New prevention scenarios: polygenic risk

Artificial intelligence in cardiovascular prevention: new ways will open new doors

Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.

Genetic influence on brain volume alterations related to self-reported childhood abuse

As an important predictor of adulthood psychopathology, self-reported childhood abuse appears heritable and is associated with brain abnormalities. However, the specific genetic mechanisms behind these brain alterations remain largely unknown. This study recruited young adults who reported different degrees of childhood abuse from the community. In order to fully understand the influence of genes on brain changes related to self-reported childhood abuse, various experiments were conducted in this study. Firstly, volume changes of gray matter and white matter related to childhood abuse were investigated by using advanced magnetic resonance imaging techniques. After sequencing the whole exons, we further investigated the relationship between polygenic risk score, brain volume alterations, and childhood abuse score. Furthermore, transcription-neuroimaging association analysis was used to identify risk genes whose expressions were associated with brain volume alterations. The gray matter volumes of left caudate and superior parietal lobule, and white matter volumes of left cerebellum and right temporal lobe-basal ganglia region were significantly correlated with the childhood abuse score. More importantly, brain volume changes mediated the influence of polygenic risk on self-reported childhood abuse. Additionally, transcription-neuroimaging association analysis reported 63 risk genes whose expression levels were significantly associated with childhood abuse-related brain volume changes. These genes are involved in multiple biological processes, such as nerve development, synaptic transmission, and cell construction. Combining data from multiple perspectives, our work provides evidence of brain abnormalities associated with childhood abuse, and further indicates that polygene genetic risk and risk gene expression may affect the occurrence of childhood abuse by brain regulation, which provides insights into the molecularpathology and neuromechanism of childhood adversity. Paying attention to the physical and mental health of high-risk children may be a fundamental way to prevent childhood abuse and promote lifelong mental health.

Contribution of brain network connectivity in predicting effects of polygenic risk and childhood trauma on state-trait anxiety

BACKGROUND

Anxiety is usually attributed to adverse environmental factors, but it is known as a polygenic inheritance disease. Gene-environment interactions on the occurrence and severity of anxiety are still unclear. The role of brain network connectivity in the gene-environment effects on anxiety has not been explored and may be key to understanding neuropathogenesis and guiding treatment.

METHODS

This study recruited 177 young adults from the community that completed functional magnetic resonance imaging, Childhood Trauma Questionnaire (CTQ), state-trait anxiety scores, and whole exome sequencing. We calculated polygenic risk score (PRS) for anxiety and the sum score of CTQ, which are genetic and environmental factors that may affect anxiety, respectively. Abnormal brain network connectivity determined by the gene-environment effects and its associations with anxiety scores were then explored.

RESULTS

Except for the main effect of PRS or CTQ on intra-network connectivity, significant interactions were found in intra-network connectivity of visual network, default mode network, self-reference network, and sensorimotor network. Moreover, altered network connectivity was related to anxious tendency. In particular, the effect of CTQ on trait anxiety was mediated by the disrupted sensorimotor network, accompanied by a significant direct effect. However, the PRS influence on anxiety was mainly mediated through sensorimotor network paths, which exceeded the direct influence and was moderated by childhood trauma levels.

CONCLUSIONS

These network-specific functional changes related to individual gene-environment risks advance our understanding of psychiatric pathogenesis of anxiety and provide new insights for clinical intervention.