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Complications
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Association of Muscle Mass Loss with Diabetes Development in Liver Transplantation Recipients
Sejeong Lee, Minyoung Lee, Young-Eun Kim, Hae Kyung Kim, Sook Jung Lee, Jiwon Kim, Yurim Yang, Chul Hoon Kim, Hyangkyu Lee, Dong Jin Joo, Myoung Soo Kim, Eun Seok Kang
Diabetes Metab J. 2024;48(1):146-156.   Published online January 3, 2024
DOI: https://doi.org/10.4093/dmj.2022.0100
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  • 3 Web of Science
  • 3 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Post-transplant diabetes mellitus (PTDM) is one of the most significant complications after transplantation. Patients with end-stage liver diseases requiring transplantation are prone to sarcopenia, but the association between sarcopenia and PTDM remains to be elucidated. We aimed to investigate the effect of postoperative muscle mass loss on PTDM development.
Methods
A total of 500 patients who underwent liver transplantation at a tertiary care hospital between 2005 and 2020 were included. Skeletal muscle area at the level of the L3–L5 vertebrae was measured using computed tomography scans performed before and 1 year after the transplantation. The associations between the change in the muscle area after the transplantation and the incidence of PTDM was investigated using a Cox proportional hazard model.
Results
During the follow-up period (median, 4.9 years), PTDM occurred in 165 patients (33%). The muscle mass loss was greater in patients who developed PTDM than in those without PTDM. Muscle depletion significantly increased risk of developing PTDM after adjustment for other confounding factors (hazard ratio, 1.50; 95% confidence interval, 1.23 to 1.84; P=0.001). Of the 357 subjects who had muscle mass loss, 124 (34.7%) developed PTDM, whereas of the 143 patients in the muscle mass maintenance group, 41 (28.7%) developed PTDM. The cumulative incidence of PTDM was significantly higher in patients with muscle loss than in patients without muscle loss (P=0.034).
Conclusion
Muscle depletion after liver transplantation is associated with increased risk of PTDM development.

Citations

Citations to this article as recorded by  
  • Triacylglycerol‐Based Insulin Resistance Indices and Post‐Transplantation Diabetes Mellitus After Liver Transplantation
    Mateo Chvatal‐Medina, Yakun Li, Adrian Post, Margery A. Connelly, Han Moshage, Stephan J. L. Bakker, Vincent E. de Meijer, Hans Blokzijl, Robin P. F. Dullaart
    Lipids.2026; 61(4): 479.     CrossRef
  • Impact of Early Post-Transplantation Diabetes Mellitus and Changes in Diabetic Status on Graft Failure and Mortality in Kidney Transplant Recipients
    Junseok Jeon, Hye Ryoun Jang, Yebin Park, Kyungho Lee, Jung Eun Lee, Kyungdo Han, Wooseong Huh
    Transplant International.2026;[Epub]     CrossRef
  • Plasma Citrate Levels Are Inversely Associated with Estimated Muscle Mass and Strength in Liver Transplant Recipients
    Yakun Li, Adrian Post, Mateo Chvatal Medina, Caecilia S. E. Doorenbos, Margery A. Connelly, Han Moshage, Stephan J. L. Bakker, Vincent E. de Meijer, Robin P. F. Dullaart
    International Journal of Molecular Sciences.2026; 27(11): 4809.     CrossRef
Brief Report
Technology/Device
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Do-It-Yourself Open Artificial Pancreas System in Children and Adolescents with Type 1 Diabetes Mellitus: Real-World Data
Min Sun Choi, Seunghyun Lee, Jiwon Kim, Gyuri Kim, Sung Min Park, Jae Hyeon Kim
Diabetes Metab J. 2022;46(1):154-159.   Published online November 23, 2021
DOI: https://doi.org/10.4093/dmj.2021.0011
  • 10,712 View
  • 242 Download
  • 11 Web of Science
  • 11 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Few studies have been conducted among Asian children and adolescents with type 1 diabetes mellitus (T1DM) using do-it-yourself artificial pancreas system (DIY-APS). We evaluated real-world data of pediatric T1DM patients using DIY-APS. Data were obtained for 10 patients using a DIY-APS with algorithms. We collected sensor glucose and insulin delivery data from each participant for a period of 4 weeks. Average glycosylated hemoglobin was 6.2%±0.3%. The mean percentage of time that glucose level remained in the target range of 70 to 180 mg/dL was 82.4%±7.8%. Other parameters including time above range, time below range and mean glucose were also within the recommended level, similar to previous commercial and DIY-APS studies. However, despite meeting the target range, unadjusted gaps were still observed between the median basal setting and temporary basal insulin, which should be handled by healthcare providers.

Citations

Citations to this article as recorded by  
  • Glycaemic Control During School Days and Holidays in Children and Adolescents With Type 1 Diabetes Using Open‐Source Android Artificial Pancreas Systems: A Real‐World Study
    Xianming Li, Xinyi Ye, Jie Tang, Qin Wang, Wenhao Zhang, Yanqin Xu, Yu Ding, Sihui Luo
    Diabetes, Obesity and Metabolism.2026; 28(9): 8359.     CrossRef
  • Efficacy and Safety of a Non-Carbohydrate Counting Meal Bolus Strategy in Adults with Type 1 Diabetes Using Open-Source Automated Insulin Delivery
    Yunying Cai, Le Ma, Lun Zhang, Qiongli Neng, Heng Su
    Diabetes Technology & Therapeutics.2025; 27(10): 831.     CrossRef
  • Efficacy and safety of a tubeless open‐source hybrid automated insulin delivery use at home among adults with type 1 diabetes mellitus: Results from a 26‐week, free‐living, randomized crossover trial
    Mengyun Lei, Ping Ling, Beisi Lin, Jing Lv, Zhigu Liu, Yongwen Zhou, Hongrong Deng, Daizhi Yang, Chaofan Wang, Xubin Yang, Jinhua Yan, Wen Xu
    Diabetes, Obesity and Metabolism.2025; 27(9): 4699.     CrossRef
  • Effective blood glucose control in Chinese children with type 1 diabetes via a do-it-yourself artificial pancreas system: a single-center study
    Lihong Yang, Fei Xie, Linqi Han, Fengyan You, Zhiqiang Wei, Caihong Liu, Chao Xu, Yan Sun
    Therapeutic Advances in Endocrinology and Metabolism.2025;[Epub]     CrossRef
  • Factors of Composite Target of Time in Range and Time Below Range Among Children and Adolescents With Type 1 Diabetes Mellitus Using Open-Source Automated Insulin Delivery
    Mengyun Lei, Yongwen Zhou, Chaofan Wang, Ping Ling, Ying Ni, Daizhi Yang, Xubin Yang, Wen Xu, Hongrong Deng, Jinhua Yan
    Endocrine Practice.2025; 31(12): 1538.     CrossRef
  • Real-world efficacy and safety of open-source automated insulin delivery for people with type 1 diabetes mellitus: Experience from mainland China
    Yongwen Zhou, Mengyun Lei, Daizhi Yang, Ping Ling, Ying Ni, Hongrong Deng, Wen Xu, Xubin Yang, Benjamin John Wheeler, Jianping Weng, Jinhua Yan
    Diabetes Research and Clinical Practice.2024; 218: 111910.     CrossRef
  • Advances in Continuous Glucose Monitoring and Integrated Devices for Management of Diabetes with Insulin-Based Therapy: Improvement in Glycemic Control
    Jee Hee Yoo, Jae Hyeon Kim
    Diabetes & Metabolism Journal.2023; 47(1): 27.     CrossRef
  • Open-source automated insulin delivery systems (OS-AIDs) in a pediatric population with type 1 diabetes in a real-life setting: the AWeSoMe study group experience
    Judith Nir, Marianna Rachmiel, Abigail Fraser, Yael Lebenthal, Avivit Brener, Orit Pinhas-Hamiel, Alon Haim, Eve Stern, Noa Levek, Tal Ben-Ari, Zohar Landau
    Endocrine.2023; 81(2): 262.     CrossRef
  • Efficacy and safety of Android artificial pancreas system use at home among adults with type 1 diabetes mellitus in China: protocol of a 26-week, free-living, randomised, open-label, two-arm, two-phase, crossover trial
    Mengyun Lei, Beisi Lin, Ping Ling, Zhigu Liu, Daizhi Yang, Hongrong Deng, Xubin Yang, Jing Lv, Wen Xu, Jinhua Yan
    BMJ Open.2023; 13(8): e073263.     CrossRef
  • Barriers to Uptake of Open-Source Automated Insulin Delivery Systems: Analysis of Socioeconomic Factors and Perceived Challenges of Caregivers of Children and Adolescents With Type 1 Diabetes From the OPEN Survey
    Antonia Huhndt, Yanbing Chen, Shane O’Donnell, Drew Cooper, Hanne Ballhausen, Katarzyna A. Gajewska, Timothée Froment, Mandy Wäldchen, Dana M. Lewis, Klemens Raile, Timothy C. Skinner, Katarina Braune
    Frontiers in Clinical Diabetes and Healthcare.2022;[Epub]     CrossRef
  • Toward Personalized Hemoglobin A1c Estimation for Type 2 Diabetes
    Namho Kim, Da Young Lee, Wonju Seo, Nan Hee Kim, Sung-Min Park
    IEEE Sensors Journal.2022; 22(23): 23023.     CrossRef

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