Inflammation and Alzheimer's disease: Possible role of periodontal disease

Department of Periodontology and Implant Dentistry, College of Dentistry, New York University, New York, NY, USA.
Alzheimer's & dementia: the journal of the Alzheimer's Association (Impact Factor: 12.41). 07/2008; 4(4):242-50. DOI: 10.1016/j.jalz.2007.08.004
Source: PubMed


The molecular and cellular mechanisms responsible for the etiology and pathogenesis of Alzheimer's disease (AD) have not been defined; however, inflammation within the brain is thought to play a pivotal role. Studies suggest that peripheral infection/inflammation might affect the inflammatory state of the central nervous system. Chronic periodontitis is a prevalent peripheral infection that is associated with gram-negative anaerobic bacteria and the elevation of serum inflammatory markers including C-reactive protein. Recently, chronic periodontitis has been associated with several systemic diseases including AD. In this article we review the pathogenesis of chronic periodontitis and the role of inflammation in AD. In addition, we propose several potential mechanisms through which chronic periodontitis can possibly contribute to the clinical onset and progression of AD. Because chronic periodontitis is a treatable infection, it might be a readily modifiable risk factor for AD.

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    • "In recent years, many studies were conducted to evaluate the correlation between systemic disease and oral health status (referred to as the oral-systemic connection). The role of oral bacteria in other types of systemic inflammation has been established[4,5], and the American Heart Association (AHA) has reported that atherosclerotic vascular disease may be associated with periodontal problems[6]. Invasive dental procedures may sometimes be performed on patients with systemic diseases, so dentists should be aware of potential complications in the management of these diseases. "

    Full-text · Article · Jan 2016
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    • "Selected biological markers for analysis were reported as relevant to LOBD and AD in published studies on inflammation based pathology (Akiyama et al., 2000;Kamer et al., 2008;Goldstein et al., 2009;Sardi et al., 2011;Lee et al., 2013;García-Bueno et al., 2014). After extracting plasma from blood samples, inflammatory cytokines interleukins 1 and 6 (IL-1 and IL-6) and tumor necrosis factor alpha (TNFα) were determined by enzyme immunoassay (EIA). "
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    ABSTRACT: Background: Late onset bipolar disorder (LOBD) is often difficult to distinguish from degenerative dementias, such as Alzheimer disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence in the elder population is not negligible and it is increasing. Both pathologies share pathophysiological neuroinflammation features. Improvements in differential diagnosis of LOBD and AD will help to select the best personalized treatment. Objective: The aim of this study is to assess the relative significance of clinical observations, neuropsychological tests, and specific blood plasma biomarkers (inflammatory and neurotrophic), separately and combined, in the differential diagnosis of LOBD versus AD. It was carried out evaluating the accuracy achieved by classification-based computer-aided diagnosis (CAD) systems based on these variables. Materials: A sample of healthy controls (HC) (n = 26), AD patients (n = 37), and LOBD patients (n = 32) was recruited at the Alava University Hospital. Clinical observations, neuropsychological tests, and plasma biomarkers were measured at recruitment time. Methods: We applied multivariate machine learning classification methods to discriminate subjects from HC, AD, and LOBD populations in the study. We analyzed, for each classification contrast, feature sets combining clinical observations, neuropsychological measures, and biological markers, including inflammation biomarkers. Furthermore, we analyzed reduced feature sets containing variables with significative differences determined by a Welch's t-test. Furthermore, a battery of classifier architectures were applied, encompassing linear and non-linear Support Vector Machines (SVM), Random Forests (RF), Classification and regression trees (CART), and their performance was evaluated in a leave-one-out (LOO) cross-validation scheme. Post hoc analysis of Gini index in CART classifiers provided a measure of each variable importance. Results: Welch's t-test found one biomarker (Malondialdehyde) with significative differences (p < 0.001) in LOBD vs. AD contrast. Classification results with the best features are as follows: discrimination of HC vs. AD patients reaches accuracy 97.21% and AUC 98.17%. Discrimination of LOBD vs. AD patients reaches accuracy 90.26% and AUC 89.57%. Discrimination of HC vs LOBD patients achieves accuracy 95.76% and AUC 88.46%. Conclusion: It is feasible to build CAD systems for differential diagnosis of LOBD and AD on the basis of a reduced set of clinical variables. Clinical observations provide the greatest discrimination. Neuropsychological tests are improved by the addition of biomarkers, and both contribute significantly to improve the overall predictive performance.
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    • "The development of treatments that delay the onset of AD by five years is estimated to potentially reduce the number of AD patients by half. Recently, considerable attention has been focused on lifestyle related diseases, including diabetes [117] and periodontitis [118] [119] [120] [121], as exacerbating factors for AD. Nakanishi and Wu focused on the strategies to prevent AD progression in their review article [122]. "
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