Amyloid and Tau Positive Mild Cognitive Impairment: Clinical and Biomarker Characteristics of Dementia Progression
Introduction
Recent advances in Alzheimer’s disease (AD) biomarkers, particularly in cerebrospinal fluid (CSF) and neuroimaging, have significantly enhanced our understanding of AD pathophysiology in humans. Biomarkers that measure β-amyloid (Aβ) deposition include amyloid positron emission tomography (PET) and CSF Aβ1–42 levels. Biomarkers for fibrillar tau include CSF phosphorylated tau (p-tau) levels and cortical tau PET uptake, while neurodegeneration or neuronal injury biomarkers include CSF total-tau (t-tau) levels, fluorodeoxyglucose (FDG) PET hypometabolism, and magnetic resonance imaging (MRI) grey matter atrophy. The A/T/N biomarker classification, where “A,” “T,” and “N” represent Aβ, tau, and neurodegeneration biomarkers, respectively, has become pivotal in AD research. The National Institute on Aging-Alzheimer’s Association (NIA-AA) research framework emphasizes biomarkers over clinical features for a biological diagnosis of AD in living humans.
Background
According to the amyloid, tau, neurodegeneration research framework classification, amyloid and tau positive (A+T+) mild cognitive impairment (MCI) individuals are defined as prodromal Alzheimer’s disease. This study aimed to compare the clinical and biomarker features between A+T+MCI individuals who progressed to progressive MCI (pMCI) and those who remained stable MCI (sMCI), and to identify relevant baseline clinical and biomarker features that could predict progression to dementia within two years.
Methods
We stratified 197 A+T+MCI individuals into pMCI (n = 64) and sMCI (n = 133) over two years. Demographics, cognitive assessment scores, CSF, and neuroimaging biomarkers (18F-florbetapir PET mean standardized uptake value ratios [SUVR] and structural MRI) were compared between pMCI and sMCI at baseline, 12-, and 24-month follow-up. Logistic regression models were used to evaluate clinical baseline and biomarker features that predicted dementia progression in A+T+MCI.
Results
pMCI individuals had higher mean 18F-florbetapir SUVR, CSF t-tau, and p-tau181P than sMCI individuals. pMCI individuals performed poorer in cognitive assessments, both global and domain-specific (memory, executive, language, attention, and visuospatial skills) than sMCI. At baseline, there were significant differences in regions of interest of structural MRI between the two groups, including bilateral amygdala, hippocampus and entorhinal, bilateral inferior lateral ventricle, left superior and middle temporal, left posterior and caudal anterior cingulate (P < 0.05). Baseline CSF t-tau levels and cognitive scores of Montreal cognitive assessment, functional assessment questionnaire, and everyday cognition by the patient’s study partner language domain could predict progression to dementia in A+T+MCI within two years.
Discussion
The current NIA-AA research framework proposes the biological definition of AD using amyloid, tau, and neurodegeneration (ATN) biomarkers to measure in vivo AD pathological changes. ATN biomarkers also support the identification of individuals at increased risk of disease progression. In this regard, A+T+N− and A+T+N+ individuals within the AD continuum have the highest risk of disease progression. Furthermore, current intervention studies focus on amyloid and tau, which may result in neuroprotection and disease modification through linked mechanisms. Therefore, in the present study, we aim to advance the current ATN literature by identifying the clinical and biomarker risk factors of dementia progression in A+T+MCI individuals within typical clinical trial periods, so as to enrich the recruitment of study populations in future clinical trials targeting amyloid and tau.
The ApoEε4 allele is a well-known genetic risk factor for AD and the ApoEε4 allele is able to predict disease progression from MCI to AD-type dementia. However, the mechanism of ApoEε4 allele in increasing risk of AD remains controversial. While the ApoEε4 allele has been linked to increased Aβ plaque deposition and decreased Aβ clearance, others have shown that the ApoEε4 allele is associated with elevated p-tau levels. There is also evidence suggesting that ApoEε4 allele may increase AD risk through pathways independent of Aβ. Li et al reported that ApoEε4 allele was associated with increased cortical thickness in brain regions vulnerable to AD pathology, such as medial and inferior temporal regions in the preclinical and early MCI stage. Our study indicates that ApoEε4 allele is not a significant predictor of disease conversion among A+T+MCI individuals, which may suggest that the effect of ApoEε4 allele on AD occurs in an earlier stage of AD, before MCI. This is consistent with the findings by van Rossum et al which showed that ApoEε4 allele was a risk factor for the development of abnormal Aβ processing but did not influence clinical progression once abnormal Aβ processing was established.
Furthermore, we found that pMCI individuals performed poorer in global cognitive assessments such as CDR-SB, ADAS-Cog, MMSE, MoCA, FAQ scores, as well as specific cognitive domains of memory, attention, executive functions, language, and processing speed compared to sMCI individuals at baseline, 1-, and 2-year follow-up. Based on the differences in the ATN biomarkers between pMCI and sMCI group as described above, pMCI individuals also have greater burden of ATN. Given that recent longitudinal studies have shown a relationship between CSF AD biomarkers and disease progression, our results further adds to the current literature by demonstrating that ATN biomarkers, which reflect AD pathophysiology, have a negative impact on cognition.
There are also discrepancies between participant’s and informant’s report of the patient’s cognitive and activities of daily living performance based on the ECog test. At baseline, the informant-reported ECog scores are higher in the pMCI group compared to the sMCI group. However, there is no difference in the self-reported ECog scores between the two groups. This may be attributed to the phenomenon whereby participants with cognitive impairment tend to under-report their symptoms, whereas their informants tend to provide a more objective account. This is in line with a prior study showing that MCI individuals report that they are performing well in financial tasks and driving when their informants report otherwise. The tendency to under-estimate problems may due to reduced awareness of cognitive dysfunction (ie, anosognosia) or an inability to accurately appraise one’s own cognitive abilities. On the other hand, available evidence has suggested that the mixed MCI group was generally more accurate in evaluating their cognitive abilities than amnestic MCI group. Given that mixed MCI group had impairments primarily in language and attention/executive functioning, it is possible they were more aware of the everyday consequences of these cognitive deficits than were those with memory deficits. The discrepancies in our result also confirmed that the participants we selected were accurately in line with the pathological characteristics of AD (amnestic MCI).
While we found significant differences in the levels of ATN biomarkers (CSF Aβ1–42, total tau, p-tau181, and mean [18F] florbetapir SUVR) between pMCI and sMCI, only CSF t-tau predicts disease progression among A+T+MCI individuals, which is classified as a marker of “N,” biomarkers of neurodegeneration or neural injury. As in 2018 NIA-AA research framework to investigate the AD continuum, the markers of “N” cannot be used to indicate Alzheimer pathophysiologic processes. However, this conclusion partly because “N” represents both AD and non-AD pathologies. Discordance was also dependent on disease stage. Indeed, our conclusion is in line with other published ADNI data of A+T+MCI. In a longitudinal observational study of brain atrophy, CSF p-tau levels, identified as “N,” can predict MRI progression in patients with mild AD dementia and in cognitively normal participants. And previous literatures have also suggested FDG-PET as predictors of short-term MCI-to-AD dementia conversion, which likewise confirm the predictive utility of “N” biomarkers. In our presentation, A+T+MCI participants are identified as having the typical AD pathologic, and in this case, “N” biomarkers are proven to provide much more powerful prediction of future cognitive decline. This is logical given that CSF t-tau, indicator of the intensity of neuronal injury at a given point in time, is the aspect of AD neuropathologic change that correlates most closely with clinical manifestations.
As we all know, structural MRI is the most commonly used technique to identify brain atrophies related to AD, which has been proved to have the power to predict AD dementia earlier, a few previous studies were also designed with the aim of predicting imminent conversion. Moreover, a somewhat surprising finding was that while we found differences in some of the MRI ROIs such as subcortical volume of bilateral amygdala, hippocampus, cortical volume and thickness average of bilateral entorhinal, subcortical volume of bilateral choroid plexus and inferior lateral ventricle between pMCI and sMCI at baseline, these differences are no longer seen at 12- and 24 months follow-up. This finding may suggest that the MRI ROIs do not predict disease progression in A+T+MCI individuals. One possible explanation is that the ROI method does not allow a comprehensive and objective assessment of the entire cortex and may not be sensitive enough to detect small and more diffuse changes that may arise over a short period of time. The progression to dementia may not be predicted simply by the atrophy of one separate area. Furthermore, in this work, we partitioned A+T+MCI into progressive pMCI and sMCI, based on the clinical follow-up in a 24-month follow-up (range 6–24 months at 6-month intervals). Follow-up time and the intervals inevitably had an effect on the results. And as AD participants were not included, we could not reflect the full view of the disease progression. These findings emphasize the complexity and spatial extent of the patterns of brain atrophy that characterize brain structure in A+T+MCI and that, in future works, together with advanced pattern analysis and recognition methods, are likely to provide powerful imaging markers for prediction and quantification of disease progression. This is also consistent with findings from a large meta-analysis not including ADNI data which show that baseline cognitive measures compared to brain volumetric markers are better predictors of disease conversion to dementia in MCI. Unlike neuroimaging measures such as volumetric MRI and PET, cognitive measures such as FAQ remain useful in predicting disease progression even in the later stages of MCI.
There are limitations in the current study. First, the ADNI database consists of highly educated individuals who volunteered to take part in the research study focusing on AD research. Therefore, findings from this cohort may not be generalized to the rest of the population. Also, the ADNI enrolled subjects only from the United States and Canada. Therefore, it is necessary to replicate our results in larger population-based cohorts and to conduct research on the related mechanisms. Second, the longitudinal follow-up period of this study is limited to two years. While this is a typical clinical period, the short duration of two years may not be sufficient for A+T+MCI individuals to progress to dementia. Therefore, future studies should include a longer follow-up period. Third, we are not able to measure sensitivity and specificity values in this study due to the small sample sizes.
In conclusion, we identified key clinical and biomarker characteristics that distinguish pMCI and sMCI individuals. Specific CSF and cognitive measures (CSF t-tau, MoCA, FAQ, and EcogSP language scores) that predict dementia progression in A+T+MCI might be useful in early treatment decisions or stratified enrollment of this population into clinical trials.
doi.org/10.1097/CM9.0000000000001496
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