Metabolic Profiling Early in Life Predicts Subclinical Atherosclerosis

Metabolic Profiling

June 30, 2011

June 30, 2011 (Gothenburg, Sweden) — A study designed to assess whether metabolic profiling provides any value beyond conventional lipid measurements suggests the metabolomics model can better stratify young patients at risk for the development of atherosclerosis.

"Atherosclerosis is characterized by a long, progressive, symptom-free period, and the extent of atherosclerosis can be measured in the subclinical state using ultrasound sonography, for instance, of the carotid artery," said lead investigator Dr Peter Würtz (University of Helsinki, Finland). "Carotid atherosclerosis as measured by ultrasound is a strong predictor of cardiovascular events. The aim of our study was to do comprehensive metabolic profiling to see whether we can determine who will develop increased carotid intima-media thickness [IMT] later in life."

In presenting the results of the study here at the European Atherosclerosis Society 2011 Congress, Würtz explained that this study was designed with primary prevention in mind and that researchers included 1570 healthy individuals aged 24 to 39 years old from the Cardiovascular Risk in Young Finns study. The group used established risk factors and circulating metabolites to predict who would develop high IMT (>90th percentile) or atherosclerotic plaque during six years of follow-up.

The metabolomics model

To heartwire , Würtz said that a metabolomics model is better able to "get into the disease processes in order to understand it on a molecular basis." With metabolic profiling, which costs approximately €20 (approximately $30 US) per sample, researchers measure the lipoprotein subclass profile, such as the different particle sizes of vLDL cholesterol, LDL cholesterol, and HDL cholesterol. Within these particles, the researchers quantify the extent of total cholesterol, free cholesterol, cholesterol esters, phospholipids, triglycerides, and the total lipid content. In addition, they also measure omega-3 and omega-6 fatty acids, as well as various metabolites abundant in serum, such as amino acids, proteins, and intermediates of metabolic processes.

For the conventional lipid measurements, including LDL cholesterol, HDL cholesterol, and triglycerides, the risk of developing high IMT/atherosclerotic plaque among those with abnormal lipid values was in the range observed in previous trials. For example, elevated LDL-cholesterol levels and higher HDL-cholesterol levels measured at baseline were associated with a 35% increased risk and a 21% lower risk, respectively, of developing subclinical atherosclerosis.

The researchers observed, however, a higher risk of developing atherosclerosis when using lipoprotein subclass particles. Logistic regression models adjusted for sex, age, body-mass index, blood pressure, and family history of disease showed that large, medium, and small LDL cholesterol was associated with a 43%, 50%, and 46% increased risk of developing IMT/atherosclerotic plaque. Small vLDL cholesterol was associated with a 39% increased risk of developing subclinical disease. Similarly, large HDL cholesterol was a better predictor of high IMT/plaque compared with conventional HDL cholesterol.

Two biomarkers, linoleic acid and docosahexaenoic acid (DHA), were also strongly correlated with subclinical atherosclerosis. High serum levels of linoleic acid were associated with an increased risk of high IMT/plaque, while DHA was protective against disease. The amino acids tyrosine and glutamine were both associated with an approximate 35% increased risk of developing atherosclerosis, a surprising finding according to Würtz, and one that he can't explain.

In a comparison of a prediction model that included the metabolic profiling against a reference model that used conventional risk factors, researchers observed a slight increase in the area under the curve and a high rate of reclassification. The net reclassification index, a measure that examines the net effect of adding a biomarker to the risk-prediction model, was a clinically significant 15.4%.

"Now, to really assess the clinical value of these results, we need to test the associations with cardiovascular end points," said Würtz. "The data we have right now are just on a subclinical basis."

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