The Association between Metabolic Syndrome and Biochemical Markers in Beijing Adolescents
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design and Participants
2.2. Anthropometric Measurement and Definition of Mets in Adolescents
2.3. Biochemical Analysis
2.4. Statistical Analysis
3. Results
3.1. General Characteristics of the Participants
3.2. The Biochemical Indexes in Mets Children and Non-Mets Children
3.3. Association between MetS and Biochemical Indexes
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Eckel, R.H.; Grundy, S.M.; Zimmet, P.Z. The metabolic syndrome. Lancet 2005, 365, 1415–1428. [Google Scholar] [CrossRef]
- Gu, D.; Reynolds, K.; Wu, X.; Chen, J.; Duan, X.; Reynolds, R.F.; Whelton, P.K.; He, J.; InterASIA Collaborative Group. Prevalence of the metabolic syndrome and overweight among adults in China. Lancet 2005, 365, 1398–1405. [Google Scholar] [PubMed]
- He, Y.N.; Zhao, W.H.; Zhao, L.Y.; Yu, D.M.; Zhang, J.; Yu, W.T.; Yang, X.G.; Ding, G.G. The epidemic status of metabolic syndrome among Chinese adolescents aged 10–17 years in 2010–2012. Zhonghua Yu Fang Yi Xue Za Zhi 2017, 51, 513–518. [Google Scholar] [PubMed]
- Trevisan, M.; Liu, J.; Bahsas, F.B.; Menotti, A.; Risk Factor and Life Expectancy Research Group. Syndrome X and mortality: A population-based study. Am. J. Epidemiol. 1998, 148, 958–966. [Google Scholar] [CrossRef] [PubMed]
- Ford, E.S.; Li, C.; Cook, S.; Choi, H.K. Serum concentrations of uric acid and the metabolic syndrome among US children and adolescents. Circulation 2007, 115, 2526–2532. [Google Scholar] [CrossRef] [PubMed]
- Johnson, R.J.; Kang, D.H.; Feig, D.; Kivlighn, S.; Kanellis, J.; Watanabe, S.; Tuttle, K.R.; Rodriguez-Iturbe, B.; Herrera-Acosta, J.; Mazzali, M. Is there a pathogenetic role for uric acid in hypertension and cardiovascular and renal disease? Hypertension 2003, 41, 1183–1190. [Google Scholar] [CrossRef] [PubMed]
- Messerli, F.H.; Frohlich, E.D.; Dreslinski, G.R.; Suarez, D.H.; Aristimuno, G.G. Serum uric acid in essential hypertension: An indicator of renal vascular involvement. Ann. Intern. Med. 1980, 93, 817–821. [Google Scholar] [CrossRef]
- Gin, H.; Rigalleau, V.; Aparicio, M. Lipids, protein intake, and diabetic nephropathy. Diabetes Metab. 2000, 26 (Suppl. 4), 45–53. [Google Scholar]
- Guijarro, C.; Kasiske, B.L.; Kim, Y.; O’Donnell, M.P.; Lee, H.S.; Keane, W.F. Early glomerular changes in rats with dietary-induced hypercholesterolemia. Am. J. Kidney Dis. 1995, 26, 152–161. [Google Scholar] [CrossRef]
- Vaziri, N.D.; Freel, R.W.; Hatch, M. Effect of chronic experimental renal insufficiency on urate metabolism. J. Am. Soc. Nephrol. 1995, 6, 1313–1317. [Google Scholar]
- The Subspecialty Group of Endocrinology, Genetics and Metabolism, Society of Pediatrics, Chinese Medical Association; The Subspecialty Group of Cardiovascular disease, Society of Pediatrics, Chinese Medical Association; The Subspecialty Group of Child Health Care, Society of Pediatrics, Chinese Medical Association. The definition and suggestion on the metabolic syndrome of Chinese children and adolescent. Chin. J. Pediatr. 2012, 50, 420–422. [Google Scholar]
- National Health Commission of People’s Republic of China. Screening for Overweight and Obesity Among School-Age Children and Adolescents; National Health Commission of People’s Republic of China: Beijing, China, 2018.
- Tsouli, S.G.; Liberopoulos, E.N.; Mikhailidis, D.P.; Athyros, V.G.; Elisaf, M.S. Elevated serum uric acid levels in metabolic syndrome: An active component or an innocent bystander? Metabolism 2006, 55, 1293–1301. [Google Scholar] [CrossRef] [PubMed]
- Weiss, R.; Dziura, J.; Burgert, T.S.; Tamborlane, W.V.; Taksali, S.E.; Yeckel, C.W.; Allen, K.; Lopes, M.; Savoye, M.; Morrison, J.; et al. Obesity and the metabolic syndrome in children and adolescents. N. Engl. J. Med. 2004, 350, 2362–2374. [Google Scholar] [CrossRef] [PubMed]
- Schwimmer, J.B.; Pardee, P.E.; Lavine, J.E.; Blumkin, A.K.; Cook, S. Cardiovascular risk factors and the metabolic syndrome in pediatric nonalcoholic fatty liver disease. Circulation 2008, 118, 277–283. [Google Scholar] [CrossRef]
- Mencin, A.A.; Lavine, J.E. Nonalcoholic fatty liver disease in children. Curr. Opin. Clin. Nutr. Metab. Care 2011, 14, 151–157. [Google Scholar] [CrossRef]
- Dixon, J.B.; Bhathal, P.S.; O’Brien, P.E. Nonalcoholic fatty liver disease: Predictors of nonalcoholic steatohepatitis and liver fibrosis in the severely obese. Gastroenterology 2001, 121, 91–100. [Google Scholar] [CrossRef]
- Schwimmer, J.B.; McGreal, N.; Deutsch, R.; Finegold, M.J.; Lavine, J.E. Influence of gender, race, and ethnicity on suspected fatty liver in obese adolescents. Pediatrics 2005, 115, e561–e565. [Google Scholar] [CrossRef]
- Graham, R.C.; Burke, A.; Stettler, N. Ethnic and sex differences in the association between metabolic syndrome and suspected nonalcoholic fatty liver disease in a nationally representative sample of US adolescents. J. Pediatr. Gastroenterol. Nutr. 2009, 49, 442–449. [Google Scholar] [CrossRef]
- Verrijken, A.; Francque, S.; Mertens, I.; Talloen, M.; Peiffer, F.; Van Gaal, L. Visceral adipose tissue and inflammation correlate with elevated liver tests in a cohort of overweight and obese patients. Int. J. Obes. 2010, 34, 899–907. [Google Scholar] [CrossRef]
- D’Adamo, E.; Cali, A.M.; Weiss, R.; Santoro, N.; Pierpont, B.; Northrup, V.; Caprio, S. Central role of fatty liver in the pathogenesis of insulin resistance in obese adolescents. Diabetes Care 2010, 33, 1817–1822. [Google Scholar] [CrossRef]
- Oliveira, A.C.; Oliveira, A.M.; Almeida, M.S.; Silva, A.M.; Adan, L.; Ladeia, A.M. Alanine aminotransferase and high sensitivity C-reactive protein: Correlates of cardiovascular risk factors in youth. J. Pediatr. 2008, 152, 337–342. [Google Scholar] [CrossRef] [PubMed]
- DeBoer, M.D. Obesity, systemic inflammation, and increased risk for cardiovascular disease and diabetes among adolescents: A need for screening tools to target interventions. Nutrition 2013, 29, 379–386. [Google Scholar] [CrossRef] [PubMed]
- Nagarajan, P.; Kumar, M.J.; Venkatesan, R.; Majundar, S.S.; Juyal, R.C. Genetically modified mouse models for the study of nonalcoholic fatty liver disease. World J. Gastroenterol. 2012, 18, 1141–1153. [Google Scholar] [CrossRef] [PubMed]
- Bremer, A.A.; Mietus-Snyder, M.; Lustig, R.H. Toward a unifying hypothesis of metabolic syndrome. Pediatrics 2012, 129, 557–570. [Google Scholar] [CrossRef] [PubMed]
- Pasceri, V.; Willerson, J.T.; Yeh, E.T. Direct proinflammatory effect of C-reactive protein on human endothelial cells. Circulation 2000, 102, 2165–2168. [Google Scholar] [CrossRef]
- Tan, K.C.; Wat, N.M.; Tam, S.C.; Janus, E.D.; Lam, T.H.; Lam, K.S. C-reactive protein predicts the deterioration of glycemia in chinese subjects with impaired glucose tolerance. Diabetes Care 2003, 26, 2323–2328. [Google Scholar] [CrossRef]
- Mahadik, S.R.; Deo, S.S.; Mehtalia, S.D. Association of adiposity, inflammation and atherosclerosis: The role of adipocytokines and CRP in Asian Indian subjects. Metab. Syndr. Relat. Disord. 2008, 6, 121–128. [Google Scholar] [CrossRef]
- Steele, C.E.; Morrell, D.; Evans, M. Metabolic syndrome and inflammatory skin conditions. Curr. Opin. Pediatr. 2019, 31, 515–522. [Google Scholar] [CrossRef]
- Rodriguez-Zuniga, M.J.M.; Garcia-Perdomo, H.A. Systematic review and meta-analysis of the association between psoriasis and metabolic syndrome. J. Am. Acad. Dermatol. 2017, 77, 657–666. [Google Scholar] [CrossRef]
- Zhang, A.; Silverberg, J.I. Association of atopic dermatitis with being overweight and obese: A systematic review and meta-analysis. J. Am. Acad. Dermatol. 2015, 72, 606–616. [Google Scholar] [CrossRef]
- Vinkel, C.; Thomsen, S.F. Risk factors for cardiovascular disease in patients with hidradenitis suppurativa. J. Eur. Acad. Dermatol. Venereol. 2017, 31, e411–e413. [Google Scholar] [CrossRef] [PubMed]
- Targher, G.; Bonapace, S.; Byrne, C.D. Does high LDL-cholesterol cause cardiovascular disease? Expert Rev. Clin. Pharmacol. 2019, 12, 91. [Google Scholar] [CrossRef] [PubMed] [Green Version]
General Information | Boys (n = 871) | Girls (n = 895) | Total Population (n = 1766) |
---|---|---|---|
Educational attainment of caregivers | |||
Primary school and below | 21 (2%) | 22 (2%) | 43 (2%) |
Middle school | 398 (46%) | 397 (44%) | 795 (45%) |
College and above | 452 (52%) | 476 (53%) | 928 (53%) |
Annual household income per capita (Yuan) | |||
< 20000 | 255 (29%) | 289 (32%) | 544 (31%) |
20000–70000 | 376 (43%) | 358 (40%) | 734 (42%) |
> 70000 | 240 (28%) | 248 (28%) | 488 (28%) |
Residence | |||
Urban | 539 (62%) | 558 (62%) | 1097 (62%) |
Suburban | 332 (38%) | 337 (38%) | 669 (38%) |
Age of participants (Years) | 11.34 ± 1.12 | 11.26 ± 1.13 | 11.30 ± 1.12 |
Height (cm) | 154.65 ± 10.56 | 153.01 ± 8.72 | 153.82 ± 9.70 |
Weight (kg) | 50.16 ± 14.95 | 46.13 ± 12.36 | 48.12 ± 13.84 |
Waist circumference (cm) | 70.77 ± 11.99 | 66.02 ± 9.65 | 68.36 ± 11.12 |
Body Mass Index (BMI) | 20.64 ± 4.45 | 19.47 ± 3.92 | 20.05 ± 4.23 |
Overweight | 184 (21%) | 122 (14%) | 306 (17%) |
Obesity | 209 (24%) | 148 (17%) | 357 (20%) |
Risk Factors of Mets | Boys (n = 871) | Girls (n = 895) | Total Population (n = 1776) |
---|---|---|---|
Waist to height ratio (WHtR) | 0.46 ± 0.07 | 0.43 ± 0.06 | 0.44 ± 0.06 |
Serum Triglycerides (mmol/L) | 0.80 ± 0.51 | 0.88 ± 0.50 | 0.84 ± 0.51 |
High Density Lipoprotein (HDL) (mmol/L) | 1.54 ± 0.34 | 1.53 ± 0.32 | 1.54 ± 0.33 |
Fasting blood glucose (mmol/L) | 5.19 ± 0.43 | 5.08 ± 0.37 | 5.14 ± 0.41 |
Systolic blood pressure (mmHg) | 113.28 ± 11.57 | 110.98 ± 10.73 | 112.11 ± 11.21 |
Diastolic blood pressure (mmHg) | 64.53 ± 7.83 | 65.60 ± 7.57 | 65.07 ± 7.71 |
MetS (%) | 38 (4%) | 21 (2%) | 59 (3%) |
Normal weight MetS (%) | 1 (0.2%) | 1 (0.2%) | 2 (0.2%) |
Overweight MetS (%) | 11 (6%) | 3 (3%) | 14 (5%) |
Obesity MetS (%) | 26 (12%) | 17 (12%) | 43 (12%) |
Urban MetS (%) | 6 (2%) | 6 (2%) | 12 (2%) |
Suburban MetS (%) | 32 (6%) | 15 (3%) | 47 (4%) |
MetS components (%) | |||
Abdominal adiposity | 296 (34%) | 242 (27%) | 538 (30%) |
High serum triglycerides (TG) | 78 (9%) | 93 (10%) | 171 (10%) |
Low HDL | 42 (5%) | 36 (4%) | 78 (4%) |
Abnormal glucose homeostasis | 118 (14%) | 69 (8%) | 187 (11%) |
Evaluated blood pressure | 80 (9%) | 41 (5%) | 121 (7%) |
Biochemical Markers | MetS Students (59) | Non-MetS Students (1707) | p-Value |
---|---|---|---|
ALT (U/L) | 23.45 ± 2.70 | 12.85 ± 0.25 | <0.01 |
AST (U/L) | 23.04 ± 1.37 | 22.07 ± 0.16 | 0.29 |
Cholesterol (mmol/L) | 4.2 ± 0.10 | 4.14 ± 0.02 | 0.73 |
SUA (umol/L) | 385.29 ± 13.25 | 320.61 ± 1.94 | <0.01 |
HDL (mmol/L) | 1.09 ± 0.19 | 1.55 ± 0.32 | <0.01 |
Homocysteine(umol/L) | 14.78 ± 0.79 | 13.38 ± 0.14 | 0.07 |
LDL (mmol/L) | 2.63 ± 0.08 | 2.28 ± 0.01 | <0.01 |
CRP (mg/L) | 1.78 ± 0.34 | 0.98 ± 0.06 | 0.01 |
Unadjusted | Model 1 | Model 2 | ||||
---|---|---|---|---|---|---|
Coef * | p | Coef | p | Coef | p | |
MetS | ||||||
ALT | 0.48 | <0.01 | 0.45 | <0.01 | 0.45 | <0.01 |
SUA | 0.18 | <0.01 | 0.14 | <0.01 | 0.15 | <0.01 |
LDL | 0.14 | <0.01 | 0.15 | <0.01 | 0.18 | <0.01 |
CRP | 0.91 | <0.01 | 0.86 | <0.01 | 0.86 | <0.01 |
Abdominal adiposity | ||||||
ALT | 0.35 | <0.01 | 0.34 | <0.01 | 0.33 | <0.01 |
SUA | 0.15 | <0.01 | 0.15 | <0.01 | 0.15 | <0.01 |
LDL | 0.11 | <0.01 | 0.10 | <0.01 | 0.11 | <0.01 |
CRP | 0.93 | <0.01 | 0.90 | <0.01 | 0.91 | <0.01 |
High serum TG | ||||||
ALT | 0.27 | <0.01 | 0.28 | <0.01 | 0.28 | <0.01 |
SUA | 0.11 | <0.01 | 0.11 | <0.01 | 0.11 | <0.01 |
LDL | 0.16 | <0.01 | 0.16 | <0.01 | 0.17 | <0.01 |
CRP | 0.40 | <0.01 | 0.43 | <0.01 | 0.42 | <0.01 |
Low HDL | ||||||
ALT | 0.12 | 0.02 | 0.12 | 0.02 | 0.11 | 0.04 |
SUA | 0.08 | <0.01 | 0.06 | 0.03 | 0.06 | 0.02 |
LDL | –0.03 | 0.29 | –0.02 | 0.46 | 0.00 | 0.94 |
CRP | 0.71 | <0.01 | 0.70 | <0.01 | 0.70 | <0.01 |
Abnormal glucose homeostasis | ||||||
ALT | 0.11 | <0.01 | 0.08 | 0.02 | 0.08 | 0.02 |
SUA | 0.05 | <0.01 | 0.01 | 0.55 | 0.01 | 0.52 |
LDL | 0.01 | 0.64 | 0.02 | 0.26 | 0.03 | 0.10 |
CRP | 0.07 | 0.42 | 0.02 | 0.84 | 0.02 | 0.79 |
Evaluated blood pressure | ||||||
ALT | 0.29 | <0.01 | 0.26 | <0.01 | 0.26 | <0.01 |
SUA | 0.17 | <0.01 | 0.13 | <0.01 | 0.13 | <0.01 |
LDL | 0.05 | 0.06 | 0.06 | 0.02 | 0.07 | 0.01 |
CRP | 0.48 | <0.01 | 0.42 | <0.01 | 0.42 | <0.01 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhao, Y.; Yu, Y.; Li, H.; Li, M.; Zhang, D.; Guo, D.; Yu, X.; Lu, C.; Wang, H. The Association between Metabolic Syndrome and Biochemical Markers in Beijing Adolescents. Int. J. Environ. Res. Public Health 2019, 16, 4557. https://doi.org/10.3390/ijerph16224557
Zhao Y, Yu Y, Li H, Li M, Zhang D, Guo D, Yu X, Lu C, Wang H. The Association between Metabolic Syndrome and Biochemical Markers in Beijing Adolescents. International Journal of Environmental Research and Public Health. 2019; 16(22):4557. https://doi.org/10.3390/ijerph16224557
Chicago/Turabian StyleZhao, Yao, Yingjie Yu, Hong Li, Mingying Li, Dongran Zhang, Dandan Guo, Xiaohui Yu, Ce Lu, and Hui Wang. 2019. "The Association between Metabolic Syndrome and Biochemical Markers in Beijing Adolescents" International Journal of Environmental Research and Public Health 16, no. 22: 4557. https://doi.org/10.3390/ijerph16224557