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2. Evaluating the associations between compliance with CKD guideline component metrics and renal outcomes

Author

Nyma, Zannatun, Kitaoka, Kaori, Yano, Yuichiro, Kanegae, Hiroshi, Bayaraa, Nomin, Kishi, Seiji, Nagasu, Hajime, Nakano, Toshiaki, Wada, Jun, Maruyama, Shoichi, Nakagawa, Naoki, Tamura, Kouichi, Yokoo, Takashi, Yanagita, Motoko, Narita, Ichiei, Yamagata, Kunihiro, Wada, Takashi, Tsuruya, Kazuhiko, Nakashima, Naoki, Isaka, Yoshitaka, Nangaku, Masaomi, Kashihara, Naoki, and Okada, Hirokazu

Published
2024
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5. Clinical questions and good practice statements of clinical practice guidelines for management of kidney injury during anticancer drug therapy 2022

Author

Yanagita, Motoko, Muto, Satoru, Nishiyama, Hiroyuki, Ando, Yuichi, Hirata, Sumio, Doi, Kent, Fujiwara, Yutaka, Hanafusa, Norio, Hatta, Takahiro, Hoshino, Junichi, Ichioka, Satoko, Inoue, Takamitsu, Ishikura, Kenji, Kato, Taigo, Kitamura, Hiroshi, Kobayashi, Yusuke, Koizumi, Yuichi, Kondoh, Chihiro, Matsubara, Takeshi, Matsubara, Kazuo, Matsumoto, Koji, Okuda, Yusuke, Okumura, Yuta, Sakaida, Emiko, Shibagaki, Yugo, Shimodaira, Hideki, Takano, Nao, Uchida, Akiko, Yakushijin, Kimikazu, Yamamoto, Takehito, Yamamoto, Kazuhiro, Yasuda, Yoshinari, Oya, Mototsugu, Okada, Hirokazu, Nangaku, Masaomi, and Kashihara, Naoki

Published
2024
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8. Chapter 1: Evaluation of kidney function in patients undergoing anticancer drug therapy, from clinical practice guidelines for the management of kidney injury during anticancer drug therapy 2022

Author

Muto, Satoru, Matsubara, Takeshi, Inoue, Takamitsu, Kitamura, Hiroshi, Yamamoto, Kazuhiro, Ishii, Taisuke, Yazawa, Masahiko, Yamamoto, Ryohei, Okada, Naoto, Mori, Kiyoshi, Yamada, Hiroyuki, Kuwabara, Takashige, Yonezawa, Atsushi, Fujimaru, Takuya, Kawano, Haruna, Yokoi, Hideki, Doi, Kent, Hoshino, Junichi, and Yanagita, Motoko

Published
2023
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9. Chapter 3: Management of kidney injury caused by cancer drug therapy, from clinical practice guidelines for the management of kidney injury during anticancer drug therapy 2022

Author

Ando, Yuichi, Nishiyama, Hiroyuki, Shimodaira, Hideki, Takano, Nao, Sakaida, Emiko, Matsumoto, Koji, Nakanishi, Koki, Sakai, Hideki, Tsukamoto, Shokichi, Komine, Keigo, Yasuda, Yoshinari, Kato, Taigo, Fujiwara, Yutaka, Koyama, Takafumi, Kitamura, Hiroshi, Kuwabara, Takashige, Yonezawa, Atsushi, Okumura, Yuta, Yakushijin, Kimikazu, Nozawa, Kazuki, Goto, Hideaki, Matsubara, Takeshi, Hoshino, Junichi, and Yanagita, Motoko

Published
2023
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14. Tertiary lymphoid tissues in kidney diseases: a perspective for the pediatric nephrologist

Author

Yoshikawa, Takahisa, Lee, Yu Ho, Sato, Yuki, and Yanagita, Motoko

Subjects
Children -- Diseases, Lymphoid tissue -- Analysis, Kidney diseases -- Diagnosis -- Care and treatment, Health
Abstract

Chronic kidney disease (CKD) is a major public health problem worldwide. In the pediatric population, CKD is also an important health issue because it causes several comorbid conditions that can have long-term consequences beyond the pediatric age. Chronic inflammation is a common pathological feature of CKD, irrespective of etiology, and leads to maladaptive repair and kidney dysfunction. Tertiary lymphoid tissues (TLTs) are ectopic lymphoid structures that develop in non-lymphoid organs under chronic inflammation caused by pathological conditions, including infections, autoimmune diseases, and cancers. TLTs in the kidneys have been poorly researched due to the lack of an animal model. We have recently found that, in aged but not young mice, TLTs develop in multiple kidney injury models, and the analysis of age-dependent TLTs has brought about several novel insights into the development and pathogenic impacts of TLTs in the kidney. Age-dependent TLT formation is also observed in human kidneys. In addition to aged kidneys, TLT development is also reported in several human kidney diseases including kidney allografts, lupus nephritis, and IgA nephropathy in both adults and children. In this review, we describe the novel findings on TLTs in the kidney obtained mainly from the analysis of age-dependent TLTs and discuss the clinical relevance of TLTs in kidney diseases., Author(s): Takahisa Yoshikawa [sup.1] , Yu Ho Lee [sup.2] , Yuki Sato [sup.1] , Motoko Yanagita [sup.1] [sup.3] Author Affiliations: (1) grid.258799.8, 0000 0004 0372 2033, Department of Nephrology, Graduate [...]

Published
2023
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17. Improved Renal Function in Initial Treatment Improves Patient Survival, Renal Outcomes, and Glucocorticoid-Related Complications in IgG4-Related Kidney Disease in Japan

Author

Mizushima, Ichiro, Saeki, Takako, Kobayashi, Daisuke, Sawa, Naoki, Hayashi, Hiroki, Taniguchi, Yoshinori, Nakata, Hirosuke, Yamada, Kazunori, Matsui, Shoko, Yasuno, Tetsuhiko, Masutani, Kosuke, Nagasawa, Tasuku, Takahashi, Hiroki, Ubara, Yoshifumi, Yanagita, Motoko, and Kawano, Mitsuhiro

Published
2024
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18. Genome-wide association study of the risk of chronic kidney disease and kidney-related traits in the Japanese population: J-Kidney-Biobank

Author

Sugawara, Yuka, Hirakawa, Yosuke, Nagasu, Hajime, Narita, Akira, Katayama, Akihiro, Wada, Jun, Shimizu, Miho, Wada, Takashi, Kitamura, Hiromasa, Nakano, Toshiaki, Yokoi, Hideki, Yanagita, Motoko, Goto, Shin, Narita, Ichiei, Koshiba, Seizo, Tamiya, Gen, Nangaku, Masaomi, Yamamoto, Masayuki, and Kashihara, Naoki

Published
2023
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29. Genetic Background and Clinicopathologic Features of Adult-onset Nephronophthisis

Author

Fujimaru, Takuya, Kawanishi, Kunio, Mori, Takayasu, Mishima, Eikan, Sekine, Akinari, Chiga, Motoko, Mizui, Masayuki, Sato, Noriaki, Yanagita, Motoko, Ooki, Yuki, Nagahama, Kiyotaka, Ohnuki, Yuko, Hamano, Naoto, Watanabe, Saki, Mochizuki, Toshio, Nagatsuji, Katsushi, Tanaka, Kenichi, Tsukamoto, Tatsuo, Tsushima, Hideo, Shimamoto, Mamiko, Tsuji, Takahiro, Kuyama, Tamaki, Kawamoto, Shinya, Maki, Kenji, Katsuma, Ai, Oishi, Mariko, Yamamoto, Kouhei, Mandai, Shintaro, Kikuchi, Hiroaki, Ando, Fumiaki, Mori, Yutaro, Susa, Koichiro, Iimori, Soichiro, Naito, Shotaro, Rai, Tatemitsu, Hoshino, Junichi, Ubara, Yoshifumi, Miyazaki, Mariko, Nagata, Michio, Uchida, Shinichi, and Sohara, Eisei

Published
2021
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32. KDIGO Controversies Conference on onco-nephrology: kidney disease in hematological malignancies and the burden of cancer after kidney transplantation

Author

Abu-Alfa, Ali K., Amer, Hatem, Beutel, Gernot, Chapman, Jeremy, Chen, Xiaohong, Chudek, Jerzy, Cosmai, Laura, Danesi, Romano, De Stefano, Filippo, Iseki, Kunitoshi, Jaimes, Edgar A., Jhaveri, Kenar D., Jurczyszyn, Artur, Kazancioğlu, Rümeyza, Kitchlu, Abhijat, Kollmannsberger, Christian, Lahoti, Amit, Li, Yang, Macía, Manuel, Matsubara, Takeshi, Mitropoulos, Dionysios, Noiri, Eisei, Perazella, Mark A., Ronco, Pierre, Rosner, Mitchell H., Soler Romeo, Maria Jose, Sprangers, Ben, Stadler, Walter M., Stevens, Paul E., Tesař, Vladimír, Torres da Costa e Silva, Verônica, Vesole, David H., Vijayan, Anitha, Viklický, Ondřej, Workeneh, Biruh T., Yanagita, Motoko, Zakharova, Elena, Małyszko, Jolanta, Bamias, Aristotelis, Danesh, Farhad R., Dębska-Ślizień, Alicja, Gallieni, Maurizio, Gertz, Morie A., Kielstein, Jan T., Tesarova, Petra, Wong, Germaine, Cheung, Michael, Wheeler, David C., Winkelmayer, Wolfgang C., and Porta, Camillo

Published
2020
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33. KDIGO Controversies Conference on onco-nephrology: understanding kidney impairment and solid-organ malignancies, and managing kidney cancer

Author

Abu-Alfa, Ali K., Amer, Hatem, Beutel, Gernot, Chapman, Jeremy, Chen, Xiaohong, Chudek, Jerzy, Cosmai, Laura, Danesi, Romano, De Stefano, Filippo, Iseki, Kunitoshi, Jaimes, Edgar A., Jhaveri, Kenar D., Jurczyszyn, Artur, Kazancioğlu, Rümeyza, Kitchlu, Abhijat, Kollmannsberger, Christian, Lahoti, Amit, Li, Yang, Macía, Manuel, Matsubara, Takeshi, Mitropoulos, Dionysios, Noiri, Eisei, Perazella, Mark A., Ronco, Pierre, Rosner, Mitchell H., Soler Romeo, Maria Jose, Sprangers, Ben, Stadler, Walter M., Stevens, Paul E., ladimír Tesař, Torres da Costa e Silva, Verônica, Vesole, David H., Vijayan, Anitha, Viklický, Ondřej, Workeneh, Biruh T., Yanagita, Motoko, Zakharova, Elena, Porta, Camillo, Bamias, Aristotelis, Danesh, Farhad R., Dębska-Ślizień, Alicja, Gallieni, Maurizio, Gertz, Morie A., Kielstein, Jan T., Tesarova, Petra, Wong, Germaine, Cheung, Michael, Wheeler, David C., Winkelmayer, Wolfgang C., and Małyszko, Jolanta

Published
2020
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35. Factors affecting the sodium‐glucose cotransporter 2 inhibitors‐related initial decline in glomerular filtration rate and its possible effect on kidney outcome in chronic kidney disease with type 2 diabetes: The Japan Chronic Kidney Disease Database

Author

Kanaoka, Tomohiko, Wakui, Hiromichi, Yano, Yuichiro, Nagasu, Hajime, Kanegae, Hiroshi, Nangaku, Masaomi, Hirakawa, Yosuke, Nakagawa, Naoki, Wada, Jun, Tsuruya, Kazuhiko, Nakano, Toshiaki, Maruyama, Shoichi, Wada, Takashi, Konishi, Masaaki, Nagahiro, Takanori, Yamagata, Kunihiro, Narita, Ichiei, Yanagita, Motoko, Terada, Yoshio, and Araki, Shinichi

Subjects
SODIUM-glucose cotransporters, CHRONIC kidney failure, GLOMERULAR filtration rate, TYPE 2 diabetes, DATABASES, RENIN-angiotensin system, DEAD
Abstract

Aim: Sodium‐glucose cotransporter 2 (SGLT2) inhibitors often cause a transient decrease in glomerular filtration rate (GFR) shortly after the initiation, referred to as the 'initial drop'. However, the clinical significance of this initial drop in real‐world practice remains unclear. Materials and Methods: Using the nationwide Japan Chronic Kidney Disease Database, we examined factors that affected the initial drop, in patients with chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM). We also evaluated the effects of the initial drop on a composite kidney outcome (a decline in GFR of ≥50% or progression to end‐stage kidney disease). Results: Data from 2053 patients with CKD and T2DM newly prescribed an SGLT2 inhibitor were analysed. The follow‐up period after SGLT2 inhibitor administration was 1015 days (interquartile range: 532, 1678). Multivariate linear regression models revealed that the concomitant use of the renin‐angiotensin system inhibitors and diuretics, urinary protein levels ≥2+, and changes in GFR before the initiation of the SGLT2 inhibitor were associated with a larger initial GFR decline (β = −0.609, p =.039; β = −2.298, p <.001; β = −0.936, p =.048; β = −0.079, p <.001, respectively). Patients in the quartile with the largest initial GFR decline experienced a higher incidence of the subsequent composite kidney outcome than those in the other quartiles (p <.001). Conclusions: The concomitant use of renin‐angiotensin system inhibitors and diuretics, higher urine protein levels and pre‐treatment GFR changes were associated with a larger initial GFR decline. Of these factors, the use of a diuretic had the largest effect. Furthermore, patients with CKD and T2DM experiencing an excessive initial GFR drop might be at a higher risk of adverse kidney outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Cardioprotective Effects of VCP Modulator KUS121 in Murine and Porcine Models of Myocardial Infarction

Author

Ide, Yuya, Horie, Takahiro, Saito, Naritatsu, Watanabe, Shin, Otani, Chiharu, Miyasaka, Yui, Kuwabara, Yasuhide, Nishino, Tomohiro, Nakao, Tetsushi, Nishiga, Masataka, Nishi, Hitoo, Nakashima, Yasuhiro, Nakazeki, Fumiko, Koyama, Satoshi, Kimura, Masahiro, Tsuji, Shuhei, Rodriguez, Randolph Ruiz, Xu, Sijia, Yamasaki, Tomohiro, Watanabe, Toshimitsu, Yamamoto, Masamichi, Yanagita, Motoko, Kimura, Takeshi, Kakizuka, Akira, and Ono, Koh

Published
2019
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48. Interpretable machine learning-based individual analysis of acute kidney injury in immune checkpoint inhibitor therapy.

Author

Sakuragi, Minoru, Uchino, Eiichiro, Sato, Noriaki, Matsubara, Takeshi, Ueda, Akihiko, Mineharu, Yohei, Kojima, Ryosuke, Yanagita, Motoko, and Okuno, Yasushi

Subjects
IMMUNE checkpoint inhibitors, ACUTE kidney failure, MACHINE learning, IMMUNE checkpoint proteins, RECEIVER operating characteristic curves, SUMATRIPTAN
Abstract

Background: Acute kidney injury (AKI) is a critical complication of immune checkpoint inhibitor therapy. Since the etiology of AKI in patients undergoing cancer therapy varies, clarifying underlying causes in individual cases is critical for optimal cancer treatment. Although it is essential to individually analyze immune checkpoint inhibitor-treated patients for underlying pathologies for each AKI episode, these analyses have not been realized. Herein, we aimed to individually clarify the underlying causes of AKI in immune checkpoint inhibitor-treated patients using a new clustering approach with Shapley Additive exPlanations (SHAP). Methods: We developed a gradient-boosting decision tree-based machine learning model continuously predicting AKI within 7 days, using the medical records of 616 immune checkpoint inhibitor-treated patients. The temporal changes in individual predictive reasoning in AKI prediction models represented the key features contributing to each AKI prediction and clustered AKI patients based on the features with high predictive contribution quantified in time series by SHAP. We searched for common clinical backgrounds of AKI patients in each cluster, compared with annotation by three nephrologists. Results: One hundred and twelve patients (18.2%) had at least one AKI episode. They were clustered per the key feature, and their SHAP value patterns, and the nephrologists assessed the clusters' clinical relevance. Receiver operating characteristic analysis revealed that the area under the curve was 0.880. Patients with AKI were categorized into four clusters with significant prognostic differences (p = 0.010). The leading causes of AKI for each cluster, such as hypovolemia, drug-related, and cancer cachexia, were all clinically interpretable, which conventional approaches cannot obtain. Conclusion: Our results suggest that the clustering method of individual predictive reasoning in machine learning models can be applied to infer clinically critical factors for developing each episode of AKI among patients with multiple AKI risk factors, such as immune checkpoint inhibitor-treated patients. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Removal rate of 5-fluorouracil and its metabolites in patients on hemodialysis: a report of two cases of colorectal cancer patients with end-stage renal failure.

Author

Imamaki, Hirotaka, Oura, Mitsuaki, Oguro, Fumiya, Nishikawa, Yoshitaka, Nakagawa, Shunsaku, Funakoshi, Taro, Kataoka, Shigeki, Horimatsu, Takahiro, Yonezawa, Atsushi, Matsubara, Takeshi, Watanabe, Norihiko, Muto, Manabu, Yanagita, Motoko, and Ozaki, Yoshinao

Subjects
IRINOTECAN, HEMODIALYSIS patients, KIDNEY failure, LIQUID chromatography-mass spectrometry, COLORECTAL cancer, CHRONIC kidney failure
Abstract

Purpose: Hyperammonemia is a serious adverse effect of 5-fluorouracil (5FU) administration. Hemodialysis can be used for its management, but detailed data on the concentrations and removal rate of 5FU and its metabolites during hemodialysis remain unclear. Here, we present two cases of hemodialysis patients with end-stage renal disease who received concurrent 5FU infusion. Methods: Blood samples were collected from the hemodialysis circuit before and after the dialyzer during day 2 hemodialysis sessions, and from the internal shunt just before and after day 4 hemodialysis sessions. The serum levels of 5FU and its metabolites—α-fluoro-β-alanine (FBAL) and monofluoroacetate (FA)—were measured using liquid chromatography-tandem mass spectrometry. Results: Seven sets of blood samples were collected for case 1; the removal rates (mean ± standard deviation) of 5FU and FBAL by the dialyzer were 81.2 ± 23.2% and 96.1 ± 8.6%, respectively (p < 0.001). Three sets of blood samples were collected for case 2; the removal rates of 5FU and FBAL were 81.7 ± 3.9% and 94.8 ± 2.7%, respectively (p = 0.03). Twenty-seven sets of blood samples were collected for case 1; reductions in blood FBAL and FA levels were 49.3 ± 8.8% (p < 0.001) and 64.2 ± 30.3% (p = 0.04), respectively. Bayesian estimation yielded similar results. Three sets of blood samples were collected for case 2; reductions in the blood FBAL and FA levels were 49.9 ± 6.9% and 50.6 ± 33.0%, respectively. Conclusion: In this study, 5FU and its metabolite FBAL were directly removed from the blood by approximately 90% during hemodialysis, and the blood levels of FBAL and FA were reduced by approximately 50% with a single hemodialysis session. [ABSTRACT FROM AUTHOR]

Published
2024
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