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 (ß = -0.36, P = 6.75e-10) and ICA (ß = -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: ß = -0.10, P = 1.63e-12; COL16A1: ß = -0.07, P = 3.14e-09; LOC105373592: ß = -0.06, P = 1.89e-05; C8orf81/LOC441376: ß = 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. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00315-7.

Development and validation of a transcriptomic signature-based model as the predictive, preventive, and personalized medical strategy for preterm birth within 7 days in threatened preterm labor women.

Preterm birth (PTB) is the leading cause of neonatal death. The essential strategy to prevent PTB is the accurate identification of threatened preterm labor (TPTL) women who will have PTB in a short time (< 7 days). Here, we aim to propose a clinical model to contribute to the effective prediction, precise prevention, and personalized medical treatment for PTB < 7 days in TPTL women through bioinformatics analysis and prospective cohort studies. In this study, the 1090 key genes involved in PTB < 7 days in the peripheral blood of TPTL women were ascertained using WGCNA. Based on this, the biological basis of immune-inflammatory activation (e.g., IFNγ and TNFα signaling) as well as immune cell disorders (e.g., monocytes and Th17 cells) in PTB < 7 days were revealed. Then, four core genes (JOSD1, IDNK, ZMYM3, and IL1B) that best represent their transcriptomic characteristics were screened by SVM and LASSO algorithm. Therefore, a prediction model with an AUC of 0.907 was constructed, which was validated in a larger population (AUC = 0.783). Moreover, the predictive value (AUC = 0.957) and clinical feasibility of this model were verified through the clinical prospective cohort we established. In conclusion, in the context of Predictive, Preventive, and Personalized Medicine (3PM), we have developed and validated a model to predict PTB < 7 days in TPTL women. This is promising to greatly improve the accuracy of clinical prediction, which would facilitate the personalized management of TPTL women to precisely prevent PTB < 7 days and improve maternal-fetal outcomes.

Modeling SARS-CoV-2 spike/ACE2 protein-protein interactions for predicting the binding affinity of new spike variants for ACE2, and novel ACE2 structurally related human protein targets, for COVID-19 handling in the 3PM context.

Aims: The rapid spread of new SARS-CoV-2 variants has highlighted the crucial role played in the infection by mutations occurring at the SARS-CoV-2 spike receptor binding domain (RBD) in the interactions with the human ACE2 receptor. In this context, it urgently needs to develop new rapid tools for quickly predicting the affinity of ACE2 for the SARS-CoV-2 spike RBD protein variants to be used with the ongoing SARS-CoV-2 genomic sequencing activities in the clinics, aiming to gain clues about the transmissibility and virulence of new variants, to prevent new outbreaks and to quickly estimate the severity of the disease in the context of the 3PM. Methods: In our study, we used a computational pipeline for calculating the interaction energies at the SARS-CoV-2 spike RBD/ACE2 protein-protein interface for a selected group of characterized infectious variants of concern/interest (VoC/VoI). By using our pipeline, we built 3D comparative models of the SARS-CoV-2 spike RBD/ACE2 protein complexes for the VoC B.1.1.7-United Kingdom (carrying the mutations of concern/interest N501Y, S494P, E484K at the RBD), P.1-Japan/Brazil (RBD mutations: K417T, E484K, N501Y), B.1.351-South Africa (RBD mutations: K417N, E484K, N501Y), B.1.427/B.1.429-California (RBD mutations: L452R), the B.1.141 (RBD mutations: N439K), and the recent B.1.617.1-India (RBD mutations: L452R; E484Q) and the B.1.620 (RBD mutations: S477N; E484K). Then, we used the obtained 3D comparative models of the SARS-CoV-2 spike RBD/ACE2 protein complexes for predicting the interaction energies at the protein-protein interface. Results: Along SARS-CoV-2 mutation database screening and mutation localization analysis, it was ascertained that the most dangerous mutations at VoC/VoI spike proteins are located mainly at three regions of the SARS-CoV-2 spike "boat-shaped" receptor binding motif, on the RBD domain. Notably, the P.1 Japan/Brazil variant present three mutations, K417T, E484K, N501Y, located along the entire receptor binding motif, which apparently determines the highest interaction energy at the SARS-CoV-2 spike RBD/ACE2 protein-protein interface, among those calculated. Conversely, it was also observed that the replacement of a single acidic/hydrophilic residue with a basic residue (E484K or N439K) at the "stern" or "bow" regions, of the boat-shaped receptor binding motif on the RBD, appears to determine an interaction energy with ACE2 receptor higher than that observed with single mutations occurring at the "hull" region or with other multiple mutants. In addition, our pipeline allowed searching for ACE2 structurally related proteins, i.e., THOP1 and NLN, which deserve to be investigated for their possible involvement in interactions with the SARS-CoV-2 spike protein, in those tissues showing a low expression of ACE2, or as a novel receptor for future spike variants. A freely available web-tool for the in silico calculation of the interaction energy at the SARS-CoV-2 spike RBD/ACE2 protein-protein interface, starting from the sequences of the investigated spike and/or ACE2 variants, was made available for the scientific community at: https://www.mitoairm.it/covid19affinities. Conclusion: In the context of the PPPM/3PM, the employment of the described pipeline through the provided webservice, together with the ongoing SARS-CoV-2 genomic sequencing, would help to predict the transmissibility of new variants sequenced from future patients, depending on SARS-CoV-2 genomic sequencing activities and on the specific amino acid replacement and/or on its location on the SARS-CoV-2 spike RBD, to put in play all the possible counteractions for preventing the most deleterious scenarios of new outbreaks, taking into consideration that a greater transmissibility has not to be necessarily related to a more severe manifestation of the disease. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-021-00267-w.

Association of systemic inflammation indices with visual field loss progression in patients with primary angle-closure glaucoma: potential biomarkers for 3P medical approaches.

RELEVANCE: Accumulating evidence suggests a dysfunction of the para-inflammation in the retinal ganglion cell layer and the optic nerve head in patients with glaucoma. Currently, circulating blood platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR) are regarded as novel indicators of systemic inflammation. Biomarkers allow early identification of patients with visual field (VF) loss progression and timely implementation of replacement therapies. OBJECTIVE: This study aimed to investigate whether higher inflammatory indices (PLR, NLR, and LMR) were associated with VF loss progression in patients with primary angle-closure glaucoma (PACG) for the predictive diagnostics, targeted prevention, and personalization of medical services. METHODS: This prospective cohort study followed up 277 patients with PACG for at least 24 months, with clinical examination and VF testing every 6 months. Inflammatory cell quantification, including platelets, neutrophils, lymphocytes, and monocytes, was measured using the Sysmex XN-A1 automated inflammatory cells quantification system. Three systemic inflammatory indices, PLR, NLR, and LMR, were determined on the basis of baseline neutrophil, lymphocyte, monocyte, and platelet counts in patients with PACG. The risk factors for PACG were analyzed using logistic regression, Cox proportional hazards regression, and the Kaplan-Meier curve. RESULTS: Our results revealed that 111 (40.07%) patients showed VF loss progression. The PLR was significantly higher (P = 0.046) in the progression group than in the non-progression group. A higher PLR (OR 1.05, 95% CI 1.01-1.08, P = 0.004) was a risk factor for PACG progression. In multivariate analyses, PLR independently predicted VF loss progression (HR 1.01, 95% CI 1.00-1.01, P = 0.04). Kaplan-Meier curve analysis showed that higher PLR indicated significantly higher rates of VF loss progression (66.91% vs. 52.90%, P = 0.03). Comparable results were observed in the male and female subgroups. CONCLUSION: Our findings revealed the significant association between a high PLR and a greater risk of VF loss progression in patients with PACG. PLR may be highly recommended as a novel predictive/diagnostic tool for the assessment of VF loss progression from the perspectives of predictive, preventive, and personalized medicine in vulnerable populations and for individual screening. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13167-021-00260-3.

The interplay between the vaginal microbiome and innate immunity in the focus of predictive, preventive, and personalized medical approach to combat HPV-induced cervical cancer.

HPVs representing the most common sexually transmitted disease are a group of carcinogenic viruses with different oncogenic potential. The immune system and the vaginal microbiome represent the modifiable and important risk factors in HPV-induced carcinogenesis. HPV infection significantly increases vaginal microbiome diversity, leading to gradual increases in the abundance of anaerobic bacteria and consequently the severity of cervical dysplasia. Delineation of the exact composition of the vaginal microbiome and immune environment before HPV acquisition, during persistent/progressive infections and after clearance, provides insights into the complex mechanisms of cervical carcinogenesis. It gives hints regarding the prediction of malignant potential. Relative high HPV prevalence in the general population is a challenge for modern and personalized diagnostics and therapeutic guidelines. Identifying the dominant microbial biomarkers of high-grade and low-grade dysplasia could help us to triage the patients with marked chances of lesion regression or progression. Any unnecessary surgical treatment of cervical dysplasia could negatively affect obstetrical outcomes and sexual life. Therefore, understanding the effect and role of microbiome-based therapies is a breaking point in the conservative management of HPV-associated precanceroses. The detailed evaluation of HPV capabilities to evade immune mechanisms from various biofluids (vaginal swabs, cervicovaginal lavage/secretions, or blood) could promote the identification of new immunological targets for novel individualized diagnostics and therapy. Qualitative and quantitative assessment of local immune and microbial environment and associated risk factors constitutes the critical background for preventive, predictive, and personalized medicine that is essential for improving state-of-the-art medical care in patients with cervical precanceroses and cervical cancer. The review article focuses on the influence and potential diagnostic and therapeutic applications of the local innate immune system and the microbial markers in HPV-related cancers in the context of 3P medicine.

Mitochondriopathies as a Clue to Systemic Disorders-Analytical Tools and Mitigating Measures in Context of Predictive, Preventive, and Personalized (3P) Medicine.

The mitochondrial respiratory chain is the main site of reactive oxygen species (ROS) production in the cell. Although mitochondria possess a powerful antioxidant system, an excess of ROS cannot be completely neutralized and cumulative oxidative damage may lead to decreasing mitochondrial efficiency in energy production, as well as an increasing ROS excess, which is known to cause a critical imbalance in antioxidant/oxidant mechanisms and a "vicious circle" in mitochondrial injury. Due to insufficient energy production, chronic exposure to ROS overproduction consequently leads to the oxidative damage of life-important biomolecules, including nucleic acids, proteins, lipids, and amino acids, among others. Different forms of mitochondrial dysfunction (mitochondriopathies) may affect the brain, heart, peripheral nervous and endocrine systems, eyes, ears, gut, and kidney, among other organs. Consequently, mitochondriopathies have been proposed as an attractive diagnostic target to be investigated in any patient with unexplained progressive multisystem disorder. This review article highlights the pathomechanisms of mitochondriopathies, details advanced analytical tools, and suggests predictive approaches, targeted prevention and personalization of medical services as instrumental for the overall management of mitochondriopathy-related cascading pathologies.