New strategies of clinical precision medicine

Public health genomics research in Italy: an overview of ongoing projects

Public health genomics (PHG) aims to integrate advances in genomic sciences into healthcare for the benefit of the general population. As in many countries, there are various research initiatives in this field in Italy, but a clear picture of the national research portfolio has never been sketched. Thus, we aimed to provide an overview of current PHG research projects at the national or international level by consultation with Italian institutional and academic experts. We included 68 PHG projects: the majority were international projects in which Italian researchers participated (n = 43), mainly funded by the European Commission, while the remainder were national initiatives (N = 25), mainly funded by central government. Funding varied considerably, from € 50,000 to € 80,803,177. Three main research themes were identified: governance (N = 20); precision medicine (PM; N = 46); and precision public health (N = 2). We found that research activities are preferentially aimed at the clinical application of PM, while other efforts deal with the governance of the complex translation of genomic innovation into clinical and public health practice. To align such activities with national and international priorities, the development of an updated research agenda for PHG is needed.

Integrating oculomics with genomics reveals imaging biomarkers for preventive and personalized prediction of arterial aneurysms

Objective

Arterial aneurysms are life-threatening but usually asymptomatic before requiring hospitalization. Oculomics of retinal vascular features (RVFs) extracted from retinal fundus images can reflect systemic vascular properties and therefore were hypothesized to provide valuable information on detecting the risk of aneurysms. By integrating oculomics with genomics, this study aimed to (i) identify predictive RVFs as imaging biomarkers for aneurysms and (ii) evaluate the value of these RVFs in supporting early detection of aneurysms in the context of predictive, preventive and personalized medicine (PPPM).

Methods

This study involved 51,597 UK Biobank participants who had retinal images available to extract oculomics of RVFs. Phenome-wide association analyses (PheWASs) were conducted to identify RVFs associated with the genetic risks of the main types of aneurysms, including abdominal aortic aneurysm (AAA), thoracic aneurysm (TAA), intracranial aneurysm (ICA) and Marfan syndrome (MFS). An aneurysm-RVF model was then developed to predict future aneurysms. The performance of the model was assessed in both derivation and validation cohorts and was compared with other models employing clinical risk factors. An RVF risk score was derived from our aneurysm-RVF model to identify patients with an increased risk of aneurysms.

Results

PheWAS identified a total of 32 RVFs that were significantly associated with the genetic risks of aneurysms. Of these, the number of vessels in the optic disc ('ntreeA') was associated with both AAA (β = -0.36, P = 6.75e-10) and ICA (β = -0.11, P = 5.51e-06). In addition, the mean angles between each artery branch ('curveangle_mean_a') were commonly associated with 4 MFS genes (FBN1: β = -0.10, P = 1.63e-12; COL16A1: β = -0.07, P = 3.14e-09; LOC105373592: β = -0.06, P = 1.89e-05; C8orf81/LOC441376: β = 0.07, P = 1.02e-05). The developed aneurysm-RVF model showed good discrimination ability in predicting the risks of aneurysms. In the derivation cohort, the C-index of the aneurysm-RVF model was 0.809 [95% CI: 0.780-0.838], which was similar to the clinical risk model (0.806 [0.778-0.834]) but higher than the baseline model (0.739 [0.733-0.746]). Similar performance was observed in the validation cohort, with a C-index of 0.798 (0.727-0.869) for the aneurysm-RVF model, 0.795 (0.718-0.871) for the clinical risk model and 0.719 (0.620-0.816) for the baseline model. An aneurysm risk score was derived from the aneurysm-RVF model for each study participant. The individuals in the upper tertile of the aneurysm risk score had a significantly higher risk of aneurysm compared to those in the lower tertile (hazard ratio = 17.8 [6.5-48.8], P = 1.02e-05).

Conclusion

We identified a significant association between certain RVFs and the risk of aneurysms and revealed the impressive capability of using RVFs to predict the future risk of aneurysms by a PPPM approach. Our finds have great potential to support not only the predictive diagnosis of aneurysms but also a preventive and more personalized screening plan which may benefit both patients and the healthcare system.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00315-7.

T cell phenotype switching in autoimmune disorders: Clinical significance of targeting metabolism

Increasing efforts points to the understanding of how to maximize the capabilities of the adaptive immune system to fight against the development of immune and inflammatory disorders. Here we focus on the role of T cells as immune cells which subtype imbalance may lead to disease onset. Specifically, we propose that autoimmune disorders may develop as a consequence of a metabolic imbalance that modulates switching between T cell phenotypes. We highlight a Systems Biology strategy that integrates computational metabolic modelling with experimental data to investigate the metabolic requirements of T cell phenotypes, and to predict metabolic genes that may be targeted in autoimmune inflammatory diseases. Thus, we propose a new perspective of targeting T cell metabolism to modulate the immune response and prevent T cell phenotype imbalance, which may help to repurpose already existing drugs targeting metabolism for therapeutic treatment.