Predictive Cardio-Metabolic Trascrictomics Trajectories in the Barilla Offspring Follow-Up Study (PREDICT-OMICS)

Predictive Cardio-Metabolic Trascrictomics Trajectories in the Barilla Offspring Follow-Up STUDY: the PREDICT-OMICS Study.

Experimental, drug-free, longitudinal, single-centre study for the prediction of cardiometabolic risk in Barilla Off-Spring Study subjects by analysing the evolution of transcriptomic signatures

Study Overview

• Status: Active, not recruiting
• Conditions: Healthy Volunteer
• Intervention / Treatment: Other: Evaluation of transcriptomic and cardio-metabolic profiles

Detailed Description

In Westernized societies, common metabolic and cardiovascular diseases have complex etiologies involving dynamic genome-metagenome-environment interactions. The early molecular alterations that initiate and sustain their progression, although still only partially understood, are thought to share common roots in the terrain of insulin resistance (IR). In recent decades, reciprocal relationships between IR and inflammation have been unraveled, leading to the general concept of "meta-inflammation". In turn, the regulatory role played by metabolic signatures in immune cells has led to the concept of "immunometabolism". In particular, in these meta-inflammation-related disorders, there is a paucity of prospective gene expression data, especially in the early stages of their natural history. Gene expression profiling of peripheral blood mononuclear cells (PBMCs) may be a useful and accessible window into the pathophysiology of processes occurring in difficult-to-access organs and tissues. In a deeply phenotyped healthy cohort, the Barilla Offspring Study, a transcriptomic signature exclusively associated with IR was found by analyzing PBMC gene expression with a novel rank-based classification method, which was also found to discriminate diseased from healthy individuals in Alzheimer's disease, chronic heart failure and type 2 diabetes. The granularity of this approach can be further improved by examining gene expression in monocytes, as cells of innate immunity and mainly implicated in inflammatory/degenerative disorders and their risk factors. In this longitudinal study, we aim to identify the transcriptomic signature(s) in circulating immune cells and inflammatory biomarkers that predict or are associated with 15-year changes in glucose tolerance and/or carotid artery atherogenic phenotype. The study has solid premises: i. The cohort of subjects offers a unique opportunity to identify PBMC transcriptomic trajectories (baseline and 15-year follow-up) that predict changes in cardiometabolic phenotype; ii. cross-sectional assessment of monocyte transcriptomic profiling in the same cohort may uncover additional lineage-specific signatures associated with different cardio-metabotypes at follow-up, allowing comparison with PBMC; iii. an innovative rank-based classification method - SCUDO and its extensions - will be used, in addition to standard methods, to compute transcriptomic analyses. The results of the study may identify cellular transcriptomic signatures and trajectories, which could link cardiometabolic phenotypes at the molecular and cellular level, highlighting possible biological mechanisms of cardiometabolic disease susceptibility and progression, and unveiling a wide range of molecular targets in PBMC and, especially, monocytes, which can be further investigated for their validity as peripheral biomarkers for risk assessment. The findings will also provide new insights into targeted pharmacological strategies for the prevention and/or treatment of cardiometabolic diseases.

• Study Type: Observational
• Enrollment (Estimated): 110

Contacts and Locations

Study Locations

• Italy
  • PR
    • Parma, PR, Italy, 43126
      • University of Parma, Department of Medicine and Surgery

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

healthy adults who participated in the cross-sectional Barilla Offspring Study in 2006-2007

Description

Inclusion Criteria:

• Being enrolled in the Barilla Off-Spring Study (2006-2007)
• Ability to understand the methods, aims and implications of the study, and to give free and informed consent

Exclusion Criteria:

• Pregnancy

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

Participants in the Barilla Offspring Study

Other: Evaluation of transcriptomic and cardio-metabolic profiles; The parameters and variables collected in 2006-2007 (T0) will be re-evaluated at the follow-up visit (T1), including demographic, anthropometric, lifestyle data (smoking habit, physical activity, sleep quality) blood pressure, standard biochemical analysis and inflammatory profile. To assess the evolution of glucose tolerance and vascular damage, metabolic (OGTT) and cardiovascular (carotid ecodoppler) profile analyses will be repeated. For gene expression analyses, in addition to messenger RNA from PBMCs (as at T0), RNA from PBMC-derived monocytes will also be extracted and sequenced at T1.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Selection of a set of candidate genes directly correlated with the development of altered metabolic phenotypes	All data related to the transcriptomics of PBMCs at T0 (2006-2007) will be retrieved. At T1, a blood sample will be collected from which PBMCs will be isolated. Total RNA will be purified and its quality tested using the Quibit fluorimetric technique and the Tape-station system. From the same blood sample, the monocyte subpopulation will also be isolated and used to identify the specific transcriptomic signature. Both RNA datasets, collected at T0 and T1, will be processed to identify informative transcriptomic signatures of baseline and follow-up conditions. The sets will be converted to obtain a profile for a symbol gene; this will allow easier comparison of the calculated transcriptomic signatures between different datasets, facilitating the biological interpretation of the results.	within six months of the enrolment visit

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Selection of a set of candidate genes directly related to the development of altered vascular phenotypes	All data related to the transcriptomics of PBMCs at T0 (2006-2007) will be retrieved. At T1, a blood sample will be collected from which PBMCs will be isolated. Total RNA will be purified and its quality tested using the Quibit fluorimetric technique and the Tape-station system. From the same blood sample, the monocyte subpopulation will also be isolated and used to identify the specific transcriptomic signature. Both RNA datasets, collected at T0 and T1, will be processed to identify informative transcriptomic signatures of baseline and follow-up conditions. The sets will be converted to obtain a profile for a symbol gene; this will allow easier comparison of the calculated transcriptomic signatures between different datasets, facilitating the biological interpretation of the results.	within six months of the enrolment visit
Selection of a set of candidate genes directly related to overt altered metabolic and/or vascular phenotypes.	All data related to the transcriptomics of PBMCs at T0 (2006-2007) will be retrieved. At T1, a blood sample will be collected from which PBMCs will be isolated. Total RNA will be purified and its quality tested using the Quibit fluorimetric technique and the Tape-station system. From the same blood sample, the monocyte subpopulation will also be isolated and used to identify the specific transcriptomic signature. Both RNA datasets, collected at T0 and T1, will be processed to identify informative transcriptomic signatures of baseline and follow-up conditions. The sets will be converted to obtain a profile for a symbol gene; this will allow easier comparison of the calculated transcriptomic signatures between different datasets, facilitating the biological interpretation of the results.	within six months of the enrolment visit
Selection of markers directly related to the development of altered metabolic and/or vascular phenotypes.	All data related to the transcriptomics of PBMCs at T0 (2006-2007) will be retrieved. At T1, a blood sample will be collected from which PBMCs will be isolated. Total RNA will be purified and its quality tested using the Quibit fluorimetric technique and the Tape-station system. From the same blood sample, the monocyte subpopulation will also be isolated and used to identify the specific transcriptomic signature. Both RNA datasets, collected at T0 and T1, will be processed to identify informative transcriptomic signatures of baseline and follow-up conditions. The sets will be converted to obtain a profile for a symbol gene; this will allow easier comparison of the calculated transcriptomic signatures between different datasets, facilitating the biological interpretation of the results.	within six months of the enrolment visit
Selection of a set of candidate genes directly related to alterations in biohumoral parameters and the inflammatory profile.	All data related to the transcriptomics of PBMCs at T0 (2006-2007) will be retrieved. At T1, a blood sample will be collected from which PBMCs will be isolated. Total RNA will be purified and its quality tested using the Quibit fluorimetric technique and the Tape-station system. From the same blood sample, the monocyte subpopulation will also be isolated and used to identify the specific transcriptomic signature. Both RNA datasets, collected at T0 and T1, will be processed to identify informative transcriptomic signatures of baseline and follow-up conditions. The sets will be converted to obtain a profile for a symbol gene; this will allow easier comparison of the calculated transcriptomic signatures between different datasets, facilitating the biological interpretation of the results.	within six months of the enrolment visit
Selection of a set of candidate genes directly related to alterations in lifestyle.	All data related to the transcriptomics of PBMCs at T0 (2006-2007) will be retrieved. At T1, a blood sample will be collected from which PBMCs will be isolated. Total RNA will be purified and its quality tested using the Quibit fluorimetric technique and the Tape-station system. From the same blood sample, the monocyte subpopulation will also be isolated and used to identify the specific transcriptomic signature. Both RNA datasets, collected at T0 and T1, will be processed to identify informative transcriptomic signatures of baseline and follow-up conditions. The sets will be converted to obtain a profile for a symbol gene; this will allow easier comparison of the calculated transcriptomic signatures between different datasets, facilitating the biological interpretation of the results.	within six months of the enrolment visit

Collaborators and Investigators

• Sponsor: Azienda Ospedaliero-Universitaria di Parma
• Collaborators: University of Parma

Publications and helpful links

General Publications

• Ahlqvist E, Storm P, Karajamaki A, Martinell M, Dorkhan M, Carlsson A, Vikman P, Prasad RB, Aly DM, Almgren P, Wessman Y, Shaat N, Spegel P, Mulder H, Lindholm E, Melander O, Hansson O, Malmqvist U, Lernmark A, Lahti K, Forsen T, Tuomi T, Rosengren AH, Groop L. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018 May;6(5):361-369. doi: 10.1016/S2213-8587(18)30051-2. Epub 2018 Mar 5.
• Scazzina F, Dei Cas A, Del Rio D, Brighenti F, Bonadonna RC. The beta-cell burden index of food: A proposal. Nutr Metab Cardiovasc Dis. 2016 Oct;26(10):872-8. doi: 10.1016/j.numecd.2016.04.015. Epub 2016 May 6.
• Bianchi C, Miccoli R, Trombetta M, Giorgino F, Frontoni S, Faloia E, Marchesini G, Dolci MA, Cavalot F, Cavallo G, Leonetti F, Bonadonna RC, Del Prato S; GENFIEV Investigators. Elevated 1-hour postload plasma glucose levels identify subjects with normal glucose tolerance but impaired beta-cell function, insulin resistance, and worse cardiovascular risk profile: the GENFIEV study. J Clin Endocrinol Metab. 2013 May;98(5):2100-5. doi: 10.1210/jc.2012-3971. Epub 2013 Mar 28.
• Myhrstad MC, Ulven SM, Gunther CC, Ottestad I, Holden M, Ryeng E, Borge GI, Kohler A, Bronner KW, Thoresen M, Holven KB. Fish oil supplementation induces expression of genes related to cell cycle, endoplasmic reticulum stress and apoptosis in peripheral blood mononuclear cells: a transcriptomic approach. J Intern Med. 2014 Nov;276(5):498-511. doi: 10.1111/joim.12217. Epub 2014 Mar 20.
• Chi H. Immunometabolism at the intersection of metabolic signaling, cell fate, and systems immunology. Cell Mol Immunol. 2022 Mar;19(3):299-302. doi: 10.1038/s41423-022-00840-x. Epub 2022 Feb 21. No abstract available.
• Adeva-Andany MM, Martinez-Rodriguez J, Gonzalez-Lucan M, Fernandez-Fernandez C, Castro-Quintela E. Insulin resistance is a cardiovascular risk factor in humans. Diabetes Metab Syndr. 2019 Mar-Apr;13(2):1449-1455. doi: 10.1016/j.dsx.2019.02.023. Epub 2019 Feb 22.

Study record dates

Study Major Dates

• Study Start (Actual): May 23, 2024
• Primary Completion (Estimated): July 1, 2025
• Study Completion (Estimated): December 31, 2025

Study Registration Dates

• First Submitted: March 10, 2025
• First Submitted That Met QC Criteria: March 10, 2025
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: March 10, 2025
• Last Verified: March 1, 2025

More Information

Terms related to this study

• Keywords: transcriptomic analysis; cardio-metabolic analysis
• Other Study ID Numbers: 604-2023; 2022FZL247 (Other Grant/Funding Number: PRIN-2022)

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No