| 1. |
- Kröger, A, et al.
(författare)
-
The severity of human peri-implantitis lesions correlates with the level of submucosal microbial sysbiosis
- 2018
-
Ingår i: Journal of Clinical Periodontology. - 0303-6979 .- 1600-051X. ; 45:12, s. 1498-1509
-
Tidskriftsartikel (refereegranskat)abstract
- AIM: To cross-sectionally analyze the submucosal microbiome of peri-implantitis (PI) lesions at different severity levels.MATERIALS AND METHODS: Microbial signatures of 45 submucosal plaque samples from untreated peri-implantitis lesions obtained from 30 non-smoking, systemically healthy subjects were assessed by 16s sequencing. Linear mixed models were used to identify taxa with differential abundance by probing depth, after correction for age, gender, and multiple samples per subject. Network analyses were performed to identify groups of taxa with mutual occurrence or exclusion. Subsequently, the effects of peri-implant probing depth on submucosal microbial dysbiosis was calculated using the microbial dysbiosis index.RESULTS: In total, we identified 337 different taxa in the submucosal microbiome of peri-implantitis. Total abundance of 12 taxa correlated significantly with increasing probing depth; a significant relationship with lower probing depth was found for 16 taxa. Network analysis identified two mutually exclusive complexes associated with shallow pockets and deeper pockets, respectively. Deeper peri-implant pockets were associated with significantly increased dysbiosis.CONCLUSION: Increases in peri-implant pocket depth are associated with substantial changes in the submucosal microbiome and increasing levels of dysbiosis. This article is protected by copyright. All rights reserved.
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| 2. |
- Kröger, A, et al.
(författare)
-
The severity of human peri-implantitis lesions correlates with the level of submucosal microbial sysbiosis
- 2018
-
Ingår i: Journal of Clinical Periodontology. - : Blackwell Munksgaard. - 0303-6979 .- 1600-051X. ; 45:12, s. 1498-1509
-
Tidskriftsartikel (refereegranskat)abstract
- AIM: To cross-sectionally analyze the submucosal microbiome of peri-implantitis (PI) lesions at different severity levels. MATERIALS AND METHODS: Microbial signatures of 45 submucosal plaque samples from untreated peri-implantitis lesions obtained from 30 non-smoking, systemically healthy subjects were assessed by 16s sequencing. Linear mixed models were used to identify taxa with differential abundance by probing depth, after correction for age, gender, and multiple samples per subject. Network analyses were performed to identify groups of taxa with mutual occurrence or exclusion. Subsequently, the effects of peri-implant probing depth on submucosal microbial dysbiosis was calculated using the microbial dysbiosis index. RESULTS: In total, we identified 337 different taxa in the submucosal microbiome of peri-implantitis. Total abundance of 12 taxa correlated significantly with increasing probing depth; a significant relationship with lower probing depth was found for 16 taxa. Network analysis identified twomutually exclusive complexes associated with shallow pockets and deeper pockets, respectively. Deeper peri-implant pockets were associated with significantly increased dysbiosis. CONCLUSION: Increases in peri-implant pocket depth are associated with substantial changes in the submucosal microbiome and increasing levels of dysbiosis. This article is protected by copyright. All rights reserved.
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| 3. |
- De Silva, Kushan, et al.
(författare)
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A combined strategy of feature selection and machine learning to identify predictors of prediabetes
- 2020
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Ingår i: JAMIA Journal of the American Medical Informatics Association. - : Oxford University Press. - 1067-5027 .- 1527-974X. ; 27:3, s. 396-406
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- De Silva, Kushan, et al.
(författare)
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A data-driven biocomputing pipeline with meta-analysis on high throughput transcriptomics to identify genome-wide miRNA markers associated with type 2 diabetes
- 2022
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Ingår i: Heliyon. - : Elsevier BV. - 2405-8440. ; 8:2, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- De Silva, Kushan, et al.
(författare)
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Causality of anthropometric markers associated with polycystic ovarian syndrome : Findings of a Mendelian randomization study
- 2022
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 17:6 June
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- De Silva, Kushan, et al.
(författare)
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Clinical notes as prognostic markers of mortality associated with diabetes mellitus following critical care : A retrospective cohort analysis using machine learning and unstructured big data
- 2021
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Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 132
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Tidskriftsartikel (refereegranskat)abstract
- 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 & rsquo; 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.
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| 7. |
- De Silva, Kushan, et al.
(författare)
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Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans : a bioinformatics analytic workflow
- 2022
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Ingår i: Functional & Integrative Genomics. - : Springer Science and Business Media LLC. - 1438-793X .- 1438-7948. ; 22:5, s. 1003-1029
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 8. |
- De Silva, Kushan, et al.
(författare)
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Nutritional markers of undiagnosed type 2 diabetes in adults : Findings of a machine learning analysis with external validation and benchmarking.
- 2021
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Ingår i: PLOS ONE. - : PLOS. - 1932-6203. ; 16:5
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 9. |
- Grant, Melissa, et al.
(författare)
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The Human Salivary Antimicrobial Peptide Profile according to the Oral Microbiota in Health, Periodontitis and Smoking.
- 2019
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Ingår i: Journal of Innate Immunity. - : S. Karger. - 1662-811X .- 1662-8128. ; 11:5, s. 432-443
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 10. |
- Jönsson, Daniel, et al.
(författare)
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Circulating Endothelial Progenitor Cells in Periodontitis
- 2014
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Ingår i: Journal of Periodontology. - : American Academy of Periodontology. - 0022-3492 .- 1943-3670. ; 85:12, s. 1739-1747
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Tidskriftsartikel (refereegranskat)abstract
- Background: Several biologically plausible mechanisms have been proposed to mediate the association between periodontitis and atherosclerotic vascular disease (AVD), including adverse effects on vascular endothelial function. Circulating endothelial progenitor cells (cEPCs) are known to contribute to vascular repair, but limited data are available regarding the relationship between cEPC levels and periodontitis. The aims of this cross-sectional study are to investigate the levels of hemangioblastic and monocytic cEPCs in patients with periodontitis and periodontally healthy controls and to associate cEPC levels with the extent and severity of periodontitis. Methods: A total of 112 individuals (56 patients with periodontitis and 56 periodontally healthy controls, aged 26 to 65 years; mean age: 43 years) were enrolled. All participants underwent a full-mouth periodontal examination and provided a blood sample. Hemangioblastic cEPCs were assessed using flow cytometry, and monocytic cEPCs were identified using immunohistochemistry in cultured peripheral blood mononuclear cells. cEPC levels were analyzed in the entire sample, as well as in a subset of 50 pairs of patients with periodontitis/periodontally healthy controls, matched with respect to age, sex, and menstrual cycle. Results: Levels of hemangioblastic cEPCs were approximately 2.3-fold higher in patients with periodontitis than periodontally healthy controls, after adjustments for age, sex, physical activity, systolic blood pressure, and body mass index (P = 0.001). A non-significant trend for higher levels of monocytic cEPCs in periodontitis was also observed. The levels of hemangioblastic cEPCs were positively associated with the extent of bleeding on probing, probing depth, and clinical attachment loss. Hemangioblastic and monocytic cEPC levels were not correlated (Spearman correlation coefficient 0.03, P = 0.77), suggesting that they represent independent populations of progenitor cells. Conclusion: These findings further support the notion that oral infections have extraoral effects and document that periodontitis is associated with a mobilization of EPCs from the bone marrow, apparently in response to systemic inflammation and endothelial injury.
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