The aim of the study is to evaluate whether the Oral Fat Loading Test (OFLT) determines a different response in terms of the quantity, quality, and kinetics of triglyceride-rich lipoproteins in subjects with T2D, prediabetics, and control subjects, and whether triglyceride-rich lipoproteins and inflammatory cytokines after OFLT are potential early biomarkers of the risk of progression to diabetes and the development of complications in a general practice setting.
To address these questions, a hybrid cohort study was designed by identifying three groups of subjects: T2D and prediabetics (exposed and near-exposed) and control subjects (unexposed).
Males and females aged 50-70 years
BMI between 25-30 kg/m2
HbA1c ≤7% for T2D
HbA1c ≤6.5% for prediabetes
Lipid-lowering therapy with ezetimibe, fenofibrate, omega-3 fatty acids, or other drugs that can interfere with lipoprotein absorption and metabolism
Chronic Kidney Disease (CKD) with estimated glomerular filtration rate (eGFR) <60 ml/min and renal impairment (e.g., uACR ≥30 mg/mmol) for 3 months or more
Secondary or syndromic forms of obesity
Patients on insulin therapy
All individuals attending the Poggio Renatico Primary Care Unit - Poggio Rete Salute Group Medicine, Ferrara Local Health Authority who have undergone at least two diagnostic tests for diabetes in the last 12 months will be contacted.
Cohort
Prospective
Oral Fat Load Test
Diagnostic testAfter a 12-hour overnight fast, each participant will be given a sugar-free mascarpone cream containing 75 g of fat. Blood samples will be collected before (OFL Time -1 minute) and 1 (OFL Time +1 hour), 2 (OFL Time +2 hours), 3 (OFL Time +3 hours), 4 (OFL Time +4 hours), and 6 (OFL Time +6 hours) hours after the OFL.
Triglyceride concentrations after oral fat loading test
Serum triglyceride concentrations measured before and 6 hours after oral fat loading test in participants with type 2 diabetes, prediabetes, and controls.
Progression from prediabetes to type 2 diabetes
Development of type 2 diabetes according to American Diabetes Association (ADA) diagnostic criteria during follow-up in participants with prediabetes.
Triglyceride-rich lipoprotein concentrations after oral fat loading test
Serum triglyceride-rich lipoprotein (TRL) concentrations measured before and 6 hours after oral fat loading test in participants with type 2 diabetes, prediabetes, and controls.
Chylomicron remnant concentrations after oral fat loading test
Chylomicron remnant concentrations measured before and 6 hours after oral fat loading test.
Interleukin-1β concentration after oral fat loading test
Plasma interleukin-1β (IL-1β) concentrations measured before and 6 hours after oral fat loading test.
Interleukin-18 (IL-18) concentration after oral fat loading test
Plasma interleukin-1β interleukin-18 (IL-18) concentrations measured before and 6 hours after oral fat loading test.
Circulating ceramide content
Plasma circulating ceramide concentrations measured in participants with type 2 diabetes, prediabetes, and controls.
Change in circulating ceramide concentrations after oral fat loading test
Plasma circulating ceramide concentrations measured before and 6 hours after oral fat loading test.
Proteomic analyses
To perform untargeted proteomic analyses on serum samples collected at baseline, T0 and postprandial peak, from subjects with diabetes, pre-diabetes, and controls, in order to identify differential protein patterns associated with the postprandial response. The aim is to identify early proteomic biomarkers of altered lipid metabolism and subclinical inflammatory signaling. Proteomic data will be integrated with bioinformatic pathway enrichment analyses to characterize the key biological processes involved, with particular focus on inflammation, oxidative stress, and lipoprotein metabolism.
Metabolomic analyses
To conduct targeted and untargeted metabolomic analyses on the same serum samples previously analyzed by proteomics, collected at baseline (T0) and postprandial peak. Metabolic profiles will be compared among subjects with type 2 diabetes (T2D), pre-diabetes, and healthy controls. Metabolomic data will be integrated with proteomic datasets using a multi-omics approach to identify early metabolic signatures of postprandial dysfunction, predictors of altered lipid response, and metabolites with potential relevance as therapeutic or diagnostic targets.
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Angelina Passaro
Lead sponsor
University Hospital of Ferrara
Sponsor institution
University Hospital of Ferrara
Collaborator
S. Anna Hospital
Collaborator
Azienda USL Ferrara
Collaborator
Università degli Studi di Ferrara
Collaborator
