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Acta Diabetologica

, Volume 55, Issue 9, pp 901–908 | Cite as

Association of branched chain amino acids related variant rs1440581 with risk of incident diabetes and longitudinal changes in insulin resistance in Chinese

  • Liping Xuan
  • Yanan Hou
  • Tiange Wang
  • Mian Li
  • Zhiyun Zhao
  • Jieli Lu
  • Yu Xu
  • Yuhong Chen
  • Lu Qi
  • Weiqing Wang
  • Yufang Bi
  • Min Xu
Original Article
  • 132 Downloads

Abstract

Aims

Previous genome-wide association studies reported rs1440581 was significantly associated with circulating branched chain amino acids (BCAAs) levels in Europeans. We aimed to investigate association of BCAAs related variant rs1440581 with incident T2D risk and longitudinal changes in glucose-related metabolic traits in a community-based prospective cohort of Chinese.

Methods

6043 non-diabetic participants aged ≥ 40 years from a community-based population at baseline were included and followed-up for 5 years. The BCAAs related variant rs1440581 was genotyped. Incident T2D was defined as fasting plasma glucose (FPG) ≥ 7.0 mmol/L or taking anti-diabetic therapy. Anthropometry and biochemical measurements were evaluated at both baseline and follow-up.

Results

576 (9.5%) participants developed T2D during the 5-year follow-up. Each C-allele was associated with a 20% higher risk of incident T2D (odds ratio = 1.20, 95% confidence interval [1.05, 1.36]) after adjustments for the confounders. We did not find a main effect of the variant on increase in fasting serum insulin (FSI) level or insulin resistance (IR). However, we found rs1440581 significantly modified effect of weight gain on increase in FSI and HOMA-IR. In the C-allele carriers, body mass index increase was associated with greater increase in Log10_FSI (β ± SE 0.027 ± 0.002) and Log10_HOMA-IR (0.030 ± 0.003), as compared to T-allele (both P for interaction = 0.003).

Conclusions

BCAAs related genetic variant rs1440581 was associated with an increased risk of incident T2D in a Chinese population. This variant might modify effect of weight gain on development in IR.

Keywords

Branched chain amino acids Genetic variant Longitudinal change Interaction Type 2 diabetes 

Notes

Acknowledgements

We thank all the study participants for their participation and contribution. We are also grateful for research team who contributed to data collection and laboratory measurement.

Author contributions

LX and YH performed/supervised genetic analyses, contributed to data interpretation, and wrote the manuscript; LX, YH, TW, ML, ZZ, JL, YX, and YC contributed to acquisition of clinical and genetic data and reviewed the manuscript; LX, YH, MX, YB, LQ and WW contributed to genetic analyses and data interpretation, and reviewed the manuscript; MX designed study, contributed to data interpretation, wrote the manuscript and takes full responsibility for the work as a whole.

Funding

This work was supported by grants from the National Natural Science Foundation of China (81471062, 81471059, 81500660 and 81500610), the Ministry of Science and Technology of China (2016YFC1305600 and 2016YFC1304904), Shanghai Jiao Tong University SMC-Chen Xing Project (2016), the Gaofeng Clinical Medicine Grant Support from the Shanghai Municipal Education Commission (20152508 and 20161301) and the Shanghai Pujiang Program (18PJ1409600).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study protocol was approved by the Institutional Review Board of Rui-Jin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine.

Informed consent

Informed consent was obtained from all individual participants included in this study.

Supplementary material

592_2018_1165_MOESM1_ESM.docx (17 kb)
Supplementary material 1 (DOCX 17 KB)

References

  1. 1.
    Newgard CB, An J, Bain JR et al (2009) A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 9:311–326CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Stancakova A, Civelek M, Saleem NK et al (2012) Hyperglycemia and a common variant of GCKR are associated with the levels of eight amino acids in 9,369 Finnish men. Diabetes 61:1895–1902CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Suhre K, Meisinger C, Doring A et al (2010) Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PloS One 5:e13953CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Kettunen J, Tukiainen T, Sarin AP et al (2012) Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet 44:269–276CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Lotta LA, Scott RA, Sharp SJ et al (2016) Genetic predisposition to an impaired metabolism of the branched-chain amino acids and risk of type 2 diabetes: a mendelian randomisation analysis. PLoS Med 13:e1002179CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Xu M, Qi Q, Liang J et al (2013) Genetic determinant for amino acid metabolites and changes in body weight and insulin resistance in response to weight-loss diets: the Preventing Overweight Using Novel Dietary Strategies (POUNDS LOST) trial. Circulation 127:1283–1289CrossRefPubMedGoogle Scholar
  7. 7.
    Goni L, Qi L, Cuervo M et al (2017) Effect of the interaction between diet composition and the PPM1K genetic variant on insulin resistance and β cell function markers during weight loss: results from the Nutrient Gene Interactions in Human Obesity: implications for dietary guidelines (NUGENOB) randomized trial. Am J Clin Nutr 106(3):902–908PubMedGoogle Scholar
  8. 8.
    Bi Y, Wang W, Xu M et al (2016) Diabetes genetic risk score modifies effect of bisphenol A exposure on deterioration in glucose metabolism. J Clin Endocrinol Metab 101:143–150CrossRefPubMedGoogle Scholar
  9. 9.
    Xu M, Lv X, Xie L et al (2016) Discrete associations of the GCKR variant with metabolic risk in a Chinese population: longitudinal change analysis. Diabetologia 59:307–315CrossRefPubMedGoogle Scholar
  10. 10.
    Xu M, Huang Y, Xie L et al (2016) Diabetes and risk of arterial stiffness: a mendelian randomization analysis. Diabetes 65:1731–1740CrossRefPubMedGoogle Scholar
  11. 11.
    Tillin T, Hughes AD, Wang Q et al (2015) Diabetes risk and amino acid profiles: cross-sectional and prospective analyses of ethnicity, amino acids and diabetes in a South Asian and European cohort from the SABRE (Southall And Brent REvisited) Study. Diabetologia 58:968–979CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Wang TJ, Larson MG, Vasan RS et al (2011) Metabolite profiles and the risk of developing diabetes. Nat Med 17:448–453CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Palmer ND, Stevens RD, Antinozzi PA et al (2015) Metabolomic profile associated with insulin resistance and conversion to diabetes in the Insulin Resistance Atherosclerosis Study. J Clin Endocrinol Metab 100:E463–E468CrossRefPubMedGoogle Scholar
  14. 14.
    Guasch-Ferre M, Hruby A, Toledo E et al (2016) Metabolomics in prediabetes and diabetes: a systematic review and meta-analysis. Diabetes Care 39:833–846CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Davey Smith G, Ebrahim S (2005) What can mendelian randomisation tell us about modifiable behavioural and environmental exposures? BMJ (Clin Res Ed) 330:1076–1079CrossRefGoogle Scholar
  16. 16.
    Didelez V, Sheehan N (2007) Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res 16:309–330CrossRefPubMedGoogle Scholar
  17. 17.
    Qi L (2009) Mendelian randomization in nutritional epidemiology. Nutr Rev 67:439–450CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Taneera J, Lang S, Sharma A et al (2012) A systems genetics approach identifies genes and pathways for type 2 diabetes in human islets. Cell Metab 16:122–134CrossRefPubMedGoogle Scholar
  19. 19.
    Lu G, Sun H, She P et al (2009) Protein phosphatase 2Cm is a critical regulator of branched-chain amino acid catabolism in mice and cultured cells. J Clin Investig 119:1678–1687CrossRefPubMedGoogle Scholar
  20. 20.
    Joshi M, Jeoung NH, Popov KM, Harris RA (2007) Identification of a novel PP2C-type mitochondrial phosphatase. Biochem Biophys Res Commun 356:38–44CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Lian K, Du C, Liu Y et al (2015) Impaired adiponectin signaling contributes to disturbed catabolism of branched-chain amino acids in diabetic mice. Diabetes 64:49–59CrossRefPubMedGoogle Scholar
  22. 22.
    Menni C, Fauman E, Erte I et al (2013) Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach. Diabetes 62:4270–4276CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Interleukin-6 Receptor Mendelian Randomisation Analysis (IL6R MR) Consortium, Swerdlow DI, Holmes MV et al (2012) The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis. Lancet 379(9822):1214–1224CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Italia S.r.l., part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, National Clinical Research Center for Metabolic Diseases, Collaborative Innovation Center of Systems Biomedicine and Shanghai Clinical Center for Endocrine and Metabolic Diseases, Rui-Jin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
  2. 2.Shanghai Institute of Endocrine and Metabolic Diseases, Rui-Jin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
  3. 3.Department of Endocrine and Metabolic Diseases, Rui-Jin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
  4. 4.Department of EpidemiologyTulane University School of Public Health and Tropical MedicineNew OrleansUSA

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