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Updated: Jan 14, 2026

The MPLEx Protocol for Multi-omic Analyses of Soil Samples
Published on: May 30, 2018
Lipidomic profiling unveils sex differences in diabetes risk: Implications for precision medicine
Ana F Pina1,2, Maria João Meneses1,3, Fabrizia Carli4
1iNOVA4Health, NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade NOVA de Lisboa, Lisbon, Portugal.
Aims/Hypothesis:
Type 2 diabetes is a multifactorial condition whose greatest impact comes from its complications. We hypothesized that distinct insulin-derived mechanisms and lipid profiles discriminate sex differences and can be used to identify subjects at higher risk to develop diabetes-related complications.
Methods:
The PREVADIAB2 study evaluated metabolic alterations after 5 years in individuals initially free of Type 2 Diabetes (PREVADIAB1). In this analysis, 953 participants were stratified into clusters using hierarchical clustering based on insulinogenic index (IGI), fasting insulin secretion rate, HOMA-IR, and fasting insulin clearance. A subset of participants (n = 488) had their lipidome assessed using LC/MS-QTOF.
Results:
Four clusters were identified: Liver Sensitive (LS), Pancreas Glucose Sensitive (PGS), Insulin Deficient (ID), and Insulin Resistant (IR), each with distinct dysglycemia risk. While metabolic features were similar across sexes, the parameter thresholds differed, resulting in sex-specific lipidomic profiles. Women exhibited higher levels of circulating dihydroceramides (5.3 ± 1.9 vs. 4.7 ± 1.8, p < .001), associated with de novo ceramide synthesis, and elevated sphingomyelins (SM), suggesting altered lipid metabolism. Conversely, the ceramide-to-SM ratio was higher in men (1.04 ± .21 vs. .90 ± .18, p < .001). Except for the LS cluster, all other clusters exhibit distinct lipid signatures associated with metabolic dysfunction, further accentuated by specific lipid profile sex differences.
Conclusions:
Distinct insulin-related metabolic features and sex identify different phenotypes with distinct lipidome profiles, highlighting the need to place prediabetes in a broader context of metabolism beyond glucose.
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