Interaction between factor V Leiden and serum LDL cholesterol increases the risk of atherosclerosis

A new, accurate predictive model for incident hypertension

OBJECTIVE

Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures.

METHODS

The primary study population consisted of 1605 normotensive individuals aged 20-79 years with 5-year follow-up from the population-based study, that is the Study of Health in Pomerania (SHIP). The initial set was randomly split into a training and a testing set. We used a probabilistic graphical model applying a Bayesian network to create a predictive model for incident hypertension and compared the predictive performance with the established Framingham risk score for hypertension. Finally, the model was validated in 2887 participants from INTER99, a Danish community-based intervention study.

RESULTS

In the training set of SHIP data, the Bayesian network used a small subset of relevant baseline features including age, mean arterial pressure, rs16998073, serum glucose and urinary albumin concentrations. Furthermore, we detected relevant interactions between age and serum glucose as well as between rs16998073 and urinary albumin concentrations [area under the receiver operating characteristic (AUC 0.76)]. The model was confirmed in the SHIP validation set (AUC 0.78) and externally replicated in INTER99 (AUC 0.77). Compared to the established Framingham risk score for hypertension, the predictive performance of the new model was similar in the SHIP validation set and moderately better in INTER99.

CONCLUSION

Data mining procedures identified a predictive model for incident hypertension, which included innovative and easy-to-measure variables. The findings promise great applicability in screening settings and clinical practice.

Pathology supported genetic testing and treatment of cardiovascular disease in middle age for prevention of Alzheimer's disease

Chronic, multi-factorial conditions caused by a complex interaction between genetic and environmental risk factors frequently share common disease mechanisms, as evidenced by an overlap between genetic risk factors for cardiovascular disease (CVD) and Alzheimer's disease (AD). Single nucleotide polymorphisms (SNPs) in several genes including ApoE, MTHFR, HFE and FTO are known to increase the risk of both conditions. The E4 allele of the ApoE polymorphism is the most extensively studied risk factor for AD and increases the risk of coronary heart disease by approximately 40%. It furthermore displays differential therapeutic responses with use of cholesterol-lowering statins and acetylcholinesterase inhibitors, which may also be due to variation in the CYP2D6 gene in some patients. Disease expression may be triggered by gene-environment interaction causing conversion of minor metabolic abnormalities into major brain disease due to cumulative risk. A growing body of evidence supports the assessment and treatment of CVD risk factors in midlife as a preventable cause of cognitive decline, morbidity and mortality in old age. In this review, the concept of pathology supported genetic testing (PSGT) for CVD is described in this context. PSGT combines DNA testing with biochemical measurements to determine gene expression and to monitor response to treatment. The aim is to diagnose treatable disease subtypes of complex disorders, facilitate prevention of cumulative risk and formulate intervention strategies guided from the genetic background. CVD provides a model to address the lifestyle link in most chronic diseases with a genetic component. Similar preventative measures would apply for optimisation of heart and brain health.

Different Genes and Polymorphisms Affecting High-Density Lipoprotein Cholesterol Levels in Greek Familial Hypercholesterolemia Patients

Familial Hypercholesterolemia (FH) is a genetic disorder characterized by high low-density lipoprotein cholesterol (LDL-C) concentrations that frequently gives rise to premature coronary artery disease. The clinical expression of FH is highly variable, even in patients carrying the same LDL receptor gene mutation. This variability may be due to environmental and other genetic factors. We investigated the effect of APOCIII T1100C, FV Gln506Arg, ADRB2 Glu27Gln, SELE Ser128Arg, SELE Leu554Phe, and ENaCa Ala663Thr polymorphisms on the HDL-C variations in 84 patients with FH. For ApoCIII T1100C, subjects with the TT genotype presented higher HDL-C levels than the other genotype groups (p = 0.046). Similarly the presence of the Gln allele in ADRB2 27 Glu/Gln heterozygotes and ADRB2 27 Gln/Gln homozygotes was associated with higher HDL-C levels (p = 0.014). Among the other polymorphisms tested, none of them were associated with variations in HDL-C levels. The influence of each polymorphism on lipid concentrations was evaluated with linear regression analyses after adjustment for age and sex. Among the variables studied including total cholesterol, LDL-C, high-density lipoprotein (HDL)-C, triglycerides, apolipoprotein A (Apo-A) and B (Apo-B), and lipoprotein alpha (LP alpha), HDL-C concentration was significantly different in models applied for polymorphisms ApoCIII T1100C, FV Gln506Arg, and ADRB2 Glu27Gln (p = 0.01, p = 0.018, p = 0.04, respectively). These results suggest that HDL-C levels in FH heterozygotes may be affected by several different genetic variants.