BACKGROUND Methods from 7 manufacturers and 1 distributor for directly measuring HDL cholesterol (C) and LDL-C were evaluated for imprecision, trueness, total error, and specificity in nonfrozen serum samples. METHODS We performed each direct method according to the manufacturer’s instructions, using a Roche/Hitachi 917 analyzer, and compared the results with those obtained with reference measurement procedures for HDL-C and LDL-C. Imprecision was estimated for 35 runs performed with frozen pooled serum specimens and triplicate measurements on each individual sample. Sera from 37 individuals without disease and 138 with disease (primarily dyslipidemic and cardiovascular) were measured by each method. Trueness and total error were evaluated from the difference between the direct methods and reference measurement procedures. Specificity was evaluated from the dispersion in differences observed. RESULTS Imprecision data based on 4 frozen serum pools showed total CVs <3.7% for HDL-C and <4.4% for LDL-C. Bias for the nondiseased group ranged from −5.4% to 4.8% for HDL-C and from −6.8% to 1.1% for LDL-C, and for the diseased group from −8.6% to 8.8% for HDL-C and from −11.8% to 4.1% for LDL-C. Total error for the nondiseased group ranged from −13.4% to 13.6% for HDL-C and from −13.3% to 13.5% for LDL-C, and for the diseased group from −19.8% to 36.3% for HDL-C and from −26.6% to 31.9% for LDL-C. CONCLUSIONS Six of 8 HDL-C and 5 of 8 LDL-C direct methods met the National Cholesterol Education Program total error goals for nondiseased individuals. All the methods failed to meet these goals for diseased individuals, however, because of lack of specificity toward abnormal lipoproteins.

Since 2009, the United States (U.S.) Food and Drug Administration (FDA) Center for Tobacco Products (CTP) has had the authority to regulate the manufacturing, distribution, and marketing of tobacco products in order to reduce the death and disease caused by tobacco use. Biomarkers of exposure pertain to actual human exposure to chemicals arising from tobacco use and could play an important role across a number of FDA regulatory activities, including assessing new and modified risk tobacco products and identifying and evaluating potential product standards. On August 3–4, 2015, FDA/CTP hosted a public workshop focused on biomarkers of exposure with participants from government, industry, academia, and other organizations. The workshop was divided into four sessions focused on: 1) approaches to evaluating and selecting biomarkers; 2) biomarkers of exposure and relationship to disease risk; 3) currently-used biomarkers of exposure and biomarkers in development; and 4) biomarkers of exposure and the assessment of smokeless tobacco and electronic nicotine delivery systems (ENDS). This paper synthesizes the main findings from the workshop and highlights research areas that could further strengthen the science around biomarkers of exposure and help determine their application in tobacco product regulation.

BACKGROUND Our objective was to evaluate the accuracy of cardiovascular disease (CVD) risk score classification by direct LDL cholesterol (dLDL-C), calculated LDL cholesterol (cLDL-C), and non–HDL cholesterol (non–HDL-C) compared to classification by reference measurement procedures (RMPs) performed at the CDC. METHODS We examined 175 individuals, including 138 with CVD or conditions that may affect LDL-C measurement. dLDL-C measurements were performed using Denka, Kyowa, Sekisui, Serotec, Sysmex, UMA, and Wako reagents. cLDL-C was calculated by the Friedewald equation, using each manufacturer’s direct HDL-C assay measurements, and total cholesterol and triglyceride measurements by Roche and Siemens (Advia) assays, respectively. RESULTS For participants with triglycerides <2.26 mmol/L (<200 mg/dL), the overall misclassification rate for the CVD risk score ranged from 5% to 17% for cLDL-C methods and 8% to 26% for dLDL-C methods when compared to the RMP. Only Wako dLDL-C had fewer misclassifications than its corresponding cLDL-C method (8% vs 17%; P <0.05). Non–HDL-C assays misclassified fewer patients than dLDL-C for 4 of 8 methods (P < 0.05). For participants with triglycerides ≥2.26 mmol/L (≥200 mg/dL) and <4.52 mmol/L (<400 mg/dL), dLDL-C methods, in general, performed better than cLDL-C methods, and non–HDL-C methods showed better correspondence to the RMP for CVD risk score than either dLDL-C or cLDL-C methods. CONCLUSIONS Except for hypertriglyceridemic individuals, 7 of 8 dLDL-C methods failed to show improved CVD risk score classification over the corresponding cLDL-C methods. Non–HDL-C showed overall the best concordance with the RMP for CVD risk score classification of both normal and hypertriglyceridemic individuals.

Tobacco-specific nitrosamines (TSNAs) are N-nitroso-derivatives of pyridine-alkaloids (e.g., nicotine) present in tobacco and cigarette smoke. Two TSNAs, N’-nitrosonornicotine (NNN) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), are included on the Food and Drug Administration’s list of harmful and potentially harmful constituents (HPHCs) in tobacco products and tobacco. The amounts of four TSNAs (NNK, NNN, N-nitrosoanabasine (NAB), and N’-nitrosoanatabine (NAT)) in the tobacco and mainstream smoke from 50 U.S. commercial cigarette brands were measured from November 15, 2011 to January 4, 2012 using a validated, HPLC-MS/MS method. Smoke samples were generated using the International Organization of Standardization (ISO) and Canadian Intense (CI) machine-smoking regimens. NNN and NAT were the most abundant TSNAs in tobacco filler and smoke across all cigarette brands whereas NNK and NAB were present in the least amounts. The average of the ratios for each TSNA in mainstream smoke to filler content is 29% by the CI smoking regimen and 13% for the ISO machine-smoking regimen. The reliability of each TSNA to predict total TSNA amounts in the filler and smoke was examined. NNN, NAT, and NAB have a moderate to high correlation (R2 = 0.61 – 0.98) and all three TSNAs individually predict total TSNAs with minimal difference between measured and predicted total TSNA amounts (error < 7.4%). NNK has weaker correlation (R2 = 0.56 – 0.82) and is a less reliable predictor of total TSNA quantities. Tobacco weight and levels of TSNAs in filler influence TSNA levels in smoke from the CI machine-smoking regimen. In contrast, filter ventilation is a major determinant of levels of TSNAs in smoke by the ISO machine-smoking regimen. Comparative analysis demonstrates substantial variability in TSNA amounts in tobacco filler and mainstream smoke yields under ISO and CI machine smoking regimens among U.S. commercial cigarette brands.

BACKGROUND External quality assessment (EQA) with commutable samples is essential for assessing the quality of assays performed by laboratories, particularly when the emphasis is on their standardization status and interchangeability of results. METHODS We used a panel of 20 fresh-frozen single-donation serum samples to assess assays for the measurement of creatinine, glucose, phosphate, uric acid, total cholesterol, HDL cholesterol, LDL cholesterol, and triglycerides. The commercial random access platforms included: Abbott Architect, Beckman Coulter AU, Ortho Vitros, Roche Cobas, Siemens Advia, and Thermo Scientific Konelab. The assessment was done at the peer group level and by comparison against the all-method trimmed mean or reference method values, where available. The considered quality indicators were intraassay imprecision, combined imprecision (including sample–matrix interference), bias, and total error. Fail/pass decisions were based on limits reflecting state-of-the-art performance, but also limits related to biological variation. RESULTS Most assays showed excellent peer performance attributes, except for HDL- and LDL cholesterol. Cases in which individual assays had biases exceeding the used limits were the Siemens Advia creatinine (−4.2%), Ortho Vitros phosphate (8.9%), Beckman Coulter AU triglycerides (5.4%), and Thermo Scientific Konelab uric acid (6.4%), which lead to considerable interassay discrepancies. Additionally, large laboratory effects were observed that caused interlaboratory differences of >30%. CONCLUSIONS The design of the EQA study was well suited for monitoring different quality attributes of assays performed in daily laboratory practice. There is a need for improvement, even for simple clinical chemistry analytes. In particular, the interchangeability of results remains jeopardized both by assay standardization issues and individual laboratory effects.