Mild Cognitive Impairment Subgroups Research Articles

Predicting Longitudinal Cognitive Decline and Alzheimer's Conversion in Mild Cognitive Impairment Patients Based on Plasma Biomarkers.

The increasing burden of Alzheimer's disease (AD) emphasizes the need for effective diagnostic and therapeutic strategies. Despite available treatments targeting amyloid beta (Aβ) plaques, disease-modifying therapies remain elusive. Early detection of mild cognitive impairment (MCI) patients at risk for AD conversion is crucial, especially with anti-Aβ therapy. While plasma biomarkers hold promise in differentiating AD from MCI, evidence on predicting cognitive decline is lacking. This study's objectives were to evaluate whether plasma protein biomarkers could predict both cognitive decline in non-demented individuals and the conversion to AD in patients with MCI. This study was conducted as part of the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD), a prospective, community-based cohort. Participants were based on plasma biomarker availability and clinical diagnosis at baseline. The study included MCI (n = 50), MCI-to-AD (n = 21), and cognitively unimpaired (CU, n = 40) participants. Baseline plasma concentrations of six proteins-total tau (tTau), phosphorylated tau at residue 181 (pTau181), amyloid beta 42 (Aβ42), amyloid beta 40 (Aβ40), neurofilament light chain (NFL), and glial fibrillary acidic protein (GFAP)-along with three derivative ratios (pTau181/tTau, Aβ42/Aβ40, pTau181/Aβ42) were analyzed to predict cognitive decline over a six-year follow-up period. Baseline protein biomarkers were stratified into tertiles (low, intermediate, and high) and analyzed using a linear mixed model (LMM) to predict longitudinal cognitive changes. In addition, Kaplan-Meier analysis was performed to discern whether protein biomarkers could predict AD conversion in the MCI subgroup. This prospective cohort study revealed that plasma NFL may predict longitudinal declines in Mini-Mental State Examination (MMSE) scores. In participants categorized as amyloid positive, the NFL biomarker demonstrated predictive performance for both MMSE and total scores of the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet (CERAD-TS) longitudinally. Additionally, as a baseline predictor, GFAP exhibited a significant association with cross-sectional cognitive impairment in the CERAD-TS measure, particularly in amyloid positive participants. Kaplan-Meier curve analysis indicated predictive performance of NFL, GFAP, tTau, and Aβ42/Aβ40 on MCI-to-AD conversion. This study suggests that plasma GFAP in non-demented participants may reflect baseline cross-sectional CERAD-TS scores, a measure of global cognitive function. Conversely, plasma NFL may predict longitudinal decline in MMSE and CERAD-TS scores in participants categorized as amyloid positive. Kaplan-Meier curve analysis suggests that NFL, GFAP, tTau, and Aβ42/Aβ40 are potentially robust predictors of future AD conversion.

Subgroups of mild cognitive impairment based on cognitive function among Chinese older adults

Abstract Background: Due to the heterogeneity of mild cognitive impairment (MCI), the key points for nonpharmaceutical therapies cannot be determined. Examining impairment characteristics among different MCI subgroups would therefore be beneficial to the development of nonpharmaceutical therapies. Objective: To characterize the subgroups of older Chinese with MCI and examine factors associated with the membership of distinct subgroups. Methods: This cross-sectional study was conducted between October 2018 and June 2022 in Fujian, East China, with 1017 older adults with MCI deemed eligible. Exclusion criteria included any diagnosis of other neurological or psychiatric conditions that could affect cognition. Participants underwent a neuropsychological test that assessed memory, language, attention, and executive function(s) and completed a questionnaire collecting their socioeconomic and clinical variables. Neuropsychological test scores were transformed into z-scores, which were then examined using latent class analysis. Associations between socioeconomic and clinical variables, and MCI subgroups were assessed using multinomial logistic regression analysis. Results: Three MCI subgroups were identified: G1 “Minimally impairment” (56.64%), G2 “Moderate impairment” (36.28%), and G3 “Worst impairment” (7.08%). Age, place of residence, marriage status, living status, and participation in intellectual activities differed significantly across the 3 MCI subgroups (P<0.05). Compared with the minimally impairment group, participants with the worst impairment were less likely to participate in intellectual activity and more likely to live in a nursing home. Conclusion: Cognitive function is a suitable foundation for establishing classifications among individuals with MCI. Nonpharmalogical therapies may target older adults at higher risk of moderate or worst impairment.

MMANet: A Multi-Task Residual Network for Alzheimer's Disease Classification and Brain Age Prediction

Objective: Alzheimer's disease (AD) is an irreversible neurodegenerative disease, while mild cognitive impairment (MCI) is a clinical precursor of AD, thus differentiation of AD, MCI and normal control (NC) from noninvasive magnetic resonance imaging (MRI) has positive clinical implications. Material and method: We utilize a 3D residual network to classify AD, MCI, and NC, and add a multiscale module to the original network to enhance the feature representation capability of the network, as well as a cross-dimensional attentional mechanism to enhance the network's attention to important brain regions. We experimentally verified that the network is more inclined to overestimate the brain age of patients in AD and MCI subgroups, thus proving that there is a high correlation between the brain age prediction task and the AD classification task. Therefore, we adopted a multi-task learning approach, using brain age prediction as a supplementary task for AD classification to reduce the risk of overfitting of the network during the training process. Results: Our method achieved 96.02% accuracy, 93.40% precision, 91.48% recall, and 92.24% F1 value in AD/MCI/NC classification. Conclusions: Ablation experiments confirmed that our proposed cross-dimensional attention and multiscale modules can improve the diagnostic performance of AD and MCI, and that multi-task learning in conjunction with brain age prediction can further improve the performance.

News event memory in amnestic and non-amnestic MCI, heritable risk for dementia, and subjective memory complaints

Robust and sensitive clinical measures are needed for more accurate and earlier detection of Alzheimer's disease (AD), for staging preclinical AD, and for gauging the efficacy of treatments. Mild impairment on episodic memory tests is thought to indicate a cognitive risk of developing AD and mild cognitive impairment (MCI), considered to be a transitional stage between normal aging and AD. Novel tests of semantic memory, such as memory for news events, are also impaired early on but have received little clinical attention even though they may provide a novel way to assess cognitive risk for AD. We examined memory for news events in older adults with normal cognition (NC, N = 34), amnestic MCI (aMCI, N = 27), or non-aMCI (N = 10) using the Retrograde Memory News Events Test (RM-NET). We asked if news event memory was sensitive to 1) aMCI and also non-aMCI, which has rarely been examined, 2) genetic risk for dementia (positive family history of any type of dementia, presence of an APOE-4 allele, or polygenic risk for AD), and 3) subjective memory functioning judgments about the past. We found that both MCI subgroups exhibited impaired RM-NET Lifespan accuracy scores together with temporally-limited retrograde amnesia. For the aMCI group amnesia extended back 45 years prior to testing, but not beyond that time frame. The extent of retrograde amnesia could not be reliably estimated in the small non-aMCI group. The effect sizes of having MCI on the RM-NET were medium for the non-aMCI group and large for the aMCI group, whereas the effect sizes of participant characteristics on RM-NET accuracy scores were small. For the combined MCI group (N = 37), news event memory was significantly related to positive family history of dementia but was not related to the more specific genetic markers of AD risk. For the NC group, news event memory was not related to any measure of genetic risk. Objective measures of past memory from the RM-NET were not related to subjective memory judgements about the present or the recent past in either group. By contrast, when individuals subjectively compared their present versus past memory abilities, there was a significant association between this judgment and objective measures of the past from the RM-NET (direct association for the NC group and inverse for the MCI group). The RM-NET holds significant promise for early identification of those with cognitive and genetic risk factors for AD and non-AD dementias.

Economic Impact of Progression from Mild Cognitive Impairment to Alzheimer Disease in the United States

BackgroundLimited evidence exists on the economic burden of individuals who progress from mild cognitive impairment (MCI) to Alzheimer disease and related dementia disorders (ADRD).ObjectivesTo assess the all-cause health care resource utilization and costs for individuals who develop ADRD following an MCI diagnosis compared to those with stable MCI.DesignThis was a retrospective cohort study from January 01, 2014, to December 31, 2019.SettingThe Merative MarketScan Commercial and Medicare Databases were used.ParticipantsIndividuals were included if they: (1) were aged 50 years or older; (2) had ≥1 claim with an MCI diagnosis based on the International Classification of Diseases, Ninth Revision (ICD-9) code of 331.83 or the Tenth Revision (ICD-10) code of G31.84; and had continuous enrollment. Individuals were excluded if they had a diagnosis of Parkinson’s disease or ADRD or prescription of ADRD medication.MeasurementsOutcomes included all-cause utilization and costs per patient per year in the first 12 months following MCI diagnosis, in total and by care setting: inpatient admissions, emergency department (ED) visits, outpatient visits, and pharmacy claims.ResultsOut of the total of 5185 included individuals, 1962 (37.8%) progressed to ADRD (MCI-to-ADRD subgroup) and 3223 (62.2%) did not (Stable MCI subgroup). Adjusted all-cause utilization was higher for all care settings in the MCI-to-ADRD subgroup compared with the Stable MCI subgroup. Adjusted all-cause mean total costs ($34599 vs $24541; mean ratio [MR], 1.41 [95% CI, 1.31–1.51]; P<.001), inpatient costs ($47463 vs $38004; MR, 1.25 [95% CI, 1.08–1.44]; P=.002), ED costs ($4875 vs $3863; MR, 1.26 [95% CI, 1.11–1.43]; P<.001), and outpatient costs ($16652 vs $13015; MR, 1.28 [95% CI, 1.20–1.37]; P<.001) were all significantly higher for the MCI-to-ADRD subgroup compared with the Stable MCI subgroup.ConclusionsIndividuals who progressed from MCI to ADRD had significantly higher health care costs than individuals with stable MCI. Early identification of MCI and delaying its progression is important to improve patient and economic outcomes.

Clinical variables contributing to the identification of biologically defined subgroups within cognitively unimpaired and mild cognitive impairment individuals.

A lack of consensus exists in linking demographic, behavioral, and cognitive characteristics to biological stages of dementia, defined by the ATN (amyloid, tau, neurodegeneration) classification incorporating amyloid, tau, and neuronal injury biomarkers. Using a random forest classifier we investigated whether 27 demographic, behavioral, and cognitive characteristics allowed distinction between ATN-defined groups with the same cognitive profile. This was done separately for three cognitively unimpaired (CU) (112 A-T-N-; 46 A+T+N+/-; 65 A-T+/-N+/-) and three mild cognitive impairment (MCI) (128 A-T-N-; 223 A+T+N+/-; 94 A-T+/-N+/-) subgroups. Classification-balanced accuracy reached 39% for the CU and 52% for the MCI subgroups. Logical Delayed Recall (explaining 16% of the variance), followed by the Alzheimer's Disease Assessment Scale 13 (14%) and Everyday Cognition Informant (10%), were the most relevant characteristics for classification of the MCI subgroups. Race and ethnicity, marital status, and Everyday Cognition Patient were not relevant (0%). The demographic, behavioral, and cognitive measures used in our model were not informative in differentiating ATN-defined CU profiles. Measures of delayed memory, general cognition, and activities of daily living were the most informative in differentiating ATN-defined MCI profiles; however, these measures alone were not sufficient to reach high classification performance.

The effect of the mind–body intervention on depression in people with cognitive impairment: A systematic review and meta‐analysis

AbstractPurposeTo assess the effect of the mind–body intervention on depression in people with cognitive impairment.MethodsFive databases (PubMed, Web of Science, Embase, Cochrane Library, and PsycINFO) were systematically searched, the Cochrane Collaboration's tool was used for quality assessment, and subgroup analyses were performed according to participant types, intervention types, and duration of interventions.FindingsA total of 16 randomized controlled trials were included, two of which were unable to obtain complete data and one had high heterogeneity. The overall meta‐analysis of 13 articles showed that the mind–body intervention was not effective in promoting depression in individuals with cognitive impairment (standardized mean difference [SMD] = −0.08; 95% confidence interval (CI) [−0.27, 0.11]; p = 0.419; I2 = 52.4%, 13 studies, 1406 participants) compared with the control group. Subgroup analysis showed that depression was significantly improved in the dementia subgroup after the mind–body intervention compared to the control group (SMD = −0.31; 95% CI [−0.57, −0.05]; p = 0.021. I2 = 29.2%, six studies, 399 participants), while no significant changes were observed in the mild cognitive impairment subgroup. Among different intervention types and durations, no intervention was found to have a significant positive effect on depression.ConclusionThe mind–body intervention is an effective intervention for improving depression in people with dementia, but its effect is not significant in individuals with overall cognitive impairment. Follow‐up high‐quality studies are needed to further verify the effects of different mind–body interventions on depression in people with different severities of cognitive impairment.Implications for Nursing Practice: By proving the effectiveness of the mind–body intervention in improving depression in people with dementia, it will provide evidence for the further development and improvement of the mode and content of the mind–body intervention and increase the attention and support of the healthcare system and society for the mind–body intervention.

Cerebrospinal and Blood Biomarkers in Alzheimer’s Disease: Did Mild Cognitive Impairment Definition Affect Their Clinical Usefulness?

Despite Alzheimer’s Disease (AD) being known from the times of Alois Alzheimer, who lived more than one century ago, many aspects of the disease are still obscure, including the pathogenesis, the clinical spectrum definition, and the therapeutic approach. Well-established biomarkers for AD come from the histopathological hallmarks of the disease, which are Aβ and phosphorylated Tau protein aggregates. Consistently, cerebrospinal fluid (CSF) Amyloid β (Aβ) and phosphorylated Tau level measurements are currently used to detect AD presence. However, two central biases affect these biomarkers. Firstly, incomplete knowledge of the pathogenesis of diseases legitimates the search for novel molecules that, reasonably, could be expressed by neurons and microglia and could be detected in blood simpler and earlier than the classical markers and in a higher amount. Further, studies have been performed to evaluate whether CSF biomarkers can predict AD onset in Mild Cognitive Impairment (MCI) patients. However, the MCI definition has changed over time. Hence, the studies on MCI patients seem to be biased at the beginning due to the imprecise enrollment and heterogeneous composition of the miscellaneous MCI subgroup. Plasma biomarkers and novel candidate molecules, such as microglia biomarkers, have been tentatively investigated and could represent valuable targets for diagnosing and monitoring AD. Also, novel AD markers are urgently needed to identify molecular targets for treatment strategies. This review article summarizes the main CSF and blood AD biomarkers, underpins their advantages and flaws, and mentions novel molecules that can be used as potential biomarkers for AD.

Longitudinal resting-state EEG in amyloid-positive patients along the Alzheimer’s disease continuum: considerations for clinical trials

BackgroundTo enable successful inclusion of electroencephalography (EEG) outcome measures in Alzheimer’s disease (AD) clinical trials, we retrospectively mapped the progression of resting-state EEG measures over time in amyloid-positive patients with mild cognitive impairment (MCI) or dementia due to AD.MethodsResting-state 21-channel EEG was recorded in 148 amyloid-positive AD patients (MCI, n = 88; dementia due to AD, n = 60). Two or more EEG recordings were available for all subjects. We computed whole-brain and regional relative power (i.e., theta (4-8 Hz), alpha1 (8-10 Hz), alpha2 (10-13 Hz), beta (13-30 Hz)), peak frequency, signal variability (i.e., theta permutation entropy), and functional connectivity values (i.e., alpha and beta corrected amplitude envelope correlation, theta phase lag index, weighted symbolic mutual information, inverted joint permutation entropy). Whole-group linear mixed effects models were used to model the development of EEG measures over time. Group-wise analysis was performed to investigate potential differences in change trajectories between the MCI and dementia subgroups. Finally, we estimated the minimum sample size required to detect different treatment effects (i.e., 50% less deterioration, stabilization, or 50% improvement) on the development of EEG measures over time, in hypothetical clinical trials of 1- or 2-year duration.ResultsWhole-group analysis revealed significant regional and global oscillatory slowing over time (i.e., increased relative theta power, decreased beta power), with strongest effects for temporal and parieto-occipital regions. Disease severity at baseline influenced the EEG measures’ rates of change, with fastest deterioration reported in MCI patients. Only AD dementia patients displayed a significant decrease of the parieto-occipital peak frequency and theta signal variability over time. We estimate that 2-year trials, focusing on amyloid-positive MCI patients, require 36 subjects per arm (2 arms, 1:1 randomization, 80% power) to detect a stabilizing treatment effect on temporal relative theta power.ConclusionsResting-state EEG measures could facilitate early detection of treatment effects on neuronal function in AD patients. Their sensitivity depends on the region-of-interest and disease severity of the study population. Conventional spectral measures, particularly recorded from temporal regions, present sensitive AD treatment monitoring markers.

Plasma biomarkers of neurodegeneration in mild cognitive impairment with Lewy bodies.

Blood biomarkers of Alzheimer's disease (AD) may allow for the early detection of AD pathology in mild cognitive impairment (MCI) due to AD (MCI-AD) and as a co-pathology in MCI with Lewy bodies (MCI-LB). However not all cases of MCI-LB will feature AD pathology. Disease-general biomarkers of neurodegeneration, such as glial fibrillary acidic protein (GFAP) or neurofilament light (NfL), may therefore provide a useful supplement to AD biomarkers. We aimed to compare the relative utility of plasma Aβ42/40, p-tau181, GFAP and NfL in differentiating MCI-AD and MCI-LB from cognitively healthy older adults, and from one another. Plasma samples were analysed for 172 participants (31 healthy controls, 48 MCI-AD, 28 possible MCI-LB and 65 probable MCI-LB) at baseline, and a subset (n = 55) who provided repeated samples after ≥1 year. Samples were analysed with a Simoa 4-plex assay for Aβ42, Aβ40, GFAP and NfL, and incorporated previously-collected p-tau181 from this same cohort. Probable MCI-LB had elevated GFAP (p < 0.001) and NfL (p = 0.012) relative to controls, but not significantly lower Aβ42/40 (p = 0.06). GFAP and p-tau181 were higher in MCI-AD than MCI-LB. GFAP discriminated all MCI subgroups, from controls (AUC of 0.75), but no plasma-based marker effectively differentiated MCI-AD from MCI-LB. NfL correlated with disease severity and increased with MCI progression over time (p = 0.011). Markers of AD and astrocytosis/neurodegeneration are elevated in MCI-LB. GFAP offered similar utility to p-tau181 in distinguishing MCI overall, and its subgroups, from healthy controls.

Grey matter volume and CSF biomarkers predict neuropsychological subtypes of MCI

There is increasing evidence of different subtypes of individuals with mild cognitive impairment (MCI). An important line of research is whether neuropsychologically-defined subtypes have distinct patterns of neurodegeneration and cerebrospinal fluid (CSF) biomarker composition. In our study, we demonstrated that MCI participants of the ADNI database (N = 640) can be discriminated into 3 coherent neuropsychological subgroups. Our clustering approach revealed amnestic MCI, mixed MCI, and cluster-derived normal subgroups. Furthermore, classification modeling revealed that specific predictive features can be used to differentiate amnestic and mixed MCI from cognitively normal (CN) controls: CSF Aβ142 concentration for the former and CSF Aβ1–42 concentration, tau concentration as well as grey matter atrophy (especially in the temporal and occipital lobes) for the latter. In contrast, participants from the cluster-derived normal subgroup exhibited an identical profile to CN controls in terms of cognitive performance, brain structure, and CSF biomarker levels. Our comprehensive data analytics strategy provides further evidence that multimodal neuropsychological subtyping is both clinically and neurobiologically meaningful.

Eye Movement Latency Coefficient of Variation as a Predictor of Cognitive Impairment: An Eye Tracking Study of Cognitive Impairment.

Studies demonstrated impairment in the control of saccadic eye movements in Alzheimer's disease (AD) and people with mild cognitive impairment (MCI) when conducting the pro-saccade and antisaccade tasks. Research showed that changes in the pro and antisaccade latencies may be particularly sensitive to dementia and general executive functioning. These tasks show potential for diagnostic use, as they provide a rich set of potential eye tracking markers. One such marker, the coefficient of variation (CV), is so far overlooked. For biological markers to be reliable, they must be able to detect abnormalities in preclinical stages. MCI is often viewed as a predecessor to AD, with certain classifications of MCI more likely than others to progress to AD. The current study examined the potential of CV scores on pro and antisaccade tasks to distinguish participants with AD, amnestic MCI (aMCI), non-amnesiac MCI (naMCI), and older controls. The analyses revealed no significant differences in CV scores across the groups using the pro or antisaccade task. Antisaccade mean latencies were able to distinguish participants with AD and the MCI subgroups. Future research is needed on CV measures and attentional fluctuations in AD and MCI individuals to fully assess this measure's potential to robustly distinguish clinical groups with high sensitivity and specificity.

Saccadic Eye Movements in Patients with Mild Cognitive Impairment: A Longitudinal Study

Saccadic eye movements are one of the sensitive and noninvasive methods to help monitor the cognitive course of mild cognitive impairment (MCI). The study aimed to evaluate both pro and anti-saccade longitudinally and the relationship between cognitive functions and eye movements in MCI subgroups and healthy controls (HCs) at a two-year follow-up. This study revealed that the anti-saccade anticipatory responses decreased in amnestic MCI (aMCI). Correct vertical pro-saccades increased in non-amnestic MCI (naMCI), while the express saccades decreased. Our study demonstrated that longer than two years of follow-up is necessary to monitor the course of MCI. Findings of the relationships between longitudinal changes of saccades and cognitive measurements demonstrated the usability of eye movements in evaluating the process of MCI.

A Pilot Study on Cerebral Blood Flow and Mini-Mental State Examination to Predict Amyloid Deposition in Preclinical Alzheimer's Disease.

Earlier differential diagnosis of dementia remains a major challenge. Although amyloid deposition by positron emission tomography is an emerging standard for the diagnosis of Alzheimer's disease, it is too expensive for routine use in clinical settings. We conducted a pilot study on the potential usefulness of single-photon emission computed tomography and the Mini-Mental State Examination to predict amyloid positron emission tomography positivity in preclinical Alzheimer's disease. Eighteen subjects, including 11 with mild cognitive impairment and 7 with subjective cognitive decline, underwent 18F-florbetapir positron emission tomography, 99mTc-ethylcysteinate dimer cerebral perfusion single-photon emission computed tomography, and the Mini-Mental State Examination. For the assessment of amyloid deposition, visual judgment as a qualitative method and a semiautomatic software analysis as a quantitative method were used. Six subjects were judged as amyloid positive, including 4 mild cognitive impairment and 2 subjective cognitive decline subjects. Compared to the amyloid positron emission tomography-negative group, this group showed a statistically significant difference in the Mini-Mental State Examination recall score [2 (1 : 3) vs. 3 (2 : 3), P = .041] and single-photon emission computed tomography findings from the amyloid-negative group. In the mild cognitive impairment subgroup, correlations were found between amyloid deposition and single-photon emission computed tomography indicators, while in the subjective cognitive decline subgroup, only the Mini-Mental State Examination recall score correlated with amyloid deposition. The Mini-Mental State Examination recall score and single-photon emission computed tomography indicators may be worthwhile for further evaluation as predictors of amyloid deposition in the preclinical stage.

Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study.

Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.

Asymmetric degeneration of the hippocampus predicts risk of Alzheimer’s disease

AbstractBackgroundAsymmetry of the hippocampus is an important finding in normal aging and Alzheimer disease (AD). However, it is largely unknown how asymmetric degeneration of the hippocampus is associated with an individual's risk of AD.MethodA deep‐learning (DL) classification model was developed to quantify degeneration of the hippocampus based on its magnetic resonance imaging (MRI) scan and binary mask. Different from most extant studies, the DL model was trained to distinguish AD patients from cognitively normal (CN) controls with their bilateral hippocampi as two separate instances for generating two classification scores (positive indicating AD‐like and negative indicating CN‐like), one for each hippocampus. An integration of the two scores of an individual’s MRI data was utilized to classify AD patients and CN controls, to predict individual mild cognitive impairment (MCI) subjects’ progression to AD dementia in a time‐to‐event analysis setting, as well as to identify subgroups of MCI subjects with distinct risk of AD dementia. Particularly, the DL classification model was trained on the ADNI 1 cohort (n=420), and then validated for distinguishing AD subjects from CN controls on the ADNI 2&amp;GO (n=469) and AIBL (n=396) cohorts and for predicting MCI subjects’ progression to AD dementia and identifying MCI subgroups on the ADNI 2&amp;GO cohort (n=439).ResultThe DL model achieved accurate classification performance with classification rates of 0.923 and 0.927 and AUC values of 0.963 and 0.961 on the ADNI‐GO&amp;2 and AIBL cohorts, respectively. The integration of the bilateral hippocampal classification scores achieved a concordance index of 0.765 for predicting individual MCI subjects’ progression to AD dementia. The MCI subgroups with bilateral AD‐like, bilateral CN‐like, and one AD‐like and one CN‐like scores were significantly different in risk of progression to AD dementia and a variety of biomarkers and cognitive scores, including Apoe4, Amyloid, tau, and ADAS‐cog score (p &lt; 0.0004).ConclusionThe present study demonstrated that the asymmetric hippocampal degeneration patterns quantified using a DL model on MRI data could achieve promising classification performance for distinguishing AD patients from CN controls and may aid the clinicians to early prediction of MCI subjects’ progression to AD dementia to facilitate personalized medicine.

Generative adversarial network constrained multiple loss autoencoder: A deep learning-based individual atrophy detection for Alzheimer's disease and mild cognitive impairment.

Exploring individual brain atrophy patterns is of great value in precision medicine for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the current individual brain atrophy detection models are deficient. Here, we proposed a framework called generative adversarial network constrained multiple loss autoencoder (GANCMLAE) for precisely depicting individual atrophy patterns. The GANCMLAE model was trained using normal controls (NCs) from the Alzheimer's Disease Neuroimaging Initiative cohort, and the Xuanwu cohort was employed to validate the robustness of the model. The potential of the model for identifying different atrophy patterns of MCI subtypes was also assessed. Furthermore, the clinical application potential of the GANCMLAE model was investigated. The results showed that the model can achieve good image reconstruction performance on the structural similarity index measure (0.929 ± 0.003), peak signal-to-noise ratio (31.04 ± 0.09), and mean squared error (0.0014 ± 0.0001) with less latent loss in the Xuanwu cohort. The individual atrophy patterns extracted from this model are more precise in reflecting the clinical symptoms of MCI subtypes. The individual atrophy patterns exhibit a better discriminative power in identifying patients with AD and MCI from NCs than those of the t-test model, with areas under the receiver operating characteristic curve of 0.867 (95%: 0.837-0.897) and 0.752 (95%: 0.71-0.790), respectively. Similar findings are also reported in the AD and MCI subgroups. In conclusion, the GANCMLAE model can serve as an effective tool for individualised atrophy detection.

The mean diffusivity of forceps minor is useful to distinguish amnestic mild cognitive impairment from mild cognitive impairment caused by cerebral small vessel disease.

In recent years, the desire to make a more fine-grained identification on mild cognitive impairment (MCI) has become apparent, the etiological diagnosis of MCI in particular. Nevertheless, new methods for the etiological diagnosis of MCI are currently insufficient. The objective of this study was to establish discriminative measures for amnestic mild cognitive impairment (a-MCI) and MCI caused by cerebral small vessel disease (CSVD). In total, 20 normal controls (NCs), 33 a-MCI patients, and 25 CSVD-MCI patients performed comprehensive neuropsychological assessments concerning global cognitive function and five cognitive domains as well as magnetic resonance imaging scan with diffusion tensor imaging (DTI). Diffusion parameters including fractional anisotropy and mean diffusivity of 20 major white matter metrics were obtained by ROI-based analyses. The neuropsychological tests and diffusion measurements were compared and binary logistic regression was used to identify the best differential indicator for the two MCI subgroups. The discriminating power was calculated by receiver operating characteristic analysis. Amnestic mild cognitive impairment group showed significant impairment in memory and language function, while CSVD-MCI group revealed more deficits in multi-cognitive domains of memory, language, attention and executive function than controls. Compared to the a-MCI, CSVD-MCI was significantly dysfunctional in the executive function. The CSVD-MCI group had decreased fractional anisotropy and increased mean diffusivity values throughout widespread white matter areas. CSVD-MCI presented more severe damage in the anterior thalamic radiation, forceps major, forceps minor and right inferior longitudinal fasciculus compared with a-MCI group. No significant neuropsychological tests were found in the binary logistic regression model, yet the DTI markers showed a higher discriminative power than the neuropsychological tests. The Stroop test errors had moderate potential (AUC = 0.747; sensitivity = 76.0%; specificity = 63.6%; P = 0.001; 95% CI: 0.617-0.877), and the mean diffusivity value of forceps minor demonstrated the highest predictive power to discriminate each MCI subtype (AUC = 0.815; sensitivity = 88.0%; specificity = 72.7%; P < 0.001; 95% CI: 0.698-0.932). The mean diffusivity of forceps minor may serve as an optimal indicator to differentiate between a-MCI and CSVD-MCI.

The predictive value of fibrinogen in the occurrence of mild cognitive impairment events in patients with diabetic peripheral neuropathy

BackgroundResearch suggests that fibrinogen (Fib) is related to mild cognitive impairment (MCI) and diabetic peripheral neuropathy (DPN) and the risk of MCI in patients with DPN is greatly increased, although no studies have evaluated the predictive value of Fib for the risk of MCI in patients with DPN.MethodsThis prospective observational clinical study enrolled 207 type 2 diabetes mellitus (T2DM) patients, who were divided into diabetes with no neuropathy (102 cases) and diabetes with neuropathy (105 cases) groups. Meanwhile, 90 healthy unrelated subjects were recruited as controls. The incidence of MCI in the DPN patients was followed up for 2 years. Divide patients in the DPN group into subgroups according to whether MCI occur, use multivariate logistic regression to analyze independent factors of MCIs in DPN patients within 2 years, and use ROC curve to analyze the predictive value of Fib for MCI in DPN patients.ResultsFib levels were significantly higher in diabetic subjects with neuropathy compared with those without (P < 0.001). In further subgroup analysis of DPN patients who were divided according to the occurrence of MCI, baseline data of the MCI subgroup showed Fib levels were higher than that in the non-MCI group while education levels declined (P < 0.001). The education level and increased Fib levels were independent factors for the occurrence of MCI within 2 years after the onset of DPN (OR = 0.769, 95% CI: 0.605 ~ 0.968, P = 0.037; OR = 2.674, 95% CI: 1.094 ~ 3.168, P = 0.002). The ROC curve indicated that the predictive value of Fib was (AUC = 0.764, 95% CI: 0.671 ~ 0.842, P < 0.001).ConclusionsFib may function as a predictor for assessing the risk of MCI in DPN patients.

Population Attributable Fractions for Modifiable Risk Factors of Incident Dementia in Cognitively Normal and Mild Cognitively Impaired Older Adults: Data from Two Cohort Studies.

Background:Recent global meta-analyses show that 40% of dementia cases can be attributed to twelve modifiable risk factors.Objective:To investigate how health promotion strategies may differ in specific populations, this study estimated population attributable fractions (PAFs) of these risk factors for dementia in cognitively normal (CN) individuals and individuals with mild cognitive impairment (MCI) in United States and Greek cohorts.Methods:We re-analyzed data from the National Alzheimer’s Coordinating Centre (NACC, n = 16,147, mean age 75.2±6.9 years, 59.0% female) and the Hellenic Longitudinal Investigation of Aging and Diet (HELIAD, n = 1,141, mean age 72.9±5.0 years, 58.0% female). PAFs for the total samples and CN and MCI subgroups were calculated based on hazard ratios for the risk of dementia and risk factor prevalence in NACC (9 risk factors) and HELIAD (10 risk factors).Results:In NACC, 2,630 participants developed MCI (25.1%) and 3,333 developed dementia (20.7%) during a mean follow-up of 4.9±3.5 years. Weighted overall PAFs were 19.4% in the total sample, 15.9% in the CN subgroup, and 3.3% in the MCI subgroup. In HELIAD, 131 participants developed MCI (11.2%) and 68 developed dementia (5.9%) during an average follow-up of 3.1±0.86 years. Weighted overall PAFs were 65.5% in the total sample, 65.8% in the CN subgroup and 64.6% in the MCI subgroup.Conclusion:Translation of global meta-analysis data on modifiable risk factors should be carefully carried out per population. The PAFs of risk factors differ substantially across populations, directing health policy making to tailored risk factor modification plans.