Journal of Clinical Nephrology and Therapeutics

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Opinion Article - Journal of Clinical Nephrology and Therapeutics (2023) Volume 7, Issue 6

Exploring novel biomarkers for early detection and prognostication of chronic kidney disease progression.

Seung Park*

Department of Internal Medicine, Yonsei University, Republic of Korea

*Corresponding Author:
Seung Park
Department of Internal Medicine, Yonsei University, Republic of Korea
Yonsei University
Republic of Korea
E-mail:seung@park.ac

Received:03-Dec-2023,Manuscript No. AACNT-24-127351; Editor assigned:04-Dec-2023,PreQC No. AACNT-24-127351(PQ); Reviewed:18-Dec-2023,QC No. AACNT-24-127351; Revised:22-Dec-2023,Manuscript No. AACNT-24-127351(R); Published:31-Dec-2023,DOI: 10.35841/aacnt-7.6.171

Citation: Park S. Exploring novel biomarkers for early detection and prognostication of chronic kidney disease progression. J Clin Nephrol Ther. 2023;7(6):171

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Introduction

The degenerative condition known as chronic kidney disease (CKD) is marked by a progressive decrease of renal function over time, which increases the risk of serious morbidity and mortality. Timely intervention and better patient outcomes depend on early detection and prognostication of CKD development. There are still limitations to the sensitivity, specificity, and predictive usefulness of current biomarkers for the diagnosis and prognosis of CKD. Investigating innovative biomarkers that can precisely identify people at risk of CKD development and forecast their clinical course is therefore imperative.[1].

The field of CKD biomarker research has made strides recently, and this review highlights those developments, concentrating on new markers that may be used for prognostication and early identification. The usefulness of several biomarker types—such as conventional blood markers, urine biomarkers, and new molecular markers—is highlighted. Furthermore discussed are the prospects and difficulties related to the clinical application and validation of these biomarkers. In general, the discovery of new biomarkers has the potential to enhance the management of chronic kidney disease (CKD) by facilitating early intervention and individualised treatment plans. To confirm these biomarkers' therapeutic usefulness and incorporate them into standard clinical practice, more investigation is required. Globally, chronic kidney disease (CKD) is a major public health concern due to its rising prevalence and high rates of morbidity and mortality. The progressive and irreversible loss of renal function that occurs with chronic kidney disease (CKD) over time can result in cardiovascular disease, end-stage renal disease (ESRD), and early mortality. The pathophysiology of CKD has been better understood, and treatment therapies have been developed, yet early detection and precise prognostication of CKD progression are still difficult to achieve. As of right now, conventional indicators including serum creatinine, estimated glomerular filtration rate (eGFR), and proteinuria are the mainstays of CKD diagnosis and prognosis. But especially in the early stages of the disease, these indicators' sensitivity, specificity, and prognostic utility are limited.[2].

Therefore, it is imperative to investigate new biomarkers that can improve CKD early detection and offer useful prognostic data on the course and fate of the illness. The discovery of new biomarkers has the potential to enhance the management of chronic kidney disease (CKD) by facilitating customized treatment plans, risk assessment, and early intervention. Newly developed biomarkers have the potential to improve clinical decision-making, ease illness monitoring, and shed light on underlying pathogenic mechanisms. Furthermore, new biomarkers may help in the creation of tailored treatments meant to halt or reduce the progression of CKD and lessen its side effects. With an emphasis on the identification and validation of novel markers for the early detection and prognostication of CKD progression, this review attempts to examine recent developments in the field of CKD biomarker research. We will go over a variety of biomarker types and highlight their potential use in various stages of chronic kidney disease (CKD). These include conventional blood markers, urine biomarkers, and new molecular markers. We will also look at the prospects and difficulties related to the clinical application and validation of these biomarkers.[3].

All things considered, the search for new CKD biomarkers offers a potential path towards bettering risk assessment, expanding our knowledge of the condition, and improving patient management. These biomarkers have the power to revolutionise the management of CKD and ultimately enhance clinical outcomes for impacted patients by illuminating novel pathways and intervention targets. In summary, the investigation of new biomarkers for the early identification and prognostication of the course of chronic kidney disease (CKD) is an important field of study with substantial ramifications for clinical practice. The current state of CKD biomarkers research is defined by continuous efforts to find biomarkers that can overcome the shortcomings of conventional indicators like proteinuria and serum creatinine. Through technological advancements and a deeper comprehension of the biology of chronic kidney disease (CKD), researchers have made significant strides in the identification and validation of prospective biomarkers in a variety of domains.[4].

Finding new biomarkers has the potential to significantly improve the management of chronic kidney disease (CKD) by allowing for early illness detection, simplifying risk assessment, and forecasting the course and consequences of the disease. Based on these indicators, early intervention techniques could help postpone or prevent the onset of complications associated with CKD, thereby improving patient quality of life and reducing healthcare burden. Moreover, by enabling individualised treatment plans catered to each patient's unique risk profile, the incorporation of innovative biomarkers into clinical practice has the potential to completely transform patient care. Biomarker-guided therapy has the potential to enhance patient adherence and treatment success by optimizing medication selection, dosage adjustment, and monitoring tactics.[5].

Conclusion

To successfully move promising biomarkers from research to clinical application, a number of obstacles must be overcome. Assay technique standardization, validation across a range of patient groups, longitudinal assessment of biomarker performance, and integration into clinical guidelines and decision-making algorithms are some of these obstacles. All things considered, there is a lot of potential for improving patient outcomes, expanding our knowledge of the illness, and increasing the search for new biomarkers for chronic kidney disease. It is imperative that scientists, physicians, business representatives, and government regulators work together and expedite the conversion of biomarker discoveries into significant clinical advantages for CKD patients. We can work towards more efficient CKD prevention, early identification, and management by utilizing the potential of innovative biomarkers, which will ultimately lessen the condition's crippling worldwide impact.

References

References

  1. Chapman AB, Devuyst O, Eckardt KU, et al. Autosomal-dominant polycystic kidney disease (ADPKD): Executive summary from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. 2015;88(1):17-27.

    2. Indexed at, Google scholar,Cross ref

  2. Yang X, Le Minh H, Cheng KT, et al. Renal compartment segmentation in DCE-MRI images. Med Image Anal. 2016;32:269-80.

    3. Indexed at, Google scholar, Cross ref

  3. Kistler AD, Poster D, Krauer F, et al. Increases in kidney volume in autosomal dominant polycystic kidney disease can be detected within 6 months.Kidney int. 2009;75(2):235-41.

    4. Indexed at, Google scholar, Cross ref

  4. Bhutani H, Smith V, Rahbari-Oskoui F, et al. A comparison of ultrasound and magnetic resonance imaging shows that kidney length predicts chronic kidney disease in autosomal dominant polycystic kidney disease. Kidney int. 2015;88(1):146-51.

    5. Indexed at, Google scholar , Cross ref

  5. Sigmund M, Ferstl R. Panel vector autoregression in R with the package panelvar. Q Rev Econ Finance. 2021; 80:693-720.

    6. Indexed at, Google scholar

  6. Cardenas CE, Yang J, Anderson BM, et al. Advances in auto-segmentation. Semin radiat oncol. 2019;29(3):185-197.

    7. Indexed at, Google scholar, Cross ref

  7. Hohmann E. Editorial commentary: Big data and machine learning in medicine. J Arthrosc Relat Surg. 2022;38(3):848-9.

    8. Indexed at, Google scholar, Cross ref

  8. Chapman AB, Guay-Woodford LM, Grantham JJ, et al. Renal structure in early autosomal-dominant polycystic kidney disease (ADPKD): The Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) cohort. Kidney int. 2003;64(3):1035-45.

    9. Indexed at, Google scholar, Cross ref

  9. Momeny M, Neshat AA, Hussain MA, et al . Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images. Comput Biol Med. 2021;136:104704.

    10. Indexed at, Google scholar , Cross ref

  10. Yang X, Le Minh H, Cheng KT, et al. Renal compartment segmentation in DCE-MRI images. Med Image Anal. 2016;32:269-80.

    11. Indexed at , Google scholar, Cross ref

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