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Endocrine Abstracts (2018) 56 GP69 | DOI: 10.1530/endoabs.56.GP69

ECE2018 Guided Posters Cardiovascular (8 abstracts)

Can monogenic severe hypertriglyceridemia be differentiated from polygenic forms through clinical features: data from APPROACH and COMPASS studies in FCS and non-FCS hypertriglyceridemic patients?

Louis O’Dea 1 , James MacDougall 2 , Andres Digenio 1 , Brant Hubbard 1 , Marcello Arca 3 , Patrick Moriarty 4 , John Kastelein 5 , Eric Bruckert 6 & Joseph Witztum 7

1AkceaTherapeutics™, Inc., Cambridge, Massachusetts, USA; 2BioBridges, Wellsley, Massachusetts, USA; 3La Sapienze University of Rome, Rome, Italy; 4University of Kansas Medical Center, Kansas City, Missouri, USA; 5Academic Medical Center (AMC), University of Amsterdam, Amsterdam, The Netherlands; 6ICAN, Paris, France; 7University of California, San Diego, California, USA.

Introduction: Differentiation between familial chylomicronemia syndrome (FCS), a rare hypertriglyceridemia, and severe hypertriglyceridemia (sHTG; non-FCS) is challenging due to overlap in triglyceride (TG) levels and symptomology but important in disease management. Clinical characteristics that allow for reliable differentiation may exist in the presenting clinical features and primary diagnostic testing. The objective of this analysis was to assess whether readily obtainable clinical information can effectively diagnose and differentiate patients with FCS from sHTG (non-FCS) based on 2 well-curated datasets arising from 2 clinical studies.

Methods: Patients from two Phase-III clinical trials of sHTG patients, one with molecularly-proven FCS and one with polygenic sHTG (non-FCS) were included in this analysis. Logistic regression analyses were performed to determine the ability of variables (individually or sets), including patient demographics, medical history and baseline lipids, to differentiate between FCS and sHTG (non-FCS) populations. For each of the logistic regression analyses, receiver operating characteristics (ROC) were employed to determine the highest accuracy (defined as the percentage of times the Actual and Predicted values match) using the predicted probability (Pr) of being in the FCS-population. Positive predictive-value (PPV), and negative predictive-value (NPV) are defined as follows: PPV is Pr (Predicted-FCS + True-FCS |Predicted-FCS); NPV is Pr(Predicted Non-FCS + True Non-FCS|Predicted Non-FCS). Optimal was defined as maximizing sensitivity + specificity.

Results: One hundred and fifty four patients (n=49 genetically confirmed FCS patients and n=105 sHTG (non-FCS) patients) were included in the analysis. Of the 154 patients, 45/49 of FCS patients and 99/105 of sHTG (non-FCS) patients were diagnosed correctly based on the model. Optimal sensitivity was 91.8%, optimal specificity was 94.3%, and accuracy was 93.5%. Fasting low-density lipoprotein-Cholesterol (LDL-C), apolipoprotein-A1 (apoA1), and apoB were determined to have the highest individual predictability with ROC area-under-the-curve values of 0.902, 0.8971, and 0.8852, respectively. FCS and sHTG (non-FCS) patients could be differentiated with an accuracy of 91.6% with a 3-variable set (apoB/LDL-C, BMI, and history of pancreatitis) and 93.5% with a 5-variable set (HDL-C and VLDL-C included). Individual variables and sets both had higher NPV than PPV.

Conclusions: Our results indicate that FCS and sHTG (non-FCS) patients can be diagnosed and differentiated with a high-degree of accuracy by analyzing readily obtainable clinical information. This suggests that where genetic testing is not available or among FCS patients who do not test positive for a known genotype, the diagnosis of FCS can be made clinically with a high-degree of certainty.

Volume 56

20th European Congress of Endocrinology

Barcelona, Spain
19 May 2018 - 22 May 2018

European Society of Endocrinology 

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