Correlation of florbetaben PET imaging and the amyloid peptide Aß42 in cerebrospinal fluid

Comparison of Diagnostic Performances Between Cerebrospinal Fluid Biomarkers and Amyloid PET in a Clinical Setting

The diagnostic performances of cerebrospinal fluid (CSF) biomarkers and amyloid positron emission tomography (PET) were compared by examining the association and concordance or discordance between CSF Aβ1-42 and amyloid PET, after determining our own cut-off values for CSF Alzheimer's disease (AD) biomarkers. Furthermore, we evaluated the ability of CSF biomarkers and amyloid PET to predict clinical progression. CSF Aβ1-42, t-tau, and p-tau levels were analyzed in 203 individuals [27 normal controls, 38 mild cognitive impairment (MCI), 62 AD dementia, and 76 patients with other neurodegenerative diseases] consecutively recruited from two dementia clinics. We used both visual and standardized uptake value ratio (SUVR)-based amyloid PET assessments for analyses. The association of CSF biomarkers with amyloid PET SUVR, hippocampal atrophy, and cognitive function were investigated by linear regression analysis, and the risk of conversion from MCI to AD dementia was assessed using a Cox proportional hazards model. CSF p-tau/Aβ1-42 and t-tau/Aβ1-42 exhibited the best diagnostic accuracies among the CSF AD biomarkers examined. Correlations were observed between CSF biomarkers and global SUVR, hippocampal volume, and cognitive function. Overall concordance and discordance between CSF Aβ1-42 and amyloid PET was 77% and 23%, respectively. Baseline positive CSF Aβ1-42 for MCI demonstrated a 5.6-fold greater conversion risk than negative CSF Aβ1-42 .  However, amyloid PET findings failed to exhibit significant prognostic value. Therefore, despite presence of a significant correlation between the CSF Aβ1-42 level and SUVR of amyloid PET, and a relevant concordance between CSF Aβ1-42 and amyloid PET, baseline CSF Aβ1-42 better predicted AD conversion.

Amyloid biomarkers as predictors of conversion from mild cognitive impairment to Alzheimer's dementia: a comparison of methods

Background

Amyloid-β (Aβ) PET is an established predictor of conversion from mild cognitive impairment (MCI) to Alzheimer’s dementia (AD). We compared three PET (including an approach based on voxel-wise Cox regression) and one cerebrospinal fluid (CSF) outcome measures in their predictive power.

Methods

Datasets were retrieved from the ADNI database. In a training dataset (N = 159), voxel-wise Cox regression and principal component analyses were used to identify conversion-related regions (Cox-VOI and AD conversion-related pattern (ADCRP), respectively). In a test dataset (N = 129), the predictive value of mean normalized ¹⁸F-florbetapir uptake (SUVR) in AD-typical brain regions (composite SUVR) or the Cox-VOI and the pattern expression score (PES) of ADCRP and CSF Aβ₄₂/Aβ₄₀ as predictors were compared by Cox models (corrected for age and sex).

Results

All four Aβ measures were significant predictors (p < 0.001). Prediction accuracies (Harrell’s c) showed step-wise significant increases from Cox-SUVR (c = 0.71; HR = 1.84 per Z-score increase), composite SUVR (c = 0.73; HR = 2.18), CSF Aβ₄₂/Aβ₄₀ (c = 0.75; HR = 3.89) to PES (c = 0.77; HR = 2.71).

Conclusion

The PES of ADCRP is the most predictive Aβ PET outcome measure, comparable to CSF Aβ₄₂/Aβ₄₀, with a slight but statistically significant advantage.

Medial Temporal Atrophy Alone is Insufficient to Predict Underlying Alzheimer's Disease Pathology

Background

The medial temporal region is the earliest affected structure in patients with Alzheimer’s disease (AD), and its atrophy is known as the hallmark of AD. This study aimed to investigate the value of medial temporal atrophy (MTA) for detecting 18F-florbetaben positron emission tomography (PET)-proven AD pathology.

Methods

We retrospectively enrolled 265 subjects complaining of cognitive decline at a dementia outpatient clinic from March 2015 to December 2017. All subjects underwent brain magnetic resonance imaging, 18F-fluorodeoxyglucose PET, and 18F-florbetaben PET at baseline. We performed multivariable logistic regression analyses on variables including age, sex, years of education, white matter hyperintensities, apolipoprotein E (APOE) genotype, and memory composite scores in various combinations to investigate whether MTA was indicative of underlying AD pathology.

Results

Our sample population of 265 patients comprised 121 with AD-related cognitive impairment, 42 with Lewy bodies-related cognitive impairment, 32 with vascular cognitive impairment, and 70 with other or undetermined pathologies. In the multivariable logistic regression analyses, MTA was not an independent predictor of underlying AD pathology (P>0.200). The predictive power of underlying AD-related cognitive impairment significantly increased when multiple variables including APOE genotype and memory composite scores were considered together (area under the curve >0.750).

Conclusion

Our results suggest that MTA alone may be insufficient to accurately predict the presence of AD pathology. It is necessary to comprehensively consider various other factors such as APOE genotype and a detailed memory function to determine whether the patient is at high risk of AD.

Clinical significance of focal ß-amyloid deposition measured by 18F-flutemetamol PET

Background

Although amyloid PET of typical Alzheimer’s disease (AD) shows diffuse ß-amyloid (Aß) deposition, some patients show focal deposition. The clinical significance of this focal Aß is not well understood. We examined the clinical significance of focal Aß deposition in terms of cognition as well as Aß and tau cerebrospinal fluid (CSF) levels. We further evaluated the order of Aß accumulation by visual assessment.

Methods

We included 310 subjects (125 cognitively unimpaired, 125 mild cognitive impairment, and 60 AD dementia) from 9 referral centers. All patients underwent neuropsychological tests and ¹⁸F-flutemetamol (FMM) PET. Seventy-seven patients underwent CSF analysis. Each FMM scan was visually assessed in 10 regions (frontal, precuneus and posterior cingulate, lateral temporal, parietal, and striatum of each hemisphere) and was classified into three groups: No-FMM, Focal-FMM (FMM uptake in 1–9 regions), and Diffuse-FMM (FMM uptake in all 10 regions).

Results

53/310 (17.1%) subjects were classified as Focal-FMM. The cognitive level of the Focal-FMM group was better than that of Diffuse-FMM group and worse than that of No-FMM group. Among the Focal-FMM group, those who had FMM uptake to a larger extent or in the striatum had worse cognitive levels. Compared to the Diffuse-FMM group, the Focal-FMM group had a less AD-like CSF profile (increased Aß42 and decreased t-tau, t-tau/Aß42). Among the Focal-FMM group, Aß deposition was most frequently observed in the frontal (62.3%) and least frequently observed in the striatum (43.4%) and temporal (39.6%) regions.

Conclusions

We suggest that focal Aß deposition is an intermediate stage between no Aß and diffuse Aß deposition. Furthermore, among patients with focal Aß deposition, those who have Aß to a larger extent and striatal involvement show clinical features close to diffuse Aß deposition. Further longitudinal studies are needed to evaluate the disease progression of patients with focal Aß deposition.

Electronic supplementary material

The online version of this article (10.1186/s13195-019-0577-x) contains supplementary material, which is available to authorized users.

Discordant amyloid-β PET and CSF biomarkers and its clinical consequences

Background

In vivo, high cerebral amyloid-β load has been associated with (i) reduced concentrations of Aβ₄₂ in cerebrospinal fluid and (ii) increased retention using amyloid-β positron emission tomography. Although these two amyloid-β biomarkers generally show good correspondence, ~ 10–20% of cases have discordant results. To assess the consequences of having discordant amyloid-β PET and CSF biomarkers on clinical features, biomarkers, and longitudinal cognitive trajectories.

Methods

We included 768 patients (194 with subjective cognitive decline (SCD), 127 mild cognitive impairment (MCI), 309 Alzheimer’s dementia (AD), and 138 non-AD) who were categorized as concordant-negative (n = 315, 41%), discordant (n = 97, 13%), or concordant-positive (n = 356, 46%) based on CSF and PET results. We compared discordant with both concordant-negative and concordant-positive groups on demographics, clinical syndrome, apolipoprotein E (APOE) ε4 status, CSF tau, and clinical and neuropsychological progression.

Results

We found an increase from concordant-negative to discordant to concordant-positive in rates of APOE ε4 (28%, 55%, 70%, Z = − 10.6, P < 0.001), CSF total tau (25%, 45%, 78%, Z = − 13.7, P < 0.001), and phosphorylated tau (28%, 43%, 80%, Z = − 13.7, P < 0.001) positivity. In patients without dementia, linear mixed models showed that Mini-Mental State Examination and memory composite scores did not differ between concordant-negative (β [SE] − 0.13[0.08], P = 0.09) and discordant (β 0.08[0.15], P = 0.15) patients (P_interaction = 0.19), while these scores declined in concordant-positive (β − 0.75[0.08] patients (P_interaction < 0.001). In patients with dementia, longitudinal cognitive scores were not affected by amyloid-β biomarker concordance or discordance. Clinical progression rates from SCD to MCI or dementia (P = 0.01) and from MCI to dementia (P = 0.003) increased from concordant-negative to discordant to concordant-positive.

Conclusions

Discordant cases were intermediate to concordant-negative and concordant-positive patients in terms of genetic (APOE ε4) and CSF (tau) markers of AD. While biomarker agreement did not impact cognition in patients with dementia, discordant biomarkers are not benign in patients without dementia given their higher risk of clinical progression.

Electronic supplementary material

The online version of this article (10.1186/s13195-019-0532-x) contains supplementary material, which is available to authorized users.

Role of Neuroimaging as a Biomarker for Neurodegenerative Diseases

It has recently been recognized that neurodegenerative diseases are caused by common cellular and molecular mechanisms including protein aggregation and inclusion body formation. Each type of neurodegenerative disease is characterized by the specific protein that aggregates. In these days, the pathway involved in protein aggregation has been elucidated. These are leading to approaches toward disease-modifying therapies. Neurodegenerative diseases are fundamentally diagnosed pathologically. Therefore, autopsy is essential for a definitive diagnosis of a neurodegenerative disease. However, recently, the development of various molecular brain imaging techniques have enabled pathological changes in the brain to be inferred even without autopsy. Some molecular imaging techniques are described as biomarker in diagnostic criteria of neurodegenerative disease. Magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), positron emission tomography (PET), and amyloid imaging are described in the diagnostic guidelines for Alzheimer’s disease in the National Institute on Aging-Alzheimer’s Association. MRI, dopamine transporter (DAT) imaging, and ¹²³I-metaiodobenzyl-guanidine (MIBG) myocardial scintigraphy listed in the guidelines for consensus clinical diagnostic criteria for dementia with Lewy bodies are described as potential biomarkers. The Movement Disorder Society Progressive Supranuclear Palsy Study Group defined MRI, SPECT/PET, DAT imaging, and tau imaging as biomarkers. Other diagnostic criteria for neurodegenerative disease described neuroimaging findings as only characteristic finding, not as biomarker. In this review, we describe the role of neuroimaging as a potential biomarker for neurodegenerative diseases.