Prediction Model of Conversion to Dementia Risk in Subjects with Amnestic Mild Cognitive Impairment: A Longitudinal, Multi-Center Clinic-Based Study

Identification of amnestic mild cognitive impairment by structural and functional MRI using a machine-learning approach

OBJECTIVE

The importance of early treatment for mild cognitive impairment (MCI) has been extensively shown. However, classifying patients presenting with memory complaints in clinical practice as having MCI vs normal results is difficult. Herein, we assessed the feasibility of applying a machine learning approach based on structural volumes and functional connectomic profiles to classify the cognitive levels of cognitively unimpaired (CU) and amnestic MCI (aMCI) groups. We further applied the same method to distinguish aMCI patients with a single memory impairment from those with multiple memory impairments.

METHODS

Fifty patients with aMCI were enrolled and classified as having either verbal or visual-aMCI (verbal or visual memory impairment), or both aMCI (verbal and visual memory impairments) based on memory test results. In addition, 26 CU patients were enrolled in the control group. All patients underwent structural T1-weighted magnetic resonance imaging (MRI) and resting-state functional MRI. We obtained structural volumes and functional connectomic profiles from structural and functional MRI, respectively, using graph theory. A support vector machine (SVM) algorithm was employed, and k-fold cross-validation was performed to discriminate between groups.

RESULTS

The SVM classifier based on structural volumes revealed an accuracy of 88.9% at classifying the cognitive levels of patients with CU and aMCI. However, when the structural volumes and functional connectomic profiles were combined, the accuracy increased to 92.9%. In the classification of verbal or visual-aMCI (n = 22) versus both aMCI (n = 28), the SVM classifier based on structural volumes revealed a low accuracy of 36.7%. However, when the structural volumes and functional connectomic profiles were combined, the accuracy increased to 53.1%.

CONCLUSION

Structural volumes and functional connectomic profiles obtained using a machine learning approach can be used to classify cognitive levels to distinguish between aMCI and CU patients. In addition, combining the functional connectomic profiles with structural volumes results in a better classification performance than the use of structural volumes alone for identifying both "aMCI versus CU" and "verbal- or visual-aMCI versus both aMCI" patients.

Prevalence, cognitive characteristics, and influencing factors of amnestic mild cognitive impairment among older adults residing in an urban community in Chengdu, China

Objective

Dementia is a significant public health concern, and mild cognitive impairment (MCI) serves as a transitional stage between normal aging and dementia. Among the various types of MCI, amnestic MCI (aMCI) has been identified as having a higher likelihood of progressing to Alzheimer's dimension. However, limited research has been conducted on the prevalence of aMCI in China. Therefore, the objective of this study is to investigate the prevalence of aMCI, examine its cognitive characteristics, and identify associated risk factors.

Methods

In this cross-sectional study, we investigated a sample of 368 older adults aged 60 years and above in the urban communities of Chengdu, China. The participants underwent a battery of neuropsychological assessments, including the Mini-Mental State Examination (MMSE), the Clinical Dementia Rating (CDR), Auditory Verbal Learning Test (AVLT), Wechsler's Logical Memory Task (LMT), Boston Naming Test (BNT) and Trail Making Test Part A (TMT-A). Social information was collected by standard questionnaire. Multiple logistic regression analysis was utilized to screen for the risk and protective factors of aMCI.

Results

The data analysis included 309 subjects with normal cognitive function and 59 with aMCI, resulting in a prevalence of 16.0% for aMCI. The average age of participants was 69.06 ± 7.30 years, with 56.0% being females. After controlling for age, gender and education, the Spearman partial correlation coefficient between various cognitive assessments and aMCI ranged from -0.52 for the long-term delayed recall scores in AVLT to 0.19 for the time-usage scores in TMT-A. The results indicated that all cognitive domains, except for naming scores (after semantic cue of BNT) and error quantity (in TMT-A), showed statistically significant associations with aMCI. Furthermore, the multiple logistic regression analysis revealed that older age (OR = 1.044, 95%CI: 1.002~1.087), lower educational level, and diabetes (OR = 2.450, 95%CI: 1.246~4.818) were risk factors of aMCI.

Conclusion

This study found a high prevalence of aMCI among older adults in Chengdu, China. Individuals with aMCI exhibited lower cognitive function in memory, language, and executive domains, with long-term delayed recall showing the strongest association. Clinicians should prioritize individuals with verbal learning and memory difficulties, especially long-term delayed recall, in clinical practice.

Genetic Predisposition for White Matter Hyperintensities and Risk of Mild Cognitive Impairment and Alzheimer's Disease: Results from the HELIAD Study

The present study investigated the association of genetic predisposition for white matter hyperintensities (WMHs) with incident amnestic mild cognitive impairment (aMCI) or Alzheimer's disease (AD), as well as whether such an association was influenced by age, sex, and cognitive reserve. Overall, 537 individuals without aMCI or dementia at baseline were included. Among them, 62 individuals developed aMCI/AD at follow up. Genetic propensity to WMH was estimated using a polygenic risk score for WMHs (PRS WMH). The association of PRS WMH with aMCI/AD incidence was examined using COX models. A higher PRS WMH was associated with a 47.2% higher aMCI/AD incidence (p = 0.015) in the fully adjusted model. Subgroup analyses showed significant results in the older age group, in which individuals with a higher genetic predisposition for WMHs had a 3.4-fold higher risk for developing aMCI/AD at follow up (p < 0.001), as well as in the lower cognitive reserve (CR, proxied by education years) group, in which individuals with a higher genetic predisposition for WMHs had an over 2-fold higher risk (p = 0.013). Genetic predisposition for WMHs was associated with aMCI/AD incidence, particularly in the group of participants with a low CR. Thus, CR might be a modifier in the relationship between genetic predisposition for WMHs and incident aMCI/AD.

Occipital Amyloid Deposition Is Associated with Rapid Cognitive Decline in the Alzheimer's Disease Continuum

BACKGROUND

Clinical significance of additional occipital amyloid-β (Aβ) plaques in Alzheimer's disease (AD) remains unclear.

OBJECTIVE

In this study, we investigated the effect of regional Aβ deposition on cognition in patients on the AD continuum, especially in the occipital region.

METHODS

We retrospectively reviewed the medical record of 208 patients with AD across the cognitive continuum (non-dementia and dementia). Multivariable linear regression analyses were performed to determine the effect of regional Aβ deposition on cognitive function. A linear mixed model was used to assess the effect of regional deposition on longitudinal changes in Mini-Mental State Examination (MMSE) scores. Additionally, the patients were dichotomized according to the occipital-to-global Aβ deposition ratio (ratio ≤1, Aβ-OCC- group; ratio >1, Aβ-OCC+ group), and the same statistical analyses were applied for between-group comparisons.

RESULTS

Regional Aβ burden itself was not associated with baseline cognitive function. In terms of Aβ-OCC group effect, the Aβ-OCC+ group exhibited a poorer cognitive performance on language function compared to the Aβ-OCC- group. High Aβ retention in each region was associated with a rapid decline in MMSE scores, only in the dementia subgroup. Additionally, Aβ-OCC+ individuals exhibited a faster annual decline in MMSE scores than Aβ-OCC- individuals in the non-dementia subgroup (β= -0.77, standard error [SE] = 0.31, p = 0.013).

CONCLUSION

The present study demonstrated that additional occipital Aβ deposition was associated with poor baseline language function and rapid cognitive deterioration in patients on the AD continuum.

Classification and prediction of cognitive trajectories of cognitively unimpaired individuals

Objectives

Efforts to prevent Alzheimer's disease (AD) would benefit from identifying cognitively unimpaired (CU) individuals who are liable to progress to cognitive impairment. Therefore, we aimed to develop a model to predict cognitive decline among CU individuals in two independent cohorts.

Methods

A total of 407 CU individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 285 CU individuals from the Samsung Medical Center (SMC) were recruited in this study. We assessed cognitive outcomes by using neuropsychological composite scores in the ADNI and SMC cohorts. We performed latent growth mixture modeling and developed the predictive model.

Results

Growth mixture modeling identified 13.8 and 13.0% of CU individuals in the ADNI and SMC cohorts, respectively, as the "declining group." In the ADNI cohort, multivariable logistic regression modeling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], p < 0.001), low baseline cognitive composite scores (β [SE]: -0.274 [0.070], p < 0.001), and reduced hippocampal volume (β [SE]: -0.952 [0.302], p = 0.002) were predictive of cognitive decline. In the SMC cohort, increased Aβ uptake (β [SE]: 2.007 [0.549], p < 0.001) and low baseline cognitive composite scores (β [SE]: -4.464 [0.758], p < 0.001) predicted cognitive decline. Finally, predictive models of cognitive decline showed good to excellent discrimination and calibration capabilities (C-statistic = 0.85 for the ADNI model and 0.94 for the SMC model).

Conclusion

Our study provides novel insights into the cognitive trajectories of CU individuals. Furthermore, the predictive model can facilitate the classification of CU individuals in future primary prevention trials.

The Seoul Neuropsychological Screening Battery (SNSB) for Comprehensive Neuropsychological Assessment

The Seoul Neuropsychological Screening Battery (SNSB) is known as a representative comprehensive neuropsychological evaluation tool in Korea since its first standardization in 2003. It was the main neuropsychological evaluation tool in the Clinical Research Center for Dementia of South Korea, a large-scale multi-center cohort study in Korea that was started in 2005. Since then, it has been widely used by dementia clinicians, and further solidified its status as a representative dementia evaluation tool in Korea. Many research results related to the SNSB have been used as a basis for the diagnosis and evaluation of patients in various clinical settings, especially, in many areas of cognitive assessment, including dementia evaluation. The SNSB version that was updated in 2012 provides psychometrically improved norms and indicators through a model-based standardization procedure based on a theoretical probability distribution in the norm's development. By providing a score for each cognitive domain, it is easier to compare cognitive abilities between domains and to identify changes in cognitive domain functions over time. Through the development of the SNSB-Core, a short form composed of core tests, which also give a composite score was provided. The SNSB is a useful test battery that provides key information on the evaluation of early cognitive decline, analysis of cognitive decline patterns, judging the severity of dementia, and differential diagnosis of dementia. This review will provide a broad understanding of the SNSB by describing the test composition, contents of individual subtests, characteristics of standardization, analysis of the changed standard score, and related studies.

Preclinical Alzheimer's dementia: a useful concept or another dead end?

The term, preclinical dementia, was introduced in 2011 when new guidelines for the diagnosis of Alzheimer's dementia (AD) were published. In the intervening 11 years, many studies have appeared in the literature focusing on this early stage. A search conducted in English on Google Scholar on 06.23.2022 using the term "preclinical (Alzheimer's) dementia" produced 121, 000 results. However, the label is arguably more relevant for research purposes, and it is possible that the knowledge gained may lead to a cure for AD. The term has not been widely adopted by clinical practitioners. Furthermore, it is still not possible to predict who, after a diagnosis of preclinical dementia, will go on to develop AD, and if so, what the risk factors (modifiable and non-modifiable) might be. This Review/Theoretical article will focus on preclinical Alzheimer's dementia (hereafter called preclinical AD). We outline how preclinical AD is currently defined, explain how it is diagnosed and explore why this is problematic at a number of different levels. We also ask the question: Is the concept 'preclinical AD' useful in clinical practice or is it just another dead end in the Holy Grail to find a treatment for AD? Specific recommendations for research and clinical practice are provided.

Association of Enlarged Perivascular Spaces With Amyloid Burden and Cognitive Decline in Alzheimer Disease Continuum

BACKGROUND AND OBJECTIVES

To investigate the effects of enlarged perivascular space (EPVS) on amyloid burden and cognitive function in Alzheimer disease (AD) continuum.

METHODS

We retrospectively reviewed 208 patients with AD across the cognitive continuum (preclinical, prodromal, and AD dementia) who showed amyloid deposition on 18F-florbetaben PET scans and 82 healthy controls. EPVSs were counted for each patient in the basal ganglia (BG), centrum semiovale (CSO), and hippocampus (HP) on axial T2-weighted images. Patients were then classified according to the number of EPVSs into the EPVS+ (>10 EPVSs) and EPVS- (0-10 EPVSs) groups for the BG and CSO, respectively. In terms of HP-EPVS, equal or more than 7 EPVSs on bilateral hemisphere were regarded as the presence of HP-EPVS. After adjusting for markers of small vessel disease (SVD), multiple linear regression analyses were performed to determine the intergroup differences in global and regional amyloid deposition and cognitive function at the time of diagnosis of AD continuum. A linear mixed model was used to assess the effects of EPVSs on the longitudinal changes in the Mini-Mental State Examination (MMSE) scores.

RESULTS

Amyloid burden at the time of diagnosis of AD continuum was not associated with the degree of BG-, CSO-, or HP-EPVS. BG-EPVS affected language and frontal/executive function via SVD markers, and HP-EPVS was associated with general cognition via SVD markers. However, CSO-EPVS was not associated with baseline cognition. A higher number of CSO-EPVS was significantly associated with a more rapid decline in MMSE scores (β = -0.58, standard error = 0.23, p = 0.011) independent of the amyloid burden. In terms of BG and HP, there was no difference between the EPVS+ and EPVS- groups in the rate of longitudinal decreases in MMSE scores.

DISCUSSION

Our findings suggest that BG-, CSO-, and HP-EPVS are not associated with baseline β-amyloid burden or cognitive function independently of SVD at the diagnosis of AD continuum. However, CSO-EPVS appears to be associated with the progression of cognitive decline in an amyloid-independent manner. Further studies are needed to investigate whether CSO-EPVS is a potential therapeutic target in patients with AD continuum.

Prediction value of the genetic risk of type 2 diabetes on the amnestic mild cognitive impairment conversion to Alzheimer’s disease

Amnestic mild cognitive impairment (aMCI) and Type 2 diabetes mellitus (T2DM) are both important risk factors for Alzheimer’s disease (AD). We aimed to investigate whether a T2DM-specific polygenic risk score (PRS_sT2DM) can predict the conversion of aMCI to AD and further explore the underlying neurological mechanism. All aMCI patients were from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database and were divided into conversion (aMCI-C, n = 164) and stable (aMCI-S, n = 222) groups. PRS_sT2DM was calculated by PRSice-2 software to explore the predictive efficacy of the aMCI conversion to AD. We found that PRS_sT2DM could independently predict the aMCI conversion to AD after removing the common variants of these two diseases. PRS_sT2DM was significantly negatively correlated with gray matter volume (GMV) of the right superior frontal gyrus in the aMCI-C group. In all aMCI patients, PRS_sT2DM was significantly negatively correlated with the cortical volume of the right superior occipital gyrus. The cortical volume of the right superior occipital gyrus could significantly mediate the association between PRS_sT2DM and aMCI conversion. Gene-based analysis showed that T2DM-specific genes are highly expressed in cortical neurons and involved in ion and protein binding, neural development and generation, cell junction and projection, and PI3K-Akt and MAPK signaling pathway, which might increase the aMCI conversion by affecting the Tau phosphorylation and amyloid-beta (Aβ) accumulation. Therefore, the PRS_sT2DM could be used as a measure to predict the conversion of aMCI to AD.

Prediction of conversion to dementia using interpretable machine learning in patients with amnestic mild cognitive impairment

Purpose

Amnestic mild cognitive impairment (aMCI) is a transitional state between normal aging and Alzheimer’s disease (AD). However, not all aMCI patients are observed to convert to AD dementia. Therefore, developing a predictive algorithm for the conversion of aMCI to AD dementia is important. Parametric methods, such as logistic regression, have been developed; however, it is difficult to reflect complex patterns, such as non-linear relationships and interactions between variables. Therefore, this study aimed to improve the predictive power of aMCI patients’ conversion to dementia by using an interpretable machine learning (IML) algorithm and to identify the factors that increase the risk of individual conversion to dementia in each patient.

Methods

We prospectively recruited 705 patients with aMCI who had been followed-up for at least 3 years after undergoing baseline neuropsychological tests at the Samsung Medical Center between 2007 and 2019. We used neuropsychological tests and apolipoprotein E (APOE) genotype data to develop a predictive algorithm. The model-building and validation datasets were composed of data of 565 and 140 patients, respectively. For global interpretation, four algorithms (logistic regression, random forest, support vector machine, and extreme gradient boosting) were compared. For local interpretation, individual conditional expectations (ICE) and SHapley Additive exPlanations (SHAP) were used to analyze individual patients.

Results

Among the four algorithms, the extreme gradient boost model showed the best performance, with an area under the receiver operating characteristic curve of 0.852 and an accuracy of 0.807. Variables, such as age, education, the scores of visuospatial and memory domains, the sum of boxes of the Clinical Dementia Rating scale, Mini-Mental State Examination, and APOE genotype were important features for creating the algorithm. Through ICE and SHAP analyses, it was also possible to interpret which variables acted as strong factors for each patient.

Conclusion

We were able to propose a predictive algorithm for each aMCI individual’s conversion to dementia using the IML technique. This algorithm is expected to be useful in clinical practice and the research field, as it can suggest conversion with high accuracy and identify the degree of influence of risk factors for each patient.

Predictive Scale for Amyloid PET Positivity Based on Clinical and MRI Variables in Patients with Amnestic Mild Cognitive Impairment

The presence of amyloid-β (Aβ) deposition is considered important in patients with amnestic mild cognitive impairment (aMCI), since they can progress to Alzheimer’s disease dementia. Amyloid positron emission tomography (PET) has been used for detecting Aβ deposition, but its high cost is a significant barrier for clinical usage. Therefore, we aimed to develop a new predictive scale for amyloid PET positivity using easily accessible tools. Overall, 161 aMCI patients were recruited from six memory clinics and underwent neuropsychological tests, brain magnetic resonance imaging (MRI), apolipoprotein E (APOE) genotype testing, and amyloid PET. Among the potential predictors, verbal and visual memory tests, medial temporal lobe atrophy, APOE genotype, and age showed significant differences between the Aβ-positive and Aβ-negative groups and were combined to make a model for predicting amyloid PET positivity with the area under the curve (AUC) of 0.856. Based on the best model, we developed the new predictive scale comprising integers, which had an optimal cutoff score ≥ 3. The new predictive scale was validated in another cohort of 98 participants and showed a good performance with AUC of 0.835. This new predictive scale with accessible variables may be useful for predicting Aβ positivity in aMCI patients in clinical practice.

Development of a machine learning model to predict mild cognitive impairment using natural language processing in the absence of screening

BACKGROUND

Patients and their loved ones often report symptoms or complaints of cognitive decline that clinicians note in free clinical text, but no structured screening or diagnostic data are recorded. These symptoms/complaints may be signals that predict who will go on to be diagnosed with mild cognitive impairment (MCI) and ultimately develop Alzheimer's Disease or related dementias. Our objective was to develop a natural language processing system and prediction model for identification of MCI from clinical text in the absence of screening or other structured diagnostic information.

METHODS

There were two populations of patients: 1794 participants in the Adult Changes in Thought (ACT) study and 2391 patients in the general population of Kaiser Permanente Washington. All individuals had standardized cognitive assessment scores. We excluded patients with a diagnosis of Alzheimer's Disease, Dementia or use of donepezil. We manually annotated 10,391 clinic notes to train the NLP model. Standard Python code was used to extract phrases from notes and map each phrase to a cognitive functioning concept. Concepts derived from the NLP system were used to predict future MCI. The prediction model was trained on the ACT cohort and 60% of the general population cohort with 40% withheld for validation. We used a least absolute shrinkage and selection operator logistic regression approach (LASSO) to fit a prediction model with MCI as the prediction target. Using the predicted case status from the LASSO model and known MCI from standardized scores, we constructed receiver operating curves to measure model performance.

RESULTS

Chart abstraction identified 42 MCI concepts. Prediction model performance in the validation data set was modest with an area under the curve of 0.67. Setting the cutoff for correct classification at 0.60, the classifier yielded sensitivity of 1.7%, specificity of 99.7%, PPV of 70% and NPV of 70.5% in the validation cohort.

DISCUSSION AND CONCLUSION

Although the sensitivity of the machine learning model was poor, negative predictive value was high, an important characteristic of models used for population-based screening. While an AUC of 0.67 is generally considered moderate performance, it is also comparable to several tests that are widely used in clinical practice.

Prediction Models for Conversion From Mild Cognitive Impairment to Alzheimer’s Disease: A Systematic Review and Meta-Analysis

Background and Purpose

Alzheimer’s disease (AD) is a devastating neurodegenerative disorder with no cure, and available treatments are only able to postpone the progression of the disease. Mild cognitive impairment (MCI) is considered to be a transitional stage preceding AD. Therefore, prediction models for conversion from MCI to AD are desperately required. These will allow early treatment of patients with MCI before they develop AD. This study performed a systematic review and meta-analysis to summarize the reported risk prediction models and identify the most prevalent factors for conversion from MCI to AD.

Methods

We systematically reviewed the studies from the databases of PubMed, CINAHL Plus, Web of Science, Embase, and Cochrane Library, which were searched through September 2021. Two reviewers independently identified eligible articles and extracted the data. We used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist for the risk of bias assessment.

Results

In total, 18 articles describing the prediction models for conversion from MCI to AD were identified. The dementia conversion rate of elderly patients with MCI ranged from 14.49 to 87%. Models in 12 studies were developed using the data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). C-index/area under the receiver operating characteristic curve (AUC) of development models were 0.67–0.98, and the validation models were 0.62–0.96. MRI, apolipoprotein E genotype 4 (APOE4), older age, Mini-Mental State Examination (MMSE) score, and Alzheimer’s Disease Assessment Scale cognitive (ADAS-cog) score were the most common and strongest predictors included in the models.

Conclusion

In this systematic review, many prediction models have been developed and have good predictive performance, but the lack of external validation of models limited the extensive application in the general population. In clinical practice, it is recommended that medical professionals adopt a comprehensive forecasting method rather than a single predictive factor to screen patients with a high risk of MCI. Future research should pay attention to the improvement, calibration, and validation of existing models while considering new variables, new methods, and differences in risk profiles across populations.

Association of Dipeptidyl Peptidase-4 Inhibitor Use and Amyloid Burden in Diabetic Patients With AD-Related Cognitive Impairment

OBJECTIVES

To investigate whether dipeptidyl peptidase-4 inhibitors (DPP-4i) have beneficial effects on amyloid aggregation and longitudinal cognitive outcome in diabetic Alzheimer's disease-related cognitive impairment (ADCI).

METHODS

We retrospectively reviewed 282 patients with ADCI who had positive scan of ¹⁸F-florbetaben amyloid PET images were classified into three groups according to a prior diagnosis of diabetes and DPP-4i use: diabetic patients being treated with (ADCI-DPP-4i⁺, n=70) or without DPP-4i (ADCI-DPP-4i^-, n=71), and non-diabetic patients (n=141). Multiple linear regression analyses were performed to determine inter-group differences in global and regional amyloid retention using standardized uptake value ratios calculated from cortical areas. We assessed the longitudinal changes in Mini-Mental State Examination (MMSE) score using a linear mixed model.

RESULTS

The ADCI-DPP-4i⁺ group had lower global amyloid burden than the ADCI-DPP-4i^- group (β = 0.075, SE = 0.024, p = 0.002) and the non-diabetic ADCI group (β = 0.054, SE = 0.021, p = 0.010) after adjusting for age, sex, education, cognitive status, and APOE ε4 carrier status. Additionally, the ADCI-DPP-4i⁺ group had lower regional amyloid burden in temporo-parietal areas than either the ADCI-DPP-4i^- group or the non-diabetic ADCI group. The ADCI-DPP-4i⁺ group showed a slower longitudinal decrease in MMSE score (β = 0.772, SE = 0.272, p = 0.005) and memory recall sub-score (β = 0.291, SE = 0.116, p = 0.012) than the ADCI-DPP-4i^- group.

CONCLUSIONS

These findings suggest that DPP-4i use is associated with low amyloid burden and favorable long-term cognitive outcome in diabetic patients with ADCI.

Implication of Small Vessel Disease MRI Markers in Alzheimer's Disease and Lewy Body Disease

BACKGROUND

Small vessel disease (SVD) magnetic resonance imaging (MRI) markers including deep and periventricular white matter hyperintensities (PWMH), lacunes, and microbleeds are frequently observed in Alzheimer's disease (AD) and Lewy body disease (LBD), but their implication has not been clearly elucidated.

OBJECTIVE

To investigate the implication of SVD MRI markers in cognitively impaired patients with AD and/or LBD.

METHODS

We consecutively recruited 57 patients with pure AD-related cognitive impairment (ADCI), 49 with pure LBD-related cognitive impairment (LBCI), 45 with mixed ADCI/LBCI, and 34 controls. All participants underwent neuropsychological tests, brain MRI, and amyloid positron emission tomography. SVD MRI markers including the severity of deep and PWMH and the number of lacunes and microbleeds were visually rated. The relationships among vascular risk factors, SVD MRI markers, ADCI, LBCI, and cognitive scores were investigated after controlling for appropriate covariates.

RESULTS

LBCI was associated with more severe PWMH, which was conversely associated with an increased risk of LBCI independently of vascular risk factors and ADCI. PWMH was associated with attention and visuospatial dysfunction independently of vascular risk factors, ADCI, and LBCI. Both ADCI and LBCI were associated with more lobar microbleeds, but not with deep microbleeds.

CONCLUSION

Our findings suggest that PWMH could reflect degenerative process related with LBD, and both AD and LBD independently increase lobar microbleeds.

A simple nomogram prediction model to identify relatively young patients with mild cognitive impairment who may progress to Alzheimer's disease

AIM

Construct a clinical predictive model based on easily accessible clinical features and imaging data to identify patients 65 years of age and younger with mild cognitive impairment(MCI) who may progress to Alzheimer's disease(AD).

METHODS

From the ADNI database, patients with MCI who were less than or equal to 65 years of age and who had been followed for 6-60 months were selected.We collected demographic data, neuropsychological test scale scores, and structural magnetic images of these patients. Clinical characteristics were then screened, and VBM and SBM analyses were performed using structural nuclear magnetic images to obtain imaging histology characteristics. Finally, predictive models were constructed combining the clinical and imaging histology characteristics.

RESULTS

The constructed nomogram has a cross-validated AUC of 0.872 in the training set and 0.867 in the verification set, and the calibration curve fits well.We also provide an online model-based forecasting tool.

CONCLUSION

The model has good performance and uses convenience,it should be able to provide assistance in clinical work to screen relatively young MCI patients who may progress to AD.

Neuropsychological Contribution to Predict Conversion to Dementia in Patients with Mild Cognitive Impairment Due to Alzheimer's Disease

BACKGROUND

Diagnosis of Alzheimer's disease (AD) confirmed by biomarkers allows the patient to make important life decisions. However, doubt about the fleetness of symptoms progression and future cognitive decline remains. Neuropsychological measures were extensively studied in prediction of time to conversion to dementia for mild cognitive impairment (MCI) patients in the absence of biomarker information. Similar neuropsychological measures might also be useful to predict the progression to dementia in patients with MCI due to AD.

OBJECTIVE

To study the contribution of neuropsychological measures to predict time to conversion to dementia in patients with MCI due to AD.

METHODS

Patients with MCI due to AD were enrolled from a clinical cohort and the effect of neuropsychological performance on time to conversion to dementia was analyzed.

RESULTS

At baseline, converters scored lower than non-converters at measures of verbal initiative, non-verbal reasoning, and episodic memory. The test of non-verbal reasoning was the only statistically significant predictor in a multivariate Cox regression model. A decrease of one standard deviation was associated with 29% of increase in the risk of conversion to dementia. Approximately 50% of patients with more than one standard deviation below the mean in the z score of that test had converted to dementia after 3 years of follow-up.

CONCLUSION

In MCI due to AD, lower performance in a test of non-verbal reasoning was associated with time to conversion to dementia. This test, that reveals little decline in the earlier phases of AD, appears to convey important information concerning conversion to dementia.

Immunomagnetic Reduction Detects Plasma Aβ1-42 Levels as a Potential Dominant Indicator Predicting Cognitive Decline

Although the concentrations of Alzheimer’s disease (AD) biomarkers Aβ_1–40, Aβ_1–42 and tau protein are very low in human plasma, ultrasensitive assays such as immunomagnetic reduction (IMR) are able to precisely quantify them. Review articles have described the detailed working mechanism of IMR and revealed the feasibility of detecting early-stage AD by assaying these plasma biomarkers with IMR. In this review, we aimed to compare the significance of these plasma biomarkers in predicting cognitive decline in patients with Down syndrome, stroke, or amnestic mild cognitive impairment based on findings in the literature. We found that plasma Aβ_1–42 might play the predominant role in predicting cognitive decline in these patients.

Prediction of poor clinical outcome in vascular cognitive impairment: TRACE-VCI study

INTRODUCTION

Prognostication in memory clinic patients with vascular brain injury (eg possible vascular cognitive impairment [VCI]) is often uncertain. We created a risk score to predict poor clinical outcome.

METHODS

Using data from two longitudinal cohorts of memory clinic patients with vascular brain injury without advanced dementia, we created (n = 707) and validated (n = 235) the risk score. Poor clinical outcome was defined as substantial cognitive decline (change of Clinical Dementia Rating ≥1 or institutionalization) or major vascular events or death. Twenty-four candidate predictors were evaluated using Cox proportional hazard models.

RESULTS

Age, clinical syndrome diagnosis, Disability Assessment for Dementia, Neuropsychiatric Inventory, and medial temporal lobe atrophy most strongly predicted poor outcome and constituted the risk score (C-statistic 0.71; validation cohort 0.78). Of note, none of the vascular predictors were retained in this model. The 2-year risk of poor outcome was 6.5% for the lowest (0-5) and 55.4% for the highest sum scores (10-13).

DISCUSSION

This is the first, validated, prediction score for 2-year clinical outcome of patients with possible VCI.

Methodological Issues in the Clinical Validation of Biomarkers for Alzheimer's Disease: The Paradigmatic Example of CSF

The use of biomarkers is profoundly transforming medical research and practice. Their adoption has triggered major advancements in the field of Alzheimer’s disease (AD) over the past years. For instance, the analysis of the cerebrospinal fluid (CSF) and neuroimaging changes indicative of neuronal loss and amyloid deposition has led to the understanding that AD is characterized by a long preclinical phase. It is also supporting the transition towards a biology-grounded framework and definition of the disease. Nevertheless, though sufficient evidence exists about the analytical validity (i.e., accuracy, reliability, and reproducibility) of the candidate AD biomarkers, their clinical validity (i.e., how well the test measures the clinical features, and the disease or treatment outcomes) and clinical utility (i.e., if and how the test improves the patient’s outcomes, confirms/changes the diagnosis, identifies at-risk individuals, influences therapeutic choices) have not been fully proven. In the present review, some of the methodological issues and challenges that should be addressed in order to better appreciate the potential benefits and limitations of AD biomarkers are discussed. The ultimate goal is to stimulate a constructive discussion aimed at filling the existing gaps and more precisely defining the directions of future research. Specifically, four main aspects of the clinical validation process are addressed and applied to the most relevant CSF biomarkers: (1) the definition of reference values; (2) the identification of reference standards for the disease of interest (i.e., AD); (3) the inclusion within the diagnostic process; and (4) the statistical process supporting the whole framework.

Prediction of fast decline in amyloid positive mild cognitive impairment patients using multimodal biomarkers

It may be possible to classify patients with Aβ positive (+) mild cognitive impairment (MCI) into fast and slow decliners according to their biomarker status. In this study, we aimed to develop a risk prediction model to predict fast decline in the Aβ+ MCI population using multimodal biomarkers. We included 186 Aβ+ MCI patients who underwent florbetapir PET, brain MRI, cerebrospinal fluid (CSF) analyses, and FDG PET at baseline. We defined conversion to dementia within 3 years (= fast decline) as the outcome. The associations of potential covariates (MCI stage, APOE4 genotype, corrected hippocampal volume (HV), FDG PET SUVR, AV45 PET SUVR, CSF Aβ, total tau (t-tau), and phosphorylated tau (p-tau)) with the outcome were tested and nomograms were constructed using logistic regression models in the training dataset (n=124, n of fast decliners=52). The model was internally validated with the testing dataset (n=62, n of fast decliners=22). The multivariable analysis (including CSF t-tau) showed that MCI stage (late MCI vs. early MCI; OR 15.88, 95% CI 4.59, 54.88), APOE4 (OR 5.65, 95% CI 1.52, 20.98), corrected HV*1000 (OR 0.22, 95% CI 0.09, 0.57), FDG SUVR*10 (OR 0.43, 95% CI 0.27, 0.71), and log_e CSF t-tau (OR 6.20, 95% CI 1.48, 25.96) were associated with being fast decliners. In the second model including CSF p-tau instead of t-tau, the above associations remained the same, with a significant association between log_e CSF p-tau (OR 4.53, 95% CI 1.26, 16.31) and fast decline. The constructed nomograms showed excellent predictive performance (90%) on validation with the testing dataset. Among Aβ+ MCI patients, our findings suggested that multimodal AD biomarkers are significantly associated with being classified as fast decliners. A nomogram incorporating these biomarkers might be useful in early treatment decisions or stratified enrollment of this population into clinical trials.

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About 40% of Aβ+ MCI patients progressed to dementia within 3 years (fast decliner).

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Neurodegeneration markers were predictive of fast decline in Aβ+ MCI patients.

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Presence of APOE4 and late MCI stage were predictive of fast decline in Aβ+ MCI patients.

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The fast decliner prediction model shows an excellent predictive performance.

Data-driven prognostic features of cognitive trajectories in patients with amnestic mild cognitive impairments

BACKGROUND

Although amnestic mild cognitive impairment (aMCI) is generally considered to be a prodromal stage of Alzheimer's disease, patients with aMCI show heterogeneous patterns of progression. Moreover, there are few studies investigating data-driven cognitive trajectory in aMCI. We therefore classified patients with aMCI based on their cognitive trajectory, measured by clinical dementia rating sum of boxes (CDR-SOB). Then, we compared the clinical and neuroimaging features among groups classified by cognitive trajectory.

METHODS

We retrospectively recruited 278 patients with aMCI who underwent three or more timepoints of neuropsychological testing. They also had magnetic resonance imaging (MRI) including structured three-dimensional volume images. Cortical thickness was measured using surface-based methods. We performed trajectory analyses to classify our aMCI patients according to their progression and investigate their cognitive trajectory using CDR-SOB.

RESULTS

Trajectory analyses showed that patients with aMCI were divided into three groups: stable (61.8%), slow decliner (31.7%), and fast decliner (6.5%). Changes throughout a mean follow-up duration of 3.7 years in the CDR-SOB for the subgroups of stable/slow/fast decliners were 1.3-, 6.4-, and 12-point increases, respectively. Decliners were older and carried apolipoprotein E4 (APOE4) genotypes more frequently than stable patients. Compared with the stable group, decliners showed a higher frequency of aMCI patients with both visual and verbal memory dysfunction, late stage aMCI, and multiple domain dysfunction. In addition, compared with the stable group, the slow decliners showed cortical thinning predominantly in bilateral parietotemporal areas, while the fast decliners showed cortical thinning predominantly in bilateral frontotemporal areas. Both decliner groups showed worse cognitive function in attention, language, visuospatial, memory, and frontal/executive domains than the stable group.

CONCLUSIONS

Our data-driven trajectory analysis provides new insights into heterogeneous cognitive trajectories of aMCI and further suggests that baseline clinical and neuroimaging profiles might predict aMCI patients with poor prognosis.