Department of Physiology Theses and Dissertationshttp://hdl.handle.net/10675.2/3203872024-03-29T09:59:37Z2024-03-29T09:59:37ZEFFECTS OF SODIUM BICARBONATE ON GLUCOSE HOMEOSTASIS AND BLOOD PRESSURE IN CHRONIC KIDNEY DISEASEMannon, Elinorhttp://hdl.handle.net/10675.2/6241422021-11-08T19:52:32Z2021-10-01T00:00:00ZEFFECTS OF SODIUM BICARBONATE ON GLUCOSE HOMEOSTASIS AND BLOOD PRESSURE IN CHRONIC KIDNEY DISEASE
Mannon, Elinor
Sodium bicarbonate (NaHCO3) is a therapeutic used in chronic kidney disease (CKD). NaHCO3 is typically used to treat metabolic acidosis, but clinical studies have indicated that NaHCO3 supplementation may slow CKD progression. As such, NaHCO3 is now given to patients with CKD to slow the decline of glomerular filtration rate. However, the consequences of chronic NaHCO3 supplementation in CKD remain unclear.
Acidosis has been associated with insulin resistance, and correction of acidosis with NaHCO3 was reported to improve insulin sensitivity. Our goal in Aim 1 was to determine whether acid and alkali loading would promote loss of acid-base homeostasis and consequently decrease insulin sensitivity. We determined that the blood glucose response to insulin is enhanced following renal mass reduction, and that this response is not reversed by an acidosis. Additionally, the development of an alkalosis did not impair the blood glucose response to insulin.
Alkali can promote potassium (K+) wasting, and an association between K+ wasting and insulin resistance has been identified in clinical and basic science research. Our goal in Aim 2 was to identify whether chronic NaHCO3 treatment may promote loss of insulin sensitivity through effects on K+ status. We determined that chronic NaHCO3 treatment impairs insulin sensitivity when combined with other K+ wasting stimuli. K+ deprivation alone also impaired the blood glucose response to insulin, however these impairments in insulin sensitivity were not directly related to decreases in intracellular [K+].
Salt-sensitivity increases as functional renal mass declines, and chronic sodium (Na+) loading with NaHCO3 may contribute to hypertension in patients with CKD. Our goal in Aim 3 was to investigate whether NaHCO3 loading promotes similar levels of Na+ and volume retention, and hypertension as sodium chloride (NaCl) loading does in a rat model of CKD. We found that NaHCO3 was pro-hypertensive, but to a lesser degree than NaCl, despite similar amounts of Na+ and volume retention.
From these studies we concluded that NaHCO3 does not improve insulin sensitivity through its effects on acid-base status. Further, access to dietary K+ may improve insulin sensitivity with chronic NaHCO3 treatment. Finally, NaHCO3 can promote hypertension in CKD.
2021-10-01T00:00:00ZA NOVEL NETWORK BASED LINEAR MODEL FOR ENRICHMENT OF SYNERGISTIC DRUG COMBINATIONSLi, Jiaqihttp://hdl.handle.net/10675.2/6241312021-11-05T13:38:44Z2021-07-01T00:00:00ZA NOVEL NETWORK BASED LINEAR MODEL FOR ENRICHMENT OF SYNERGISTIC DRUG COMBINATIONS
Li, Jiaqi
Drug combination therapies can improve drug efficacy, reduce drug dosage, and
overcome drug resistance with respect to cancer treatments. Current research strategies to
determine which drug combinations have a synergistic effect rely mainly on clinical or
empirical experience and screening predefined pools of drugs. Given the number of
possible drug combinations, the speed and scope to find new drug combinations are very
limited using these methods. Due to the exponential growth in these combinatorials, it is
difficult to test all possible outcomes in the lab. Several large-scale public genomic and
phenotypic resources that provide data from single drug-treated cells as well as data from
small molecules deliver a wealth of cellular response information. This data gives
opportunity to overcome limitations of the current methods. The development of a new
strategy for advanced data processing and analysis that includes a computational
prediction algorithm is highly desirable. Because of this, a program was written that
predicts synergistic drug combinations using gene regulatory network knowledge and an
operational module unit (OMU) system generated from single drug genomic and
phenotypic data. As a proof of principle, we applied the pipeline to a group of anticancer
drugs and demonstrated how the algorithm could help researchers efficiently find
possible synergistic drug combinations using single drug data to evaluate all possible
drug pairs.
2021-07-01T00:00:00ZMODELING THE SIMULTANEOUS EFFECTS OF COPY NUMBER VARIATION AND METHYLATION ON GENE EXPRESSION USING NEXT GENERATION SEQUENCING DATAClaussen, Henryhttp://hdl.handle.net/10675.2/6241282021-12-06T21:16:04Z2021-07-01T00:00:00ZMODELING THE SIMULTANEOUS EFFECTS OF COPY NUMBER VARIATION AND METHYLATION ON GENE EXPRESSION USING NEXT GENERATION SEQUENCING DATA
Claussen, Henry
The collection and order of nucleobases in a strand of DNA, called the primary
sequence, is one of the most important pieces of information in the study of the human
body. The proteins which regulate all biological functions in the body are synthesized
based on the structure of the DNA molecule. The next generation sequencing (NGS)
process of sequencing RNA transcripts, known as RNA-seq, has become a powerful
alternative to traditional microarray technology. NGS is used to measure the levels of
gene expression, detect structural DNA variations from the human reference genome,
and uncover the epigenetic modifications of methylation. Despite its prevalence in
genetic research, RNA-seq data suffers from the statistical complication known as
”large p small n” where the predictor variables greatly outnumber the subjects in a
study.
In this research we propose combining all three types of data into a
multivariate linear model. With the implementation of a variable selection process
for preliminary dimension reduction and the application of a Group LASSOapproach, we hope to reduce the complexity and dimension of NGS data to a
manageable and, most importantly, interpretable level. Changes in gene expression
levels have been linked with the development of harmful diseases such as cancer. A
successful model will provide insight on the simultaneous effects that methylation
and structural variation have on gene expression in the body.
2021-07-01T00:00:00ZPredictive Inference for Linear and Circular Concomitants with Biomedical ApplicationsHowie, Melissahttp://hdl.handle.net/10675.2/6241272021-12-06T21:21:36Z2021-07-01T00:00:00ZPredictive Inference for Linear and Circular Concomitants with Biomedical Applications
Howie, Melissa
Let (X_i, Y_i), for i=1,...,n, be a random sample from a bivariate distribution. If the sample is ordered with respect to one of the variables, say X, then the rth ordered X-value is called the rth order statistic and is denoted X_{r:n}. The Y-value corresponding to this value is called the concomitant of the rth order statistic and is denoted Y_{[r:n]}. In biomedical research, there is an interest in predicting the concomitant variable corresponding to the rth order statistic of the other variable. For example, one may be interested in predicting the time at which a patient has the peak blood pressure or the mercury level in fish where the water is most polluted.
One such distribution of interest is the bivariate exponential conditionals distribution (BEC), whose conditional distributions are both exponential. The asymptotic predictive distribution of the concomitants of order statistics from the BEC is derived. The results are used in a prediction problem involving the mercury concentration in largemouth bass sampled from Florida lakes, as a function of surface water pollution level.
Clinicians are often confronted with data such that one variable is linear and the other variable is circular, i.e., measured as an angle. A particular linear-circular distribution of interest is the exponential circular normal distribution. The predictive distribution of concomitants of order statistics from the exponential circular normal distribution is derived. The results are applied to predicting the future value of time at maximum heart rate in subjects from the Augusta Heart Study, a longitudinal study of normotensive children with verified family histories of cardiovascular diseases (e.g., hypertension and premature myocardial infarction).
2021-07-01T00:00:00Z