The gut microbiota as a contributing factor to antipsychotic-induced weight gain and metabolic dysfunction

Schizophrenia represents one of the world’s most devastating illnesses due to its often lifelong course and debilitating nature. The treatment of schizophrenia has vastly improved over recent decades with the discovery of several antipsychotic compounds; however these drugs are not without adverse effects that must be addressed to maximize their therapeutic value. Newer, atypical, antipsychotics are associated with a compilation of serious metabolic side effects including weight gain, insulin resistance, fat deposition, glucose dysregulation and ensuing co-morbidities such as type II diabetes mellitus. The mechanisms underlying these side effects remain to be fully elucidated and adequate interventions are lacking. Further understanding of the factors that contribute these side effects is therefore required in order to develop effective adjunctive therapies and to potentially design antipsychotic drugs in the future with reduced impact on the metabolic health of patients. We investigated if the gut microbiota represented a novel mechanism contributing to the metabolic dysfunction associated with atypical antipsychotics. The gut microbiota comprises the bacteria that exist symbiotically within the gastrointestinal tract, and has been shown in recent years to be involved in several aspects of energy balance and metabolism. We have demonstrated that administration of certain antipsychotics in the rat results in an altered microbiota profile and, moreover, that the microbiota is required for the full scale of metabolic dysfunction to occur. We have further shown that specific antibiotics can attenuate certain aspects of olanzapine and risperidone–induced metabolic dysfunction, in particular fat deposition and adipose tissue inflammation. Mechanisms underlying this novel link appear to involve energy utilization via expression of lipogenic genes as well as reduced inflammatory tone. Taken together, these data indicate that the gut microbiota is an important factor involved in the myriad of metabolic complications associated with antipsychotic therapy. Furthermore, these data support the future investigation of microbial-based therapeutics for not only antipsychotic-induced weight gain but also for tackling the global obesity epidemic.

Context-aware real-time assistant architecture for pervasive healthcare

The aging population in many countries brings into focus rising healthcare costs and pressure on conventional healthcare services. Pervasive healthcare has emerged as a viable solution capable of providing a technology-driven approach to alleviate such problems by allowing healthcare to move from the hospital-centred care to self-care, mobile care, and at-home care. The state-of-the-art studies in this field, however, lack a systematic approach for providing comprehensive pervasive healthcare solutions from data collection to data interpretation and from data analysis to data delivery. In this thesis we introduce a Context-aware Real-time Assistant (CARA) architecture that integrates novel approaches with state-of-the-art technology solutions to provide a full-scale pervasive healthcare solution with the emphasis on context awareness to help maintaining the well-being of elderly people. CARA collects information about and around the individual in a home environment, and enables accurately recognition and continuously monitoring activities of daily living. It employs an innovative reasoning engine to provide accurate real-time interpretation of the context and current situation assessment. Being mindful of the use of the system for sensitive personal applications, CARA includes several mechanisms to make the sophisticated intelligent components as transparent and accountable as possible, it also includes a novel cloud-based component for more effective data analysis. To deliver the automated real-time services, CARA supports interactive video and medical sensor based remote consultation. Our proposal has been validated in three application domains that are rich in pervasive contexts and real-time scenarios: (i) Mobile-based Activity Recognition, (ii) Intelligent Healthcare Decision Support Systems and (iii) Home-based Remote Monitoring Systems.