Miller, Joshua R.

Abstract Background We determined whether circulating cardiac troponin I–cardiac troponin T–troponin C (ITC) complex containing long-form cardiac troponin T (long-T ITC) complex and ratios of high-sensitivity cardiac troponin I (hs-cTnI) and high-sensitivity cardiac troponin T (hs-cTnT) could differentiate myocardial infarction (MI) from chronic and acute non-MI myocardial injuries in patients with suspected acute coronary syndrome. Methods We measured long-T ITC complex, hs-cTnI, and hs-cTnT (Mindray) at 0- and 2-h in consecutive patients enrolled in the Mindray hs-cTnI Assay Analytical and Clinical Evaluation for the Diagnosis and RIsk Assessment of Myocardial InfarctIon (MERITnI) trial. Results Among the 882 subjects studied (median 3.0 h presentation), 47 had acute MI (5.3%), 116 had acute non-MI myocardial injury, and 90 had chronic myocardial injury. At 0 and 2 h, areas under the curve (AUC) significantly differentiated acute MI from chronic myocardial injury for long-T ITC complex (0.72 and 0.81, respectively; P = 0.0082) but not acute non-MI myocardial injury (AUC, 0.65 and 0.69, respectively; P = 0.23). 0 and 2 h AUCs for long-T ITC complex/hs-cTnT and hs-cTnI/hs-cTnT ratios also significantly differentiated acute MI from chronic myocardial injury (AUC, 0.79 and 0.86, respectively) and acute non-MI myocardial injury (AUC, 0.68 and 0.71, respectively). There was no biomarker concentration or ratio value discrimination between type 1 and type 2 MIs. Conclusions Long-T ITC concentrations and long-T ITC complex/hs-cTnT and hs-cTnI/hs-cTnT ratios may clinically assist in differentiating acute MI from non-MI myocardial injuries when evaluating patients with suspected acute coronary syndrome. ClinicalTrials.gov Registration Number: NCT05853042.

Abstract Quantifying urine protein is critical for detecting and monitoring kidney disease. However, screens for proteinuria are unable to detect low levels of albuminuria. For this reason, our institution has long measured urinary total protein and osmolality on routine urinalysis specimens (RUS) and calculated a predicted 24-hour protein excretion (PER) based upon the protein to osmolality ratio (POR). Within the last decade, sodium-glucose cotransporter-2 inhibitors (SGLT2i) have been approved by the United States Food and Drug Administration (FDA) as a treatment for adults with type 2 diabetes mellitus, and these agents appear to be beneficial for many forms of proteinuric renal disease and confer renal protection. Therefore, we investigated whether the expected glucosuria and increase in urine osmolality while on SGLT2i would significantly alter the POR, and hence predicted PER, via a retrospective analysis of residual timed and randomly collected urine specimens. Between January 1st, 2014, until January 29th, 2025, 5820 patients with nearly contemporaneous (collected within one week of each other) timed collections and RUS were identified that included 5656 not on SGLT2i (Cohort A) and 164 patients prescribed SGLT2i (Cohort B). Total protein concentrations were assessed using a Roche Cobas c701 by using benzethonium chloride. The osmolality was determined via freezing point depression using a Precision Systems Inc Multi-OSMETTE, whereas glucose measurements were made using either an Arkray AX-4030 with AUTION Stick 9EB test strips or a Clinitek Status with Multistix 10 SG reagent strips. Predicted 24-hour protein results were then calculated from the POR using a published formula based upon prior clinical studies: 10(x·[log(protein/osmolality)]+y where x is 0.908 or 0.953 for males and females, respectively, and y is 2.8254 or 2.9739 for males and females, respectively. In Cohort A, there was good correlation between the predicted and measured 24-hour PER (Spearman correlation coefficient, ρ = 0.78), although the predicted PER underestimated the measured PER (Passing-Bablok regression, y = 1.54x-20.97 mg/24 hr). Importantly, a strong correlation (ρ = 0.93) and similar distribution (p=0.17) were found between the predicted and measured PER in Cohort B, with a much-reduced proportional bias (y = 1.06x+62.96 mg/24 hr). Furthermore, comparison of the difference between the predicted and measured PER between the two cohorts demonstrated the same prediction accuracy (p = 0.65), with a median difference (interquartile range) of 79 mg/24 hr (256) and 109 mg/24 hr (480) for Cohort A and B, respectively. Finally, a strong correlation (ρ = 0.85), similar distribution (p = 0.23), and little bias (y = 0.96x+28.14 mg/24 hr) existed between the predicted and measured PER for all patients with overt glucosuria (urine glucose ≥1000 mg/dL, n = 348). In summary, these data suggest that the established formula for predicted PER on RUS is equally valid for patients on or off SGLT2i, and that this result is a useful clinical estimating tool.

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.