Linguistic profiles, brain metabolic patterns and rates of amyloid-β biomarker positivity in patients with mixed primary progressive aphasia

Data-driven subtypes of mixed semantic-logopenic primary progressive aphasia: Linguistic features, biomarker profiles and brain metabolic patterns

Mixed primary progressive aphasia (mPPA) accounts for a substantial proportion of primary progressive aphasia (PPA) cases. However, the lack of a standardised definition of this condition has resulted in misclassification of PPA cases. In this study, we enrolled 55 patients diagnosed with PPA, comprising 12 semantic variant (svPPA), 23 logopenic variant (lvPPA), and 20 mPPA cases with linguistic characteristics consistent with both svPPA and lvPPA (s/lvPPA). All patients underwent language assessments, evaluation of Alzheimer's disease biomarkers (via cerebrospinal fluid analysis or Amyloid-PET), and 18F-FDG-PET brain scans. An agglomerative hierarchical clustering (AHC) analysis based on linguistic characteristics revealed two distinct clusters within the s/lvPPA group: cluster k1 (n = 10) displayed an AD-like biomarker profile, with lower levels of Aβ42 and Aβ42/Aβ40 ratio, along with higher levels of t-tau and p-tau compared to cluster k2 (n = 10). Interestingly, k1 exhibited linguistic features that were similar to those of svPPA. Both clusters exhibited extensive temporoparietal hypometabolism. These findings support the hypothesis that a subgroup of s/lvPPA may represent a clinical manifestation of AD-related PPA.

Heterogeneity and overlap in the continuum of linguistic profile of logopenic and semantic variants of primary progressive aphasia: a Profile Analysis based on Multidimensional Scaling study

BACKGROUND

Primary progressive aphasia (PPA) diagnostic criteria underestimate the complex presentation of semantic (sv) and logopenic (lv) variants, in which symptoms partially overlap, and mixed clinical presentation (mixed-PPA) and heterogenous profile (lvPPA +) are frequent. Conceptualization of similarities and differences of these clinical conditions is still scarce.

METHODS

Lexical, semantic, phonological, and working memory errors from nine language tasks of sixty-seven PPA were analyzed using Profile Analysis based on Multidimensional Scaling, which allowed us to create a distributed representation of patients' linguistic performance in a shared space. Patients had been studied with [18F] FDG-PET. Correlations were performed between metabolic and behavioral data.

RESULTS

Patients' profiles were distributed across a continuum. All PPA, but two, presented a lexical retrieval impairment, in terms of reduced production of verbs and nouns. svPPA patients occupied a fairly clumped space along the continuum, showing a preponderant semantic deficit, which correlated to fusiform gyrus hypometabolism, while only few presented working memory deficits. Adjacently, lvPPA + presented a semantic impairment combined with phonological deficits, which correlated with metabolism in the anterior fusiform gyrus and posterior middle temporal gyrus. Starting from the shared phonological deficit side, a large portion of the space was occupied by all lvPPA, showing a combination of phonological, lexical, and working memory deficits, with the latter correlating with posterior temporo-parietal hypometabolism. Mixed PPA did not show unique profile, distributing across the space.

DISCUSSION

Different clinical PPA entities exist but overlaps are frequent. Identifying shared and unique clinical markers is critical for research and clinical practice. Further research is needed to identify the role of genetic and pathological factors in such distribution, including also higher sample size of less represented groups.

Primary progressive aphasia: six questions in search of an answer

Here, we review recent progress in the diagnosis and management of primary progressive aphasia-the language-led dementias. We pose six key unanswered questions that challenge current assumptions and highlight the unresolved difficulties that surround these diseases. How many syndromes of primary progressive aphasia are there-and is syndromic diagnosis even useful? Are these truly 'language-led' dementias? How can we diagnose (and track) primary progressive aphasia better? Can brain pathology be predicted in these diseases? What is their core pathophysiology? In addition, how can primary progressive aphasia best be treated? We propose that pathophysiological mechanisms linking proteinopathies to phenotypes may help resolve the clinical complexity of primary progressive aphasia, and may suggest novel diagnostic tools and markers and guide the deployment of effective therapies.

Plasma neurofilament light chain predicts Alzheimer's disease in patients with subjective cognitive decline and mild cognitive impairment: A cross-sectional and longitudinal study

BACKGROUND AND PURPOSE

We aimed to evaluate the accuracy of plasma neurofilament light chain (NfL) in predicting Alzheimer's disease (AD) and the progression of cognitive decline in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI).

METHODS

This longitudinal cohort study involved 140 patients (45 with SCD, 73 with MCI, and 22 with AD dementia [AD-D]) who underwent plasma NfL and AD biomarker assessments (cerebrospinal fluid, amyloid positron emission tomography [PET], and 18 F-fluorodeoxyglucose-PET) at baseline. The patients were rated according to the amyloid/tau/neurodegeneration (A/T/N) system and followed up for a mean time of 2.72 ± 0.95 years to detect progression from SCD to MCI and from MCI to AD. Forty-eight patients (19 SCD, 29 MCI) also underwent plasma NfL measurements 2 years after baseline.

RESULTS

At baseline, plasma NfL detected patients with biomarker profiles consistent with AD (A+/T+/N+ or A+/T+/N-) with high accuracy (area under the curve [AUC] 0.82). We identified cut-off values of 19.45 pg/mL for SCD and 20.45 pg/mL for MCI. During follow-up, nine SCD patients progressed to MCI (progressive SCD [p-SCD]), and 14 MCI patients developed AD dementia (progressive MCI [p-MCI]). The previously identified cut-off values provided good accuracy in identifying p-SCD (80% [95% confidence interval 65.69: 94.31]). The rate of NfL change was higher in p-MCI (3.52 ± 4.06 pg/mL) compared to non-progressive SCD (0.81 ± 1.25 pg/mL) and non-progressive MCI (-0.13 ± 3.24 pg/mL) patients. A rate of change lower than 1.64 pg/mL per year accurately excluded progression from MCI to AD (AUC 0.954).

CONCLUSION

Plasma NfL concentration and change over time may be a reliable, non-invasive tool to detect AD and the progression of cognitive decline at the earliest stages of the disease.

Comparing two picture naming tasks in primary progressive aphasia: Insights from behavioural and neural results

Picture naming tests are widely used to evaluate language impairments in neurodegenerative diseases, especially in Primary Progressive Aphasia (PPA). The available tests differ for many factors affecting the performance, e.g. format of stimuli and their psycholinguistic properties. We aim to identify the most appropriate naming test to be used on PPA according to the clinical and research demands. We investigated the behavioural characteristics, i.e. proportion of correct responses and error type, and their neural correlates in two Italian naming tests, CaGi naming (CaGi) and naming subtest of the Screening for Aphasia in NeuroDegeneration battery (SAND), administered to 52 PPA patients who underwent an FDG-PET scan. We analysed the effectiveness of the tests in distinguishing PPA versus controls and among PPA variants, considering the psycholinguistic variables affecting performance. We explored the brain metabolic correlates of behavioural performance in the tests. SAND, differently from CaGi, has time limits for the response and its items are less frequent and acquired later. SAND and CaGi differed in terms of number of correct responses and error profile, suggesting a higher difficulty to name SAND items compared to CaGi. Semantic errors predominated in CaGi, while anomic and semantic errors were equally frequent in SAND. Both tests distinguished PPA from controls, but SAND outperformed CaGi in discriminating among PPA variants. FDG-PET imaging revealed a shared metabolic involvement of temporal areas associated with lexico-semantic processing, encompassing anterior fusiform, temporal pole, and extending to posterior fusiform in sv-PPA. Concluding, a picture naming test with response time limit and items which are less frequent and acquired later in life, as SAND, may be effective at highlighting subtle distinctions between PPA variants, improving the diagnosis. Conversely, a naming test without time limit for the response, as CaGi, may be useful for a better characterization of the nature of the naming impairment at the behavioural level, eliciting more naming errors than anomia, possibly helping in the development of rehabilitation protocols.

Matching Clinical Diagnosis and Amyloid Biomarkers in Alzheimer's Disease and Frontotemporal Dementia

BACKGROUND

The aims of this study were to compare the diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values (PPV, NPV) of different cerebrospinal fluid (CSF) amyloid biomarkers and amyloid-Positron Emission Tomography (PET) in patients with a clinical diagnosis of Alzheimer's disease (AD) and Frontotemporal Dementia (FTD); to compare concordance between biomarkers; and to provide an indication of their use and interpretation.

METHODS

We included 148 patients (95 AD and 53 FTD), who underwent clinical evaluation, neuropsychological assessment, and at least one amyloid biomarker (CSF analysis or amyloid-PET). Thirty-six patients underwent both analyses. One-hundred-thirteen patients underwent Apolipoprotein E (ApoE) genotyping.

RESULTS

Amyloid-PET presented higher diagnostic accuracy, sensitivity, and NPV than CSF Aβ_1-42 but not Aβ_42/40 ratio. Concordance between CSF biomarkers and amyloid-PET was higher in FTD patients compared to AD cases. None of the AD patients presented both negative Aβ biomarkers.

CONCLUSIONS

CSF Aβ_42/40 ratio significantly increased the diagnostic accuracy of CSF biomarkers. On the basis of our current and previous data, we suggest a flowchart to guide the use of biomarkers according to clinical suspicion: due to the high PPV of both amyloid-PET and CSF analysis including Aβ_42/40, in cases of concordance between at least one biomarker and clinical diagnosis, performance of the other analysis could be avoided. A combination of both biomarkers should be performed to better characterize unclear cases. If the two amyloid biomarkers are both negative, an underlying AD pathology can most probably be excluded.