Potential Role of Serum and Urinary Biomarkers in Diagnosis and Prognosis of Diabetic Nephropathy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Diabetic nephropathy (DN) is a progressive kidney disease caused by alterations in kidney architecture and function, and constitutes one of the leading causes of end-stage renal disease (ESRD). The purpose of this review is to summarize the state of the art of the DN-biomarker field with a focus on the new strategies that enhance the sensitivity of biomarkers to predict patients who will develop DN or are at risk of progressing to ESRD. OBJECTIVE: In this review, we provide a description of the pathophysiology of DN and propose a panel of novel putative biomarkers associated with DN pathophysiology that have been increasingly investigated for diagnosis, to predict disease progression or to provide efficient personal treatment. METHODS: We performed a review of the literature with PubMed and Google Scholar to collect baseline data about the pathophysiology of DN and biomarkers associated. We focused our research on new and emerging biomarkers of DN. KEY FINDINGS: In this review, we summarized the critical signaling pathways and biological processes involved in DN and highlighted the pathogenic mediators of this disease. We next proposed a large review of the major advances that have been made in identifying new biomarkers which are more sensitive and reliable compared with currently used biomarkers. This includes information about emergent biomarkers such as functional noncoding RNAs, microRNAs, long noncoding RNAs, exosomes, and microparticles. LIMITATIONS: Despite intensive strategies and constant investigation, no current single treatment has been able to reverse or at least mitigate the progression of DN, or reduce the morbidity and mortality associated with this disease. Major difficulties probably come from the renal disease being heterogeneous among the patients. IMPLICATIONS: Expanding the proteomics screening, including oxidative stress and inflammatory markers, along with metabolomics approaches may further improve the prognostic value and help in identifying the patients with diabetes who are at high risk of developing kidney diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".