Contribution of the genes of the renin-angiotensin system to interindividual differences in blood pressure levels in Caucasians from Rochester, Minnesota

The Renin-Angiotensin system (RAS) regulates blood pressure through its effects on vascular tone, renal hemodynamics, and renal sodium and fluid balance. The genes encoding the four major components of the RAS, angiotensinogen, renin, angiotensin I-converting enzyme (ACE), and angiotensin II receptor type 1 (AT1), have been investigated as candidate genes in the pathogenesis of essential hypertension. However, studies have primarily focused on small samples of diseased individuals, and, therefore, have provided little information about the determinants of interindividual variation in blood pressure (BP) in the general population.^ Using data from a large population-based sample from Rochester, MN, I have evaluated the contribution of variation in the region of the RAS genes to interindividual variation in systolic, diastolic, and mean arterial pressure in the population-at-large. Marker genotype data from four polymorphisms located within or very near these genes were first collected on 3,974 individuals from 583 randomly ascertained three-generation pedigrees. Haseman-Elston regression and variance component methods of linkage analysis were then carried out to estimate the proportion of interindividual variance in BP attributable to the effects of variation at these four measured loci.^ A significant effect of the ACE locus on interindividual variation in mean arterial pressure (MAP) was detected in a sample of siblings belonging to the youngest generation. After allowing for measured covariates, this effect accounted for 15-25% of the interindividual variance in MAP, and was even greater in a subset with a positive family history of hypertension. When gender-specific analyses were carried out, this effect was significant in males but not in females. Extended pedigree analyses also provided evidence for an effect of the ACE locus on interindividual variation in MAP, but no difference between males and females was observed. Circumstantial evidence suggests that the ACE gene itself may be responsible for the observed effects on BP, although the possibility that other genes in the region may be at play cannot be excluded.^ No definitive evidence for an effect of the renin, angiotensinogen, or AT1 loci on interindividual variation in BP was obtained in this study, suggesting that the impact of these genes on BP may not be great in the Caucasian population-at-large. However, this does not preclude a larger effect of these genes in some subsets of individuals, especially among those with clinically manifest hypertension or coronary heart disease, or in other populations. ^

Resting and reactive blood pressure as predictors of ambulatory blood pressure in older hypertensive adults

Background. A review of the literature suggests that Hypertension (HTN) in older adults is associated with sympathetic stimulation that results in increasing blood pressure (BP) reactivity. If clinical assessment of BP captured sympathetic stimulation, it would be valuable for hypertension management. ^ Objectives. The study examined whether reactive change scores from a short BPR protocol, resting blood pressure (BP), or resting pulse pressure (PP) is a better predictor of 24 hour ambulatory BP and BP load in cardiac patients. ^ Method. The study used a single-group design, with both an experimental clinical component and an observational field component. Both components used repeated measurement methods. The study population consisted of 45 adult patients with a mean age of 64.6 ± 8.5 years who were diagnosed with cardiac disease and who were taking anti-hypertensive medication. Blood pressure reactivity was operationalized with a speech protocol. During the speech protocol, BP was measured with an automatic device (Dinamap 825XT) while subjects talked about their health and about their usual day. Twenty-four hour ambulatory BP measurement (Spacelabs 90207 monitor) followed the speech protocol. ^ Results. Resting SBP and resting PP were significant predictors of 24-hour SBP, and resting SBP was a significant predictor of SBP load. No predictors were significant of 24-hour DBP or DBP load. ^ Conclusions. Initial resting BP and PP may be used in clinical settings to assess hypertension management. Future studies are necessary to confirm the ability of resting BP to predict ABP and BP load in older, medicated hypertensives. ^

Gene by smoking interaction in blood pressure

Hypertension (HT) is mediated by the interaction of many genetic and environmental factors. Previous genome-wide linkage analysis studies have found many loci that show linkage to HT or blood pressure (BP) regulation, but the results were generally inconsistent. Gene by environment interaction is among the reasons that potentially explain these inconsistencies between studies. Here we investigate influences of gene by smoking (GxS) interaction on HT and BP in European American (EA), African American (AA) and Mexican American (MA) families from the GENOA study. A variance component-based method was utilized to perform genome-wide linkage analysis of systolic blood pressure (SBP), diastolic blood pressure (DBP), and HT status, as well as bivariate analysis for SBP and DBP for smokers, non-smokers, and combined groups. The most significant results were found for SBP in MA. The strongest signal was for chromosome 17q24 (LOD = 4.2), increased to (LOD = 4.7) in bivariate analysis but there was no evidence of GxS interaction at this locus (p = 0.48). Two signals were identified only in one group: on chromosome 15q26.2 (LOD = 3.37) in non-smokers and chromosome 7q21.11 (LOD = 1.4) in smokers, both of which had strong evidence for GxS interaction (p = 0.00039 and 0.009 respectively). There were also two other signals, one on chromosome 20q12 (LOD = 2.45) in smokers, which became much higher in the combined sample (LOD = 3.53), and one on chromosome 6p22.2 (LOD = 2.06) in non-smokers. Neither peak had very strong evidence for GxS interaction (p = 0.08 and 0.06 respectively). A fine mapping association study was performed using 200 SNPs in 30 genes located under the linkage signals on chromosomes 15 and 17. Under the chromosome 15 peak, the association analysis identified 6 SNPs accounting for a 7 mmHg increase in SBP in MA non-smokers. For the chromosome 17 linkage peak, the association analysis identified 3 SNPs accounting for a 6 mmHg increase in SBP in MA. However, none of these SNPs was significant after correcting for multiple testing, and accounting for them in the linkage analysis produced very small reductions in the linkage signal. ^ The linkage analysis of BP traits considering the smoking status produced very interesting signals for SBP in the MA population. The fine mapping association analysis gave some insight into the contribution of some SNPs to two of the identified signals, but since these SNPs did not remain significant after multiple testing correction and did not explain the linkage peaks, more work is needed to confirm these exploratory results and identify the culprit variations under these linkage peaks. ^

Data quality in trauma transfusion studies and the impact of missing data on predicting massive transfusion

Maximizing data quality may be especially difficult in trauma-related clinical research. Strategies are needed to improve data quality and assess the impact of data quality on clinical predictive models. This study had two objectives. The first was to compare missing data between two multi-center trauma transfusion studies: a retrospective study (RS) using medical chart data with minimal data quality review and the PRospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study with standardized quality assurance. The second objective was to assess the impact of missing data on clinical prediction algorithms by evaluating blood transfusion prediction models using PROMMTT data. RS (2005-06) and PROMMTT (2009-10) investigated trauma patients receiving ≥ 1 unit of red blood cells (RBC) from ten Level I trauma centers. Missing data were compared for 33 variables collected in both studies using mixed effects logistic regression (including random intercepts for study site). Massive transfusion (MT) patients received ≥ 10 RBC units within 24h of admission. Correct classification percentages for three MT prediction models were evaluated using complete case analysis and multiple imputation based on the multivariate normal distribution. A sensitivity analysis for missing data was conducted to estimate the upper and lower bounds of correct classification using assumptions about missing data under best and worst case scenarios. Most variables (17/33=52%) had <1% missing data in RS and PROMMTT. Of the remaining variables, 50% demonstrated less missingness in PROMMTT, 25% had less missingness in RS, and 25% were similar between studies. Missing percentages for MT prediction variables in PROMMTT ranged from 2.2% (heart rate) to 45% (respiratory rate). For variables missing >1%, study site was associated with missingness (all p≤0.021). Survival time predicted missingness for 50% of RS and 60% of PROMMTT variables. MT models complete case proportions ranged from 41% to 88%. Complete case analysis and multiple imputation demonstrated similar correct classification results. Sensitivity analysis upper-lower bound ranges for the three MT models were 59-63%, 36-46%, and 46-58%. Prospective collection of ten-fold more variables with data quality assurance reduced overall missing data. Study site and patient survival were associated with missingness, suggesting that data were not missing completely at random, and complete case analysis may lead to biased results. Evaluating clinical prediction model accuracy may be misleading in the presence of missing data, especially with many predictor variables. The proposed sensitivity analysis estimating correct classification under upper (best case scenario)/lower (worst case scenario) bounds may be more informative than multiple imputation, which provided results similar to complete case analysis.^