Background
Diabetes and periodontitis are chronic non‐communicable diseases independently associated with mortality and have a bidirectional relationship.
Aims
To update the evidence for their ...epidemiological and mechanistic associations and re‐examine the impact of effective periodontal therapy upon metabolic control (glycated haemoglobin, HbA1C).
Epidemiology
There is strong evidence that people with periodontitis have elevated risk for dysglycaemia and insulin resistance. Cohort studies among people with diabetes demonstrate significantly higher HbA1C levels in patients with periodontitis (versus periodontally healthy patients), but there are insufficient data among people with type 1 diabetes. Periodontitis is also associated with an increased risk of incident type 2 diabetes.
Mechanisms
Mechanistic links between periodontitis and diabetes involve elevations in interleukin (IL)‐1‐β, tumour necrosis factor‐α, IL‐6, receptor activator of nuclear factor‐kappa B ligand/osteoprotegerin ratio, oxidative stress and Toll‐like receptor (TLR) 2/4 expression.
Interventions
Periodontal therapy is safe and effective in people with diabetes, and it is associated with reductions in HbA1C of 0.27–0.48% after 3 months, although studies involving longer‐term follow‐up are inconclusive.
Conclusions
The European Federation of Periodontology (EFP) and the International Diabetes Federation (IDF) report consensus guidelines for physicians, oral healthcare professionals and patients to improve early diagnosis, prevention and comanagement of diabetes and periodontitis.
Periodontal infections are hypothesized to increase the risk of adverse systemic outcomes through inflammatory mechanisms. The magnitude of effect, if any, of anti-infective periodontal treatment on ...systemic inflammation is unknown, as are the patient populations most likely to benefit. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to test the hypothesis that anti-infective periodontal treatment reduces systemic c-reactive protein (CRP).
MEDLINE, EMBASE, CENTRAL and CINAHL databases were searched using sensitivity-enhancing search terms. Eligible RCTs enrolled patients with periodontal infection, compared a clearly defined anti-infective periodontal intervention (experimental group) to an "inactive control" (no periodontal intervention) or to an "active control" (lower treatment intensity than the experimental group). Mean differences in final CRP values at the earliest post-treatment time point (typically 1-3 months) between experimental and control groups were analyzed using random-effects regression. Among 2,753 possible studies 20 were selected, which included 2,561 randomized patients(median=57). Baseline CRP values were >3.0 mg/L in 40% of trials. Among studies with a control group receiving no treatment, the mean difference in CRP final values among experimental treatment vs. control groups was -0.37 mg/L 95%CI=-0.64, -0.11, (P=0.005), favoring experimental treatment. Trials for which the experimental group received antibiotics had stronger effects (P for interaction=0.03) and the mean difference in CRP final values among experimental treatment vs. control was -0.75 mg/L 95%CI=-1.17,-0.33. No treatment effect was observed among studies using an active treatment comparator. Treatment effects were stronger for studies that included patients with co-morbidities vs. studies that included "systemically healthy" patients, although the interaction was not significant (P=0.48).
Anti-infective periodontal treatment results in short-term modest reductions in systemic CRP.
Diabetes and periodontitis are chronic non-communicable diseases independently associated with mortality and have a bidirectional relationship.
To update the evidence for their epidemiological and ...mechanistic associations and re-examine the impact of effective periodontal therapy upon metabolic control (glycated haemoglobin, HbA1C).
There is strong evidence that people with periodontitis have elevated risk for dysglycaemia and insulin resistance. Cohort studies among people with diabetes demonstrate significantly higher HbA1C levels in patients with periodontitis (versus periodontally healthy patients), but there are insufficient data among people with type 1 diabetes. Periodontitis is also associated with an increased risk of incident type 2 diabetes.
Mechanistic links between periodontitis and diabetes involve elevations in interleukin (IL)-1-β, tumour necrosis factor-α, IL-6, receptor activator of nuclear factor-kappa B ligand/osteoprotegerin ratio, oxidative stress and Toll-like receptor (TLR) 2/4 expression.
Periodontal therapy is safe and effective in people with diabetes, and it is associated with reductions in HbA1C of 0.27–0.48% after 3 months, although studies involving longer-term follow-up are inconclusive.
The European Federation of Periodontology (EFP) and the International Diabetes Federation (IDF) report consensus guidelines for physicians, oral healthcare professionals and patients to improve early diagnosis, prevention and comanagement of diabetes and periodontitis.
The weathering hypothesis states that chronic exposure to social and economic disadvantage leads to accelerated decline in physical health outcomes and could partially explain racial disparities in a ...wide array of health conditions. This systematic review summarizes the literature empirically testing the weathering hypothesis and assesses the quality of the evidence regarding weathering as a determinant of racial disparities in health.
Databases (Web of Science, Ovid MEDLINE, PubMed, and Embase) were searched for studies published in English up to July 1, 2017. Studies that tested the weathering hypothesis for any physical health outcome and included at least one socially or economically disadvantaged group (e.g., Blacks) for whom the weathering hypothesis applies were assessed for eligibility. Threats to validity were assessed using the Quality in Prognostic Studies tool.
The 41 included studies were rated as having overall good methodological quality. Most studies found evidence in support of the weathering hypothesis, although the magnitude of support varied by the health outcome and population studied.
Future evaluations of the weathering hypothesis should include an examination of additional health outcomes and interrogate mechanisms that could link weathering to poor health.
Dimension reduction of high‐dimensional microbiome data facilitates subsequent analysis such as regression and clustering. Most existing reduction methods cannot fully accommodate the special ...features of the data such as count‐valued and excessive zero reads. We propose a zero‐inflated Poisson factor analysis model in this paper. The model assumes that microbiome read counts follow zero‐inflated Poisson distributions with library size as offset and Poisson rates negatively related to the inflated zero occurrences. The latent parameters of the model form a low‐rank matrix consisting of interpretable loadings and low‐dimensional scores that can be used for further analyses. We develop an efficient and robust expectation‐maximization algorithm for parameter estimation. We demonstrate the efficacy of the proposed method using comprehensive simulation studies. The application to the Oral Infections, Glucose Intolerance, and Insulin Resistance Study provides valuable insights into the relation between subgingival microbiome and periodontal disease.
Trimethylamine-N-oxide (TMAO)-a gut-microbiota metabolite-is a biomarker of cardiometabolic risk. No studies have investigated TMAO as an early biomarker of longitudinal glucose increase or prevalent ...impaired glucose regulation. In a longitudinal cohort study, 300 diabetes-free men and women (77%) aged 20-55 years (mean = 34±10) were enrolled at baseline and re-examined at 2-years to investigate the association between TMAO and biomarkers of diabetes risk. Plasma TMAO was measured using Ultra Performance Liquid Chromatography-Mass Spectrometry. After an overnight fast, FPG was measured longitudinally, HbA1C and insulin were measured only at baseline. Insulin resistance was defined using HOMA-IR. Multivariable generalized linear models regressed; i) FPG change (year 2 minus baseline) on baseline TMAO tertiles; and ii) HOMA-IR and HbA1c on TMAO tertiles. Multivariable relative risk regressions modeled prevalent prediabetes across TMAO tertiles. Mean values of 2-year longitudinal FPG±SE across tertiles of TMAO were 86.6±0.9, 86.7±0.9, 86.4±0.9 (p = 0.98). Trends were null for FPG, HbA1c, HOMA-IR, cross-sectionally. The prevalence ratio of prediabetes among participants in 2nd and 3rd TMAO tertiles (vs. the 1st) were 1.94 95%CI 1.09-3.48 and 1.41 95%CI: 0.76-2.61. TMAO levels are associated with increased prevalence of prediabetes in a nonlinear fashion but not with insulin resistance or longitudinal FPG change.
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.
To investigate the causality of central- and general obesity on PCOS using surrogate anthropometric markers.
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.
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.
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.
Impaired glucose regulation (IGR) is common world-wide, and is correlated with Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the virus that causes Coronavirus disease 2019 (COVID-19). ...However, no systematic reviews are available on the topic, and little is known about the strength of the evidence underlying published associations. The current systematic review identified consistent, reproducible associations but several limitations were observed including: (1) a consistent lack of robust confounder adjustment for risk factors collected prior to infection; (2) lack of data on insulin resistance or glycemia measures Hemoglobin A1c (HbA1c) or glucose; (3) few studies considering insulin resistance, glucose or HbA1c values in the clinically normal range as a predictor of SARS-CoV-2 risk; (4) few studies assessed the role of IGR as a risk factor for infection among initially uninfected samples; (5) a paucity of population-based data considering SARS-CoV-2 as a risk factor for the onset of IGR. While diabetes status is a clear predictor of poor prognosis following a SARS-CoV-2 infection, causal conclusions are limited. It is uncertain whether interventions targeting dysglycemia to improve SARS-CoV-2 outcomes have potential to be effective, or if risk assessment should include biomarkers of diabetes risk (ie, insulin and glucose or HbA1c) among diabetes-free individuals. Future studies with robust risk factor data collection, among population-based samples with pre-pandemic assessments will be important to inform these questions.
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