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Predicting diagnosis 4 years prior to Alzheimer's disease incident.

ABSTRACT: This study employed a deep learning longitudinal model, graph convolutional and recurrent neural network (graph-CNN-RNN), on a series of brain structural MRI scans for AD prognosis. It characterized whole-brain morphology via incorporating longitudinal cortical and subcortical morphology and defined a probabilistic risk for the prediction of AD as a function of age prior to clinical diagnosis. The graph-CNN-RNN model was trained on half of the Alzheimer's Disease Neuroimaging Initiative dataset (ADNI, n = 1559) and validated on the other half of the ADNI dataset and the Open Access Series of Imaging Studies-3 (OASIS-3, n = 930). Our findings demonstrated that the graph-CNN-RNN can reliably and robustly diagnose AD at the accuracy rate of 85% and above across all the time points for both datasets. The graph-CNN-RNN predicted the AD conversion from 0 to 4 years before the AD onset at ∼80% of accuracy. The AD probabilistic risk was associated with clinical traits, cognition, and amyloid burden assessed using [18F]-Florbetapir (AV45) positron emission tomography (PET) across all the time points. The graph-CNN-RNN provided the quantitative trajectory of brain morphology from prognosis to overt stages of AD. Such a deep learning tool and the AD probabilistic risk have great potential in clinical applications for AD prognosis.

SUBMITTER: Qiu A

PROVIDER: S-EPMC8958535 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Predicting diagnosis 4 years prior to Alzheimer's disease incident.

Qiu Anqi A Xu Liyuan L Liu Chaoqiang C

NeuroImage. Clinical 20220324

This study employed a deep learning longitudinal model, graph convolutional and recurrent neural network (graph-CNN-RNN), on a series of brain structural MRI scans for AD prognosis. It characterized whole-brain morphology via incorporating longitudinal cortical and subcortical morphology and defined a probabilistic risk for the prediction of AD as a function of age prior to clinical diagnosis. The graph-CNN-RNN model was trained on half of the Alzheimer's Disease Neuroimaging Initiative dataset ...[more]

PMID: 35344803

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Project description:BackgroundPatients with young onset Alzheimer's disease (YOAD) face long diagnostic delays. Prescription medication use may provide insights into early signs and symptoms, which may help facilitate timely diagnosis.MethodsIn a register-based nested case-control study, we examined medication use for everyone diagnosed with YOAD in a Danish memory clinic during 2016-2020 compared to cognitively healthy controls. Prescription medication use were grouped into 13 overall categories (alimentary tract and metabolism, blood and blood forming organs, cardiovascular system, dermatologicals, genitourinary system and sex hormones, systemic hormonal preparations, antiinfectives for systemic use, antineoplastic and immunomodulating agents, musculo-skeletal system, nervous system, antiparasitic products, respiratory system, and sensory organs). Further stratifications were done for predetermined subcategories with a use-prevalence of at least 5% in the study population. Conditional logistic regression produced odds ratios, which given the use of incidence-density matching is interpretable as incidence rate ratios (IRRs). The association between prescription medication use and subsequent YOAD diagnosis was examined in the entire 10-year study period and in three time-intervals.ResultsThe study included 1745 YOAD cases and 5235 controls. In the main analysis, several overall categories showed significant associations with YOAD in one or more time-intervals, namely blood and blood forming organs and nervous system. Prescription medication use in the nervous system category was increased for YOAD cases compared to controls already 10->5 years prior to diagnosis (IRR 1.17, 95% CI 1.05-1.31), increasing to 1.57 (95% CI 1.39-1.78) in the year preceding diagnosis. This was largely driven by antidepressant and antipsychotic use, and especially prominent for first-time users.ConclusionsIn this study, medication use in several categories was associated with YOAD. Onset of treatment-requiring psychiatric symptoms such as depression or psychosis in mid-life may serve as potential early indicators of YOAD.

Project description:BackgroundA lot of attention has been paid to the relationship of blood pressure and dementia because epidemiological research has reported conflicting evidence. Observational data has shown that midlife hypertension is a risk factor for cognitive decline and dementia later in life, whereas there is evidence that low blood pressure is predictive in later life. The aim of the present study was to examine the association between dementia and blood pressure measured up to 27 years (mean 17.6 years) prior to ascertainment.MethodsIn Nord-Trøndelag County, Norway, incident dementia data were collected during 1995-2011, and the diagnoses were validated by a panel of experts in the field. By using the subjects' personal identification numbers, the dementia data were linked to data from the Nord-Trøndelag Health Study (the HUNT Study), a large, population-based health study performed in 1984-1986 (HUNT 1) and 1995-1997 (HUNT 2). A total of 24,638 participants of the HUNT Study were included in the present study, 579 of whom were diagnosed with Alzheimer disease, mixed Alzheimer/vascular dementia, or vascular dementia. Multiple logistic regression analyses were conducted to analyze the association between dementia and blood pressure data from HUNT 1 and HUNT 2.ResultsOver the age of 60 years, consistent inverse associations were observed between systolic blood pressure and all-cause dementia, mixed Alzheimer/vascular dementia, and Alzheimer disease, but not with vascular dementia, when adjusting for age, sex, education, and other relevant covariates. This was observed for systolic blood pressure in both HUNT 1 and HUNT 2, regardless of antihypertensive medication use. There was an adverse association between systolic blood pressure, pulse pressure, and Alzheimer disease in individuals treated with antihypertensive medication under the age of 60 years.ConclusionsOur data are in line with those in previous studies demonstrating an inverse association between dementia and systolic blood pressure in individuals over the age of 60 years. We cannot exclude a survival effect, however. Among middle-aged subjects (<60 years), elevated systolic blood pressure and pulse pressure were associated with eventual Alzheimer disease in individuals who reported using antihypertensive medication.

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Predicting diagnosis 4 years prior to Alzheimer's disease incident.

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Predicting diagnosis 4 years prior to Alzheimer's disease incident.

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