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.

Analysing energy detector diversity receivers for spectrum sensing

The analysis of energy detector systems is a well studied topic in the literature: numerous models have been derived describing the behaviour of single and multiple antenna architectures operating in a variety of radio environments. However, in many cases of interest, these models are not in a closed form and so their evaluation requires the use of numerical methods. In general, these are computationally expensive, which can cause difficulties in certain scenarios, such as in the optimisation of device parameters on low cost hardware. The problem becomes acute in situations where the signal to noise ratio is small and reliable detection is to be ensured or where the number of samples of the received signal is large. Furthermore, due to the analytic complexity of the models, further insight into the behaviour of various system parameters of interest is not readily apparent. In this thesis, an approximation based approach is taken towards the analysis of such systems. By focusing on the situations where exact analyses become complicated, and making a small number of astute simplifications to the underlying mathematical models, it is possible to derive novel, accurate and compact descriptions of system behaviour. Approximations are derived for the analysis of energy detectors with single and multiple antennae operating on additive white Gaussian noise (AWGN) and independent and identically distributed Rayleigh, Nakagami-m and Rice channels; in the multiple antenna case, approximations are derived for systems with maximal ratio combiner (MRC), equal gain combiner (EGC) and square law combiner (SLC) diversity. In each case, error bounds are derived describing the maximum error resulting from the use of the approximations. In addition, it is demonstrated that the derived approximations require fewer computations of simple functions than any of the exact models available in the literature. Consequently, the regions of applicability of the approximations directly complement the regions of applicability of the available exact models. Further novel approximations for other system parameters of interest, such as sample complexity, minimum detectable signal to noise ratio and diversity gain, are also derived. In the course of the analysis, a novel theorem describing the convergence of the chi square, noncentral chi square and gamma distributions towards the normal distribution is derived. The theorem describes a tight upper bound on the error resulting from the application of the central limit theorem to random variables of the aforementioned distributions and gives a much better description of the resulting error than existing Berry-Esseen type bounds. A second novel theorem, providing an upper bound on the maximum error resulting from the use of the central limit theorem to approximate the noncentral chi square distribution where the noncentrality parameter is a multiple of the number of degrees of freedom, is also derived.

The impact of whey protein isolate on energy balance regulation

Using C57BL/6J mice fed whey protein isolate (WPI) enriched high fat (HFD) or low-fat diets (LFD), this study tested the hypothesis that WPI directly impacts on adiposity by influencing lipid metabolism. WPI suppressed HFD-induced body fat and increased lean mass at 8 weeks of dietary challenge despite elevated plasma triacylglycerol (TAG) levels, suggesting reduced TAG storage. WPI reduced HFD-associated hypothalamic leptin and insulin receptor (IR) mRNA expression, and prevented HFD-associated reductions in adipose tissue IR and glucose transporter 4 expression. These effects were largely absent at 21 weeks of HFD feeding, however WPI increased lean mass and cause a trend towards decreased fat mass, with notable increased Lactobacillus and decreased Clostridium gut bacterial species. Increasing the protein to carbohydrate ratio enhanced the above effects, and shifted the gut microbiota composition away from the HFD group. Seven weeks of WPI intake with a LFD decreased insulin signalling gene expression in the adipose tissue in association with an increased fat accumulation. WPI reduced intestinal weight and length, suggesting a potential functional relationship between WPI, gastro-intestinal morphology and insulin related signalling in the adipose. Extending the study to 15 weeks, did not affect adipose fat weight, but decreased energy intake, weight gain and intestinal length. The functionality of protein sensing lysophosphatidic acid receptor 5 (LPA5) in 3T3-L1 pre-adipocytes was assessed. Over-expression of the receptor in 3T3-L1 pre-adipocytes provided a growth advantage to the cells and suppressed cellular differentiation into mature fat cells. In conclusion, the data demonstrates WPI impacts on adiposity by influencing lipid metabolism in a temporal manner, resulting possibly due to changes in lean mass, hypothalamic and adipose gene expression, gut microbiota and gastrointestinal morphology. The data also showed LPA5 is a novel candidate in regulating of preadipocyte growth and differentiation, and may mediate dietary protein effects on adipose tissue.