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

Abstract

Aims/hypothesisWe evaluated seasonal HbA1c changes in children with type 1 diabetes and its relation with measures of weather conditions.MethodsHbA1c 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.ResultsA total of 3,935 HbA1c measurements in 589 school (≥7 years) and 88 preschool (<7 years) children were analysed. Mean (±SD) HbA1c level for the whole study period was 7.65 ± 1.12%. The lowest HbA1c levels were observed in late summer and the highest in winter months, with differences consistently exceeding 0.44%. Autocorrelation analysis of HbA1c 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 HbA1c 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/interpretationSeasonal changes of HbA1c 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 HbA1c levels as their primary outcome, as well as HbA1c-based diagnosis of diabetes.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-010-2013-4) contains supplementary material, which is available to authorised users.

Glycated haemoglobin variations in paediatric type 1 diabetes: the impact of season, gender and age

“…Despite the fact that the present study contained a large amount of HbA1c measurements, a lot more than in most paediatric studies (4, 23), we could not confirm the seasonal variations during follow up that were found in other studies (4,23). Children and adolescents with type 1 diabetes in Sweden are intensively treated with multiple insulin injections or insulin pump therapy and active self-control.…”

“…Seasonal variations in glycated haemoglobin (HbA1c) at follow-up have been observed in adults and children with type 1 diabetes, with the lowest values during the summer and the highest levels during the winter (1)(2)(3)(4)(5). Some studies have found the greatest seasonal variations in older age groups (4), but no gender differences have been reported.…”

“…Some studies have found the greatest seasonal variations in older age groups (4), but no gender differences have been reported. It has been suggested that these variations may be due to increased physical activity and fewer viral infections in the summer months and a colder climate during late autumn and winter (4,5). It is not just HbA1c that is subject to seasonal variations.…”

Non-dipping and arterial hypertension depend on clinical factors rather than on genetic variability of ACE and RGS2 genes in patients with type 1 diabetes

“…Reference values for healthy people estimated by the local laboratory were from 4.3 to 5.7 %. The within-run coefficient of variation (CV) determined by the manufacturer was 1.05 % for normal patients and 0.94 % for diabetic patients; the between-run CV was 1.61 and 1.16 % for normal and for diabetic patients, respectively, as reported previously [16]. In all patients, the estimation of albumin excretion rate (AER) was obtained from overnight urine sample.…”

Assessing seasonality in clinical research

“…However, in clinical research it is little used. Of 12 seasonality papers in Medline (4 -15) , only two (12,15) applied autocorrelation, while the rest just reported the presence of a signifi cant difference between the mean data of different seasons. Autocorrelation does not demonstrate signifi cant differences between seasons, but rather signifi cantly repetitive patterns, meaning patterns that are stronger repetitive than could happen by chance.…”