Aneta Aleksandra Nielsen

This was an exploratory, prospective, longitudinal, cohort study that aimed to establish “healthy” reference levels related to growth parameters and glucose metabolites in preterm infants. This was conducted to further investigate growth and metabolic disturbances potentially related to neonatal illness. The study sample consisted of 108 preterm infants born before 32 weeks in 2018–2019 in the Capital Region of Denmark. Repetitive blood samples were acquired at the neonatal wards, while clinical data were obtained from the regional hospital medical record system. Thirty-four “healthy” preterm infants (31%) were identified. The “ill” infants were divided into four subgroups dependent on gestational age and small for gestational age. Reference levels for the growth parameters and metabolic biomarkers glucose, albumin, and adiponectin, and two glucose control indicators, glycated albumin and fructosamine, were determined for the “healthy” and “ill” subgroups. The “ill” extremely preterm infants had increased glucose levels (mean difference 0.71 mmol/L, 95% CI 0.23; 1.18 mmol/L) and glycated albumin (corrected; %) (mean difference 0.92 mmol/L, 95% CI 0.38 mmol/L;1.47 mmol/L) compared to the “healthy” infants. In “ill” extremely preterm infants and “ill” very preterm infants born small for gestational age, levels of biomarkers containing proteins were decreased. In the “Ill” extremely preterm infants and infants born small for gestational age, postnatal growth was continuously decreased throughout the postconceptional period. The short-term glucose-control indicator, glycated albumin (corrected; %), reflected well the high glucose levels due to its correction for the depleted plasma-protein pool.

We evaluated the impact of including haemolobbing A1c (HbA1c) measurements in a regional algorithm for identification of diabetics by comparing the population identified by the regional algorithm with diabetics registered in the National Danish Diabetes Register (NDR) relative to prevalence, co-morbidity and five-year mortality rate. The regional (County of Vejle) and national diabetes populations were compared per the inclusion date of 31 December 2006 limited to persons residing in four municipalities in the County of Vejle, Denmark. A total of 14,998 diabetics were identified by the regional algorithm, of whom 11,499 (prevalence 4.1%) resided in the four municipalities. The total number of diabetics registered in the NDR was 227,621 in Denmark, of whom 10,976 (prevalence 4.0%) resided in the four municipalities. The regional diabetics (2,802 persons) not identified in the NDR population had a significantly lower mortality rate (57%) than the diabetics (2,279 persons) in the NDR population not identified by the regional algorithm. The significantly higher mortality in the NDR population not identified by the regional algorithm may stem from differences between the components of the two algorithms, i.e. frequency of glucose measurements in the NDR versus frequency of HbA1c measurements including elevated values in the regional algorithm. The NDR algorithm, which includes the use of frequency of glucose measurements without a value over the diagnostic threshold, identified about 21% of persons who probably had their glucose measured for other reasons than diabetes. The Danish Council for Research and Innovation, Region of Southern Denmark's PhD Fund, University of Southern Denmark and the Research Fund, Vejle Hospital. not relevant.

In Denmark, the use of HbA1c in the diagnosis of diabetes was adopted from March 2012. We evaluated the change in the number of diabetes cases diagnosed by haemoglobin A1c (HbA1c) versus fasting venous plasma glucose (FPG), and estimated the influence of analytical variation and bias on the HbA1c-based prevalence of diabetes. The study population constituted 4239 individuals not known to have diabetes randomly selected from all inhabitants aged 25-75 years in the former County of Vejle, Denmark. The number of undiagnosed patients with diabetes in the study population using FPG or HbA1c as the diagnostic criterion was estimated. Furthermore, changes in the analytical bias and coefficient of variation (CV) for HbA1c analysis were simulated and the effect on the number of diabetes cases was observed. Changing the diagnostic test from FPG to HbA1c reduced the number of patients with diabetes by approximately 46% based on one measurement. The predictive value of one test of HbA1c was 91% versus only 66% for one test of FPG. Analytical variation had a much greater impact on the number of patients with diabetes than bias. At a bias of 0%, an increase of CVanalytical from 2.7% to 3.7% increased the number of diabetes cases by 90%. In the study population, the percentage of undiagnosed patients with diabetes aged 25-75 years was reduced from 3.6% (95% CI 3.0%-4.2%) based on one FPG measurement (FPG ≥7.0 mmol/L) to only 1.9% (95% CI 1.5%-2.3%) if the diagnosis of diabetes was based on the criterion of HbA1c ≥48 mmol/mol (6.5% DCCT).