Diabetologia

, Volume 56, Issue 10, pp 2176–2180

Family history of diabetes is associated with higher risk for prediabetes: a multicentre analysis from the German Center for Diabetes Research

Authors

  • Robert Wagner
    • Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical ChemistryEberhard Karls University
    • German Center for Diabetes Research (DZD)
  • Barbara Thorand
    • German Center for Diabetes Research (DZD)
    • Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology II
  • Martin A. Osterhoff
    • German Center for Diabetes Research (DZD)
    • Department of Clinical NutritionGerman Institute of Human Nutrition
    • Department of Endocrinology, Diabetes and NutritionCharité-University-Medicine Berlin
  • Gabriele Müller
    • German Center for Diabetes Research (DZD)
    • Institute for Medical Informatics and Biometrics, Technical University Dresden, Medical Faculty Carl Gustav Carus
  • Anja Böhm
    • Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical ChemistryEberhard Karls University
    • German Center for Diabetes Research (DZD)
  • Christa Meisinger
    • German Center for Diabetes Research (DZD)
    • Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology II
  • Bernd Kowall
    • German Center for Diabetes Research (DZD)
    • Institute of Biometrics and Epidemiology, German Diabetes CenterLeibniz Center for Diabetes Research at Heinrich Heine University
  • Wolfgang Rathmann
    • German Center for Diabetes Research (DZD)
    • Institute of Biometrics and Epidemiology, German Diabetes CenterLeibniz Center for Diabetes Research at Heinrich Heine University
  • Florian Kronenberg
    • Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical PharmacologyInnsbruck Medical University
  • Harald Staiger
    • Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical ChemistryEberhard Karls University
    • German Center for Diabetes Research (DZD)
    • Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen
  • Norbert Stefan
    • Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical ChemistryEberhard Karls University
    • German Center for Diabetes Research (DZD)
    • Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen
  • Michael Roden
    • German Center for Diabetes Research (DZD)
    • Institute of Clinical Diabetology, German Diabetes CenterLeibniz Center for Diabetes Research at Heinrich Heine University
  • Peter E. Schwarz
    • German Center for Diabetes Research (DZD)
    • Department for Prevention & Care of Diabetes, Technical University Dresden, Medical Faculty Carl Gustav Carus
  • Andreas F. Pfeiffer
    • German Center for Diabetes Research (DZD)
    • Department of Clinical NutritionGerman Institute of Human Nutrition
    • Department of Endocrinology, Diabetes and NutritionCharité-University-Medicine Berlin
  • Hans-Ulrich Häring
    • Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical ChemistryEberhard Karls University
    • German Center for Diabetes Research (DZD)
    • Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen
    • Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical ChemistryEberhard Karls University
    • German Center for Diabetes Research (DZD)
    • Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen
Short Communication

DOI: 10.1007/s00125-013-3002-1

Cite this article as:
Wagner, R., Thorand, B., Osterhoff, M.A. et al. Diabetologia (2013) 56: 2176. doi:10.1007/s00125-013-3002-1

Abstract

Aims/hypothesis

Prediabetes is a collective term for different subphenotypes (impaired glucose tolerance [IGT] and/or impaired fasting glucose [IFG]) with different pathophysiologies. A positive family history for type 2 diabetes (FHD) is associated with increased risk for type 2 diabetes. We assumed that it would also associate with prediabetes, but wondered whether all subphenotypes are related to a positive family history.

Methods

In a study population of 8,106 non-diabetic individuals of European origin collected from four study centres (normal glucose tolerance, NGT n = 5,482, IFG and/or IGT n = 2,624), we analysed whether having at least one first degree relative with diabetes is associated with prediabetes. The analyses were performed using the same models in each population separately. Afterwards, a meta-analysis was performed.

Results

FHD was significantly associated with the risk for prediabetes (IFG and/or IGT, OR 1.40; 95% CI 1.27, 1.54). This association remained significant in multivariable logistic regression models including sex, age and BMI (OR 1.26; 95% CI 1.14, 1.40). When different prediabetic outcomes were considered separately, the association was found for isolated IFG (OR 1.37; 95% CI 1.20, 1.57), isolated IGT (OR 1.25; 95% CI 1.07, 1.46) as well as for the combination IFG+IGT (OR 1.64; 95% CI 1.40, 1.93). After stratification on BMI, association between FHD and prediabetes was seen only in non-obese individuals (BMI < 30 kg/m2).

Conclusions/interpretation

We found that FHD is an important risk factor for prediabetes, especially for combined IGT and IFG. Its relevance seems to be more evident in the non-obese.

Keywords

DZDFamily historyGerman Center for Diabetes ResearchIFGIGTMeta-analysisPrediabetes

Abbreviations

DZD

Deutsches Zentrum für Diabetesforschung (German Center for Diabetes Research)

FHD

Family history of diabetes

IFG

Impaired fasting glycaemia

iIFG

Isolated impaired fasting glycaemia

IGT

Impaired glucose tolerance

iIGT

Isolated impaired glucose tolerance

KORA

Cooperative Research in the Region of Augsburg

MeSyBePo

Metabolic Syndrome Berlin Potsdam

NGT

Normal glucose tolerance

PRAEDIAS

Prävention des Diabetes – Selbst aktiv werden (Active in Diabetes Prevention)

TUEF

Tübingen family study

Introduction

Prediabetes is a high-risk state for diabetes affecting approximately 470 million people worldwide. However, it is unclear whether all of its subcategories (isolated impaired fasting glucose [iIFG], isolated impaired glucose tolerance [iIGT], and their combination [IFG+IGT]) share the same pathophysiological background with diabetes. The progression rates of prediabetic conditions to diabetes are strikingly different [1].

A positive family history of type 2 diabetes (FHD) nearly doubles the risk of diabetes in the offspring [2]. As FHD is associated with all characteristic features of diabetes pathophysiology, it may well be that individuals with FHD are at increased risk of prediabetes.

In this study, we performed a meta-analysis from four German studies comprising 8,106 non-diabetic individuals seeking answers for the question whether FHD is associated with prediabetes and whether its subcategories behave differently. We furthermore tested whether these associations are modified by variables such as sex, age and obesity.

Methods

Participants

Data from four pre-existing cohort studies conducted by partner institutes of the German Center for Diabetes Research (Deutsches Zentrum für Diabetesforschung [DZD]) were used in this meta-analysis. The analysis comprised individuals without previously diagnosed diabetes who underwent 75 g OGTTs in the morning, after at least 10 h fasting. Individuals with incidental diabetes (fasting glucose ≥7.0 or 2 h glucose ≥11.1 mmol/l) were excluded. Prediabetes was taken as including IFG and/or IGT, defined according to the recommendations of the ADA [3].

Background information on each study population is provided in electronic supplementary material (ESM) Table 1.

The investigations were performed in accordance with the Declaration of Helsinki, and all studies were approved by local ethics committees. All participants provided written informed consent.

Characteristics of the participants for each study are shown in Table 1, with glucose tolerance data provided in ESM Table 2.
Table 1

Characteristics of the participants of the four study populations

 

TUEF

MeSyBePo

KORA

PRAEDIAS

NGT

IFG, IGT or IFG+IGT

p

NGT

IFG, IGT or IFG+IGT

p

NGT

IFG, IGT or IFG+IGT

p

NGT

IFG, IGT or IFG+IGT

p

n (%)

1,456

(71%)

601

(29%)

 

1,331 (65%)

719

(35%)

 

1,622

(71%)

666 (29%)

 

1,073

(63%)

638

(37%)

 

Sex (f/m)

(female%)

976/480

(67%)

393/208

(65%)

0.47

922/409

(69%)

496/223

(69%)

0.47

957/665

(59%)

260/406

(39%)

<0.0001

635/438

(59%)

328/310

(51%)

0.02

Age (years)

37 ± 12

45 ± 13

<0.0001

49 ± 14

56 ± 12

<0.0001

50 ± 11

57 ± 10

<0.0001

45 ± 18

55 ± 17

<0.0001

BMI (kg/m2)

28.3 ± 7.7

34.5 ± 10.8

<0.0001

28.5 ± 6.0

31.3 ± 6.8

<0.0001

26.2 ± 4.3

29.0 ± 4.8

<0.0001

26.0 ± 4.7

27.8 ± 4.8

<0.0001

Family history of diabetes

Yes/No (n/n, % yes)

606/850

(42%)

309/292

(51%)

<0.0001

424/907

(32%)

280/439

(39%)

<0.001

387/1,235

(24%)

205/461

(31%)

0.0006

396/677

(37%)

279/359

(44%)

0.06

Fasting plasma/serum glucose (mmol/l)

4.9 ± 0.4

5.7 ± 0.5

<0.0001

4.8 ± 0.4

5.5 ± 0.6

<0.0001

5.0 ± 0.3

5.8 ± 0.5

<0.0001

5.0 ± 0.3

5.8 ± 0.5

<0.0001

Postchallange (2 h) plasma/serum glucose (mmol/l)

5.7 ± 1.1

7.9 ± 1.6

<0.0001

6.1 ± 1.0

8.2 ± 1.4

<0.0001

5.3 ± 1.1

7.2 ± 1.8

<0.0001

5.5 ± 1.2

7.7 ± 1.7

<0.0001

Fasting plasma insulin (pmol/l)

60 ± 50

99 ± 76

<0.0001

57 ± 38

75 ± 48

<0.0001

46 ± 161

82 ± 357

<0.0001

67 ± 50

92 ± 66

<0.0001

Post-challenge (2 h) plasma insulin (pmol/l)

367 ± 352

738 ± 649

<0.0001

304 ± 271

591 ± 428

<0.0001

349 ± 436

601 ± 675

<0.0001

264 ± 203

509 ± 429

<0.0001

Definition of FHD

For this meta-analysis, FHD was uniformly defined as at least one first degree relative with type 2 diabetes. Participants were asked about first degree relatives with type 2 diabetes (parents, siblings or children) using questionnaires or personal interviews. The questionnaire in the Cooperative Research in the Region of Augsburg (KORA) study comprised only parents and siblings.

Statistical analyses

Data are given as means ± SD. Means were compared with t tests and the Wilcoxon test, as appropriate. Binary outcomes were tested using Fisher’s exact test. Multivariable logistic regression models were used to analyse associations between FHD and prediabetes. Prediabetes categories were applied as dichotomous outcomes (prediabetes vs normal glucose tolerance [NGT], iIFG vs NGT, iIGT vs NGT, IFG+IGT vs NGT). Covariates with non-normal distribution were loge-transformed to approximate normal distribution in the models. ORs and their 95% CIs have been calculated separately in each centre. Age and BMI were dichotomised for interaction tests. For age, a cut-off of 45 years, a numeral close to both the weighted average of the four study populations and the median age in Germany, was used. For BMI, we applied the clinical cut-off for obesity (30 kg/m2).

Calculations were carried out using JMP 8.0 (SAS Institute, Cary, NC, USA) in TUEF (Tübingen Family Study), SAS 9.2 (SAS Institute), in KORA, and IBM SPSS Statistics 18 and 19 (IBM, Ehningen, Germany) in PRAEDIAS (Prävention des Diabetes – Selbst aktiv werden [Active in Diabetes Prevention]) and MeSyBePo (Metabolic Syndrome Berlin Potsdam).

Logistic regression results from all centres were pooled with MIX 2.0 Pro (Version 2.0.1.4, www.biostatxl.com). Fixed-effects models were used throughout the study. Weighting was performed with the Mantel–Haenszel method. Heterogeneity was low (p for Cochrane’s Q >0.05) for all analysed outcome variables, except for iIGT (Q = 12.6, p = 0.006).

Results

FHD was significantly associated with the risk of prediabetes in each single study as well as the meta-analysis (OR 1.40; 95% CI 1.27, 1.54, p < 0.001), see Fig. 1a–d. This association remained significant in the multivariable logistic regression model adjusting for the covariables sex, age and BMI (OR 1.26; 95% CI 1.14, 1.40, p ≤ 0.001). When stratifying for prediabetes subcategories, the association with FHD was established for iIFG (OR 1.37; 95% CI 1.20, 1.57, p < 0.001), iIGT (OR 1.25; 95% CI 1.07, 1.46, p < 0.001) and IFG+IGT (OR 1.64; 95% CI 1.40, 1.93, p < 0.001) in meta-analyses. While the association remained significant in iIFG (OR 1.26; 95% CI 1.09, 1.45, p < 0.001) and IFG+IGT (OR 1.47; 95% CI 1.23, 1.76, p < 0.001) after adjusting for sex, age and BMI, the association of FHD with iIGT was no longer significant after adjustment for these confounders (OR 1.11; 95% CI 0.94, 1.3, p = 0.21).
https://static-content.springer.com/image/art%3A10.1007%2Fs00125-013-3002-1/MediaObjects/125_2013_3002_Fig1_HTML.gif
Fig. 1

Association of a positive family history of diabetes with prediabetes (a), isolated IFG (b), isolated IGT (c) and combined IFG+IGT (d)

We further tested the interaction of FHD with sex, age and obesity in all centres for different prediabetes subcategories (see ESM Table 3). Interaction or a trend for interaction (p < 0.10) with consistent effect direction was observed between FHD and BMI for the determination of prediabetes in two out of four studies. In a subsequent meta-analysis of BMI-stratified subpopulations, the association of FHD with prediabetes was seen only in the non-obese subpopulations, but not in the obese subpopulations (see ESM Figure 1).

Discussion

In the present analysis of 8,106 individuals characterised by OGTT in four DZD centres, we found that FHD is associated with a 40% increased risk of having prediabetes. When taking additional risk factors such as obesity and age in a multivariable model into account, the strength of the association was attenuated to 26%.

An earlier study from Sweden also found a 50% increased risk of prediabetes in participants with FHD [4]. In our meta-analysis, the OR was lowest for iIGT (1.25) compared with iIFG (1.37) and IFG+IGT (1.64). Given that IGT implies a higher conversion rate to diabetes than IFG [1], its weaker association with FHD was surprising. As IFG is predominantly associated with hepatic insulin resistance while IGT is often associated with muscle insulin resistance [5] as well as impaired insulin secretion, one may speculate that FHD might have a stronger link to hepatic insulin resistance. Of note, the lower OR of iIGT is particularly striking in the PRAEDIAS and MeSyBePo studies, and the relatively large inter-centre differences lead to an increased heterogeneity for this variable in the meta-analysis.

An analysis of the Nurses’ Health Study showed that BMI accounted for 21% of the association between FHD and diabetes in women [6]. In the European Prospective Investigation into Cancer and Nutrition (EPIC)–InterAct study, however, lifestyle, anthropometric and genetic risk factors did not sufficiently explain the excess risk associated with FHD [7]. Our data suggest that FHD is associated with prediabetes in non-obese rather than in obese individuals. This might indicate that the effect of FHD on prediabetes becomes readily measurable only when not overshadowed by strong risk factors such as obesity. Most diabetes risk questionnaires, including the German Diabetes Risk Score [8] and the Finnish Findrisk [9], heavily rely on markers of obesity. The predictive value of such diabetes risk questionnaires is improved by adding information on family history of diabetes [10]. This improvement could be more striking in the non-obese.

Limitations of our study include its cross-sectional nature and the combination of a population-based study (KORA) with cohort studies specifically recruiting persons with a high risk for type 2 diabetes. In addition, the method of ascertainment of FHD was not identical in each centre.

The association of FHD with increased risk for prediabetes points towards an important role for FHD in the early pathogenesis of diabetes.

Acknowledgements

We thank all the research volunteers for their participation.

Funding

This work was supported by a grant from the German Federal Ministry of Education and Research to the German Center for Diabetes Research.

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Contribution statement

RW contributed to data acquisition, analysis, interpretation of data, and drafted and wrote the manuscript. BT contributed to data analysis, interpretation of data, and wrote the manuscript. MAO contributed to data analysis and edited the manuscript. GM, MR, PES and AFP contributed to data acquisition and critically revised the manuscript. AB and HS contributed to data acquisition, interpretation of data, and critically revised the manuscript. CM and BK contributed to data analysis and critically revised the manuscript. WR and FK contributed to data analysis, interpretation of data, and critically revised the manuscript. NS contributed to data acquisition, analysis and critically revised the manuscript. HUH designed the study and critically revised the manuscript. AF designed the study, contributed to data analysis and interpretation of data, and wrote the manuscript. All authors approved the final version of the manuscript to be published.

Supplementary material

125_2013_3002_MOESM1_ESM.pdf (257 kb)
ESM Table 1(PDF 256 kb)
125_2013_3002_MOESM2_ESM.pdf (219 kb)
ESM Table 2(PDF 219 kb)
125_2013_3002_MOESM3_ESM.pdf (355 kb)
ESM Table 3(PDF 354 kb)
125_2013_3002_MOESM4_ESM.pdf (400 kb)
ESM Fig. 1(PDF 400 kb)

Copyright information

© Springer-Verlag Berlin Heidelberg 2013