1, 95% CI = 1.1–9.6; p = 0.046), but only SNAP was significantly different from the LL group. These results support different neurobiological pathways to AD beyond the amyloid hypothesis, highlighting the alternative “neurodegeneration-first pathway” for further investigation.

Keywords

Alzheimer’s disease amyloid cerebrospinal fluid biomarkers mild cognitive impairment neurodegeneration

INTRODUCTION

Alzheimer’s disease (AD) is the leading cause of dementia worldwide and the most common neurodegenerative disease, affecting 5 to 7% of people over the age of sixty [1]. Dementia in general, and AD in particular, is considered a public global health priority considering its high prevalence, economic impact, and the associated dependency leading to social exclusion [2]. This immense burden emphasizes the urgent need for strategies that prevent or modify disease progression. As first identified by Alois Alzheimer in 1906 [3], the neuropathological hallmarks of AD are the amyloid-β (Aβ) senile plaques and tau-containing neurofibrillary tangles. The relationship between senile plaques and neurofibrillary tangles puzzled Alzheimer’s and launched the debate about which one of these proteins represents the crucial pathogenic process in AD. The amyloid cascade hypothesis proposes that Aβ is the key pathological hallmark of AD [4]. This hypothesis succeeded and was largely supported by the discovery that the major genetic mutations in early onset familial AD are all related to an abnormal Aβ processing, further warranted by the demonstration of Aβ neuronal toxicity [5]. However, familial AD accounts for less than 1% of disease cases [6], and considering that AD is an exclusively human disease, carriers of these autosomal dominant mutations or transgenic animal models carrying the same errors have been the only available models to investigate the early pathological mechanisms or surrogates of these events (biomarkers). These studies mainly pinpoint the role of amyloid [7], though results are inevitably biased and prone to circularity, and there is no overwhelming evidence that amyloid changes represent the crucial pathogenic process in the most prevalent sporadic forms of AD. Even so, the Aβ cascade framework dominated the field for the last 25 years, and fostered conceptual biomarker models like that proposed by Jack and colleagues [8] to describe the hypothetical sequence of dynamic biomarker changes in the order of brain amyloidosis, neurodegeneration, memory deficit (mild cognitive impairment, MCI) and clinical dysfunction (dementia state). Moreover, this hypothesis promoted the development of clinical trials aiming to reduce the generation of Aβ, facilitate its clearance, or prevent the aggregation of the peptide. Most disappointingly, trials of anti-Aβ therapy in symptomatic patients did not produce clinical benefits, despite some evidence of Aβ clearance [9].

Despite the therapeutic failure, the interest in capturing the earliest stages of AD, supported by new available biomarkers of the disease like the cerebrospinal fluid (CSF) biomarkers, PET imaging and evidence of hippocampal atrophy on MRI, radically changed our diagnostic focus that has moved from the phase of dementia to prodromal or pre-symptomatic stages. The classical CSF biomarkers for AD are Aβ₄₂, which is found in low concentrations in AD due to brain amyloid deposition, total tau (t-Tau) at high concentrations representing cortical neuronal loss, and phosphorylated tau (p-Tau) also at high concentrations, reflecting cortical tangle formation [10]. These amyloid and neuronal injury markers have been incorporated in new diagnostic criteria, like those proposed by the National Institute of Aging-Alzheimer Association (NIA-AA) for MCI [11] or preclinical states [12] to increase the confidence that subjects with prodromal dementia have AD as the underlying cause. Longitudinal cohort studies using these criteria are becoming available and represent an opportunity to investigate the intensity of the underlying biological processes (amyloidosis versus neurodegeneration) and their relevance for the progression to dementia and AD [13 –18].

With this specific purpose, we re-evaluated our cohort of amnestic MCI with available CSF biomarkers to classify subjects according to the NIA-AA MCI-sub-groups or stages, aiming to establish its prognostic value for Alzheimer-type dementia at follow-up and at the same time to test the proposed hypothetical model of biomarkers sequence conforming the amyloid cascade.

MATERIALS AND METHODS

Subjects

In 2003, we started a longitudinal assessment of patients with the diagnosis of amnestic MCI at the Dementia Clinic, Neurology Department of Coimbra University Hospital. This cohort already includes 400 MCI patients, but for this specific investigation, we only considered 217 that underwent lumbar puncture with CSF biomarkers assessment at the initial evaluation (obligatory inclusion criterion) and were enrolled until December 2016. The baseline study and follow-up protocol have been previously published [19, 20]. In brief, the patients were enrolled in a systematic way and had biannual clinical observation and annual neuropsychological and functional evaluations in order to detect progression to dementia. Cases that were followed-up with this comprehensive protocol until they developed dementia or until they had been cognitively stable for at least 2 years comprise the longitudinal study-group. This group was further dichotomized between those that were cognitively stable and those that developed dementia due to Alzheimer’s disease, according to the National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders (NINCDS-ADRDA) [21] and more recently to the 2011 NIA-AA criteria [22]. As we stated, for the biomarker-based subject classification, we used the classical CSF biomarkers for AD, operationalized according to the framework of the NIA-AA criteria for MCI and preclinical forms [11 , 23]. Subjects were classified in the low-AD-likelihood group if both amyloid (i.e., CSF Aβ₄₂) and neuronal injury markers (i.e., CSF t-tau and/or p-tau) were normal (LL), in the high-AD-likelihood group if both amyloid and at least one neuronal injury marker were abnormal (HL), or in one of the two conflicting biomarker groups [Isolated Amyloid Pathology (IAP) group if the amyloid marker was abnormal and neuronal injury markers normal, Suspected Non-Alzheimer Pathophysiology (SNAP) group if at least one neuronal injury marker was abnormal and the amyloid marker normal].

This study was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and was approved by the Ethics Board of Coimbra University Hospital. All subjects or responsible caregivers, whichever appropriate, gave their informed consent.

Clinical and neuropsychological procedures

MCI patients included in this study were of the amnestic type and the diagnosis was made in accordance with the criteria defined by Petersen [24] and more recently the framework for MCI due to AD, proposed by NIA-AA criteria [11]. Diagnostic investigation included a standard clinical evaluation, an extensive cognitive and staging assessment, standard laboratory tests, imaging studies (CT or MRI and SPECT), CSF analysis, APOE genotyping, and eventually PiB-PET (12 patients). At baseline, a neurologist completed a medical history with the patient and the caregiver and conducted a general physical, neurological, and psychiatric examination as well as a comprehensive diagnostic battery-protocol, including: 1) Cognitive instruments as the Mini-Mental State Evaluation (MMSE) [25], Portuguese version [26]; The Montreal Cognitive Assessment (MoCA) [27], Portuguese version [28]; the Alzheimer Disease Assessment Scale-Cognitive (ADAS - Cog) [29, 30], Portuguese version [31]; and a comprehensive neuropsychological battery with normative data for the Portuguese population (BLAD) [32] exploring memory (Wechsler Memory Scale sub-tests) and other cognitive domains (including language, praxis, executive functions and visuo-constructive tests); 2) Standard staging scales which provide objective information about subject performance in various domains, including the Clinical Dementia Rating (CDR) [33], Portuguese version [34] for global staging; the Disability Assessment for Dementia (DAD) [35], Portuguese version [36] for evaluation of functional status; the Neuropsychiatric Inventory (NPI) [37], Portuguese version [38] to characterize the psychopathological profile and the Geriatric Depression Scale (GDS-30) [39], Portuguese version [40] to exclude Major Depression.

All the available information (baseline cognitive test, staging scales, clinical laboratory and imaging studies) was used to reach a consensus diagnosis. A similar approach was used for follow-up annually evaluations. The baseline inclusion criteria for amnestic MCI were those proposed by Petersen [24] and were operationalized as this: 1) A subjective complaint of memory decline (reported by the subject or an informant); 2) An objective memory impairment (considered when scores on standard Wechsler memory tests were >1.5 SDs below age/education adjusted norms) with or without deficits in other cognitive domains; 3) Normal general cognition suggested by normal scores in the MMSE and MoCA using the Portuguese cut off scores [26, 41]; 4) Largely normal daily life activities, evaluated with a functional scale – DAD; 5) Absence of dementia, indicated by a CDR rating of 0.5. As exclusion criteria for enrolment we considered a significant underlying medical or neurological illness revealed by lab tests or imaging; a relevant psychiatric disease, including major depression, suggested in the medical interview and confirmed by the GDS; CT or MRI demonstration of significant vascular burden [42] (large cortico-subcortical infarct; extensive subcortical white matter lesions superior to 25%; uni- or bilateral thalamic lacunes; lacune in head of caudate nucleus; more than 2 lacunes).

The patients were observed every 6 months and clinical evaluation of progression was conducted annually, with a brief cognitive and functional status reassessment, including the MMSE, MoCA, ADAS-Cog, and the CDR. Dementia was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders – fourth edition (DSM-IV-TR) criteria [43] and AD, according to specific criteria [21, 22]. Conversion to AD required meeting clinical diagnostic criteria for probable AD and was confirmed by the coordinator of the clinical study (IS). As these criteria are not fully operational and the conversion status decision has some uncertainty and subjectivity, patients in this study were classified as having undergone conversion based on 1) Objective evidence by cognitive testing of decline to dementia using the MMSE, MoCA, and the ADAS-COG scores and qualitative evaluation (i.e., impairment of memory plus another domain); and 2) Changes in global CDR rating from 0.5 to 1 or more, confirming the cognitive profile of dementia and loss of autonomy.

Laboratory determinations

CSF samples were collected from patients as part of their routine clinical diagnosis investigation. Pre-analytical and analytical procedures were done in accordance with the Alzheimer’s Association guidelines for CSF biomarker determination [44]. Briefly, CSF samples were collected in sterile polypropylene tubes, immediately centrifuged at 1800 g for 10 min at 4°C, aliquoted into polypropylene tubes and stored at –80°C until analysis. CSF Aβ₄₂, t-Tau, and p-Tau were measured separately by commercially available sandwich ELISA kits (Innotest, Innogenetics/Fujirebio, Ghent, Belgium), as previously described [45, 46]. External quality control of the assays was performed under the scope of the Alzheimer’s Association Quality Control Program for CSF Biomarkers [44]. CSF biomarkers were classified as normal/abnormal according to previously reported laboratory reference values [47].

For APOE genotyping, peripheral blood samples were also collected into EDTA tubes and genomic DNA was isolated from leucocytes using the DNA isolation kit for mammalian blood (Roche Diagnostics, GmbH, Manheim, Germany), as described by the manufacturer. The analysis of the two polymorphisms, rs429358 and rs7412, at codons 112 and 158, respectively was performed by PCR-RFLP assay, as previously described [48].

Statistical analysis

Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS, version 20.0) (IBM SPSS, Chicago, IL). Normality of continuous variables was assessed by the Kolmogorov-Smirnov test. For normally distributed continuous variables one-way ANOVA followed either by the Bonferroni or the Games-Howell post-test was performed to assess the statistical significance of the difference between means. When continuous variables did not show normal distribution, the Kruskal-Waalis test was used, followed by the Dunn-Bonferronni post-test. Group differences between categorical variables were examined using the χ² test. Binary logistic regression analysis was used to identify predictive markers of conversion to AD, with conversion as dependent variable and age, gender, education, ApoE genotype, baseline MMSE, CSF Aβ₄₂, t-Tau, and p-Tau levels as independent variables. Survival analysis was used to assess the probability of conversion to AD in the different MCI groups. Kaplan – Meier survival curves were plotted and the survival distributions in the different subgroups were compared by the log-rank test. Survival time was calculated as the interval from the initial baseline evaluation to the diagnosis of dementia. For patients who remained non-demented, survival time was censored at the date of the last clinical assessment. A Cox proportional hazards model, corrected for age, gender, education, ApoE genotype, and baseline MMSE score was used to test the predictive ability for Alzheimer’s disease-type dementia of the different MCI groups.

RESULTS

Cohort data

At baseline, 217 MCI patients were included (138 females, 79 males), with ages ranging from 40 to 85 years (mean 67.3±9.4), a mean education level of 6.3±4.1 years, and a mean longitudinal follow-up of 4.2±3.4 (0.5–13.0) years. Demographic, clinical, genetics, and biomarker data of the baseline study population are presented in Table 1. Concerning cognitive scores, the mean MMSE score was 26.1±3.3, a value above the international cut off for dementia (<24/30) [25] and the same applies to the mean values on the MoCA (17.6±5.6) and the ADAS-COG (11.7±6.0), both above the cut-offs for dementia, proposed for the Portuguese population, respectively <17 and >12 points [49, 50]. Regarding biological parameters, 43% were carriers of at least one ApoE ɛ4 allele—the typical ApoE genotyping of AD spectrum disorders [51], the mean level of CSF Aβ₄₂ (667±310 pg/ml) was in the normal range for our center (>542 pg/ml), while t-Tau (371±260) and p-Tau (50±27) values were both above the respective reference values (i.e., <212 pg/ml and <32 pg/ml) [47]. Noteworthy, in this baseline study population, the percentage of patients with injury markers (63%) was higher than those with amyloidosis (47%).

Table 1

Demographic, clinical, genetic, and biomarker data of the study population

	MCI (n = 217)
Gender (M/F)	79/138
Age (y)	67.3±9.4
Age onset (y)	64.6±9.3
Education (y)	6.3±4.1
MMSE	26.1±3.3
MoCA	17.6±5.6
ADAS-Cog	11.7±6.0
ApoE ɛ4 (%)	43%
Aβ₄₂ (pg/mL)	667±310
t-Tau (pg/mL)	371±260
p-Tau (pg/mL)	50±27

Data are expressed as mean±S.D, except for ApoE that is expressed as percentage of ɛ4 carries. M, male; F, female; MMSE, Mini-Mental State Examination, higher scores correspond to better performance; MoCA, Montreal Cognitive Assessment, higher scores correspond to better performance; ADAS-Cog, Alzheimer Disease Assessment Scale-Cognitive, lower scores correspond to better performance.

Cohort classification

Using CSF biomarkers operationalized according to the framework of the NIA-AA criteria for MCI, 81 (37%) were classified in the high-AD-likelihood group (HL - subjects with both amyloid and injury markers), 22 (10%) in the IAP group, 57 (26%) in the SNAP group, and a similar number - 57 (26%) had neither amyloid nor neurodegeneration-biomarkers, being classified in the low-AD-likelihood group (LL) (Table 2). As expected, differences were significant in terms of amyloid levels between HL and IAP versus SNAP and LL, as well as between SNAP and HL versus IAP and LL in terms of neuronal injury markers. When comparing HL with SNAP subjects, both t-tau and p-tau levels were slightly but significantly higher in the HL group and on the contrary, no difference in Aβ₄₂ levels was seen between HL and IAP groups.

Table 2

CSF biomarker profile of the different MCI subgroups

	low-AD likelihood	high-AD likelihood	IAP	SNAP
n (%)	57 (26%)	81 (37%)	22 (10%)	57 (26%)
Aβ₄₂ (pg/mL)	909±269	424±123^***	481±320^***;§§§	843±220^γγγ
t-Tau (pg/mL)	163±44	555±281^***	141±63^{γγγ ;§§§}	405±171^***;γγ
p-Tau (pg/mL)	30±10	67±30^***	30±9^{γγγ ;§§§}	52±21^***;γγ

Data are expressed as mean±S.D. AD, Alzheimer’s disease; IAP, Isolated Amyloid Pathology; SNAP, Suspected Non-Alzheimer Pathophysiology. ^***p < 0.001 versus low-AD-likelihood. ^γγp < 0.005 versus high-AD-likelihood. ^γγγp < 0.001 versus high-AD-likelihood. ^§§§p < 0.001 versus SNAP.

Longitudinal study

Of the 217 MCI patients enrolled, 29 had a follow-up <2 years, 3 died, 18 were drop-outs, and 2 patients were excluded from the further analysis because, although their clinical presentation was amnestic MCI, they developed frontotemporal dementia and in fact they were carriers of C9orf72 mutation. The remaining 165 subjects with a follow-up ≥2 years (mean follow-up time: 5.0±3.2 years) comprised the longitudinal study-group, which was further dichotomized between those that were cognitively stable in the last observation, 80 (48%), and those that progressed to dementia due to AD, 85 (52%). A logistic regression model was employed to identify the best predictors of conversion to AD. We included age, gender, education, ApoE genotype, baseline MMSE, and CSF Aβ₄₂, t-Tau, and p-Tau values as variables in the equation and obtained a reasonable fit (Nagelkerkes R² = 0.585), with an overall accuracy of 83%. We verified that the variables that were contributing significantly to the model classification were age (p = 0.004; OR = 1.099, 95% CI = 1.031 – 1.171), CSF Aβ₄₂ (p = 0.001; OR = 0.994, 95% CI = 0.991 – 0.998), and t-tau (p = 0.003; OR = 1.008, 95% CI = 1.003–1.013). Although ApoE ɛ4 was much more represented in the group of MCI patients that converted to AD (58% versus 26%; p < 0.001), this variable was not identified as a significant predictor of conversion to AD in our model.

The longitudinal study-group was again classified in MCI subtypes according to CSF biomarkers, with an equivalent distribution to the baseline sub-grouping: HL group 69 (42%), IAP group 17 (10%), SNAP group 42 (26%), and in the LL group 37 (22%). In Table 3, we present the demographic, clinical, and genetics data of these groups. There were no significant differences regarding gender, years of education, and time of follow-up, but the HL and SNAP patients were older at baseline and at onset of the symptoms and this difference reached statistical significance in the comparison with the LL group (p≤0.001). Regarding the cognitive tests, the MMSE mean score was significantly lower in the HL group in comparison with all the other groups (p≤0.001), the MoCA mean score was also lower in this group in comparison with the LL group (p < 0.001) and the same applies to ADAS-Cog mean score, that was higher in HL group versus LL group (p = 0.001), indicating again greater cognitive impairment. Subjects in the HL group were also more often APOE ɛ4 carriers (63%) and more likely to progress to AD (80%), than all other biomarker groups: IAP - 47%, SNAP - 40%, LL- 14%. Conversion rates were similar in the IAP and SNAP groups, but significantly different from the other two groups.

Table 3

Demographic, clinical, and genetic data of the MCI subgroups with clinical follow-up

	low-AD likelihood	high-AD likelihood	IAP	SNAP	p value
n (%)	37 (22%)	69 (42%)	17 (10%)	42 (26%)
Gender (M/F)	13/24	32/37	6/11	12/30	0.282
Age (y)	63.5±9.8	70.0±7.7^**	66.4±8.9	70.4±8.8^*	0.001
Age onset (y)	60.2±10.0	67.4±6.9^**	61.7±9.3	67.8±9.0^*	<0.001
Education (y)	7.8±4.5	6.0±3.9	6.1±4.0	5.4±3.9	0.051
MMSE	27.3±3.6	24.8±3.0^***	27.6±2.2^γγ	27.0±3.2^γγγ	<0.001
MoCA	21.0±5.0	15.8±5.2^***	17.9±7.2	18.1±5.0	<0.001
ADAS-Cog	9.0±4.4	14.1±4.9^**	10.6±5.8	10.6±7.2	<0.001
ApoE ɛ4 (%)	24%	63% ^*	25% ^γ	33% ^γ	<0.001
Follow-up time (years)	5.0±3.6	4.7±3.0	5.6±3.9	5.1±3.1	0.584
Conversion to AD [n (%)]	5(14%)	55(80%)^***	8(47%)^*,γ	17(40%)^*,γγγ	<0.001

Data are expressed as mean±S.D, except for ApoE that is expressed as percentage of ɛ4 carries and conversion to AD that is expressed as percentage of patients that converted. M, male; F, female; AD, Alzheimer’s disease; IAP, Isolated Amyloid Pathology; SNAP, Suspected Non-Alzheimer Pathophysiology; MMSE, Mini-Mental State Examination, higher scores correspond to better performance; MoCA, Montreal Cognitive Assessment, higher scores correspond to better performance; ADAS-Cog, Alzheimer Disease Assessment Scale-Cognitive, lower scores correspond to better performance. ^*p < 0.05 versus low-AD-likelihood. ^**p < 0.005 versus low-AD-likelihood. ^***p < 0.001 versus low-AD-likelihood. ^γp < 0.05 versus high-AD-likelihood. ^γγp < 0.005 versus high-AD-likelihood. ^γγγp < 0.001 versus high-AD-likelihood.

Survival analysis

Since the conversion to dementia occurred at different moments of the follow-up time, a survival analysis was performed. Kaplan– Meier survival curves for the probability of conversion to AD plotted according to MCI groups are depicted in Fig. 1. The HL group was significantly associated with an estimated shorter time of conversion to AD (3.9±0.4 years; 95% CI = 3.1 – 4.7) than the LL group (12.5±1.4 years; 95% CI = 9.7–15.2; p < 0.001). Estimated time to conversion was not different between the IAP and SNAP groups (7.7±1.5 years; 95% CI = 4.8–10.7 and 8.4±1.0 years, 95% CI = 6.3–10.4, respectively), but was significantly different from the HL group and LL group (p < 0.01 and p < 0.05, respectively). Cox regression models, with age, gender, education, ApoE genotype, and baseline MMSE score taken into account, showed that MCI patients belonging to the HL subtype had the highest risk of progression to AD (hazard ratio 6.1, 95% CI 2.1–18.0, p = 0.001), compared with patients classified in the LL group (reference). MCI patients classified in the IAP and SNAP subtypes presented a very similar risk of progression to AD, that was significantly increased in comparison with the LL subtype only in the patients classified as SNAP (SNAP: hazard ratio 3.1, 95% CI 1.1–9.6; p = 0.046; IAP: hazard ratio 2.6, 95% CI 0.7–9.3, p = 0.141). Risk of progression to AD also failed to reach statistical significance difference between the HL group and the IAP (p = 0.091) or the SNAP group (p = 0.062).

Fig.1

Kaplan– Meier survival curves for the probability of conversion to Alzheimer’s disease (AD) plotted according to the different mild cognitive impairment subgroups. LL, low-AD-likelihood; HL, high-AD-likelihood; IAP, Isolated Amyloid Pathology; SNAP, Suspected Non-Alzheimer Pathophysiology. Log-Rank (Mantel-Cox): p < 0.001.

DISCUSSION

In recent years, several biomarkers have been developed for AD and some of them, like the CSF biomarkers, have been incorporated in recent diagnostic criteria defining groups or states of risk of progression to dementia. This enormous progress allowed their use by clinicians as surrogates of outcome to diagnose and potentially help to treat the disease at the mildly symptomatic stage of MCI. Our study was developed in this specific context of routine clinical practice and since we enrolled patients in a systematic way, our cohort may be considered representative of an ordinary tertiary Memory Clinic, surpassing the selection biases of investigational studies. In this context, the mean age of our cohort is lower than in community studies, although according to previously work from our group, there were no major biological distinction between younger and older MCI patients [52]. In line with the approach of a proxy routine clinical practice, we included exclusively patients with MCI (not investigational preclinical states) and because AD (and not other forms of dementia) is the main focus of discussion around the implementation of biomarkers in the referred setting, we only considered amnestic MCI subjects at baseline and those that progressed to dementia due AD at the follow-up of at least 2 years. Previous studies with cross-sectional data or longitudinal follow-up have examined the frequency of biomarker stages and its prognosis [13 –18]. One uncontroversial finding of these studies, also confirmed in this work, is that subjects with both amyloid and neuronal injuries markers have the highest risk of progression, with 80% of patients in our HL group developing AD in the next 4 years. This information is highly valuable for monitoring MCI patients in clinical practice and for the selection of participants in clinical trials.

Concerning the key importance of amyloidosis in the pathogenesis of AD, namely as “an amyloid-first pathway” as suggested by the cascade hypothesis [4], the state of the art is more controversial. As we referred, this hypothesis was driven by the discovery that the major genetic mutations in familial AD are all related to an abnormal Aβ processing [6]. Likewise, a recent investigation of the dynamic of biomarkers in patients with the genetic variants of AD (autosomal mutations in PSEN1, PSEN2, and APP genes), highlights the deposition of amyloid as the earliest finding and the first component of a biomarker model with three sequential phases: active amyloidosis; a stable plateau of amyloid deposition; and a further stage of progressive neurodegeneration and cognitive decline [7]. This profile of “an amyloid-first pathway” in early-onset sporadic forms (<65 years) is also implicit in the longitudinal cohort of the Mayo Clinic Study of Aging [18], which assessed transition rates between biomarker states and dementia by age. This work showed that the transition rate between biomarker negative subjects to incident amyloidosis was most common in the 60–75 age-range and plateaued after the seventies. In our opinion, the strongest support for the cascade hypothesis in late-onset forms of AD, comes from studies showing that positivity of amyloid biomarkers may precede cognitive impairment by several decades [17, 53]. Returning to our results, it is remarkable that only 47% of our baseline MCI-cohort had abnormal low levels of CSF Aβ₄₂ and mainly in association with injury biomarkers (37%). In fact, the isolated amyloid pathology group (IAP) represented only 10% of the total cohort or the follow-up cohort and in line with other studies, these patients tended to be younger than those belonging to other pathological groups. In addition, the rate of conversion to AD of the IAP group (47%) and estimated mean time of conversion (7.7±1.5) was quite similar to the “only neurodegeneration group” (SNAP), with conversion rate and estimated mean time of conversion of 40% and 8.4±1.0, respectively, indicating an equivalent risk of progression to AD. However, according to the Cox Regression model, only the SNAP group had a statistically increased risk of conversion in relation of the LL group. Moreover, our logistic regression model identified both CSF-Aβ₄₂ and t-Tau as predictors of conversion to AD. Our interpretation of these results is that amyloidosis is intimately related to the neurodegenerative process, especially in younger patients, though the Aβ pathway is not necessary and probably is not the mostly relevant pathological event in these late-onset forms of sporadic AD.

The biomarker dynamic model of Jack and colleagues [8] and the NIA-AA criteria framework [23] propose a staging method based on the conception that biomarkers of AD follow an invariable temporal sequence in accordance with the amyloid cascade hypothesis. In line with this model, we would expect that the profile of “amyloidosis-only” or “amyloidosis plus neurodegeneration” would be dominant at the prodromal state, which was indeed corroborated in some longitudinal studies [14 , 18]. However, in our clinical practice we were frequently confronted with the opposite scenario: patients with typical neuropsychological and neuroimaging features of AD presenting exclusively CSF neurodegeneration biomarkers. Our results expand this empirical notion, showing that the percentage of MCI patients with injury markers within the baseline-cohort (63%) was higher than those with amyloidosis (47%) and the same applies to the follow-up cohort—68% versus 52%, respectively. Concerning the prognosis, SNAP and IAP groups were equivalent in terms of risk of progression to AD, but only SNAP had significantly increased risk in relation to the LL group. The relevance of neurodegeneration is further confirmed by the results of the regression model, showing that t-Tau is a reliable predictor of progression to AD. This unexpected high prevalence of the SNAP group has been confirmed in studies with preclinical forms [15, 16] as well as in MCI stage [13] and remarkably, this last large MCI multicenter study, also showed that the 3-year progression rate to Alzheimer-type dementia was as high in the SNAP group (24%), as in the isolated amyloid pathology group (22%). The authors admitted several explanations for this “intriguing finding”: that these subjects could have comorbidities concurring for the progression to Alzheimer-type dementia; clinical misclassification or atypical forms of AD; or that CSF Aβ₄₂ cut-offs may have been too conservative. None of these hypotheses fits our data: we extensively excluded other brain comorbidities (namely vascular disease); we only considered typical amnestic patients and those with a further clinical diagnosis of dementia due to AD; the mean amyloid level of our SNAP group was not transitional and in fact was quite similar to the LL group. Besides, of the 12 patients that also performed PiB-PET, we verified a total concordance between biomarkers, with 10/12 being classified as amyloid positive in both assessments and 2/10 as negative. Although we focus our discussion on recent studies [13 –18], it is challenging to compare or conciliate our results with some of them and to interpret potential discrepancies. In fact, these studies are quite diverse in terms of target population (clinical versus community and/or preclinical versus MCI), the established outcomes (AD versus unspecified dementia) and they use different biomarkers that may reflect different processes or might became abnormal at different stages of the disease [54]. However, there seems to be a trend indicating that older AD patients may not exhibit a florid amyloid response. For example, in the Mayo Clinic Study of Aging [18], which is clearly aligned with the amyloid hypothesis, it is noteworthy that the sample included a large group of older individuals with “only neurodegeneration markers” (equivalent to SNAP) and that transition to dementia almost always required neurodegeneration. Thus, in our opinion, there is overwhelming information supporting different neurobiological pathways to sporadic AD beyond the amyloid hypothesis, highlighting the alternative “neurodegeneration-first pathway” for further investigation.

We believe that the added value of the present study, in addition to the strengths already emphasized along the text, is the holistic and rigorous methodology adopted to define stages and progression, obviating misclassifications; the use of neuropsychological instruments well-validated for the Portuguese population and administered by the same experienced team of neuropsychologists, which may improve reliability and diagnostic consistency; the exclusive use of CSF biomarkers, with quantitative and standardized cut-offs, which may also improve the reliability of the results. However, some limitations of the current study must be addressed. First of all, since only the amnestic subtype of MCI was considered, the generalization of the results to other forms of MCI should be cautious; similarly, results might be different when imaging biomarkers are considered; classification in biomarker sub-groups could be affected by cognitive performance and mainly by subjects-demographic characteristics, which deserves further investigation. In fact, the evidence that ApoE ɛ4 was not identified as a significant predictor of conversion to AD in our cohort might be explained by the high prevalence of young patients, outside the age range of major ApoE influence, considering the pleiotropy effect of this polymorphism [53, 55]. Finally, to increase the strength of results we will need a longer follow-up and a more robust sample.

In conclusion, this study produced several evidences that patients with only neuronal injury markers, in our opinion, erroneously designated of Suspected Non-AD-Pathophysiology (SNAP), represent a prevalent group in the MCI stage and have a risk of progression to AD comparable to those with Isolated Amyloid Pathology (IAP). This brings together arguments for the investigation of key mechanisms of the AD pathophysiology, independently of the amyloid response. Moreover, the SNAP group seems to be the ideal target to explore new or more accurate biomarkers, including tau PET imaging, and for the development of innovative and successful therapeutic interventions.

DISCLOSURE STATEMENT

Authors’ disclosures available online (https://www.j-alz.com/manuscript-disclosures/17-9908).

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