Abstract:In recent years it has been shown that vitamin D deficiency is associated with an increased incidence as well as the progression of a broad range of diseases including osteoporosis, rickets, cardiovascular disease, autoimmune disease, multiple sclerosis and cancer.

Clinical laboratories frequently encounter samples showing significant haemolysis, icterus or lipaemia. Technical advances, utilizing spectrophotometric measurements on automated chemistry analysers, allow rapid and accurate identification of such samples. However, accurate quantification of haemolysis, icterus and lipaemia interference is of limited value if laboratories do not set rational alert limits, based on sound interference testing experiments. Furthermore, in the context of increasing consolidation of laboratories and the formation of laboratory networks, there is an increasing requirement for harmonization of the handling of haemolysis, icterus and lipaemia-affected samples across different analytical platforms. Harmonization may be best achieved by considering both the analytical aspects of index measurement and the possible variations in the effects of haemolysis, icterus and lipaemia interferences on assays from different manufacturers. Initial verification studies, followed up with ongoing quality control testing, can help a laboratory ensure the accuracy of haemolysis, icterus and lipaemia index results, as well as assist in managing any biases in index results from analysers from different manufacturers. Similarities, and variations, in the effect of haemolysis, icterus and lipaemia interference in assays from different manufacturers can often be predicted from the mechanism of interference. Nevertheless, interference testing is required to confirm expected similarities or to quantify differences. It is important that laboratories are familiar with a number of interference testing protocols and the particular strengths and weaknesses of each. A rigorous approach to all aspects of haemolysis, icterus and lipaemia interference testing allows the analytical progress in index measurement to be translated into improved patient care.

Folate deficiency has been linked to diverse clinical manifestations and despite the importance of accurate assessment of folate status, the best test for routine use is uncertain. Both serum and red cell folate assays are widely available in clinical laboratories; however, red cell folate is the more time-consuming and costly test. This review sought to evaluate whether the red cell assay demonstrated superior performance characteristics to justify these disadvantages. Red cell folate, but not serum folate, measurements demonstrated analytical variation due to sample pre-treatment parameters, oxygen saturation of haemoglobin and haematocrit. Neither marker was clearly superior in characterising deficiency but serum folate more frequently showed the higher correlation with homocysteine, a sensitive marker of deficiency. Similarly, both serum and red cell folate were shown to increase in response to folic acid supplementation. However, serum folate generally gave the greater response and was able to distinguish different supplementation doses. The C677T polymorphism of methylenetetrahydrofolate reductase alters the distribution of folate forms in red cells and may thereby cause further analytical variability in routine red cell folate assays. Overall, serum folate is cheaper and faster to perform than red cell folate, is influenced by fewer analytical variables and provides an assessment of folate status that may be superior to red cell folate.

Aging is a complex biological process characterized by a progressive decline of organ functions leading to an increased risk of age-associated diseases and death. Decades of intensive research have identified a range of molecular and biochemical pathways contributing to aging. However, many aspects regarding the regulation and interplay of these pathways are insufficiently understood. Telomere dysfunction and genomic instability appear to be of critical importance for aging at a cellular level. For example, age-related diseases and premature aging syndromes are frequently associated with telomere shortening. Telomeres are repetitive nucleotide sequences that together with the associated sheltrin complex protect the ends of chromosomes and maintain genomic stability. Recent studies suggest that micronutrients, such as vitamin D, folate and vitamin B12, are involved in telomere biology and cellular aging. In particular, vitamin D is important for a range of vital cellular processes including cellular differentiation, proliferation and apoptosis. As a result of the multiple functions of vitamin D it has been speculated that vitamin D might play a role in telomere biology and genomic stability. Here we review existing knowledge about the link between telomere biology and cellular aging with a focus on the role of vitamin D. We searched the literature up to November 2014 for human studies, animal models and in vitro experiments that addressed this topic.

No abstract

Variations in design among automated 25-OHD assays influence their performance characteristics. Consideration of the details of assay design is therefore important when selecting and validating new assays.

Background: It is difficult for clinical laboratories to identify samples that are labelled with the details of an incorrect patient. Many laboratories screen for these errors with delta checks, with final decision-making based on manual review of results by laboratory staff. Machine learning (ML) models have been shown to outperform delta checks for identifying these errors. However, a comparison of ML models to human-level performance has not yet been made. Methods: Deidentified data for current and previous (within seven days) electrolytes, urea and creatinine results was used in the computer simulation of mislabelled samples. Eight different ML models were developed on 127,256 sets of results using different algorithms: artificial neural network (ANN), extreme gradient boosting, support vector machine, random forest, logistic regression, k-nearest neighbours and two decision trees (one complex and one simple). A separate test dataset (n = 14,140) was used to evaluate the performance of these models as well as laboratory staff volunteers, who manually reviewed a random subset of this data (n = 500). Results: The best performing ML model was the ANN (92.1% accuracy), with the simple decision tree demonstrating the poorest accuracy (86.5%). The accuracy of laboratory staff for identifying mislabelled samples was 77.8%. Conclusions: The results of this preliminary investigation suggest that even relatively simple ML models can exceed human performance for identifying mislabelled samples. ML techniques should be considered for implementation in clinical laboratories to assist with error identification.

Reference intervals are relied upon by clinicians when interpreting their patients’ test results. Therefore, laboratorians directly contribute to patient care when they report accurate reference intervals. The traditional approach to establishing reference intervals is to perform a study on healthy volunteers. However, the practical aspects of the staff time and cost required to perform these studies make this approach difficult for clinical laboratories to routinely use. Indirect methods for deriving reference intervals, which utilise patient results stored in the laboratory’s database, provide an alternative approach that is quick and inexpensive to perform. Additionally, because large amounts of patient data can be used, the approach can provide more detailed reference interval information when multiple partitions are required, such as with different age-groups. However, if the indirect approach is to be used to derive accurate reference intervals, several considerations need to be addressed. The laboratorian must assess whether the assay and patient population were stable over the study period, whether data ‘clean-up’ steps should be used prior to data analysis and, often, how the distribution of values from healthy individuals should be modelled. The assumptions and potential pitfalls of the particular indirect technique chosen for data analysis also need to be considered. A comprehensive understanding of all aspects of the indirect approach to establishing reference intervals allows the laboratorian to harness the power of the data stored in their laboratory database and ensure the reference intervals they report are accurate.