Min Xu1,2,3, Xiaofei Lv1,2,3, Lan Xie4, Xiaolin Huang1,2,3, Ya Huang1,2,3, Ying Chen1,2,3, Kui Peng1,2,3, Po Wang1,2,3, Weiqing Wang1,2,3, Lu Qi5, Yufang Bi6,7,8, Yimin Sun9,10, Guang Ning1,2,3. 1. State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of the Ministry of Health, National Clinical Research Center for Metabolic Diseases, Collaborative Innovation Center of Systems Biomedicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 2. Shanghai Institute of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 3. Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Rui-Jin 2nd Road, Shanghai, 200025, China. 4. Department of Biomedical Engineering, Medical Systems Biology Research Center, Tsinghua University School of Medicine, 18 Life Science Park Road, Beijing, 100084, China. 5. Department of Nutrition, Harvard School of Public Health, Boston, MA, USA. 6. State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of the Ministry of Health, National Clinical Research Center for Metabolic Diseases, Collaborative Innovation Center of Systems Biomedicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. byf10784@rjh.com.cn. 7. Shanghai Institute of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. byf10784@rjh.com.cn. 8. Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Rui-Jin 2nd Road, Shanghai, 200025, China. byf10784@rjh.com.cn. 9. Department of Biomedical Engineering, Medical Systems Biology Research Center, Tsinghua University School of Medicine, 18 Life Science Park Road, Beijing, 100084, China. ymsun@capitalbio.com. 10. National Engineering Research Center for Beijing Biochip Technology, Beijing, China. ymsun@capitalbio.com.
Abstract
AIMS/HYPOTHESIS: Glucokinase regulatory protein gene (GCKR) variant rs780092 is a novel genetic variant associated with serum triacylglycerol (TG) identified in a genome-wide association study in East Asians. We aimed to investigate associations of rs780092 with incident type 2 diabetes and dyslipidaemia, and the longitudinal changes in glucose and lipid levels. METHODS: A community-based prospective cohort study was conducted at baseline in 2008, including 5,613 non-diabetic participants (37% male, mean age 57.6 years) with 5 years of follow-up. Blood glucose and lipid was measured at baseline and follow-up. RESULTS: Each rs780092 T-allele was associated with a 17% lower risk of incident type 2 diabetes (HR 0.83 [95% CI 0.73, 0.95]) and 36% higher risk of incident hypertriacylglycerolaemia (OR 1.36 [95% CI 1.08, 1.72]), after adjustment for baseline fasting glucose and TG and other confounders. The T-allele was associated with a 5 year increasing level of log10 TG (β ± SE, 0.01 ± 0.004, p = 0.005). Mediation analysis showed that both baseline TG and the 5 year increase in log10 TG were significant mediators in the associations of rs780092 with risk of diabetes. The risk of incident type 2 diabetes associated with 1 SD increase in total and LDL-cholesterol was 35% and 22% lower in TT carriers compared with CC carriers, respectively (both p for interaction ≤ 0.04). CONCLUSIONS/ INTERPRETATION: The GCKR rs780092 variant showed opposite-directional associations with type 2 diabetes and hypertriacylglycerolaemia in a Chinese population. Both baseline level and 5 year change in serum TG were mediators of the association between the genetic variant and type 2 diabetes.
AIMS/HYPOTHESIS: Glucokinase regulatory protein gene (GCKR) variant rs780092 is a novel genetic variant associated with serum triacylglycerol (TG) identified in a genome-wide association study in East Asians. We aimed to investigate associations of rs780092 with incident type 2 diabetes and dyslipidaemia, and the longitudinal changes in glucose and lipid levels. METHODS: A community-based prospective cohort study was conducted at baseline in 2008, including 5,613 non-diabeticparticipants (37% male, mean age 57.6 years) with 5 years of follow-up. Blood glucose and lipid was measured at baseline and follow-up. RESULTS: Each rs780092 T-allele was associated with a 17% lower risk of incident type 2 diabetes (HR 0.83 [95% CI 0.73, 0.95]) and 36% higher risk of incident hypertriacylglycerolaemia (OR 1.36 [95% CI 1.08, 1.72]), after adjustment for baseline fasting glucose and TG and other confounders. The T-allele was associated with a 5 year increasing level of log10 TG (β ± SE, 0.01 ± 0.004, p = 0.005). Mediation analysis showed that both baseline TG and the 5 year increase in log10 TG were significant mediators in the associations of rs780092 with risk of diabetes. The risk of incident type 2 diabetes associated with 1 SD increase in total and LDL-cholesterol was 35% and 22% lower in TT carriers compared with CC carriers, respectively (both p for interaction ≤ 0.04). CONCLUSIONS/ INTERPRETATION: The GCKRrs780092 variant showed opposite-directional associations with type 2 diabetes and hypertriacylglycerolaemia in a Chinese population. Both baseline level and 5 year change in serum TG were mediators of the association between the genetic variant and type 2 diabetes.
Entities:
Keywords:
GCKR; Single-nucleotide polymorphism; Triacylglycerol; Type 2 diabetes
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