Elias MF, Elias PK, Sullivan LM, Wolf PA, D’Agostino RB. Lower cognitive function and the presence of obesity and hypertension: The Framingham Heart Study

Department of Mathematics and Statistics, Statistics and Consulting Unit, Boston University, MA 02215, USA.
International Journal of Obesity (Impact Factor: 5.39). 03/2003; 27(2):260-8. DOI: 10.1038/sj.ijo.802225
Source: PubMed

ABSTRACT To determine the independent effects of obesity and hypertension on cognitive functioning.
Using a prospective design, male (n=551) and female (n=872) participants of the Framingham Heart Study were classified by presence or absence of obesity and hypertension based on data collected over an 18-y surveillance period. All subjects were free from dementia, stroke, and clinically diagnosed cardiovascular disease up to the time of cognitive testing. Statistical models were adjusted for age, education, occupation, cigarette smoking, alcohol consumption, total cholesterol, and a diagnosis of type II diabetes. Body mass index status (nonobese or obese) and blood pressure status (normotensive or hypertensive) were then related to cognitive performance (learning, memory, executive functioning, and abstract reasoning) on tests administered 4-6 y later.
Adverse effects of obesity and hypertension on cognitive performance were observed for men only. Obese and hypertensive men performed more poorly than men classified as either obese or hypertensive, and the best performance was observed in nonobese, normotensive men.
The adverse effects of obesity and hypertension in men are independent and cumulative with respect to cognitive deficit.

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Available from: Merrill F Elias, May 12, 2014
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    • "A growing body of evidence indicates that cognitive impairment is associated with obesity, metabolic syndrome, and type 2 diabetes (T2D) (Elias et al. 2003; Hassing et al. 2004; Calvo-Ochoa and Arias 2014), suggesting common yet undefined pathogenic mechanisms among the conditions. However, the cellular and molecular mechanisms driving these peripherally induced CNS deficits remain elusive in this emerging area of interest. "
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    ABSTRACT: Compelling evidence indicates that type 2 diabetes mellitus (T2D), insulin resistance (IR), and metabolic syndrome are often accompanied by cognitive impairment. However, the mechanistic link between these metabolic abnormalities and CNS dysfunction requires further investigations. Here, we evaluated whether adipose tissue (AT) IR and related metabolic alterations resulted in CNS changes by studying synapse lipid composition and function in the adipocyte-specific ecto-nucleotide pyrophosphate phosphodiesterase overexpressing transgenic (AtENPP1-Tg) mouse, a model characterized by white adipocyte IR, systemic IR, and ectopic fat deposition. When fed a high-fat diet (HFD), AtENPP1-Tg mice recapitulate essential features of the human metabolic syndrome, making them an ideal model to characterize peripherally induced CNS deficits. Using a combination of gas chromatography and western blot analysis, we found evidence of altered lipid composition, including decreased phospholipids and increased triglycerides (TG) and fatty acid (FFA) in hippocampal synaptosomes isolated from HFD-fed AtENPP1-Tg mice. These changes were associated with impaired basal synaptic transmission at the Schaffer collaterals to hippocampal cornu ammonis 1 (CA1) synapses, decreased phosphorylation of the GluN1 glutamate receptor subunit, down-regulation of insulin receptor expression and up-regulation of the FFA receptor 1. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
    Journal of Neurochemistry 02/2015; 133(1). DOI:10.1111/jnc.13043 · 4.24 Impact Factor
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    • "Recently, our group examined GM changes in structural MRI data from elderly subjects with different degrees of cardiovascular risk (de Toledo Ferraz Alves et al., 2011) using the Framingham Coronary Heart Disease Risk [FCHDR (Grundy et al., 1998; Wilson et al., 1998)] score, a composite index comprising five clinical factors (age, blood pressure, diabetes mellitus, smoking status, and cholesterol levels ). Given recent findings regarding cardiovascular risk effects on cognitive performance (Mosconi, 2005; Obisesan et al., 2008; Fitzpatrick et al., 2009; Purandare, 2009; Scarmeas et al., 2009; Buchman et al., 2012), the FCHDR score has presented excellent potential for investigations involving brain imaging (Elias et al., 2003; Jeerakathil et al., 2004; Massaro et al., 2004; Seshadri et al., 2004; DeCarli et al., 2005; Romero et al., 2009). For the study by de Toledo Ferraz Alves et al. (2011), dementiafree individuals aged from 66 to 75 years were recruited from the database " São Paulo Aging and Health Study " (SPAH) (Scazufca and Seabra, 2008; Scazufca et al., 2008) and divided into three subgroups (high-risk, medium-risk, and low-risk) according to their FCHDR scores and gender (Table 1). "
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    ABSTRACT: Recent literature has presented evidence that cardiovascular risk factors (CVRF) play an important role on cognitive performance in elderly individuals, both those who are asymp-tomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer's disease (AD) and vascular dementia in the presence of a high degree of cardiovascular risk. In the present paper, we explore the association between CVRF and brain changes using pattern recognition techniques applied to structural MRI and the Framingham score (a composite measure of cardiovascular risk largely used in epidemiological studies) in a sample of healthy elderly individuals. We aim to answer the following questions: is it possible to decode (i.e., to learn information regarding cardio-vascular risk from structural brain images) enabling individual predictions? Among clinical measures comprising the Framingham score, are there particular risk factors that stand as more predictable from patterns of brain changes? Our main findings are threefold: (i) we verified that structural changes in spatially distributed patterns in the brain enable statistically significant prediction of Framingham scores. This result is still significant when controlling for the presence of the APOE 4 allele (an important genetic risk factor for both AD and cardiovascular disease). (ii) When considering each risk factor singly, we found different levels of correlation between real and predicted factors; however, single factors were not significantly predictable from brain images when considering APOE4 allele presence as covariate. (iii) We found important gender differences, and the possible causes of that finding are discussed.
    Frontiers in Aging Neuroscience 12/2014; 6(300). DOI:10.3389/fnagi.2014.00300 · 2.84 Impact Factor
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    • "In one study (Kuo et al., 2006) of men and women between the ages of 65 and 94, BMI had no effect on measures of explicit memory (the Hopkins Verbal Learning Test word lists, the Rey Auditory- Verbal Learning, and the Rivermead Behavioral Memory Test paragraph recall task). In contrast, obese men between the ages of 32–62 displayed deficits in both immediate and delayed recall on a logical memory task (Elias et al., 2003). In another study in which age was entered as a covariate, a negative correlation was observed between BMI and both immediate and delayed recall of a list of words at a 5-year follow-up (Cournot et al., 2006). "
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    ABSTRACT: Obesity has been associated with impaired executive functions including working memory. Less explored is the influence of obesity on learning and memory. In the current study we assessed stimulus reward association learning, explicit learning and memory and working memory in healthy weight, overweight and obese individuals. Explicit learning and memory did not differ as a function of group. In contrast, working memory was significantly and similarly impaired in both overweight and obese individuals compared to the healthy weight group. In the first reward association learning task the obese, but not healthy weight or overweight participants consistently formed paradoxical preferences for a pattern associated with a negative outcome (fewer food rewards). To determine if the deficit was specific to food reward a second experiment was conducted using money. Consistent with experiment 1, obese individuals selected the pattern associated with a negative outcome (fewer monetary rewards) more frequently than healthy weight individuals and thus failed to develop a significant preference for the most rewarded patterns as was observed in the healthy weight group. Finally, on a probabilistic learning task, obese compared to healthy weight individuals showed deficits in negative, but not positive outcome learning. Taken together, our results demonstrate deficits in working memory and stimulus reward learning in obesity and suggest that obese individuals are impaired in learning to avoid negative outcomes.
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