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Predicting potential biomarkers for early diabetic nephropathy in children with Type I Diabetes Mellitus

Fu, Haoyi (2017) Predicting potential biomarkers for early diabetic nephropathy in children with Type I Diabetes Mellitus. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

Type 1 Diabetes Mellitus (T1D) is a common form of Diabetes Mellitus worldwide and can cause long-term complications, especially in children. Diabetic nephropathy (DN) is the leading cause of mortality in T1D. The non-invasive gold standard for screening, monitoring, and predicting progression of DN is the assessment of albuminuria. However, it has been shown to lack sensitivity and specificity for early pathological manifestations of the disease. Other biomarkers including α-klotho, serum uric acid and estimated glomerular filtration rate (GFR) might potentially have a better ability to detect onset of DN earlier. The goal of this study is to gain a better understanding of how these biomarkers are associated with demographic and clinical characteristics in children.
Data from 97 children, age 10 or more years with a T1D duration of at least 2 years, were collected at Children’s Hospital of Pittsburgh over a 4 month period. Correlations and univariable regression models were built to detect whether significant relationships between these biomarkers and demographic and clinical predictors were present. Multivariate regression models for each of the biomarkers were constructed and the cross-validation method was used to validate the models. After selecting the final models, linear regression assumptions were checked and model diagnostics were performed to detect problematic data points.
The final model for α-klotho contained the variables of hemoglobin A1c, growth velocity, triglycerides, total cholesterol, HDL and central obesity. For estimated GFR, the model included hemoglobin A1c, diastolic blood pressure percentile, growth velocity, albumin creatinine ratio (ACR), creatinine, total cholesterol and central obesity. The final model for serum uric acid included hemoglobin A1c, diabetic duration years, age, ACR, creatinine, triglycerides, total cholesterol, HDL, central obesity and waist percentile. Model fit criteria for all three multivariate models were largely improved compared to univariable models. Model diagnostics showed few problematic data points and linear regression assumptions for all three best models were not violated.
Public Health Significance: Although these biomarkers have been studied in adults with respect to screening, monitoring, and predicting progression of DN, less work has been done in pediatric populations. The work here provides a better understanding of the relationship between these biomarkers, and the demographic and clinical characteristics of children with T1D. The regression model validation techniques employed provide models that are not overly optimistic with respect to prediction. These methods are also more appropriate for studies with smaller sample sizes as found in this study.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Fu, Haoyihaf40@pitt.eduhaf40
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorArena, Vincentarena@pitt.eduarena
Committee MemberYouk, Adaayouk@pitt.eduayouk
Committee MemberLibman, IngridIngrid.Libman@chp.eduIngrid.Libman
Date: 30 August 2017
Date Type: Publication
Defense Date: 21 June 2017
Approval Date: 30 August 2017
Submission Date: 5 June 2017
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 112
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Type I Diabetes Mellitus, Diabetic Nephropathy, alpha-klotho, serum uric acid, eGFR
Date Deposited: 30 Aug 2017 21:39
Last Modified: 30 Aug 2018 05:15
URI: http://d-scholarship.pitt.edu/id/eprint/32354

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