Clinical correlates of renal dysfunction in hypertensive patients without cardiovascular complications: the REDHY study

Our study was aimed to assess the clinical correlates of different degrees of renal dysfunction in a wide group of non-diabetic hypertensive patients, free from cardiovascular (CV) complications and known renal diseases, participating to the REDHY (REnal Dysfunction in HYpertension) study. A total of 1856 hypertensive subjects (mean age: 47±14 years), attending our hypertension centre, were evaluated. The glomerular filtration rate (GFR) was estimated by the simplified Modification of Diet in Renal Disease Study prediction equation. A 24-h urine sample was collected to determine albumin excretion rate (AER). Albuminuria was defined as an AER greater than 20 μg min−1. We used the classification proposed by the US National Kidney Foundation's guidelines for chronic kidney disease (CKD) to define the stages of renal function impairment. In multiple logistic regression analysis, the probability of having stage 1 and stage 2 CKD was significantly higher in subjects with greater values of systolic blood pressure (SBP) and with larger waist circumference. SBP was also positively related to stage 3 CKD. Stage 3 and stages 4–5 CKD were inversely associated with waist circumference and directly associated with serum uric acid. Age was inversely related to stage 1 CKD and directly related to stage 3 CKD. The factors associated with milder forms of kidney dysfunction are, in part, different from those associated with more advanced stages of renal function impairment

Cystatin C and Cardiovascular Disease

Background Epidemiological studies show that high circulating cystatin C is associated with risk of cardiovascular disease (CVD), independent of creatinine-based renal function measurements. It is unclear whether this relationship is causal, arises from residual confounding, and/or is a consequence of reverse causation. Objectives The aim of this study was to use Mendelian randomization to investigate whether cystatin C is causally related to CVD in the general population. Methods We incorporated participant data from 16 prospective cohorts (n = 76,481) with 37,126 measures of cystatin C and added genetic data from 43 studies (n = 252,216) with 63,292 CVD events. We used the common variant rs911119 in CST3 as an instrumental variable to investigate the causal role of cystatin C in CVD, including coronary heart disease, ischemic stroke, and heart failure. Results Cystatin C concentrations were associated with CVD risk after adjusting for age, sex, and traditional risk factors (relative risk: 1.82 per doubling of cystatin C; 95% confidence interval [CI]: 1.56 to 2.13; p = 2.12 × 10−14). The minor allele of rs911119 was associated with decreased serum cystatin C (6.13% per allele; 95% CI: 5.75 to 6.50; p = 5.95 × 10−211), explaining 2.8% of the observed variation in cystatin C. Mendelian randomization analysis did not provide evidence for a causal role of cystatin C, with a causal relative risk for CVD of 1.00 per doubling cystatin C (95% CI: 0.82 to 1.22; p = 0.994), which was statistically different from the observational estimate (p = 1.6 × 10−5). A causal effect of cystatin C was not detected for any individual component of CVD. Conclusions Mendelian randomization analyses did not support a causal role of cystatin C in the etiology of CVD. As such, therapeutics targeted at lowering circulating cystatin C are unlikely to be effective in preventing CVD

Relation between QT and RR intervals is highly individual among healthy subjects: implications for heart rate correction of the QT interval

Objective: To compare the QT/RR relation in healthy subjects in order to investigate the differences in optimum heart rate correction of the QT interval. Methods: 50 healthy volunteers (25 women, mean age 33.6 (9.5) years, range 19–59 years) took part. Each subject underwent serial 12 lead electrocardiographic monitoring over 24 hours with a 10 second ECG obtained every two minutes. QT intervals and heart rates were measured automatically. In each subject, the QT/RR relation was modelled using six generic regressions, including a linear model (QT = β + α × RR), a hyperbolic model (QT = β + α/RR), and a parabolic model (QT = β × RR(α)). For each model, the parallelism and identity of the regression lines in separate subjects were statistically tested. Results: The patterns of the QT/RR relation were very different among subjects. Regardless of the generic form of the regression model, highly significant differences were found not only between the regression lines but also between their slopes. For instance, with the linear model, the individual slope (parameter α) of any subject differed highly significantly (p < 0.000001) from the linear slope of no fewer than 21 (median 32) other subjects. The linear regression line of 20 subjects differed significantly (p < 0.000001) from the linear regression lines of each other subject. Conversion of the QT/RR regressions to QTc heart rate correction also showed substantial intersubject differences. Optimisation of the formula QTc = QT/RR(α) led to individual values of α ranging from 0.234 to 0.486. Conclusion: The QT/RR relation exhibits a very substantial intersubject variability in healthy volunteers. The hypothesis underlying each prospective heart rate correction formula that a “physiological” QT/RR relation exists that can be mathematically described and applied to all people is incorrect. Any general heart rate correction formula can be used only for very approximate clinical assessment of the QTc interval over a narrow window of resting heart rates. For detailed precise studies of the QTc interval (for example, drug induced QT interval prolongation), the individual QT/RR relation has to be taken into account