Background
Diagnostic accuracy for the early detection of mild cognitive impairment (MCI) is critical both in the clinical and research settings. Our aim was to evaluate the diagnostic performance of ...the ALTOIDA‐iADL test in subjects with non‐degenerative MCI and prodromal (pAD) and mild (mAD) Alzheimer's disease.
Methods
ALTOIDA‐iADL is a 10‐minute administrable cognitive test, which assesses activities of daily living in the form of an augmented virtual reality game. The task consists of placing and finding virtual objects in a real environment and provides a final score (the NeuroMotor Index; NMI). The NMI is obtained by weighting multi‐modal information such as hands’ micromovements, walking bouts and speed, reaction time and navigation trajectory (among others), and represents the overall outcome of the individual task performance. Fifty‐one participants were included and classified according to cerebrospinal fluid (CSF) AD biomarkers: MCI (n = 22; age: 68.2; MMSE: 26.5), pAD (n = 15; age: 69.4; MMSE: 24.0) and mAD (n = 14; age: 70.6; MMSE: 20.7).
Results
The NMI allowed differentiating between subjects with absence (Aβ‐) and presence (Aβ+) of abnormalities in the amyloid biomarker (p < 0.01). Also, differences were found between the MCI group and the pAD (p < 0.01) and mAD (p < 0.01) groups (Fig. 1). ROC curves showed good diagnostic accuracy of the NMI in the discrimination between the Aβ‐ and Aβ+ (AUC = 0.777; p < 0.01), MCI and pAD (AUC = 0.781; p < 0.01) and MCI and mAD (AUC = 0.772; p < 0.01). The NMI did not discriminate between the pAD and mAD (AUC = 0.557; p = 0.61) groups (Fig. 2). The NMI correlated with CSF NfL levels (r = ‐.456; p < 0.05) and the MMSE score (r = .432; p < 0.01), showing an association with the degree of cognitive impairment.
Conclusions
ALTOIDA‐iADL is useful in the differential diagnosis between patients with non‐degenerative MCI and prodromal and mild Alzheimer's disease. Its performance is related to the degree of impairment in cognitive screening tests and with biomarkers of axonal damage/neurodegeneration.
Background
The ongoing COVID‐19 pandemic and related care policies have affected dementia patients. The characteristics of early‐onset dementia (EOD, <65 years) patients in 2020 may provide insights ...on how to rearrange the provision of care.
Method
We retrospectively reviewed, from 2016 to 2020, the demographic and clinical data of the new referrals at our EOD clinic (Hospital Clínic Barcelona). We used Fisher’s Exact test and Mann–Whitney U test in R4.0.2 (http://www.R‐project.org/) to analyze differences between 2020 and the period 2016‐2019.
Result
In 2020, we did not visit any new referral from 15th march to 31th may. We evaluated 104 patients in 2020 and 392 patients in 2016‐2019 (mean=98(SD=11.8) patients/year). No differences were found in age at onset (AAO), sex, diagnostic delay and MMSE score (Table1). Significant differences were found in the diagnoses obtained in each period (p<0.000005, Figure1A). In 2020, 19.2% of the patients were diagnosed with neurodegenerative diseases (ND), 48.1% with non‐neurodegenerative diseases (NND) and 32.7% with subjective cognitive decline (SCD). On contrast, in 2016‐2019, 26% of the patients were diagnosed with ND, 22.2% with NND and 51.8% with SCD. Compared to 2016‐2019, ND, but not SCD or NND, presented longer diagnostic delay in 2020 (p<0.0005, Figure1B). ND, NND and SCD did not show differences between periods in AAO, sex or MMSE.
We did not find differences in the type of ND in each period (Figure1A). Compared to 2016‐2019, Frontotemporal Lobar Degeneration (FTLD) presented longer diagnostic delay in 2020 (p<0.005, Figure1B) while ND subgroups did not show differences in AAO, sex or MMSE. Cognitive disturbances in recovered COVID‐19 patients accounted for 16% of NND in 2020 N=8, AAO 50.63(12), 63% female, MMSE 26.8(2.3).
Conclusion
In 2020, albeit we were forced to stop our normal activity during 2.5 months, we visited a similar number of patients among which we observed an increase in NND, including cognitive disturbances in patients with recovered COVID‐19. On contrast, we found a reduction in SCD and, to a lesser extent, ND. ND showed a longer diagnostic delay in 2020 that mainly affected FTLD. Whether COVID‐19 pandemic entails a diagnostic delay in dementia patients must be confirmed in 2021.
Synaptic damage, axonal neurodegeneration, and neuroinflammation are common features in Alzheimer's disease (AD), frontotemporal dementia (FTD), and Creutzfeldt-Jakob disease (CJD).
Unicentric cohort ...of 353 participants included healthy control (HC) subjects, AD continuum stages, genetic AD and FTD, and FTD and CJD. We measured cerebrospinal fluid neurofilament light (NF-L), neurogranin (Ng), 14-3-3, and YKL-40 proteins.
Biomarkers showed differences in HC subjects versus AD, FTD, and CJD. Disease groups differed between them except AD versus FTD for YKL-40. Only NF-L differed between all stages within the AD continuum. AD and FTD symptomatic mutation carriers presented differences with respect to HC subjects. Applying the AT(N) system, 96% subjects were positive for neurodegeneration if 14-3-3 was used, 94% if NF-L was used, 62% if Ng was used, and 53% if YKL-40 was used.
Biomarkers of synapse and neurodegeneration differentiate HC subjects from neurodegenerative dementias and between AD, FTD, and CJD. NF-L and 14-3-3 performed similar to total tau when AT(N) system was applied.
•Neurofilament light (NF-L) levels are increased in neurodegenerative dementias.•14-3-3 protein is increased in Alzheimer's disease (AD) and frontotemporal dementia (FTD).•Neurogranin is decreased in FTD and increased in AD and Creutzfeldt-Jakob disease.•NF-L and 14-3-3 are good neurodegeneration markers when applied in the AT(N) system.•Only cerebrospinal fluid NF-L levels tracked disease progression in AD.
Prior studies have described distinct patterns of brain gray matter and white matter alterations in Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD), as well as differences in ...their cerebrospinal fluid (CSF) biomarkers profiles. We aim to investigate the relationship between early‐onset AD (EOAD) and FTLD structural alterations and CSF biomarker levels. We included 138 subjects (64 EOAD, 26 FTLD, and 48 controls), all of them with a 3T MRI brain scan and CSF biomarkers available (the 42 amino acid‐long form of the amyloid‐beta protein Aβ42, total‐tau protein T‐tau, neurofilament light chain NfL, neurogranin Ng, and 14‐3‐3 levels). We used FreeSurfer and FSL to obtain cortical thickness (CTh) and fraction anisotropy (FA) maps. We studied group differences in CTh and FA and described the “AD signature” and “FTLD signature.” We tested multiple regression models to find which CSF‐biomarkers better explained each disease neuroimaging signature. CTh and FA maps corresponding to the AD and FTLD signatures were in accordance with previous literature. Multiple regression analyses showed that the biomarkers that better explained CTh values within the AD signature were Aβ and 14‐3‐3; whereas NfL and 14‐3‐3 levels explained CTh values within the FTLD signature. Similarly, NfL levels explained FA values in the FTLD signature. Ng levels were not predictive in any of the models. Biochemical markers contribute differently to structural (CTh and FA) changes typical of AD and FTLD.
Accelerated long‐term forgetting (ALF) refers to a rapid loss of information over days or weeks despite normal acquisition/encoding. Notwithstanding its potential relevance as a presymptomatic marker ...of cognitive dysfunction, no study has addressed the relationship between ALF and Alzheimer’s disease (AD) biomarkers. We examined ALF in APOE ɛ4 carriers versus noncarriers, and its relationships with AD cerebrospinal fluid (CSF) biomarkers. We found ALF over three months in APOE ɛ4 carriers (F(1,19) = 5.60; P < 0.05; Cohen’s d = 1.08), and this performance was associated with abnormal levels of the CSF Aβ42/ptau ratio (r = −.614; P < 0.01). Our findings indicate that ALF is detectable in at‐risk individuals, and that there is a relationship between ALF and the pathophysiological processes underlying AD.
Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and ...to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T‐T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2‐year follow‐up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross‐sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross‐sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross‐sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross‐sectional and longitudinal data.
We use machine learning tools in a multidisease approach to assess differential discrimination of Alzheimer's disease and frontotemporal dementia. Importantly, our approach includes a feature reduction strategy, making it computationally efficient and it also considers the study of associated brain patterns to gain explainability of the algorithm.
Sex differences in early‐onset Alzheimer's disease Contador, José; Pérez‐Millan, Agnès; Guillén, Nuria ...
European journal of neurology,
December 2022, 2022-12-00, 20221201, Letnik:
29, Številka:
12
Journal Article
Recenzirano
Background and purpose
Sex is believed to drive heterogeneity in Alzheimer's disease (AD), although evidence in early‐onset AD (EOAD; <65 years) is scarce.
Methods
We included 62 EOAD patients and 44 ...healthy controls (HCs) with core AD cerebrospinal fluid (CSF) biomarkers, neurofilament light chain levels, neuropsychological assessment, and 3‐T magnetic resonance imaging. We measured cortical thickness (CTh) and hippocampal subfield volumes (HpS) using FreeSurfer. Adjusted linear models were used to analyze sex‐differences and the relationship between atrophy and cognition.
Results
Compared to same‐sex HCs, female EOAD subjects showed greater cognitive impairment and broader atrophy burden than male EOAD subjects. In a direct female‐EOAD versus male‐EOAD comparison, there were slight differences in temporal CTh, with no differences in cognition or HpS. CSF tau levels were higher in female EOAD than in male EOAD subjects. Greater atrophy was associated with worse cognition in female EOAD subjects.
Conclusions
At diagnosis, there are sex differences in the pattern of cognitive impairment, atrophy burden, and CSF tau in EOAD, suggesting there is an influence of sex on pathology spreading and susceptibility to the disease in EOAD.
Objectives
We studied a sample of cognitively unimpaired individuals, with and without subjective cognitive decline (SCD), in order to investigate accelerated long‐term forgetting (ALF) and to ...explore the relationships between objective and subjective cognitive performance and cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers.
Methods
Fifty‐two individuals were included and SCD was quantified through the Subjective Cognitive Decline Questionnaire (SCD‐Q), using its validated cutoff to classify participants as Low SCD‐Q (n = 21) or High SCD‐Q (n = 31). These groups were further subdivided according to the presence or absence of abnormal levels of CSF Aβ42. Objective cognitive performance was assessed with the Ancient Farming Equipment Test (AFE‐T), a new highly‐demanding test that calls for acquisition and retention of novel object/name pairs and allows measuring ALF over a 6‐month period.
Results
The High SCD‐Q group showed a significantly higher free forgetting rate at 3 months compared to the Low SCD‐Q (F 1,44 = 4.72; p < 0.05). When stratifying by amyloid status, High SCD‐Q/Aβ+ showed a significantly lower performance than High SCD‐Q/Aβ–on the final free and cued learning scores (F 1,27 = 6.44, p < 0.05 and F 1,27 = 7.51, p < 0.05, respectively), the 1‐week free and cued recall (F 1,24 = 4.49; p < 0.05 and F 1,24 = 7.10; p < 0.01, respectively), the 1‐week cued forgetting rate (F 1,24 = 5.13; p < 0.05), and the 3‐month cued recall (F 1,24 = 4.27; p < 0.05). Linear regression analyses showed that higher SCD‐Q scores were associated with higher forgetting rates on the AFE‐T (β = −0.212; p < 0.05).
Conclusions
It is possible to detect ALF in individuals with high SCD ratings, appearing especially in those with abnormal CSF Aβ42 levels. Both in research and the clinical field, there is an increasing need of using more demanding cognitive measures, such as the AFE‐T, for identifying and tracking the earliest cognitive changes in these populations.
Key Points
We assessed accelerated long‐term forgetting (ALF) in subjective cognitive decline (SCD)
We found ALF over 3 months in individuals with high SCD ratings
Individuals with high SCD ratings and abnormal Aβ42 levels displayed higher forgetting rates
ALF might be a potential marker of subtle cognitive dysfunction in the AD continuum
Background and purpose
How the APOE genotype can differentially affect cortical and subcortical memory structures in biomarker‐confirmed early‐onset (EOAD) and late‐onset (LOAD) Alzheimer's disease ...(AD) was assessed.
Method
Eighty‐seven cerebrospinal fluid (CSF) biomarker‐confirmed AD patients were classified according to their APOE genotype and age at onset. 28 were EOAD APOE4 carriers (+EOAD), 21 EOAD APOE4 non‐carriers (–EOAD), 23 LOAD APOE4 carriers (+LOAD) and 15 LOAD APOE4 non‐carriers (–LOAD). Grey matter (GM) volume differences were analyzed using voxel‐based morphometry in Papez circuit regions. Multiple regression analyses were performed to determine the relation between GM volume loss and cognition.
Results
Significantly more mammillary body atrophy in +EOAD compared to –EOAD is reported. The medial temporal and posterior cingulate cortex showed less GM in +LOAD compared to –LOAD. Medial temporal GM volume loss was also found in +EOAD compared to –LOAD. With an exception for +EOAD, medial temporal GM was strongly associated with episodic memory in the three groups, whilst posterior cingulate cortex GM volume was more related with visuospatial abilities. Visuospatial abilities and episodic memory were also associated with the anterior thalamic nucleus in –LOAD.
Conclusions
Our results show that the APOE genotype has a significant effect on GM integrity as a function of age of disease onset. Specifically, whilst LOAD APOE4 genotype is mostly associated with increased medial temporal and parietal atrophy compared to –LOAD, for EOAD APOE4 might have a more specific effect on subcortical (mammillary body) structures. The findings suggest that APOE genotype needs to be taken into account when classifying patients by age at onset.
Background
MRI atrophy predicts cognitive status in AD. However, this relationship has not been investigated in early-onset AD (EOAD, < 65 years) patients with a biomarker-based diagnosis.
Methods
...Forty eight EOAD (MMSE ≥ 15; A + T + N +) and forty two age-matched healthy controls (HC; A − T − N −) from a prospective cohort underwent full neuropsychological assessment, 3T-MRI scan and lumbar puncture at baseline. Participants repeated the cognitive assessment annually. We used linear mixed models to investigate whether baseline cortical thickness (CTh) or subcortical volume predicts two-year cognitive outcomes in the EOAD group.
Results
In EOAD, hemispheric CTh and ventricular volume at baseline were associated with global cognition, language and attentional/executive functioning 2 years later (
p
< 0.0028). Regional CTh was related to most cognitive outcomes (
p
< 0.0028), except verbal/visual memory subtests. Amygdalar volume was associated with letter fluency test (
p
< 0.0028). Hippocampal volume did not show significant associations.
Conclusion
Baseline hemispheric/regional CTh, ventricular and amygdalar volume, but not the hippocampus, predict two-year cognitive outcomes in EOAD.
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