Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration

Dissecting the clinical and pathological prognosis of MCI patients who reverted to normal cognition: a longitudinal study

BACKGROUND

Controversy existed in the prognosis of reversion from mild cognitive impairment (MCI) to normal cognition (NC). We aim to depict the prognostic characteristics of cognition, neuroimaging, and pathology biomarkers, as well as the risk of Alzheimer's disease (AD) dementia for MCI reverters.

METHODS

A total of 796 non-demented participants (mean age = 73.3 years, female = 54.4%, MCI = 55.7%), who were divided into MCI reverters (n = 109), stable MCI (n = 334), and stable NC (n = 353) based on 2-year diagnosis changes, were subsequently followed up for 6 years. Cox proportional hazard regression models were applied to assess the AD dementia hazard. Linear mixed-effect models were used to evaluate the differences in changing rates of cognitive scores, brain volumes, brain metabolism, and AD biomarkers among three groups.

RESULTS

The 2-year MCI reversion rate was 18.17%. MCI reversion was associated with an 89.6% lower risk of AD dementia (HR = 0.104, 95% confidence interval = [0.033, 0.335], p < 0.001) than stable MCI. No significant difference in incident AD risk was found between MCI reverters and stable NC (p = 0.533). Compared to stable MCI, reverters exhibited slower decreases in cognitive performance (false discovery rate corrected p value [FDR-Q] < 0.050), brain volumes (FDR-Q < 0.050), brain metabolism (FDR-Q < 0.001), and levels of cerebrospinal fluid β-amyloid1-42 (FDR-Q = 0.008). The above-mentioned differences were not found between MCI reverters and stable NC (FDR-Q > 0.050).

CONCLUSIONS

Reversion from MCI to NC predicts a favorable prognosis of pathological, neurodegenerative, and cognitive trajectory.

Detecting short-interval longitudinal cortical atrophy in neurodegenerative dementias via cluster scanning: A proof of concept

Regional brain atrophy estimated from structural magnetic resonance imaging (MRI) is a widely used measure of neurodegeneration in Alzheimer's disease (AD), Frontotemporal Lobar Degeneration (FTLD), and other dementias. Yet, traditional MRI-derived morphometric estimates are susceptible to measurement errors, posing a challenge for reliably detecting longitudinal atrophy, particularly over short intervals. Here, we examined the utility of multiple MRI scans acquired in rapid succession (i.e., cluster scanning) for detecting longitudinal cortical atrophy over 3- and 6-month intervals within individual patients. Four individuals with mild cognitive impairment or mild dementia likely due to AD or FTLD participated in this study. At baseline, 3 months, and 6 months, structural MRI data were collected on a 3 Tesla scanner using a fast 1.2-mm T1-weighted multi-echo magnetization-prepared rapid gradient echo (MEMPRAGE) sequence (acquisition time = 2'23"). At each timepoint, participants underwent up to 32 MEMPRAGE scans acquired in four separate sessions over two days. Using linear mixed-effects models, phenotypically vulnerable cortical ("core atrophy") regions exhibited statistically significant longitudinal atrophy in all participants (i.e., decreased cortical thickness) by 3 months and further demonstrated preferential vulnerability compared to control regions in three of the participants over at least one of the 3-month intervals. These findings provide proof-of-concept evidence that pooling multiple morphometric estimates derived from cluster scanning can detect longitudinal cortical atrophy over short intervals in individual patients with neurodegenerative dementias.

White matter injury, plasma Alzheimer's disease, and neurodegenerative biomarkers on cognitive decline in community-dwelling older adults: A 10-year longitudinal study

INTRODUCTION

This study aimed to investigate the synergistic impact of white matter injury, Alzheimer's disease, and neurodegenerative pathology on long-term cognitive decline and dementia risk in older adults.

METHODS

We included 262 dementia-free participants with baseline and follow-up interviews (2010-2021). At baseline, peak width of skeletonized mean diffusivity (PSMD) was assessed from diffusion tensor imaging. Plasma phosphorylated tau 217 (p-tau217) and neurofilament light chain (NfL) were measured using a single-molecule immune-array assay. Cognitive function was evaluated using Mini-Mental State Examination (MMSE) and domain-specific cognitive tests.

RESULTS

Participants with high-level PSMD, p-tau217, and NfL showed the fastest decline of MMSE (β = -0.30) and the highest dementia incidence of 3.54/100 person-years. A combination model with three markers demonstrated a good predictive value for dementia, incorporating age, sex, education, and apolipoprotein E (area under the curve = 0.93, 95% confidence interval = 0.86, 0.99).

DISCUSSION

Combining co-pathology markers may identify individuals with a high risk of cognitive decline.

HIGHLIGHTS

Peak width of skeletonized mean diffusivity (PSMD) was correlated with long-term cognitive decline, and this correlation was modified by plasma phosphorylated tau (p-tau)217 and neurofilament light chain (NfL). Participants with high levels of PSMD, p-tau217, and NfL showed the fastest cognitive decline and the highest risk of dementia. A combination of the three markers exhibited a good predictive value of incident dementia over a 10-year follow-up period.

Association of small vessel disease progression with longitudinal cognitive decline across mild cognitive impairment

BACKGROUND

Cerebral small vessel disease (SVD) is the leading cause of vascular dementia. However, it is unclear whether the individual SVD or global SVD progression correlates with cognitive decline across mild cognitive impairment (MCI) subjects.

OBJECTIVE

To investigate the association of small vessel disease progression with longitudinal cognitive decline across MCI.

METHODS

We included 432 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, with 151 participants in the cognitively normal (CN) group and 281 participants in the MCI group. We evaluated magnetic resonance imaging-based SVD markers in both CN and MCI groups and explored their associations with 12-and 24-month cognitive decline using linear mixing effect (LME) models.

RESULTS

In the CN group, cerebral microbleed (CMB) progression was associated with the decline in language function (p < 0.05), and deep white matter hyperintensity (WMH) progression was associated with a decline in memory function (p < 0.05). In the MCI group, CMB progression was associated with a decline in memory function (p < 0.05) and lacunes progression was associated with executive function (p < 0.05), whereas the progression of global SVD score was not related to longitudinal cognitive function.

CONCLUSIONS

The progression of CMB and WMH had an impact on cognitive decline in both CN and MCI groups, and lacunes progression only had an association with cognitive decline in the MCI group. Our study suggested that individual SVD markers may have a higher predictive value in longitudinal cognition compared with global SVD burden.

Physical activity and the outcome of cognitive trajectory: a machine learning approach

BACKGROUND

Physical activity (PA) may have an impact on cognitive function. Machine learning (ML) techniques are increasingly used in dementia research, e.g., for diagnosis and risk stratification. Less is known about the value of ML for predicting cognitive decline in people with dementia (PwD). The aim of this study was to use an ML approach to identify variables associated with a multimodal PA intervention that may impact cognitive changes in PwD, i.e., by distinguishing between cognitive decliners and non-decliners.

METHODS

This is a secondary, exploratory analysis using data from a Randomized Controlled Trial that included a 16-week multimodal PA intervention for the intervention group (IG) and treatment as usual for the control group (CG) in nursing homes. Predictors included in the ML models were related to the intervention (e.g., adherence), physical performance (e.g., mobility, balance), and pertinent health-related variables (e.g., health status, dementia form and severity). Primary outcomes were global and domain-specific cognitive performance (i.e., attention/ executive function, language, visuospatial skills, memory) assessed by standardized tests. A Support Vector Machine model was used to perform the classification of each primary outcome into the two classes of decline and non-decline. GridSearchCV with fivefold cross-validation was used for model training, and area under the ROC curve (AUC) and accuracy were calculated to assess model performance.

RESULTS

The study sample consisted of 319 PwD (IG, N = 161; CG, N = 158). The proportion of PwD experiencing cognitive decline, in the different domains measured, ranged from 27-48% in CG, and from 23-49% in IG, with no statistically significant differences and no time*group effects. ML models showed accuracy and AUC values ranging from 40.6-75.6. The strongest predictors of cognitive decline or non-decline were performance of activities of daily living in IG and CG, and adherence and mobility in IG.

CONCLUSIONS

ML models showed moderate performance, suggesting that the selected variables only had limited value for classification, with adherence and performance of activities of daily living appearing to be predictors of cognitive decline. While the study provides preliminary evidence of the potential use of ML approaches, larger studies are needed to confirm our observations and to include other variables in the prediction of cognitive decline, such as emotional health or biomarker abnormalities.

Associations of neurocognitive and neuropsychiatric patterns with brain structural biomarkers and dementia risk: A latent class analysis

BACKGROUND

Neurocognitive and neuropsychiatric symptoms are essential clinical manifestations of age-related cognitive impairment, yet their patterns of co-existence remain unclear through the cognitive continuum.

OBJECTIVE

To examine the associations of person-centered cluster-derived patterns, based on a comprehensive collection of domain-specific cognitive and neuropsychiatric assessments, with neuroimaging markers and dementia risk.

METHODS

641 participants were included in the analysis from memory clinics in Singapore. Latent class analysis was applied to define clusters of individuals with different clinical patterns. The associations between identified clinical groups with neuroimaging markers of cerebrovascular diseases and neurodegeneration were analyzed using logistic regression models. Cox proportional hazard models were applied for incident dementia.

RESULTS

Three latent classes differing in neurocognitive and neuropsychiatric impairment were identified (Class 1 "memory impairment only"; Class 2 "global cognitive impairment"; Class 3 "global cognitive and neuropsychiatric impairment"). Compared with Class 1, Class 2 and 3 were associated with smaller brain volumes, moderate-to-severe cortical atrophy and medial temporal lobe atrophy, and the presence of all cerebrovascular lesions. Moreover, compared with Class 2, Class 3 had smaller brain volumes, moderate-to-severe cortical atrophy and presence of intracranial stenosis. Additionally, compared to Class 1, Class 2 (hazard ratio [HR] = 3.84, 95%CI 2.11-7.00), and Class 3 (HR = 6.92, 95%CI 2.84-16.83) showed an increased risk of incident dementia.

CONCLUSIONS

Participants characterized by multi-domain cognitive impairment and co-occurrence of cognitive and neuropsychiatric impairment showed the highest risk of incident dementia, which may be attributed to both neurodegenerative and cerebrovascular pathologies.

Biomarker pathway heterogeneity of amyloid-positive individuals

INTRODUCTION

In amyloid-positive individuals, disease-related biomarker heterogeneity is understudied.

METHODS

We used Subtype and Stage Inference (SuStaIn) to identify data-driven subtypes among cerebrospinal fluid (CSF) amyloid beta (1-42)-positive individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNIGO/2 [n = 376]). Variables included: CSF phosphorylated tau (p-tau181), hippocampal and whole-brain volume, logical memory (LM), composite Trail Making Test score, and white matter hyperintensity (WMH) volumes. CSF amyloid-negative, apolipoprotein E ε4 non-carrier cognitively unimpaired controls (n = 86) were used to calculate z scores.

RESULTS

One subtype (n = 145) had early LM changes, with later p-tau and WMH changes. A second subtype (n = 88) had early WMH changes, were older, and more hypertensive. A third subtype (n = 100) had early p-tau changes, and reflected typical Alzheimer's disease. Some amyloid positive (n = 43) individuals were similar to the amyloid-negative group.

DISCUSSION

This work identified heterogeneity in individuals who are conventionally considered homogeneous, which is likely driven by co-pathologies including cerebrovascular disease.

HIGHLIGHTS

Data-driven modeling identified marker heterogeneity in amyloid-positive individuals. Heterogeneity reflected Alzheimer's disease-like, vascular-like, and mixed pathology presentations. Some amyloid-positive individuals were more similar to amyloid-negative controls. Vascular pathology plays a key role in understanding heterogeneity in those on the amyloid pathway.

Neurocognitive profiles are associated with subsequent brain integrity in a sample of Hispanics/Latinos: Findings from the SOL-INCA-MRI study (HCHS/SOL)

The Hispanic/Latino population is one of the largest and most diverse ethnoracial groups in the United States at high risk for dementia. We examined cognitive constructs and associations with subsequent hippocampal volume (HV) and white matter hyperintensity volume (WMHV). Participants were from the Hispanic Community Health Study/Study of Latinos-Magnetic Resonance Imaging Study (n = 2029). We examined confirmatory factor analysis and longitudinal invariance using neurocognitive scores at Visits 1 (2008-2011) and 2 (2014-2018) and path analyses. We obtained a longitudinally invariant two-factor episodic memory (EM) and working memory (WM) construct. Lower EM profile at both visits was associated with greater WMHV and smaller HV at Visit 2. Lower WM profile at both visits was associated with larger WMHV and smaller HV at Visit 2. Neurocognitive profiles were associated with subsequent neurodegeneration in a sample of Hispanics/Latinos. Identifying neurocognitive risk profiles may lead to early detection and intervention, and significantly impact the course of neurodegeneration.

Highlights

Cognitive profiles predict brain integrity up to 10 years later.We observed two-factor latent memory constructs and longitudinal invariance.These findings were observed in a Hispanic/Latino cohort.

Clinically feasible automated MRI volumetry of the brain as a prognostic marker in subjective and mild cognitive impairment

Background/aims

The number of patients suffering from cognitive decline and dementia increases, and new possible treatments are being developed. Thus, the need for time efficient and cost-effective methods to facilitate an early diagnosis and prediction of future cognitive decline in patients with early cognitive symptoms is becoming increasingly important. The aim of this study was to evaluate whether an MRI based software, NeuroQuant® (NQ), producing volumetry of the hippocampus and whole brain volume (WBV) could predict: (1) conversion from subjective cognitive decline (SCD) at baseline to mild cognitive impairment (MCI) or dementia at follow-up, and from MCI at baseline to dementia at follow-up and (2) progression of cognitive and functional decline defined as an annual increase in the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) score.

Methods

MRI was performed in 156 patients with SCD or MCI from the memory clinic at Oslo University Hospital (OUH) that had been assessed with NQ and had a clinical follow-up examination. Logistic and linear regression analyses were performed with hippocampus volume and WBV as independent variables, and conversion or progression as dependent variables, adjusting for demographic and other relevant covariates including Mini-Mental State Examination-Norwegian Revised Version score (MMSE-NR) and Apolipoprotein E ɛ4 (APOE ɛ4) carrier status.

Results

Hippocampus volume, but not WBV, was associated with conversion to MCI or dementia, but neither were associated with conversion when adjusting for MMSE-NR. Both hippocampus volume and WBV were associated with progression as measured by the annual change in CDR-SB score in both unadjusted and adjusted analyses.

Conclusion

The results indicate that automated regional MRI volumetry of the hippocampus and WBV can be useful in predicting further cognitive decline in patients with early cognitive symptoms.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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%).

CONCLUSIONS

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 interaction of global small vessel disease burden and Alzheimer's disease pathologies do not change the independent association of amyloid-beta with hippocampal volume: A longitudinal study on mild cognitive impairment subjects

The purpose of this study was to investigate whether the co-existence of global small vessel disease (SVD) burdens and Alzheimer's disease (AD) pathologies change hippocampal volume (HV) and cognitive function of mild cognitive impairment (MCI) subjects. We obtained MRI images, cerebrospinal fluid biomarkers (Aβ1-42 and p-tau), and neuropsychological tests of 310 MCI subjects from ADNI. The global SVD score was assessed. We used linear regression and linear mixing effect to analyze the effects of global SVD burdens, AD pathologies, and their interactions (SVD*AD) on baseline and longitudinal HV and cognition respectively. We used simple mediation effect to analyze the influencing pathways. After adjusting for global SVD and SVD*AD, Aβ remained independently correlated with baseline and longitudinal HV (std β = 0.294, p = .007; std β = 0.292, p < .001), indicating that global SVD did not affect the correlation between Aβ and HV. Global SVD score was correlated with longitudinal but not baseline HV (std β = 0.470, p = .050), suggesting that global SVD may be more representative of long-term permanent impairment. Global SVD, AD pathologies, and SVD*AD were independently correlated with baseline and longitudinal cognitions, in which the association of Aβ (B = 0.005, 95% CI: 0.005; 0.024) and p-tau (B = -0.002, 95% CI: -0.004; -0.000) with cognition were mediated by HV, suggesting that HV is more likely to explain the progression caused by AD pathology than SVD. The co-existence of global SVD and AD pathologies did not affect the individual association of Aβ on HV; HV played a more important role in the influence of AD pathology on cognition than in SVD.