Alzheimer's Disease Signature Research Articles

340-OR: ADA Presidents' Select Abstract: Structural Neuroimaging and Cognitive Function in Long-Duration Type 1 Diabetes

Cognitive decline is an increasingly important complication in people with type 1 diabetes (T1D) as they age. We previously reported that compared with nondiabetic people, those with long-duration T1D have impaired cognitive function, similar to people with type 2 diabetes. To characterize factors associated with cognitive decline in people with T1D, 52 participants of the Joslin Medalist Study (“Medalists”) with T1D≥50 years, and 20 age-matched nondiabetic controls, underwent clinical evaluation, cognitive assessments, and neuroimaging (Alzheimer Disease Neuroimaging Initiative-3 protocol). Compared to controls, Medalists had 49.4 (±21.9) cm3 (p=0.03) lower total brain volume, and 2.7 (±0.8) cm3, 3.0 (±1.5) cm3 and 3.1 (±1.5) cm3 lower volumes (p<0.05), respectively, in the deep gray matter region (thalamus, putamen, caudate and globus pallidus), the Alzheimer disease signature region (hippocampus, parahippocampus, entorhinal cortex, inferior parietal lobule, precuneus and cuneus), and the occipital lobe. No significant differences were observed in frontal, temporal, or parietal lobe volumes. In assessing small vessel disease, the number of white matter hyperintensities (WMHs) or microvascular hemorrhages did not differ between Medalists and controls, nor did regional cerebral blood flow measures (via arterial spin labeling). Among Medalists, reduced total and all regional brain volumes were associated (p<0.05) with worse motor skills, female sex, lower education status, increased age, and longer diabetes duration. Increased WMHs were associated with worse renal function and higher coronary artery calcification scores. Volumes did not associate with other cognitive domains (recall, working memory, or executive function). These findings suggest that cognitive decline in people with long-duration T1D is related mainly to abnormalities in parenchymal CNS rather than neurovascular changes. Definitive confirmation of this finding by brain histopathology is underway. Disclosure H. Shah: None. M. Desalvo: None. A. Haidar: None. S. Jangolla: None. M. Yu: None. J. Gauthier: None. N.A. Ziemniak: None. I. Wu: None. T. Billah: None. L. Ning: None. A. Adam: None. Y. Rathi: None. G.L. King: Research Support; Janssen Research & Development, LLC. Funding National Institute of Diabetes and Digestive and Kidney Diseases (3P30DK036836-34S1); Thomas J. Beatson, Jr. Foundation

MicroRNA (miRNA) Complexity in Alzheimer's Disease (AD).

AD is a complex, progressive, age-related neurodegenerative disorder representing the most common cause of senile dementia and neurological dysfunction in our elderly domestic population. The widely observed heterogeneity of AD is a reflection of the complexity of the AD process itself and the altered molecular-genetic mechanisms operating in the diseased human brain and CNS. One of the key players in this complex regulation of gene expression in human pathological neurobiology are microRNAs (miRNAs) that, through their actions, shape the transcriptome of brain cells that normally associate with very high rates of genetic activity, gene transcription and messenger RNA (mRNA) generation. The analysis of miRNA populations and the characterization of their abundance, speciation and complexity can further provide valuable clues to our molecular-genetic understanding of the AD process, especially in the sporadic forms of this common brain disorder. Current in-depth analyses of high-quality AD and age- and gender-matched control brain tissues are providing pathophysiological miRNA-based signatures of AD that can serve as a basis for expanding our mechanistic understanding of this disorder and the future design of miRNA- and related RNA-based therapeutics. This focused review will consolidate the findings from multiple laboratories as to which are the most abundant miRNA species, both free and exosome-bound in the human brain and CNS, which miRNA species appear to be the most prominently affected by the AD process and review recent developments and advancements in our understanding of the complexity of miRNA signaling in the hippocampal CA1 region of AD-affected brains.

Association of Arterial Stiffness and Atherosclerotic Burden With Brain Structural Changes Among Japanese Men.

Background Little is known regarding whether arterial stiffness and atherosclerotic burden are each independently associated with brain structural changes. Simultaneous assessments of both arterial stiffness and atherosclerotic burden in associations with brain could provide insights into the mechanisms of brain structural changes. Methods and Results Using data from the SESSA (Shiga Epidemiological Study of Subclinical Atherosclerosis), we analyzed data among 686 Japanese men (mean [SD] age, 67.9 [8.4] years; range, 46-83 years) free from history of stroke and myocardial infarction. Brachial-ankle pulse wave velocity and coronary artery calcification on computed tomography scans were measured between March 2010 and August 2014. Brain volumes (total brain volume, gray matter, Alzheimer disease signature and prefrontal) and brain vascular damage (white matter hyperintensities) were quantified using brain magnetic resonance imaging from January 2012 through February 2015. In multivariable adjustment models including mean arterial pressure, when brachial-ankle pulse wave velocity and coronary artery calcification were entered into the same models, the β (95% CI) for Alzheimer disease signature volume for each 1-SD increase in brachial-ankle pulse wave velocity was -0.33 (-0.64 to -0.02), and the unstandardized β (95% CI) for white matter hyperintensities for each 1-unit increase in coronary artery calcification was 0.68 (0.05-1.32). Brachial-ankle pulse wave velocity and coronary artery calcification were not statistically significantly associated with total brain and gray matter volumes. Conclusions Among Japanese men, higher arterial stiffness was associated with lower Alzheimer disease signature volumes, whereas higher atherosclerotic burden was associated with brain vascular damage. Arterial stiffness and atherosclerotic burden may be independently associated with brain structural changes via different pathways.

Development and validation of an automatic classification algorithm for the diagnosis of Alzheimer's disease using a high-performance interpretable deep learning network.

To develop and validate an automatic classification algorithm for diagnosing Alzheimer's disease (AD) or mild cognitive impairment (MCI). This study evaluated a high-performance interpretable network algorithm (TabNet) and compared its performance with that of XGBoost, a widely used classifier. Brain segmentation was performed using a commercially approved software. TabNet and XGBoost were trained on the volumes or radiomics features of 102 segmented regions for classifying subjects into AD, MCI, or cognitively normal (CN) groups. The diagnostic performances of the two algorithms were compared using areas under the curves (AUCs). Additionally, 20 deep learning-based AD signature areas were investigated. Between December 2014 and March 2017, 161 AD, 153 MCI, and 306 CN cases were enrolled. Another 120 AD, 90 MCI, and 141 CN cases were included for the internal validation. Public datasets were used for external validation. TabNet with volume features had an AUC of 0.951 (95% confidence interval [CI], 0.947-0.955) for AD vs CN, which was similar to that of XGBoost (0.953 [95% CI, 0.951-0.955], p = 0.41). External validation revealed the similar performances of two classifiers using volume features (0.871 vs. 0.871, p = 0.86). Likewise, two algorithms showed similar performances with one another in classifying MCI. The addition of radiomics data did not improve the performance of TabNet. TabNet and XGBoost focused on the same 13/20 regions of interest, including the hippocampus, inferior lateral ventricle, and entorhinal cortex. TabNet shows high performance in AD classification and detailed interpretation of the selected regions. Using a high-performance interpretable deep learning network, the automatic classification algorithm assisted in accurate Alzheimer's disease detection using 3D T1-weighted brain MRI and detailed interpretation of the selected regions. • MR volumetry data revealed that TabNet had a high diagnostic performance in differentiating Alzheimer's disease (AD) from cognitive normal cases, which was comparable with that of XGBoost. • The addition of radiomics data to the volume data did not improve the diagnostic performance of TabNet. • Both TabNet and XGBoost selected the clinically meaningful regions of interest in AD, including the hippocampus, inferior lateral ventricle, and entorhinal cortex.

Tau accumulates differently in four subtypes of Alzheimer's disease

AbstractBackgroundHeterogeneity in Alzheimer's disease (AD) has been reported on the basis of clinical, neuropathological, and neuroimaging data. However, most of the indices, including cerebral atrophy evaluated using magnetic resonance imaging and amyloid β (Aβ) accumulation detected using positron emission tomography (PET), lack sensitivity, and specificity for categorization.AimHerein, we used a novel PET ligand for tau to estimate the differential distribution of tau in the subtypes of AD.MethodsPatients with posterior cortical atrophy (PCA; n = 3), frontal variant of AD (FAD; n = 1), logopenic variant primary progressive aphasia (LPPA; n = 2), typical AD (TAD; n = 6), and healthy controls (HC; n = 12) were studied. Aβ and tau accumulation were evaluated using [11C]PiB and [11C]PBB3, respectively.ResultsAmyloid β accumulation was confirmed in all PCA, FAD, LPPA, and TAD cases. Tau accumulation was dominantly high in the occipital lobes in the PCA, strikingly high in the frontal lobes in the FAD, and moderately high in the angular gyrus of the dominant hemisphere in the LPPA. Tau accumulation in TAD cases was significantly higher than age‐dependent tau accumulation in HC in these subtype‐specific regions as well as in AD signature regions. Glucose utilization was reversely correlated with PBB3 accumulation in the subtype‐specific regions.ConclusionsTau accumulates differently in the four subtypes of AD, suggesting that tau pathology may be closely associated with unique clinical features.

Aberrant protein S-nitrosylation contributes to hyperexcitability-induced synaptic damage in Alzheimer's disease: Mechanistic insights and potential therapies.

Alzheimer's disease (AD) is arguably the most common cause of dementia in the elderly and is marked by progressive synaptic degeneration, which in turn leads to cognitive decline. Studies in patients and in various AD models have shown that one of the early signatures of AD is neuronal hyperactivity. This excessive electrical activity contributes to dysregulated neural network function and synaptic damage. Mechanistically, evidence suggests that hyperexcitability accelerates production of reactive oxygen species (ROS) and reactive nitrogen species (RNS) that contribute to neural network impairment and synapse loss. This review focuses on the pathways and molecular changes that cause hyperexcitability and how RNS-dependent posttranslational modifications, represented predominantly by protein S-nitrosylation, mediate, at least in part, the deleterious effects of hyperexcitability on single neurons and the neural network, resulting in synaptic loss in AD.

Effective Preprocessing of Single-Cell RNA-Seq for Unravelling Alzheimer's Disease Signatures.

The development in the field of biomedical technology has brought significant progress in the diagnosis and prediction of many complex diseases. Part of this development is the single-cell RNA sequencing analysis, which allows the study of a complex disease in great depth at the cellular level. Such analyses can decipher the mechanisms that cause complex diseases, such as Alzheimer's disease (AD). However, the increasing depth in the collection of single-cell RNA sequencing data implies, in addition to greater challenges, the production of a large amount of information, which needs careful analysis. Toward this direction, we examine the approach to single-cell RNA sequencing data through the development of an exploratory data analysis methodology. For this purpose, a combination of various tools is presented for their effective and efficient processing. At the same time, reference is made to the relevant biological concepts, the goals and challenges of the studies, and the workflows of sequencing, preprocessing, and analysis of the data. Our framework is applied to Alzheimer's disease data providing evidence that such data are quite complex while the appropriate preprocess step can boost the machine learning processes for identifying AD signatures.

Diagnostic Performance of a Tablet Computer-Based Cognitive Screening Test for Identification of Amnestic Mild Cognitive Impairment.

Early and appropriate diagnosis of amnestic mild cognitive impairment (aMCI) is clinically important because aMCI is considered the prodromal stage of dementia caused by Alzheimer's disease (AD). aMCI is assessed using the comprehensive neuropsychological (NP) battery, but it is rater-dependent and does not provide quick results. Thus, we investigated the performance of the computerized cognitive screening test (Inbrain Cognitive Screening Test; Inbrain CST) in the diagnosis of aMCI and compared its performance to that of the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) test (CERAD-K), a comprehensive and pencil-and-paper NP test. A total of 166 participants were included in this cross-sectional study. The participants were recruited as part of a prospective, community-based cohort study for MCI (PREcision medicine platform for mild cognitive impairment on multi-omics, imaging, evidence-based R&BD; PREMIER). All participants were assessed using the CERAD-K and the Inbrain CST. The Inbrain CST comprised seven subtests that assessed the following five cognitive domains: attention, language, visuospatial, memory, and executive functions. Seventy-six participants underwent brain magnetic resonance imaging and [18F]-flutemetamol positron emission tomography (PET). We evaluated the diagnostic performance of the Inbrain CST for the identification of aMCI by comparing the findings with those of CERAD-K. We also determined the characteristics of aMCI patients as defined by the CERAD-K and Inbrain CST. Of the 166 participants, 93 were diagnosed with aMCI, while 73 were cognitively unimpaired. The sensitivity of the Inbrain CST for aMCI diagnosis was 81.7%, and its specificity was 84.9%. Positive and negative predictive values were 87.4% and 78.5%, respectively. The diagnostic accuracy was 83.1%, and the error rate was 16.9%. Demographic and clinical characteristics between individuals with aMCI defined by the Inbrain CST and CERAD-K were not significantly different. The frequency of positive amyloid PET scan, the hippocampal/parahippocampal volumes, and AD signature cortical thickness did not differ between the patients with aMCI defined by CERAD-K and those with aMCI defined by the Inbrain CST. The Inbrain CST showed sufficient sensitivity, specificity, and positive and negative predictive values for diagnosing objective memory impairment in aMCI. In addition, aMCI patients identified by CERAD-K and the Inbrain CST showed comparable clinical and neuroimaging characteristics. Therefore, the Inbrain CST can be considered an alternative test to supplement the limitations of existing pencil-and-paper NP tests.

Validity and Performance of Blood Biomarkers for Alzheimer Disease to Predict Dementia Risk in a Large Clinic-Based Cohort.

Blood biomarkers for Alzheimer disease (AD) have consistently proven to be associated with CSF or PET biomarkers and effectively discriminate AD from other neurodegenerative diseases. Our aim was to test their utility in clinical practice, from a multicentric unselected prospective cohort where patients presented with a large spectrum of cognitive deficits or complaints. The MEMENTO cohort enrolled 2,323 outpatients with subjective cognitive complaint (SCC) or mild cognitive impairment (MCI) consulting in 26 French memory clinics. Participants had neuropsychological assessments, MRI, and blood sampling at baseline. CSF sampling and amyloid PET were optional. Baseline blood Aβ42/40 ratio, total tau, p181-tau, and neurofilament light chain (NfL) were measured using a Simoa HD-X analyzer. An expert committee validated incident dementia cases during a 5-year follow-up period. Overall, 2,277 individuals had at least 1 baseline blood biomarker available (n = 357 for CSF subsample, n = 649 for PET subsample), among whom 257 were diagnosed with clinical AD/mixed dementia during follow-up. All blood biomarkers but total tau were mildly correlated with their equivalence in the CSF (r = 0.33 to 0.46, p < 0.0001) and were associated with amyloid-PET status (p < 0.0001). Blood p181-tau was the best blood biomarker to identify amyloid-PET positivity (area under the curve = 0.74 [95% CI = 0.69; 0.79]). Higher blood and CSF p181-tau and NfL concentrations were associated with accelerated time to AD dementia onset with similar incidence rates, whereas blood Aβ42/40 was less efficient than CSF Aβ42/40. Blood p181-tau alone was the best blood predictor of 5-year AD/mixed dementia risk (c-index = 0.73 [95% CI = 0.69; 0.77]); its accuracy was higher in patients with clinical dementia rating (CDR) = 0 (c-index = 0.83 [95% CI = 0.69; 0.97]) than in patients with CDR = 0.5 (c-index = 0.70 [95% CI = 0.66; 0.74]). A "clinical" reference model (combining demographics and neuropsychological assessment) predicted AD/mixed dementia risk with a c-index = 0.88 [95% CI = 0.86-0.91] and performance increased to 0.90 [95% CI = 0.88; 0.92] when adding blood p181-tau + Aβ42/40. A "research" reference model (clinical model + apolipoprotein E genotype and AD signature on MRI) had a c-index = 0.91 [95% CI = 0.89-0.93] increasing to 0.92 [95% CI = 0.90; 0.93] when adding blood p181-tau + Aβ42/40. Chronic kidney disease and vascular comorbidities did not affect predictive performances. In a clinic-based cohort of patients with SCC or MCI, blood biomarkers may be good hallmarks of underlying pathology but add little to 5-year dementia risk prediction models including traditional predictors.

Survey on Feature Detection of Alzheimer’s disease in Early Stage by Particle Size Distribution in Feature Selection Optimization Procedure

The rising incidence of Alzheimer's Disease (AD) and moderate impairments of scan results in the modern world have piqued the interest of scientists in the field of neuroimaging-based diagnostics. Neuroimaging allows for the quantification of pathological alterations in the brain that has been associated to AD. Through the use of categorization frameworks, which provide diagnostic and prognostic tools, these measurements have been quickly included into the signatures of AD in recent years. The purpose of this article is to summarise research on Alzheimer's disease that used optimization strategies for feature selection. To address the problem of excessive model complexity when using ML techniques, this work presents a novel approach to feature selection. Several stages of Alzheimer's disease and a state of altered brain function that is clinically similar to AD but less severe have been described. The effectiveness of an AD's classification in these approaches is evaluated using a wrapper-based feature selection mechanism. Then, a suggested Social Spider Metaheuristic algorithm has been employed to zero down on the most crucial characteristics for making a correct AD diagnosis.

Survey on Feature Detection of Alzheimer’s disease in Early Stage by Particle Size Distribution in Feature Selection Optimization Procedure

The rising incidence of Alzheimer's Disease (AD) and moderate impairments of scan results in the modern world have piqued the interest of scientists in the field of neuroimaging-based diagnostics. Neuroimaging allows for the quantification of pathological alterations in the brain that has been associated to AD. Through the use of categorization frameworks, which provide diagnostic and prognostic tools, these measurements have been quickly included into the signatures of AD in recent years. The purpose of this article is to summarise research on Alzheimer's disease that used optimization strategies for feature selection. To address the problem of excessive model complexity when using ML techniques, this work presents a novel approach to feature selection. Several stages of Alzheimer's disease and a state of altered brain function that is clinically similar to AD but less severe have been described. The effectiveness of an AD's classification in these approaches is evaluated using a wrapper-based feature selection mechanism. Then, a suggested Social Spider Metaheuristic algorithm has been employed to zero down on the most crucial characteristics for making a correct AD diagnosis.

Vascular burden and cognition: Mediating roles of neurodegeneration and amyloid PET.

It remains unclear to what extent cerebrovascular burden relates to amyloid beta (Aβ) deposition, neurodegeneration, and cognitive dysfunction in mixed disease populations with small vessel disease and Alzheimer's disease (AD) pathology. In 120 subjects, we investigated the association of vascular burden (white matter hyperintensity [WMH] volumes) with cognition. Using mediation analyses, we tested the indirect effects of WMH on cognition via Aβ deposition (18 F-AV45 positron emission tomography [PET]) and neurodegeneration (cortical thickness or 18 F fluorodeoxyglucose PET) in AD signature regions. We observed that increased total WMH volume was associated with poorer performance in all tested cognitive domains, with the strongest effects observed for semantic fluency. These relationships were mediated mainly via cortical thinning, particularly of the temporal lobe, and to a lesser extent serially mediated via Aβ and cortical thinning of AD signature regions. WMH volumes differentially impacted cognition depending on lobar location and Aβ status. In summary, our study suggests mainly an amyloid-independent pathway in which vascular burden affects cognitive function via localized neurodegeneration. HIGHLIGHTS: Alzheimer's disease often co-exists with vascular pathology. We studied a unique cohort enriched for high white matter hyperintensities (WMH). High WMH related to cognitive impairment of semantic fluency and executive function. This relationship was mediated via temporo-parietal atrophy rather than metabolism. This relationship was, to lesser extent, serially mediated via amyloid beta and atrophy.

Herpes Viruses in the Baltimore Longitudinal Study of Aging: Associations With Brain Volumes, Cognitive Performance, and Plasma Biomarkers.

Although an infectious etiology of Alzheimer disease (AD) has received renewed attention with a particular focus on herpes viruses, the longitudinal effects of symptomatic herpes virus (sHHV) infection on brain structure and cognition remain poorly understood, as does the effect of sHHV on AD/neurodegeneration biomarkers. We used a longitudinal, community-based cohort to characterize the association of sHHV diagnoses with changes in 3 T MRI brain volume and cognitive performance. In addition, we related sHHV to cross-sectional differences in plasma biomarkers of AD (β-amyloid [Aβ]42/40), astrogliosis (glial fibrillary acidic protein [GFAP]), and neurodegeneration (neurofilament light [NfL]). Baltimore Longitudinal Study of Aging participants were recruited from the community and assessed with serial brain MRIs and cognitive examinations over an average of 3.4 (SD = 3.2) and 8.6 (SD = 7.7) years, respectively. sHHV classification used International Classification of Diseases, Ninth Revision codes documented at comprehensive health and functional screening evaluations at each study visit. Linear mixed-effects and multivariable linear regression models were used in analyses. A total of 1,009 participants were included in the primary MRI analysis, 98% of whom were cognitively normal at baseline MRI (mean age = 65.7 years; 54.8% female). Having a sHHV diagnosis (N = 119) was associated with longitudinal reductions in white matter volume (annual additional rate of change -0.34 cm3/y; p = 0.035), particularly in the temporal lobe. However, there was no association between sHHV and changes in total brain, total gray matter, or AD signature region volumes. Among the 119 participants with sHHV, exposure to antiviral treatment attenuated declines in occipital white matter (p = 0.04). Although the sHHV group had higher cognitive scores at baseline, sHHV diagnosis was associated with accelerated longitudinal declines in attention (annual additional rate of change -0.01 Z-score/year; p = 0.008). In addition, sHHV diagnosis was associated with elevated plasma GFAP, but not related to Aβ42/40 and NfL levels. These findings suggest an association of sHHV infection with white matter volume loss, attentional decline, and astrogliosis. Although the findings link sHHV to several neurocognitive features, the results do not support an association between sHHV and AD-specific disease processes.

Association of liver disease with brain volume loss, cognitive decline, and plasma neurodegenerative disease biomarkers

Although liver dysfunction has been implicated in Alzheimer's disease (AD), it remains unknown how liver disease may influence the trajectory of brain and cognitive changes in older adults. We related self-reported liver disease to longitudinal measures of brain structure and cognition, as well as baseline measures of plasma AD/neurodegeneration biomarkers in the Baltimore Longitudinal Study of Aging. Liver disease was identified using ICD-9 classification codes. Brain volume and cognition were assessed serially using 3T-MRI and a cognitive battery. 1008, 2157, and 780 participants were included in the MRI, cognitive, and plasma biomarker analysis, respectively. After adjustment for confounders, liver disease was associated with accelerated decline in total brain and white matter volume, but not total gray matter or AD signature region volume. Although liver disease showed no relationship with domain-specific cognitive decline or plasma biomarkers, participants with a history of hepatitis demonstrated accelerated decline in verbal fluency and elevated neurofilament light. Results suggest all-cause liver disease may accelerate brain volume loss but does not appear to promote AD-specific neurocognitive changes.

The cognitive performance in the Phototest is predictor ofbiological markers of Alzheimer's disease.

The abnormal cerebrospinal fluid levels of biomarkers, such as β-amyloid and phosphorylated tau (pTau), support the biological diagnosis of Alzheimer Disease (AD) independently of its clinical stage. However, this invasive exam cannot be extensively applied and requires previous sound clinical screen that can be based on brief, well validated cognitive tests, such as the Phototest. To evaluate the association of partial (naming [NA], total recall [TR], free recall [FR], and verbal fluency) and total scores of the Phototest with the biological diagnosis of AD and the potential use of this test as a screening tool in the clinical work up. Retrospective study of Individuals attending a Memory Clinic who were applied the Phototest and classified, according to cerebrospinal fluid biomarkers (β-amyloid1-42 and pTau), in the biological AD continuum stage (ContAD) as "no AD" (A-), "AD changes" (A+T-) or "AD" (A+T+). Multivariate analyses were conducted with one fixed factor, ContAD, and partial and total Phototest scores. The area under the receiver operating characteristics curve (AUC) was calculated to estimate the capacity of Phototest scores to predict amyloidosis (A+) and AD. The study included 170 individuals (92 A-, 23 A+T- and 55 A+T+). FR (7.9, 0.01 [F,p]) and TR (8.1, 0.001) scores were associated with ContAD and had a moderate ability (AUC 0.71-0.74) to detect the presence of "A+" or "AD". Partial memory scores of Phototest are associated with ContAD. They predict acceptably the presence of abnormal levels of β-amyloid and AD signature in CSF and can be useful to support further biological diagnostic tests.

Blood Neuroexosomal Mitochondrial Proteins Predict Alzheimer Disease in Diabetes.

There is accumulating evidence that mitochondrial dysfunction is associated with the contribution of diabetes to Alzheimer disease (AD) progression. Neuronal mitochondrial proteins are found in plasma neuronal-derived exosomes (NDEs) at levels that reflect those in brain neurons. Here, we tested the performance of mitochondrial proteins in plasma NDEs to predict cognitive decline and brain injury in participants with diabetes. The study participants with type 2 diabetes mellitus (T2DM) included 41 cognitively normal control subjects, 97 individuals with mild cognitive impairment (MCI) (68 individuals with stable MCI; 29 individuals with progressive MCI), and 36 patients with AD dementia. Plasma neuroexosomal proteins were measured by ELISA kits. Spearman correlation was used to test associations between plasma neuroexosomal mitochondrial proteins and other core biomarkers of AD. Diagnostic accuracy for progressive MCI and AD was obtained for mitochondrial proteins using receiver operating characteristic curve analyses. The associations of mitochondrial proteins with the conversion from MCI to AD were assessed by Cox proportional hazard regression analysis. Plasma levels of neuroexosomal NADH ubiquinone oxidoreductase core subunit S3 (NDUFS3) and succinate dehydrogenase complex subunit B (SDHB) were significantly lower in patients with T2DM with AD dementia and progressive MCI than in cognitively normal subjects (P < 0.001 for both groups). We also found that plasma neuroexosomal NDUFS3 and SDHB levels were lower in progressive MCI subjects than in stable MCI subjects. Both plasma neuroexosomal NDUFS3 and SDHB offer diagnostic utility for AD. Low plasma neuroexosomal SDHB levels significantly predicted conversion from MCI to AD. In addition, low mitochondrial protein levels were associated with the rate of hippocampal and gray matter atrophy and reduced AD signature cortical thickness in progressive MCI over the follow-up period. These data suggest that both plasma neuroexosomal NDUFS3 and SDHB are already increased at the early clinical stage of AD, and indicate the promise of plasma neuroexosomal mitochondrial proteins as diagnostic and prognostic biomarkers for the earliest symptomatic stage of AD in participants with diabetes.

Association of Performance on the Financial Capacity Instrument-Short Form With Brain Amyloid Load and Cortical Thickness in Older Adults.

To investigate the association of the Financial Capacity Instrument-Short Form (FCI-SF) performance and timing total scores with brain β-amyloid and cortical thickness in cognitively unimpaired (CU) (at baseline) older adults. A total of 309 participants (aged 70 years or older) of the Mayo Clinic Study of Aging underwent 11C-Pittsburgh compound B PET amyloid imaging and MRI, and completed the FCI-SF. Abnormal amyloid PET was defined as standardized uptake value ratio ≥1.48 in an Alzheimer disease (AD)-related region of interest and reduced AD signature cortical thickness as ≤2.68 mm (neurodegeneration). A cohort of 218 (of the 309) participants had follow-up visits (every 15 months) with FCI-SF data for longitudinal analysis (number of visits including baseline, median [range]: 2 [2-4]). In the analysis, we used linear regression and mixed-effects models adjusted for age, sex, education, apolipoprotein E ε4 allele status, global cognitive z score, and previous FCI-SF testing. Participants' mean age (SD) was 80.2 (4.8) years (56.3% male individuals). In cross-sectional analysis, abnormal amyloid PET (vs normal) was associated with a lower FCI-SF total score and slower total composite time. In longitudinal analysis, FCI-SF total score declined faster (difference in annualized rate of change, beta coefficient [β] [95% confidence interval (CI)] = -1.123 [-2.086 to -0.161]) and FCI-SF total composite time increased faster (difference in annualized rate of change, β [95% CI] = 16.274 [5.951 to 26.597]) for participants with neurodegeneration at baseline (vs those without). Participants who exhibited both abnormal amyloid PET and neurodegeneration at baseline had a greater increase in total composite time when compared with the group without abnormal amyloid and without neurodegeneration (difference in annualized rate of change, β [95% CI] = 16.750 [3.193 to 30.307]). Performance and processing speed on the FCI-SF were associated with imaging biomarkers of AD pathophysiology in CU (at baseline) older adults. Higher burdens of imaging biomarkers were associated with longitudinal worsening on FCI-SF performance. Additional research is needed to delineate further these associations and their predictive utility at the individual person level.

A 1-year randomized controlled trial of a nutritional blend to improve nutritional biomarkers and prevent cognitive decline among community-dwelling older adults: The Nolan Study.

IntroductionThis study aimed to test the efficacy of a nutritional blend (NB) in improving nutritional biomarkers and preventing cognitive decline among older adults.MethodsA 1‐year randomized, double‐blind, multicenter, placebo‐controlled trial with 362 adults (58.6% female, mean 78.3 years, SD = 4.8) receiving an NB or placebo. Erythrocyte ω‐3 index and homocysteine concentrations were primary outcomes. Other outcomes included Patient‐Reported Outcomes Measurement Information System (PROMIS) Applied Cognition‐Abilities, composite cognitive score (CCS), Cognitive Function Instrument (CFI) self‐assessment and study partner, hippocampal volume (HV), and Alzheimer's disease signature cortical thickness (CT).ResultsA total of 305 subjects completed the follow‐up. Supplementation increased ω‐3 index and decreased homocysteine, but did not affect CCS, CFI self‐assessment, HV, and CT. Placebo improved and treatment did not change PROMIS at 1 month. Intervention showed a positive effect on CFI study partner.DiscussionAlthough improving nutritional biomarkers, this 1‐year trial with a multi‐nutrient novel approach was not able to show effects on cognitive outcomes among older adults.

Toward the Identification of Neurophysiological Biomarkers for Alzheimer's Disease in Down Syndrome: A Potential Role for Cross-Frequency Phase-Amplitude Coupling Analysis.

Cross-frequency coupling (CFC) mechanisms play a central role in brain activity. Pathophysiological mechanisms leading to many brain disorders, such as Alzheimer's disease (AD), may produce unique patterns of brain activity detectable by electroencephalography (EEG). Identifying biomarkers for AD diagnosis is also an ambition among research teams working in Down syndrome (DS), given the increased susceptibility of people with DS to develop early-onset AD (DS-AD). Here, we review accumulating evidence that altered theta-gamma phase-amplitude coupling (PAC) may be one of the earliest EEG signatures of AD, and therefore may serve as an adjuvant tool for detecting cognitive decline in DS-AD. We suggest that this field of research could potentially provide clues to the biophysical mechanisms underlying cognitive dysfunction in DS-AD and generate opportunities for identifying EEG-based biomarkers with diagnostic and prognostic utility in DS-AD.

Chapter 31 - Alzheimer disease and neuroplasticity

Alzheimer's disease (AD) is considered the most harmful form of dementia in the elderly population. At present, there are no effective treatments and this is likely due to the incomplete understanding of the pathophysiology. Recent data indicate that synaptic dysfunction could be a central element of AD pathophysiology. It was found that a synaptic breakdown is an early event that heralds neuronal degeneration. Transcranial magnetic stimulation (TMS) has been recently introduced as a novel approach to identify the early signatures of synaptic dysfunction characterizing AD pathophysiology. In this chapter, we review the new neurophysiologic signatures of AD that have been emphasized by TMS studies. We show how TMS measurement of neuroplasticity identified long-term potentiation (LTP)-like cortical plasticity as a key element of AD synaptic dysfunction. These measurements are useful to increase the accuracy of differential diagnosis, predict disease progression, and anticipate response to therapy. Moreover, enhancing neuroplasticity holds as a promising therapeutic approach to improve cognition in AD. In recent years, studies showed treatments with multiple sessions of rTMS can influence cognition in people with neurodegenerative diseases. In the second part of this chapter, we also consider novel therapeutic approaches based on the clinical use of rTMS.