Lipidomic Profiling Unveils Sex Differences in Diabetes Risk: Implications for Precision Medicine

Abstract

AbstractType 2 diabetes (T2D) is a multifactorial condition whose greatest impact comes from its complications. Not only impaired glucose homeostasis, but also lipid alterations have a relevant role, with insulin derived mechanisms behind this milieu, i.e., glycemia and lipidemia. Thus, we hypothesized that a) distinct glucose and lipid profiles and b) sex differences, particularly in lipids patterns, may be used to identify subjects at higher risk to develop T2D.The PREVADIAB2 study evaluated metabolic alterations after 5 years in subjects without T2D when participating to PREVADIAB1. Herein, 953 subjects from the PREVADIAB2 cohort were stratified using a hierarchical clustering algorithm, informed by HOMA-IR, IGI,fISR andfIC. The resulting clusters were profiled and the lipidome of a subset (n=488) was assessed by LC/MS-QTOF.We identified four clusters, named according to their main metabolic features: Liver Sensitive (LS); Pancreas Glucose Sensitive (PGS); Insulin Deficient (ID); and Insulin Resistant (IR). These cluster metabolic patterns were similar between sexes. However, men and women had distinct parameters cut-offs and lipidomic profiles. Overall, women presented higher long chain ceramides. Nonetheless, men had higher ceramide to sphingomyelin ratio and higher lysophosphatidylcholine to phosphatidylcholine ratio. For both genders, the LS cluster had the most advantageous lipid profile, whereas the other clusters presented lipid specificities towards dysmetabolism. This work shows that clustering individuals by distinct insulin-related metabolic features and sex identifies different phenotypes with distinct lipidome profile, thus demonstrating the importance of placing diabetes in a broader context of metabolism beyond glucose.