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.
SETTINGS: Health screening programme conducted in three inland settlements in the east coast of Malaysia and Peninsular Malaysia.
SUBJECTS: 150 Negritos who were still living in three inland settlements in the east coast of Malaysia and 1227 Malays in Peninsular Malaysia. These subjects were then categorised into MS and non-MS groups based on the International Diabetes Federation (IDF) consensus worldwide definition of MS and were recruited between 2010 and 2015. The subjects were randomly selected and on a voluntary basis.
PRIMARY AND SECONDARY OUTCOME MEASURES: This study was a cross-sectional study. Serum samples were collected for analysis of inflammatory (hsCRP), endothelial activation (sICAM-1) and prothrombogenesis [lp(a)] biomarkers.
RESULTS: MS was significantly higher among the Malays compared with Negritos (27.7%vs12.0%). Among the Malays, MS subjects had higher hsCRP (p=0.01) and sICAM-1 (p<0.05) than their non-MS counterpart. There were no significant differences in all the biomarkers between MS and the non-MS Negritos. However, when compared between ethnicity, all biomarkers were higher in Negritos compared with Malays (p<0.001). Binary logistic regression analysis affirmed that Negritos were an independent predictor for Lp(a) concentration (p<0.001).
CONCLUSIONS: This study suggests that there may possibly be a genetic influence other than lifestyle, which could explain the lack of difference in biomarkers concentration between MS and non-MS Negritos and for Negritos predicting Lp(a).
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.
AIMS: 1) To compare the effects of different TCT isomers on inflammation, endothelial activation, and endothelial nitric oxide synthase (eNOS). 2) To identify the two most potent TCT isomers in stimulated human endothelial cells. 3) To investigate the effects of TCT isomers on NFκB activation, and protein and gene expression levels in stimulated human endothelial cells.
METHODS: Human umbilical vein endothelial cells were incubated with various concentrations of TCT isomers or α-TOC (0.3-10 µM), together with lipopolysaccharides for 16 h. Supernatant cells were collected and measured for protein and gene expression of cytokines (interleukin-6, or IL-6; tumor necrosis factor-alpha, or TNF-α), adhesion molecules (intercellular cell adhesion molecule-1, or ICAM-1; vascular cell adhesion molecule-1, or VCAM-1; and e-selectin), eNOS, and NFκB.
RESULTS: δ-TCT is the most potent TCT isomer in the inhibition of IL-6, ICAM-1, VCAM-1, and NFκB, and it is the second potent in inhibiting e-selectin and eNOS. γ-TCT isomer is the most potent isomer in inhibiting e-selectin and eNOS, and it is the second most potent in inhibiting is IL-6, VCAM-1, and NFκB. For ICAM-1 protein expression, the most potent is δ-TCT followed by α-TCT. α- and β-TCT inhibit IL-6 at the highest concentration (10 µM) but enhance IL-6 at lower concentrations. γ-TCT markedly increases eNOS expression by 8-11-fold at higher concentrations (5-10 µM) but exhibits neutral effects at lower concentrations.
CONCLUSION: δ- and γ-TCT are the two most potent TCT isomers in terms of the inhibition of inflammation and endothelial activation whilst enhancing eNOS, possibly mediated via the NFκB pathway. Hence, there is a great potential for TCT isomers as anti-atherosclerotic agents.