Pei-Ning Wang

Background and ObjectivesVascular cognitive impairment and dementia (VCID), a major contributor of dementia, remains underrecognized, owing to the absence of universally accepted diagnostic criteri...

Effective treatment for Alzheimer’s disease (AD) remains an unmet need. Thus, identifying patients with mild cognitive impairment (MCI) who are at high-risk of progressing to AD is crucial for early intervention. Blood-based transcriptomics analyses were performed using a longitudinal study cohort to compare progressive MCI (P-MCI, n = 28), stable MCI (S-MCI, n = 39), and AD patients (n = 49). Statistical DESeq2 analysis and machine learning methods were employed to identify differentially expressed genes (DEGs) and develop prediction models. We discovered a remarkable gender-specific difference in DEGs that distinguish P-MCI from S-MCI. Machine learning models achieved high accuracy in distinguishing P-MCI from S-MCI (AUC 0.93), AD from S-MCI (AUC 0.94), and AD from P-MCI (AUC 0.92). An 8-gene signature was identified for distinguishing P-MCI from S-MCI. Blood-based transcriptomic biomarker signatures show great utility in identifying high-risk MCI patients, with mitochondrial processes emerging as a crucial contributor to AD progression.

Assessing dementia conversion in patients with mild cognitive impairment (MCI) remains challenging owing to pathological heterogeneity. While many MCI patients ultimately proceed to Alzheimer’s disease (AD), a subset of patients remain stable for various times. Our aim was to characterize the plasma metabolites of nineteen MCI patients proceeding to AD (P-MCI) and twenty-nine stable MCI (S-MCI) patients by untargeted metabolomics profiling. Alterations in the plasma metabolites between the P-MCI and S-MCI groups, as well as between the P-MCI and AD groups, were compared over the observation period. With the help of machine learning-based stratification, a 20-metabolite signature panel was identified that was associated with the presence and progression of AD. Furthermore, when the metabolic signature panel was used for classification of the three patient groups, this gave an accuracy of 73.5% using the panel. Moreover, when specifically classifying the P-MCI and S-MCI subjects, a fivefold cross-validation accuracy of 80.3% was obtained using the random forest model. Importantly, indole-3-propionic acid, a bacteria-generated metabolite from tryptophan, was identified as a predictor of AD progression, suggesting a role for gut microbiota in AD pathophysiology. Our study establishes a metabolite panel to assist in the stratification of MCI patients and to predict conversion to AD.

Subjective memory complaint (SMC), a self‐perceived worsening in memory capacity concurrent with normal performance on standardized cognitive assessments, is considered a risk factor for the development of Alzheimer's disease (AD). Deficient sensory gating (SG), referring to the lack of automatic inhibition of neural responses to the second identical stimulus, has been documented in prodromal and incident AD patients. However, it remains unknown whether the cognitively normal elderly with SMC demonstrate alterations of SG function compared with those without SMC. A total of 19 healthy controls (HC) and 16 SMC subjects were included in the present study. Neural responses to the auditory paired‐stimulus paradigm were recorded by the magnetoencephalography and analyzed by the distributed source imaging method of minimum norm estimate. The SG of M50 and M100 components were measured using the amplitude ratio of the second response over the first response at the cortical level. Compared to HC, subjects with SMC showed significantly increased M50 SG ratios in the inferior parietal lobule (IPL). Furthermore, M50 SG ratios in the right IPL yielded an acceptable discriminative ability to distinguish SMC from HC. However, we did not find a significant association between SG ratios and cognitive function requiring inhibitory control either in the HC or SMC group. In conclusion, although SMC subjects have intact cognitive functioning revealed by objective neuropsychological tests, their deficits in automatic inhibitory function could be detected through neurophysiological recordings. Our results suggest that altered brain function occurs in SMC prior to the obvious decline of cognitive performance.

Fibromyalgia (FM) is a stress-related chronic pain disorder with common cognitive complaints. This study characterized cognitive dysfunction in patients with FM and explored whether these changes are linked to altered cortisol levels. Consecutive 44 patients with FM and 48 healthy controls were enrolled for the assessments of subjective and objective cognitive functions and diurnal levels of salivary cortisol (sampled at awakening, 30 min after awakening, 3 pm, and bedtime). All measurements were compared between the groups and evaluated for clinical correlation. The FM group had more subjective cognitive complaints and performed poorer in objective cognitive testing in memory (delayed recall in Chinese Version Verbal Learning Test and Taylor Complex Figure Test), language (Boston Naming Test), and executive domains (Wisconsin Card Sorting Test) after adjustments for education. The diurnal cortisol levels of patients with FM tended to be lower, especially at 30 min after awakening and bedtime. Moreover, moderate positive correlations existed between the Chinese Version Verbal Learning Test, Boston Naming Test and the morning cortisol levels within the FM group. We suggested the altered cognitive function in FM may be linked to stress maladaptation. Future studies are warranted to elucidate whether stress management improves cognitive performance in patients with FM.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of toxic misfolded proteins, which are believed to have propagated from disease-specific epicenters through their corresponding large-scale structural networks in the brain. Although previous cross-sectional studies have identified potential AD-associated epicenters and corresponding brain networks, it is unclear whether these networks are associated with disease progression. Hence, this study aims to identify the most vulnerable epicenters and corresponding large-scale structural networks involved in the early stages of AD and to evaluate its associations with multiple cognitive domains using longitudinal study design. Annual neuropsychological and MRI assessments were obtained from 23 patients with AD, 37 patients with amnestic mild cognitive impairment (MCI), and 33 healthy controls (HC) for 3 years. Candidate epicenters were identified as regions with faster decline rate in the gray matter volume (GMV) in patients with MCI who progressed to AD as compared to those regions in patients without progression. These epicenters were then further used as pre-defined regions of interest to map the synchronized degeneration network (SDN) in HCs. Spatial similarity, network preference and clinical association analyses were used to evaluate the specific roles of the identified SDNs. Our results demonstrated that the hippocampus and posterior cingulate cortex (PCC) were the most vulnerable AD-associated epicenters. The corresponding PCC-SDN showed significant spatial association with the patterns of GMV atrophy rate in each patient group and the overlap of these patterns was more evident in the advanced stages of the disease. Furthermore, individuals with a higher GMV atrophy rate of the PCC-SDN also showed faster decline in multiple cognitive domains. In conclusion, our findings suggest the PCC and hippocampus are two vulnerable regions involved early in AD pathophysiology. However, the PCC-SDN, but not hippocampus-SDN, was more closely associated with AD progression. These results may provide insight into the pathophysiology of AD from large-scale network perspective.

Patients with amnestic mild cognitive impairment (aMCI) demonstrate significant cognitive deficits, especially in the memory aspect. The memory deficiency might be attributed to the difficulties in the inhibitory function to suppress redundant stimuli. Sensory gating (SG) refers to the attenuation of neural responses to the second identical stimulus in a paired-click paradigm, in which auditory stimuli are delivered in pairs with inter-stimulus intervals (ISI) of 500 ms and inter-pair intervals of 6–8 s. It is considered as an electrophysiological signal to reflect the brain’s automatic response to gate out repetitive sensory inputs. However, there has been no study systematically investigating SG function in aMCI patients. Thus, the present study used magnetoencephalography (MEG) to record neuromagnetic responses to a paired-click paradigm in 23 healthy controls (HC) and 26 aMCI patients. The Stimulus 2/Stimulus 1 (S2/S1) amplitude ratio was used to represent the SG function. Compared to HC, aMCI patients showed M50 SG deficits in the left inferior frontal gyrus (IFG) and right inferior parietal lobule (IPL). M100 SG defects were also observed in the right IPL. Based on the ROIs showing significant between-group SG differences, we found that a more deficient M50 SG function in the right IPL was associated with poorer performance in the immediate recall of Logic Memory (LM), Chinese Version Verbal Learning Test (CVVLT) and Digit Span Backward (DSB) Test. Furthermore, the M50 SG ratios of the right IPL together with the neuropsychological performance of LM and CVVLT demonstrated very good accuracy in the discrimination of aMCI from HC. In conclusion, compared to HC, aMCI patients showed a significant SG deficit in the right IPL, which was correlated with the auditory short-term memory function. We suggest the combination of SG in the right IPL, LM and CVVLT to be sensitive indicators to differentiate aMCI patients from HC.

Introduction: Due to the high cost and high failure rate of ascertaining amyloid positron emission tomography positivity (PET+) in patients with earlier stage Alzheimer’s disease (AD), an effective pre-screening tool for amyloid PET scans is needed. Methods: Patients with mild cognitive impairment (n = 33, 24.2% PET+, 42% females, age 74.4 ± 7.5, MMSE 26.8 ± 1.9) and mild dementia (n = 19, 63.6% PET+, 36.3% females, age 73.0 ± 9.3, MMSE 22.6 ± 2.0) were recruited. Amyloid PET imaging, Apolipoprotein E (APOE) genotyping, and plasma amyloid β (Aβ)_1–40, Aβ_1–42, and total tau protein quantification by immunomagnetic reduction (IMR) method were performed. Receiver operating characteristics (ROC) analysis and Youden’s index were performed to identify possible cut-off points, clinical sensitivities/specificities, and areas under the curve (AUCs). Results: Amyloid PET+ participants had lower plasma Aβ_1–42 levels than amyloid PET-negative (PET−) subjects. APOE ε4 carriers had higher plasma Aβ_1–42 than non-carriers. We developed an algorithm involving the combination of plasma Aβ_1–42 and APOE genotyping. The success rate for detecting amyloid PET+ patients effectively increased from 42.3 to 70.4% among clinically suspected MCI and mild dementia patients. Conclusions: Our results demonstrate the possibility of utilizing APOE genotypes in combination with plasma Aβ_1–42 levels as a pre-screening tool for predicting the positivity of amyloid PET findings in early stage dementia patients.