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