Improving Assessment of Drug Safety Through Proteomics: Early Detection and Mechanistic Characterization of the Unforeseen Harmful Effects of Torcetrapib

Proteomic Assessment of the Risk of Secondary Cardiovascular Events among Individuals with CKD

Key Points

Machine learning and large-scale proteomics led to a 16-protein secondary cardiovascular risk model in patients with CKD. Hepatic fibrosis and liver X receptor activation represented biologic pathways that link kidney disease and risk of secondary cardiovascular events. An understanding of the circulating proteins associated with secondary cardiovascular events may help to identify novel therapeutic targets.

Background

Cardiovascular risk models have been developed primarily for incident events. Well-performing models are lacking to predict secondary cardiovascular events among people with a history of coronary heart disease, stroke, or heart failure who also have CKD. We sought to develop a proteomic risk score for cardiovascular events in individuals with CKD and a history of cardiovascular disease.

Methods

We measured 4638 plasma proteins among 1067 participants from the Chronic Renal Insufficiency Cohort (CRIC) and 536 individuals from the Atherosclerosis Risk in Communities (ARIC) Cohort. All had non–dialysis-dependent CKD and coronary heart disease, heart failure, or stroke at study baseline. A proteomic risk model for secondary cardiovascular events was derived by elastic net regression in CRIC, validated in ARIC, and compared with clinical models. Biologic mechanisms of secondary events were characterized through proteomic pathway analysis.

Results

A 16-protein risk model was superior to the Framingham Risk Score for secondary events, including a modified score that included eGFR. In CRIC, the annualized area under the receiver operating characteristic curve (area under the curve) within 1–5 years ranged between 0.77 and 0.80 for the protein model and 0.57 and 0.72 for the clinical models. These findings were replicated in the ARIC validation cohort. Biologic pathway analysis identified pathways and proteins for cardiac remodeling and fibrosis, vascular disease, and thrombosis.

Conclusions

The proteomic risk model for secondary cardiovascular events outperformed clinical models on the basis of traditional risk factors and eGFR.

Atlas of the plasma proteome in health and disease in 53,026 adults

Large-scale proteomics studies can refine our understanding of health and disease and enable precision medicine. Here, we provide a detailed atlas of 2,920 plasma proteins linking to diseases (406 prevalent and 660 incident) and 986 health-related traits in 53,026 individuals (median follow-up: 14.8 years) from the UK Biobank, representing the most comprehensive proteome profiles to date. This atlas revealed 168,100 protein-disease associations and 554,488 protein-trait associations. Over 650 proteins were shared among at least 50 diseases, and over 1,000 showed sex and age heterogeneity. Furthermore, proteins demonstrated promising potential in disease discrimination (area under the curve [AUC] > 0.80 in 183 diseases). Finally, integrating protein quantitative trait locus data determined 474 causal proteins, providing 37 drug-repurposing opportunities and 26 promising targets with favorable safety profiles. These results provide an open-access comprehensive proteome-phenome resource (https://proteome-phenome-atlas.com/) to help elucidate the biological mechanisms of diseases and accelerate the development of disease biomarkers, prediction models, and therapeutic targets.

HDL-free cholesterol influx into macrophages and transfer to LDL correlate with HDL-free cholesterol content

High-density lipoprotein (HDL)-free cholesterol (FC) transfers to other lipoproteins and cells, the former by a spontaneous mechanism and the latter by both spontaneous and receptor-mediated mechanisms. Macrophages are an important cell type in all stages of atherosclerotic cardiovascular disease (ASCVD), and the magnitude of FC efflux from macrophages to HDL, a metric of HDL function, inversely associated with several metrics of ASCVD. Very high plasma HDL concentrations are associated with increased all-cause and ASCVD mortality, suggesting that the reverse process, FC influx from HDL into macrophages, is atherogenic. We hypothesize that HDL-FC is a metric of dysfunctional HDL, and when combined with HDL particle number (HDL-P), is an ASCVD risk factor. The magnitude of FC influx from HDL to macrophages is expected to be a function of HDL-P and HDL-FC content. Here we show that plasma HDL-FC content varies 2-fold among normolipidemic human subjects and linearly correlates with low-density lipoprotein (LDL)-FC content. The influx of HDL-FC into macrophages and transfer to LDL increase linearly with HDL-FC. As expected, the influx of HDL-FC into macrophages and the transfer to LDL are positively correlated. These data support the hypothesis that high HDL FC content is a marker for dysfunctional HDL, resulting in greater influx into macrophages and greater HDL-FC transfer to LDL. HDL-FC transfer to LDL is a valid surrogate for influx into macrophages. This study of HDL composition and function of normolipidemic subjects provides the basis for further investigation and establishment of HDL-FC content as an ASCVD risk factor.

Crossing the Halfway Point: Aptamer-Based, Highly Multiplexed Assay for the Assessment of the Proteome

Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.

Circadian misalignment disrupts biomarkers of cardiovascular disease risk and promotes a hypercoagulable state

The circadian system regulates 24-h time-of-day patterns of cardiovascular physiology, with circadian misalignment resulting in adverse cardiovascular risk. Although many proteins in the coagulation-fibrinolysis axis show 24-h time-of-day patterns, it is not understood if these temporal patterns are regulated by circadian or behavioral (e.g., sleep and food intake) cycles, or how circadian misalignment influences these patterns. Thus, we utilized a night shiftwork protocol to analyze circadian versus behavioral cycle regulation of 238 plasma proteins linked to cardiovascular physiology. Six healthy men aged 26.2 ± 5.6 years (mean ± SD) completed the protocol involving two baseline days with 8-h nighttime sleep opportunities (circadian alignment), a transition to shiftwork day, followed by 2 days of simulated night shiftwork with 8-h daytime sleep opportunities (circadian misalignment). Plasma was collected for proteomics every 4 h across 24 h during baseline and during daytime sleep and the second night shift. Cosinor analyses identified proteins with circadian or behavioral cycle-regulated 24-h time-of-day patterns. Five proteins were circadian regulated (plasminogen activator inhibitor-1, angiopoietin-2, insulin-like growth factor binding protein-4, follistatin-related protein-3, and endoplasmic reticulum resident protein-29). No cardiovascular-related proteins showed regulation by behavioral cycles. Within the coagulation pathway, circadian misalignment decreased tissue factor pathway inhibitor, increased tissue factor, and induced a 24-h time-of-day pattern in coagulation factor VII (all FDR < 0.10). Such changes in protein abundance are consistent with changes observed in hypercoagulable states. Our analyses identify circadian regulation of proteins involved in cardiovascular physiology and indicate that acute circadian misalignment could promote a hypercoagulable state, possibly contributing to elevated cardiovascular disease risk among shift workers.

Incident heart failure in chronic kidney disease: proteomics informs biology and risk stratification

BACKGROUND AND AIMS

Incident heart failure (HF) among individuals with chronic kidney disease (CKD) incurs hospitalizations that burden patients and health care systems. There are few preventative therapies, and the Pooled Cohort equations to Prevent Heart Failure (PCP-HF) perform poorly in the setting of CKD. New drug targets and better risk stratification are urgently needed.

METHODS

In this analysis of incident HF, SomaScan V4.0 (4638 proteins) was analysed in 2906 participants of the Chronic Renal Insufficiency Cohort (CRIC) with validation in the Atherosclerosis Risk in Communities (ARIC) study. The primary outcome was 14-year incident HF (390 events); secondary outcomes included 4-year HF (183 events), HF with reduced ejection fraction (137 events), and HF with preserved ejection fraction (165 events). Mendelian randomization and Gene Ontology were applied to examine causality and pathways. The performance of novel multi-protein risk models was compared to the PCP-HF risk score.

RESULTS

Over 200 proteins were associated with incident HF after adjustment for estimated glomerular filtration rate at P < 1 × 10-5. After adjustment for covariates including N-terminal pro-B-type natriuretic peptide, 17 proteins remained associated at P < 1 × 10-5. Mendelian randomization associations were found for six proteins, of which four are druggable targets: FCG2B, IGFBP3, CAH6, and ASGR1. For the primary outcome, the C-statistic (95% confidence interval [CI]) for the 48-protein model in CRIC was 0.790 (0.735, 0.844) vs. 0.703 (0.644, 0.762) for the PCP-HF model (P = .001). C-statistic (95% CI) for the protein model in ARIC was 0.747 (0.707, 0.787).

CONCLUSIONS

Large-scale proteomics reveal novel circulating protein biomarkers and potential mediators of HF in CKD. Proteomic risk models improve upon the PCP-HF risk score in this population.

Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review

Cardiovascular diseases remain the main cause of death worldwide which makes it essential to better understand, diagnose, and treat atherosclerosis. Artificial intelligence (AI) and novel technological solutions offer us new possibilities and enable the practice of individually tailored medicine. The study was performed using the PRISMA protocol. As of January 10, 2023, the analysis has been based on a review of 457 identified articles in PubMed and MEDLINE databases. The search covered reviews, original articles, meta-analyses, comments, and editorials published in the years 2009-2023. In total, 123 articles met inclusion criteria. The results were divided into the subsections presented in the review (genome-wide association studies, radiomics, and other studies). This paper presents actual knowledge concerning atherosclerosis, in silico, and big data analyses in cardiology that affect the way medicine is practiced in order to create an individual approach and adjust the therapy of atherosclerosis.

The pathophysiology of excess plasma-free cholesterol

PURPOSE OF REVIEW

Several large studies have shown increased mortality due to all-causes and to atherosclerotic cardiovascular disease. In most clinical settings, plasma HDL-cholesterol is determined as a sum of free cholesterol and cholesteryl ester, two molecules with vastly different metabolic itineraries. We examine the evidence supporting the concept that the pathological effects of elevations of plasma HDL-cholesterol are due to high levels of the free cholesterol component of HDL-C.

RECENT FINDINGS

In a small population of humans, a high plasma HDL-cholesterol is associated with increased mortality. Similar observations in the HDL-receptor deficient mouse (Scarb1 -/- ), a preclinical model of elevated HDL-C, suggests that the pathological component of HDL in these patients is an elevated plasma HDL-FC.

SUMMARY

Collective consideration of the human and mouse data suggests that clinical trials, especially in the setting of high plasma HDL, should measure free cholesterol and cholesteryl esters and not just total cholesterol.

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.

Proteomics of CKD progression in the chronic renal insufficiency cohort

Progression of chronic kidney disease (CKD) portends myriad complications, including kidney failure. In this study, we analyze associations of 4638 plasma proteins among 3235 participants of the Chronic Renal Insufficiency Cohort Study with the primary outcome of 50% decline in estimated glomerular filtration rate or kidney failure over 10 years. We validate key findings in the Atherosclerosis Risk in the Communities study. We identify 100 circulating proteins that are associated with the primary outcome after multivariable adjustment, using a Bonferroni statistical threshold of significance. Individual protein associations and biological pathway analyses highlight the roles of bone morphogenetic proteins, ephrin signaling, and prothrombin activation. A 65-protein risk model for the primary outcome has excellent discrimination (C-statistic[95%CI] 0.862 [0.835, 0.889]), and 14/65 proteins are druggable targets. Potentially causal associations for five proteins, to our knowledge not previously reported, are supported by Mendelian randomization: EGFL9, LRP-11, MXRA7, IL-1 sRII and ILT-2. Modifiable protein risk markers can guide therapeutic drug development aimed at slowing CKD progression.

Proteomic cardiovascular risk assessment in chronic kidney disease

AIMS

Chronic kidney disease (CKD) is widely prevalent and independently increases cardiovascular risk. Cardiovascular risk prediction tools derived in the general population perform poorly in CKD. Through large-scale proteomics discovery, this study aimed to create more accurate cardiovascular risk models.

METHODS AND RESULTS

Elastic net regression was used to derive a proteomic risk model for incident cardiovascular risk in 2182 participants from the Chronic Renal Insufficiency Cohort. The model was then validated in 485 participants from the Atherosclerosis Risk in Communities cohort. All participants had CKD and no history of cardiovascular disease at study baseline when ∼5000 proteins were measured. The proteomic risk model, which consisted of 32 proteins, was superior to both the 2013 ACC/AHA Pooled Cohort Equation and a modified Pooled Cohort Equation that included estimated glomerular filtrate rate. The Chronic Renal Insufficiency Cohort internal validation set demonstrated annualized receiver operating characteristic area under the curve values from 1 to 10 years ranging between 0.84 and 0.89 for the protein and 0.70 and 0.73 for the clinical models. Similar findings were observed in the Atherosclerosis Risk in Communities validation cohort. For nearly half of the individual proteins independently associated with cardiovascular risk, Mendelian randomization suggested a causal link to cardiovascular events or risk factors. Pathway analyses revealed enrichment of proteins involved in immunologic function, vascular and neuronal development, and hepatic fibrosis.

CONCLUSION

In two sizeable populations with CKD, a proteomic risk model for incident cardiovascular disease surpassed clinical risk models recommended in clinical practice, even after including estimated glomerular filtration rate. New biological insights may prioritize the development of therapeutic strategies for cardiovascular risk reduction in the CKD population.

Robust and High-Throughput Analytical Flow Proteomics Analysis of Cynomolgus Monkey and Human Matrices with Zeno SWATH Data Independent Acquisition

Modern mass spectrometers routinely allow deep proteome coverage in a single experiment. These methods are typically operated at nano and micro flow regimes, but they often lack throughput and chromatographic robustness, which is critical for large-scale studies. In this context, we have developed, optimized and benchmarked LC-MS methods combining the robustness and throughput of analytical flow chromatography with the added sensitivity provided by the Zeno trap across a wide range of cynomolgus monkey and human matrices of interest for toxicological studies and clinical biomarker discovery. SWATH data independent acquisition (DIA) experiments with Zeno trap activated (Zeno SWATH DIA) provided a clear advantage over conventional SWATH DIA in all sample types tested with improved sensitivity, quantitative robustness and signal linearity as well as increased protein coverage by up to 9-fold. Using a 10-min gradient chromatography, up to 3,300 proteins were identified in tissues at 2 μg peptide load. Importantly, the performance gains with Zeno SWATH translated into better biological pathway representation and improved the ability to identify dysregulated proteins and pathways associated with two metabolic diseases in human plasma. Finally, we demonstrate that this method is highly stable over time with the acquisition of reliable data over the injection of 1,000+ samples (14.2 days of uninterrupted acquisition) without the need for human intervention or normalization. Altogether, Zeno SWATH DIA methodology allows fast, sensitive and robust proteomic workflows using analytical flow and is amenable to large-scale studies. This work provides detailed method performance assessment on a variety of relevant biological matrices and serves as a valuable resource for the proteomics community.

Neuroblastoma suppressor of tumorigenicity 1 is a circulating protein associated with progression to end-stage kidney disease in diabetes

Circulating proteins associated with transforming growth factor-β (TGF-β) signaling are implicated in the development of diabetic kidney disease (DKD). It remains to be comprehensively examined which of these proteins are involved in the pathogenesis of DKD and its progression to end-stage kidney disease (ESKD) in humans. Using the SOMAscan proteomic platform, we measured concentrations of 25 TGF-β signaling family proteins in four different cohorts composed in total of 754 Caucasian or Pima Indian individuals with type 1 or type 2 diabetes. Of these 25 circulating proteins, we identified neuroblastoma suppressor of tumorigenicity 1 (NBL1, aliases DAN and DAND1), a small secreted protein known to inhibit members of the bone morphogenic protein family, to be most strongly and independently associated with progression to ESKD during 10-year follow-up in all cohorts. The extent of damage to podocytes and other glomerular structures measured morphometrically in 105 research kidney biopsies correlated strongly with circulating NBL1 concentrations. Also, in vitro exposure to NBL1 induced apoptosis in podocytes. In conclusion, circulating NBL1 may be involved in the disease process underlying progression to ESKD, and its concentration in circulation may identify subjects with diabetes at increased risk of progression to ESKD.

A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk

A reliable, individualized, and dynamic surrogate of cardiovascular risk, synoptic for key biologic mechanisms, could shorten the path for drug development, enhance drug cost-effectiveness and improve patient outcomes. We used highly multiplexed proteomics to address these objectives, measuring about 5000 proteins in each of 32,130 archived plasma samples from 22,849 participants in nine clinical studies. We used machine learning to derive a 27-protein model predicting 4-year likelihood of myocardial infarction, stroke, heart failure, or death. The 27 proteins encompassed 10 biologic systems, and 12 were associated with relevant causal genetic traits. We independently validated results in 11,609 participants. Compared to a clinical model, the ratio of observed events in quintile 5 to quintile 1 was 6.7 for proteins versus 2.9 for the clinical model, AUCs (95% CI) were 0.73 (0.72 to 0.74) versus 0.64 (0.62 to 0.65), c-statistics were 0.71 (0.69 to 0.72) versus 0.62 (0.60 to 0.63), and the net reclassification index was +0.43. Adding the clinical model to the proteins only improved discrimination metrics by 0.01 to 0.02. Event rates in four predefined protein risk categories were 5.6, 11.2, 20.0, and 43.4% within 4 years; median time to event was 1.71 years. Protein predictions were directionally concordant with changed outcomes. Adverse risks were predicted for aging, approaching an event, anthracycline chemotherapy, diabetes, smoking, rheumatoid arthritis, cancer history, cardiovascular disease, high systolic blood pressure, and lipids. Reduced risks were predicted for weight loss and exenatide. The 27-protein model has potential as a "universal" surrogate end point for cardiovascular risk.

The amniotic fluid proteome changes with gestational age in normal pregnancy: a cross-sectional study

The cell-free transcriptome in amniotic fluid (AF) has been shown to be informative of physiologic and pathologic processes in pregnancy; however, the change in AF proteome with gestational age has mostly been studied by targeted approaches. The objective of this study was to describe the gestational age-dependent changes in the AF proteome during normal pregnancy by using an omics platform. The abundance of 1310 proteins was measured on a high-throughput aptamer-based proteomics platform in AF samples collected from women during midtrimester (16-24 weeks of gestation, n = 15) and at term without labor (37-42 weeks of gestation, n = 13). Only pregnancies without obstetrical complications were included in the study. Almost 25% (320) of AF proteins significantly changed in abundance between the midtrimester and term gestation. Of these, 154 (48.1%) proteins increased, and 166 (51.9%) decreased in abundance at term compared to midtrimester. Tissue-specific signatures of the trachea, salivary glands, brain regions, and immune system were increased while those of the gestational tissues (uterus, placenta, and ovary), cardiac myocytes, and fetal liver were decreased at term compared to midtrimester. The changes in AF protein abundance were correlated with those previously reported in the cell-free AF transcriptome. Intersecting gestational age-modulated AF proteins and their corresponding mRNAs previously reported in the maternal blood identified neutrophil-related protein/mRNA pairs that were modulated in the same direction. The first study to utilize an aptamer-based assay to profile the AF proteome modulation with gestational age, it reveals that almost one-quarter of the proteins are modulated as gestation advances, which is more than twice the fraction of altered plasma proteins (~ 10%). The results reported herein have implications for future studies focused on discovering biomarkers to predict, monitor, and diagnose obstetrical diseases.

High-density lipoproteins, reverse cholesterol transport and atherogenesis

Plasma HDL-cholesterol concentrations correlate negatively with the risk of atherosclerotic cardiovascular disease (ASCVD). According to a widely cited model, HDL elicits its atheroprotective effect through its role in reverse cholesterol transport, which comprises the efflux of cholesterol from macrophages to early forms of HDL, followed by the conversion of free cholesterol (FCh) contained in HDL into cholesteryl esters, which are hepatically extracted from the plasma by HDL receptors and transferred to the bile for intestinal excretion. Given that increasing plasma HDL-cholesterol levels by genetic approaches does not reduce the risk of ASCVD, the focus of research has shifted to HDL function, especially in the context of macrophage cholesterol efflux. In support of the reverse cholesterol transport model, several large studies have revealed an inverse correlation between macrophage cholesterol efflux to plasma HDL and ASCVD. However, other studies have cast doubt on the underlying reverse cholesterol transport mechanism: in mice and humans, the FCh contained in HDL is rapidly cleared from the plasma (within minutes), independently of esterification and HDL holoparticle uptake by the liver. Moreover, the reversibility of FCh transfer between macrophages and HDL has implicated the reverse process - that is, the transfer of FCh from HDL to macrophages - in the aetiology of increased ASCVD under conditions of very high plasma HDL-FCh concentrations.

Comprehensive Search for Novel Circulating miRNAs and Axon Guidance Pathway Proteins Associated with Risk of ESKD in Diabetes

BACKGROUND

Mechanisms underlying the pro gression of diabetic kidney disease to ESKD are not fully understood.

METHODS

We performed global microRNA (miRNA) analysis on plasma from two cohorts consisting of 375 individuals with type 1 and type 2 diabetes with late diabetic kidney disease, and targeted proteomics analysis on plasma from four cohorts consisting of 746 individuals with late and early diabetic kidney disease. We examined structural lesions in kidney biopsy specimens from the 105 individuals with early diabetic kidney disease. Human umbilical vein endothelial cells were used to assess the effects of miRNA mimics or inhibitors on regulation of candidate proteins.

RESULTS

In the late diabetic kidney disease cohorts, we identified 17 circulating miRNAs, represented by four exemplars (miR-1287-5p, miR-197-5p, miR-339-5p, and miR-328-3p), that were strongly associated with 10-year risk of ESKD. These miRNAs targeted proteins in the axon guidance pathway. Circulating levels of six of these proteins-most notably, EFNA4 and EPHA2-were strongly associated with 10-year risk of ESKD in all cohorts. Furthermore, circulating levels of these proteins correlated with severity of structural lesions in kidney biopsy specimens. In contrast, expression levels of genes encoding these proteins had no apparent effects on the lesions. In in vitro experiments, mimics of miR-1287-5p and miR-197-5p and inhibitors of miR-339-5p and miR-328-3p upregulated concentrations of EPHA2 in either cell lysate, supernatant, or both.

CONCLUSIONS

This study reveals novel mechanisms involved in progression to ESKD and points to the importance of systemic factors in the development of diabetic kidney disease. Some circulating miRNAs and axon guidance pathway proteins represent potential targets for new therapies to prevent and treat this condition.

Molecular Aspects of Lifestyle and Environmental Effects in Patients With Diabetes: JACC Focus Seminar

Diabetes is characterized as an integrated condition of dysregulated metabolism across multiple tissues, with well-established consequences on the cardiovascular system. Recent advances in precision phenotyping in biofluids and tissues in large human observational and interventional studies have afforded a unique opportunity to translate seminal findings in models and cellular systems to patients at risk for diabetes and its complications. Specifically, techniques to assay metabolites, proteins, and transcripts, alongside more recent assessment of the gut microbiome, underscore the complexity of diabetes in patients, suggesting avenues for precision phenotyping of risk, response to intervention, and potentially novel therapies. In addition, the influence of external factors and inputs (eg, activity, diet, medical therapies) on each domain of molecular characterization has gained prominence toward better understanding their role in prevention. Here, the authors provide a broad overview of the role of several of these molecular domains in human translational investigation in diabetes.

Prognostic Metrics Associated with Inflammation and Atherosclerosis Signaling Evaluate the Burden of Adverse Clinical Outcomes in Ischemic Stroke Patients

BACKGROUND

Conventional prognostic risk factors can only partly explain the adverse clinical outcomes after ischemic stroke. We aimed to establish a set of prognostic metrics and evaluate its public health significance on the burden of adverse clinical outcomes of ischemic stroke.

METHODS

All patients were from the China Antihypertensive Trial in Acute Ischemic Stroke (CATIS). We established prognostic metrics of ischemic stroke from 20 potential biomarkers in a propensity-score-matched extreme case sample (n = 146). Pathway analysis was conducted using Ingenuity Pathway Analysis. In the whole CATIS population (n = 3575), we evaluated effectiveness of these prognostic metrics and estimated their population-attributable fractions (PAFs) related to the risk of clinical outcomes. The primary outcome was a composite outcome of death or major disability (modified Rankin Scale score ≥3) at 3 months after stroke.

RESULTS

Matrix metalloproteinase-9 (MMP-9), S100A8/A9, high-sensitivity C-reactive protein (hsCRP), and growth differentiation factor-15 (GDF-15) were selected as prognostic metrics for ischemic stroke. Pathway analysis showed significant enrichment in inflammation and atherosclerosis signaling. All 4 prognostic metrics were independently associated with poor prognosis of ischemic stroke. Compared with patients having 1 or 0 high-level prognostic metrics, those with 4 had higher risk of primary outcome (OR: 3.84, 95%CI: 2.67-5.51; PAF: 37.4%, 95%CI: 19.5%-52.9%).

CONCLUSION

The set of prognostic metrics, enriching in inflammation and atherosclerosis signaling, could effectively predict the prognosis at 3 months after ischemic stroke and would provide additional information for the burden of adverse clinical outcomes among patients with ischemic stroke.

General Perspectives for the Treatment of Atherosclerosis

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Atherosclerosis, a cardiovascular disease, is at the top of the list among the diseases leading to death. Although the biochemical and pathophysiological cascades involved within the development of atherosclerosis have been identified clearly, its nature is quite complex to be treated with a single agent targeting a pathway. Therefore, many natural and synthetic compounds have been suggested for the treatment of the disease. The majority of the drugs employed target one of the single components of the pathological outcomes, resulting in many times less effective and longterm treatments. In most cases, treatment options prevent further worsening of the symptoms rather than a radical treatment. Consequently, the current review has been prepared to focus on the validated and non-validated targets of atherosclerosis as well as the alternative treatment options such as hydroxymethyl glutaryl coenzyme A (HMG-CoA) reductase inhibitors, acyl-CoA cholesterol acyl transferase (ACAT) inhibitors, lipoprotein lipase stimulants, bile acid sequestrants, and some antioxidants. Related to the topic, both synthetic compounds designed employing medicinal chemistry skills and natural molecules becoming more popular in drug development are scrutinized in this mini review.

Omics and Cardiometabolic Disease Risk Prediction

Risk assessments are integral for the prevention and management of cardiometabolic disease (CMD). However, individuals may develop CMD without traditional risk factors, necessitating the development of novel biomarkers to aid risk prediction. The emergence of omic technologies, including genomics, proteomics, and metabolomics, has allowed for assessment of orthogonal measures of cardiometabolic risk, potentially improving the ability for novel biomarkers to refine disease risk assessments. While omics has shed light on novel mechanisms for the development of CMD, its adoption in clinical practice faces significant challenges. We review select omic technologies and cardiometabolic investigations for risk prediction, while highlighting challenges and opportunities for translating findings to clinical practice.

Prioritizing Candidates of Post-Myocardial Infarction Heart Failure Using Plasma Proteomics and Single-Cell Transcriptomics

Supplemental Digital Content is available in the text.

Background:

Heart failure (HF) is the most common long-term complication of acute myocardial infarction (MI). Understanding plasma proteins associated with post-MI HF and their gene expression may identify new candidates for biomarker and drug target discovery.

Methods:

We used aptamer-based affinity-capture plasma proteomics to measure 1305 plasma proteins at 1 month post-MI in a New Zealand cohort (CDCS [Coronary Disease Cohort Study]) including 181 patients post-MI who were subsequently hospitalized for HF in comparison with 250 patients post-MI who remained event free over a median follow-up of 4.9 years. We then correlated plasma proteins with left ventricular ejection fraction measured at 4 months post-MI and identified proteins potentially coregulated in post-MI HF using weighted gene co-expression network analysis. A Singapore cohort (IMMACULATE [Improving Outcomes in Myocardial Infarction through Reversal of Cardiac Remodelling]) of 223 patients post-MI, of which 33 patients were hospitalized for HF (median follow-up, 2.0 years), was used for further candidate enrichment of plasma proteins by using Fisher meta-analysis, resampling-based statistical testing, and machine learning. We then cross-referenced differentially expressed proteins with their differentially expressed genes from single-cell transcriptomes of nonmyocyte cardiac cells isolated from a murine MI model, and single-cell and single-nucleus transcriptomes of cardiac myocytes from murine HF models and human patients with HF.

Results:

In the CDCS cohort, 212 differentially expressed plasma proteins were significantly associated with subsequent HF events. Of these, 96 correlated with left ventricular ejection fraction measured at 4 months post-MI. Weighted gene co-expression network analysis prioritized 63 of the 212 proteins that demonstrated significantly higher correlations among patients who developed post-MI HF in comparison with event-free controls (data set 1). Cross-cohort meta-analysis of the IMMACULATE cohort identified 36 plasma proteins associated with post-MI HF (data set 2), whereas single-cell transcriptomes identified 15 gene-protein candidates (data set 3). The majority of prioritized proteins were of matricellular origin. The 6 most highly enriched proteins that were common to all 3 data sets included well-established biomarkers of post-MI HF: N-terminal B-type natriuretic peptide and troponin T, and newly emergent biomarkers, angiopoietin-2, thrombospondin-2, latent transforming growth factor-β binding protein-4, and follistatin-related protein-3, as well.

Conclusions:

Large-scale human plasma proteomics, cross-referenced to unbiased cardiac transcriptomics at single-cell resolution, prioritized protein candidates associated with post-MI HF for further mechanistic and clinical validation.

Impact of Kidney Function on the Blood Proteome and on Protein Cardiovascular Risk Biomarkers in Patients With Stable Coronary Heart Disease

Background

Chronic kidney disease (CKD) confers increased cardiovascular risk, not fully explained by traditional factors. Proteins regulate biological processes and inform the risk of diseases. Thus, in 938 patients with stable coronary heart disease from the Heart and Soul cohort, we quantified 1054 plasma proteins using modified aptamers (SOMAscan) to: (1) discern how reduced glomerular filtration influences the circulating proteome, (2) learn of the importance of kidney function to the prognostic information contained in recently identified protein cardiovascular risk biomarkers, and (3) identify novel and even unique cardiovascular risk biomarkers among individuals with CKD.

Methods and Results

Plasma protein levels were correlated to estimated glomerular filtration rate (eGFR) using Spearman‐rank correlation coefficients. Cox proportional hazard models were used to estimate the association between individual protein levels and the risk of the cardiovascular outcome (first among myocardial infarction, stroke, heart failure hospitalization, or mortality). Seven hundred and nine (67.3%) plasma proteins correlated with eGFR at P<0.05 (ρ 0.06–0.74); 218 (20.7%) proteins correlated with eGFR moderately or strongly (ρ 0.2–0.74). Among the previously identified 196 protein cardiovascular biomarkers, just 87 remained prognostic after correction for eGFR. Among patients with CKD (eGFR <60 mL/min per 1.73 m²), we identified 21 protein cardiovascular risk biomarkers of which 8 are unique to CKD.

Conclusions

CKD broadly alters the composition of the circulating proteome. We describe protein biomarkers capable of predicting cardiovascular risk independently of glomerular filtration, and those that are prognostic of cardiovascular risk specifically in patients with CKD and even unique to patients with CKD.

Metabolomics and Proteomics in Type 2 Diabetes

The persistent increase in the worldwide burden of type 2 diabetes mellitus (T2D) and the accompanying rise of its complications, including cardiovascular disease, necessitates our understanding of the metabolic disturbances that cause diabetes mellitus. Metabolomics and proteomics, facilitated by recent advances in high-throughput technologies, have given us unprecedented insight into circulating biomarkers of T2D even over a decade before overt disease. These markers may be effective tools for diabetes mellitus screening, diagnosis, and prognosis. As participants of metabolic pathways, metabolite and protein markers may also highlight pathways involved in T2D development. The integration of metabolomics and proteomics with genomics in multiomics strategies provides an analytical method that can begin to decipher causal associations. These methods are not without their limitations; however, with careful study design and sample handling, these methods represent powerful scientific tools that can be leveraged for the study of T2D. In this article, we aim to give a timely overview of circulating metabolomics and proteomics findings with T2D observed in large human population studies to provide the reader with a snapshot into these emerging fields of research.

Aptamer-Based Proteomic Platform Identifies Novel Protein Predictors of Incident Heart Failure and Echocardiographic Traits

BACKGROUND

We used a large-scale, high-throughput DNA aptamer-based discovery proteomic platform to identify circulating biomarkers of cardiac remodeling and incident heart failure (HF) in community-dwelling individuals.

METHODS

We evaluated 1895 FHS (Framingham Heart Study) participants (age 55±10 years, 54% women) who underwent proteomic profiling and echocardiography. Plasma levels of 1305 proteins were related to echocardiographic traits and to incident HF using multivariable regression. Statistically significant protein-HF associations were replicated in the HUNT (Nord-Trøndelag Health) study (n=2497, age 63±10 years, 43% women), and results were meta-analyzed. Genetic variants associated with circulating protein levels (pQTLs) were related to echocardiographic traits in the EchoGen (n=30 201) and to incident HF in the CHARGE (n=20 926) consortia.

RESULTS

Seventeen proteins associated with echocardiographic traits in cross-sectional analyses (false discovery rate <0.10), and 8 of these proteins had pQTLs associated with echocardiographic traits in EchoGen (P<0.0007). In Cox models adjusted for clinical risk factors, 29 proteins demonstrated associations with incident HF in FHS (174 HF events, mean follow-up 19 [limits, 0.2-23.7] years). In meta-analyses of FHS and HUNT, 6 of these proteins were associated with incident HF (P<3.8×10-5; 3 with higher risk: NT-proBNP [N-terminal proB-type natriuretic peptide], TSP2 [thrombospondin-2], MBL [mannose-binding lectin]; and 3 with lower risk: ErbB1 [epidermal growth factor receptor], GDF-11/8 [growth differentiation factor-11/8], and RGMC [hemojuvelin]). For 5 of the 6 proteins, pQTLs were associated with echocardiographic traits (P<0.0006) in EchoGen, and for RGMC, a protein quantitative trait loci was associated with incident HF (P=0.001).

CONCLUSIONS

A large-scale proteomics approach identified new predictors of cardiac remodeling and incident HF. Future studies are warranted to elucidate how biological pathways represented by these proteins may mediate cardiac remodeling and HF risk and to assess if these proteins can improve HF risk prediction.

Is proteomics of value in cardiovascular risk assessment?

PURPOSE OF REVIEW

To briefly summarize recently published evidence in the field of cardiovascular proteomics, focusing on its ability to improve cardiovascular risk stratification and critically discussing still open and burning issues and future perspectives of proteomics research.

RECENT FINDINGS

Several epidemiological studies have demonstrated an improvement in cardiovascular risk prediction beyond traditional risk factors by adding novel biomarkers, identified by both discovery and targeted proteomics. However, only a moderate improvement in risk discrimination over clinical variables was observed. Moreover, despite different outcomes there was also a strong overlap of identified candidates, with several of them being already well established cardiovascular risk markers such as growth differentiation factor 15, natriuretic peptides, C-reactive protein, interleukins, and metalloproteases.

SUMMARY

Although proteomics plays a crucial role in biomarker discovery, the modest discriminative ability of this technique raises the possibility that there are still hidden mechanisms in protein regulatory networks, which urgently need to be evaluated to improve a cardiovascular risk assessment to a clinically significant extent.

Plasma protein patterns as comprehensive indicators of health

Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3-10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12-16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.

Future Directions of Pharmacovigilance Studies Using Electronic Medical Recording and Human Genetic Databases

Adverse drug reactions (ADRs) constitute key factors in determining successful medication therapy in clinical situations. Integrative analysis of electronic medical record (EMR) data and use of proper analytical tools are requisite to conduct retrospective surveillance of clinical decisions on medications. Thus, we suggest that electronic medical recording and human genetic databases are considered together in future directions of pharmacovigilance. We analyzed EMR-based ADR studies indexed on PubMed during the period from 2005 to 2017 and retrospectively acquired 1161 (29.6%) articles describing drug-induced adverse reactions (e.g., liver, kidney, nervous system, immune system, and inflammatory responses). Of them, only 102 (8.79%) articles contained useful information to detect or predict ADRs in the context of clinical medication alerts. Since insufficiency of EMR datasets and their improper analyses may provide false warnings on clinical decision, efforts should be made to overcome possible problems on data-mining, analysis, statistics, and standardization. Thus, we address the characteristics and limitations on retrospective EMR database studies in hospital settings. Since gene expression and genetic variations among individuals impact ADRs, pharmacokinetics, and pharmacodynamics, appropriate paths for pharmacovigilance may be optimized using suitable databases available in public domain (e.g., genome-wide association studies (GWAS), non-coding RNAs, microRNAs, proteomics, and genetic variations), novel targets, and biomarkers. These efforts with new validated biomarker analyses would be of help to repurpose clinical and translational research infrastructure and ultimately future personalized therapy considering ADRs.

A signature of circulating inflammatory proteins and development of end-stage renal disease in diabetes

Chronic inflammation is postulated to be involved in development of end stage renal disease (ESRD) in diabetes, but which specific circulating inflammatory proteins contribute to this risk remains unknown. To study this we examined 194 circulating inflammatory proteins in subjects from three independent cohorts with Type 1 and Type 2 diabetes. In each cohort we identified an extremely robust Kidney Risk Inflammatory Signature (KRIS) consisting of 17 novel proteins enriched for TNF Receptor Superfamily members that was associated with a 10-year risk of ESRD. All these proteins had a systemic, non-kidney source. Our prospective study findings provide strong evidence that KRIS proteins contribute to the inflammatory process underlying ESRD development in both types of diabetes. These proteins may be used as new therapeutic targets, new prognostic tests for high risk of ESRD and as surrogate outcome measures where changes in KRIS levels during intervention can reflect the tested therapy’s effectiveness.

Proteomic profiling of circulating proteins in subjects from three independent cohorts with type 1 and type 2 diabetes, identified an extremely robust inflammatory signature, consisting of 17 proteins enriched for TNF Receptor Superfamily members that was associated with a 10-year risk of end-stage renal disease.

Reproducibility and Variability of Protein Analytes Measured Using a Multiplexed Modified Aptamer Assay

BACKGROUND

There is growing interest in the use of multiplexed aptamer-based assays for large-scale proteomic studies. However, the analytic, short- and long-term variation of the measured proteins is largely uncharacterized.

METHODS

We quantified 4001 plasma protein analytes from 42 participants in the Atherosclerosis Risk in Communities (ARIC) Study in split samples and at multiple visits using a multiplexed modified aptamer assay. We calculated the CV, Spearman correlation, and intraclass correlation (ICC) between split samples and evaluated the short-term (4-9 weeks) and long-term (approximately 20 years) variability using paired t-tests with log-transformed protein concentrations and Bonferroni-corrected significance thresholds. We performed principal component (PC) analysis of protein analyte concentrations and evaluated their associations with age, sex, race, and estimated glomerular filtration rate (eGFR).

RESULTS

The mean baseline age was 57 years at the first visit, 43% of participants were male and 57% were white. Among 3693 protein analytes that passed quality control, half (n = 1846) had CVs < 5.0%, Spearman correlations > 0.89, and ICCs > 0.96 among the split samples. Over the short term, only 1 analyte had a statistically significant difference between the 2 time points, whereas, over approximately 20 years, 866 analytes (23.4%) had statistically significant differences (P < 1.4 × 10^-5, 681 increased, 185 decreased). PC1 had high correlations with age (-0.73) and eGFR (0.60). PC2 had moderate correlation with male sex (0.18) and white race (0.31).

CONCLUSIONS

Multiplexed modified aptamer technology can assay thousands of proteins with excellent precision. Our results support the potential for large-scale studies of the plasma proteome over the lifespan.

Proteomics: Clinical and research applications in respiratory diseases

The proteome is the study of the protein content of a definable component of an organism in biology. However, the tissue-specific expression of proteins and the varied post-translational modifications, splice variants and protein-protein complexes that may form, make the study of protein a challenging yet vital tool in answering many of the unanswered questions in medicine and biology to date. Indeed, the spatial, temporal and functional composition of proteins in the human body has proven difficult to elucidate for many years. Given the effect of microRNA and epigenetic regulation on silencing and enhancing gene transcription, the study of protein arguably provides more accurate information on homeostasis and perturbation in health and disease. There have been significant advances in the field of proteomics in recent years, with new technologies and platforms available to the research community. In this review, we briefly discuss some of these new technologies and developments in the context of respiratory disease. We also discuss the types of data science approaches to analyses and interpretation of the large volumes of data generated in proteomic studies. We discuss the application of these technologies with regard to respiratory disease and highlight the potential for proteomics in generating major advances in the understanding of respiratory pathophysiology into the future.

Systems-Wide Approaches in Induced Pluripotent Stem Cell Models

Human induced pluripotent stem cells (iPSCs) provide a renewable supply of patient-specific and tissue-specific cells for cellular and molecular studies of disease mechanisms. Combined with advances in various omics technologies, iPSC models can be used to profile the expression of genes, transcripts, proteins, and metabolites in relevant tissues. In the past 2 years, large panels of iPSC lines have been derived from hundreds of genetically heterogeneous individuals, further enabling genome-wide mapping to identify coexpression networks and elucidate gene regulatory networks. Here, we review recent developments in omics profiling of various molecular phenotypes and the emergence of human iPSCs as a systems biology model of human diseases.

Precision Medicine and Personalized Medicine in Cardiovascular Disease

Precision medicine aims to offer "the right treatment to the right patient at the right time." In cardiovascular medicine the potential of precision medicine applies to all stages of the disease development and includes risk prediction, preventative measures, and targeted therapeutic approaches. Precision medicine will benefit from new developments in the area of genomics and other omics but equally heavily depends on established biomarkers, functional tests, and imaging. Cardiovascular medicine often relies on noninvasive diagnostic procedures and symptom-based disease management. In contrast, other clinical disciplines including oncology and immunology have already moved to molecular diagnostics that lend themselves to precision medicine approaches. There are opportunities to implement precision medicine approaches by focusing on common diseases such as hypertension, conditions with diagnostic and prognostic uncertainty such as angina, and conditions that are associated with high mortality and involve costly and potentially harmful interventions such as dilated cardiomyopathy and cardiac resynchronization therapy. Sex and gender issues have not yet been fully explored in precision medicine although the opportunity to use molecular data to more accurately manage men and women with cardiovascular disease has been acknowledged. A mindshift is required in order to fully exploit the potential of precision medicine to tackle the global burden of cardiovascular diseases.