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Articles by C. Herder
Total Records ( 3 ) for C. Herder
  B. Thorand , A. Zierer , J. Baumert , C. Meisinger , C. Herder and W. Koenig
  Aims  Adipocyte-derived hormones seem to be involved in the development of Type 2 diabetes. Therefore, we assessed the association between the proinflammatory adipokine leptin and incident Type 2 diabetes, taking into account interactions between leptin and the anti-inflammatory adipokine adiponectin.

Methods  Using a case-cohort design, serum levels of adipokines were measured in 460 cases with incident Type 2 diabetes and 1474 non-cases selected from a source population of 7936 middle-aged subjects participating in the population-based Monitoring of Trends and Determinants in Cardiovascular Disease (MONICA)/Cooperative Health Research in the Region of Augsburg (KORA) Augsburg cohort study between 1984 and 1995 and followed up until 2002 (mean follow-up 10.9±4.7 years).

Results  High leptin and low adiponectin levels were associated with an increased Type 2 diabetes risk. The multivariable adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) comparing tertile extremes were 1.71 (1.12-2.63) for leptin (top vs. bottom tertile) and 2.65 (1.88-3.76) for adiponectin (bottom vs. top tertile), respectively. There was a significant interaction between leptin and adiponectin, with highest diabetes risk being observed in individuals with high leptin and low adiponectin levels (P = 0.029 for interaction).While the addition of adiponectin to a basic risk factor model improved model prediction (Δ area under the curve 0.011), the change in model prediction was only marginal after the addition of leptin (Δ area under the curve 0.002).

Conclusions  Our findings indicate that the two adipokines leptin and adiponectin interact in modulating Type 2 diabetes risk, but adiponectin is more strongly associated with Type 2 diabetes risk than leptin.

  W. Rathmann , B. Kowall , M. Heier , C. Herder , R. Holle , B. Thorand , K. Strassburger , A. Peters , H.-E. Wichmann , G. Giani and C. Meisinger
  Background  The aim was to derive Type 2 diabetes prediction models for the older population and to check to what degree addition of 2-h glucose measurements (oral glucose tolerance test) and biomarkers improves the predictive power of risk scores which are based on non-biochemical as well as conventional clinical parameters.

Methods  Oral glucose tolerance tests were carried out in a population-based sample of 1353 subjects, aged 55-74 years (62% response) in Augsburg (Southern Germany) from 1999 to 2001. The cohort was reinvestigated in 2006-2008. Of those individuals without diabetes at baseline, 887 (74%) participated in the follow-up. Ninety-three (10.5%) validated diabetes cases occurred during the follow-up. In logistic regression analyses for model 1, variables were selected from personal characteristics and additional variables were selected from routinely measurable blood parameters (model 2) and from 2-h glucose, adiponectin, insulin and homeostasis model assessment of insulin resistance (HOMA-IR) (model 3).

Results  Age, sex, BMI, parental diabetes, smoking and hypertension were selected for model 1. Model 2 additionally included fasting glucose, HbA1c and uric acid. The same variables plus 2-h glucose were selected for model 3. The area under the receiver operating characteristic curve significantly increased from 0.763 (model 1) to 0.844 (model 2) and 0.886 (model 3) (P < 0.01). Biomarkers such as adiponectin and insulin did not improve the predictive abilities of models 2 and 3. Cross-validation and bootstrap-corrected model performance indicated high internal validity.

Conclusions  This longitudinal study in an older population provides models to predict the future risk of Type 2 diabetes. The OGTT, but not biomarkers, improved discrimination of incident diabetes.

  A. Icks , B. Albers , B. Haastert , S. Pechlivanis , B. Bokhof , U. Slomiany , R. Erbel , K.-H. Jockel , J. Kruse , B. Nowotny , C. Herder , G. Giani and S. Moebus
  Aims  Cross-sectional studies have consistently reported evidence for an association between diabetes and depressive disorders. However, only limited prospective studies have examined this association, reporting conflicting results. In a population-based cohort study, we compared cumulative incidences of diabetes between participants with and without high depressive symptoms.

Method  We analysed the 5-year follow-up data from the German Heinz Nixdorf Recall study of 3547 participants without diabetes at baseline [mean age 58.8 (sd 7.6) years, 47.5% male]. Depressive symptoms were defined using the Centre for Epidemiologic Studies Depression scale (cut point ≥ 17). Diabetes (diagnosed or previously undetected) was identified by self-reported physician-diagnosed diabetes, medication and high blood glucose levels. We estimated 5-year cumulative incidences with 95% confidence intervals and fitted multiple logistic regression models to calculate the odds ratios, adjusted for age, sex, physical activity, smoking, living with or without partner, and educational level.

Results  The cumulative incidence of diabetes was 9.2% (95% CI 6.3-12.8) in participants with high depressive symptoms at baseline and 9.0% (95% CI 8.0-10.0) in participants without these symptoms. The age- and sex-adjusted odds ratio of diabetes in participants with depressive symptoms compared with those without was 1.13 [95% CI 0.77-1.68; fully adjusted 1.11 (95% CI 0.74-1.65)]. These results did not substantially change in several additional sensitivity analyses.

Conclusion  Our study did not show a significantly increased risk of developing diabetes in individuals with high depressive symptoms compared with those without high depressive symptoms during a 5-year follow-up period.

 
 
 
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