School of Public Health (Boisvenue, Johnson, Yeung), and Department of Computing Science (Oliva), Faculty of Science, and Department of Family Medicine (Manca), Faculty of Medicine & Dentistry, University of Alberta; Northern Alberta Primary Care Research Network (Manca); Division of Endocrinology and Metabolism (Yeung), Department of Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alta. firstname.lastname@example.org.
The prevalence of metabolic syndrome is growing worldwide, yet remains underinvestigated in Canadian young adults. We sought to explore the use of a harmonized case definition specific to early-onset metabolic syndrome and determine its feasibility in assessing the prevalence of metabolic syndrome among electronic medical record (EMR) data of young adults in Northern Alberta.
We conducted a cross-sectional study using a sample of EMR data from young adult patients aged 18-40 years and residing in Northern Alberta, who had an encounter with a participating primary care clinic between June 29, 2015, and June 29, 2018. Physical examination, laboratory investigation and disease diagnosis data were collected. A case definition and algorithm were developed to assess the feasibility of identifying metabolic syndrome, including measures for body mass index (BMI), blood pressure (BP), dysglycemia, hypertriglyceridemia, high-density lipoprotein cholesterol, diabetes and hypertension.
Among 15 766 young adults, the case definition suggested the prevalence of metabolic syndrome was 4.4%, 95% confidence interval (CI) 4.1%-4.7%. The most frequent 3-factor combination (41.6%, 95% CI 37.9%-45.3%) of metabolic syndrome criteria consisted of being overweight or obese, having elevated BP and hypertriglyceridemia. Half of metabolic syndrome cases (51.3%, 95% CI 47.6%-55.0%) were missing measures for fasting blood glucose, and one-fifth were missing a hemoglobin A1c (HbA1c) level. Notably, most young adults with a BMI of 25 or greater were missing HbA1c (68.7%, 95% CI 67.6%-69.8%), fasting blood glucose (84.0%, 95% CI 83.2%-84.8%) and triglyceride testing (79.0%, 95% CI 78.1%-79.9%).
We have shown that our case definition is feasible in identifying early-onset metabolic syndrome using EMR data; however, the degree of missing data limits the feasibility in assessing prevalence. Further investigation is required to validate this case definition for metabolic syndrome in the EMR data, which may involve comparing this definition to other validated metabolic syndrome case definitions.