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

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

Involvement of Reactive Oxygen Species in a Feed-Forward Mechanism of Na/K-ATPase Mediated Signaling

Cardiotonic steroids (such as ouabain) signaling through Na/K-ATPase regulate sodium reabsorption in the renal proximal tubule. We report here that reactive oxygen species are required to initiate ouabain-stimulated Na/K-ATPase·c-Src signaling. Pretreatment with the antioxidant N-acetyl-L-cysteine prevented ouabain-stimulated Na/K-ATPase·c-Src signaling, protein carbonylation, redistribution of Na/K-ATPase and sodium/proton exchanger isoform 3, and inhibition of active transepithelial 22Na+ transport. Disruption of the Na/K-ATPase·c-Src signaling complex attenuated ouabain-stimulated protein carbonylation. Ouabain-stimulated protein carbonylation is reversed after removal of ouabain, and this reversibility is largely independent of de novo protein synthesis and degradation by either the lysosome or the proteasome pathways. Furthermore, ouabain stimulated direct carbonylation of two amino acid residues in the actuator domain of the Na/K-ATPase α1 subunit. Taken together, the data indicate that carbonylation modification of the Na/K-ATPase α1 subunit is involved in a feed-forward mechanism of regulation of ouabain-mediated renal proximal tubule Na/K-ATPase signal transduction and subsequent sodium transport

Building digital twins of the human immune system: toward a roadmap

Digital twins, customized simulation models pioneered in industry, are beginning to be deployed in medicine and healthcare, with some major successes, for instance in cardiovascular diagnostics and in insulin pump control. Personalized computational models are also assisting in applications ranging from drug development to treatment optimization. More advanced medical digital twins will be essential to making precision medicine a reality. Because the immune system plays an important role in such a wide range of diseases and health conditions, from fighting pathogens to autoimmune disorders, digital twins of the immune system will have an especially high impact. However, their development presents major challenges, stemming from the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient’s immune state in vivo. This perspective outlines a roadmap for meeting these challenges and building a prototype of an immune digital twin. It is structured as a four-stage process that proceeds from a specification of a concrete use case to model constructions, personalization, and continued improvement

Protein Carbonylation of an Amino Acid Residue of the Na/K‐ATPase α1 Subunit Determines Na/K‐ATPase Signaling and Sodium Transport in Renal Proximal Tubular Cells

Background We have demonstrated that cardiotonic steroids, such as ouabain, signaling through the Na/K‐ATPase, regulate sodium reabsorption in the renal proximal tubule. By direct carbonylation modification of the Pro222 residue in the actuator (A) domain of pig Na/K‐ATPase α1 subunit, reactive oxygen species are required for ouabain‐stimulated Na/K‐ATPase/c‐Src signaling and subsequent regulation of active transepithelial 22Na+ transport. In the present study we sought to determine the functional role of Pro222 carbonylation in Na/K‐ATPase signaling and sodium handling. Methods and Results Stable pig α1 knockdown LLC‐PK1‐originated PY‐17 cells were rescued by expressing wild‐type rat α1 and rat α1 with a single mutation of Pro224 (corresponding to pig Pro222) to Ala. This mutation does not affect ouabain‐induced inhibition of Na/K‐ATPase activity, but abolishes the effects of ouabain on Na/K‐ATPase/c‐Src signaling, protein carbonylation, Na/K‐ATPase endocytosis, and active transepithelial 22Na+ transport. Conclusions Direct carbonylation modification of Pro224 in the rat α1 subunit determines ouabain‐mediated Na/K‐ATPase signal transduction and subsequent regulation of renal proximal tubule sodium transport

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

HIV and pre-neoplastic and neoplastic lesions of the cervix in South Africa: a case-control study

BACKGROUND: Cervical cancer and infection with human immunodeficiency virus (HIV) are both major public health problems in South Africa. The aim of this study was to determine the risk of cervical pre-cancer and cancer among HIV positive women in South Africa. METHODS: Data were derived from a case-control study that examined the association between hormonal contraceptives and invasive cervical cancer. The study was conducted in the Western Cape (South Africa), from January 1998 to December 2001. There were 486 women with invasive cervical cancer, 103 control women with atypical squamous cells of undetermined significance (ASCUS), 53 with low-grade squamous intraepithelial lesions (LSIL), 50 with high-grade squamous intraepithelial lesions (HSIL) and 1159 with normal cytology. Odds ratios (OR) and 95% confidence intervals (CI) were calculated using multiple logistic regression. RESULTS: The adjusted odds ratios associated with HIV infection were: 4.4 [95% CI (2.3 – 8.4) for ASCUS, 7.4 (3.5 – 15.7) for LSIL, 5.8 (2.4 – 13.6) for HSIL and 1.17 (0.75 – 1.85) for invasive cervical cancer. HIV positive women were nearly 5 times more likely to have high-risk human papillomavirus infection (HR-HPV) present compared to HIV negative women [OR 4.6 (95 % CI 2.8 – 7.5)]. Women infected with both HIV and high-risk HPV had a more than 40 fold higher risk of SIL than women infected with neither of these viruses. CONCLUSION: HIV positive women were at an increased risk of cervical pre-cancer, but did not demonstrate an excess risk of invasive cervical cancer. An interaction between HIV and HR-HPV infection was demonstrated. Our findings underscore the importance of developing locally relevant screening and management guidelines for HIV positive women in South Africa

Associations of maternal BMI and gestational weight gain with neonatal adiposity in the Healthy Start study

Background: Maternal obesity and weight gain during pregnancy are risk factors for child obesity. Associations may be attributable to causal effects of the intrauterine environment or genetic and postnatal environmental factors