Journal of Clinical Nephrology and Therapeutics

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

Personalized medicine approaches in the management of glomerular diseases: A review of current evidence and future directions.

Esubalew Mulatu*

Department of Public Health, Debre Berhan University, Ethiopia

*Corresponding Author:
Esubalew Mulatu
Department of Public Health, Debre Berhan University, Ethiopia
Debre Berhan University
Ethiopia
E-mail:seung@park.ac

Received:03-Dec-2023,Manuscript No. AACNT-24-127531; Editor assigned:04-Dec-2023,PreQC No. AACNT-24-127531(PQ); Reviewed:18-Dec-2023,QC No. AACNT-24-127531; Revised:22-Dec-2023,Manuscript No. AACNT-24-127531(R); Published:31-Dec-2023,DOI: 10.35841/aacnt-7.6.177

Citation: Mulatu E. Personalized medicine approaches in the management of glomerular diseases: A review of current evidence and future directions. J Clin Nephrol Ther. 2023;7(6):177

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Introduction

A diverse range of kidney ailments known as glomerular diseases are distinguished by glomerular inflammation, damage, and malfunction. Immunosuppression and symptom control have been the mainstays of traditional treatment approaches for glomerular disorders; nevertheless, patient response to therapy can vary greatly, underscoring the need for personalised medicine approaches. With an emphasis on recent findings and potential paths for future research, this review offers a thorough summary of personalised medicine techniques in the treatment of glomerular disorders. We go through how genetic testing, biomarkers, molecular profiling, imaging methods, and predictive modelling affect therapy choices and illness prognoses. Furthermore, we investigate the possibility of precision medicine approaches, such as tailored treatment algorithms and targeted treatments, to maximise therapeutic efficacy and reduce treatment-associated toxicity.[1].

Additionally, we look at the opportunities and difficulties related to adopting. By integrating personalized medicine approaches into routine clinical care, clinicians can tailor treatment strategies to individual patients, optimize therapeutic outcomes, and ultimately improve the management of glomerular diseases. Further research is warranted to validate the clinical utility of personalized medicine approaches and to identify novel biomarkers and therapeutic targets for personalized interventions in glomerular diseases. The term "glomerular diseases" refers to a broad range of kidney problems that are typified by inflammation, damage, and malfunction of the kidney's filtration units, or glomeruli. These disorders include, but are not limited to, lupus nephritis (LN), membranous nephropathy (MN), IgA nephropathy (IgAN), and focal segmental glomerulosclerosis (FSGS). Due to their progressive loss of kidney function, increasing morbidity and mortality, and end-stage renal disease (ESRD), glomerular disorders pose a substantial burden on public health.[2].

Empirical immunosuppressive medications, including corticosteroids, immunosuppressants, and cytotoxic drugs, have been the mainstay of traditional treatment approaches for glomerular disorders. These therapies aim to reduce proteinuria and suppress immune-mediated inflammation. Although some patients have shown these treatments to be effective, response rates differ greatly, and many people develop treatment resistance, relapses, or negative side effects. Furthermore, existing therapeutic approaches lack specificity and may not address the underlying molecular mechanisms driving disease pathogenesis. Personalised medicine techniques have garnered increasing attention in the management of glomerular disorders in recent years. Precision medicine, another name for personalised medicine, is the practice of customising medical treatments for individual patients according to their distinct clinical, molecular, and genetic traits. Through an understanding of the biological pathways that underlie the pathophysiology of each patient's condition, personalised medicine techniques present opportunities to maximise treatment selection, forecast response to treatment, and reduce side effects associated with treatment.[3].

The goal of this review is to give a thorough overview of personalised medicine strategies for glomerular disease management. We will go over the available data as well as projected developments in several important fields, including as genetic testing, molecular profiling, biomarkers, imaging modalities, and predictive modelling, and how they may affect patient outcomes and treatment choices. Furthermore, we shall explore the challenges and opportunities associated with the implementation of personalized medicine approaches in clinical practice, including technological limitations, ethical considerations, and healthcare disparities. This review aims to educate physicians, researchers, and policymakers about the potential of personalised medicine to transform the treatment of glomerular diseases and enhance patient outcomes by summarising the most recent research and outlining promising future approaches.[4].

Because glomerular diseases vary widely in terms of how they manifest, develop, and respond to treatment, they provide substantial clinical management issues. While beneficial in many situations, traditional therapeutic approaches frequently lack specificity and might result in less than ideal results as well as unfavourable side effects. By customising medicines for each patient according to their own genetic, molecular, and clinical profiles, personalised medicine techniques present a promising paradigm change in the management of glomerular disorders. Even with personalised medicine's potential, there are still a number of issues that need to be resolved. Technological constraints, such as the cost and accessibility of molecular profiling and genetic testing, may prevent personalised medicine techniques from being widely used. To guarantee equal access to personalised medicine interventions, ethical factors such as patient privacy and consent and healthcare disparities must also be properly taken into account.[5].

Conclusion

In addition, more investigation is required to confirm the clinical benefit of personalised medicine strategies and find new therapeutic targets and biomarkers for tailored treatments in glomerular illnesses. Translating research discoveries into clinical practice and advancing the field of personalised medicine need concerted efforts by researchers, physicians, patients, and policymakers. In summary, personalised medicine strategies have enormous potential to transform the treatment of renal diseases by enabling more precise, effective, and individualized treatments. By leveraging the latest advances in genetics, molecular biology, and clinical informatics, personalized medicine has the potential to transform patient care and improve outcomes for individuals affected by glomerular diseases.

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 atGoogle scholar,Cross ref

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

  5. 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.
  6. Indexed atGoogle scholarCross ref

  7. 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.
  8. Indexed atGoogle scholar , Cross ref

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

  11. Cardenas CE, Yang J, Anderson BM, et al. Advances in auto-segmentation. Semin radiat oncol. 2019;29(3):185-197.
  12. Indexed atGoogle scholarCross ref

  13. Hohmann E. Editorial commentary: Big data and machine learning in medicine. J Arthrosc Relat Surg. 2022;38(3):848-9.
  14. Indexed atGoogle scholarCross ref

  15. 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.
  16. Indexed atGoogle scholarCross ref

  17. 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.
  18. Indexed atGoogle scholar , Cross ref

  19. Yang X, Le Minh H, Cheng KT, et al. Renal compartment segmentation in DCE-MRI images. Med Image Anal. 2016;32:269-80.
  20. Indexed at , Google scholarCross ref

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