Predictive Cardio-Metabolic Trascrictomics Trajectories in the Barilla Offspring Follow-Up Study

Healthy Volunteer

transcriptomic analysis, cardio-metabolic analysis

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

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.

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