ApolipoproteinA1-75 G/A (M1-) polymorphism and Lipoprotein(a); Anti- vs. Pro-Atherogenic properties

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Heritability and genetic correlations of heart rate variability at rest and during stress in the Oman Family Study

Introduction:Individual differences in heart rate variability (HRV) can be partly attributed to genetic factors that may be more pronounced during stress. Using data from the Oman Family Study (OFS), we aimed to estimate and quantify the relative contribution of genes and environment to the variance of HRV at rest and during stress; calculate the overlap in genetic and environmental influences on HRV at rest and under stress using bivariate analyses of HRV parameters and heart rate (HR).Methods:Time and frequency domain HRV variables and average HR were measured from beat-to-beat HR obtained from electrocardiogram recordings at rest and during two stress tests [mental: Word Conflict Test (WCT) and physical: Cold Pressor Test (CPT)] in the OFS - a multigenerational pedigree consisting of five large Arab families with a total of 1326 participants. SOLAR software was used to perform quantitative genetic modelling.Results:Heritability estimates for HRV and HR ranged from 0.11 to 0.31 for rest, 0.09-0.43 for WCT, and 0.07-0.36 for CPT. A large part of the genetic influences during rest and stress conditions were shared with genetic correlations ranging between 0.52 and 0.86 for rest-WCT and 0.60-0.92 for rest-CPT. Nonetheless, genetic rest-stress correlations for most traits were significantly smaller than 1 indicating some stress-specific genetic effects.Conclusion:Genetic factors significantly influence HRV and HR at rest and under stress. Most of the genetic factors that influence HRV at rest also influence HRV during stress tests, although some unique genetic variance emerges during these challenging conditions

N-Acetyltransferase Polymorphism Among Northern Sudanese

Interindividual and interethnic differences in allele frequencies of N-acetyltransferase (NAT2) single nucleotide polymorphisms (SNPs) are responsible for phenotypic variability of adverse drug reactions and susceptibility to cancer. We genotyped the seven NAT2 common SNPs in 127 randomly selected unrelated northern Sudanese subjects using allele-specific and RFLP polymerase chain reaction (PCR) based methods. Molecular genotyping was enough to designate alleles for 41 individuals unambiguously, whereas 63 individuals’ alleles were inferred from haplotypes previously described. In the remaining 23 individuals, however, the phase of the SNPs could not be decided because of multiple SNP heterozygotes. Using computational methods in the HAP and Phase programs, we confirmed the inferred alleles of the 62 individuals and predicted the remaining 23 ambiguous alleles. Twelve NAT2 alleles were identified. Four alleles coded for rapid acetylators (18%), and eight alleles coded for slow acetylators (82%). Two genotypes coded for rapid acetylation (3.9%), 10 for intermediate acetylation (27.6%), and 13 for slow acetylation (68.5%). The G191A African SNP and the G857A predominantly Asian SNP were each detected at a low frequency of 3.1%. The combination of molecular and computational analysis was useful in resolving ambiguous genotypes of NAT2 in multiple SNP heterozygotes. Among the northern Sudanese the SNPs associated with slow acetylation are more prevalent than in Caucasians and Asians. This and other African studies are suggestive of an African origin for NAT2-associated polymorphism

Lipids-Risk Categories in Omani Type 2 Diabetics : Impact of the National Cholesterol Educational Program

Objective: To evaluate the impact of the National Cholesterol Educational Program Adult Treatment Panel III (ATP III) and the Framingham Offspring Study on Omani diabetic subjects. Methods: 221 subjects with type 2 diabetes (86 females and 135 males) and 156 non-diabetic subjects (70 females and 86 males) aged 30-70 years attending Sultan Qaboos University Hospital between 1999-2002 were recruited. Lipid profile, glucose, %HbA1c, apoprotein A-1 and apoproteinB were measured. Low density lipoprotein was calculated using the Friedwald formula. ATP-III and Framingham Offspring Study guidelines were used to classify lipid parameters into coronary heart disease-risk categories. Results: Diabetic compared to non-diabetic subjects had significantly higher triglycerides of >1.7 mmol/L (p=0.01) and lower low density lipoprotein cholesterol of >4.2 mmol/L (p=0.012 ) and, in female subjects only, lower high density lipoprotein cholesterol of <0.05 mmo/L for ( p<0.0001). In addition, 57% of diabetic subjects had abnormal aplipoproteinB of >1.2 g/L compared to 49% of non-diabetic subjects. Combined raised levels of triglycerides, apolipoproteinB and low levels of high density lipoprotein were found in 42% of diabetic compared to 26% of the non-diabetic subjects (p=0.05). Diabetic subjects had significantly higher (p=0.008) NCEP risk-score for coronary artery disease, however, only 34% conformed to a NCEP 10-year-risk score of >10%. Conclusion: A substantial proportion of the Omani diabetic subjects were dyslipidaemic according to the ATP III guidelines. This study recommends the implementation of a lower cut-off threshold for starting lipid-modifying agents for Omani diabetics when using the 10-year Framingham Risk Scoring equation.

Apolipoprotein A1 Gene Polymorphisms at the 75 bp and 83/ 84 bp Polymorphic Sites in Healthy Omanis Compared with World Populations

The relative frequencies of the *A allele of the APOA1 gene at - 75 bp (M1- ) and the C or T+83/ 84 bp allele (M2- ) varied significantly between populations. We found the frequencies of M1- and M2- to be 0.22 and 0.067, respectively, in 150 healthy Omanis. These frequencies were compared to frequencies found in other world populations

Isochromosome 9q as a sole anomaly in an Omani boy with acute lymphoblastic leukaemia

This report describes a case of acute lymphoblastic leukaemia in which isochromosome 9q (i(9q)) was the sole acquired cytogenetic abnormality. The Immunophenotype showed positivity for CD3, CD4, CD5, CD7, CD8, CD10, CD71, CD117 and TdT, consistent with T cell acute lymphoblastic leukaemia (ALL). The chromosomal analysis of bone marrow showed 46,XY,i(9)(q10) in all the metaphases analysed. The bone marrow morphology was ALL-L2 as per the French–American–British criteria. Isochromosomes are rare chromosomal abnormalities in childhood ALL and the effect of i(9q) is not well established. The patient’s good response to therapy with normal cytogenetics within a month of induction, and disease-free survival after bone marrow transplant are indicative of a good prognosis in such cases

Apolipoprotein E Polymorphism in Omani Dyslipidemic Patients With and Without Coronary Artery Disease

Apolipoprotein E (APOE) polymorphism is a predictor of interindividual variability in plasma levels of lipids and lipoproteins and a predictor of risk of coronary artery disease (CAD). We studied the relationship between APOE polymorphism and lipid profiles and risk of CAD in Omani dyslipidemic patients. This retrospective study included 244 dyslipidemic patients, of whom 67 had CAD. Fasting blood glucose, lipids, and plasma lipoprotein levels were measured using standard methods, and APOE genotypes were detected by PCR-RFLP. The dyslipidemic patients had the following APOE allele frequencies: APOE*2, 0.030; APOE*3, 0.894; and APOE*4, 0.076. APOE allele frequencies between patients with and without CAD showed no significant differences. Compared to APOE*3/*3 homozygotes, APOE*4 allele patients had higher mean levels of low-density lipoprotein (LDL) cholesterol (p = 0.014), apoB (p = 0.031), lower mean levels of apoA1 ( p = 0.043), and a trend of higher mean level of total cholesterol ( p = 0.084). Thirty-one percent of patients with CAD had the APOE*4 allele compared to 26% with the APOE*3 allele, but this difference was not significant. Compared with APOE*3/*3 homozygotes, patients with the APOE*4 allele had 1.3 times higher risk for CAD after ignoring dyslipidemia, but this risk was modified after adjusting for dyslipidemia. In conclusion, among dyslipidemic patients, carriers of APOE*4 compared to homozygous carriers of APOE*3 had significantly higher levels of LDL cholesterol and apoB, but no relationship with CAD was found

Estimate of the HOMA-IR Cut-off Value Identifying Subjects at Risk of Insulin Resistance Using a Machine Learning Approach

Objectives: This study describes an unsupervised machine learning approach used to estimate the homeostatic model assessment-insulin resistance (HOMA-IR) cut-off for identifying subjects at risk of IR in a given ethnic group based on the clinical data of a representative sample. Methods: The approach was applied to analyse the clinical data of individuals with Arab ancestors, which was obtained from a family study conducted in Nizwa, Oman, between January 2000 and December 2004. First, HOMA-IR-correlated variables were identified to which a clustering algorithm was applied. Two clusters having the smallest overlap in their HOMA-IR values were retrieved. These clusters represented the samples of two populations, which are insulin-sensitive subjects and individuals at risk of IR. The cut-off value was estimated from intersections of the Gaussian functions, thereby modelling the HOMA-IR distributions of these populations. Results: A HOMA-IR cut-off value of 1.62 ± 0.06 was identified. The validity of this cut-off was demonstrated by showing the following: 1) that the clinical characteristics of the identified groups matched the published research findings regarding IR; 2) that a strong relationship exists between the segmentations resulting from the proposed cut-off and those resulting from the two-hour glucose cut-off recommended by the World Health Organization for detecting prediabetes. Finally, the method was also able to identify the cut-off values for similar problems (e.g. fasting sugar cut-off for prediabetes). Conclusion: The proposed method defines a HOMA-IR cut-off value for detecting individuals at risk of IR. Such methods can identify high-risk individuals at an early stage, which may prevent or delay the onset of chronic diseases such as type 2 diabetes. Keywords: Unsupervised Machine Learning; Cluster Analysis; Insulin Resistance; Diabetes Mellitus, Type II