Vanderboom, Patrick M.

Abstract Background Duplicate measurements can be used to characterize assay imprecision, identify outliers, and estimate error rates. The benefits of duplicate analysis, however, need to be balanced against the increased financial cost. The objective of this study was to evaluate the need for continued replicate measurement in a laboratory developed assay measuring anti-drug antibodies to infliximab (ATI). To this end, duplicate patient sample measurements from the ATI assay over a two-year period were analyzed to calculate outlier and error frequencies. Methods ATI were measured by electrochemiluminescence (ECL) on the MesoScale Discovery (MSD) platform, with a standard curve from 20–1250 U/mL using commercially available monoclonal ATIs. All patient samples tested for ATI over a 27-month period of time (n = 9582) were subject to inclusion in this study. Only patient samples with ATI levels within the analytical measuring range (AMR) of the assay, however, were selected for further analysis. Samples were divided equally into 7 bins across the measuring range based on mean replicate concentration. Within-run imprecision was determined for each bin using the standard deviation (combined from all replicates using sum of squares) and mean concentration derived from all replicates. Outliers were defined as replicate measurements with a percent difference greater than ±3 SD of the calculated assay imprecision. Errors were defined as samples which had replicate values on opposite sides of the clinical cutoff of positive ATI (≥50 U/mL) and a percent difference greater than 3SD of the assay imprecision. Results Of the 9582 total patient samples analyzed, 2467 had ATI values within the AMR and 1655 had levels over the clinical cut-off of 50 U/mL (17.3% positivity rate). Within-run imprecision determined from the mean and combined SD of the duplicate measurements ranged from 15.7% at the lowest concentration bin (20–27.5 U/mL) to 10.0% at the highest concentration bin (358–1243 U/mL). The mean percentage of outliers in each bin ranged from 0.8%–3.4% resulting in an overall outlier frequency of 2.1% when considering all samples within the AMR. There were 66 samples within the AMR which demonstrated qualitative discrepancies between the replicates. However, only 21 (31.8%) of these samples, had a percent agreement greater than 3SD of the assay imprecision resulting in an overall error rate of 0.2% when considering all 9582 samples. Conclusion In this study, the analysis of replicate patient samples enabled the establishment of a precision profile and estimation of error rates for a laboratory developed test measuring ATI. The calculated error rate of 0.2%, however, is well within the reported range of most clinical assays indicating that the continued practice of duplicate measurement may not be necessary. Additionally, updating the laboratory protocol to eliminate replicate testing would have positive impact in laboratory workflow, cost, and turn-around-time for results.

Abstract Funding Acknowledgements Type of funding sources: Private hospital(s). Main funding source(s): Mayo Clinic Department of Laboratory Medicine and Pathology. Background Elevated lipoprotein(a) [Lp(a)] is associated with increased risk for atherosclerotic cardiovascular disease (ASCVD). Treatment of patients with elevated Lp(a) usually includes marked reduction in low density lipoprotein cholesterol (LDL-C). However, there are uncertainties about whether all increases in Lp(a) are equivalent in there risk relationship. Purpose The aim of this investigation was to risk stratify patients with elevated Lp(a) mass [Lp(a)-M] based on Lp(a) cholesterol (Lp(a)-C) measurements. We assessed coronary artery disease (CAD) by coronary angiogram and major adverse cardiac events (MACE) in a cohort of patients with elevated Lp(a) mass (≥30mg/dL) over a 15 year followup. Methods Electrophoretic and immunologic assays were used to measure Lp(a)-C and Lp(a)-M, respectively. Univariate and multivariate logistic regression analysis was employed to test for association between Lp(a) and CAD in a cohort patients with elevated Lp(a)-M (≥30mg/dL). The relationship between Lp(a) and time to major cardiac events was evaluated using cox regression analysis. Receiver operator curve (ROC) analyses were used to identify optimal cutoff values for Lp(a)-M and C for predicting CAD in our model. Results We analyzed 179 patients with elevated Lp(a)-M. Cutoff values of 3.3mg/dL for Lp(a)-C and 42.1mg/dL for Lp(a)-M optimally predicted CAD in an ROC analysis. Of these patients, 84 were found to have Lp(a)-C < 3.3mg/dL (low Lp(a)-C), while 95 patients had Lp(a)-C levels ≥ 3.3mg/dL (high Lp(a)-C). High Lp(a)-C was associated with CAD identified on coronary angiography in univariate (OR = 2.17, 95% CI = 1.18 – 4.00, p = 0.013) and multivariate (OR = 3.36, 95% CI = 1.64 – 6.89, p < 0.001) models controlling for traditional risk factors. In univariate analyses high Lp(a)-C was also associated with MACE (HR = 2.38, 95% CI = 1.19 – 4.75, p = 0.01) while Lp(a)-M ≥ 42.1mg/dL (high Lp(a)-M) was not (HR = 1.68, 95% CI = 0.82 – 3.44, p = 0.15) (Table 1, Figure 1). Lp(a)-C remained significantly correlated with MACE after adjusting for traditional risk factors. When included in a model together, Lp(a)-C maintained its significance while Lp(a)-M failed to predict MACE, indicating that Lp(a)-C is a stronger predictor of MACE than Lp(a)-M. Conclusion This study demonstrates that patients with elevated Lp(a)-M and high Lp(a)-C are approximately 2.5 times more likely to encounter MACE over time when compared to patients with elevated Lp(a)-M and low Lp(a)-C. Our investigation suggests that Lp(a)-C can be used to risk stratify patients with elevated Lp(a)-M and identify those at higher risk.

Abstract Cholestatic liver diseases, such as primary sclerosing cholangitis and primary biliary cirrhosis, can lead to serum accumulation of lipoprotein X (LpX). LpX is a multilamellar particle high in cholesterol but lacking structural apolipoproteins A1 or B. The absence of ApoB results in no negative feedback on cholesterol biosynthesis and prevents LpX clearance from the liver. While clinical signs and symptoms typically precede laboratory findings, it is possible that in medically complex patients the identification of LpX could be the first observation of cholestatic liver disease. Traditional laboratory methods are insufficient to identify LpX as it is of similar density to low-density lipoprotein (LDL). LpX contains a high concentration of cholesterol which is erroneously reported as LDL-C by routine clinical methods. As LpX is a rare complication of liver disease, clinicians may presume the elevation is a coincidental familial hypercholesterolemia rather than a sequela of liver disease. Currently, lipoprotein gel electrophoresis is the only laboratory method to identify LpX. In this method only performed in specialty lipid laboratories, LpX is readily identified by its unique reverse electrophoretic mobility relative to other lipoproteins. The objective of this study was to characterize lipid panels from LpX-positive samples and develop a suitable mechanism to identify LpX-containing samples with good clinical validity. From 21,377 clinical electrophoresis results reported between Nov 2011 to Nov 2021, LpX was identified in 157 serum samples. Overall, patients with LpX were younger (median 44y vs. 55y, p<0.0001) with significantly higher total cholesterol (812mg/dL vs 190mg/dL, p<0.0001) and lower high density lipoprotein-cholesterol (HDL-C; 3mg/dL vs 45mg/dL, p<0.0001). Data were randomly split (70/30) into training (n=14,964) and testing (n=6,413) cohorts. Receiver operator characteristic curve analysis identified optimal thresholds of 22.5 mg/dL HDL-C (AUC = 0.94) and 378.5 mg/dL total cholesterol (AUC = 0.91), as well as a nonHDL-C/HDL-C ratio of 9.2 (AUC = 0.995). Applying these cutoffs to the testing cohort achieved a sensitivity/specificity of 98%/81% for HDL-C, 96%/87% for total cholesterol, and 98%/94% for nonHDL-C/HDL-C ratio. A multivariate model combining these three parameters showed a sensitivity/specificity of 97%/85%, respectively. In conclusion, low HDL-C, elevated total cholesterol and a ratio of nonHDL-C/HDL-C >9.2 are associated with the presence of LpX. The ratio of nonHDL-C/HDL-C is the most sensitive and specific predictor of LpX. If confirmed in other cohorts, laboratories could include a reporting comment on lipid panels with a nonHDL-C/HDL-C ratio >9.2 cautioning a high suspicion for the presence of LpX and recommending confirmatory testing. It may also be prudent to caution that LDL-C results may not be accurate. In conclusion, identifying patients with high suspicion of LpX based on abnormal lipid panel results may aide in clinical diagnosis, even when an assay to detect LpX is not readily available.

AbstractObjectiveThe health benefits of exercise are well documented, but several exercise‐response parameters are attenuated in individuals with obesity. The goal of this pilot study was to identify molecular mechanisms that may influence exercise response with obesity.MethodsA multi‐omics comparison of the transcriptome, proteome, and phosphoproteome in muscle from a preliminary cohort of lean individuals (n = 4) and individuals with obesity (n = 4) was performed, before and after a single bout of 30 minutes of unilateral cycling at 70% maximal oxygen uptake (VO2 peak). Mass spectrometry and RNA sequencing were used to interrogate the proteome, phosphoproteome, and transcriptome from muscle biopsy tissue.ResultsThe main findings are that individuals with obesity exhibited transcriptional and proteomic signatures consistent with reduced mitochondrial function, protein synthesis, and glycogen synthesis. Furthermore, individuals with obesity demonstrated markedly different transcriptional, proteomic, and phosphoproteomic responses to exercise, particularly biosynthetic pathways of glycogen synthesis and protein synthesis. Casein kinase II subunit alpha and glycogen synthase kinase‐3β signaling was identified as exercise‐response pathways that were notably altered by obesity.ConclusionsOpportunities to enhance exercise responsiveness by targeting specific molecular pathways that are disrupted in skeletal muscle from individuals with obesity await a better understanding of the precise molecular mechanisms that may limit exercise‐response pathways in obesity.

Abstract Background We evaluated the analytical sensitivity and specificity of 4 rapid antigen diagnostic tests (Ag RDTs) for severe acute respiratory syndrome coronavirus 2, using reverse transcription quantitative PCR (RT-qPCR) as the reference method and further characterizing samples using droplet digital quantitative PCR (ddPCR) and a mass spectrometric antigen test. Methods Three hundred fifty (150 negative and 200 RT-qPCR positive) residual PBS samples were tested for antigen using the BD Veritor lateral flow (LF), ACON LF, ACON fluorescence immunoassay (FIA), and LumiraDx FIA. ddPCR was performed on RT-qPCR–positive samples to quantitate the viral load in copies/mL applied to each Ag RDT. Mass spectrometric antigen testing was performed on PBS samples to obtain a set of RT-qPCR–positive, antigen-positive samples for further analysis. Results All Ag RDTs had nearly 100% specificity compared to RT-qPCR. Overall analytical sensitivity varied from 66.5% to 88.3%. All methods detected antigen in samples with viral load >1 500 000 copies/mL RNA, and detected ≥75% of samples with viral load of 500 000 to 1 500 000 copies/mL. The BD Veritor LF detected only 25% of samples with viral load between 50 000 to 500 000 copies/mL, compared to 75% for the ACON LF device and >80% for LumiraDx and ACON FIA. The ACON FIA detected significantly more samples with viral load <50 000 copies/mL compared to the BD Veritor. Among samples with detectable antigen and viral load <50 000 copies/mL, sensitivity of the Ag RDT varied between 13.0% (BD Veritor) and 78.3% (ACON FIA). Conclusions Ag RDTs differ significantly in analytical sensitivity, particularly at viral load <500 000 copies/mL.