Annals of Laboratory Medicine最新文献

Background: Korea National Health and Nutrition Examination Survey (KNHANES) triglyceride testing changed from the glycerol blanking method (2005-2021) to the glycerol nonblanking method (2022). We converted triglyceride data from 2005-2021 to that obtained since 2022 with different analytical methods.

Methods: To develop a conversion equation, 98 fresh serum specimen pairs were compared using Passing-Bablok regression analysis. Implications of the conversion equation on epidemiological data were evaluated using KNHANES data from 2019-2021. Bias estimations determined using the Lipid Standardization Program (LSP) of the United States Centers for Disease Control and Prevention (CDC) enhanced the accuracy and comparability of the triglyceride results.

Results: Triglyceride concentrations measured via the glycerol non-blanking method were 10.7 mg/dL (0.12 mmol/L, 10.0%) higher than those from the glycerol blanking method, with a 9.9 mg/dL (0.11 mmol/L, 5.0%) difference at a concentration of 200 mg/dL (2.26 mmol/L, N=98). The conversion equation y (glycerol non-blanking, 2022)=11.94+0.99x (glycerol blanking, 2005-2021) changed the mean triglyceride concentrations of the KNHANES 2019-2021 data (N=16,015) from 123.7 mg/dL (1.40 mmol/L, 95% confidence interval [CI]: 122.2-125.1 mg/dL [1.38-1.41 mmol/L]) to 134.3 mg/dL (1.52 mmol/L, 95% CI: 132.9-135.8 mg/dL [1.50-1.53 mmol/L]). Since 2022, bias monitoring using the CDC's LSP has remained within a 5.0% limit.

Conclusions: KNHANES triglyceride values in 2022 (non-blanking) were substantially higher than those from 2005-2021 (blanking). Conversion equations helped effectively adjust 2005-2021 data. Researchers should consider adjusting the KNHANES triglyceride data based on their study characteristics.

Current ABO titration methods lack standardization and harmonization. We analyzed the consistency of ABO antibody titer testing among Korean laboratories and discussed future directions for standardization by analyzing external quality control data collected by the Korean Association of External Quality Assessment Service over 5 yrs (2019-2023). The analysis included the number of participating institutions and methods, as well as the proportion of acceptable results. To compare column agglutination technology (CAT) and tube methods, we created a normalized variable: ([log₂ titer of laboratory test result]-[mean of log₂ titer for the peer group]). The number of participating institutions and methods increased over time. The use of CAT methods expanded, whereas that of tube methods declined. The proportion of acceptable results ranged from 84.0% to 100%, with no significant differences between CAT and tube methods. An F-test revealed no significant variance differences among institutions using these methods. Tube methods demonstrated lower variance in anti-human globulin testing, and room temperature tube methods exhibited lower variance than that of CAT methods. Domestic laboratories demonstrated highquality performance in ABO antibody titer testing, with no significant differences in acceptable result rates or variance across methods. Continuous efforts toward standardization remain essential.

Background: Multilocus sequence typing (MLST) is well-established for Pasteurella multocida but remains undeveloped for Pasteurella canis. We established MLST for P. canis using isolates from humans and companion animals in Japan and Korea to gain insights into its population biology.

Methods: We analyzed 39 and 22 isolates from companion animals and humans, respectively. We selected seven housekeeping genes-adk, aroA, deoD, gdhA, g6pd, mdh, and pgi-used in P. multocida MLST. Primer pairs for PCR amplification and sequencing were designed based on conserved sites in 10 whole-genome sequences. We determined fragment sequences, variable sites, allelic profiles, and sequence types (STs) of each isolate. A phylogenetic tree of concatenated sequences was constructed using the goeBURST algorithm to identify STs and clonal complexes (CCs). ompA, encoding outer membrane protein A, was genotyped for molecular characterization.

Results: The sequenced fragment lengths and allele numbers of the seven genes were 424, 451, 483, 439, 429, 419, and 440 bp and 16, 13, 15, 18, 22, 19, and 18, respectively. ST1-ST47, including CC2, CC10, CC18, CC31, and CC33, were diversely distributed among the isolates from different hosts/countries. In the seven-gene phylogenetic tree, apart from P. multocida, all isolates clustered together. goeBURST diagrams revealed diverse ST distributions among different hosts (animal/human) and countries (Japan/Korea/ others). We found clusters 1-4 in ompA genotyping, indicating that MLST discrimination is higher than ompA typing discrimination.

Conclusions: We established MLST for P. canis isolates from humans and companion animals in Japan and Korea, thereby providing a robust tool for population biology studies.

Measurement results of biological samples are not perfect and vary because of numerous factors related to the biological samples themselves and the measurement procedures used to analyze them. The imprecision in patients' laboratory data arising from the measurement procedure, known as analytical variation, depends on the conditions under which the data are collected. Additionally, the sample type and sampling time significantly affect patients' laboratory results, particularly in serial measurements using samples collected at different time points. For accurate interpretation of patients' laboratory data, imprecision-both its analytical and biological components-should be properly evaluated and incorporated into data management. With advancements in measurement technologies, analytical imprecision can be minimized to an insignificant level compared to biological imprecision, which is inherent to all biomolecules because of the dynamic nature of metabolism. This review addresses: (i) the theoretical background of variation, (ii) the statistical and metrological evaluation of measurement variation, (iii) the assessment of variation under different conditions in medical laboratories, (iv) the impact of measurement variation on clinical decisions, (v) the influence of biases on measurement variation, and (vi) the variability of analytes in human metabolism. Collectively, both analytical and biological imprecision are inseparable aspects of all measurements in biological samples, with biological imprecision serving as the foundation of personalized laboratory medicine.

Pharmacogenomics is a rapidly evolving field with a strong foundation in basic science dating back to 1960. Pharmacogenomic findings have been translated into clinical care through collaborative efforts of clinical practitioners, pharmacists, clinical laboratories, and research groups. The methods used have transitioned from targeted genotyping of relatively few variants in individual genes to multiplexed multi-gene panels, and sequencingbased methods are likely on the horizon; however, no system exists for classifying and reporting rare variants identified via sequencing-based approaches. Laboratory testing in pharmacogenomics is complex for several genes, including cytochrome P450 2D6 (CYP2D6), HLA-A, and HLA-B , owing to a high degree of polymorphisms, homology with other genes, and copy-number variation. These loci require specialized methods and familiarity with each gene, which may persist during the transition to next-generation sequencing. Increasing implementation across laboratories and clinical facilities has required cooperative efforts to develop standard testing targets, nomenclature, and reporting practices and guidelines for applying the results clinically. Beyond standardization, harmonization between pharmacogenomics and the broader field of genomic medicine may be essential for facilitating further adoption and realizing the full potential of personalized medicine. In this review, we describe the evolution of clinical laboratory testing for pharmacogenomics, including standardization efforts and the anticipated transition from targeted genotyping to sequencing-based pharmacogenomics. We speculate on potential upcoming developments, including pharmacoepigenetics, improved understanding of the impact of non-coding variants, use of large-scale functional genomics to characterize rare variants, and a renewed interest in polygenic risk or combinatorial approaches, which will drive the progression of the field.