Diabetes & Metabolism Journal

Brief Report

Metabolic Risk/Epidemiology
Trends in the Prevalence of Obesity and Its Phenotypes Based on the Korea National Health and Nutrition Examination Survey from 2007 to 2017 in Korea: Sang Ouk Chin, You-Cheol Hwang, Hong-Yup Ahn, Ji Eun Jun, In-Kyung Jeong, Kyu Jeung Ahn, Ho Yeon Chung; Diabetes Metab J. 2022;46(5):808-812. Published online March 8, 2022; DOI: https://doi.org/10.4093/dmj.2021.0226

4,824 View
227 Download
3 Web of Science
3 Crossref

Abstract PDF Supplementary Material PubReader ePub

This study used data from the Korea National Health and Nutrition Examination Survey IV–VII from 2007 to identify the prevalence of obesity and its phenotypes (metabolically unhealthy obesity [MUO] and metabolically healthy obesity [MHO]) and their secular changes. The prevalence of obesity in Korea increased with significant secular changes observed (β=0.326, P trend <0.01) between 2007 and 2017, and especially in men (β=0.682, P trend <0.001) but not in women. The changes in the prevalence of obesity during the study period were different between men and women (P=0.001). The prevalence of MUO significantly increased only in men (β=0.565, P trend <0.01), while that of MHO increased only in women (β=0.179, P<0.05), especially in the younger age group (β=0.308, P<0.01).

Citations

Citations to this article as recorded by

Link between obesity and growth in children and adolescents
Hae Sang Lee
Journal of the Korean Medical Association.2024; 67(5): 330. CrossRef
Hormonal Gut–Brain Signaling for the Treatment of Obesity
Eun Roh, Kyung Mook Choi
International Journal of Molecular Sciences.2023; 24(4): 3384. CrossRef
Differences of Regional Fat Distribution Measured by Magnetic Resonance Imaging According to Obese Phenotype in Koreans
Ha-Neul Choi, Hyunjung Lim, Young-Seol Kim, Sang-Youl Rhee, Jung-Eun Yim
Metabolic Syndrome and Related Disorders.2022; 20(10): 551. CrossRef

Original Articles

Obesity and Metabolic Syndrome
Air Pollution Has a Significant Negative Impact on Intentional Efforts to Lose Weight: A Global Scale Analysis: Morena Ustulin, So Young Park, Sang Ouk Chin, Suk Chon, Jeong-taek Woo, Sang Youl Rhee; Diabetes Metab J. 2018;42(4):320-329. Published online April 24, 2018; DOI: https://doi.org/10.4093/dmj.2017.0104

5,095 View
43 Download
6 Web of Science
8 Crossref

Abstract PDF PubReader

Background

Air pollution causes many diseases and deaths. It is important to see how air pollution affects obesity, which is common worldwide. Therefore, we analyzed data from a smartphone application for intentional weight loss, and then we validated them.

Methods

Our analysis was structured in two parts. We analyzed data from a cohort registered to a smartphone application in 10 large cities of the world and matched it with the annual pollution values. We validated these results using daily pollution data in United States and matching them with user information. Body mass index (BMI) variation between final and initial login time was considered as outcome in the first part, and daily BMI in the validation. We analyzed: daily calories intake, daily weight, daily physical activity, geographical coordinates, seasons, age, gender. Weather Underground application programming interface provided daily climatic values. Annual and daily values of particulate matter PM10 and PM2.5 were extracted. In the first part of the analysis, we used 2,608 users and then 995 users located in United States.

Results

Air pollution was highest in Seoul and lowest in Detroit. Users decreased BMI by 2.14 kg/m² in average (95% confidence interval, −2.26 to −2.04). From a multilevel model, PM10 (β=0.04, P=0.002) and PM2.5 (β=0.08, P<0.001) had a significant negative effect on weight loss when collected per year. The results were confirmed with the validation (β_AQI*time=1.5×10^–5; P<0.001) by mixed effects model.

Conclusion

This is the first study that shows how air pollution affects intentional weight loss applied on wider area of the world.

Citations

Citations to this article as recorded by

What could be the reasons for not losing weight even after following a weight loss program?
Jyoti Dabas, S. Shunmukha Priya, Akshay Alawani, Praveen Budhrani
Journal of Health, Population and Nutrition.2024;[Epub] CrossRef
Subchronic exposure to 1,2-naphthoquinone induces adipose tissue inflammation and changes the energy homeostasis of mice, partially due to TNFR1 and TLR4
Clílton Kraüss de Oliveira Ferreira, Clara Machado Campolim, Olívia Pizetta Zordão, Fernando Moreira Simabuco, Chadi Pellegrini Anaruma, Rodrigo Martins Pereira, Vitor Ferreira Boico, Luiz Guilherme Salvino, Maíra Maftoum Costa, Nathalia Quintero Ruiz, Le
Toxicology Reports.2023; 11: 10. CrossRef
Effects of Ambient Particulate Matter (PM2.5) Exposure on Calorie Intake and Appetite of Outdoor Workers
Thavin Kumar Mathana Sundram, Eugenie Sin Sing Tan, Hwee San Lim, Farahnaz Amini, Normina Ahmad Bustami, Pui Yee Tan, Navedur Rehman, Yu Bin Ho, Chung Keat Tan
Nutrients.2022; 14(22): 4858. CrossRef
Efficiency in reducing air pollutants and healthcare expenditure in the Seoul Metropolitan City of South Korea
Subal C. Kumbhakar, Jiyeon An, Masoomeh Rashidghalam, Almas Heshmati
Environmental Science and Pollution Research.2021; 28(20): 25442. CrossRef
Seasonal variation and trends in the Internet searches for losing weight: An infodemiological study
Ying Teng, Shun-Wei Huang, Zhen Li, Qiao-Mei Xie, Man Zhang, Qiu-Yue Lou, Fang Wang, Yan-Feng Zou
Obesity Research & Clinical Practice.2020; 14(3): 225. CrossRef
Estimation of health benefits from air quality improvement using the MODIS AOD dataset in Seoul, Korea
Daeun Kim, Jeongyeong Kim, Jaehwan Jeong, Minha Choi
Environmental Research.2019; 173: 452. CrossRef
Appropriate Medical Technology in the Era of the 4th Industrial Revolution
Sang Youl Rhee
The Korean Journal of Medicine.2019; 94(5): 387. CrossRef
Can Air Pollution Biologically Hinder Efforts to Lose Body Weight?
Duk-Hee Lee
Diabetes & Metabolism Journal.2018; 42(4): 282. CrossRef

A Smartphone Application Significantly Improved Diabetes Self-Care Activities with High User Satisfaction: Yu Jin Kim, Sang Youl Rhee, Jong Kyu Byun, So Young Park, Soo Min Hong, Sang Ouk Chin, Suk Chon, Seungjoon Oh, Jeong-taek Woo, Sung Woon Kim, Young Seol Kim; Diabetes Metab J. 2015;39(3):207-217. Published online April 22, 2015; DOI: https://doi.org/10.4093/dmj.2015.39.3.207

10,837 View
71 Download
39 Web of Science
48 Crossref

Abstract PDF PubReader

Background

We developed for the first time a smartphone application designed for diabetes self-management in Korea and registered a patent for the relevant algorithm. We also investigated the user satisfaction with the application and the change in diabetes related self-care activities after using the application.

Methods

We conducted a questionnaire survey on volunteers with diabetes who were using the application. Ninety subjects responded to the questionnaire between June 2012 and March 2013. A modified version of the Summary of Diabetes Self-Care Activities (SDSCA) was used in this study.

Results

The survey results exhibited a mean subject age of 44.0 years old, and males accounted for 78.9% of the subjects. Fifty percent of the subjects had diabetes for less than 3 years. The majority of respondents experienced positive changes in their clinical course after using the application (83.1%) and were satisfied with the structure and completeness of the application (86.7%). Additionally, the respondents' answers indicated that the application was easy to use (96.7%) and recommendable to others (97.7%) and that they would continue using the application to manage their diabetes (96.7%). After using the Diabetes Notepad application, diabetes related self-care activities assessed by SDSCA displayed statistically significant improvements (P<0.05), except for the number of days of drinking.

Conclusion

This smartphone-based application can be a useful tool leading to positive changes in diabetes related self-care activities and increase user satisfaction.

Citations

Citations to this article as recorded by

Time Efficiency, Reliability, and User Satisfaction of the Tooth Memo App for Recording Oral Health Information: Cross-Sectional Questionnaire Study
Palinee Detsomboonrat, Pagaporn Pantuwadee Pisarnturakit
JMIR Formative Research.2024; 8: e56143. CrossRef
Weight Management Health Note, a Mobile Health Platform for Obesity Management Developed by the Korean Society for the Study of Obesity
Yujung Lee, Hyunji Sang, Sunyoung Kim, Doo Ah Choi, Sang Youl Rhee
Journal of Obesity & Metabolic Syndrome.2024; 33(1): 1. CrossRef
Relationship between Diabetes Self-Management and the Use of Health Care Apps: A Cross-Sectional Study
Satoshi Inagaki, Kenji Kato, Kozue Abe, Hiroaki Takahashi, Tomokazu Matsuda
ACI Open.2023; 07(01): e23. CrossRef
The effect of mHealth program on behavior modification and health outcomes among patients with diabetes: A randomized controlled trial study
Mohd Khairul Zul Hasymi Firdaus, Piyanuch Jittanoon, Umaporn Boonyasopun, Muhammad Kamil Che Hasan
Belitung Nursing Journal.2023; 9(5): 437. CrossRef
Clinical Effects of a Home Care Pilot Program for Patients with Type 1 Diabetes Mellitus: A Retrospective Cohort Study
Sejeong Lee, KyungYi Kim, Ji Eun Kim, Yura Hyun, Minyoung Lee, Myung-Il Hahm, Sang Gyu Lee, Eun Seok Kang
Diabetes & Metabolism Journal.2023; 47(5): 693. CrossRef
Mobile-Based Application Interventions to Enhance Cancer Control and Care in Low- and Middle-Income Countries: A Systematic Review
Andrew Donkor, Jennifer Akyen Ayitey, Prince Nyansah Adotey, Esther Oparebea Ofori, Doris Kitson-Mills, Verna Vanderpuye, Samuel Yaw Opoku, Tim Luckett, Meera R. Agar, Penelope Engel-Hills
International Journal of Public Health.2023;[Epub] CrossRef
Effect of an Integrative Mobile Health Intervention in Patients With Hypertension and Diabetes: Crossover Study
Sang Woo Oh, Kyoung-Kon Kim, Sung Soo Kim, Su Kyung Park, Sangshin Park
JMIR mHealth and uHealth.2022; 10(1): e27192. CrossRef
Evaluation of a Smart After-Care Program for Patients with Lung Cancer: A Prospective, Single-Arm Pilot Study
Hee Chul Yang, Seung Hyun Chung, Ji Sung Yoo, Boram Park, Moon Soo Kim, Jong Mog Lee
Journal of Chest Surgery.2022; 55(2): 108. CrossRef
Personalized Nutrition for the Prevention and Treatment of Metabolic Diseases: Opportunities and Perspectives
I. N. Napolsky, P. V. Popova
Russian Journal for Personalized Medicine.2022; 2(1): 15. CrossRef
The effect of a mobile application on the foot care of individuals with type 2 diabetes: A randomised controlled study
Berna Dincer, Nefise Bahçecik
Health Education Journal.2021; 80(4): 425. CrossRef
Effectiveness of Disease-Specific mHealth Apps in Patients With Diabetes Mellitus: Scoping Review
Claudia Eberle, Maxine Löhnert, Stefanie Stichling
JMIR mHealth and uHealth.2021; 9(2): e23477. CrossRef
Diabetology 4.0: Scoping Review of Novel Insights and Possibilities Offered by Digitalization
Claudia Eberle, Stefanie Stichling, Maxine Löhnert
Journal of Medical Internet Research.2021; 23(3): e23475. CrossRef
Mobile Healthcare System Provided by Primary Care Physicians Improves Quality of Diabetes Care
Tae Jung Oh, Jie-Eun Lee, Seok Kim, Sooyoung Yoo, Hak Chul Jang
CardioMetabolic Syndrome Journal.2021; 1(1): 88. CrossRef
A Personalized Mobile Health Program for Type 2 Diabetes During the COVID-19 Pandemic: Single-Group Pre–Post Study
Ian Yi Han Ang, Kyle Xin Quan Tan, Clive Tan, Chiew Hoon Tan, James Wei Ming Kwek, Joanne Tay, Sue Anne Toh
JMIR Diabetes.2021; 6(3): e25820. CrossRef
The use of mobile health interventions for gestational diabetes mellitus: a descriptive literature review
Maryam Zahmatkeshan, Somayyeh Zakerabasali, Mojtaba Farjam, Yousef Gholampour, Maryam Seraji, Azita Yazdani
Journal of Medicine and Life.2021; 14(2): 131. CrossRef
Methods and Measures Used to Evaluate Patient-Operated Mobile Health Interventions: Scoping Literature Review
Meghan Bradway, Elia Gabarron, Monika Johansen, Paolo Zanaboni, Patricia Jardim, Ragnar Joakimsen, Louise Pape-Haugaard, Eirik Årsand
JMIR mHealth and uHealth.2020; 8(4): e16814. CrossRef
Effect of a Mobile Phone–Based Glucose-Monitoring and Feedback System for Type 2 Diabetes Management in Multiple Primary Care Clinic Settings: Cluster Randomized Controlled Trial
Yeoree Yang, Eun Young Lee, Hun-Sung Kim, Seung-Hwan Lee, Kun-Ho Yoon, Jae-Hyoung Cho
JMIR mHealth and uHealth.2020; 8(2): e16266. CrossRef
Methods and Evaluation Criteria for Apps and Digital Interventions for Diabetes Self-Management: Systematic Review
Dillys Larbi, Pietro Randine, Eirik Årsand, Konstantinos Antypas, Meghan Bradway, Elia Gabarron
Journal of Medical Internet Research.2020; 22(7): e18480. CrossRef
Evaluating the effect of a smartphone app-based self-management program for people with COPD: A randomized controlled trial
Soo Kyung Park, Cho Hee Bang, Seung Hyeun Lee
Applied Nursing Research.2020; 52: 151231. CrossRef
Development of an mHealth application for family carers of people with dementia: A study protocol
Sarath Rathnayake, Wendy Moyle, Cindy Jingwen Jones, Pauline Calleja
Collegian.2019; 26(2): 295. CrossRef
Use of Weight-Management Mobile Phone Apps in Saudi Arabia: A Web-Based Survey
Ghadeer S Aljuraiban
JMIR mHealth and uHealth.2019; 7(2): e12692. CrossRef
Appropriate Medical Technology in the Era of the 4th Industrial Revolution
Sang Youl Rhee
The Korean Journal of Medicine.2019; 94(5): 387. CrossRef
Popular Diabetes Apps and the Impact of Diabetes App Use on Self-Care Behaviour: A Survey Among the Digital Community of Persons With Diabetes on Social Media
Mihiretu M. Kebede, Claudia R. Pischke
Frontiers in Endocrinology.2019;[Epub] CrossRef
Development and Evaluation: The Satisfaction of Using an Oral Health Survey Mobile Application
Palinee Detsomboonrat, Pagaporn Pantuwadee Pisarnturakit
Telemedicine and e-Health.2019; 25(1): 55. CrossRef
Usability of a Disease Management Mobile Application as Perceived by Patients With Diabetes
Roqaieh Janatkhah, Rasoul Tabari-Khomeiran, Attaolah Asadi-Louyeh, Ehsan Kazemnejad
CIN: Computers, Informatics, Nursing.2019; 37(8): 413. CrossRef
Structure and Characteristics of Diabetes Self-management Applications
Sara Angelini, Gregorio Marco Alicastro, Sara Dionisi, Marco Di Muzio
CIN: Computers, Informatics, Nursing.2019; 37(7): 340. CrossRef
Digital Health Transformation of Integrated Care in Europe: Overarching Analysis of 17 Integrated Care Programs
Erik Baltaxe, Thomas Czypionka, Markus Kraus, Miriam Reiss, Jan Erik Askildsen, Renata Grenkovic, Tord Skogedal Lindén, János György Pitter, Maureen Rutten-van Molken, Oscar Solans, Jonathan Stokes, Verena Struckmann, Josep Roca, Isaac Cano
Journal of Medical Internet Research.2019; 21(9): e14956. CrossRef
A Mobile Prenatal Care App to Reduce In-Person Visits: Prospective Controlled Trial
Kathryn I Marko, Nihar Ganju, Jill M Krapf, Nancy D Gaba, James A Brown, Joshua J Benham, Julia Oh, Lorna M Richards, Andrew C Meltzer
JMIR mHealth and uHealth.2019; 7(5): e10520. CrossRef
Users’ preferences and design recommendations to promote engagements with mobile apps for diabetes self-management: Multi-national perspectives
Mary D. Adu, Usman H. Malabu, Aduli E. O. Malau-Aduli, Bunmi S. Malau-Aduli, Simone Borsci
PLOS ONE.2018; 13(12): e0208942. CrossRef
Developing and Clinical Application of a Smartphone Mobile Mood Chart Application in Korean for Patients with Bipolar Disorder
Kayoung Song, Saejeong Lee, Woon Yoon, Changyoon Kim, Yeonho Joo, Jungsun Lee, Myong-Wuk Chon
Journal of Korean Neuropsychiatric Association.2018; 57(3): 244. CrossRef
mHealth Tools for the Self-Management of Patients With Multimorbidity in Primary Care Settings: Pilot Study to Explore User Experience
Anum Irfan Khan, Ashlinder Gill, Cheryl Cott, Parminder Kaur Hans, Carolyn Steele Gray
JMIR mHealth and uHealth.2018; 6(8): e171. CrossRef
Rules of engagement in mobile health: what does mobile health bring to research and theory?
Cigdem Sahin
Contemporary Nurse.2018; 54(4-5): 374. CrossRef
Diabetes Mellitus m-Health Applications: A Systematic Review of Features and Fundamentals
Ericles Andrei Bellei, Daiana Biduski, Nathália Pinto Cechetti, Ana Carolina Bertoletti De Marchi
Telemedicine and e-Health.2018; 24(11): 839. CrossRef
Current Status and Effects of Nutrition Education Programs for Diabetic Patients in Korea
Hae Jin Kang
The Journal of Korean Diabetes.2018; 19(2): 106. CrossRef
Mobile direct observation of therapy (MDOT) - A rapid systematic review and pilot study in children with asthma
Michael D. Shields, Fahad ALQahtani, Michael P. Rivey, James C. McElnay, Jacobus P. van Wouwe
PLOS ONE.2018; 13(2): e0190031. CrossRef
App-basiertes Selbstmonitoring bei Typ-2-Diabetes
A. Steinert, M. Haesner, E. Steinhagen-Thiessen
Zeitschrift für Gerontologie und Geriatrie.2017; 50(6): 516. CrossRef
Smartphone App Use for Diabetes Management: Evaluating Patient Perspectives
Kirstie Lithgow, Alun Edwards, Doreen Rabi
JMIR Diabetes.2017; 2(1): e2. CrossRef
Needs for Development of IT-based Nutritional Management Program for Women with Gestational Diabetes Mellitus
Chan-Jung Han, Sun-Young Lim, Eunsuk Oh, Yoon-Hee Choi, Kun-Ho Yoon, Jin-Hee Lee
Korean Journal of Community Nutrition.2017; 22(3): 207. CrossRef
Semantic representation and processing of hypoglycemic events derived from wearable sensor data
Jean-Paul Calbimonte, Jean-Eudes Ranvier, Fabien Dubosson, Karl Aberer, Antonis Bikakis, Thanos G. Stavropoulos, Georgios Meditskos
Journal of Ambient Intelligence and Smart Environments.2017; 9(1): 97. CrossRef
Acceptability and Feasibility of a Smartphone Application for 5th, 6th Grade Elementary Students to Prevent Childhood Obesity; a Qualitative Study
Ji-Hye Jung, Young-Gyu Cho, Da-Ye Ji, Jae-Heon Kang
Korean Journal of Health Promotion.2016; 16(4): 251. CrossRef
m-santé francophone et diabète : mise au point
M. Halbron, M. Joubert, E. Sonnet
Médecine des Maladies Métaboliques.2016; 10(3): 243. CrossRef
Successful weight reduction and maintenance by using a smartphone application in those with overweight and obesity
Sang Ouk Chin, Changwon Keum, Junghoon Woo, Jehwan Park, Hyung Jin Choi, Jeong-taek Woo, Sang Youl Rhee
Scientific Reports.2016;[Epub] CrossRef
The Wills Eye Glaucoma App: Interest of Patients and Their Caregivers in a Smartphone-based and Tablet-based Glaucoma Application
Michael Waisbourd, Hermandeep Dhami, Chen Zhou, Michael Hsieh, Pramod Abichandani, Michael J. Pro, Marlene R. Moster, L. Jay Katz, Lisa A. Hark, Jonathan S. Myers
Journal of Glaucoma.2016; 25(9): e787. CrossRef
Testing the Feasibility of Remote Patient Monitoring in Prenatal Care Using a Mobile App and Connected Devices: A Prospective Observational Trial
Kathryn I Marko, Jill M Krapf, Andrew C Meltzer, Julia Oh, Nihar Ganju, Anjali G Martinez, Sheetal G Sheth, Nancy D Gaba
JMIR Research Protocols.2016; 5(4): e200. CrossRef
Social Networking Services-Based Communicative Care for Patients with Diabetes Mellitus in Korea
Hun-Sung Kim, Yoo Jeong, Sun Baik, So Yang, Tong Kim, Hyunah Kim, Hyunyong Lee, Seung-Hwan Lee, Jae Cho, In-Young Choi, Kun-Ho Yoon
Applied Clinical Informatics.2016; 07(03): 899. CrossRef
Efficacy of a New Medical Information system, Ubiquitous Healthcare Service with Voice Inception Technique in Elderly Diabetic Patients
Kyoung Min Kim, Kyeong Seon Park, Hyun Ju Lee, Yun Hee Lee, Ji Seon Bae, Young Joon Lee, Sung Hee Choi, Hak Chul Jang, Soo Lim
Scientific Reports.2015;[Epub] CrossRef
Six-month Outcomes of Mobile Phone Application-based Self-management in a Patient with Type 2 Diabetes
Mi Kyeong Hong, Young Yun Cho, Mi Yong Rha, Jae Hyeon Kim, Moon-Kyu Lee
Clinical Nutrition Research.2015; 4(3): 201. CrossRef
Physical Activity Capture Technology With Potential for Incorporation Into Closed-Loop Control for Type 1 Diabetes
Vikash Dadlani, James A. Levine, Shelly K. McCrady-Spitzer, Eyal Dassau, Yogish C. Kudva
Journal of Diabetes Science and Technology.2015; 9(6): 1208. CrossRef

Risk Factors for the Progression of Intima-Media Thickness of Carotid Arteries: A 2-Year Follow-Up Study in Patients with Newly Diagnosed Type 2 Diabetes: Sang Ouk Chin, Jin Kyung Hwang, Sang Youl Rhee, Suk Chon, You-Cheol Hwang, Seungjoon Oh, Kyu Jeung Ahn, Ho Yeon Chung, Jeong-taek Woo, Sung-Woon Kim, Young Seol Kim, Ja-Heon Kang, In-Kyung Jeong; Diabetes Metab J. 2013;37(5):365-374. Published online October 17, 2013; DOI: https://doi.org/10.4093/dmj.2013.37.5.365

5,397 View
40 Download
6 Crossref

Abstract PDF Supplementary Material PubReader

Background

Intima-media thickness (IMT) of the carotid arteries is known to have a positive correlation with the risk of cardiovascular disease. This study was designed to identify risk factors affecting the progression of carotid IMT in patients with type 2 diabetes mellitus (T2DM).

Methods

Patients with newly diagnosed T2DM with carotid IMT measurements were enrolled, and their clinical data and carotid IMT results at baseline and 2 years later were compared.

Results

Of the 171 patients, 67.2% of males and 50.8% of females had abnormal baseline IMT of the left common carotid artery. At baseline, systolic blood pressure, body mass index and smoking in male participants, and fasting plasma glucose and glycated hemoglobin levels in females were significantly higher in patients with abnormal IMT than in those with normal IMT. Low density lipoprotein cholesterol (LDL-C) levels in males and high density lipoprotein cholesterol (HDL-C) levels in females at the 2-year follow-up were significantly different between the nonprogression and the progression groups. Reduction of the United Kingdom Prospective Diabetes Study (UKPDS) 10-year coronary heart disease (CHD) risk score after 2 years was generally higher in the nonprogression group than the progression group.

Conclusion

LDL-C levels in males and HDL-C levels in females at the 2-year follow-up were significantly different between participants with and without progression of carotid IMT. Furthermore, a reduction in the UKPDS 10-year CHD risk score appeared to delay the advancement of atherosclerosis. Therefore, the importance of establishing the therapeutic goal of lipid profiles should be emphasized to prevent the progression of carotid IMT in newly diagnosed T2DM patients.

Citations

Citations to this article as recorded by

Comparison of the Effectiveness of Low Carbohydrate Versus Low Fat Diets, in Type 2 Diabetes: Systematic Review and Meta-Analysis of Randomized Controlled Trials
Tanefa A. Apekey, Maria J. Maynard, Monia Kittana, Setor K. Kunutsor
Nutrients.2022; 14(20): 4391. CrossRef
Nomogram Based on Risk Factors for Type 2 Diabetes Mellitus Patients with Coronary Heart Disease

Rong Shi, Birong Wu, Zheyun Niu, Hui Sun, Fan Hu
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2020; Volume 13: 5025. CrossRef
HMGA1 Mediated High-Glucose-Induced Vascular Smooth Muscle Cell Proliferation in Diabetes Mellitus: Association Between PI3K/Akt Signaling and HMGA1 Expression
Qinghai Zhang, Ling Chen, Zhibo Zhao, Ying Wu, Jing Zhong, Gebo Wen, Renxian Cao, Xuyu Zu, Jianghua Liu
DNA and Cell Biology.2018; 37(4): 389. CrossRef
Relationship between frequency of hypoglycemic episodes and changes in carotid atherosclerosis in insulin-treated patients with type 2 diabetes mellitus
Tomoya Mita, Naoto Katakami, Toshihiko Shiraiwa, Hidenori Yoshii, Nobuichi Kuribayashi, Takeshi Osonoi, Hideaki Kaneto, Keisuke Kosugi, Yutaka Umayahara, Masahiko Gosho, Iichiro Shimomura, Hirotaka Watada
Scientific Reports.2017;[Epub] CrossRef
Impact of carotid atherosclerosis detection on physician and patient behavior in the management of type 2 diabetes mellitus: a prospective, observational, multicenter study
In-Kyung Jeong, Sin-Gon Kim, Dong Hyeok Cho, Chong Hwa Kim, Chul Sik Kim, Won-Young Lee, Kyu-Chang Won, Doo-Man Kim
BMC Cardiovascular Disorders.2016;[Epub] CrossRef
The effect of fibroblast growth factors and advanced glycation end-products on the intima-media complex thickness in patients with coronary heart disease and type 2 diabetes
Ekaterina Vladimirovna Ivannikova, Victor Yurievich Kalashnikov, Olga Mikhailovna Smirnova, Alexander Borisovich Kuznetsov, Сергей Anatolievich Terekhin, Alexander Viktorovich Il'in
Diabetes mellitus.2014; 17(2): 47. CrossRef

Hemoglobin A1c May Be an Inadequate Diagnostic Tool for Diabetes Mellitus in Anemic Subjects: Jung Il Son, Sang Youl Rhee, Jeong-taek Woo, Jin Kyung Hwang, Sang Ouk Chin, Suk Chon, Seungjoon Oh, Sung Woon Kim, Young Seol Kim; Diabetes Metab J. 2013;37(5):343-348. Published online October 17, 2013; DOI: https://doi.org/10.4093/dmj.2013.37.5.343

6,346 View
74 Download
35 Crossref

Abstract PDF PubReader

Background

Recently, a hemoglobin A1c (HbA1c) level of 6.5% has been determined to be a criterion for diabetes mellitus (DM), and it is a widely used marker for the diagnosis of DM. However, HbA1c may be influenced by a number of factors. Anemia is one of the most prevalent diseases with an influence on HbA1c; however, its effect on HbA1c varies based on the variable pathophysiology of anemia. The aim of this study was to determine the effect of anemia on HbA1c levels.

Methods

Anemic subjects (n=112) and age- and sex-matched controls (n=217) who were drug naive and suspected of having DM were enrolled. The subjects underwent an oral glucose tolerance test and HbA1c simultaneously. We compared mean HbA1c and its sensitivity and specificity for diagnosing DM between each subgroup.

Results

Clinical characteristics were found to be similar between each subgroup. Also, when glucose levels were within the normal range, the difference in mean HbA1c was not significant (P=0.580). However, when plasma glucose levels were above the diagnostic cutoff for prediabetes and DM, the mean HbA1c of the anemic subgroup was modestly higher than in the nonanemic group. The specificity of HbA1c for diagnosis of DM was significantly lower in the anemic subgroup (P<0.05).

Conclusion

These results suggest that the diagnostic significance of HbA1c might be limited in anemic patients.

Citations

Citations to this article as recorded by

Unlocking Optimal Glycemic Interpretation: Redefining HbA1c Analysis in Female Patients With Diabetes and Iron‐Deficiency Anemia Using Machine Learning Algorithms
Kadra Mohamed Abdillahi, Fatma Ceyla Eraldemir, Irfan Kösesoy
Journal of Clinical Laboratory Analysis.2024;[Epub] CrossRef
Establishment of a feline glycated hemoglobin reference interval for a novel dried‐blood‐spot assay and the effects of anemia on assay results
Jocelyn Mott, Jacqueline K. Dolan, Chen Gilor, Shir Gilor
Veterinary Clinical Pathology.2023; 52(3): 531. CrossRef
Waist circumference and glycaemia are strong predictors of progression to diabetes in individuals with prediabetes in sub-Saharan Africa: 4-year prospective cohort study in Malawi
Wisdom P. Nakanga, Amelia C. Crampin, Joseph Mkandawire, Louis Banda, Rob C. Andrews, Andrew T. Hattersley, Moffat J. Nyirenda, Lauren R. Rodgers, Julia Robinson
PLOS Global Public Health.2023; 3(9): e0001263. CrossRef
Implications of Iron Deficiency Anaemia on Glycemic Dynamics in Diabetes Mellitus: A Critical Risk Factor in Cardiovascular Disease
Eman Elsheikh, Sereen S Aljohani , Munirah M Alshaikhmubarak, Meshari A Alhawl, Alhanouf W Alsubaie, Norah Alsultan, Asmaa F Sharif, Sayed Ibrahim Ali
Cureus.2023;[Epub] CrossRef
Serum Iron Profile in Type 2 Diabetes, A Role Beyond Anemic Marker!
Happy Chutia, Sungdirenla Jamir, Md Yasir, Gautam Handique
The Journal of Medical Research.2023; 9(5): 129. CrossRef
Large-scale retrospective analyses of the effect of iron deficiency anemia on hemoglobin A1c concentrations
Lokinendi V. Rao, George W. Pratt, Caixia Bi, Martin H. Kroll
Clinica Chimica Acta.2022; 529: 21. CrossRef
Glycemic Abnormalities Assessment on Children and Adolescents with Beta-Thalassemia Major
Siska Mayasari Lubis, Bidasari Lubis, Nadhira Anindita Ralena
Open Access Macedonian Journal of Medical Sciences.2022; 10(B): 697. CrossRef
Integrity loss of glycosylated hemoglobin with deepening anemia
Bünyamin AYDIN, Aysun GÖNDEREN
Journal of Health Sciences and Medicine.2022; 5(3): 839. CrossRef
Effect of anemia and erythrocyte indices on hemoglobin A1c levels among pregnant women
Zong-Hui Guo, Huai-Liang Tian, Xiao-Qian Zhang, Deng-Han Zhang, Zhi-Min Wang, Kun Wang, Wen-Wen Su, Fei Chen
Clinica Chimica Acta.2022; 534: 1. CrossRef
Genetic association of anthropometric traits with type 2 diabetes in ethnically endogamous Sindhi families
Manju Mamtani, Manisha T. Jaisinghani, Sujeet G. Jaiswal, Kanchan V. Pipal, Ashwini A. Patel, Hemant Kulkarni, Lee-Ling Lim
PLOS ONE.2021; 16(9): e0257390. CrossRef
Influence of red blood cell indices on HbA1c performance in detecting dysglycaemia in a Singapore preconception cohort study
See Ling Loy, Jinjie Lin, Yin Bun Cheung, Aravind Venkatesh Sreedharan, Xinyi Chin, Keith M. Godfrey, Kok Hian Tan, Lynette Pei-Chi Shek, Yap Seng Chong, Melvin Khee-Shing Leow, Chin Meng Khoo, Yung Seng Lee, Shiao-Yng Chan, Ngee Lek, Jerry Kok Yen Chan,
Scientific Reports.2021;[Epub] CrossRef
Increased risks of different grades of non‐alcoholic fatty liver disease in prediabetic subjects with impaired fasting glucose and glucose tolerance, including the isolated glycosylated hemoglobin levels of 5.7–6.4% in a Chinese population
Chung‐Hao Li, Yu‐Tsung Chou, Wei‐Chen Shen, Feng‐Hwa Lu, Yi‐Ching Yang, Jin‐Shang Wu, Chih‐Jen Chang
Journal of Diabetes Investigation.2020; 11(5): 1336. CrossRef
Evaluation of continuous glucose monitoring system for detection of alterations in glucose homeostasis in pediatric patients with β-thalassemia major
Mona H. El-Samahy, Azza A. Tantawy, Amira A. Adly, Abeer A. Abdelmaksoud, Eman A. Ismail, Nouran Y. Salah
Pediatric Diabetes.2019; 20(1): 65. CrossRef
Glycosylated Hemoglobin in Subjects Affected by Iron-Deficiency Anemia
Jari Intra, Giuseppe Limonta, Fabrizio Cappellini, Maria Bertona, Paolo Brambilla
Diabetes & Metabolism Journal.2019; 43(4): 539. CrossRef
The role of telenursing in the management of diabetes：A systematic review and meta‐analysis
Sa Yang, Qiuhuan Jiang, Hongfang Li
Public Health Nursing.2019; 36(4): 575. CrossRef
Glycated haemoglobin and iron deficiency anaemia: a case‐control study
Jari Intra, Giuseppe Limonta, Fabrizio Cappellini, Maria Bertona, Paolo Brambilla
Practical Diabetes.2018; 35(3): 90. CrossRef
Intravenous Ferric Carboxymaltose in Patients with Type 2 Diabetes Mellitus and Iron Deficiency: CLEVER Trial Study Design and Protocol
Christoph Schindler, Andreas L. Birkenfeld, Markolf Hanefeld, Ulrike Schatz, Carsta Köhler, Martin Grüneberg, Diethelm Tschöpe, Matthias Blüher, Christoph Hasslacher, Stefan R. Bornstein
Diabetes Therapy.2018; 9(1): 37. CrossRef
Effectiveness of smartphone technologies on glycaemic control in patients with type 2 diabetes: systematic review with meta‐analysis of 17 trials
I. X. Y. Wu, J. C. Y. Kee, D. E. Threapleton, R. C. W. Ma, V. C. K. Lam, E. K. P. Lee, S. Y. S. Wong, V. C. H. Chung
Obesity Reviews.2018; 19(6): 825. CrossRef
Definition, Classification and Diagnosis of Diabetes, Prediabetes and Metabolic Syndrome
Zubin Punthakee, Ronald Goldenberg, Pamela Katz
Canadian Journal of Diabetes.2018; 42: S10. CrossRef
Iron status and its association with HbA1c levels in Dutch children with diabetes mellitus type 1
Marjolijn D. Akkermans, E. C. A. Mieke Houdijk, Boudewijn Bakker, Agnes Clement-de Boers, Daniëlle C. M. van der Kaay, Martine C. de Vries, M. Claire Woltering, Dick Mul, Johannes B. van Goudoever, Frank Brus
European Journal of Pediatrics.2018; 177(4): 603. CrossRef
Association between Hemoglobin and Hemoglobin A1c: A Data-Driven Analysis of Health Checkup Data in Japan
Masato Takeuchi, Koji Kawakami
Journal of Clinical Medicine.2018; 7(12): 539. CrossRef
Glycated haemoglobin A1c(HbA1c) for detection of diabetes mellitus and impaired fasting glucose in Malawi: a diagnostic accuracy study
Sujit D Rathod, Amelia C Crampin, Crispin Musicha, Ndoliwe Kayuni, Louis Banda, Jacqueline Saul, Estelle McLean, Keith Branson, Shabbar Jaffar, Moffat J Nyirenda
BMJ Open.2018; 8(5): e020972. CrossRef
Sex and age affect agreement between fasting plasma glucose and glycosylated hemoglobin for diagnosis of dysglycemia
Mercedes Lorenzo-Medina, Begoña Uranga, Antonio Rus, Rosa Martínez, Carolina Puertas, María Dolores Blanco, Ernesto Casís, Rosa Corcoy
Endocrinología, Diabetes y Nutrición.2017; 64(7): 345. CrossRef
Sex and age affect agreement between fasting plasma glucose and glycosylated hemoglobin for diagnosis of dysglycemia
Mercedes Lorenzo-Medina, Begoña Uranga, Antonio Rus, Rosa Martínez, Carolina Puertas, María Dolores Blanco, Ernesto Casís, Rosa Corcoy
Endocrinología, Diabetes y Nutrición (English ed.).2017; 64(7): 345. CrossRef
Impact of Mean Cell Hemoglobin on Hb A1c–Defined Glycemia Status
Santiago Rodriguez-Segade, Javier Rodriguez Garcia, José M García-López, Francisco Gude, Felipe F Casanueva, Santiago RS-Alonso, Félix Camiña
Clinical Chemistry.2016; 62(12): 1570. CrossRef
Effect of iron deficiency anaemia on HbA1c levels is dependent on the degree of anaemia
Juliana Frezza Silva, Ana Laura Pimentel, Joíza Lins Camargo
Clinical Biochemistry.2016; 49(1-2): 117. CrossRef
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 & Metabolism Journal.2016; 40(2): 167. CrossRef
Non-alcoholic fatty liver disease associated with increased arterial stiffness in subjects with normal glucose tolerance, but not pre-diabetes and diabetes
Chieh-Ying Chou, Yi-Ching Yang, Jin-Shang Wu, Zih-Jie Sun, Feng-Hwa Lu, Chih-Jen Chang
Diabetes and Vascular Disease Research.2015; 12(5): 359. CrossRef
Effects of diabetes definition on global surveillance of diabetes prevalence and diagnosis: a pooled analysis of 96 population-based studies with 331 288 participants
G Danaei, S Fahimi, Y Lu, B Zhou, K Hajifathalian, M Di Cesare, WC Lo, B Reis-Santos, MJ Cowan, JE Shaw, J Bentham, JK Lin, H Bixby, D Magliano, P Bovet, JJ Miranda, YH Khang, GA Stevens, LM Riley, MK Ali, M Ezzati, ZA Abdeen, KA Kadir, M Abu-Rmeileh, B A
The Lancet Diabetes & Endocrinology.2015; 3(8): 624. CrossRef
Derivation and validation of an HbA1c optimal cutoff for diagnosing prediabetes in a South African mixed ancestry population
Annalise E. Zemlin, Tandi E. Matsha, Andre P. Kengne, Rajiv T. Erasmus
Clinica Chimica Acta.2015; 448: 215. CrossRef
Factors affecting A1C in non-diabetic individuals: Review and meta-analysis
Gabriela Cavagnolli, Ana Laura Pimentel, Priscila Aparecida Correa Freitas, Jorge Luiz Gross, Joíza Lins Camargo
Clinica Chimica Acta.2015; 445: 107. CrossRef
Limitations of Hemoglobin A1c for the Diagnosis of Posttransplant Diabetes Mellitus
Ivar Anders Eide, Thea Anine Strøm Halden, Anders Hartmann, Anders Åsberg, Dag Olav Dahle, Anna V Reisæter, Trond Jenssen
Transplantation.2015; 99(3): 629. CrossRef
The effect of anaemia and abnormalities of erythrocyte indices on HbA1c analysis: a systematic review
Emma English, Iskandar Idris, Georgina Smith, Ketan Dhatariya, Eric S. Kilpatrick, W. Garry John
Diabetologia.2015; 58(7): 1409. CrossRef
Relationship between Hb and HbA1c in Japanese adults: An analysis of the 2009 Japan Society of Ningen Dock database
Eiko Takahashi, Kengo Moriyama, Minoru Yamakado
Diabetes Research and Clinical Practice.2014; 104(3): e64. CrossRef
Diagnosing Diabetes with Hemoglobin A1c: Current Debates and Considerations for Anemic Patients
Tae Hyuk Kim, Sung Hee Choi
Diabetes & Metabolism Journal.2013; 37(5): 340. CrossRef

First
Prev
Page of 1
Next
Last

Search