# Adiposity-obesity-related subgroups discovery and metabolomics analysis for chronic kidney disease

> **NIH NIH F31** · UNIVERSITY OF PENNSYLVANIA · 2020 · $45,520

## Abstract

Project Summary
Chronic kidney disease (CKD) is prevalent in the U.S. population and is a major cause of end-stage renal
disease (ESRD), cardiovascular disease morbidity, and mortality. Current gaps exist in characterizing the
heterogeneity of CKD population. Obesity further increases the risks of adverse outcomes among people with
impaired kidney function. However, the mechanism between obesity and CKD is not fully understood, and
numerous studies have observed “obesity paradox” or survival advantage being associated with higher body
fat among advanced CKD patients. In this context, the Chronic Renal Insufficiency Cohort (CRIC) Study
provides an ideal opportunity to study the mechanism of CKD with its rich clinical information and population
characteristics, as well as its availability of non-targeted high-dimensional metabolomics data.
To better understand the “adiposity-obesity-related” (AOR) mechanisms of CKD and to discover novel
biomarkers, we propose to identify distinct subgroups based on AOR attributes and to perform metabolomics
analysis on the identified subgroups and CKD outcomes. The overall goal of this research and the training plan
is to execute three studies as the subjects of a doctoral dissertation in metabolomics analysis and CKD
epidemiology. First, in Aim 1, we will identify latent AOR subgroups in the CRIC CKD population using the
data-driven approach of consensus clustering. We will also evaluate the utility and the independent risk
discrimination performance of the AOR subgroups with key CKD outcomes (CKD progression, CVD, and
death), using Cox regression model with adjustment for known CKD risk factors. Next, in Aim 2, we will
perform metabolomics analysis and identify metabolites and metabolomic patterns that are associated with the
different AOR subgroups. In response to the statistical challenges in analyzing the high-dimensional
metabolomics data, we will implement both the machine learning random-forest algorithm and the conventional
univariate and multivariate regression analysis. Finally, in Aim 3, we will investigate the relationship between
CKD outcomes and the metabolites and metabolomic patterns identified in Aim 2, using Cox regression model,
extended by pathway analysis and mediation analysis.
Mining high-dimensional metabolomics data with AOR phenotypes in the CRIC Study holds promise to expand
our knowledge of pathophysiology and molecular characterization of CKD. Future studies can be built upon
these findings to improve more effective and personalized CKD management in the setting of the obesity
epidemic. A rigorous curriculum including didactic and experiential learning in biomedical data mining,
statistics, and advanced epidemiology will round out the applicant's training, preparing her to be an
independent and collaborative investigator and a kidney disease epidemiologist at the completion of his PhD.

## Key facts

- **NIH application ID:** 9964494
- **Project number:** 5F31DK122683-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Zihe Zheng
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $45,520
- **Award type:** 5
- **Project period:** 2019-06-01 → 2021-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9964494

## Citation

> US National Institutes of Health, RePORTER application 9964494, Adiposity-obesity-related subgroups discovery and metabolomics analysis for chronic kidney disease (5F31DK122683-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9964494. Licensed CC0.

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