The potential impact of salivary peptides in periodontitis.

Periodontitis is a complex immune-inflammatory condition characterized by the disruption of the periodontal ligament and subsequent formation of periodontal pockets, and by alveolar bone loss, often resulting in tooth loss. A myriad of factors, namely, genetic, metabolic, immunological, and inflammatory, is associated with progression of periodontitis. Periodontitis is also associated with systemic conditions such as neoplastic disorders, obesity, and diabetes. The current diagnosis of this disease relies on clinical measurements such as clinical attachment loss and probing depth, which have poor precision due to patient, operator and probe-related factors. Thus, there is a need to develop reliable, objective, and reproducible biomarkers for early diagnosis of periodontitis. In this regard, saliva, with contributions from the gingival crevicular fluid, holds great potential. However, most of the information on biomarkers of periodontium-related salivary proteins has come from studies on the molecular pathogenesis of periodontitis. In periodontitis, a more holistic approach, such as the use of -omics technologies, for biomarker discovery, is needed. Herein, we review the biomarkers proposed to date for the assessment of periodontitis, with emphasis on the role of salivary peptides in periodontitis and their assessment by high-throughput saliva proteomics. We also discuss the challenges pertaining to the identification of new periodontitis biomarkers in saliva.

Ghrelin and adipokines: An overview of their physiological role, antimicrobial activity and impact on cardiovascular conditions.

The human body has many different hormones that interact with each other and with other factors such as proteins, cell receptors and metabolites. There is still a limited understanding of some of the underlying biological mechanisms of some hormones. In the past decades, science and technology have made major advancements in regard to innovation and knowledge in fields such as medicine. However, some conditions are complex and have many variables that their full picture is still unclear, even though some of these conditions have an alarming rate of incidence and serious health consequences. Conditions such as type 2 diabetes, obesity, nonalcoholic liver disease (NAFLD), cancer in its different forms and even mental conditions, such as Alzheimer's disease, are some of the most common diseases in the 21st century. These conditions are relevant not only because of their high incidence on the general population, but also because of their severity. In this chapter, we present an overview of cardiovascular (CV) diseases. According to the World Health Organization (WHO), cardiovascular diseases, such as coronary artery disease (CAD), heart attack, cardiomyopathy and heart failure (among others), are the number one cause of death worldwide. In 2016, it was estimated that 17.9 million people died from CV diseases, representing more than 30% of all global deaths. Approximately 95% of people who died from CV diseases were so-called "premature deaths" because were referenced to individuals under the age of 70 years old. In this chapter we described some of the hormones that may have an impact on CV diseases, including ghrelin, a peptide that is mostly produced in the stomach, known to induce hunger. Ghrelin is linked to an increase in body fat, i.e., adipose tissue in animals. For this reason, we also included the adipokines leptin, adiponectin and resistin. The main objectives of this chapter are to present the state of the art knowledge concerning the mechanisms of each hormone relevant to CV diseases; to compile data and results that further elucidate the relevance of these peptides for several physiological events, conditions and diseases; and to discuss the metabolic impact of each hormone. We established connections between multiple peptides and the underlying condition/disease with tools such as STRING, referring to research using databases, such as UniProt, DisGeNET and Proteomics DB. Fig. 1 shows a network that summarizes the information presented in this chapter, which serves as a visual representation.