Dataset Information

Predicting cognitive decline: Which is more useful, baseline amyloid levels or longitudinal change?

ABSTRACT: The use of biomarkers for the early detection of Alzheimer's disease (AD) is crucial for developing potential therapeutic treatments. Positron Emission Tomography (PET) is a well-established tool used to detect β-amyloid (Aβ) plaques in the brain. Previous studies have shown that cross-sectional biomarkers can predict cognitive decline (Schindler et al.,2021). However, it is still unclear whether longitudinal Aβ-PET may have additional value for predicting time to cognitive impairment in AD. The current study aims to evaluate the ability of baseline- versus longitudinal rate of change in-¹¹C-Pittsburgh compound B (PiB) Aβ-PET to predict cognitive decline. A cohort of 153 participants who previously underwent PiB-PET scans and comprehensive clinical assessments were used in this study. Our analyses revealed that baseline Aβ is significantly associated with the rate of change in cognitive composite scores, with cognition declining more rapidly when baseline PiB Aβ levels were higher. In contrast, no signification association was identified between the rate of change in PiB-PET Aβ and cognitive decline. Additionally, the ability of the rate of change in the PiB-PET measures to predict cognitive decline was significantly influenced by APOE ε4 carrier status. These results suggest that a single PiB-PET scan is sufficient to predict cognitive decline and that longitudinal measures of Aβ accumulation do not improve the prediction of cognitive decline once someone is amyloid positive.

SUBMITTER: Chen G

PROVIDER: S-EPMC10788301 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Predicting cognitive decline: Which is more useful, baseline amyloid levels or longitudinal change?

Chen Gengsheng G McKay Nicole S NS Gordon Brian A BA Liu Jingxia J Joseph-Mathurin Nelly N Schindler Suzanne E SE Hassenstab Jason J Aschenbrenner Andrew J AJ Wang Qing Q Schultz Stephanie A SA Su Yi Y LaMontagne Pamela J PJ Keefe Sarah J SJ Massoumzadeh Parinaz P Cruchaga Carlos C Xiong Chengjie C Morris John C JC Benzinger Tammie L S TLS

NeuroImage. Clinical 20231215

The use of biomarkers for the early detection of Alzheimer's disease (AD) is crucial for developing potential therapeutic treatments. Positron Emission Tomography (PET) is a well-established tool used to detect β-amyloid (Aβ) plaques in the brain. Previous studies have shown that cross-sectional biomarkers can predict cognitive decline (Schindler et al.,2021). However, it is still unclear whether longitudinal Aβ-PET may have additional value for predicting time to cognitive impairment in AD. The ...[more]

PMID: 38150745

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Project description:ObjectiveTo delineate the trajectories of Aβ42 level in cerebrospinal fluid (CSF), fludeoxyglucose F18 (FDG) uptake using positron emission tomography, and hippocampal volume using magnetic resonance imaging and their relative associations with cognitive change at different stages in aging and Alzheimer disease (AD).DesignCohort study.SettingThe 59 study sites for the Alzheimer's Disease Neuroimaging Initiative.ParticipantsA total of 819 participants 55 to 90 years of age with normal cognition, mild cognitive impairment, and AD who were followed up during the period from 2005 to 2007.Main outcome measuresRates of change in level of Aβ42 in CSF, FDG uptake, hippocampal volume, and the Alzheimer Disease's Assessment Scale-cognitive subscale score during up to 36 months of follow-up by diagnostic group as well as prediction of cognitive change by each biomarker.ResultsReductions in the level of Aβ42 in CSF were numerically greater in participants with normal cognition than in participants with mild cognitive impairment or AD; whereas both glucose metabolic decline and hippocampal atrophy were significantly slower in participants with normal cognition than in participants with mild cognitive impairment or AD. Positive APOE4 status accelerated hippocampal atrophic changes in participants with mild cognitive impairment or AD, but did not modify rates of change in level of Aβ42 in CSF or FDG uptake. The Alzheimer Disease's Assessment Scale-cognitive subscale scores were related only to the baseline level of Aβ42 in CSF and the baseline FDG uptake in participants with normal cognition, which were about equally associated with change in FDG uptake and hippocampal volume in participants with mild cognitive impairment and best modeled by change in FDG uptake in participants with AD.ConclusionTrajectories of Aβ42 level in CSF, FDG uptake, and hippocampal volume vary across different cognitive stages. The longitudinal patterns support a hypothetical sequence of AD pathology in which amyloid deposition is an early event before hypometabolism or hippocampal atrophy, suggesting that biomarker prediction for cognitive change is stage dependent.

Project description:BackgroundIn Alzheimer's disease, amyloid- β (A β) peptides aggregate in the lowering CSF amyloid levels - a key pathological hallmark of the disease. However, lowered CSF amyloid levels may also be present in cognitively unimpaired elderly individuals. Therefore, it is of great value to explain the variance in disease progression among patients with A β pathology.MethodsA cohort of n=2293 participants, of whom n=749 were A β positive, was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study heterogeneity in disease progression for individuals with A β pathology. The analysis used baseline clinical variables including demographics, genetic markers, and neuropsychological data to predict how the cognitive ability and AD diagnosis of subjects progressed using statistical models and machine learning. Due to the relatively low prevalence of A β pathology, models fit only to A β-positive subjects were compared to models fit to an extended cohort including subjects without established A β pathology, adjusting for covariate differences between the cohorts.ResultsA β pathology status was determined based on the A β42/A β40 ratio. The best predictive model of change in cognitive test scores for A β-positive subjects at the 2-year follow-up achieved an R2 score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F1 score of 0.791. A β-positive subjects declined faster on average than those without A β pathology, but the specific level of CSF A β was not predictive of progression rate. When predicting cognitive score change 4 years after baseline, the best model achieved an R2 score of 0.325 and it was found that fitting models to the extended cohort improved performance. Moreover, using all clinical variables outperformed the best model based only on a suite of cognitive test scores which achieved an R2 score of 0.228.ConclusionOur analysis shows that CSF levels of A β are not strong predictors of the rate of cognitive decline in A β-positive subjects when adjusting for other variables. Baseline assessments of cognitive function accounts for the majority of variance explained in the prediction of 2-year decline but is insufficient for achieving optimal results in longer-term predictions. Predicting changes both in cognitive test scores and in diagnosis provides multiple perspectives of the progression of potential AD subjects.

Project description:As a potential preclinical stage of Alzheimer's dementia, subjective cognitive decline (SCD) reveals a higher risk of future cognitive decline and conversion to dementia. However, it has not been clear whether SCD status increases the clinical progression of older adults in the context of amyloid deposition, cerebrovascular disease (CeVD), and psychiatric symptoms. We identified 99 normal controls (NC), 15 SCD individuals who developed mild cognitive impairment in the next 2 years (P-SCD), and 54 SCD individuals who did not (S-SCD) from ADNI database with both baseline and 2-year follow-up data. Total white matter hyperintensity (WMH), WMH in deep (DWMH) and periventricular (PWMH) regions, and voxel-wise grey matter volumes were compared among groups. Furthermore, using structural equation modelling method, we constructed path models to explore SCD-related brain changes longitudinally and to determine whether baseline SCD status, age, and depressive symptoms affect participants' clinical outcomes. Both SCD groups showed higher baseline amyloid PET SUVR, baseline PWMH volumes, and larger increase of PWMH volumes over time than NC. In contrast, only P-SCD had higher baseline DWMH volumes and larger increase of DWMH volumes over time than NC. No longitudinal differences in grey matter volume and amyloid was observed among NC, S-SCD, and P-SCD. Our path models demonstrated that SCD status contributed to future WMH progression. Further, baseline SCD status increases the risk of future cognitive decline, mediated by PWMH; baseline depressive symptoms directly contribute to clinical outcomes. In conclusion, both S-SCD and P-SCD exhibited more severe CeVD than NC. The CeVD burden increase was more pronounced in P-SCD. In contrast with the direct association of depressive symptoms with dementia severity progression, the effects of SCD status on future cognitive decline may manifest via CeVD pathologies. Our work highlights the importance of multi-modal longitudinal designs in understanding the SCD trajectory heterogeneity, paving the way for stratification and early intervention in the preclinical stage. PRACTITIONER POINTS: Both S-SCD and P-SCD exhibited more severe CeVD at baseline and a larger increase of CeVD burden compared to NC, while the burden was more pronounced in P-SCD. Baseline SCD status increases the risk of future PWMH and DWMH volume accumulation, mediated by baseline PWMH and DWMH volumes, respectively. Baseline SCD status increases the risk of future cognitive decline, mediated by baseline PWMH, while baseline depression status directly contributes to clinical outcome.

Dataset Information

Predicting cognitive decline: Which is more useful, baseline amyloid levels or longitudinal change?

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Predicting cognitive decline: Which is more useful, baseline amyloid levels or longitudinal change?

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