Lower prediagnostic serum 25-hydroxyvitamin D concentration is associated with higher risk of insulin-requiring diabetes: a nested case-control study.

AIMS/HYPOTHESIS: Low serum 25-hydroxyvitamin D [25(OH)D] concentration may increase risk of insulin-requiring diabetes. METHODS: A nested case-control study was performed using serum collected during 2002-2008 from military service members. One thousand subjects subsequently developed insulin-requiring diabetes. A healthy control was individually matched to each case on blood-draw date (±2 days), age (±3 months), length of service (±30 days) and sex. The median elapsed time between serum collection and first diagnosis of diabetes was 1 year (range 1 month to 10 years). Statistical analysis used matched pairs and conditional logistic regression. RESULTS: ORs for insulin-requiring diabetes by quintile of serum 25(OH)D, from lowest to highest, were 3.5 (95% CI 2.0, 6.0), 2.5 (1.5, 4.2), 0.8 (0.4, 1.4), 1.1 (0.6, 2.8) and 1.0 (reference) (p (trend) <0.001). The quintiles (based on fifths using serum 25(OH)D concentration in the controls) of serum 25(OH)D in nmol/l, were <43 (median 28), 43-59 (median 52), 60-77 (median 70), 78-99 (median 88) and ≥100 (median 128). CONCLUSIONS/INTERPRETATION: Individuals with lower serum 25(OH)D concentrations had higher risk of insulin-requiring diabetes than those with higher concentrations. A 3.5-fold lower risk was associated with a serum 25(OH)D concentration ≥60 nmol/l.

Modelling indoor electromagnetic fields (EMF) from mobile phone base stations for epidemiological studies.

Radio frequency electromagnetic fields (RF-EMF) from mobile phone base stations can be reliably modelled for outdoor locations, using 3D radio wave propagation models that consider antenna characteristics and building geometry. For exposure assessment in epidemiological studies, however, it is especially important to determine indoor exposure levels as people spend most of their time indoors. We assessed the accuracy of indoor RF-EMF model predictions, and whether information on building characteristics could increase model accuracy. We performed 15-minute spot measurements in 263 rooms in 101 primary schools and 30 private homes in Amsterdam, the Netherlands. At each measurement location, we collected information on building characteristics that can affect indoor exposure to RF-EMF, namely glazing and wall and window frame materials. Next, we modelled RF-EMF at the measurement locations with the 3D radio wave propagation model NISMap. We compared model predictions with measured values to evaluate model performance, and explored if building characteristics modified the association between modelled and measured RF-EMF using a mixed effect model. We found a Spearman correlation of 0.73 between modelled and measured total downlink RF-EMF from base stations. The average modelled and measured RF-EMF were 0.053 and 0.041mW/m(2), respectively, and the precision (standard deviation of the differences between predicted and measured values) was 0.184mW/m(2). Incorporating information on building characteristics did not improve model predictions. Although there is exposure misclassification, we conclude that it is feasible to reliably rank indoor RF-EMF from mobile phone base stations for epidemiological studies.

Geospatial modelling of electromagnetic fields from mobile phone base stations.

There is concern that exposure to radio frequency electromagnetic fields (RF-EMF) from mobile phone base stations might lead to adverse health effects. In order to assess potential health risks, reliable exposure assessment is necessary. Geospatial exposure modelling is a promising approach to quantify ambient exposure to RF-EMF for epidemiological studies involving large populations. We modelled RF-EMF for Amsterdam, The Netherlands by using a 3D RF-EMF model (NISMap). We subsequently compared modelled results to RF-EMF measurements in five areas with differing built-up characteristics (e.g., low-rise residential, high-rise commercial). We performed, in each area, repeated continuous measurements along a predefined ~2 km long path. This mobile monitoring approach captures the high spatial variability in electric field strengths. The modelled values were in good agreement with the measurements. We found a Spearman correlation of 0.86 for GSM900 and 0.85 for UMTS between modelled and measured values. The average measured GSM900 field strength was 0.21 V/m, and UMTS 0.09 V/m. The model underestimated the GSM900 field strengths by 0.07 V/m, and slightly overestimated the UMTS field strengths by 0.01 V/m. NISMap provides a reliable way of assessing environmental RF-EMF exposure for epidemiological studies of RF-EMF and health in urban areas.

The consequences of swim, cycle, and run performance on overall result in elite olympic distance triathlon.

This study examined the consequences of performance in swim, cycle, and run phases on overall race finish in an elite "draft legal" Olympic distance (OD) triathlon. The subjects were 24 male athletes grouped by rank order into the top 50 % (n = 12) and bottom 50 % (n = 12) of the race population. Swimming velocity (m x s (-1)), cycling speed (km x h (-1)), and running velocity (m x s (-1)) were measured at regular intervals using a global positioning system, chip timing system, and video analysis. Actual rank after each stage and overall was obtained from the race results and video analysis. The top 50 % athletes overall swam faster over the first 400 m of the swim phase (p > 0.05). Their swim ranking was lower (p < 0.01) than the bottom 50 % athletes after this stage. There were no significant differences in actual race position between the groups after the cycle. However, the bottom 50 % athletes after the swim stage cycled faster (p < 0.01) at 13.4 km of the cycle. Speed at 13.4 km of the cycle stage was inversely correlated (r = 0.60, p < 0.01) to running performance. Performance (rank and velocity) in the running stage was highly correlated with overall race result (r = 0.86 and - 0.53, respectively, both p > 0.01). It appears that inferior swimming performance can result in a tactic that involves greater work in the initial stages of the cycle stage of elite OD racing, and may influence subsequent running performance.