Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes

Plasma proteomic signatures for type 2 diabetes and related traits in the UK Biobank cohort

OBJECTIVE

The plasma proteome holds promise as a diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of proteins to predict type 2 diabetes and related traits.

STUDY DESIGN

We analyzed clinical, genetic, and proteomic data from three UK Biobank subcohorts for associations with truncal fat, estimated maximum oxygen consumption (VO2max), and type 2 diabetes. Using least absolute shrinkage and selection operator (LASSO) regression, we compared predictive performance of each trait between data types. The benefit of proteomic signatures (PSs) over the type 2 diabetes clinical risk score, QDiabetes was evaluated. Two-sample Mendelian randomization (MR) identified potentially causal proteins for each trait.

RESULTS

LASSO-derived PSs improved prediction of truncal fat and VO2max over clinical and genetic factors. We observed a modest improvement in type 2 diabetes prediction over the QDiabetes score when combining a PS derived for type 2 diabetes that was further augmented with fat- and fitness-associated PSs. Two-sample MR identified a few proteins as potentially causal for each trait.

CONCLUSION

Plasma PSs modestly improve type 2 diabetes prediction beyond clinical and genetic factors. Candidate causally associated proteins deserve further study as potential novel therapeutic targets for type 2 diabetes.

Omics Approach for Personalised Prevention of Type 2 Diabetes Mellitus for African and European Populations (OPTIMA): a protocol paper

INTRODUCTION

The prevalence of type 2 diabetes (T2D) within sub-Saharan Africa (SSA) is increasing. Despite the pathophysiology of T2D differing by ethnicity and sex, risk stratification and guidelines for the prevention of T2D are generic, relying on evidence from studies including predominantly Europeans. Accordingly, this study aims to develop ethnic-specific and sex-specific risk prediction models for the early detection of dysglycaemia (impaired glucose tolerance and T2D) to inform clinically feasible, culturally acceptable and cost-effective risk management and prevention strategies using dietary modification in SSA and European populations.

METHODS AND ANALYSIS

This multinational collaboration will include the prospective cohort data from two African cohorts, the Middle-Aged Soweto Cohort from South Africa and the Research on Obesity and Diabetes among African Migrants Prospective cohort from Ghana and migrants living in Europe, and a Swedish cohort, the Pre-Swedish CArdioPulmonary bioImage Study. Targeted proteomics, as well as targeted and untargeted metabolomics, will be performed at baseline to discover known and novel ethnic-specific and sex-specific biomarkers that predict incident dysglycaemia in the different longitudinal cohorts. Dietary patterns that explain maximum variation in the biomarker profiles and that associate with dysglycaemia will be identified in the SSA and European cohorts and used to build the prototypes for dietary interventions to prevent T2D. A comparative cost-effectiveness analysis of the dietary interventions will be estimated in the different populations. Finally, the perceptions of at-risk participants and healthcare providers regarding ethnic-specific and sex-specific dietary recommendations for the prevention of T2D will be assessed using focus group discussions and in-depth interviews in South Africa, Ghana, Germany (Ghanaian migrants) and Sweden.

ETHICS AND DISSEMINATION

Ethical clearance has been obtained from all participating sites. The study results will be disseminated at scientific conferences and in journal publications, and through community engagement events and diabetes organisations in the respective countries.

Overcoming Missing Data: Accurately Predicting Cardiovascular Risk in Type 2 Diabetes, A Systematic Review

Understanding is limited regarding strategies for addressing missing value when developing and validating models to predict cardiovascular disease (CVD) in type 2 diabetes mellitus (T2DM). This study aimed to investigate the presence of and approaches to missing data in these prediction models. The MEDLINE electronic database was systematically searched for English-language studies from inception to June 30, 2024. The percentages of missing values, missingness mechanisms, and missing data handling strategies in the included studies were extracted and summarized. This study included 51 articles published between 2001 and 2024, involving 19 studies that focused solely on prediction model development, and 16 and 16 studies that incorporated internal and external validation, respectively. Most articles reported missing data in the development (n = 40/51) and external validation (n = 12/16) stages. Furthermore, the missing data were addressed in 74.5% of development studies and 68.8% of validation studies. Imputation emerged as the predominant method employed for both development (27/40) and validation (7/12) purposes, followed by deletion (17/40 and 4/12, respectively). During the model development phase, the number of studies reported missing data increased from 9 out of 15 before 2016 to 31 out of 36 in 2016 and subsequent years. Although missing values have received much attention in CVD risk prediction models in patients with T2DM, most studies lack adequate reporting on the methodologies used for addressing the missing data. Enhancing the quality assurance of prediction models necessitates heightened clarity and the utilization of suitable methodologies to handle missing data effectively.

Application of proteomics for novel drug discovery and risk prediction optimisation in stroke and myocardial infarction: a review of in-human studies

The use of proteomics in human studies investigating stroke and myocardial infarction (MI) has been increasing, prompting a review of the literature. This revealed proteinaceous biomarkers of stroke from thrombi, brain tissue, cells, and particles, some of which cross the blood-brain barrier (BBB). Several proteins were also implicated in coronary artery disease (CAD), which often underlies MI, cholesterol transportation, and inflammation. Furthermore, the platelet proteome revealed itself as a potential therapeutic target, along with differentially expressed proteins associated with MI progression. Moreover, proteomic data enhanced the performance of conventional risk scores and causal protein discovery has improved interventions and drug development for patients with MI and other conditions. These findings suggest that proteomics holds much promise for future stroke and MI research.

Risk prediction of ischemic heart disease using plasma proteomics, conventional risk factors and polygenic scores in Chinese and European adults

Plasma proteomics could enhance risk prediction for multiple diseases beyond conventional risk factors or polygenic scores (PS). To assess utility of proteomics for risk prediction of ischemic heart disease (IHD) compared with conventional risk factors and PS in Chinese and European populations. A nested case-cohort study measured plasma levels of 2923 proteins using Olink Explore panel in ~ 4000 Chinese adults (1976 incident IHD cases and 2001 sub-cohort controls). We used conventional and machine learning (Boruta) methods to develop proteomics-based prediction models of IHD, with discrimination assessed using area under the curve (AUC), C-statistics and net reclassification index (NRI). These were compared with conventional risk factors and PS in Chinese and in 37,187 Europeans. Overall, 446 proteins were associated with IHD (false discovery rate < 0.05) in Chinese after adjustment for conventional cardiovascular disease risk factors. Proteomic risk models alone yielded higher C-statistics for IHD than conventional risk factors or PS (0.855 [95%CI 0.841-0.868] vs. 0.845 [0.829-0.860] vs 0.553 [0.528-0.578], respectively). Addition of 446 proteins to PS improved C-statistics to 0.857 (0.843-0.871) and NRI by 109.1%; and addition to conventional risk factors improved C-statistics to 0.868 (0.854-0.882) and NRI by 86.9%. Boruta analysis identified 30 proteins accounting for ~ 90% of improvement in NRI for IHD conferred by all 2923 proteins. Similar proteomic panels yielded comparable improvements in risk prediction of IHD in Europeans. Plasma proteomics improved risk prediction of IHD beyond conventional risk factors and PS and could enhance precision medicine approaches for primary prevention of IHD.

Plasma proteomic signatures for type 2 diabetes mellitus and related traits in the UK Biobank cohort

Aims/hypothesis

The plasma proteome holds promise as a diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict type 2 diabetes mellitus (T2DM) and related traits.

Methods

Clinical, genetic, and high-throughput proteomic data from three subcohorts of UK Biobank participants were analyzed for association with dual-energy x-ray absorptiometry (DXA) derived truncal fat (in the adiposity subcohort), estimated maximum oxygen consumption (VO2max) (in the fitness subcohort), and incident T2DM (in the T2DM subcohort). We used least absolute shrinkage and selection operator (LASSO) regression to assess the relative ability of non-proteomic and proteomic variables to associate with each trait by comparing variance explained (R2) and area under the curve (AUC) statistics between data types. Stability selection with randomized LASSO regression identified the most robustly associated proteins for each trait. The benefit of proteomic signatures (PSs) over QDiabetes, a T2DM clinical risk score, was evaluated through the derivation of delta (Δ) AUC values. We also assessed the incremental gain in model performance metrics using proteomic datasets with varying numbers of proteins. A series of two-sample Mendelian randomization (MR) analyses were conducted to identify potentially causal proteins for adiposity, fitness, and T2DM.

Results

Across all three subcohorts, the mean age was 56.7 years and 54.9% were female. In the T2DM subcohort, 5.8% developed incident T2DM over a median follow-up of 7.6 years. LASSO-derived PSs increased the R2 of truncal fat and VO2max over clinical and genetic factors by 0.074 and 0.057, respectively. We observed a similar improvement in T2DM prediction over the QDiabetes score [Δ AUC: 0.016 (95% CI 0.008, 0.024)] when using a robust PS derived strictly from the T2DM outcome versus a model further augmented with non-overlapping proteins associated with adiposity and fitness. A small number of proteins (29 for truncal adiposity, 18 for VO2max, and 26 for T2DM) identified by stability selection algorithms offered most of the improvement in prediction of each outcome. Filtered and clustered versions of the full proteomic dataset supplied by the UK Biobank (ranging between 600-1,500 proteins) performed comparably to the full dataset for T2DM prediction. Using MR, we identified 4 proteins as potentially causal for adiposity, 1 as potentially causal for fitness, and 4 as potentially causal for T2DM.

Conclusions/Interpretation

Plasma PSs modestly improve the prediction of incident T2DM over that possible with clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of these signatures in predicting the risk of T2DM over the standard practice of using the QDiabetes score. Candidate causally associated proteins identified through MR deserve further study as potential novel therapeutic targets for T2DM.

A plasma proteomic signature for atherosclerotic cardiovascular disease risk prediction in the UK Biobank cohort

Background

While risk stratification for atherosclerotic cardiovascular disease (ASCVD) is essential for primary prevention, current clinical risk algorithms demonstrate variability and leave room for further improvement. The plasma proteome holds promise as a future diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict ASCVD.

Method

Clinical, genetic, and high-throughput plasma proteomic data were analyzed for association with ASCVD in a cohort of 41,650 UK Biobank participants. Selected features for analysis included clinical variables such as a UK-based cardiovascular clinical risk score (QRISK3) and lipid levels, 36 polygenic risk scores (PRSs), and Olink protein expression data of 2,920 proteins. We used least absolute shrinkage and selection operator (LASSO) regression to select features and compared area under the curve (AUC) statistics between data types. Randomized LASSO regression with a stability selection algorithm identified a smaller set of more robustly associated proteins. The benefit of plasma proteins over standard clinical variables, the QRISK3 score, and PRSs was evaluated through the derivation of Δ AUC values. We also assessed the incremental gain in model performance using proteomic datasets with varying numbers of proteins. To identify potential causal proteins for ASCVD, we conducted a two-sample Mendelian randomization (MR) analysis.

Result

The mean age of our cohort was 56.0 years, 60.3% were female, and 9.8% developed incident ASCVD over a median follow-up of 6.9 years. A protein-only LASSO model selected 294 proteins and returned an AUC of 0.723 (95% CI 0.708-0.737). A clinical variable and PRS-only LASSO model selected 4 clinical variables and 20 PRSs and achieved an AUC of 0.726 (95% CI 0.712-0.741). The addition of the full proteomic dataset to clinical variables and PRSs resulted in a Δ AUC of 0.010 (95% CI 0.003-0.018). Fifteen proteins selected by a stability selection algorithm offered improvement in ASCVD prediction over the QRISK3 risk score [Δ AUC: 0.013 (95% CI 0.005-0.021)]. Filtered and clustered versions of the full proteomic dataset (consisting of 600-1,500 proteins) performed comparably to the full dataset for ASCVD prediction. Using MR, we identified 11 proteins as potentially causal for ASCVD.

Conclusion

A plasma proteomic signature performs well for incident ASCVD prediction but only modestly improves prediction over clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of this signature in predicting the risk of ASCVD over the standard practice of using the QRISK3 score.

Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review

BACKGROUND

A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation.

METHODS

PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789).

RESULTS

In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively.

CONCLUSION

AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.

An elevated urinary albumin-to-creatinine ratio increases the risk of incident cardia-cerebrovascular disease in individuals with type 2 diabetes

AIMS

We aimed to explore the associations between urine albumin-to-creatinine ratio (uACR) and cardia-cerebrovascular disease (CVD) in Chinese population with type 2 diabetes(T2D).

METHODS

We included 8975 participants with T2D but free of prevalent CVD (including myocardial infarction, ischemic and hemorrhagic stroke) at baseline from Kailuan study who were assessed with uACR between 2014 and 2016. The participants were divided into three groups based on their baseline uACR: normal (< 3 mg/mmol), microalbuminuria (3-30 mg/mmol), and macroalbuminuria (≥ 30 mg/mmol). Cox regression models and restricted cubic spline were used to evaluate the hazard ratios (HRs) and 95% confidence intervals (CIs) of incident CVD. The area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to see if incorporating uACR into existing models could improve performance.

RESULTS

During a median follow-up of 4.05 years, 560 participants developed first CVD event (6.24%). After adjustment for potential confounders, participants with microalbuminuria had higher risks of CVD compared with normal uACR, with HRs of 1.57(95% CI 1.04-2.37) for myocardial infarction, 1.24(95% CI 1.00-1.54) for ischemic stroke,1.62(95% CI 0.73-3.61) for hemorrhagic stroke, and 1.30(95% CI 1.07-1.57) for total CVD. The risks gradually attenuated with uACR increase, with HRs of 2.86(95% CI 1.63-5.00) for myocardial infarction, 2.46(95% CI 1.83-3.30) for ischemic stroke, 4.69(95% CI 1.72-12.78) for hemorrhagic stroke, and 2.42(95% CI 1.85-3.15) for total CVD in macroalbuminuria. The addition of uACR to established CVD risk models improved the CVD risk prediction efficacy.

CONCLUSIONS

Increasing uACR, even below the normal range, is an independent risk factor for new-onset CVD in T2D population. Furthermore, uACR could improve the risk prediction for CVD among community based T2D patients.

Targeting IL-12 family cytokines: A potential strategy for type 1 and type 2 diabetes mellitus

Diabetes is a common metabolic disease characterized by an imbalance in blood glucose levels. The pathogenesis of diabetes involves the essential role of cytokines, particularly the IL-12 family cytokines. These cytokines, which have a similar structure, play multiple roles in regulating the immune response. Recent studies have emphasized the importance of IL-12 family cytokines in the development of both type 1 and type 2 diabetes mellitus. As a result, they hold promise as potential therapeutic targets for the treatment of these conditions. This review focuses on the potential of targeting IL-12 family cytokines for diabetes therapy based on their roles in the pathogenesis of both types of diabetes. We have summarized various therapies that target IL-12 family cytokines, including drug therapy, combination therapy, cell therapy, gene therapy, cytokine engineering therapy, and gut microbiota modulation. By analyzing the advantages and disadvantages of these therapies, we have evaluated their feasibility for clinical application and proposed possible solutions to overcome any challenges. In conclusion, targeting IL-12 family cytokines for diabetes therapy provides updated insights into their potential benefits, such as controlling inflammation, preserving islet β cells, reversing the onset of diabetes, and impeding the development of diabetic complications.

Proteomics and lipidomics in atherosclerotic cardiovascular disease risk prediction

Given the limited accuracy of clinically used risk scores such as the Systematic COronary Risk Evaluation 2 system and the Second Manifestations of ARTerial disease 2 risk scores, novel risk algorithms determining an individual's susceptibility of future incident or recurrent atherosclerotic cardiovascular disease (ASCVD) risk are urgently needed. Due to major improvements in assay techniques, multimarker proteomic and lipidomic panels hold the promise to be reliably assessed in a high-throughput routine. Novel machine learning-based approaches have facilitated the use of this high-dimensional data resulting from these analyses for ASCVD risk prediction. More than a dozen of large-scale retrospective studies using different sets of biomarkers and different statistical methods have consistently demonstrated the additive prognostic value of these panels over traditionally used clinical risk scores. Prospective studies are needed to determine the clinical utility of a biomarker panel in clinical ASCVD risk stratification. When combined with the genetic predisposition captured with polygenic risk scores and the actual ASCVD phenotype observed with coronary artery imaging, proteomics and lipidomics can advance understanding of the complex multifactorial causes underlying an individual's ASCVD risk.

TRAIL or TRAIL-R2 as a Predictive Biomarker for Mortality or Cardiovascular Events: A Systematic Review and Meta-analysis

ABSTRACT

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and TRAIL-receptor-2 (TRAIL-R2) are associated with atherosclerosis. This meta-analysis aimed to investigate the potential association between TRAIL/TRAIL-R2 with mortality or cardiovascular (CV) events. PubMed, Embase, and Cochrane Library were searched for reports published up to May 2021. Reports were included when the association between TRAIL or TRAIL-R2 and mortality or CV events was reported. Considering the heterogeneity between studies, we used the random-effects model for all analyses. Ultimately, the meta-analysis included 18 studies (16,295 patients). The average follow-up ranged from 0.25 to 10 years. Decreased TRAIL levels were negatively associated with all-cause mortality [rank variable, hazard ratio (HR), 95% CI, 2.93, 1.94-4.42; I2 = 0.0%, Pheterogeneity = 0.835]. Increased TRAIL-R2 levels were positively associated with all-cause mortality (continuous variable, HR, 95% CI, 1.43, 1.23-1.65; I2 = 0.0%, Pheterogeneity = 0.548; rank variable, HR, 95% CI, 7.08, 2.70-18.56; I2 = 46.5%, Pheterogeneity = 0.154), CV mortality (continuous variable, HR, 95% CI, 1.33, 1.14-1.57; I2 = 0.0%, Pheterogeneity = 0.435), myocardial infarction (continuous variable, HR, 95% CI, 1.23, 1.02-1.49; rank variable, HR, 95% CI, 1.49, 1.26-1.76; I2 = 0.7%, Pheterogeneity = 0.402), and new-onset heart failure (rank variable, HR, 95% CI, 3.23, 1.32-7.87; I2 = 83.0%, Pheterogeneity = 0.003). In conclusion, decreased TRAIL was negatively associated with all-cause mortality, and increased TRAIL-R2 was positively associated with all-cause mortality, CV mortality, myocardial infarction, and heart failure.

Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review

BACKGROUND

With the rising prevalence of diabetes, machine learning (ML) models have been increasingly used for prediction of diabetes and its complications, due to their ability to handle large complex data sets. This study aims to evaluate the quality and performance of ML models developed to predict microvascular and macrovascular diabetes complications in an adult Type 2 diabetes population.

METHODS

A systematic review was conducted in MEDLINE®, Embase®, the Cochrane® Library, Web of Science®, and DBLP Computer Science Bibliography databases according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. Studies that developed or validated ML prediction models for microvascular or macrovascular complications in people with Type 2 diabetes were included. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC). An AUC >0.75 indicates clearly useful discrimination performance, while a positive mean relative AUC difference indicates better comparative model performance.

RESULTS

Of 13 606 articles screened, 32 studies comprising 87 ML models were included. Neural networks (n = 15) were the most frequently utilized. Age, duration of diabetes, and body mass index were common predictors in ML models. Across predicted outcomes, 36% of the models demonstrated clearly useful discrimination. Most ML models reported positive mean relative AUC compared with non-ML methods, with random forest showing the best overall performance for microvascular and macrovascular outcomes. Majority (n = 31) of studies had high risk of bias.

CONCLUSIONS

Random forest was found to have the overall best prediction performance. Current ML prediction models remain largely exploratory, and external validation studies are required before their clinical implementation.

PROTOCOL REGISTRATION

Open Science Framework (registration number: 10.17605/OSF.IO/UP49X).

Are the results from a multiplex proteomic assay and a conventional immunoassay for NT-proBNP and GDF-15 comparable?

BACKGROUND

We aimed to compare absolute plasma concentrations of N-terminal pro-brain natriuretic peptide (NT-proBNP) and growth differentiation factor 15 (GDF-15) obtained by a conventional immunoassay with the corresponding relative concentrations from a proximity extension assay (PEA) and compare the prognostic impact of the protein levels obtained from these assays.

METHODS

We evaluated 437 patients with peripheral arterial disease (PAD) and a population-based cohort of 643 individuals without PAD. Correlations were calculated using Spearman's rank correlation coefficients (rho). The discriminatory accuracy of the protein levels to predict future cardiovascular events was analyzed with Cox regression and presented as time-dependent areas under the receiver-operator-characteristic curves (tdAUCs).

RESULTS

For NT-proBNP, the two assays correlated with rho 0.93 and 0.93 in the respective cohort. The PEA values leveled off at higher values in both cohorts. The corresponding correlations for GDF-15 were 0.91 and 0.89. At 5 years follow-up, the tdAUCs in the patient cohort were similar for NT-proBNP and GDF-15 regardless of assay used (0.65-0.66). The corresponding tdAUCs in the population-based cohort were between 0.72 and 0.77.

CONCLUSION

Except for the highest levels of NT-proBNP, we suggest that PEA data for NT-proBNP and GDF-15 reliably reflects absolute plasma levels and contains similar prognostic information.

Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review

Prediction model has been the focus of studies since the last century in the diagnosis and prognosis of various diseases. With the advancement in computational technology, machine learning (ML) has become the widely used tool to develop a prediction model. This review is to investigate the current development of prediction model for the risk of cardiovascular disease (CVD) among type 2 diabetes (T2DM) patients using machine learning. A systematic search on Scopus and Web of Science (WoS) was conducted to look for relevant articles based on the research question. The risk of bias (ROB) for all articles were assessed based on the Prediction model Risk of Bias Assessment Tool (PROBAST) statement. Neural network with 76.6% precision, 88.06% sensitivity, and area under the curve (AUC) of 0.91 was found to be the most reliable algorithm in developing prediction model for cardiovascular disease among type 2 diabetes patients. The overall concern of applicability of all included studies is low. While two out of 10 studies were shown to have high ROB, another studies ROB are unknown due to the lack of information. The adherence to reporting standards was conducted based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) standard where the overall score is 53.75%. It is highly recommended that future model development should adhere to the PROBAST and TRIPOD assessment to reduce the risk of bias and ensure its applicability in clinical settings. Potential lipid peroxidation marker is also recommended in future cardiovascular disease prediction model to improve overall model applicability.

Increased serum S100A12 levels are associated with higher risk of acute heart failure in patients with type 2 diabetes

AIMS

The hyperglycaemic stress induces the release of inflammatory proteins such as S100A12, one of the endogenous ligands of the receptors for advanced glycation end products (RAGE). Chronic activation of RAGE has multiple deleterious effects in target tissues such as the heart and the vessels by promoting oxidative stress, inflammation by the release of cytokines, macrophages infiltration, and vascular cell migration and proliferation, causing ultimately endothelial cell and cardiomyocyte dysfunction. The aim of our study was to investigate the prognostic value of circulating S100A12 beyond established cardiovascular risk factors (CVRF) for heart failure (HF) and major adverse cardiovascular events (MACE) in a cohort of patients with type 2 diabetes.

METHODS AND RESULTS

Serum S100A12 concentrations were measured at baseline in 1345 type 2 diabetes patients (58% men, 64 ± 11 years) recruited in the SURDIAGENE prospective cohort. Endpoints were the occurrence of acute HF requiring hospitalization (HHF) and MACE. We used a proportional hazard model adjusted for established CVRF (age, sex, duration of diabetes, estimated glomerular filtration rate, albumin/creatinine ratio, history of coronary artery disease) and serum S100A12. During the median follow-up of 84 months, 210 (16%) and 505 (38%) patients developed HHF and MACE, respectively. Baseline serum S100A12 concentrations were associated with an increased risk of HHF [hazard ratio (HR) (95% confidence interval) 1.28 (1.01-1.62)], but not MACE [1.04 (0.90-1.20)]. After adjustment for CVRF, S100A12 concentrations remained significantly associated with an increased risk of HHF [1.29 (1.01-1.65)]. In a sub-analysis, patients with high probability of pre-existing HF [N terminal pro brain natriuretic peptide (NT-proBNP) >1000 pg/mL, n = 87] were excluded. In the remaining 1258 patients, the association of serum S100A12 with the risk of HHF tended to be more pronounced [1.39 (1.06-1.83)]. When including the gold standard HF marker NT-proBNP in the model, the prognostic value of S100A12 for HHF did not reach significance. Youden method performed at 7 years for HHF prediction yielded an optimal cut-off for S100A12 concentration of 49 ng/mL (sensitivity 53.3, specificity 52.2). Compared with those with S100A12 ≤ 49 ng/mL, patients with S100A12 > 49 ng/mL had a significantly increased risk of HHF in the univariate model [HR = 1.58 (1.19-2.09), P = 0.0015] but also in the multivariate model [HR = 1.63 (1.23-2.16), P = 0.0008]. After addition of NT-proBNP to the multivariate model, S100A12 > 49 ng/mL remained associated with an increased risk of HHF [HR = 1.42 (1.07-1.90), P = 0.0160]. However, the addition of S100A12 categories on top of multivariate model enriched by NT-pro BNP did not improve the ability of the model to predict HHF (relative integrated discrimination improvement = 1.9%, P = 0.1500).

CONCLUSIONS

In patients with type 2 diabetes, increased serum S100A12 concentration is independently associated with risk of HHF, but not with risk of MACE. Compared with NT-proBNP, the potential clinical interest of S100A12 for the prediction of HF events remains limited. However, S100A12 could be a candidate for a multimarker approach for HF risk assessment in diabetic patients.

Precision prognostics for the development of complications in diabetes

Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.

Novel Biomarkers Detected by Proteomics Predict Death and Cardiovascular Events in Hemodialysis Patients

End-stage kidney disease increases mortality and the risk of cardiovascular (CV) disease. It is crucial to explore novel biomarkers to predict CV disease in the complex setting of patients receiving hemodialysis (HD). This study investigated the association between 92 targeted proteins with all-cause death, CV death, and composite vascular events (CVEs) in HD patients. From December 2010 to March 2011, 331 HD patients were included and followed prospectively for 5 years. Serum was analyzed for 92 CV-related proteins using Proseek Multiplex Cardiovascular I panel, a high-sensitivity assay based on proximity extension assay (PEA) technology. The association between biomarkers and all-cause death, CV death, and CVEs was evaluated using Cox-regression analyses. Of the PEA-based proteins, we identified 20 proteins associated with risk of all-cause death, 7 proteins associated with risk of CV death, and 17 proteins associated with risk of CVEs, independent of established risk factors. Interleukin-8 (IL-8), T-cell immunoglobulin and mucin domain 1 (TIM-1), and C-C motif chemokine 20 (CCL20) were associated with increased risk of all-cause death, CV death, and CVE in multivariable-adjusted models. Stem cell factor (SCF) and Galanin peptides (GAL) were associated with both decreased risk of all-cause death and CV death. In conclusion, IL-8, TIM-1, and CCL20 predicted death and CV outcomes in HD patients. Novel findings were that SCF and GAL were associated with a lower risk of all-cause death and CV death. The SCF warrants further study with regard to its possible biological effect in HD patients.

Associations between biomarkers of multimorbidity burden and mortality risk among patients with acute dyspnea

The patients' burden of comorbidities is a cornerstone in risk assessment, clinical management and follow-up. The aim of this study was to evaluate if biomarkers associated with comorbidity burden can predict outcome in acute dyspnea patients. We included 774 patients with dyspnea admitted to an emergency department and measured 80 cardiovascular protein biomarkers in serum collected at admission. The number of comorbidities for each patient were added, and a multimorbidity score was created. Eleven of the 80 biomarkers were independently associated with the multimorbidity score and their standardized and weighted values were summed into a biomarker score of multimorbidities. The biomarker score and the multimorbidity score, expressed per standard deviation increment, respectively, were related to all-cause mortality using Cox Proportional Hazards Model. During long-term follow-up (2.4 ± 1.5 years) 45% of the patients died and during short-term follow-up (90 days) 12% died. Through long-term follow-up, in fully adjusted models, the HR (95% CI) for mortality concerning the biomarker score was 1.59 (95% CI 1348-1871) and 1.18 (95% CI 1035-1346) for the multimorbidity score. For short-term follow-up, in the fully adjusted model, the biomarker score was strongly related to 90-day mortality (HR 1.98, 95% CI 1428-2743), whereas the multimorbidity score was not significant. Our main findings suggest that the biomarker score is superior to the multimorbidity score in predicting long and short-term mortality. Measurement of the biomarker score may serve as a biological fingerprint of the multimorbidity score at the emergency department and, therefore, be helpful for risk prediction, treatment decisions and need of follow-up both in hospital and after discharge from the emergency department.

Cardiovascular risk in patients with type 2 diabetes: A systematic review of prediction models

AIMS

To identify all cardiovascular disease risk prediction models developed in patients with type 2 diabetes or in the general population with diabetes as a covariate updating previous studies, describing model performance and analysing both their risk of bias and their applicability METHODS: A systematic search for predictive models of cardiovascular risk was performed in PubMed. The CHARMS and PROBAST guidelines for data extraction and for the assessment of risk of bias and applicability were followed. Google Scholar citations of the selected articles were reviewed to identify studies that conducted external validations.

RESULTS

The titles of 10,556 references were extracted to ultimately identify 19 studies with models developed in a population with diabetes and 46 studies in the general population. Within models developed in a population with diabetes, only six were classified as having a low risk of bias, 17 had a favourable assessment of applicability, 11 reported complete model information, and also 11 were externally validated.

CONCLUSIONS

There exists an overabundance of cardiovascular risk prediction models applicable to patients with diabetes, but many have a high risk of bias due to methodological shortcomings and independent validations are scarce. We recommend following the existing guidelines to facilitate their applicability.

Current Knowledge of Selected Cardiovascular Biomarkers in Pediatrics: Kidney Injury Molecule-1, Salusin-α and -β, Uromodulin, and Adropin

Cardiovascular diseases are the leading cause of morbidity and mortality in the modern world. Their common denominator is atherosclerosis, a process beginning in childhood. In pediatrics, the aim of preventive measures is to recognize children and adolescents at risk for accelerated atherosclerosis and possible premature cardiovascular events in adulthood. Several diagnostic procedures and biomarkers are available for cardiovascular risk assessment in adults. However, reliable markers in pediatrics are still insufficiently studied. In this contribution, we discuss five potential biomarkers of particular interest: kidney injury molecule-1, salusin-α and -β, uromodulin, and adropin. Studies regarding the pediatric population are scarce, but they support the evidence from studies in the adult population. These markers might entail both a prognostic and a therapeutic interest.

Novel Cardiovascular Risk Factors in Patients with Diabetic Kidney Disease

Patients with diabetic kidney disease (DKD) are at very high risk for cardiovascular events. Only part of this increased risk can be attributed to the presence of diabetes mellitus (DM) and to other DM-related comorbidities, including hypertension and obesity. The identification of novel risk factors that underpin the association between DKD and cardiovascular disease (CVD) is essential for risk stratification, for individualization of treatment and for identification of novel treatment targets.In the present review, we summarize the current knowledge regarding the role of emerging cardiovascular risk markers in patients with DKD. Among these biomarkers, fibroblast growth factor-23 and copeptin were studied more extensively and consistently predicted cardiovascular events in this population. Therefore, it might be useful to incorporate them in risk stratification strategies in patients with DKD to identify those who would possibly benefit from more aggressive management of cardiovascular risk factors.

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.

Identification of Dysregulated Complement Activation Pathways Driven by N-Glycosylation Alterations in T2D Patients

Diabetes has become a major public health concern worldwide, most of which are type 2 diabetes (T2D). The diagnosis of T2D is commonly based on plasma glucose levels, and there are no reliable clinical biomarkers available for early detection. Recent advances in proteome technologies offer new opportunity for the understanding of T2D; however, the underlying proteomic characteristics of T2D have not been thoroughly investigated yet. Here, using proteomic and glycoproteomic profiling, we provided a comprehensive landscape of molecular alterations in the fasting plasma of the 24 Chinese participants, including eight T2D patients, eight prediabetic (PDB) subjects, and eight healthy control (HC) individuals. Our analyses identified a diverse set of potential biomarkers that might enhance the efficiency and accuracy based on current existing biological indicators of (pre)diabetes. Through integrative omics analysis, we showed the capability of glycoproteomics as a complement to proteomics or metabolomics, to provide additional insights into the pathogenesis of (pre)diabetes. We have newly identified systemic site-specific N-glycosylation alterations underlying T2D patients in the complement activation pathways, including decreased levels of N-glycopeptides from C1s, MASP1, and CFP proteins, and increased levels of N-glycopeptides from C2, C4, C4BPA, C4BPB, and CFH. These alterations were not observed at proteomic levels, suggesting new opportunities for the diagnosis and treatment of this disease. Our results demonstrate a great potential role of glycoproteomics in understanding (pre)diabetes and present a new direction for diabetes research which deserves more attention.

Plasma Protein Profile of Incident Myocardial Infarction, Ischemic Stroke, and Heart Failure in 2 Cohorts

Background The aim is to study common etiological pathways for 3 major cardiovascular diseases (CVD), as reflected in multiple proteins. Methods and Results Eighty-four proteins were measured using the proximity extension technique in 870 participants in the PIVUS (Prospective Investigation of Uppsala Seniors Study) cohort on 3 occasions (age 70, 75, and 80 years). The sample was followed for incident myocardial infarction, ischemic stroke or heart failure. The same proteins were measured in an independent validation sample, the ULSAM (Uppsala Longitudinal Study of Adult Men) cohort in 595 participants at age 77. During a follow-up of up to 15 years in PIVUS and 9 years in ULSAM, 222 and 167 individuals experienced a CVD. Examining associations with the 3 outcomes separately in a meta-analysis of the 2 cohorts, 6 proteins were related to incident myocardial infarction, 25 to heart failure, and 8 proteins to ischemic stroke following adjustment for traditional risk factors. Growth differentiation factor 15 and tumor necrosis factor-related apoptosis-inducing ligand receptor 2 were related to all 3 CVDs. Including estimated glomerular filtration rate in the models attenuated some of these relationships. Fifteen proteins were related to a composite of all 3 CVDs using a discovery/validation approach when adjusting for traditional risk factors. A selection of 7 proteins by lasso in PIVUS improved discrimination of incident CVD by 7.3% compared with traditional risk factors in ULSAM. Conclusions We discovered and validated associations of multiple proteins with incident CVD. Only a few proteins were associated with all 3 diseases: myocardial infarction, ischemic stroke, and heart failure.

Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach

Cardiovascular disease (CVD) is a major complication of type 2 diabetes mellitus (T2DM). In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network (DNN) to develop a CVD risk prediction model and explored the bio-psycho-social contributors to the CVD risk among patients with T2DM. From 2017 to 2020, 834 patients with T2DM were recruited from the Department of Endocrinology, Affiliated Hospital of Harbin Medical University, China. In this cross-sectional study, the patients' bio-psycho-social information was collected through clinical examinations and questionnaires. The dataset was randomly split into a 75% train set and a 25% test set. DNN was implemented at the best performance on the train set and applied on the test set. The receiver operating characteristic curve (ROC) analysis was used to evaluate the model performance. Of participants, 272 (32.6%) were diagnosed with CVD. The developed ensemble model for CVD risk achieved an area under curve score of 0.91, accuracy of 87.50%, sensitivity of 88.06%, and specificity of 87.23%. Among patients with T2DM, the top five predictors in the CVD risk model were body mass index, anxiety, depression, total cholesterol, and systolic blood pressure. In summary, machine learning models can provide an automated identification mechanism for patients at CVD risk. Integrated treatment measures should be taken in health management, including clinical care, mental health improvement, and health behavior promotion.

Methodological considerations for identifying multiple plasma proteins associated with all-cause mortality in a population-based prospective cohort

Novel methods to characterize the plasma proteome has made it possible to examine a wide range of proteins in large longitudinal cohort studies, but the complexity of the human proteome makes it difficult to identify robust protein-disease associations. Nevertheless, identification of individuals at high risk of early mortality is a central issue in clinical decision making and novel biomarkers may be useful to improve risk stratification. With adjustment for established risk factors, we examined the associations between 138 plasma proteins measured using two proximity extension assays and long-term risk of all-cause mortality in 3,918 participants of the population-based Malmö Diet and Cancer Study. To examine the reproducibility of protein-mortality associations we used a two-step random-split approach to simulate a discovery and replication cohort and conducted analyses using four different methods: Cox regression, stepwise Cox regression, Lasso-Cox regression, and random survival forest (RSF). In the total study population, we identified eight proteins that associated with all-cause mortality after adjustment for established risk factors and with Bonferroni correction for multiple testing. In the two-step analyses, the number of proteins selected for model inclusion in both random samples ranged from 6 to 21 depending on the method used. However, only three proteins were consistently included in both samples across all four methods (growth/differentiation factor-15 (GDF-15), N-terminal pro-B-type natriuretic peptide, and epididymal secretory protein E4). Using the total study population, the C-statistic for a model including established risk factors was 0.7222 and increased to 0.7284 with inclusion of the most predictive protein (GDF-15; P < 0.0001). All multiple protein models showed additional improvement in the C-statistic compared to the single protein model (all P < 0.0001). We identified several plasma proteins associated with increased risk of all-cause mortality independently of established risk factors. Further investigation into the putatively causal role of these proteins for longevity is needed. In addition, the examined methods for identifying multiple proteins showed tendencies for overfitting by including several putatively false positive findings. Thus, the reproducibility of findings using such approaches may be limited.

TRAIL and Cardiovascular Disease-A Risk Factor or Risk Marker: A Systematic Review

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a pro-apoptotic protein showing broad biological functions. Data from animal studies indicate that TRAIL may possibly contribute to the pathophysiology of cardiomyopathy, atherosclerosis, ischemic stroke and abdominal aortic aneurysm. It has been also suggested that TRAIL might be useful in cardiovascular risk stratification. This systematic review aimed to evaluate whether TRAIL is a risk factor or risk marker in cardiovascular diseases (CVDs) focusing on major adverse cardiovascular events. Two databases (PubMed and Cochrane Library) were searched until December 2020 without a year limit in accordance to the PRISMA guidelines. A total of 63 eligible original studies were identified and included in our systematic review. Studies suggest an important role of TRAIL in disorders such as heart failure, myocardial infarction, atrial fibrillation, ischemic stroke, peripheral artery disease, and pulmonary and gestational hypertension. Most evidence associates reduced TRAIL levels and increased TRAIL-R2 concentration with all-cause mortality in patients with CVDs. It is, however, unclear whether low TRAIL levels should be considered as a risk factor rather than a risk marker of CVDs. Further studies are needed to better define the association of TRAIL with cardiovascular diseases.

Longitudinal plasma protein profiling of newly diagnosed type 2 diabetes

BACKGROUND

Comprehensive proteomics profiling may offer new insights into the dysregulated metabolic milieu of type 2 diabetes, and in the future, serve as a useful tool for personalized medicine. This calls for a better understanding of circulating protein patterns at the early stage of type 2 diabetes as well as the dynamics of protein patterns during changes in metabolic status.

METHODS

To elucidate the systemic alterations in early-stage diabetes and to investigate the effects on the proteome during metabolic improvement, we measured 974 circulating proteins in 52 newly diagnosed, treatment-naïve type 2 diabetes subjects at baseline and after 1 and 3 months of guideline-based diabetes treatment, while comparing their protein profiles to that of 94 subjects without diabetes.

FINDINGS

Early stage type 2 diabetes was associated with distinct protein patterns, reflecting key metabolic syndrome features including insulin resistance, adiposity, hyperglycemia and liver steatosis. The protein profiles at baseline were attenuated during guideline-based diabetes treatment and several plasma proteins associated with metformin medication independently of metabolic variables, such as circulating EPCAM.

INTERPRETATION

The results advance our knowledge about the biochemical manifestations of type 2 diabetes and suggest that comprehensive protein profiling may serve as a useful tool for metabolic phenotyping and for elucidating the biological effects of diabetes treatments.

FUNDING

This work was supported by the Swedish Heart and Lung Foundation, the Swedish Research Council, the Erling Persson Foundation, the Knut and Alice Wallenberg Foundation, and the Swedish state under the agreement between the Swedish government and the county councils (ALF-agreement).

Predictors of HbA1c among Adipocytokine Biomarkers in African-American Men with Varied Glucose Tolerance

This study explored adipocytokine associations with acute and chronic hyperglycemia in African-American men (AAM). Fourteen adipocytokines were measured from men with normal glucose tolerance (NGT) or type 2 diabetes (T2D, drug-naïve MF(-) or using metformin MF(+)). Acute and chronic hyperglycemia were evaluated by 120 min oral glucose tolerance test (OGTT) and glycohemoglobin A1c (HbA1c). AAM with T2D (n = 21) compared to NGT (n = 20) were older, had higher BMI and slightly higher glucose and insulin. In the fasted state, TNF-α, IL-6, PAI-1, IL-13, adiponectin, adipsin, and lipocalin were lower in T2D vs. NGT. At 120 min post-glucose load, TNF-α, IL-6, IL-13, IL-8, PAI-1, adiponectin, adipsin, lipocalin, and resistin were lower in T2D vs. NGT. There were no statistical differences for GM-CSF, IL-7, IL-10, IP-10, and MCP-1. Regression analysis showed that fasting IL-8, TNF-α, adiponectin, lipocalin, resistin, adipsin, and PAI-1 were associated with HbA1c. After adjusting for age, BMI, glucose tolerance, and metformin use, only adipsin remained significantly associated with HbA1c (p = 0.021). The model including adipsin, TNF-α, age, BMI, and group designation (i.e., NGT, MF(-), MF(+)) explained 86% of HbA1c variability. The data suggested that adipsin could be associated with HbA1c in AAM with varied glucose tolerance.

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.

Improved cardiovascular risk prediction using targeted plasma proteomics in primary prevention

AIMS

In the era of personalized medicine, it is of utmost importance to be able to identify subjects at the highest cardiovascular (CV) risk. To date, single biomarkers have failed to markedly improve the estimation of CV risk. Using novel technology, simultaneous assessment of large numbers of biomarkers may hold promise to improve prediction. In the present study, we compared a protein-based risk model with a model using traditional risk factors in predicting CV events in the primary prevention setting of the European Prospective Investigation (EPIC)-Norfolk study, followed by validation in the Progressione della Lesione Intimale Carotidea (PLIC) cohort.

METHODS AND RESULTS

Using the proximity extension assay, 368 proteins were measured in a nested case-control sample of 822 individuals from the EPIC-Norfolk prospective cohort study and 702 individuals from the PLIC cohort. Using tree-based ensemble and boosting methods, we constructed a protein-based prediction model, an optimized clinical risk model, and a model combining both. In the derivation cohort (EPIC-Norfolk), we defined a panel of 50 proteins, which outperformed the clinical risk model in the prediction of myocardial infarction [area under the curve (AUC) 0.754 vs. 0.730; P < 0.001] during a median follow-up of 20 years. The clinically more relevant prediction of events occurring within 3 years showed an AUC of 0.732 using the clinical risk model and an AUC of 0.803 for the protein model (P < 0.001). The predictive value of the protein panel was confirmed to be superior to the clinical risk model in the validation cohort (AUC 0.705 vs. 0.609; P < 0.001).

CONCLUSION

In a primary prevention setting, a proteome-based model outperforms a model comprising clinical risk factors in predicting the risk of CV events. Validation in a large prospective primary prevention cohort is required to address the value for future clinical implementation in CV prevention.

Anti-inflammatory cytokines IL-27, IL-10, IL-1Ra and TGF-β in subjects with increasing grades of glucose intolerence (DM-LTB-2)

Anti-inflammatory cytokines act as double edged swords- they can dampen inflammation but can also suppress immunity. The role played by these cytokines in latent TB infected (LTBI) subjects, with various grades of glucose intolerance was studied. Both serum levels and recall-secretion of IL-27, IL-10, IL-1Ra and TGF-β in Normal Glucose Tolerance (NGT), Pre-Diabetes (PDM), Newly diagnosed Diabetes (NDM) and Known Diabetes (KDM) subjects, both with and without LTBI (n = 382), were quantified by ELISA. All the subjects were screened for LTBI by QuantiFERON-TB Gold test. Serum levels of IL-27, IL-10 and IL-1Ra were significantly elevated in the LTB^-PDM, compared to LTB^-NGT group. Increased IL-27 and IL-10 levels and decreased levels of TGF-β were seen in the LTB^-NDM, compared to LTB^-NGT group. Decreased serum levels of IL-27 and increased levels of IL-1Ra and TGF-β were seen in the LTB^-KDM, compared to LTB^-NGT group. TB antigens induced the secretion of IL-1Ra in LTB⁺ subjects in the NGT, PDM and NDM groups, but not in the KDM group. Co-morbidity with LTBI brought about (diabetic) stage-specific modulation, in these cytokine levels. Major defects in the circulating levels and recall secretion of anti-inflammatory cytokines, as seen in LTB⁺KDM subjects, could fuel DM-TB synergy.

Using proximity extension proteomics assay to discover novel biomarkers associated with circulating leptin levels in patients with type 2 diabetes

We aimed to discover novel associations between leptin and circulating proteins which could link leptin to the development of cardiovascular disease in patients with type 2 diabetes (T2DM). In a discovery phase, we investigated associations between 88 plasma proteins, assessed with a proximity extension assay, and plasma leptin in a cohort of middle-aged patients with T2DM. Associations passing the significance threshold of a False discovery rate of 5% (corresponding to p < 0.0017) were replicated in patients with T2DM in an independent cohort. We also investigated if proteins mediated the longitudinal association between plasma leptin and the incidence of major cardiovascular events (MACE). One protein, adipocyte fatty acid binding protein (A-FABP), was significantly associated with leptin in both the discovery phase [95% CI (0.06, 0.17) p = 0.00002] and the replication cohort [95% CI (0.12, 0.39) p = 0.0003]. Multiplicative interaction analyses in the two cohorts suggest a stronger association between A-FABP and leptin in men than in women. In longitudinal analyses, the association between leptin and MACE was slightly attenuated after adding A-FABP to the multivariate model. Our analysis identified a consistent association between leptin and A-FABP in two independent cohorts of patients with T2DM, particularly in men.Trial registration: ClinicalTrials.gov identifier NCT01049737.

Use of Glycated Hemoglobin (A1c) as a Biomarker for Vascular Risk in Type 2 Diabetes: Its Relationship with Matrix Metalloproteinases-2, -9 and the Metabolism of Collagen IV and Elastin

Background and objectives: HbA1c measurements may be useful not only in optimizing glycemic control but also as a tool for managing overall vascular risk in patients with diabetes. In the present study, we investigate the clinical significance of HbA1c as a biomarker for hyperglycemia-induced vascular damages in type 2 diabetes (T2D) based on the levels of matrix metalloproteinases-2, -9 (MMP-2, MMP-9), anti-collagen IV (ACIV), and anti-elastin (AE) antibodies (Abs) IgM, IgG, and IgA, and CIV-derived peptides (CIV-DP) reflecting collagen and elastin turnover in the vascular wall. The aim is to show the relationship of hyperglycemia with changes in the levels of vascular markers and the dynamics of this relationship at different degrees of glycemic control reported by HbA1c levels. Materials and Methods: To monitor elastin and collagen IV metabolism, we measured serum levels of these immunological markers in 59 patients with T2D and 20 healthy control subjects with an ELISA. Results: MMP-2, MMP-9, and the AEAbs IgA levels were significantly higher in diabetic patients than in control subjects, whereas those of the AEAbs IgM, ACIVAbs IgM, and CIV-DP were significantly lower. MMP-9 levels were significantly lower at HbA1c values >7.5%. Conclusions: A set of three tested markers (MMP-2, MMP-9, and AEAbs IgA) showed that vascular damages from preceding long-term hyperglycemia begin to dominate at HbA1c values ≥7.5%, which is the likely cut-point to predict increased vascular risk.

Proteomic analysis reveals sex-specific biomarker signature in postural orthostatic tachycardia syndrome

Background

Postural orthostatic tachycardia syndrome (POTS) is a variant of cardiovascular (CV) autonomic disorder of unknown etiology characterized by an excessive heart rate increase on standing and orthostatic intolerance. In this study we sought to identify novel CV biomarkers potentially implicated in POTS pathophysiology.

Methods

We conducted a nested case-control study within the Syncope Study of Unselected Population in Malmö (SYSTEMA) cohort including 396 patients (age range, 15–50 years) with either POTS (n = 113) or normal hemodynamic response during passive head-up-tilt test (n = 283). We used a targeted approach to explore changes in cardiovascular proteomics associated with POTS through a sequential two-stage process including supervised principal component analysis and univariate ANOVA with Bonferroni correction.

Results

POTS patients were younger (26 vs. 31 years; p < 0.001) and had lower BMI than controls. The discovery algorithm identified growth hormone (GH) and myoglobin (MB) as the most specific biomarker fingerprint for POTS. Plasma level of GH was higher (9.37 vs 8.37 of normalised protein expression units (NPX); p = 0.002), whereas MB was lower (4.86 vs 5.14 NPX; p = 0.002) in POTS compared with controls. In multivariate regression analysis, adjusted for age and BMI, and stratified by sex, lower MB level in men and higher GH level in women remained independently associated with POTS.

Conclusions

Cardiovascular proteomics analysis revealed sex-specific biomarker signature in POTS featured by higher plasma level of GH in women and lower plasma level of MB in men. These findings point to sex-specific immune-neuroendocrine dysregulation and deconditioning as potentially key pathophysiological traits underlying POTS.

Circulating TNF receptors and risk of renal disease progression, cardiovascular disease events and mortality in patients with diabetes: a systematic review and meta-analysis

PURPOSE

Inflammation plays an important role in the pathogenesis of diabetes complications. This study aims to assess the association between circulating inflammatory biomarkers TNF receptors (TNFRs) and the risk of renal disease progression, cardiovascular disease (CVD) events, and mortality in patients with diabetes.

METHODS

PubMed and Embase databases were comprehensively searched up to March 2019. Data were extracted independently by two reviewers. A random effects model was performed for the pooled analyses.

RESULTS

Five studies in 3316 subjects assessed TNFRs with renal disease in patients with type 1 diabetes and showed both TNFR-1 and TNFR-2 were consistently associated with the renal outcomes. Fourteen studies in 7696 subjects evaluated TNFRs in patients with type 2 diabetes. The pooled risk ratio per doubling increase in TNFR-1 and TNFR-2 for renal disease progression was more than two (2.64 [1.98, 3.52] and 2.23 [1.69, 2.94]). The subgroup analyses and sensitivity analyses further illustrated these results of renal outcome and its robustness. Moreover, higher TNFR-1 and TNFR-2 was also significantly associated with CVD events and mortality in patients with type 2 diabetes.

CONCLUSIONS

Circulating TNFR-1 and TNFR-2 are independently associated with higher risk of renal disease progression, CVD events, and mortality in patients with diabetes and might contribute to the clinical risk assessment in the future.

Introducing Serine as Cardiovascular Disease Biomarker Candidate via Pathway Analysis

Background:

The rate of death due to cardiovascular disease (CVD) is growing. Investigations about CVD that leading to introduce varieties of metabolites is available. The monitoring of these metabolites to find effective ones in the future of clinic applications is the main aim of this study.

Materials and Methods:

Numbers of 34 metabolites for the CVD are extracted from literature and designated for interaction determinations by MetScape V 3.1.3. The compound-reaction-enzyme-gene network was constructed and the pathways were analyzed. Based on the presence of metabolites in the pathways the critical compounds were determined.

Results:

Pathway analysis revealed 18 disturbed pathways related to the CVD. glycerophospholipid metabolism pathway including 27 compounds is related to the 9 queried metabolites. L-Serine which was communed between 5 pathways and also was presented in the largest pathway was identified as the critical compound.

Conclusion:

It can be concluded that L-Serine is a proper biomarker candidate for CVD diagnosis and also patients follow up approaches.

The plasma protein profile and cardiovascular risk differ between intima-media thickness of the common carotid artery and the bulb: A meta-analysis and a longitudinal evaluation

BACKGROUND AND AIMS

Genetic loci associated with CHD show different relationships with intima-media thickness in the common carotid artery (IMT-CCA) and in the bulb (IMT-bulb). We evaluated if IMT-CCA and IMT-bulb differ also with respect to circulating protein profiles and risk of incident atherosclerotic disease.

METHODS

In three Swedish cohorts (MDC, IMPROVE, PIVUS, total n > 7000), IMT-CCA and IMT-bulb were assessed by ultrasound at baseline, and 86 cardiovascular-related proteins were analyzed. In the PIVUS study only, IMT-CCA and IMT-bulb were investigated in relation to incident atherosclerotic disease over 10 years of follow-up.

RESULTS

In a meta-analysis of the analysis performed separately in the cohorts, three proteins, matrix metalloproteinase-12 (MMP-12), hepatocyte growth factor (HGF) and N-terminal pro-B-type natriuretic peptide (NT-proBNP), were associated with IMT-CCA when adjusted for traditional cardiovascular risk factors. Five proteins were associated with IMT-bulb (MMP-12, growth/differentiation factor 15 (GDF-15), osteoprotegerin, growth hormone and renin). Following adjustment for cardiovascular risk factors, IMT-bulb was significantly more closely related to incident stroke or myocardial infarction (total number of cases, 111) than IMT-CCA in the PIVUS study (HR 1.51 for 1 SD, 95%CI 1.21-1.87, p < 0.001 vs HR 1.17, 95%CI 0.93-1.47, p = 0.16). MMP-12 levels were related to this combined end-point (HR 1.30, 95%CI 1.08-1.56, p = 0.0061).

CONCLUSIONS

Elevated levels of MMP-12 were associated with both IMT-CCA and IMT-bulb, but other proteins were significantly related to IMT in only one of these locations. The finding that IMT-bulb was more closely related to incident atherosclerotic disease than IMT-CCA emphasizes a difference between these measurements of IMT.

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.

Association between Biomarkers of Low-grade Inflammation and Sex Hormones in Women with Polycystic Ovary Syndrome

Objective Women with polycystic ovary syndrome (PCOS) have higher circulating levels of C-reactive protein, but the relationship between inflammation and endocrine function in PCOS remains poorly understood. Thus, this study aimed to investigate the association between low-grade inflammation and sex hormones in women with PCOS.

Design and Patients A comprehensive panel of biomarkers of inflammation was measured in serum of 63 women with PCOS using proximity extension assay technology. Associations of 65 biomarkers with sex hormones were assessed without and with adjustment for age and body mass index (BMI).

Results In the unadjusted analysis, 20 biomarkers were positively correlated with 17-OH-progesterone (17-OH-P), 14 with prolactin and 6 with free testosterone, whereas inverse associations were found for 16 biomarkers with sex hormone-binding globulin (SHBG), 6 with luteinizing hormone (LH) and 6 with estrogen (all p<0.05). Among the positive associations, correlations were mainly found for five chemokines (CXCL11, CCL4, MCP-4/CCL13, CXCL5, CXCL6) and for VEGF-A, LAP-TGFβ1, TNFSF14 and MMP-1. Inverse associations with sex hormones were mainly present for two chemokines (CXCL1, MCP-2/CCL8), CDCP1, CST5 and CSF-1. Adjustment for age and BMI reduced the number of biomarker associations for SHBG and estrogen, but had hardly any impact on associations with 17-OH-P, prolactin, free testosterone and LH.

Conclusion Women with PCOS feature BMI-independent associations between biomarkers of inflammation and certain sex steroid and hypophyseal hormones. Most of these inflammation-related biomarkers were chemokines, which may be relevant as potential mediators of the increased cardiometabolic risk of women with PCOS.

Analysis of S100A12 plasma levels in hyperlipidemic subjects with or without familial hypercholesterolemia

AIMS

Inflammation is a key regulatory process that links hypercholesterolemia and immune mechanisms promoting atherosclerosis. Inflammatory biomarkers may be helpful to better define the atherosclerotic burden in patients with high cholesterol levels such as familial hypercholesterolemia (FH). Our aim was to evaluate the concentration of S100A12 protein in FH patients and its association with pulse wave velocity (PWV).

METHODS

We measured glucose and lipid profile, S100A12, sRAGE, esRAGE and PWV in 39 patients with a genetically confirmed diagnosis of FH and 39 hypercholesterolemic subjects without a clinical diagnosis of FH (Dutch score ≤ 3). All subjects were on statin treatment at the time of the enrollment.

RESULTS

No difference of glucose and lipid profile was found in the two groups. FH patients had higher S100A12 plasma levels than non-FH subjects (12.87 ± 4.82 vs. 8.57 ± 4.87 ng/mL, p < 0.01). No difference of hs-CRP, sRAGE and esRAGE was found between the two groups. Also, PWV was higher in FH patients than non-FH subjects (8.63 ± 0.92 vs. 6.68 ± 0.73 m/s, p < 0.05). Finally, S100A12 was independently correlated with age (p < 0.01), genetic mutation (p < 0.01) and PWV (p < 0.001).

CONCLUSIONS

FH patients exhibited higher S100A12 levels than non-FH subjects. A novel vascular inflammation pathway, other than hs-CRP, might be useful to better characterize cardiovascular risk profile.

Dietary Pattern Specific Protein Biomarkers for Cardiovascular Disease: A Cross-Sectional Study in 2 Independent Cohorts

Background Mechanisms related to the influence of diet on the development of cardiovascular disease are not entirely understood, and protein biomarkers may help to understand these pathways. Studies of biomarkers identified with multiplex proteomic methods and dietary patterns are largely lacking. Methods and Results Dietary patterns were generated through principal component analysis in 2 population-based Swedish cohorts, the EpiHealth (EpiHealth study; n=20 817 men and women) and the SMCC (Swedish Mammography Cohort Clinical [n=4650 women]). A set of 184 protein cardiovascular disease biomarkers were measured with 2 high-throughput, multiplex immunoassays. Discovery and replication multivariable linear regression analyses were used to investigate the associations between the principal component analysis-generated dietary patterns and the cardiovascular disease-associated protein biomarkers, first in the EpiHealth (n=2240) and then in the Swedish Mammography Cohort Clinical. Four main dietary patterns were identified in the EpiHealth, and 3 patterns were identified in the Swedish Mammography Cohort Clinical. The healthy and the Western/traditional patterns were found in both cohorts. In the EpiHealth, 57 protein biomarkers were associated with 3 of the dietary patterns, and 41 of these associations were replicated in the Swedish Mammography Cohort Clinical, with effect estimates ranging from 0.057 to 0.083 (P-value range, 5.0×10-2-1.4×10-9) for each SD increase in the relative protein concentration. Independent associations were established between dietary patterns and the 21 protein biomarkers. Two proteins, myeloperoxidase and resistin, were associated with both the healthy and the light meal pattern but in opposite directions. Conclusions We have discovered and replicated independent associations between dietary patterns and 21 biomarkers linked to cardiovascular disease, which have a role in the pathways related to inflammation, endothelial and immune function, cell adhesion, and metabolism.

C-peptide predicts all-cause and cardiovascular death in a cohort of individuals with newly diagnosed type 2 diabetes. The Skaraborg diabetes register

AIMS

To study the association between baseline level of C-peptide and all-cause death, cardiovascular death and cardiovascular complications among persons with newly diagnosed type 2 diabetes.

METHODS

The Skaraborg Diabetes Register contains data on baseline C-peptide concentrations among 398 persons <65 years with newly diagnosed type 2 diabetes 1996-1998. National registries were used to determine all-cause death, cardiovascular death and incidence of myocardial infarction and ischemic stroke until 31 December 2014. The association between baseline C-peptide and outcomes were evaluated with adjustment for multiple confounders by Cox regression analysis. Missing data were handled by multiple imputation.

RESULTS

In the imputed and fully adjusted model there was a significant association between 1 nmol/l increase in C-peptide concentration and all-cause death (HR 2.20, 95% CI 1.49-3.25, p < 0.001, number of events = 104), underlying cardiovascular death (HR 2.69, 1.49-4.85, p = 0.001, n = 35) and the composite outcome of underlying cardiovascular death, myocardial infarction or ischemic stroke (HR 1.61, 1.06-2.45, p = 0.027, n = 90).

CONCLUSIONS

Elevated C-peptide levels at baseline in persons with newly diagnosed type 2 diabetes are associated with increased risk of all-cause and cardiovascular mortality. C-peptide might be used to identify persons at high risk of cardiovascular complications and premature death.

General and Abdominal Obesity and Incident Distal Sensorimotor Polyneuropathy: Insights Into Inflammatory Biomarkers as Potential Mediators in the KORA F4/FF4 Cohort

OBJECTIVE

To investigate the associations between different anthropometric measurements and development of distal sensorimotor polyneuropathy (DSPN) considering interaction effects with prediabetes/diabetes and to evaluate subclinical inflammation as a potential mediator.

RESEARCH DESIGN AND METHODS

This study was conducted among 513 participants from the Cooperative Health Research in the Region of Augsburg (KORA) F4/FF4 cohort (aged 62-81 years). Anthropometry was measured at baseline. Incident DSPN was defined by neuropathic impairments using the Michigan Neuropathy Screening Instrument at baseline and follow-up. Associations between anthropometric measurements and DSPN were estimated by multivariable logistic regression. Potential differences by diabetes status were assessed using interaction terms. Mediation analysis was conducted to determine the mediation effect of subclinical inflammation in these associations.

RESULTS

After a mean follow-up of 6.5 years, 127 cases with incident DSPN were detected. Both general and abdominal obesity were associated with development of DSPN. The odds ratios (95% CI) of DSPN were 3.06 (1.57; 5.97) for overweight, 3.47 (1.72; 7.00) for obesity (reference: normal BMI), and 1.22 (1.07; 1.38) for 5-cm differences in waist circumference, respectively. Interaction analyses did not indicate any differences by diabetes status. Two chemokines (C-C motif chemokine ligand 7 [CCL7] and C-X-C motif chemokine ligand 10 [CXCL10]) and one neuron-specific marker (Delta/Notch-like epidermal growth factor-related receptor [DNER]) were identified as potential mediators, which explained a proportion of the total effect up to 11% per biomarker.

CONCLUSIONS

General and abdominal obesity were associated with incident DSPN among individuals with and without diabetes, and this association was partly mediated by inflammatory markers. However, further mechanisms and biomarkers should be investigated as additional mediators to explain the remainder of this association.