Ge Li1, Ling Zhong1, Lanwen Han2, Yonghui Wang2, Bo Li1, Dongmei Wang1, Yanglu Zhao3, Yu Li1, Qian Zhang1, Lu Qi4,5, John R Speakman6,7, Steven M Willi8, Ming Li9, Shan Gao10. 1. Department of Endocrinology, NHC Key Laboratory of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China. 2. Department of Endocrinology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100043, China. 3. Epidemiology Department, Fielding School of Public Health, University of California Los Angeles, LA, 90024, USA. 4. Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA. 5. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA. 6. Institute of Biological and Environmental Sciences, Aberdeen University, Aberdeen, UK. 7. State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China. 8. Department of Endocrinology, The Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. 9. Department of Endocrinology, NHC Key Laboratory of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China. liming@pumch.cn. 10. Department of Endocrinology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100043, China. gaoshanmw@163.com.
Abstract
BACKGROUND/ OBJECTIVES: Adiponectin represents an important link between adipose tissue dysfunction and cardiometabolic risk in obesity; however, there is a lack of data on the effects of adiponectin-related genetic variations and gene-diet interactions on metabolic disorders in children. We aimed to investigate possible interactions between adiponectin-related genetic variants and habitual dietary patterns on metabolic health among children with normal weight versus overweight/obesity, and whether these effects in childhood longitudinally contribute to metabolic risk at follow-up. SUBJECTS/ METHODS: In total, 3,317 Chinese children aged 6-18 at baseline and 339 participants at 10-year follow-up from the Beijing Child and Adolescent Metabolic Syndrome study cohort were included. Baseline lifestyle factors, plasma adiponectin levels, and six adiponectin-related genetic variants resulting from GWAS in East Asians (loci in/near ADIPOQ, CDH13, WDR11FGF, CMIP, and PEPD) were assessed for their associations with the metabolic disorders. Being metabolically unhealthy was defined by exhibiting any metabolic syndrome component. RESULTS: Among the six loci, ADIPOQ rs6773957 (OR 1.26, 95% CI:1.07-1.47, P = 0.004) and adiponectin receptor CDH13 rs4783244 (0.82, 0.69-0.96, P = 0.017) were correlated with metabolic risks independent of lifestyle factors in normal-weight children, but the associations were less obvious in those with overweight/obesity. A significant interaction between rs6773957 and diet (Pinteraction = 0.004) for metabolic health was observed in normal-weight children. The adiponectin-decreasing allele of rs6773957 was associated with greater metabolic risks in individuals with unfavorable diet patterns (P < 0.001), but not in those with healthy patterns (P > 0.1). A similar interaction effect was observed using longitudinal data (Pinteraction = 0.029). CONCLUSIONS: These findings highlight a novel gene-diet interaction on the susceptibility to cardiometabolic disorders, which has a long-term impact from childhood onward, particularly in those with normal weight. Personalized dietary advice in these individuals may be recommended as an early possible therapeutic measure to improve metabolic health.
BACKGROUND/ OBJECTIVES: Adiponectin represents an important link between adipose tissue dysfunction and cardiometabolic risk in obesity; however, there is a lack of data on the effects of adiponectin-related genetic variations and gene-diet interactions on metabolic disorders in children. We aimed to investigate possible interactions between adiponectin-related genetic variants and habitual dietary patterns on metabolic health among children with normal weight versus overweight/obesity, and whether these effects in childhood longitudinally contribute to metabolic risk at follow-up. SUBJECTS/ METHODS: In total, 3,317 Chinese children aged 6-18 at baseline and 339 participants at 10-year follow-up from the Beijing Child and Adolescent Metabolic Syndrome study cohort were included. Baseline lifestyle factors, plasma adiponectin levels, and six adiponectin-related genetic variants resulting from GWAS in East Asians (loci in/near ADIPOQ, CDH13, WDR11FGF, CMIP, and PEPD) were assessed for their associations with the metabolic disorders. Being metabolically unhealthy was defined by exhibiting any metabolic syndrome component. RESULTS: Among the six loci, ADIPOQ rs6773957 (OR 1.26, 95% CI:1.07-1.47, P = 0.004) and adiponectin receptor CDH13 rs4783244 (0.82, 0.69-0.96, P = 0.017) were correlated with metabolic risks independent of lifestyle factors in normal-weight children, but the associations were less obvious in those with overweight/obesity. A significant interaction between rs6773957 and diet (Pinteraction = 0.004) for metabolic health was observed in normal-weight children. The adiponectin-decreasing allele of rs6773957 was associated with greater metabolic risks in individuals with unfavorable diet patterns (P < 0.001), but not in those with healthy patterns (P > 0.1). A similar interaction effect was observed using longitudinal data (Pinteraction = 0.029). CONCLUSIONS: These findings highlight a novel gene-diet interaction on the susceptibility to cardiometabolic disorders, which has a long-term impact from childhood onward, particularly in those with normal weight. Personalized dietary advice in these individuals may be recommended as an early possible therapeutic measure to improve metabolic health.
Authors: Wenjie Ma; Tao Huang; Yan Zheng; Molin Wang; George A Bray; Frank M Sacks; Lu Qi Journal: J Clin Endocrinol Metab Date: 2016-04-07 Impact factor: 5.958
Authors: C Weyer; T Funahashi; S Tanaka; K Hotta; Y Matsuzawa; R E Pratley; P A Tataranni Journal: J Clin Endocrinol Metab Date: 2001-05 Impact factor: 5.958
Authors: K G M M Alberti; Robert H Eckel; Scott M Grundy; Paul Z Zimmet; James I Cleeman; Karen A Donato; Jean-Charles Fruchart; W Philip T James; Catherine M Loria; Sidney C Smith Journal: Circulation Date: 2009-10-05 Impact factor: 29.690
Authors: Hala B AlEssa; Vasanti S Malik; Changzheng Yuan; Walter C Willett; Tianyi Huang; Frank B Hu; Deirdre K Tobias Journal: Am J Clin Nutr Date: 2016-12-14 Impact factor: 7.045
Authors: He Gao; Tove Fall; Rob M van Dam; Allan Flyvbjerg; Björn Zethelius; Erik Ingelsson; Sara Hägg Journal: Diabetes Date: 2012-12-28 Impact factor: 9.461
Authors: Jacob M Keaton; Chuan Gao; Meijian Guan; Jacklyn N Hellwege; Nicholette D Palmer; James S Pankow; Myriam Fornage; James G Wilson; Adolfo Correa; Laura J Rasmussen-Torvik; Jerome I Rotter; Yii-Der I Chen; Kent D Taylor; Stephen S Rich; Lynne E Wagenknecht; Barry I Freedman; Maggie C Y Ng; Donald W Bowden Journal: Genet Epidemiol Date: 2018-04-24 Impact factor: 2.344