Genetic linkage of serum homocysteine in Dominican families: the Family Study of Stroke Risk and Carotid Atherosclerosis

Nicotinamide N-Methyltransferase: Genomic Connection to Disease

Single-nucleotide polymorphisms (SNPs) in and around the nicotinamide N-methyltransferase (NNMT) gene are associated with a range of cancers and other diseases and conditions. The data on these associations have been assembled, and their strength discussed. There is no evidence that the presence of either the major or minor base in any SNP affects the expression of nicotinamide N-methyltransferase. Nevertheless, suggestions have been put forward that some of these SNPs do affect NNMT expression and thus homocysteine metabolism. An alternative idea involving non-coding messenger RNAs (mRNAs) is suggested as a possible mechanism whereby health is influenced. It is postulated that these long, non-coding NNMT mRNAs may exert deleterious effects by interfering with the expression of other genes. Neither hypothesis, however, has experimental proof, and further work is necessary to elucidate NNMT genetic interactions.

Medical genetics and genomic medicine in the Dominican Republic: challenges and opportunities

Medical genetics and genomic medicine in the Dominican Republic: challenges and opportunities.

Genetic insights into cardiometabolic risk factors

Many biochemical traits are recognised as risk factors, which contribute to or predict the development of disease. Only a few are in widespread use, usually to assist with treatment decisions and motivate behavioural change. The greatest effort has gone into evaluation of risk factors for cardiovascular disease and/or diabetes, with substantial overlap as 'cardiometabolic' risk. Over the past few years many genome-wide association studies (GWAS) have sought to account for variation in risk factors, with the expectation that identifying relevant polymorphisms would improve our understanding or prediction of disease; others have taken the direct approach of genomic case-control studies for the corresponding diseases. Large GWAS have been published for coronary heart disease and Type 2 diabetes, and also for associated biomarkers or risk factors including body mass index, lipids, C-reactive protein, urate, liver function tests, glucose and insulin. Results are not encouraging for personal risk prediction based on genotyping, mainly because known risk loci only account for a small proportion of risk. Overlap of allelic associations between disease and marker, as found for low density lipoprotein cholesterol and heart disease, supports a causal association, but in other cases genetic studies have cast doubt on accepted risk factors. Some loci show unexpected effects on multiple markers or diseases. An intriguing feature of risk factors is the blurring of categories shown by the correlation between them and the genetic overlap between diseases previously thought of as distinct. GWAS can provide insight into relationships between risk factors, biomarkers and diseases, with potential for new approaches to disease classification.

Traditional risk factors are not major contributors to the variance in carotid intima-media thickness

BACKGROUND AND PURPOSE

Carotid intima-media thickness (cIMT) was a widely accepted ultrasound marker of subclinical atherosclerosis in the past. Although traditional risk factors may explain ≈50% of the variance in plaque burden, they may not explain such a high proportion of the variance in IMT, especially when measured in plaque-freel ocations. We aimed this study to identify individuals with cIMT unexplained by traditional risk factors for future environmental and genetic research.

METHODS

As part of the Northern Manhattan Study, 1790 stroke-free individuals (mean age, 69±9 years; 60% women; 61% Hispanic; 19% black; 18% white) were assessed for cIMT using B-mode carotid ultrasound. Multiple linear regression models were evaluated: (1) incorporating prespecified traditional risk factors; and (2) including less traditional factors, such as inflammation biomarkers, adiponectin, homocysteine, and kidney function. Standardized cIMT residual scores were constructed to select individuals with unexplained cIMT.

RESULTS

Mean total cIMT was 0.92±0.09 mm. The traditional model explained 11% of the variance in cIMT. Age (7%), male sex (3%), glucose (<1%), pack-years of smoking (<1%), and low-density lipoprotein cholesterol (<1%) were significant contributing factors. The model, including inflammatory biomarkers, explained 16% of the variance in cIMT. Adiponectin was the only additional significant contributor to the variance in cIMT. We identified 358 individuals (20%) with cIMT unexplained by the investigated risk factors.

CONCLUSIONS

Vascular risk factors explain only a small proportion of variance in cIMT. Identification of novel genetic and environmental factors underlying unexplained subclinical atherosclerosis is of utmost importance for future effective prevention of vascular disease.