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Ghanem A, Borghol AH, Munairdjy Debeh FG, Paul S, AlKhatib B, Harris PC, Garimella PS, Hanna C, Kline TL, Dahl NK, Chebib FT. Biomarkers of Kidney Disease Progression in ADPKD. Kidney Int Rep 2024; 9:2860-2882. [PMID: 39435347 PMCID: PMC11492289 DOI: 10.1016/j.ekir.2024.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 06/10/2024] [Accepted: 07/08/2024] [Indexed: 10/23/2024] Open
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic kidney disorder and the fourth leading cause of kidney failure (KF) in adults. Characterized by a reduction in glomerular filtration rate (GFR) and increased kidney size, ADPKD exhibits significant variability in progression, highlighting the urgent need for reliable and predictive biomarkers to optimize management and treatment approaches. This review explores the roles of diverse biomarkers-including clinical, genetic, molecular, and imaging biomarkers-in evaluating disease progression and customizing treatments for ADPKD. Clinical biomarkers such as biological sex, the predicting renal outcome in polycystic kidney disease (PROPKD) score, and body mass index are shown to correlate with disease severity and progression. Genetic profiling, particularly distinguishing between truncating and non-truncating pathogenic variants in the PKD1 gene, refines risk assessment and prognostic precision. Advancements in imaging significantly enhance our ability to assess disease severity. Height-adjusted total kidney volume (htTKV) and the Mayo imaging classification (MIC) are foundational, whereas newer imaging biomarkers, including texture analysis, total cyst number (TCN), cyst-parenchyma surface area (CPSA), total cyst volume (TCV), and cystic index, focus on detailed cyst characteristics to offer deeper insights. Molecular biomarkers (including serum and urinary markers) shed light on potential therapeutic targets that could predict disease trajectory. Despite these advancements, there is a pressing need for the development of response biomarkers in both the adult and pediatric populations, which can evaluate the biological efficacy of treatments. The holistic evaluation of these biomarkers not only deepens our understanding of kidney disease progression in ADPKD, but it also paves the way for personalized treatment strategies aiming to significantly improve patient outcomes.
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Affiliation(s)
- Ahmad Ghanem
- Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USA
| | - Abdul Hamid Borghol
- Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USA
| | | | - Stefan Paul
- Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USA
| | - Bassel AlKhatib
- Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USA
| | - Peter C. Harris
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA
| | - Pranav S. Garimella
- Division of Nephrology and Hypertension, University of California San Diego, San Diego, California, USA
| | - Christian Hanna
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA
- Division of Pediatric Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA
| | - Timothy L. Kline
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA
- Division of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Neera K. Dahl
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA
| | - Fouad T. Chebib
- Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida, USA
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Nawaz MZ, Khalid HR, Shahbaz S, Al-Ghanim KA, Pugazhendhi A, Zhu D. Discovery of putative inhibitors of human Pkd1 enzyme: Molecular docking, dynamics and simulation, QSAR, and MM/GBSA. ENVIRONMENTAL RESEARCH 2024; 257:119336. [PMID: 38838751 DOI: 10.1016/j.envres.2024.119336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 05/08/2024] [Accepted: 06/02/2024] [Indexed: 06/07/2024]
Abstract
Polycystic kidney disease is the most prevalent hereditary kidney disease globally and is mainly linked to the overexpression of a gene called PKD1. To date, there is no effective treatment available for polycystic kidney disease, and the practicing treatments only provide symptomatic relief. Discovery of the compounds targeting the PKD1 gene by inhibiting its expression under the disease condition could be crucial for effective drug development. In this study, a molecular docking and molecular dynamic simulation, QSAR, and MM/GBSA-based approaches were used to determine the putative inhibitors of the Pkd1 enzyme from a library of 1379 compounds. Initially, fourteen compounds were selected based on their binding affinities with the Pkd1 enzyme using MOE and AutoDock tools. The selected drugs were further investigated to explore their properties as drug candidates and the stability of their complex formation with the Pkd1 enzyme. Based on the physicochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, and toxicity profiling, two compounds including olsalazine and diosmetin were selected for the downstream analysis as they demonstrated the best drug-likeness properties and highest binding affinity with Pkd1 in the docking experiment. Molecular dynamic simulation using Gromacs further confirmed the stability of olsalazine and diosmetin complexes with Pkd1 and establishing interaction through strong bonding with specific residues of protein. High biological activity and binding free energies of two complexes calculated using 3D QSAR and Schrodinger module, respectively further validated our results. Therefore, the molecular docking and dynamics simulation-based in-silico approach used in this study revealed olsalazine and diosmetin as potential drug candidates to combat polycystic kidney disease by targeting Pkd1 enzyme.
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Affiliation(s)
- Muhammad Zohaib Nawaz
- International Joint Laboratory on Synthetic Biology and Biomass Biorefinery, Biofuels Institute, School of Emergency Management, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang, 212013, China
| | - Hafiz Rameez Khalid
- International Joint Laboratory on Synthetic Biology and Biomass Biorefinery, Biofuels Institute, School of Emergency Management, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang, 212013, China
| | - Sabeen Shahbaz
- Department of Biochemistry, University of Agriculture, Faisalabad, 38040, Pakistan
| | - Khalid A Al-Ghanim
- Department of Zoology, College of Science, King Saud University, P.O. Box 11451, Riyadh, Saudi Arabia
| | - Arivalagan Pugazhendhi
- School of Engineering, Lebanese American University, Byblos, Lebanon; University Centre for Research & Development, Department of Civil Engineering, Chandigarh University, Mohali, 140103, India.
| | - Daochen Zhu
- International Joint Laboratory on Synthetic Biology and Biomass Biorefinery, Biofuels Institute, School of Emergency Management, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang, 212013, China.
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Zhu C, He X, Blumenfeld JD, Hu Z, Dev H, Sattar U, Bazojoo V, Sharbatdaran A, Aspal M, Romano D, Teichman K, Ng He HY, Wang Y, Soto Figueroa A, Weiss E, Prince AG, Chevalier JM, Shimonov D, Moghadam MC, Sabuncu M, Prince MR. A Primer for Utilizing Deep Learning and Abdominal MRI Imaging Features to Monitor Autosomal Dominant Polycystic Kidney Disease Progression. Biomedicines 2024; 12:1133. [PMID: 38791095 PMCID: PMC11118119 DOI: 10.3390/biomedicines12051133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
Abdominal imaging of autosomal dominant polycystic kidney disease (ADPKD) has historically focused on detecting complications such as cyst rupture, cyst infection, obstructing renal calculi, and pyelonephritis; discriminating complex cysts from renal cell carcinoma; and identifying sources of abdominal pain. Many imaging features of ADPKD are incompletely evaluated or not deemed to be clinically significant, and because of this, treatment options are limited. However, total kidney volume (TKV) measurement has become important for assessing the risk of disease progression (i.e., Mayo Imaging Classification) and predicting tolvaptan treatment's efficacy. Deep learning for segmenting the kidneys has improved these measurements' speed, accuracy, and reproducibility. Deep learning models can also segment other organs and tissues, extracting additional biomarkers to characterize the extent to which extrarenal manifestations complicate ADPKD. In this concept paper, we demonstrate how deep learning may be applied to measure the TKV and how it can be extended to measure additional features of this disease.
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Affiliation(s)
- Chenglin Zhu
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Xinzi He
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14853, USA
- Cornell Tech, Cornell University, Ithaca, NY 10044, USA
| | - Jon D. Blumenfeld
- The Rogosin Institute, New York, NY 10021, USA; (J.D.B.); (J.M.C.); (D.S.)
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Zhongxiu Hu
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Hreedi Dev
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Usama Sattar
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Vahid Bazojoo
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Arman Sharbatdaran
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Mohit Aspal
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Dominick Romano
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Kurt Teichman
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Hui Yi Ng He
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Yin Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Andrea Soto Figueroa
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Erin Weiss
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Anna G. Prince
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - James M. Chevalier
- The Rogosin Institute, New York, NY 10021, USA; (J.D.B.); (J.M.C.); (D.S.)
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Daniil Shimonov
- The Rogosin Institute, New York, NY 10021, USA; (J.D.B.); (J.M.C.); (D.S.)
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Mina C. Moghadam
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
| | - Mert Sabuncu
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
- Cornell Tech, Cornell University, Ithaca, NY 10044, USA
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Martin R. Prince
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (C.Z.); (X.H.); (Z.H.); (H.D.); (U.S.); (V.B.); (A.S.); (M.A.); (D.R.); (K.T.); (H.Y.N.H.); (Y.W.); (A.S.F.); (E.W.); (A.G.P.); (M.C.M.)
- Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA
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He QH, Feng JJ, Wu LC, Wang Y, Zhang X, Jiang Q, Zeng QY, Yin SW, He WY, Lv FJ, Xiao MZ. Deep learning system for malignancy risk prediction in cystic renal lesions: a multicenter study. Insights Imaging 2024; 15:121. [PMID: 38763985 PMCID: PMC11102892 DOI: 10.1186/s13244-024-01700-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 04/15/2024] [Indexed: 05/21/2024] Open
Abstract
OBJECTIVES To develop an interactive, non-invasive artificial intelligence (AI) system for malignancy risk prediction in cystic renal lesions (CRLs). METHODS In this retrospective, multicenter diagnostic study, we evaluated 715 patients. An interactive geodesic-based 3D segmentation model was created for CRLs segmentation. A CRLs classification model was developed using spatial encoder temporal decoder (SETD) architecture. The classification model combines a 3D-ResNet50 network for extracting spatial features and a gated recurrent unit (GRU) network for decoding temporal features from multi-phase CT images. We assessed the segmentation model using sensitivity (SEN), specificity (SPE), intersection over union (IOU), and dice similarity (Dice) metrics. The classification model's performance was evaluated using the area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA). RESULTS From 2012 to 2023, we included 477 CRLs (median age, 57 [IQR: 48-65]; 173 men) in the training cohort, 226 CRLs (median age, 60 [IQR: 52-69]; 77 men) in the validation cohort, and 239 CRLs (median age, 59 [IQR: 53-69]; 95 men) in the testing cohort (external validation cohort 1, cohort 2, and cohort 3). The segmentation model and SETD classifier exhibited excellent performance in both validation (AUC = 0.973, ACC = 0.916, Dice = 0.847, IOU = 0.743, SEN = 0.840, SPE = 1.000) and testing datasets (AUC = 0.998, ACC = 0.988, Dice = 0.861, IOU = 0.762, SEN = 0.876, SPE = 1.000). CONCLUSION The AI system demonstrated excellent benign-malignant discriminatory ability across both validation and testing datasets and illustrated improved clinical decision-making utility. CRITICAL RELEVANCE STATEMENT In this era when incidental CRLs are prevalent, this interactive, non-invasive AI system will facilitate accurate diagnosis of CRLs, reducing excessive follow-up and overtreatment. KEY POINTS The rising prevalence of CRLs necessitates better malignancy prediction strategies. The AI system demonstrated excellent diagnostic performance in identifying malignant CRL. The AI system illustrated improved clinical decision-making utility.
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Affiliation(s)
- Quan-Hao He
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Jia-Jun Feng
- Department of Medical Imaging, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Ling-Cheng Wu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Yun Wang
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Xuan Zhang
- Department of Urology, Yongchuan Hospital of Chongqing Medical University, Chongqing, China
| | - Qing Jiang
- Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qi-Yuan Zeng
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Si-Wen Yin
- Department of Urology, Chongqing University Fuling Hospital, Chongqing, People's Republic of China
| | - Wei-Yang He
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
| | - Ming-Zhao Xiao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
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Nakashima M, Suga N, Ikeda Y, Yoshikawa S, Matsuda S. Inspiring Tactics with the Improvement of Mitophagy and Redox Balance for the Development of Innovative Treatment against Polycystic Kidney Disease. Biomolecules 2024; 14:207. [PMID: 38397444 PMCID: PMC10886467 DOI: 10.3390/biom14020207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 01/31/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Polycystic kidney disease (PKD) is the most common genetic form of chronic kidney disease (CKD), and it involves the development of multiple kidney cysts. Not enough medical breakthroughs have been made against PKD, a condition which features regional hypoxia and activation of the hypoxia-inducible factor (HIF) pathway. The following pathology of CKD can severely instigate kidney damage and/or renal failure. Significant evidence verifies an imperative role for mitophagy in normal kidney physiology and the pathology of CKD and/or PKD. Mitophagy serves as important component of mitochondrial quality control by removing impaired/dysfunctional mitochondria from the cell to warrant redox homeostasis and sustain cell viability. Interestingly, treatment with the peroxisome proliferator-activated receptor-α (PPAR-α) agonist could reduce the pathology of PDK and might improve the renal function of the disease via the modulation of mitophagy, as well as the condition of gut microbiome. Suitable modulation of mitophagy might be a favorable tactic for the prevention and/or treatment of kidney diseases such as PKD and CKD.
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Affiliation(s)
| | | | | | | | - Satoru Matsuda
- Department of Food Science and Nutrition, Nara Women’s University, Kita-Uoya Nishimachi, Nara 630-8506, Japan
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Wigerinck S, Gregory AV, Smith BH, Iliuta IA, Hanna C, Chedid M, Kaidbay HDN, Senum SR, Shukoor S, Harris PC, Torres VE, Kline TL, Chebib FT. Evaluation of advanced imaging biomarkers at kidney failure in patients with ADPKD: a pilot study. Clin Kidney J 2023; 16:1691-1700. [PMID: 37779848 PMCID: PMC10539251 DOI: 10.1093/ckj/sfad114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Indexed: 10/03/2023] Open
Abstract
Background Autosomal dominant polycystic kidney disease (ADPKD) presents with variable disease severity and progression. Advanced imaging biomarkers may provide insights into cystic and non-cystic processes leading to kidney failure in different age groups. Methods This pilot study included 39 ADPKD patients with kidney failure, stratified into three age groups (<46, 46-56, >56 years old). Advanced imaging biomarkers were assessed using an automated instance cyst segmentation tool. The biomarkers were compared with an age- and sex-matched ADPKD cohort in early chronic kidney disease (CKD). Results Ht-total parenchymal volume correlated negatively with age at kidney failure. The median Ht-total parenchymal volume was significantly lower in patients older than 56 years. Cystic burden was significantly higher at time of kidney failure, especially in patients who reached it before age 46 years. The cyst index at kidney failure was comparable across age groups and Mayo Imaging Classes. Advanced imaging biomarkers showed higher correlation with Ht-total kidney volume in early CKD than at kidney failure. Cyst index and parenchymal index were relatively stable over 5 years prior to kidney failure, whereas Ht-total cyst volume and cyst parenchymal surface area increased significantly. Conclusion Age-related differences in advanced imaging biomarkers suggest variable pathophysiological mechanisms in ADPKD patients with kidney failure. Further studies are needed to validate the utility of these biomarkers in predicting disease progression and guiding treatment strategies.
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Affiliation(s)
- Stijn Wigerinck
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
- Faculty of Medicine, Catholic University of Leuven, Leuven, Belgium
| | | | - Byron H Smith
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Ioan-Andrei Iliuta
- Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL, USA
| | - Christian Hanna
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
- Division of Pediatric Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Maroun Chedid
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | | | - Sarah R Senum
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA
| | - Shebaz Shukoor
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Peter C Harris
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA
| | - Vicente E Torres
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | | | - Fouad T Chebib
- Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL, USA
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Prince MR, Weiss E, Blumenfeld JD. Size Matters: How to Characterize ADPKD Severity by Measuring Total Kidney Volume. J Clin Med 2023; 12:6068. [PMID: 37763007 PMCID: PMC10532118 DOI: 10.3390/jcm12186068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
Following patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD) has been challenging because serum biomarkers such as creatinine often remain normal until relatively late in the disease [...].
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Affiliation(s)
- Martin R. Prince
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA;
- Division of General Medicine, Columbia College of Physicians and Surgeons, New York, NY 10027, USA
| | - Erin Weiss
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA;
| | - Jon D. Blumenfeld
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA;
- The Rogosin Institute, New York, NY 10065, USA
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Gkika V, Louka M, Tsagkatakis M, Tsirpanlis G. The Efficacy, the Treatment Response and the Aquaretic Effects of a Three-Year Tolvaptan Regimen in Polycystic Kidney Disease Patients. Clin Pract 2023; 13:1035-1042. [PMID: 37736928 PMCID: PMC10514807 DOI: 10.3390/clinpract13050092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/17/2023] [Accepted: 08/18/2023] [Indexed: 09/23/2023] Open
Abstract
Tolvaptan, a selective vasopressin V2 receptor antagonist, is the first and only approved specific treatment for Autosomal-Dominant Polycystic Kidney Disease (ADPKD), and is used in current clinical practice. Real clinical data are missing. In this retrospective study, 41 ADPKD patients received tolvaptan for 3 years, from 2018 to 2021. Total kidney volume (TKV) was measured using Magnetic Resonance Imaging, at initiation and at the end of the treatment period. A complete biochemistry/hematology profile and a 24 h urine volume collection were performed monthly for the first 18 months and every 3 months thereafter. At the end of the treatment period, the median (IQR) estimated Glomerular Filtration Rate (e-GFR) was 5.3 (-1.3, 8.7) mL/min higher than the expected e-GFR decline without treatment, while the prediction for End Stage Chronic Kidney Disease (ESKD) had been prolonged by 1 (0, 2) year. Total Kidney Volume did not change significantly (2250 (1357) mL at 3 years of treatment vs. 2180 (1091) mL expected without treatment, p = 0.48). Younger patients with a relatively preserved e-GFR, lower hypertension burden, better familiar renal prognosis and more severe imaging data showed better outcomes. The aquaretic adverse effects of tolvaptan did not affect renal function and electrolyte balance in 51 patients, in a follow-up period of 18 months. Consequently, tolvaptan seems to be effective in preventing progression of ADPKD when administered in a timely manner in patients with better familiar renal history, shorter hypertension duration and worse imaging profile. Increased diuresis does not affect treatment efficacy.
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Affiliation(s)
- Vasiliki Gkika
- Department of Nephrology, General Hospital of Athens “G. Gennimatas”, 11527 Athens, Greece; (V.G.); (M.L.)
| | - Michaela Louka
- Department of Nephrology, General Hospital of Athens “G. Gennimatas”, 11527 Athens, Greece; (V.G.); (M.L.)
| | | | - George Tsirpanlis
- Department of Nephrology, General Hospital of Athens “G. Gennimatas”, 11527 Athens, Greece; (V.G.); (M.L.)
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Caroli A, Kline TL. Abdominal Imaging in ADPKD: Beyond Total Kidney Volume. J Clin Med 2023; 12:5133. [PMID: 37568535 PMCID: PMC10420262 DOI: 10.3390/jcm12155133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 08/02/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023] Open
Abstract
In the context of autosomal dominant polycystic kidney disease (ADPKD), measurement of the total kidney volume (TKV) is crucial. It acts as a marker for tracking disease progression, and evaluating the effectiveness of treatment strategies. The TKV has also been recognized as an enrichment biomarker and a possible surrogate endpoint in clinical trials. Several imaging modalities and methods are available to calculate the TKV, and the choice depends on the purpose of use. Technological advancements have made it possible to accurately assess the cyst burden, which can be crucial to assessing the disease state and helping to identify rapid progressors. Moreover, the development of automated algorithms has increased the efficiency of total kidney and cyst volume measurements. Beyond these measurements, the quantification and characterization of non-cystic kidney tissue shows potential for stratifying ADPKD patients early on, monitoring disease progression, and possibly predicting renal function loss. A broad spectrum of radiological imaging techniques are available to characterize the kidney tissue, showing promise when it comes to non-invasively picking up the early signs of ADPKD progression. Radiomics have been used to extract textural features from ADPKD images, providing valuable information about the heterogeneity of the cystic and non-cystic components. This review provides an overview of ADPKD imaging biomarkers, focusing on the quantification methods, potential, and necessary steps toward a successful translation to clinical practice.
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Affiliation(s)
- Anna Caroli
- Bioengineering Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 24020 Ranica, BG, Italy
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10
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Kistler AD. Function follows form: the quest for the best prognostic imaging biomarker in ADPKD. Kidney Int 2023; 104:239-241. [PMID: 37479385 DOI: 10.1016/j.kint.2023.05.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/26/2023] [Accepted: 05/31/2023] [Indexed: 07/23/2023]
Abstract
Total kidney volume represents the most solid prognostic biomarker for autosomal dominant polycystic kidney disease, because it mirrors cyst growth that precedes kidney function decline. Considerable variability of glomerular filtration rate trajectories, however, remains unexplained by total kidney volume, and its calculation is time-consuming. Using deep learning algorithms, Gregory et al. determined total kidney volume and other, novel, imaging-based biomarkers. They achieved automation and improved prognostic accuracy for long-term kidney function loss, yet the study leaves some open questions and room for further improvement.
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Affiliation(s)
- Andreas D Kistler
- Department of Medicine, Cantonal Hospital Frauenfeld, Spital Thurgau AG, Frauenfeld, Switzerland; University of Zurich, Zurich, Switzerland.
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11
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Lanktree MB. Evidence for Kidney Volume as a Measure of ADPKD Severity "Marches On" in the OVERTURE Study. Kidney Int Rep 2023; 8:951-953. [PMID: 37180501 PMCID: PMC10166780 DOI: 10.1016/j.ekir.2023.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023] Open
Affiliation(s)
- Matthew B. Lanktree
- Departments of Medicine and Health Research Methodology, Evidence and Impact, Division of Nephrology, St. Joseph’s Healthcare Hamilton & McMaster University, Hamilton, Ontario, Canada
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