Clinical measurement in assessing obesity and the prediction of biomarkers in association to cardovascular diseases and type-II diabetes

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR CONSULTATION

HIV, cardiovascular diseases, and chronic arsenic exposure co-exist in a positive synergy

Recent epidemiological evidences indicate that arsenic exposure increases risk of atherosclerosis, cardio vascular diseases (CVD) such as hypertension, atherosclerosis, coronary artery disease (CAD) and microangiopathies in addition to the serious global health concern related to its carcinogenic effects. In experiments on animals, acute and chronic exposure to arsenic directly correlates with cardiac tachyarrhythmia, and atherogenesis in a concentration and duration dependent manner. Moreover, the other effects of long-term arsenic exposure include induction of non-insulin dependent diabetes by mechanisms yet to be understood. On the other hand, there are controversial issues, gaps in knowledge, and future research priorities in accelerated incidences of CVD and mortalities in patients with HIV who are under long-termanti-retroviral therapy (ART). Although, both HIV infection itself and various components of ART initiate significant pathological alterations in the myocardium and the vasculature, simultaneous environmental exposure to arsenic which is more convincingly being recognized as a facilitator of HIV viral cycling in the infected immune cells, may contribute an additional layer of adversity in these patients. A high degree of suspicion and early screening may allow appropriate interventional guidelines to improve the quality of lives of those affected. In this mini-review which have been fortified with our own preliminary data, we will discuss some of the key current understating of chronic arsenic exposure, and its possible impact on the accelerated HIV/ART induced CVD. The review will conclude with notes on recent developments in mathematical modeling in this field that probabilistically forecast incidence prevalence as functions of aging and life style parameters, most of which vary with time themselves; this interdisciplinary approach provides a complementary kernel to conventional biology.