Differentiating Familial Chylomicronemia Syndrome From Multifactorial Severe Hypertriglyceridemia by Clinical...

Differentiation between familial chylomicronemia syndrome (FCS, type 1 hyperlipoproteinemia), a rare metabolic disorder, and the more common multifactorial severe hypertriglyceridemia (sHTG, type 5 hyperlipoproteinemia) is challenging because of their overlapping symptoms but important in patient management.

To assess whether readily obtainable clinical information beyond triglycerides can effectively diagnose and differentiate patients with FCS from those with sHTG, based on well-curated data from two intervention studies of these conditions.

The analysis included 154 patients from two phase 3 clinical trials of patients with sHTG, one cohort with genetically confirmed FCS (n = 49) and one with multifactorial sHTG (n = 105). Logistic regression analyses were performed to determine the ability of variables (patient demographics, medical history, and baseline lipids, individually or in sets) to differentiate the patient populations. Receiver operating characteristics were used to determine the variable sets with the highest accuracy (percentage of times actual values matched predicted) and optimal sensitivity and specificity.

The primary model diagnosed 45 of 49 patients with FCS and 99 of 105 patients with sHTG correctly. Optimal sensitivity for all available parameters (n = 17) was 91.8%, optimal specificity was 94.3%, and accuracy was 93.5%. Fasting low-density lipoprotein cholesterol (LDL-C) provided the highest individual predictability. However, a three-variable set of ultracentrifugally measured LDL-C, body mass index, and pancreatitis history differentiated the diseases with a near similar accuracy of 91.0%, and adding high-density lipoprotein cholesterol and very low-density lipoprotein cholesterol for a five-variable set provided a small incremental increase in accuracy (92.2%).

In the absence of genetic testing, hypertriglyceridemic patients with FCS and sHTG can be differentiated with a high degree of accuracy by analyzing readily obtainable clinical information.