Article

The 'accelerator hypothesis': relationship between weight, height, body mass index and age at diagnosis in a large cohort of 9,248 German and Austrian children with type 1 diabetes mellitus

University Children's Hospital, University of Erlangen-Nuremberg, Loschgestr.15, 91054, Erlangen, Germany.
Diabetologia (Impact Factor: 6.88). 01/2006; 48(12):2501-4. DOI: 10.1007/s00125-005-0033-2
Source: PubMed

ABSTRACT The aim of this study was to investigate whether either increased weight or BMI are associated with the earlier manifestation of type 1 diabetes mellitus in children.
We evaluated anthropometric measurements in a large cohort of 9,248 patients of European extraction who were diagnosed in the years 1990-2003 in 116 pediatric clinics throughout Germany and Austria.
Patients were divided into four groups according to age (0-4.9 years, 5-9.9 years, 10-14.9 years and 15-20 years). Significantly higher standard deviation scores (SDSs) for weight and BMI at diabetes onset were found for both boys and girls in the three younger age groups (up to 14.9 years of age) compared with the reference population (p<0.00001). In addition, the BMI SDS and the weight SDS were significantly higher in the 0-4.9-years age group than in all other groups (p<0.00001), and BMI SDS at onset gradually decreased with increasing age at manifestation (p<0.0001). Over the >10-year study period, there was a continuous rise in the weight-SDS and the BMI-SDS in the cohort (p<0.0001), especially in the 5-9.9-years and the 10-14.9-years age groups. Multivariate analysis revealed a significant influence of male sex and of year of manifestation on BMI SDS (p<0.0001) and demonstrated a negative association between the patients' BMI SDS and age at diagnosis, with a mean annual decrease in BMI SDS of -0.0248 (95% CI -0.0294 to -0.0202, p<0.0001).
A higher BMI was associated with a younger age at diabetes onset. Increased weight gain could therefore be a risk factor for the early manifestation of type 1 diabetes.

0 Bookmarks
 · 
79 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: The past twenty years of research on leptin has provided crucial information on the link between metabolic state and immune system function. Adipocytes influence not only the endocrine system but also the immune response, through several cytokine-like mediators known as adipokines, which include leptin. Initially described as an antiobesity hormone, leptin has subsequently been shown also to influence hematopoiesis, thermogenesis, reproduction, angiogenesis, and more importantly immune homeostasis. As a cytokine, leptin can affect thymic homeostasis and the secretion of acute-phase reactants such as interleukin-1 (IL-1) and tumor-necrosis factor-alpha (TNF-α). Leptin links nutritional status and proinflammatory T helper 1 (Th1) immune responses and the decrease in leptin plasma concentration during food deprivation leads to impaired immune function. Conversely, elevated circulating leptin levels in obesity appear to contribute to the low-grade inflammatory background which makes obese individuals more susceptible to increased risk of developing cardiovascular diseases, diabetes, or degenerative disease including autoimmunity and cancer. In this review, we provide an overview of recent advances on the role of leptin in the pathogenesis of several autoimmune disorders that may be of particular relevance in the modulation of the autoimmune attack through metabolic-based therapeutic approaches. Copyright © 2014 Elsevier Inc. All rights reserved.
    Metabolism: clinical and experimental 10/2014; DOI:10.1016/j.metabol.2014.10.014 · 3.61 Impact Factor
  • Source
    Pediatric Diabetes 09/2014; 15 Suppl 20:270-8. DOI:10.1111/pedi.12183 · 2.13 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: BACKGROUND: Severe hypoglycemia is a major complication of insulin treatment in patients with type 1 diabetes, limiting full realization of glycemic control. It has been shown in the past that low levels of hemoglobin A1c (HbA1c), a marker of average plasma glucose, predict a high risk of severe hypoglycemia, but it is uncertain whether this association still exists. Based on advances in diabetes technology and pharmacotherapy, we hypothesized that the inverse association between severe hypoglycemia and HbA1c has decreased in recent years. METHODS AND FINDINGS: We analyzed data of 37,539 patients with type 1 diabetes (mean age ± standard deviation 14.4±3.8 y, range 1-20 y) from the DPV (Diabetes Patienten Verlaufsdokumentation) Initiative diabetes cohort prospectively documented between January 1, 1995, and December 31, 2012. The DPV cohort covers an estimated proportion of >80% of all pediatric diabetes patients in Germany and Austria. Associations of severe hypoglycemia, hypoglycemic coma, and HbA1c levels were assessed by multivariable regression analysis. From 1995 to 2012, the relative risk (RR) for severe hypoglycemia and coma per 1% HbA1c decrease declined from 1.28 (95% CI 1.19-1.37) to 1.05 (1.00-1.09) and from 1.39 (1.23-1.56) to 1.01 (0.93-1.10), respectively, corresponding to a risk reduction of 1.2% (95% CI 0.6-1.7, p<0.001) and 1.9% (0.8-2.9, p<0.001) each year, respectively. Risk reduction of severe hypoglycemia and coma was strongest in patients with HbA1c levels of 6.0%-6.9% (RR 0.96 and 0.90 each year) and 7.0%-7.9% (RR 0.96 and 0.89 each year). From 1995 to 2012, glucose monitoring frequency and the use of insulin analogs and insulin pumps increased (p<0.001). Our study was not designed to investigate the effects of different treatment modalities on hypoglycemia risk. Limitations are that associations between diabetes education and physical activity and severe hypoglycemia were not addressed in this study. CONCLUSIONS: The previously strong association of low HbA1c with severe hypoglycemia and coma in young individuals with type 1 diabetes has substantially decreased in the last decade, allowing achievement of near-normal glycemic control in these patients. Please see later in the article for the Editors' Summary.
    PLoS Medicine 10/2014; 2014 Oct 7;11(10):e1001742. doi: 10.1371/journal.pmed.1001742. eCollection 2014 Oct.. DOI:10.1371/journal.pmed.1001742 · 15.25 Impact Factor

Full-text (2 Sources)

Download
21 Downloads
Available from
Aug 11, 2014