Elizabeth L. Yanik, PhD

Assistant Professor
Orthopaedic Surgery

Biomedical Informatics and Data Science Program
Human and Statistical Genetics Program

Research Abstract:

My independent research is currently focused on discovering new insights into the etiology and progression of rotator cuff disease in order to directly inform clinical decision-making and prevention approaches. Rotator cuff disease is the most frequent cause of shoulder disability, and injuries of the shoulder lead to higher median days away from work than any other body part. Rotator cuff surgery rates are also increasing, leading to significant burdens on the healthcare system. Given the high costs and morbidity of rotator cuff disease, it will be important to identify high-risk subgroups accurately to enable prevention efforts. This requires knowledge of important risk factors as well as the creation and validation of predictive models in large, prospective studies.

As one part of this research, I’ve currently obtained data from the UK Biobank, a large prospective, population-based cohort of 500,000 people with extensive data on demographics, behaviors, occupation, hospital diagnoses (including >3,000 symptomatic rotator cuff disease diagnoses), and complete genotype information for >800,000 genetic markers. Several studies are planned using this resource. First, a number of individual characteristics that have been identified as potential risk factors in laboratory settings or case-control studies, such as smoking, body mass index, and hypertension, have been collected as a part of the standard baseline visit for the cohort (which
includes extensive surveys and measurements for height, weight, and blood pressure). As such, the UK Biobank will allow us to verify and more accurately estimate the magnitude of these associations with incident rotator cuff disease. Second, the extensive genetic information available for study participants will allow us to conduct a genome-wide association study with rotator cuff disease and to develop a polygenic risk score. Previous studies have identified genetic contributors to rotator cuff disease, but no previous study has been as large or included measurement of such a large number of genetic markers. Third, a job exposure matrix will be used to link information provided by study participants on current job title and work history to data on corresponding upper extremity burdens. This will allow a better understanding of the relationship between occupational exposures and rotator cuff disease risk. These studies will further enable the development of prediction models that combine important risk factors and the evaluation of potentially important interactions between genetic and environmental risk.

My research team has also recently completed a study prospectively collecting saliva samples for genotyping from patients presenting with atraumatic rotator cuff tears to our department’s clinical providers. We are evaluating the associations between rotator cuff-related SNPs and patient characteristics such as: age at symptomatic tear diagnosis, tear size at diagnosis, and bilaterality.

My research has a number of potential future implications. A better understanding of genetic susceptibility may be able to guide prevention interventions towards high risk individuals, and in the era of CRISPR/Cas9, could also lead directly to novel treatments. Other identified risk factors, such as tobacco use, may be more easily modifiable and may point to the utility of already existing public health interventions to reduce the rate of symptomatic rotator cuff tears. In particular, my investigation of occupational risk factors for rotator cuff disease may help inform future worksite interventions. Finally, many of these factors, both genetic and non-genetic, are likely to also play important roles in determining treatment outcomes. Further study can help guide treatment decisions with the aim of ultimately optimizing patient outcomes.

Selected Publications:

Yanik EL, Keener JD, Stevens MJ, Walker-Bone KE, Dale AM, Ma Y, Colditz GA, Wright RW, Saccone NL, Jain NB, Evanoff BA. Occupational demands associated with rotator cuff disease surgery in the UK Biobank. Scand J Work Environ Health. 2022. doi:10.5271/sjweh.4062 [online first article]

Yanik EL, Evanoff BA, Dale AM, Ma Y, Walker-Bone KE. Occupational characteristics associated with SARS-CoV-2 infection in the UK Biobank during August-November 2020: a cohort study. BMC Public Health. 2022;22(1):1884. doi: 10.1186/s12889-022-14311-5. PMCID: PMC9549452

Yanik EL, Steven MJ, Harris EC, Walker-Bone KE, Dale AM, Ma Y, Colditz GA, Evanoff BA. Physical work exposure matrix for use in the UK Biobank. Occup Med. 2022 Feb; 72(2): 132-141. PubMed Central PMCID: PMC8863087.

Yanik EL, Keener JD, Lin SJ, Coldtiz GA, Wright RW, Evanoff BA, Jain NB, Saccone NL. Identification of a Novel Genetic Marker for Degenerative Rotator Cuff Disease Surgery in the UK Biobank. J Bone Joint Surg Am. 2021 Jul. 103(14): 1259-1267. PubMed Central PMCID: PMC8282705.

Yanik EL, Kelly MP, Lurie JD, Baldus CR, Shaffrey CI, Schwab FJ, Bess S, Lenke LG, Labore A, Bridwell KH. Effect modifiers for patient-reported outcomes in operatively and nonoperatively treated patients with adult symptomatic lumber scoliosis: a combined analysis of randomized and observational cohorts. J Neurosurg Spine. 2020 Mar 6; 1-10. PubMed PMID: 32114531.

Yanik EL, Colditz GA, Wright RW, Saccone NL, Evanoff BA, Jain NB, Dale AM, Keener JD. Risk factors for rotator cuff disease in a population-based cohort. Bone Joint J. 2020 Mar; 102-B(3): 352-359. PubMed Central PMCID: PMC7552877.

Last Updated: 11/7/2022 3:26:22 PM

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