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Obesity and Alzheimer's disease, does the obesity paradox really exist? A magnetic resonance imaging study.

ABSTRACT: Mid-life obesity is an established risk factor for Alzheimer's disease (AD) dementia, whereas late-life obesity has been proposed as a protective state. Weight loss, which predates cognitive decline, might explain this obesity paradox on AD risk. We aimed to assess the impact of late life obesity on brain structure taking into account weight loss as a potential confounder. We included 162 elderly controls of the Alzheimer's Disease Neuroimaging Initiative (ADNI) with available 3T MRI scan. Significant weight loss was defined as relative weight loss ?5% between the baseline and last follow-up visit. To be able to capture weight loss, only subjects with a minimum clinical and anthropometrical follow-up of 12 months were included. Individuals were categorized into three groups according to body mass index (BMI) at baseline: normal-weight (BMI<25 Kg/m2), overweight (BMI 25-30 Kg/m2) and obese (BMI>30 Kg/m2). We performed both an interaction analysis between obesity and weight loss, and stratified group analyses in the weight-stable and weigh-loss groups. We found a significant interaction between BMI and weight loss affecting brain structure in widespread cortical areas. The stratified analyses showed atrophy in occipital, inferior temporal, precuneus and frontal regions in the weight stable group, but increased cortical thickness in the weight-loss group. In conclusion, our data support that weight loss negatively confounds the association between late-life obesity and brain atrophy. The obesity paradox on AD risk might be explained by reverse causation.

SUBMITTER: Pegueroles J

PROVIDER: S-EPMC6205180 | biostudies-other | 2018 Oct

REPOSITORIES: biostudies-other

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Obesity and Alzheimer's disease, does the obesity paradox really exist? A magnetic resonance imaging study.

Pegueroles Jordi J Jiménez Amanda A Vilaplana Eduard E Montal Victor V Carmona-Iragui María M Pané Adriana A Alcolea Daniel D Videla Laura L Casajoana Anna A Clarimón Jordi J Ortega Emilio E Vidal Josep J Blesa Rafael R Lleó Alberto A Fortea Juan J

Oncotarget 20181005 78

Mid-life obesity is an established risk factor for Alzheimer's disease (AD) dementia, whereas late-life obesity has been proposed as a protective state. Weight loss, which predates cognitive decline, might explain this obesity paradox on AD risk. We aimed to assess the impact of late life obesity on brain structure taking into account weight loss as a potential confounder. We included 162 elderly controls of the Alzheimer's Disease Neuroimaging Initiative (ADNI) with available 3T MRI scan. Signi ...[more]

PMID: 30410669

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Project description:Diagnosing Alzheimer's disease (AD) in the preclinical stage offers opportunities for early intervention; however, there is currently a lack of convenient biomarkers to facilitate the diagnosis. Using radiomics analysis, we aimed to determine whether the features extracted from multiparametric magnetic resonance imaging (MRI) can be used as potential biomarkers. This study was part of the Sino Longitudinal Study on Cognitive Decline project (NCT03370744), a prospective cohort study. All participants were cognitively healthy at baseline. Cohort 1 (n = 183) was divided into individuals with preclinical AD (n = 78) and controls (n = 105) using amyloid-positron emission tomography, and this cohort was used as the training dataset (80%) and validation dataset (the remaining 20%); cohort 2 (n = 51) was selected retrospectively and divided into "converters" and "nonconverters" according to individuals' future cognitive status, and this cohort was used as a separate test dataset; cohort three included 37 converters (13 from the Alzheimer's Disease Neuroimaging Initiative) and was used as another test set for independent longitudinal research. We extracted radiomics features from multiparametric MRI scans from each participant, using t-tests, autocorrelation tests, and three independent selection algorithms. We then established two classification models (support vector machine [SVM] and random forest [RF]) to verify the efficiency of the retained features. Five-fold cross-validation and 100 repetitions were carried out for the above process. Furthermore, the acquired stable high-frequency features were tested in cohort three by paired two-sample t-tests and survival analyses to identify whether their levels changed with cognitive decline and impact conversion time. The SVM and RF models both showed excellent classification efficiency, with an average accuracy of 89.7-95.9% and 87.1-90.8% in the validation set and 81.9-89.1% and 83.2-83.7% in the test set, respectively. Three stable high-frequency features were identified, all based on the structural MRI modality: the large zone high-gray-level emphasis feature of the right posterior cingulate gyrus, the variance feature of the left superior parietal gyrus, and the coarseness feature of the left posterior cingulate gyrus; their levels were correlated with amyloid-β deposition and predicted future cognitive decline (areas under the curve 0.649-0.761). In addition, levels of the variance feature at baseline decreased with cognitive decline and could affect the conversion time (p < 0.05). In conclusion, this exploratory study shows that the radiomics features of multiparametric MRI scans could represent potential biomarkers of preclinical AD.

Dataset Information

Obesity and Alzheimer's disease, does the obesity paradox really exist? A magnetic resonance imaging study.

Publications

Obesity and Alzheimer's disease, does the obesity paradox really exist? A magnetic resonance imaging study.

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