Dataset Information

Incorporating a Genetic Risk Score Into Coronary Heart Disease Risk Estimates: Effect on Low-Density Lipoprotein Cholesterol Levels (the MI-GENES Clinical Trial).

ABSTRACT: Whether knowledge of genetic risk for coronary heart disease (CHD) affects health-related outcomes is unknown. We investigated whether incorporating a genetic risk score (GRS) in CHD risk estimates lowers low-density lipoprotein cholesterol (LDL-C) levels.Participants (n=203, 45-65 years of age, at intermediate risk for CHD, and not on statins) were randomly assigned to receive their 10-year probability of CHD based either on a conventional risk score (CRS) or CRS + GRS ((+)GRS). Participants in the (+)GRS group were stratified as having high or average/low GRS. Risk was disclosed by a genetic counselor followed by shared decision making regarding statin therapy with a physician. We compared the primary end point of LDL-C levels at 6 months and assessed whether any differences were attributable to changes in dietary fat intake, physical activity levels, or statin use. Participants (mean age, 59.4±5 years; 48% men; mean 10-year CHD risk, 8.5±4.1%) were allocated to receive either CRS (n=100) or (+)GRS (n=103). At the end of the study period, the (+)GRS group had a lower LDL-C than the CRS group (96.5±32.7 versus 105.9±33.3 mg/dL; P=0.04). Participants with high GRS had lower LDL-C levels (92.3±32.9 mg/dL) than CRS participants (P=0.02) but not participants with low GRS (100.9±32.2 mg/dL; P=0.18). Statins were initiated more often in the (+)GRS group than in the CRS group (39% versus 22%, P<0.01). No significant differences in dietary fat intake and physical activity levels were noted.Disclosure of CHD risk estimates that incorporated genetic risk information led to lower LDL-C levels than disclosure of CHD risk based on conventional risk factors alone.URL: http://www.clinicaltrials.gov. Unique identifier: NCT01936675.

SUBMITTER: Kullo IJ

PROVIDER: S-EPMC4803581 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Incorporating a Genetic Risk Score Into Coronary Heart Disease Risk Estimates: Effect on Low-Density Lipoprotein Cholesterol Levels (the MI-GENES Clinical Trial).

Kullo Iftikhar J IJ Jouni Hayan H Austin Erin E EE Brown Sherry-Ann SA Kruisselbrink Teresa M TM Isseh Iyad N IN Haddad Raad A RA Marroush Tariq S TS Shameer Khader K Olson Janet E JE Broeckel Ulrich U Green Robert C RC Schaid Daniel J DJ Montori Victor M VM Bailey Kent R KR

Circulation 20160225 12

<h4>Background</h4>Whether knowledge of genetic risk for coronary heart disease (CHD) affects health-related outcomes is unknown. We investigated whether incorporating a genetic risk score (GRS) in CHD risk estimates lowers low-density lipoprotein cholesterol (LDL-C) levels.<h4>Methods and results</h4>Participants (n=203, 45-65 years of age, at intermediate risk for CHD, and not on statins) were randomly assigned to receive their 10-year probability of CHD based either on a conventional risk sco ...[more]

PMID: 26915630

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Project description:Objective- The cell-cholesterol efflux capacity of HDL (high-density lipoprotein) is inversely associated with coronary heart disease risk. ABCA1 (ATP-binding cassette transporter A1) plays a crucial role in cholesterol efflux from macrophages to preβ-1-HDL. We tested the hypothesis that coronary heart disease patients have functionally abnormal preβ-1-HDL. Approach and Results- HDL cell-cholesterol efflux capacity via the ABCA1 and the SR-BI (scavenger receptor class B type I) pathways, HDL antioxidative capacity, apo (apolipoprotein) A-I-containing HDL particles, and inflammatory- and oxidative-stress markers were measured in a case-control study of 100 coronary heart disease cases and 100 sex-matched controls. There were significant positive correlations between ABCA1-dependent cholesterol efflux and the levels of small lipid-poor preβ-1 particles ( R2=0.535) and between SR-BI-dependent cholesterol efflux and the levels of large lipid-rich (α-1+α-2) HDL particles ( R2=0.712). Cases had significantly higher (87%) preβ-1 concentrations than controls, but the functionality of their preβ-1 particles (preβ-1 concentration normalized ABCA1-dependent efflux capacity) was significantly lower (-31%). Cases had significantly lower (-12%) mean concentration of large HDL particles, but the functionality of their particles (α-1+α-2 concentration normalized SR-BI-dependent efflux capacity) was significantly higher (22%) compared with that of controls. HDL antioxidative capacity was significantly lower (-16%) in cases than in controls. There were no significant correlations between either preβ-1 functionality or large HDL particle functionality with HDL antioxidative capacity or the concentrations of inflammatory- and oxidative-stress markers. Conclusions- HDL cell-cholesterol efflux capacity is significantly influenced by both the concentration and the functionality of specific HDL particles participating in cell-cholesterol efflux. Coronary heart disease patients have higher than normal preβ-1 concentrations with decreased functionality and lower than normal large HDL particle concentrations with enhanced functionality.

Project description:Background: Coronary artery disease (CAD) is the leading cause of death worldwide, which has a long asymptomatic period of atherosclerosis. Thus, it is crucial to develop efficient strategies or biomarkers to assess the risk of CAD in asymptomatic individuals. Methods: A total of 356 consecutive CAD patients and 164 non-CAD controls diagnosed using coronary angiography were recruited. Blood lipids, other baseline characteristics, and clinical information were investigated in this study. In addition, low-density lipoprotein cholesterol (LDL-C) subfractions were classified and quantified using the Lipoprint system. Based on these data, we performed comprehensive analyses to investigate the risk factors for CAD development and to predict CAD risk. Results: Triglyceride, LDLC-3, LDLC-4, LDLC-5, LDLC-6, and total small and dense LDL-C were significantly higher in the CAD patients than those in the controls, whereas LDLC-1 and high-density lipoprotein cholesterol (HDL-C) had significantly lower levels in the CAD patients. Logistic regression analysis identified male [odds ratio (OR) = 2.875, P < 0.001], older age (OR = 1.018, P = 0.025), BMI (OR = 1.157, P < 0.001), smoking (OR = 4.554, P < 0.001), drinking (OR = 2.128, P < 0.016), hypertension (OR = 4.453, P < 0.001), and diabetes mellitus (OR = 8.776, P < 0.001) as clinical risk factors for CAD development. Among blood lipids, LDLC-3 (OR = 1.565, P < 0.001), LDLC-4 (OR = 3.566, P < 0.001), and LDLC-5 (OR = 6.866, P < 0.001) were identified as risk factors. To predict CAD risk, six machine learning models were constructed. The XGboost model showed the highest AUC score (0.945121), which could distinguish CAD patients from the controls with a high accuracy. LDLC-4 played the most important role in model construction. Conclusions: The established models showed good performance for CAD risk prediction, which can help screen high-risk CAD patients in asymptomatic population, so that further examination and prevention treatment might be taken before any sudden or serious event.

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Dataset Information

Incorporating a Genetic Risk Score Into Coronary Heart Disease Risk Estimates: Effect on Low-Density Lipoprotein Cholesterol Levels (the MI-GENES Clinical Trial).

Publications

Incorporating a Genetic Risk Score Into Coronary Heart Disease Risk Estimates: Effect on Low-Density Lipoprotein Cholesterol Levels (the MI-GENES Clinical Trial).

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