HbA1c levels in schoolchildren with type 1 diabetes are seasonally variable and dependent on weather conditions

Aims/hypothesis: We evaluated seasonal HbA$_{1c}$ changes in children with type 1 diabetes and its relation with measures of weather conditions. Methods: HbA$_{1c}$ changes over more than 3 years were evaluated in type 1 diabetic patients who were younger than 18 years and had diabetes duration of more than 12 months, and correlated with measures of weather conditions (ambient temperature, hours of sunshine and solar irradiance). After comparison of autocorrelation patterns, patterns of metabolic control and meteorological data were evaluated using Spearman rank correlation. Results: A total of 3,935 HbA$_{1c}$ measurements in 589 school (≥7 years) and 88 preschool (<7 years) children were analysed. Mean (±SD) HbA$_{1c}$ level for the whole study period was 7.65±1.12%. The lowest HbA$_{1c}$ levels were observed in late summer and the highest in winter months, with differences consistently exceeding 0.44%. Autocorrelation analysis of HbA$_{1c}$ levels in schoolchildren showed a sine-wave pattern with a cycle length of roughly 12 months, which mirrored changes in ambient temperature. Strong negative correlations of HbA$_{1c}$ with ambient temperature (R=−0.56; p=0.0002), hours of sunshine (R=−0.52; p=0.0007) and solar irradiance (R=−0.52; p=0.0006) were present in schoolchildren, but not in preschoolers (p≥0.29 for each correlation). Conclusions/interpretation: Seasonal changes of HbA$_{1c}$ levels in schoolchildren with type 1 diabetes are a significant phenomenon and should be considered in patient education and diabetes management. They may potentially affect the results of clinical trials using HbA$_{1c}$ levels as their primary outcome, as well as HbA$_{1c}$-based diagnosis of diabetes

Early Markers of Glycaemic Control in Children with Type 1 Diabetes Mellitus

Background: Type 1 diabetes mellitus (T1DM) may lead to severe long-term health consequences. In a longitudinal study, we aimed to identify factors present at diagnosis and 6 months later that were associated with glycosylated haemoglobin (HbA 1c) levels at 24 months after T1DM diagnosis, so that diabetic children at risk of poor glycaemic control may be identified. Methods: 229 children,15 years of age diagnosed with T1DM in the Auckland region were studied. Data collected at diagnosis were: age, sex, weight, height, ethnicity, family living arrangement, socio-economic status (SES), T1DM antibody titre, venous pH and bicarbonate. At 6 and 24 months after diagnosis we collected data on weight, height, HbA 1c level, and insulin dose. Results: Factors at diagnosis that were associated with higher HbA1c levels at 6 months: female sex (p,0.05), lower SES (p,0.01), non-European ethnicity (p,0.01) and younger age (p,0.05). At 24 months, higher HbA1c was associated with lower SES (p,0.001), Pacific Island ethnicity (p,0.001), not living with both biological parents (p,0.05), and greater BMI SDS (p,0.05). A regression equation to predict HbA1c at 24 months was consequently developed. Conclusions: Deterioration in glycaemic control shortly after diagnosis in diabetic children is particularly marked in Pacific Island children and in those not living with both biological parents. Clinicians need to be aware of factors associated wit

Mathematical modelling of clostridial acetone-butanol-ethanol fermentation

Clostridial acetone-butanol-ethanol (ABE) fermentation features a remarkable shift in the cellular metabolic activity from acid formation, acidogenesis, to the production of industrial-relevant solvents, solventogensis. In recent decades, mathematical models have been employed to elucidate the complex interlinked regulation and conditions that determine these two distinct metabolic states and govern the transition between them. In this review, we discuss these models with a focus on the mechanisms controlling intra- and extracellular changes between acidogenesis and solventogenesis. In particular, we critically evaluate underlying model assumptions and predictions in the light of current experimental knowledge. Towards this end, we briefly introduce key ideas and assumptions applied in the discussed modelling approaches, but waive a comprehensive mathematical presentation. We distinguish between structural and dynamical models, which will be discussed in their chronological order to illustrate how new biological information facilitates the ‘evolution’ of mathematical models. Mathematical models and their analysis have significantly contributed to our knowledge of ABE fermentation and the underlying regulatory network which spans all levels of biological organization. However, the ties between the different levels of cellular regulation are not well understood. Furthermore, contradictory experimental and theoretical results challenge our current notion of ABE metabolic network structure. Thus, clostridial ABE fermentation still poses theoretical as well as experimental challenges which are best approached in close collaboration between modellers and experimentalists

Metabolic control as reflectet by HbA1c in children, adolescents and young adults with type-1 diabetes mellitus: combined longitudinal analysis including 27,035 patients from 207 centers in Germany and Austria during the last decade.

INTRODUCTION: While the central role of HbA1c levels for the prediction of micro- and macrovascular complications in patients with type 1 diabetes is generally accepted; recommendations in current guidelines and the level of metabolic control actually achieved during routine care differ widely. Limited information is available on factors that influence metabolic control in the pediatric age group and during the transition from pediatric to adult diabetes care. In a large prospective multicenter database (DPV-Wiss), 338,330 individual HbA1c measurements from 27,035 patients with type-1 diabetes (94,074 observation years) were recorded between 1995 and 2005. Data were anonymously transmitted from 207 institutions. HbA1c values were mathematically standardized to the DCCT normal range (4.05-6.05%). The SAS 9.1 software was used for statistical analysis using nonparametric statistics. Median HbA1c for all measurements was 7.8%, with a strong effect of diabetes duration: median HbA1c at onset was 9.1%, during the first 2 years of diabetes 7.1% with a subsequent increase to 7.9% in patients beyond the remission phase (>2 years, 20,314 patients); a strong age dependency was present. HbA1c above the recommended guidelines was found in 23%. For all age groups, girls/women had higher HbA1c values compared to boys (mean difference 0.1%, p<0.0001). Seasonal variation was remarkably small with the lowest HbA1c values in September (mean: 7.86%) and highest values in January (8.08%; p<0.0001). Some improvement in HbA1c was observed comparing three periods: 1995-1997, 1998-2000 and 2001-2005; after remission the median HbA1c decreases from 8.5% to 7.6%. In a multivariate model, a significant influence on HbA1c was detected for age (p<0001), duration of diabetes (p<0.0001), gender (p<0.02), minority status (p<0.0001), season (p<0.0001), treatment period (p<0.0001), insulin therapy (p<0.0001) and center effect (p<0.0001). CONCLUSIONS: Both patient-related and treatment-related variables have a strong influence on metabolic control achieved in pediatric and young adult patients with T1DM. In contrast to wide-spread belief, metabolic control is only marginally better in summer compared to winter. Some improvement in metabolic control was observed during the last 10 years