Tests Emerging to Advance Cardiovascular Risk Assessment
From - Diagnostic Testing & Emerging Technologies While testing for cardiovascular conditions does not garner the headlines that oncology testing does, several recent studies demonstrate how the introduction of genetic testing and… . . . read more
While testing for cardiovascular conditions does not garner the headlines that oncology testing does, several recent studies demonstrate how the introduction of genetic testing and other new testing methods can improve cardiovascular risk assessment and diagnoses. DTET highlights some recent testing developments in the field of cardiovascular medicine.
Common Variants Contribute to Early-Onset Coronary Artery Disease
Diagnostic workup of early-onset coronary artery disease (EOCAD) should include determination of a polygenic risk score resulting from the cumulative risk posed by a high number of common genetic risk variants, according to a study published Jan. 8 in Circulation: Genomic and Precision Medicine. The authors say that the combined effect of these common variants on coronary artery disease risk may be more prevalent than high heritability, monogenic disorders like familial hypercholesterolemia.
The study calculated a genetic risk score for 111,418 British participants from the UK Biobank cohort. The genetic risk score was based on the presence of 182 independent variants associated with coronary artery disease (GRS182). Genotyping used the Affymetrix UK Biobank Lung Exome Variant Evaluation Axiom array or the Affymetrix UK Biobank Axiom Array. Participants with documented obstructive coronary artery disease were identified through codes for coronary artery bypass grafting or coronary angioplasty with or without stenting.
The researchers found that 96 individuals from the large cohort had EOCAD (77 men and 19 women). Participants with a diagnosis of EOCAD had a significantly higher GRS182 compared to those without EOCAD. An increase of one standard deviation in GRS182 corresponded to an 84 percent increased risk or EOCAD. The prevalence of a polygenic contribution that increased EOCAD risk similar to those with familial hypercholesterolemia was estimated at 1 in 53. Individuals with documented obstructive CAD, regardless of age of onset, also had a higher GRS182 versus controls.
“The increase in genetic risk was independent of other known risk factors, suggesting that testing for multiple genetic differences is clinically useful to evaluate risk and guide management,” said senior author Guillaume Paré, M.D., from McMaster University in Canada, in a statement. “Combining polygenic screening with current testing for familial hypercholesterolemia could potentially increase five-fold the number of cases for which a genetic explanation can be found.”
Adding Genetic Test Helps Pinpoint Cause of Stroke
The vast majority of spontaneous intracerebral hemorrhages have no underlying macrovascular cause. However, certain types of stroke, namely lobar spontaneous intracerebral hemorrhages with cerebral amyloid angiopathy (CAA), are associated with a higher risk of recurrent stroke than those associated with arteriolosclerosis.
“Identifying the cause of a brain hemorrhage is important to planning patient care.”
—Mark A. Rodrigues, M.B.Ch.B.
New research suggests that adding a simple genetic test to a CT scan evaluation improves prediction of CAA-associated lobar intracerebral hemorrhage, which may ultimately impact treatment decisions.
The study included consecutive adult patients with first-ever intracerebral hemorrhage confirmed by CT. Two neuroradiologists independently evaluated reformatted head CT images. APOE genotype analysis was also conducted. CT and genetic features were used to inform development of a model for identifying lobar intracerebral hemorrhage associated with CAA.
The researchers found that participants with lobar intracerebral hemorrhage and moderate or severe CAA were significantly more likely to be APOE ɛ4 carriers. Additionally, these participants were significantly more likely to have specific CT characteristics, including a strictly lobar intracerebral haemorrhage, subarachnoid haemorrhage, and finger-like projections from the intracerebral hemorrhage than participants with lobar intracerebral hemorrhage and absent or mild CAA.
“Identifying the cause of a brain hemorrhage is important to planning patient care,” says Mark A. Rodrigues, M.B.Ch.B., the lead author of the study. “Our findings suggest that the combination of routine CT scanning with APOE gene testing can identify those whose ICH has been caused by CAA, a group who may be more at risk of another ICH or dementia.”
Novel, Adaptable Cholesterol Estimation Best in Nonfasting Samples
Novel adaptable low-density lipoprotein cholesterol (LDL-CN) estimation is more accurate in nonfasting patient samples than the classic Friedewald method (LDL-CF), according to a study published Jan. 2 in Circulation. This advantage is particularly noticeable in cases of low LDL-C and high triglycerides.
“In making evidence-based decisions about lipid-lowering therapy, clinicians and patients can place greater confidence in LDL-C results from nonfasting samples that are calculated with the novel method of LDL-C estimation compared with the classic Friedewald equation,” write the authors led by Vasanth Sathiyakumar, M.D., from Johns Hopkins University in Baltimore, Md.
The study evaluated samples from 1,545,634 patients (959,153 fasting for 10– 12 hours and 586,481 nonfasting) participating in the Very Large Database of Lipids study. Rapid ultracentrifugation was used to directly measure LDL-C content (LDL-CD). Accuracy was defined as the percentage of LDL-CD falling within an estimated LDL-C (LDL-CN or LDL-CF) category. The magnitude of differences between LDL-CD and estimated LDL-C (both methods) were stratified by LDL-C and triglyceride categories.
The researchers found that in both fasting and nonfasting samples, accuracy was significantly higher with the novel method across all clinical LDL-C categories, compared with the Friedewald estimation. For samples with LDL-C less than 70 mg/dL, nonfasting LDL-CN accuracy was significantly superior to LDL-CF accuracy (92 versus 71 percent). In this lower LDL-C range, 19 percent of fasting and 30 perecent of nonfasting patients had differences between LDL-CF and LDL-CD that exceeded mg/dL. In comparison, using the novel estimation, only 2 percent and 3 percent of patients, respectively, had similar differences.
Takeaway: Several recent studies demonstrate how the introduction of genetic testing and other new testing methods can improve cardiovascular risk assessment.
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