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Original Article
Diabetes, obesity and metabolism
Triglyceride-Glucose Index Predicts Future Atherosclerotic Cardiovascular Diseases: A 16-Year Follow-up in a Prospective, Community-Dwelling Cohort Study
Joon Ho Moon, Yongkang Kim, Tae Jung Oh, Jae Hoon Moon, Soo Heon Kwak, Kyong Soo Park, Hak Chul Jang, Sung Hee Choi, Nam H. Cho
Endocrinol Metab. 2023;38(4):406-417.   Published online August 3, 2023
DOI: https://doi.org/10.3803/EnM.2023.1703
  • 2,587 View
  • 161 Download
  • 3 Web of Science
  • 5 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
While the triglyceride-glucose (TyG) index is a measure of insulin resistance, its association with cardiovascular disease (CVD) has not been well elucidated. We evaluated the TyG index for prediction of CVDs in a prospective large communitybased cohort.
Methods
Individuals 40 to 70 years old were prospectively followed for a median 15.6 years. The TyG index was calculated as the Ln [fasting triglycerides (mg/dL)×fasting glucose (mg/dL)/2]. CVDs included any acute myocardial infarction, coronary artery disease or cerebrovascular disease. We used a Cox proportional hazards model to estimate CVD risks according to quartiles of the TyG index and plotted the receiver operating characteristics curve for the incident CVD.
Results
Among 8,511 subjects (age 51.9±8.8 years; 47.5% males), 931 (10.9%) had incident CVDs during the follow-up. After adjustment for age, sex, body mass index, diabetes mellitus, hypertension, total cholesterol, smoking, alcohol, exercise, and C-reactive protein, subjects in the highest TyG quartile had 36% increased risk of incident CVD compared with the lowest TyG quartile (hazard ratio, 1.36; 95% confidence interval, 1.10 to 1.68). Carotid plaque, assessed by ultrasonography was more frequent in subjects in the higher quartile of TyG index (P for trend=0.049 in men and P for trend <0.001 in women). The TyG index had a higher predictive power for CVDs than the homeostasis model assessment of insulin resistance (HOMA-IR) (area under the curve, 0.578 for TyG and 0.543 for HOMA-IR). Adding TyG index on diabetes or hypertension alone gave sounder predictability for CVDs.
Conclusion
The TyG index is independently associated with future CVDs in 16 years of follow-up in large, prospective Korean cohort.

Citations

Citations to this article as recorded by  
  • Construction and validation of a nomogram for predicting diabetes remission at 3 months after bariatric surgery in patients with obesity combined with type 2 diabetes mellitus
    Kaisheng Yuan, Bing Wu, Ruiqi Zeng, Fuqing Zhou, Ruixiang Hu, Cunchuan Wang
    Diabetes, Obesity and Metabolism.2024; 26(1): 169.     CrossRef
  • Association between the triglyceride glucose index and chronic total coronary occlusion: A cross-sectional study from southwest China
    Kaiyong Xiao, Huili Cao, Bin Yang, Zhe Xv, Lian Xiao, Jianping Wang, Shuiqing Ni, Hui Feng, Zhongwei He, Lei Xv, Juan Li, Dongmei Xv
    Nutrition, Metabolism and Cardiovascular Diseases.2024; 34(4): 850.     CrossRef
  • The association between TyG and all-cause/non-cardiovascular mortality in general patients with type 2 diabetes mellitus is modified by age: results from the cohort study of NHANES 1999–2018
    Younan Yao, Bo Wang, Tian Geng, Jiyan Chen, Wan Chen, Liwen Li
    Cardiovascular Diabetology.2024;[Epub]     CrossRef
  • Triglyceride-glucose index predicts type 2 diabetes mellitus more effectively than oral glucose tolerance test-derived insulin sensitivity and secretion markers
    Min Jin Lee, Ji Hyun Bae, Ah Reum Khang, Dongwon Yi, Mi Sook Yun, Yang Ho Kang
    Diabetes Research and Clinical Practice.2024; 210: 111640.     CrossRef
  • Evaluation of the novel three lipid indices for predicting five- and ten-year incidence of cardiovascular disease: findings from Kerman coronary artery disease risk factors study (KERCADRS)
    Alireza Jafari, Hamid Najafipour, Mitra Shadkam, Sina Aminizadeh
    Lipids in Health and Disease.2023;[Epub]     CrossRef
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Review Article
Diabetes, Obesity and Metabolism
Lipoprotein Lipase: Is It a Magic Target for the Treatment of Hypertriglyceridemia
Joon Ho Moon, Kyuho Kim, Sung Hee Choi
Endocrinol Metab. 2022;37(4):575-586.   Published online August 29, 2022
DOI: https://doi.org/10.3803/EnM.2022.402
  • 7,651 View
  • 434 Download
  • 11 Web of Science
  • 14 Crossref
AbstractAbstract PDFPubReader   ePub   
High levels of triglycerides (TG) and triglyceride-rich lipoproteins (TGRLs) confer a residual risk of cardiovascular disease after optimal low-density lipoprotein cholesterol (LDL-C)–lowering therapy. Consensus has been made that LDL-C is a non-arguable primary target for lipid lowering treatment, but the optimization of TGRL for reducing the remnant risk of cardiovascular diseases is urged. Omega-3 fatty acids and fibrates are used to reduce TG levels, but many patients still have high TG and TGRL levels combined with low high-density lipoprotein concentration that need to be ideally treated. Lipoprotein lipase (LPL) is a key regulator for TGs that hydrolyzes TGs to glycerol and free fatty acids in lipoprotein particles for lipid storage and consumption in peripheral organs. A deeper understanding of human genetics has enabled the identification of proteins regulating the LPL activity, which include the apolipoproteins and angiopoietin-like families. Novel therapeutic approach such as antisense oligonucleotides and monoclonal antibodies that regulate TGs have been developed in recent decades. In this article, we focus on the biology of LPL and its modulators and review recent clinical application, including genetic studies and clinical trials of novel therapeutics. Optimization of LPL activity to lower TG levels could eventually reduce incident atherosclerotic cardiovascular disease in conjunction with successful LDL-C reduction.

Citations

Citations to this article as recorded by  
  • The chylomicron saga: time to focus on postprandial metabolism
    Alejandro Gugliucci
    Frontiers in Endocrinology.2024;[Epub]     CrossRef
  • Sanghuangporus vaninii extract ameliorates hyperlipidemia in rats by mechanisms identified with transcriptome analysis
    Ning Gao, Yuanzhen Liu, Guangjie Liu, Bo Liu, Yupeng Cheng
    Food Science & Nutrition.2024;[Epub]     CrossRef
  • Targeting host-specific metabolic pathways—opportunities and challenges for anti-infective therapy
    Monika I. Konaklieva, Balbina J. Plotkin
    Frontiers in Molecular Biosciences.2024;[Epub]     CrossRef
  • Obesity, dyslipidemia, and cardiovascular disease: A joint expert review from the obesity medicine association and the National Lipid Association 2024
    Harold Edward Bays, Carol Kirkpatrick, Kevin C. Maki, Peter P. Toth, Ryan T. Morgan, Justin Tondt, Sandra Michelle Christensen, Dave Dixon, Terry A. Jacobson
    Obesity Pillars.2024; : 100108.     CrossRef
  • Role of Fenofibrate Use in Dyslipidemia and Related Comorbidities in the Asian Population: A Narrative Review
    Chaicharn Deerochanawong, Sin Gon Kim, Yu-Cheng Chang
    Diabetes & Metabolism Journal.2024; 48(2): 184.     CrossRef
  • Xanthohumol, a prenylated chalcone, regulates lipid metabolism by modulating the LXRα/RXR-ANGPTL3-LPL axis in hepatic cell lines and high-fat diet-fed zebrafish models
    Wan-Yun Gao, Pei-Yi Chen, Hao-Jen Hsu, Je-Wen Liou, Chia-Ling Wu, Ming-Jiuan Wu, Jui-Hung Yen
    Biomedicine & Pharmacotherapy.2024; 174: 116598.     CrossRef
  • High producer variant of lipoprotein lipase may protect from hepatocellular carcinoma in alcohol-associated cirrhosis
    Franziska Schmalz, Janett Fischer, Hamish Innes, Stephan Buch, Christine Möller, Madlen Matz-Soja, Witigo von Schönfels, Benjamin Krämer, Bettina Langhans, Alexandra Klüners, Michael Soyka, Felix Stickel, Jacob Nattermann, Christian P. Strassburg, Thomas
    JHEP Reports.2023; 5(4): 100684.     CrossRef
  • Measurement of Serum Low Density Lipoprotein Cholesterol and Triglyceride-Rich Remnant Cholesterol as Independent Predictors of Atherosclerotic Cardiovascular Disease: Possibilities and Limitations
    Dieter Lütjohann, Hans-Ulrich Klör, Frans Stellaard
    Nutrients.2023; 15(9): 2202.     CrossRef
  • Influence of antipsychotic medications on hyperlipidemia risk in patients with schizophrenia: evidence from a population-based cohort study and in vitro hepatic lipid homeostasis gene expression
    Tien-Yuan Wu, Ni Tien, Cheng-Li Lin, Yu-Cun Cheah, Chung Y. Hsu, Fuu-Jen Tsai, Yi-Jen Fang, Yun-Ping Lim
    Frontiers in Medicine.2023;[Epub]     CrossRef
  • Triglyceride-Rich Lipoprotein Metabolism: Key Regulators of Their Flux
    Alejandro Gugliucci
    Journal of Clinical Medicine.2023; 12(13): 4399.     CrossRef
  • Sugar and Dyslipidemia: A Double-Hit, Perfect Storm
    Alejandro Gugliucci
    Journal of Clinical Medicine.2023; 12(17): 5660.     CrossRef
  • Dyslipidemia in Patients with Chronic Kidney Disease: An Updated Overview
    Sang Heon Suh, Soo Wan Kim
    Diabetes & Metabolism Journal.2023; 47(5): 612.     CrossRef
  • Peroxisome Proliferator-Activated Receptor α in Lipoprotein Metabolism and Atherosclerotic Cardiovascular Disease
    Elena Valeria Fuior, Evangelia Zvintzou, Theodosios Filippatos, Katerina Giannatou, Victoria Mparnia, Maya Simionescu, Anca Violeta Gafencu, Kyriakos E. Kypreos
    Biomedicines.2023; 11(10): 2696.     CrossRef
  • Developing a model to predict the early risk of hypertriglyceridemia based on inhibiting lipoprotein lipase (LPL): a translational study
    Julia Hernandez-Baixauli, Gertruda Chomiciute, Juan María Alcaide-Hidalgo, Anna Crescenti, Laura Baselga-Escudero, Hector Palacios-Jordan, Elisabet Foguet-Romero, Anna Pedret, Rosa M. Valls, Rosa Solà, Miquel Mulero, Josep M. Del Bas
    Scientific Reports.2023;[Epub]     CrossRef
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Original Article
APOA5 Polymorphism Is Associated with Metabolic Syndrome in Korean Postmenopausal Women.
Doh Hee Kim, Seung Hee Lee, Kyung Hoon Han, Chae Bong Kim, Kwan Young Song, Sook Cho, Kye Heui Lee
Endocrinol Metab. 2012;27(4):276-281.   Published online December 20, 2012
DOI: https://doi.org/10.3803/EnM.2012.27.4.276
  • 4,639 View
  • 23 Download
  • 2 Crossref
AbstractAbstract PDF
BACKGROUND
Menopause is an independent risk factor in metabolic syndrome which induced an alteration of the lipid metabolism by hormonal changes. Apolipoprotein A5 gene (APOA5) was related to the regulation of triglyceride and high density lipoprotein cholesterol (HDL-C) level with biosynthesis and decomposition. This study was conducted to investigate the relationship between APOA5 polymorphism and metabolic syndrome in Korean postmenopausal women. METHODS: This study included 307 postmenopausal women with anthropometric and biochemical measurement in 2010-2011. The polymorphism of APOA5 was analyzed by polymerase chain reaction-restriction fragment length polymorphism method with MseI restriction enzyme. RESULTS: The metabolic syndrome prevalence with TT genotype was significantly lower than the frequency in those with TC/CC (27.09%, 38.46%, and 45.71% for TT, TC, and CC, respectively; P < 0.05). Multiple regression analysis of metabolic syndrome risk factors indicated that postmenopausal women with CC genotype had a higher risk with 3 times than that in TT genotype (P < 0.05). APOA5 C carriers showed an increased risk of triglyceride level (odd ratio, 2.93 and 1.85 for CC and TC+CC, respectively; P < 0.05). Interestingly, HDL-C was related to triglyceride directly in comparison to APOA5. CONCLUSION: The results of this study indicate that APOA5 has an influence on serum triglyceride and HDL-C, which contribute to metabolic syndrome in Korean postmenopausal women.

Citations

Citations to this article as recorded by  
  • Effects of a 3-year dietary intervention on age-related changes in triglyceride and apolipoprotein A-V levels in patients with impaired fasting glucose or new-onset type 2 diabetes as a function of the APOA5 -1131 T > C polymorphism
    Minjoo Kim, Jey Sook Chae, Miri Kim, Sang-Hyun Lee, Jong Ho Lee
    Nutrition Journal.2014;[Epub]     CrossRef
  • APOA5Polymorphism Is Associated with Metabolic Syndrome in Korean Postmenopausal Women
    Mi Hae Seo, Won Young Lee
    Endocrinology and Metabolism.2012; 27(4): 274.     CrossRef
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