Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| University Hospital of Ferrara | OTHER |
| S. Anna Hospital | OTHER |
| Azienda USL Ferrara | OTHER_GOV |
| Università degli Studi di Ferrara |
Not provided
Not provided
Not provided
Not provided
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).
Diabetes is a serious chronic disease characterized by elevated blood glucose levels resulting from abnormal pancreatic β-cell biology in relation to insulin action.
According to estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), diabetes is the eighth leading cause of death and disability worldwide, affecting nearly 460 million people of all ages and in all countries in 2019. It is currently estimated that approximately 529 million people of all ages worldwide are living with diabetes, with a global age-standardized prevalence of 6.1%.
Estimates from the International Diabetes Federation (IDF) indicate that global health expenditure on diabetes reached 910 billion euros in 2021 and is projected to exceed 1,000 billion euros by 2045.
Diabetes is also a major risk factor for ischemic heart disease and stroke, which according to GBD estimates are the first and second leading causes of disease globally. Diabetes itself is associated with increased mortality compared with non-diabetic individuals, worsens prognosis for all other diseases, increases premature mortality (Years of Life Lost, YLL), years lived with disability (Years Lived with Disability, YLD), and loss of healthy life years (Disability-Adjusted Life Years, DALY). Globally, diabetes accounted for 37.8 million YLL due to premature death and 41.4 million YLD, for a total of 79.2 million DALYs in 2021. Type 2 diabetes accounted for the vast majority of YLL, YLD, and DALYs attributable to diabetes. The global age-standardized DALY rate for diabetes was 915 per 100,000, with YLL and YLD rates of 437 and 478 per 100,000, respectively. In Italy, age-standardized DALYs in 2021 were 521.1 thousand, representing an 11.5% reduction compared with 2010, whereas increases were observed in the rest of Western Europe, in high-income countries, and globally.
Type 1 diabetes (T1D) and type 2 diabetes (T2D) are the most common forms of the disease and are diagnosed according to well-defined criteria reported in the operational manual. T1D often develops during childhood, whereas T2D has a strong genetic component and is strongly associated with obesity and a sedentary lifestyle. Cases of T2D account for approximately 96% of all diabetes cases.
Between 20% and 25% of adults with diabetes meet laboratory criteria for the diagnosis but have not been formally diagnosed (undiagnosed diabetes). Several studies have shown that individuals may spend 5-6 years in an asymptomatic phase of prediabetes and T2D prior to diagnosis, during which microvascular and macrovascular complications may already develop.
Prediabetes refers to individuals whose glucose levels do not meet diagnostic criteria for diabetes but who exhibit abnormal carbohydrate metabolism. Prediabetes is defined by impaired fasting glucose (IFG), and/or impaired glucose tolerance (IGT) following an oral glucose load, and/or HbA1c values between 5.7% and 6.4%, as reported in the operational manual.
Prediabetes should not be considered a distinct clinical entity, but rather a risk factor for progression to diabetes and cardiovascular disease (CVD). Prediabetes is associated with obesity, particularly abdominal or visceral obesity, dyslipidemia characterized by elevated triglycerides and/or low HDL cholesterol, and hypertension. The presence of prediabetes should prompt comprehensive cardiovascular risk factor screening.
Although prevention and management strategies differ across diabetes types, established approaches exist to reduce disease burden, including control of risk factors for the development and progression of T2D and improvement of healthcare system infrastructure.
T2D and prediabetes are characterized by insulin resistance (IR) in multiple cellular systems, including hepatocytes, adipocytes, and myocytes, excessive hepatic glucose production, altered lipid metabolism, and progressive impairment of insulin secretion. IR represents a central mechanism not only in diabetes but also in obesity and metabolic syndrome, and is associated with an increased risk of microvascular and macrovascular complications. While hyperglycemia, hypertension, kidney disease, and dyslipidemia are considered traditional CVD risk factors in diabetes, an association between IR and CVD has been increasingly recognized even in the absence of overt diabetes. IR is linked to alterations in lipid and lipoprotein metabolism, resulting in atherogenic dyslipidemia. Atherogenic dyslipidemia is highly prevalent in patients with T2D.
IR plays a major role in the metabolism of triglyceride-rich lipoproteins of hepatic origin, particularly very low-density lipoproteins (VLDL), including increased hepatic VLDL triglyceride synthesis. A key mechanism underlying increased VLDL triglyceride production is the accelerated lipolysis of stored triglycerides in adipose tissue, leading to increased free fatty acid flux to the liver. In insulin-resistant adipocytes, lipolysis is dysregulated, resulting in continuous release of free fatty acids into circulation. In hepatocytes, this promotes increased triglyceride synthesis, contributing both to intracellular lipid accumulation and hepatic steatosis, as well as enhanced VLDL production. Increased VLDL triglyceride synthesis is variably associated with increased hepatic production of apolipoprotein B-100. Overall, this results in hypertriglyceridemia, increased numbers of apoB-100-containing particles, and reduced HDL cholesterol concentrations. IR is also associated with increased hepatic triglyceride lipase activity, which may accelerate HDL clearance and reduce HDL cholesterol levels. In addition to increased VLDL synthesis, IR is associated with impaired VLDL clearance in skeletal muscle and adipose tissue due to reduced activity of lipoprotein lipases.
The lipid abnormalities of atherogenic dyslipidemia are not only quantitative but also qualitative and kinetic. IR is associated with changes in the average particle size of lipoproteins and likely in the lipoprotein lipidome. Lipids constitute a major and heterogeneous family of biomolecules within the metabolome. Complex lipids can be classified into multiple classes and subclasses, including triacylglycerols, diacylglycerols, phosphatidylcholines, phosphatidylethanolamines, ceramides, sphingomyelins, and cholesterol esters. Abnormal lipid profiles are associated with several diseases, including metabolic syndrome, T2D, cancer, nephropathy, and cardiovascular and neurodegenerative diseases.
A key feature of atherogenic dyslipidemia in T2D and IR is postprandial hyperlipidemia, which plays a fundamental role in the development of cardiovascular disease. Postprandial serum triglyceride levels vary considerably depending on meal composition and time elapsed after food intake. Due to this variability, assessment of postprandial hyperlipidemia requires an oral fat loading test (OFLT). Elevated postprandial triglyceride levels reflect increased concentrations of triglyceride-rich lipoproteins, including chylomicrons, VLDL, and their remnants. Intestinally synthesized chylomicrons transport dietary triglycerides to peripheral tissues in the postprandial state. Reduced lipoprotein lipase activity associated with IR impairs triglyceride hydrolysis from chylomicrons, leading to altered postprandial chylomicron responses. Measurement of apolipoprotein B48, non-fasting triglycerides, non-HDL cholesterol, and remnant cholesterol is essential for identifying postprandial hyperlipidemia.
Multiple factors contribute to chylomicron production during the postprandial phase. Substantial evidence supports the physiological role of glucagon-like peptides, microsomal triglyceride transfer protein, and the central role of apolipoprotein B48 in chylomicron synthesis and postprandial kinetics.
Just as postprandial triglycerides are an independent predictor of coronary artery disease, remnant lipoproteins possess multiple atherogenic properties and are associated with increased all-cause mortality in patients with ischemic heart disease and with the development of coronary artery disease, even after adjustment for major risk factors.
Diagnosis of postprandial hyperlipidemia requires an OFLT; however, there is no consensus on the optimal timing of postprandial measurements or on meal standardization. The test is time-consuming and requires prolonged rest. Despite these limitations, the OFLT remains the only available test for assessing postprandial hyperlipidemia.
Dyslipidemia and chronic inflammation are considered the main drivers of atherosclerotic plaque formation in diabetes. Atherosclerosis is accompanied by local inflammation within the vascular wall due to endothelial dysfunction and vascular smooth muscle cell involvement. Components of the diabetic milieu, oxidative stress, and other factors are believed to damage vascular endothelial cells, leading to increased expression of adhesion molecules and secretion of chemokines, promoting monocyte adhesion. Adherent monocytes migrate into the subendothelial space and differentiate into macrophages, which release cytokines such as interleukin-1β, interleukin-18, tumor necrosis factor-α, and interferon-γ, amplifying the inflammatory process. Compared with chylomicrons and VLDL, remnant lipoproteins can penetrate the arterial wall and do not require oxidation for macrophage uptake. Remnants enhance monocyte rolling, adhesion, and transmigration on endothelial cells and are involved in inflammation, platelet activation, and endothelial dysfunction through activation of transcription factors such as NF-κB. Adipocyte dysfunction associated with obesity is an integral component of T2D pathogenesis and, together with atherogenic dyslipidemia, promotes chronic systemic inflammation that contributes to insulin resistance, β-cell dysfunction, and ultimately T2D. This chronic inflammatory state contributes to long-term diabetic complications.
Despite substantial evidence supporting a strong relationship among insulin resistance, inflammation, and dyslipidemia, the determinants of progression from isolated insulin resistance to prediabetes, overt T2D, and T2D with complications remain unclear. Moreover, investigation of the postprandial phase, which occupies a large portion of the day, may provide critical insights into whether postprandial metabolism differentiates prediabetes from clinically overt diabetes, thereby elucidating pathophysiological mechanisms and informing dietary and pharmacological strategies to reduce disease progression.
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). The subjects were recruited from the patient datasets of the Poggio Renatico Primary Care Unit - Poggio Rete Salute Group Medicine, Ferrara Local Health Authority, with the support of a propensity score matching analysis. In an initial cross-sectional phase, the differential response to OFLT will be studied in the three groups (TRL, TRL remnants, lipidoma, inflammatory cytokines, etc.). The second prospective phase will observe up to 36 months whether the response to OFLT of triglyceride-rich lipoproteins and inflammatory cytokines are potential early biomarkers of the risk of progression to diabetes and the development of complications.
Main Objectives:
Primary (Cross-sectional phase) To evaluate the odds ratio (OR) that TG after OFLT is higher in T2D, in good glycemic control (A1c <7%) and treated with "standard of care" and in prediabetics compared to controls.
(Prospective phase) To evaluate whether TG after OFLT are early biomarkers of risk of progression to diabetes (ADA criteria) in prediabetic subjects treated with "standard of care" in a 36-month follow-up.
Secondary (Cross-sectional phase) To evaluate the odds ratio (OR) that TRLs after OFLT are higher in T2D, in good glycemic control (A1c <7%) and treated with "standard of care" and in prediabetics compared to controls.
To evaluate the odds ratio (OR) that remnant CM after OFLT are higher in T2D, in good glycemic control (A1c <7%) and treated with "standard of care" and in prediabetics compared to controls.
To evaluate whether the levels of interleukin-1β (IL-1β), interleukin-18 (IL-18), tumor necrosis factor-α (TNF-α), measured after 6 hours from OFLT, change from baseline.
To evaluate the odds ratio (OR) that cytokine levels after OFLT are higher in T2D, in good glycemic control (A1c <7%) and treated with "standard of care" and in prediabetics compared to controls.
(Prospective phase) To evaluate whether TG after OFLT are early biomarkers of risk of progression to nephropathy defined as a doubling of the urine albumin-creatinine ratio (uACR) >2 times compared to baseline and/or progression to a higher stage of renal disease; estimated glomerular filtration rate, eGFR <60 ml/min and uACR ≥30 mg/mmol for 3 months or more in diabetic subjects, in good glycemic control (A1c <7%), treated with "standard of care" over a 36-month follow-up.
To evaluate whether TG after OFLT are early biomarkers of risk of developing major cardiovascular events (Major Adverse Cardiac Events, five-point MACE: nonfatal stroke, nonfatal myocardial infarction, cardiovascular death, admission for unstable angina or heart failure) in diabetic subjects treated with "standard of care" in a 36-month follow-up.
To evaluate whether TRL after OFLT are early biomarkers of risk of progression to diabetes (ADA criteria[5]) in prediabetic subjects treated with "standard of care" in a 36-month follow up.
To evaluate whether TRL after OFLT are early biomarkers of risk of progression to nephropathy defined as uACR, >2 times compared to baseline and/or progression to a higher stage of renal disease (eGFR <60 ml/min and uACR ≥30 mg/mmol for 3 months or more) in diabetic subjects, in good glycemic control (A1c <7%), treated with "standard of care" in a 36-month follow-up.
To evaluate whether TRL after OFLT are early biomarkers of risk of developing major cardiovascular events (Major Adverse Cardiac Events, five-point MACE: nonfatal stroke, nonfatal myocardial infarction, cardiovascular death, admission for unstable angina or heart failure) in diabetic subjects, with good glycemic control (A1c <7%), treated with "standard of care" in a 36-month follow-up.
To evaluate whether remnant CM after OFLT are early biomarkers of risk of progression to diabetes (ADA criteria) in prediabetic subjects treated with "standard of care" in a 36-month follow-up.
To evaluate whether remnant CM after OFLT are early biomarkers of risk of progression to nephropathy defined as uACR, >2 times compared to baseline and/or progression to a higher stage of renal disease (eGFR <60 ml/min and uACR ≥30 mg/mmol for 3 months or more) in diabetic subjects, in good glycemic control (A1c <7%), treated with "standard of care" in a 36-month follow-up.
To evaluate whether remnant CM after OFLT are early biomarkers of risk of developing major cardiovascular events (Major Adverse Cardiac Events, five-point MACE: nonfatal stroke, nonfatal myocardial infarction, cardiovascular death, admission for unstable angina or heart failure) in diabetic subjects, with good glycemic control (A1c <7%), treated with "standard of care" in a 36-month follow-up.
To evaluate whether changes in cytokine levels predict the risk of progression to diabetes (ADA criteria) in diabetic subjects with good glycemic control (A1c <7%), treated with "standard of care" over a 36-month follow-up.
To evaluate whether changes in cytokine levels predict the risk of progression to uACR-defined nephropathy, >2 times compared to baseline, and/or progression to a higher stage of renal disease (eGFR <60 ml/min and uACR ≥30 mg/mmol for 3 months or more) in diabetic subjects with good glycemic control (A1c <7%), treated with "standard of care" over a 36-month follow-up.
To evaluate whether variations in cytokine levels predict the risk of developing major cardiovascular events (Major Adverse Cardiac Events, five-point MACE: nonfatal stroke, nonfatal myocardial infarction, cardiovascular death, admission for unstable angina or heart failure) in diabetic subjects with good glycemic control (A1c <7%), treated with "standard of care" in a 36-month follow-up.
Others (Cross sectional Phase) To evaluate whether circulating ceramide content is different in T2D, prediabetic, and control subjects.
To evaluate whether circulating ceramide content, measured 6 hours after OFLT, changes compared to baseline.
(Prospective Phase) To evaluate whether baseline circulating ceramide content predicts the risk of progression to diabetes (ADA criteria) in diabetic subjects with good glycemic control (A1c <7%), treated with "standard of care" over a 36-month follow-up.
To evaluate whether baseline circulating ceramide content predicts the risk of progression to nephropathy defined as uACR >2 times baseline and/or progression to a higher stage of renal disease (eGFR <60 ml/min and uACR ≥30 mg/mmol for 3 months or more) in diabetic subjects with good glycemic control (A1c <7%), treated with "standard of care" over a 36-month follow-up.
To evaluate whether baseline circulating ceramide content predicts the risk of developing major cardiovascular events (Major Adverse Cardiac Events, five-point MACE: nonfatal stroke, nonfatal myocardial infarction, cardiovascular death, admission for unstable angina or heart failure) in diabetic subjects with good glycemic control (A1c <7%), treated with "standard of care" over a 36-month follow-up.
Statistical analysis Variables will be expressed as mean ± standard deviation (SD) or median and 95% confidence intervals (95% CI), while categorical variables will be expressed as frequencies. The Shapiro-Wilk test will be used to assess the normality of continuous variables. Variables with symmetric distribution will be presented as mean ± standard deviation (SD), whereas non-normally distributed variables will be expressed as median and interquartile range [Q1-Q3]. Categorical data will be expressed as counts and percentages (%). Non-normally distributed variables will be log-transformed before parametric statistical analyses.
Percentages will be compared using the chi-square test, Fisher's exact test, or Yates' correction, as appropriate. Continuous variables will be compared using Student's t-test or Mann-Whitney/Kruskal-Wallis tests, as appropriate. Box plots will be used to compare continuous variables among groups.
Chi-square tests for risk estimates and one-vs-one analyses between the main endpoints will also be performed, with calculation of relative Odds Ratios (OR) and 95% confidence intervals (95% CI). ORs and 95% CIs will be estimated using unadjusted logistic regression models considering diabetes conversion and development of complications as dependent variables, and age, sex, 5-point MACE events, and other potential confounding variables as independent variables.
Overall survival curves will be generated using the Kaplan-Meier method and compared among subgroups using the log-rank test. A p-value <0.05 will be considered statistically significant.
Correlations between continuous variables will be assessed using Pearson correlation tests for normally distributed variables, whereas non-normally distributed variables will be analyzed after log-transformation or using non-parametric tests (Spearman correlation test). Results will be reported as correlation coefficient (r) and p-value.
For all variables of interest, absolute (Δ) and percentage (Δ%) changes between baseline, Time+6 months, Time+12 months, Time+24 months, and Time+36 months will be calculated. Changes over time will be analyzed using repeated-measures t-tests or general linear models (GLM) for repeated measures, including within-subject and between-subject analyses.
Statistical analyses will be performed using SPSS software version 29.0 (SPSS Inc., Chicago, IL, USA). When relevant, effect size measures will also be reported.
Study Team PI per AOUFe e UniFe - Prof. Angelina Passaro, MD, PhD PI per AUSLFe - Dott. Luca Catapano, MD CoPI per UniFe - Dott. Juana Maria Sanz Molina, MSc, PhD e CoPI per UniFe -- Prof. Domenico Sergi, MSc, PhD CoPI per AOUFe - Dott. Edoardo Dalla Nora, MD, PhD CoPI per AUSLFe - Dott.ssa Eleonora Petrucci, MD Researcher And Primary Study Coordinator - Fabiola Castaldo, MSc Researcher - Simona Colombari, MSc Researcher - Sharon Angelini, MSc
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Controls |
| ||
| Prediabetics |
| ||
| Diabetics |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Oral Fat Load Test | Diagnostic Test | After 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | From enrollment after two years at the end of the enrollment |
| 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. | At 36-month follow-up |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | From enrollment after two years at the end of the enrollment |
| Measure | Description | Time Frame |
|---|---|---|
| Circulating ceramide content | Plasma circulating ceramide concentrations measured in participants with type 2 diabetes, prediabetes, and controls. | From enrollment after two years at the end of the enrollment |
| Change in circulating ceramide concentrations after oral fat loading test |
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
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.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Fabiola Castaldo, Pharmacy | Contact | +39 3348330516 | fabiola.castaldo@unife.it | |
| Juana M Sanz Molina, Chemistry | Contact | szj@unife.it |
| Name | Affiliation | Role |
|---|---|---|
| Angelina Passaro, Degree in Medicine and Surgery | University Hospital of Ferrara "Sant'Anna" | Study Chair |
| Luca Catapano, Degree in Medicine and Surgery | Azienda USL Ferrara | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University Hospital "Sant'Anna" of Ferrara | Recruiting | Ferrara | Ferrara | 44124 | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 27095446 | Background | Leon-Acuna A, Alcala-Diaz JF, Delgado-Lista J, Torres-Pena JD, Lopez-Moreno J, Camargo A, Garcia-Rios A, Marin C, Gomez-Delgado F, Caballero J, Van-Ommen B, Malagon MM, Perez-Martinez P, Lopez-Miranda J. Hepatic insulin resistance both in prediabetic and diabetic patients determines postprandial lipoprotein metabolism: from the CORDIOPREV study. Cardiovasc Diabetol. 2016 Apr 19;15:68. doi: 10.1186/s12933-016-0380-y. | |
| 12898469 |
| Label | URL |
|---|---|
| Website with useful information about the study that can be shared with the patients | View source |
Not provided
Sensitive participant data will not be shared. However, the management and sharing of non-identifiable data will fully comply with the FAIR principles (Findable, Accessible, Interoperable, Reusable), ensuring traceability, interoperability, and reusability of the data for scientific research purposes, in accordance with applicable data protection regulations
Not provided
Not provided
Not provided
Not provided
Not provided
| OTHER |
Not provided
Not provided
Not provided
"Blood samples will be collected before and at 1, 2, 3, 4, and 6 hours after Oral fat Load Test. Also blood samples, performed in patients fasting for at least 12 hours will be repeated after 6, 12, 24 and 36 months from the baseline visit at follow-up visit.
Vacutainer that will be collected:
Vacutainer Tube - EDTA for plasma Vacutainer Serum Tubes - Red tube with added silica particles to trigger clotting, without anticoagulants for serum The serum/plasma obtained after centrifugation at 3000 rpm for 15 minutes will be stored in cryovials and preserved at -80°C
30-50 ml of midstream morning urine will also be collected
|
| Chylomicron remnant concentrations after oral fat loading test |
Chylomicron remnant concentrations measured before and 6 hours after oral fat loading test. |
| From enrollment after two years at the end of the enrollment |
| Interleukin-1β concentration after oral fat loading test | Plasma interleukin-1β (IL-1β) concentrations measured before and 6 hours after oral fat loading test. | From enrollment after two years at the end of the enrollment |
| 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. | From enrollment after two years at the end of the enrollment |
| Tumor necrosis factor-α (TNF-α) concentration after oral fat loading test | Tumor necrosis factor-α (TNF-α) concentrations measured before and 6 hours after oral fat loading test. | From enrollment after two years at the end of the enrollment |
| Progression of diabetic nephropathy during follow-up | Progression of diabetic nephropathy defined as doubling of urine albumin-creatinine ratio compared to baseline, progression to a higher stage of renal disease, estimated glomerular filtration rate below 60 mL/min, and/or urine albumin-creatinine ratio ≥30 mg/mmol for at least 3 months. | 36-month follow-up |
| Major adverse cardiovascular events (5-point MACE) | Occurrence of major adverse cardiovascular events defined as nonfatal stroke, nonfatal myocardial infarction, cardiovascular death, hospitalization for unstable angina, or hospitalization for heart failure during follow-up. | 36-month follow-up |
Plasma circulating ceramide concentrations measured before and 6 hours after oral fat loading test. |
| From enrollment after two years at the end of the enrollment |
| 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. | From enrollment after two years at the end of the enrollment |
| 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. | From enrollment after two years at the end of the enrollment |
| In vitro model using VLDL | To develop in vitro cellular models to investigate the pathophysiological mechanisms underlying postprandial metabolic dysfunction. Human endothelial cells (HUVEC) and human monocytic cells (U937) will be established and exposed to postprandial VLDL isolated from patient serum samples. Cells will be incubated for 4 hours with VLDL isolated from patient serum. Following incubation, total RNA will be extracted and real-time PCR analyses will be performed to assess the gene expression of selected target genes, including IL-6, IL-1β, VCAM-1, E-selectin (ELAM-1), ICAM-1, and P-SEL. The impact of postprandial VLDL will be evaluated in terms of endothelial activation, expression of adhesion molecules, monocytic inflammatory responses, and induction of oxidative stress-related signals. Cellular responses will be correlated with proteomic and metabolomic profiles, as well as with clinical parameters of the subjects, in order to identify causal mechanisms and targetable biological pathways. | From enrollment after two years at the end of the enrollment |
| Extracellullar vescicles content | To evaluate whether the composition of extracellular vesicles differs among subjects with type 2 diabetes, prediabetes, and healthy controls. | From enrollment after two years at the end of the enrollment |
| Extracellular vescicles after OFL | To assess whether circulating extracellular vesicle levels measured 6 hours after the Oral Fat Loading Test (OFLT) differ from baseline values. | From enrollment after two years at the end of the enrollment |
| Fast Protein Liquid Chromatography analysis | Separation and characterization of the different protein and lipoprotein fractions in serum samples from enrolled subjects at baseline (T0) and at the triglyceride peak time point after OFL | From enrollment after two years at the end of the enrollment |
| RNA-seq analysis | Total RNA will be extracted from buffy coat samples obtained from peripheral blood at T0 and at peak triglicerydes time and used for transcriptomic analyses | From enrollment after two years at the end of the enrollment |
| RNA-Seq on Huvec and VLDL | Total RNA will be extracted from Huvec cells trated with VLDL extracted from serum at T0 and at peak truglicerydes time and used for transcriptomic analyses. | From enrollment after two years at the end of the enrollment |
| Angelina Passaro, Degree in Medicine and Surgery | University Hospital of Ferrara "Sant'Anna" | Principal Investigator |
| Angelina Passaro, Degree in Medicine and Surgery | University Hospital of Ferrara "Sant'Anna" | Study Director |
| Nucleo di Cure Primarie di Poggio Renatico - Medicina di Gruppo Poggio Rete Salute Via Salvo D'Acquisto, 1/a | Recruiting | Poggio Renatico | Ferrara | 44023 | Italy |
|
| Background |
| Carstensen M, Thomsen C, Hermansen K. Incremental area under response curve more accurately describes the triglyceride response to an oral fat load in both healthy and type 2 diabetic subjects. Metabolism. 2003 Aug;52(8):1034-7. doi: 10.1016/s0026-0495(03)00155-0. |
| 37247330 | Background | SCORE2-Diabetes Working Group and the ESC Cardiovascular Risk Collaboration. SCORE2-Diabetes: 10-year cardiovascular risk estimation in type 2 diabetes in Europe. Eur Heart J. 2023 Jul 21;44(28):2544-2556. doi: 10.1093/eurheartj/ehad260. |
| 34120177 | Background | SCORE2 working group and ESC Cardiovascular risk collaboration. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J. 2021 Jul 1;42(25):2439-2454. doi: 10.1093/eurheartj/ehab309. |
| 8150893 | Background | Seidenberg M, Haltiner A, Taylor MA, Hermann BB, Wyler A. Development and validation of a Multiple Ability Self-Report Questionnaire. J Clin Exp Neuropsychol. 1994 Feb;16(1):93-104. doi: 10.1080/01688639408402620. |
| 34829831 | Background | Hasheminasabgorji E, Jha JC. Dyslipidemia, Diabetes and Atherosclerosis: Role of Inflammation and ROS-Redox-Sensitive Factors. Biomedicines. 2021 Nov 3;9(11):1602. doi: 10.3390/biomedicines9111602. |
| 34182846 | Background | Groenen AG, Halmos B, Tall AR, Westerterp M. Cholesterol efflux pathways, inflammation, and atherosclerosis. Crit Rev Biochem Mol Biol. 2021 Aug;56(4):426-439. doi: 10.1080/10409238.2021.1925217. Epub 2021 Jun 28. |
| 36362288 | Background | Yanai H, Adachi H, Hakoshima M, Katsuyama H. Atherogenic Lipoproteins for the Statin Residual Cardiovascular Disease Risk. Int J Mol Sci. 2022 Nov 4;23(21):13499. doi: 10.3390/ijms232113499. |
| 26106188 | Background | Xiao C, Dash S, Morgantini C, Adeli K, Lewis GF. Gut Peptides Are Novel Regulators of Intestinal Lipoprotein Secretion: Experimental and Pharmacological Manipulation of Lipoprotein Metabolism. Diabetes. 2015 Jul;64(7):2310-8. doi: 10.2337/db14-1706. |
| 37762244 | Background | Yanai H, Adachi H, Hakoshima M, Katsuyama H. Postprandial Hyperlipidemia: Its Pathophysiology, Diagnosis, Atherogenesis, and Treatments. Int J Mol Sci. 2023 Sep 11;24(18):13942. doi: 10.3390/ijms241813942. |
| 37697054 | Background | Hornburg D, Wu S, Moqri M, Zhou X, Contrepois K, Bararpour N, Traber GM, Su B, Metwally AA, Avina M, Zhou W, Ubellacker JM, Mishra T, Schussler-Fiorenza Rose SM, Kavathas PB, Williams KJ, Snyder MP. Dynamic lipidome alterations associated with human health, disease and ageing. Nat Metab. 2023 Sep;5(9):1578-1594. doi: 10.1038/s42255-023-00880-1. Epub 2023 Sep 11. |
| 15983261 | Background | Festa A, Williams K, Hanley AJ, Otvos JD, Goff DC, Wagenknecht LE, Haffner SM. Nuclear magnetic resonance lipoprotein abnormalities in prediabetic subjects in the Insulin Resistance Atherosclerosis Study. Circulation. 2005 Jun 28;111(25):3465-72. doi: 10.1161/CIRCULATIONAHA.104.512079. |
| 30328521 | Background | Bjornstad P, Eckel RH. Pathogenesis of Lipid Disorders in Insulin Resistance: a Brief Review. Curr Diab Rep. 2018 Oct 17;18(12):127. doi: 10.1007/s11892-018-1101-6. |
| 22796579 | Background | Sparks JD, Sparks CE, Adeli K. Selective hepatic insulin resistance, VLDL overproduction, and hypertriglyceridemia. Arterioscler Thromb Vasc Biol. 2012 Sep;32(9):2104-12. doi: 10.1161/ATVBAHA.111.241463. Epub 2012 Jul 12. |
| 25725623 | Background | Verges B. Pathophysiology of diabetic dyslipidaemia: where are we? Diabetologia. 2015 May;58(5):886-99. doi: 10.1007/s00125-015-3525-8. Epub 2015 Mar 1. |
| 8994405 | Background | Grundy SM. Small LDL, atherogenic dyslipidemia, and the metabolic syndrome. Circulation. 1997 Jan 7;95(1):1-4. doi: 10.1161/01.cir.95.1.1. No abstract available. |
| 36507639 | Background | ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, Collins BS, Hilliard ME, Isaacs D, Johnson EL, Kahan S, Khunti K, Leon J, Lyons SK, Perry ML, Prahalad P, Pratley RE, Seley JJ, Stanton RC, Gabbay RA, on behalf of the American Diabetes Association. 1. Improving Care and Promoting Health in Populations: Standards of Care in Diabetes-2023. Diabetes Care. 2023 Jan 1;46(Supple 1):S10-S18. doi: 10.2337/dc23-S001. |
| 29107454 | Background | Gedebjerg A, Almdal TP, Berencsi K, Rungby J, Nielsen JS, Witte DR, Friborg S, Brandslund I, Vaag A, Beck-Nielsen H, Sorensen HT, Thomsen RW. Prevalence of micro- and macrovascular diabetes complications at time of type 2 diabetes diagnosis and associated clinical characteristics: A cross-sectional baseline study of 6958 patients in the Danish DD2 cohort. J Diabetes Complications. 2018 Jan;32(1):34-40. doi: 10.1016/j.jdiacomp.2017.09.010. Epub 2017 Sep 19. |
| 31901175 | Background | Whicher CA, O'Neill S, Holt RIG. Diabetes in the UK: 2019. Diabet Med. 2020 Feb;37(2):242-247. doi: 10.1111/dme.14225. |
| 34883189 | Background | Ogurtsova K, Guariguata L, Barengo NC, Ruiz PL, Sacre JW, Karuranga S, Sun H, Boyko EJ, Magliano DJ. IDF diabetes Atlas: Global estimates of undiagnosed diabetes in adults for 2021. Diabetes Res Clin Pract. 2022 Jan;183:109118. doi: 10.1016/j.diabres.2021.109118. Epub 2021 Dec 6. |
| 36507649 | Background | ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, Collins BS, Hilliard ME, Isaacs D, Johnson EL, Kahan S, Khunti K, Leon J, Lyons SK, Perry ML, Prahalad P, Pratley RE, Seley JJ, Stanton RC, Gabbay RA, on behalf of the American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes-2023. Diabetes Care. 2023 Jan 1;46(Suppl 1):S19-S40. doi: 10.2337/dc23-S002. |
| 37356446 | Background | GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023 Jul 15;402(10397):203-234. doi: 10.1016/S0140-6736(23)01301-6. Epub 2023 Jun 22. |
| 34879977 | Background | Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, Stein C, Basit A, Chan JCN, Mbanya JC, Pavkov ME, Ramachandaran A, Wild SH, James S, Herman WH, Zhang P, Bommer C, Kuo S, Boyko EJ, Magliano DJ. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022 Jan;183:109119. doi: 10.1016/j.diabres.2021.109119. Epub 2021 Dec 6. |
| 27061677 | Background | NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet. 2016 Apr 9;387(10027):1513-1530. doi: 10.1016/S0140-6736(16)00618-8. Epub 2016 Apr 6. |
| 33069326 | Background | GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020 Oct 17;396(10258):1204-1222. doi: 10.1016/S0140-6736(20)30925-9. |
| ID | Term |
|---|---|
| D011236 | Prediabetic State |
| D003924 | Diabetes Mellitus, Type 2 |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D004700 | Endocrine System Diseases |
Not provided
Not provided