Objectives Aims of this SR were to assess the association of Periodontitis (PD) with Chronic Kidney Disease (CKD) and with different CKD stages. Materials and methods MEDLINE, Cochrane Central Register of Trials and EMBASE, up to April 4, 2021 were searched. RCTs, prospective and retrospective cohort studies, case–control studies and cross‐sectional studies were considered. JBI’s Critical Appraisal Tool for risk of bias assessment was used. The risk of PD was calculated using the Mantel–Haenszel odds ratios (MH‐OR); weighted mean difference for clinical attachment level (CAL) and periodontal probing depth (PPD) were also evaluated. Results Out of 1949 titles screened, 142 full texts were evaluated and 17 studies were included. CKD was associated to higher risk of PD (MH‐OR = 2.36, [95% C.I. 1.25, 4.44]; p = 0.008), higher mean CAL (WMD = 0.41 mm [95% C.I. 0.22, 0.60]; p < 0.0001) and mean PPD (WMD = 0.25 mm [95% C.I. 0.03, 0.47]; p = 0.02) compared to healthy individuals. Severe CKD (stages 4–5 vs 2–3) resulted at higher risk of PD (MH‐OR = 2.21, [95% C.I. 1.07, 4.54]; p = 0.03). Heterogeneity and risk of bias were high. Conclusions An association between PD and CKD was found. It could be appropriate to consider PD a frequent CKD comorbidity.

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Aim: To evaluate outcome measures, methods of assessment, and analysis in clinical studies on fixed single-and multiple-unit implant restorations.Materials and Methods: Three independent electronic database searches (MEDLINE, EMBASE, and Cochrane) were done to identify prospective and retrospective clinical studies published from January 2011 up to June 2021 with ≥20 patients and minimum 1-year follow-up period on technical and clinical outcomes of implant-supported single crowns (SCs) and partial fixed dental prostheses (P-FDPs). An entire data extraction was performed to identify primarily the most reported outcome measures and later to define the choice of assessment methods of those outcome measures. The outcomes were analysed descriptively, and the strength of association was evaluated using the Pearson chi-square test (p ≤ .05). Results:In a total 531 studies, 368 on SCs (69.3%), 70 on P-FDPs (13.1%), and 93 on both restoration types (17.5%) were included; 56.3% of all studies did not clearly define a primary outcome. The most frequent primary outcome was marginal bone level (MBL) (55.2%) followed by implant survival (5.3%), professional aesthetic evaluation (3.4%), and technical complications (2.1%). Peri-implant indices were the most reported secondary outcome (55.1%), followed by implant survival (39.9%), MBL (36%), and implant success (26.4%). Prosthetic failure (seven studies [3.9%]) was one of the least reported outcome measures. Conclusions:Outcome measures and their assessment methods showed high heterogeneity among studies. Primary outcomes were not often defined clearly, and the most frequently selected primary outcome was marginal bone loss. Prosthetic outcomes, implant survival, and patient-related outcomes were only infrequently reported.

Aims Periodontal disease (PD) is a chronic inflammation of periodontal tissue associated with infection from specific anaerobic pathogens contained in dental plaque. Both type 1 and type 2 diabetes are associated with an increased prevalence of PDs. A two-way relationship between diabetes and periodontitis has been proposed, with diabetes increasing the risk for periodontitis, and periodontal inflammation negatively affecting glycaemic control. To date, the relationship between PD and glucose variability in type 1 diabetes has not been evaluated. To investigate the prevalence of PD in patients with type 1 diabetes and its association with glycemic control and glucose variability. Methods In this cross-sectional study, all enrolled patients were scheduled to attend both a diabetologic and a periodontal visit. HbA1c, glucose coefficient of variation (CV), loss of clinical attachment (CAL), and periodontal probing depth (PPD) were collected. Results 136 patients were included in the analysis. The prevalence of PD was 63%. A significant correlation was found between mean CAL and glucose CV (r = 0.31, p = 0.002), but not with HbA1c. Mean PPD was also associated with glucose CV (r = 0.27 and 0.044), but not with HbA1c. In a multiple linear regression model, with mean CAL as dependent variable, age, glucose CV, and smoking habit resulted significantly associated (r = 0.23, p = 0.013; r = 0.33, p = 0.001; r = 0.34, p < 0.001, respectively). Assuming mean PPD as dependent variable, multiple linear regression analysis showed a significant association with glucose CV and smoking habits only. Conclusions PD is associated with glucose variability in patients with type 1 diabetes also after adjusting for the main confounders.