Association between periodontitis and systemic medication intake: A case- control study

BackgroundTo investigate the frequency of systemic drugs taken by elderly patients with or without periodontitis and the possible association between medication consumption and the severity of periodontitis.MethodsA total of 1221 patients, including 608 with generalized moderate to severe periodontitis (periodontitis group) and 613 age- and gender- matched individuals with healthy periodontium (healthy group) were selected. Systemic conditions, medications and periodontal status were recorded. Medication intake frequency (%) was compared using unconditional logistic regression.ResultsThe top three most common medications were angiotensin- converting enzyme (ACE) inhibitors (17.9%), antidepressants (17.8%), and lipid- lowering medications (16.5%). Both ACE inhibitors and antidepressants showed statistically higher intake frequency in the periodontitis group relative to healthy controls (21.5% versus 14.4%; odds ratio [OR] = 1.64), (21.1% versus 14.5%, OR = 1.57) (P < 0.01). Additionally, intake of oral hypoglycemic agents, calcium channel blockers (CCB), insulin, and diuretics were significantly higher in the periodontitis group with OR = 2.49, 2.32, 2.08 and 1.79, respectively (P < 0.05). Several medications demonstrated a disease severity- dependent association comparing generalized severe periodontitis with moderate periodontitis and healthy group: oral hypoglycemic agents (17.4% versus 16.8% versus 8.0%), CCB (14.8% versus 14.4% versus 8.0%) and anticonvulsants (13.4% versus 7.7% versus 6.4%) with OR of 2.43, 1.99, and 2.28 (severe periodontitis versus healthy group), respectively.ConclusionThere was a significantly higher frequency of medication intake related to cardiovascular disease and diabetes in patients with periodontitis. A disease severity- dependence with medication intake frequency was also noted. This study provides indirect evidence for the possible relationship between systemic diseases and periodontitis.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163409/2/jper10532_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163409/1/jper10532.pd

Use of ILâ 1 β, ILâ 6, TNFâ α, and MMPâ 8 biomarkers to distinguish periâ implant diseases: A systematic review and metaâ analysis

ObjectiveTo investigate the use of periâ implant crevicular fluid (PICF) interleukinâ 1β (ILâ 1β), ILâ 6, tumor necrosis factorâ α (TNFâ α), and matrix metalloproteinaseâ 8 (MMPâ 8) biomarkers in distinguishing between healthy implants (H), periâ implant mucositis (MU), and periâ implantitis (PI).Material and MethodsElectronic using three databases (Pubmed, EMBASE, and Cochrane) and manual searches were conducted for articles published up to March 2018 by two independent calibrated reviewers. Metaâ analyses using a randomâ effects model were conducted for each of the cytokines; ILâ 1β, ILâ 6, and TNFâ α, to analyze standardized mean difference (SMD) between H and MU, MU and PI, H and PI with their associated 95% confidence intervals (CI). Qualitative assessment of MMPâ 8 was provided consequent to the lack of studies that provide valid data for a metaâ analysis.ResultsNineteen articles were included in this review. ILâ 1β, ILâ 6, and TNFâ α, levels were significantly higher in MU than H groups (SMD: 1.94; 95% CI: 0.87, 3.35; Pâ <â .001, SMD: 1.17; 95% CI: 0.16, 3.19; Pâ =â .031 and SMD: 3.91; 95% CI: 1.13, 6.70; Pâ =â .006, respectively). Similar results were obtained with PI compared to H sites (SMD: 2.21, 95% CI: 1.32, 3.11; Pâ <â .001, SMD: 1.72; 95% CI: 0.56, 2.87; Pâ =â .004 and SMD: 3.78; 95% CI: 1.67, 5.89; Pâ <â .001, respectively). ILâ 6 was statistically higher in PI than MU sites (SMDâ =â 1.46; 95% CI: 0.36, 2.55; Pâ =â .009); while ILâ 1à increase was not significant. Despite absence of metaâ analysis, MMPâ 8 show to be a promising biomarker in detection of PI in literature.ConclusionWithin the limitations of this study, proâ inflammatory cytokines in PICF, such as ILâ 1à and ILâ 6, can be used as adjunct tools to clinical parameters to differentiate H from MU and PI.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147839/1/cid12694_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147839/2/cid12694.pd

Preventive healthcare policies in the US: solutions for disease management using Big Data Analytics

Abstract Data-driven healthcare policy discussions are gaining traction after the Covid-19 outbreak and ahead of the 2020 US presidential elections. The US has a hybrid healthcare structure; it is a system that does not provide universal coverage, albeit few years ago enacted a mandate (Affordable Care Act-ACA) that provides coverage for the majority of Americans. The US has the highest health expenditure per capita of all western and developed countries; however, most Americans don’t tap into the benefits of preventive healthcare. It is estimated that only 8% of Americans undergo routine preventive screenings. On a national level, very few states (15 out of the 50) have above-average preventive healthcare metrics. In literature, many studies focus on the cure of diseases (research areas such as drug discovery and disease prediction); whilst a minority have examined data-driven preventive measures—a matter that Americans and policy makers ought to place at the forefront of national issues. In this work, we present solutions for preventive practices and policies through Machine Learning (ML) methods. ML is morally neutral, it depends on the data that train the models; in this work, we make the case that Big Data is an imperative paradigm for healthcare. We examine disparities in clinical data for US patients by developing correlation and imputation methods for data completeness. Non-conventional patterns are identified. The data lifecycle followed is methodical and deliberate; 1000+ clinical, demographical, and laboratory variables are collected from the Centers for Disease Control and Prevention (CDC). Multiple statistical models are deployed (Pearson correlations, Cramer’s V, MICE, and ANOVA). Other unsupervised ML models are also examined (K-modes and K-prototypes for clustering). Through the results presented in the paper, pointers to preventive chronic disease tests are presented, and the models are tested and evaluated.http://deepblue.lib.umich.edu/bitstream/2027.42/174005/1/40537_2020_Article_315.pd

Clustering by periodontitis- associated factors: A novel application to NHANES data

BackgroundUnsupervised clustering is a method used to identify heterogeneity among groups and homogeneity within a group of patients. Without a prespecified outcome entry, the resulting model deciphers patterns that may not be disclosed using traditional methods. This is the first time such clustering analysis is applied in identifying unique subgroups at high risk for periodontitis in National Health and Nutrition Examination Surveys (NHANES 2009 to 2014 data sets using >500 variables.MethodsQuestionnaire, examination, and laboratory data (33 tables) for >1,000 variables were merged from 14,072 respondents who underwent clinical periodontal examination. Participants with - ¥6 teeth and available data for all selected categories were included (N = 1,222). Data wrangling produced 519 variables. k- means/modes clustering (k = 2:14) was deployed. The optimal k- value was determined through the elbow method, formula = - (xi2) - ((- xi)2 /n). The 5- cluster model showing the highest variability (63.08%) was selected. The 2012 Centers for Disease Control and Prevention/American Academy of Periodontology (AAP) and 2018 European Federation of Periodontology/AAP periodontitis case definitions were applied.ResultsCluster 1 (n = 249) showed the highest prevalence of severe periodontitis (43%); 39% self- reported - fair- general health; 55% had household income <$35,000/year; and 48% were current smokers. Cluster 2 (n = 154) had one participant with periodontitis. Cluster 3 (n = 242) represented the greatest prevalence of moderate periodontitis (53%). In Cluster 4 (n = 35) only one participant had no periodontitis. Cluster 5 (n = 542) was the systemically healthiest with 77% having no/mild periodontitis.ConclusionClustering of NHANES demographic, systemic health, and socioeconomic data effectively identifies characteristics that are statistically significantly related to periodontitis status and hence detects subpopulations at high risk for periodontitis without costly clinical examinations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/169254/1/jper10715.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/169254/2/jper10715-sup-0008-SuppMat8.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/169254/3/jper10715_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/169254/4/jper10715-sup-0009-SuppMat9.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/169254/5/jper10715-sup-0007-SuppMat7.pd

The Role of Epigenetics in Periodontal and Systemic Diseases and Smoking: A Systematic Review

The aims of this systematic review were to identify and synthesize the evidence for an association in DNA methylation/histone modifications between periodontal diseases and systemic diseases/smoking. Electronic database searches using relevant search terms in PubMed, Embase, MEDLINE, CINAHL, Web of Science, Scopus, and SciELO, and manual searches, were independently conducted to identify articles meeting the inclusion criteria. Nine studies of 1482 participants were included. Periodontitis was compared to metabolic disorders, rheumatoid arthritis (RA), cancer, and smokers, as well as healthy controls. Substantial variation regarding the reporting of sample sizes and patient characteristics, statistical analyses, and methodology was found. IL6 and TNF were modified similarly in RA and periodontitis. While TIMP-3 and GSTP-1 were significantly lower in periodontitis patients and controls than in cancer, SOCS-1, RMI2, CDH1, and COX2 were modified similarly in both cancer and periodontitis. While TLR4 in and CXCL8 were affected in periodontitis independent of smoking habit, smoking might change the transcription and methylation states of ECM organization-related genes, which exacerbated the periodontal condition. There was some evidence, albeit inconsistent, for an association between DNA methylation and periodontal diseases and systemic diseases or smokers compared to healthy patients or non-smokers