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Sung-Hee Ihm  (Ihm SH) 2 Articles
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A Potential Issue with Screening Prediabetes or Diabetes Using Serum Glucose: A Delay in Diagnosis
Jun Goo Kang, Cheol-Young Park, Sung-Hee Ihm, Sung Woo Park
Diabetes Metab J. 2016;40(5):414-417.   Published online September 1, 2016
DOI: https://doi.org/10.4093/dmj.2016.40.5.414
  • 3,977 View
  • 53 Download
  • 11 Web of Science
  • 11 Crossref
AbstractAbstract PDFPubReader   

The aim of this study was to compare the fasting serum glucose level with the fasting plasma glucose level for diagnosing hyperglycemic states in real-life clinical situations. Additionally, we investigated a usual delay in sample processing and how such delays can impact the diagnosis of hyperglycemic states. Among 1,254 participants who had normoglycemia or impaired fasting glucose (IFG) assessed by the fasting serum glucose level, 20.9% were newly diagnosed with diabetes based on the plasma fasting glucose level. Of the participants with normoglycemia, 62.1% and 14.2% were newly diagnosed with IFG and diabetes, respectively, according to the plasma fasting glucose level. In our clinical laboratory for performing health examinations, the time delay from blood sampling to glycemic testing averaged 78±52 minutes. These findings show that the ordinary time delay for sample processing of the serum glucose for screening hyperglycemic states may be an important reason for these diagnoses to be underestimated in Korea.

Citations

Citations to this article as recorded by  
  • Comparing glycemic traits in defining diabetes among rural Chinese older adults
    Pin Wang, Yuanjing Li, Mingqi Wang, Lin Song, Yi Dong, Xiaolei Han, Jaakko Tuomilehto, Yongxiang Wang, Yifeng Du, Chengxuan Qiu, Fredirick Lazaro mashili
    PLOS ONE.2024; 19(1): e0296694.     CrossRef
  • An Overview of Pre-Analytical Factors Impacting Metabolomics Analyses of Blood Samples
    Amy Thachil, Li Wang, Rupasri Mandal, David Wishart, Tom Blydt-Hansen
    Metabolites.2024; 14(9): 474.     CrossRef
  • Serum glucose, a cost-effective alternate of plasma glucose in diagnosing and monitoring diabetes mellitus
    Vivek Pant, Anders Kallner
    International Journal of Diabetes in Developing Countries.2023; 43(3): 377.     CrossRef
  • Hyperglycemia and prematurity: a narrative review
    Dimitrios Angelis, Mambarambath A. Jaleel, Luc P. Brion
    Pediatric Research.2023; 94(3): 892.     CrossRef
  • Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021
    Kanyin Liane Ong, Lauryn K Stafford, Susan A McLaughlin, Edward J Boyko, Stein Emil Vollset, Amanda E Smith, Bronte E Dalton, Joe Duprey, Jessica A Cruz, Hailey Hagins, Paulina A Lindstedt, Amirali Aali, Yohannes Habtegiorgis Abate, Melsew Dagne Abate, Mo
    The Lancet.2023; 402(10397): 203.     CrossRef
  • PM2.5 exposure increases the risk of preterm birth in pre-pregnancy impaired fasting glucose women: A cohort study in a Southern province of China
    Zhijiang Liang, Lina Zhao, Jialing Qiu, Xinhong Zhu, Min Jiang, Guocheng Liu, Qingguo Zhao
    Environmental Research.2022; 204: 112403.     CrossRef
  • The impact of maternal prepregnancy impaired fasting glucose on preterm birth and large for gestational age: a large population-based cohort study
    Jie Tang, Xinhong Zhu, Mingzhen Li, Dongming Huang, Qingguo Zhao
    American Journal of Obstetrics and Gynecology.2020; 222(3): 265.e1.     CrossRef
  • Utility of point‐of‐care vs reference laboratory testing for the evaluation of glucose levels
    O. M. Andriankaja, F. J. Muñoz‐Torres, J. L. Vergara, C. M. Pérez, K. Joshipura
    Diabetic Medicine.2019; 36(5): 626.     CrossRef
  • Pre-analytical factors in blood glucose measurement
    Alpesh Goyal, Yashdeep Gupta
    Diabetes Research and Clinical Practice.2019; 158: 107802.     CrossRef
  • Long-term stability of glucose: glycolysis inhibitor vs. gel barrier tubes
    Theresa Winter, Anke Hannemann, Juliane Suchsland, Matthias Nauck, Astrid Petersmann
    Clinical Chemistry and Laboratory Medicine (CCLM).2018; 56(8): 1251.     CrossRef
  • Blood Glucose Measurement: Is Serum Equal to Plasma?
    Hye Soon Kim
    Diabetes & Metabolism Journal.2016; 40(5): 365.     CrossRef
Epidemiology
Glycosylated Hemoglobin Threshold for Predicting Diabetes and Prediabetes from the Fifth Korea National Health and Nutrition Examination Survey
Sangmo Hong, Jun Goo Kang, Chul Sik Kim, Seong Jin Lee, Cheol-Young Park, Chang Beom Lee, Sung-Hee Ihm
Diabetes Metab J. 2016;40(2):167-170.   Published online April 5, 2016
DOI: https://doi.org/10.4093/dmj.2016.40.2.167
  • 3,407 View
  • 41 Download
  • 1 Web of Science
  • 2 Crossref
AbstractAbstract PDFPubReader   

We aimed to estimate the threshold level of glycosylated hemoglobin (HbA1c) for the fasting plasma glucose of 100 and 126 mg/dL in the Korean adult population, using the 2011 Korea National Health and Nutrition Examination Survey. A total of 4,481 participants over 19 years of age without diabetic medications and conditions to influence the interpretation of HbA1c levels, such as anemia, renal insufficiency, liver cirrhosis, and cancers, were analyzed. A point-wise area under the receiver operating characteristic curve was used to estimate the optimal HbA1c cutoff value. A HbA1c threshold of 6.35% was optimal for predicting diabetes with a sensitivity of 86.9% and a specificity of 99.1%. Furthermore, the threshold of HbA1c was 5.65% for prediabetes, with a sensitivity of 69.3% and a specificity of 71%. Further prospective studies are needed to evaluate the HbA1c cutoff point for diagnosing prediabetes and diabetes in the Korean population.

Citations

Citations to this article as recorded by  
  • The Cutoff Value of HbA1c in Predicting Diabetes and Impaired Fasting Glucose
    Seyoung Kwon, Youngak Na
    The Korean Journal of Clinical Laboratory Science.2017; 49(2): 114.     CrossRef
  • Cutoff Point of HbA1c for Diagnosis of Diabetes Mellitus in Chinese Individuals
    Bing Wang, Ming-Chuan Liu, Xin-Yu Li, Xu-Han Liu, Qiu-Xia Feng, Lu Lu, Zhu Zhu, Ying-Shu Liu, Wei Zhao, Zheng-Nan Gao, Noel Christopher Barengo
    PLOS ONE.2016; 11(11): e0166597.     CrossRef

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