Brain aging mechanisms with mechanical manifestations

Blinkouskaya, Yana; Caçoilo, Andreia; Gollamudi, Trisha; Jalalian, Shima; Weickenmeier, Johannes

doi:10.1016/j.mad.2021.111575

Cited by 93 publications

(75 citation statements)

References 303 publications

(421 reference statements)

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“…In the DNN model, the subjects' age was the most important variable in estimating the GM-BHQ. These results are consistent with those of previous studies demonstrating that aging plays the most important role in cerebral atrophy ( 28 , 29 ). In addition, our recent studies have shown that there is a strong negative correlation between subjects' age and cognitive function, as represented by the MMSE score ( 1 ).…”

Section: Discussionsupporting

confidence: 93%

Estimation of Human Cerebral Atrophy Based on Systemic Metabolic Status Using Machine Learning

Sakatani

Oyama

et al. 2022

Front. Neurol.

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BackgroundBased on the assumption that systemic metabolic disorders affect cognitive function, we have developed a deep neural network (DNN) model that can estimate cognitive function based on basic blood test data that do not contain dementia-specific biomarkers. In this study, we used the same DNN model to assess whether basic blood data can be used to estimate cerebral atrophy.MethodsWe used data from 1,310 subjects (58.32 ± 12.91years old) enrolled in the Brain Doc Bank. The average Mini Mental State Examination score was 28.6 ± 1.9. The degree of cerebral atrophy was determined using the MRI-based index (GM-BHQ). First, we evaluated the correlations between the subjects' age, blood data, and GM-BHQ. Next, we developed DNN models to assess the GM-BHQ: one used subjects' age and blood data, while the other used only blood data for input items.ResultsThere was a negative correlation between age and GM-BHQ scores (r = -0.71). The subjects' age was positively correlated with blood urea nitrogen (BUN) (r = 0.40), alkaline phosphatase (ALP) (r = 0.22), glucose (GLU) (r = 0.22), and negative correlations with red blood cell counts (RBC) (r = −0.29) and platelet counts (PLT) (r = −0.26). GM-BHQ correlated with BUN (r = −0.30), GLU (r = −0.26), PLT (r = 0.26), and ALP (r = 0.22). The GM-BHQ estimated by the DNN model with subject age exhibited a positive correlation with the ground truth GM-BHQ (r = 0.70). Furthermore, even if the DNN model without subject age was used, the estimated GM-BHQ showed a significant positive correlation with ground truth GM-BHQ (r = 0.58). Age was the most important variable for estimating GM-BHQ.DiscussionAging had the greatest effect on cerebral atrophy. Aging also affects various organs, such as the kidney, and causes changes in systemic metabolic status, which may contribute to cerebral atrophy and cognitive impairment. The DNN model may serve as a new screening test for dementia using basic blood tests for health examinations. Finally, the blood data reflect systemic metabolic disorders in each subject—this method may thus contribute to personalized care.

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Section: Discussionsupporting

confidence: 93%

Estimation of Human Cerebral Atrophy Based on Systemic Metabolic Status Using Machine Learning

Sakatani

Oyama

et al. 2022

Front. Neurol.

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“…Aging is associated with specific alterations in signal transduction pathways, which culminates in memory impairments 43 . To detect the molecular and cellular mechanisms underlying this physiological event is a big challenge.…”

Section: Resultsmentioning

confidence: 99%

mGluR5 ablation leads to age-related synaptic plasticity impairments and does not improve Huntington’s disease phenotype

Souza

Ferreira-Vieira

Maciel

et al. 2022

Sci Rep

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Glutamate receptors, including mGluR5, are involved in learning and memory impairments triggered by aging and neurological diseases. However, each condition involves distinct molecular mechanisms. It is still unclear whether the mGluR5 cell signaling pathways involved in normal brain aging differ from those altered due to neurodegenerative disorders. Here, we employed wild type (WT), mGluR5−/−, BACHD, which is a mouse model of Huntington’s Disease (HD), and mGluR5−/−/BACHD mice, at the ages of 2, 6 and 12 months, to distinguish the mGluR5-dependent cell signaling pathways involved in aging and neurodegenerative diseases. We demonstrated that the memory impairment exhibited by mGluR5−/− mice is accompanied by massive neuronal loss and decreased dendritic spine density in the hippocampus, similarly to BACHD and BACHD/mGluR5−/− mice. Moreover, mGluR5 ablation worsens some of the HD-related alterations. We also show that mGluR5−/− and BACHD/mGluR5−/− mice have decreased levels of PSD95, BDNF, and Arc/Arg3.1, whereas BACHD mice are mostly spared. PSD95 expression was affected exclusively by mGluR5 ablation in the aging context, making it a potential target to treat age-related alterations. Taken together, we reaffirm the relevance of mGluR5 for memory and distinguish the mGluR5 cell signaling pathways involved in normal brain aging from those implicated in HD.

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“…Each brain's morphology is unique, and aging changes brain morphology in both healthy and pathological conditions ( 26 ). Heart failure, atrial fibrillation, and renal insufficiency may contribute to acute stroke with increasing age, especially in patients over 85 years of age ( 27 ).…”

Section: Discussionmentioning

confidence: 99%

High serum amyloid A predicts risk of cognitive impairment after lacunar infarction: Development and validation of a nomogram

Pan

et al. 2022

Front. Neurol.

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BackgroundPost-stroke cognitive impairment (PSCI) after lacunar infarction was worth attention in recent years. An easy-to-use score model to predict the risk of PSCI was rare. This study aimed to explore the association between serum amyloid A (SAA) and cognitive impairment, and it also developed a nomogram for predicting the risk of PSCI in lacunar infarction patients.MethodsA total of 313 patients with lacunar infarction were enrolled in this retrospective study between January 2021 and December 2021. They were divided into a training set and a validation set at 70%:30% randomly. The Chinese version of the Mini-Mental State Examination (MMSE) was performed to identify cognitive impairment 3 months after discharge. Univariate and multivariate logistic regression analyses were used to determine the independent risk factors for PSCI in the training set. A nomogram was developed based on the five variables, and the calibration curve and the receiver operating characteristic (ROC) curve were drawn to assess the predictive ability of the nomogram between the training set and the validation set. The decision curve analysis (DCA) was also conducted in both sets.ResultsIn total, 52/313 (16.61%) participants were identified with PSCI. The SAA levels in patients with PSCI were significantly higher than non-PSCI patients in the training set (P < 0.001). After multivariate analysis, age, diabetes mellitus, white blood count, cystatin C, and SAA were independent risk predictors of PSCI. The nomogram demonstrated a good discrimination performance between the training set (AUC = 0.860) and the validation set (AUC = 0.811). The DCA showed that the nomogram had a well clinical utility in the two sets.ConclusionThe increased SAA is associated with PSCI in lacunar infarction patients, and the nomogram developed with SAA can increase prognostic information for the early detection of PSCI.

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Brain aging mechanisms with mechanical manifestations

Cited by 93 publications

References 303 publications

Estimation of Human Cerebral Atrophy Based on Systemic Metabolic Status Using Machine Learning

Estimation of Human Cerebral Atrophy Based on Systemic Metabolic Status Using Machine Learning

mGluR5 ablation leads to age-related synaptic plasticity impairments and does not improve Huntington’s disease phenotype

High serum amyloid A predicts risk of cognitive impairment after lacunar infarction: Development and validation of a nomogram

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