Skip to main content
Top
Published in: Diabetologia 5/2015

Open Access 01-05-2015 | Article

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

Authors: Therese Tillin, Alun D. Hughes, Qin Wang, Peter Würtz, Mika Ala-Korpela, Naveed Sattar, Nita G. Forouhi, Ian F. Godsland, Sophie V. Eastwood, Paul M. McKeigue, Nish Chaturvedi

Published in: Diabetologia | Issue 5/2015

Login to get access

Abstract

Aims/hypothesis

South Asian individuals have an increased risk of diabetes compared with Europeans that is unexplained by obesity and traditional or established metabolic measures. Circulating amino acids (AAs) may provide additional explanatory insights. In a unique cohort of European and South Asian men, we compared cross-sectional associations between AAs, metabolic and obesity traits, and longitudinal associations with incident diabetes.

Methods

Nuclear magnetic spectroscopy was used to measure the baseline (1988–1991) levels of nine AAs in serum samples from a British population-based cohort of 1,279 European and 1,007 South Asian non-diabetic men aged 40–69 years. Follow-up was complete for 19 years in 801 European and 643 South Asian participants.

Results

The serum concentrations of isoleucine, phenylalanine, tyrosine and alanine were significantly higher in South Asian men, while cross-sectional correlations of AAs with glycaemia and insulin resistance were similar in the two ethnic groups. However, most AAs were less strongly correlated with measures of obesity in the South Asian participants. Diabetes developed in 227 (35%) South Asian and 113 (14%) European men. Stronger adverse associations were observed between branched chain and aromatic AAs and incident diabetes in South Asian men. Tyrosine was a particularly strong predictor of incident diabetes in South Asian individuals, even after adjustment for metabolic risk factors, including obesity and insulin resistance (adjusted OR for a 1 SD increment, 1.47, 95% CI 1.17,1.85, p = 0.001) compared with Europeans (OR 1.10, 0.87, 1.39, p = 0.4; p = 0.045 for ethnicity × tyrosine interaction).

Conclusions/interpretation

Branched chain and aromatic AAs, particularly tyrosine, may be a focus for identifying novel aetiological mechanisms and potential treatment targets for diabetes in South Asian populations and may contribute to their excess risk of diabetes.
Appendix
Available only for authorised users
Literature
1.
go back to reference Shaw JE, Sicree RA, Zimmet PZ (2010) Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract 87:4–14CrossRefPubMed Shaw JE, Sicree RA, Zimmet PZ (2010) Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract 87:4–14CrossRefPubMed
2.
go back to reference Anand SS, Yusuf S, Vuksan V et al (2000) Differences in risk factors, atherosclerosis, and cardiovascular disease between ethnic groups in Canada: the Study of Health Assessment and Risk in Ethnic groups (SHARE). Lancet 356:279–284CrossRefPubMed Anand SS, Yusuf S, Vuksan V et al (2000) Differences in risk factors, atherosclerosis, and cardiovascular disease between ethnic groups in Canada: the Study of Health Assessment and Risk in Ethnic groups (SHARE). Lancet 356:279–284CrossRefPubMed
3.
go back to reference Tillin T, Hughes AD, Godsland IF et al (2013) Insulin resistance and truncal obesity as important determinants of the greater incidence of diabetes in Indian Asians and African Caribbeans compared with Europeans: the Southall And Brent REvisited (SABRE) cohort. Diabetes Care 36:383–393CrossRefPubMedCentralPubMed Tillin T, Hughes AD, Godsland IF et al (2013) Insulin resistance and truncal obesity as important determinants of the greater incidence of diabetes in Indian Asians and African Caribbeans compared with Europeans: the Southall And Brent REvisited (SABRE) cohort. Diabetes Care 36:383–393CrossRefPubMedCentralPubMed
4.
go back to reference 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 9:311–326CrossRefPubMedCentralPubMed 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 9:311–326CrossRefPubMedCentralPubMed
6.
7.
go back to reference Tai ES, Tan ML, Stevens RD et al (2010) Insulin resistance is associated with a metabolic profile of altered protein metabolism in Chinese and Asian-Indian men. Diabetologia 53:757–767CrossRefPubMedCentralPubMed Tai ES, Tan ML, Stevens RD et al (2010) Insulin resistance is associated with a metabolic profile of altered protein metabolism in Chinese and Asian-Indian men. Diabetologia 53:757–767CrossRefPubMedCentralPubMed
8.
go back to reference Floegel A, Stefan N, Yu Z et al (2012) Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes 62:639–648CrossRefPubMed Floegel A, Stefan N, Yu Z et al (2012) Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes 62:639–648CrossRefPubMed
9.
go back to reference Huffman KM, Shah SH, Stevens RD et al (2009) Relationships between circulating metabolic intermediates and insulin action in overweight to obese, inactive men and women. Diabetes Care 32:1678–1683CrossRefPubMedCentralPubMed Huffman KM, Shah SH, Stevens RD et al (2009) Relationships between circulating metabolic intermediates and insulin action in overweight to obese, inactive men and women. Diabetes Care 32:1678–1683CrossRefPubMedCentralPubMed
10.
go back to reference 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 61:1895–1902CrossRefPubMedCentralPubMed 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 61:1895–1902CrossRefPubMedCentralPubMed
11.
go back to reference Gogna N, Krishna M, Oommen AM, Dorai K (2015) Investigating correlations in the altered metabolic profiles of obese and diabetic subjects in a South Indian Asian population using an NMR-based metabolomic approach. Mol Biosyst 11:595–606CrossRefPubMed Gogna N, Krishna M, Oommen AM, Dorai K (2015) Investigating correlations in the altered metabolic profiles of obese and diabetic subjects in a South Indian Asian population using an NMR-based metabolomic approach. Mol Biosyst 11:595–606CrossRefPubMed
12.
go back to reference Tillin T, Forouhi NG, McKeigue PM, Chaturvedi N (2010) Southall And Brent REvisited: Cohort profile of SABRE, a UK population-based comparison of cardiovascular disease and diabetes in people of European, Indian Asian and African Caribbean origins. Int J Epidemiol 41:33–42CrossRefPubMedCentralPubMed Tillin T, Forouhi NG, McKeigue PM, Chaturvedi N (2010) Southall And Brent REvisited: Cohort profile of SABRE, a UK population-based comparison of cardiovascular disease and diabetes in people of European, Indian Asian and African Caribbean origins. Int J Epidemiol 41:33–42CrossRefPubMedCentralPubMed
13.
go back to reference Baecke JA, Burema J, Frijters JE (1982) A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr 36:936–942PubMed Baecke JA, Burema J, Frijters JE (1982) A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr 36:936–942PubMed
14.
go back to reference World Health Organization (1999) Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. WHO, Geneva World Health Organization (1999) Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. WHO, Geneva
15.
go back to reference Levy JC, Matthews DR, Hermans MP (1998) Correct homeostasis model assessment (HOMA) evaluation uses the computer program. Diabetes Care 21:2191–2192CrossRefPubMed Levy JC, Matthews DR, Hermans MP (1998) Correct homeostasis model assessment (HOMA) evaluation uses the computer program. Diabetes Care 21:2191–2192CrossRefPubMed
16.
go back to reference Matsuda M, DeFronzo RA (1999) Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 22:1462–1470CrossRefPubMed Matsuda M, DeFronzo RA (1999) Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 22:1462–1470CrossRefPubMed
17.
go back to reference DeFronzo RA, Matsuda M (2010) Reduced time points to calculate the composite index. Diabetes Care 33:e93CrossRefPubMed DeFronzo RA, Matsuda M (2010) Reduced time points to calculate the composite index. Diabetes Care 33:e93CrossRefPubMed
18.
go back to reference Soininen P, Kangas AJ, Wurtz P et al (2009) High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. Analyst 134:1781–1785CrossRefPubMed Soininen P, Kangas AJ, Wurtz P et al (2009) High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. Analyst 134:1781–1785CrossRefPubMed
19.
go back to reference Kettunen J, Tukiainen T, Sarin AP et al (2012) Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet 44:269–276CrossRefPubMedCentralPubMed Kettunen J, Tukiainen T, Sarin AP et al (2012) Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet 44:269–276CrossRefPubMedCentralPubMed
20.
21.
go back to reference Fisher R (1921) On the probable error of a coefficient deduced from a small sample. Metron 1:3–32 Fisher R (1921) On the probable error of a coefficient deduced from a small sample. Metron 1:3–32
23.
go back to reference Newsom R (2010) Comparing the predictive powers of survival models using Harrell's C or Somers' D. Stata J 10:339–358 Newsom R (2010) Comparing the predictive powers of survival models using Harrell's C or Somers' D. Stata J 10:339–358
24.
go back to reference Pencina MJ, Agostino RBD, Agostino RBD, Vasan RS (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27:157–172CrossRefPubMed Pencina MJ, Agostino RBD, Agostino RBD, Vasan RS (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27:157–172CrossRefPubMed
25.
go back to reference Fine JP, Gray R (1999) A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94:496–509CrossRef Fine JP, Gray R (1999) A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94:496–509CrossRef
26.
go back to reference Wurtz P, Soininen P, Kangas AJ et al (2013) Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care 36:648–655CrossRefPubMedCentralPubMed Wurtz P, Soininen P, Kangas AJ et al (2013) Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care 36:648–655CrossRefPubMedCentralPubMed
27.
go back to reference Fiehn O, Garvey WT, Newman JW, Lok KH, Hoppel CL, Adams SH (2010) Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS ONE 5:e15234CrossRefPubMedCentralPubMed Fiehn O, Garvey WT, Newman JW, Lok KH, Hoppel CL, Adams SH (2010) Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS ONE 5:e15234CrossRefPubMedCentralPubMed
28.
go back to reference Persson M, Nilsson JA, Nelson JJ, Hedblad B, Berglund G (2007) The epidemiology of Lp-PLA(2): distribution and correlation with cardiovascular risk factors in a population-based cohort. Atherosclerosis 190:388–396CrossRefPubMed Persson M, Nilsson JA, Nelson JJ, Hedblad B, Berglund G (2007) The epidemiology of Lp-PLA(2): distribution and correlation with cardiovascular risk factors in a population-based cohort. Atherosclerosis 190:388–396CrossRefPubMed
29.
go back to reference Krebs M, Krssak M, Bernroider E et al (2002) Mechanism of amino acid-induced skeletal muscle insulin resistance in humans. Diabetes 51:599–605CrossRefPubMed Krebs M, Krssak M, Bernroider E et al (2002) Mechanism of amino acid-induced skeletal muscle insulin resistance in humans. Diabetes 51:599–605CrossRefPubMed
30.
go back to reference She P, Van HC, Reid T, Hutson SM, Cooney RN, Lynch CJ (2007) Obesity-related elevations in plasma leucine are associated with alterations in enzymes involved in branched-chain amino acid metabolism. Am J Physiol Endocrinol Metab 293:E1552–E1563CrossRefPubMedCentralPubMed She P, Van HC, Reid T, Hutson SM, Cooney RN, Lynch CJ (2007) Obesity-related elevations in plasma leucine are associated with alterations in enzymes involved in branched-chain amino acid metabolism. Am J Physiol Endocrinol Metab 293:E1552–E1563CrossRefPubMedCentralPubMed
31.
go back to reference Tremblay F, Brule S, Hee US et al (2007) Identification of IRS-1 Ser-1101 as a target of S6K1 in nutrient- and obesity-induced insulin resistance. Proc Natl Acad Sci U S A 104:14056–14061CrossRefPubMedCentralPubMed Tremblay F, Brule S, Hee US et al (2007) Identification of IRS-1 Ser-1101 as a target of S6K1 in nutrient- and obesity-induced insulin resistance. Proc Natl Acad Sci U S A 104:14056–14061CrossRefPubMedCentralPubMed
32.
33.
go back to reference Petersen KF, Dufour S, Feng J et al (2006) Increased prevalence of insulin resistance and nonalcoholic fatty liver disease in Asian-Indian men. Proc Natl Acad Sci U S A 103:18273–18277CrossRefPubMedCentralPubMed Petersen KF, Dufour S, Feng J et al (2006) Increased prevalence of insulin resistance and nonalcoholic fatty liver disease in Asian-Indian men. Proc Natl Acad Sci U S A 103:18273–18277CrossRefPubMedCentralPubMed
34.
go back to reference Anand SS, Tarnopolsky MA, Rashid S et al (2011) Adipocyte hypertrophy, fatty liver and metabolic risk factors in South Asians: the Molecular Study of Health and Risk in Ethnic Groups (mol-SHARE). PLoS ONE 6:e22112CrossRefPubMedCentralPubMed Anand SS, Tarnopolsky MA, Rashid S et al (2011) Adipocyte hypertrophy, fatty liver and metabolic risk factors in South Asians: the Molecular Study of Health and Risk in Ethnic Groups (mol-SHARE). PLoS ONE 6:e22112CrossRefPubMedCentralPubMed
35.
go back to reference Eastwood SV, Tillin T, Wright A et al (2014) Thigh fat and muscle each contribute to excess cardiometabolic risk in South Asians, independent of visceral adipose tissue. Obesity (Silver Spring) 22:2071–2079CrossRef Eastwood SV, Tillin T, Wright A et al (2014) Thigh fat and muscle each contribute to excess cardiometabolic risk in South Asians, independent of visceral adipose tissue. Obesity (Silver Spring) 22:2071–2079CrossRef
36.
go back to reference Chowdhury B, Lantz H, Sjostrom L (1996) Computed tomography-determined body composition in relation to cardiovascular risk factors in Indian and matched Swedish males. Metabolism 45:634–644CrossRefPubMed Chowdhury B, Lantz H, Sjostrom L (1996) Computed tomography-determined body composition in relation to cardiovascular risk factors in Indian and matched Swedish males. Metabolism 45:634–644CrossRefPubMed
37.
go back to reference Chandalia M, Lin P, Seenivasan T et al (2007) Insulin resistance and body fat distribution in South Asian men compared to Caucasian men. PLoS ONE 2:e812CrossRefPubMedCentralPubMed Chandalia M, Lin P, Seenivasan T et al (2007) Insulin resistance and body fat distribution in South Asian men compared to Caucasian men. PLoS ONE 2:e812CrossRefPubMedCentralPubMed
38.
go back to reference Fernstrom JD, Wurtman RJ, Hammarstrom-Wiklund B, Rand WM, Munro HN, Davidson CS (1979) Diurnal variations in plasma concentrations of tryptophan, tyrosine, and other neutral amino acids: effect of dietary protein intake. Am J Clin Nutr 32:1912–1922PubMed Fernstrom JD, Wurtman RJ, Hammarstrom-Wiklund B, Rand WM, Munro HN, Davidson CS (1979) Diurnal variations in plasma concentrations of tryptophan, tyrosine, and other neutral amino acids: effect of dietary protein intake. Am J Clin Nutr 32:1912–1922PubMed
39.
go back to reference Weller LA, Margen S, Calloway DH (1969) Variation in fasting and postprandial amino acids of men fed adequate or protein-free diets. Am J Clin Nutr 22:1577–1583PubMed Weller LA, Margen S, Calloway DH (1969) Variation in fasting and postprandial amino acids of men fed adequate or protein-free diets. Am J Clin Nutr 22:1577–1583PubMed
40.
go back to reference Nasset ES, Heald FP, Calloway DH, Margen S, Schneeman P (1979) Amino acids in human blood plasma after single meals of meat, oil, sucrose and whiskey. J Nutr 109:621–630PubMed Nasset ES, Heald FP, Calloway DH, Margen S, Schneeman P (1979) Amino acids in human blood plasma after single meals of meat, oil, sucrose and whiskey. J Nutr 109:621–630PubMed
41.
go back to reference Riggio O, Merli M, Pieche U et al (1989) Circadian rhythmicity of plasma amino acid variations in healthy subjects. Recenti Prog Med 80:591–593PubMed Riggio O, Merli M, Pieche U et al (1989) Circadian rhythmicity of plasma amino acid variations in healthy subjects. Recenti Prog Med 80:591–593PubMed
42.
go back to reference Ashley DV, Barclay DV, Chauffard FA, Moennoz D, Leathwood PD (1982) Plasma amino acid responses in humans to evening meals of differing nutritional composition. Am J Clin Nutr 36:143–153PubMed Ashley DV, Barclay DV, Chauffard FA, Moennoz D, Leathwood PD (1982) Plasma amino acid responses in humans to evening meals of differing nutritional composition. Am J Clin Nutr 36:143–153PubMed
Metadata
Title
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
Authors
Therese Tillin
Alun D. Hughes
Qin Wang
Peter Würtz
Mika Ala-Korpela
Naveed Sattar
Nita G. Forouhi
Ian F. Godsland
Sophie V. Eastwood
Paul M. McKeigue
Nish Chaturvedi
Publication date
01-05-2015
Publisher
Springer Berlin Heidelberg
Published in
Diabetologia / Issue 5/2015
Print ISSN: 0012-186X
Electronic ISSN: 1432-0428
DOI
https://doi.org/10.1007/s00125-015-3517-8

Other articles of this Issue 5/2015

Diabetologia 5/2015 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine

Highlights from the ACC 2024 Congress

Year in Review: Pediatric cardiology

Watch Dr. Anne Marie Valente present the last year's highlights in pediatric and congenital heart disease in the official ACC.24 Year in Review session.

Year in Review: Pulmonary vascular disease

The last year's highlights in pulmonary vascular disease are presented by Dr. Jane Leopold in this official video from ACC.24.

Year in Review: Valvular heart disease

Watch Prof. William Zoghbi present the last year's highlights in valvular heart disease from the official ACC.24 Year in Review session.

Year in Review: Heart failure and cardiomyopathies

Watch this official video from ACC.24. Dr. Biykem Bozkurt discusses last year's major advances in heart failure and cardiomyopathies.