Association between Serum Gamma-Glutamyltransferase and Prevalence of Metabolic Syndrome Using Data from the Korean Genome and Epidemiology Study
The aim of this study was to determine whether there is a positive correlation between gamma-glutamyltransferase (GGT) levels and the prevalence of metabolic syndrome and whether GGT can be used as an easily checkable metabolic index using data from the large-scale Korean Genome and Epidemiology Stu...
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| Published in | Endocrinology and metabolism (Seoul) Vol. 34; no. 4; pp. 390 - 397 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Korea (South)
Korean Endocrine Society
01.12.2019
대한내분비학회 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2093-596X 2093-5978 2093-5978 |
| DOI | 10.3803/EnM.2019.34.4.390 |
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| Abstract | The aim of this study was to determine whether there is a positive correlation between gamma-glutamyltransferase (GGT) levels and the prevalence of metabolic syndrome and whether GGT can be used as an easily checkable metabolic index using data from the large-scale Korean Genome and Epidemiology Study (KoGES).
We obtained data of 211,725 participants of the KoGES. The collected data included age, sex, height, weight, waist circumference, and various biochemical characteristics, including serum GGT levels. The data of study participants who ingested more than 40 g/day of alcohol and who were diagnosed with metabolic syndrome at baseline was excluded. We analyzed the prevalence of metabolic syndrome according to GGT quartiles in both genders.
The GGT level was significantly higher in subjects with metabolic syndrome compared to normal subjects (37.92±48.20 mg/dL vs. 25.62±33.56 mg/dL). The prevalence of metabolic syndrome showed a stepwise increase with GGT quartiles in both male and female subjects. Compared to the lowest GGT quartile, the odds ratio was 1.534 (95% confidence interval [CI], 1.432 to 1.643), 1.939 (95% CI, 1.811 to 2.076), and 2.754 (95% CI, 2.572 to 2.948) in men and 1.155 (95% CI, 1.094 to 1.218), 1.528 (95% CI, 1.451 to 1.609), and 2.022 (95% CI, 1.921 to 2.218) in women with increasing GGT quartile. The cutoff value of GGT predicting risk of metabolic syndrome was 27 IU/L in men and 17 IU/L in women.
We suggested that GGT could be an easily checkable marker for the prediction of metabolic syndrome. |
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| AbstractList | The aim of this study was to determine whether there is a positive correlation between gamma-glutamyltransferase (GGT) levels and the prevalence of metabolic syndrome and whether GGT can be used as an easily checkable metabolic index using data from the large-scale Korean Genome and Epidemiology Study (KoGES).
We obtained data of 211,725 participants of the KoGES. The collected data included age, sex, height, weight, waist circumference, and various biochemical characteristics, including serum GGT levels. The data of study participants who ingested more than 40 g/day of alcohol and who were diagnosed with metabolic syndrome at baseline was excluded. We analyzed the prevalence of metabolic syndrome according to GGT quartiles in both genders.
The GGT level was significantly higher in subjects with metabolic syndrome compared to normal subjects (37.92±48.20 mg/dL vs. 25.62±33.56 mg/dL). The prevalence of metabolic syndrome showed a stepwise increase with GGT quartiles in both male and female subjects. Compared to the lowest GGT quartile, the odds ratio was 1.534 (95% confidence interval [CI], 1.432 to 1.643), 1.939 (95% CI, 1.811 to 2.076), and 2.754 (95% CI, 2.572 to 2.948) in men and 1.155 (95% CI, 1.094 to 1.218), 1.528 (95% CI, 1.451 to 1.609), and 2.022 (95% CI, 1.921 to 2.218) in women with increasing GGT quartile. The cutoff value of GGT predicting risk of metabolic syndrome was 27 IU/L in men and 17 IU/L in women.
We suggested that GGT could be an easily checkable marker for the prediction of metabolic syndrome. BackgroundThe aim of this study was to determine whether there is a positive correlation between gamma-glutamyltransferase (GGT) levels and the prevalence of metabolic syndrome and whether GGT can be used as an easily checkable metabolic index using data from the large-scale Korean Genome and Epidemiology Study (KoGES).MethodsWe obtained data of 211,725 participants of the KoGES. The collected data included age, sex, height, weight, waist circumference, and various biochemical characteristics, including serum GGT levels. The data of study participants who ingested more than 40 g/day of alcohol and who were diagnosed with metabolic syndrome at baseline was excluded. We analyzed the prevalence of metabolic syndrome according to GGT quartiles in both genders.ResultsThe GGT level was significantly higher in subjects with metabolic syndrome compared to normal subjects (37.92±48.20 mg/dL vs. 25.62±33.56 mg/dL). The prevalence of metabolic syndrome showed a stepwise increase with GGT quartiles in both male and female subjects. Compared to the lowest GGT quartile, the odds ratio was 1.534 (95% confidence interval [CI], 1.432 to 1.643), 1.939 (95% CI, 1.811 to 2.076), and 2.754 (95% CI, 2.572 to 2.948) in men and 1.155 (95% CI, 1.094 to 1.218), 1.528 (95% CI, 1.451 to 1.609), and 2.022 (95% CI, 1.921 to 2.218) in women with increasing GGT quartile. The cutoff value of GGT predicting risk of metabolic syndrome was 27 IU/L in men and 17 IU/L in women.ConclusionWe suggested that GGT could be an easily checkable marker for the prediction of metabolic syndrome. Background: The aim of this study was to determine whether there is a positive correlation between gamma-glutamyltransferase(GGT) levels and the prevalence of metabolic syndrome and whether GGT can be used as an easily checkable metabolic index usingdata from the large-scale Korean Genome and Epidemiology Study (KoGES). Methods: We obtained data of 211,725 participants of the KoGES. The collected data included age, sex, height, weight, waist circumference, and various biochemical characteristics, including serum GGT levels. The data of study participants who ingested morethan 40 g/day of alcohol and who were diagnosed with metabolic syndrome at baseline was excluded. We analyzed the prevalence ofmetabolic syndrome according to GGT quartiles in both genders. Results: The GGT level was significantly higher in subjects with metabolic syndrome compared to normal subjects (37.92±48.20mg/dL vs. 25.62±33.56 mg/dL). The prevalence of metabolic syndrome showed a stepwise increase with GGT quartiles in bothmale and female subjects. Compared to the lowest GGT quartile, the odds ratio was 1.534 (95% confidence interval [CI], 1.432 to1.643), 1.939 (95% CI, 1.811 to 2.076), and 2.754 (95% CI, 2.572 to 2.948) in men and 1.155 (95% CI, 1.094 to 1.218), 1.528 (95%CI, 1.451 to 1.609), and 2.022 (95% CI, 1.921 to 2.218) in women with increasing GGT quartile. The cutoff value of GGT predictingrisk of metabolic syndrome was 27 IU/L in men and 17 IU/L in women. Conclusion: We suggested that GGT could be an easily checkable marker for the prediction of metabolic syndrome. KCI Citation Count: 0 The aim of this study was to determine whether there is a positive correlation between gamma-glutamyltransferase (GGT) levels and the prevalence of metabolic syndrome and whether GGT can be used as an easily checkable metabolic index using data from the large-scale Korean Genome and Epidemiology Study (KoGES).BACKGROUNDThe aim of this study was to determine whether there is a positive correlation between gamma-glutamyltransferase (GGT) levels and the prevalence of metabolic syndrome and whether GGT can be used as an easily checkable metabolic index using data from the large-scale Korean Genome and Epidemiology Study (KoGES).We obtained data of 211,725 participants of the KoGES. The collected data included age, sex, height, weight, waist circumference, and various biochemical characteristics, including serum GGT levels. The data of study participants who ingested more than 40 g/day of alcohol and who were diagnosed with metabolic syndrome at baseline was excluded. We analyzed the prevalence of metabolic syndrome according to GGT quartiles in both genders.METHODSWe obtained data of 211,725 participants of the KoGES. The collected data included age, sex, height, weight, waist circumference, and various biochemical characteristics, including serum GGT levels. The data of study participants who ingested more than 40 g/day of alcohol and who were diagnosed with metabolic syndrome at baseline was excluded. We analyzed the prevalence of metabolic syndrome according to GGT quartiles in both genders.The GGT level was significantly higher in subjects with metabolic syndrome compared to normal subjects (37.92±48.20 mg/dL vs. 25.62±33.56 mg/dL). The prevalence of metabolic syndrome showed a stepwise increase with GGT quartiles in both male and female subjects. Compared to the lowest GGT quartile, the odds ratio was 1.534 (95% confidence interval [CI], 1.432 to 1.643), 1.939 (95% CI, 1.811 to 2.076), and 2.754 (95% CI, 2.572 to 2.948) in men and 1.155 (95% CI, 1.094 to 1.218), 1.528 (95% CI, 1.451 to 1.609), and 2.022 (95% CI, 1.921 to 2.218) in women with increasing GGT quartile. The cutoff value of GGT predicting risk of metabolic syndrome was 27 IU/L in men and 17 IU/L in women.RESULTSThe GGT level was significantly higher in subjects with metabolic syndrome compared to normal subjects (37.92±48.20 mg/dL vs. 25.62±33.56 mg/dL). The prevalence of metabolic syndrome showed a stepwise increase with GGT quartiles in both male and female subjects. Compared to the lowest GGT quartile, the odds ratio was 1.534 (95% confidence interval [CI], 1.432 to 1.643), 1.939 (95% CI, 1.811 to 2.076), and 2.754 (95% CI, 2.572 to 2.948) in men and 1.155 (95% CI, 1.094 to 1.218), 1.528 (95% CI, 1.451 to 1.609), and 2.022 (95% CI, 1.921 to 2.218) in women with increasing GGT quartile. The cutoff value of GGT predicting risk of metabolic syndrome was 27 IU/L in men and 17 IU/L in women.We suggested that GGT could be an easily checkable marker for the prediction of metabolic syndrome.CONCLUSIONWe suggested that GGT could be an easily checkable marker for the prediction of metabolic syndrome. |
| Author | Hyon, Dae Sung Han, Sul Ki Koh, Sang Baek Kim, Jang Young Lee, Mi Young Kim, Hae Kyung Huh, Ji Hye |
| AuthorAffiliation | 4 Center for Global Health and Social Medicine, Institute of Poverty Alleviation and International Development, Yonsei University, Seoul, Korea 1 Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea 2 Department of Preventive Medicine and Institute of Occupational Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea 3 Institute of Genomic Cohort, Yonsei University Wonju College of Medicine, Wonju, Korea |
| AuthorAffiliation_xml | – name: 2 Department of Preventive Medicine and Institute of Occupational Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea – name: 4 Center for Global Health and Social Medicine, Institute of Poverty Alleviation and International Development, Yonsei University, Seoul, Korea – name: 3 Institute of Genomic Cohort, Yonsei University Wonju College of Medicine, Wonju, Korea – name: 1 Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea |
| Author_xml | – sequence: 1 givenname: Mi Young orcidid: 0000-0002-0967-9350 surname: Lee fullname: Lee, Mi Young organization: Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea – sequence: 2 givenname: Dae Sung surname: Hyon fullname: Hyon, Dae Sung organization: Department of Preventive Medicine and Institute of Occupational Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea – sequence: 3 givenname: Ji Hye surname: Huh fullname: Huh, Ji Hye organization: Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea – sequence: 4 givenname: Hae Kyung surname: Kim fullname: Kim, Hae Kyung organization: Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea – sequence: 5 givenname: Sul Ki surname: Han fullname: Han, Sul Ki organization: Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea – sequence: 6 givenname: Jang Young surname: Kim fullname: Kim, Jang Young organization: Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea., Institute of Genomic Cohort, Yonsei University Wonju College of Medicine, Wonju, Korea – sequence: 7 givenname: Sang Baek orcidid: 0000-0001-5609-6521 surname: Koh fullname: Koh, Sang Baek organization: Department of Preventive Medicine and Institute of Occupational Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea., Institute of Genomic Cohort, Yonsei University Wonju College of Medicine, Wonju, Korea., Center for Global Health and Social Medicine, Institute of Poverty Alleviation and International Development, Yonsei University, Seoul, Korea |
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| CitedBy_id | crossref_primary_10_1177_1742271X221122585 crossref_primary_10_1186_s13098_021_00710_y crossref_primary_10_1177_14791641241252553 crossref_primary_10_1089_met_2020_0095 crossref_primary_10_1089_met_2023_0055 crossref_primary_10_5812_hepatmon_121021 crossref_primary_10_1007_s10067_021_05861_x crossref_primary_10_1007_s40200_022_01124_x crossref_primary_10_3389_fonc_2021_731144 crossref_primary_10_4103_jdmimsu_jdmimsu_239_23 crossref_primary_10_7759_cureus_41060 crossref_primary_10_1186_s13098_023_01000_5 crossref_primary_10_1186_s13098_023_01200_z crossref_primary_10_3748_wjg_v27_i2_176 |
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| Keywords | Metabolic syndrome Korean Genome and Epidemiology Study Gamma-glutamyltransferase |
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| Title | Association between Serum Gamma-Glutamyltransferase and Prevalence of Metabolic Syndrome Using Data from the Korean Genome and Epidemiology Study |
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