Clinical chemistry最新文献

Background: Untargeted metabolomics has shown promise in expanding screening and diagnostic capabilities for inborn errors of metabolism (IEMs). However, inter-batch variability remains a major barrier to its implementation in the clinical laboratory, despite attempts to address this through normalization techniques. We have developed a sustainable, matrix-matched reference material (RM) using the iterative batch averaging method (IBAT) to correct inter-batch variability in liquid chromatography-high-resolution mass spectrometry-based untargeted metabolomics for IEM screening.

Methods: The RM was created using pooled batches of remnant plasma specimens. The batch size, number of batch iterations per RM, and stability compared to a conventional pool of specimens were determined. The effectiveness of the RM for correcting inter-batch variability in routine screening was evaluated using plasma collected from a cohort of phenylketonuria (PKU) patients.

Results: The RM exhibited lower metabolite variability between iterations over time compared to metabolites from individual batches or individual specimens used for its creation. In addition, the mean variation across amino acid (n = 19) concentrations over 12 weeks was lower for the RM (CVtotal = 8.8%; range 4.7%-25.3%) compared to the specimen pool (CVtotal = 24.6%; range 9.0%-108.3%). When utilized in IEM screening, RM normalization minimized unwanted inter-batch variation and enabled the correct classification of 30 PKU patients analyzed 1 month apart from 146 non-PKU controls.

Conclusions: Our RM minimizes inter-batch variability in untargeted metabolomics and demonstrated its potential for routine IEM screening in a cohort of PKU patients. It provides a practical and sustainable solution for data normalization in untargeted metabolomics for clinical laboratories.

Background: Immunoglobulin (Ig) measurements in the clinical laboratory have been traditionally performed by nephelometry, turbidimetry, electrophoresis, and ELISA assays. Mass spectrometry (MS) measurements have the potential to provide deeper insights on the nature of these markers.

Content: Different approaches-top-down, middle-down, or bottom-up-have been described for measuring specific Igs for endogenous monoclonal immunoglobulins (M-proteins) and exogenous therapeutic monoclonal antibody therapies (t-mAbs). Challenges arise in distinguishing the Ig of interest from the polyclonal Ig background. MS is emerging as a practical method to provide quantitative analysis and information about structural and clonal features that are not easily determined by current clinical laboratory methods. This review discusses clinically implemented examples, including isotyping and quantification of M-proteins and quantitation of t-mAbs within the polyclonal Ig background, as examples of how MS can enhance our detection and characterization of Igs.

Summary: This review of current clinically available MS proteomic tests for Igs highlights both analytical and nonanalytical challenges for implementation. Given the new insight into Igs from these methods, it is hoped that vendors, laboratorians, healthcare providers, and payment systems can work to overcome these challenges and advance the care of patients.

Background: Obesity, defined by body mass index (BMI) alone, is a metabolically heterogeneous disorder with distinct cardiovascular manifestations across individuals. This study aimed to investigate the associations of a proteomic signature of BMI with risk of major subtypes of cardiovascular disease (CVD).

Methods: A total of 40 089 participants from UK Biobank, free of CVD at baseline, had complete data on proteomic data measured by the Olink assay. A BMI-proteomic score (pro-BMI score) was calculated from 67 pre-identified plasma proteins associated with BMI.

Results: A higher pro-BMI score was significantly associated with higher risks of ischemic heart disease (IHD) and heart failure (HF), but not with risk of stroke. Comparing the highest with the lowest quartiles, the adjusted hazard ratio (HR) for IHD was 1.49 (95% CI, 1.32-1.67) (P-trend < 0.001), and the adjusted HR for HF was 1.52 (95% CI, 1.25-1.85) (P-trend < 0.001). Further analyses showed that the association of pro-BMI score with HF risk was largely driven by the actual BMI, whereas the association of the pro-BMI score with IHD risk was independent of actual BMI and waist-to-hip ratio (WHR). The association between pro-BMI score and IHD risk appeared to be stronger in the normal BMI group than other BMI groups (P-interaction = 0.004) and stronger in the normal WHR group than the high WHR group (P-interaction = 0.049).

Conclusions: Higher pro-BMI score is significantly associated with higher IHD risk, independent of actual BMI levels. Our findings suggest that plasma proteins hold promise as complementary markers for diagnosing obesity and may facilitate personalized interventions.