Clinical and molecular characterization of 2 novel<i> LPL</i> variants in familial chylomicronemia syndrome

Agirbasli D., Karaoglan B., Ozalp N. H., KALAYCI A.

JOURNAL OF CLINICAL LIPIDOLOGY, cilt.20, sa.7, ss.1508-1514, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 7
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1016/j.jacl.2026.05.009
  • Dergi Adı: JOURNAL OF CLINICAL LIPIDOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, EMBASE, MEDLINE
  • Sayfa Sayıları: ss.1508-1514
  • İstanbul Üniversitesi Adresli: Hayır

Özet

Familial chylomicronemia syndrome (FCS) (OMIM #238600) is a rare autosomal recessive disorder characterized by severe hypertriglyceridemia and chylomicronemia due to profound reduction of lipoprotein lipase (LPL). Here, we report 2 unrelated cases of FCS caused by novel homozygous variants in the LPL gene, expanding the known mutational spectrum of this condition. The first case involved a 43-year-old man with longstanding severe hypertriglyceridemia and recurrent acute pancreatitis. Genetic analysis revealed a novel homozygous frameshift variant, c.1172_1173insTCCACAAATA AGA (p.Leu392ProfsTer25), in the LPL gene predicted to result in loss of function. The second case was a female infant presenting with extreme neonatal hypertriglyceridemia, in whom a homozygous missense variant, c.296T > C (p.Leu99Pro), in the LPL gene was identified. Both variants were absent from population databases and identified as novel candidate variants. These cases underscore that phenotypic variability in FCS, ranging from severe neonatal presentation to adult-onset recurrent pancreatitis, is shaped by both the type of the variant and the functional domain involved. Our findings underscore the significance of domain-specific interpretation in genotype-phenotype correlations and identify novel candidate variants for future functional validation. (c) 2026 National Lipid Association. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.