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  • 1
    In: Investigative Radiology, Ovid Technologies (Wolters Kluwer Health), Vol. 55, No. 4 ( 2020-4), p. 217-225
    Abstract: Autosomal dominant polycystic kidney disease (ADPKD) is a chronic progressive disorder with a significant disease burden leading to end-stage renal disease in more than 75% of the affected individuals. Although prediction of disease progression is highly important, all currently available biomarkers—including height-adjusted total kidney volume (htTKV)—have important drawbacks in the everyday clinical setting. Thus, the purpose of this study was to evaluate T2 mapping as a source of easily obtainable and accurate biomarkers, which are needed for improved patient counseling and selection of targeted treatment options. Materials and Methods A total of 139 ADPKD patients from The German ADPKD Tolvaptan Treatment Registry and 10 healthy controls underwent magnetic resonance imaging on a clinical 1.5-T system including acquisition of a Gradient-Echo-Spin-Echo T2 mapping sequence. The ADPKD patients were divided into 3 groups according to kidney cyst fraction (0%–35%, 36%–70%, 〉 70%) as a surrogate marker for disease severity. The htTKV was calculated based on standard T2-weighted imaging. Mean T2 relaxation times of both kidneys (kidney-T2) as well as T2 relaxation times of the residual kidney parenchyma (parenchyma-T2) were measured on the T2 maps. Results Calculation of parenchyma-T2 was 6- to 10-fold faster than determination of htTKV and kidney-T2 (0.78 ± 0.14 vs 4.78 ± 1.17 minutes, P 〈 0.001; 0.78 ± 0.14 vs 7.59 ± 1.57 minutes, P 〈 0.001). Parenchyma-T2 showed a similarly strong correlation to cyst fraction ( r = 0.77, P 〈 0.001) as kidney-T2 ( r = 0.76, P 〈 0.001), the strongest correlation to the serum-derived biomarker copeptin ( r = 0.37, P 〈 0.001), and allowed for the most distinct separation of patient groups divided according to cyst fraction. In contrast, htTKV showed an only moderate correlation to cyst fraction ( r = 0.48, P 〈 0.001). These observations were even more evident when considering only patients with preserved kidney function. Conclusions The rapidly assessable parenchyma-T2 shows a strong association with disease severity early in disease and is superior to htTKV when it comes to correlation with renal cyst fraction.
    Type of Medium: Online Resource
    ISSN: 1536-0210 , 0020-9996
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2020
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  • 2
    In: Nephrology Dialysis Transplantation, Oxford University Press (OUP), Vol. 37, No. Supplement_3 ( 2022-05-03)
    Abstract: Over the last years, there has been increasing evidence that defects in the energy metabolism of polycystic kidney disease (PKD) cyst lining cells, especially increased glucose dependency and defects in fatty acid oxidation, may underlie the pathogenesis of ADPKD, Rowe et al. (Defective glucose metabolism in polycystic kidney disease identifies a new therapeutic strategy. Nat Med 2013; 19(4): 488–493). Based on these data, ketogenic dietary interventions have effectively been used in PKD animal models, Kipp et al. (A mild reduction of food intake slows disease progression in an orthologous mouse model of polycystic kidney disease. Am J Physiol Renal Physiol 2016;310(8):F726-F31), Torres et al. (Ketosis ameliorates renal cyst growth in polycystic kidney disease. Cell Metab 2019;30(6):1007–23 e5) and Warner et al. (Food restriction ameliorates the development of polycystic kidney disease. J Am Soc Nephrol 2016;27(5):1437–1447). As a first-in-human pilot study, the RESET-PKD study now strives to translate these promising results in the clinical setting. METHOD The present study enrolled 10 ADPKD patients with rapid disease progression. After a screening visit (V1), patients followed their usual carbohydrate-rich diet for up to 4 weeks. In a second visit (V2), patients chose to either perform a 3-day water fast (WF) or a 14-day ketogenic diet (KD) until a third study visit (V3), after which they returned to their normal high-carbohydrate diet for 3–6 weeks until a final visit (V4). At all visits, total kidney volume (TKV) and total liver volume (TLV) were monitored using MRI; anthropometric parameters, body composition and biochemical parameters (i.e. serum creatinine, complete blood cell count, glucose, β-hydroxybutyrate in fingerstick blood and acetone in breath) were measured. Ketone bodies were evaluated at all visits and in between. Feasibility was examined using respective questionnaires. Undesirable effects such as feelings of hunger and discomfort were documented in a study diary. RESULTS All participants (KD: n = 5, WF: n = 5; age: 39.8 ± 11.6 years; eGFR: 82 ± 23.5 mL/min; TKV: 2224 ± 1156 mL) were classified as Mayo Class 1C to 1E. Serum creatinine values were not altered by the ketogenic dietary interventions (V2: 1.17 ± 0.33 mg/dL versus V3: 1.20 ± 0.35 mg/dL, P = 0.826), but serum glucose levels decreased significantly (V2: 84 ± 3 mg/dL, V3: 70 ± 13 mg/dL, P = 0.004). BHB blood levels as well as acetone levels in breath increased in both study arms (V1 to V2 average acetone: 2.6 ± 1.18 ppm, V2 to V3: 22.8 ± 11.9 ppm, P & lt; 0.0001; V1 to V2 average BHB: 0.22 ± 0.08 mmol/L, V2 to V3: 1.89 ± 0.92 mmol/L, P & lt; 0.0001). Nine out of 10 patients reached a ketogenic state during the intervention and 90% evaluated ketogenic interventions as being feasible while 80% of patients both reached the metabolic endpoint and rated ketogenic dietary interventions as feasible. TKV changes during KD and WF did not show any significant differences compared to the period on carbohydrate-rich diet (ΔTKV V1 to V2: 0.75 ± 0.54%, ΔTKV V2 to V3: −1.06 ± 1.16%). CONCLUSION The RESET-PKD study demonstrates feasibility of short-term ketogenic interventions both regarding reliable attainment of ketosis and patient-reported feasibility of this intervention in everyday life. In this proof of principle study, short-term ketogenic interventions could not show a significant reduction in TKV. However, this endpoint is likely to require long-term exposure to ketogenesis. Future large-scale clinical trials examining such long-term dietary interventions—e.g. the KETO-ADPKD study—will be of great importance to further evaluate long-term feasibility and the therapeutic potential of ketogenic diets in ADPKD.
    Type of Medium: Online Resource
    ISSN: 0931-0509 , 1460-2385
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
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  • 3
    In: Nephrology Dialysis Transplantation, Oxford University Press (OUP), Vol. 38, No. 7 ( 2023-06-30), p. 1623-1635
    Abstract: Ketogenic dietary interventions (KDI) have been shown to be effective in animal models of polycystic kidney disease (PKD), but data from clinical trials are lacking. Methods Ten autosomal dominant PKD (ADPKD) patients with rapid disease progression were enrolled at visit V1 and initially maintained a carbohydrate-rich diet. At V2, patients entered one of the two KDI arms: a 3-day water fast (WF) or a 14-day ketogenic diet (KD). At V3, they resumed their normal diet for 3–6 weeks until V4. At each visit, magnetic resonance imaging kidney and liver volumetry was performed. Ketone bodies were evaluated to assess metabolic efficacy and questionnaires were used to determine feasibility. Results All participants [KD n = 5, WF n = 5; age 39.8 ± 11.6 years; estimated glomerular filtration rate 82 ± 23.5 mL/min/1.73 m2; total kidney volume (TKV) 2224 ± 1156 mL] were classified as Mayo Class 1C–1E. Acetone levels in breath and beta-hydroxybutyrate (BHB) blood levels increased in both study arms (V1 to V2 average acetone: 2.7 ± 1.2 p.p.m., V2 to V3: 22.8 ± 11.9 p.p.m., P = .0006; V1 to V2 average BHB: 0.22 ± 0.08 mmol/L, V2 to V3: 1.88 ± 0.93 mmol/L, P = .0008). Nine of 10 patients reached a ketogenic state and 9/10 evaluated KDIs as feasible. TKV did not change during this trial. However, we found a significant impact on total liver volume (ΔTLV V2 to V3: −7.7%, P = .01), mediated by changes in its non-cystic fraction. Conclusions RESET-PKD demonstrates that short-term KDIs potently induce ketogenesis and are feasible for ADPKD patients in daily life. While TLV quickly changed upon the onset of ketogenesis, changes in TKV may require longer-term interventions.
    Type of Medium: Online Resource
    ISSN: 0931-0509 , 1460-2385
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
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  • 4
    In: Kidney360, Ovid Technologies (Wolters Kluwer Health), Vol. 3, No. 12 ( 2022-12), p. 2048-2058
    Abstract: We developed a model for automated kidney and liver volumetry in ADPKD to provide assistance with time-consuming volumetry. The model works in both coronal and axial planes and was tested in the real-life setting using large multicentric cohorts. The trained model is published along with the code to allow for further joint development and integration into commercial software packages. Background Imaging-based total kidney volume (TKV) and total liver volume (TLV) are major prognostic factors in autosomal dominant polycystic kidney disease (ADPKD) and end points for clinical trials. However, volumetry is time consuming and reader dependent in clinical practice. Our aim was to develop a fully automated method for joint kidney and liver segmentation in magnetic resonance imaging (MRI) and to evaluate its performance in a multisequence, multicenter setting. Methods The convolutional neural network was trained on a large multicenter dataset consisting of 992 MRI scans of 327 patients. Manual segmentation delivered ground-truth labels. The model’s performance was evaluated in a separate test dataset of 93 patients (350 MRI scans) as well as a heterogeneous external dataset of 831 MRI scans from 323 patients. Results The segmentation model yielded excellent performance, achieving a median per study Dice coefficient of 0.92–0.97 for the kidneys and 0.96 for the liver. Automatically computed TKV correlated highly with manual measurements (intraclass correlation coefficient [ICC]: 0.996–0.999) with low bias and high precision (−0.2%±4% for axial images and 0.5%±4% for coronal images). TLV estimation showed an ICC of 0.999 and bias/precision of −0.5%±3%. For the external dataset, the automated TKV demonstrated bias and precision of −1%±7%. Conclusions Our deep learning model enabled accurate segmentation of kidneys and liver and objective assessment of TKV and TLV. Importantly, this approach was validated with axial and coronal MRI scans from 40 different scanners, making implementation in clinical routine care feasible. Clinical Trial registry name and registration number: The German ADPKD Tolvaptan Treatment Registry (AD[H]PKD), NCT02497521
    Type of Medium: Online Resource
    ISSN: 2641-7650
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2022
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