MCI Group Research Articles

Cognitive performance and plasma ADAM10 levels in low‐educated older adults with normal cognition, mild cognitive impairment, and Alzheimer’s disease

AbstractBackgroundADAM10 has been investigated as a potential biomarker for Alzheimer’s disease (AD) (Monteiro et al., 2021). However, most of the production in the literature comes from high‐income countries, where the population usually has high educational attainment (Mukadam et al., 2019). Evidence in the literature suggests an effect of education on cognitive performance; thus, low‐educated older adults tend to have worse scores on cognitive tests. Also, low education has been associated with an increased risk of dementia (Mukadam et al., 2019; Suemoto et al., 2022). However, little evidence is available on plasma ADAM10 levels in a sample of older adults with low educational attainment.MethodParticipants were divided into three groups: Healthy (n = 33), MCI (n = 27), and AD (n = 30). Cognitive performance was assessed through MMSE and ACE‐R. SDS‐PAGE and Western‐Blotting were performed to obtain ADAM10 levels in plasma.ResultParticipants were primarily female (62.1%), the mean age was 74.5 (8.2), and the average years of education was 4.6 (4.3). Healthy participants performed better than the MCI group on MMSE (p = 0.04). Compared to the healthy participants, the AD group was older (p = 0.00); had more comorbidities (p = 0.02), was prescribed more medications (p = 0.00); had worse performance both on MMSE (p = 0.00) and on ACE‐R (p = 0.003); and had higher levels of plasma ADAM10 (p = 0.033). Significant differences between MCI and AD groups for age (p = 0.01), years of education (p = 0.008), medication use (p = 0.02), and MMSE score (p = 0.00) were observed. The logistic regression analysis indicated that having lower levels of plasma ADAM10 decreases the chances of an older adult having a diagnosis of AD by 82.3% (Exp(B) = 0.177; p = 0.005; 95%CI [0.053‐0.593]).ConclusionOur results suggested healthy low‐educated older adults had less plasma ADAM10 compared to individuals with AD. Also, despite low education, cognitive test scores differ for each group.

Comparison of sleep and wake EEG biomarkers in mild cognitive impairment and Alzheimer’s disease

AbstractBackgroundEEG slowing, a biomarker of cognitive decline in Alzheimer’s disease (AD), has been reported in wake or sleep EEG [1,2,3]. However, the relation between wake and sleep EEG and their comparative sensitivity to early stage of cognitive decline is less understood. In this study we compared sleep and wake EEG biomarkers using a common set of endpoints.MethodsParticipants included individuals with mild cognitive impairment (MCI, n = 30, ages 53‐88), Alzheimer’s disease dementia (ADem, n = 15, ages 60‐84), and controls with normal cognition (HC, n = 55, ages 50‐84). Twenty‐channel resting‐state EEG with eyes‐closed was collected during 5‐minutes of wakefulness (STAT X24). Sleep EEG recordings from AF7‐AF8 were collected in‐home and staged using Sleep ProfilerTM. Power spectral densities (PSD) and the derived Theta‐Alpha ratios were computed for wake EEG sites (counterpart bipolar montage), with the wake FP1‐FP2 compared by stage to the sleep EEG at AF7‐AF8. Significant differences in Theta‐Alpha ratios between MCI or ADem vs. HC groups were identified using independent t‐test (p<0.05) with the normalized effect size measured using Hedges‐g (E.S.).ResultsCorrelation coefficients between wake Alpha, Theta power and non‐REM sleep were: r = 0.83, 0.75 for stage N1, r = 0.71, 0.66 for stage N2, and r = 0.55, 0.50 for stage N3. Based on the frontal Theta‐Alpha ratios, the ADem group showed significant EEG slowing during both wake (E.S. = 0.73) and sleep (E.S. = 1.65, 1.31, 0.61, 1.71 for N1, N2, N3, and REM) as compared to the HC, while MCI group only exhibited EEG slowing during sleep (E.S. = 0.56, 0.77, 0.51 for N1, N2 and REM). Temporal (T5‐T6) wake EEG manifested significant slowing in both ADem (E.S. = 1.21) and MCI (E.S. = 0.56) groups. Compared to the HC, ADem patients exhibited significantly less sleep time (E.S. = 0.91, p = 0.002) and less stage REM (E.S. = 1.0, p = 0.001).ConclusionsCognitive decline in the AD spectrum is associated with common signatures in both sleep and wake EEG. However, the magnitude of these effects and their spatial distribution may be different in sleep versus wakefulness. These findings support a multi modal approach to neurophysiological assessment using both sleep and wakefulness data.

Clinical Manifestations.

Recent studies comparing non-Hispanic Whites (NHW) to non-Hispanic Blacks (NHB) with cognitive impairment found differences in associations between neuropsychological performance and ratings of daily function. Evaluating differences in these relationships by race and ethnicity may be important to interpreting disparities in clinical research outcomes for people with Alzheimer's disease and related disorders. We compared demographic and performance variables across consensus diagnostic groups (CN, impaired-not-MCI, MCI, or dementia) at baseline in NHB (N = 1,609; 15.62%), Hispanic (N = 706, 6.86%) and NHW (N = 7,984,77.52%) adults in the NACC cohort, using data from Version 3.0 of the UDS, collected at the 37 NIA-ADRCs from March 2015 to June 2022 (alz.washington.edu). Spearman's rank correlation examined the associations between CDR-GS, MoCA, and FAS scores by race and ethnicity within consensus diagnostic groups. Among 10,299 participants, 5,964 (57.91%) were women, mean age at baseline was 69.34 (±9.80), and mean education was 16.02 (±2.81) years. Significant differences in gender proportions between race and ethnic groups were observed. NHB had a higher proportion of female participants (χ2 = 233.39 <.0001); and reported the lowest FAS scores (Z = 200.67, P < .001). Hispanic participants had completed fewer years of education (Z = 3999.92, p = <.0001); and reported higher depressive symptoms (Z = 39.81, p = <.0001). Significant correlations were observed between all assessments by race and ethnic groups. When stratified by consensus group, in the NHW and NHB subsamples, CDR and MoCA scores associations were significant except in the "Impaired not MCI" groups. In the Hispanic subsample, significant correlations were only observed in the dementia group. After adjusting for the effects of GDS scores, correlations retained significance except in the NHB MCI group. Significant correlations between CDR and FAS scores were observed except in the "Impaired not MCI" group in the Hispanic subsample. These associations were retained after controlling for depressive symptoms except in the Hispanic MCI group. Associations between cognitive and functional performance measures varied by racial and ethnic group when stratified by consensus groups in the NACC dataset. Specific ethnic, racial and socio-cultural factors, as well as implicit/explicit examiner bias that may drive differences in performance warrant further study.

Monoaminergic Disfunctions and Misfolded Proteins levels in the Alzheimer’s Disease Continuum

AbstractBackgroundMonoaminergic systems are pharmacologically modulable and therefore both a target for symptomatic therapies and a window for the study of disease progression. Although these mechanisms are unclear. We explore the progression and the distribution of monoaminergic deficits in the Alzheimer’s Disease (AD) continuum, along with the accumulation of misfolded pathological proteins.MethodWe included 50 patients with Alzheimer’s Disease (AD) and 42 Healthy Controls (HC). We acquired brain [18F]FDG‐PET images as a marker of neurodegeneration and MMSE as a measure of global cognitive functioning. We performed voxel‐based analysis between AD vs. HC. Using JuSpace toolbox we explored the correlation between [18F]FDG‐PET images and PET maps of dopamine (DAT), serotonin (SERT), noradrenaline (NAT) and choline vesicular (VAChT) transporters cortical distribution. Moreover, we evaluated relative hypometabolism in the main cholinergic nucleus (Meynert nucleus). We divided in two groups the AD patients based on MMSE score (AD Dementia ‐ADD‐ &lt;24; MCI ≥24), to explore the association between monoaminergic disfunction and pathological proteins burden, across the AD continuum.ResultSample characteristics are provided in Table 1. Results from voxel‐based analysis are shown in Figure 1. In the whole AD group, the distribution of relative cortical hypometabolism was associated with a significant reduction of VAChT availability (p = 0.018). This association remained significant when considering the ADD (p = 0.006), but not the MCI group. Figure 2.β‐amyloid42 levels and β‐amyloid42/40 ratio were inversely correlated with VAChT in the ADD group (p = 0.003; p = 0.038, respectively). No correlation was observed in the MCI group. Lastly, there was no correlation between Meynert nucleus relative hypometabolism and misfolded proteins levels in all groups.ConclusionIn the ADD phase we showed a significant damage in the cortical regions targeted by the cholinergic system. This result was not evident among the other systems studied, nor in the MCI phase. This provides an explanation of the reduction of the cholinesterase inhibitors efficiency in the dementia phase. Moreover, we showed that β‐amyloid accumulation was not associated with Meynert Nucleus degeneration, but that impacted on cholinergic function via damage accumulation in the cortical areas targeted by the cholinergic system.

Fully automatic deep‐learning based segmentation of Hippocampal subfields from standard resolution T1W MRI in Alzheimer’s Disease

AbstractBackgroundHippocampal subfield volumes are of interest in Alzheimer’s disease (AD) as sub‐regions are affected at different points in the disease trajectory and may provide increased sensitivity as biomarkers compared to the whole hippocampus alone. Subfield volume estimation typically requires a high‐resolution T2‐weighted MRI to ensure accurate anatomical parcellation; however, this increases scan times and may not be present in historical datasets. Automatic methods exist for subfield segmentation from mono‐modal standard resolution T1W MRI, with deep‐learning approaches providing the most promising results to date. Here we extend such work employing deep learning approaches to provide flexible hippocampal subfield segmentation of standard resolution (1mm3) T1W MRI.MethodWe trained a collection of deep‐learning segmentation models to perform binary segmentation of the CA1‐3, CA4/DG, Subiculum, and whole Hippocampus, using a publicly available dataset of 25 subjects with manually segmented region labels on T1 MRI (1mm3). Multilabel segmentations were generated from combination of segmentation outputs.ResultTotal hippocampal and subfield multi‐label segmentations were generated for all test subjects in each cross validated loop and DICE overlap with the ground truth label computed. Preliminary results, computed using a 5‐fold cross validation, report mean DICE in whole Hippocampus = 0.904(std: 0.014), CA1‐3 = 0.842(std:0.019), CA4/DG = 0.767(std:0.04), and Subiculum = 0.791(std:0.033).Segmentation models were applied to 947 subjects with standard T1 MRI from the ADNI cohort and either a CN or MCI label. Subjects were selected with pre‐existing hippocampal subfield volumes, provided by ADNI, computed with the ASHS method using a multi‐modal T1 and hi‐res T2 MRI image. While the proposed method and ASHS employ subtly different parcellation schema we note significant correlation across similar regions between methods.Logistic regression of ICV corrected volumes and MMSE, correcting for age and sex, reveal both ASHS and the proposed method can significantly discriminate between CN and MCI groups (Table 1), with subfields areas providing greater discrimination that of the whole hippocampus.ConclusionHere we present preliminary results for a flexible deep‐learning approach to segmentation of hippocampal subfields from standard resolution T1 MRI alone; indicating comparable performance for group discrimination to an established multi‐modal standard.

Hippocampus‐associated tract degradation follows differential DTI patterns and is influenced by amyloid in MCI patients

AbstractBackgroundDiffusion tensor imaging (DTI) has been leveraged to understand microstructural alterations to limbic and cortico‐cortical tracts in Alzheimer’s disease (AD). However, investigation into the key changes and patterns of fractional anisotropy (FA) and mean diffusivity (MD) metrics in the early stages of MCI has not been widely investigated. This study aimed to establish whether the early DTI white matter tract changes in MCI patients are decoupled and whether amyloid positivity (Aβ+/Aβ‐) is associated with early tract degradation.Method171 participants (103 MCI &amp; 73 CN) were recruited from the AD Neuroimaging Initiative (ADNI; adni.loni.usc.edu) database. For each participant, FA and MD maps were obtained at their baseline scan and voxel wise analysis was carried out using FSL (https://fsl.fmrib.ox.ac.uk/fsl/) and Tract‐Based Spatial Statistics (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TBSS). Students t‐tests were conducted on the FA and MD changes in hippocampal WM tracts, previously identified as areas of potential early degradation. Comparisons between CN and MCI groups were made along Aβ+ and Aβ‐ groups.ResultWe found evidence for WM degradation, indicated by lower FA scores in MCI participants compared with controls, in the specific regions of interest of the fornix (p = 0.004) and tapetum (p = 0.012). Mean diffusivity was increased in the MCI group compared to CN in areas of the fornix (p &lt; 0.001), consistent with FA alterations, yet differed from areas identified in FA changes owing to changes in the uncinate fasciculus (p = 0.022) and cingulum (p = 0.003). Comparison of DTI metrics in the fornix between amyloid status groups revealed that Aβ+ participants had significantly lower FA compared to Aβ‐ participants (p = 0.047), but MD did not significantly differ.ConclusionThese results confirm the notion that WM tract integrity degradation in the tract apparatus surrounding the hippocampus occur early in the AD trajectory. However, results indicate that FA alterations occur in different patterns to that of MD and that amyloid may influence these patterns. This supports the idea that efforts to develop therapeutics which maintain WM tract integrity early may be key to slowing AD progression.

Exploring the association between VLOM ratio and years of education in MCI and normal aging urban cohort

AbstractBackgroundMild Cognitive Impairment (MCI) is a transition state of cognitive decline from healthy brain to dementia. It is a prodrome of Alzheimer’s disease. Verbal‐Language/Orientation‐Memory (VLOM) ratio, a simply calculated index compares its language and memory scores helps to differentiate between Alzheimer’s disease and frontotemporal dementia (Mathuranath, et al.,2000). VLOM ratio is helpful in prognosticating and planning future management of the disease. The previous studies have shown the association of scores in cognitive domains with years of education (Nieto A, et al.,2016). Therefore, the objective of the study is to explore the association of years of education with VLOM ratio in MCI participants and normal participants.MethodCross sectional analysis was done using the baseline data from the cohort study Tata Longitudinal Study of Ageing (TLSA) from urban Bengaluru, India. Clinical dementia rating scale (CDR) was used to classify participants into normal and MCI category. CDR score of 0 was considered as normal, score of 0.5 as MCI. ACE III‐R (Addenbrooke’s Cognitive Examination III‐R) a neuropsychological test was administered and verbal fluency plus language to orientation plus memory (VLOM) ratio was calculated. Further, years of education was classified into three levels and comparison of VLOM ratio was made within these levels of education groups in both MCI and normal participants. Mann Whitney U test was used to compare the VLOM ratio between MCI and normal participants. VLOM ratio and years of education among the MCI and normal participants was compared using Kruskal‐Wallis test.ResultsAmong 953 participants, 69 had MCI and 884 participants had normal cognition. Comparison of VLOM ratio between MCI and normal participants showed a significant difference, with higher VLOM ratio in MCI participants (Median (IQR)) = 0.97 (0.11) vs 0.95 (0.09); p = 0.01). There was no significant difference in VLOM ratio between years of education in both normal and MCI participants.ConclusionThe VLOM ratio found to be higher in MCI group indicating that the scores in orientation and memory domain might be lower in MCI compared to Normal participants. There was no association of VLOM ratio with years of education in normal and MCI participants.

Plasma p‐tau 181 outperforms FDG‐PET and plasma‐NfL in the diagnosis of biological AD

AbstractBackgroundTo compare the performance of plasma p‐tau181, plasma‐NfL and FDG‐PET in the diagnosis of biological AD.MethodWe included 787 participants (cognitively unimpaired (CU, n = 251), mild cognitive impairment (MCI, n = 412), and Alzheimer’s dementia (AD, n = 124) from the ADNI database. Participants underwent measures of plasma p‐tau181, plasma‐NfL, CSF P‐tau181, [18F]FDG‐PET, amyloid‐PET and cognitive screening tests. ROC analyses were used to determine diagnostic accuracy of plasma P‐tau181, NfL and [18F]FDG‐PET using clinical diagnosis and core AD biomarkers (amyloid‐PET and CSF P‐tau181) as reference standards. We assessed whether plasma P‐tau181 and [18F]FDG‐PET diagnostic accuracies were significantly different with a bootstrap‐based test (pROC package in R). Second, correlations of [18F]FDG‐PET, plasma‐NfL, plasma P‐tau181 with P‐tau181 CSF, Aβ‐PET, and cognitive performance were evaluated. Correlation coefficients were compared via Cocor package, a statistical framework for comparing associations between intercorrelated measurements.ResultAcross the total sample we observed that plasma P‐tau181, plasma‐NfL, and [18F]FDG‐PET were able to identify individuals with CSF evidence of AD as well as participants who are amyloid‐PET+. In the MCI group, plasma P‐tau181 outperformed [18F]FDG‐PET and plasma‐NfL in identifying AD pathophysiology measured via CSF P‐tau181 (p = 0.0007), and amyloid‐PET (p = 0.001). We also observed that both plasma P‐tau181, plasma‐NfL and [18F]FDG‐PET were associated with AD pathophysiology measured by core AD biomarkers (CSF and amyloid‐PET). However, [18F]FDG‐PET was more closely associated with cognitive outcomes than plasma P‐tau181 and plasma NfL (MoCA: p &lt; 0.0001; MMSE: p&lt; 0.0001; CDR‐SB: p &lt; 0.0001)ConclusionThis study investigated the diagnostic properties of plasma P‐tau181 and two neurodegenerative biomarkers (plasma‐NfL and [18F]FDG‐PET) as well as their to identify biological AD. While plasma P‐tau181, plasma‐NfL concentrations and [18F]FDG‐PET were associated with AD pathophysiology measured by core AD biomarkers (CSF and amyloid‐PET), plasma P‐tau181 outperformed [18F]FDG‐PET and plasma‐NfL in identifying individuals with AD pathophysiology. However, we also observed that [18F]FDG‐PET was more strongly associated with neuropsychological assessments than plasma P‐tau181 and plasma‐NfL. Taken together, our study suggests that plasma P‐tau181 may aid in the evaluation of individuals by identifying those with underlying AD pathophysiology.

Cardiovascular and Metabolic Disorders in Mild Cognitive Impairment among Older African‐American Adults

AbstractBackgroundMild Cognitive Impairment (MCI) is one of the early predictors of Alzheimer’s disease (AD) and dementia. Assessing comorbidities associated with an MCI diagnosis is crucial not only for diagnostic accuracy but also for understanding the role of comorbidities for evident cognitive impairment. In this study we investigated the association between MCI (amnestic and/or non‐amnestic) and primary comorbidities of hypertension (HTN), hyperlipidaemia (HLD), and Diabetes Mellitus (DM).MethodsWe recruited 116 community‐dwelling older African American adults with subjective complaints (103 females, 13 males, mean age = 73.93, range = 65‐91) through the Wayne State Institute of Gerontology and Michigan Alzheimer’s Disease Research Center. Participants were consensus diagnosed as 57 healthy controls (HC), 32 amnestic MCI (aMCI), and 27 nonamnestic MCI (naMCI) based on NAAC Unified Data Set3 protocol. NIH Toolbox‐Cognition battery (NIHTB‐C) also was administered, but not used for consensus.ResultsAcross all the participants, UDS3 forms noted 69% with hypertension (HTN), 56% hyperlipidaemia (HLD), and 28% Type II Diabetes. Among the normal group: 65% had HTN, 51% had HLD, and 28% had DM. Among those diagnosed with aMCI: 66% had HTN, 53% had HLD, and 34% had DM. Among naMCI: 81% had HTN, 63% had HLD, and 22% had DM. Comorbidities did not differ significantly across diagnostic groups. Regarding NIHTB‐C, significant ANOVA interactions did show that aMCI and naMCI with DM were lower in Fluid Intelligence than scores for controls with DM (p = .017). Also, as compared to controls with HTN, naMCI with HTN were lower on Fluid Intelligence (p = .024).ConclusionOur findings did not demonstrate differences in cardiovascular‐ or diabetes‐related comorbidities across MCI groups and controls in this group of community‐dwelling African Americans. However, DM and HTN, but not HLD, did affect the relationship among the diagnostic groups for fluid intelligence, a summary measure of higher cognitive functioning. This suggests that comorbid medical factors can affect cognitive performance, particularly for MCI groups. Further research is needed to fully understand the scope of risk factors influencing cognitive decline in African Americans. Further analyses will assess relationships between cognitive diagnoses, race, health conditions, and demographic risk factors.

Early changes of fecal short‐chain fatty acid levels in patients with mild cognitive impairments

AbstractBackgroundMild cognitive impairment (MCI) is with great risk for the conversion to Alzheimer’s disease (AD). The aim of present study wanted to explore whether fecal Short‐chain fatty acids (SCFAs) could be used as the biomarker for the identification of MCI patients and to examine the relationship between fecal SCFAs and amyloid‐β deposition in the brain.MethodsA cohort of 32 MCI patients and 27 NC were recruited in our study. Fecal levels of SCFAs were measured using chromatography and mass spectrometry. Disease duration, ApoE genotype, body mass index, constipation, and diabetes were evaluated. To assess cognitive impairment, we used the Mini‐Mental Status Examination (MMSE). To assess brain atrophy, the degree of medial temporal atrophy (MTA score, Grade 0‐4) was measured by structural MRI. β‐amyloid positron emission tomography (PET) with 18F‐AV‐45 was performed in seven MCI patients to detect and quantify Aβ deposition in the brain.ResultsCompared with NC, MCI patients had significantly lower fecal levels of acetic acid, butyric acid, and caproic acid. Among fecal SCFAs, acetic acid performed the best in discriminating MCI from NC, achieved an AUC of 0.752 (p = 0.001, 95% CI: 0.628‐0.876), specificity of 66.7% and sensitivity of 75%. By combining fecal levels of acetic acid, butyric acid and caproic acid, the diagnostic specificity was significantly improved, reaching 88.9%. To better verify the diagnostic performance of SCFAs, we randomly assigned 80% of participants into training dataset and 20% into testing dataset. Only acetic acid showed significantly difference between these two groups in the training dataset. Based on the fecal levels of acetic acid, we achieved the ROC curve. Next, the ROC curve was evaluated in the independent test data and 66.7% (4 in 6) of MCI patients, and 80% (4 in 5) of NC could be identified correctly. Subgroup analysis showed that reduced fecal SCFAs in MCI group were negatively associated with Aβ deposition in cognition‐related brain regions.ConclusionOur findings suggest that gut metabolite SCFAs have the potential to serve as early diagnostic biomarkers for distinguishing patients with MCI from NC and could serve as potential targets for preventing AD.

Locus coeruleus metabolic changes as a predictor of tau accumulation and cognitive decline

AbstractBackgroundThe locus coeruleus (LC), the primary noradrenergic nucleus in the brainstem, is among the earliest brain structures to accumulate tau pathology, one of the neuropathologic hallmarks of Alzheimer’s disease (AD). Tau pathology in the earlier stages and significant cell loss in the disease’s later stages can affect the metabolic turnover of the LC. In addition, alterations in the LC’s metabolic turnover have been associated with tau formation and propagation. We investigated differences in LC glucose metabolism across AD clinical diagnostic and biomarker groups and its association with longitudinal tau, beta‐amyloid, and cognitive measurements.MethodWe investigated LC FDG‐PET signal of 295 participants from the ADNI‐1 and ADNI‐2/GO cohorts, who were equally distributed across the CSF beta‐amyloid (A) and phosphorylated‐tau (T) biomarker groups (Table 1). An in‐house developed joint‐entropy‐penalized image deblurring algorithm was used to quantify Positron Emission Tomography (PET) signal within small brain regions (Figure 2A). ANCOVA was used to investigate diagnostic and biomarker‐based group differences in LC metabolism. Post‐hoc Tukey contrasts corrected for multiple comparisons in subsequent pairwise analyses. Linear mixed‐effects models investigated the predictive value of LC metabolism on the rate of change in CSF‐based p‐tau, beta‐amyloid and cognitive scores.ResultLow LC FDG‐PET signal was observed in the amyloid and tau‐positive group compared to the other groups (p&lt;0.0001). Pair‐wise differences between the diagnostic groups did not survive correction for multiple comparisons. Combining biomarker and diagnostic status, we observed lower LC metabolism in A+T+ MCI and AD groups compared to CN and MCI A+T‐ and A‐T‐ groups (p&lt;0.001; Figure 1B). Lower LC metabolism predicted accumulation of p‐tau (t(127) = ‐2.65, p = 0.009) and PACC decline (t(128) = 2.2, p = 0.029; Figure 2). LC metabolism did not significantly predict changes in beta‐amyloid levels or MMSE scores.ConclusionThis is the first study relating in‐vivo spatially specific measures of LC metabolism to AD‐related biomarker status. Low LC metabolism was most pronounced in those with evidence of underlying AD pathologic change and cognitive impairment. Furthermore, lower LC metabolism predicted greater CSF‐based p‐tau accumulation and declining cognition over time, providing an early marker of AD progression.

AMYPAD: Correlation of Amyloid PET results with anxiety and depressive symptoms in SCD+, MCI and dementia patients

AbstractBackgroundDepression and anxiety are two frequent neuropsychiatric symptoms in Alzheimer’s disease (AD). Moreover, depressive symptoms and anxiety are frequently associated with greater amyloid deposition and risk of Alzheimer’s dementia. The aim of the present study is to assess the association between anxiety and depression and amyloid burden.MethodIn the AMPYAD study, participants with subjective cognitive decline (SCD+, n = 244), mild cognitive impairment (MCI, n = 341) and dementia (n = 255) were enrolled in eight European memory clinics between 2018 and 2020. They underwent three study visits (baseline, six months, thirteen months) with collection of cognitive data and symptoms of depression and anxiety (Hospital Anxiety and Depression Scale, HADS) as well as measures of quality of life and coping strategies. Amyloid burden was obtained as the standardized centiloid values (CL). Correlation analyses were carried out for HADS‐Scores and CL. We analyzed the progression of depressive and anxiety symptoms over time between patients with and without presence of amyloid (CL≥21) using mixed‐model‐ANOVA. All analyses were corrected for time of PET result disclosure.ResultA total of 840 patients (46.2% female; mean age of 70.9 (SD = 7.8)) were included. Among SCD+ patients, CL values were positively correlated with baseline anxiety scores (r(216) = .14, p = .038). In contrast, MCI and dementia patients showed a negative correlation between CL and depressive symptoms (r(296) = ‐.16, p = .005; r(209) = ‐.23, p&lt;.001). In SCD+, there was no significant interaction between time and CL status for anxiety (p = .512), but a significant main effect of time (F(2,302) = 3.4, p = .036, η² = .02), indicating a decline in anxiety symptoms over time. A trend indicated higher anxiety for amyloid positive participants, but the effect missed statistical significance (p = .068). For depressive symptoms, we found no main effects and no significant interaction of time and CL status.ConclusionThe AMYPAD study contains a large amyloid‐PET data set of patients from eight European memory clinics. We observed an association of increased anxiety scores with amyloid load in SCD+, but reduced symptoms of depression with higher amyloid load in the MCI and dementia group. These findings and their longitudinal development over time will be put into a broader context of patient characteristics.

The cortical glucose metabolism signatures of cognitive resilience in amyloid positive cognitively unimpaired elderly adults

AbstractBackgroundβ‐amyloid (Aβ) positive (A+) cognitively unimpaired (CU) individuals who maintain normal cognitive function at follow‐up may have good cognitive resilience (CR). A+/CU Individuals with high CR may have hypermetabolism. In this study, we explored the cortical glucose metabolism features of CR in A+/CU elderly adults.MethodWe analyzed 855 ADNI participants with Aβ PET (18F‐florbetapir (FBP) or 18F‐florbetaben (FBB)), CSF p‐Tau181, 18F‐fluorodeoxyglucose (FDG) PET and longitudinal residual hippocampal volume (rHCV) and cognitive assessments. A+/T+/N+ were defined as AD summary cortical standardized uptake value ratio (SUVR) for FBP ≥1.11 or FBB ≥1.08, CSF p‐Tau≥23pg/ml, and rHCV≤‐0.67 respectively. We compared intensity‐ and spatial‐normalized FDG SUVR images between 96 A‐/T‐/N‐/CU individuals (Reference group) and 68 A+/CU individuals who maintained CU at follow‐up regardless of T+ or N+ (CR group). Cortical regions with higher FDG SUVR in the CR group were identified as the CR_metaROI. Baseline CR_metaROI FDG‐SUVRs were compared between different stages of AD. Finally, we determined whether CR_metaROI FDG‐SUVR can modulate the association of Aβ PET and CSF p‐Tau with longitudinal changes of cognition, controlling for age, sex, and education.ResultCR_metaROI was primarily located in the precentral and postcentral regions (Fig. 1). AD dementia patients had lower CR_metaROI FDG SUVRs than the CU (standardized β (βstd) = ‐0.326[95% confidence interval (ci): ‐0.534, ‐0.119], p = 0.002) and MCI (βstd = ‐0.303[95%ci: ‐0.496, ‐0.111], p = 0.002) groups (Fig. 2A), and A+/CU group had higher (βstd = 0.605[95%ci: 0.352, 0.859], p&lt;0.001) CR_metaROI FDG SUVRs than the A‐/CU group (Fig. 2B). A‐/T+ and A+/T‐ individuals had lower (βstd = ‐0.248[95%ci: ‐0.4474, ‐0.022], p = 0.031) and higher (βstd = 0.305[95%ci: 0.093, 0.516], p = 0.005) CR_metaROI FDG SUVRs than the A‐/T‐ group respectively (Fig. 2C). The A+/T‐/N+ group had higher (βstd = 0.466[95%ci: 0.153, 0.779], p = 0.004) CR_metaROI FDG SUVRs than the A‐/T‐/N‐group (Fig. 2D). Moreover, higher baseline metaROI FDG‐SUVRs were associated with slower rates of decline in memory, executive function and preclinical Alzheimer cognitive composite scores upon the same levels of Aβ PET (Fig. 3A‐C) and CSF p‐Tau (Fig. 3D‐F).ConclusionWe identified a metabolism signature of CR in A+/CU elderly adults. These findings may provide novel insights into understanding the hypermetabolism patterns of CR in aging people.

Longitudinal Elastic Shape Analysis of Brain Subcortical Structures

AbstractBackgroundOver the past 30 years, MRI has become a ubiquitous tool for accurately visualizing the development of the brain’s subcortical structures. However, the quantification of subcortical structures is still in its infancy due to challenges in shape extraction, representation, and modeling.MethodA simple and efficient framework of longitudinal elastic shape analysis (LESA) is developed for subcortical structures. By employing the elastic shape analysis of static surfaces using the square root normal field (SRNF) representations, we computed shape summaries of surfaces and represented the complex subcortical structures using a small number of basis functions. Further, we statistically modeled the sparse longitudinal data to predict the spatial‐temporal shape changes of subcortical structures. We also conducted the temporal registration using the square‐root velocity function (SRVF) framework to quantify the accelerated aging of Alzheimer’s disease (AD).ResultWe applied LESA to analyze longitudinal neuroimaging data sets and showcased its wide applications in estimating continuous shape trajectories, building life‐span growth patterns, and comparing shape differences among different groups. Using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we found that from 60 to 90 years old, the AD group always had the largest left ventricle (LV) surface area and the smallest left hippocampus (LHC) surface area, followed by the MCI and NC groups. The enlargement of LV and the atrophy of LHC had different speeds among different groups. Most of the shape‐changing differences between the AD and NC groups were visible in the anterior and posterior ends of LV. The atrophy of LHC happened mainly at the posterior end with aging. The AD group had the sharpest posterior end, and the NC group deformed the least with aging. Compared with the NC group, the AD group showed an average accelerated enlargement of 2.91 years and accelerated shape change of 2.76 years in LV; an average accelerated atrophy of 2.56 years and accelerated shape change of 2.59 years in LHC. By constructing the life‐span growth trajectories, we observed increases in shape changes’ speed in both LV and LHC after 60 years old.ConclusionAD can significantly speed up the shape change of ventricle and hippocampus compared with normal aging.

Posterior cingulate cortex microRNA alterations in cognitive resilience, mild cognitive impairment, and Alzheimer’s disease

AbstractBackgroundMicroRNA (miRNA) dysregulation is linked to Alzheimer’s disease (AD) pathophysiology. We recently reported a role for miR‐298 and miR‐20b in AD risk and amyloid and tau metabolism. To examine the extent to which miRNAs are dysregulated during the earliest stages of AD, we quantified miRNA transcript levels in postmortem samples of posterior cingulate cortex (PCC), a default mode network (DMN) hub that underlies autobiographical memory, in normal subjects and those who died within the AD continuum.MethodPCC samples were obtained from Rush ROS/MAP participants who came to autopsy with a diagnosis of a) no cognitive impairment and low pathology (NCI‐LP, Braak stage I/II, n = 12), b) NCI with high pathology (NCI‐HP, Braak stage IV, representing cognitive resilience, n = 8), c) mild cognitive impairment (MCI, n = 10), or d) dementia due to AD (n = 9). Illumina‐based sequencing of total RNA was performed and trimmed reads with a size of 15‐31 bases were analyzed.ResultDifferential expression analysis (FDR ≤ 0.04) revealed 42 miRNAs significantly dysregulated among the NCI (independent of Braak stage), MCI, and AD groups. Two miRNAs, miR‐99a (FDR‐adjusted p = 0.01) and miR‐664b (p = 0.005), were downregulated in AD vs NCI and MCI, while miR‐30a (p = 0.006), miR‐374a (p = 0.004), and miR‐501 (p = 0.005) were significantly upregulated in MCI vs. NCI and AD. Three miRNAs were significantly downregulated in NCI‐HP vs. NCI‐LP: miR‐103a (p = 0.04), miR‐211 (p = 0.03), and miR‐4443 (p = 0.03). These miRNAs may operate in resilience‐related pathways. Correlation analysis revealed that decreasing PCC levels of miR‐664b levels were associated with poorer performance on antemortem tests of episodic memory (r = 0.41, p = 0.009), semantic memory (r = 0.44, p = 0.004), and visuospatial ability (r = 0.45, p = 0.004).ConclusionTaken together, these data may identify potential pathogenic or protective miRNA‐related mechanisms contributing to PCC and DMN function during the progression of AD, which may inform biomarker and intervention strategies. Ongoing analysis will identify functional pathway enrichment and miRNA targets.

Novel EEG tools to predict early cognitive impairments in Parkinson’s disease

AbstractBackgroundParkinson’s disease (PD) is a neurodegenerative disorder mainly characterised by motor symptoms but often also associated with dementia, increasing in likelihood with duration of disease. Electroencephalography (EEG) can aid in the early diagnosis of those at risk of developing dementia. Therefore, we analysed EEG recordings across conscious states from PD patients with and without cognitive symptoms (Latreille et al in 2016, https://doi.org/10.1093/brain/aww018).MethodBaseline data (REM sleep, NREM sleep, WAKE) from 38 PD patients (15 with and 23 without mild cognitive impairment) and 27 healthy subjects provided power spectra (qEEG), relative band power, as well as dominant frequency and its standard deviation to investigate markers for cognitive impairment. At follow‐up (an average 4.5 years after baseline), 11 patients developed dementia (PDD). Connectivity measures for left and right occipital and central electrodes were obtained as described in Crouch et al, 2018, https://doi.org/10.1038/s41598‐018‐19707‐1.ResultqEGG analyses confirmed previous reports of enhanced theta power during REM, NREM and WAKE for patients who developed PDD. A reduction in alpha power during WAKE was observed for MCI and PDD groups when compared to controls. Alpha rhythms generally decrease with age, alongside enhanced theta power, and this outcome may contribute to the global cognitive status of patients. A reduced and less variable dominant frequency was evident for MCI and PDD patients during WAKE, indicative of reduced flexibility of the network. Finally, rPDC detected disease‐specific changes, such as enhanced cross‐hemispheric connectivity in PDD in the alpha band during NREM sleep that indicate a lack of dynamism in the network. Also noted was the diminished interhemispheric communication in delta power between both central and occipital channels in PDD during WAKE.ConclusionTogether, we here confirm that changes of theta and alpha power are optimal discriminatory biomarkers for PD with cognitive decline (with MCI and at risk of dementia). Also, spectral and rPDC analyses indicate that dementia in PD is preceded by a loss of dynamism in EEG activity during MCI stages, both at the electrode and network level.

The effect of short Aβ(1‐38) peptides on cognitive decline and conversion to AD dementia: longitudinal data using the DELCODE cohort

AbstractBackgroundThe current research consensus is that long Aβ(1‐42) peptides are neurotoxic pathological hallmarks of AD. Interestingly, short Aβ peptides, such as Aβ(1‐38), appear to be less neurotoxic and may counteract AD neuropathology. However, research on Aβ(1‐38) is scarce and their effect on AD pathophysiology in humans in vivo remains unclear. Here, we investigate whether higher relative concentrations of Aβ(1‐38) levels in the cerebrospinal fluid (CSF), compared to Aβ(1‐42) alone, with phospho Tau (pTau) or with both, protect against cognitive decline and risk of conversion to AD dementia.MethodParticipants with subjective cognitive decline (n = 56), mild cognitive impairment (n = 60) and AD dementia (n = 61) from the DELCODE study with a likely pathological Aβ42/P‐tau ratio of &gt; 9.68 at baseline were included. Cognitive performance was assessed annually for five years with the Mini‐Mental State Examination (MMSE) and the Preclinical Alzheimer Cognitive Composite (i.e., PACC5), which was derived from the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Conversion to AD dementia was assessed with the Clinical Dementia Rating (CDR) and NINCDS‐ARDA criteria. CSF was collected at baseline. Data was analysed using statistical model fitting procedures for linear mixed models, i.e., Likelihood Ratio (LR) testing. Additionally, we controlled for baseline diagnostic status and the relative effect of Aβ(1‐42) and pTau.ResultHigher Aβ(1‐38) baseline levels were associated with better follow‐up performance on the PACC5 in all three groups across the AD continuum (p &lt; .001). For the MMSE, only the MCI group showed significantly better follow‐up performance (p = .003), and a trend was found in the other groups (SCD: p = .090, AD p = .099). Furthermore, higher Aβ(1‐38) baseline levels decreased risk of conversion to AD dementia within three years by 88% (p = .003) in the SCD and MCI groups.ConclusionAcross the AD spectrum, higher Aβ(1‐38) levels protect against cognitive decline and reduce the risk of conversion to AD dementia. This could inform individual risk prediction in memory clinic patients.

Visualizing Braak stages with deformation‐based morphometry in super‐sampled MRI

AbstractBackgroundUnlike MRI data, histological data collected post‐mortem can’t be used to study the longitudinal formation of neurofibrillary tangles (NFT) and corresponding brain changes over time for individuals. Although structural MRI offers the possibility of non‐invasive longitudinal antemortem imaging, its resolution is a limiting factor. Using image processing and super‐sampling techniques, we examined the possibility of employing structural MRI to observe the NFT footprints in the brain.MethodData included baseline MRI scans of 1473 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset including ADNI1, 2, and GO. Voxel size was super‐sampled to 0.5×0.5×0.5mm (Manjón et al. 2010) before non‐linear registration to an ADNI‐based unbiased symmetric MRI template. The resulting deformation fields were used to compute the Jacobian determinant map for each subject as a proxy for local volume. For each subject, the sex‐ and age‐corrected z‐scored Jacobian maps were then calculated and averaged for each diagnostic group: normal subjects(CN), early MCI, late MCI, and AD. The average map for CN group was subtracted from the other average maps to show the AD‐stage differences.ResultThe Braak tau staging pattern (Braak &amp; Braak, 1995) is seen in average Jacobian maps as the disease progresses. Subjects in the MCI groups showed on average more pronounced neurodegeneration in the transentorhinal and entorhinal region (Braak Stages 1‐2), followed by degeneration in the hippocampus (Stages 3‐4). Later on, this neurodegeneration progresses toward the neocortex (Stages 5‐6). Figure 1 illustrates these results: A) the template, B)EMCI vs CN, C) LMCI vs EMCI, D) LMCI vs CN, E) AD vs CN. Red color shows an increase and blue indicates a decrease in local volume.ConclusionUsing novel image processing techniques, we were able to verify the staging of AD‐related neurofibrillary pathology progression in MRI data. This shows that structural MRI data, although limited in resolution, can still offer in‐depth insight into the brain antemortem which will in return allow the longitudinal and possible lifespan studies with an accuracy close to histological data.

Cross-cultural effects of reminiscence therapy on life satisfaction and autobiographical memory of older adults: a pilot study across Mexico and Spain

BackgroundThere are increasing reports on the cognitive and emotional benefits of positive reminiscence therapy in older people. The objective of this study is to assess the differential improvement of the quality of life for older people in different vital situations (three different types of aging) and from different countries by implementing a positive reminiscence therapy program (REMPOS).MethodsThe participants were 144 older adults above the age of 65, 77 participants from Spain (45 experimental groups, 32 control groups) and 67 from Mexico (34 experimental groups, 33 control groups). The participants were recruited from nursing and retirement homes. A factorial randomized design with pre–post measurement with three independent variables: country (Mexico, Spain), condition (experimental, control), and types of aging (healthy aging, HA., mild cognitive impairment, MCI., Alzheimer’s disease, AD). The experimental groups received REMPOS therapy and control groups received standard cognitive stimulation program. The quality of life was measured with the Life Satisfaction Inventory for adults (LSI-A) and autobiographical memory test (AMT) before and after REMPOS therapy.ResultsThe REMPOS intervention showed significantly higher positive effects than the control condition on the recall of specific positive memories across countries and types of aging, except for the Spanish MCI group. Life satisfaction in the Alzheimer’s and MCI group only improved with REMPOS in the Mexican sample.ConclusionsThe REMPOS effects showed generalizable effects across countries, but the cross-cultural differences shown highlight the necessity of running studies to test those differential effects.

Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer’s disease: findings based on urine proteomics and machine learning

BackgroundAlzheimer’s disease is a prevalent disease with a heavy global burden. Proteomics is the systematic study of proteins and peptides to provide comprehensive descriptions. Aiming to obtain a more accurate and convenient clinical diagnosis, researchers are working for better biomarkers. Urine is more convenient which could reflect the change of disease at an earlier stage. Thus, we conducted a cross-sectional study to investigate novel diagnostic panels.MethodsWe firstly enrolled participants from China-Japan Friendship Hospital from April 2022 to November 2022, collected urine samples, and conducted an LC–MS/MS analysis. In parallel, clinical data were collected, and clinical examinations were performed. After statistical and bioinformatics analyses, significant risk factors and differential urinary proteins were determined. We attempt to investigate diagnostic panels based on machine learning including LASSO and SVM.ResultsFifty-seven AD patients, 43 MCI patients, and 62 CN subjects were enrolled. A total of 3366 proteins were identified, and 608 urine proteins were finally included in the analysis. There were 33 significantly differential proteins between the AD and CN groups and 15 significantly differential proteins between the MCI and CN groups. AD diagnostic panel included DDC, CTSC, EHD4, GSTA3, SLC44A4, GNS, GSTA1, ANXA4, PLD3, CTSH, HP, RPS3, CPVL, age, and APOE ε4 with an AUC of 0.9989 in the training test and 0.8824 in the test set while MCI diagnostic panel included TUBB, SUCLG2, PROCR, TCP1, ACE, FLOT2, EHD4, PROZ, C9, SERPINA3, age, and APOE ε4 with an AUC of 0.9985 in the training test and 0.8143 in the test set. Besides, diagnostic proteins were weakly correlated with cognitive functions.ConclusionsIn conclusion, the procedure is convenient, non-invasive, and useful for diagnosis, which could assist physicians in differentiating AD and MCI from CN.