Better biomarkers are urgently needed to improve diagnosis, guide molecularly targeted therapy and monitor activity and therapeutic response across a wide spectrum of disease. Proteomics methods ...based on mass spectrometry hold special promise for the discovery of novel biomarkers that might form the foundation for new clinical blood tests, but to date their contribution to the diagnostic armamentarium has been disappointing. This is due in part to the lack of a coherent pipeline connecting marker discovery with well-established methods for validation. Advances in methods and technology now enable construction of a comprehensive biomarker pipeline from six essential process components: candidate discovery, qualification, verification, research assay optimization, biomarker validation and commercialization. Better understanding of the overall process of biomarker discovery and validation and of the challenges and strategies inherent in each phase should improve experimental study design, in turn increasing the efficiency of biomarker development and facilitating the delivery and deployment of novel clinical tests.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a secreted protein that enhances degradation of the LDL receptor. While agents that inhibit PCSK9 markedly reduce atherogenic lipoproteins and ...show great promise for event reduction, it is unknown whether plasma PCSK9 levels predict incident cardiovascular events.
In a nested case-control evaluation conducted in a prospective cohort of >28 000 initially healthy American women, we measured plasma concentrations of PCSK9 at baseline among 358 participants who subsequently developed major cardiovascular events (cases) and among 358 age, smoking, and hormone replacement therapy matched participants who remained free of disease during 17 years of follow-up (controls). Proprotein convertase subtilisin/kexin type 9 level was not significantly related to smoking status, hypertension, obesity, or a family history of premature cardiovascular disease but was positively associated with apolipoprotein B-100 (r = 0.20, P< 0.001), and triglycerides (r = 0.13, P = 0.004). No associations were observed between PCSK9 and apo A1, HDLC, lipoprotein(a), or high-sensitivity C-reactive protein. Despite modest positive association with atherogenic lipids, baseline levels of PCSK9 did not predict the first cardiovascular events; the odds ratios (ORs) for future vascular events for the lowest (referent) to highest baseline quartiles of PCSK9 were 1.0, 0.94, 0.98, and 1.15 (P-trend = 0.53). In contrast, the corresponding ORs for baseline apo B levels were 1.0, 1.14, 1.34, and 1.94 (P-trend = 0.002).
In a large-scale primary prevention cohort, plasma levels of PCSK9 measured at baseline did not predict future cardiovascular events.
Incomplete reporting has been identified as a major source of avoidable waste in biomedical research. Essential information is often not provided in study reports, impeding the identification, ...critical appraisal, and replication of studies. To improve the quality of reporting of diagnostic accuracy studies, the Standards for Reporting of Diagnostic Accuracy Studies (STARD) statement was developed. Here we present STARD 2015, an updated list of 30 essential items that should be included in every report of a diagnostic accuracy study. This update incorporates recent evidence about sources of bias and variability in diagnostic accuracy and is intended to facilitate the use of STARD. As such, STARD 2015 may help to improve completeness and transparency in reporting of diagnostic accuracy studies.
The European Atherosclerosis Society-European Federation of Clinical Chemistry and Laboratory Medicine Consensus Panel aims to provide recommendations to optimize atherogenic lipoprotein ...quantification for cardiovascular risk management.
We critically examined LDL cholesterol, non-HDL cholesterol, apolipoprotein B (apoB), and LDL particle number assays based on key criteria for medical application of biomarkers. (
) Analytical performance: Discordant LDL cholesterol quantification occurs when LDL cholesterol is measured or calculated with different assays, especially in patients with hypertriglyceridemia >175 mg/dL (2 mmol/L) and low LDL cholesterol concentrations <70 mg/dL (1.8 mmol/L). Increased lipoprotein(a) should be excluded in patients not achieving LDL cholesterol goals with treatment. Non-HDL cholesterol includes the atherogenic risk component of remnant cholesterol and can be calculated in a standard nonfasting lipid panel without additional expense. ApoB more accurately reflects LDL particle number. (
) Clinical performance: LDL cholesterol, non-HDL cholesterol, and apoB are comparable predictors of cardiovascular events in prospective population studies and clinical trials; however, discordance analysis of the markers improves risk prediction by adding remnant cholesterol (included in non-HDL cholesterol) and LDL particle number (with apoB) risk components to LDL cholesterol testing. (
) Clinical and cost-effectiveness: There is no consistent evidence yet that non-HDL cholesterol-, apoB-, or LDL particle-targeted treatment reduces the number of cardiovascular events and healthcare-related costs than treatment targeted to LDL cholesterol.
Follow-up of pre- and on-treatment (measured or calculated) LDL cholesterol concentration in a patient should ideally be performed with the same documented test method. Non-HDL cholesterol (or apoB) should be the secondary treatment target in patients with mild to moderate hypertriglyceridemia, in whom LDL cholesterol measurement or calculation is less accurate and often less predictive of cardiovascular risk. Laboratories should report non-HDL cholesterol in all standard lipid panels.
IntroductionStandards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency of reporting in studies investigating diagnostic test accuracy. ...However, its current form, STARD 2015 does not address the issues and challenges raised by artificial intelligence (AI)-centred interventions. As such, we propose an AI-specific version of the STARD checklist (STARD-AI), which focuses on the reporting of AI diagnostic test accuracy studies. This paper describes the methods that will be used to develop STARD-AI.Methods and analysisThe development of the STARD-AI checklist can be distilled into six stages. (1) A project organisation phase has been undertaken, during which a Project Team and a Steering Committee were established; (2) An item generation process has been completed following a literature review, a patient and public involvement and engagement exercise and an online scoping survey of international experts; (3) A three-round modified Delphi consensus methodology is underway, which will culminate in a teleconference consensus meeting of experts; (4) Thereafter, the Project Team will draft the initial STARD-AI checklist and the accompanying documents; (5) A piloting phase among expert users will be undertaken to identify items which are either unclear or missing. This process, consisting of surveys and semistructured interviews, will contribute towards the explanation and elaboration document and (6) On finalisation of the manuscripts, the group’s efforts turn towards an organised dissemination and implementation strategy to maximise end-user adoption.Ethics and disseminationEthical approval has been granted by the Joint Research Compliance Office at Imperial College London (reference number: 19IC5679). A dissemination strategy will be aimed towards five groups of stakeholders: (1) academia, (2) policy, (3) guidelines and regulation, (4) industry and (5) public and non-specific stakeholders. We anticipate that dissemination will take place in Q3 of 2021.
National Cholesterol Education Program (NCEP) guidelines recommend development of direct assays for LDL cholesterol (LDL-C) measurement, but it is unclear how these assays compare with Friedewald ...calculation in predicting cardiovascular disease (CVD).
In a study of 27 331 healthy women with triglycerides <or=4.52 mmol/L (<or=400 mg/dL), baseline fasting Friedewald LDL-C was compared with fasting and nonfasting direct homogenous measurement for incident CVD during an 11-year period.
Fasting LDL-C measurements obtained by the 2 methods were highly correlated (r = 0.976, P < 0.001). Compared with fasting Friedewald LDL-C, mean fasting direct LDL-C was 0.15 mmol/L (5.6 mg/dL) lower and nonfasting direct LDL-C 0.30 mmol/L (11.5 mg/dL) lower, both P < 0.0001. The adjusted hazard ratio per 1-SD increment was 1.23 95% CI 1.15-1.32; 1-SD 0.88 mmol/L (34.1 mg/dL) for fasting direct LDL-C and 1.22 95% CI 1.14-1.30; 1-SD 0.90 mmol/L (34.9 mg/dL) for fasting Friedewald. Nonfasting LDL-C was not associated with CVD by either method. Fasting LDL-C measurements fell into the same NCEP risk category with either method for 79.3% of participants, whereas they differed by 1 NCEP category for 20.7% of participants, with most classified into a lower-risk category by direct LDL-C.
The association of LDL-C with CVD by the 2 methods was nearly identical in fasting samples. However, the lower direct LDL-C concentrations may misclassify many individuals into a lower NCEP category. Moreover, the lack of association of nonfasting direct LDL-C with CVD raises questions regarding the clinical utility of a direct assay for LDL-C in nonfasting blood samples.
Doxorubicin causes cardiac injury and cardiomyopathy in children with acute lymphoblastic leukemia (ALL). Measuring biomarkers during therapy might help individualize treatment by immediately ...identifying cardiac injury and cardiomyopathy.
Children with high-risk ALL were randomly assigned to receive doxorubicin alone (n = 100; 75 analyzed) or doxorubicin with dexrazoxane (n = 105; 81 analyzed). Echocardiograms and serial serum measurements of cardiac troponin T (cTnT; cardiac injury biomarker), N-terminal pro-brain natriuretic peptide (NT-proBNP; cardiomyopathy biomarker), and high-sensitivity C-reactive protein (hsCRP; inflammatory biomarker) were obtained before, during, and after treatment.
cTnT levels were increased in 12% of children in the doxorubicin group and in 13% of the doxorubicin-dexrazoxane group before treatment but in 47% and 13%, respectively, after treatment (P = .005). NT-proBNP levels were increased in 89% of children in the doxorubicin group and in 92% of children in the doxorubicin-dexrazoxane group before treatment but in only 48% and 20%, respectively, after treatment (P = .07). The percentage of children with increased hsCRP levels did not differ between groups at any time. In the first 90 days of treatment, detectable increases in cTnT were associated with abnormally reduced left ventricular (LV) mass and LV end-diastolic posterior wall thickness 4 years later (P < .01); increases in NT-proBNP were related to an abnormal LV thickness-to-dimension ratio, suggesting LV remodeling, 4 years later (P = .01). Increases in hsCRP were not associated with any echocardiographic variables.
cTnT and NT-proBNP may hold promise as biomarkers of cardiotoxicity in children with high-risk ALL. Definitive validation studies are required to fully establish their range of clinical utility.
In an analysis from the Framingham Heart Study, 10 biomarkers were evaluated for their ability to predict clinical outcomes. The addition of biomarkers to conventional risk factors resulted in ...significant increases in the hazard ratios for death and major cardiovascular events but only small increases in the C statistic for each end point. The incremental value of the assessment of biomarkers to the evaluation of established risk factors appears to be modest.
The addition of biomarkers to conventional risk factors resulted in significant increases in the hazard ratios for death and major cardiovascular events but only small increases in the C statistic for each end point.
Established cardiovascular risk factors, including dyslipidemia, smoking, hypertension, and diabetes mellitus, have been incorporated into algorithms for risk assessment in the general population,
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but these characteristics do not fully explain cardiovascular risk.
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There is substantial interest in the use of newer biomarkers to identify persons who are at risk for the development of cardiovascular disease and who could be targeted for preventive measures.
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Many individual biomarkers have been related to cardiovascular risk in ambulatory persons, including levels of C-reactive protein,
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B-type natriuretic peptide,
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fibrinogen,
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d-dimer,
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and homocysteine.
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Measurement of several biomarkers simultaneously (the “multimarker” approach) could enhance . . .
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