Diabetes & Metabolism Journal

Original Articles

Metabolic Risk/Epidemiology
Temporal Changes in Resting Heart Rate and Risk of Diabetes Mellitus: Mi Kyoung Son, Kyoungho Lee, Hyun-Young Park; Diabetes Metab J. 2024;48(4):752-762. Published online February 2, 2024; DOI: https://doi.org/10.4093/dmj.2023.0305

2,021 View
184 Download

Abstract PDF Supplementary Material PubReader ePub: Background
To investigate the association between the time-varying resting heart rate (RHR) and change in RHR (∆RHR) over time and the risk of diabetes mellitus (DM) by sex.
Methods
We assessed 8,392 participants without DM or atrial fibrillation/flutter from the Korean Genome and Epidemiology Study, a community-based prospective cohort study that was initiated in 2001 to 2002. The participants were followed up until December 31, 2018. Updating RHR with biennial in-study re-examinations, the time-varying ∆RHR was calculated by assessing the ∆RHR at the next follow-up visit.
Results
Over a median follow-up of 12.3 years, 1,345 participants (16.2%) had DM. As compared with RHR of 60 to 69 bpm, for RHR of ≥80 bpm, the incidence of DM was significantly increased for both male and female. A drop of ≥5 bpm in ∆RHR when compared with the stable ∆RHR group (–5< ∆RHR <5 bpm) was associated significantly with lower risk of DM in both male and female. However, an increase of ≥5 bpm in ∆RHR was significantly associated with higher risk of DM only in female, not in male (hazard ratio for male, 1.057 [95% confidence interval, 0.869 to 1.285]; and for female, 1.218 [95% confidence interval, 1.008 to 1.471]).
Conclusion
In this community-based longitudinal cohort study, a reduction in ∆RHR was associated with a decreased risk of DM, while an increase in ∆RHR was associated with an increased risk of DM only in female.

Metabolic Risk/Epidemiology
Iron Overload and the Risk of Diabetes in the General Population: Results of the Chinese Health and Nutrition Survey Cohort Study: He Gao, Jinying Yang, Wenfei Pan, Min Yang; Diabetes Metab J. 2022;46(2):307-318. Published online March 7, 2022; DOI: https://doi.org/10.4093/dmj.2020.0287

6,664 View
242 Download
19 Web of Science
19 Crossref

Abstract PDF Supplementary Material PubReader ePub

Background
Recent studies have found that there are significant associations between body iron status and the development of diabetes. In the present study, we aimed to analyze the association among iron overload (IO), insulin resistance (IR), and diabetes in Chinese adults, and to explore the sex difference.
Methods
Men and women (age >19 years) who participated in the Chinese Health and Nutrition Survey and did not have diabetes at baseline were followed between 2009 and 2015 (n=5,779). Over a mean of 6 years, 75 participants were diagnosed with incident diabetes. Logistic regression was used to assess the risk factors associated with IO. Cox proportional hazard regression was used to estimate the risk of incident diabetes and to determine whether the risk differed among subgroups. Causal mediation analysis (CMA) was used to explore the mechanism linking IO and diabetes.
Results
According to sex-stratified multivariable-adjusted Cox proportional hazards regression, IO increased the risk of incident diabetes. Women with IO had a higher risk of diabetes than men. Subgroup analysis with respect to age showed that the association between IO and diabetes was stronger in older women and younger men (P<0.001). CMA showed that liver injury (alanine transaminase) and lipid metabolism abnormalities (triglyceride, apolipoprotein B) contributed to the association between IO and diabetes.
Conclusion
IO is associated with diabetes and this association is sex-specific. IO may indirectly induce IR via liver injury and lipid metabolism abnormalities, resulting in diabetes.

Citations

Citations to this article as recorded by

Role of nutrition in diabetes mellitus and infections
Xue-Lu Yu, Li-Yun Zhou, Xiao Huang, Xin-Yue Li, Ming-Ke Wang, Ji-Shun Yang
World Journal of Clinical Cases.2025;[Epub] CrossRef
Micronutrient Patterns and Low Intake of Vitamin A, Vitamin D, Folate, Magnesium, and Potassium Among Prediabetes and Type 2 Diabetes Patients
Oana C Iatcu, Andrei Lobiuc, Mihai Covasa
Cureus.2024;[Epub] CrossRef
The levels, single and multiple health risk assessment of 23 metals in enteral nutrition formulas
Burhan Basaran, Hulya Turk
Food and Chemical Toxicology.2024; 192: 114914. CrossRef
Quantitative susceptibility mapping for iron monitoring of multiple subcortical nuclei in type 2 diabetes mellitus: a systematic review and meta-analysis
Sana Mohammadi, Sadegh Ghaderi, Fatemeh Sayehmiri, Mobina Fathi
Frontiers in Endocrinology.2024;[Epub] CrossRef
Iron Overload Induces Hepatic Ferroptosis and Insulin Resistance by Inhibiting the Jak2/stat3/slc7a11 Signaling Pathway
Manqiu Mo, Ling Pan, Ling Deng, Min Liang, Ning Xia, Yuzhen Liang
Cell Biochemistry and Biophysics.2024; 82(3): 2079. CrossRef
Diatom Chaetoceros Sp. as an Efficient Biological Antidote in Iron Toxicity: In‐Vitro and In‐Vivo Experiments
Zeinab Janahmadi, Shadi Talebi, Fatemeh Farjadian, Safieh Momeni
ChemistrySelect.2024;[Epub] CrossRef
Endogenous iron biomineralization in the mouse spleen of metabolic diseases
Ruowen Guo, Lei Zhang, Dongsheng Song, Biao Yu, Chao Song, Hanxiao Chen, Wenjing Xie, Chuanlin Feng, Guofeng Cheng, Kejun Hu, Jialiang Jiang, Zhe Qu, Haifeng Du, Xin Zhang
Fundamental Research.2024;[Epub] CrossRef
The Association Between Dietary Iron, the SNP of the JAZF1 rs864745, and Glucose Metabolism in a Chinese Population
Zihan Hu, Hongwei Liu, Baozhang Luo, Chunfeng Wu, Changyi Guo, Zhengyuan Wang, Jiajie Zang, Fan Wu, Zhenni Zhu
Nutrients.2024; 16(22): 3831. CrossRef
Plasma Ferritin Concentrations in the General Population: A Cross-Sectional Analysis of Anthropometric, Metabolic, and Dietary Correlates
Cara Övermöhle, Sabina Waniek, Gerald Rimbach, Katharina Susanne Weber, Wolfgang Lieb
The Journal of Nutrition.2023; 153(5): 1524. CrossRef
Association of Body Iron Metabolism with Type 2 Diabetes Mellitus in Chinese Women of Childbearing Age: Results from the China Adult Chronic Disease and Nutrition Surveillance (2015)
Jie Feng, Xiaoyun Shan, Lijuan Wang, Jiaxi Lu, Yang Cao, Lichen Yang
Nutrients.2023; 15(8): 1935. CrossRef
Iron overload induces islet β cell ferroptosis by activating ASK1/P-P38/CHOP signaling pathway
Ling Deng, Man-Qiu Mo, Jinling Zhong, Zhengming Li, Guoqiao Li, Yuzhen Liang
PeerJ.2023; 11: e15206. CrossRef
The role of ferroptosis in metabolic diseases
Ling Xie, Bin Fang, Chun Zhang
Biochimica et Biophysica Acta (BBA) - Molecular Cell Research.2023; 1870(6): 119480. CrossRef
Epidemiological and transcriptome data identify potential key genes involved in iron overload for type 2 diabetes
Xuekui Liu, Xiu Hong, Shiqiang Jiang, Rui Li, Qian Lv, Jie Wang, Xiuli Wang, Manqing Yang, Houfa Geng, Yang Li
Diabetology & Metabolic Syndrome.2023;[Epub] CrossRef
Association between serum iron and liver transaminases based on a large adult women population
Andong He, Zhuoping Zhou, Lili Huang, Ka Cheuk Yip, Jing Chen, Ruiling Yan, Ruiman Li
Journal of Health, Population and Nutrition.2023;[Epub] CrossRef
Association between serum ferritin and uric acid levels and nonalcoholic fatty liver disease in the Chinese population
Fangli Zhou, Xiaoli He, Dan Liu, Yan Ye, Haoming Tian, Li Tian
PeerJ.2023; 11: e16267. CrossRef
The Role of Iron Overload in Diabetic Cognitive Impairment: A Review
Ji-Ren An, Qing-Feng Wang, Gui-Yan Sun, Jia-Nan Su, Jun-Tong Liu, Chi Zhang, Li Wang, Dan Teng, Yu-Feng Yang, Yan Shi
Diabetes, Metabolic Syndrome and Obesity.2023; Volume 16: 3235. CrossRef
The Association Between METS-IR and Serum Ferritin Level in United States Female: A Cross-Sectional Study Based on NHANES
Han Hao, Yan Chen, Ji Xiaojuan, Zhang Siqi, Chu Hailiang, Sun Xiaoxing, Wang Qikai, Xing Mingquan, Feng Jiangzhou, Ge Hongfeng
Frontiers in Medicine.2022;[Epub] CrossRef
Research Progress on Relationship Between Iron Overload and Lower Limb Arterial Disease in Type 2 Diabetes Mellitus
Zhongjing Wang, Shu Fang, Sheng Ding, Qin Tan, Xuyan Zhang
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2022; Volume 15: 2259. CrossRef
Iron deficiency in cardiac surgical patients
L Hof, O Old, A.U. Steinbicker, P Meybohm, S Choorapoikayil, K Zacharowski
Acta Anaesthesiologica Belgica.2022; 73(4): 235. CrossRef

Metabolic Risk/Epidemiology
Sex Differences in the Effects of CDKAL1 Variants on Glycemic Control in Diabetic Patients: Findings from the Korean Genome and Epidemiology Study: Hye Ah Lee, Hyesook Park, Young Sun Hong; Diabetes Metab J. 2022;46(6):879-889. Published online February 8, 2022; DOI: https://doi.org/10.4093/dmj.2021.0265

65,535 View
190 Download
1 Web of Science
1 Crossref

Abstract PDF Supplementary Material PubReader ePub

Background
Using long-term data from the Korean Genome and Epidemiology Study, we defined poor glycemic control and investigated possible risk factors, including variants related to type 2 diabetes mellitus (T2DM). In addition, we evaluated interaction effects among risk factors for poor glycemic control.
Methods
Among 436 subjects with newly diagnosed diabetes, poor glycemic control was defined based on glycosylated hemoglobin trajectory patterns by group-based trajectory modeling. For the variants related to T2DM, genetic risk scores (GRSs) were calculated and divided into quartiles. Risk factors for poor glycemic control were assessed using a logistic regression model.
Results
Of the subjects, 43% were in the poor-glycemic-control group. Body mass index (BMI) and triglyceride (TG) were associated with poor glycemic control. The risk for poor glycemic control increased by 11.0% per 1 kg/m² increase in BMI and by 3.0% per 10 mg/dL increase in TG. The risk for GRS with poor glycemic control was sex-dependent (P_interaction=0.07), and a relationship by GRS quartiles was found in females but not in males. Moreover, the interaction effect was found to be significant on both additive and multiplicative scales. The interaction effect was evident in the variants of cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like (CDKAL1).
Conclusion
Females with risk alleles of variants in CDKAL1 associated with T2DM had a higher risk for poor glycemic control than males.

Citations

Citations to this article as recorded by

Hepatic Cdkal1 deletion regulates HDL catabolism and promotes reverse cholesterol transport
Dan Bi An, Soo-jin Ann, Seungmin Seok, Yura Kang, Sang-Hak Lee
Atherosclerosis.2023; 375: 21. CrossRef

Metabolic Risk/Epidemiology
Longitudinal Change in Myocardial Function and Clinical Parameters in Middle-Aged Subjects: A 3-Year Follow-up Study: Dong-Hyuk Cho, Hyung Joon Joo, Mi-Na Kim, Hee-Dong Kim, Do-Sun Lim, Seong-Mi Park; Diabetes Metab J. 2021;45(5):719-729. Published online June 15, 2021; DOI: https://doi.org/10.4093/dmj.2020.0132

5,359 View
121 Download
3 Web of Science
3 Crossref

Graphical Abstract Abstract PDF PubReader ePub

Background
Metabolic syndrome (MetS) is closely associated with the aging process. However, changes in metabolic conditions and cardiac function that occur in middle aged population remain unclear. We evaluated longitudinal changes in metabolic parameters and cardiac function during a 3-year period in subjects with suspected MetS.
Methods
We studied 191 participants with suspected MetS at baseline and after 3 years. Anthropometric parameters, including waist circumference (WC), and metabolic parameters, including fasting blood glucose and lipid profile were measured. Conventional echocardiography with two-dimensional speckle tracking was performed.
Results
Mean age was 56.2±4.4 years, and there were 97 women (50.8%). Men had increased WC and triglycerides (TG) (WC 91.2±6.8 cm vs. 84.0±8.0 cm, P<0.001; TG 184.4±116.3 mg/dL vs. 128.2±53.6 mg/dL, P<0.001), and reduced global longitudinal strain (GLS) (–15.4%±2.1% vs. –17.1%±2.0%, P<0.001) compared to women. After 3.4 years, values of WC and TG did not change in men but increased in women (all P<0.05). The absolute value of left ventricular (LV) GLS did not change in men but was reduced in women (P=0.011). Change in TG was independently associated with worsening of LV GLS only in women (standardized β, –0.309; 95% confidence interval, –0.130 to –0.009; P=0.025).
Conclusion
In middle aged population, a vulnerable period for metabolic disturbance, cardiac remodeling tended to progress, which was prominent in women. Progression of adiposity and dyslipidemia after menopause may accelerate subclinical cardiac remodeling in middle-aged women. Lifestyle modification and medical interventions may help prevent further cardiac dysfunction in these subjects.

Citations

Citations to this article as recorded by

Positive additive interaction effects of age, sex, obesity, and metabolic syndrome on left ventricular dysfunction
Dan Zhou, Zhongwen Ye, Zhiqiang Nie, Chaolei Chen, Songyuan Luo, Mengqi Yan, Jiabin Wang, Yingqing Feng
Journal of Diabetes.2024;[Epub] CrossRef
Epicardial Adipose Tissue and Heart Failure, Friend or Foe?
Dong-Hyuk Cho, Seong-Mi Park
Diabetes & Metabolism Journal.2024; 48(3): 373. CrossRef
Lung-Heart Outcomes and Mortality through the 2020 COVID-19 Pandemic in a Prospective Cohort of Breast Cancer Radiotherapy Patients
Vincent Vinh-Hung, Olena Gorobets, Nele Adriaenssens, Hilde Van Parijs, Guy Storme, Dirk Verellen, Nam P. Nguyen, Nicolas Magne, Mark De Ridder
Cancers.2022; 14(24): 6241. CrossRef

Obesity and Metabolic Syndrome
The Relationship between Thyroid Function and Different Obesity Phenotypes in Korean Euthyroid Adults: Jeong Mi Kim, Bo Hyun Kim, Hyungi Lee, Eun Heui Kim, Mijin Kim, Jong Ho Kim, Yun Kyung Jeon, Sang Soo Kim, In Joo Kim, Yong Ki Kim; Diabetes Metab J. 2019;43(6):867-878. Published online April 3, 2019; DOI: https://doi.org/10.4093/dmj.2018.0130

7,166 View
79 Download
20 Web of Science
19 Crossref

Abstract PDF Supplementary Material PubReader

Background

Thyroid disease and metabolic syndrome are both associated with cardiovascular disease. The aim of this study was to investigate the correlation between thyroid hormones and obesity sub-phenotypes using nationwide data from Korea, a country known to be iodine replete.

Methods

This study was based on data obtained from the sixth Korea National Health and Nutrition Examination Survey, administered from 2013 to 2015. A total of 13,873 participants aged ≥19 years were included, and classified into four groups: metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity (MUNO), and metabolically unhealthy obesity (MUO) by body fat on the basis of body mass index and metabolic health.

Results

At baseline, serum free thyroxine (fT4) values were significantly higher in the MHNO phenotype (MHNO, 1.27±0.01 ng/dL; MHO, 1.25±0.01 ng/dL; MUNO, 1.24±0.01 ng/dL; MUO, 1.24±0.01 ng/dL, P<0.001) in total study population. However, this significant association no longer remained after adjustment for age, urine iodine concentration, and smoking (P=0.085). After adjustment for confounders, statistically significant association was observed between lower thyroid stimulating hormone (TSH) and MHNO phenotype (P=0.044). In men participants (not women), higher fT4 values were significantly associated with MHNO phenotype (P<0.001). However, no significant association was observed between thyroid function (TSH or fT4) and obesity phenotypes in groups classified by age (cutoff age of 55 years).

Conclusion

Although there was a difference by age and sex, we found that the decrease of TSH and the increase of fT4 values were associated with MHNO.

Citations

Citations to this article as recorded by

Causal association between obesity and hypothyroidism: a two-sample bidirectional Mendelian randomization study
Yingkun Qiu, Qinyu Liu, Yinghua Luo, Jiadi Chen, Qingzhu Zheng, Yuping Xie, Yingping Cao
Frontiers in Endocrinology.2024;[Epub] CrossRef
Exploring the diagnostic performance of machine learning in prediction of metabolic phenotypes focusing on thyroid function
Hyeong Jun Ahn, Kyle Ishikawa, Min-Hee Kim, Vijayalakshmi Kakulapati
PLOS ONE.2024; 19(6): e0304785. CrossRef
Association between thyroid hormone resistance and obesity: a cross‐sectional study and mouse stimulation test
Zhihui Wang, Huimin Yu, Kai Wang, Junming Han, Yongfeng Song
Obesity.2024; 32(8): 1483. CrossRef
Association between sensitivity to thyroid hormone indices and type 2 diabetic microvascular complications in euthyroid patients
Junping Zhang, Zhanpeng Luo, Jie Zhang, Ruohao Zhang, Xinnan Liu, Jinying Wang, Shasha He, Jiao Wang, Dandan Chen, Cuiyun Chen, Jiancheng Wang, Shen Chen, Jixiong Xu
Scientific Reports.2024;[Epub] CrossRef
Association between thyroid function and obesity phenotypes in healthy euthyroid individuals: an investigation based on Tehran Thyroid Study
Behnaz Abiri, Amirhossein Ramezani Ahmadi, Maryam Mahdavi, Atieh Amouzegar, Majid Valizadeh
European Journal of Medical Research.2023;[Epub] CrossRef
Characteristics of the metabolically unhealthy phenotype in menopausal resistance training practitioners
Ana Carla Leocadio de Magalhaes, Vilma Fernandes Carvalho, Sabrina Pereira da Cruz, Andréa Ramalho
Nutrición Hospitalaria.2023;[Epub] CrossRef
A systematic review and meta-analysis investigating the relationship between metabolic syndrome and the incidence of thyroid diseases
Heba Alwan, Valerie Aponte Ribero, Orestis Efthimiou, Cinzia Del Giovane, Nicolas Rodondi, Leonidas Duntas
Endocrine.2023; 84(2): 320. CrossRef
Higher Sensitivity to Thyroid Hormones May Be Linked to Maintaining the Healthy Metabolic Condition in People with Obesity: New Insight from NHANES
Ying-shan Liu, Xiao-cong Liu, Jian Kuang, Hai-xia Guan
Obesity Facts.2023; 16(5): 497. CrossRef
Is there a link between obesity phenotype and thyroid diseases? A mini-review of current concepts
Ewa Malwina Milewska-Kobos, Ewelina Szczepanek-Parulska, Marek Ruchala
Postępy Higieny i Medycyny Doświadczalnej.2023; 77(1): 107. CrossRef
Sex-specific Association of Subclinical Hypothyroidism With Incident Metabolic Syndrome: A Population-based Cohort Study
Zhiyuan Wu, Yue Jiang, Di Zhou, Shuo Chen, Yu Zhao, Haiping Zhang, Yue Liu, Xia Li, Wei Wang, Jingbo Zhang, Xiaoping Kang, Lixin Tao, Bo Gao, Xiuhua Guo
The Journal of Clinical Endocrinology & Metabolism.2022; 107(6): e2365. CrossRef
Determination of age and sex specific TSH and FT4 reference limits in overweight and obese individuals in an iodine-replete region: Tehran Thyroid Study (TTS)
Hengameh Abdi, Bita Faam, Safoora Gharibzadeh, Ladan Mehran, Maryam Tohidi, Fereidoun Azizi, Atieh Amouzegar
Endocrine Research.2021; 46(1): 37. CrossRef
Association of Metabolic Obesity Phenotypes and Total Testosterone in Chinese Male Population
Luna Liu, Shuang Liu, Qianmei Song, Dandan Luo, Yu Su, Xiangyu Qi, Qian Wang, Jing Ning, Youyuan Lv, Qingbo Guan
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2021; Volume 14: 399. CrossRef
Insulin Resistance in Association with Thyroid Function, Psychoemotional State, and Cardiovascular Risk Factors
Nijole Kazukauskiene, Aurelija Podlipskyte, Giedrius Varoneckas, Narseta Mickuviene
International Journal of Environmental Research and Public Health.2021; 18(7): 3388. CrossRef
Association between different obesity phenotypes and hypothyroidism: a study based on a longitudinal health management cohort
Yupeng Wang, Haiyan Lin, Qihang Li, Liying Guan, Meng Zhao, Fang Zhong, Jing Liu, Zhongshang Yuan, Honglin Guo, Yongfeng Song, Ling Gao, Jiajun Zhao
Endocrine.2021; 72(3): 688. CrossRef
Causal Association Between Serum Thyrotropin and Obesity: A Bidirectional, Mendelian Randomization Study
Xichang Wang, Xiaotong Gao, Yutong Han, Fan Zhang, Zheyu Lin, Hong Wang, Weiping Teng, Zhongyan Shan
The Journal of Clinical Endocrinology & Metabolism.2021; 106(10): e4251. CrossRef
Effect of body mass index on peak growth hormone level after growth hormone stimulation test in children with short stature
Na Yeong Lee, Sung Eun Kim, Seulki Kim, Moon Bae Ahn, Shin Hee Kim, Won Kyoung Cho, Kyoung Soon Cho, Min Ho Jung, Byung-Kyu Suh
Annals of Pediatric Endocrinology & Metabolism.2021; 26(3): 192. CrossRef
Interaction effect of obesity and thyroid autoimmunity on the prevalence of hyperthyrotropinaemia
Xiaoyong Guo, Zhao He, Shanshan Shao, Yilin Fu, Dongmei Zheng, Lu Liu, Ling Gao, Liying Guan, Meng Zhao, Jiajun Zhao
Endocrine.2020; 68(3): 573. CrossRef
The role of thyroid hormone in metabolism and metabolic syndrome
Patrícia de Fátima dos Santos Teixeira, Patrícia Borges dos Santos, Carmen Cabanelas Pazos-Moura
Therapeutic Advances in Endocrinology and Metabolism.2020;[Epub] CrossRef
Characteristics of Serum Thyroid Hormones in Different Metabolic Phenotypes of Obesity
Xiaomin Nie, Xiaojing Ma, Yiting Xu, Yun Shen, Yufei Wang, Yuqian Bao
Frontiers in Endocrinology.2020;[Epub] CrossRef

Genome-Wide Association Study Identifies Two Novel Loci with Sex-Specific Effects for Type 2 Diabetes Mellitus and Glycemic Traits in a Korean Population: Min Jin Go, Joo-Yeon Hwang, Tae-Joon Park, Young Jin Kim, Ji Hee Oh, Yeon-Jung Kim, Bok-Ghee Han, Bong-Jo Kim; Diabetes Metab J. 2014;38(5):375-387. Published online October 17, 2014; DOI: https://doi.org/10.4093/dmj.2014.38.5.375

6,564 View
46 Download
29 Web of Science
26 Crossref

Abstract PDF PubReader

Background

Until recently, genome-wide association study (GWAS)-based findings have provided a substantial genetic contribution to type 2 diabetes mellitus (T2DM) or related glycemic traits. However, identification of allelic heterogeneity and population-specific genetic variants under consideration of potential confounding factors will be very valuable for clinical applicability. To identify novel susceptibility loci for T2DM and glycemic traits, we performed a two-stage genetic association study in a Korean population.

Methods

We performed a logistic analysis for T2DM, and the first discovery GWAS was analyzed for 1,042 cases and 2,943 controls recruited from a population-based cohort (KARE, n=8,842). The second stage, de novo replication analysis, was performed in 1,216 cases and 1,352 controls selected from an independent population-based cohort (Health 2, n=8,500). A multiple linear regression analysis for glycemic traits was further performed in a total of 14,232 nondiabetic individuals consisting of 7,696 GWAS and 6,536 replication study participants. A meta-analysis was performed on the combined results using effect size and standard errors estimated for stage 1 and 2, respectively.

Results

A combined meta-analysis for T2DM identified two new (rs11065756 and rs2074356) loci reaching genome-wide significance in CCDC63 and C12orf51 on the 12q24 region. In addition, these variants were significantly associated with fasting plasma glucose and homeostasis model assessment of β-cell function. Interestingly, two independent single nucleotide polymorphisms were associated with sex-specific stratification in this study.

Conclusion

Our study showed a strong association between T2DM and glycemic traits. We further observed that two novel loci with multiple diverse effects were highly specific to males. Taken together, these findings may provide additional insights into the clinical assessment or subclassification of disease risk in a Korean population.

Citations

Citations to this article as recorded by

Family history of type 2 diabetes and the risk of type 2 diabetes among young and middle‐aged adults
Ken R. Smith, Huong Meeks, David Curtis, Barbara B. Brown, Kyle Kole, Lori Kowaleski‐Jones
Chronic Diseases and Translational Medicine.2024;[Epub] CrossRef
Relationship between HECTD4 gene variants, obesity, and coffee consumption
Min-Jeong Kim, Hyun-Seok Jin, Yong-Bin Eom
European Journal of Clinical Nutrition.2024;[Epub] CrossRef
Interactions between Bitter Taste Receptor Gene Variants and Dietary Intake Are Associated with the Incidence of Type 2 Diabetes Mellitus in Middle-Aged and Older Korean Adults
Kyung Won Lee, Dayeon Shin
International Journal of Molecular Sciences.2023; 24(3): 2199. CrossRef
Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study
Mahdi Akbarzadeh, Nadia Alipour, Hamed Moheimani, Asieh Sadat Zahedi, Firoozeh Hosseini-Esfahani, Hossein Lanjanian, Fereidoun Azizi, Maryam S. Daneshpour
Journal of Translational Medicine.2022;[Epub] CrossRef
The potential effects of HECTD4 variants on fasting glucose and triglyceride levels in relation to prevalence of type 2 diabetes based on alcohol intake
Yoo Jeong Lee, Hansongyi Lee, Han Byul Jang, Min-Gyu Yoo, Sumin Im, Soo Kyung Koo, Hye-Ja Lee
Archives of Toxicology.2022; 96(9): 2487. CrossRef
Impaired fasting glucose and development of chronic kidney disease in non-diabetic population: a Mendelian randomization study
Hyoungnae Kim, Suyeon Park, Soon Hyo Kwon, Jin Seok Jeon, Dong Cheol Han, Hyunjin Noh
BMJ Open Diabetes Research & Care.2020; 8(1): e001395. CrossRef
Interactions of Habitual Coffee Consumption by Genetic Polymorphisms with the Risk of Prediabetes and Type 2 Diabetes Combined
Taiyue Jin, Jiyoung Youn, An Na Kim, Moonil Kang, Kyunga Kim, Joohon Sung, Jung Eun Lee
Nutrients.2020; 12(8): 2228. CrossRef
Genetic predisposition in type 2 diabetes: A promising approach toward a personalized management of diabetes
Mahmoud M. Sirdah, N. Scott Reading
Clinical Genetics.2020; 98(6): 525. CrossRef
Association of transcription factor 7-like 2 (TCF7L2) gene polymorphism with type 2 diabetes mellitus in Chinese Korean ethnicity population
Kui-Chen Zhou, Hong-Wei Liu, Chen Wang, Yan-Jun Fu, Feng Jin
Medicine.2019; 98(5): e14288. CrossRef
Association of Fasting Glucose Level with Neutrophil-Lymphocyte Ratio Compared to Leukocyte Count and Serum C-Reactive Protein
Jin-Kyu Kim, Ah-Young Lee, Jee-Hyun Kang, Byung-Yeon Yu, Seong-Ju Kim
Korean Journal of Family Medicine.2018; 39(1): 42. CrossRef
New Common and Rare Variants Influencing Metabolic Syndrome and Its Individual Components in a Korean Population
Ho-Sun Lee, Yongkang Kim, Taesung Park
Scientific Reports.2018;[Epub] CrossRef
Association of genetic variants in RETN, NAMPT and ADIPOQ gene with glycemic, metabolic traits and diabetes risk in a Chinese population
Qiang Zhou, Bo Chen, Tianxing Ji, Miaoshan Luo, Jiandong Luo
Gene.2018; 642: 439. CrossRef
Opposite Genetic Effects of CMIP Polymorphisms on the Risk of Type 2 Diabetes and Obesity: A Family-Based Study in China
Yaying Cao, Tao Wang, Yiqun Wu, Juan Juan, Xueying Qin, Xun Tang, Tao Wu, Yonghua Hu
International Journal of Molecular Sciences.2018; 19(4): 1011. CrossRef
Type 2 Diabetes Genetic Variants and Risk of Diabetic Retinopathy
Yong He Chong, Qiao Fan, Yih Chung Tham, Alfred Gan, Shu Pei Tan, Gavin Tan, Jie Jin Wang, Paul Mitchell, Tien Yin Wong, Ching-Yu Cheng
Ophthalmology.2017; 124(3): 336. CrossRef
Precision Nutrition: A Review of Personalized Nutritional Approaches for the Prevention and Management of Metabolic Syndrome
Juan De Toro-Martín, Benoit Arsenault, Jean-Pierre Després, Marie-Claude Vohl
Nutrients.2017; 9(8): 913. CrossRef
10-year trajectory of β-cell function and insulin sensitivity in the development of type 2 diabetes: a community-based prospective cohort study
Jung Hun Ohn, Soo Heon Kwak, Young Min Cho, Soo Lim, Hak Chul Jang, Kyong Soo Park, Nam H Cho
The Lancet Diabetes & Endocrinology.2016; 4(1): 27. CrossRef
Sex and Gender Differences in Risk, Pathophysiology and Complications of Type 2 Diabetes Mellitus
Alexandra Kautzky-Willer, Jürgen Harreiter, Giovanni Pacini
Endocrine Reviews.2016; 37(3): 278. CrossRef
No Interaction with Alcohol Consumption, but Independent Effect of C12orf51 (HECTD4) on Type 2 Diabetes Mellitus in Korean Adults Aged 40-69 Years: The KoGES_Ansan and Ansung Study
Jihye Kim, Bermseok Oh, Ji Eun Lim, Mi Kyung Kim, C. Mary Schooling
PLOS ONE.2016; 11(2): e0149321. CrossRef
Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes
Sungkyoung Choi, Sunghwan Bae, Taesung Park
Genomics & Informatics.2016; 14(4): 138. CrossRef
Analysis of multiple related phenotypes in genome-wide association studies
Sohee Oh, Iksoo Huh, Seung Yeoun Lee, Taesung Park
Journal of Bioinformatics and Computational Biology.2016; 14(05): 1644005. CrossRef
Recent progress in genetic and epigenetic research on type 2 diabetes
Soo Heon Kwak, Kyong Soo Park
Experimental & Molecular Medicine.2016; 48(3): e220. CrossRef
Statistical power considerations in genotype-based recall randomized controlled trials
Naeimeh Atabaki-Pasdar, Mattias Ohlsson, Dmitry Shungin, Azra Kurbasic, Erik Ingelsson, Ewan R. Pearson, Ashfaq Ali, Paul W. Franks
Scientific Reports.2016;[Epub] CrossRef
The role of vitamin D, obesity and physical exercise in regulation of glycemia in Type 2 Diabetes Mellitus patients
Abdulbari Bener, Abdulla O.A.A. Al-Hamaq, Eda Merve Kurtulus, Waleed K. Abdullatef, Mahmoud Zirie
Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2016; 10(4): 198. CrossRef
Recent advances in understanding the genetic architecture of type 2 diabetes
Karen L. Mohlke, Michael Boehnke
Human Molecular Genetics.2015; 24(R1): R85. CrossRef
Letter: Genome-Wide Association Study Identifies Two Novel Loci with Sex-Specific Effects for Type 2 Diabetes Mellitus and Glycemic Traits in a Korean Population (Diabetes Metab J2014;38:375-87)
Soo Heon Kwak
Diabetes & Metabolism Journal.2014; 38(6): 484. CrossRef
Response: Genome-Wide Association Study Identifies Two Novel Loci with Sex-Specific Effects for Type 2 Diabetes Mellitus and Glycemic Traits in a Korean Population (Diabetes Metab J2014;38:375-87)
Min Jin Go, Bong-Jo Kim
Diabetes & Metabolism Journal.2014; 38(6): 487. CrossRef

Sex Hormone Binding Globulin, Body Fat Distribution and Insulin Resistance in Premenopausal Women.: Young Sook Lee, Hye Jin Lee, Jee Young Oh, Young Sun Hong, Yeon Ah Sung; Korean Diabetes J. 2003;27(1):63-72. Published online February 1, 2003

1,638 View
22 Download

Abstract PDF: BACKGROUND
Low levels of sex-hormone binding globulin (SHBG), an indirect index of androgenicity, have been reported to be associated with obesity, especially central obesity. In women, increased androgenicity is related to hyperinsulinemia, impaired glucose tolerance and the development of type 2 diabetes mellitus. Recent studies have suggested that the relationship between SHBG and insulin resistance was mediated by the change in total or visceral adiposity, and that ethnical differences in the relationship between sex hormone and body fat distribution might exist. METHODS: We examined the associations of SHBG to the body fat distribution and insulin resistance in Korean premenopausal women. The fasting serum level of SHBG was measured by RIA, and the insulin sensitivity by the minimal model derived sensitivity index (SI), using the insulin modified intravenous glucose tolerance test. The amount of body fat, and its distribution, were assessed by anthropometric measurement, bioelectric impedance analyses, and computed tomography at the level of the umbilicus. RESULTS: 1. SHBG was significantly inversely correlated with the body mass index (BMI), waist circumference, visceral fat area, and fasting insulin levels, and was significantly positively correlated to the SI. 2. SHBG was significantly lower in premenopausal women with an impaired glucose tolerance, compared to those with a normal glucose tolerance, and significantly lower in those with hypertension (systolic BP> or =140 mmHg or diastolic BP> or =90 mmHg), compared to those with normal blood pressure. SHBG was also significantly lower in persons with central obesity(waist circumference > or = 80 cm) compared to those without. 3. In a multiple linear regression analysis, the SI was significantly associated with SHBG, after adjustment for age, BMI, systolic blood pressure, triglycerides, HDL- cholesterol, and percentage body fat, but this association disappeared after additional adjustment for visceral fat area. 4. In a multiple linear regression analysis, the fasting plasma insulin, BMI and percentage body fat were significant independent factors associated with SHBG. CONCLUSION: Increased androgenicity as assessed by decreased serum SHBG concentrations, is strongly associated with an unfavorable body fat distribution, hypertension, glucose intolerance, hyperinsulinemia, and insulin resistance.

Free Testosterone and Sex Hormone-Binding Globulin Level in Type 2 Diabetic Men.: Ki Deuk Nam, Young Seol Kim, Cheol Young Park, Seung Joon Oh, Deog Yoon Kim, Jeong Taek Woo, Sung Woon Kim, In Myung Yang, Jin Woo Kim, Young Kil Choi; Korean Diabetes J. 2000;24(6):699-707. Published online January 1, 2001

1,417 View
19 Download

Abstract PDF: BACKGROUND
Insulin resistance is a risk factor for cardiovascular disease and type 2 diabetes mellitus. There are many previous studies indicating that insulin lowers serum sex hormone-binding globulin levels, and there is inverse correlation between insulin resistance and serum sex hormone-binding globulin levels in women. However, in men, a limited number of studies are available to explain the effect of sex hormone on age and insulin. Therefore, the present study was undertaken to investigate the relationship among free testosterone, hormone- binding globulin and age in type 2 diabetic men and control subjects. METHOD: Age, body mass index, total cholesterol, triglyceride, fasting blood sugar, and insulin concentrations were examined on 89 type 2 diabetic men and 47 control subjects. The free testosterone level was measured by commercially available double-antibody system (Radioimmunoassay). The sex hormone-binding globulin level was also measured by commercially available double-antibody system(Immunoradiometric assay). RESULTS: 1) Sex hormone-binding globulin level was significantly increased in patients with type 2 diabetes. However, there was no significant difference in free testosterone level between the two groups. 2) Sex hormone-binding globulin was positively correlated with age (r=0.4, p<0.001) in patients with type 2 diabetes. Sex hormone-binding globulin and free testos terone were not correlated with age in control sujects. 3) Free testosterone and sex hormone-binding globulin concentrations were not significantly related to serum insulin concentration after adjusting for age and body mass index. CONCLUSIONS: We observed increased sex hormone-binding globulin concentration in diabetes man, and was a positively related to age. Further studies are needed to understand the relationships between age, insulin resistance, testosterone, and sex hormone-binding globulin concentrations.

First
Prev
Page of 1
Next
Last

Search