RESUMO
BACKGROUND: Recent magnetic resonance imaging (MRI) studies have established that brain iron accumulation might accelerate cognitive decline in Alzheimer's disease (AD) patients. Both normal aging and AD are associated with cerebral atrophy in specific regions. However, no studies have investigated aging- and AD-selective iron deposition-related cognitive changes during normal aging. Here, we applied quantitative susceptibility mapping (QSM) to detect iron levels in cortical signature regions and assessed the relationships among iron, atrophy, and cognitive changes in older adults. METHODS: In this Taizhou Imaging Study, 770 older adults (mean age 62.0 ± 4.93 years, 57.5% women) underwent brain MRI to measure brain iron and atrophy, of whom 219 underwent neuropsychological tests nearly every 12 months for up to a mean follow-up of 2.68 years. Global cognition was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Domain-specific cognitive scores were obtained from MoCA subscore components. Regional analyses were performed for cortical regions and 2 signature regions where atrophy affected by aging and AD only: Aging (AG) -specific and AD signature meta-ROIs. The QSM and cortical morphometry means of the above ROIs were also computed. RESULTS: Significant associations were found between QSM levels and cognitive scores. In particular, after adjusting for cortical thickness of regions of interest (ROIs), participants in the upper tertile of the cortical and AG-specific signature QSM exhibited worse ZMMSE than did those in the lower tertile [ ß = -0.104, p = 0.026; ß = -0.118, p = 0.021, respectively]. Longitudinal analysis suggested that QSM values in all ROIs might predict decline in ZMoCA and key domains such as attention and visuospatial function (all p < 0.05). Furthermore, iron levels were negatively correlated with classic MRI markers of cortical atrophy (cortical thickness, gray matter volume, and local gyrification index) in total, AG-specific signature and AD signature regions (all p < 0.05). CONCLUSION: AG- and AD-selective iron deposition was associated with atrophy and cognitive decline in elderly people, highlighting its potential as a neuroimaging marker for cognitive aging.
Assuntos
Atrofia , Encéfalo , Envelhecimento Cognitivo , Ferro , Imageamento por Ressonância Magnética , Testes Neuropsicológicos , Humanos , Feminino , Masculino , Imageamento por Ressonância Magnética/métodos , Pessoa de Meia-Idade , Idoso , Ferro/metabolismo , Encéfalo/diagnóstico por imagem , Encéfalo/metabolismo , Encéfalo/patologia , Atrofia/patologia , Envelhecimento Cognitivo/fisiologia , China , Envelhecimento/patologia , Disfunção Cognitiva/diagnóstico por imagem , Disfunção Cognitiva/metabolismoRESUMO
Mutations in Presenilin-1 (PSEN1) have been found to be associated with very early onset Alzheimer's disease (VEOAD). Here, we reported two patients with VEOAD caused by de novo PSEN1 mutations. A 33-year-old man with a de novo p.F177S mutation in PSEN1 presented with progressive decline in memory and daily function. A 37-year-old woman with a de novo PSEN1 p.L381V mutation presented with onset memory impairment, developed cerebellar syndrome, rigidity, and spastic paraparesis. The Amyloid/Tau/Neurodegeneration (ATN) biomarker profiles of both patients were Aâ+âTâ+â(N)+. Our finding increases the genetic knowledge of VEOAD and extends the ethnic distribution of PSEN1 mutations.
Assuntos
Doença de Alzheimer/genética , Mutação , Presenilina-1/genética , Adulto , Idade de Início , Doença de Alzheimer/complicações , Biomarcadores , Doenças Cerebelares/etiologia , Feminino , Predisposição Genética para Doença , Humanos , Masculino , Paraparesia Espástica/etiologiaRESUMO
BACKGROUND: Post-stroke cognitive impairment (PSCI) significantly affects stroke survivors' quality of life and rehabilitation. A risk model identifying cognitive decline at admission would help to improve early detection and management of post-stroke patients. OBJECTIVE: To develop a new clinical risk score for ischemic stroke survivors in predicting 6-12 months PSCI. METHODS: We prospectively enrolled 179 patients diagnosed with acute ischemic stroke within a 7-day onset. Data were analyzed based on baseline demographics, clinical risk factors, and radiological parameters. Logistic regression and area under the receiver operating curve (AUROC) were used to evaluate model efficiency. RESULTS: One hundred forty-five subjects completed a 6-12-month follow-up visit, and 77 patients (53.1%) were diagnosed with PSCI. Age (ß=â0.065, ORâ=â1.067, 95% CIâ=â1.016-1.120), years of education (ß=â-0.346, ORâ=â0.707, 95% CIâ=â0.607-0.824), periventricular hyperintensity grading (ß=â1.253, ORâ=â3.501, 95% CIâ=â1.652-7.417), diabetes mellitus (ß=â1.762, ORâ=â5.825, 95% CIâ=â2.068-16.412), and the number of acute nonlacunar infarcts (ß=â0.569, ORâ=â1.766, 95% CIâ=â1.243-2.510) were independently associated with 6-12 month PSCI, constituting a model with optimal predictive efficiency (AUCâ=â0.884, 95% CIâ=â0.832-0.935). CONCLUSIONS: The optimized risk model was effective in screening stroke survivors at high risk of developing 6-12 months PSCI in a simple and pragmatic way. It could be a potential tool to identify patients with a high risk of PSCI at an early stage in clinical practice after further independent external cohort validation.