Why Does Obesity Lead to Hypertension? Further Lessons from the Intersalt Study.

Objectives To analyze correlations between major determinants of blood pressure (BP), in efforts to generate and compare predictive models that explain for variance in systolic, diastolic, and mean BP amongst participants of the Intersalt study. Methods Data from the Intersalt study, consisting of nearly 10,000 subjects from 32 different countries, were reviewed and analyzed. Published mean values of 24 hour urinary electrolyte excretion (Na+, K+), 24 hour urine creatinine excretion, body mass index (BMI, kg/m^2), and blood pressure data were extracted and imported into Matlab™ for stepwise linear regression analysis. Results As shown earlier, strong correlations between urinary sodium excretion (UNaV) and systolic, diastolic and mean blood pressure were noted as well as between UNaV and the age dependent increase in systolic blood pressure. Of interest, BMI and urinary creatinine excretion rate (UCrV) also both correlated with systolic blood pressure, but the ratio of BMI/UCrV, constructed to be a measure of obesity, correlated negatively with systolic blood pressure. Conclusions Our results offer population-based evidence suggesting that increased size due to muscle mass rather than adiposity may correspond more to blood pressure. Additional data bases will need to be sampled and analyzed to further validate these observations

Why Does Obesity Lead to Hypertension? Further Lessons from the Intersalt Study

Objectives To analyze correlations between major determinants of blood pressure (BP), in efforts to generate and compare predictive models that explain for variance in systolic, diastolic, and mean BP amongst participants of the Intersalt study. Methods Data from the Intersalt study, consisting of nearly 10,000 subjects from 32 different countries, were reviewed and analyzed. Published mean values of 24 hour urinary electrolyte excretion (Na+, K+), 24 hour urine creatinine excretion, body mass index (BMI, kg/m^2), and blood pressure data were extracted and imported into Matlab™ for stepwise linear regression analysis. Results As shown earlier, strong correlations between urinary sodium excretion (UNaV) and systolic, diastolic and mean blood pressure were noted as well as between UNaV and the age dependent increase in systolic blood pressure. Of interest, BMI and urinary creatinine excretion rate (UCrV) also both correlated with systolic blood pressure, but the ratio of BMI/UCrV, constructed to be a measure of obesity, correlated negatively with systolic blood pressure. Conclusions Our results offer population-based evidence suggesting that increased size due to muscle mass rather than adiposity may correspond more to blood pressure. Additional data bases will need to be sampled and analyzed to further validate these observations

Predicting Adverse Outcomes in Chronic Kidney Disease Using Machine Learning Methods: Data from the Modification of Diet in Renal Disease

Background: Understanding factors which predict progression of renal failure is of great interest to clinicians. Objectives: We examined machine learning methods to predict the composite outcome of death, dialysis or doubling of serum creatinine using the modification of diet in renal disease (MDRD) data set. Methods: We specifically evaluated a generalized linear model, a support vector machine, a decision tree, a feed-forward neural network and a random forest evaluated within the context of 10 fold validation using the CARET package available within the open source architecture R program. Results: We found that using clinical parameters available at entry into the study, these computer learning methods trained on 70% of the MDRD population had prediction accuracies ranging from 66-77% on the remaining 30%. Although the support vector machine methodology appeared to have the highest accuracy, all models studied worked relatively well. Conclusions: These results illustrate the utility of employing machine learning methods within R to address the prediction of long term clinical outcomes using initial clinical measurements

Adiposity Predicts Pulse Pressure in Subjects with Chronic Kidney Disease: Data from the Modification of Diet in Renal Disease

Obesity is a known risk factor for hypertension, but the mechanisms by which this occurs are still unclear. As the body mass index (BMI) is frequently used to define obesity, but the BMI does not distinguish between adipose and other tissues, we sought to develop another index of obesity. We decided to look at the ratio of BMI to urinary creatinine excretion as the latter measurement is believed to be an index of muscle mass. We used data from the modification of diet in renal disease (MDRD) study as urinary creatinine collections and blood pressure measurements were readily available in this data set. The UCRV correlated well with lean body mass determined by anthropomorphic measurements available on this data set. We found that the BMI/UCRV ratio correlated with either percent body fat (PBF) or total body fat calculated as the product of PBF and weight. We also found that the BMI/UCRV ratio correlated significantly with systolic, diastolic and especially pulse pressure in this population. These data suggest that adipocyte mass has a relationship to blood pressure in subjects with renal disease. Should these data be confirmed in other populations, the BMI/UCRV ratio may prove to be a useful measurement in patients at risk for hypertension and other cardiovascular diseases

Predicting Adverse Outcomes in Chronic Kidney Disease Using Machine Learning Methods: Data from the Modification of Diet in Renal Disease

Background: Understanding factors which predict progression of renal failure is of great interest to clinicians. Objectives: We examined machine learning methods to predict the composite outcome of death, dialysis or doubling of serum creatinine using the modification of diet in renal disease (MDRD) data set. Methods: We specifically evaluated a generalized linear model, a support vector machine, a decision tree, a feed-forward neural network and a random forest evaluated within the context of 10 fold validation using the CARET package available within the open source architecture R program. Results: We found that using clinical parameters available at entry into the study, these computer learning methods trained on 70% of the MDRD population had prediction accuracies ranging from 66-77% on the remaining 30%. Although the support vector machine methodology appeared to have the highest accuracy, all models studied worked relatively well. Conclusions: These results illustrate the utility of employing machine learning methods within R to address the prediction of long term clinical outcomes using initial clinical measurements

Attenuation of Na/K-ATPase Mediated Oxidant Amplification with pNaKtide Ameliorates Experimental Uremic Cardiomyopathy

We have previously reported that the sodium potassium adenosine triphosphatase (Na/K-ATPase) can effect the amplification of reactive oxygen species. In this study, we examined whether attenuation of oxidant stress by antagonism of Na/K-ATPase oxidant amplification might ameliorate experimental uremic cardiomyopathy induced by partial nephrectomy (PNx). PNx induced the development of cardiac morphological and biochemical changes consistent with human uremic cardiomyopathy. Both inhibition of Na/K-ATPase oxidant amplification with pNaKtide and induction of heme oxygenase-1 (HO-1) with cobalt protoporphyrin (CoPP) markedly attenuated the development of phenotypical features of uremic cardiomyopathy. In a reversal study, administration of pNaKtide after the induction of uremic cardiomyopathy reversed many of the phenotypical features. Attenuation of Na/K-ATPase oxidant amplification may be a potential strategy for clinical therapy of this disorder

Metabolic Syndrome and Salt-Sensitive Hypertension in Polygenic Obese TALLYHO/JngJ Mice: Role of Na/K-ATPase Signaling

We have demonstrated that Na/K-ATPase acts as a receptor for reactive oxygen species (ROS), regulating renal Na+ handling and blood pressure. TALLYHO/JngJ (TH) mice are believed to mimic the state of obesity in humans with a polygenic background of type 2 diabetes. This present work is to investigate the role of Na/K-ATPase signaling in TH mice, focusing on susceptibility to hypertension due to chronic excess salt ingestion. Age-matched male TH and the control C57BL/6J (B6) mice were fed either normal diet or high salt diet (HS: 2, 4, and 8% NaCl) to construct the renal function curve. Na/K-ATPase signaling including c-Src and ERK1/2 phosphorylation, as well as protein carbonylation (a commonly used marker for enhanced ROS production), were assessed in the kidney cortex tissues by Western blot. Urinary and plasma Na+ levels were measured by flame photometry. When compared to B6 mice, TH mice developed salt-sensitive hypertension and responded to a high salt diet with a significant rise in systolic blood pressure indicative of a blunted pressure-natriuresis relationship. These findings were evidenced by a decrease in total and fractional Na+ excretion and a right-shifted renal function curve with a reduced slope. This salt-sensitive hypertension correlated with changes in the Na/K-ATPase signaling. Specifically, Na/K-ATPase signaling was not able to be stimulated by HS due to the activated baseline protein carbonylation, phosphorylation of c-Src and ERK1/2. These findings support the emerging view that Na/K-ATPase signaling contributes to metabolic disease and suggest that malfunction of the Na/K-ATPase signaling may promote the development of salt-sensitive hypertension in obesity. The increased basal level of renal Na/K-ATPase-dependent redox signaling may be responsible for the development of salt-sensitive hypertension in polygenic obese TH mice