Yang Feng, Yuechen Shi, Kexin Ma, Jiaming Xiao, Ming Liu, Yuqing Yi, Xiaoyu Zhang, Ke Wang, Zhenming Gao
{"title":"基于计算机断层扫描的肌间脂肪组织分析及其在肾移植后糖尿病中的预测作用。","authors":"Yang Feng, Yuechen Shi, Kexin Ma, Jiaming Xiao, Ming Liu, Yuqing Yi, Xiaoyu Zhang, Ke Wang, Zhenming Gao","doi":"10.1016/j.asjsur.2024.08.075","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>While body mass index (BMI) is the most widely used indicator as a measure of obesity factors in post-transplantation diabetes mellitus (PTDM), body composition is a more accurate measure of obesity. This study aims to investigate the effects of Computed tomography (CT)--based morphemic factors on PTDM and establish a prediction model for PTDM after kidney transplantation.</p><p><strong>Methods: </strong>The pre-transplant data and glycemic levels of kidney transplant recipients (June 2021 to July 2023) were retrospectively and prospectively collected. Univariate and multivariate analyses were conducted to analyze the relationship between morphemic factors and PTDM at one month, six months, and one year after hospital discharge. Subsequently, a one-year risk prediction model based on morphemic factors was developed.</p><p><strong>Results: </strong>The study consisted of 131 participants in the one-month group, where Hemoglobin A1c (HbA1c) (p = 0.02) was identified as the risk factor for PTDM. In the six-month group, 129 participants were included, and the intermuscular adipose tissue (IMAT) area (p = 0.02) was identified as the risk factor for PTDM. The one-year group had 128 participants, and the risk factors for PTDM were identified as body mass index (BMI) (p = 0.02), HbA1c (p = 0.01), and IMAT area (p = 0.007). HbA1c (%) and IMAT area were included in the risk prediction Model for PTDM in the one-year group with AUC = 0.716 (95 % CI 0.591-0.841, p = 0.001).</p><p><strong>Conclusions: </strong>Compared to BMI and other morphemic factors, this study demonstrated that the IMAT area was the most potential predictor of PTDM.</p><p><strong>Clinical trial notation: </strong>Chictr.org (ChiCTR2300078639).</p>","PeriodicalId":55454,"journal":{"name":"Asian Journal of Surgery","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computed tomography-based intermuscular adipose tissue analysis and its predicting role in post-kidney transplantation diabetes mellitus.\",\"authors\":\"Yang Feng, Yuechen Shi, Kexin Ma, Jiaming Xiao, Ming Liu, Yuqing Yi, Xiaoyu Zhang, Ke Wang, Zhenming Gao\",\"doi\":\"10.1016/j.asjsur.2024.08.075\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>While body mass index (BMI) is the most widely used indicator as a measure of obesity factors in post-transplantation diabetes mellitus (PTDM), body composition is a more accurate measure of obesity. This study aims to investigate the effects of Computed tomography (CT)--based morphemic factors on PTDM and establish a prediction model for PTDM after kidney transplantation.</p><p><strong>Methods: </strong>The pre-transplant data and glycemic levels of kidney transplant recipients (June 2021 to July 2023) were retrospectively and prospectively collected. Univariate and multivariate analyses were conducted to analyze the relationship between morphemic factors and PTDM at one month, six months, and one year after hospital discharge. Subsequently, a one-year risk prediction model based on morphemic factors was developed.</p><p><strong>Results: </strong>The study consisted of 131 participants in the one-month group, where Hemoglobin A1c (HbA1c) (p = 0.02) was identified as the risk factor for PTDM. In the six-month group, 129 participants were included, and the intermuscular adipose tissue (IMAT) area (p = 0.02) was identified as the risk factor for PTDM. The one-year group had 128 participants, and the risk factors for PTDM were identified as body mass index (BMI) (p = 0.02), HbA1c (p = 0.01), and IMAT area (p = 0.007). HbA1c (%) and IMAT area were included in the risk prediction Model for PTDM in the one-year group with AUC = 0.716 (95 % CI 0.591-0.841, p = 0.001).</p><p><strong>Conclusions: </strong>Compared to BMI and other morphemic factors, this study demonstrated that the IMAT area was the most potential predictor of PTDM.</p><p><strong>Clinical trial notation: </strong>Chictr.org (ChiCTR2300078639).</p>\",\"PeriodicalId\":55454,\"journal\":{\"name\":\"Asian Journal of Surgery\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Asian Journal of Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.asjsur.2024.08.075\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SURGERY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Journal of Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.asjsur.2024.08.075","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SURGERY","Score":null,"Total":0}
Computed tomography-based intermuscular adipose tissue analysis and its predicting role in post-kidney transplantation diabetes mellitus.
Background: While body mass index (BMI) is the most widely used indicator as a measure of obesity factors in post-transplantation diabetes mellitus (PTDM), body composition is a more accurate measure of obesity. This study aims to investigate the effects of Computed tomography (CT)--based morphemic factors on PTDM and establish a prediction model for PTDM after kidney transplantation.
Methods: The pre-transplant data and glycemic levels of kidney transplant recipients (June 2021 to July 2023) were retrospectively and prospectively collected. Univariate and multivariate analyses were conducted to analyze the relationship between morphemic factors and PTDM at one month, six months, and one year after hospital discharge. Subsequently, a one-year risk prediction model based on morphemic factors was developed.
Results: The study consisted of 131 participants in the one-month group, where Hemoglobin A1c (HbA1c) (p = 0.02) was identified as the risk factor for PTDM. In the six-month group, 129 participants were included, and the intermuscular adipose tissue (IMAT) area (p = 0.02) was identified as the risk factor for PTDM. The one-year group had 128 participants, and the risk factors for PTDM were identified as body mass index (BMI) (p = 0.02), HbA1c (p = 0.01), and IMAT area (p = 0.007). HbA1c (%) and IMAT area were included in the risk prediction Model for PTDM in the one-year group with AUC = 0.716 (95 % CI 0.591-0.841, p = 0.001).
Conclusions: Compared to BMI and other morphemic factors, this study demonstrated that the IMAT area was the most potential predictor of PTDM.
期刊介绍:
Asian Journal of Surgery, launched in 1978, is the official peer-reviewed open access journal of the Asian Surgical Association, the Taiwan Robotic Surgery Association, and the Taiwan Society of Coloproctology. The Journal is published monthly by Elsevier and is indexed in SCIE, Medline, ScienceDirect, Scopus, Embase, Current Contents, PubMed, Current Abstracts, BioEngineering Abstracts, SIIC Data Bases, CAB Abstracts, and CAB Health.
ASJSUR has a growing reputation as an important medium for the dissemination of cutting-edge developments in surgery and its related disciplines in the Asia-Pacific region and beyond. Studies on state-of-the-art surgical innovations across the entire spectrum of clinical and experimental surgery are particularly welcome.
The journal publishes original articles, review articles, and case reports that are of exceptional and unique importance. The journal publishes original articles, review articles, and case reports that are of exceptional and unique importance.