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Hong-Yup Ahn  (Ahn HY) 3 Articles
Metabolic Risk/Epidemiology
Metabolic Dysfunction-Associated Fatty Liver Disease and Mortality: A Population-Based Cohort Study
Kyung-Soo Kim, Sangmo Hong, Hong-Yup Ahn, Cheol-Young Park
Diabetes Metab J. 2023;47(2):220-231.   Published online January 12, 2023
  • 65,535 View
  • 282 Download
  • 9 Web of Science
  • 10 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
We investigated whether metabolic dysfunction-associated fatty liver disease (MAFLD) is associated with an elevated risk of all-cause and cardiovascular mortality using a large-scale health examination cohort.
A total of 394,835 subjects in the Kangbuk Samsung Health Study cohort were enrolled from 2002 to 2012. Participants were categorized by the presence of nonalcoholic fatty liver disease (NAFLD) and MAFLD as follows: normal subjects; patients with both NAFLD and MAFLD; patients with NAFLD only; and patients with MAFLD only. Cox proportional hazards models were used to analyze the risk of mortality.
During a median 5.7 years of follow-up, 20.69% was patients with both NAFLD and MAFLD, 1.51% was patients with NAFLD only, and 4.29% was patients with MAFLD only. All-cause and cardiovascular death was higher in patients with MAFLD than those without MAFLD (P<0.001, respectively). In patients with MAFLD only, the hazard ratio (HR) of all-cause and cardiovascular death was 1.35 (95% confidence interval [CI], 1.13 to 1.60) and 1.90 (95% CI, 1.26 to 2.88) after adjusting for age, which lost its statistical significance by multivariable adjustments. Compared to patients with less than two components of metabolic dysfunction, patients with more than two components of metabolic dysfunction were a higher risk of cardiovascular death (HR, 2.05; 95% CI, 1.25 to 3.38) and only women with more than two components of metabolic dysfunction were a higher risk of all-cause death (HR, 1.44; 95% CI, 1.02 to 2.03).
MAFLD criteria could identify a high-risk group for all-cause and cardiovascular death.


Citations to this article as recorded by  
  • Mortality in metabolic dysfunction-associated steatotic liver disease: A nationwide population-based cohort study
    Eugene Han, Byung-Wan Lee, Eun Seok Kang, Bong-Soo Cha, Sang Hoon Ahn, Yong-ho Lee, Seung Up Kim
    Metabolism.2024; 152: 155789.     CrossRef
  • Association of non-alcoholic fatty liver disease with cardiovascular disease and all cause death in patients with type 2 diabetes mellitus: nationwide population based study
    Kyung-Soo Kim, Sangmo Hong, Kyungdo Han, Cheol-Young Park
    BMJ.2024; : e076388.     CrossRef
  • Sex differences in mortality and liver‐related events in non‐alcoholic fatty liver disease: A systematic review and meta‐analysis
    Huimin Zhou, Haiyan Chen, Hanxiao Lu, Bo Wu, Shuo Zhang, Yuanlong Gu, Guangwen Zhou, Jie Xiang, Jun Yang
    Liver International.2024;[Epub]     CrossRef
  • Association between dietary carbohydrate to fiber ratio and metabolic dysfunction associated fatty liver disease in adults: evidence from the NHANES 2017–2020
    Zhenmin Liu, Taiyong Fang
    Journal of Health, Population and Nutrition.2024;[Epub]     CrossRef
  • Comparison of Outcomes Between Metabolic Dysfunction-Associated Fatty Liver Disease and Non-alcoholic Fatty Liver Disease: A Meta-Analysis
    Ghazala S Virk, Jaahnavi Vajje, Nausheen K Virk, Raam Mannam, Wajeeh Rehman, Naglaa G Ghobriel , Irfan-ud-din Mian, Muhammad Usama
    Cureus.2023;[Epub]     CrossRef
  • Trends in prevalence and all-cause mortality of metabolic dysfunction-associated fatty liver disease among adults in the past three decades: Results from the NHANES study
    Zhi-Qin Xie, Hong-Xia Li, Bing-Kun Wang, Zhao-Ming Yang, Zi-Yu Zhang, Wen-Liang Tan, Wen-Xin Li, Qing-Bin Wang, Lei Yang, Hong-Kai Zhuang, Chen-Wei Tang, Chang-Zhen Shang, Ya-Jin Chen
    European Journal of Internal Medicine.2023; 110: 62.     CrossRef
  • Comparing the Mortality Risk between Metabolic Dysfunction-Associated Fatty Liver Disease and Non-Alcoholic Fatty Liver Disease
    Han Na Jung, Chang Hee Jung
    Diabetes & Metabolism Journal.2023; 47(2): 198.     CrossRef
  • Increased expression of sodium-glucose cotransporter 2 and O-GlcNAcylation in hepatocytes drives non-alcoholic steatohepatitis
    Hye Jin Chun, Eun Ran Kim, Minyoung Lee, Da Hyun Choi, Soo Hyun Kim, Eugene Shin, Jin-Hong Kim, Jin Won Cho, Dai Hoon Han, Bong-Soo Cha, Yong-ho Lee
    Metabolism.2023; 145: 155612.     CrossRef
  • Current understanding and future perspectives on the impact of changing NAFLD to MAFLD on global epidemiology and clinical outcomes
    Karl Vaz, Daniel Clayton-Chubb, Ammar Majeed, John Lubel, David Simmons, William Kemp, Stuart K. Roberts
    Hepatology International.2023; 17(5): 1082.     CrossRef
  • Mitochondrial Quality Control: Its Role in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)
    Soyeon Shin, Jaeyoung Kim, Ju Yeon Lee, Jun Kim, Chang-Myung Oh
    Journal of Obesity & Metabolic Syndrome.2023; 32(4): 289.     CrossRef
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
  • 3,849 View
  • 215 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   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 to this article as recorded by  
  • 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
Comparison of the Usefulness of the Updated Homeostasis Model Assessment (HOMA2) with the Original HOMA1 in the Prediction of Type 2 Diabetes Mellitus in Koreans
Young Seok Song, You-Cheol Hwang, Hong-Yup Ahn, Cheol-Young Park
Diabetes Metab J. 2016;40(4):318-325.   Published online May 27, 2016
  • 4,325 View
  • 80 Download
  • 39 Web of Science
  • 42 Crossref
AbstractAbstract PDFPubReader   

The original homeostasis model assessment (HOMA1) and the updated HOMA model (HOMA2) have been used to evaluate insulin resistance (IR) and β-cell function, but little is known about the usefulness of HOMA2 for the prediction of diabetes in Koreans. The aim of this study was to demonstrate the usefulness of HOMA2 as a predictor of type 2 diabetes mellitus in Koreans without diabetes.


The study population consisted of 104,694 Koreans enrolled at a health checkup program and followed up from 2001 to 2012. Participants were divided into a normal glucose tolerance (NGT) group and a pre-diabetes group according to fasting glucose and glycosylated hemoglobin levels. Anthropometric and laboratory data were measured at the baseline checkup, and HOMA values were calculated at the baseline and follow-up checkups. The hazard ratios (HRs) of the HOMA1 and HOMA2 values and the prevalence of diabetes at follow-up were evaluated using a multivariable Cox proportional hazards model and Kaplan-Meier analysis.


After adjusting for several diabetes risk factors, all of the HOMA values except 1/HOMA1-β and 1/HOMA2-β in the NGT group were significant predictors of the progression to diabetes. In the NGT group, there was no significant difference in HOMA1-IR (HR, 1.09; 95% confidence interval [CI], 1.04 to 1.14) and HOMA2-IR (HR, 1.11; 95% CI, 1.04 to 1.19). However, in the pre-diabetes group, 1/HOMA2-β was a more powerful marker (HR, 1.29; 95% CI, 1.26 to 1.31) than HOMA1-IR (HR, 1.23; 95% CI, 1.19 to 1.28) or 1/HOMA1-β (HR, 1.14; 95% CI, 1.12 to 1.16). In the non-diabetic group (NGT+pre-diabetes), 1/HOMA2-β was also a stronger predictor of diabetes (HR, 1.27; 95% CI, 1.25 to 1.29) than HOMA1-IR (HR, 1.14; 95% CI, 1.12 to 1.15) or 1/HOMA1-β (HR, 1.13; 95% CI, 1.11 to 1.14).


HOMA2 is more predictive than HOMA1 for the progression to diabetes in pre-diabetes or non-diabetic Koreans.


Citations to this article as recorded by  
  • Non-linear associations of HOMA2-IR with all-cause mortality in general populations: insights from NHANES 1999–2006
    Aikai Zhang, Lingchen Huang, Min Tang
    BMC Public Health.2024;[Epub]     CrossRef
  • An acute exercise at low to moderate intensity attenuated postprandial lipemia and insulin responses
    Lisa L. Ji, Vicki S. Fretwell, Abel Escamilla, Wanxiang Yao, Tianou Zhang, Meizi He, John Q. Zhang
    Journal of Exercise Science & Fitness.2024; 22(1): 14.     CrossRef
  • Insufficient compensatory pancreatic β-cells function might be closely associated with hyperuricemia in U.S. adults: evidence from the National Health and Nutrition Examination Survey
    Tianran Shen, Qiutong Zheng, Liling Zhong, Xia Zeng, Xiaojing Yuan, Fengxin Mo, Shiheng Zhu, Wenhan Yang, Qingsong Chen
    BMC Public Health.2024;[Epub]     CrossRef
  • Glycated haemoglobin, HOMA2‐B, C‐peptide to glucose ratio and type 2 diabetes clusters as predictors for therapy failure in individuals with type 2 diabetes without insulin therapy: A registry analysis
    Faisal Aziz, Christoph Sternad, Caren Sourij, Lisa Knoll, Harald Kojzar, Anna Schranz, Alexandra Bürger, Harald Sourij, Felix Aberer
    Diabetes, Obesity and Metabolism.2024; 26(3): 1082.     CrossRef
  • Nghiên cứu tương quan giữa chỉ số đề kháng insulin và chỉ số khối cơ thể ở bệnh nhân tiền đái tháo đường có tăng huyết áp
    Linh Dương
    Journal of Clinical Medicine- Hue Central Hospital.2024;[Epub]     CrossRef
  • Bridelia ferruginea inhibits key carbohydrate digesting enzyme and intestinal glucose absorption and modulates glucose metabolism in diabetic rats
    Olajumoke Oyebode, Lindiwe Zuma, Ochuko Lucky Erukainure, Neil Koorbanally, Md. Shahidul Islam
    Archives of Physiology and Biochemistry.2023; 129(3): 671.     CrossRef
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    Xiliang Zhao, Jin Wang, Quan Li, Yicong Ye, Yong Zeng
    Cardiovascular Drugs and Therapy.2023; 37(3): 487.     CrossRef
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    Shang-Feng Tsai, Chao-Tung Yang, Wei-Ju Liu, Chia-Lin Lee
    eClinicalMedicine.2023; 58: 101934.     CrossRef
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    Qi‐chao Sun, Jie Liu, Ran Meng, Ning Zhang, Jing Yao, Fan Yang, Da‐long Zhu
    Journal of Diabetes Investigation.2023; 14(8): 953.     CrossRef
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    Maximiliane Chiara Hammel, Robert Stein, Jürgen Kratzsch, Mandy Vogel, Alexander J. Eckert, Rima Destya Triatin, Marco Colombo, Christof Meigen, Ronny Baber, Juraj Stanik, Ulrike Spielau, Anette Stoltze, Kerstin Wirkner, Anke Tönjes, Harold Snieder, Reinh
    The Lancet Regional Health - Europe.2023; 30: 100652.     CrossRef
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    Adisa Poljo, Stephan Kopf, Alba Sulaj, Stephanie Roessler, Thomas Albrecht, Benjamin Goeppert, Sarah Bojko, Beat P. Müller-Stich, Adrian T. Billeter
    Surgery for Obesity and Related Diseases.2023; 19(12): 1421.     CrossRef
  • Glutamine Defended the Kidneys Versus Lead Intoxication Via Elevating Endogenous Antioxidants, Reducing Inflammation and Carbonyl Stress, as well as Improving Insulin Resistance and Dyslipidemia
    Sina Mahdavifard, Najafzadeh Nowruz
    Biological Trace Element Research.2023;[Epub]     CrossRef
  • Sex and genetic background define the metabolic, physiologic, and molecular response to protein restriction
    Cara L. Green, Heidi H. Pak, Nicole E. Richardson, Victoria Flores, Deyang Yu, Jay L. Tomasiewicz, Sabrina N. Dumas, Katherine Kredell, Jesse W. Fan, Charlie Kirsh, Krittisak Chaiyakul, Michaela E. Murphy, Reji Babygirija, Gregory A. Barrett-Wilt, Joshua
    Cell Metabolism.2022; 34(2): 209.     CrossRef
  • Associations of the HOMA2‐%B and HOMA2‐IR with progression to diabetes and glycaemic deterioration in young and middle‐aged Chinese
    Baoqi Fan, Hongjiang Wu, Mai Shi, Aimin Yang, Eric S. H. Lau, Claudia H. T. Tam, Dandan Mao, Cadmon K. P. Lim, Alice P. S. Kong, Ronald C. W. Ma, Elaine Chow, Andrea O. Y. Luk, Juliana C. N. Chan
    Diabetes/Metabolism Research and Reviews.2022;[Epub]     CrossRef
  • Triglyceride and glucose index is a simple and easy‐to‐calculate marker associated with nonalcoholic fatty liver disease
    Kyung‐Soo Kim, Sangmo Hong, Hong‐Yup Ahn, Cheol‐Young Park
    Obesity.2022; 30(6): 1279.     CrossRef
  • Protective effect of acetylcysteine, histidine, and their combination against diabetes vascular complications in type-2 diabetic rats via reducing NF-kβ pathway signaling
    Sina Mahdavifard, Manochehr Nakhjavani
    Journal of Diabetes & Metabolic Disorders.2022; 21(2): 1233.     CrossRef
  • Visceral fat might impact left ventricular remodeling through changes in arterial stiffness in type 2 diabetes: A cross-sectional study
    Haishan Huang, Jing Jin, Yanshan Chen, Lina Wang, Jingyi Zhong, Zhenguo Chen, Lingling Xu
    International Journal of Cardiology.2022; 368: 78.     CrossRef
  • The effect of berberine and fenugreek seed co-supplementation on inflammatory factor, lipid and glycemic profile in patients with type 2 diabetes mellitus: a double-blind controlled randomized clinical trial
    Shima Nematollahi, Gholam Reza Pishdad, Mehrnoosh Zakerkish, Foroogh Namjoyan, Kambiz Ahmadi Angali, Fatemeh Borazjani
    Diabetology & Metabolic Syndrome.2022;[Epub]     CrossRef
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    Frontiers in Pediatrics.2022;[Epub]     CrossRef
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    Acta Diabetologica.2022; 60(2): 305.     CrossRef
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    Journal of Diabetes Investigation.2021; 12(4): 557.     CrossRef
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    Stroke.2017; 48(11): 2999.     CrossRef
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Diabetes Metab J : Diabetes & Metabolism Journal