Safety and effectiveness in an uncontrolled setting of glucagon-like-peptide-1 receptor agonists in patients with familial partial lipodystrophy: Real-life experience from a national reference network.

Aim: To describe the effects of Glucagon-like peptide-1 receptor agonists (GLP-1RA) in patients with familial partial lipodystrophy (FPLD) assessed in a real-life setting in a national reference network.

Patients And Methods: We retrospectively collected clinical and metabolic parameters in patients with FPLD in the French lipodystrophy reference network, who initiated GLP-1RA. Data were recorded before, at one-year (12 ± 6 months) and at the latest follow-up on GLP-1RA therapy (≥18 months).

Results: Seventy-six patients (89.4% of women), diagnosed with LMNA-related FPLD2 (n = 57), PPARG-related FPLD3 (n = 4), PLIN1-related FPLD4 (n = 5) or FPLD1 (n = 10) initiated GLP-1RA therapy between 2008 and 2024. Patients were aged a median (IQR) 48 years (34.5-57), body mass index (BMI) was 26.0 kg/m (23.9-29.5), HbA1c 8.3% (7.5-9.3), triglycerides 2.31 mmol/L (1.62-3.88). GLP-1RA were used in addition to previously used antidiabetics, 50% of patients being insulin-treated. After one year with GLP-1RA therapy, BMI, HbA1c and triglycerides significantly decreased to 25.6 kg/m (22.7-29.1), 7.3% (6.6-8.3) and 1.97 mmol/L (1.5-3.2) respectively (p < 0.001, p < 0.001 and p < 0.01, respectively), without significant changes in other antidiabetic and lipid-lowering drugs. Gamma-glutamyl-transferase and alanine-aminotransferase levels also significantly decreased. Effects on HbA1c, BMI and triglycerides persisted in the long term. One case of acute pancreatitis occurred during follow-up, associated with severe hypertriglyceridemia in a non-observant patient. Gastrointestinal symptoms affected 34% of patients, leading to GLP-1RA withdrawal in six patients.

Conclusion: GLP-1RA significantly improved BMI, HbA1c and triglycerides in a large majority of patients with FPLD. Larger and prospective controlled studies are warranted for identification of predictive factors and safety.