Skip to main content
Top
Published in: Diabetologia 3/2016

Open Access 01-03-2016 | Article

Innate biology versus lifestyle behaviour in the aetiology of obesity and type 2 diabetes: the GLACIER Study

Authors: Alaitz Poveda, Robert W. Koivula, Shafqat Ahmad, Inês Barroso, Göran Hallmans, Ingegerd Johansson, Frida Renström, Paul W. Franks

Published in: Diabetologia | Issue 3/2016

Login to get access

Abstract

Aims/hypothesis

We compared the ability of genetic (established type 2 diabetes, fasting glucose, 2 h glucose and obesity variants) and modifiable lifestyle (diet, physical activity, smoking, alcohol and education) risk factors to predict incident type 2 diabetes and obesity in a population-based prospective cohort of 3,444 Swedish adults studied sequentially at baseline and 10 years later.

Methods

Multivariable logistic regression analyses were used to assess the predictive ability of genetic and lifestyle risk factors on incident obesity and type 2 diabetes by calculating the AUC.

Results

The predictive accuracy of lifestyle risk factors was similar to that yielded by genetic information for incident type 2 diabetes (AUC 75% and 74%, respectively) and obesity (AUC 68% and 73%, respectively) in models adjusted for age, age2 and sex. The addition of genetic information to the lifestyle model significantly improved the prediction of type 2 diabetes (AUC 80%; p = 0.0003) and obesity (AUC 79%; p < 0.0001) and resulted in a net reclassification improvement of 58% for type 2 diabetes and 64% for obesity.

Conclusions/interpretation

These findings illustrate that lifestyle and genetic information separately provide a similarly high degree of long-range predictive accuracy for obesity and type 2 diabetes.
Appendix
Available only for authorised users
Literature
2.
go back to reference Morris AP, Voight BF, Teslovich TM et al (2012) Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 44:981–990PubMedCentralCrossRefPubMed Morris AP, Voight BF, Teslovich TM et al (2012) Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 44:981–990PubMedCentralCrossRefPubMed
3.
go back to reference Scott RA, Lagou V, Welch RP et al (2012) Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet 44:991–1005PubMedCentralCrossRefPubMed Scott RA, Lagou V, Welch RP et al (2012) Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet 44:991–1005PubMedCentralCrossRefPubMed
4.
go back to reference Talmud PJ, Cooper JA, Morris RW et al (2015) Sixty-five common genetic variants and prediction of type 2 diabetes. Diabetes 64:1830–1840CrossRefPubMed Talmud PJ, Cooper JA, Morris RW et al (2015) Sixty-five common genetic variants and prediction of type 2 diabetes. Diabetes 64:1830–1840CrossRefPubMed
5.
go back to reference Talmud PJ, Hingorani AD, Cooper JA et al (2010) Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ 340:b4838PubMedCentralCrossRefPubMed Talmud PJ, Hingorani AD, Cooper JA et al (2010) Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ 340:b4838PubMedCentralCrossRefPubMed
6.
go back to reference Meigs JB, Shrader P, Sullivan LM et al (2008) Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med 359:2208–2219PubMedCentralCrossRefPubMed Meigs JB, Shrader P, Sullivan LM et al (2008) Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med 359:2208–2219PubMedCentralCrossRefPubMed
7.
go back to reference Kurbasic A, Poveda A, Chen Y et al (2014) Gene–lifestyle interactions in complex diseases: design and description of the GLACIER and VIKING studies. Curr Nutr Rep 3:400–411 Kurbasic A, Poveda A, Chen Y et al (2014) Gene–lifestyle interactions in complex diseases: design and description of the GLACIER and VIKING studies. Curr Nutr Rep 3:400–411
8.
go back to reference Hallmans G, Agren A, Johansson G et al (2003) Cardiovascular disease and diabetes in the Northern Sweden Health and Disease Study Cohort—evaluation of risk factors and their interactions. Scand J Public Health Suppl 61:18–24CrossRefPubMed Hallmans G, Agren A, Johansson G et al (2003) Cardiovascular disease and diabetes in the Northern Sweden Health and Disease Study Cohort—evaluation of risk factors and their interactions. Scand J Public Health Suppl 61:18–24CrossRefPubMed
9.
go back to reference WHO (1999) Definition, diagnosis and classification of diabetes mellitus and its complications: part 1: diagnosis and classification of diabetes mellitus. World Health Organization, Geneva WHO (1999) Definition, diagnosis and classification of diabetes mellitus and its complications: part 1: diagnosis and classification of diabetes mellitus. World Health Organization, Geneva
10.
go back to reference Expert Committee on the Diagnosis and Classification of Diabetes Mellitus (2003) Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 26(Suppl 1):S5–20 Expert Committee on the Diagnosis and Classification of Diabetes Mellitus (2003) Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 26(Suppl 1):S5–20
11.
go back to reference Franks PW, Rolandsson O, Debenham SL et al (2008) Replication of the association between variants in WFS1 and risk of type 2 diabetes in European populations. Diabetologia 51:458–463PubMedCentralCrossRefPubMed Franks PW, Rolandsson O, Debenham SL et al (2008) Replication of the association between variants in WFS1 and risk of type 2 diabetes in European populations. Diabetologia 51:458–463PubMedCentralCrossRefPubMed
12.
go back to reference Renstrom F, Payne F, Nordstrom A et al (2009) Replication and extension of genome-wide association study results for obesity in 4923 adults from northern Sweden. Hum Mol Genet 18:1489–1496PubMedCentralCrossRefPubMed Renstrom F, Payne F, Nordstrom A et al (2009) Replication and extension of genome-wide association study results for obesity in 4923 adults from northern Sweden. Hum Mol Genet 18:1489–1496PubMedCentralCrossRefPubMed
13.
go back to reference Voight BF, Kang HM, Ding J et al (2012) The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet 8, e1002793PubMedCentralCrossRefPubMed Voight BF, Kang HM, Ding J et al (2012) The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet 8, e1002793PubMedCentralCrossRefPubMed
14.
go back to reference Fontaine-Bisson B, Renstrom F, Rolandsson O et al (2010) Evaluating the discriminative power of multi-trait genetic risk scores for type 2 diabetes in a northern Swedish population. Diabetologia 53:2155–2162PubMedCentralCrossRefPubMed Fontaine-Bisson B, Renstrom F, Rolandsson O et al (2010) Evaluating the discriminative power of multi-trait genetic risk scores for type 2 diabetes in a northern Swedish population. Diabetologia 53:2155–2162PubMedCentralCrossRefPubMed
15.
go back to reference Johansson I, Hallmans G, Wikman A, Biessy C, Riboli E, Kaaks R (2002) Validation and calibration of food-frequency questionnaire measurements in the Northern Sweden Health and Disease cohort. Public Health Nutr 5:487–496CrossRefPubMed Johansson I, Hallmans G, Wikman A, Biessy C, Riboli E, Kaaks R (2002) Validation and calibration of food-frequency questionnaire measurements in the Northern Sweden Health and Disease cohort. Public Health Nutr 5:487–496CrossRefPubMed
16.
go back to reference Johansson I, Van Guelpen B, Hultdin J, Johansson M, Hallmans G, Stattin P (2010) Validity of food frequency questionnaire estimated intakes of folate and other B vitamins in a region without folic acid fortification. Eur J Clin Nutr 64:905–913CrossRefPubMed Johansson I, Van Guelpen B, Hultdin J, Johansson M, Hallmans G, Stattin P (2010) Validity of food frequency questionnaire estimated intakes of folate and other B vitamins in a region without folic acid fortification. Eur J Clin Nutr 64:905–913CrossRefPubMed
17.
go back to reference Wennberg M, Vessby B, Johansson I (2009) Evaluation of relative intake of fatty acids according to the Northern Sweden FFQ with fatty acid levels in erythrocyte membranes as biomarkers. Public Health Nutr 12:1477–1484CrossRefPubMed Wennberg M, Vessby B, Johansson I (2009) Evaluation of relative intake of fatty acids according to the Northern Sweden FFQ with fatty acid levels in erythrocyte membranes as biomarkers. Public Health Nutr 12:1477–1484CrossRefPubMed
18.
go back to reference Hallal PC, Victora CG (2004) Reliability and validity of the International Physical Activity Questionnaire (IPAQ). Med Sci Sports Exerc 36:556CrossRefPubMed Hallal PC, Victora CG (2004) Reliability and validity of the International Physical Activity Questionnaire (IPAQ). Med Sci Sports Exerc 36:556CrossRefPubMed
19.
go back to reference Craig CL, Marshall AL, Sjostrom M et al (2003) International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 35:1381–1395CrossRefPubMed Craig CL, Marshall AL, Sjostrom M et al (2003) International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 35:1381–1395CrossRefPubMed
20.
go back to reference Nettleton JA, Hivert MF, Lemaitre RN et al (2013) Meta-analysis investigating associations between healthy diet and fasting glucose and insulin levels and modification by loci associated with glucose homeostasis in data from 15 cohorts. Am J Epidemiol 177:103–115PubMedCentralCrossRefPubMed Nettleton JA, Hivert MF, Lemaitre RN et al (2013) Meta-analysis investigating associations between healthy diet and fasting glucose and insulin levels and modification by loci associated with glucose homeostasis in data from 15 cohorts. Am J Epidemiol 177:103–115PubMedCentralCrossRefPubMed
21.
go back to reference Nordic Council of Ministers (2012) Nordic nutrition recommendations 2012. Integrating nutrition and physical activity. Nordic Council of Ministers. Copenhagen Nordic Council of Ministers (2012) Nordic nutrition recommendations 2012. Integrating nutrition and physical activity. Nordic Council of Ministers. Copenhagen
22.
go back to reference DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefPubMed DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefPubMed
23.
go back to reference Pencina MJ, D’Agostino RB Sr, Steyerberg EW (2011) Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 30:11–21PubMedCentralCrossRefPubMed Pencina MJ, D’Agostino RB Sr, Steyerberg EW (2011) Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 30:11–21PubMedCentralCrossRefPubMed
24.
25.
go back to reference Siddiqi N (2006) Credit risk scorecards: developing and implementing intelligent credit scoring. Wiley, New Jersey Siddiqi N (2006) Credit risk scorecards: developing and implementing intelligent credit scoring. Wiley, New Jersey
27.
go back to reference Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575PubMedCentralCrossRefPubMed Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575PubMedCentralCrossRefPubMed
28.
go back to reference R Development Core Team (2008) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria R Development Core Team (2008) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria
29.
go back to reference SAS Institute (2011) The SAS system for Windows, version 9.4. SAS Institute, Cary, NC, USA SAS Institute (2011) The SAS system for Windows, version 9.4. SAS Institute, Cary, NC, USA
30.
go back to reference Mozaffarian D, Wilson PW, Kannel WB (2008) Beyond established and novel risk factors: lifestyle risk factors for cardiovascular disease. Circulation 117:3031–3038CrossRefPubMed Mozaffarian D, Wilson PW, Kannel WB (2008) Beyond established and novel risk factors: lifestyle risk factors for cardiovascular disease. Circulation 117:3031–3038CrossRefPubMed
31.
go back to reference Lyssenko V, Jonsson A, Almgren P et al (2008) Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med 359:2220–2232CrossRefPubMed Lyssenko V, Jonsson A, Almgren P et al (2008) Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med 359:2220–2232CrossRefPubMed
32.
go back to reference Renstrom F, Shungin D, Johansson I et al (2011) Genetic predisposition to long-term nondiabetic deteriorations in glucose homeostasis: Ten-year follow-up of the GLACIER study. Diabetes 60:345–354PubMedCentralCrossRefPubMed Renstrom F, Shungin D, Johansson I et al (2011) Genetic predisposition to long-term nondiabetic deteriorations in glucose homeostasis: Ten-year follow-up of the GLACIER study. Diabetes 60:345–354PubMedCentralCrossRefPubMed
33.
go back to reference Knowler WC, Barrett-Connor E, Fowler SE et al (2002) Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 346:393–403CrossRefPubMed Knowler WC, Barrett-Connor E, Fowler SE et al (2002) Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 346:393–403CrossRefPubMed
34.
go back to reference Aschard H, Chen J, Cornelis MC, Chibnik LB, Karlson EW, Kraft P (2012) Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases. Am J Hum Genet 90:962–972PubMedCentralCrossRefPubMed Aschard H, Chen J, Cornelis MC, Chibnik LB, Karlson EW, Kraft P (2012) Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases. Am J Hum Genet 90:962–972PubMedCentralCrossRefPubMed
Metadata
Title
Innate biology versus lifestyle behaviour in the aetiology of obesity and type 2 diabetes: the GLACIER Study
Authors
Alaitz Poveda
Robert W. Koivula
Shafqat Ahmad
Inês Barroso
Göran Hallmans
Ingegerd Johansson
Frida Renström
Paul W. Franks
Publication date
01-03-2016
Publisher
Springer Berlin Heidelberg
Published in
Diabetologia / Issue 3/2016
Print ISSN: 0012-186X
Electronic ISSN: 1432-0428
DOI
https://doi.org/10.1007/s00125-015-3818-y

Other articles of this Issue 3/2016

Diabetologia 3/2016 Go to the issue
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.