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Energy intake and expenditure in patients with Alzheimer's disease and mild cognitive impairment: the NUDAD project.

ABSTRACT: BACKGROUND:Malnutrition is common in patients with Alzheimer's disease (AD) dementia and mild cognitive impairment (MCI) and is associated with institutionalization and increased mortality. Malnutrition is the result of a negative energy balance, which could be due to reduced dietary intake and/or higher energy expenditure. To study underlying mechanisms for malnutrition, we investigated dietary intake and resting energy expenditure (REE) of patients with AD dementia, MCI, and controls. In addition, we studied associations of global cognition (Mini-Mental State Examination (MMSE)) and AD biomarkers with dietary intake and REE. METHODS:We included 219 participants from the NUDAD project, 71 patients with AD dementia (age 68?±?8?years, 58% female, MMSE 24?±?3), 52 with MCI (67?±?8?years, 42% female, MMSE 26?±?2), and 96 controls (62?±?7?years, 52% female, MMSE 28?±?2). We used a 238-item food frequency questionnaire to assess dietary intake (energy, protein, carbohydrate, and fat). In a subgroup of 92 participants (30 patients with AD dementia, 22 with MCI, and 40 controls) we measured REE with indirect calorimetry. Between-group differences in dietary intake and REE were tested with ANOVAs. In the total sample, linear regression analyses were used to explore potential associations of MMSE score and AD biomarkers with dietary intake and REE. All analyses were adjusted for age, sex, education, and body mass index or fat-free mass. RESULTS:Patients with AD dementia and MCI did not differ from controls in total energy intake (1991?±?71 and 2172?±?80 vs 2022?±?61?kcal/day, p?>?0.05) nor in protein, carbohydrate, or fat intake. Patients with AD dementia and MCI had a higher REE than controls (1704?±?41 and 1754?±?47 vs 1569?±?34?kcal/day, p?

SUBMITTER: Doorduijn AS

PROVIDER: S-EPMC7520025 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Energy intake and expenditure in patients with Alzheimer's disease and mild cognitive impairment: the NUDAD project.

Doorduijn Astrid S AS de van der Schueren Marian A E MAE van de Rest Ondine O de Leeuw Francisca A FA Hendriksen Heleen M A HMA Teunissen Charlotte E CE Scheltens Philip P van der Flier Wiesje M WM Visser Marjolein M

Alzheimer's research & therapy 20200926 1

<h4>Background</h4>Malnutrition is common in patients with Alzheimer's disease (AD) dementia and mild cognitive impairment (MCI) and is associated with institutionalization and increased mortality. Malnutrition is the result of a negative energy balance, which could be due to reduced dietary intake and/or higher energy expenditure. To study underlying mechanisms for malnutrition, we investigated dietary intake and resting energy expenditure (REE) of patients with AD dementia, MCI, and controls. ...[more]

PMID: 32979927

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Project description:Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attention recently. So far, multiple biomarkers have been shown to be sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI non-converters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers.

Dataset Information

Energy intake and expenditure in patients with Alzheimer's disease and mild cognitive impairment: the NUDAD project.

Publications

Energy intake and expenditure in patients with Alzheimer's disease and mild cognitive impairment: the NUDAD project.

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