Neuroimaging Characteristics of Frailty Status in Patients with Alzheimer's Disease

Factors and brain imaging features associated with cognition in oldest-old patients with Alzheimer-type dementia

BACKGROUND

The underlying pathophysiology of cognitive dysfunction in oldest-old patients with Alzheimer-type dementia (AD) has not been clarified to date. We aimed to determine the factors and brain imaging features associated with cognition in oldest-old patients with AD.

METHODS

We enrolled 456 consecutive outpatients with probable AD (145 men and 311 women, age range: 51-95 years). Demographic factors, such as education level, disease duration at initial visit, body mass index, comorbidities, frailty, and leisure activity, and brain imaging features, including severity of medial temporal lobe (MTL) atrophy, white matter lesions and infarcts, and frequency of posterior cerebral hypoperfusion were compared among pre-old (≤ 74 years), old (75 to 84 years), and oldest-old (≥ 85 years) subgroups.

RESULTS

The oldest-old subgroup showed significantly longer disease duration, lower education level, more severe frailty, less leisure activity, worse cognitive impairment, a tendency of slower progression of cognitive decline, greater MTL atrophy, more severe white matter hyperintensities and infarcts, and lower frequency of posterior hypoperfusion than the younger age subgroups. Regarding the brain imaging subtypes, there were significantly more patients with the limbic-predominant subtype and fewer patients with the hippocampal-sparing subtype in the oldest-old AD group than the pre-old AD group.

CONCLUSIONS

Oldest-old patients with AD show different factors and brain imaging features associated with cognition from pre-old and old patients. Our results are expected to provide useful information towards understanding the pathophysiology of oldest-old patients with AD, and for determining their clinical diagnosis and appropriate management methods.

Laser tweezers Raman spectroscopy combined with machine learning for diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is a common nervous system disease to affect mostly elderly people over the age of 65 years. However, the diagnosis of AD is mainly depend on the imaging examination, clinical assessments and neuropsychological tests, which may get error diagnosis results and are not able to detect early AD. Here, a rapid, non-invasive, and high accuracy diagnostic method for AD especially early AD is provided based on the laser tweezers Raman spectroscopy (LTRS) combined with machine learning algorithms. AD platelets from different 3xTg-AD transgenic rats at different stages of disease are captured to collect high signal-to-noise ratio Raman signals without contact by LTRS, which is then combined with partial least squares discriminant analysis (PLS-DA), support vector machine (SVM) and principal component analysis (PCA)-canonical discriminate function (CDA) for classification. The results show that the normal and diseased platelets at 3-, 6- and 12-month AD are successfully distinguished and the accuracy is 91%, 68% and 97% respectively, which demonstrates the suggested method can provide a precise detection for AD diagnosis at early, middle and advanced stages.

Risk factors and a nomogram for frailty in Chinese older patients with Alzheimer's disease: A single-center cross-sectional study

This study aimed to determine the risk factors of frailty in Chinese older patients with Alzheimer's disease (AD) and then construct a nomogram for frailty in this population. A total of 205 eligible older AD patients were recruited. Patients' general demographic characteristics were collected through a self-designed questionnaire. A nomogram was constructed for frailty based on the risk factors identified from the multivariate analysis. The discrimination and calibration capabilities of this nomogram were assessed with the C-index and calibration curve, respectively. The results showed that older age, no regular exercise habit, severe cognitive decline, and low social support were identified as important risk factors of frailty in AD patients. The C-index of the nomogram was 0.884 by bootstrapping validation, and the calibration curve of the nomogram showed high coherence between the predicted and actual probabilities of frailty. In conclusion, this nomogram was validated to have favorable discrimination and calibration capabilities.

Frailty, MRI, and FDG-PET Measures in an Australian Memory Clinic Cohort

Given that the global population is aging, the number of age-related syndromes, such as frailty, is expected to rise in conjunction. Frailty is characterized by the loss of homeostatic reserve, rendering the individual vulnerable to poor health outcomes. Many biological mechanisms have been proposed to contribute to frailty. However, few studies have assessed the associations between frailty and brain diseases or neuroimaging biomarkers. Aims: The aims of this study were to measure the prevalence of frailty in a memory clinic and to examine associations between frailty and brain changes found on magnetic resonance imaging (MRI) and 18-F deoxyglucose (FDG) positron emission tomography (PET) in memory clinic attendees. Methods: A 54-items Frailty Index was retrospectively assessed for all clinic attendees from 2014. Frailty was defined as FI > 0.25. MR images were analyzed for stroke, cerebral small vessel disease [CSVD, including cerebral microbleeds (CMBs), cortical superficial siderosis (CSS), and white matter hyperintensity (WMH)], and neurodegenerative changes [MRI: mesial temporal atrophy (MTA), FDG-PET: regional hypometabolism], blind to clinical findings. Results: There were 209 clinic attendees in 2014, of whom 121 had MRI performed. The prevalence of frailty (using FI) in the memory clinic in 2014 was 38.3% overall (patients without MRI: 43.2%, patients with MRI 34.7%, p = 0.25). Frailty was associated with presence of deep WMH, increased severity of periventricular WMH, and presence of CSS, but not neurodegeneration markers (MTA atrophy/FDG-PET hypometabolism). Conclusion: The findings support the idea that previously reported associations between frailty and imaging evidence of CSVD in other cohorts are also relevant to the Australian clinic setting. Given that a large proportion of memory clinic attendees are frail, there may be opportunities for interventions to reduce preventable adverse health outcomes, such as falls and fractures, and reduce the prevalence and impact of frailty in this cohort.

Differentiating Mild Cognitive Impairment, Alzheimer's Disease, and Dementia With Lewy Bodies Using Cingulate Island Sign on Perfusion IMP-SPECT

The cingulate island sign (CIS) on fludeoxyglucose (FDG)-positron emission tomography (PET) is a supporting biomarker of dementia with Lewy bodies (DLB). Its diagnostic accuracy has only been investigated in FDG-PET, however. The present prospective study compared the CIS on I-iodoamphetamine-single photon emission computed tomography (SPECT) among patients with mild cognitive impairment (MCI), AD, or DLB. Fifty-eight patients with MCI, 42 with probable AD, and 58 with probable DLB were enrolled. The "CIScore" used to evaluate the CIS was defined as the ratio of volume of interest (VOI)-1 (indicating posterior cingulate gyrus [PCG]) to VOI-2 (area of significantly reduced regional cerebral blood perfusion [rCBF] in DLB patients compared with in healthy controls). It was calculated using eZIS software. The CIScore for MCI, DLB, and AD was 0.22, 0.23, and 0.28, respectively. The CIScore in the AD group was significantly higher than that in the DLB or MCI groups (AD vs. DLB: p < 0.001, AD vs. MCI: p < 0.005). This suggests that the CIScore can discriminate DLB from AD, if the decrease in rCBF in the PCG is similar between them. We believe that it is difficult to identify MCI based on the CIScore, as the decrease in rCBF in the PCG is not severe. The diagnostic accuracy of the CIScore may be low as it often shows an increase in elderly DLB patients, in whom the pathologically common form is most prevalent (1). Further study should include assessment of multiple components such as symptom classification and age.