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Do Epilepsy Patients with Cognitive Impairment Have Alzheimer's Disease-like Brain Metabolism?

ABSTRACT: Although not classically considered together, there is emerging evidence that Alzheimer's disease (AD) and epilepsy share a number of features and that each disease predisposes patients to developing the other. Using machine learning, we have previously developed an automated fluorodeoxyglucose positron emission tomography (FDG-PET) reading program (i.e., MAD), and demonstrated good sensitivity (84%) and specificity (95%) for differentiating AD patients versus healthy controls. In this retrospective chart review study, we investigated if epilepsy patients with/without mild cognitive symptoms also show AD-like metabolic patterns determined by the MAD algorithm. Scans from a total of 20 patients with epilepsy were included in this study. Because AD diagnoses are made late in life, only patients aged ≥40 years were considered. For the cognitively impaired patients, four of six were identified as MAD+ (i.e., the FDG-PET image is classified as AD-like by the MAD algorithm), while none of the five cognitively normal patients was identified as MAD+ (χ² = 8.148, p = 0.017). These results potentially suggest the usability of FDG-PET in prognosticating later dementia development in non-demented epilepsy patients, especially when combined with machine learning algorithms. A longitudinal follow-up study is warranted to assess the effectiveness of this approach.

SUBMITTER: He M

PROVIDER: S-EPMC10135603 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Do Epilepsy Patients with Cognitive Impairment Have Alzheimer's Disease-like Brain Metabolism?

He Michael M Kolesar Tiffany A TA Goertzen Andrew L AL Ng Marcus C MC Ko Ji Hyun JH

Biomedicines 20230406 4

Although not classically considered together, there is emerging evidence that Alzheimer's disease (AD) and epilepsy share a number of features and that each disease predisposes patients to developing the other. Using machine learning, we have previously developed an automated fluorodeoxyglucose positron emission tomography (FDG-PET) reading program (i.e., MAD), and demonstrated good sensitivity (84%) and specificity (95%) for differentiating AD patients versus healthy controls. In this retrospec ...[more]

PMID: 37189726

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Project description:IntroductionBrain age is a machine learning-derived estimate that captures lower brain volume. Previous studies have found that brain age is significantly higher in mild cognitive impairment and Alzheimer's disease (AD) compared to healthy controls. Few studies have investigated changes in brain age longitudinally in MCI and AD. We hypothesized that individuals with MCI and AD would show heightened brain age over time and across the lifespan. We also hypothesized that both MCI and AD would show faster rates of brain aging (higher slopes) over time compared to healthy controls.MethodsWe utilized data from an archival dataset, mainly Alzheimer's disease Neuroimaging Initiative (ADNI) 1 with 3Tesla (3 T) data which totaled 677 scans from 183 participants. This constitutes a secondary data analysis on existing data. We included control participants (healthy controls or HC), individuals with MCI, and individuals with AD. We predicted brain age using a pre-trained model and tested for accuracy. We investigated cross-sectional differences in brain age by group [healthy controls or HC, mild cognitive impairment (MCI), and AD]. We conducted longitudinal modeling of age and brain age by group using time from baseline in one model and chronological age in another model.ResultsWe predicted brain age with a mean absolute error (MAE) < 5 years. Brain age was associated with age across the study and individuals with MCI and AD had greater brain age on average. We found that the MCI group had significantly higher rates of change in brain age over time compared to the HC group regardless of individual chronologic age, while the AD group did not differ in rate of brain age change.DiscussionWe replicated past studies that showed that MCI and AD had greater brain age than HC. We additionally found that this was true over time, both groups showed higher brain age longitudinally. Contrary to our hypothesis, we found that the MCI, but not the AD group, showed faster rates of brain aging. We essentially found that while the MCI group was actively experiencing faster rates of brain aging, the AD group may have already experienced this acceleration (as they show higher brain age). Individuals with MCI may experience higher rates of brain aging than AD and controls. AD may represent a homeostatic endpoint after significant neurodegeneration. Future work may focus on individuals with MCI as one potential therapeutic option is to alter rates of brain aging, which ultimately may slow cognitive decline in the long-term.

Project description:BackgroundAll cells accumulate insoluble protein aggregates throughout their lifespan. While many studies have characterized the canonical disease-associated protein aggregates, such as those associated with amyloid plaques, additional, undefined proteins aggregate in the brain and may be directly associated with disease and lifespan.MethodsA proteomics approach was used to identify a large subset of insoluble proteins in the mild cognitively impaired (MCI) and Alzheimer's disease (AD) human brain. Cortical samples from control, MCI, and AD patients were separated into detergent-soluble and detergent-insoluble fractions, and high-resolution LC/MS/MS technology was used to determine which proteins became more insoluble in the disease state. Bioinformatics analyses were used to determine if the alteration of protein aggregation between AD and control patients was associated with any specific biological process. Western blots were used to validate the proteomics data and to assess the levels of secondary protein modifications in MCI and AD.ResultsThere was a stage-dependent increase in detergent-insoluble proteins, with more extreme changes occurring in the AD cohort. Glycolysis was the most significantly overrepresented gene ontology biological process associated with the alteration of protein aggregation between AD and control patients. It was further shown that many low molecular weight proteins that were enriched in the AD brain were also highly aggregated, migrating on SDS-PAGE far above their predicted molecular masses. Glucose-6-phosphate isomerase, ubiquitin carboxyl-terminal hydrolase isoenzyme L1 (UCHL1/PARK5), and the DNA damage repair enzyme KU70 were among the top insoluble proteins identified by proteomics and validated by Western blot to be increased in the insoluble fractions of both MCI and AD brain samples.ConclusionsDiverse proteins became more detergent-insoluble in the brains of both MCI and AD patients compared to age-matched controls, suggesting that multiple proteins aggregate in these diseases, likely posing a direct toxic insult to neurons. Furthermore, detergent-insoluble proteins included those with important biological activities for critical cellular processes such as energetics, proteolysis, and DNA damage repair. Thus, reduced protein solubility likely promotes aggregation and limits functionality, reducing the efficiency of multiple aspects of cell physiology. Pharmaceutical interventions that increase autophagy may provide a useful therapeutic treatment to combat protein aggregation.

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Do Epilepsy Patients with Cognitive Impairment Have Alzheimer's Disease-like Brain Metabolism?

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Do Epilepsy Patients with Cognitive Impairment Have Alzheimer's Disease-like Brain Metabolism?

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