OUP accepted manuscript

Blood protein assessment of leading incident diseases and mortality in the UK Biobank

The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (n = 47,600). We report 3,209 associations between 963 protein levels and 21 incident outcomes. Next, protein-based scores (ProteinScores) are developed using penalized Cox regression. When applied to test sets, six ProteinScores improve the area under the curve estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c-a clinical marker used to monitor and diagnose type 2 diabetes. The performance of scores using metabolomic and proteomic features is also compared. These data characterize early proteomic contributions to major age-related diseases, demonstrating the value of the plasma proteome for risk stratification.

Autoimmune diseases and atherosclerotic cardiovascular disease

Autoimmune diseases are associated with a dramatically increased risk of atherosclerotic cardiovascular disease and its clinical manifestations. The increased risk is consistent with the notion that atherogenesis is modulated by both protective and disease-promoting immune mechanisms. Notably, traditional cardiovascular risk factors such as dyslipidaemia and hypertension alone do not explain the increased risk of cardiovascular disease associated with autoimmune diseases. Several mechanisms have been implicated in mediating the autoimmunity-associated cardiovascular risk, either directly or by modulating the effect of other risk factors in a complex interplay. Aberrant leukocyte function and pro-inflammatory cytokines are central to both disease entities, resulting in vascular dysfunction, impaired resolution of inflammation and promotion of chronic inflammation. Similarly, loss of tolerance to self-antigens and the generation of autoantibodies are key features of autoimmunity but are also implicated in the maladaptive inflammatory response during atherosclerotic cardiovascular disease. Therefore, immunomodulatory therapies are potential efficacious interventions to directly reduce the risk of cardiovascular disease, and biomarkers of autoimmune disease activity could be relevant tools to stratify patients with autoimmunity according to their cardiovascular risk. In this Review, we discuss the pathophysiological aspects of the increased cardiovascular risk associated with autoimmunity and highlight the many open questions that need to be answered to develop novel therapies that specifically address this unmet clinical need.

Plasma proteins associate with carotid plaques and predict incident atherosclerotic cardiovascular events

PURPOSE

Performing non-invasive carotid imaging is challenging, owing inter-operator variability and organizational barriers, but plasma proteomics can offer an alternative. We sought plasma proteins that associate with the presence of carotid plaques, their number and predict the incidence of clinically overt atherosclerotic cardiovascular events (ASCVD) above currently recognized risk factors in "apparently healthy" subjects.

METHODS

We studied the plasma levels of 368 proteins in 664 subjects from the PLIC study, who underwent an ultrasound imaging screening of the carotids to check for the presence of plaques. We clustered, by artificial intelligence (A.I.), the proteins that associate with the presence, the number of plaques and that predict incident ASCVDs over 22 years (198 events were registered).

FINDINGS

299/664 subjects had at least 1 carotid plaque (1+) (77 with only one plaque, 101 with 2 plaques, 121 with ≥3 plaques (3+)). The remaining 365 subjects with no plaques acted as controls. 106 proteins were associated with 1+ plaques, but 97 proteins significantly predicted 3+ plaques only (AUC = 0.683 (0.601-0.785), p < 0.001), when considered alone. A.I. underscored 87 proteins that improved the performance of the classical risk factors both in detecting 3+ plaques (AUC = 0.918 (0.887-0.943) versus risk factors alone, AUC = 0.760 (0.716-0.801), p < 0.001) and in predicting the incident ASCVD (AUC = 0.739 (0.704-0.773) vs risk factors alone AUC = 0.559 (0.521-0.598), p < 0.001). The chemotaxis/migration of leukocytes and interleukins/cytokines signaling were biological pathways mostly represented by these proteins.

DISCUSSION AND CONCLUSIONS

Plasma proteomics marks the number of carotid plaques and improve the prediction of incidence ASCVDs in apparently healthy subjects.

Enhanced vasorin signaling mitigates adverse cardiovascular remodeling

Arterial stiffening is a critical risk factor contributing to the exponential rise in age-associated cardiovascular disease incidence. This process involves age-induced arterial proinflammation, collagen deposition, and calcification, which collectively contribute to arterial stiffening. The primary driver of proinflammatory processes leading to collagen deposition in the arterial wall is the transforming growth factor-beta1 (TGF-β1) signaling. Activation of this signaling is pivotal in driving vascular extracellular remodeling, eventually leading to arterial fibrosis and calcification. Interestingly, the glycosylated protein vasorin (VASN) physically interacts with TGF-β1, and functionally restraining its proinflammatory fibrotic signaling in arterial walls and vascular smooth muscle cells (VSMCs). Notably, as age advances, matrix metalloproteinase type II (MMP-2) is activated, which effectively cleaves VASN protein in both arterial walls and VSMCs. This age-associated/MMP-2-mediated decrease in VASN levels exacerbates TGF-β1 activation, amplifying arterial fibrosis and calcification in the arterial wall. Importantly, TGF-β1 is a downstream molecule of the angiotensin II (Ang II) signaling pathway in the arterial wall and VSMCs, which is modulated by VASN. Indeed, chronic administration of Ang II to young rats significantly activates MMP-2 and diminishes the VASN expression to levels comparable to untreated older control rats. This review highlights and discusses the role played by VASN in mitigating fibrosis and calcification by alleviating TGF-β1 activation and signaling in arterial walls and VSMCs. Understanding these molecular physical and functional interactions may pave the way for establishing VASN-based therapeutic strategies to counteract adverse age-associated cardiovascular remodeling, eventually reducing the risk of cardiovascular diseases.

Dynamic molecular signatures of acute myocardial infarction based on transcriptomics and metabolomics

Acute myocardial infarction (AMI) commonly precedes ventricular remodeling, heart failure. Few dynamic molecular signatures have gained widespread acceptance in mainstream clinical testing despite the discovery of many potential candidates. These unmet needs with respect to biomarker and drug discovery of AMI necessitate a prioritization. We enrolled patients with AMI aged between 30 and 70. RNA-seq analysis was performed on the peripheral blood mononuclear cells collected from the patients at three time points: 1 day, 7 days, and 3 months after AMI. PLC/LC-MS analysis was conducted on the peripheral blood plasma collected from these patients at the same three time points. Differential genes and metabolites between groups were screened by bio-informatics methods to understand the dynamic changes of AMI in different periods. We obtained 15 transcriptional and 95 metabolite expression profiles at three time points after AMI through high-throughput sequencing. AMI-1d: enrichment analysis revealed the biological features of 1 day after AMI primarily included acute inflammatory response, elevated glycerophospholipid metabolism, and decreased protein synthesis capacity. Phosphatidylcholine (PC) and phosphatidylethanolamine (PE) might stand promising biomarkers to differentiate post-AMI stage. Anti-inflammatory therapy during the acute phase is an important direction for preventing related pathology. AMI-7d: the biological features of this stage primarily involved the initiation of cardiac fibrosis response and activation of platelet adhesion pathways. Accompanied by upregulated TGF-beta signaling pathway and ECM receptor interaction, GP5 help assess platelet activation, a potential therapeutic target to improve haemostasis. AMI-3m: the biological features of 3 months after AMI primarily showed a vascular regeneration response with VEGF signaling pathway, NOS3 and SHC2 widely activated, which holds promise for providing new therapeutic approaches for AMI. Our analysis highlights transcriptional and metabolomics signatures at different time points after MI, which deepens our understanding of the dynamic biological responses and associated molecular mechanisms that occur during cardiac repair.

Role of Brain-Derived Neurotrophic Factor in Anxiety or Depression After Percutaneous Coronary Intervention

Anxiety or depression after percutaneous coronary intervention (PCI) is one of the key clinical problems in cardiology that need to be solved urgently. Brain-derived neurotrophic factor (BDNF) may be a potential biomarker for the pathogenesis and treatment of anxiety or depression after PCI. This article reviews the correlation between BDNF and cardiovascular system and nervous system from the aspects of synthesis, release and action site of BDNF, and focuses on the latest research progress of the mechanism of BDNF in anxiety or depression after PCI. It includes the specific mechanisms by which BDNF regulates the levels of inflammatory factors, reduces oxidative stress damage, and mediates multiple signaling pathways. In addition, this review summarizes the therapeutic potential of BDNF as a potential biomarker for anxiety or depression after PCI.

Association of inflammatory markers with muscle and cognitive function in early and late-aging older adults

OBJECTIVES

Age-related loss in muscle and cognitive function is common in older adults. Numerous studies have suggested that inflammation contributes to the decline in physical performance and increased frailty in older adults. We sought to investigate the relationship of inflammatory markers, including CRP, IL-6, IL-10, TNF-α, TNFR1, and TNFR2, with muscle and cognitive function in frail early-aging and non-frail late-aging older adults.

DESIGN

Secondary analysis of a cross-sectional study.

SETTINGS AND PARTICIPANTS

Two hundred community-dwelling older men and women were included. They had been recruited in two groups based on age and functional status: 100 early-agers (age 65-75, who had poor functional status, and more co-morbidities) and 100 late-agers (older than 75 years, who were healthier and had better functional status).

MEASUREMENTS

We assessed CRP, IL-6, IL-10, TNF-α, TNFR1, TNFR2, grip strength, Short Physical Performance Battery (SPPB) score, and cognitive function. We used correlation coefficients, partial correlations, and regression modeling adjusted for age, BMI, gender, and exercise frequency.

RESULTS

The mean age in the two groups were 70.4 and 83.2, respectively. In regression models adjusting for age, BMI, gender and exercise frequency, early-agers demonstrated significant associations between inflammatory markers and outcomes. Each mg/dl of CRP was associated with (regression coefficient ± standard error) -0.6 ± 0.2 kg in grip strength (p = 0.0023). Similarly, each pg/mL of TNF-α was associated with -1.4 ± 0.7 (p = 0.0454), each 500 pg/mL of TNFR1 was associated with -1.9 ± 0.6 (p = 0.0008), and each 500 pg/mL of TNFR2 was associated with -0.5 ± 0.2 (p = 0.0098) in grip strength. Each 500 pg/mL of TNFR1 was associated with -0.4 ± 0.2 point in SPPB (p = 0.0207) and each pg/mL in IL-10 with 0.2 ± 0.1 point in MoCA (p = 0.0475). In late-agers, no significant correlation was found between any of the inflammatory markers and functional outcomes.

CONCLUSION

In early-agers with frailty and more co-morbidities, the inflammatory markers CRP, TNF-α, TNFR1, and TNFR2 were associated with grip strength, TNFR1 was correlated with physical performance, and IL-10 was correlated with cognitive function. However, in healthier late-agers, no relationship was found between inflammatory markers and muscle or cognitive function. Our findings suggest presence of a relationship between inflammation and loss of muscle performance and cognitive function in frailer and sicker individuals, regardless of their chronological age.

Proteomic scrutiny of nasal microbiomes: implications for the clinic

INTRODUCTION

The nasal cavity is the initial site of the human respiratory tract and is one of the habitats where microorganisms colonize. The findings from a growing number of studies have shown that the nasal microbiome is an important factor for human disease and health. 16S rRNA sequencing and metagenomic next-generation sequencing (mNGS) are the most commonly used means of microbiome evaluation. Among them, 16S rRNA sequencing is the primary method used in previous studies of nasal microbiomes. However, neither 16S rRNA sequencing nor mNGS can be used to analyze the genes specifically expressed by nasal microorganisms and their functions. This problem can be addressed by proteomic analysis of the nasal microbiome.

AREAS COVERED

In this review, we summarize current advances in research on the nasal microbiome, introduce the methods for proteomic evaluation of the nasal microbiome, and focus on the important roles of proteomic evaluation of the nasal microbiome in the diagnosis and treatment of related diseases.

EXPERT OPINION

The detection method for microbiome-expressed proteins is known as metaproteomics. Metaproteomic analysis can help us dig deeper into the nasal microbiomes and provide new targets and ideas for clinical diagnosis and treatment of many nasal dysbiosis-related diseases.

A review of disease risk prediction methods and applications in the omics era

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

Proteomic study of left ventricle and cortex in rats after myocardial infarction

Myocardial infarction (MI) induces neuroinflammation indirectly, chronic neuroinflammation may cause neurodegenerative diseases. Changes in the proteomics of heart and brain tissue after MI may shed new light on the mechanisms involved in neuroinflammation. This study explored brain and heart protein changes after MI with a data-independent acquisition (DIA) mode proteomics approach. Permanent ligation of the left anterior descending coronary artery (LAD) was performed in the heart of rats, and the immunofluorescence of microglia in the brain cortex was performed at 1d, 3d, 5d, and 7d after MI to detect the neuroinflammation. Then proteomics was accomplished to obtain the vital proteins in the heart and brain post-MI. The results show that the number of microglia was significantly increased in the Model-1d group, the Model-3d group, the Model-5d group, and the Model-7d group compared to the Sham group. Various proteins were obtained through DIA proteomics. Linking to key targets of brain disease, 14 proteins were obtained in the brain cortex. Among them, elongation of very long chain fatty acids protein 5 (ELOVL5) and ATP-binding cassette subfamily G member 4 (ABCG4) were verified through western blotting (WB). The results of WB were consistent with the proteomics results. Therefore, these proteins may be related to the pathogenesis of neuroinflammation after MI.

Longevity-associated BPIFB4 gene counteracts the inflammatory signaling

BACKGROUND

Increased levels of pro-inflammatory proteins in plasma can be detected in older individuals and associate with the so called chronic low-grade inflammation, which contributes to a faster progression of aged-related cardiovascular (CV) diseases, including frailty, neurodegeneration, gastro-intestinal diseases and disorders reflected by alterations in the composition of gut microbiota. However, successful genetic programme of long-living individuals alters the trajectory of the ageing process, by promoting an efficient immune response that can counterbalance deleterious effects of inflammation and the CV complications. This is the case of BPIFB4 gene in which, homozygosity for a four single-nucleotide polymorphism (SNP) haplotype, the Longevity-Associated Variant (LAV) correlates with prolonged health span and reduced risk of CV complications and inflammation. The relation between LAV-BPIFB4 and inflammation has been proven in different experimental models, here we hypothesized that also human homozygous carriers of LAV-BPIFB4 gene may experience a lower inflammatory burden as detected by plasma proteomics that could explain their favourable CV risk trajectory over time. Moreover, we explored the therapeutic effects of LAV-BPIFB4 in inflammatory disease and monolayer model of intestinal barrier.

RESULTS

We used high-throughput proteomic approach to explore the profiles of circulating proteins from 591 baseline participants selected from the PLIC cohort according to the BPIFB4 genotype to identify the signatures and differences of BPIFB4 genotypes useful for health and disease management. The observational analysis identified a panel of differentially expressed circulating proteins between the homozygous LAV-BPIFB4 carriers and the other alternative BPIFB4 genotypes highlighting in the latter ones a higher grade of immune-inflammatory markers. Moreover, in vitro studies performed on intestinal epithelial organs from inflammatory bowel disease (IBD) patients and monolayer model of intestinal barrier demonstrated the benefit of LAV-BPIFB4 treatment.

CONCLUSIONS

Homozygosity for LAV-BPIFB4 results in the attenuation of inflammation in PLIC cohort and IBD patients providing preliminary evidences for its therapeutic use in inflammatory disorders that need to be further characterized and confirmed by independent studies.

Different Anti-inflammatory Drugs on High-Sensitivity C-Reactive Protein in Patients After Percutaneous Coronary Intervention: A Pilot Randomized Clinical Trial

ABSTRACT

Colchicine reduces atherothrombotic cardiovascular events in coronary artery disease because of its anti-inflammatory effect. However, the effects of the other anti-inflammatory drugs in coronary artery disease remain unclear. This study included 132 patients aged 18-80 years who completed the planned percutaneous coronary interventions and were treated with aggressive secondary prevention strategies for 4 weeks. The subjects were randomly assigned to 1 of the following treatment groups for 4 weeks: (1) control: no additional intervention; (2) colchicine: 0.5 mg once a day; (3) tranilast: 0.1 g thrice a day; or (4) oridonin: 0.5 g thrice a day. The primary outcome was the percentage change in high-sensitivity C-reactive protein (hsCRP) levels at the end of 4 weeks. In total, 109 patients completed the study. The mean age was 58.33 years, 81 (74.31%) were male, and 28 (25.69%) were female. The percentage changes in hsCRP after 4 weeks of treatment were -11.62%, -48.28%, -21.60%, and -7.81%, in the control, colchicine, tranilast, and the oridonin groups, respectively. Compared with the control group, only the colchicine group showed significantly greater reduction in hsCRP levels ( P = 0.022). In targeted proteomic analysis, proteins associated with neutrophil activation (azurocidin, myeloperoxidase, and myeloblastin), platelet aggregation (glycoprotein VI), and endothelial damage (galectin-3) were reduced with colchicine therapy. These results show that of 3 anti-inflammatory drugs only colchicine could reduce hsCRP in patients after percutaneous coronary interventions.

Adopting artificial intelligence in cardiovascular medicine: a scoping review

Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.

The Association Between S100A12 Protein and C-Reactive Protein with Malignant Ventricular Arrhythmias Following Acute Myocardial Infarction in the Elderly

Objective

To investigate the association of S100A12 protein and C-reactive protein (CRP) with the onset of malignant ventricular arrhythmias (MVA) after acute myocardial infarction (AMI) in the elderly.

Methods

A total of 159 elderly AMI patients admitted to Chongming Hospital affiliated to Shanghai University of Medicine & Health Sciences from January 2018 to January 2023 were enrolled in the study. CRP levels were determined using an automatic biochemical analyzer, and S100A12 levels were measured using enzyme-linked immunosorbent assay (ELISA). Patients were categorized based on the Lown classification into groups without MVA and with MVA. Univariate analysis was initially performed to identify independent variables, followed by multivariate logistic regression to determine the risk factors for malignant ventricular arrhythmias post-AMI. The predictive value of S100A12 protein and CRP for malignant ventricular arrhythmias after acute myocardial infarction in the elderly was analyzed using the receiver operating characteristic (ROC) curve.

Results

Among the 159 patients with AMI, 27 (17%) had MVA. Multivariate logistic regression analysis indicated that both S100A12 protein and CRP could be independent risk factors for malignant ventricular arrhythmias following acute myocardial infarction in the elderly (p < 0.05). The area under the ROC curve showed the area under the curve (AUC) for S100A12 protein to be 0.7147, for CRP 0.7356, and for the combined diagnosis 0.8350 (p < 0.05).

Conclusion

S100A12 protein and CRP are independent risk factors for MVA after MI in the elderly. The combined application of S100A12 protein and CRP has higher diagnostic sensitivity and specificity.

C-reactive protein modifies lipoprotein(a)-related risk for coronary heart disease: the BiomarCaRE project

BACKGROUND AND AIMS

Recent investigations have suggested an interdependence of lipoprotein(a) [Lp(a)]-related risk for cardiovascular disease with background inflammatory burden. The aim the present analysis was to investigate whether high-sensitive C-reactive protein (hsCRP) modulates the association between Lp(a) and coronary heart disease (CHD) in the general population.

METHODS

Data from 71 678 participants from 8 European prospective population-based cohort studies were used (65 661 without/6017 with established CHD at baseline; median follow-up 9.8/13.8 years, respectively). Fine and Gray competing risk-adjusted models were calculated according to accompanying hsCRP concentration (<2 and ≥2 mg/L).

RESULTS

Among CHD-free individuals, increased Lp(a) levels were associated with incident CHD irrespective of hsCRP concentration: fully adjusted sub-distribution hazard ratios [sHRs (95% confidence interval)] for the highest vs. lowest fifth of Lp(a) distribution were 1.45 (1.23-1.72) and 1.48 (1.23-1.78) for a hsCRP group of <2 and ≥2 mg/L, respectively, with no interaction found between these two biomarkers on CHD risk (Pinteraction = 0.82). In those with established CHD, similar associations were seen only among individuals with hsCRP ≥ 2 mg/L [1.34 (1.03-1.76)], whereas among participants with a hsCRP concentration <2 mg/L, there was no clear association between Lp(a) and future CHD events [1.29 (0.98-1.71)] (highest vs. lowest fifth, fully adjusted models; Pinteraction = 0.024).

CONCLUSIONS

While among CHD-free individuals Lp(a) was significantly associated with incident CHD regardless of hsCRP, in participants with CHD at baseline, Lp(a) was related to recurrent CHD events only in those with residual inflammatory risk. These findings might guide adequate selection of high-risk patients for forthcoming Lp(a)-targeting compounds.

Excessive daytime sleepiness, morning tiredness, and prognostic biomarkers in patients with chronic coronary syndrome

BACKGROUND

Sleep-related breathing disorders (SRBD) are related to cardiovascular outcomes in patients with chronic coronary syndrome (CCS). Whether SRBD-related symptoms are associated with prognostic biomarkers in patients with CCS is not established.

METHODS

Associations between frequency (never/rarely, sometimes, often, always) of self-reported SRBD-related symptoms (excessive daytime sleepiness [EDS]; morning tiredness [MT]; loud snoring; multiple awakenings/night; gasping, choking, or apnea when asleep) and levels of biomarkers related to cardiovascular prognosis (high-sensitivity C-reactive protein [hs-CRP], interleukin 6 [IL-6], high-sensitivity cardiac troponin T [hs-cTnT], N-terminal pro B-type natriuretic peptide [NT-proBNP], cystatin C, growth differentiation factor 15 [GDF-15] and lipoprotein-associated phospholipase A2 activity) were assessed at baseline in 15,640 patients with CCS on optimal secondary preventive therapy in the STABILITY trial. Cross-sectional associations were assessed by adjusted linear regression models testing for trends with the never/rarely category serving as reference.

RESULTS

EDS was associated (geometric mean ratio, 95% confidence interval) with increased levels of IL-6 (often 1.07 [1.03-1.10], always 1.15 [1.10-1.21]), GDF-15 (often 1.03 [1.01-1.06], always 1.07 [1.03-1.11]), NT-proBNP (always 1.22 [1.12-1.33]), and hs-cTnT (always 1.07 [1.01-1.12]). MT was associated with increased levels of IL-6 (often 1.05 [1.01-1.09], always 1.09 [1.04-1.15]), and GDF-15 (always 1.06 [1.03-1.10]). All symptoms were to some degree associated with higher levels of hs-CRP and loud snoring was also associated with decreased levels of NT-proBNP and hs-cTnT.

CONCLUSIONS

In patients with CCS, stepwise increased frequency of SRBD-related symptoms, such as EDS and MT, were associated with gradually higher levels of IL-6 and GDF-15, each reflecting distinct pathophysiological pathways.

Risk stratification of papillary thyroid cancers using multidimensional machine learning

BACKGROUND

Papillary thyroid cancer (PTC) is one of the most common endocrine malignancies with different risk levels. However, preoperative risk assessment of PTC is still a challenge in the worldwide. Here, the authors first report a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) by multidimensional features including clinical indicators, immune indices, genetic feature, and proteomics.

MATERIALS AND METHODS

The 558 patients collected from June 2013 to November 2020 were allocated to three groups: the discovery set [274 patients, 274 formalin-fixed paraffin-embedded (FFPE)], the retrospective test set (166 patients, 166 FFPE), and the prospective test set (118 patients, 118 fine-needle aspiration). Proteomic profiling was conducted by FFPE and fine-needle aspiration tissues from the patients. Preoperative clinical information and blood immunological indices were collected. The BRAFV600E mutation were detected by the amplification refractory mutation system.

RESULTS

The authors developed a machine learning model of 17 variables based on the multidimensional features of 274 PTC patients from a retrospective cohort. The PRAC-PTC achieved areas under the curve (AUC) of 0.925 in the discovery set and was validated externally by blinded analyses in a retrospective cohort of 166 PTC patients (0.787 AUC) and a prospective cohort of 118 PTC patients (0.799 AUC) from two independent clinical centres. Meanwhile, the preoperative predictive risk effectiveness of clinicians was improved with the assistance of PRAC-PTC, and the accuracies reached at 84.4% (95% CI: 82.9-84.4) and 83.5% (95% CI: 82.2-84.2) in the retrospective and prospective test sets, respectively.

CONCLUSION

This study demonstrated that the PRAC-PTC that integrating clinical data, gene mutation information, immune indices, high-throughput proteomics and machine learning technology in multicentre retrospective and prospective clinical cohorts can effectively stratify the preoperative risk of PTC and may decrease unnecessary surgery or overtreatment.

Elevated soluble LOX-1 predicts risk of first-time myocardial infarction

BACKGROUND

There is an unmet clinical need for novel therapies addressing the residual risk in patients receiving guideline preventive therapy for coronary heart disease. Experimental studies have identified a pro-atherogenic role of the oxidized LDL receptor LOX-1. We investigated the association between circulating soluble LOX-1 (sLOX-1) and the risk for development of myocardial infarction.

METHODS

The study subjects (n = 4658) were part of the Malmö Diet and Cancer study. The baseline investigation was carried out 1991-1994 and the incidence of cardiovascular events monitored through national registers during a of 19.5 ± 4.9 years follow-up. sLOX-1 and other biomarkers were analyzed by proximity extension assay and ELISA in baseline plasma.

RESULTS

Subjects in the highest tertile of sLOX-1 had an increased risk of myocardial infarction (hazard ratio (95% CI) 1.76 (1.40-2.21) as compared with those in the lowest tertile. The presence of cardiovascular risk factors was related to elevated sLOX-1, but the association between sLOX-1 and risk of myocardial infarction remained significant when adjusting for risk factors.

CONCLUSIONS

In this prospective population study we found an association between elevated sLOX-1, the presence of carotid disease and the risk for first-time myocardial infarction. Taken together with previous experimental findings of a pro-atherogenic role of LOX-1, this observation supports LOX-1 inhibition as a possible target for prevention of myocardial infarction.

Integrative proteomic analyses across common cardiac diseases yield new mechanistic insights and enhanced prediction

Cardiac diseases represent common highly morbid conditions for which underlying molecular mechanisms remain incompletely understood. Here, we leveraged 1,459 protein measurements in 44,313 UK Biobank participants to characterize the circulating proteome associated with incident coronary artery disease, heart failure, atrial fibrillation, and aortic stenosis. Multivariable-adjusted Cox regression identified 820 protein-disease associations-including 441 proteins-at Bonferroni-adjusted P <8.6×10 -6 . Cis -Mendelian randomization suggested causal roles that aligned with epidemiological findings for 6% of proteins identified in primary analyses, prioritizing novel therapeutic targets for different cardiac diseases (e.g., interleukin-4 receptor for heart failure and spondin-1 for atrial fibrillation). Interaction analyses identified seven protein-disease associations that differed Bonferroni-significantly by sex. Models incorporating proteomic data (vs. clinical risk factors alone) improved prediction for coronary artery disease, heart failure, and atrial fibrillation. These results lay a foundation for future investigations to uncover novel disease mechanisms and assess the clinical utility of protein-based prevention strategies for cardiac diseases.

Multi-proteomic Biomarker Risk Scores for Predicting Risk and Guiding Therapy in Patients with Coronary Artery Disease

PURPOSE OF REVIEW

Patients with established coronary artery disease (CAD) are at high residual risk for adverse events, despite guideline-based treatments. Herein, we aimed to determine whether risk scores based on multiple circulating biomarkers that represent activation of various pathophysiologically important pathways involved in atherosclerosis and myocardial dysfunction help identify those at greatest residual risk.

RECENT FINDINGS

Numerous circulating proteins, representing dysregulation of the pathways involved in the development and stability of coronary and myocardial diseases, have been identified. When aggregated together, biomarker risk scores (BRS) more accurately stratify patients with established CAD that may help target interventions in those individuals who are at elevated risk. Moreover, intensification of guideline-based therapies has been associated with parallel improvements in both BRS and outcomes, indicating that these risk scores may be employed clinically to target therapy. Multi-protein BRS are predictive of risk, independent of, and in addition to traditional risk factor assessments in patients with CAD. Those with elevated risk may benefit from optimization of therapies, and improvements in the BRS will identify those with improved outcomes.

Machine Learning in Cardiovascular Risk Prediction and Precision Preventive Approaches

PURPOSE OF REVIEW

In this review, we sought to provide an overview of ML and focus on the contemporary applications of ML in cardiovascular risk prediction and precision preventive approaches. We end the review by highlighting the limitations of ML while projecting on the potential of ML in assimilating these multifaceted aspects of CAD in order to improve patient-level outcomes and further population health.

RECENT FINDINGS

Coronary artery disease (CAD) is estimated to affect 20.5 million adults across the USA, while also impacting a significant burden at the socio-economic level. While the knowledge of the mechanistic pathways that govern the onset and progression of clinical CAD has improved over the past decade, contemporary patient-level risk models lag in accuracy and utility. Recently, there has been renewed interest in combining advanced analytic techniques that utilize artificial intelligence (AI) with a big data approach in order to improve risk prediction within the realm of CAD. By virtue of being able to combine diverse amounts of multidimensional horizontal data, machine learning has been employed to build models for improved risk prediction and personalized patient care approaches. The use of ML-based algorithms has been used to leverage individualized patient-specific data and the associated metabolic/genomic profile to improve CAD risk assessment. While the tool can be visualized to shift the paradigm toward a patient-specific care, it is crucial to acknowledge and address several challenges inherent to ML and its integration into healthcare before it can be significantly incorporated in the daily clinical practice.

Plasma proteomic profiles predict individual future health risk

Developing a single-domain assay to identify individuals at high risk of future events is a priority for multi-disease and mortality prevention. By training a neural network, we developed a disease/mortality-specific proteomic risk score (ProRS) based on 1461 Olink plasma proteins measured in 52,006 UK Biobank participants. This integrative score markedly stratified the risk for 45 common conditions, including infectious, hematological, endocrine, psychiatric, neurological, sensory, circulatory, respiratory, digestive, cutaneous, musculoskeletal, and genitourinary diseases, cancers, and mortality. The discriminations witnessed high accuracies achieved by ProRS for 10 endpoints (e.g., cancer, dementia, and death), with C-indexes exceeding 0.80. Notably, ProRS produced much better or equivalent predictive performance than established clinical indicators for almost all endpoints. Incorporating clinical predictors with ProRS enhanced predictive power for most endpoints, but this combination only exhibited limited improvement when compared to ProRS alone. Some proteins, e.g., GDF15, exhibited important discriminative values for various diseases. We also showed that the good discriminative performance observed could be largely translated into practical clinical utility. Taken together, proteomic profiles may serve as a replacement for complex laboratory tests or clinical measures to refine the comprehensive risk assessments of multiple diseases and mortalities simultaneously. Our models were internally validated in the UK Biobank; thus, further independent external validations are necessary to confirm our findings before application in clinical settings.

Prognostic significance of inflammation in patients with coronary artery disease at low residual inflammatory risk

Patients with coronary artery disease (CAD) at low residual inflammatory risk are often overlooked in research and practice. This study examined the associations between fourteen inflammatory indicators and all-cause mortality in 5,339 CAD patients with baseline high-sensitivity C-reactive protein (hsCRP) <2 mg/L who received percutaneous coronary intervention and statin and aspirin therapy. The median follow-up time was 2.1 years. Neutrophil-derived systemic inflammatory response index (SIRI) yielded the strongest and most robust association with all-cause mortality among all indicators. Lower hsCRP remained to be associated with a lower risk of all-cause mortality. A newly developed comprehensive inflammation score (CIS) showed better predictive performance than other indicators, which was validated by an independent external cohort. In conclusion, neutrophil-derived indicators, particularly SIRI, strongly predicted all-cause mortality independent of hsCRP in CAD patients at low residual inflammatory risk. CIS may help identify individuals with inflammation burdens that cannot be explained by hsCRP alone.

Development and validation of a quantitative Proximity Extension Assay instrument with 21 proteins associated with cardiovascular risk (CVD-21)

BACKGROUND

Treatment of cardiovascular diseases (CVD) is a substantial burden to healthcare systems worldwide. New tools are needed to improve precision of treatment by optimizing the balance between efficacy, safety, and cost. We developed a high-throughput multi-marker decision support instrument which simultaneously quantifies proteins associated with CVD.

METHODS AND FINDINGS

Candidate proteins independently associated with different clinical outcomes were selected from clinical studies by the screening of 368 circulating biomarkers. We then custom-designed a quantitative PEA-panel with 21 proteins (CVD-21) by including recombinant antigens as calibrator samples for normalization and absolute quantification of the proteins. The utility of the CVD-21 tool was evaluated in plasma samples from a case-control cohort of 4224 patients with chronic coronary syndrome (CCS) using multivariable Cox regression analyses and machine learning techniques. The assays in the CVD-21 tool gave good precision and high sensitivity with lower level of determination (LOD) between 0.03-0.7 pg/ml for five of the biomarkers. The dynamic range for the assays was sufficient to accurately quantify the biomarkers in the validation study except for troponin I, which in the modeling was replaced by high-sensitive cardiac troponin T (hs-TnT). We created seven different multimarker models, including a reference model with NT-proBNP, hs-TnT, GDF-15, IL-6, and cystatin C and one model with only clinical variables, for the comparison of the discriminative value of the CVD-21 tool. All models with biomarkers including hs-TnT provided similar discrimination for all outcomes, e.g. c-index between 0.68-0.86 and outperformed models using only clinical variables. Most important prognostic biomarkers were MMP-12, U-PAR, REN, VEGF-D, FGF-23, TFF3, ADM, and SCF.

CONCLUSIONS

The CVD-21 tool is the very first instrument which with PEA simultaneously quantifies 21 proteins with associations to different CVD. Novel pathophysiologic and prognostic information beyond that of established biomarkers were identified by a number of proteins.

Serum proteomic profiling of carotid arteriopathy: A population outcome study

BACKGROUND AND AIMS

Circulating proteins reflecting subclinical vascular disease may improve prediction of atherosclerotic cardiovascular disease (ASCVD). We applied feature selection and unsupervised clustering on proteomic data to identify proteins associated with carotid arteriopathy and construct a protein-based classifier for ASCVD event prediction.

METHODS

491 community-dwelling participants (mean age, 58 ± 11 years; 51 % women) underwent carotid ultrasonography and proteomic profiling (CVD II panel, Olink Proteomics). ASCVD outcome was collected (median follow-up time: 10.2 years). We applied partial least squares (PLS) to identify proteins linked to carotid intima-media thickness (cIMT). Next, we assessed the association between future ASCVD events and protein-based phenogroups derived by unsupervised clustering (Gaussian Mixture modelling) based on proteins selected in PLS.

RESULTS

PLS identified 19 proteins as important, which were all associated with cIMT in multivariable-adjusted linear regression. 8 of the 19 proteins were excluded from the clustering analysis because of high collinearity. Based on the 11 remaining proteins, the clustering algorithm subdivided the cohort into two phenogroups. Compared to the first phenogroup (n = 177), participants in the second phenogroup (n = 314) presented: i) a more unfavorable lipid profile with higher total cholesterol and triglycerides and lower HDL cholesterol (p ≤ 0.014); ii) higher cIMT (p = 0.0020); and iii) a significantly higher risk for future ASCVD events (multivariable-adjusted hazard ratio (95 % CI) versus phenogroup 1: 2.05 (1.26-3.52); p = 0.0093). The protein-based phenogrouping supplemented ACC/AHA 10-year ASCVD risk scoring for prediction of a first ASCVD event.

CONCLUSIONS

Focused protein-based phenogrouping identified individuals at high risk for future ASCVD and may complement current risk stratification strategies.

Culprit plaque morphology determines inflammatory risk and clinical outcomes in acute coronary syndrome

AIMS

Rupture of the fibrous cap (RFC) and erosion of an intact fibrous cap (IFC) are the two predominant mechanisms causing acute coronary syndromes (ACS). It is uncertain whether clinical outcomes are different following RFC-ACS vs. IFC-ACS and whether this is affected by a specific inflammatory response. The prospective, translational OPTIcal-COherence Tomography in Acute Coronary Syndrome study programme investigates the impact of the culprit lesion phenotype on inflammatory profiles and prognosis in ACS patients.

METHODS AND RESULTS

This analysis included 398 consecutive ACS patients, of which 62% had RFC-ACS and 25% had IFC-ACS. The primary endpoint was a composite of cardiac death, recurrent ACS, hospitalization for unstable angina, and target vessel revascularization at 2 years [major adverse cardiovascular events (MACE+)]. Inflammatory profiling was performed at baseline and after 90 days. Patients with IFC-ACS had lower rates of MACE+ than those with RFC-ACS (14.3% vs. 26.7%, P = 0.02). In 368-plex proteomic analyses, patients with IFC-ACS showed lower inflammatory proteome expression compared with those with RFC-ACS, including interleukin-6 and proteins associated with the response to interleukin-1β. Circulating plasma levels of interleukin-1β decreased from baseline to 3 months following IFC-ACS (P < 0.001) but remained stable following RFC-ACS (P = 0.25). Interleukin-6 levels decreased in patients with RFC-ACS free of MACE+ (P = 0.01) but persisted high in those with MACE+.

CONCLUSION

This study demonstrates a distinct inflammatory response and a lower risk of MACE+ following IFC-ACS. These findings advance our understanding of inflammatory cascades associated with different mechanisms of plaque disruption and provide hypothesis generating data for personalized anti-inflammatory therapeutic allocation to ACS patients, a strategy that merits evaluation in future clinical trials.

Prediction of coronary artery disease using urinary proteomics

AIMS

Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD.

METHODS AND RESULTS

Urinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78-0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66-0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47-0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80-0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26-1.89, P < 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25-0.95, P = 0.001; 0.64, 95% CI: 0.28-0.98, P = 0.001, correspondingly).

CONCLUSION

A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention.

Early signaling of inflammation in patients following traumatic injury with accurately estimated time of injury by profiling C-reactive protein levels

BACKGROUND

Despite its widespread use, the precise dynamics of CRP response in clinical practice remain poorly defined. We employed a novel quadratic model to explore the time-course analysis of CRP values in trauma patients with known precise time of injury.

METHODS

Relevant data on all adult patients admitted to our hospital following traumatic incidents between January 1st 2010 to December 31, 2020 were retrospectively collected. Those with a documented time of injury and who underwent CRP evaluation within the first 24 h since injury were studied.

RESULTS

Based on the findings from our annual health check-up center, we established a reference upper normal CRP value of 12.99 mg/L. Within the first 7 h after injury, the CRP levels of 8-9% of the 1545 study patients exceeded the reference threshold. The proportion of patients with CRP levels > 12.99 mg/L increased to 18.5% at 8-9 h later and rose sharply to 91.6% at 22-24 h later. Our quadratic model yielded the equation: CRP = 5.122-0.528xTime + 0.139xTime 2. It accounted for > 40% of the variance in CRP levels (R2 = 42.4%).

CONCLUSIONS

Clear and prominent CRP elevations following atraumatic event are detected only 9-12 h following the insult. This novel finding has crucial implications for accurate CRP assessment of inflammatory responses to physical injuries.

Combining Polygenic and Proteomic Risk Scores With Clinical Risk Factors to Improve Performance for Diagnosing Absence of Coronary Artery Disease in Patients With de novo Chest Pain

BACKGROUND

Patients with de novo chest pain, referred for evaluation of possible coronary artery disease (CAD), frequently have an absence of CAD resulting in millions of tests not having any clinical impact. The objective of this study was to investigate whether polygenic risk scores and targeted proteomics improve the prediction of absence of CAD in patients with suspected CAD, when added to the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS).

METHODS

Genotyping and targeted plasma proteomics (N=368 proteins) were performed in 1440 patients with symptoms suspected to be caused by CAD undergoing coronary computed tomography angiography. Based on individual genotypes, a polygenic risk score for CAD (PRSCAD) was calculated. The prediction was performed using combinations of PRSCAD, proteins, and PMRS as features in models using stability selection and machine learning.

RESULTS

Prediction of absence of CAD yielded an area under the curve of PRSCAD-model, 0.64±0.03; proteomic-model, 0.58±0.03; and PMRS model, 0.76±0.02. No significant correlation was found between the genetic and proteomic risk scores (Pearson correlation coefficient, -0.04; P=0.13). Optimal predictive ability was achieved by the full model (PRSCAD+protein+PMRS) yielding an area under the curve of 0.80±0.02 for absence of CAD, significantly better than the PMRS model alone (P<0.001). For reclassification purpose, the full model enabled down-classification of 49% (324 of 661) of the 5% to 15% pretest probability patients and 18% (113 of 611) of >15% pretest probability patients.

CONCLUSIONS

For patients with chest pain and low-intermediate CAD risk, incorporating targeted proteomics and polygenic risk scores into the risk assessment substantially improved the ability to predict the absence of CAD. Genetics and proteomics seem to add complementary information to the clinical risk factors and improve risk stratification in this large patient group.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT02264717.

Evaluation of Large-Scale Proteomics for Prediction of Cardiovascular Events

Importance

Whether protein risk scores derived from a single plasma sample could be useful for risk assessment for atherosclerotic cardiovascular disease (ASCVD), in conjunction with clinical risk factors and polygenic risk scores, is uncertain.

Objective

To develop protein risk scores for ASCVD risk prediction and compare them to clinical risk factors and polygenic risk scores in primary and secondary event populations.

Design, Setting, and Participants

The primary analysis was a retrospective study of primary events among 13 540 individuals in Iceland (aged 40-75 years) with proteomics data and no history of major ASCVD events at recruitment (study duration, August 23, 2000 until October 26, 2006; follow-up through 2018). We also analyzed a secondary event population from a randomized, double-blind lipid-lowering clinical trial (2013-2016), consisting of individuals with stable ASCVD receiving statin therapy and for whom proteomic data were available for 6791 individuals.

Exposures

Protein risk scores (based on 4963 plasma protein levels and developed in a training set in the primary event population); polygenic risk scores for coronary artery disease and stroke; and clinical risk factors that included age, sex, statin use, hypertension treatment, type 2 diabetes, body mass index, and smoking status at the time of plasma sampling.

Main Outcomes and Measures

Outcomes were composites of myocardial infarction, stroke, and coronary heart disease death or cardiovascular death. Performance was evaluated using Cox survival models and measures of discrimination and reclassification that accounted for the competing risk of non-ASCVD death.

Results

In the primary event population test set (4018 individuals [59.0% women]; 465 events; median follow-up, 15.8 years), the protein risk score had a hazard ratio (HR) of 1.93 per SD (95% CI, 1.75 to 2.13). Addition of protein risk score and polygenic risk scores significantly increased the C index when added to a clinical risk factor model (C index change, 0.022 [95% CI, 0.007 to 0.038]). Addition of the protein risk score alone to a clinical risk factor model also led to a significantly increased C index (difference, 0.014 [95% CI, 0.002 to 0.028]). Among White individuals in the secondary event population (6307 participants; 432 events; median follow-up, 2.2 years), the protein risk score had an HR of 1.62 per SD (95% CI, 1.48 to 1.79) and significantly increased C index when added to a clinical risk factor model (C index change, 0.026 [95% CI, 0.011 to 0.042]). The protein risk score was significantly associated with major adverse cardiovascular events among individuals of African and Asian ancestries in the secondary event population.

Conclusions and Relevance

A protein risk score was significantly associated with ASCVD events in primary and secondary event populations. When added to clinical risk factors, the protein risk score and polygenic risk score both provided statistically significant but modest improvement in discrimination.

Optimizing a Diagnostic Model of Periodontitis by Using Targeted Proteomics

Periodontitis (PD), a widespread chronic infectious disease, compromises oral health and is associated with various systemic conditions and hematological alterations. Yet, to date, it is not clear whether serum protein profiling improves the assessment of PD. We collected general health data, performed dental examinations, and generated serum protein profiles using novel Proximity Extension Assay technology for 654 participants of the Bialystok PLUS study. To evaluate the incremental benefit of proteomics, we constructed two logistic regression models assessing the risk of having PD according to the CDC/AAP definition; the first one contained established PD predictors, and in addition, the second one was enhanced by extensive protein information. We then compared both models in terms of overall fit, discrimination, and calibration. For internal model validation, we performed bootstrap resampling (n = 2000). We identified 14 proteins, which improved the global fit and discrimination of a model of established PD risk factors, while maintaining reasonable calibration (area under the curve 0.82 vs 0.86; P < 0.001). Our results suggest that proteomic technologies offer an interesting advancement in the goal of finding easy-to-use and scalable diagnostic applications for PD that do not require direct examination of the periodontium.

Modeling Clinical Phenotype Variability: Consideration of Genomic Variations, Computational Methods, and Quantitative Proteomics

Advances in biomedical and computer technologies have presented the modeling community the opportunity for mechanistically modeling and simulating the variability in a disease phenotype or in a drug response. The capability to quantify response variability can inform a drug development program. Quantitative systems pharmacology scientists have published various computational approaches for creating virtual patient populations (VPops) to model and simulate drug response variability. Genomic variations can impact disease characteristics and drug exposure and response. Quantitative proteomics technologies are increasingly used to facilitate drug discovery and development and inform patient care. Incorporating variations in genomics and quantitative proteomics may potentially inform creation of VPops to model and simulate virtual patient trials, and may help account for, in a predictive manner, phenotypic variations observed clinically.

Identification of thrombotic biomarkers in orthopedic surgery patients by plasma proteomics

BACKGROUND

Due to the poor specificity of D-dimer, more accurate thrombus biomarkers are clinically needed to improve the diagnostic power of VTE.

METHODS

The plasma samples were classified into low-risk group (n = 6) and high-risk group (n = 6) according to the Caprini Thrombosis Risk Assessment Scale score. Data-independent acquisition mass spectrometry (DIA-MS) was performed to identify the proteins in the 12 plasma samples. Bioinformatics analysis including volcano plot, heatmap, KEGG pathways and chord diagram analysis were drawn to analyze the significantly differentially expressed proteins (DEPs) between the two groups. Then, another 26 plasma samples were collected to verify the key proteins as potential biomarkers of VTE in orthopedic surgery patients.

RESULTS

A total of 371 proteins were identified by DIA-MS in 12 plasma samples. Volcano plotting showed that there were 30 DEPs. KEGG pathway enrichment analysis revealed that the DEPs were majorly involved in the blood coagulation pathway. The chord diagram analysis demonstrated that proteins SAA1, VWF, FLNA, ACTB, VINC, F13B, F13A and IPSP in the DEPs were significantly related to blood coagulation. VWF and F13B were selected for validation experiments. ELISA test showed that, as compared with those in the low-risk group, the level of VWF in the high-risk sera was significantly increased.

CONCLUSIONS

The level of VWF in the high-risk group of thrombosis after orthopedic surgery was significantly higher than that in the low-risk group of preoperative thrombosis, suggesting that VWF may be used as a potential thrombus biomarker in orthopedic surgery patients.

From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators

PURPOSE OF REVIEW

'Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of 'omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of 'omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed.

RECENT FINDINGS

Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.

Growth differentiation factor 15 and cardiovascular risk: individual patient meta-analysis

AIMS

Levels of growth differentiation factor 15 (GDF-15), a cytokine secreted in response to cellular stress and inflammation, have been associated with multiple types of cardiovascular (CV) events. However, its comparative prognostic performance across different presentations of atherosclerotic cardiovascular disease (ASCVD) remains unknown.

METHODS AND RESULTS

An individual patient meta-analysis was performed using data pooled from eight trials including 53 486 patients. Baseline GDF-15 concentration was analyzed as a continuous variable and using established cutpoints (<1200 ng/L, 1200-1800 ng/L, > 1800 ng/L) to evaluate its prognostic performance for CV death/hospitalization for heart failure (HHF), major adverse cardiovascular events (MACE), and their components using Cox models adjusted for clinical variables and established CV biomarkers. Analyses were further stratified on ASCVD status: acute coronary syndrome (ACS), stabilized after recent ACS, and stable ASCVD. Overall, higher GDF-15 concentration was significantly and independently associated with an increased rate of CV death/HHF and MACE (P < 0.001 for each). However, while GDF-15 showed a robust and consistent independent association with CV death and HHF across all presentations of ASCVD, its prognostic association with future myocardial infarction (MI) and stroke only remained significant in patients stabilized after recent ACS or with stable ASCVD [hazard ratio (HR): 1.24, 95% confidence interval (CI): 1.17-1.31 and HR: 1.16, 95% CI: 1.05-1.28 for MI and stroke, respectively] and not in ACS (HR: 0.98, 95% CI: 0.90-1.06 and HR: 0.87, 95% CI: 0.39-1.92, respectively).

CONCLUSION

Growth differentiation factor 15 consistently adds prognostic information for CV death and HHF across the spectrum of ASCVD. GDF-15 also adds prognostic information for MI and stroke beyond clinical risk factors and cardiac biomarkers but not in the setting of ACS.

Appraisal of Cardiovascular Risk Factors, Biomarkers, and Ocular Imaging in Cardiovascular Risk Prediction

Cardiovascular disease remains a leading cause of death worldwide despite the use of available cardiovascular disease risk prediction tools. Identification of high-risk individuals via risk stratification and screening at sub-clinical stages, which may be offered by ocular screening, is important to prevent major adverse cardiac events. Retinal microvasculature has been widely researched for potential application in both diabetes and cardiovascular disease risk prediction. However, the conjunctival microvasculature as a tool for cardiovascular disease risk prediction remains largely unexplored. The purpose of this review is to evaluate the current cardiovascular risk assessment methods, identifying gaps in the literature that imaging of the ocular microcirculation may have the potential to fill. This review also explores the themes of machine learning, risk scores, biomarkers, medical imaging, and clinical risk factors. Cardiovascular risk classification varies based on the population assessed, the risk factors included, and the assessment methods. A more tailored, standardised and feasible approach to cardiovascular risk prediction that utilises technological and medical imaging advances, which may be offered by ocular imaging, is required to support cardiovascular disease prevention strategies and clinical guidelines.

Transcriptomic-based clustering of human atherosclerotic plaques identifies subgroups with different underlying biology and clinical presentation

Histopathological studies have revealed key processes of atherosclerotic plaque thrombosis. However, the diversity and complexity of lesion types highlight the need for improved sub-phenotyping. Here we analyze the gene expression profiles of 654 advanced human carotid plaques. The unsupervised, transcriptome-driven clustering revealed five dominant plaque types. These plaque phenotypes were associated with clinical presentation and showed differences in cellular compositions. Validation in coronary segments showed that the molecular signature of these plaques was linked to coronary ischemia. One of the plaque types with the most severe clinical symptoms pointed to both inflammatory and fibrotic cell lineages. Further, we did a preliminary analysis of potential circulating biomarkers that mark the different plaques phenotypes. In conclusion, the definition of the plaque at risk for a thrombotic event can be fine-tuned by in-depth transcriptomic-based phenotyping. These differential plaque phenotypes prove clinically relevant for both carotid and coronary artery plaques and point to distinct underlying biology of symptomatic lesions.

Proteomics, dietary intake, and changes in cardiometabolic health within a behavioral weight-loss intervention: A pilot study

OBJECTIVE

Identifying associations among circulating proteins, dietary intakes, and clinically relevant indicators of cardiometabolic health during weight loss may elucidate biologically relevant pathways affected by diet, allowing for an incorporation of precision nutrition approaches when designing future interventions. This study hypothesized that plasma proteins would be associated with diet and cardiometabolic health indicators within a behavioral weight-loss intervention.

METHODS

This secondary data analysis included participants (n = 20, mean [SD], age: 40.1 [9.5] years, BMI: 34.2 [4.0] kg/m2 ) who completed a 1-year behavioral weight-loss intervention. Cardiovascular disease-related plasma proteins, diet, and cardiometabolic health indicators were evaluated at baseline and 3 months. Associations were determined via linear regression and integrated networks created using Visualization Of LineAr Regression Elements (VOLARE).

RESULTS

A total of 16 plasma proteins were associated with ≥1 diet or health indicator at baseline (p < 0.001); changes in 42 proteins were associated with changes in diet or health indicators from baseline to 3 months (p < 0.005). Baseline tumor necrosis factor receptor superfamily member 10C (TNFRSF10C) was associated with intakes of dark green vegetables (r = -0.712), and fatty acid-binding protein 4 (FABP4) was associated with intakes of unsweetened coffee (r = -0.689). Changes in refined-grain intakes were associated with changes in scavenger receptor cysteine-rich type 1 protein M130 (CD163; r = 0.725), interleukin-1 receptor type 1 (IL1R-T1; r = 0.624), insulin (r = 0.656), and triglycerides (r = 0.648).

CONCLUSIONS

Circulating cardiovascular disease-related proteins were associated with diet and cardiometabolic health indicators prior to and in response to weight loss.