Blood biomarker panel recommended for personalized prediction, prognosis, and prevention of complications associated with abdominal aortic aneurysm

Identification and Evaluation of Blood-Based Biomarkers for Abdominal Aortic Aneurysm

INTRODUCTION

Blood-based biomarkers for abdominal aortic aneurysm (AAA) have been studied individually; however, we considered a panel of proteins to investigate AAA prognosis and its potential to improve predictive accuracy.

MATERIALS AND METHODS

Using a prospectively recruited cohort of patients with/without AAA (n = 452), we conducted a prognostic study to develop a model that accurately predicts AAA outcomes using clinical features and circulating biomarker levels. Serum concentrations of 9 biomarkers were measured at baseline, and the cohort was followed for 2 years. The primary outcome was major adverse aortic event (MAAE; composite of rapid AAA expansion [>0.5 cm/6 months or >1 cm/12 months], AAA intervention, or AAA rupture). Using 10-fold cross-validation, we trained a random forest model to predict 2 year MAAE using (1) clinical characteristics, (2) biomarkers, and (3) clinical characteristics and biomarkers.

RESULTS

Two-year MAAE occurred in 114 (25%) patients. Two proteins were significantly elevated in patients with AAA compared with those without AAA (angiopoietin-2 and aggrecan), composing the protein panel. For predicting 2 year MAAE, our random forest model achieved area under the receiver operating characteristic curve (AUROC) 0.74 using clinical features alone, and the addition of the 2-protein panel improved performance to AUROC 0.86.

CONCLUSIONS

Using a combination of clinical/biomarker data, we developed a model that accurately predicts 2 year MAAE.

Blood immunological profile of abdominal aortic aneurysm based on autoimmune injury

Abdominal aortic aneurysm (AAA) occupies a large part of aorta aneurysm, and if there's no timely intervention or treatment, the risks of rupture and death would rise sharply. With the depth of research in AAA, more and more evidence showed correlations between AAA and autoimmune injury. Currently, a variety of bioactive peptides and cells have been confirmed to be related with AAA progression. Despite the tremendous progress, more than half researches were sampling from lesion tissues, which would be difficult to obtain. Given that the intrusiveness and convenience, serological test take advantages in initial diagnosis. Here we review blood biomarkers associated with autoimmune injury work in AAA evolution, aiming to make a profile on blood immune substances of AAA and provide a thought for potential clinical practice.

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.

Serum Lipid Oxidative Stress Products as Risk Factors Are the Candidate Predictive Biomarkers for Human Abdominal Aortic Aneurysms

This research was designed to determine the association of serum lipid peroxidation products with disease severity in patients with abdominal aortic aneurysm (AAA). In total, 76 pairs of AAA cases as well as matched controls were enrolled in our research using propensity score matching (PSM). And their malondialdehyde (MDA), lipid hydroperoxide (LPO), and glutathione peroxidase (GSH-Px) levels were also detected through enzyme-linked immunosorbent assay (ELISA). Additionally, the relative clinical data of enrolled participants were extracted. The serum biomarker concentrations were measured in 76 patients with AAAs (diameter between 30 and 54 mm, n = 54; diameter ≥55 mm, n = 22) and 76 control patients from observational cohort study. After PSM adjustment for clinical variables, including age, gender, heart ratio, body mass index, smoking, hypertension, diabetes mellitus, coronary heart disease, and stroke, the serum MDA and LPO among AAA cases were remarkably increased compared with those from the normal patients. Inversely, serum GSH-Px was significantly decreased in patients with AAA compared to the control group. Besides, the serum levels of MDA and LPO were independently associated with AAA risk. Typically, there was significantly positive correlation between MDA level and LPO level (R = 0.358) but negative correlation of MDA level with GSH-Px (R = -0.203) level in patients with AAA. Meanwhile, the area under the receiver operating characteristic curve was 0.965 when MDA was used to diagnose AAA, and the optimal threshold value was 0.242 nmol/mL. Moreover, serum MDA level was significantly increased in cases with rupture AAA compared to those in selective AAA cases. Logistic regression analysis suggested that a higher serum MDA level indicated an elevated risk of AAA rupture (odds ratio = 2.536; 95% CI: 1.037-6.203; P =0.041). Our present findings suggest that serum peroxidation contents were evidently changed among AAA cases. Serum MDA and LPO concentrations could be used to predict disease severity in patients with AAA. Moreover, serum MDA may serve as the candidate biomarker for diagnosis of AAA and accurate identification of increased risks of AAA rupture.

Psychological symptoms in Chinese nurses may be associated with predisposition to chronic disease: a cross-sectional study of suboptimal health status

BACKGROUND

Suboptimal health status (SHS) is a reversible state between ideal health and illness and it can be effectively reversed by risk prediction, disease prevention, and personalized medicine under the global background of predictive, preventive, and personalized medicine (PPPM) concepts. More and more Chinese nurses have been troubled by psychological symptoms (PS). The correlation between PS and SHS is unclear in nurses. The purpose of current study is to investigate the prevalence of SHS and PS in Chinese nurses and the relationship between SHS and PS along with predisposing factors as well as to discuss the feasibility of improving health status and preventing diseases according to PPPM concepts in Chinese nurses.

METHODS

A cross-sectional study was conducted with the cluster sampling method among 9793 registered nurses in Foshan city, China. SHS was evaluated with the Suboptimal Health Status Questionnaire-25 (SHSQ-25). Meanwhile, the PS of depression and anxiety were evaluated with Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) self-assessment questionnaires. The relationship between PS and SHS in Chinese nurses was subsequently analyzed.

RESULTS

Among the 9793 participants, 6107 nurses were included in the final analysis. The prevalence of SHS in the participants was 74.21% (4532/6107) while the symptoms of depression and anxiety were 47.62% (2908/6107) and 24.59% (1502/6107) respectively. The prevalence of SHS in the participants with depression and anxiety was significantly higher than those without the symptoms of depression (83.3% vs 16.7%, P < 0.001) and anxiety (94.2% vs 5.8%, P < 0.0001). The ratio of exercise habit was significantly lower than that of non-exercise habit (68.8% vs 78.4%, P < 0.001) in SHS group.

CONCLUSIONS

There is a high prevalence of SHS and PS in Chinese nurses. PS in Chinese nurses are associated with SHS. Physical exercise is a protective factor for SHS and PS so that the exercise should be strongly recommended as a valuable preventive measure well in the agreement with PPPM philosophy. Along with SDS and SAS, SHSQ-25 should also be highly recommended and applied as a novel predictive/preventive tool for the health measures from the perspectives of PPPM in view of susceptible population and individual screening, the predisposition to chronic disease preventing, personalization of intervention, and the ideal health state restoring.

Liquid biopsy technologies for hematological diseases

Since the discovery of circulating tumor cells in 1869, technological advances in studying circulating biomarkers from patients' blood have made the diagnosis of nonhematologic cancers less invasive. Technological advances in the detection and analysis of biomarkers provide new opportunities for the characterization of other disease types. When compared with traditional biopsies, liquid biopsy markers, such as exfoliated bladder cancer cells, circulating cell-free DNA (cfDNA), and extracellular vesicles (EV), are considered more convenient than conventional biopsies. Liquid biopsy markers undoubtedly have the potential to influence disease management and treatment dynamics. Our main focuses of this review will be the cell-based, gene-based, and protein-based key liquid biopsy markers (including EV and cfDNA) in disease detection, and discuss the research progress of these biomarkers used in conjunction with liquid biopsy. First, we highlighted the key technologies that have been broadly adopted used in hematological diseases. Second, we introduced the latest technological developments for the specific detection of cardiovascular disease, leukemia, and coronavirus disease. Finally, we concluded with perspectives on these research areas, focusing on the role of microfluidic technology and artificial intelligence in point-of-care medical applications. We believe that the noninvasive capabilities of these technologies have great potential in the development of diagnostics and can influence treatment options, thereby advancing precision disease management.

Discovery of novel biomarkers for atherosclerotic aortic aneurysm through proteomics-based assessment of disease progression

Since aortic aneurysms (AAs) are mostly asymptomatic, but they have a high mortality rate upon rupture, their detection and progression evaluation are clinically important issues. To discover diagnostic biomarkers for AA, we performed proteome analysis of aortic media from patients with thoracic atherosclerotic AA (TAAA), comparing protein levels between the aneurysm and normal tissue areas. After hierarchical clustering analysis of the proteome analysis data, tissue samples were classified into three groups, regardless of morphological features. This classification was shown to reflect disease progression stage identified by pathological examination. This proteomics-based staging system enabled us to identify more significantly altered proteins than the morphological classification system. In subsequent data analysis, Niemann-Pick disease type C2 protein (NPC2) and insulin-like growth factor-binding protein 7 (IGFBP7) were selected as novel biomarker candidates for AA and were compared with the previously reported biomarker, thrombospondin 1 (THBS1). Blood concentrations of NPC2 and IGFBP7 were significantly increased, while THBS1 levels were decreased in TAAA and abdominal atherosclerotic AA patients. Receiver operating characteristic analysis of AA patients and healthy controls showed that NPC2 and IGFBP7 have higher specificity and sensitivity than THBS1. Thus, NPC2 and IGFBP7 are promising biomarkers for the detection and progression evaluation of AA.

Abdominal aortic aneurysm in prostate cancer patients: the "road map" from incidental detection to advanced predictive, preventive, and personalized approach utilizing common follow-up for both pathologies

Abdominal aortic aneurysm (AAA) is often a hidden pathological process showing no clinical symptoms. Genetic burden, smoking, male gender, age > 65 years, and white race have been identified as the main risk factors. A regular screening program has been introduced but is, as yet, unclear and is not performed in most countries. Prostate cancer is the most frequent male malignant disease in Western countries. Prostate cancer is a disease of older age with a median primary diagnosis of over 60 years. In recent years, advanced imaging methods have been established as important diagnostic tools in prostate cancer diagnostics. The incidental detection of AAA during diagnostic imaging performed due to prostate cancer diagnosis could reveal some asymptomatic aneurysms. Using our experience, the incidental detection of AAA during 18F-fluoromethylcholine PET/CT imaging, performed due to the staging, follow-up, and restaging of the prostate cancer, was reworked into a regular tool of secondary prevention within the framework of personalized medicine strategies. Experience with this type of AAA detection is demonstrated by a cohort of 500 patients who underwent 18F-fluorometylcholine PET/CT examination due to the staging or restaging of prostate cancer. A total of 28 aneurysms were detected (26 aneurysms < 50 mm, 2 aneurysms > 50 mm). In 2 cases (diameter < 50 mm), serious complications were found (penetrating aortic ulcer). The detection and monitoring of AAA in patients undergoing 18F-fluorometylcholine PET/CT due to the prostate cancer offers the possibility of a secondary prevention of AAA, patient stratification, and common follow-up for both pathologies.

Inflammatory cell infiltrates, hypoxia, vascularization, pentraxin 3 and osteoprotegerin in abdominal aortic aneurysms - A quantitative histological study

Information about the tissue characteristics of abdominal aortic aneurysms (AAAs), some of which may be reflected in the serum, can help to elucidate AAA pathogenesis and identify new AAA biomarkers. This information would be beneficial not only for diagnostics and follow-up but also for potential therapeutic intervention. Therefore, the aim of our study was to compare the expression of structural proteins, immune factors (T and B lymphocytes, macrophages, neutrophils and pentraxin 3 (PTX3)), osteoprotegerin (OPG), microvessels and hypoxic cells in AAA and nonaneurysmal aortic walls. We examined specimens collected during surgery for AAA repair (n = 39) and from the abdominal aortas of kidney donors without AAA (n = 8). Using histochemical and immunohistochemical methods, we quantified the areas positive for smooth muscle actin, desmin, elastin, collagen, OPG, CD3, CD20, MAC387, myeloperoxidase, PTX3, and hypoxia-inducible factor 1-alpha and the density of CD31-positive microvessels. AAA samples contained significantly less actin, desmin, elastin and OPG, more collagen, macrophages, neutrophils, T lymphocytes, B lymphocytes, hypoxic cells and PTX3, and a greater density of vasa vasorum (VV) than those in non-AAA samples. Hypoxia positively correlated with actin and negatively correlated with collagen. Microvascular density was related to inflammatory cell infiltrates, hypoxia, PTX3 expression and AAA diameter. The lower OPG expression in AAAs supports the notion of its protective role in AAA remodeling. AAA contained altered amounts of structural proteins, implying reduced vascular elasticity. PTX3 was upregulated in AAA and colocalized with inflammatory infiltrates. This evidence supports further evaluation of PTX3 as a candidate marker of AAA. The presence of aortic hypoxia, despite hypervascularization, suggests that hypoxia-induced neoangiogenesis may play a role in AAA pathogenesis. VV angiogenesis of the AAA wall increases its vulnerability.