Uremic symptoms, including fatigue, nausea, anorexia, pruritus, decreased mental acuity, poor sleep, and
paresthesia, represent a significant burden on the quality of life for millions of US adults living with chronic
kidney disease (CKD). The observation that many uremic symptoms improve following kidney transplantation
suggests that retained solutes play a causal role in their pathogenesis. However, cross-sectional studies do not
show a consistent association between kidney function and uremic symptoms, suggesting that other
determinants must also be present. In order to guide management strategies, research is needed to evaluate
the association between kidney function and uremic symptoms and identify any modifiable factors that
additionally influence this relationship. Furthermore, the distinct disrupted metabolic pathways that contribute to
the development of uremic symptoms are unknown. Metabolomics technology, which involves the
comprehensive analysis of metabolites in a biological specimen, has the potential to rapidly accelerate the
identification of uremic retention solutes that drive symptoms, leading to novel therapeutic strategies. The
Chronic Renal Insufficiency Cohort (CRIC), an NIDDK-sponsored study of 3,939 individuals with CKD that has
over 14 years of longitudinal assessments of kidney function and uremic symptoms, creates the ideal
opportunity to complete our overall objective: to identify determinants of and outcomes associated with
longitudinal trends in uremic symptoms and gain insight into their metabolic causes. Our central hypothesis is
that beyond metrics of kidney function (e.g. estimated glomerular filtration rate [eGFR]), novel modifiable
factors, both clinical and metabolite, will be associated with uremic symptoms, and that symptom trends over
time can identify patients at increased risk of adverse outcomes. In Aim 1 of the study, we will quantify the
association between changes in eGFR and changes in uremic symptoms and the association of uremic
symptoms with dialysis initiation. In Aim 2 we will utilize the available plasma metabolomics data for a subset of
CRIC participants (N=1,800) to identify metabolites associated with uremic symptoms and will evaluate
whether metabolite markers of symptoms are also associated with the risk of dialysis initiation. These studies
will result in a comprehensive picture of the prognosis for patients with uremic symptoms and provide insight
into associations between disrupted metabolic pathways and symptom development. Such information stands
to influence clinical practice as well as lead to novel therapeutics aimed at directly improving the quality of life
for this patient population. The research aims are integrated into a comprehensive training plan that includes
practical mentored experiences and a Master’s Degree in Public Health and will provide Dr. Wulczyn the
opportunity to 1) learn advanced principals of biostatistical and epidemiological methods, including mixed
effects modelling and survival analysis, 2) apply machine learning algorithms to predictive modelling, and 3)
advance towards independence as a clinical investigator.