Search

Background: Periodontal disease is associated with cardiovascular disease (CVD) but it is unknown if periodontal disease severity is associated with asymptomatic carotid plaque. The aim of the current population-based, observational study was to investigate if signs of periodontal disease are associated with the occurrence of carotid plaque and total plaque area (TPA).Methods: The Malmö Offspring Study (MOS) is a population-based study. MOS participants underwent a thorough cardiovascular phenotyping, including carotid ultrasonography. The Malmö Offspring Dental Study (MODS) invited participants of MOS for dental examination, including periodontal charting. Multivariable regression models were used to analyse the presence of carotid plaque and TPA in relation to periodontal parameters.Results: In all, 831 MODS participants were recruited, out of which 495 belonged to the children generation with mean age of 53 years, 63% had carotid plaque and 38% had moderate or severe periodontal disease. In models adjusted for CVD risk factors, the OR for having carotid plaque in subjects with vs without periodontal disease was 1.75 (95% CI: 1.11-2.78). In a linear model with TPA as dependent and number of periodontal pockets ≥ 4 mm as independent variable, the adjusted beta-coefficient was 0.34 mm2 (95% CI 0.16-0.52).Conclusion: Individuals within the highest quartile of periodontal pockets are expected to have 9 mm2 larger TPA compared to those without pockets. Our results suggest that intervention studies addressing periodontal disease could be useful for prevention of CVD. [ABSTRACT FROM AUTHOR]

Objectives: The objectives were to: (i) assess the accuracy of dental data for adults obtained from the Swedish Quality Register on Caries and Periodontitis (SKaPa); (ii) explore whether Latent Class Analysis (LCA) can identify groups of people based on caries data; and (iii) characterise the dental, medical and behavioural characteristics of people in the LCA-derived classes., Methods: Caries data from the SKaPa register were compared with clinical data collected by five experienced dentists in a nested subgroup of the Malmö Offspring Study (MOS), namely the Malmö Offspring Dental Study (MODS) (n = 724) for validation. Dental data from SKaPa were then used to classify 61 984 adult participants of the Västerbotten Intervention Programme (VIP) into five classes using LCA and DMFS-based quintile ranking, respectively. Dental status (including caries progression over 5 years), medical, anthropometric and behavioural characteristics were compared between the groups. Analyses were replicated in 2767 adults in the MOS., Results: DMFS-scores and number of teeth recorded within -2 to +2 years showed excellent agreement between the SKaPa and reference data with intra-class correlations > 0.90. The five LCA classes differed in mean DMFS from 10.0 to 94.4. There were strong associations between LCA class and health, and health and behavioural measures respectively, including some associations that were not detected using DMFS-ranked quintile groups. LCA class was associated with incremental change in DMFS, DFS, and number of teeth. The results in the MOS cohort were consistent with the results in the VIP cohort., Conclusions: Dental data for adults from the SKaPa registry were considered accurate within 2 years of recording. The LCA approach can classify participants into caries subtypes based on dental charting. These groups differ in health and behavioural characteristics and future caries increment. The LCA approach may capture some information that is missing from DMFS-ranked quintile groups, but is also heavily influenced by total DMFS, meaning that applying LCA in cumulative, highly age-determined diseases, such as caries, is a challenge., (© 2024 The Author(s). Community Dentistry and Oral Epidemiology published by John Wiley & Sons Ltd.)

Medication can affect the gut microbiota composition and function. The aim of this study was to investigate connections between use of common non-antibiotic medicines and the gut microbiota composition and function in a large Swedish cohort (N = 2223). Use of 67 medications and polypharmacy (≥ 5 medications), based on self-reported and prescription registry data, were associated with the relative abundance of 881 gut metagenomic species (> 5% prevalence) and 103 gut metabolic modules (GMMs). Altogether, 97 associations of 26 medications with 40 species and of four medications with five GMMs were observed (false discovery rate < 5%). Several earlier findings were replicated like the positive associations of proton pump inhibitors (PPIs) with numerous oral species, and those of metformin with Escherichia species and with lactate consumption I and arginine degradation II. Several new associations were observed between, among others, use of antidepressants, beta-blockers, nonsteroidal anti-inflammatory drugs and calcium channel blockers, and specific species. Polypharmacy was positively associated with Enterococcus faecalis, Bacteroides uniformis, Rothia mucilaginosa, Escherichia coli and Limosilactobacillus vaginalis, and with 13 GMMs. We confirmed several previous findings and identified numerous new associations between use of medications/polypharmacy and the gut microbiota composition and functional potential. Further studies are needed to confirm the new findings., (© 2024. The Author(s).)

Objective: The aim was to optimize diagnostics for carotid artery calcifications (CACs) on panoramic radiographs (PRs) to identify cardiovascular disease (CVD) by investigating how 4 defined CAC shapes are associated with ultrasound (US) findings indicating CVD., Study Design: The study included 414 participants (802 neck sides) from the Malmö Offspring Dental Study, examined with PRs. The PRs were assessed for CAC shapes stratified into 4 categories: single, scattered, vessel-width defining, and vessel-outlining. The carotid arteries were examined with US for signs of CVD: the presence of plaques, largest individual area of a plaque, number of plaques, and percentage reduction of the lumen. Associations between the different CAC categories and US characteristics were analyzed., Results: All categories of CAC were significantly associated with a higher degree of US findings indicating CVD compared with no CAC (P < .001). The most significant differences were found for vessel-outlining CAC, with the mean of the largest individual plaque area of 17.9 vs 2.3 mm 2 , mean number of plaques 1.6 vs 0.2, and mean percentage reduction of the lumen 24.1% vs 3.5% (all P < .001)., Conclusions: Independent of shape, CACs detected on PRs were associated with a higher degree of US findings of CVD. This was most pronounced for vessel-outlining CAC. With refined differential diagnostics of CACs in PRs, dentists may contribute to improved identification of patients in need of cardiovascular prevention., Competing Interests: Declaraiton of interest None., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Aim: To investigate the association between periodontitis and lung function in the Malmö Offspring Dental Study., Materials and Methods: In all 1001 individuals (49.9% female, mean age: 44.6) from Malmö Offspring Dental Study were included. Periodontitis was assessed by a full-mouth examination protocol including bleeding on probing and classified according to the American Academy of Periodontology/Center for Disease Control definitions. Forced expiratory volume in 1 s (FEV 1 ) and forced vital capacity (FVC) were expressed as absolute values and %predicted according to Global Lung Function Initiative reference values. FEV 1 , FVC and FEV 1 /FVC were analysed in relation to periodontal status using linear regression., Results: Severe periodontitis was found in 7% of the population. Adjusted regression models showed significant associations between lung function and severe periodontitis with 2.1 unit lower FEV 1 /FVC ratio (95% CI: -3.91, -0.23) and odds ratio (adjusted) of 2.56 (95% CI: 1.40, 4.75, p = .003) for airflow obstruction (FEV 1 /FVC less than the lower limit of normal) if having severe periodontitis. Lower values of %predicted FEV 1 and %predicted FVC, but not FEV 1 /FVC, were found in individuals with >25% bleeding on probing., Conclusions: Severe periodontitis was associated with lower FEV 1 /FVC ratio and airflow obstruction in the present cohort. More large-scale prospective studies and intervention studies are required for a comprehensive evaluation., (© 2023 The Authors. Journal of Clinical Periodontology published by John Wiley & Sons Ltd.)

Background: Gut microbiota have been implicated in atherosclerotic disease, but their relation with subclinical coronary atherosclerosis is unclear. This study aimed to identify associations between the gut microbiome and computed tomography-based measures of coronary atherosclerosis and to explore relevant clinical correlates., Methods: We conducted a cross-sectional study of 8973 participants (50 to 65 years of age) without overt atherosclerotic disease from the population-based SCAPIS (Swedish Cardiopulmonary Bioimage Study). Coronary atherosclerosis was measured using coronary artery calcium score and coronary computed tomography angiography. Gut microbiota species abundance and functional potential were assessed with shotgun metagenomics sequencing of fecal samples, and associations with coronary atherosclerosis were evaluated with multivariable regression models adjusted for cardiovascular risk factors. Associated species were evaluated for association with inflammatory markers, metabolites, and corresponding species in saliva., Results: The mean age of the study sample was 57.4 years, and 53.7% were female. Coronary artery calcification was detected in 40.3%, and 5.4% had at least 1 stenosis with >50% occlusion. Sixty-four species were associated with coronary artery calcium score independent of cardiovascular risk factors, with the strongest associations observed for Streptococcus anginosus and Streptococcus oralis subsp oralis ( P <1×10 -5 ). Associations were largely similar across coronary computed tomography angiography-based measurements. Out of the 64 species, 19 species, including streptococci and other species commonly found in the oral cavity, were associated with high-sensitivity C-reactive protein plasma concentrations, and 16 with neutrophil counts. Gut microbial species that are commonly found in the oral cavity were negatively associated with plasma indole propionate and positively associated with plasma secondary bile acids and imidazole propionate. Five species, including 3 streptococci, correlated with the same species in saliva and were associated with worse dental health in the Malmö Offspring Dental Study. Microbial functional potential of dissimilatory nitrate reduction, anaerobic fatty acid β-oxidation, and amino acid degradation were associated with coronary artery calcium score., Conclusions: This study provides evidence of an association of a gut microbiota composition characterized by increased abundance of Streptococcus spp and other species commonly found in the oral cavity with coronary atherosclerosis and systemic inflammation markers. Further longitudinal and experimental studies are warranted to explore the potential implications of a bacterial component in atherogenesis., Competing Interests: Disclosures S. Pita, N. Nielsen, and Drs Eklund, Holm, and Nielsen are employees of Clinical Microbiomics A/S, where samples were processed and DNA extraction and estimations of relative abundance of the metagenomics species were done. Dr Ärnlöv has received lecture fees from Novartis and AstraZeneca and has served on advisory boards for AstraZeneca and Boehringer Ingelheim, all unrelated to the article. Dr Nilsson has received lecture fees from Novartis, Novo Nordisk, Amgen, and Boehringer Ingelheim. The other authors declare no competing interests.

Type 2 diabetes (T2D) has a complex etiology which is not yet fully elucidated. The identification of gene perturbations and hub genes of T2D may deepen our understanding of its genetic basis. We aimed to identify highly perturbed genes and hub genes associated with T2D via an extensive bioinformatics analytic workflow consisting of five steps: systematic review of Gene Expression Omnibus and associated literature; identification and classification of differentially expressed genes (DEGs); identification of highly perturbed genes via meta-analysis; identification of hub genes via network analysis; and downstream analysis of highly perturbed genes and hub genes. Three meta-analytic strategies, random effects model, vote-counting approach, and p value combining approach, were applied. Hub genes were defined as those nodes having above-average betweenness, closeness, and degree in the network. Downstream analyses included gene ontologies, Kyoto Encyclopedia of Genes and Genomes pathways, metabolomics, COVID-19-related gene sets, and Genotype-Tissue Expression profiles. Analysis of 27 eligible microarrays identified 6284 DEGs (4592 downregulated and 1692 upregulated) in four tissue types. Tissue-specific gene expression was significantly greater than tissue non-specific (shared) gene expression. Analyses revealed 79 highly perturbed genes and 28 hub genes. Downstream analyses identified enrichments of shared genes with certain other diabetes phenotypes; insulin synthesis and action-related pathways and metabolomics; mechanistic associations with apoptosis and immunity-related pathways; COVID-19-related gene sets; and cell types demonstrating over- and under-expression of marker genes of T2D. Our approach provided valuable insights on T2D pathogenesis and pathophysiological manifestations. Broader utility of this pipeline beyond T2D is envisaged., (© 2022. The Author(s).)

Introduction: Using body mass index (BMI) as a proxy, previous Mendelian randomization (MR) studies found total causal effects of general obesity on polycystic ovarian syndrome (PCOS). Hitherto, total and direct causal effects of general- and central obesity on PCOS have not been comprehensively analyzed., Objectives: To investigate the causality of central- and general obesity on PCOS using surrogate anthropometric markers., Methods: Summary GWAS data of female-only, large-sample cohorts of European ancestry were retrieved for anthropometric markers of central obesity (waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR)) and general obesity (BMI and its constituent variables-weight and height), from the IEU Open GWAS Project. As the outcome, we acquired summary data from a large-sample GWAS (118870 samples; 642 cases and 118228 controls) within the FinnGen cohort. Total causal effects were assessed via univariable two-sample Mendelian randomization (2SMR). Genetic architectures underlying causal associations were explored. Direct causal effects were analyzed by multivariable MR modelling., Results: Instrumental variables demonstrated no weak instrument bias (F > 10). Four anthropometric exposures, namely, weight (2.69-77.05), BMI (OR: 2.90-4.06), WC (OR: 6.22-20.27), and HC (OR: 6.22-20.27) demonstrated total causal effects as per univariable 2SMR models. We uncovered shared and non-shared genetic architectures underlying causal associations. Direct causal effects of WC and HC on PCOS were revealed by two multivariable MR models containing exclusively the anthropometric markers of central obesity. Other multivariable MR models containing anthropometric markers of both central- and general obesity showed no direct causal effects on PCOS., Conclusions: Both and general- and central obesity yield total causal effects on PCOS. Findings also indicated potential direct causal effects of normal weight-central obesity and more complex causal mechanisms when both central- and general obesity are present. Results underscore the importance of addressing both central- and general obesity for optimizing PCOS care., Competing Interests: The authors have declared that no competing interests exist.

Aim: The metabolite 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid (CMPF) is a fatty fish-intake biomarker. We investigated the association between plasma levels of CMPF in relation to gingival inflammation and periodontitis case definition, as well as the extent and severity variables., Materials and Methods: The Malmö Offspring Study is a population-based study, and the Malmö Offspring Dental Study (MODS) is its dental arm, including periodontal charting. Plasma CMPF was measured using liquid chromatography-mass spectrometry and studied in relation to periodontal diagnosis and parameters using multivariable linear or logistic regression modelling adjusting for age, sex, education, body mass index, fasting glucose, and smoking., Results: Metabolite data were available for 922 MODS participants. Higher CMPF levels were associated with less gingival inflammation (β = -2.12, p = .002) and lower odds of severe periodontitis (odds ratio [OR] = 0.74, 95% confidence interval [CI]: 0.56 to 0.98). Higher CMPF levels were also associated with more teeth (β = 0.19, p = .001), lower number of periodontal pockets (≥4 mm) (β = -1.07, p = .007), and lower odds of having two or more periodontal pockets of ≥6 mm (OR = 0.80, 95% CI: 0.65 to 0.98) in fully adjusted models., Conclusions: CMPF, a validated biomarker of fatty fish consumption, is associated with less periodontal inflammation and periodontitis. Residual confounding cannot be ruled out, and future studies are warranted., (© 2022 The Authors. Journal of Clinical Periodontology published by John Wiley & Sons Ltd.)

Objective: The primary aim of this current systematic review and meta-analysis was to evaluate the potential microbiological effect of probiotics on the implant microbiota. The secondary aim was to evaluate if probiotics have any effect as an adjunct to non-surgical peri-implant treatment in reducing peri-implant mucositis and peri-implantitis clinical parameters-bleeding on probing, modified Gingival Index, and pocket depth., Methods: The research focus questions were constructed in accordance with the Participants, Intervention, Comparison, and Outcomes (PICO) criteria, and a PROSPERO protocol was registered. A comprehensive systematic search in MEDLINE via the PubMed, Scopus, and Web of Science Core Collection databases was conducted. Two independent reviewers screened the reports based on the PICO criteria-inclusion and exclusion criteria., Results: In total, 467 records were identified, and ultimately, 7 papers were included: 3 papers in the qualitative synthesis of microbiological effect and 4 in the meta-analysis synthesis on pocket depth. The data synthesis showed that probiotics had no detectable effect on the implant microflora, and in the following data synthesis, no clinical peri-implantitis variable showed a significantly beneficial effect from probiotics in the test group compared to the control group., Conclusion: Within the limitations of this review, the oral implant microflora is not affected by probiotics nor do probiotics add any effect to the conventional non-surgical treatment of peri-implant mucositis and peri-implantitis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Sayardoust, Johansson and Jönsson.)

Background: MicroRNAs (miRNAs) are sought-after biomarkers of complex, polygenic diseases such as type 2 diabetes (T2D). Data-driven biocomputing provides robust and novel avenues for synthesizing evidence from individual miRNA seq studies., Objective: To identify miRNA markers associated with T2D, via a data-driven, biocomputing approach on high throughput transcriptomics., Materials and Methods: The pipeline consisted of five sequential steps using miRNA seq data retrieved from the National Center for Biotechnology Information Gene Expression Omnibus platform: systematic review; identification of differentially expressed miRNAs (DE-miRNAs); meta-analysis of DE-miRNAs; network analysis; and downstream analyses. Three normalization algorithms (trimmed mean of M-values; upper quartile; relative log expression) and two meta-analytic algorithms (robust rank aggregation; Fisher's method of p -value combining) were integrated into the pipeline. Network analysis was conducted on miRNet 2.0 while enrichment and over-representation analyses were conducted on miEAA 2.0 ., Results: A total of 1256 DE-miRNAs (821 downregulated; 435 upregulated) were identified from 5 eligible miRNA seq datasets (3 circulatory; 1 adipose; 1 pancreatic). The meta-signature comprised 9 miRNAs ( hsa-miR-15b-5p; hsa-miR-33b-5p; hsa-miR-106b-3p; hsa-miR-106b-5p; hsa-miR-146a-5p; hsa-miR-483-5p; hsa-miR-539-3p; hsa-miR-1260a; hsa-miR-4454 ), identified via the two meta-analysis approaches. Two hub nodes ( hsa-miR-106b-5p; hsa-miR-15b-5p ) with above-average degree and betweenness centralities in the miRNA-gene interactions network were identified. Downstream analyses revealed 5 highly conserved- ( hsa-miR-33b-5p; hsa-miR-15b-5p; hsa-miR-106b-3p; hsa-miR-106b-5p; hsa-miR-146a-5p ) and 7 highly confident- ( hsa-miR-33b-5p; hsa-miR-15b-5p; hsa-miR-106b-3p; hsa-miR-106b-5p; hsa-miR-146a-5p; hsa-miR-483-5p; hsa-miR-539-3p ) miRNAs. A total of 288 miRNA-disease associations were identified, in which 3 miRNAs ( hsa-miR-15b-5p; hsa-miR-106b-3p; hsa-miR-146a-5p ) were highly enriched., Conclusions: A meta-signature of DE-miRNAs associated with T2D was discovered via in-silico analyses and its pathobiological relevance was validated against corroboratory evidence from contemporary studies and downstream analyses. The miRNA meta-signature could be useful for guiding future studies on T2D. There may also be avenues for using the pipeline more broadly for evidence synthesis on other conditions using high throughput transcriptomics., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

Purpose: Infections from the oral microbiome may lead to exacerbations of chronic obstructive pulmonary disease (COPD). We investigated whether advanced dental cleaning could reduce exacerbation frequency. Secondary outcomes were disease-specific health status, lung function, and whether the bacterial load and composition of plaque microbiome at baseline were associated with a difference in outcomes., Patients and Methods: One-hundred-one primary and secondary care patients with COPD were randomized to intervention with advanced dental cleaning or to dental examination only, repeated after six months. At baseline and at 12 months, data of exacerbations, lung function, COPD Assessment Test (CAT) score, and periodontal status were collected from questionnaires, record review, and periodontal examination. Student's t -test and Mann-Whitney-U (MWU) test compared changes in outcomes. The primary outcome variable was also assessed using multivariable linear regression with adjustment for potential confounders. Microbiome analyses of plaque samples taken at baseline were performed using Wilcoxon signed ranks tests for calculation of alpha diversity, per mutational multivariate analysis of variance for beta diversity, and receiver operating characteristic curves for prediction of outcomes based on machine learning models., Results: In the MWU test, the annual exacerbation frequency was significantly reduced in patients previously experiencing frequent exacerbations (p = 0.020) and in those with repeated advanced dental cleaning (p = 0.039) compared with the non-treated control group, but not in the total population including both patients with a single and repeated visits (p = 0.207). The result was confirmed in multivariable linear regression, where the risk of new exacerbations was significantly lower in patients both in the intention to treat analysis (regression coefficient 0.36 (95% CI 0.25-0.52), p < 0.0001) and in the population with repeated dental cleaning (0.16 (0.10-0.27), p < 0.0001). The composition of microbiome at baseline was moderately predictive of an increased risk of worsened health status at 12 months (AUC = 0.723)., Conclusion: Advanced dental cleaning is associated with a reduced frequency of COPD exacerbations. Regular periodontal examination and dental cleaning may be of clinical importance to prevent COPD exacerbations., Competing Interests: The authors report no conflicts of interest in this work., (© 2021 Sundh et al.)

Objective: To evaluate the influence of implant and prosthetic components on peri-implant tissue health. A further aim was to evaluate peri-implant soft-tissue changes following surgical peri-implantitis treatment., Materials and Methods: Group discussions based on two systematic reviews (SR) and one critical review (CR) addressed (i) the influence of implant material and surface characteristics on the incidence and progression of peri-implantitis, (ii) implant and restorative design elements and the associated risk for peri-implant diseases, and (iii) peri-implant soft-tissue level changes and patient-reported outcomes following peri-implantitis treatment. Consensus statements, clinical recommendations, and implications for future research were discussed within the group and approved during plenary sessions., Results: Data from preclinical in vivo studies demonstrated significantly greater radiographic bone loss and increased area of inflammatory infiltrate at modified compared to non-modified surface implants. Limited clinical data did not show differences between modified and non-modified implant surfaces in incidence or progression of peri-implantitis (SR). There is some evidence that restricted accessibility for oral hygiene and an emergence angle of >30 combined with a convex emergence profile of the abutment/prosthesis are associated with an increased risk for peri-implantitis (CR). Reconstructive therapy for peri-implantitis resulted in significantly less soft-tissue recession, when compared with access flap. Implantoplasty or the adjunctive use of a barrier membrane had no influence on the extent of peri-implant mucosal recession following peri-implantitis treatment (SR)., Conclusions: Prosthesis overcontouring and impaired access to oral hygiene procedures increases risk for peri-implantitis. When indicated, reconstructive peri-implantitis treatment may facilitate the maintenance of post-operative peri-implant soft-tissue levels., (© 2021 The Authors. Clinical Oral Implants Research published by John Wiley & Sons Ltd.)

Objectives: Using a nationally-representative, cross-sectional cohort, we examined nutritional markers of undiagnosed type 2 diabetes in adults via machine learning., Methods: A total of 16429 men and non-pregnant women ≥ 20 years of age were analysed from five consecutive cycles of the National Health and Nutrition Examination Survey. Cohorts from years 2013-2016 (n = 6673) was used for external validation. Undiagnosed type 2 diabetes was determined by a negative response to the question "Have you ever been told by a doctor that you have diabetes?" and a positive glycaemic response to one or more of the three diagnostic tests (HbA1c > 6.4% or FPG >125 mg/dl or 2-hr post-OGTT glucose > 200mg/dl). Following comprehensive literature search, 114 potential nutritional markers were modelled with 13 behavioural and 12 socio-economic variables. We tested three machine learning algorithms on original and resampled training datasets built using three resampling methods. From this, the derived 12 predictive models were validated on internal- and external validation cohorts. Magnitudes of associations were gauged through odds ratios in logistic models and variable importance in others. Models were benchmarked against the ADA diabetes risk test., Results: The prevalence of undiagnosed type 2 diabetes was 5.26%. Four best-performing models (AUROC range: 74.9%-75.7%) classified 39 markers of undiagnosed type 2 diabetes; 28 via one or more of the three best-performing non-linear/ensemble models and 11 uniquely by the logistic model. They comprised 14 nutrient-based, 12 anthropometry-based, 9 socio-behavioural, and 4 diet-associated markers. AUROC of all models were on a par with ADA diabetes risk test on both internal and external validation cohorts (p>0.05)., Conclusions: Models performed comparably to the chosen benchmark. Novel behavioural markers such as the number of meals not prepared from home were revealed. This approach may be useful in nutritional epidemiology to unravel new associations with type 2 diabetes., Competing Interests: The authors have declared that no competing interests exist.

Background: Clinical notes are ubiquitous resources offering potential value in optimizing critical care via data mining technologies., Objective: To determine the predictive value of clinical notes as prognostic markers of 1-year all-cause mortality among people with diabetes following critical care., Materials and Methods: Mortality of diabetes patients were predicted using three cohorts of clinical text in a critical care database, written by physicians (n = 45253), nurses (159027), and both (n = 204280). Natural language processing was used to pre-process text documents and LASSO-regularized logistic regression models were trained and tested. Confusion matrix metrics of each model were calculated and AUROC estimates between models were compared. All predictive words and corresponding coefficients were extracted. Outcome probability associated with each text document was estimated., Results: Models built on clinical text of physicians, nurses, and the combined cohort predicted mortality with AUROC of 0.996, 0.893, and 0.922, respectively. Predictive performance of the models significantly differed from one another whereas inter-rater reliability ranged from substantial to almost perfect across them. Number of predictive words with non-zero coefficients were 3994, 8159, and 10579, respectively, in the models of physicians, nurses, and the combined cohort. Physicians' and nursing notes, both individually and when combined, strongly predicted 1-year all-cause mortality among people with diabetes following critical care., Conclusion: Clinical notes of physicians and nurses are strong and novel prognostic markers of diabetes-associated mortality in critical care, offering potentially generalizable and scalable applications. Clinical text-derived personalized risk estimates of prognostic outcomes such as mortality could be used to optimize patient care., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

As cardio metabolic disease manifestations tend to cluster in families there is a need to better understand the underlying mechanisms in order to further develop preventive strategies. In fact, genetic markers used in genetic risk scores, important as they are, will not be able alone to explain these family clusters. Therefore, the search goes on for the so called missing heritability to better explain these associations. Shared lifestyle and social conditions in families, but also early life influences may be of importance. Gene-environmental interactions should be explored. In recent years interest has grown for the role of diet-microbiota associations, as microbiota patterns may be shared by family members. In the Malmö Offspring Study that started in 2013, we have so far been able to examine about 4700 subjects (18-71 years) representing children and grandchildren of index subjects from the first generation, examined in the Malmö Diet Cancer Study during 1991 to 1996. This will provide rich data and opportunities to analyse family traits of chronic disease across three generations. We will provide extensive genotyping and phenotyping including cardiovascular and respiratory function, as well as markers of glucose metabolism. In addition, also cognitive function will be assessed. A 4-day online dietary recall will be conducted and gut as well as oral microbiota analysed. The ambition is to provide one of the first large-scale European family studies with individual data across three generations, which could deepen our knowledge about the role of family traits for chronic disease and its underlying mechanisms.

Objective: To identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population., Materials and Methods: We analyzed n = 6346 men and women enrolled in the National Health and Nutrition Examination Survey 2013-2014. Prediabetes was defined using American Diabetes Association guidelines. The sample was randomly partitioned to training (n = 3174) and internal validation (n = 3172) sets. Feature selection algorithms were run on training data containing 156 preselected exposure variables. Four machine learning algorithms were applied on 46 exposure variables in original and resampled training datasets built using 4 resampling methods. Predictive models were tested on internal validation data (n = 3172) and external validation data (n = 3000) prepared from National Health and Nutrition Examination Survey 2011-2012. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Predictors were assessed by odds ratios in logistic models and variable importance in others. The Centers for Disease Control (CDC) prediabetes screening tool was the benchmark to compare model performance., Results: Prediabetes prevalence was 23.43%. The CDC prediabetes screening tool produced 64.40% AUROC. Seven optimal (≥ 70% AUROC) models identified 25 predictors including 4 potentially novel associations; 20 by both logistic and other nonlinear/ensemble models and 5 solely by the latter. All optimal models outperformed the CDC prediabetes screening tool (P < 0.05)., Discussion: Combined use of feature selection and machine learning increased predictive performance outperforming the recommended screening tool. A range of predictors of prediabetes was identified., Conclusion: This work demonstrated the value of combining feature selection with machine learning to identify a wide range of predictors that could enhance prediabetes prediction and clinical decision-making., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Cardiovascular disease is a worldwide human condition which has multiple underlying contributing factors: one of these is long-term increased blood pressure-hypertension. Nitric oxide (NO) is a small nitrogenous radical species that has a number of physiological functions including vasodilation. It can be produced enzymatically through host nitric oxide synthases and by an alternative nitrate-nitrite-NO pathway from ingested inorganic nitrate. It was discovered that this route relies on the ability of the oral microbiota to reduce nitrate to nitrite and NO. Next generation sequencing has been used over the past two decades to gain deeper insight into the microbes involved, their location and the effect of their removal from the oral cavity. This review article presents this research and comments briefly on future directions.

Antimicrobial peptides (AMPs) are a diverse family of peptides that defend the mucosal surfaces of the oral cavity and other locations. Many AMPs have multiple functions and properties that influence aspects of innate defense and colonization by microorganisms. The human oral cavity is home to the second-most diverse microbiome, and the health of the mouth is influenced by the presence of these bacteria as well as by extrinsic factors such as periodontitis and smoking. This study hypothesized that the AMP profile is different in the presence of extrinsic factors and that this would also be reflected in the bacteria present. The AMP profile was analyzed by quantitative selected-reaction-monitoring mass spectrometry analysis and 40 bacterial species were quantified by DNA-DNA hybridization in saliva donated by 41 individuals. Periodontal status was assessed through dental examination and smoking status through medical charting. Periodontal health (in nonsmokers) was associated with a higher abundance of ribonuclease 7, protachykinin 1, β-defensin 128, lipocalin 1, bactericidal permeability-increasing protein fold-containing family B member 3, and bone-marrow proteoglycan. Nonsmoking periodontal disease was associated with an abundance of neutrophil defensin 1 and cathelicidin. However, 7 AMPs were overabundant in periodontal disease in smokers: adrenomedullin, eosinophil peroxidase, 3 different histones, myeloperoxidase, and neutrophil defensin 1. There were no differentially abundant AMPs in smokers versus nonsmokers with periodontal health. Correlation network inference of healthy nonsmokers, healthy smokers, nonsmoking periodontitis, or smoking periodontitis donors demonstrated very different networks growing in complexity with increasing numbers of stressors. The study highlights the importance of the interaction between the oral cavity and its resident microbiota and how this may be influenced by periodontal disease and smoking., (© 2018 The Author(s) Published by S. Karger AG, Basel.)

The cause of irritable bowel syndrome (IBS), a chronic disorder characterized by abdominal pain and disturbed bowel habits, is largely unknown. It is believed to be related to physical properties in the gut, central mechanisms in the brain, psychological factors, or a combination of these. To understand the relationships within the gut-brain axis with respect to IBS, large numbers of measurements ranging from stool samples to functional magnetic resonance imaging are collected from patients with IBS and healthy controls. As such, IBS is a typical example in medical research where research turns into a big data analysis challenge. In this chapter we demonstrate the power of interactive visual data analysis and exploration to generate an environment for scientific reasoning and hypothesis formulation for data from multiple sources with different character. Three case studies are presented to show the utility of the presented work.

Periodontitis is a complex chronic inflammatory noncommunicable disease, initiated by the development of a dysbiotic microbial plaque biofilm below the gingival margin. Whilst the pathogenic biofilm is a "necessary cause" of periodontitis, it is insufficient on its own to cause the disease, and a destructive immune-inflammatory response is a key to the translation of risk to destructive events. Other exposures or "component causes" include individual genetic predisposition, lifestyle (including smoking and nutrition), and environmental factors. Dietary nutrients are essential for life as they provide crucial energy sources in the form of macronutrients, as well as important cofactors in the form of micronutrients, which regulate the functionality of enzymes during the regulation of anabolic and catabolic processes in human cells. Moreover, micronutrients can regulate gene transcription factors, such as the proinflammatory nuclear factor kappa B and the anti-inflammatory nuclear factor (erythroid-derived 2)-like 2. This review focuses on the role of vitamins (vitamin A, carotenoids, the vitamin B complex, vitamins C, D, and E, and coenzyme Q10) and minerals (calcium, magnesium, iron, zinc, potassium, copper, manganese, and selenium) in human physiology and the impact of their deficiencies upon periodontal health and disease., (© 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objective: To evaluate the accuracy and precision of objects produced by additive manufacturing systems (AM) for use in dentistry and to compare with subtractive manufacturing systems (SM)., Methods: Ten specimens of two geometrical objects were produced by five different AM machines and one SM machine. Object A mimics an inlay-shaped object, while object B imitates a four-unit bridge model. All the objects were sorted into different measurement dimensions (x, y, z), linear distances, angles and corner radius., Results: None of the additive manufacturing or subtractive manufacturing groups presented a perfect match to the CAD file with regard to all parameters included in the present study. Considering linear measurements, the precision for subtractive manufacturing group was consistent in all axes for object A, presenting results of <0.050mm. The additive manufacturing groups had consistent precision in the x-axis and y-axis but not in the z-axis. With regard to corner radius measurements, the SM group had the best overall accuracy and precision for both objects A and B when compared to the AM groups., Significance: Within the limitations of this in vitro study, the conclusion can be made that subtractive manufacturing presented overall precision on all measurements below 0.050mm. The AM machines also presented fairly good precision, <0.150mm, on all axes except for the z-axis. Knowledge regarding accuracy and precision for different production techniques utilized in dentistry is of great clinical importance. The dental community has moved from casting to milling and additive techniques are now being implemented. Thus all these production techniques need to be tested, compared and validated., (Copyright © 2018 The Academy of Dental Materials. Published by Elsevier Inc. All rights reserved.)

Objective: Regulation of immune-like cell properties of periodontal ligament (PDL) cells is not understood. We investigate the importance of secretory leukocyte protease inhibitor (SLPI) for production of pro-inflammatory cytokines in human PDL cells., Materials and Methods: PDL cells were isolated from teeth extracted for orthodontic reasons. Cellular location of SLPI was investigated by immunocytochemistry. Cytokine transcript and protein expression were assessed by quantitative real-time RT-PCR and Western blotting. SLPI gene activity was knocked-down by siRNA. NF-κB signaling was assessed by measuring IκBα, and phosphorylated p65 and p105 protein expression., Results: PDL cells showed cytoplasmic expression of SLPI. Cellular expression level of SLPI negatively correlated to LPS-induced stimulation of IL-6 and MCP-1. Both SLPI gene activity and protein were reduced by about 70% in PDL cells treated with SLPI siRNA compared to cells treated with non-coding construct. Treatment with SLPI siRNA was associated with up-regulation of both basal and LPS-stimulated IL-6, MCP-1 and TLRs mRNA expression. The up-regulation of MCP-1 transcript in SLPI siRNA-treated cells was confirmed on protein level. SLPI siRNA-treatment enhanced the phosphorylated NF-κB p105 protein expression., Conclusions: SLPI regulates PDL cell pro-inflammatory cytokine expression and modulates NF-κB signaling, suggesting that SLPI governs the immune cell-like properties of PDL cells.

In this work we present a volume exploration method designed to be used by novice users and visitors to science centers and museums. The volumetric digitalization of artifacts in museums is of rapidly increasing interest as enhanced user experience through interactive data visualization can be achieved. This is, however, a challenging task since the vast majority of visitors are not familiar with the concepts commonly used in data exploration, such as mapping of visual properties from values in the data domain using transfer functions. Interacting in the data domain is an effective way to filter away undesired information but it is difficult to predict where the values lie in the spatial domain. In this work we make extensive use of dynamic previews instantly generated as the user explores the data domain. The previews allow the user to predict what effect changes in the data domain will have on the rendered image without being aware that visual parameters are set in the data domain. Each preview represents a subrange of the data domain where overview and details are given on demand through zooming and panning. The method has been designed with touch interfaces as the target platform for interaction. We provide a qualitative evaluation performed with visitors to a science center to show the utility of the approach.