Constance Chu, M.D. on Blood Tests for “Pre-Osteoarthritis” to Help Prevent OA

by Elizabeth Hofheinz, M.P.H., M.Ed., October 4, 2019

Constance Chu, M.D., professor and vice chair of Research in the Department of Orthopedic Surgery at Stanford University doesn’t “horse around” when it comes to joint preservation and osteoarthritis (OA) prevention. Dr. Chu, a Kappa Delta Award winner for her work on visualizing “pre-osteoarthritis” (‘pre-OA’) using quantitative MRI, notes that her team is now advancing the field of OA prevention with development of blood testing protocols to help provide early warning of joint ‘aging’ and increasing OA risk.

Early detection system…

Dr. Chu: “We have shown that quantitative MRI mapping can reveal cartilage changes long before the onset of clinical OA. My team and I are also working to clarify and enhance the use of serum biomarkers to assess OA disease state and risks of progression. We are on our way towards having an early detection system to identify ‘pre-OA’, which I have defined as signs of heightened OA risk occurring before the onset of irreversible clinical disease.”

Describing one of her many epiphanies, Dr. Chu states, “Years ago it hit me that while we often speak of precursors to many diseases (i.e, pre-diabetes), you didn’t hear anyone talking about ‘pre-OA.’ I became curious about uncovering and describing such a condition.”

Noting the complexity of a disease involving mechanical, biological, and structural components, Dr. Chu says, there isn’t a good blood test for OA risk. “We have blood sugar tests for diabetes and cholesterol tests for heart disease. For OA, whether biomarkers go down or up varies by disease state. Making things more complicated is that it is difficult to get a ‘normal’ values because if you measure my levels and measure yours, or measure them at different times of the day, they are going to be different.”

Team members Tom Andriacchi and Jennifer Hledik came up with the idea of using a mechanical stimulus to provoke an OA biomarker response. This “OA biomarker stress test” is similar to a cardiac stress test where putting someone on a treadmill can reveal EKG changes that aren’t present at rest. In one of “Team Chu’s” studies, “Mechanically Stimulated Biomarkers Signal Cartilage Changes Over 5 years Consistent With Disease Progression in Medial Knee Osteoarthritis Patients” (Journal of Orthopaedic Research, March 2018), they measured a collagen degradation marker and a chondroitin sulfate synthesis marker in 16 (human) patients at two timepoints after a 30-minute walk.

“We were looking for a link between changes in these serum biomarkers and cartilage thickness changes five years later,” states Dr. Chu, “and we found them. Specifically, changes to these biomarkers after walking correlated with an MRI measure of OA disease state.”

“An increase in the degradation biomarker correlated with cartilage thinning of the lateral tibia, while an increase in the synthesis marker correlated with cartilage thickening of the lateral femur. Patients with increased degradation biomarkers had greater medial tibial cartilage thinning than those showing a decrease in the same marker.”

Back to the horses…

Dr. Chu: “One of our new DOD funded projects is a collaboration with veterinarian Laurie Goodrich and colleagues at Colorado State University where we will have the opportunity to measure the changes in these biomarkers during OA development. Through this project, we hope to get a better idea of how these biomarkers change from pre-OA to early OA to established OA as determined by structural means.”

And now the elephants…

“It’s like the parable of the blind men and the elephant…OA is such a lengthy, large and diverse disease process and most of the time we are just looking at the part we can see and feel—which is typically the end stage of the disease. Early stage OA appears completely different from end-stage OA and symptomatic disease can behave completely different from one week to the next. Thus, you have to step back and look at the whole elephant in order to integrate many viewpoints and lines of work to make real progress.”

“As you look into the earlier stages, the variability in the appearance of the disease and what you are going to measure in the blood changes. So it’s the same metric that could be higher in the early stage and lower in the later stage, and then go up in end stage OA. In my opinion there is not one correct biomarker that’s going to tell us everything we want to know about OA…you must consider a panel of biomarkers. Ideally, this panel will include structural, mechanical, and biological components that predict clinical disease.”

Elaborating on their process, Dr. Chu notes, “We conduct a structural assessment using varying forms of imaging and obtain measurements such as gait analysis on how the joint functions. Then we put what we find in the blood within the context of the structure or function and that’s when we begin to obtain meaningful correlations.”

Moving the needle…

Asked what her lab would require in order to move the needle on this work, Dr. Chu stated, “We need grants and funding to be able to collect samples and imaging from many additional patients and samples. Over a 5 to 10-year period we can see the disease move from pre-OA to OA in some people. Hopefully, we will also see stable disease and even resolution of pre-OA in others. Then we could feed that information through a machine learning algorithm to start seeing what relationships emanate from looking at the same joint from multiple points of view. At that point we may start seeing meaningful clusters and be able to identify more predictors of OA.”

Then, says Dr. Chu, when patients ask, ‘Where will I be in 5/10 years’ we will have a better answer. We would be able to tell someone, ‘Your test results fall within a range showing higher or lower risk for developing OA within a few years.’ Then we could target higher risk people for early intervention. We could then use the same markers to monitor treatment effects and see if they remain in the higher risk group or move into a lower risk group.”

Observing a wide-ranging trend, she notes, “There are a number of very large partnerships, such as those with Apple or Google, where entities gather a lot of information and then ‘crunch’ the data to see what relationships show up between people and what they do. Perhaps we may have an opportunity to leverage some of these large, ongoing studies to help us better understand how OA develops, how to treat it, and how to prevent it.”