Predictor Of Cognitive Decline Research Articles

Can Depressive Symptomatology at Diagnosis Predict Cognitive and Functional Decline Over 1 Year in Rural Canadian Patients With Dementia?

Depressive symptomatology is often associated with the onset of dementia, although the exact form and directionality of this association is still unclear. The aim of this study is to investigate whether depressive symptomatology at the time of dementia diagnosis was predictive of cognitive, functional, and behavioral decline over 1 year. In a Rural and Remote Memory Clinic, 375 patients consecutively diagnosed with mild cognitive impairment, Alzheimer disease, or non-Alzheimer disease dementia completed the Center for Epidemiological Studies Depression Scale at first visit and 1-year follow-up to assess depressive symptomatology. The same cohort was evaluated for cognitive, functional, and behavioral decline through the completion of 5 clinical tests performed at the first visit and at 1-year follow-up. Depressive symptomatology at time of dementia diagnosis did not predict cognitive or functional decline over 1 year, although increases in depressive symptomatology over 1 year significantly correlated with higher caregiver ratings of neuropsychiatric symptom severity and related distress over that time. Increasingly severe depressive symptomatology over 1 year correlated with greater caregiver distress. This study points the way for future studies delineating the relationship between depression, dementia progression, and caregiver distress.

The Early Detection of Neurodegenerative diseases initiative: an international and multidisciplinary effort for transforming the early detection of dementia‐causing diseases

AbstractBackgroundAround 50 million people have dementia worldwide, with nearly 10 million new cases every year. Diagnosis is complex and often relies on expensive and invasive measures, with most patients accessing medical support when they already experience symptoms.MethodThe Early Detection of Neurodegenerative diseases (EDoN) initiative, spearheaded by Alzheimer’s Research UK, brings together over 60 experts from 49 universities, research projects, patient cohorts and technology providers to create machine learning models to detect the earliest stages of dementia‐causing diseases. EDoN has reviewed behavioural and physiological modalities with the strongest association with pre‐clinical disease.ResultOver 140 modalities were identified from the review and were shortlisted to create the version 1 digital toolkit. This first version includes Mezurio and Longevity smartphone apps, a Fitbit charge 4 activity tracker and Dreem 3 sleep headband. This Toolkit was further refined through patient and public involvement studies and collects 26 measures related to 7 aspects of behaviour and physiology (cognition, neural activity, physical activity, heart rate, fine motor movement, sleep, language and speech). The Toolkit is now being used to collect digital data in four international cohorts (Boston University Alzheimer’s Disease Research Center ‐ BU ADRC; The predictors of COgnitive DECline in attenders of memory clinic using digital devices ‐ CODEC‐2; Western Australia Memory Study ‐ WAMS; Healthy Brain Aging ‐ HBA), alongside prospective and retrospective clinical data, to inform the development of machine learning models.ConclusionEDoN will build models with digital markers, validating them against other biomarkers to predict dementia subtypes and individualised disease trajectories. Based on the outputs of the initial models, EDoN will go through a series of iterations of cohort engagement, modality and tool refinement, and data collection. Workstreams are underway to inform data security, privacy, ethics and open policy research, as well as considering the integration of the final EDoN Toolkit into healthcare systems globally. EDoN aims to deliver a cost‐effective, low burden and population‐wide method for early detection of dementia‐causing diseases that will benefit the public, patients, carers, researchers and clinicians, as well as the broader healthcare system and the delivery of new therapies.

The Total CES‐D score from the 14‐item version as a predictor of cognitive decline

AbstractBackgroundDepressive symptomatology is associated with cognitive decline and greater risk for dementia. Studies have examined this association using self‐report measures such as the Centre for Epidemiologic Studies‐Depression Scale (CES‐D; Radloff, 1991). Although the CES‐D was originally designed as a 20‐item scale, certain items have been reported to misrepresent depressive symptoms due to sex and cultural differences, which are addressed in the 14‐item version of this measure (Carleton et al., 2013). The aim of this study was to examine how the CES‐D total score from the 14‐item version compared to the 20‐item version in predicting progression to cognitive decline from a cognitively unimpaired baseline, across genders.MethodData were extracted from the Wisconsin Registry for Alzheimer’s Prevention. A total of 1,055 participants were included who were cognitively unimpaired and stable at baseline and had at least one follow‐up assessment. Bivariate logistic regression analyses were conducted using follow‐up consensus diagnosis as outcome (cognitively unimpaired stable vs. cognitive unimpaired declining, mild cognitive impairment or dementia); baseline total CES‐D scores from either the 14‐item or 20‐item version were used as predictors in separate analyses; age at last follow‐up assessment, age difference between baseline and last follow‐up assessment, years of education, and polygenic risk score were covariates (see Table 1).ResultIn female participants, we observed that the logistic regression model with the total CES‐D score from the 14‐item version was statistically significant (χ2(5) = 54.674, p = .001) and explained 15.2% (Nagelkerke R2) of the variance in predicting cognitive decline; this result was comparable to that with the 20‐item version, which was also statistically significant (χ2(5) = 53.276, p = .001) and explained 14.8% of the variance. In male participants, the model with the total score from the 14‐item version was statistically significant (χ2(5) = 14.846, p = .011) and explained 9.9% of the variance, whereas the model with the total score from the 20‐item version was statistically significant (χ2(5) = 14.070, p = .015) and explained 9.4% of the variance.ConclusionOur findings suggest that the total CES‐D score from the 14‐item version is comparable to, if not slightly better than, the score from the 20‐item version in predicting progression to a clinical level of cognitive decline in both males and females.

Serum TCA cycle metabolites in Lewy bodies dementia and Alzheimer's disease: Network analysis and cognitive prognosis

Abnormalities in the Tri-Carboxylic-Acid (TCA) cycle have been documented in dementia. Through network analysis, TCA cycle metabolites could indirectly reflect known dementia-related abnormalities in biochemical pathways, and key metabolites might be associated with prognosis. This study analyzed TCA cycle metabolites as predictors of cognitive decline in a mild dementia cohort and explored potential interactions with the diagnosis of Lewy Body Dementia (LBD) or Alzheimer's Disease (AD) and APOE-ε4 genotype. We included 145 mild dementia patients (LBD = 59; AD = 86). Serum TCA cycle metabolites were analyzed at baseline, and partial correlation networks were conducted. Cognitive performance was measured annually over 5-years with the Mini-mental State Examination. Longitudinal mixed-effects Tobit models evaluated each baseline metabolite as a predictor of 5-years cognitive decline. APOE-ε4 and diagnosis interactions were explored. Results showed comparable metabolite concentrations in LBD and AD. Multiple testing corrected networks showed larger coefficients for a negative correlation between pyruvate – succinate and positive correlations between fumarate – malate and citrate – Isocitrate in both LBD and AD. In the total sample, adjusted mixed models showed significant associations between baseline citrate concentration and longitudinal MMSE scores. In APOE-ε4 carriers, baseline isocitrate predicted MMSE scores. We conclude that, in mild dementia, serum citrate concentrations could be associated with subsequent cognitive decline, as well as isocitrate concentrations in APOE-ε4 carriers. Downregulation of enzymatic activity in the first half of the TCA cycle (decarboxylating dehydrogenases), with upregulation in the latter half (dehydrogenases only), might be indirectly reflected in serum TCA cycle metabolites' networks.

Prediction of Cognitive Decline in Parkinson's Disease Using Clinical and DAT SPECT Imaging Features, and Hybrid Machine Learning Systems.

We aimed to predict Montreal Cognitive Assessment (MoCA) scores in Parkinson's disease patients at year 4 using handcrafted radiomics (RF), deep (DF), and clinical (CF) features at year 0 (baseline) applied to hybrid machine learning systems (HMLSs). 297 patients were selected from the Parkinson's Progressive Marker Initiative (PPMI) database. The standardized SERA radiomics software and a 3D encoder were employed to extract RFs and DFs from single-photon emission computed tomography (DAT-SPECT) images, respectively. The patients with MoCA scores over 26 were indicated as normal; otherwise, scores under 26 were indicated as abnormal. Moreover, we applied different combinations of feature sets to HMLSs, including the Analysis of Variance (ANOVA) feature selection, which was linked with eight classifiers, including Multi-Layer Perceptron (MLP), K-Neighbors Classifier (KNN), Extra Trees Classifier (ETC), and others. We employed 80% of the patients to select the best model in a 5-fold cross-validation process, and the remaining 20% were employed for hold-out testing. For the sole usage of RFs and DFs, ANOVA and MLP resulted in averaged accuracies of 59 ± 3% and 65 ± 4% for 5-fold cross-validation, respectively, with hold-out testing accuracies of 59 ± 1% and 56 ± 2%, respectively. For sole CFs, a higher performance of 77 ± 8% for 5-fold cross-validation and a hold-out testing performance of 82 + 2% were obtained from ANOVA and ETC. RF+DF obtained a performance of 64 ± 7%, with a hold-out testing performance of 59 ± 2% through ANOVA and XGBC. Usage of CF+RF, CF+DF, and RF+DF+CF enabled the highest averaged accuracies of 78 ± 7%, 78 ± 9%, and 76 ± 8% for 5-fold cross-validation, and hold-out testing accuracies of 81 ± 2%, 82 ± 2%, and 83 ± 4%, respectively. We demonstrated that CFs vitally contribute to predictive performance, and combining them with appropriate imaging features and HMLSs can result in the best prediction performance.

Baseline Differences in Driving Frequency as a Predictor of Cognitive Decline and Alzheimer's Disease.

To extend prior research by examining daily driving frequency as a predictor of cognitive decline and later diagnosis of Alzheimer's disease. 1,426 older adults completed batteries of questionnaires and neuropsychological tests at baseline and yearly follow-ups (M = 6.8, SD = 4.9). Linear mixed effects models were estimated to examine whether daily driving frequency at baseline was predictive of cognitive decline while accounting for IADLs, mobility, depression, and demographics. Cox regression was used to examine driving frequency as a predictor of Alzheimer's disease diagnosis. Less daily driving frequency was associated with greater decline in all cognitive domains over time except for working memory. Although driving frequency was associated with these changes in cognition, it did not uniquely predict the development of Alzheimer's disease when accounting for other factors (eg, other IADLs). Our findings extend prior research linking driving cessation to greater levels of cognitive decline. Future work might benefit from examining the utility of driving habits (especially changes in driving) as measures of everyday functioning in older adult evaluations.

5613510 EFFECTS OF REGIONAL BRAIN VOLUME ON COGNITION IN SICKLE CELL ANAEMIA: A DEVELOPMENTAL PERSPECTIVE

Cognitive decline, related at least in part to reduced processing speed and lower working memory, is a major problem in paediatric and adult patients with sickle cell anaemia (SCA)1,2 and affects quality of life. Multiple studies investigating the association between quantitative and qualitative neuroimaging findings and cognition have had mixed results. Hence, the aetiology of cognitive decline in this population is not clearly understood. Cerebral atrophy on magnetic resonance imaging is noted in the literature but few studies have investigated the association between brain volumes and cognition. White matter microstructural integrity and white matter hyperintensities have been associated with reduced processing speed index (PSI).3 However no study has investigated the association between white matter volumes and PSI in the SCA population. Total subcortical volumes have also been noted to be lower in SCA children and adults. Only one study has investigated the association between total subcortical volumes and working memory index (WMI) in adults.4 Moreover, several studies in the past have noted age related decline in total brain volume as well neurodevelopmental delay in grey matter volume growth.5,6 Most of the developmental trajectory data in the literature comes from longitudinal data sets with limited samples and without control groups. Hence, for the purpose of this study, we investigated the association between regional brain volumes (white matter volumes and total subcortical volumes) and cognition (processing speed index and working memory index) in 129 SCA patients aged 8 – 64 years (66 male) and 50 controls (21 males). Participants were enrolled in two studies - the Sleep Asthma Cohort (SAC) and the Prevention of Morbidity in SCA (POMS) trial. We hypothesized that white matter volumes would predict processing speed index and total subcortical volumes would predict working memory index in SCA patients as well as controls. We also hypothesized that the developmental trajectories for regional brain volumes and cognitive variables would differ between patients and controls. Separate multiple linear regression models for males and females revealed that WMV predicted processing speed index(p=.019) and total subcortical volumes predicted working memory index (p=.025) in male patients only. Age (p=.003) and socioeconomic status (p=.03) also independently predicted PSI in male patients. Individual subcortical regions predicting WMI were statistically significant for left and right thalamus only in male patients. Haemoglobin and age emerged as the best predictors of cognitive decline in patients. Comparison of developmental trajectories between patients and controls revealed that only processing speed was significantly delayed at 8 years. The rate of development for all variables was lower in SCA patients and age-related decline was accelerated in patients as compared to controls. We speculate that early cerebral haemodynamic compromise may contribute to long term cognitive decline in patients. We also highlight that male SCA patients are particularly vulnerable to SCA disease severity. Hence, timely intervention to stabilise haemoglobin levels, especially in males, should be considered. Limitations include lack of stratification by SCI and age (adults and children separately) and the possibility that we were underpowered to detect effects in females.

Prediction of cognitive decline in Parkinson’s disease (PD) patients with electroencephalography (EEG) connectivity characterized by time-between-phase-crossing (TBPC)

The aim of the study is to identify the dynamic change pattern of EEG to predict cognitive decline in patients with Parkinson’s disease. Here we demonstrate that the quantification of synchrony-pattern changes across the scalp, measured using electroencephalography (EEG), offers an alternative approach of observing an individual’s functional brain organization. This method, called “Time-Between-Phase-Crossing” (TBPC), is based on the same phenomenon as the phase-lag-index (PLI); it also considers intermittent changes in the signals of phase differences between pairs of EEG signals, but additionally analyzes dynamic connectivity changes. We used data from 75 non-demented Parkinson’s disease patients and 72 healthy controls, who were followed over a period of 3 years. Statistics were calculated using connectome-based modeling (CPM) and receiver operating characteristic (ROC). We show that TBPC profiles, via the use of intermittent changes in signals of analytic phase differences of pairs of EEG signals, can be used to predict cognitive decline in Parkinson’s disease (p < 0.05).

Association of retinal optical coherence tomography metrics and polygenic risk scores with cognitive function and future cognitive decline

PurposeTo evaluate the potential of retinal optical coherence tomography (OCT) measurements and polygenic risk scores (PRS) to identify people at risk of cognitive impairment.MethodsUsing OCT images from 50 342 UK...

Plasma biomarkers for diagnosis of Alzheimer's disease and prediction of cognitive decline in individuals with mild cognitive impairment.

The last few years have seen major advances in blood biomarkers for Alzheimer's Disease (AD) with the development of ultrasensitive immunoassays, promising to transform how we diagnose, prognose, and track progression of neurodegenerative dementias. We evaluated a panel of four novel ultrasensitive electrochemiluminescence (ECL) immunoassays against presumed CNS derived proteins of interest in AD in plasma [phosphorylated-Tau181 (pTau181), total Tau (tTau), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP)]. Two sets of banked plasma samples from the Massachusetts Alzheimer's Disease Research Center's longitudinal cohort study were examined: A longitudinal prognostic sample (n = 85) consisting of individuals with mild cognitive impairment (MCI) and 4 years of follow-up and a cross-sectional sample (n = 238) consisting of individuals with AD, other neurodegenerative diseases (OND), and normal cognition (CN). Participants with MCI who progressed to dementia due to probable AD during follow-up had higher baseline plasma concentrations of pTau181, NfL, and GFAP compared to non-progressors. The best prognostic discrimination was observed with pTau181 (AUC = 0.83, 1.7-fold increase) and GFAP (AUC = 0.83, 1.6-fold increase). Participants with autopsy- and/or biomarker verified AD had higher plasma levels of pTau181, tTau and GFAP compared to CN and OND, while NfL was elevated in AD and further increased in OND. The best diagnostic discrimination was observed with pTau181 (AD vs CN: AUC = 0.90, 2-fold increase; AD vs. OND: AUC = 0.84, 1.5-fold increase) but tTau, NfL, and GFAP also showed good discrimination between AD and CN (AUC = 0.81-0.85; 1.5-2.2 fold increase). These new ultrasensitive ECL plasma assays for pTau181, tTau, NfL, and GFAP demonstrated diagnostic utility for detection of AD. Moreover, the absolute baseline plasma levels of pTau181 and GFAP reflect cognitive decline over the next 4 years, providing prognostic information that may have utility in both clinical practice and clinical trial populations.

Dissipating the fog: Cognitive trajectories and risk factors 1 year after COVID-19 hospitalization.

Cognitive impairment is common after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, associations between post-hospital discharge risk factors and cognitive trajectories have not been explored. A total of 1105 adults (mean age ± SD 64.9 ± 9.9 years, 44% women, 63% White) with severe coronavirus disease 2019 (COVID-19) were evaluated for cognitive function 1 year after hospital discharge. Scores from cognitive tests were harmonized, and clusters of cognitive impairment were defined using sequential analysis. Three groups of cognitive trajectories were observed during the follow-up: no cognitive impairment, initial short-term cognitive impairment, and long-term cognitive impairment. Predictors of cognitive decline after COVID-19 were older age (β=-0.013, 95% CI =-0.023;-0.003), female sex (β=-0.230, 95% CI=-0.413;-0.047), previous dementia diagnosis or substantial memory complaints (β=-0.606, 95% CI =-0.877;-0.335), frailty before hospitalization (β=-0.191, 95% CI =-0.264;-0.119), higher platelet count (β=-0.101, 95% CI =-0.185;-0.018), and delirium (β=-0.483, 95% CI =-0.724;-0.244). Post-discharge predictors included hospital readmissions and frailty. Cognitive impairment was common and the patterns of cognitive trajectories depended on sociodemographic, in-hospital, and post-hospitalization predictors. Cognitive impairment after coronavirus disease 2019 (COVID-19) hospital discharge was associated with higher age, less education, delirium during hospitalization, a higher number of hospitalizations post discharge, and frailty before and after hospitalization. Frequent cognitive evaluations for 12-month post-COVID-19 hospitalization showed three possible cognitive trajectories: no cognitive impairment, initial short-term impairment, and long-term impairment. This study highlights the importance of frequent cognitive testing to determine patterns of COVID-19 cognitive impairment, given the high frequency of incident cognitive impairment 1 year after hospitalization.

Cretan Aging Cohort-Phase III: Methodology and Descriptive Characteristics of a Long-Term Longitudinal Study on Predictors of Cognitive Decline in Non-Demented Elderly from Crete, Greece.

Identifying modifiable factors that may predict long-term cognitive decline in the elderly with adequate daily functionality is critical. Such factors may include poor sleep quality and quantity, sleep-related breathing disorders, inflammatory cytokines and stress hormones, as well as mental health problems. This work reports the methodology and descriptive characteristics of a long-term, multidisciplinary study on modifiable risk factors for cognitive status progression, focusing on the 7-year follow-up. Participants were recruited from a large community-dwelling cohort residing in Crete, Greece (CAC; Cretan Aging Cohort). Baseline assessments were conducted in 2013-2014 (Phase I and II, circa 6-month time interval) and follow-up in 2020-2022 (Phase III). In total, 151 individuals completed the Phase III evaluation. Of those, 71 were cognitively non-impaired (CNI group) in Phase II and 80 had been diagnosed with mild cognitive impairment (MCI). In addition to sociodemographic, lifestyle, medical, neuropsychological, and neuropsychiatric data, objective sleep was assessed based on actigraphy (Phase II and III) and home polysomnography (Phase III), while inflammation markers and stress hormones were measured in both phases. Despite the homogeneity of the sample in most sociodemographic indices, MCI persons were significantly older (mean age = 75.03 years, SD = 6.34) and genetically predisposed for cognitive deterioration (APOE ε4 allele carriership). Also, at follow-up, we detected a significant increase in self-reported anxiety symptoms along with a substantial rise in psychotropic medication use and incidence of major medical morbidities. The longitudinal design of the CAC study may provide significant data on possible modifiable factors in the course of cognitive progression in the community-dwelling elderly.

Association between endocrine therapy and cognitive decline in older women with early breast cancer: Findings from the prospective CLIMB study

IntroductionStudies investigating the long-term effects of breast cancer treatment on cognition in older women with breast cancer are lacking, even though preserving cognition is highly valued by the older population. Specifically, concerns have been raised regarding the detrimental effects of endocrine therapy (ET) on cognition. Therefore, we investigated cognitive functioning over time and predictors for cognitive decline in older women treated for early breast cancer. MethodsWe prospectively enrolled Dutch women aged ≥70 years with stage I-III breast cancer in the observational CLIMB study. The Mini-Mental State Examination (MMSE) was performed before ET initiation and after 9, 15 and 27 months. Longitudinal MMSE scores were analysed and stratified for ET. Linear mixed models were used to identify possible predictors of cognitive decline. ResultsAmong the 273 participants, the mean age was 76 years (standard deviation 5), and 48% received ET. The mean baseline MMSE score was 28.2 (standard deviation 1.9). Cognition did not decline to clinically meaningful differences, irrespective of ET. MMSE scores of women with pre-treatment cognitive impairments slightly improved over time (significant interaction terms) in the entire cohort and in women receiving ET. High age, low educational level and impaired mobility were independently associated with declining MMSE scores over time, although the declines were not clinically meaningful. ConclusionCognition of older women with early breast cancer did not decline in the first two years after treatment initiation, irrespective of ET. Our findings suggest that the fear of declining cognition does not justify the de-escalation of breast cancer treatment in older women.

Prediction of Longitudinal Cognitive Decline in Preclinical Alzheimer Disease Using Plasma Biomarkers

Alzheimer disease (AD) pathology starts with a prolonged phase of β-amyloid (Aβ) accumulation without symptoms. The duration of this phase differs greatly among individuals. While this disease phase has high relevance for clinical trial designs, it is currently unclear how to best predict the onset of clinical progression. To evaluate combinations of different plasma biomarkers for predicting cognitive decline in Aβ-positive cognitively unimpaired (CU) individuals. This prospective population-based prognostic study evaluated data from 2 prospective longitudinal cohort studies (the Swedish BioFINDER-1 and the Wisconsin Registry for Alzheimer Prevention [WRAP]), with data collected from February 8, 2010, to October 21, 2020, for the BioFINDER-1 cohort and from August 11, 2011, to June 27, 2021, for the WRAP cohort. Participants were CU individuals recruited from memory clinics who had brain Aβ pathology defined by cerebrospinal fluid (CSF) Aβ42/40 in the BioFINDER-1 study and by Pittsburgh Compound B (PiB) positron emission tomography (PET) in the WRAP study. A total of 564 eligible Aβ-positive and Aβ-negative CU participants with available relevant data from the BioFINDER-1 and WRAP cohorts were included in the study; of those, 171 Aβ-positive participants were included in the main analyses. Baseline P-tau181, P-tau217, P-tau231, glial fibrillary filament protein, and neurofilament light measured in plasma; CSF biomarkers in the BioFINDER-1 cohort, and PiB PET uptake in the WRAP cohort. The primary outcome was longitudinal measures of cognition (using the Mini-Mental State Examination [MMSE] and the modified Preclinical Alzheimer Cognitive Composite [mPACC]) over a median of 6 years (range, 2-10 years). The secondary outcome was conversion to AD dementia. Baseline biomarkers were used in linear regression models to predict rates of longitudinal cognitive change (calculated separately). Models were adjusted for age, sex, years of education, apolipoprotein E ε4 allele status, and baseline cognition. Multivariable models were compared based on model R2 coefficients and corrected Akaike information criterion. Among 171 Aβ-positive CU participants included in the main analyses, 119 (mean [SD] age, 73.0 [5.4] years; 60.5% female) were from the BioFINDER-1 study, and 52 (mean [SD] age, 64.4 [4.6] years; 65.4% female) were from the WRAP study. In the BioFINDER-1 cohort, plasma P-tau217 was the best marker to predict cognitive decline in the mPACC (model R2 = 0.41) and the MMSE (model R2 = 0.34) and was superior to the covariates-only models (mPACC: R2 = 0.23; MMSE: R2 = 0.04; P < .001 for both comparisons). Results were validated in the WRAP cohort; for example, plasma P-tau217 was associated with mPACC slopes (R2 = 0.13 vs 0.01 in the covariates-only model; P = .01) and MMSE slopes (R2 = 0.29 vs 0.24 in the covariates-only model; P = .046). Sparse models were identified with plasma P-tau217 as a predictor of cognitive decline. Power calculations for enrichment in hypothetical clinical trials revealed large relative reductions in sample sizes when using plasma P-tau217 to enrich for CU individuals likely to experience cognitive decline over time. In this study, plasma P-tau217 predicted cognitive decline in patients with preclinical AD. These findings suggest that plasma P-tau217 may be used as a complement to CSF or PET for participant selection in clinical trials of novel disease-modifying treatments.

Visualization of atrophy of medial temporal lobes and the septal nuclei in patients with transient ischaemic attack and controls

IntroductionTransient ischaemic attack (TIA) is associated with increased risk of cognitive decline and dementia as early as one-year post-event. Regional brain atrophy measurements may predict future cognitive decline. Aims: 1) To determine whether Medial Temporal Atrophy (MTA) scores and interseptal distance (ISD) measurements are greater in patients with TIA compared to controls; and 2) To determine whether MTA and ISD predicts cognitive change one year after TIA. MethodsBaseline demographic, vascular risk factors, structural imaging and cognitive tests scores were compared between 103 Patients with TIA and 103 age-and-sex-matched controls from the Predementia Neuroimaging of Transient Ischaemic Attack (PREVENT) Study. MTA was assessed using the Schelten's Scale, and ISD was calculated as the distance between the septal nucleus of each hemisphere. Multiple linear regression models were used to evaluate how MTA and ISD related to cognitive change after adjusting for covariates. ResultsPatients with TIA had larger ISD measurements (1.4 mm [SD=1.2] vs. 0.9 mm [SD=1.0]); p < 0.001) and higher right/left MTA scores (both p < 0.05) compared to controls. At baseline, controls performed significantly better on the RAVLT (total recall), BVMT (total and delayed recall) and the Trail Making Task (A and B) compared to patients with TIA. However, at one-year follow-up there was no evidence of decline in the patients with TIA compared with controls. Higher MTA and ISD scores were not associated with cognitive decline. ConclusionsPatients with TIA had higher MTA scores and ISD measurements than controls, but neither were predictors of cognitive decline at one year. Future studies with longer follow-up periods will be required to determine whether higher MTA scores and ISD predict risk of cognitive decline in patients with TIA.

Is there a link between heart rate variability and cognitive decline? A cross-sectional study on patients with mild cognitive impairment and cognitively healthy controls.

Given that, up to date, there is no effective strategy to treat dementia, a timely start of interventions in a prodromal stage such as mild cognitive impairment (MCI) is considered an important option to lower the overall societal burden. Although autonomic functions have been related to cognitive performance, both aspects have rarely been studied simultaneously in MCI. The aim of the present study was to investigate cardiac autonomic control in older adults with and without MCI. Cardiac autonomic control was assessed by means of heart rate variability (HRV) at resting state and during cognitive tasks in 22 older adults with MCI and 29 healthy controls (HCs). Resting HRV measurement was performed for 5 minutes during a sitting position. Afterwards, participants performed three PC-based tasks to probe performance in executive functions and language abilities (i.e., Stroop, N-back, and a verbal fluency task). Participants with MCI showed a significant reduction of HRV in the frequency-domain (high frequency power) and nonlinear indices (SD2, D2, and DFA1) during resting state compared to HCs. Older individuals with MCI exhibited decreases in RMSSD and increases in DFA1 from resting state to Stroop and N-back tasks, reflecting strong vagal withdrawal, while this parameter remained stable in HCs. The results support the presence of autonomic dysfunction at the early stage of cognitive impairment. Heart rate variability could help in the prediction of cognitive decline as a noninvasive biomarker or as a tool to monitor the effectiveness of therapy and prevention of neurodegenerative diseases.

INTERMUSCULAR ADIPOSITY: A NOVEL RISK FACTOR FOR COGNITIVE DECLINE IN A BIRACIAL COHORT OF OLDER ADULTS

Abstract Skeletal muscle and brain health both decline with age. Poorer skeletal muscle characteristics may be early markers of cognitive decline. The Health ABC study obtained repeated measures of thigh intermuscular adiposity via CT (IMAT, Years 1 and 6) and Mini-Mental State Exam (MMSE, Years 1 through 10), in 1634 adults (69-79 years, 48% women, 35% black). Linear mixed effects models accounted for change (Years 1 and 6) in weight, muscle (strength, area), and adiposity characteristics (abdominal subcutaneous, visceral, total fat mass) and for dementia risk factors (education, APOE4, diabetes, hypertension, physical activity). IMAT increased by 0.97 cm2/year (SD:1.16), and MMSE declined by 0.4 points/year (0.02). Each SD of IMAT corresponded to a MMSE decline of 0.22 points/year (p&amp;lt;0.0001), similar in adjusted models and stratified by race or gender. Aging-related IMAT increase may be a novel predictor of cognitive decline beyond traditional risk factors, with potential implications for muscle-brain cross talk.

Prediction of Cognitive Decline by Behavioral Symptoms in Neuropsychiatric Disorders

Background: Neuropsychiatric disorders are described by their neurological, behavioral, and cognitive symptoms. However, behavioral symptoms may often be overlooked due to the current approach in neurology. Objectives: This study investigated the relationship between behavioral symptoms and cognitive functioning in neurological disorders. The second aim was to predict neurocognitive patterns by behavioral symptoms as independent variables. Methods: Behavioral symptoms were collected based on semi-structured neuropsychiatric interviews with 211 patients admitted to the neuropsychiatry department of Ayatollah Kashani hospital in Isfahan by both a neuropsychiatry fellow and an attending neuropsychiatrist. A neuropsychiatry fellow assessed all patients using the neuropsychiatry unit cognitive (NUCog) assessment tool. We used a generalized linear model (GLM) to indicate the effect of behavioral symptoms on the risk of decline in cognitive domains. Due to the use of all available samples, this study had no age limit, and the patients were 15 to 92 years old. Results: The regression coefficient of NUCog subscale scores for behavioral symptoms using GLM revealed that education level had a positive relationship with the scores of attention (P &lt; 0.001), visuoconstruction (P &lt; 0.001), memory (P &lt; 0.001), executive function (P &lt; 0.001), language (P &lt; 0.001), and the total score of NUCog (P &lt; 0.001). Patients with apathy had lower scores on the memory subscale (P = 0.002) and total NUCog (P = 0.021). Similarly, patients with delusion had lower scores on memory (P = 0.006) and executive function (P = 0.026). There was a negative relationship between agitation and attention (P = 0.049), visuoconstruction (P = 0.015), memory (P = 0.018), executive function (P = 0.005), and total score of NUCog (P = 0.007). Sleep disturbances were accompanied by lower memory scores (P = 0.056) and lower mean NUCog scores (P = 0.052). Visual hallucination was associated with declined performance in attention (P = 0.057). Conclusions: Behavioral assessment can help predict cognitive patterns in patients with neurobehavioral syndromes.

Cognitive decline in Autosomal Dominant Alzheimer’s Disease and its relation to TauPET and fMRI Complexity in the Hippocampus

AbstractBackgroundIn recent years the neurofibrillary tangle pathology detected with tauPET imaging has been found to be closely related to cognitive decline in Alzheimer’s Disease. However, PET scans are expensive and involve radioactive tracers. We and others have proposed the complexity of rs‐fMRI using MultiScaleEntropy (MSE) as an index for neuronal function. Reduced entropy values have been associated with aging, APOEɛ4 genotype and cognitive function in AD. Here we hypothesize that the BOLD signal complexity is sensitive to tau‐related neuronal injury and cognitive decline in AD.MethodData (N=44, Table1) was obtained from the Estudio de la Enfermedad de Alzheimer en Jalisciences (EEAJ) study, which examines persons at‐risk for autosomal‐dominant AD. All subjects had T1, tauPET (18F‐AV1451) and two resting‐state fMRI scans. FMRI data underwent standard preprocessing before computing MSE. SUVR images were calculated with inferior cerebellum as reference region. Correlations between SUVR tauPET and MSE were calculated voxel‐wise including age, gender and gray ‐matter volume as covariates. Mediation effects in hippocampal clusters between MSE, tauPET and cognitive function (MMSE) were tested in SAS (95% confidence‐interval was estimated with 1000 bootstrapping samples).ResultWe observed gradually decreasing entropy and gradually increasing tauPET from NC to MCI and AD (Fig1). This inverse relationship was significant (p&lt;0.05,corrected) in hippocampus, lateral temporal lobe and anterior cingulate cortex (Fig2). Mediator analysis revealed significant indirect (mediation) effects of PET to the association between MSE and MMSE (log‐transformed) in both hippocampi: Left: total 1.37 95%CI (0.6,2.14) indirect 0.86(0.27,1.85) and Right: total 1.49(0.66,2.31) indirect 1.04(0.48,1.84).ConclusionOur findings show that there is a statistically significant total effect between MSE and cognitive decline that is largely mediated by tauPET, with 63% and 70% of total effect for right and left hippocampus respectively. These results support the relevance of hippocampal tauopathy on cognitive decline. Moreover, complexity of rs‐fMRI is associated with both regional tau protein accumulation and cognitive decline, and thus could provide a safe and cost‐effective alternative marker for regional neuropathology and prediction of cognitive decline in AD.

Speed of cognitive decline in biomarker stratified SCD subgroups

AbstractBackgroundSubjective cognitive decline (SCD) is associated with an increased risk of cognitive decline and dementia, specifically in cases in the Alzheimer’s continuum (amyloid positive, A+). In the present study we tested, if further stratification with additional markers is useful for identifying fast progressors within SCD‐A+.MethodThe analysis is based on 170 SCD case of the multicenter memory clinic based DELCODE study in Germany. The following markers were used as predictors of cognitive decline: number of SCDplus criteria, CSF Aß42/Aß42, tTau, pTau181, APOE status and hippocampal volume. The outcome was the preclinical Alzheimer’s cognitive composite 5 (PACC5) over up to 5 years. Univariate and multivariate hierarchical regression models were calculated with continuous (a) and dichotomized (b) variables.ResultIn the univariate analyses significant or close to significant associations of Aß42/Aß42 (a:p = 0.012; b:p = 0.040), tTau (a:p = 0.034; b:p = 0.053) and pTau181 (a:p = 0.057; b:p = 0.019) with cognitive decline were observed. In the hierarchical model with continuous variables, only Aß42/Aß40 reached significance (p = 0.016); while dichotomized variables Aß42/Aß40 (p = 0,074) and pTau181 (p = 0.068) reached trend level significance.ConclusionWithin SCD‐A+, those who are also pTau positive show greatest decline. This finding is in agreement with the concept of A+ and T+ indicating full Alzheimer’s disease pathology and is important for the design of clinical trials in SCD.