Author(s): Natalie VanderNoot
Mentor(s): Lynn Gerber, Health Administration and Policy
|Sarcopenia is the loss of muscle mass, function, and strength, associated with aging. Recent studies have associated sarcopenia with chronic diseases independent of age. Diagnosing sarcopenia is treatable and if reversed may reduce all-cause mortality. Three criteria comprise the diagnosis of sarcopenia: muscle strength, muscle mass, and muscle function/activity. In this project, we establish a series of cutoff values for each of these categories, using grip strength to measure muscle strength, output from bioelectrical impedance analysis for muscle mass, and the adjusted activity scores from the Human Activity Profile(HAP) questionnaire. The sample includes 187 patients from a variety of studies at the Beatty Liver and Obesity Program at Inova Fairfax Hospital, 50% of whom have non-alcoholic fatty liver disease (NAFLD), 25% have hepatitis C virus (HCV), and 25% that are healthy controls. Cutoffs for sarcopenia were set at the bottom half of activity scores, the bottom half of percent lean mass separated by sex, and a value below 0 for grip strength normalized by age and sex. Fatigue was measured in self-reported fatigue survey scores, and ELISA immunoassays were used to analyze the blood concentration of certain myokines, proteins released by the muscles. In the 86 patients with all variables available, 20 were classified in the sarcopenia group. While there is no evidence of a difference in self-reported fatigue in patients with sarcopenia, lower activity measured by the HAP correlates with increased fatigue. Irisin and FGF myokines were moderately related to sarcopenia (p = .0598 and p = .0707 respectively), and BDNF was positively correlated with adjusted activity scores on the HAP (p < .0001). This study describes correlations among diagnostic criteria, symptoms and myokines that may constitute a biosignature that better identifies sarcopenia. Better classification may assist in identifying at risk populations and enabling early intervention.|
|My name is Natalie VanderNoot and I am a biology major at George Mason University. I am mentored by Dr. Lynn Gerber from the Department of Health Administration and Policy who holds a joint appointment at Inova Fairfax Hospital in Falls Church, VA. Together we investigated how to define sarcopenia. |
Sarcopenia is a loss of muscle function and strength, typically in older patients or in chronically ill patients of all ages. To the right you can see a muscle biopsy of young, healthy muscle and you can also see the stark difference in atrophied muscle that we would consider to be sarcopenia. Our question is: how can we identify patients with sarcopenia?
We’ve attempted to measure sarcopenia in the three broadly used categories to define it, which are muscle strength, muscle mass, muscle function and activity. To measure muscle strength we used grip strength measured in pounds which you can see to the left in the image of the grip strength monitor that we use. The patient squeezes this monitor as hard as they can in their dominant hand and we can read the force applied in the meter on the top.
To measure muscle mass, we use bioelectrical impedance analysis, which outputs the percent lean mass and percent fat mass of the person.
And to measure the muscle activity, we use the adjusted activity scores from the human activity profile survey. These self reported scores give us an idea of what physical activities patients are able to complete in their everyday lives.
Our study includes 187 patients from studies at the Betty and Guy Beatty Liver and Obesity Research Program at Inova Fairfax Hospital.
46 of the patients are healthy controls, 47 are hepatitis c virus patients, and 94 are non-alcoholic fatty liver disease patients.
After removing the patients who were missing any of the three sarcopenia measures and blood samples, 86 patients remained. 22 of which were healthy controls, 36 of which were hepatitis c virus patients, and 28 that were non-alcoholic fatty liver disease patients.
When the measures of sarcopenia were collected, I split into the bottom quarter, bottom third, bottom half, bottom two-thirds, and bottom three-quarters. I used the bottom half (marked in bold) to cut-off values of what we would consider low for that group and what we would use as an identifier of sarcopenia.
The only difference here is that for relative grip strength, I actually used zero since this value is already normalized on a normal distribution
To identify sarcopenia in the population, I used this decision tree. I started with patients who had a low percent lean mass and then looked to their grip strength, which would measure their overall muscle strength and then their activity scores which we used as a measure of their overall muscle activity.
and then looked to their grip strength, which would measure their overall muscle strength, and then their activity scores which we used as a measure of their overall muscle activity.
Patients who were low in all three categories are considered to have severe sarcopenia, While patients who are low in percent lean mass and either grip strength or activity
are considered to have sarcopenia. And patients who have a low percent lean mass but normal grip strength and normal activity scores are considered to have pre-sarcopenia
With those 20 patients that we identified to have sarcopenia, we wanted to see if fatigue or blood test results were associated with the classification We investigated the concentrations of myokines, which are proteins released by the muscles that circulate in the blood and found that irisin and FGF were moderately associated with sarcopenia.
There is no evidence, however, of self-reported fatigue in sarcopenia patients being any higher than patients without sarcopenia. In the future we’d like to investigate self-reported physical activity and it’s relationship with fatigue and blood tests. We’d also like to use these defined sarcopenia characteristics on a larger sample to investigate further any relationships of symptoms or fatigue in a larger sample size.
Thank you very much for coming to my presentation and I would also like to thank my mentors and the other scientists at Inova Fairfax Hospital who helped me with this project
As well as Dr. Karen Lee from the OSCAR office and the Undergraduate Research Scholars Program for providing funding