A new equation for small dense low-density lipoprotein cholesterol (sdLDL-C), based on the standard lipid panel, may help improve atherosclerotic cardiovascular disease (ASCVD) risk stratification (Clin Chem 2021; doi.org/10.1093/clinchem/hvab048).
Because research has shown an association between sdLDL-C and ASCVD, the National Cholesterol Education Program has recognized sdLDL-C as an emerging risk factor. But its utility as an independent ASCVD risk factor has not been fully established. Measuring sdLDL-C also has been labor-intensive, and until recently, estimating it required additional tests. However, researchers have used a Food and Drug Administration-approved, fully automatic direct method for measuring sdLDL-C in recent studies that show it is a stronger risk factor for ASCVD than LDL-C.
The researchers describe a new sdLDL-C equation based only on results of the standard lipid panel, which includes total cholesterol, high density LDL-C, and triglycerides (TG). Based on observed relationships between different lipid concentrations and cholesterol on large buoyancy (lb) LDL-C and sdLDL-C, the equation uses two terms. The first (1.44 x LDL-C) accounts for individuals with high levels of LDL-C with more lbLDL-C. Larger LDL particles contain more cholesterol, the researchers note. The second term (0.14 X 1n(TG) x LDL-C) is an interaction term between LDL-C and TG. It accounts for a greater fraction of cholesterol in sdLDL than lbLDL as TG increases.
The researchers used sdLDL-C and lbLDL-C as risk enhancer tests in the National Heart and Nutrition Examination Survey (NHANES) and examined their association with ASCVD in the Multi-Ethnic Study of Atherosclerosis (MESA). The lbLDL-C equation was more accurate, with an R2 of 0.933 and slope of 0.94, compared to the sdLDL-C equation with an R2 of 0.745 and slope of
0.73. Using the 80th percentile concentration, 46 mg/dL, as a cutpoint, sdLDL-C identified in NHANES pointed to additional high risk patients not identified by other risk-enhancer tests based on TG, LDL-C, apolipoprotein B, and non-HDL-C. Univariate survival-curve analysis showed that estimated sdLDL-C was better than other risk-enhancer tests in predicting ASCVD events in MESA participants.
After multivariate adjustment for other known ASCVD risk factors, estimated sdLDL-C had the strongest association with ASCVD compared to other lipid parameters, including measurements of actual sdLDL-C
New Assays Provide Better Specificity and Selectivity for Abeta1-42 and Abeta1-40
Researchers have developed a highly specific blood test that could be used in studies of Alzheimer’s disease therapies (Sci Rep 2021;11:9736). The assays measure full-length Abeta1-42 and Abeta1-40 peptides in amyloid plaques found in the brains of people with Alzheimer's disease. These peptides are used to monitor treatment response and are usually measured in cerebrospinal fluid or by positron emission tomography. Both tests are invasive and expensive.
Researchers aimed to validate the assays, called ready-to-use Amyblood tests, and explore their clinical value in a cohort of 43 patients and 42 controls who had also had cerebrospinal fluid (CSF) testing via the commercially available Quanterix Simoa trilex kit and Euroimmun ELISA assays. The researchers compared linearity and intra- and inter-assay percent coefficient of variation (%CV) among the three assays.
Amyblood, Quanterix triplex, and ELISA showed similar linearity (96%–122%) and intra-assay %CV of 3.1% or less. Using Amyblood, the researchers measured a minor nonspecific signal of +2.4 pg/mL Abeta1-42 when incubated with 60 pg/mL Abeta1-40. Researchers saw a substantial nonspecific signal of +24.7 pg/mL Abetax-42 when measuring 40 pg/mL Abeta3-42 with the Quanterix triplex. Selectivity for Abeta1-42 at physiological Abeta1-42 and Abeta1-40 concentrations was 125% for Amyblood and 163% for Quanterix. Amyblood and Quanterix ratios and ELISA Abeta1-42 concentration differentiated samples from Alzheimer’s disease patients and controls.
These validation data show that the Amyblood assays are suitable for measuring 1-42 and 1-40 amyloid isoforms in blood, the researchers wrote.
A next step in developing Amyblood assays is leveraging the multiplexing possibilities of Simoa technology to simultaneously detect multiple biomarkers and to reflect different aspects of Alzheimer’s disease within one assay run. These measures would save time and resources, the researchers said.
Technique May Improve Next-Generation Sequencing and Increase Its Use
A new technology helps overcome the inefficiency and high error rates that have limited the clinical application of next-generation sequencing (NGS) techniques, according to a recent paper (Nat Biotechnol 2021; doi.org/10.1038/s41587-021-00900-z).
Despite improved baseline error rates in NGS technology, identifying and quantifying low-frequency mutations remains challenging. Meanwhile, NGS is not very useful for detecting rare mutations, especially in liquid biopsies. In response, researchers developed what they call the Safer Sequencing System (SaferSeqS). SaferSeqS allows efficient tagging of both DNA strands in each original molecule present in an individual's blood sample with a unique barcode. The double-stranded DNA molecule’s structural redundancy allows the researchers to distinguish real mutations from errors, an approach called duplex sequencing. If both strands of a DNA molecule contain the same mutation, it is far more likely that the test has found a real mutation and not an error.
The researchers, who described SaferSeqS in a previously published study, built upon their technique by constructing a library via in situ generation of double-stranded molecular bar codes, and by adding target enrichment via anchored polymerase chain reaction (PCR) and in silico reconstruction of template molecules.
The researchers used this enhanced SafeSeqS assay to retest 74 blood samples from cancer patients with false negative results on CancerSEEK, a single multiplex PCR blood test that screens for eight common types of cancer. Using SaferSeqS, the researchers found previously undetectable mutations in 58% of the newly tested samples.
On the basis of both the current and previously published study, the researchers concluded that SaferSeqS reduces the error rate of existing mutation-detection approaches more than 100-fold. SaferSeqS is highly scalable, cost-effective, and amenable to high-throughput automation, the researchers added.