Metabolomics and Proteomics in Chronic Kidney Disease and Diabetic Kidney Disease
A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Endocrinology and Clinical Metabolic Research".
Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 6386
Special Issue Editor
2. Research Unit Molecular Epidemiology/Deputy Head—Diabetes Research Unit, Institute of Epidemiology, Helmholtz Munich, Germany
Interests: cardiometabolic health; cohort studies; molecular epidemiology; omics
Special Issue Information
Dear Colleagues,
Chronic Kidney Disease (CKD) has become a silent epidemic on the rise, accompanied by the increase in prevalence of associated risk factors such as diabetes, obesity and cardiovascular disease. In particular, Diabetes Kidney Disease (DKD), a common complication of diabetes, accounts for 30-50% of CKD cases, holding the leading cause of end-stage kidney disease and representing an independent risk factor for cardiovascular and all-cause mortality. However, aberrant levels of diagnostic or staging markers of kidney function (such as creatinine, cystatin c, albumin) are only present in late stages of the disease and no targeted therapies exist for CKD beyond the management of traditional cardiorenal risk factors. Therefore, there is an increasing necessity in kidney research to identify novel biomarkers for early kidney impairment (in particular among people with diabetes) and deliver better insight into pathophysiological pathways.
The recent developments of high throughput technologies in metabolomics and proteomics research have open unprecedented avenues in the discovery of new biomarkers for CKD/DKD screening, diagnosis and prognosis. Moreover, new pathophysiological insights are uncovered by integrating genomics or experimental work to these investigations. Assessing the causal directionality of the associations through Mendelian Randomization approaches (for e.g. in population studies) has proven to a be an effective toolset to drive new knowledge. Additionally, extending traditional statistical analyses with Machine Learning tools (including random forest, support vector machines, K -means clustering et cet.) have consistently shown better performance in predicting disease signatures.
In this special issue, we would like to invite articles that investigate proteomics or metabolomics with CKD/DKD development or progression in clinical or cohort studies that make use of all the available toolset of complementary analyses in omics research.
Dr. Jana Nano
Guest Editor
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Keywords
- proteomis
- metabolomics
- chronic kidney disease
- diabetic kidney disease
- genomics
- biomarkers
- causality
- machine learning
- experimental validation
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