Open Access

SAA1 gene variants and childhood obesity in China

Lipids in Health and Disease201312:161

https://doi.org/10.1186/1476-511X-12-161

Received: 18 September 2013

Accepted: 5 October 2013

Published: 30 October 2013

Abstract

Background

Obesity increases the risk for insulin resistance and metabolic syndrome in both adults and children. SAA is a member of apolipoprotein and plays an important role in maintaining glucose and lipid homeostasis. The purpose of this study was to assess SAA1 allelic variants with obesity in young school-age children.

Methods

A total of 520 consecutive children ages 5–15 years were recruited. Children were divided based on BMI z score into Obese (OB; BMI z score ≥1.65; n = 253) and non-obese (NOB; n = 267). Four SNPs of the human SAA1 gene (rs12218, rs4638289, rs7131332 and rs11603089) were genotyped by use of polymerase chain reaction – restriction fragment length polymorphism (PCR-RFLP) method.

Results

Compared to NOB, circulating SAA levels were increased in OB, as were LDL-C, TG and TC concentration. Obese children showed increased frequency of rs12218 and rs4638289 polymorphism compared to control children. There were no differences between OB and NOB for the other 2 polymorphisms. Only the rs4638289 polymorphism showed significant contributions to higher SAA plasma levels.

Conclusions

SAA1 genetic polymorphism was associated with obesity in Chinese children.

Keywords

Genetic polymorphisms Serum amyloid A Obesity Childhood

Childhood obesity is a serious public health problem that has reached epidemic proportions all over the world [1, 2]. In children, the presence of obesity has been associated with increased levels of high sensitivity CRP (hsCRP) [3], as well as other inflammatory mediators [48], all of which promote the development of endothelial and metabolic dysfunction [912]. Serum amyloid A (SAA) is not only a sensitive acute phase proteins in plasma, but also an apolipoprotein that can replace apolipoprotein A1 (apoA1) as the major apolipoprotein of HDL-C, particularly during the acute phase response [13]. Several studies suggested that SAA is associated with obesity in adult [14]. And a meta-analysis found a strong association between body mass index and SAA levels [15]. Previous studies have demonstrated that SAA1 genetic polymorphism can change the plasma SAA levels [16]. Therefore, the genetic polymorphisms in SAA1 may be associated with obesity. Furthermore, several studies have reported that rs12218 in SAA1 gene was associated with carotid atherosclerosis [17], HDL-C concentration [18, 19] and peripheral arterial disease [20]. However, the relationship between SAA gene polymorphism and obesity remains unknown.

In the present study, we aimed to investigate the relation between SAA1 genetic polymorphism and obesity in Chinese children.

Results and discussion

A total of 253 OB children and 267 age-, gender- and ethnicity-matched NOB children were recruited during January to July 2012. The demographic characteristics of this cohort are shown in Table 1. As anticipated, OB children had higher LDL-C, TC and TG levels compared to NOB children (Table 1). OB children also had significantly higher levels of SAA than NOB (Table 1).
Table 1

Characteristics of the participants

Groups

N

Age (years)

BMI Kg/m2

SBP (mmol/L)

DBP (mmol/L)

GLU (mmol/L)

TG (mmol/L)

TC (mmol/L)

HDL-C (mmol/L)

LDL-C (mmol/L)

OB group

253

13.2 ± 0.9

21.1 ± 2.6

99.5 ± 12.6

61.0 ± 7.9

4.43 ± 0.47

0.61 ± 0.28

2.8 ± 0.7

0.87 ± 0.23

1.39 ± 0.49

NOB group

267

13.4 ± 1.0

17.1 ± 1.4

97.7 ± 12.7

61.4 ± 7.0

4.35 ± 0.48

0.52 ± 0.17

2.6 ± 0.6

0.88 ± 0.22

1.25 ± 0.36

P

 

0.102

<0.001

0.113

0.506

0.055

<0.001

0.001

0.734

<0.001

The frequency of each of the SAA polymorphisms is shown in Table 2 for OB and NOB children. Obese children showed increased frequency of rs12218 and rs4638289 polymorphism compared to control children. There were no differences between OB and NOB for the other 2 polymorphisms. Only the rs4638289 polymorphism showed significant contributions to higher SAA plasma levels (Table 3).
Table 2

Distributions of SAA1 genotypes (OB = 253, NOB = 267)

SNPs

Allels (1/2)

Groups

Genotypes (n, %)

P value

1/1

1/2

2/2

rs11603089

A/G

NOB group

192 (0.72)

70 (0.26)

5 (0.02)

0.302

  

OB group

172 (0.68)

71 (0.28)

10 (0.04)

 

rs4638289

A/T

NOB group

107 (0.40)

134 (0.50)

26 (0.10)

0.001

  

OB group

84 (0.33)

114 (0.44)

55 (0.23)

 

rs12218

C/T

NOB group

152 (0.57)

102 (0.38)

13 (0.05)

0.014

  

OB group

129 (0.51)

94 (0.37)

30 (0.12)

 

rs7131332

A/G

NOB group

67 (0.25)

160 (0.60)

40 (0.15)

0.897

  

OB group

65 (0.24)

147 (0.58)

41 (0.18)

 
Table 3

SAA concentration between each genotypes ( M + SD; ug/mL)

Group

n

rs11603089

rs4638289

rs12218

rs7131332

AA

AG

GG

AA

AT

TT

CC

CT

TT

AA

AG

GG

OB group

253

71.16 ± 23.22*

68.03 ± 22.11*

70.13 ± 24.13*

62.62 ± 23.22*

78.26 ± 24.32*

88.24 ± 30.13*

73.15 ± 25.23*

70.03 ± 23.44*

69.18 ± 25.14*

68.64 ± 24.91*

73.65 ± 25.86*

70.60 ± 23.12*

NOB group

267

51.22 ± 21.13

52.02 ± 22.11

53.12 ± 24.87

45.54 ± 20.53

59.12 ± 24.86

62.44 ± 24.34

55.33 ± 24.31

50.34 ± 26.15

55.29 ± 22.96

52.71 ± 21.43

50.1 ± 24.49

53.71 ± 26.05

Note: compared to NOB group, *P < 0.05; Compared to AA genotype of rs4638289, P < 0.05.

In the present study, we found that both rs12218 and rs4638289 polymorphism in the SAA1 gene were associated with OB in Chinese Children. And the SAA plasma levels are significantly higher in obese children. Furthermore, there was significant difference in SAA plasma levels between each genotype of rs4638289 in SAA1 gene.

As described previously [18, 20], SAA1 encodes one important inflammation factor-SAA.. Recently, Xie et al. reported that rs12218 polymorphism in SAA1 gene was associated with IMT [17], HDL-C [18, 19], Ankle-brachial index (ABI) [20], and plasma uric acid levels [18] which was related to cardiovascular disease in adults. And Zhang et al. reported SAA1 gene polymorphism was associated with cerebral infarction [21]. Xu et al. also reported SAA1 gene polymorphism was associated with lipid levels [19]. However, the relationship between SAA gene polymorphism and obesity in Children remains unclear.

Inflammation is important in the pathogenesis of atherosclerosis and obesity [15]. In 2000, Yamada et al. [22] reported that the SAA1 genetic polymorphism influences the plasma concentration of SAA. In the present study, we performed a case–control study to observe the relationship between SAA1 genetic polymorphism and obesity. We found rs12218 CC genotype and rs4638289 TT genotype are very common in the obesity patients than that in the control subjects. Furthermore, we also find the rs4638289 was associated with SAA level, but the rs12218 was not found to be associated with SAA level in the present study. An elevated level of SAA causes amyloidosis and is a risk factor for atherosclerosis and its clinical complications, type 2 diabetes, as well as various malignancies. The previous study [23] described the first genome-wide association study on baseline SAA concentrations. In a meta-analysis of four genome-wide scans totalling 4,212 participants of European descent, the authors identified two novel genetic susceptibility regions on chromosomes 11 and 1 to be associated with baseline SAA concentrations. The chromosome 11 region contains the serum amyloid A1 gene and the adjacent genes and explains a high percentage of the total estimated heritability. In their study, the also found rs463889 was associated with SAA concentration. Our result was in line with their report. However, the mechanism which may link SAA1 genetic polymorphism to obesity is still unknown. The change of plasma concentration of SAA resulting from the genetic polymorphism of SAA1 may be a possible mechanism which merits further investigation.

In conclusion, SAA1 genetic polymorphism was associated with obesity in Chinese children.

Subjects and methods

Subjects

The study was approved by the Wuhan University Human Research Committee, and informed consent was obtained from the legal caregiver of each participant. Consecutive children between the ages of 5 to 15 years attending public schools in Wuhan city were invited to participate in the study, after they underwent a school based health screening, which included height and weight measurements. Based on such screening, children were identified when their BMI z score was ≥ 1.65 (OB) and age-, gender-, ethnicity-, and area-of residence-matched children with BMI z scores <1.65 (NOB) were then identified and recruited to serve as controls. Of note, all children were otherwise healthy, and were representative of the demographic characteristics of the general population of the city of Wuhan. Children were excluded if they had known diabetes or pre-diabetes, any defined genetic abnormality or underlying systemic disease including hypertension, or if they were within a month from any acute infectious process.

Genotyping

The selection and genotyping of SNPs was performed according to Xie et al.’s protocol [20]. Briefly, we analyzed four tagging SNPs (rs12218, rs4638289, rs7131332 and rs11603089) in the present study. Genomic DNA was extracted from the peripheral blood leukocytes using a DNA extraction Kit (Beijing Bioteke Co. Ltd). Genotyping was confirmed by polymerase chain reaction (PCR) – restriction fragment length polymorphism (RFLP) analysis. The primer pair sequences, annealing temperatures and restriction enzymes for these four SNPs were described in the previous study [1820].

Anthropometry

To verify the school-health screening initial reports, children were weighed in a calibrated scale to the nearest 0.1 kg and height (to 0.1 cm) was measured with a stadiometer (Holtain, Crymych, UK). Body mass index (BMI) was calculated and BMI z-score was computed using CDC 2000 growth standards http://www.cdc.gov/growthcharts and online software http://www.cdc.gov/epiinfo. A BMI z score ≥ 1.65 was considered as fulfilling the criteria for obesity.

Blood based assays

Blood samples were drawn by venipuncture in the morning after an overnight fast. Blood samples were immediately centrifuged and plasma was frozen at -80°C until assay. Serum lipids including total cholesterol, high-density lipoprotein (HDL) cholesterol, calculated low-density lipoprotein cholesterol (LDL), and triglycerides (TG) were assessed using Flex Reagent Cartridges (Dade Behring). Plasma SAA level was measured using enzyme-linked immunosorbent assay (ELISA kit, Beijing Nothern Biotechnology Institute, Beijing, China).

Statistical analyses

Analyses were carried out using SPSS version 17.0 (SPSS, Chicago, IL, USA). The Hardy–Weinberg equilibrium was assessed by chi-square analyses. The differences in the distribution of genotypes between OB and NOB were analyzed using the chi-square test. General linear model (GLM) analysis was performed to test for associations between SNP genotypes and BMI after adjusting for confounding variables. Normality was assessed by plotting the residuals. Correction for multiple testing was applied for the number of individual risk factors per genotype to achieve a probability value for significance to be assumed before analysis was undertaken.

Abbreviations

SAA: 

Serum amyloid A

OB: 

Obesity

TG: 

Triglycerides

TC: 

Total cholesterol

HDL-C: 

High-density lipoprotein

LDL-C: 

Low-density lipoprotein.

Declarations

Authors’ Affiliations

(1)
Department of Pediatric, RenMin Hospital of Wuhan University

References

  1. Wang Y, Beydoun MA: The obesity epidemic in the United States-gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiol Rev. 2007, 29: 6-28. 10.1093/epirev/mxm007View ArticlePubMedGoogle Scholar
  2. Ogden CL, Carroll MD, Flegal KM: High body mass index for age among US children and adolescents, 2003–2006. JAMA. 2008, 299: 2401-5. 10.1001/jama.299.20.2401View ArticlePubMedGoogle Scholar
  3. Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB: Low-grade systemic inflammation in overweight children. Pediatrics. 2001, 107 (1): E13- 10.1542/peds.107.1.e13View ArticlePubMedGoogle Scholar
  4. Nemet D, Wang P, Funahashi T, Matsuzawa Y, Tanaka S, Engelman L, Cooper DM: Adipocytokines, body composition, and fitness in children. Pediatr Res. 2003, 53: 148-152. 10.1203/00006450-200301000-00025View ArticlePubMedGoogle Scholar
  5. Halle M, Korsten-Reck U, Wolfarth B, Berg A: Low-grade systemic inflammation in overweight children: impact of physical fitness. Exerc Immunol Rev. 2004, 10: 66-74.PubMedGoogle Scholar
  6. Süheyl Ezgü F, Hasanoğlu A, Tümer L, Ozbay F, Aybay C, Gündüz M: Endothelial activation and inflammation in prepubertal obese Turkish children. Metabolism. 2005, 54 (10): 1384-9. 10.1016/j.metabol.2005.05.003View ArticlePubMedGoogle Scholar
  7. Juonala M, Viikari JS, Rönnemaa T, Taittonen L, Marniemi J, Raitakari OT: Childhood C-reactive protein in predicting CRP and carotid intimamedia thickness in adulthood: the Cardiovascular Risk in Young Finns Study. Arterioscler Thromb Vasc Biol. 2006, 26 (8): 1883-8. 10.1161/01.ATV.0000228818.11968.7aView ArticlePubMedGoogle Scholar
  8. Brasil AR, Norton RC, Rossetti MB, Leão E, Mendes RP: C-reactive protein as an indicator of low intensity inflammation in children and adolescents with and without obesity. J Pediatr (Rio J). 2007, 83 (5): 477-80.View ArticleGoogle Scholar
  9. Sbarbati A, Osculati F, Silvagni D, Benati D, Galiè M, Camoglio FS, Rigotti G, Maffeis C: Obesity and inflammation: evidence for an elementary lesion. Pediatrics. 2006, 117 (1): 220-3. 10.1542/peds.2004-2854View ArticlePubMedGoogle Scholar
  10. Reinehr T, Kiess W, de Sousa G, Stoffel-Wagner B, Wunsch R: Intima media thickness in childhood obesity: relations to inflammatory marker, glucose metabolism, and blood pressure. Metabolism. 2006, 55 (1): 113-8. 10.1016/j.metabol.2005.07.016View ArticlePubMedGoogle Scholar
  11. Valle Jiménez M, Estepa RM, Camacho RM, Estrada RC, Luna FG, Guitarte FB: Endothelial dysfunction is related to insulin resistance and inflammatory biomarker levels in obese prepubertal children. Eur J Endocrinol. 2007, 156 (4): 497-502. 10.1530/EJE-06-0662View ArticlePubMedGoogle Scholar
  12. Lee S, Bacha F, Gungor N, Arslanian S: Comparison of different definitions of pediatric metabolic syndrome: relation to abdominal adiposity, insulin resistance, adiponectin, and inflammatory biomarkers. J Pediatr. 2008, 152 (2): 177-84. 10.1016/j.jpeds.2007.07.053View ArticlePubMedGoogle Scholar
  13. Ishii W, Liepnieks JJ, Yamada T, Benson MD, Kluve-Beckerman B: Human SAA1-derived amyloid deposition in cell culture: a consistent model utilizing human peripheral blood mononuclear cells and serum-free medium. Amyloid. 2013, 20 (2): 61-71. 10.3109/13506129.2013.775941View ArticlePubMedGoogle Scholar
  14. Jahangiri A, Wilson PG, Hou T, Brown A, King VL, Tannock LR: Serum amyloid A is found on ApoB-containing lipoproteins in obese humans with diabetes. Obesity (Silver Spring). 2013, 21 (5): 993-6. doi:10.1002/oby.20126View ArticleGoogle Scholar
  15. Zhao Y, He X, Shi X, Huang C, Liu J, Zhou S, Heng CK: Association between serum amyloid A and obesity: a meta-analysis and systematic review. Inflamm Res. 2010, 59 (5): 323-34. 10.1007/s00011-010-0163-yView ArticlePubMedGoogle Scholar
  16. Xie X, Ma YT, Yang YN, Li XM, Zheng YY, Fu ZY, Ma X, Liu F, Huang Y, Chen BD: SAA1 genetic polymorphisms are associated with plasma glucose concentration in non-diabetic subjects. Clin Chem Lab Med. 2013, 1-4. doi:10.1515/cclm-2013-0097Google Scholar
  17. Xie X, Ma YT, Yang YN, Fu ZY, Li XM, Huang D, Ma X, Chen BD, Liu F: Polymorphisms in the SAA1/2 gene are associated with carotid intima media thickness in healthy Han Chinese subjects: the Cardiovascular Risk Survey. PLoS One. 2010, 5 (11): e13997-doi:10.1371/journal.pone.0013997PubMed CentralView ArticlePubMedGoogle Scholar
  18. Xie X, Ma YT, Yang YN, Li XM, Fu ZY, Zheng YY, Ma X, Chen BD, Liu F, Huang Y, Yu ZX, Chen Y: Serum uric acid levels are associated with polymorphism in the SAA1 gene in Chinese subjects. PLoS One. 2012, 7 (6): e40263-doi:10.1371/journal.pone.0040263PubMed CentralView ArticlePubMedGoogle Scholar
  19. Xu XL, Sun XT, Pang L, Huang G, Huang J, Shi M, Wang YQ: Rs12218 In SAA1 gene was associated with serum lipid levels. Lipids Health Dis. 2013, 12 (1): 116-doi:10.1186/1476-511X-12-116PubMed CentralView ArticlePubMedGoogle Scholar
  20. Xie X, Ma YT, Yang YN, Fu ZY, Li XM, Zheng YY, Huang D, Ma X, Chen BD, Liu F: Polymorphisms in the SAA1 gene are associated with ankle-to-brachial index in Han Chinese healthy subjects. Blood Press. 2011, 20 (4): 232-8. doi:10.3109/08037051.2011.566244View ArticlePubMedGoogle Scholar
  21. Zhang LJ, Yuan B, Li HH, Tao SB, Yan HQ, Chang L, Zhao JH: Associations of genetic polymorphisms of SAA1 with cerebral infarction. Lipids Health Dis. 2013, 12 (1): 130- 10.1186/1476-511X-12-130PubMed CentralView ArticlePubMedGoogle Scholar
  22. Yamada T: Analysis of serum amyloid A1 exon 4 polymorphism in Japanese population. Amyloid. 2000, 7 (2): 118-20. 10.3109/13506120009146248View ArticlePubMedGoogle Scholar
  23. Marzi C, Albrecht E, Hysi PG, Lagou V, Waldenberger M: Genome-Wide Association Study Identifies Two Novel Regions at 11p15.5-p13 and 1p31 with Major Impact on Acute-Phase Serum Amyloid A. PLoS Genet. 2010, 6 (11): e1001213- 10.1371/journal.pgen.1001213PubMed CentralView ArticlePubMedGoogle Scholar

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© Zhang et al.; licensee BioMed Central Ltd. 2013

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