Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography images from the Alzheimer's Disease Neuroimaging Initiative (ADNI)

Seeing beyond the symptoms: biomarkers and brain regions linked to cognitive decline in Alzheimer's disease

Objective

Early Alzheimer's disease (AD) diagnosis remains challenging, necessitating specific biomarkers for timely detection. This study aimed to identify such biomarkers and explore their associations with cognitive decline.

Methods

A cohort of 1759 individuals across cognitive aging stages, including healthy controls (HC), mild cognitive impairment (MCI), and AD, was examined. Utilizing nine biomarkers from structural MRI (sMRI), diffusion tensor imaging (DTI), and positron emission tomography (PET), predictions were made for Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale Sum of Boxes (CDRSB), and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS). Biomarkers included four sMRI (e.g., average thickness [ATH]), four DTI (e.g., mean diffusivity [MD]), and one PET Amyloid-β (Aβ) measure. Ensemble regression tree (ERT) technique with bagging and random forest approaches were applied in four groups (HC/MCI, HC/AD, MCI/AD, and HC/MCI/AD).

Results

Aβ emerged as a robust predictor of cognitive scores, particularly in late-stage AD. Volumetric measures, notably ATH, consistently correlated with cognitive scores across early and late disease stages. Additionally, ADAS demonstrated links to various neuroimaging biomarkers in all subject groups, highlighting its efficacy in monitoring brain changes throughout disease progression. ERT identified key brain regions associated with cognitive scores, such as the right transverse temporal region for Aβ, left and right entorhinal cortex, left inferior temporal gyrus, and left middle temporal gyrus for ATH, and the left uncinate fasciculus for MD.

Conclusion

This study underscores the importance of an interdisciplinary approach in understanding AD mechanisms, offering potential contributions to early biomarker development.

Diagnostic performance of the cingulate island sign ratio for differentiating dementia with Lewy bodies from Alzheimer's disease changes depending on the mini-mental state examination score

BACKGROUND

The cingulate island sign (CIS) ratio is a diagnostic adjunct for differentiating dementia with Lewy bodies (DLB) from Alzheimer's disease (AD). A recent study showed that the CIS ratio in DLB changed depending on the Mini-Mental State Examination (MMSE) score. We aimed to evaluate whether the diagnostic performance (sensitivity and specificity) of the CIS ratio for differentiating DLB from AD changes depending on the MMSE score.

METHODS

Twenty-two patients with DLB and 26 amyloid-positive patients with AD, who underwent 18F-FDG PET and completed an MMSE examination, were classified into three groups according to MMSE scores: Group A (MMSE >24), Group B (20 ≤ MMSE ≤24), and Group C (MMSE <20). In each group, we compared the CIS ratio between patients with DLB and AD and conducted receiver operating characteristic (ROC) curve analysis to calculate the sensitivity and specificity.

RESULTS

Within Group B, the CIS ratio in DLB was significantly higher than that in AD (p = 0.0005), but not within Groups A (p = 0.5117) and C (p = 0.8671). ROC curve analyses showed that the sensitivities and specificities of the CIS ratio for differentiating DLB from AD were 66.7% and 77.8% in Group A, 91.7% and 100.0% in Group B, and 75.0% and 66.7% in Group C, respectively.

CONCLUSIONS

The present study suggests that the diagnostic performance of the CIS ratio for differentiating DLB from AD changes depending on the MMSE score, with higher sensitivity and specificity at MMSE scores of 20-24.

GLP-1 Analogs, SGLT-2, and DPP-4 Inhibitors: A Triad of Hope for Alzheimer's Disease Therapy

Alzheimer's is a prevalent, progressive neurodegenerative disease marked by cognitive decline and memory loss. The disease's development involves various pathomechanisms, including amyloid-beta accumulation, neurofibrillary tangles, oxidative stress, inflammation, and mitochondrial dysfunction. Recent research suggests that antidiabetic drugs may enhance neuronal survival and cognitive function in diabetes. Given the well-documented correlation between diabetes and Alzheimer's disease and the potential shared mechanisms, this review aimed to comprehensively assess the potential of new-generation anti-diabetic drugs, such as GLP-1 analogs, SGLT-2 inhibitors, and DPP-4 inhibitors, as promising therapeutic approaches for Alzheimer's disease. This review aims to comprehensively assess the potential therapeutic applications of novel-generation antidiabetic drugs, including GLP-1 analogs, SGLT-2 inhibitors, and DPP-4 inhibitors, in the context of Alzheimer's disease. In our considered opinion, antidiabetic drugs offer a promising avenue for groundbreaking developments and have the potential to revolutionize the landscape of Alzheimer's disease treatment.

GlyNAC (Glycine and N-Acetylcysteine) Supplementation in Old Mice Improves Brain Glutathione Deficiency, Oxidative Stress, Glucose Uptake, Mitochondrial Dysfunction, Genomic Damage, Inflammation and Neurotrophic Factors to Reverse Age-Associated Cognitive Decline: Implications for Improving Brain Health in Aging

Cognitive decline frequently occurs with increasing age, but mechanisms contributing to age-associated cognitive decline (ACD) are not well understood and solutions are lacking. Understanding and reversing mechanisms contributing to ACD are important because increased age is identified as the single most important risk factor for dementia. We reported earlier that ACD in older humans is associated with glutathione (GSH) deficiency, oxidative stress (OxS), mitochondrial dysfunction, glucose dysmetabolism and inflammation, and that supplementing GlyNAC (glycine and N-acetylcysteine) improved these defects. To test whether these defects occur in the brain in association with ACD, and could be improved/reversed with GlyNAC supplementation, we studied young (20-week) and old (90-week) C57BL/6J mice. Old mice received either regular or GlyNAC supplemented diets for 8 weeks, while young mice received the regular diet. Cognition and brain outcomes (GSH, OxS, mitochondrial energetics, autophagy/mitophagy, glucose transporters, inflammation, genomic damage and neurotrophic factors) were measured. Compared to young mice, the old-control mice had significant cognitive impairment and multiple brain defects. GlyNAC supplementation improved/corrected the brain defects and reversed ACD. This study finds that naturally-occurring ACD is associated with multiple abnormalities in the brain, and provides proof-of-concept that GlyNAC supplementation corrects these defects and improves cognitive function in aging.

Orthogonal latent space learning with feature weighting and graph learning for multimodal Alzheimer's disease diagnosis

Recent studies have shown that multimodal neuroimaging data provide complementary information of the brain and latent space-based methods have achieved promising results in fusing multimodal data for Alzheimer's disease (AD) diagnosis. However, most existing methods treat all features equally and adopt nonorthogonal projections to learn the latent space, which cannot retain enough discriminative information in the latent space. Besides, they usually preserve the relationships among subjects in the latent space based on the similarity graph constructed on original features for performance boosting. However, the noises and redundant features significantly corrupt the graph. To address these limitations, we propose an Orthogonal Latent space learning with Feature weighting and Graph learning (OLFG) model for multimodal AD diagnosis. Specifically, we map multiple modalities into a common latent space by orthogonal constrained projection to capture the discriminative information for AD diagnosis. Then, a feature weighting matrix is utilized to sort the importance of features in AD diagnosis adaptively. Besides, we devise a regularization term with learned graph to preserve the local structure of the data in the latent space and integrate the graph construction into the learning processing for accurately encoding the relationships among samples. Instead of constructing a similarity graph for each modality, we learn a joint graph for multiple modalities to capture the correlations among modalities. Finally, the representations in the latent space are projected into the target space to perform AD diagnosis. An alternating optimization algorithm with proved convergence is developed to solve the optimization objective. Extensive experimental results show the effectiveness of the proposed method.

Brain Networks, Clinical Manifestations, and Neuroimaging of Cognitive Disorders: The Role of Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Other Advanced Neuroimaging Tests

In this article, we briefly discuss imaging modalities used in clinical settings for neuroanatomical characterization and for diagnosis of the underlying disease. We then discuss how each neuroimaging tool can be used in the context of clinical syndromes. The major underlying causes relevant to our discussion include Alzheimer disease, Lewy body disease, cerebrovascular disease, frontotemporal degeneration, autoimmune diseases, and systemic or metabolic derangements.

Multimodality imaging of neurodegenerative disorders with a focus on multiparametric magnetic resonance and molecular imaging

Neurodegenerative diseases afflict a large number of persons worldwide, with the prevalence and incidence of dementia rapidly increasing. Despite their prevalence, clinical diagnosis of dementia syndromes remains imperfect with limited specificity. Conventional structural-based imaging techniques also lack the accuracy necessary for confident diagnosis. Multiparametric magnetic resonance imaging and molecular imaging provide the promise of improving specificity and sensitivity in the diagnosis of neurodegenerative disease as well as therapeutic monitoring of monoclonal antibody therapy. This educational review will briefly focus on the epidemiology, clinical presentation, and pathologic findings of common and uncommon neurodegenerative diseases. Imaging features of each disease spanning from conventional magnetic resonance sequences to advanced multiparametric methods such as resting-state functional magnetic resonance imaging and arterial spin labeling imaging will be described in detail. Additionally, the review will explore the findings of each diagnosis on molecular imaging including single-photon emission computed tomography and positron emission tomography with a variety of clinically used and experimental radiotracers. The literature and clinical cases provided demonstrate the power of advanced magnetic resonance imaging and molecular techniques in the diagnosis of neurodegenerative diseases and areas of future and ongoing research. With the advent of combined positron emission tomography/magnetic resonance imaging scanners, hybrid protocols utilizing both techniques are an attractive option for improving the evaluation of neurodegenerative diseases.

Differential impact of body mass index and leptin on baseline and longitudinal positron emission tomography measurements of the cerebral metabolic rate for glucose in amnestic mild cognitive impairment

Introduction

Several studies have suggested that greater adiposity in older adults is associated with a lower risk of Alzheimer's disease (AD) related cognitive decline, some investigators have postulated that this association may be due to the protective effects of the adipose tissue-derived hormone leptin. In this study we sought to demonstrate that higher body mass indices (BMIs) are associated with greater baseline FDG PET measurements of the regional cerebral metabolic rate for glucose (rCMRgl), a marker of local neuronal activity, slower rCMRgl declines in research participants with amnestic mild cognitive impairment (aMCI). We then sought to clarify the extent to which those relationships are attributable to cerebrospinal fluid (CSF) or plasma leptin concentrations.

Materials and methods

We used baseline PET images from 716 73 ± 8 years-old aMCI participants from the AD Neuroimaging Initiative (ADNI) of whom 453 had follow up images (≥6 months; mean follow up time 3.3 years). For the leptin analyses, we used baseline CSF samples from 81 of the participants and plasma samples from 212 of the participants.

Results

As predicted, higher baseline BMI was associated with greater baseline CMRgl measurements and slower declines within brain regions preferentially affected by AD. In contrast and independently of BMI, CSF, and plasma leptin concentrations were mainly related to less baseline CMRgl within mesocorticolimbic brain regions implicated in energy homeostasis.

Discussion

While higher BMIs are associated with greater baseline CMRgl and slower declines in persons with aMCI, these associations appear not to be primarily attributable to leptin concentrations.

Prodromal characteristics of dementia with Lewy bodies: baseline results of the MEMENTO memory clinics nationwide cohort

Background

Isolated subjective cognitive impairment (SCI) and mild cognitive impairment (MCI) are the prodromal phases of dementia with Lewy bodies (DLB). MEMENTO is a nationwide study of patients with SCI and MCI with clinic, neuropsychology, biology, and brain imaging data. We aimed to compare SCI and MCI patients with symptoms of prodromal DLB to others in this study at baseline.

Methods

Participants of the French MEMENTO cohort study were recruited for either SCI or MCI. Among them, 892 were included in the Lewy sub-study, designed to search specifically for symptoms of DLB. Probable prodromal DLB diagnosis (pro-DLB group) was done using a two-criteria cutoff score among the four core clinical features of DLB. This Pro-DLB group was compared to two other groups at baseline: one without any core symptoms (NS group) and the one with one core symptom (1S group). A comprehensive cognitive battery, questionnaires on behavior, neurovegetative and neurosensory symptoms, brain 3D volumetric MRI, CSF, FDG PET, and amyloid PET were done.

Results

The pro-DLB group comprised 148 patients (16.6%). This group showed more multidomain (59.8%) MCI with slower processing speed and a higher proportion of patients with depression, anxiety, apathy, constipation, rhinorrhea, sicca syndrome, and photophobia, compared to the NS group. The pro-DLB group had isolated lower P-Tau in the CSF (not significant after adjustments for confounders) and on brain MRI widening of sulci including fronto-insular, occipital, and olfactory sulci (FDR corrected), when compared to the NS group. Evolution to dementia was not different between the three groups over a median follow-up of 2.6 years.

Conclusions

Patients with symptoms of prodromal DLB are cognitively slower, with more behavioral disorders, autonomic symptoms, and photophobia. The occipital, fronto-insular, and olfactory bulb involvement on brain MRI was consistent with symptoms and known neuropathology. The next step will be to study the clinical, biological, and imaging evolution of these patients.

Trial registration

Clinicaltrials.gov, NCT01926249

The road to precision medicine: Eliminating the "One Size Fits All" approach in Alzheimer's disease

The expeditious advancement of Alzheimer's Disease (AD) is a threat to the global healthcare system, that is further supplemented by therapeutic failure. The prevalence of this disorder has been expected to quadrupole by 2050, thereby exerting a tremendous economic pressure on medical sector, worldwide. Thus, there is a dire need of a change in conventional approaches and adopt a novel methodology of disease prevention, treatment and diagnosis. Precision medicine offers a personalized approach to disease management, It is dependent upon genetic, environmental and lifestyle factors associated with the individual, aiding to develop tailored therapeutics. Precision Medicine Initiatives are launched, worldwide, to facilitate the integration of personalized models and clinical medicine. The review aims to provide a comprehensive understanding of the neuroinflammatory processes causing AD, giving a brief overview of the disease interventions. This is further followed by the role of precision medicine in AD, constituting the genetic perspectives, operation of personalized form of medicine and optimization of clinical trials with the 3 R's, showcasing an in-depth understanding of this novel approach in varying aspects of the healthcare industry, to provide an opportunity to the global AD researchers to elucidate suitable therapeutic regimens in clinically and pathologically complex diseases, like AD.

Correlation Between Urine Formaldehyde and Cognitive Abilities in the Clinical Spectrum of Alzheimer’s Disease

Urine-based formaldehyde has been reported to be a potential biomarker for Alzheimer’s disease (AD). However, there is a lack of research about the correlation between urine formaldehyde and cognitive abilities in the clinical spectrum of AD, especially the preclinical period. The relationship of urine formaldehyde with APOE genotype, brain Aβ status and plasma pathological markers in AD are also not clear. This study intends to explore the correlation between urine formaldehyde and cognitive abilities throughout the AD continuum, to evaluate the role of APOE genotype and Aβ accumulation on urine formaldehyde, and further to clarify the relationship between urine formaldehyde level and AD plasma pathological markers. We recruited 72 cognitively normal controls (NC), 110 subjective cognitive decline (SCD), 140 objectively defined subtle cognitive decline (Obj-SCD), 171 mild cognitive impairment (MCI) and 136 AD dementia participants. Next, we collected the data of clinical materials, neuropsychological examination, APOE genotyping, urine formaldehyde concentration, 18F-florbetapir PET imaging and plasma biomarkers. Compared with NC, Obj-SCD and MCI groups, the level of urine formaldehyde was found to be significantly upregulated in SCD group. In addition, the level of urine formaldehyde was significantly higher in AD group compared to both NC and MCI groups. Further subgroup analysis showed that, the level of urine formaldehyde was higher in APOE ε4+ subgroup compared to APOE ε4– subgroup in both NC and AD groups. There was no difference in urine formaldehyde level between the brain Aβ+ subgroup and Aβ– subgroup in each group. In addition, regression analysis showed urine formaldehyde level was correlated with gender, plasma Aβ42 and p-Tau181/T-tau. The dynamic change of urine formaldehyde in the AD continuum could be used as a potential biomarker, and combined with comprehensive cognitive evaluation could become a useful method to distinguish SCD from NC and Obj-SCD, and to distinguish MCI from AD.

Neuroprotective Effect of SGLT2 Inhibitors

Patients with diabetes are at higher risk of cardiovascular diseases and cognitive impairment. SGLT2 inhibitors (Empagliflozin, Canagliflozin, Dapagliflozin, Ertugliflozin, Sotagliflozin) are newer hypoglycemic agents with many pleiotropic effects. In this review, we discuss their neuroprotective potential. SGLT2 inhibitors (SGLT2i) are lipid-soluble and reach the brain/serum ratio from 0.3 to 0.5. SGLT receptors are present in the central nervous system (CNS). Flozins are not fully SGLT2-selective and have an affinity for the SGLT1 receptor, which is associated with protection against ischemia/reperfusion brain damage. SGLT2i show an anti-inflammatory and anti-atherosclerotic effect, including reduction of proinflammatory cytokines, M2 macrophage polarization, JAK2/STAT1 and NLRP3 inflammasome inhibition, as well as cIMT regression. They also mitigate oxidative stress. SGLT2i improve endothelial function, prevent remodeling and exert a protective effect on the neurovascular unit, blood-brain barrier, pericytes, astrocytes, microglia, and oligodendrocytes. Flozins are also able to inhibit AChE, which contributes to cognitive improvement. Empagliflozin significantly increases the level of cerebral BDNF, which modulates neurotransmission and ensures growth, survival, and plasticity of neurons. Moreover, they may be able to restore the circadian rhythm of mTOR activation, which is quite a novel finding in the field of research on metabolic diseases and cognitive impairment. SGLT2i have a great potential to protect against atherosclerosis and cognitive impairment in patients with type 2 diabetes mellitus.

Distinct Functional and Metabolic Alterations of DMN Subsystems in Alzheimer's Disease: A Simultaneous FDG-PET/fMRI Study

The default mode network (DMN) dysfunction has been widely identified in Alzheimer's disease (AD). Increasing evidence has shown that the functional heterogeneity of DMN has been associated with distinct cognitive functions. The pathophysiological changes of these two DMN subsystems, i.e., anterior DMN (aDMN) and posterior DMN (pDMN), also showed different patterns in the AD patients. Yet the underlying metabolic mechanism remains not clear. In this work, we performed a simultaneous FDG-PET/fMRI study, to investigate the distinct functional and metabolic alterations of DMN subsystems in AD. Significantly decreased functional connectivity strength (FCS) in pDMN but not aDMN was found in AD patients. The retaining connectivity in aDMN might represent a compensatory strategy. Concurrently, significant glucose hypometabolism was shown in pDMN and aDMN of AD patients, respectively. Moreover, the reduction of FCS in pDMN was positively correlated with MMSE score of patients. Our study suggests that resting state functional connectivity and glucose metabolism changed differently in the aDMN and pDMN of AD. Our findings brought new insights in understanding the underlying metabolism changes along with functional alterations in AD.

The Neuropsychological Correlates of Brain Perfusion and Gray Matter Volume in Alzheimer's Disease

BACKGROUND

Neuropsychological tests, structural neuroimaging, and functional neuroimaging are employed as diagnostic and monitoring biomarkers of patients with Alzheimer's disease (AD)Objective:We aimed to elucidate the similarities and differences in neuropsychological tests and neuroimaging with the use of the Mini-Mental State Examination (MMSE), Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-cog), structural magnetic resonance image (MRI), and perfusion single photon emission computed tomography (SPECT), and parametric image analyses to understand its role in AD.

METHODS

Clinically-diagnosed AD patients (n = 155) were scanned with three-dimensional T1-weighted MRI and N-isopropyl-p-[123I] iodoamphetamine SPECT. Statistical parametric mapping 12 was used for preprocessing images, statistical analyses, and voxel-based morphometry for gray matter volume analyses. Group comparison (AD versus healthy controls), multiple regression analyses with MMSE, ADAS-cog total score, and ADAS-cog subscores as variables, were performed.

RESULTS

The AD group showed bilateral hippocampal volume reduction and hypoperfusion in the bilateral temporo-parietal lobe and posterior midline structures. Worse MMSE and ADAS-cog total score were associated with bilateral temporo-parietal volume loss and hypoperfusion. MMSE, but not ADAS-cog, was associated with the posterior midline structures. The ADAS-cog subscores were associated with the temporal volume, while perfusion analyses were linked to the left temporo-parietal region with the language function and right analogous region with the constructional praxis subscore.

CONCLUSION

MMSE and ADAS-cog are associated with temporo-parietal regions, both in volume and perfusion. The MMSE score is associated with posterior midline structures and linked to an abnormal diagnostic AD pattern. Perfusion image analyses better represents the cognitive function in AD patients.

The relationship of glucose-stimulated insulin secretion to cerebral glucose metabolism and cognition in healthy middle-aged and older adults

Insulin resistance (IR) has been related to reduced cerebral glucose metabolism in regions identified as hypometabolic in Alzheimer's clinical syndrome. Insulin secretion (IS) has been less studied than IR despite findings that decreased IS is an early indicator of future type 2 diabetes and a potential predictor of Alzheimer's clinical syndrome. We investigated whether higher IR and lower IS would be associated with greater age-related reductions in regional cerebral glucose metabolism and worse cognitive performance. Two-hour oral glucose tolerance testing and 18F-fluorodeoxyglucose positron emission tomography were performed on 1-2 occasions on a sample of healthy middle-aged and older adults from the Wisconsin Alzheimer's Disease Research Center. Neuropsychological tests were completed during Alzheimer's Disease Research Center Clinical Core visits. Pattern of findings suggested that lower (not higher) IS was related to higher regional cerebral glucose metabolism in middle aged but not older adults, and lower (not higher) IS was also related to better immediate recall. In the context of healthy insulin sensitivity, lower IS may be beneficial to brain health.

Glycolytic Metabolism, Brain Resilience, and Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of age-related dementia. Despite decades of research, the etiology and pathogenesis of AD are not well understood. Brain glucose hypometabolism has long been recognized as a prominent anomaly that occurs in the preclinical stage of AD. Recent studies suggest that glycolytic metabolism, the cytoplasmic pathway of the breakdown of glucose, may play a critical role in the development of AD. Glycolysis is essential for a variety of neural activities in the brain, including energy production, synaptic transmission, and redox homeostasis. Decreased glycolytic flux has been shown to correlate with the severity of amyloid and tau pathology in both preclinical and clinical AD patients. Moreover, increased glucose accumulation found in the brains of AD patients supports the hypothesis that glycolytic deficit may be a contributor to the development of this phenotype. Brain hyperglycemia also provides a plausible explanation for the well-documented link between AD and diabetes. Humans possess three primary variants of the apolipoprotein E (ApoE) gene - ApoE∗ϵ2, ApoE∗ϵ3, and ApoE∗ϵ4 - that confer differential susceptibility to AD. Recent findings indicate that neuronal glycolysis is significantly affected by human ApoE isoforms and glycolytic robustness may serve as a major mechanism that renders an ApoE2-bearing brain more resistant against the neurodegenerative risks for AD. In addition to AD, glycolytic dysfunction has been observed in other neurodegenerative diseases, including Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis, strengthening the concept of glycolytic dysfunction as a common pathway leading to neurodegeneration. Taken together, these advances highlight a promising translational opportunity that involves targeting glycolysis to bolster brain metabolic resilience and by such to alter the course of brain aging or disease development to prevent or reduce the risks for not only AD but also other neurodegenerative diseases.

Prediabetes Is Associated With Brain Hypometabolism and Cognitive Decline in a Sex-Dependent Manner: A Longitudinal Study of Nondemented Older Adults

Although type 2 diabetes is a well-known risk factor for Alzheimer's disease (AD), little is known about how its precursor-prediabetes-impacts neuropsychological function and brain health. Thus, we examined the relationship between prediabetes and AD-related biological and cognitive/clinical markers in a well-characterized sample drawn from the Alzheimer's Disease Neuroimaging Initiative. Additionally, because women show higher rates of AD and generally more atherogenic lipid profiles than men, particularly in the context of diabetes, we examined whether sex moderates any observed associations. The total sample of 911 nondemented and non-diabetic participants [normal control = 540; mild cognitive impairment (MCI) = 371] included 391 prediabetic (fasting blood glucose: 100-125 mg/dL) and 520 normoglycemic individuals (age range: 55-91). Linear mixed effects models, adjusted for demographics and vascular and AD risk factors, examined the independent and interactive effects of prediabetes and sex on 2-6 year trajectories of FDG-PET measured cerebral metabolic glucose rate (CMRglu), hippocampal/intracranial volume ratio (HV/IV), cerebrospinal fluid phosphorylated tau-₁₈₁/amyloid-β_1-42 ratio (p-tau₁₈₁/Aβ_1-42), cognitive function (executive function, language, and episodic memory) and the development of dementia. Analyses were repeated in the MCI subsample. In the total sample, prediabetic status had an adverse effect on CMRglu across time regardless of sex, whereas prediabetes had an adverse effect on executive function across time in women only. Within the MCI subsample, prediabetic status was associated with lower CMRglu and poorer executive function and language performance across time within women, whereas these associations were not seen within men. In the total sample and MCI subsample, prediabetes did not relate to HV/IV, p-tau₁₈₁/Aβ_1-42, memory function or dementia risk regardless of sex; however, among incident dementia cases, prediabetic status related to earlier age of dementia onset in women but not in men. Results suggest that prediabetes may affect cognition through altered brain metabolism, and that women may be more vulnerable to the negative effects of glucose intolerance.

Blinded Clinical Evaluation for Dementia of Alzheimer's Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods

BACKGROUND

Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. However, to enable the translation of these methods and test their usefulness in clinical practice, it is crucial to conduct independent validation on real clinical samples, which has yet to be properly delineated in the current literature.

OBJECTIVE

In this paper, we present our efforts to enable such clinical translational through the evaluation and comparison of two machine-learning methods for discrimination between dementia of Alzheimer's type (DAT) and Non-DAT controls.

METHODS

FDG-PET-based dementia scores were generated on an independent clinical sample whose clinical diagnosis was blinded to the algorithm designers. A feature-engineered approach (multi-kernel probability classifier) and a non-feature-engineered approach (3D convolutional neural network) were analyzed. Both classifiers were pre-trained on cognitively normal subjects as well as subjects with DAT. These two methods provided a probabilistic dementia score for this previously unseen clinical data. Performance of the algorithms were compared against ground-truth dementia rating assessed by experienced nuclear physicians.

RESULTS

Blinded clinical evaluation on both classifiers showed good separation between the cognitively normal subjects and the patients diagnosed with DAT. The non-feature-engineered dementia score showed higher sensitivity among subjects whose diagnosis was in agreement between the machine-learning models, while the feature-engineered approach showed higher specificity in non-consensus cases.

CONCLUSION

In this study, we demonstrated blinded evaluation using data from an independent clinical sample for assessing the performance in DAT classification models in a clinical setting. Our results showed good generalizability for two machine-learning approaches, marking an important step for the translation of pre-trained machine-learning models into clinical practice.

Putative Survival Advantages in Young Apolipoprotein ɛ4 Carriers are Associated with Increased Neural Stress

Inheritance of a single copy of the apolipoprotein E (APOE) ɛ4 allele increases risk of Alzheimer’s disease (AD) by 3-4-fold, with homozygosity associated with a 12-16-fold increase in risk, relative to ɛ3 allele homozygosity. There is a decreased risk associated with the APOE ɛ2 allele. The pathological consequence of APOE genotype has led to intense efforts to understand the mechanistic basis of the interplay between APOE status and loss of synapses. Numerous ɛ4 allele-related associations have been reported with the potential relevance of these associations to the pathogenesis of AD unknown at this time. In primarily young subjects, we have reviewed a representative body of literature on ɛ4 allele-associations related to the following: cardiovascular responses; impacts on reproduction and fetal development; co-morbidities; resistance to infectious disease; responses to head injury; biochemical differences possibly related to neural stress; and brain structure-function differences. In addition, the literature on the association between the ɛ4 allele and cognitive performance has been reviewed comprehensively. The weight-of-the-evidence supports the hypothesis that possession of the ancestral ɛ4 allele in youth is associated with improved fitness during fetal development, infancy, and youth relative to the more recently appearing ɛ3 allele, at the expense of decreased fitness in old age, which is substantially improved by the ɛ3 allele. However, possession of the ɛ4 allele is also associated with higher levels of synaptic macromolecular turnover, which likely stresses basic cellular neuroplasticity mechanisms. Clinical trials of potential AD therapeutics should consider APOE status as an enrollment criterion.

Neuronostatin Promotion Soluble Aβ1-42 Oligomers: Induced Dysfunctional Brain Glucose Metabolism in Mice

Neuronostatin (NST) is an endogenous peptide hormone, it has the ability to improve oligomeric Aβ (oAβ)-induced cognitive impairments and increase blood glucose levels in mice. However, the relationship between NST and oAβ regarding brain glucose metabolism has not yet been established. The present study defined the contributions of NST and oAβ in the brain glucose metabolism in mice. It was found that i.c.v. co-administration of NST (3 nmol/mouse) and oAβ (1 nmol/mouse) decreased the mRNA expressions of glucose-6-phosphate dehydrogenase and phosphofructokinase. The treatments were observed to reduce ATP production and the enzyme activities of glucose-6-phosphate dehydrogenase and hexokinase in both the cortex and hippocampus. Simultaneously, co-injection of NST and oAβ inhibited the mRNA and protein expression of glucose transporters GLUT3 and GLUT1 in the cortex and hippocampus. NST promoted the oAβ-induced decreased the cortical NeuN staining, while oAβ increased the levels of NST in both the cortex and hippocampus. I.c.v. co-administration of NST and oAβ led to increase the levels of GPR107 expression and the phosphorylation of PKA, Akt, PERK and eIF-2α in the cortex. These findings suggest that NST promoted oAβ-induced dysfunctional glucose metabolism through the GPR107/PKA/Akt signaling pathway and PERK/eIF2α axis in the brain, which thus contributes to metabolic dysfunction and Alzheimer's disease (AD) pathophysiology.

Ketones improves Apolipoprotein E4-related memory deficiency via sirtuin 3

Background: Apolipoprotein E4 (ApoE4) is the major genetic risk factor of Alzheimer’s disease (AD). ApoE4 carriers have cerebral hypometabolism which is thought as a harbinger of AD. Our previous studies indicated ketones improved mitochondria energy metabolism via sirtuin 3 (Sirt3). However, it is unclear whether ketones upregulate Sirt3 and improve ApoE4-related learning and memory deficits.

Results: Ketones improved learning and memory abilities of ApoE4 mice but not ApoE3 mice. Sirt3, synaptic proteins, the NAD⁺/ NADH ratio, and ATP production were significantly increased in the hippocampus and the cortex from ketone treatment.

Methods: Human ApoE3 and ApoE4 transgenic mice (9-month-old) were treated with either ketones or normal saline by daily subcutaneous injections for 3 months (ketones, beta-hydroxybutyrate (BHB): 600 mg/kg/day; acetoacetate (ACA): 150 mg/kg/day). Learning and memory ability of these mice were assessed. Sirt3 protein, synaptic proteins (PSD95, Synaptophysin), the NAD⁺/ NADH ratio, and ATP levels were measured in the hippocampus and the cortex.

Conclusion: Our current studies suggest that ketones improve learning and memory abilities of ApoE4 transgenic mice. Sirt3 may mediate the neuroprotection of ketones by increasing neuronal energy metabolism in ApoE4 transgenic mice. This provides the foundation for Sirt3’s potential role in the prevention and treatment of AD.

Brain angiotensin II and angiotensin IV receptors as potential Alzheimer's disease therapeutic targets

Alzheimer's disease (AD) is a neurodegenerative disorder that is multifactorial in nature. Yet, despite being the most common form of dementia in the elderly, AD's primary cause remains unknown. As such, there is currently little to offer AD patients as the vast majority of recently tested therapies have either failed in well-controlled clinical trials or inadequately treat AD. Recently, emerging preclinical and clinical evidence has associated the brain renin angiotensin system (RAS) to AD pathology. Accordingly, various components of the brain RAS were shown to be altered in AD patients and mouse models, including the angiotensin II type 1 (AT1R), angiotensin IV receptor (AT4R), and Mas receptors. Collectively, the changes observed within the RAS have been proposed to contribute to many of the neuropathological hallmarks of AD, including the neuronal, cognitive, and vascular dysfunctions. Accumulating evidence has additionally identified antihypertensive medications targeting the RAS, particularly angiotensin receptor blockers (ARBs) and angiotensin-converting enzyme inhibitors (ACEIs), to delay AD onset and progression. In this review, we will discuss the emergence of the RAS's involvement in AD and highlight putative mechanisms of action underlying ARB's beneficial effects that may explain their ability to modify the risk of developing AD or AD progression. The RAS may provide novel molecular targets for recovering memory pathways, cerebrovascular function, and other pathological landmarks of AD.

The Dichotomy of Alzheimer's Disease Pathology: Amyloid-β and Tau

In this issue, an article by Tiepolt et al. shows that PET scanning using [¹¹C]PiB can demonstrate both cerebral blood flow (CBF) changes and amyloid-β (Aβ) deposition in patients with mild cognitive dysfunction or mild dementia of Alzheimer’s disease (AD). The CBF changes can be determined because the early scan counts (1–9 minutes) reflect the flow of the radiotracer in the blood passing through the brain, while the Aβ levels are measured by later scan counts (40–70 minutes) after the radiotracer has been cleared from regions to which the radiotracer did not bind. Thus, two different diagnostic measures are obtained with a single injection. Unexpectedly, the mild patients with Aβ positivity had scan data with only a weak relationship to memory, while the relationships to executive function and language function were relatively strong. This divergence of findings from studies of severely impaired patients highlights the importance of determining how AD pathology affects the brain. A possibility suggested in this commentary is that Aβ deposits occur early in AD and specifically in critical areas of the neocortex affected only later by the neurofibrillary pathology indicating a different role of the amyloid-β protein precursor (AβPP) in the development of those neocortical regions, and a separate component of AD pathology may selectively impact functions of these neocortical regions. The effects of adverse AβPP metabolism in the medial temporal and brainstem regions occur later possibly because of different developmental issues, and the later, different pathology is clearly more cognitively and socially devastating.

An Efficient Combination among sMRI, CSF, Cognitive Score, and APOE ε4 Biomarkers for Classification of AD and MCI Using Extreme Learning Machine

Alzheimer's disease (AD) is the most common cause of dementia and a progressive neurodegenerative condition, characterized by a decline in cognitive function. Symptoms usually appear gradually and worsen over time, becoming severe enough to interfere with individual daily tasks. Thus, the accurate diagnosis of both AD and the prodromal stage (i.e., mild cognitive impairment (MCI)) is crucial for timely treatment. As AD is inherently dynamic, the relationship between AD indicators is unclear and varies over time. To address this issue, we first aimed at investigating differences in atrophic patterns between individuals with AD and MCI and healthy controls (HCs). Then we utilized multiple biomarkers, along with filter- and wrapper-based feature selection and an extreme learning machine- (ELM-) based approach, with 10-fold cross-validation for classification. Increasing efforts are focusing on the use of multiple biomarkers, which can be useful for the diagnosis of AD and MCI. However, optimum combinations have yet to be identified and most multimodal analyses use only volumetric measures obtained from magnetic resonance imaging (MRI). Anatomical structural MRI (sMRI) measures have also so far mostly been used separately. The full possibilities of using anatomical MRI for AD detection have thus yet to be explored. In this study, three measures (cortical thickness, surface area, and gray matter volume), obtained from sMRI through preprocessing for brain atrophy measurements; cerebrospinal fluid (CSF), for quantification of specific proteins; cognitive score, as a measure of cognitive performance; and APOE ε4 allele status were utilized. Our results show that a combination of specific biomarkers performs well, with accuracies of 97.31% for classifying AD vs. HC, 91.72% for MCI vs. HC, 87.91% for MCI vs. AD, and 83.38% for MCIs vs. MCIc, respectively, when evaluated using the proposed algorithm. Meanwhile, the areas under the curve (AUC) from the receiver operating characteristic (ROC) curves combining multiple biomarkers provided better classification performance. The proposed features combination and selection algorithm effectively classified AD and MCI, and MCIs vs. MCIc, the most challenging classification task, and therefore could increase the accuracy of AD classification in clinical practice. Furthermore, we compared the performance of the proposed method with SVM classifiers, using a cross-validation method with Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets.

An update on blood-based biomarkers for non-Alzheimer neurodegenerative disorders

Cerebrospinal fluid analyses and neuroimaging can identify the underlying pathophysiology at the earliest stage of some neurodegenerative disorders, but do not have the scalability needed for population screening. Therefore, a blood-based marker for such pathophysiology would have greater utility in a primary care setting and in eligibility screening for clinical trials. Rapid advances in ultra-sensitive assays have enabled the levels of pathological proteins to be measured in blood samples, but research has been predominantly focused on Alzheimer disease (AD). Nonetheless, proteins that were identified as potential blood-based biomarkers for AD, for example, amyloid-β, tau, phosphorylated tau and neurofilament light chain, are likely to be relevant to other neurodegenerative disorders that involve similar pathological processes and could also be useful for the differential diagnosis of clinical symptoms. This Review outlines the neuropathological, clinical, molecular imaging and cerebrospinal fluid features of the most common neurodegenerative disorders outside the AD continuum and gives an overview of the current status of blood-based biomarkers for these disorders.

Effects of the 5-HT2A and DRD3 genotypes on cortical morphology and functional connectivity density in drug-naïve first episode schizophrenia

The 5-hydroxytryptamine 2A receptor (5-HT2A) and dopamine D3 receptor (DRD3) have been extensively studied as promising candidate genes for schizophrenia. Magnetic resonance imaging studies have demonstrated that schizophrenia is associated with widespread structural and functional abnormalities in the brain. Serotonin and dopamine receptors play crucial roles in the development of the human cerebral cortex and brain activity. However, how the 5-HT2A and DRD3 genes impact brain structure and function in schizophrenia remains unknown. In the present study, we investigated the main effect of disease state and the interaction effect between disease state and genotype of these two genes on cortical volume, thickness, surface area and functional connectivity density (FCD) in fifty-five drug-naïve first episode schizophrenia patients and fifty-three healthy controls. We found that the differences in local FCD (lFCD) and global FCD (gFCD) between patients and healthy controls were predominantly located in brain hub regions. The significant interaction effects of disease state and 5-HT2A and DRD3 genes on brain structure and function were mainly located in the temporal cortex. Our findings may help to improve the understanding of the relationship between 5-HT2A and DRD3 genotypes and schizophrenia pathogenesis.

Interaction Between BDNF Val66Met and APOE4 on Biomarkers of Alzheimer's Disease and Cognitive Decline

BACKGROUND

Whether brain-derived neurotrophic factor (BDNF) Met carriage impacts the risk or progression of Alzheimer's disease (AD) is unknown.

OBJECTIVE

To evaluate the interaction of BDNF Met and APOE4 carriage on cerebral metabolic rate for glucose (CMRgl), amyloid burden, hippocampus volume, and cognitive decline among cognitively unimpaired (CU) adults enrolled in the Arizona APOE cohort study.

METHODS

114 CU adults (mean age 56.85 years, 38% male) with longitudinal FDG PET, magnetic resonance imaging, and cognitive measures were BDNF and APOE genotyped. A subgroup of 58 individuals also had Pittsburgh B (PiB) PET imaging. We examined baseline CMRgl, PiB PET amyloid burden, CMRgl, and hippocampus volume change over time, and rate of change in cognition over an average of 15 years.

RESULTS

Among APOE4 carriers, BDNF Met carriers had significantly increased amyloid deposition and accelerated CMRgl decline in regions typically affected by AD, but without accompanying acceleration of cognitive decline or hippocampal volume changes and with higher baseline frontal CMRgl and slower frontal decline relative to the Val/Val group. The BDNF effects were not found among APOE4 non-carriers.

CONCLUSION

Our preliminary studies suggest that there is a weak interaction between BDNF Met and APOE4 on amyloid-β plaque burden and longitudinal PET measurements of AD-related CMRgl decline in cognitively unimpaired late-middle-aged and older adults, but with no apparent effect upon rate of cognitive decline. We suggest that cognitive effects of BDNF variants may be mitigated by compensatory increases in frontal brain activity-findings that would need to be confirmed in larger studies.

Apolipoprotein E regulates mitochondrial function through the PGC-1α-sirtuin 3 pathway

Cerebral hypometabolism is a pathophysiological hallmark of Alzheimer’s disease (AD). Our previous studies found that a mitochondrial protein, sirtuin3 (Sirt3), was down-regulated in human AD postmortem brains. Sirt3 protected neurons against oligo-amyloid β-42 induced hypometabolism in human Apolipoprotein E4 (ApoE4) transgenic mice. However, how ApoE affects mitochondrial function and its proteins such as Sirt3 remains unclear.

We characterized and compared levels of Sirt3 and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α, a Sirt3 activator), oxidative stress proteins, synaptic proteins, cognitive task performance and ATP production in 12-month old human ApoE4 and ApoE3 transgenic mice, and assessed changes in Sirt3 expression on cellular metabolism in primary neurons from ApoE4 and ApoE3 transgenic mice.

Compared to ApoE3 mice, Sirt3 and PGC-1α levels were significantly lower in ApoE4 mice. Learning and memory, synaptic proteins, the NAD+/ NADH ratios, and ATP production were significantly lower in ApoE4 mice as well. Sirt3 knockdown reduced the oxygen consumption and ATP production in primary neurons with the human ApoE3, while Sirt3 overexpression protected these damages in ApoE4 neurons.

Our findings suggest that ApoE4 suppresses mitochondrial function via the PGC-1α- Sirt3 pathway. This discovery provides us novel therapeutic targets for the treatment and prevention of AD.

Metabolic Syndrome, Brain Insulin Resistance, and Alzheimer's Disease: Thioredoxin Interacting Protein (TXNIP) and Inflammasome as Core Amplifiers

Empirical evidence indicates a strong association between insulin resistance and pathological alterations related to Alzheimer's disease (AD) in different cerebral regions. While cerebral insulin resistance is not essentially parallel with systemic metabolic derangements, type 2 diabetes mellitus (T2DM) has been established as a risk factor for AD. The circulating "toxic metabolites" emerging in metabolic syndrome may engage several biochemical pathways to promote oxidative stress and neuroinflammation leading to impair insulin function in the brain or "type 3 diabetes". Thioredoxin-interacting protein (TXNIP) as an intracellular amplifier of oxidative stress and inflammasome activation may presumably mediate central insulin resistance. Emerging data including those from our recent studies has demonstrated a sharp TXNIP upregulation in stroke, aging and AD and well underlining the significance of this hypothesis. With the main interest to illustrate TXNIP place in type 3 diabetes, the present review primarily briefs the potential mechanisms contributing to cerebral insulin resistance in a metabolically deranged environment. Then with a particular focus on plausible TXNIP functions to drive and associate with AD pathology, we present the most recent evidence supporting TXNIP as a promising therapeutic target in AD as an age-associated dementia.

Can the emerging field of immunometabolism provide insights into neuroinflammation?

In the past few years it has become increasingly clear that an understanding of the interaction between metabolism and immune function can provide an insight into cellular responses to challenges. Significant progress has been made in terms of how macrophages are metabolically re-programmed in response to inflammatory stimuli but, to date, little emphasis has been placed on evaluating equivalent changes in microglia. The need to make progress is driven by the fact that, while microglial activation and the cell's ability to adopt an inflammatory phenotype is necessary to fulfil the neuroprotective function of the cell, persistent activation of microglia and the associated neuroinflammation is at the heart of several neurodegenerative diseases. Understanding the metabolic changes that accompany microglial responses may broaden our perspective on how dysfunction might arise and be tempered. This review will evaluate the current literature that addresses the interplay between inflammation and metabolic reprogramming in microglia, reflecting on the parallels that exist with macrophages. It will consider the changes that take place with age including those that have been reported in neurons and astrocytes with the development of non-invasive imaging techniques, and reflect on the literature that is currently available relating to metabolic reprogramming of microglia with age and in neurodegeneration. Finally it will consider the possibility that manipulating microglial metabolism may provide a valuable approach to modulating neuroinflammation.

Decoupling of Local Metabolic Activity and Functional Connectivity Links to Amyloid in Alzheimer's Disease

BACKGROUND

Both ongoing local metabolic activity (LMA) and corresponding functional connectivity (FC) with remote brain regions are progressively impaired in Alzheimer's disease (AD), particularly in the posterior default mode network (pDMN); however, it is unknown how these impairments interact. It is well known that decreasing mean synaptic activity of a region, i.e., decreasing LMA, reduces the region's sensitivity to afferent input from other regions, i.e., FC.

OBJECTIVE

We hypothesized progressive decoupling between LMA and FC in AD, which is linked to amyloid-β pathology (Aβ).

METHODS

Healthy adults (n=20) and Aβ+patients without memory impairment (n=9), early MCI (n=21), late MCI (n=18) and AD (n=22) were assessed by resting-state fMRI, FDG-PET, and AV-45-PET to measure FC, LMA, and Aβ of the pDMN. Coupling between LMA and FC (rLA/FC) was estimated by voxelwise correlation.

RESULTS

RLMA/FC decreased with disease severity (F=20.09, p<0.001). This decrease was specifically associated with pDMN Aβ (r=-0.273, p=0.029) but not global Aβ (r=-0.112, p=0.378) and with the impact of Aβ on FC (i.e., rAβ/FC,r=-0.339; p=0.006). In multiple regression models rLMA/FC was also associated with memory impairment, reduced cognitive speed and flexibility, outperforming global Aβ, pDMN Aβ, pDMN LMA, and pDMN FC, respectively.

CONCLUSION

Results demonstrate increasing decoupling of LMA from its FC in AD. Data suggest that decoupling is driven by local Aβ and contributes to memory decline.

Alzheimer's Disease: From Firing Instability to Homeostasis Network Collapse

Alzheimer's disease (AD) starts from pure cognitive impairments and gradually progresses into degeneration of specific brain circuits. Although numerous factors initiating AD have been extensively studied, the common principles underlying the transition from cognitive deficits to neuronal loss remain unknown. Here we describe an evolutionarily conserved, integrated homeostatic network (IHN) that enables functional stability of central neural circuits and safeguards from neurodegeneration. We identify the critical modules comprising the IHN and propose a central role of neural firing in controlling the complex homeostatic network at different spatial scales. We hypothesize that firing instability and impaired synaptic plasticity at early AD stages trigger a vicious cycle, leading to dysregulation of the whole IHN. According to this hypothesis, the IHN collapse represents the major driving force of the transition from early memory impairments to neurodegeneration. Understanding the core elements of homeostatic control machinery, the reciprocal connections between distinct IHN modules, and the role of firing homeostasis in this hierarchy has important implications for physiology and should offer novel conceptual approaches for AD and other neurodegenerative disorders.

Predicting Imminent Progression to Clinically Significant Memory Decline Using Volumetric MRI and FDG PET

BACKGROUND

Brain imaging measurements can provide evidence of possible preclinical Alzheimer's disease (AD). Their ability to predict individual imminent clinical conversion remains unclear.

OBJECTIVE

To investigate the ability of pre-specified volumetric magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) measurements to predict which cognitively unimpaired older participants would subsequently progress to amnestic mild cognitive impairment (aMCI) within 2 years.

METHODS

From an apolipoprotein E4 (APOE4) enriched prospective cohort study, 18 participants subsequently progressed to the clinical diagnosis of aMCI or probable AD dementia within 1.8±0.8 years (progressors); 20 participants matched for sex, age, education, and APOE allele dose remained cognitively unimpaired for at least 4 years (nonprogressors). A complementary control group not matched for APOE allele dose included 35 nonprogressors. Groups were compared on baseline FDG-PET and MRI measures known to be preferentially affected in the preclinical and clinical stages of AD and by voxel-wise differences in regional gray matter volume and glucose metabolism. Receiver Operating Characteristic, binary logistic regression, and leave-one-out procedures were used to predict clinical outcome for the a priori measures.

RESULTS

Compared to non-progressors and regardless of APOE-matching, progressors had significantly reduced baseline MRI and PET measurements in brain regions preferentially affected by AD and reduced hippocampal volume was the strongest predictor of an individual's imminent progression to clinically significant memory decline (79% sensitivity/78% specificity among APOE-matched cohorts).

CONCLUSION

Regional MRI and FDG-PET measurements may be useful in predicting imminent progression to clinically significant memory decline.

Brain insulin resistance and altered brain glucose are related to memory impairments in schizophrenia

Memory is robustly impaired in schizophrenia (SZ) and related to functional outcome. Memory dysfunction has been shown to be related to altered brain glucose metabolism and brain insulin resistance in animal models and human studies of Alzheimer's disease. In this study, differences in brain glucose using magnetic resonance spectroscopy (MRS) and blood Extracellular Vesicle (EV) biomarkers of neuronal insulin resistance (i.e. Akt and signaling effectors) between SZ and controls were investigated, as well as whether these measures were related to memory impairments. Neuronal insulin resistance biomarkers showed a trend for being lower in SZ compared to controls, and memory measures were lower in SZ compared to controls. Occipital cortex glucose was higher in SZ compared to controls indicating lower brain glucose utilization. Linear regression analyses revealed significant relationships between neuronal insulin resistance biomarkers, memory measures, and brain glucose. More specifically, p70S6K, an insulin signaling effector, was related to verbal learning and brain MRS glucose in the SZ group. For the first time, we show that memory impairments in SZ may be related to brain glucose and brain insulin resistance. These data suggest that brain insulin resistance may play a role in the pathophysiology of learning and memory dysfunction in SZ.

Neuroimaging in aging and neurologic diseases

Neuroimaging biomarkers for neurologic diseases are important tools, both for understanding pathology associated with cognitive and clinical symptoms and for differential diagnosis. This chapter explores neuroimaging measures, including structural and functional measures from magnetic resonance imaging (MRI) and molecular measures primarily from positron emission tomography (PET), in healthy aging adults and in a number of neurologic diseases. The spectrum covers neuroimaging measures from normal aging to a variety of dementias: late-onset Alzheimer's disease [AD; including mild cognitive impairment (MCI)], familial and nonfamilial early-onset AD, atypical AD syndromes, posterior cortical atrophy (PCA), logopenic aphasia (lvPPA), cerebral amyloid angiopathy (CAA), vascular dementia (VaD), sporadic and familial behavioral-variant frontotemporal dementia (bvFTD), semantic dementia (SD), progressive nonfluent aphasia (PNFA), frontotemporal dementia with motor neuron disease (FTD-MND), frontotemporal dementia with amyotrophic lateral sclerosis (FTD-ALS), corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), dementia with Lewy bodies (DLB), Parkinson's disease (PD) with and without dementia, and multiple systems atrophy (MSA). We also include a discussion of the appropriate use criteria (AUC) for amyloid imaging and conclude with a discussion of differential diagnosis of neurologic dementia disorders in the context of neuroimaging.

Radiogenomics for Precision Medicine With a Big Data Analytics Perspective

Precision medicine promises better healthcare delivery by improving clinical practice. Using evidence-based substratification of patients, the objective is to achieve better prognosis, diagnosis, and treatment that will transform existing clinical pathways toward optimizing care for the specific needs of each patient. The wealth of today's healthcare data, often characterized as big data, provides invaluable resources toward new knowledge discovery that has the potential to advance precision medicine. The latter requires interdisciplinary efforts that will capitalize the information, know-how, and medical data of newly formed groups fusing different backgrounds and expertise. The objective of this paper is to provide insights with respect to the state-of-the-art research in precision medicine. More specifically, our goal is to highlight the fundamental challenges in emerging fields of radiomics and radiogenomics by reviewing the case studies of Cancer and Alzheimer's disease, describe the computational challenges from a big data analytics perspective, and discuss standardization and open data initiatives that will facilitate the adoption of precision medicine methods and practices.

Cognitive, behavioral and metabolic effects of oral galactose treatment in the transgenic Tg2576 mice

Alzheimer's disease (AD) is the most common neurodegenerative disorder associated with insulin resistance and glucose hypometabolism in the brain. Oral administration of galactose, a nutrient that provides an alternative source of energy, prevents and ameliorates early cognitive impairment in a streptozotocin-induced model (STZ-icv) of the sporadic AD (sAD). Here we explored the influence of 2-month oral galactose treatment (200 mg/kg/day) in the familial AD (fAD) by using 5- (5M) and 10- (10M) month-old transgenic Tg2576 mice mimicking the presymptomatic and the mild stage of fAD, and compared it to that observed in 7-month old STZ-icv rats mimicking mild-to-moderate sAD. Cognitive and behavioral performance was tested by Morris Water Maze, Open Field and Elevated Plus Maze tests, and metabolic status by intraperitoneal glucose tolerance test and fluorodeoxyglucose Positron-Emission Tomography scan. The level of insulin, glucagon-like peptide-1 (GLP-1) and soluble amyloid β1-42 (sAβ1-42) was measured by ELISA and the protein expression of insulin receptor (IR), glycogen synthase kinase-3β (GSK-3β), and pre-/post-synaptic markers by Western blot analysis. Although galactose normalized alterations in cerebral glucose metabolism in all Tg2576 mice (5M+2M; 10M+2M) and STZ-icv rats, it did not improve cognitive impairment in either model. Improvement of reduced grooming behavior and normalization in reduced plasma insulin levels were seen only in 5M+2M Tg2576 mice while in 10M+2M Tg2576 mice oral galactose induced metabolic exacerbation at the level of plasma insulin, GLP-1 homeostasis and glucose intolerance, and additionally increased hippocampal sAβ1-42 level, decreased IR expression and increased GSK-3β activity. The results indicate that therapeutic potential of oral galactose seems to depend on the stage and the type/model of AD and to differ in the absence and the presence of AD-like pathology.

Prognosis of conversion of mild cognitive impairment to Alzheimer's dementia by voxel-wise Cox regression based on FDG PET data

Aim

The value of ¹⁸F-fluorodeoxyglucose (FDG) PET for the prognosis of conversion from mild cognitive impairment (MCI) to Alzheimer's dementia (AD) is controversial. In the present work, the identification of cerebral metabolic patterns with significant prognostic value for conversion of MCI patients to AD is investigated with voxel-based Cox regression, which in contrast to common categorical comparisons also utilizes time information.

Methods

FDG PET data of 544 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were randomly split into two equally-sized datasets (training and test). Within a median follow-up duration of 47 months (95% CI: 46–48 months) 181 patients developed AD. In the training dataset, voxel-wise Cox regressions were used to identify regions associated with conversion of MCI to AD. These were compared to regions identified by a classical group comparison (analysis of covariance (ANCOVA) with statistical parametric mapping (SPM) 8) between converters and non-converters (both adjusted for apolipoprotein E (APOE) genotype, mini-mental state examination (MMSE) score, age, sex and education). In the test dataset, normalized FDG uptake within significant brain regions from voxel-wise Cox- and ANCOVA analyses (Cox- and ANCOVA- regions of interest (ROI), respectively) and clinical variables APOE status, MMSE score and education were tested in different Cox models (adjusted for age, sex) including: (1) only clinical variables, (2) only normalized FDG uptake in ANCOVA-ROI, (3) only normalized FDG uptake from Cox-ROI, (4) clinical variables plus FDG uptake in ANCOVA-ROI, (5) clinical variables plus FDG uptake from Cox-ROI.

Results

Conversion-related regions with relative hypometabolism comprised parts of the temporo-parietal and posterior cingulate cortex/precuneus for voxel-wise ANCOVA, plus frontal regions for voxel-wise Cox regression (both p < .01, false discovery rate (FDR) corrected). The clinical-only model (1) and the models based on normalized FDG uptake from Cox-ROI only (2) and ANCOVA-ROI only (3) all significantly predicted conversion to AD (Wald Test (WT): p < .001). The clinical model (1) was significantly improved by adding imaging information in model (4) (Akaike information criterion (AIC) relative likelihood (RL) (1) vs (4): RL < 0.018). There were no significant differences between models (2) and (3), as well as (4) and (5).

Conclusions

Voxel-wise Cox regression identifies conversion-related patterns of cerebral glucose metabolism, but is not superior to classical group contrasts in this regard. With imaging information from both FDG PET patterns, the prediction of conversion to AD was improved.

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Voxel-wise Cox regression identifies regions relevant for development AD.

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Hypometabolism of these regions poses a significant hazard for AD development.

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Inclusion of FDG PET data improves the accuracy of prognosis significantly.

Diabetes and Alzheimer's Disease: A Link not as Simple as it Seems

Type 2 diabetes mellitus is associated with an increased risk to develop Alzheimer disease, however, the underlying mechanisms for this association are still unclear. In this review we will provide a critical overview of the major findings coming from clinical studies and animal models.

Imaging and Molecular Mechanisms of Alzheimer's Disease: A Review

Alzheimer's disease is the most common form of dementia and is a significant burden for affected patients, carers, and health systems. Great advances have been made in understanding its pathophysiology, to a point that we are moving from a purely clinical diagnosis to a biological one based on the use of biomarkers. Among those, imaging biomarkers are invaluable in Alzheimer's, as they provide an in vivo window to the pathological processes occurring in Alzheimer's brain. While some imaging techniques are still under evaluation in the research setting, some have reached widespread clinical use. In this review, we provide an overview of the most commonly used imaging biomarkers in Alzheimer's disease, from molecular PET imaging to structural MRI, emphasising the concept that multimodal imaging would likely prove to be the optimal tool in the future of Alzheimer's research and clinical practice.

Longitudinal effects of aging on 18F-FDG distribution in cognitively normal elderly individuals

Previous studies of aging effects on fluorine-18-labeled fluorodeoxyglucose (¹⁸F-FDG) distribution have employed cross-sectional designs. We examined aging effects on ¹⁸F-FDG distribution using both cross-sectional and longitudinal assessments. We obtained two ¹⁸F-FDG positron emission tomography scans at two different time points from 107 cognitively normal elderly participants. The participants’ mean ages at baseline and second scans were 67.9 and 75.7, respectively. The follow-up period ranged from 4 to 11 years with a mean of 7.8 years. The voxel-wise analysis revealed significant clusters in which ¹⁸F-FDG uptake was decreased between baseline and second scans (p < 0.05, family-wise error corrected) in the anterior cingulate cortex (ACC), posterior cingulate cortex/precuneus (PCC/PC), and lateral parietal cortex (LPC). The cross-sectional analysis of ¹⁸F-FDG uptake and age showed significant correlations in the ACC (p = 0.016) but not the PCC/PC (p = 0.240) at baseline, and in the ACC (p = 0.004) and PCC/PC (p = 0.002) at the second scan. The results of longitudinal assessments suggested that ¹⁸F-FDG uptake in the ACC, PCC/PC, and LPC decreased with advancing age in cognitively normal elderly individuals, and those of the cross-sectional assessments suggested that the trajectories of age-associated ¹⁸F-FDG decreases differed between the ACC and PCC/PC.

Altered functional connectivity of the default mode network by glucose loading in young, healthy participants

Background

The functional connectivity of the default mode network (DMN) decreases in patients with Alzheimer’s disease (AD) as well as in patients with type 2 diabetes mellitus (T2DM). Altered functional connectivity of the DMN is associated with cognitive impairment. T2DM is a known cause of cognitive dysfunction and dementia in the elderly, and studies have established that T2DM is a risk factor for AD. In addition, recent studies with positron emission tomography demonstrated that increased plasma glucose levels decrease neuronal activity, especially in the precuneus/posterior cingulate cortex (PC/PCC), which is the functional core of the DMN. These findings prompt the question of how increased plasma glucose levels decrease neuronal activity in the PC/PCC. Given the association among DMN, AD, and T2DM, we hypothesized that increased plasma glucose levels decrease the DMN functional connectivity, thus possibly reducing PC/PCC neuronal activity. We conducted this study to test this hypothesis.

Results

Twelve young, healthy participants without T2DM and insulin resistance were enrolled in this study. Each participant underwent resting-state functional magnetic resonance imaging in both fasting and glucose loading conditions to evaluate the DMN functional connectivity. The results showed that the DMN functional connectivity in the PC/PCC was significantly lower in the glucose loading condition than in the fasting condition (P = 0.014).

Conclusions

Together with previous findings, the present results suggest that decreased functional connectivity of the DMN is possibly responsible for reduced PC/PCC neuronal activity in healthy individuals with increased plasma glucose levels.

Cerebral glucose metabolic prediction from amnestic mild cognitive impairment to Alzheimer's dementia: a meta-analysis

Brain ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET) has been utilized to monitor disease conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer’s dementia (AD). However, the conversion patterns of FDG-PET metabolism across studies are not conclusive. We conducted a voxel-wise meta-analysis using Seed-based d Mapping that included 10 baseline voxel-wise FDG-PET comparisons between 93 aMCI converters and 129 aMCI non-converters from nine longitudinal studies. The most robust and reliable metabolic alterations that predicted conversion from aMCI to AD were localized in the left posterior cingulate cortex (PCC)/precuneus. Furthermore, meta-regression analyses indicated that baseline mean age and severity of cognitive impairment, and follow-up duration were significant moderators for metabolic alterations in aMCI converters. Our study revealed hypometabolism in the left PCC/precuneus as an early feature in the development of AD. This finding has important implications in understanding the neural substrates for AD conversion and could serve as a potential imaging biomarker for early detection of AD as well as for tracking disease progression at the predementia stage.

Biomarkers in Alzheimer's, Frontotemporal, Lewy Body, and Vascular Dementias

This article reviews the current evidence base for biomarkers of the most common causes of dementia in later life: Alzheimer's disease (AD), frontotemporal lobar degenerations, Lewy body dementias, and vascular cognitive impairment and dementia. Biomarkers are objectively measurable indicators of normal physiology, pathological processes, or response to an intervention. Ideally, they are sensitive, specific, easy to obtain, and closely reflect the underlying biological processes of interest. While such markers are well established and in broad clinical use for common disorders in general medicine (e.g., thallium stress tests for coronary artery disease or serum blood urea nitrogen and creatinine for renal failure), analogous, validated markers for AD or other common dementias are limited, although biomarkers in research settings and specialty dementia clinics are progressing toward clinical use. By way of introducing current and future biomarkers for dementias of later life, this article will benefit the practicing clinician by increasing awareness of the availability and utility of current and emerging biomarkers in dementia diagnosis and prognosis and for monitoring new disease-modifying therapeutics that arrive in the clinic over the coming decade.