Dataset Information

LDL phenotype in subjects with mild cognitive impairment and Alzheimer's disease.

ABSTRACT: BACKGROUND:Centenarians with normal cognitive function have a "longevity phenotype" characterized by large low-density lipoproteins (LDL) and high-density lipoproteins (HDL) and low incidence of metabolic syndrome, hypertension, and cognitive impairment. Alzheimer's disease (AD) is associated with a number of cardiovascular risk factors, but it is not known if they have or lack the "longevity phenotype". OBJECTIVE:The study was designed to determine LDL size and body fat content and distribution in subjects with mild cognitive impairment (MCI) and AD. RESULTS:Fifty-eight persons with MCI or AD (cases) and 42 control subjects of similar age had measurement of LDL size and lipoprotein lipids after a 12 h fast and analysis of body composition by dual x-ray absorptiometry. Cases had small LDL size more often than controls (73% versus 66%) associated with significantly higher triglycerides, lower HDL cholesterol, and higher triglyceride/HDL cholesterol ratio (p ? 0.02). Cases with large LDL had a better lipoprotein profile than those with small LDL. Cases and controls had similar percent body fat, fat index, and lean mass index. Forty-seven percent of cases and 39% of controls were obese. CONCLUSION:The prevalence of small LDL phenotype in MCI and AD cases contrasts with the "longevity phenotype" reported for centenarians with preserved cognitive function. The small LDL phenotype is an atherogenic lipoprotein profile found in metabolic syndrome, type 2 diabetes, and insulin resistance. It is now also reported in persons with MCI and AD.

SUBMITTER: Vikarunnessa S

PROVIDER: S-EPMC4082186 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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LDL phenotype in subjects with mild cognitive impairment and Alzheimer's disease.

Vikarunnessa Sheikh S Weiner Myron F MF Vega Gloria Lena GL

Journal of Alzheimer's disease : JAD 20130101 3

<h4>Background</h4>Centenarians with normal cognitive function have a "longevity phenotype" characterized by large low-density lipoproteins (LDL) and high-density lipoproteins (HDL) and low incidence of metabolic syndrome, hypertension, and cognitive impairment. Alzheimer's disease (AD) is associated with a number of cardiovascular risk factors, but it is not known if they have or lack the "longevity phenotype".<h4>Objective</h4>The study was designed to determine LDL size and body fat content a ...[more]

PMID: 23635406

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Project description:ObjectiveTo estimate the incidence of psychotic symptoms in Alzheimer's disease.MethodsThe study consists of 776 elderly subjects presenting to the Alzheimer Disease Research Center at the University of Pittsburgh (Pittsburgh, Pennsylvania) between May 9, 2000, and August 19, 2014. All participants were diagnosed with mild cognitive impairment (National Institute on Aging-Alzheimer's Association workgroup criteria) or possible or probable Alzheimer's disease (National Institute of Neurologic and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association criteria) and were without psychosis at entry. Psychotic symptoms were evaluated using the Consortium to Establish a Registry for Alzheimer's Disease Behavioral Rating Scale every 6 months. One-, 3- and 5-year cumulative incidences of psychosis were calculated.ResultsThe 1-year psychosis incidence was 10% (95% CI, 8%-12%), and this annual rate remained remarkably consistent at 3 and 5 years. Psychosis incidence was related to cognitive status at all time points. However, the incidence rate reached a plateau during the disease course. Cumulative psychosis incidence at 5 years was 61% (95% CI, 52%-69%) in individuals with moderate to severe Alzheimer's disease, not statistically significantly different from the cumulative incidence at 3 years in this group, which was 48% (95% CI, 40%-55%) or from the 5-year incidence in individuals who entered the study with mild Alzheimer's disease, which was 48% (95% CI, 41%-56%).ConclusionsPsychosis in Alzheimer's disease has been associated with a number of adverse clinical outcomes. We provide estimates of the risk of psychosis onset within clinically defined subgroups of individuals, a tool clinicians can use in treatment planning. Anticipating which subjects are at high risk for psychosis and the poor outcomes associated with it can help with family education and support decisions to implement nonpharmacologic strategies that may reduce or prevent symptoms.

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

LDL phenotype in subjects with mild cognitive impairment and Alzheimer's disease.

Publications

LDL phenotype in subjects with mild cognitive impairment and Alzheimer's disease.

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