Evidence for the link between periodontal disease and several systemic diseases is growing rapidly. Diabetes mellitus is a systemic disease with several major complications affecting both the quality and length of life causing morbidity and mortality. Periodontitis, one of these complications, is a chronic infection associated with substantial morbidity in the form of tooth loss and that affects the quality of life directly. The association between diabetes and inflammatory periodontal disease has been studied extensively. The relationship between these two conditions appears bidirectional. The presence of one condition tends to promote the other and the meticulous management of either may assist treatment of the other. It also provides a perfect example of a cyclical association, whereby a systemic disease predisposes the individual to oral infections, and once the oral infection is established, it exacerbates the systemic disease. This review focuses to explain the interrelationship between the two based on information in the literature and the potential common immunoregulatory connections involved, exploring the mechanisms through which periodontal infection can contribute to the low-grade general inflammation associated with diabetes. Keywords: Diabetes mellitus; inflammation; insulin resistance; periodontitis.

Objective Incidence of youth‐onset diabetes in India has not been well described. Comparison of incidence, across diabetes registries, has the potential to inform hypotheses for risk factors. We sought to compare the incidence of diabetes in the U.S.‐based registry of youth onset diabetes (SEARCH) to the Registry of Diabetes with Young Age at Onset (YDR—Chennai and New Delhi regions) in India. Methods We harmonized data from both SEARCH and YDR to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Data were from youth registered with incident diabetes (2006‐2012). Denominators were from census and membership data. We calculated diabetes incidence by averaging the total cases across the entire follow‐up period and dividing this by the estimated census population corresponding to the source population for case ascertainment. Incidence was calculated for each of the registries and compared by type and within age and sex categories using a 2‐sided, skew‐corrected inverted score test. Results Incidence of type 1 was higher in SEARCH (21.2 cases/100 000 [95% CI: 19.9, 22.5]) than YDR (4.9 cases/100 000 [95% CI: 4.3, 5.6]). Incidence of type 2 diabetes was also higher in SEARCH (5.9 cases/100 000 [95% CI: 5.3, 6.6] in SEARCH vs 0.5/cases/100 000 [95% CI: 0.3, 0.7] in YDR). The age distribution of incident type 1 diabetes cases was similar across registries, whereas type 2 diabetes incidence was higher at an earlier age in SEARCH. Sex differences existed in SEARCH only, with a higher rate of type 2 diabetes among females. Conclusion The incidence of youth‐onset type 1 and 2 diabetes was significantly different between registries. Additional data are needed to elucidate whether the differences observed represent diagnostic delay, differences in genetic susceptibility, or differences in distribution of risk factors.

The cure fraction models are generally used to model lifetime data with long term survivors. In a cohort of cancer patients, it has been observed that due to the development of new drugs some patients are cured permanently, and some are not cured. The patients who are cured permanently are called cured or long term survivors while patients who experience the recurrence of the disease are termed as susceptibles or uncured. Thus, the population is divided into two groups: a group of cured individuals and a group of susceptible individuals. The proportion of cured individuals after the treatment is typically known as the cure fraction. In this paper, we have introduced a three parameter Gompertz (viz. scale, shape and acceleration) or generalized Gompertz distribution in the presence of cure fraction, censored data and covariates for estimating the proportion of cure fraction through Bayesian Approach. Inferences are obtained using the standard Markov Chain Monte Carlo technique in openBUGS software.

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