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.
CONCLUSION: Low serum HDL-c concentration is strongly correlated with enhanced status of inflammation, endothelial activation and oxidative stress. It is also an independent predictor for enhanced inflammation and endothelial activation, which are pivotal in the pathogenesis of atherosclerosis and atherosclerosis-related complications.
AIM: This study aims to investigate the genetic polymorphisms of CYP3A5 among the Orang Asli in Peninsular Malaysia using a next generation sequencing platform.
METHODS: Genomic DNAs were extracted from blood samples of the three main Orang Asli tribes and whole-genome sequencing was performed.
RESULTS: A total of 61 single nucleotide polymorphisms were identified and all the SNPs were located in introns except rs15524, which is in the 3'UTR, and 11 of these polymorphisms were novel. Two allelic variants and three genotypes were identified in the Orang Asli. The major allelic variant was the non-functional CYP3A5*3 (66.4%). The percentages of Orang Asli with CYP3A5*3/*3 (47.2%) and CYP3A5*1/*3 (38.1%) genotypes are more than twice the percentage of Orang Asli with CYP3A5*1/*1 (14.8%) genotype. Almost half of the Orang Asli harboured CYP3A5 non-expressor genotype (CYP3A5*3/*3).
CONCLUSIONS: The predominance of the CYP3A5 non-expressor genotype among the Orang Asli was unravelled and the findings in this study may serve as a guide for the optimisation of pharmacotherapy for the Orang Asli community.
METHOD: For designing and modeling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. Different Machine learning-based feature ranking techniques were investigated to identify the important MNSI features associated with DSPN diagnosis. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading using the best performing top-ranked MNSI features.
RESULTS: Top-10 ranked features from MNSI features: Appearance of Feet (R), Ankle Reflexes (R), Vibration perception (L), Vibration perception (R), Appearance of Feet (L), 10-gm filament (L), Ankle Reflexes (L), 10-gm filament (R), Bed Cover Touch, and Ulceration (R) were identified as important features for identifying DSPN by Multi-Tree Extreme Gradient Boost model. The nomogram-based prediction model exhibited an accuracy of 97.95% and 98.84% for the EDIC test set and an independent test set, respectively. A DSPN severity score technique was generated for MNSI from the DSPN severity prediction model. DSPN patients were stratified into four severity levels: absent, mild, moderate, and severe using the cut-off values of 17.6, 19.1, 20.5 for the DSPN probability less than 50%, 75%-90%, and above 90%, respectively.
CONCLUSIONS: The findings of this work provide a machine learning-based MNSI severity grading system which has the potential to be used as a secondary decision support system by health professionals in clinical applications and large clinical trials to identify high-risk DSPN patients.
METHODS: Genomic DNAs were extracted from the blood samples followed by whole-genome sequencing. The reads were aligned to the reference human genome hg19 and variants in the CYP2D6 gene were analyzed. CYP2D6*5 and duplication of CYP2D6 were analyzed using previously established methods.
RESULTS: A total of 72 single nucleotide polymorphisms were identified. CYP2D6*1, *2, *4, *5, *10,*41, and duplication of the gene were found in the Orang Asli, whereby CYP2D6*2 and *41 alleles are reported for the first time in the Malaysian population.
CONCLUSION: The findings in this study provide insights into the genetic polymorphisms of CYP2D6 in the Orang Asli of Peninsular Malaysia.