Abdurrahman Coskun

A checklist for critical appraisal of studies of biological variation

Abstract Data on biological variation are used for many purposes in laboratory medicine but concern exists over the validity of the data reported in some studies. A critical appraisal checklist has been produced by a working group established by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) to enable standardised assessment of existing and future publications of biological variation data. The checklist identifies key elements to be reported in studies to enable safe accurate and effective transport of biological variation data sets across healthcare systems. The checklist is mapped to the domains of a minimum data set required to enable this process.

Biological Variation Estimates Obtained from 91 Healthy Study Participants for 9 Enzymes in Serum

BACKGROUNDnWe sought to develop estimates of biological variation (BV) for 9 enzymes in blood serum as part of the European Biological Variation Study.nnnMETHODSnNinety-one healthy study participants (38 male and 53 female, 21-69 years old) were phlebotomized in each of 10 consecutive weeks at 6 European laboratories. The same preanalytical sample-handling protocol was followed at each center before transport to San Raffaele Hospital, Milan, Italy, for analysis. Sera were stored at -80 °C before analysis in duplicate within a single run on an ADVIA 2400 Clinical Chemistry System (Siemens Healthcare) following a protocol designed to minimize analytical imprecision. Assay traceability was established using frozen sera with target values assigned by reference methods. The results were subjected to outlier analysis before CV-ANOVA to deliver valid BV estimates. Results for 9 enzymes were subsequently partitioned for graphical display allowing visual assessment of the effects of country of origin, sex, and age on BV estimates.nnnRESULTSnWe found no effect of country upon the observed variation, but overall sex-related differences were evident for alanine amino transferase (ALT), γ-glutamyl transferase (GGT), and creatine kinase (CK). The following estimates for within-subject BV (CVI) and between-subject BV (CVG), respectively, were obtained: ALT: 9.3%, 28.2%; aspartate aminotransferase: 9.5%, 20.3%; GGT: 8.9%, 41.7%; alkaline phosphatase : 5.3%, 24.9%; lactate dehydrogenase: 5.2%, 12.6%; CK: 14.5%, 31.5%; amylase: 6.8%, 30.4%; pancreatic α-amylase: 6.3%, 24.9%; and lipase (LIP): 7.7%, 23.8%.nnnCONCLUSIONSnAll CVI and some CVG estimates were lower than those reported in the online BV 2014 updated database. Analytical performance specifications derived from BV can be applied internationally.

The EuBIVAS Project: Within–and Between-Subject Biological Variation Data for Serum Creatinine Using Enzymatic and Alkaline Picrate Methods and Implications for Monitoring

BACKGROUNDnThe European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) European Biological Variation Study (EuBIVAS) has been established to deliver rigorously determined biological variation (BV) indices. EuBIVAS determined BV for serum creatinine using the enzymatic and alkaline picrate measurement methods.nnnMETHODnIn total, 91 healthy individuals (38 males, 53 females; age range, 21-69 years) were bled for 10 consecutive weeks at 6 European laboratories. An equivalent protocol was followed at each center. Sera were stored at -80 °C before analysis. Analyses for each patient were performed in duplicate within a single run on an ADVIA 2400 system (San Raffaele Hospital, Milan). The data were subjected to outlier and homogeneity analysis before performing CV-ANOVA to determine BV and analytical variation (CVA) estimates with confidence intervals (CI).nnnRESULTSnThe within-subject BV estimates [CVI (95% CI)] were similar for enzymatic [4.4% (4.2-4.7)] and alkaline picrate [4.7% (4.4-4.9)] methods and lower than the estimate presently available online (CVI = 5.9%). No significant male/female BV differences were found. Significant differences were observed in mean creatinine values between men and women and between Turkish individuals and those of other nationalities. Between-subject BV (CVG) estimates, stratified accordingly, produced CVG values similar to historical BV data. CVA was 1.1% for the enzymatic and 4.4% for alkaline picrate methods, indicating that alkaline picrate methods fail to fulfill analytical performance specifications for imprecision (CVAPS).nnnCONCLUSIONSnThe serum creatinine CVI obtained by EuBIVAS specifies a more stringent CVAPS than previously identified. The alkaline picrate method failed to meet this CVAPS, raising questions regarding its future use.

Iodine status in Turkish populations and exposure to iodide uptake inhibitors.

Perchlorate, nitrate, and thiocyanate are competitive inhibitors of the sodium iodide symporter of the thyroid membrane. These inhibitors can decrease iodine uptake by the symporter into the thyroid gland and may disrupt thyroid function. This study assesses iodine status and exposure to iodide uptake inhibitors of non-pregnant and non-lactating adult women living in three different cities in Turkey (Istanbul, Isparta and Kayseri). We measured iodine and iodide uptake inhibitors in 24-hr urines collected from study participants (Nu200a=u200a255). All three study populations were mildly iodine deficient, with median urinary iodine (UI) levels of 77.5 µg/L in Istanbul, 58.8 µg/L in Isparta, and 69.8 µg/L in Kayseri. Perchlorate doses were higher in the study population (median 0.13 µg/kg/day), compared with a reference population (median 0.059 µg/kg/day), but lower than the U.S. EPA reference dose (0.7 µg/kg/day). Urinary thiocyanate levels increased with increasing exposure to tobacco smoke, with non-smokers (268 µg/L) significantly lower than light smokers (1110 µg/L), who were significantly lower than heavy smokers (2410 µg/L). This pilot study provides novel data indicating that study participants were moderately iodine deficient and had higher intakes of the iodide uptake inhibitor perchlorate compared with a reference population. Further investigation is needed to characterize the thyroid impact resulting from iodine deficiency coupled with exposure to iodide uptake inhibitors such as perchlorate, thiocyanate and nitrate.

A new approach to calculating the Sigma Metric in clinical laboratories

In clinical laboratories, the performance of a process as Sigma Metric (SM) is calculated by the equations derived by Westgard. In the present study, we found that the Westgard equations do not reflect the real performance of the process and that the SM calculated using these equations is lower than the real SM. We measured the substance concentration of ten analytes (glucose, urea, creatinine, cholesterol, calcium, magnesium, phosphorus, LDH, sodium, and potassium) in serum and calculated the SM for each using the Westgard equations and z transformations. The SM values for the same measurand using the Westgard equations based on either the absolute or the relative (percentage) results were not equal to each other, and those related to calcium and sodium were even lower than 0. The SM obtained from the z transformation was higher than that from the Westgard equations, and none were lower than 0. We concluded that the equations suggested by Westgard to calculate the SM do not cover all of the data produced by the process and do not reflect reality. From our research, the z transformation was the optimum method of calculating the actual SM for the process.

Clinical applications of MALDI imaging technologies in cancer and neurodegenerative diseases

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) enables localization of analytes of interest along with histology. More specifically, MALDI-IMS identifies the distributions of proteins, peptides, small molecules, lipids, and drugs and their metabolites in tissues, with high spatial resolution. This unique capacity to directly analyze tissue samples without the need for lengthy sample preparation reduces technical variability and renders MALDI-IMS ideal for the identification of potential diagnostic and prognostic biomarkers and disease gradation. MALDI-IMS has evolved rapidly over the last decade and has been successfully used in both medical and basic research by scientists worldwide. In this review, we explore the clinical applications of MALDI-IMS, focusing on the major cancer types and neurodegenerative diseases. In particular, we re-emphasize the diagnostic potential of IMS and the challenges that must be confronted when conducting MALDI-IMS in clinical settings. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Post‐translational modifications of transthyretin affect the triiodonine‐binding potential

Transthyretin (TTR) is a visceral protein, which facilitates the transport of thyroid hormones in blood and cerebrospinal fluid. The homotetrameric structure of TTR enables the simultaneous binding of two thyroid hormones per molecule. Each TTR subunit provides a single cysteine residue (Cys10), which is frequently affected by oxidative post‐translational modifications. As Cys10 is part of the thyroid hormone‐binding channel within the TTR molecule, PTM of Cys10 may influence the binding of thyroid hormones. Therefore, we analysed the effects of Cys10 modification with sulphonic acid, cysteine, cysteinylglycine and glutathione on binding of triiodothyronine (T3) by molecular modelling. Furthermore, we determined the PTM pattern of TTR in serum of patients with thyroid disease by immunoprecipitation and mass spectrometry to evaluate this association in vivo. The in silico assays demonstrated that oxidative PTM of TTR resulted in substantial reorganization of the intramolecular interactions and also affected the binding of T3 in a chemotype‐ and site‐specific manner with S‐glutathionylation as the most potent modulator of T3 binding. These findings were supported by the in vivo results, which indicated thyroid function‐specific patterns of TTR with a substantial decrease in S‐sulphonated, S‐cysteinylglycinated and S‐glutathionylated TTR in hypothyroid patients. In conclusion, this study provides evidence that oxidative modifications of Cys10 seem to affect binding of T3 to TTR probably because of the introduction of a sterical hindrance and induction of conformational changes. As oxidative modifications can be dynamically regulated, this may represent a sensitive mechanism to adjust thyroid hormone availability.

Reference interval of pregnancy-associated plasma protein-A in healthy men and non-pregnant women

OBJECTIVEnThe serum pregnancy-associated plasma protein-A (PAPP-A) concentration is a predictor of ischemic cardiac events and renal impairment. However, the reference interval of PAPP-A has not been determined. This study determined the reference interval of PAPP-A in men and non-pregnant women.nnnMETHODSnThe study enrolled 126 apparently healthy individuals (52 males and 74 females). The mean age of the men and women was 34.7 (range 20-66) years and 34.6 (range 18-65) years, respectively. Serum PAPP-A concentrations were determined using an ultrasensitive enzyme-linked immunoassay kit. Reference intervals were calculated using the bootstrap method.nnnRESULTSnThe results for three subjects were outliers, so the reference interval of PAPP-A was calculated using the data for 123 subjects. PAPP-A was undetectable in 26 subjects. The reference interval of PAPP-A for men and women (with the 90% confidence interval) was <22.9 ng/mL (19.7-23.3) and <33.6 ng/mL (25.2-36.7), respectively. In male subjects, serum PAPP-A levels of smokers [3.10 (UD, 7.30)ng/mL] were significantly lower than that of non-smokers [11.00 (UD, 24.4)ng/mL] (p<0.001) and there was a positive correlation between serum PAPP-A levels and subjects age (r=0.439; p<0.001).nnnCONCLUSIONSnThe reference interval of PAPP-A differed for men and non-pregnant women. In clinical practice, <22.9 ng/mL for men and <33.6 ng/mL for non-pregnant women may be used as reference intervals for PAPP-A.

Six Sigma as a Quality Management Tool: Evaluation of Performance in Laboratory Medicine

In medical school, the first concept expressed to students is a Latin phrase, primum non nocere, meaning “first, do no harm.” This phrase is well known among health workers and dates back to Hipocrates. However, in reality, the situation is slightly different. According to the report of the Institute of Medicine, each year in the USA, approximately 98,000 people die from medical errors (Kohn et al., 2000). Unfortunately, more people have died each year during mid-1990s from medical errors than from AIDS or breast cancer (Kohn et al., 2000). Despite this situation, we cannot say that adequate attention has been paid to the application of high standards in the healthcare sector to effectively prevent medical errors. Yet in industry, for more than a century, modern quality control has been applied to prevent errors and produce high quality goods. The result of these long-term efforts is that in many companies, the rate of errors approaches a negligible level. Regrettably, we cannot say the same thing for medical services, because the components that produce errors or defects in medical services are many more than those involved in any industrial or business sector. Despite these facts, it is clear that the quality of medical services is more important than the quality of most other goods. Consequently, healthcare professionals must pay more attention to quality than any industrial professionals do. Among healthcare services, clinical laboratories are particularly important because physicians make their decisions mostly in accordance with laboratory results (Forsman, 1996). In this context, accurate test results are crucial for physicians and their patients. First, the laboratory must be able to produce an accurate test result before any other dimension of quality becomes important. From this point of view, the evaluation of laboratory performance is critical to maintaining accurate laboratory results (Coskun, 2007). In clinical laboratories, we traditionally divide the total testing processes into three phases: pre-analytical, analytical, and post-analytical phases. However, the selection and interpretation of tests are also prone to errors and must be considered in the total testing process. For this reason, in laboratory medicine, we analyze the total testing process in five phases: pre-preanalytical, pre-analytical, analytical, post-analytical, and post-post-analytical phases 13

The comparison of parametric and nonparametric bootstrap methods for reference interval computation in small sample size groups

According to the IFCC, to determine the population-based reference interval (RI) of a test, 120 reference individuals are required. However, for some age groups such as newborns and preterm babies, it is difficult to obtain enough reference individuals. In this study, we consider both parametric and nonparametric bootstrap methods for estimating RIs and the associated confidence intervals (CIs) in small sample size groups. We used data from four different tests [glucose, creatinine, blood urea nitrogen (BUN), and triglycerides], each in 120 individuals, to calculate the RIs and the associated CIs using nonparametric and parametric approaches. Also for each test, we selected small groups (mxa0=xa020, 30,…, 120) from among the 120 individuals and applied parametric and nonparametric bootstrap methods. The glucose and creatinine data were normally distributed, and the parametric bootstrap method provided more precise RIs (i.e., the associated CIs were narrower). In contrast, the BUN and triglyceride data were not normally distributed, and the nonparametric bootstrap method provided better results. With the bootstrap methods, the RIs and CIs of small groups were similar to those of the 120 subjects required for the nonparametric method, with a slight loss of precision. For original data with normal or close to normal distribution, the parametric bootstrap approach should be used, instead of nonparametric methods. For original data that deviate significantly from a normal distribution, the nonparametric bootstrap should be applied. Using the bootstrap methods, fewer samples are required for computing RIs, with only a slightly increased uncertainty around the end points.