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| Name | Class |
|---|---|
| Capital Institute of Pediatrics, China | OTHER |
| Beijing Chao Yang Hospital | OTHER |
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Base on enriched resources from the Metabolic Syndrome cohort in children, a long-term prospective cohort study will be carried out. This cohort is a unique biochemical and genetic database of Chinese population with large number of subjects in the world. By collecting information of disease history and lifestyle, measuring clinical and metabolic parameters, especially biomarkers which can reflect the underlying mechanism of insulin resistance and metabolic syndrome, we intend to sort out some unique biochemical and genetic markers for Chinese population.
A representative sample of 19,593 school children, aged 6-18 years, were chosen from four of the eight urban districts and three of seven rural districts in the Beijing area between April and October, 2004. Among these children and adolescents, 4,500 were recognized as having risk factors defined by the presence of any one of the following: overweight, total cholesterol (TC) ≥ 5.2 mmol/L, triglyceride (TG) ≥ 1.7 mmol/L or fasting glucose (FG) ≥ 5.6 mmol/L based on initial finger capillary blood tests. Moreover, all subjects at increased risk for metabolic syndrome, together with a parallel reference population of 1,095 children, were invited to undergo medical examinations for verification based on venipuncture blood samples. Clinical data, biomarkers including adipokines, and lifestyle factors such as physical activity and diet were measured and documented. Genetic variants previously reported from genome-wide association study (GWAS) of obesity and diabetes and DNA-methylation were also assessed. Further, high-throughput analysis of proteomics and metabolomics of the blood samples were conducted. Cross-sectional and follow-up evaluations will be undertaken. The unique biochemical and genetic markers for Chinese population will be identified in the BCAMS study. The biomarkers will build a solid foundation for progressive study on mechanism of metabolic diseases and lead to early prediction of these diseases.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| No intervention | Other | No intervention |
| Measure | Description | Time Frame |
|---|---|---|
| Metabolic syndrome | The presence of pediatric metabolic syndrome (MS) at baseline was defined by the modified criteria of Adult Treatment Panel III (ATP III). | Baseline |
| Measure | Description | Time Frame |
|---|---|---|
| Metabolic syndrome | Metabolic syndrome in adolescents and adults after follow-up was defined by the harmonized definition. | At 10-year follow-up |
| Obesity | The participants' height and weight were measured under standardized conditions by trained staff. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m^2). Normal weight, overweight and obesity were defined by age- and gender-specific BMI percentiles according to the criteria from the Working Group on Obesity in China. |
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Inclusion Criteria:
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School children, aged 6-18 years, from four of the eight urban districts and three of seven rural districts in the Beijing area
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| Name | Affiliation | Role |
|---|---|---|
| Ming Li, MD | Peking Union Medical College Hospital | Principal Investigator |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 26913634 | Background | Li L, Yin J, Cheng H, Wang Y, Gao S, Li M, Grant SF, Li C, Mi J, Li M. Identification of Genetic and Environmental Factors Predicting Metabolically Healthy Obesity in Children: Data From the BCAMS Study. J Clin Endocrinol Metab. 2016 Apr;101(4):1816-25. doi: 10.1210/jc.2015-3760. Epub 2016 Feb 25. | |
| 23514611 | Background |
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| ID | Term |
|---|---|
| D009765 | Obesity |
| D008659 | Metabolic Diseases |
| ID | Term |
|---|---|
| D050177 | Overweight |
| D044343 | Overnutrition |
| D009748 | Nutrition Disorders |
| D009750 | Nutritional and Metabolic Diseases |
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Blood sample
| Baseline |
| Obesity | The participants' height and weight were measured under standardized conditions by trained staff. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m^2). | At10-year follow-up |
| Insulin resistance | Insulin resistance index was calculated by homeostasis model assessment of insulin resistance (HOMA-IR), HOMA-IR = fasting insulin (mU/L) × FG (mmol/L) / 22.5. | Baseline |
| Insulin resistance | Insulin resistance index was calculated by homeostasis model assessment of insulin resistance (HOMA-IR), HOMA-IR = fasting insulin (mU/L) × FG (mmol/L) / 22.5. | At 10-year follow-up |
| Hypertension | Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured in the right arm three times, 10 minutes apart, and the average of the three measurements was used in the analysis.Hypertension is defined by SBP / DBP ≥ 90th percentile for age, gender for subjects less than 18 years. | Baseline |
| Hypertension | Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured in the right arm three times, 10 minutes apart, and the average of the three measurements was used in the analysis.Hypertension is defined by SBP ≥ 130 mmHg or DBP ≥ 85 mmHg for adults. | At 10-year follow-up |
| Hyperglycemia | The concentrations of plasma glucose (mmol/L). | Baseline |
| Hyperglycemia | The concentrations of plasma glucose (mmol/L). | At 10-year follow-up |
| Triglyceride (TG) | The concentrations of plasma triglyceride (TG) (mmol/L). | Baseline |
| Triglyceride (TG) | The concentrations of plasma triglyceride (TG) (mmol/L). | At 10-year follow-up |
| Total cholesterol (TC) | The concentrations of plasma total cholesterol (TC) (mmol/L). | Baseline |
| Total cholesterol (TC) | The concentrations of plasma total cholesterol (TC) (mmol/L). | At 10-year follow-up |
| High-density lipoprotein cholesterol(HDL-C) | The concentrations of plasma high-density lipoprotein cholesterol(HDL-C) (mmol/L). | Baseline |
| High-density lipoprotein cholesterol(HDL-C) | The concentrations of plasma high-density lipoprotein cholesterol(HDL-C) (mmol/L). | At 10-year follow-up |
| Low-density lipoprotein cholesterol (LDL-C) | The concentrations of plasma triglyceride low-density lipoprotein cholesterol (LDL-C) (mmol/L). | Baseline |
| Low-density lipoprotein cholesterol (LDL-C) | The concentrations of plasma triglyceride low-density lipoprotein cholesterol (LDL-C) (mmol/L). | At 10-year follow-up |
| Left ventricular mass | Assessment by a non-invasive transthoracic echocardiogram using a LOGIQ P5 B-mode ultrasonogram equipped (LOGIQ P5, GE Ultrasound, Korea) with a 2.5-3.5 MHz probe. | At 10-year follow-up |
| Non Alcoholic Fatty Liver Disease | Nonalcoholic fatty liver disease (NAFLD) was diagnosed by B ultrasonography according to the 2010 Prevention and Treatment Guidelines for NAFLD published by the Society of Hepatology, Chinese Medical Association. | At 10-year follow-up |
| Self-concept | The Chinese version of the Self-Description Questionnaire II (SDQ-II) was used to assess self-concept. | At 10-year follow-up |
| Wang Q, Yin J, Xu L, Cheng H, Zhao X, Xiang H, Lam HS, Mi J, Li M. Prevalence of metabolic syndrome in a cohort of Chinese schoolchildren: comparison of two definitions and assessment of adipokines as components by factor analysis. BMC Public Health. 2013 Mar 21;13:249. doi: 10.1186/1471-2458-13-249. |
| 28329079 | Background | Li L, Fu J, Yu XT, Li G, Xu L, Yin J, Cheng H, Hou D, Zhao X, Gao S, Li W, Li C, Grant SFA, Li M, Xiao Y, Mi J, Li M. Sleep Duration and Cardiometabolic Risk Among Chinese School-aged Children: Do Adipokines Play a Mediating Role? Sleep. 2017 May 1;40(5):zsx042. doi: 10.1093/sleep/zsx042. |
| 27966597 | Background | Feng D, Zhang J, Fu J, Wu H, Wang Y, Li L, Zhao Y, Li M, Gao S. Association between sleep duration and cardiac structure in youths at risk for metabolic syndrome. Sci Rep. 2016 Dec 14;6:39017. doi: 10.1038/srep39017. |
| 29020116 | Background | Li G, Xu L, Zhao Y, Li L, Fu J, Zhang Q, Li N, Xiao X, Li C, Mi J, Gao S, Li M. Leptin-adiponectin imbalance as a marker of metabolic syndrome among Chinese children and adolescents: The BCAMS study. PLoS One. 2017 Oct 11;12(10):e0186222. doi: 10.1371/journal.pone.0186222. eCollection 2017. |
| 29212175 | Background | Fu J, Li G, Li L, Yin J, Cheng H, Han L, Zhang Q, Li N, Xiao X, Grant SFA, Li M, Gao S, Mi J, Li M. The role of established East Asian obesity-related loci on pediatric leptin levels highlights a neuronal influence on body weight regulation in Chinese children and adolescents: the BCAMS study. Oncotarget. 2017 Aug 24;8(55):93593-93607. doi: 10.18632/oncotarget.20547. eCollection 2017 Nov 7. |
| 27716289 | Background | Fu J, Hou C, Li L, Feng D, Li G, Li M, Li C, Gao S, Li M. Vitamin D modifies the associations between circulating betatrophin and cardiometabolic risk factors among youths at risk for metabolic syndrome. Cardiovasc Diabetol. 2016 Oct 6;15(1):142. doi: 10.1186/s12933-016-0461-y. |
| 28139438 | Background | Li G, Yin J, Fu J, Li L, Grant SFA, Li C, Li M, Mi J, Li M, Gao S. FGF21 deficiency is associated with childhood obesity, insulin resistance and hypoadiponectinaemia: The BCAMS Study. Diabetes Metab. 2017 Jun;43(3):253-260. doi: 10.1016/j.diabet.2016.12.003. Epub 2017 Jan 27. |
| 35145478 | Derived | Wang D, Feng D, Wang Y, Dong P, Wang Y, Zhong L, Li B, Fu J, Xiao X, Speakman JR, Li M, Gao S. Angiopoietin-Like Protein 8/Leptin Crosstalk Influences Cardiac Mass in Youths With Cardiometabolic Risk: The BCAMS Study. Front Endocrinol (Lausanne). 2022 Jan 25;12:788549. doi: 10.3389/fendo.2021.788549. eCollection 2021. |
| 35002951 | Derived | Wu Y, Zhong L, Li G, Han L, Fu J, Li Y, Li L, Zhang Q, Guo Y, Xiao X, Qi L, Li M, Gao S, Willi SM. Puberty Status Modifies the Effects of Genetic Variants, Lifestyle Factors and Their Interactions on Adiponectin: The BCAMS Study. Front Endocrinol (Lausanne). 2021 Dec 24;12:737459. doi: 10.3389/fendo.2021.737459. eCollection 2021. |
| 32049638 | Derived | Li Y, Feng D, Esangbedo IC, Zhao Y, Han L, Zhu Y, Fu J, Li G, Wang D, Wang Y, Li M, Gao S, Willi SM. Insulin resistance, beta-cell function, adipokine profiles and cardiometabolic risk factors among Chinese youth with isolated impaired fasting glucose versus impaired glucose tolerance: the BCAMS study. BMJ Open Diabetes Res Care. 2020 Feb;8(1):e000724. doi: 10.1136/bmjdrc-2019-000724. |
| 31285522 | Derived | Fu J, Wang Y, Li G, Han L, Li Y, Li L, Feng D, Wu Y, Xiao X, Li M, Grant SFA, Li M, Gao S. Childhood sleep duration modifies the polygenic risk for obesity in youth through leptin pathway: the Beijing Child and Adolescent Metabolic Syndrome cohort study. Int J Obes (Lond). 2019 Aug;43(8):1556-1567. doi: 10.1038/s41366-019-0405-1. Epub 2019 Jul 8. |
| 30811103 | Derived | Wu Y, Yu X, Li Y, Li G, Cheng H, Xiao X, Mi J, Gao S, Willi SM, Li M. Adipose Tissue Mediates Associations of Birth Weight with Glucose Metabolism Disorders in Children. Obesity (Silver Spring). 2019 May;27(5):746-755. doi: 10.1002/oby.22421. Epub 2019 Feb 27. |
| 30093511 | Derived | Li G, Han L, Wang Y, Zhao Y, Li Y, Fu J, Li M, Gao S, Willi SM. Evaluation of ADA HbA1c criteria in the diagnosis of pre-diabetes and diabetes in a population of Chinese adolescents and young adults at high risk for diabetes: a cross-sectional study. BMJ Open. 2018 Aug 8;8(8):e020665. doi: 10.1136/bmjopen-2017-020665. |
| D001835 |
| Body Weight |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |