OBJECTIVES: (1) To compare the concentrations of biomarkers of inflammation, endothelial activation and oxidative stress in subjects with low HDL-c compared to normal HDL-c; (2) To examine the association and correlation between HDL-c and these biomarkers and (3) To determine whether HDL-c is an independent predictor of these biomarkers.

METHODS: 422 subjects (mean age±SD = 43.2±11.9 years) of whom 207 had low HDL-c concentrations (HDL-c <1.0 mmol/L and <1.3 mmol/L for males and females respectively) and 215 normal controls (HDL-c ≥1.0 and ≥1.3 mmol/L for males and females respectively) were recruited in this study. The groups were matched for age, gender, ethnicity, smoking status, diabetes mellitus and hypertension. Fasting blood samples were collected for analysis of biomarkers of inflammation [high-sensitivity C-reactive protein (hsCRP) and Interleukin-6 (IL-6)], endothelial activation [soluble Vascular Cell Adhesion Molecule-1 (sVCAM-1), soluble Intercellular Adhesion Molecule-1 (sICAM-1) and E-selectin)] and oxidative stress [F2-Isoprostanes, oxidized Low Density Lipoprotein (ox-LDL) and Malondialdehyde (MDA)].

RESULTS: Subjects with low HDL-c had greater concentrations of inflammation, endothelial activation and oxidative stress biomarkers compared to controls. There were negative correlations between HDL-c concentration and biomarkers of inflammation (IL-6, p = 0.02), endothelial activation (sVCAM-1 and E-selectin, p = 0.029 and 0.002, respectively), and oxidative stress (MDA and F2-isoprostane, p = 0.036 and <0.0001, respectively). Multiple linear regression analysis showed HDL-c as an independent predictor of IL-6 (p = 0.02) and sVCAM-1 (p<0.03) after correcting for various confounding factors.

METHODS: A total of 149 patients were included in the study. HBA and HBB mutations were characterised using multiplex PCR, Sanger sequencing and multiplex ligationdependent probe amplification. In addition, 35 HbF polymorphisms were genotyped using mass spectrometry and PCR-restriction fragment length polymorphism (PCRRFLP). The genotype-phenotype association was analysed using SPSS version 22.

RESULTS: Twenty-one HBB mutations were identified in the study population. Patients with HBB mutations had heterogeneous phenotypic severity due to the presence of other secondary modifiers. Co-inheritance of α-thalassemia (n = 12) alleviated disease severity of β-thalassemia. In addition, three polymorphisms (HBS1LMYB, rs4895441 [P = 0.008, odds ratio (OR) = 0.38 (0.18, 0.78)], rs9376092 [P = 0.030, OR = 0.36 (0.14, 0.90)]; and olfactory receptor [OR51B2] rs6578605 [P = 0.018, OR = 0.52 (0.31, 0.89)]) were associated with phenotypic severity. Secondary analysis of the association between single-nucleotide polymorphisms with HbF levels revealed three nominally significant SNPs: rs6934903, rs9376095 and rs9494149 in HBS1L-MYB.

METHODS: REDISCOVER, a prospective study, enrolled 11,288 adults where sociodemographic data, anthropometric and blood pressure measurements, fasting lipid profile and glucose, and history of diabetes, hypertension, and smoking were obtained. The cross-sectional analytic sample presented in this article comprised 10,482 participants from baseline recruitment. The data was analysed by descriptive statistics and multivariable logistic regression.

RESULTS: The overall prevalence of elevated TC, elevated LDL-c, elevated TG, low HDL-c, and elevated non-HDL-c were 64.0% (95% CI 63.0-65.0), 56.7% (CI 55.7-57.7), 37.4% (CI 36.5-38.4), 36.2% (CI 35.2-37.1), and 56.2% (CI 55.3-57.2), respectively. Overweight, obesity, and central obesity were highly prevalent and significantly associated with elevated TC and all dyslipidaemia subtypes. Older age was associated with elevated TC, elevated LDL-c and elevated non-HDL-c. Hypertension was associated with elevated TC, elevated TG, and elevated non-HDL-c, while diabetes was associated with elevated TG and low HDL-c.

METHODOLOGY: Using the software-development-life-cycle (SDLC) iterative model, storyboard and wireframe were drafted; and a mock prototype was designed to illustrate the content and function graphically. Subsequently, a working prototype was developed. Qualitative studies using the 'think-aloud' and cognitive-task-analysis methods were conducted for the utility and usability testing. Topic guide was based on the 10-Nielsen's-Heuristic-Principles. Utility testing was conducted among PCP in which they 'thought-aloud' while performing tasks using the mobile app. Usability testing was conducted among MetS patients after they were given the app for 3 weeks. They 'thought-aloud' while performing tasks using the app. Interviews were audio- and video-recorded, and transcribed verbatim. Thematic content analysis was performed.

RESULT: Seven PCP and nine patients participated in the utility and usability testing, respectively. Six themes (efficiency of use, user control and freedom, appearance and aesthetic features, clinical content, error prevention, and help and documentation) emerged. PCP found the mobile app attractive and relevant sections were easy to find. They suggested adding 'zoom/swipe' functions and some parts needed bigger fonts. Patients commented that the app was user-friendly, has nice interface, and straightforward language. It helped them understand their health better. Based on these findings, the mobile app was refined.

METHODS: Seven hundered fifty five individuals from specialist clinics and community health screenings with LDL-c level ≥ 4.0 mmol/L were selected and diagnosed as FH using the DLCC, the SB Register criteria, the US MEDPED and the JFHMC. The sensitivity, specificity, efficiency, positive and negative predictive values of individuals screened with the SB register criteria, US MEDPED and JFHMC were assessed against the DLCC.