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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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  • 124 Download
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.
Others
Satisfaction Survey on Information Technology-Based Glucose Monitoring System Targeting Diabetes Mellitus in Private Local Clinics in Korea
Hun-Sung Kim, So Jung Yang, Yoo Jin Jeong, Young-Eun Kim, Seok-Won Hong, Jae Hyoung Cho
Diabetes Metab J. 2017;41(3):213-222.   Published online June 21, 2017
DOI: https://doi.org/10.4093/dmj.2017.41.3.213
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  • 40 Download
  • 8 Web of Science
  • 8 Crossref
AbstractAbstract PDFPubReader   
Background

Private local clinics in Korea have little experience with information technology (IT)-based glucose monitoring (ITGM). Our aim is to examine user satisfaction and the possibility of using ITGM service practically.

Methods

Patients sent their blood glucose levels to physicians in local clinics. The physicians reviewed the blood glucose values online and provided personal consultations through text messaging or phone calls. Thereafter, a satisfaction survey on the ITGM service, the modified Morisky scale, and patient assessment of chronic illness care were administered.

Results

One hundred and seventy patients from seven private local clinics used the ITGM. Overall satisfaction, including that about the ITGM service, the device, and its usefulness, was rated higher than “mostly satisfied” (score 4.2±0.8 out of 5.0) and even higher among the elderly. Satisfaction was positively associated with age, especially in those older than 60 years. The main reason for intent for future use of the service was the time/place flexibility. Highly motivated patients tended to answer positively regarding information satisfaction (P=0.0377).

Conclusion

Our study is the first to investigate ITGM satisfaction in private local clinics. The feasibility of users utilizing ITGM should be clarified, and future clinical research on the service's clinical effects and cost-benefit analysis is needed.

Citations

Citations to this article as recorded by  
  • Exploring Quantitative Methodologies for Assessing the Environmental, Social, and Economic Impacts of Telemedicine: A Literature Review
    Anna Savoldelli, Daniele Landi, Caterina Rizzi
    Sustainability.2024; 16(6): 2438.     CrossRef
  • Telemedicine for Diabetes Mellitus Management in Older Adults: A Systematic Review
    Luca Schiliró Tristão, Guilherme Tavares, Francisco Tustumi, Wanderley Marques Bernardo, Márcio Luis Duarte, Maria Stella Peccin, Lucas Ribeiro dos Santos
    Current Diabetes Reviews.2023;[Epub]     CrossRef
  • The effectiveness of mobile application for monitoring diabetes mellitus and hypertension in the adult and elderly population: systematic review and meta-analysis
    Emily de Souza Ferreira, Fernanda de Aguiar Franco, Marina Marilac dos Santos Lara, André Amaral Levcovitz, Mateus Araújo Dias, Tiago Ricardo Moreira, Ary Henrique Morais de Oliveira, Rosângela Minardi Mitre Cotta
    BMC Health Services Research.2023;[Epub]     CrossRef
  • Effects of the Physician–Primary-Healthcare Nurse Telemedicine Model (P-NTM) on Medication Adherence and Health-Related Quality of Life (HRQoL) of Patients with Chronic Disease at Remote Rural Areas
    Mi Young Kwak, Eun Jeong Hwang, Tae Ho Lee
    International Journal of Environmental Research and Public Health.2021; 18(5): 2502.     CrossRef
  • Lessons from Use of Continuous Glucose Monitoring Systems in Digital Healthcare
    Hun-Sung Kim, Kun-Ho Yoon
    Endocrinology and Metabolism.2020; 35(3): 541.     CrossRef
  • Apprehensions about Excessive Belief in Digital Therapeutics: Points of Concern Excluding Merits
    Hun-Sung Kim
    Journal of Korean Medical Science.2020;[Epub]     CrossRef
  • Peripartum Management of Gestational Diabetes Using a Digital Health Care Service: A Pilot, Randomized Controlled Study
    Ji-Hee Sung, Da Young Lee, Kyoung Pil Min, Cheol-Young Park
    Clinical Therapeutics.2019; 41(11): 2426.     CrossRef
  • Mobile Phone–Based Telemedicine Practice in Older Chinese Patients with Type 2 Diabetes Mellitus: Randomized Controlled Trial
    Chenglin Sun, Lin Sun, Shugang Xi, Hong Zhang, Huan Wang, Yakun Feng, Yufeng Deng, Haimin Wang, Xianchao Xiao, Gang Wang, Yuan Gao, Guixia Wang
    JMIR mHealth and uHealth.2019; 7(1): e10664.     CrossRef

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