Polychlorinated biphenyls (PCBs) were widely used in industrial and commercial applications, until they were banned in the late 1970s as a result of their significant environmental pollution. PCBs in the environment gained scientific interest because of their persistence and the potential threats they pose to humans. Traditionally, human exposure to PCBs was linked to dietary ingestion. Inhalational exposure to these contaminants is often overlooked. This review discusses the occurrence and distribution of PCBs in environmental matrices and their associated health impacts. Severe PCB contamination levels have been reported in e-waste recycling areas. The occurrence of high PCB levels, notably in urban and industrial areas, might result from extensive PCB use and intensive human activity. Furthermore, PCB contamination in the indoor environment is ten-fold higher than outdoors, which may present expose risk for humans through the inhalation of contaminated air or through the ingestion of dust. In such settings, the inhalation route may contribute significantly to PCB exposure. The data on human health effects due to PCB inhalation are scarce. More epidemiological studies should be performed to investigate the inhalation dose and response mechanism and to evaluate the health risks. Further studies should also evaluate the health impact of prolonged low-concentration PCB exposure.

Introduction: Low health literacy (HL) is one of the contributing factors that leads to negative diabetic outcome. Quality of life (QOL) is a state of complete physical, mental and social wellbeing. There is limited evidence on association of HL and QOL among type 2 diabetes mellitus (T2DM), therefore the objectives of this study were to identify the HL and QOL status, to determine the association between HL and QOL and factors associated with QOL among T2DM patients. Methods: This was a cross sectional study involving patients with T2DM from two public primary care clinics. HL and QOL were measured using translated and validatedHLS – Asia Q16 and DQoL-BCI questionnaires respectively among T2DM patients. Results: The mean HL score was 12.39 (3.34), 17.7 % had “inadequate HL”, 25.7% had “problematic HL” and 56.6% had “sufficient HL”. The mean DQoL-BCI score was 32.09 (6.51). Lower QOL level was negatively associated with age (b -0.140; CI: -0.190, - 0.090; p<0.001), not obese (b -1.476; CI: -2.605, - 0.347; p<0.011), and HL level (b -0.425; CI: -0.59, - 0.259; p<0.001). Lower QOL level was positively associated with uncontrolled glycaemic status (HbA1c >6.5%)(b 1.308; CI: -0.042, 2.659; p<0.058), treatment with insulin (b 4.163; CI:1.538, 6.788; p<0.002) and combination treatment of insulin and oral hypoglycaemic agents (OHA) (b 2.450; CI:1.145, 3.756; p<0.001). Conclusions: This study demonstrated that age, body mass index, HL, glycaemic control, treatment with OHA and insulin were significantly associated with QOL. This suggest the importance of identifying high risk patients with poor QOL for targeted intervention.

Introduction: Metabolic syndrome (MetS) is a global healthcare burden associated with increased risk of atherosclerosis (ATH). The relationship between atherogenic lipoprotein and oxidative stress biomarkers with clinical risk factors of MetS have not been fully explored.  Therefore, the objective of this study is to determine the correlation between small dense low-density lipoprotein (sdLDL-c) and isoprostane (ISP) with MetS criteria and comparing these biomarkers between MetS and non-MetS. Materials and Methods: This was a cross sectional study involving 67 MetS and 43 non-MetS diagnosed by JIS criteria 2009. Demographic details and anthropometric measurements were recorded. Blood samples were collected to analyse serum plasma glucose, direct LDL, calculated sdLDL-c and ISP. Results: Mean serum sdLDL-c and ISP levels were significantly higher among those with MetS compared to non-MetS (1.14+0.44 mmol/L vs 0.87±0.38 mmol/L respectively, p=0.005).  Similarly, mean serum ISP concentration was higher among MetS compared to non-MetS (884.40+602.69 ng/L vs 657.89±616.42 ng/L respectively, p= 0.054). sdLDL-c was positively correlated with TG in the MetS (Pearson correlation 0.501, p<.001) whilst HDL-c was positively correlated with sdLDL-c among the non-MetS (Pearson Correlation 0.422, p<.005). Conclusion: This study highlights the correlation between sdLDL-c and TG in among MetS, emphasizing the need to closely monitor and manage TG among this cohort to reduce the risk of ATH. It was also noted that HDL-c showed positive correlation with sdLDL-c among non-MetS. This discordant finding suggests that HDL-c itself may not be causally associated with cardiovascular benefits and that perhaps HDL-c subfractions may be a better approach to determine cardioprotective effects of HDL-c.