Gut Microbiota and Metabolic Disorders

Obesity and its co-morbidities, such as metabolic syndrome (MetS), non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes, have increased over the last few decades like an epidemic. So far the mechanisms of many metabolic diseases are not known in detail and currently there are not enough effective means to prevent and treat them. Several recent studies have shown that the unbalanced gut microbiota composition (GMC) and activity have an influence on the fat accumulation in the body. Further, it seems that the GMC of obese individuals differs from the lean. The aim of this study was to investigate whether there are differences between the GMC of metabolically impaired overweight/obese (MetS group), metabolically healthy overweight/obese and normal-weight individuals. In addition, the mechanisms by which the gut bacteria as well as their specific structures, such as flagellin (FLG) that stimulates the Toll-like receptor 5 (TLR5) affect metabolism, were investigated both in vivo and in vitro in human adipocytes and hepatocytes. The results of this study show that the abundance of certain gram-positive bacteria belonging to the Clostridial cluster XIV was higher in the MetS group subjects compared to their metabolically healthy overweight/obese and lean counterparts. Metabolically impaired subjects tended to also have a greater abundance of potentionally inflammatory Enterobacteria in their gut and thus seemed to have aberrant GMC. In addition, it was found that subjects with a high hepatic fat content (HHFC group) had less Faecalibacterium prausnitzii in their gut than individuals with low hepatic fat content. Further gene expression analysis revealed that the HHFC group also had increased inflammation cascades in their adipose tissue. Additionally, metabolically impaired individuals displayed an increased expression of FLG-recognizing TLR5 in adipose tissue, and the TLR5 expression levels associated positively both with liver fat content and insulin resistance in humans. These changes in the adipose tissue may further contribute to the impaired metabolism observed, such as insulin resistance and dyslipidemia. In vitro -studies showed that the FLG-induced TLR5 activation in adipocytes enhanced the hepatic fat accumulation by decreasing insulin signaling and mitochondrial functions and increasing triglyceride synthesis due to increased glycerol secretion from adipocytes. In conclusion, the findings of this study suggest that it may be possible that the novel prevention and personalized treatment strategies based on GM modulation will succesfully be developed for obesity and metabolic disorders in the future.

Adherence to Treatment in Psychotic Disorders - Development of user-centered mobile health intervention

The aim of this study was to explore adherence to treatment among people with psychotic disorders through the development of user-centered mobile technology (mHealth) intervention. More specifically, this study investigates treatment adherence as well as mHealth intervention and the factors related to its possible usability. The data were collected from 2010 to 2013. First, patients’ and professionals’ perceptions of adherence management and restrictive factors of adherence were described (n = 61). Second, objectives and methods of the intervention were defined based on focus group interviews and previously used methods. Third, views of patients and professionals about barriers and requirements of the intervention were described (n = 61). Fourth, mHealth intervention was evaluated based on a literature review (n = 2) and patients preferences regarding the intervention (n = 562). Adherence management required support in everyday activities, social networks and maintaining a positive outlook. The factors restricting adherence were related to illness, behavior and the environment. The objective of the intervention was to support the intention to follow the treatment guidelines and recommendations with mHealth technology. The barriers and requirements for the use of the mHealth were related to technology, organizational issues and the users themselves. During the course of the intervention, 33 (6%) out of 562 participants wanted to edit the content, timing or amount of the mHealth tool, and 23 (4%) quit the intervention or study before its conclusion. According to the review, mHealth interventions were ineffective in promoting adherence. Prior to the intervention, participants perceived that adherence could be supported, and the use of mHealth as a part of treatment was seen as an acceptable and efficient method for doing so. In conclusion, the use of mHealth may be feasible among people with psychotic disorders. However, clear evidence for its effectiveness in regards to adherence is still currently inconclusive.