Plasma trimethylamine N-oxide (TMAO): associations with cognition, neuroimaging, and dementia.

BACKGROUND: The gut-derived metabolite Trimethylamine N-oxide (TMAO) and its precursors - betaine, carnitine, choline, and deoxycarnitine - have been associated with an increased risk of cardiovascular disease, but their relation to cognition, neuroimaging markers, and dementia remains uncertain. METHODS: In the population-based Rotterdam Study, we used multivariable regression models to study the associations between plasma TMAO, its precursors, and cognition in 3,143 participants. Subsequently, we examined their link to structural brain MRI markers in 2,047 participants, with a partial validation in the Leiden Longevity Study (n = 318). Among 2,517 participants, we assessed the risk of incident dementia using multivariable Cox proportional hazard models. Following this, we stratified the longitudinal associations by medication use and sex, after which we conducted a sensitivity analysis for individuals with impaired renal function. RESULTS: Overall, plasma TMAO was not associated with cognition, neuroimaging markers or incident dementia. Instead, higher plasma choline was significantly associated with poor cognition (adjusted mean difference: -0.170 [95% confidence interval (CI) -0.297;-0.043]), brain atrophy and more markers of cerebral small vessel disease, such as white matter hyperintensity volume (0.237 [95% CI: 0.076;0.397]). By contrast, higher carnitine concurred with lower white matter hyperintensity volume (-0.177 [95% CI: -0.343;-0.010]). Only among individuals with impaired renal function, TMAO appeared to increase risk of dementia (hazard ratio (HR): 1.73 [95% CI: 1.16;2.60]). No notable differences were observed in stratified analyses. CONCLUSIONS: Plasma choline, as opposed to TMAO, was found to be associated with cognitive decline, brain atrophy, and markers of cerebral small vessel disease. These findings illustrate the complexity of relationships between TMAO and its precursors, and emphasize the need for concurrent study to elucidate gut-brain mechanisms.

A multimodal machine learning model for predicting dementia conversion in Alzheimer's disease.

Alzheimer's disease (AD) accounts for 60-70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10-15% of people annually convert to AD. We aimed to investigate the most robust model and modality combination by combining multi-modality image features based on demographic characteristics in six machine learning models. A total of 196 subjects were enrolled from four hospitals and the Alzheimer's Disease Neuroimaging Initiative dataset. During the four-year follow-up period, 47 (24%) patients progressed from MCI to AD. Volumes of the regions of interest, white matter hyperintensity, and regional Standardized Uptake Value Ratio (SUVR) were analyzed using T1, T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRIs, and amyloid PET (αPET), along with automatically provided hippocampal occupancy scores (HOC) and Fazekas scales. As a result of testing the robustness of the model, the GBM model was the most stable, and in modality combination, model performance was further improved in the absence of T2-FLAIR image features. Our study predicts the probability of AD conversion in MCI patients, which is expected to be useful information for clinician's early diagnosis and treatment plan design.

Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction.

Predicting the course of neurodegenerative disorders early has potential to greatly improve clinical management and patient outcomes. A key challenge for early prediction in real-world clinical settings is the lack of labeled data (i.e., clinical diagnosis). In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i.e., trajectories) and stratifies individuals without using clinical diagnosis for model training. MTM learns the relationship between states comprising expensive, invasive biomarkers (ß-amyloid, grey matter density) and readily obtainable cognitive observations. MTM training on trajectories stratifies individuals into clinically meaningful clusters more reliably than MTM training on baseline data alone and is robust to missing data (i.e., cognitive data alone or single assessments). Extracting an individualized cognitive health index (i.e., MTM-derived cluster membership index) allows us to predict progression to AD more precisely than standard clinical assessments (i.e., cognitive tests or MRI scans alone). Importantly, MTM generalizes successfully from research cohort to real-world clinical data from memory clinic patients with missing data, enhancing the clinical utility of our approach. Thus, our multimodal trajectory modeling approach provides a cost-effective and non-invasive tool for early dementia prediction without labeled data (i.e., clinical diagnosis) with strong potential for translation to clinical practice.

Neuroinflammation in dementia: A meta-analysis of PET imaging studies.

BACKGROUND: Dementia is a major public health challenge for aging societies worldwide. Neuroinflammation is thought to be a key factor in dementia development. The aim of this study was to comprehensively assess translocator protein (TSPO) expression by positron emission tomography (PET) imaging to reveal the characteristics of neuroinflammation in dementia. METHODS: We used a meta-analysis to retrieve literature on TSPO expression in dementia using PET imaging technology, including but not limited to the quality of the study design, sample size, and the type of TSPO ligand used in the study. For the included studies, we extracted key data, including TSPO expression levels, clinical characteristics of the study participants, and specific information on brain regions. Meta-analysis was performed using R software to assess the relationship between TSPO expression and dementia. RESULTS: After screening, 12 studies that met the criteria were included. The results of the meta-analysis showed that the expression level of TSPO was significantly elevated in patients with dementia, especially in the hippocampal region. The OR in the hippocampus was 1.50 with a 95% CI of 1.09 to 1.25, indicating a significant increase in the expression of TSPO in this region compared to controls. Elevated levels of inflammation in the prefrontal lobe and cingulate gyrus are associated with cognitive impairment in patients. This was despite an OR of 1.00 in the anterior cingulate gyrus, indicating that TSPO expression in this region did not correlate significantly with the findings. The overall heterogeneity test showed I² = 51%, indicating moderate heterogeneity. CONCLUSION: This study summarizes the existing literature on TSPO expression in specific regions of the brain in patients with dementia, and also provides some preliminary evidence on the possible association between neuroinflammation and dementia. However, the heterogeneity of results and limitations of the study suggest that we need to interpret these findings with caution. Future studies need to adopt a more rigorous and consistent methodological design to more accurately assess the role of neuroinflammation in dementia, thereby providing a more reliable evidence base for understanding pathological mechanisms and developing potential therapeutic strategies.

MRI-derived brain iron, grey matter volume, and risk of dementia and Parkinson's disease: Observational and genetic analysis in the UK Biobank cohort.

BACKGROUND: Iron overload is observed in neurodegenerative diseases, especially Alzheimer's disease (AD) and Parkinson's disease (PD). Homozygotes for the iron-overload (haemochromatosis) causing HFE p.C282Y variant have increased risk of dementia and PD. Whether brain iron deposition is causal or secondary to the neurodegenerative processes in the general population is unclear. METHODS: We analysed 39,533 UK Biobank participants of European genetic ancestry with brain MRI data. We studied brain iron estimated by R2* and quantitative susceptibility mapping (QSM) in 8 subcortical regions: accumbens, amygdala, caudate, hippocampus, pallidum, putamen, substantia nigra, and thalamus. We performed genome-wide associations studies (GWAS) and used Mendelian Randomization (MR) methods to estimate the causal effect of brain iron on grey matter volume, and risk of AD, non-AD and PD. We also used MR to test whether genetic liability to AD or PD causally increased brain iron (R2* and QSM). FINDINGS: In GWAS of R2* and QSM we replicated 83% of previously reported genetic loci and identified 174 further loci across all eight brain regions. Higher genetically predicted brain iron, using both R2* and QSM, was associated with lower grey matter volumes in the caudate, putamen and thalamus (e.g., Beta-putamenQSM: -0.37, p = 2*10-46). Higher genetically predicted thalamus R2* was associated with increased risk of non-AD dementia (OR 1.36(1.16;1.60), p = 2*10-4) but not AD (p > 0.05). In males, genetically predicted putamen R2* increased non-AD dementia risk, but not in females. Higher genetically predicted iron in the caudate, putamen, and substantia nigra was associated with an increased risk of PD (Odds Ratio QSM âˆ¼ substantia-nigra 1.21(1.07;1.37), p = 0.003). Genetic liability to AD or PD was not associated with R2* or QSM in the dementia or PD-associated regions. INTERPRETATION: Our genetic analysis supports a causal effect of higher iron deposition in specific subcortical brain regions for Parkinson's disease, grey matter volume, and non-Alzheimer's dementia.

Hypoperfusion in Alzheimer's Disease-Prone Regions and Dementia Conversion in Parkinson's Disease.

PURPOSE OF THE REPORT: Although early detection of individuals at risk of dementia conversion is important in patients with Parkinson's disease (PD), there is still no consensus on neuroimaging biomarkers for predicting future cognitive decline. We aimed to investigate whether cerebral perfusion patterns on early-phase 18 F-N-(3-fluoropropyl)-2ß-carboxymethoxy-3ß-(4-iodophenyl) nortropane ( 18 F-FP-CIT) PET have the potential to serve as a neuroimaging predictor for early dementia conversion in patients with PD. MATERIALS AND METHODS: In this retrospective analysis, we enrolled 187 patients with newly diagnosed PD who underwent dual-phase 18 F-FP-CIT PET at initial assessment and serial cognitive assessments during the follow-up period (>5 years). Patients with PD were classified into 2 groups: the PD with dementia (PDD)-high-risk (PDD-H; n = 47) and the PDD-low-risk (PDD-L; n = 140) groups according to dementia conversion within 5 years of PD diagnosis. We explored between-group differences in the regional uptake in the early-phase 18 F-FP-CIT PET images. We additionally performed a linear discriminant analysis to develop a prediction model for early PDD conversion. RESULTS: The PDD-H group exhibited hypoperfusion in Alzheimer's disease (AD)-prone regions (inferomedial temporal and posterior cingulate cortices, and insula) compared with the PDD-L group. A prediction model using regional uptake in the right entorhinal cortex, left amygdala, and left isthmus cingulate cortex could optimally distinguish the PDD-H group from the PDD-L group. CONCLUSIONS: Regional hypoperfusion in the AD-prone regions on early-phase 18 F-FP-CIT PET can be a useful biomarker for predicting early dementia conversion in patients with PD.

Radiographers' experiences in working with patients living with dementia in Norway - A qualitative study.

INTRODUCTION: Imaging departments are seeing an increase in the number of patients living with dementia (PWD), driven by the ageing population and diagnostic benefits offered by medical imaging. This study explored radiographers' experiences during imaging examinations for PWD. METHODS: A semi-structured interview guide comprising questions about radiographers' experiences, knowledge concerning PWD, challenges faced, and departmental initiatives was developed. Eight radiographers were interviewed, four working in MRI or general imaging, including CT and four in nuclear medicine, at three hospital trusts in Norway. Data analysis was conducted using inductive content analysis as described by Elo and Kyngäs, following a three-step process of preparation, organising and reporting. The qualified radiographers coded, categorised, and defined the themes and sub-themes to report on the findings. RESULTS: Three main categories emerged: 1. Radiographers' experiences, which included overall challenges and the radiographers' attitudes. 2. Measures undertaken, outlining the actions radiographers take during procedures, and 3.Competencies, highlighting the knowledge possessed by radiographers. Organisational challenges, such as the absence of overarching protocols and insufficient training for radiographers related to PWD, posed difficulties in effectively conducting procedures. Creating a calm environment, collaborating with caregivers, scheduling adequate time for examinations, and possessing good communication skills were viewed as facilitators for conducting examinations successfully. CONCLUSION: Radiographers perceived imaging of patients living with dementia to be generally uncomplicated. However, challenges in planning for and communicating with patients, particularly for advanced examinations or acute settings, were reported. Establishing dementia-friendly departments and training radiographers in specific communication techniques could be beneficial. IMPLICATIONS FOR PRACTICE: There is a need for more dementia-friendly imaging departments and communication training for radiographers working with PWD.

Union is strength: the combination of radiomics features and 3D-deep learning in a sole model increases diagnostic accuracy in demented patients: a whole brain 18FDG PET-CT analysis.

OBJECTIVE: FDG PET imaging plays a crucial role in the evaluation of demented patients by assessing regional cerebral glucose metabolism. In recent years, both radiomics and deep learning techniques have emerged as powerful tools for extracting valuable information from medical images. This article aims to provide a comparative analysis of radiomics features, 3D-deep learning convolutional neural network (CNN) and the fusion of them, in the evaluation of 18F-FDG PET whole brain images in patients with dementia and normal controls. METHODS: 18F-FDG brain PET and clinical score were collected in 85 patients with dementia and 125 healthy controls (HC). Patients were assigned to various form of dementia on the basis of clinical evaluation, follow-up and voxels comparison with HC using a two-sample Student's t -test, to determine the regions of brain involved. Radiomics analysis was performed on the whole brain after normalization to an optimized template. After selection using the minimum redundancy maximum relevance method and Pearson's correlation coefficients, the features obtained were added to a neural network model to find the accuracy in classifying HC and demented patients. Forty subjects not included in the training were used to test the models. The results of the three models (radiomics, 3D-CNN, combined model) were compared with each other. RESULTS: Four radiomics features were selected. The sensitivity was 100% for the three models, but the specificity was higher with radiomics and combined one (100% vs. 85%). Moreover, the classification scores were significantly higher using the combined model in both normal and demented subjects. CONCLUSION: The combination of radiomics features and 3D-CNN in a single model, applied to the whole brain 18FDG PET study, increases the accuracy in demented patients.

Risk of conversion to mild cognitive impairment or dementia among subjects with amyloid and tau pathology: a systematic review and meta-analysis.

BACKGROUND: Measurement of beta-amyloid (Aß) and phosphorylated tau (p-tau) levels offers the potential for early detection of neurocognitive impairment. Still, the probability of developing a clinical syndrome in the presence of these protein changes (A+ and T+) remains unclear. By performing a systematic review and meta-analysis, we investigated the risk of mild cognitive impairment (MCI) or dementia in the non-demented population with A+ and A- alone and in combination with T+ and T- as confirmed by PET or cerebrospinal fluid examination. METHODS: A systematic search of prospective and retrospective studies investigating the association of Aß and p-tau with cognitive decline was performed in three databases (MEDLINE via PubMed, EMBASE, and CENTRAL) on January 9, 2024. The risk of bias was assessed using the Cochrane QUIPS tool. Odds ratios (OR) and Hazard Ratios (HR) were pooled using a random-effects model. The effect of neurodegeneration was not studied due to its non-specific nature. RESULTS: A total of 18,162 records were found, and at the end of the selection process, data from 36 cohorts were pooled (n= 7,793). Compared to the unexposed group, the odds ratio (OR) for conversion to dementia in A+ MCI patients was 5.18 [95% CI 3.93; 6.81]. In A+ CU subjects, the OR for conversion to MCI or dementia was 5.79 [95% CI 2.88; 11.64]. Cerebrospinal fluid Aß42 or Aß42/40 analysis and amyloid PET imaging showed consistent results. The OR for conversion in A+T+ MCI subjects (11.60 [95% CI 7.96; 16.91]) was significantly higher than in A+T- subjects (2.73 [95% CI 1.65; 4.52]). The OR for A-T+ MCI subjects was non-significant (1.47 [95% CI 0.55; 3.92]). CU subjects with A+T+ status had a significantly higher OR for conversion (13.46 [95% CI 3.69; 49.11]) than A+T- subjects (2.04 [95% CI 0.70; 5.97]). Meta-regression showed that the ORs for Aß exposure decreased with age in MCI. (beta = -0.04 [95% CI -0.03 to -0.083]). CONCLUSIONS: Identifying Aß-positive individuals, irrespective of the measurement technique employed (CSF or PET), enables the detection of the most at-risk population before disease onset, or at least at a mild stage. The inclusion of tau status in addition to Aß, especially in A+T+ cases, further refines the risk assessment. Notably, the higher odds ratio associated with Aß decreases with age. TRIAL REGISTRATION: The study was registered in PROSPERO (ID: CRD42021288100).

Volumetric brain MRI signatures of heart failure with preserved ejection fraction in the setting of dementia.

Heart failure with preserved ejection fraction (HFpEF) is an important, emerging risk factor for dementia, but it is not clear whether HFpEF contributes to a specific pattern of neuroanatomical changes in dementia. A major challenge to studying this is the relative paucity of datasets of patients with dementia, with/without HFpEF, and relevant neuroimaging. We sought to demonstrate the feasibility of using modern data mining tools to create and analyze clinical imaging datasets and identify the neuroanatomical signature of HFpEF-associated dementia. We leveraged the bioinformatics tools at Vanderbilt University Medical Center to identify patients with a diagnosis of dementia with and without comorbid HFpEF using the electronic health record. We identified high resolution, clinically-acquired neuroimaging data on 30 dementia patients with HFpEF (age 76.9 ± 8.12 years, 61% female) as well as 301 age- and sex-matched patients with dementia but without HFpEF to serve as comparators (age 76.2 ± 8.52 years, 60% female). We used automated image processing pipelines to parcellate the brain into 132 structures and quantify their volume. We found six regions with significant atrophy associated with HFpEF: accumbens area, amygdala, posterior insula, anterior orbital gyrus, angular gyrus, and cerebellar white matter. There were no regions with atrophy inversely associated with HFpEF. Patients with dementia and HFpEF have a distinct neuroimaging signature compared to patients with dementia only. Five of the six regions identified in are in the temporo-parietal region of the brain. Future studies should investigate mechanisms of injury associated with cerebrovascular disease leading to subsequent brain atrophy.

Relationship of Perivascular Space Markers With Incident Dementia in Cerebral Small Vessel Disease.

BACKGROUND: Recent studies, using diffusion tensor image analysis along the perivascular space (DTI-ALPS), suggest impaired perivascular space (PVS) function in cerebral small vessel disease, but they were cross-sectional, making inferences on causality difficult. We determined associations between impaired PVS, measured using DTI-ALPS and PVS volume, and cognition and incident dementia. METHODS: In patients with lacunar stroke and confluent white matter hyperintensities, without dementia at baseline, recruited prospectively in a single center, magnetic resonance imaging was performed annually for 3 years, and cognitive assessments, including global, memory, executive function, and processing speed, were performed annually for 5 years. We determined associations between DTI-ALPS and PVS volume with cerebral small vessel disease imaging markers (white matter hyperintensity volume, lacunes, and microbleeds) at baseline and with changes in imaging markers. We determined whether DTI-ALPS and PVS volume at baseline and change over 3 years predicted incident dementia. Analyses were controlled for conventional diffusion tensor image metrics using 2 markers (median mean diffusivity [MD] and peak width of skeletonized MD) and adjusted for age, sex, and vascular risk factors. RESULTS: A total of 120 patients, mean age 70.0 years and 65.0% male, were included. DTI-ALPS declined over 3 years, while no change in PVS volume was found. Neither DTI-ALPS nor PVS volume was associated with cerebral small vessel disease imaging marker progression. Baseline DTI-ALPS was associated with changes in global cognition (ß=0.142, P=0.032), executive function (ß=0.287, P=0.027), and long-term memory (ß=0.228, P=0.027). Higher DTI-ALPS at baseline predicted a lower risk of dementia (hazard ratio, 0.328 [0.183-0.588]; P<0.001), and this remained significant after including median MD as a covariate (hazard ratio, 0.290 [0.139-0.602]; P<0.001). Change in DTI-ALPS predicted dementia conversion (hazard ratio, 0.630 [0.428-0.964]; P=0.048), but when peak width of skeletonized MD and median MD were entered as covariates, the association was not significant. There was no association between baseline PVS volume, or PVS change over 3 years, and conversion to dementia. CONCLUSIONS: DTI-ALPS predicts future dementia risk in patients with lacunar strokes and confluent white matter hyperintensities. However, the weakening of the association between change in DTI-ALPS and incident dementia after controlling for peak width of skeletonized MD and median MD suggests part of the signal may represent conventional diffusion tensor image metrics. PVS volume is not a predictor of future dementia risk.

MRI index of glymphatic system mediates the influence of locus coeruleus on cognition in Parkinson's disease.

INTRODUCTION: Although locus coeruleus (LC) has been demonstrated to play a critical role in the cognitive function of Parkinson's disease (PD), the underlying mechanism has not been elucidated. The objective was to investigate the relationship among LC degeneration, cognitive performance, and the glymphatic function in PD. METHODS: In this retrospective study, 71 PD subjects (21 with normal cognition; 29 with cognitive impairment (PD-MCI); 21 with dementia (PDD)) and 26 healthy controls were included. All participants underwent neuromelanin-sensitive magnetic resonance imaging (NM-MRI) and diffusion tensor image scanning on a 3.0 T scanner. The brain glymphatic function was measured using diffusion along the perivascular space (ALPS) index, while LC degeneration was estimated using the NM contrast-to-noise ratio of LC (CNRLC). RESULTS: The ALPS index was significantly lower in both the whole PD group (P = 0.04) and the PDD subgroup (P = 0.02) when compared to the controls. Similarly, the CNRLC was lower in the whole PD group (P < 0.001) compared to the controls. In the PD group, a positive correlation was found between the ALPS index and both the Montreal Cognitive Assessment (MoCA) score (r = 0.36; P = 0.002) and CNRLC (r = 0.26; P = 0.03). Mediation analysis demonstrated that the ALPS index acted as a significant mediator between CNRLC and the MoCA score in PD subjects. CONCLUSION: The ALPS index, a neuroimaging marker of glymphatic function, serves as a mediator between LC degeneration and cognitive function in PD.

Real-word application of the AT(N) classification and disease-modifying treatment eligibility in a hospital-based cohort.

BACKGROUND AND OBJECTIVES: The AT(N) classification system stratifies patients based on biomarker profiles, including amyloid-beta deposition (A), tau pathology (T), and neurodegeneration (N). This study aims to apply the AT(N) classification to a hospital-based cohort of patients with cognitive decline and/or dementia, within and outside the Alzheimer's disease (AD) continuum, to enhance our understanding of the multidimensional aspects of AD and related disorders. Furthermore, we wish to investigate how many cases from our cohort would be eligible for the available disease modifying treatments, such as aducanemab and lecanemab. METHODS: We conducted a retrospective evaluation of 429 patients referred to the Memory Center of IRCCS San Raffaele Hospital in Milan. Patients underwent clinical/neuropsychological assessments, lumbar puncture, structural brain imaging, and positron emission tomography (FDG-PET). Patients were stratified according to AT(N) classification, group comparisons were performed and the number of eligible cases for anti-ß amyloid monoclonal antibodies was calculated. RESULTS: Sociodemographic and clinical features were similar across groups. The most represented group was A + T + N + accounting for 38% of cases, followed by A + T - N + (21%) and A - T - N + (20%). Although the clinical presentation was similar, the A + T + N + group showed more severe cognitive impairment in memory, language, attention, executive, and visuospatial functions compared to other AT(N) groups. Notably, T + patients demonstrated greater memory complaints compared to T - cases. FDG-PET outperformed MRI and CT in distinguishing A + from A - patients. Although 61% of the observed cases were A + , only 17% of them were eligible for amyloid-targeting treatments. DISCUSSION: The AT(N) classification is applicable in a real-world clinical setting. The classification system provided insights into clinical management and treatment strategies. Low cognitive performance and specific regional FDG-PET hypometabolism at diagnosis are highly suggestive for A + T + or A - T + profiles. This work provides also a realistic picture of the proportion of AD patients eligible for disease modifying treatments emphasizing the need for early detection.

Brain artery diameters and risk of dementia and stroke.

INTRODUCTION: We tested the association of brain artery diameters with dementia and stroke risk in three distinct population-based studies using conventional T2-weighted brain magnetic resonance imaging (MRI) images. METHODS: We included 8420 adults > 40 years old from three longitudinal population-based studies with brain MRI scans. We estimated and meta-analyzed the hazard ratios (HRs) of the brain and carotids and basilar diameters associated with dementia and stroke. RESULT: Overall and carotid artery diameters > 95th percentile increased the risk for dementia by 1.74 (95% confidence interval [CI], 1.13-2.68) and 1.48 (95% CI, 1.12-1.96) fold, respectively. For stroke, meta-analyses yielded HRs of 1.59 (95% CI, 1.04-2.42) for overall arteries and 2.11 (95% CI, 1.45-3.08) for basilar artery diameters > 95th percentile. DISCUSSION: Individuals with dilated brain arteries are at higher risk for dementia and stroke, across distinct populations. Our findings underline the potential value of T2-weighted brain MRI-based brain diameter assessment in estimating the risk of dementia and stroke.

The usefulness of the retina for identifying people with type 2 diabetes with prodromal stages of dementia.

Type 2 diabetes (T2D) is associated with cognitive impairment and dementia. The detection of cognitive impairment is important because this population is at higher risk of experiencing difficulties in the self-management of diabetes. Mild cognitive impairment (MCI) often remains undiagnosed due to lack of simple tools for screening at large scale. This represents an important gap in the patients' management because subjects with diabetes and MCI are at high risk of progressing to dementia. Due to its developmental origin as a brain-derived tissue, the retina has been proposed as a potential means of non-invasive and readily accessible exploration of brain pathology. Recent evidence showed that retinal imaging and/or functional tests are correlated with the cognitive function and brain changes in T2D. Simple retinal functional tests (i.e. retinal microperimetry) have proven to be useful as reliable tool for the cognitive evaluation and monitoring in patients with T2D>65 years. This review gives an overall update on the usefulness of retinal imaging in identifying patients with T2D at risk of developing dementia.

Infection, delirium, and risk of dementia in patients with and without white matter disease on previous brain imaging: a population-based study.

BACKGROUND: The increased risk of dementia after delirium and infection might be influenced by cerebral white matter disease (WMD). In patients with transient ischaemic attack (TIA) and minor stroke, we assessed associations between hospital admissions with delirium and 5-year dementia risk and between admissions with infection and dementia risk, stratified by WMD severity (moderate or severe vs absent or mild) on baseline brain imaging. METHODS: We included patients with TIA and minor stroke (National Institutes of Health Stroke Score <3) from the Oxford Vascular Study (OXVASC), a longitudinal population-based study of the incidence and outcomes of acute vascular events in a population of 94 567 individuals, with no age restrictions, attending eight general practices in Oxfordshire, UK. Hospitalisation data were obtained through linkage to the Oxford Cognitive Comorbidity, Frailty, and Ageing Research Database-Electronic Patient Records (ORCHARD-EPR). Brain imaging was done using CT and MRI, and WMD was prospectively graded according to the age-related white matter changes (ARWMC) scale and categorised into absent, mild, moderate, or severe WMD. Delirium and infection were defined by ICD-10 coding supplemented by hand-searching of hospital records. Dementia was diagnosed using clinical or cognitive assessment, medical records, and death certificates. Associations between hospitalisation with delirium and hospitalisation with infection, and post-event dementia were assessed using time-varying Cox analysis with multivariable adjustment, and all models were stratified by WMD severity. FINDINGS: From April 1, 2002, to March 31, 2012, 1369 individuals were prospectively recruited into the study. Of 1369 patients (655 with TIA and 714 with minor stroke, mean age 72 [SD 13] years, 674 female and 695 male, and 364 with moderate or severe WMD), 209 (15%) developed dementia. Hospitalisation during follow-up occurred in 891 (65%) patients of whom 103 (12%) had at least one delirium episode and 236 (26%) had at least one infection episode. Hospitalisation without delirium or infection did not predict subsequent dementia (HR 1·01, 95% CI 0·86-1·20). In contrast, hospitalisation with delirium predicted subsequent dementia independently of infection in patients with and without WMD (2·64, 1·47-4·74; p=0·0013 vs 3·41, 1·91-6·09; p<0·0001) especially in those with unimpaired baseline cognition (cognitive test score above cutoff; 4·01, 2·23-7·19 vs 3·94, 1·95-7·93; both p≤0·0001). However, hospitalisation with infection only predicted dementia in those with moderate or severe WMD (1·75, 1·04-2·94 vs 0·68, 0·39-1·20; pdiff=0·023). INTERPRETATION: The increased risk of dementia after delirium is unrelated to the presence of WMD, whereas infection increases risk only in patients with WMD, suggesting differences in underlying mechanisms and in potential preventive strategies. FUNDING: National Institute for Health and Care Research and Wellcome Trust.

Long-Term Longitudinal Course of Cognitive and Motor Symptoms in Patients With Cerebral Small Vessel Disease.

BACKGROUND AND OBJECTIVES: Patients with cerebral small vessel disease (SVD) show a heterogenous clinical course. The aim of the current study was to investigate the longitudinal course of cognitive and motor function in patients who developed parkinsonism, dementia, both, or none. METHODS: Participants were from the Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort study, a prospective cohort of patients with SVD. Parkinsonism and dementia were, respectively, diagnosed according to the UK Parkinson's Disease Society brain bank criteria and the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria for major neurocognitive disorder. Linear and generalized linear mixed-effect analyses were used to study the longitudinal course of motor and cognitive tasks. RESULTS: After a median follow-up of 12.8 years (interquartile range 10.2-15.3), 132 of 501 (26.3%) participants developed parkinsonism, dementia, or both. Years before diagnosis of these disorders, participants showed distinct clinical trajectories from those who developed none: Participant who developed parkinsonism had an annual percentage of 22% (95% CI 18%-27%) increase in motor part of the Unified Parkinson's Disease Rating Scale score. This was significantly higher than the 16% (95% CI 14%-18%) of controls, mainly because of a steep increase in bradykinesia and posture and gait disturbances. When they developed dementia as well, the increase in Timed Up and Go Test time of 0.73 seconds per year (95% CI 0.58-0.87) was significantly higher than the 0.20 seconds per year increase (95% CI 0.16-0.23) of controls. All groups, including the participants who developed parkinsonism without dementia, showed a faster decline in executive function compared with controls: Annual decline in Z-score was -0.07 (95% CI -0.10 to -0.05), -0.09 (95% CI -0.11 to -0.08), and -0.11 (95% CI -0.14 to -0.08) for participants who developed, respectively, parkinsonism, dementia, and both parkinsonism and dementia. These declines were all significantly faster than the annual decline in Z-score of 0.07 (95% CI -0.10 to -0.05) of controls. DISCUSSION: A distinct pattern in deterioration of clinical markers is visible in patients with SVD, years before the diagnosis of parkinsonism and dementia. This knowledge aids early identification of patients with a high risk of developing these disorders.

Diagnostic performance of molecular imaging methods in predicting the progression from mild cognitive impairment to dementia: an updated systematic review.

PURPOSE: Epidemiological and logistical reasons are slowing the clinical validation of the molecular imaging biomarkers in the initial stages of neurocognitive disorders. We provide an updated systematic review of the recent advances (2017-2022), highlighting methodological shortcomings. METHODS: Studies reporting the diagnostic accuracy values of the molecular imaging techniques (i.e., amyloid-, tau-, [18F]FDG-PETs, DaT-SPECT, and cardiac [123I]-MIBG scintigraphy) in predicting progression from mild cognitive impairment (MCI) to dementia were selected according to the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) method and evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Main eligibility criteria were as follows: (1) ≥ 50 subjects with MCI, (2) follow-up ≥ 3 years, (3) gold standard: progression to dementia or diagnosis on pathology, and (4) measures of prospective accuracy. RESULTS: Sensitivity (SE) and specificity (SP) in predicting progression to dementia, mainly to Alzheimer's dementia were 43-100% and 63-94% for [18F]FDG-PET and 64-94% and 48-93% for amyloid-PET. Longitudinal studies were lacking for less common disorders (Dementia with Lewy bodies-DLB and Frontotemporal lobe degeneration-FTLD) and for tau-PET, DaT-SPECT, and [123I]-MIBG scintigraphy. Therefore, the accuracy values from cross-sectional studies in a smaller sample of subjects (n > 20, also including mild dementia stage) were chosen as surrogate outcomes. DaT-SPECT showed 47-100% SE and 71-100% SP in differentiating Lewy body disease (LBD) from non-LBD conditions; tau-PET: 88% SE and 100% SP in differentiating DLB from Posterior Cortical Atrophy. [123I]-MIBG scintigraphy differentiated LBD from non-LBD conditions with 47-100% SE and 71-100% SP. CONCLUSION: Molecular imaging has a moderate-to-good accuracy in predicting the progression of MCI to Alzheimer's dementia. Longitudinal studies are sparse in non-AD conditions, requiring additional efforts in these settings.

Early prediction of dementia using fMRI data with a graph convolutional network approach.

Objective. Alzheimer's disease is a progressive neurodegenerative dementia that poses a significant global health threat. It is imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions to prevent further deterioration of dementia. This study focuses on the early prediction of dementia utilizing Magnetic Resonance Imaging (MRI) data, using the proposed Graph Convolutional Networks (GCNs).Approach. Specifically, we developed a functional connectivity (FC) based GCN framework for binary classifications using resting-state fMRI data. We explored different types and processing methods of FC and evaluated the performance on the OASIS-3 dataset. We developed the GCN model for two different purposes: (1) MCI diagnosis: classifying MCI from normal controls (NCs); and (2) dementia risk prediction: classifying NCs from subjects who have the potential for developing MCI but have not been clinically diagnosed as MCI.Main results. The results of the experiments revealed several important findings: First, the proposed GCN outperformed both the baseline GCN and Support Vector Machine (SVM). It achieved the best average accuracy of 80.3% (11.7% higher than the baseline GCN and 23.5% higher than SVM) and the highest accuracy of 91.2%. Secondly, the GCN framework with (absolute) individual FC performed slightly better than that with global FC generally. However, GCN using global graphs with appropriate connectivity can achieve equivalent or superior performance to individual graphs in some cases, which highlights the significance of suitable connectivity for achieving performance. Additionally, the results indicate that the self-network connectivity of specific brain network regions (such as default mode network, visual network, ventral attention network and somatomotor network) may play a more significant role in GCN classification.Significance. Overall, this study offers valuable insights into the application of GCNs in brain analysis and early diagnosis of dementia. This contributes significantly to the understanding of MCI and has substantial potential for clinical applications in early diagnosis and intervention for dementia and other neurodegenerative diseases. Our code for GCN implementation is available at:https://github.com/Shuning-Han/FC-based-GCN.

Common infections and neuroimaging markers of dementia in three UK cohort studies.

INTRODUCTION: We aimed to investigate associations between common infections and neuroimaging markers of dementia risk (brain volume, hippocampal volume, white matter lesions) across three population-based studies. METHODS: We tested associations between serology measures (pathogen serostatus, cumulative burden, continuous antibody responses) and outcomes using linear regression, including adjustments for total intracranial volume and scanner/clinic information (basic model), age, sex, ethnicity, education, socioeconomic position, alcohol, body mass index, and smoking (fully adjusted model). Interactions between serology measures and apolipoprotein E (APOE) genotype were tested. Findings were meta-analyzed across cohorts (Nmain  = 2632; NAPOE-interaction  = 1810). RESULTS: Seropositivity to John Cunningham virus associated with smaller brain volumes in basic models (ß = -3.89 mL [-5.81, -1.97], Padjusted  < 0.05); these were largely attenuated in fully adjusted models (ß = -1.59 mL [-3.55, 0.36], P = 0.11). No other relationships were robust to multiple testing corrections and sensitivity analyses, but several suggestive associations were observed. DISCUSSION: We did not find clear evidence for relationships between common infections and markers of dementia risk. Some suggestive findings warrant testing for replication.