Jaffe, Allan S.

Background: With demographic trends transitioning towards older adults, dementia and cognitive impairment are predicted to increase dramatically. We aim to elucidate the relationship between subclinical myocardial injury, as indicated by asymptomatic increased levels of high-sensitivity cardiac Troponin (hs-cTnT), and cognitive performance. Methods: We studied MESA participants from Exam 1 (2000-02) to Exam 6 (2016-18), categorizing them based on baseline hs-cTnT at Exam 1. Cognitive decline was defined as a decrease of ≥5 units in CASI between Exam 5&6. We used Pearson correlation and linear regression to analyze the association of hs-cTnT levels with CASI scores, and logistic regression to examine the association with cognitive decline. We also explored the relationship between different hs-cTnT categories and cognitive measures. Models were adjusted for demographics, lifestyle, APOE status, comorbidities, and medication use ( Figure ). Results: 4445 participants had both baseline hs-cTnT and valid Exam 5 CASI scores while only 1776 participants had valid Exam 6 CASI scores. Cohort was predominantly female (53%), mean age of 60 years, and 27% had at least one APOE e4 allele. Median hs-cTnT was 6.32 ng/L, and median CASI scores were 89 at Exam 5 and 91.5 at Exam 6. We found a negative correlation between the log-hs-cTnT at Exam 1 and Exam 5 with CASI scores at Exam 5. An increase of one unit in log-hs-cTnT level was associated with a decrease of 0.67 (CI -1.17, -0.17) in CASI and corresponded to higher odds (OR: 1.44; CI 1.05-1.95) of cognitive decline, after adjusting for covariates. When comparing various categories of hs-cTnT with category 1, we observe that the odd of cognitive decline increases as the category increases. However, this trend does not hold for category 5 with significantly lower sample size ( Figure ). Conclusion: Our findings indicate an inverse relationship between hs-cTnT and cognitive function years later, which is significantly attenuated by known risk factors for cognitive decline. Further research is needed to determine hs-cTnT as a predictor of future global cognitive functions.

Abstract Background/Introduction The use of a single low high-sensitivity cardiac troponin measurement to rule out myocardial infarction (MI) at presentation is advocated by international guidelines. Studies have not systematically compared high-sensitivity cardiac troponin (hs-cTn) I and T assays to determine which values should be used to rule out MI in patients with suspected acute coronary syndrome. Methods In a pooled analysis from three prospective observational cohort studies the lowest cTn concentration was determined that gave a negative predictive value (NPV) of ≥99.5% for MI or death at 30 days on presentation to the emergency department. EDTA or lithium heparin plasma or serum samples collected at presentation were measured using five hs-cTnI assays (Abbott ARCHITECT i2000, Beckman Coulter Access 2, Siemens Atellica IM, Ortho-Clinical Diagnostics VITROS 5600, LSI Medience PATHFAST) and one hs-cTnT assay (Roche Cobas e411). The diagnostic outcome of MI or death at 30 days was adjudicated according to the Fourth Universal Definition of MI. Optimal thresholds were determined to identify the highest proportion of patients that could be considered for discharge for a NPV of ≥99.5% for each assay. Results Among 941 patients, MI or death at 30 days occurred in 113 (12%). Optimized thresholds varied between assays ranging from <2 to <7 ng/L (Table) that identified between 22% and 60% of patients as low risk (0-1%) for MI or death at 30 days. Conclusions Our data identify differences in the effectiveness of thresholds between hs-cTnI and hs-cTnT assays for the same safety performance to rule out MI on presentation using a single measurement. In some cases, these thresholds differ from those used in clinical practice. Due to lack of assay standardization, optimal decision thresholds should be defined and validated for each high sensitivity cTn assay.

Abstract Background Diagnosing myocardial infarction (MI) in patients with chronic kidney disease (CKD) is difficult as these patients often have elevated baseline high-sensitivity cardiac troponin T (hs-cTnT) concentrations. Methods Observational U.S. cohort study of emergency department (ED) patients undergoing hs-cTnT measurement. Cases with >1 hs-cTnT increase >99th percentile were adjudicated following the Fourth Universal Definition of MI. Diagnostic performance of baseline and serial 2- and 6-hour hs-cTnT thresholds for ruling-in acute MI was compared between those without and with CKD (eGFR <60 ml/min/1.73m2). Results The study cohort included 1992 patients, amongst whom 501 (25%) had CKD. There were 701 (82%) patients with myocardial injury, 64 (7.5%) with type 1, and 91 (11%) with type 2 MI. In CKD patients with baseline hs-cTnT thresholds of >52, >100, >200 or >300 ng/L, PPVs for acute MI were 37% [95% CI 28-46], 53% (95% CI 39-67), 73% (95% CI 50-89) and 80% (95% CI 44-98), and in those without CKD, 64% (95% CI 50-77), 69% (95% CI 49-85), 59% (95% CI 33-82) and 54% (95% CI 25-81). In CKD patients with a 2-hour hs-cTnT delta of >10, >20 or >30 ng/L, the corresponding PPVs were 65% (95% CI 50-78), 86% (95% CI 68-96) and 88% (95% CI 68-97), and in those without CKD, 65% (95% CI 51-78), 73% (95% CI 57-86) and 75% (95% CI 58-88) (Figure). Conclusion Diagnostic performance of standard baseline and serial 2-hour hs-cTnT thresholds to rule-in MI is suboptimal in patients with CKD. It significantly improves when using higher baseline thresholds and delta values.

Abstract Background Patients at high risk for Atherosclerotic Cardiovascular Disease (ASCVD), who do not achieve adequate lowering of LDL-cholesterol (LDL-C) with maximally tolerated statins or who are statin-intolerant, are often treated with proprotein convertase subtilisin/kexin (type 9) serine protease inhibitor (PCSK9-inh) therapy. The LDL-C reference method, Beta-Quantitation (BQ), is not routinely performed in these patients. Because low values are measured less accurately with current methods, a variety of equations have been developed to calculate LDL-C from the standard lipid panel test results (total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C)). We further modified the Sampson LDL-C (S-LDL-C) equation by a novel approach by combining logistic regression (LR) and linear regression to minimize errors when compared to BQ for low LDL-C. Methods A dataset of BQ results from Mayo Medical Labs was divided randomly into a training set (TG ≤ 1500 mg/dL, TC ≤ 1050 mg/dL, n=19,639) and a validation set (TG ≤ 800 mg/dL, BQ-LDL-C ≤ 130 mg/dL, n=12,910). The training dataset was analyzed by LR, using BQ-LDL-C to classify patients as being above or below the 70 mg/dL cut-point. Dependent variables in the LR model were the S-LDL-C, HDL-C, TC, TG and an interaction term (TG x NonHDL-C). The sum of the exponential terms in the trained LR equation for the dependent variables were then fitted by least squares linear regression to match BQ-LDL-C to develop the modified S-LDL-C equation (mS-LDL-C). We used the validation dataset to compare Friedewald (F-LDL-C), extended Martin (eM-LDL-C), Sampson (S-LDL-C) and modified Sampson (mS-LDL-C) equations by Cohen’s kappa coefficient for agreement with BQ-LDL-C and normalized Matthew’s correlation coefficient (MCC), which considers any imbalances in the number of false high versus false low results. Results The mS-LDL-C equation (mS-LDL-C = (TC/0.9423 - HDL-C/0.9582 - TG/9.397 - ((TGxNonHDL-C)/1305) + TG2/8569) - 5.688), primarily differs from the S-LDL-C equation in the value of the coefficients that include TG as a variable. For S-LDL-C ≤100 mg/dL, mS-LDL-C had the best concordance with BQ-LDL-C in terms of the total percent of patients misclassified as either high or low (mS-LDL-C: 21.4%, S-LDL-C: 24.4%, eM-LDL-C: 25.4% F-LDL-C: 30.8%). mS-LDL-C also had the best kappa score when compared to BQ (mS-LDL-C: 0.833, S-LDL-C: 0.810, eM-LDL-C: 0.789, F-LDL-C: 0.743). MCC was also the highest for mS-LDL-C (mS-LDL-C: 0.916, S-LDL-C: 0.906, eM-LDL-C: 0.894, F-LDL-C: 0.879). Finally, mS-LDL-C also showed the best concordance with BQ-LDL-C when an alternative lower LDL-C therapeutic decision cut-point of 55 mg/dL is used (kappa of 0.766 versus 0.724) but showed an increasingly poorer performance than S-LDL-C for higher LDL-C whenever it exceeded 150 mg/dL. Conclusions A strategy to maximize the overall accuracy of estimated LDL-C could provide an automatic reflex in a laboratory information system to the mS-LDL-C equation whenever S-LDL-C is <100 mg/dL. By doing so, the use of the more accurate mS-LDL-C equation for low LDL-C should improve PCSK9-inh therapeutic decision making over the other LDL-C equations.

Abstract Background Low-density lipoprotein-cholesterol (LDL-C) is the main univariate risk marker recommended for initial atherosclerotic cardiovascular disease (ASCVD) risk stratification. LDL, however, transports other constituents that can serve as pro-atherogenic risk markers. Like LDL-C, the triglyceride content of LDL (LDL-TG) can also be estimated from the lipid panel. Using data from UK Biobank (UKB), we compared LDL-C to LDL-TG for predicting future ASCVD events. Methods A UKB dataset (N=270,436) of primary prevention individuals not on lipid lowering therapy was used for analysis. LDL-C was calculated by the following equations: Friedewald, Extended Martin-Hopkins, Sampson-NIH, and Enhanced Sampson-NIH. LDL-TG was calculated by our newly devised equation (eLDL-TG = TG/38.46 + nonHDL-C/5.75 + (TG/nonHDL-C)/0.103 + (1/HDL-C)/0.0041 - 2.95)), which was derived from a dyslipidemic cohort (N=40,202) analyzed by the beta-quantification reference method. Results Although there was only a modest comparison between eLDL-TG and measured LDL-TG (R2=0.601, slope=0.611), eLDL-TG appeared to be a better risk marker than LDL-C in UKB. When the population was divided into quintiles and analyzed by survival curve analysis, eLDL-TG was better than LDL-C in segregating patients into low- and high-risk groups (Figure). Similar findings on the relative value of LDL-TG over other risk markers were also found when analyzed by ROC analysis (AUC: eLDL-TG>HDL-C>remnant-cholesterol>NonHDL-C>TG>apoB>Sampson-NIH-LDL-C). By Cox-proportional hazard analysis eLDL-TG showed alongside apoB and NonHDL-C the strongest association with future ASCVD events. Discordance analysis showed that when stratified as LDL-TG/LDL-C as High/Low there was an 26% increased risk for ASCVD, and when as High/High showed an 40% increased risk for ASCVD. Conclusions Like LDL-C, LDL-TG levels can also be estimated from the standard lipid panel. If our findings hold in other populations, the superiority of LDL-TG over LDL-C as a risk marker suggest that the reporting of calculated LDL-TG along with LDL-C could be a new strategy for improving ASCVD risk stratification.

Abstract Background Patients with elevated free glycerol can have falsely elevated triglyceride concentrations known as pseudohypertriglyceridemia. Most clinical laboratory triglycerides methods measure the concentration of glycerol released following the enzymatic hydrolysis of triglycerides to quantitate triglycerides. Thus, increased free glycerol can lead to falsely increased triglyceride concentration. Identifying and measuring the interference is necessary to effectively manage patient care interventions. A glycerol-corrected triglyceride assay was discontinued from Roche Diagnostics in 2021. We created a process to determine glycerol-corrected triglycerides by measuring glycerol directly using an assay from Randox Laboratories. Methods Glycerol (Randox Laboratories) and nonglycerol-blanked triglycerides (Roche Diagnostics) were measured in serum using the Roche Cobas c501. Stability (n=10) and expected values (n=206) were determined using serum from healthy volunteers. Linearity (n=5) and precision (5 replicates on 2 runs over 5 days) were determined using residual clinical serum spiked with exogenous glycerol as necessary to span the analytical measuring range. Glycerol corrected triglyceride concentrations were calculated by subtracting glycerol (mmol/L) from triglyceride (mmol/L) and converting to mg/dL (x88.59). Samples previously measured using the Triglyceride/Glycerol Blanked Reagent (n=53, Roche Diagnostics) were used to assess accuracy. (Figure 1) Results Glycerol measures were stable 3 days refrigerated and 30 days frozen. Precision coefficient of variation was 1.0% within run and 7.1% within lab. Expected values were 10 - 109 mcmol/L, and the method was linear between 2 - 3,971 mcmol/L. The average bias between methods was 11.0% with slope of 1.02 and R2>0.97 (Figure 1). Conclusions Glycerol can be accurately measured using the Randox method and the result can be used to generate an accurate glycerol-corrected triglyceride concentration.

Abstract Background Beta-quantification (BQ) is the reference method for LDL-C but is not widely available. It is difficult, therefore, for clinical laboratories to independently assess the accuracy of their methods for either calculating or directly measuring LDL-C. Our goal was to investigate if an interrelationship between the tests in the standard lipid panel could be used to compare the performance of different LDL-C methods against BQ, specifically in samples with hypertriglyceridemia, a major cause of bias for LDL-C testing. Methods BQ results from Mayo Medical Labs (n=39,667) and the NIH (n=18,338) were used to identify a linear relationship by least-squares regression analysis between lipid panel test results and LDL-C to facilitate a statistical comparison between different LDL-C methods. Results It is well established that the number of VLDL particles increases as TG increases, because VLDL is the main carrier of TG. As a consequence, the ratio of LDL-C to non-HDL-C is inversely related to TG, which can be made linear by taking the square root of TG (Figure). Nearly identical regression equations were found for this relationship, using two different BQ datasets. When LDL-C was calculated by either Sampson equation, the LDL-C/non-HDL-C regression lines nearly overlapped with the one for BQ. In contrast, the Martin equation showed a positive bias with increasing TG, whereas the Friedewald equation showed a negative bias, consistent with past reports on their LDL-C biases with hypertriglyceridemia. In the UKBiobank, the Beckman direct LDL-C showed a positive bias for TG, which was confirmed in CAP proficiency test surveys. By simulation analysis, approximately 80 samples with high (>200 mg/dL) and low (<100 mg/dL) TG is statistically sufficient to assess if the LDL-C/non-HDL-C regressions lines of LDL-C methods differ from BQ. Conclusions The LDL-C/non-HDL-C ratio method is a simple way to examine LDL-C methods for bias on hypertriglyceridemic samples.

Abstract Background Calculated LDL cholesterol (LDL-C) is a standard component of the lipid panel and an important risk factor in managing atherosclerotic cardiovascular disease (ASCVD). Due to the assumptions in the derivation of the Friedwald formula, fasting (≥8 hours) and triglyceride levels ≤400mg/dL have long been recommended. More recent data have shown that in most cases fasting has minimal effect on results, leading to the endorsement of non-fasting lipid panels for routine evaluation of ASCVD risk in clinical practice guidelines. Furthermore, newer equations (e.g. Sampson-NIH) allow for more accurate estimation of LDL-C even in the presence of hypertriglyceridemia. Mayo Clinic has implemented the Sampson-NIH LDL-C calculation and removed fasting/non-fasting designation from lipid panel orders. We hypothesized that the change to fasting designation would allow more afternoon draws for routine lipid panels and that increased non-fasting collections may increase triglyceride levels, but that the Sampson-NIH equation would prevent bias in LDL-C estimation. In order to test these hypotheses, we analyzed patient data from 1 year prior and 1 year post-implementation. Methods Results of lipid panels performed for one year preceding (n=334,719) and one year after (n=381,804) removal of the fasting/non-fasting designation and implementation of Sampson-NIH equation were collected. Median, 10th, and 90th percentile of draw time, triglyceride, and LDL-C calculated by either Sampson-NIH or Friedwald formulas were compared by quantile regression. Results Draw times shifted post-implementation at the 10th percentile (7:06 vs. 7:10, p<0.0001), median (8:49 vs. 9:08, p<0.0001) and 90th (12:02 vs. 13:46, p<0.0001). No difference in median triglyceride measurement was observed between pre-and post-implementation (110mg/dL), but slight differences were observed in the 10th and 90th percentile values (60 vs 59mg/dL and 224 vs 229mg/dL, p<0.0001). No difference was observed in 10th, 50th, or 90th percentile LDL values when calculated by Sampson-NIH equation (56, 99, 153mg/dL); however, if the Friedwald formula is used, a slight shift is observed in the 10th and 50th percentile LDL values (54 vs. 53, 97 vs. 96; p<0.0001) but not the 90th (150mg/dL). Conclusions Changing to the Sampson/NIH formula for LDL-C and removing fasting vs. non-fasting designation from routine lipid panels had minimal effect on reported triglyceride and LDL-C values, while allowing increased flexibility in draw time and reporting of LDL-C on patients with triglycerides ≥400mg/dL. Patients with TG ≥400mg/dL represented 1.7% of the dataset overall, including 5,387 patients before the change in which LDL-C could not be reported and 6,697 patients in the year post-implementation for whom LDL-C was reported based on the Sampson-NIH formula. Using an equation that is robust in patients with elevated triglyceride levels and removing fasting requirements from routine lipid panels allows greater flexibility and convenience for patients while allowing for more even distribution of phlebotomy workload and reporting of LDL-C results for patients with hypertriglyceridemia without evidence of clinically significant bias in reported results.

BACKGROUND: Cardiac troponin (cTn) is key in diagnosing myocardial infarction (MI). After MI, the clinically observed half-life of cTn has been reported to be 7 to 20 hours, but this estimate reflects the combined elimination and simultaneous release of cTn from cardiomyocytes. More precise timing of myocardial injuries necessitates separation of these 2 components. We used a novel method for determination of isolated cTn elimination kinetics in humans. METHODS: Patients with MI were included within 24 hours after revascularization and underwent plasmapheresis to obtain plasma with a high cTn concentration. After at least 3 weeks, patients returned for an autologous plasma retransfusion followed by blood sampling for 8 hours. cTn was measured with 5 different high-sensitivity cTn assays. RESULTS: Of 25 included patients, 20 participants (mean age, 64.5 years; SD, 8.2 years; 4 women [20%]) received a retransfusion after a median of 5.8 weeks (interquartile range, 5.0–6.9 weeks) after MI. After retransfusion of a median of 620 mL (range, 180–679 mL) autologous plasma, the concentration of cTn in participants’ blood increased 4 to 445 times above the upper reference level of the 5 high-sensitivity cTn assays. The median elimination half-life ranged from 134.1 minutes (95% CI, 117.8–168.0) for the Elecsys high-sensitivity cTnT assay to 239.7 minutes (95% CI, 153.7–295.1) for the Vitros high-sensitivity cTnI assay. The median clearance of cTnI ranged from 40.3 mL/min (95% CI, 32.0–44.9) to 52.7 mL/min (95% CI, 42.2–57.8). The clearance of cTnT was 77.0 mL/min (95% CI, 45.2–95.0). CONCLUSIONS: This novel method showed that the elimination half-life of cTnI and cTnT was 5 to 16 hours shorter than previously reported. This indicates a considerably longer duration of cardiomyocyte cTn release after MI than previously thought. Improved knowledge of timing of myocardial injury may call for changes in the management of MI and other disorders with myocardial injury.