Special Issue "Metabolomics and Proteomics in Chronic Kidney Disease and Diabetic Kidney Disease"
Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 4147
2. Research Unit Molecular Epidemiology/Deputy Head—Diabetes Research Unit, Institute of Epidemiology, Helmholtz Munich, Germany
Interests: cardiometabolic health; cohort studies; molecular epidemiology; omics
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- chronic kidney disease
- diabetic kidney disease
- machine learning
- experimental validation