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Proteins in Blood of Heart Disease Patients May Predict Adverse Events

Proteins in Blood of Heart Disease Patients May Predict Adverse Events

Proteins in Blood of Heart Disease Patients May Predict Adverse Events

Proteins in Blood of Heart Disease Patients May Predict Adverse Events

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Measuring the levels of a small number of proteins in the blood of patients with coronary heart disease may eventually allow doctors to more reliably assess the risk of future heart attacks, heart failure, strokes, and death in these patients, according to new research led by a UC San Francisco cardiologist. By identifying patients at either low or high risk of these events, physicians could more precisely tailor therapies, which can be costly or carry risks of their own, to each individual.

“The traditional approach would say that these patients are all pretty high risk compared to healthy people, and that they should probably all be treated the same way,” said Peter Ganz, MD, professor of medicine at UCSF and chief of cardiology at the Priscilla Chan and Mark Zuckerberg San Francisco General Hospital and Trauma Center, a UCSF partner hospital. “But some new or emerging therapies are quite expensive, and others have significant side effects. We wanted to figure out if proteins in blood could predict cardiovascular risk in this population, and if so, whether a small number of these proteins could be used to mathematically construct a prediction model.”

In the new research, published in the June 21, 2016 online issue of JAMA, the research team began by measuring levels of more than 1,000 different proteins in blood samples obtained as baseline measures in the UCSF-based “Heart and Soul” study, which has followed the cardiovascular health of study participants for as long as 11 years.

The proteins were selected in an unbiased, “agnostic” manner, Ganz said, as the researchers were interested in their potential predictive power, irrespective of any biological role they might play in heart disease. The abundance of the proteins was measured using tools developed by SomaLogic, a Colorado company creating new methods of protein analysis for basic research, diagnosis, and therapy. SomaLogic’s David G. Sterling, PhD, and Stephen A. Williams, MD, PhD, are co-authors of the new JAMA study.

Each of the more than 900 Heart and Soul participants whose blood was analyzed in the new work had been diagnosed with “stable coronary heart disease”—a history of heart attack, occluded coronary arteries, bypass surgery, or related signs of cardiovascular disease.

The baseline Heart and Soul blood samples were collected between 2000 and 2002, and the researchers employed a four-year “prediction horizon time,” meaning that they checked whether patients experienced adverse cardiovascular events in the four years following the donation of blood required for enrollment in the study.

Ganz said the team was surprised to discover that about 200 of the original 1,000 proteins targeted had at least some predictive value. But because measuring such a large number of proteins is clinically impractical, the team used quality control procedures, as well as statistical methods suggested by co-author Mark R. Segal, PhD, professor of biostatistics and director of UCSF’s Center for Bioinformatics and Molecular Biostatistics, to winnow that list down to just nine proteins.

The model derived from this pared-down list proved more accurate at predicting cardiovascular events than the risk factors that are commonly considered in clinical practice. “Traditional risk factors—blood pressure, cholesterol levels, smoking, diabetes—work pretty well in predicting outcomes in healthy people,” Ganz said, “but not in those who already have heart disease.”

As noted, all the individuals whose blood was studied had the same diagnosis—stable coronary heart disease—but a “prognostic index” the researchers derived from measurements of the nine proteins revealed as much as a tenfold difference in their risk of adverse cardiovascular events: about 70 percent of those in the group predicted to be at highest risk suffered heart attacks, strokes, or other events, compared with only six percent of the lowest-risk group.

To ensure these findings were valid, the team tested the nine-protein model using a separate set of nearly 1,000 blood samples obtained for a study called HUNT3, an initiative of the Norwegian University of Science and Technology (NTNU). Even though those samples were collected using quite different procedures than those used in the Heart and Soul study, the nine-protein prediction model held up, with a similarly dramatic difference between high- and low-risk individuals.

Though the nine-protein test cannot yet finely discriminate among all levels of risk between the highest and lowest levels, Ganz said that knowing which patients fall into these two extremes is clinically valuable in itself, especially as novel therapies for cardiovascular disease enter the marketplace from clinical trials.

Recently approved, highly effective cholesterol-lowering medications known as PCSK9 inhibitors, for example, cost about $10,000 per year, and they must be given by regular injections, making them most suitable for patients at very high risk of cardiovascular events.

Methotrexate, commonly used to tamp down inflammation in cancer and in rheumatoid arthritis, is now in clinical trials in low doses to treat heart disease. But because the drug exerts its effects by suppressing the immune system, it makes patients more susceptible to infections. A simple protein-based test could allow physicians to weigh this side effect against the benefit the treatment would bring to an individual patient.

Ganz says the new study is the latest product of an unusually fruitful eight-year collaboration with SomaLogic, an academic-industry partnership that began after a chance encounter with a relative of the company’s founders at the UCSF Department of Medicine’s Grand Rounds.

Joining Ganz, Segal, Sterling, and Williams in the research were Bettina Heidecker, MD, a former UCSF clinical fellow now at the University of Zurich; Kristian Hveem, MD, PhD, and Christian Jonasson, PhD, of NTNU; and Shintaro Kato of NEC Corporation of America.

SomaLogic provided funding for protein analysis, and also provided payment to Segal for statistical analysis, and to Hveem and Jonasson for HUNT3 blood samples and database information. The Heart and Soul and HUNT3 studies are each supported by several government and foundation grants in the US and Norway, respectively.

“Precision medicine is being able to tell an individual patient, ‘You are at very high risk, medium risk, or very low risk,’ and that patient may opt to be treated differently from other patients with the same diagnosis,” Ganz said. “If I were able to tell a patient with stable heart disease that he or she has a 70 percent chance that something bad will happen, versus a six percent chance, that could very well inform his or her choice of treatment, and that makes this a potentially useful approach.”