Periodontitis and systemic diseases:A record of discussions of working group 4 of the Joint EFP/AAP Workshop on Periodontitis and Systemic Diseases

Background There has been an explosion in research into possible associations between periodontitis and various systemic diseases and conditions. Aim To review the evidence for associations between periodontitis and various systemic diseases and conditions, including chronic obstructive pulmonary disease (COPD), pneumonia, chronic kidney disease, rheumatoid arthritis, cognitive impairment, obesity, metabolic syndrome and cancer, and to document headline discussions of the state of each field. Periodontal associations with diabetes, cardiovascular disease and adverse pregnancy outcomes were not discussed by working group 4. Results Working group 4 recognized that the studies performed to date were largely cross-sectional or case-control with few prospective cohort studies and no randomized clinical trials. The best current evidence suggests that periodontitis is characterized by both infection and pro-inflammatory events, which variously manifest within the systemic diseases and disorders discussed. Diseases with at least minimal evidence of an association with periodontitis include COPD, pneumonia, chronic kidney disease, rheumatoid arthritis, cognitive impairment, obesity, metabolic syndrome and cancer. The working group agreed that there is insufficient evidence to date to infer causal relationships with the exception that organisms originating in the oral microbiome can cause lung infections. Conclusions The group was unanimous in their opinion that the reported associations do not imply causality, and establishment of causality will require new studies that fulfil the Bradford Hill or equivalent criteria. Precise and community-agreed case definitions of periodontal disease states must be implemented systematically to enable consistent and clearer interpretations of studies of the relationship to systemic diseases. The members of the working group were unanimous in their opinion that to develop data that best inform clinicians, investigators and the public, studies should focus on robust disease outcomes and avoid surrogate endpoints. It was concluded that because of the relative immaturity of the body of evidence for each of the purported relationships, the field is wide open and the gaps in knowledge are large. © 2013 European Federation of Periodontology and American Academy of Periodontology.

Outlier effects on robust joint modelling of longitudinal and survival data

Robust joint modelling is an emerging field of research. Through the advancements in electronic patient healthcare records, the popularly of joint modelling approaches has grown rapidly in recent years providing simultaneous analysis of longitudinal and survival data. This research advances previous work through the development of a novel robust joint modelling methodology for one of the most common types of standard joint models, that which links a linear mixed model with a Cox proportional hazards model. Through t-distributional assumptions, longitudinal outliers are accommodated with their detrimental impact being down weighed and thus providing more efficient and reliable estimates. The robust joint modelling technique and its major benefits are showcased through the analysis of Northern Irish end stage renal disease patients. With an ageing population and growing prevalence of chronic kidney disease within the United Kingdom, there is a pressing demand to investigate the detrimental relationship between the changing haemoglobin levels of haemodialysis patients and their survival. As outliers within the NI renal data were found to have significantly worse survival, identification of outlying individuals through robust joint modelling may aid nephrologists to improve patient's survival. A simulation study was also undertaken to explore the difference between robust and standard joint models in the presence of increasing proportions and extremity of longitudinal outliers. More efficient and reliable estimates were obtained by robust joint models with increasing contrast between the robust and standard joint models when a greater proportion of more extreme outliers are present. Through illustration of the gains in efficiency and reliability of parameters when outliers exist, the potential of robust joint modelling is evident. The research presented in this thesis highlights the benefits and stresses the need to utilise a more robust approach to joint modelling in the presence of longitudinal outliers.