Matthias B Schulze

German Diabetes Center, Düsseldorf, North Rhine-Westphalia, Germany

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Publications (173)987.81 Total impact

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    ABSTRACT: Sub-Saharan Africa is facing a double burden of malnutrition: vitamin A deficiency (VAD) prevails, whereas the nutrition-related chronic conditions type 2 diabetes (T2D) and hypertension are emerging. Serum retinol-a VAD marker-increases in kidney disease and decreases in inflammation, which can partly be attributed to alterations in the vitamin A-transport proteins retinol-binding protein 4 (RBP4) and prealbumin. Kidney dysfunction and inflammation commonly accompany T2D and hypertension. Among urban Ghanaians, we investigated the associations of T2D and hypertension with serum retinol as well as the importance of kidney function and inflammation in this regard. A hospital-based, case-control study in individuals for risk factors of T2D, hypertension, or both was conducted in Kumasi, Ghana (328 controls, 197 with T2D, 354 with hypertension, and 340 with T2D plus hypertension). In 1219 blood samples, serum retinol, RBP4, and prealbumin were measured. Urinary albumin and estimated glomerular filtration rate (eGFR) defined kidney function. C-reactive protein (CRP) >5 mg/L indicated inflammation. We identified associations of T2D and hypertension with retinol by linear regression and calculated the contribution of RBP4, prealbumin, urinary albumin, eGFR, and CRP to these associations as the percentages of the explained variance of retinol. VAD (retinol <1.05 μmol/L) was present in 10% of this predominantly female, middle-aged, overweight, and deprived population. Hypertension, but not T2D, was positively associated with retinol (β: 0.12; 95% CI: 0.08, 0.17), adjusted for age, sex, socioeconomic factors, anthropometric measurements, and lifestyle. In addition to RBP4 (72%) and prealbumin (22%), the effect of increased retinol on individuals with hypertension was mainly attributed to impaired kidney function (eGFR: 30%; urinary albumin: 5%) but not to inflammation. In patients with hypertension, VAD might be underestimated because of increased serum retinol in the context of kidney dysfunction. Thus, the interpretation of serum retinol in sub-Saharan Africa should account for hypertension status. © 2015 American Society for Nutrition.
    American Journal of Clinical Nutrition 05/2015; DOI:10.3945/ajcn.114.101345 · 6.92 Impact Factor
  • PLoS ONE 05/2015; 10(5):e0127368. DOI:10.1371/journal.pone.0127368 · 3.53 Impact Factor
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    ABSTRACT: An association between desaturase activity and risk of type 2 diabetes (T2D) has been found in epidemiologic studies, but little is known about potential mediators of this association. We aimed to investigate the potential role of diabetes-related biomarkers as mediators of the association between estimated Δ5 desaturase (D5D), Δ6 desaturase (D6D), and stearoyl-CoA desaturase (SCD) activity and T2D risk. We analyzed a case-cohort study (subcohort: n = 1533; verified incident T2D cases: n = 400), nested within the European Prospective Investigation into Cancer and Nutrition-Potsdam Study involving 27,548 middle-aged participants. We evaluated the impact of adjustment for several T2D-related biomarkers reflecting liver fat accumulation [reflected by γ-glutamyltransferase (GGT), alanine transaminase (ALT), fetuin-A, and the algorithm-based fatty liver index (FLI)], dyslipidemia (high-density lipoprotein cholesterol, triglycerides), inflammation [C-reactive protein (CRP)], and adiponectin on the association between D5D, D6D, and SCD activity, estimated with fatty acid product-to-precursor ratios derived from erythrocyte membrane proportions, and T2D risk. Estimated D5D activity was inversely associated with T2D risk, whereas D6D and SCD activities were positively associated with risk of T2D [HRs (95% CIs) (highest vs. lowest tertile): 0.51 (0.36, 0.73), 1.68 (1.18, 2.39), and 1.82 (1.29, 2.58), respectively]. The association between estimated D5D, D6D, and SCD activities and risk of T2D was statistically significantly and markedly attenuated after adjustment for the FLI and, to a lesser extent, after adjustment for triglycerides, whereas adjustment for other desaturase-associated biomarkers (CRP, fetuin-A, ALT, and GGT) did not lead to appreciable attenuations. Liver fat accumulation, as reflected by the FLI, and dyslipidemia, as reflected by triglycerides, may partly explain the association between estimated D5D, D6D, and SCD activity and T2D risk. © 2015 American Society for Nutrition.
    American Journal of Clinical Nutrition 05/2015; DOI:10.3945/ajcn.114.102707 · 6.92 Impact Factor
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    ABSTRACT: Habitual red meat consumption was consistently related to a higher risk of type 2 diabetes in observational studies. Potentially underlying mechanisms are unclear. This study aimed to identify blood metabolites that possibly relate red meat consumption to the occurrence of type 2 diabetes. Analyses were conducted in the prospective European Prospective Investigation into Cancer and Nutrition-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2681, including 688 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Total red meat consumption was defined as energy-standardized summed intake of unprocessed and processed red meats. Concentrations of 14 amino acids, 17 acylcarnitines, 81 glycerophospholipids, 14 sphingomyelins, and ferritin were determined in serum samples from baseline. These biomarkers were considered potential mediators of the relation between total red meat consumption and diabetes risk in Cox models. The proportion of diabetes risk explainable by biomarker adjustment was estimated in a bootstrapping procedure with 1000 replicates. After adjustment for age, sex, lifestyle, diet, and body mass index, total red meat consumption was directly related to diabetes risk [HR for 2 SD (11 g/MJ): 1.26; 95% CI: 1.01, 1.57]. Six biomarkers (ferritin, glycine, diacyl phosphatidylcholines 36:4 and 38:4, lysophosphatidylcholine 17:0, and hydroxy-sphingomyelin 14:1) were associated with red meat consumption and diabetes risk. The red meat-associated diabetes risk was significantly (P < 0.001) attenuated after simultaneous adjustment for these biomarkers [biomarker-adjusted HR for 2 SD (11 g/MJ): 1.09; 95% CI: 0.86, 1.38]. The proportion of diabetes risk explainable by respective biomarkers was 69% (IQR: 49%, 106%). In our study, high ferritin, low glycine, and altered hepatic-derived lipid concentrations in the circulation were associated with total red meat consumption and, independent of red meat, with diabetes risk. The red meat-associated diabetes risk was largely attenuated after adjustment for selected biomarkers, which is consistent with the presumed mediation hypothesis. © 2015 American Society for Nutrition.
    American Journal of Clinical Nutrition 05/2015; 101(6). DOI:10.3945/ajcn.114.099150 · 6.92 Impact Factor
  • Kristin Mühlenbruch, Matthias Schulze
    Diabetes aktuell 05/2015; 13(02):63-64. DOI:10.1055/s-0035-1552945
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    ABSTRACT: The fatty liver index (FLI) predicts fatty liver by using BMI, waist circumference, γ-glutamyltransferase and triglycerides. We investigated the association between the FLI and the risk of type 2 diabetes and evaluated to what extent single FLI components contribute to the diabetes risk. We analysed a case-cohort study (random sub-cohort: 1922; incident cases: 563) nested within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study. The proportion of exposure effect (PEE) explained by single FLI components was evaluated and effect decomposition using inverse probability weighting (IPW) was applied. Women and men with a FLI ≥60 compared to those with a FLI <30 had a multivariable-adjusted Hazard Ratio (HR) of 17.6; 95% confidence interval (CI) 11.1-28.0 and HR: 10.9; 95% CI 6.22-19.2, respectively. Adjustment for BMI or waist circumference attenuated this association in men [PEEBMI (95% CI) = 53.8% (43.9%-65.8%); PEEwaist (95% CI) = 54.8% (44.2%-68.8%)]. In women, adjustment for waist circumference attenuated the association to a lesser degree than in men [PEEwaist (95% CI) = 31.1%; (21.9%-43.1%)] while BMI had no appreciable effect [PEEBMI (95% CI) = 11.0% (2.68%-21.0%)]. γ-glutamyltransferase and triglycerides showed only a small attenuation in women [PEEGGT(95% CI) = 3.11% (-0.72%-4.48%); PEETG (95% CI) = 6.36% (3.81%-9.92%)] and in men [PEEGGT = 0%; PEETG (95% CI) = 6.23% (2.03%-11.8%)]. In women, the total effect was decomposed into a direct effect and 4 indirect effects (HRBMI = 1.10; HRwaist = 1.28; HRGGT = 0.97 and HRTG = 1.03). In men, the 4 indirect effects were HRBMI = 1.25; HRwaist = 1.29; HRGGT = 0.97 and HRTG = 0.99. These data suggest that the FLI, as a proxy for fatty liver, is associated with risk of type 2 diabetes. This association is only partly explained by standard estimates of overall and abdominal body fatness, particularly among women.
    PLoS ONE 04/2015; 10(4):e0124749. DOI:10.1371/journal.pone.0124749 · 3.53 Impact Factor
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    ABSTRACT: A proportion of type 2 diabetes cases arise from normal-weight individuals who can therefore be considered to be "metabolically unhealthy normal-weight" (MUH-NW). It remains unclear which factors account for this access risk. Our aims were to identify risk factors for type 2 diabetes in normal-weight individuals and to compare the strengths of their associations with type 2 diabetes to that observed in overweight and obese participants. A case-cohort, including 2027 sub-cohort participants and 706 incident type 2 cases, was designed within the population-based European Prospective Investigation into Cancer and Nutrition Potsdam study. Adjusted means and relative frequencies of anthropometric, lifestyle and biochemical risk factors were calculated in groups stratified by BMI and incident diabetes status. Cox regressions were applied to evaluate associations between these variables and diabetes risk stratified by BMI category. MUH-NW individuals were characterized by known diabetes risk factors, e.g. they were significantly more likely to be male, former smokers, hypertensive, and less physically active compared to normal-weight individuals without incident diabetes. Higher waist circumference (women: 75.5 vs. 73.1cm; men: 88.0 vs. 85.1cm), higher HbA1c (6.1 vs. 5.3%), higher triglycerides (1.47 vs. 1.11mmol/l), and higher levels of high sensitive C-reactive protein (0.81 vs. 0.51mg/l) as well as lower levels of HDL-cholesterol (1.28 vs. 1.49mmol/l) and adiponectin (6.32 vs. 8.25μg/ml) characterized this phenotype. Stronger associations with diabetes among normal-weight participants compared to overweight and obese (p for interaction<0.05) were observed for height, waist circumference, former smoking, and hypertension. Normal-weight individuals who develop diabetes have higher levels of diabetes risk factors, however, frequently still among the normal range. Still, hypertension, elevated HbA1c and lifestyle risk factors might be useful indicators of risk. Copyright © 2015. Published by Elsevier Inc.
    Metabolism: clinical and experimental 03/2015; 64(8). DOI:10.1016/j.metabol.2015.03.009 · 3.61 Impact Factor
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    ABSTRACT: The Net Reclassification Improvement (NRI) has become a popular metric for evaluating improvement in disease prediction models through the past years. The concept is relatively straightforward but usage and interpretation has been different across studies. While no thresholds exist for evaluating the degree of improvement, many studies have relied solely on the significance of the NRI estimate. However, recent studies recommend that statistical testing with the NRI should be avoided. We propose using confidence ellipses around the estimated values of event and non-event NRIs which might provide the best measure of variability around the point estimates. Our developments are illustrated using practical examples from EPIC-Potsdam study.
    European Journal of Epidemiology 02/2015; 30(4). DOI:10.1007/s10654-015-0001-1 · 5.15 Impact Factor
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    ABSTRACT: Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=-0.09±0.01 mmol l(-1), P=3.4 × 10(-12)), T2D risk (OR[95%CI]=0.86[0.76-0.96], P=0.010), early insulin secretion (β=-0.07±0.035 pmolinsulin mmolglucose(-1), P=0.048), but higher 2-h glucose (β=0.16±0.05 mmol l(-1), P=4.3 × 10(-4)). We identify a gene-based association with FG at G6PC2 (pSKAT=6.8 × 10(-6)) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l(-1), P=1.3 × 10(-8)). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.
    Nature Communications 01/2015; 6:5897. DOI:10.1038/ncomms6897 · 10.74 Impact Factor
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    ABSTRACT: Application of metabolite profiling could expand the etiological knowledge of type 2 diabetes mellitus (T2D). However, few prospective studies apply broad untargeted metabolite profiling to reveal the comprehensive metabolic alterations preceding the onset of T2D. We applied untargeted metabolite profiling in serum samples obtained from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort comprising 300 individuals who developed T2D after a median follow-up time of 6 years and 300 matched controls. For that purpose, we used ultra-performance liquid chromatography-mass spectrometry with a protocol specifically designed for large-scale metabolomics studies with regard to robustness and repeatability. After multivariate classification to select metabolites with the strongest contribution to disease classification, we applied multivariable-adjusted conditional logistic regression to assess the association of these metabolites with T2D. Among several alterations in lipid metabolism, there was an inverse association with T2D for metabolites chemically annotated as lysophosphatidylcholine(dm16:0) and phosphatidylcholine(0-20:0)/0-20:0). Hexose sugars were positively associated with T2D, whereas higher concentrations of a sugar alcohol and a deoxyhexose sugar reduced the odds of diabetes by approximately 60% and 70%, respectively. Furthermore, there was suggestive evidence for a positive association of the circulating purine nucleotide isopentenyladenosine-5'-monophosphate with incident T2D. This study constitutes one of the largest metabolite profiling approaches of T2D biomarkers in a prospective study population. The findings might help generate new hypotheses about diabetes etiology and develop further targeted studies of a smaller number of potentially important metabolites. © 2014 American Association for Clinical Chemistry.
    Clinical Chemistry 12/2014; 61(3). DOI:10.1373/clinchem.2014.228965 · 7.77 Impact Factor
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    ABSTRACT: Background/Objectives:Diets high in saturated and trans fat and low in unsaturated fat may increase type 2 diabetes (T2D) risk, but studies on foods high in fat per unit weight are sparse. We assessed whether the intake of vegetable oil, butter, margarine, nuts and seeds and cakes and cookies is related to incident T2D.Subjects/Methods:A case-cohort study was conducted, nested within eight countries of the European Prospective Investigation into Cancer (EPIC), with 12 403 incident T2D cases and a subcohort of 16 835 people, identified from a cohort of 340 234 people. Diet was assessed at baseline (1991-1999) by country-specific questionnaires. Country-specific hazard ratios (HRs) across four categories of fatty foods (nonconsumers and tertiles among consumers) were combined with random-effects meta-analysis.Results:After adjustment not including body mass index (BMI), nonconsumers of butter, nuts and seeds and cakes and cookies were at higher T2D risk compared with the middle tertile of consumption. Among consumers, cakes and cookies were inversely related to T2D (HRs across increasing tertiles 1.14, 1.00 and 0.92, respectively; P-trend <0.0001). All these associations attenuated upon adjustment for BMI, except the higher risk of nonconsumers of cakes and cookies (HR 1.57). Higher consumption of margarine became positively associated after BMI adjustment (HRs across increasing consumption tertiles: 0.93, 1.00 and 1.12; P-trend 0.03). Within consumers, vegetable oil, butter and nuts and seeds were unrelated to T2D.Conclusions:Fatty foods were generally not associated with T2D, apart from weak positive association for margarine. The higher risk among nonconsumers of cakes and cookies needs further explanation.European Journal of Clinical Nutrition advance online publication, 26 November 2014; doi:10.1038/ejcn.2014.249.
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    ABSTRACT: The fluidity of cell membranes has been hypothesised as an important link in the association of fatty acids (FAs) with diabetes risk. The lipophilic index, which can be derived from the FA profile of blood or tissues, has recently been proposed as a novel measure of cell membrane FA fluidity. In this study we aimed to evaluate the lipophilic index in relation to the incidence of type 2 diabetes.
    Diabetologia 10/2014; 58(2). DOI:10.1007/s00125-014-3421-7 · 6.88 Impact Factor
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    ABSTRACT: The aims of this study were to investigate the association between smoking and incident type 2 diabetes, accounting for a large number of potential confounding factors, and to explore potential effect modifiers and intermediate factors.
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    ABSTRACT: Dietary patterns have been associated with the incidence of type 2 diabetes, but little is known about the impact of ethnicity on this relationship. This study evaluated the association between four a priori dietary quality indexes and risk of type 2 diabetes among white individuals, Japanese-Americans and Native Hawaiians in the Hawaii component of the Multiethnic Cohort.
    Diabetologia 10/2014; DOI:10.1007/s00125-014-3404-8 · 6.88 Impact Factor
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    ABSTRACT: Conflicting evidence exists regarding the association between saturated fatty acids (SFAs) and type 2 diabetes. In this longitudinal case-cohort study, we aimed to investigate the prospective associations between objectively measured individual plasma phospholipid SFAs and incident type 2 diabetes in EPIC-InterAct participants.
    The Lancet Diabetes & Endocrinology 08/2014; DOI:10.1016/S2213-8587(14)70146-9 · 9.19 Impact Factor
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    ABSTRACT: The inverse association between coffee consumption and the risk of type 2 diabetes (T2D) is well established; however, little is known about potential mediators of this association.OBJECTIVE: We aimed to investigate the association between coffee consumption and diabetes-related biomarkers and their potential role as mediators of the association between coffee consumption and T2D.DESIGN: We analyzed a case-cohort study (subcohort: n = 1610; verified incident T2D cases: n = 417) nested within the European Prospective Investigation into Cancer and Nutrition-Potsdam study involving 27,548 middle-aged participants. Habitual coffee consumption was assessed with a validated, semiquantitative food-frequency questionnaire. We evaluated the association between coffee consumption and several T2D-related biomarkers, such as liver markers (reflected by γ-glutamyltransferase, fetuin-A, and sex hormone-binding globulin), markers of dyslipidemia (high-density lipoprotein cholesterol and triglycerides), inflammation [C-reactive protein (CRP)], an adipokine (adiponectin), and metabolites, stratified by sex.RESULTS: Coffee consumption was inversely associated with diacyl-phosphatidylcholine C32:1 in both sexes and with phenylalanine in men, as well as positively associated with acyl-alkyl-phosphatidylcholines C34:3, C40:6, and C42:5 in women. Furthermore, coffee consumption was inversely associated with fetuin-A (P-trend = 0.06) and CRP in women and γ-glutamyltransferase and triglycerides in men. Coffee consumption tended to be inversely associated with T2D risk in both sexes, reaching significance only in men [HR (95% CI): women: ≥4 compared with >0 to <2 cups coffee/d: 0.78 (0.46, 1.33); men: ≥5 compared with >0 to <2 cups coffee/d: 0.40 (0.19, 0.81)]. The association between coffee consumption and T2D risk in men was slightly reduced after adjustment for phenylalanine or lipid markers.CONCLUSIONS: Coffee consumption was inversely associated with a diacyl-phosphatidylcholine and liver markers in both sexes and positively associated with certain acyl-alkyl-phosphatidylcholines in women. Furthermore, coffee consumption showed an inverse trend with CRP in women and with triglycerides and phenylalanine in men. However, these markers explained only to a small extent the inverse association between long-term coffee consumption and T2D risk.
    American Journal of Clinical Nutrition 07/2014; 100(3). DOI:10.3945/ajcn.113.080317 · 6.92 Impact Factor
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    ABSTRACT: We aimed to validate genetic variants as instruments for insulin resistance and secretion, to characterise their association with intermediate phenotypes, and to investigate their role in T2D risk among normal-weight, overweight and obese individuals.We investigated the association of genetic scores with euglycaemic-hyperinsulinaemic clamp- and OGTT-based measures of insulin resistance and secretion, and a range of metabolic measures in up to 18,565 individuals. We also studied their association with T2D risk among normal-weight, overweight and obese individuals in up to 8,124 incident T2D cases. The insulin resistance score was associated with lower insulin sensitivity measured by M/I value (β in SDs-per-allele [95%CI]:-0.03[-0.04,-0.01];p=0.004). This score was associated with lower BMI (-0.01[-0.01,-0.0;p=0.02) and gluteofemoral fat-mass : -0.03[-0.05,-0.02;p=1.4x10(-6)), and with higher ALT (0.02[0.01,0.03];p=0.002) and gamma-GT (0.02[0.01,0.03];p=0.001). While the secretion score had a stronger association with T2D in leaner individuals (pinteraction=0.001), we saw no difference in the association of the insulin resistance score with T2D among BMI- or waist-strata(pinteraction>0.31). While insulin resistance is often considered secondary to obesity, the association of the insulin resistance score with lower BMI and adiposity and with incident T2D even among individuals of normal weight highlights the role of insulin resistance and ectopic fat distribution in T2D, independently of body size.
    Diabetes 06/2014; 63(12). DOI:10.2337/db14-0319 · 8.47 Impact Factor
  • Janine Kröger, Matthias B Schulze
    Current opinion in lipidology 06/2014; 25(3):228-9. DOI:10.1097/MOL.0000000000000078 · 5.80 Impact Factor
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    ABSTRACT: Aims Several published diabetes prediction models include information about family history of diabetes. The aim of this study was to extend the previously developed German Diabetes Risk Score (GDRS) with family history of diabetes and to validate the updated GDRS in the Multinational MONItoring of trends and determinants in CArdiovascular Diseases (MONICA)/German Cooperative Health Research in the Region of Augsburg (KORA) study. Methods We used data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study for extending the GDRS, including 21,846 participants. Within 5 years of follow-up 492 participants developed diabetes. The definition of family history included information about the father, the mother and/or sibling/s. Model extension was evaluated by discrimination and reclassification. We updated the calculation of the score and absolute risks. External validation was performed in the MONICA/KORA study comprising 11,940 participants with 315 incident cases after 5 years of follow-up. Results The basic ROC-AUC of 0.856 (95%-CI: 0.842-0.870) was improved by 0.007 (0.003-0.011) when parent and sibling history was included in the GDRS. The net reclassification improvement was 0.110 (0.072-0.149), respectively. For the updated score we demonstrated good calibration across all tenths of risk. In MONICA/KORA, the ROC-AUC was 0.837 (0.819-0.855); regarding calibration we saw slight overestimation of absolute risks. Conclusions: Inclusion of the number of diabetes-affected parents and sibling history improved the prediction of type 2 diabetes. Therefore, we updated the GDRS algorithm accordingly. Validation in another German cohort study showed good discrimination and acceptable calibration for the vast majority of individuals.
    Diabetes research and clinical practice 06/2014; 104(3). DOI:10.1016/j.diabres.2014.03.013 · 2.54 Impact Factor
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    ABSTRACT: Background: Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention.
    PLoS Medicine 05/2014; 11(5). DOI:10.1371/journal.pmed.1001647 · 15.25 Impact Factor

Publication Stats

9k Citations
987.81 Total Impact Points

Institutions

  • 2012–2015
    • German Diabetes Center
      Düsseldorf, North Rhine-Westphalia, Germany
  • 1999–2015
    • German Institute of Human Nutrition
      • • Department of Molecular Epidemiology
      • • Department of Epidemiology
      Berlín, Berlin, Germany
  • 2013
    • Medical Research Council (UK)
      Londinium, England, United Kingdom
  • 2008–2011
    • University of Technology Munich
      • • Chair of Public Health Nutrition
      • • Department of Nutrition and Food Sciences
      München, Bavaria, Germany
  • 2003–2005
    • Massachusetts Department of Public Health
      Boston, Massachusetts, United States
  • 2002–2005
    • Harvard Medical School
      • Division of Nutrition
      Boston, Massachusetts, United States
    • Universität Bremen
      Bremen, Bremen, Germany
  • 2004
    • Simmons College
      • Program in Nutrition and Dietetics
      Boston, Massachusetts, United States
  • 1999–2003
    • German Cancer Research Center
      • Division of Cancer Epidemiology
      Heidelberg, Baden-Wuerttemberg, Germany