ESPE Abstracts (2018) 89 P-P1-110

Lipid Accumulation Product is a Predictor of Non-alcoholic Fatty Liver Disease in Childhood Obesity

Bahar Ozcabia, Salih Demirhanb, Mesut Akyolc, Hatice Ozturkmen Akayd & Ayla Guvena

aZeynep Kamil Maternity and Childrens Diseases Research and Training Hospital Division of Pediatric Endocrinology, Istanbul, Turkey; bZeynep Kamil Maternity and Childrens Diseases Research and Training Hospital Department of Pediatrics, Istanbul, Turkey; cYıldırım Beyazıt University Department of Biostatistics, Ankara, Turkey; dZeynep Kamil Maternity and Childrens Diseases Research and Training Hospital Department of Radiology, Istanbul, Turkey

Objectives: We aimed to evaluate the performance of lipid accumulation product (LAP) to predict non-alcoholic fatty liver disease (NAFLD) in obese children.

Methods: Eighty obese chidren (39 girl) were included in this study (6–18 years). Height, weight, body mass index (BMI), waist circumference (WC), puberty stage, blood pressure (n=28), fasting glucose, fasting insulin, HOMA-IR, alanine aminotransferase (ALT), aspartate aminotransferase (AST) (n=30), uric asid (n=77), cholesterol, triglyceride, HDL-cholesterol (HDL-C) (n=79), LDL-cholesterol (LDL-C) (n=79) values were obtained from the medical records. SDS and percentiles were calculated. LAP was calculated as [WC(cm)-58)]×triglyceride concentration (mmol/L) in girls; [WC(cm)-65)]×triglyceride concentration (mmol/L) in boys. Other two variant LAP values were described according to 3% (minLAP) and 50% (adjLAP) of WC values previously considered for age and gender in childhood. Aterogenic index (AI:Cholesterol/HDL-C) (n=79)was defined. NAFLD was showed by ultrasound. The AUC and appropriate cutoff points for LAP, adjLAP and minLAP were calculated by ROC analysis.

Results: Gender, puberty stage, weight SDS, BMI, BMI SDS, BMI %, WC, fasting insulin, HOMA-IR, ALT, uric acid, LAP, adjLAP and minLAP values were significantly different in chidren with and without NAFLD (P<0.005). LAP showed a positive and moderate correlation with puberty stage (ρ=0.409; P<0.001), fasting insulin (ρ=0.507; P<0.001), HOMA-IR (ρ=0.470; P<0.001), uric acid (ρ=0.522; P<0.001), AI (ρ=0.494; P<0.001) and a weak negative correlation with HDL-C (ρ=−3.833; P<0.001). Similar results were detected for minLAP and adjLAP. It was found that LAP values could be used to diagnose hepatosteatosis (AUC=0.698; P=0.002). Sensitivity and specificity values for LAP ≥ 42.70 cases were found as 53.7% and 84.6%, respectivly. The cut-off points for LAP were AUC=0.704; P=0.033 in males and AUC=0.693; P=0.013 in pubertal. While the cutoff point for adjLAP ≥ 40.05 (AUC=0.691; P=0.003), sensitivity (58.5%) and specificity (74.4%) were calculated. While the cutoff point for minLAP ≥ 53.47 (AUC=0.673; P=0.0083), sensitivity (56.1%) and specificity (76.9%) were found.

Conclusions: LAP is a is a powerfull and easy tool to predict NAFLD in childhood and is correlated with AI and uric acid level. This is the first study assessing the accuracy of LAP in childhood obesity.

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