Citations

Abstract Background Anti-citrullinated peptide antibodies (ACPA) (tested as anti-cyclic citrullinated peptide [CCP] antibodies) are frequently ordered in the evaluation of patients with suspected rheumatoid arthritis (RA) and considered to be specific biomarkers for RA. The 2010 American College of Rheumatology/European Alliance of Associations for Rheumatology (ACR/EULAR) classification criteria for RA include both ACPA and rheumatoid factor in their scoring system, despite limited standardization of commercially available anti-CCP antibody tests. In an attempt to overcome this limitation, the classification criteria recommends using multiples of the assay-specific upper limit of normal (ULN) to normalize results and standardize classification. This study evaluates the correlation between normalized results from different assays and the concordance in result classification according to the ACR/EULAR criteria. Methods A total of 265 samples with ACPA ordered as part of routine clinical care were tested on four commercial anti-CCP antibody assays, including QUANTA Lite CCP3 IgG ELISA (Werfen) (Negative: <20 Units), QUANTA Flash CCP3 chemiluminescence immunoassay (Werfen) (Negative: <20 Chemiluminescent Units), EUROIMMUN Anti-CCP ELISA (IgG) (EUROIMMUN) (Negative: <=5 Relative Units/mL), and Elecsys anti-CCP electrochemiluminescence immunoassay (Roche) (Negative: <17.0 Units/mL). The QUANTA Lite and EUROIMMUN ELISA were performed on the Agility platform (Dynex), QUANTA Flash was performed on BioFlash platform (Werfen), and Elecsys anti-CCP was performed on Cobas e601 platform (Roche). All testing was performed according to manufacturer’s instructions. Results from each assay were converted to the multiples of assay-specific ULN as the normalized results. Regression analysis was performed, and the coefficient of determination (R²) was calculated to assess the correlation between paired assays. The concordance in result classification across assays was also investigated using both assay specific cut-offs and the ACR/EULAR criteria-defined cut-offs: Negative (<=1xULN), Low positive (>1xULN and <3xULN), High positive (>=3xULN). Results Using assay-specific cut-offs, 63 samples were negative on all assays. 202 samples were positive on at least one assay, of which 79 (39%) were positive across all assays. The overall concordance of qualitative result was 54% ([63+79]/265). After result normalization using multiples of assay-specific ULNs, the highest correlation was observed between the EUROIMMUN ELISA and Elecsys anti-CCP (R²=0.83), despite differences in methodology and manufacturer. The lowest correlation was observed between QUANTA Lite and QUANTA Flash (R²=0.49), even though both are from the same manufacturer although methodologies differ. When applying the criteria-defined cut-offs, 64 samples were negative on all assays. 130 samples were classified as low positive on at least one assay; however, only 3 (2%) samples were classified as low positive across all assays. 103 samples were classified as high positive on at least one assay, of which 54 (52%) were classified as high positive across all assays. With result normalization, the overall concordance in result classification across all assays using the criteria-defined cut-offs were 46% ([64+3+54]/265). Conclusion Despite normalization using multiplies of assay-specific ULN values, the inter-assay correlation remains suboptimal, and the concordance in result classification is limited. These findings suggest that the anti-CCP antibody results from different assays may not be interchangeable even after normalization as recommended by current criteria, potentially impacting disease classification.

Abstract Background Celiac disease (CeD) is a chronic autoimmune disorder that can affect people of any age. Both ingestion of gluten and the presence of human leukocyte antigens (HLA)-DQ2 or DQ8 are required for disease development. In adults, correlation between HLA-DQ2/DQ8 allele combinations and tissue transglutaminase (tTG) antibody positivity has been well described. In comparison, this association in pediatric patients is less defined. In this study, we evaluated the roles of HLA-DQ2 and HLA-DQ8 as predictors of tTG-IgA positivity in a pediatric cohort, aiming to enhance understanding of CeD risk assessment in this diagnostic group. Methods Retrospective data were collected from individuals who underwent CeD diagnostic testing at Mayo Clinic Laboratories between 2011 and 2024; all patients were tested for total IgA, HLA-DQ typing, and tTG-IgA. Total IgA was measured using quantitative nephelometry on the BN™ II System (Siemens); HLA-DQ typing was performed using reverse sequence-specific oligonucleotide (SSO) DNA typing (LABType SSO kits, One Lambda, Thermo). tTG-IgA was tested using an enzyme-linked immunosorbent assay (QUANTA Lite® h-tTG IgA, Werfen Diagnostics). Those with total IgA below the age-specific reference interval were excluded from this analysis. Logistic regression was used to evaluate the association between HLA-DQ haplotypes and tTG-IgA positivity to assess the risk gradient across different HLA-DQ genotypes in children. Results The cohort identified in this study consisted of 115,499 individuals for whom total IgA, HLA-DQ typing and tTG-IgA testing was performed. Within this cohort, 20,676 (17.9%) were pediatric patients (age<=18 at the time of testing). Pediatric patients tested for CeD exhibited a slightly higher prevalence of at least one copy of HLA-DQ2 or DQ8 compared to adults (57.6% vs 55.4%, p<0.001). Additionally, these patients showed significantly higher rates of tTG-IgA positive across all genotypes, particularly those carrying HLA-DQ2.5 or DQ8, with positivity rates 1.2 to 2.3 times higher than in adults (p<=0.01). However, in non-permissive genotypes, the positivity rates were 0.3% (p=0.95), for both populations, indicating comparable specificity. Individuals with homozygous HLA-DQ2.5/DQ2.5 and HLA-DQ2.5/DQ2.2 showed the highest risks for tTG-IgA positivity with ORs of 85.7 (95% CI: 55.1-137.7) and 78.5 (95% CI: 51.3-124.3), respectively. Moderate risk was observed for HLA-DQ2.5/DQ8 with an OR of 49.0 (95% CI: 32.1-77.3), followed by single copy HLA-DQ2.5 (OR=35.0, 95% CI: 24.0-53.4) and homozygous HLA-DQ8/DQ8 (OR=21.7, 95% CI: 11.7-39.4). Although single copies of HLA-DQ2.2 and HLA-DQ8 showed lower risks with ORs of 5.4 and 8.9 (95% CI: 3.2-9.2 and 5.7–13.9), their combination increased the OR to 22.6 (95% CI: 13.0-39.2), similar to homozygous HLA-DQ8. Conclusion This large cohort study demonstrates the predictive value of HLA-DQ genotypes for pediatric CeD seropositivity. Notably, pediatric patients showed slightly higher prevalence and positivity rates compared to adults, particularly among those with homozygous HLA-DQ2.5/DQ2.5 and HLA-DQ2.5/DQ2.2 genotypes, which exhibited the highest risks for tTG-IgA positivity. The findings confirm that HLA-DQ2.5 is the main driver of serology positivity and suggest that HLA-DQ8 also influences serological positivity in pediatric patients, which can enhance targeted screening and potentially inform decisions regarding the necessity of duodenal biopsy.

Abstract Background The Freelite serum free light chain (FLC) assay has been widely used for detecting and monitoring monoclonal gammopathies. Reference intervals (RIs) established by Katzmann et al. in 2002 have served as the standard values for interpreting kappa/lambda FLC ratios. However, multiple reports suggested an assay drift and clinical laboratories started to move towards locally established RIs given the challenge in verifying original RIs. The assay drift over time increased kappa concentration measurements and was attributed to an interaction between the kappa FLC calibrator and serum proteins, including alpha-1-antitrypsin. A stabilized calibrator was developed to improve consistency of results with original RI. The objective of this study was to assess whether the new updated kappa calibrator would warrant a RI update. Methods Healthy adult donors’ serum samples were selected from the institution quality unit. Exclusion criteria included autoimmune diseases, cancers and renal disease. FLC measurements were obtained on the Optilite by turbidimetry using routine reagents for kappa (old lot); the new lot of reformulated kappa calibrator, and a single measurement for lambda FLC. FLC ratios were calculated with the old lot and new lot. All samples were tested by MASS-FIX to confirm absence of a monoclonal protein. Bootstrap resampling (10,000 replicates) estimated standard errors, with statistical significance defined as p < 0.05. Clinical significance was assessed per CLSI EP28-A3 guidelines. A RI was considered for partitioning by sex if the mean difference between males and females exceeded 25% of the RI width or if >4% of either sex had values beyond RI limits. Similar tests were applied to age groups (<60 and =60 years). Results 465 donors (48.2% male, 51.8% female) aged 30-89 years were used. Sixteen donors with incidental monoclonal proteins identified by MASS-FIX were excluded, for a final n=449. No clinically significant relationships of results were found with sex or age. The updated kappa calibrator resulted in a measurable shift down in FLC values with the new lot of reagents. The kappa FLC concentration and corresponding FLC ratio decreased by a median -12.1%, see table 1 for absolute values When reviewing Optilite results of the new lot, 98/449 samples were outside the kappa concentration original RI (21%). Lambda concentration RI was shifted up on Optilite when compared to original RI but passes verification (1.6% of samples, acceptance criteria < or =10%). 19 samples (4.2%) were outside the original RI for the FLC ratio of 0.26 to 1.65 (all >1.65), which was originally established as a 100% diagnostic range. The calibrator reformulation, while decreasing test results by >10%, was not sufficient to verify the original RI for kappa and still left a few cases outside the original diagnostic range for the FLC ratio. Conclusion This study assessed the impact of the stabilized calibrator on RIs for the Optilite Freelite assay. These findings suggest revisiting existing RIs to ensure continued accuracy in clinical interpretation and should be further evaluated with additional lots of reagents for robustness.

Abstract Background To develop and validate a sensitive live cell-binding assay using flow cytometry to detect a3ß4-ganglionic nicotinic acetylcholine receptor (gnAChR) antibodies for diagnosis of autoimmune autonomic ganglionopathy (AAG, a subset of autoimmune autonomic neuropathies). The current radioimmunoprecipitation assay (RIPA) method has high false positive rate and utilizes radioactivity. Methods HEK293 cells were transiently transfected to express human a3ß4-gnAChR subunits, along with NACHO and RIC3 chaperons, and GFP for detection. Transfected cells were treated with 1mM nicotine liquid for 18-22 hours prior to lifting cells for flow cytometry. The IBI (IgG Binding Index) was then calculated as the median fluorescence intensity (MFI) of AF647 of the a3ß4-gnAChR subunits expressing cells divided by the MFI of the AF647 HEK293 expressing cells. Humanized AChR-specific monoclonal antibody (mAb03) was used as a single-point calibrator. The cut-off index (COI) ratio is calculated by dividing the IBI obtained from a sample by the established IBI cut-off value of the mAB03 calibrator set by the assay. A ratio >1 indicates a positive result, while a ratio below 1 is negative. The study tested sera from 83 Mayo Clinic patients suspected of having autoimmune autonomic ganglionopathy (AAG), with a total of 100 samples collected over several years (40 from patients with clinically confirmed AAG, and 60 from patients without AAG), all previously positive for a3-gnAChR antibodies by RIPA. Additionally, 130 control samples were included: 90 from healthy individuals, 22 with hypergammaglobulinemia, and 18 with systemic lupus erythematosus. Results Median RIPA a3ß4-gnAChR value among 40 AAG patient samples was 2.58 nmol/L (range, 0.06-29.1 nmol/L) and among 60 non-AAG patients was 0.14 nmol/L (range, 0.01-4.43 nmol/L). The mAB03 calibrator median IBI value across assays was 2.1 (range, 1.6-2.7). The COI ratio was calculated using the calibrated IBI values. Thirty-eight of 40 AAG patient samples were considered positive, resulting in median IBI of 3.10 (range, 1.29-6.71); sensitivity 95%. None of non-AAG patients and controls were considered positive; median IBI of 0.38 (range, 0.14-0.51); specificity 100%. The two negative AAG patient samples (IBI, 0.43 and 0.43) were from patients in remission (treated with immune suppressants), and had RIPA titers of 0.06 nmol/L and 0.74 nmol/L. Conclusion The a3ß4-gnAChR-IgG flow cytometry assay has high sensitivity and specificity for AAG-related a3-AChR antibodies. It was able to distinguish AAG patients from non-AAG patients and controls effectively. The flow cytometry assay’s cut-off index ratio effectively identifies AAG patients while remaining negative for non-AAG and control samples.

Abstract Background The use of autonomous mobile robots (AMRs) to move materials/supplies is long-standing in manufacturing/warehouse settings and is expanding into hospitals and clinical laboratories. The recently launched Proxie robot (Collaborative Robotics i.e. Cobot) is an AMR that can autonomously move existing carts between defined locations. However, AMRs pose new challenges compared to traditional lab automation. The aims of this study were to: i) perform functional validation to verify Proxie*s manufacturer performance specifications within the laboratory, ii) assess the risk of using Proxie to deliver laboratory specimens, and iii) pilot the use of the Proxie robot to autonomously deliver loaded carts from the Pathology case intake area to the sorting station at Mayo Clinic Laboratories. Methods Functional validation of Proxie consisted of verifying manufacturer’s performance specifications for the following robot parameters (5 test runs): maximum speed, emergency-stop, and ability to maintain localization, travel autonomously, stop and/or avoid moving/fixed objects, and park/unpark. Risk assessment for the robotic use case was performed by a multi-disciplinary team utilizing an AMR risk assessment tool developed by the institutional robotics group. Hazards were categorized as mechanical, electrical, noise, ergonomic, environmental, material/substance or combination risks. Injury severity, exposure(frequency), and avoidance scores were assigned to each hazard to determine negligible-, low-, medium-, high- or very high-risk levels. Proxie’s workstream was evaluated weekly over four weeks by measuring number of cart deliveries, total time/miles traveled, % autonomous deliveries(goal >80%), % on-time deliveries, average±standard deviation(SD) time (min:sec±min:sec) for delivery/return trips, and dwell times at intake/sorting stations. Results Manufacturer’s performance specifications were verified in 100% of test runs. Proxie traveled at human walking speed, stopped within 1 second of emergency stop being enabled, maintained localization, and traveled autonomously for =5 consecutive workflow cycles. Proxie navigated through spaces =4’ wide along fixed pathways and stopped safely when encountering obstacles. Proxie navigated around people and carts in the main 6’ hallway if they were blocking <2’ hallway width. For objects blocking >2’, Proxie stopped and resumed motion after the object moved. After task completion, Proxie side-shifted into the AMR parking space stopping no closer than 7.8” from any object. Twenty-four potential hazards were identified through the risk assessment tool with all risk levels scored as negligible or low. Proxie completed 304 cart deliveries/returns operating 17:35 hours and traveling 11.5 miles during the pilot. The % autonomous deliveries for week 1-4 was 82%,88%,91% and 88%, respectively with =99% on-time deliveries. Delivery from pathology case intake to sorting station averaged 5:43±0:25 min and return trips averaged 3:32±0:34. Proxie dwell times were 13:10±0:33 and 37:34±0:23 min at the pathology intake and sorting stations, respectively. Conclusions The Proxie AMR was capable of successfully and autonomously moving carts with laboratory specimens between the pathology intake and sorting stations of the laboratory during the pilot study. Adding Proxie to this workstream translated to eliminating 5,800 steps and 4 hours 24 min of human cart-pushing per week. Incorporating Proxie into laboratory operations as a cart-moving AMR would increase efficiency and allow laboratory staff to focus on technical, non-repetitive work.

Abstract Background Hemolysis, a common pre-analytical interferent in clinical laboratory testing, can significantly affect accuracy and reliability of laboratory results. One test particularly susceptible to interference is direct bilirubin (D-bil). For example, during December 2024, 40% (500/1239) of specimens rejected due to hemolysis were orders for D-bil. The recently released Roche (Roche Diagnostics) direct bilirubin reagent (BILD2) has a lower threshold for hemolysis (H-index [HI]=25)) than the previous generation (HI=30). The aims of our study were to (1) establish concentration-specific hemolysis thresholds for D-bil and (2) investigate the instrument’s reduced volume function (dilution) as a possible mitigation strategy for samples with hemolysis indices exceeding the established thresholds. Methods Residual serum specimens from physician-ordered D-bil testing were used and analyzed on Roche cobas c701 and c501 analyzers. The instrument’s decreased volume function, intended for results outside the measuring range, was used as a 1:2 dilution and validated using 9 non-hemolyzed negative control specimens with D-bil results from 9.8-13.4 mg/dL. Recovery within 10% neat value was considered acceptable. EDTA whole blood was used to prepare hemolysate pools by washing red cells and lysing by freezing (-80 °C). Twenty-six serum pools with varying D-bil concentrations (0.2-3.4 mg/dL) were prepared. Each pool was split into 10 aliquots and spiked with increasing amounts of hemolysate, not exceeding 10% serum to interferent ratio. The decreased volume function (dilution) was utilized on a subset of the hemolysate-spiked aliquots (N=55) with D-bil results from 0.2-2.5 mg/dL. HI values were estimated to be 50% of neat HI due to the instrument’s inability to measure indices on specimens tested with the decreased volume function. Acceptance criteria for experiments utilizing hemolyzed specimens were ±0.1 mg/dL or 20% of neat sample. Results All non-hemolyzed specimens tested with the decreased volume function passed within acceptance criteria with average recovery of 109% (range: 106-110%) from undiluted specimens. Serum pools with D-bil <0.3 mg/dL (N=7) spiked with hemolysate (HI range: 7-191) had an average difference of -0.1 mg/dL (range: -0.2-0.0 mg/dL) from non-hemolyzed pools, D-bil pools 0.4-0.9 mg/dL (N=10; HI range: 6-173) had an average difference of -0.2 mg/dL (range: -0.5-0.0 mg/dL), and D-bil pools >1.0 mg/dL (N=9; HI range: 5-182) had an average difference of -20.7% (range –45.5-0.0%). Serum pools with D-bil <0.3 mg/dL (N=23; HI range: 43-191) tested using the decreased volume function had an average difference –0.1 mg/dL (range: -0.2-0.0 mg/dL; HI range: 22-96) from the non-hemolyzed specimens, D-bil pools 0.4-0.9 mg/dL (N=20; HI range: 39-173) had an average difference -0.1 mg/dL (-0.3-0.0 mg/dL; HI range: 19-86), and D-bil pools >1.0 mg/dL (N=12; HI range: 37-182 ) had an average difference of -0.2 mg/dL (-0.5-0.2 mg/dL; HI range: 19-91). Conclusion We established a new hemolysis interference threshold of 75 for samples with D-bil <0.3 mg/dL. Using the decreased volume function, we expanded the hemolysis interference threshold to HI=100 for specimens =0.3 mg/dL. All specimens with HI>100 will result in a recollection. With these changes, we estimate that we can reduce our D-bil recollections by up to 6% over six months.

Abstract Background Lymphocyte function and immune competence can be measured by proliferative response to stimuli and is useful in several clinical contexts including primary (or genetic) and secondary immunodeficiencies (e.g., immunosuppression following transplantation). Proliferation assays have traditionally been prepared manually due to the difficulty in maintaining the sterile environment required to prevent bacterial and fungal impact on cell growth. On most automated liquid handlers, the sample and reagents are placed inside a cabinet whose inner surfaces include rails, pullies, pumps, and circuitry that face the sample and are not amenable to sterilization. The Andrew+ (Waters, MA) is a small surface cleanable liquid handler with all machinery enclosed. We demonstrate here the use of an Andrew+ placed in a biological safety cabinet (BSC) to perform sterile assay setup. Methods Peripheral blood monocytes (PBMCs) from five individuals were manually generated via buffy coat isolation, then brought to standardized concentration. An Andrew+ was placed in a 4’ BSC and all surfaces disinfected with Oxivir Tb (Diversey, SC). The Andrew+ prepared three serial dilutions of two stimulant reagents (phytohemagglutinin (PHA) and pokeweed (PWM)), then dispensed stimulant and control reagents horizontally and PBMCs vertically to a 48 well plate. This process was duplicated manually from the same specimens to create a reference plate. The plates were incubated at 37°C and 5% CO2 for three days, then pulsed with EdU (Thermo Fisher, MA) and incubated for one additional day. Cells were harvested and counted via flow cytometry. Overall time and hands-on time were measured. Results A total of five samples were tested inside a using both manual and automation setup under eight conditions including with isotype control, media without stimulant, three concentrations of PHA, and three concentrations of PWM. The percent proliferation for various cell types as induced by each stimulant was calculated from the difference of the highest stimulant signal from the background (media-only) signal. Comparing the automated and manual method results showed a slope of 1.0217 and R2 of 0.9966. For a simulated run of 6 samples, technologist time for automated steps was under 8 minutes with a total time of 56 minutes. When these steps were performed manually, the time was 30 minutes. Automation increased total time by 26 minutes but saved 22 minutes of technologist hands-on time. Conclusion The Andrew+ was able to perform sterile setup of a cell culture assay. While slower overall than manual processing, technologist time was reduced. Ergonomic, inter-tech variability, and sample transcription error issues were subjectively observed to be less likely. The Andrew+ can work with up to four “dominos” (reagent or tip blocks) in width and up to three dominos in depth. Non-custom, standard depth BSCs from a variety of manufacturers (Labconco, Nuaire, ThermoFisher) have room for one domino in depth so, except for timings performed in this evaluation, all steps were broken down to be performed with a max of four dominos. Efficient sterile use of the system would require an extended depth BSC.

Abstract Background Community Paramedic (CP) programs use specially trained paramedics to provide healthcare in the community to increase access to care, reduce unnecessary emergency department visits, and improve timeliness of care. Mayo Clinic in Rochester, MN recently launched a CP Program to provide in-home care for patients who require regular intravenous medications, wound care, diuresis for heart failure, medication reconciliation, care plan compliance/education. CP utilizes i-STAT1 CHEM8+ and Lactate (CG4+) testing (Abbott Point of Care, Abbott Park, IL) to make therapy decisions primarily for heart failure patients based on rapid laboratory results. Having results available at the point of care (POC) eliminates delays between testing and treatments, expediting care for the patient. Prior to using i-STAT, CP traveled to the patient’s residence, drew blood tubes, delivered the tubes to Mayo Clinic, waited for test results, returned to the patient’s home, and provided treatment. In this observational study, we assessed the benefit of in-home POC i-STAT1 testing performed by our CP Program to improve patient care and increase efficiency. Methods Patients within a 40-mile radius of Mayo Clinic were eligible to receive CP service. Whole blood venous specimens were collected and tested using the i-STAT1 CHEM8+ metabolic panel cartridge according to manufacturer instructions to measure: ionized calcium, chloride, glucose, hematocrit, total carbon dioxide, blood urea nitrogen, creatinine, potassium, sodium, anion gap while the i-STAT1 CG4+ cartridge measures lactate. The laboratory information system was used to extract laboratory results, and Excel (Microsoft, Redmond, WA) was used to filter data and perform calculations. Results November 2022 through December 2024, CP performed testing with 353 i-STAT cartridges (3862 tests) on 102 unique patients who each had 1-12 separate orders. Patient ages were 25 to 97 years. 41.2% (1590/3862) of the individual results obtained were outside of our reference intervals (23.0% high, 18.0% low), with 10.8% being ionized calcium, 10.2% chloride, 10.3% glucose, 13.0% hematocrit, 7.6% total carbon dioxide, 18.2% blood urea nitrogen, 16.4% creatinine, 6.7% potassium and 6.9% sodium. Additionally, 0.13% of results were critically high (ionized calcium, potassium, and sodium) and 0.10% were critically low (potassium and ionized calcium). Hypokalemic patients were sent to the infusion therapy center for IV potassium. Patients experiencing hyperkalemia were retested and sent to the emergency department (ED). Hyponatremic and hypercalcemic patients were sent to the ED for further treatment. Abnormal (non-critical) results were reviewed by CP personnel to make treatment recommendations or medication adjustments. Conclusion i-STAT1 home testing performed by CP facilitated rapid therapeutic interventions and expedited decision-making. POC testing has increased the group’s efficiency—alleviating the need to transport specimens to the central laboratory and wait on test results before returning to the patient for treatment. Paramedicine emphasizes a proactive and preventative approach to medicine. We anticipate that this model of care and testing will expand to cover other chronic health conditions due to the program’s early success.

Abstract Background Diagnosis of VZV CNS vasculitis (CNS-VZV) is aided by detecting intrathecally (i.t.) synthesized VZV-specific antibodies (Abs) through testing paired serum (S) and CSF specimens and determining an antibody index (AI). Currently, laboratories in the United States do not provide AI testing for VZV. The currently used methods (e.g., EIAs, IFAs) lack clinical utility as they cannot discriminate if VZV Abs in the CSF are i.t. produced due to CNS infection or whether they are present due to passive diffusion across the blood-CSF-barrier. To optimize diagnostic options for CNS-VZV, we evaluated a quantitative VZV IgG ELISA (Euroimmun AG, Lübeck, Germany) to calculate AI values using Reiber’s formula on paired S/CSF specimens to assess the diagnostic accuracy of this method. Methods The clinical sensitivity cohort consisted of four patients diagnosed with CNS-VZV (meningitis or vasculitis) as determined by symptoms, imaging, response to antiviral therapy, and/or supporting test results (e.g., lymphocytic pleocytosis, elevated protein in CSF). The patient group for clinical specificity included 11 patients with suspected CNS-VZV who ultimately had an unclear (n=4) or non-CNS-VZV (n=7) diagnosis. The reference range cohort included paired clinical S/CSF samples from 25 patients without infectious diseases testing. Paired S/CSF samples were processed on the same batch using a fully automated ELISA analyzer capable of onboard, ad hoc runtime dilution preparation (AP22 ELITE, DAS ITALY). The AI values were calculated using the Reiber formula, and sensitivity (Sn), specificity (Sp), and positive (PPV) and negative (NPV) predictive values were calculated. The AI was calculated using the following equations: If Total IgG CSF (mg/dL):Total IgG serum (mg/dL) < Quotient limit Total IgG, then AI = (VZV IgG Ab CSF [units × dilution]:VZV IgG serum [units × dilution])/(Total IgG CSF [mg/dL]:Total IgG serum [mg/dL]). If Total IgG CSF (mg/dL):Total IgG serum (mg/dL) > Quotient limit Total, then AI = (VZV IgG Ab CSF [units × dilution]:VZV IgG serum [units × dilution])/Quotient limit Total IgG. Quotient limit Total IgG = 0.93 × [(Qalbumin2) + (6 × 10-6)]1/2 - 1.7 ×10-3. Qalbumin = Albumin CSF (mg/dL):Albumin serum (mg/dL). Results The VZV IgG ELISA with Reiber AI calculation method showed Sn, Sp, PPV, and NPV values of 100% (4/4), 91% (10/11), 80% (4/5), and ?0 (10/10), respectively. One of the Sp patients had an equivocal AI result and was classified as negative, whereas the Sp patient with a positive AI result had an unclear diagnosis. VZV AI results for 96% (24/25) of the reference range patients were negative, with one equivocal. The mean AI of the 25 patients was 1.05, and the lower and upper 3 standard deviation limits were 0.6 and 1.5, respectively. These values match the AI qualitative interpretive criteria recommended by Reiber (0.6 to <1.3 = negative, 1.3 to 1.5 = equivocal, >1.5 AI = positive). Conclusion We show that the VZV IgG ELISA, alongside the Reiber formula for AI calculation, is effective for identifying patients with i.t. synthesized VZV IgG Abs. This method can assist clinicians in evaluating patients with suspected CNS-VZV infections who are negative by alternative methods.

Abstract Background Insulin resistance leads to changes in lipid and lipoprotein metabolism prior to developing prediabetes, including an increase in the concentration of large very-low-density lipoprotein particles (LVLDL-p), the mean size of VLDL (VLDL-s), and the concentration of small low-density lipoprotein particles (sLDL-p). Conversely, there is a decrease in the concentration of large high-density lipoprotein particles (LHDL-p), mean size of HDL (HDL-s), and mean size of LDL (LDL-s). Clinically validated studies show that proton nuclear magnetic resonance (NMR) can measure these six parameters and derive a lipoprotein insulin resistance (LP-IR) score, ranging from 0 to 100, with higher scores indicating greater risk for insulin resistance. The reference method for calculating LP-IR relies on a proprietary NMR spectrometer, spectral deconvolution method, and algorithm. This study seeks to determine if lipoprotein subfraction parameters measured from a commercially available NMR system can produce LP-IR scores comparable to the reference method. Methods Serum lipoprotein NMR spectra were collected using a Bruker Ascend 600 MHz magnet, Bruker Avance NEO 500 spectrometer, and the Numares AXINON lipoFIT System to report the six LP-IR score parameters between January 2023 and January 2025 (Cohort A, n = 2,474). During the same period, LP-IR score and parameters were collected using the Vantera Clinical Analyzer (400 MHz) with the LipoProfile LP4 method (Cohort B, n = 2,959). Linear regression of decile-matched data for each parameter yielded adjustment equations to align lipoFIT values with the LipoProfile method. LP-IR scores were then calculated for both adjusted and unadjusted values using a previously published LP-IR scoring algorithm. Finally, LP-IR scores were compared for sera with measures from both instruments was (Cohort C, n = 108). Results The median concentrations of sLDL-p, LDL-s, VLDL-s, HDL-S matched between Cohort A and B (<1% difference). Significant differences were noted for LVLDL-p and LHDL-p, with percent differences of 21.5% and 23.5%, respectively. Despite closely matching medians, the distributions varied greatly, with a mean interquartile range (IQR) percent difference across measures of 31.2% (Table 1). After distribution matching, all parameter median and mean IQR percent differences fell to <5% and <10%, respectively, between Cohort A and Cohort B. In Cohort C the LP-IR score matched well (y=1.1x-15, r=0.92; 95%CI 0.88, 0.94). Following distribution matching adjustment of lipoFIT values for Cohort C, new calculated LP-IR scores matched with reduced bias (y=0.94x-1.2) while correlation was slightly reduced (r=0.88 [0.83, 0.92]). By comparison, adjustments made via scaling lipoFIT values proportionally by the ratio of the medians between Cohort A and Cohort B yielded calculated LP-IR scores in Cohort C that maintained a similar bias prior to adjustments (y=1.2x-21), highlighting the ability of distribution matching to reduce bias. Conclusion Adjustments to measured lipoprotein parameters from a commercially available NMR spectrometer can be achieved via distribution matching. These adjustments greatly reduce the bias between a commercially available method and algorithm compared to the reference method, allowing for wider adoption of LP-IR score reporting.