Dataset Information

Gray Matter Age Prediction as a Biomarker for Risk of Dementia.

ABSTRACT: The gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as a biomarker for early-stage neurodegeneration. However, owing to the lack of large longitudinal studies, it has been challenging to validate this link. We aimed to investigate the utility of such a gap as a risk biomarker for incident dementia using a deep learning approach for predicting brain age based on MRI-derived gray matter (GM). We built a convolutional neural network (CNN) model to predict brain age trained on 3,688 dementia-free participants of the Rotterdam Study (mean age 66 ± 11 y, 55% women). Logistic regressions and Cox proportional hazards were used to assess the association of the age gap with incident dementia, adjusted for age, sex, intracranial volume, GM volume, hippocampal volume, white matter hyperintensities, years of education, and APOE ?4 allele carriership. Additionally, we computed the attention maps, which shows which regions are important for age prediction. Logistic regression and Cox proportional hazard models showed that the age gap was significantly related to incident dementia (odds ratio [OR] = 1.11 and 95% confidence intervals [CI] = 1.05-1.16; hazard ratio [HR] = 1.11, and 95% CI = 1.06-1.15, respectively). Attention maps indicated that GM density around the amygdala and hippocampi primarily drove the age estimation. We showed that the gap between predicted and chronological brain age is a biomarker, complimentary to those that are known, associated with risk of dementia, and could possibly be used for early-stage dementia risk screening.

SUBMITTER: Wang J

PROVIDER: S-EPMC6800321 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Gray Matter Age Prediction as a Biomarker for Risk of Dementia.

Wang Johnny J Knol Maria J MJ Tiulpin Aleksei A Dubost Florian F de Bruijne Marleen M Vernooij Meike W MW Adams Hieab H H HHH Ikram M Arfan MA Niessen Wiro J WJ Roshchupkin Gennady V GV

Proceedings of the National Academy of Sciences of the United States of America 20191001 42

The gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as a biomarker for early-stage neurodegeneration. However, owing to the lack of large longitudinal studies, it has been challenging to validate this link. We aimed to investigate the utility of such a gap as a risk biomarker for incident dementia using a deep learning approach for predicting brain age based on MRI-derived gray matter (GM). We built a convolutional neural network (CNN) model ...[more]

PMID: 31575746

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Project description:BACKGROUND:Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) risk score is the only currently available midlife risk score for dementia. We compared CAIDE to Framingham cardiovascular Risk Score (FRS) and FINDRISC diabetes score as predictors of dementia and assessed the role of age in their associations with dementia. We then examined whether these risk scores were associated with dementia in those free of cardiometabolic disease over the follow-up. METHODS:A total of 7553 participants, 39-63?years in 1991-1993, were followed for cardiometabolic disease (diabetes, coronary heart disease, stroke) and dementia (N?=?318) for a mean 23.5?years. Cox regression was used to model associations of age at baseline, CAIDE, FRS, and FINDRISC risk scores with incident dementia. Predictive performance was assessed using Royston's R2, Harrell's C-index, Akaike's information criterion (AIC), the Greenwood-Nam-D'Agostino (GND) test, and calibration-in-the-large. Age effect was also assessed by stratifying analyses by age group. Finally, in multistate models, we examined whether cardiometabolic risk scores were associated with incidence of dementia in persons who remained free of cardiometabolic disease over the follow-up. RESULTS:Among the risk scores, the predictive performance of CAIDE (C-statistic?=?0.714; 95% CI 0.690-0.739) and FRS (C-statistic?=?0.719; 95% CI 0.693-0.745) scores was better than FINDRISC (C-statistic?=?0.630; 95% CI 0.602-0.659); p?<?0.001), AIC difference >?3; R2 32.5%, 32.0%, and 12.5%, respectively. When the effect of age in these risk scores was removed by drawing data on risk scores at age 55, 60, and 65?years, the association with dementia in all age groups remained for FRS and FINDRISC, but not for CAIDE. Only FRS at age 55 was associated with dementia in persons who remained free of cardiometabolic diseases prior to dementia diagnosis while no such association was observed at older ages for any risk score. CONCLUSIONS:Our analyses of CAIDE, FRS, and FINDRISC show the FRS in midlife to predict dementia as well as the CAIDE risk score, its predictive value being also evident among individuals who did not develop cardiometabolic events. The importance of age in the predictive performance of all three risk scores highlights the need for the development of multivariable risk scores in midlife for primary prevention of dementia.

Project description:RATIONALE:Heart failure (HF) following heart damage leads to a decreased blood flow due to a reduced pump efficiency of the heart muscle. A consequence can be insufficient oxygen supply to the organism including the brain. While HF clearly shows neurological symptoms, such as fatigue, nausea, and dizziness, the implications for brain structure are not well understood. Few studies show regional gray matter decrease related to HF; however, the underlying mechanisms leading to the observed brain changes remain unclear. OBJECTIVE:To study the relationship between impaired heart function, hampered blood circulation, and structural brain change in a case-control study. METHODS AND RESULTS:Within a group of 80 patients of the Leipzig Heart Center, we investigated a potential correlation between HF biomarkers and the brain's gray matter density (GMD) obtained by magnetic resonance imaging. We observed a significant positive correlation between cardiac ejection fraction and GMD across the whole frontal and parietal medial cortex reflecting the consequence of HF onto the brain's gray matter. Moreover, we also obtained a relationship between GMD and the NT-proBNP (N-terminal prohormone of brain natriuretic peptide)-a biomarker that is used for screening, diagnosis, and prognosis of HF. Here, we found a significant negative correlation between NT-proBNP and GMD in the medial and posterior cingulate cortex but also in precuneus and hippocampus, which are key regions implicated in structural brain changes in dementia. CONCLUSIONS:We obtained significant correlations between brain structure and markers of heart failure including ejection fraction and NT-proBNP. A diminished GMD was found with decreased ejection fraction and increased NT-proBNP in wide brain regions including the whole frontomedian cortex as well as hippocampus and precuneus. Our observations might reflect structural brain damage in areas that are related to cognition; however, whether these structural changes facilitate the development of cognitive alterations has to be proven by further longitudinal studies.

Dataset Information

Gray Matter Age Prediction as a Biomarker for Risk of Dementia.

Publications

Gray Matter Age Prediction as a Biomarker for Risk of Dementia.

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