Cost-effectiveness of using amyloid positron emission tomography in individuals with mild cognitive impairment

Background Amyloid positron emission tomography (PET) makes it possible to diagnose Alzheimer’s disease (AD) in its prodromal phase including mild cognitive impairment (MCI). This study evaluated the cost-effectiveness of including amyloid-PET for assessing individuals with MCI. Methods The target population was 60-year-old patients who were diagnosed with MCI. We constructed a Markov model for the natural history of AD with the amyloid positivity (AP). Because amyloid-PET can detect the AP MCI state, AD detection can be made faster by reducing the follow-up interval for a high-risk group. The health outcomes were evaluated in quality-adjusted life years (QALYs) and the final results of cost-effectiveness analysis were presented in the form of the Incremental Cost-Effectiveness Ratio (ICER). To handle parameter uncertainties, one-way sensitivity analyses for various variables were performed. Results Our model showed that amyloid-PET increased QALYs by 0.003 in individuals with MCI. The estimated additional costs for adopting amyloid-PET amounted to a total of 1250 USD per patient when compared with the cost when amyloid-PET is not adopted. The ICER was 3,71,545 USD per QALY. According to the sensitivity analyses, treatment effect of Donepezil and virtual intervention effect in MCI state were the most influential factors. Conclusions In our model, using amyloid-PET at the MCI stage was not cost-effective. Future advances in management of cognitive impairment would enhance QALYs, and consequently improve cost-effectiveness.

Patterns of Cerebrospinal Fluid Biomarkers and Amyloid Positron Emission Tomography in a Patient with Cerebral Amyloid Angiopathy-Related Inflammation

Cerebral amyloid angiopathy-related inflammation (CAA-RI) is a distinct subset of cerebral amyloid angiopathy characterized by the auto-inflammatory response to amyloid-laden small arteries of cerebral cortex and leptomeninges. Clinical features include cognitive-behavioral change, headache, focal neurologic deficits and seizure. Because anti-inflammatory treatments can rapidly relieve neurologic symptoms, early diagnosis is critical. Herein, we report a CAA-RI case with distinct laboratory findings of a decreased cerebrospinal fluid amyloid beta 1-42 level and relatively reduced florbetaben uptake in the focal inflammatory lesion during the acute phase of CAA-RI.

Amyloid PET-Positive Predictability of Machine Learning Algorithm Based on MDS-OAβ Levels

Background: The Multimer Detection System-Oligomeric amyloid-β (MDS-OAβ) level is a valuable blood-based biomarker for Alzheimer’s disease (AD). We used machine learning algorithms trained using multi-center datasets to examine whether blood MDS-OAβ values can predict AD-associated changes in the brain.Methods: A logistic regression model using TensorFlow (ver. 2.3.0) was applied to data obtained from 163 participants (amyloid positron emission tomography [PET]-positive and -negative findings in 102 and 61 participants, respectively). Algorithms with various combinations of features (MDS-OAβ levels, age, gender, and anticoagulant type) were tested 50 times on each dataset. Results: The predictive accuracy, sensitivity, and specificity values of blood MDS-OAβ levels for amyloid PET positivity were 78.16±4.97%, 83.87±9.40%, and 70.00±13.13%, respectively.Conclusions: The findings from this multi-center machine learning-based study suggest that MDS-OAβ values may be used to predict amyloid PET-positivity.

Altered Gut Microbiota in Adults with Subjective Cognitive Decline: The SILCODE Study

Background: Subjective cognitive decline (SCD) is the earliest symptomatic manifestation of preclinical Alzheimer’s disease (AD). Gut microbiota may serve as a susceptibility factor for AD. Altered gut microbiota has been reported in patients with mild cognitive impairment (MCI) and AD dementia. However, whether gut microbial compositions changed in SCD remains largely unknown. Objective: To characterize the gut microbiota in SCD. Methods: In this study, a total of 105 participants including 38 normal controls (NC), 53 individuals with SCD, and 14 patients with cognitive impairment (CI) were recruited. Gut microbiota of all participants isolated from fecal samples were investigated using 16S ribosomal RNA (rRNA) Illumina Miseq sequencing technique. The gut microbial compositions were compared among the three groups, and the association between altered gut microbiota and cognitive performance was analyzed. To validate the alteration of gut microbiota in SCD, we conducted amyloid positron emission tomography (PET) in selected participants and further compared the gut microbiota among subgroups. Results: The abundance of phylum Firmicutes, class Clostridia, order Clostridiales, family Ruminococcaceae, and genus Faecalibacterium showed a trend toward a progressive decline from NC to SCD and CI. Specifically, the abundance of the anti-inflammatory genus Faecalibacterium was significantly decreased in SCD compared with NC. In addition, altered bacterial taxa among the three groups were associated with cognitive performance. The findings were validated in SCD participants with positive amyloid evidence. Conclusion: The composition of gut microbiota is altered in individuals with SCD. This preliminary study will provide novel insights into the pathophysiological mechanism of AD.