INTRODUCTION:Associations between plasma proteomic profiles and left atrial (LA) strain may provide insight into biological processes underlying variation in atrial function in adults without overt cardiovascular disease. OBJECTIVES:To assess the associations between circulating proteins and phasic LA strain and to explore the potential influence of cardiometabolic factors on these relationships. PATIENTS AND METHODS:Participants were drawn from the population cohort of the Bialystok Polish Longitudinal University Study (Bialystok PLUS, 2018 to 2024), including adults aged 35-70 years who had complete echocardiographic data and available plasma samples for proteomic profiling. Individuals with overt cardiovascular, inflammatory, neoplastic, or neurodegenerative disease were excluded. LA reservoir (LASr), conduit (LAScd), and contraction (LASct) strain were assessed using speckle-tracking echocardiography. Plasma proteomic profiling was performed using the Olink® Reveal platform (Olink Proteomics AB, Uppsala, Sweden). Associations between 1034 proteins and LA strain parameters were analyzed using age-adjusted linear models with false discovery rate (FDR) correction (q ≤ 0.10). Additional analyses evaluated the influence of cardiometabolic risk factors and overlap with conventional echocardiographic parameters. RESULTS:The study included 414 adults (median age, 51 years; 47.3% men). After age adjustment, significant associations were observed exclusively for LAScd, with 30 proteins meeting the FDR threshold, whereas no proteins were related to LASr or LASct. The proteins linked to LAScd were mainly involved in metabolic regulation, inflammation, cellular stress responses, and tissue remodeling. The proteomic signature of LAScd showed only partial overlap with conventional echocardiographic phenotypes. After further adjustment for cardiometabolic risk factors, no associations remained significant after FDR correction. CONCLUSIONS:In adults without overt cardiovascular disease, LAScd was the only component of LA strain associated with a distinct age-adjusted plasma proteomic profile. The observed associations were substantially influenced by cardiometabolic risk factors, suggesting that the identified proteomic patterns primarily reflect underlying cardiometabolic processes. Further longitudinal studies are needed to determine the clinical significance of these findings.

Age-related macular degeneration (AMD) and cardiovascular disease (CVD) share numerous risk factors; however, protein biomarkers for AMD are lacking. We investigated whether circulating cardiovascular peptide biomarkers are associated with AMD in the population-based Białystok PLUS cohort. This cross-sectional analysis included 699 participants aged ≥ 50 years (AMD + = 93; AMD⁻ = 606) examined between 2018 and 2023. AMD was graded from fundus photos with the use of the Wisconsin and modified International Classification systems. Ninety-two cardiovascular proteins were quantified in serum with the Olink Target Cardiovascular III panel. Age-adjusted linear or logistic regressions assessed biomarker-AMD associations, and receiver-operating-characteristic (ROC) curves evaluated discriminative performance. After adjustment, AMD+ participants exhibited lower galectin-4 (β = - 0.15, p = 0.043) and TNF-receptor-superfamily-member-10 C (TNFRSF10C) (β = - 0.17, p = 0.037) concentrations and higher von Willebrand factor (vWF) levels (β = 0.22, p = 0.036) versus AMD⁻ individuals. Galectin-4, TNFRSF10C, and vWF predicted AMD with areas under the ROC curve of 0.613 (95% confidence interval [CI] 0.516-0.709), 0.606 (0.520-0.692), and 0.594 (0.500-0.687), respectively. Optimal cut-offs were 4.05 NPX for galectin-4, 5.09 NPX for TNFRSF10C, and 8.03 NPX for vWF, yielding sensitivities/specificities of 57%/63%, 58%/62% and 55%/63%, respectively. Elevated vWF and reduced galectin-4 and TNFRSF10C are independently associated with AMD, suggesting overlapping vascular, inflammatory and apoptotic pathways with CVD. Incorporation of these peptides into risk-stratification algorithms could enhance early AMD detection and motivate mechanistic studies targeting the TRAIL-TNFRSF10C axis and galectin-mediated signaling.

BackgroundWhite matter hyperintensities (WMHs), commonly seen in brain magnetic resonance imaging (MRI), are linked to cognitive decline and may be influenced by cardiovascular risk factors. This study explores the relationship between diabetes, prediabetes, metabolic syndrome, and the presence of WMHs in an apparently healthy population.MethodsThe study group includes 735 adult participants without neurological or severe cardiovascular diseases. During the visit, participants took part in detailed clinical examination (medical history, biochemical analysis, carotid arteries ultrasound, and brain MRI). WMHs were quantified by the SAMSEG tool implemented in Freesurfer software.ResultsParticipants’ median age was 45 (range, 36–58) years, 341 (46.39%) were men, 58 (7.9%) had diagnosed diabetes, 345 (46.94%) had diagnosed prediabetes, and 91 (12.38%) individuals fulfilled two definitions of prediabetes—simultaneously impaired glucose tolerance (IGT) and impaired fasting glucose (IFG). Univariate analysis presented a positive association between plasma glucose concentrations, glycated hemoglobin, diabetes mellitus, prediabetes, and metabolic syndrome and WMHs (p < 0.05). Multivariate analysis (R2adj. = 0.33) presented an association between glucose metabolism disorders (diabetes mellitus or fulfilling two definitions of prediabetes, β = 2.77, p = 0.006) and WMHs.ConclusionPatients with glucose metabolism disorders, not only those with diabetes but also those fulfilling definitions of prediabetes IGT and IFG simultaneously, have significantly larger volumes of WMHs. Patients with diabetes or prediabetes may benefit from the comprehensive management of cardiovascular risk factors, not only to limit the risk of cardiovascular disease but also to potentially reduce the risk of cognitive impairment.

Despite the availability of numerous lipid-lowering agents, the treatment of lipid disorders remains a public health challenge. A substantial portion of patients, especially those with severe dyslipidemia or familial hypercholesterolemia (FH), fail to achieve the LDL-C goal. The leading causes of suboptimal LDL-C control include underprescription and poor adherence; however, in rare cases, it may result from an unusual biological response to treatment. In the presented case, a 78-year-old female with a history of transient ischemic attack and myocardial infarction was diagnosed with a heterozygous variant of FH and true statin intolerance following trials of simvastatin, rosuvastatin and pitavastatin. Initially, inclisiran was added to ezetimibe, leading to an unexpected increase in LDL-C. Due to the patient’s refusal of another statin re-challenge and the unavailability of bempedoic acid, nutraceuticals were introduced. After 6 months, inclisiran was discontinued because only a 22% reduction in LDL-C was achieved, likely attributable to the nutraceutical’s effect. Another PCSK9 inhibitor, evolocumab, was subsequently initiated. Shortly after the treatment onset, the patient complained of paraesthesia in the upper extremities and discontinued therapy. LDL-C levels increased by 7% after one month of treatment with evolocumab. The patient refused treatment with lipid apheresis. Possible causes of poor response to PCSK9 inhibitors include elevated lipoprotein(a) and FH.

BACKGROUND:Guidelines recommend using the SMART2 model, estimating the risk of recurrent cardiovascular (CV) events, to support treatment decisions in patients with established atherosclerotic CV disease (ASCVD). They further outline that adding biomarkers, comorbidities, anthropometric, and social factors may improve these predictions. AIMS:To investigate the added predictive value of guideline-outlined factors including biomarkers, comorbidities, anthropometric, and social factors on top of the SMART2 model using an approach enabling their use as add-on predictors. METHODS:Patients aged 40-80 with ASCVD were included from 11 cohorts (n=179,382 with 25,789 recurrent CV events). Additional factors included biomarkers (troponin I, NT-proBNP, albuminuria), comorbidities (heart failure, atrial fibrillation, coronary multivessel disease), anthropometric measurements (body-mass index, waist and hip circumference), social (employment, education), and other factors (former smoking, parental CV history). Cross-cohort availability of these factors ranged from 2 cohorts for albuminuria to all cohorts for BMI. These factors were assessed as add-on predictors to the SMART2 model using Fine-Gray models with SMART2 coefficients as offset with recurrent CV events as primary outcome (non-fatal myocardial infarction or stroke, or CV death). Added predictive value was assessed through cohort cross validation by change (Δ) in C--statistic, calibration, and net benefit through decision curve analysis. RESULTS:Sub distribution hazard ratios for additional factors ranged from 0.77 [95% confidence interval 0.75-0.80] for employment status to 1.69 [1.63-1.76] for heart failure history. ΔC-statistic was largest for NT-proBNP (0.0127 [0.0060-0.0193]) and troponin I (0.0100 [0.0020-0.0181]), with statistically significant but smaller ΔC-statistics for employment, heart failure, and atrial fibrillation. Calibration was adequate before and after integration of additional factors. Decision curve analysis demonstrated added net benefit beyond SMART2 for NT-proBNP, heart failure history, atrial fibrillation, albuminuria, current employment, coronary multivessel disease, and education level across clinically relevant thresholds up to 40% predicted risk. CONCLUSIONS:The flexible add-on of guideline-outlined factors on top of SMART2 enables more personalised and improved estimation of recurrent CV event risk in patients with established ASCVD.

BACKGROUND AND AIMS:The 2021 ESC guidelines on cardiovascular (CV) disease prevention recommend the SMART-REACH lifetime risk model to guide treatment decisions in patients with established atherosclerotic CV disease. The aim was to develop the SMART-REACH2 model for estimating lifetime risk of recurrent CV events and treatment benefits in patients with established atherosclerotic CV disease, with systematic recalibration to the four European and other global risk regions. METHODS:SMART-REACH2 was derived in 8708 individuals aged 40-90 years with coronary, cerebrovascular, peripheral artery disease and/or abdominal aortic aneurysm from the UCC-SMART cohort. Sex-stratified, cause-specific Cox models for recurrent CV events and non-CV death were fitted using age as timescale and routinely available predictors. Recurrent CV events were defined as a composite of myocardial infarction, stroke, or CV death. Recalibration was based on representative cohorts per risk region. External validation was performed in 2 085 780 patients from 54 countries; model performance was assessed by calibration plots and Harrell's C-statistic. RESULTS:In the derivation cohort, 2057 recurrent CV events occurred over a median follow-up of 8.5 years (25th-75th: 4.3-13.0). In external validation, 307 706 events occurred. The pooled C-statistic was 0.68 (95% confidence interval 0.66-0.69) and ranged from 0.66 (0.64-0.69) for European low-risk region up to 0.72 (0.66-0.78) for Latin America, with adequate calibration across risk regions. Performance was consistent across sexes and CV disease subtypes. Using SMART-REACH2, estimated potential gains in CV disease-free life expectancy for a 50-year-old example patient receiving intensified preventive treatment (15 mmHg systolic blood pressure and 1.0 mmol/L low-density lipoprotein cholesterol reduction) ranged from 2 years in the low-risk region to 4.4 years in the very-high-risk region. CONCLUSIONS:The updated SMART-REACH2 model accounts for geographical and sex-specific variations and allows estimation of short-term and lifetime risk of recurrent CV events and treatment benefits, facilitating shared decision-making as recommended by guidelines.

Preclinical atherosclerosis and prediabetes are key targets of preventive medicine as their prevalence rises. Therefore, it is crucial to identify early processes and limit confounders such as lipid-lowering or antidiabetic therapy and advanced atherosclerosis. Proteomics enables the identification of biomarkers and molecular pathways related to atherogenesis in prediabetes. To investigate the relationship between prediabetes and preclinical atherosclerosis in apparently healthy individuals using a comprehensive proteomic approach. This cross-sectional, population-based study included 389 participants (mean age 49 ± 10 years; 47

INTRODUCTION:Metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated alcohol‑related liver disease (MetALD) are increasingly recognized conditions worldwide. However, epidemiologic data from Central and Eastern Europe are lacking. OBJECTIVES:This study aimed to assess the prevalence, clinical profiles, and liver fibrosis burden of MASLD and MetALD in a representative population from Central and Eastern Europe. PATIENTS AND METHODS:This study is part of the Bialystok PLUS (Polish Longitudinal University Study), an ongoing, prospective population‑based cohort study. A random sample of 2456 adult residents of the city of Białystok, Poland, was analyzed. Anthropometric, epidemiologic, clinical, and lifestyle‑related parameters were assessed. The participants were categorized into the MASLD, MetALD, and control groups. Differences in metabolic comorbidities and liver fibrosis severity were evaluated in each cohort. RESULTS:The prevalence of MASLD was 22.72%, while MetALD accounted for 2.08% of the population. MASLD was predominantly associated with obesity and diabetes mellitus, whereas MetALD showed a higher prevalence of arterial hypertension and dyslipidemia. Significant liver fibrosis was more frequent in MetALD (17.8%) and MASLD (15.6%), as compared with the controls (5.1%; P <0.001 and P = 0.005, respectively). The highest cirrhosis rates were observed in 4.44% of the MetALD participants, 0.58% of the MASLD patients, and 0.12% of the controls (P = 0.001). Risk factors for significant fibrosis were analyzed across all groups. CONCLUSIONS:MASLD and MetALD represent a substantial and distinct health burden not only in the Polish population but likely across Central and Eastern Europe, given shared epidemiologic patterns of metabolic dysfunction and similar alcohol consumption. Understanding the interplay between these factors is crucial for improving diagnosis establishment, treatment strategies, and public health policies in this region.

Postprandial variability in glucose and protein levels is one of the elements of insulin resistance (IR) and prediabetes, which is an area precursor to type 2 diabetes mellitus (DM). The objective of the study was a comprehensive proteomic analysis according to glucose tolerance in the general population who did not self-report DM or other diseases. We used Olink® Reveal, a novel, high-throughput platform by Olink Proteomics based on their Proximity Extension Assay (PEA), to identify levels of 1034 circulating proteins in small volumes (4 µL) of plasma samples. The study enrolled 508 participants (mean age 52 ± 10.5 years, 47.2% men) from the population-based study, Bialystok PLUS Polish Longitudinal University Study. The study population was categorized according to glucose metabolism in comparison to impaired fasting blood glucose (IFG), impaired glucose tolerance (IGT), and newly diagnosed DM. Analysis of variance (ANOVA) adjusted for age, weight, fat mass, lean mass, and body mass index (BMI), identified 19 proteins significantly associated with categories of glucose tolerance. Of the five markers with the greatest ability to distinguish newly diagnosed diabetes from non-diabetic participants, paralemmin 2 performed best (AUC = 0.81; 77% sensitivity, 75% specificity), whereas furin was the most accurate for detecting any abnormal glucose regulation (AUC = 0.69). A linear regression model adjusted for the same confounding factors showed statistically significant associations between HbA1c levels and 37 proteins. Our findings highlight multiple proteins with significantly different levels across categories of glucose tolerance, especially between the healthy controls and the group with newly diagnosed DM. The consistent patterns of protein level differences, independent of body composition, suggest potential involvement in the progression of glucose metabolism disturbances and provide unique insights into pathomechanisms. These findings identify PALM2, FURIN, PDZK1, ACAA1, and IL18R1 as potential biomarkers of early dysglycemia.

To combat online health misinformation effectively, bridging knowledge across computer science and medical sciences disciplines is required. This paper provides a comprehensive overview of research supporting the credibility evaluation of medical content from both perspectives. It aims to illuminate critical gaps between the two approaches and propose solutions to fill them. First, we reviewed existing toolkits and guidelines created by medical experts to assess the credibility and quality of Online Health Information (OHI). We reviewed n1=28 papers from the medical domain. Second, we examined n2=82 articles describing the efforts of computer scientists to assess OHI automatically or semi-automatically. We grouped those articles by their aim, algorithms, types of utilized datasets, and features extracted from sample OHI. Among our most essential findings lies the conclusion that few recent computational studies leverage expert-developed medical credibility assessment tools in constructing datasets. Datasets have significant differences in annotation protocols, basic definitions, data structures, and more. Last but not least, many classification models focus solely on textual rather than multimodal OHI features, despite the rise of image and video misinformation. This survey highlights the urgent need to integrate credibility evaluation techniques from medicine into computational pipelines and unify methodologies for future classification experiments. Additionally, the survey provides a foundation to guide future cross-disciplinary efforts combining medical expertise with AI scalability.

Background: The Mediterranean diet is considered one of the healthiest and safest diets for preventing chronic diseases. The primary objective of this study was to assess the association between adherence to the Mediterranean diet and the occurrence of prediabetes in a representative population of Bialystok, Poland. Prediabetes is a condition characterized by elevated blood glucose levels that are higher than normal but not yet in the diabetic range, indicating an increased risk of developing type 2 diabetes. Methods: The study participants were selected into healthy control (HC) and prediabetic (PreD) groups based on age and gender. Biochemical measurements included total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), fasting glucose (FG), glycated hemoglobin (HbA1c), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6). Additionally, blood pressure, handgrip strength, anthropometric parameters, and body composition were measured. Information on patients’ social data, medical history, and lifestyle history was collected using questionnaires developed for this study. A standardized questionnaire, the Satisfaction with Life Scale (SWLS), was used to assess life satisfaction. Dietary total antioxidant capacity (DTAC) and dietary total polyphenol intake (DTPI) were determined using a 3-day nutritional interview and appropriate databases containing information on polyphenols and the antioxidant potential of food products. To assess adherence to the Mediterranean diet recommendations, a 9-item Mediterranean Diet Index (MDI) was used. Results: It was found that the mean MDI for the entire group was low (3.98 ± 1.74), and the HC was characterized by a significantly higher MDI compared to the PreD. A statistically significant positive correlation was found between MDI and HDL-C, whereas a negative correlation was found between MDI and FG, homeostatic model assessment for insulin resistance (HOMA-IR), diastolic blood pressure (DBP), IL-6, body mass index (BMI), waist-hip ratio (WHR), waist circumference, visceral fat mass, android/gynoid fat ratio. Conclusions: Abdominal obesity was shown to significantly reduce life satisfaction. In model 3, after adjusting for age, sex, dietary energy intake, alcohol consumption, and smoking, each additional MDI point indicated a 10% lower risk of prediabetes.

Prediabetes and preclinical atherosclerosis are interrelated conditions contributing to cardiovascular risk, even in apparently healthy individuals. Metabolomics provides insights into the early metabolic alterations underpinning these diseases. This study aimed to investigate the shared and distinct metabolic signatures associated with prediabetes and preclinical atherosclerosis in a population with low to moderate cardiovascular risk, using a targeted metabolomic approach. A cross-sectional analysis was performed on 447 participants (mean age 39.7 ± 9.6 years) from the Białystok PLUS cohort. Prediabetes was diagnosed based on HbA1c and OGTT criteria. Preclinical atherosclerosis was assessed by carotid ultrasound. Targeted metabolomics profiling encompassed 434 metabolites and 218 metabolite sums or ratios using HPLC–MS/MS. Statistical analyses included ANOVA, linear regression, correlation analysis, and metabolite set enrichment analysis (MSEA). Prediabetes was significantly associated with preclinical atherosclerosis (30.8

Left ventricular hypertrophy (LVH) represents a significant risk factor for cardiovascular disease (CVD). Electrocardiography (ECG) is a commonly used diagnostic tool for LVH, however, its accuracy and sensitivity are limited. The objective of this study was to evaluate the diagnostic efficacy of ECG indices in the diagnosis of LVH in the Polish and German population-based studies. The Białystok Plus study (Poland) was conducted in 2017-2024. 2603 volunteers randomly chosen from the local population aged between 20 to 79 were examined. The SHIP-TREND study (Germany) was performed between 2008 and 2012. Data were obtained from 4420 volunteers randomly chosen from the population aged between 20 to 79. Exclusion criteria were incomplete data, QRS complex duration ≥ 120 ms, fascicular blocks, bundle branch blocks, paced rhythm. ECG was performed with LVH defined as >35 mm for men and women using the Sokolow–Lyon index (SV1 + RV5 or V6), as >28mm for men, >20mm for women using Cornell index (RaVL+SV3), 17mm for both sexes using Lewis index [(RI + SIII) – (RIII + SI)]. In echocardiography (ECHO), the left ventricular mass (LVM) was calculated using the Devereux Formula. The left ventricular mass index (LVMI) was calculated by the formula LVM/BSA. The LVH was defined as LVMI ≥115 g/m2 for man and ≥95 g/m2 for women. 1871 individuals from the Białystok Plus study and 2708 individuals from the SHIP-TREND study were included in the study. The mean age of the Białystok Plus study population was 48.5 ± 15.0 years, while in the SHIP-TREND population it was 48.8 ± 14.3 years. The male population constituted 44.0% and 43.3% of the Białystok Plus and SHIP-TREND populations, respectively. The prevalence of LVH on ECG using the Sokolow-Lyon index was 4.2% and 0.2% in the Polish population and German population, respectively. The prevalence of LVH on ECG using the Cornell index in the Polish population was 1.4%, while in the German population it was 4.0%. The prevalence of LVH on ECG using the Lewis index in the Polish population was 2.4%, while in the German population it reached 4.7%. The percentage of individuals with LVH according to ECHO in the Białystok Plus population was 8.9%, while in the SHIP-TREND population it reached 28.8%. The receiver operating characteristics (ROC) curve analysis of the Sokolow–Lyon index did not show a predictive ability to diagnose LVH in the populations under examination. The AUC values were not significantly higher than 0.5. Sensitivity of the generally accepted cut-off was alarmingly poor for Sokolov-Lyon and Lewis criteria, specificity, however, was good (Table 1 and Figure 1). A review of the ECG guidelines is recommended, with emphasis of the limitations of these ECG parameters. Novel clinical and ECG markers of LVH should be investigated to improve early and accurate identification of individuals at risk.

INTRODUCTION:Metabolic syndrome (MetS) is a growing global health concern characterized by adiposity, elevated blood pressure, and lipid and glucose metabolism abnormalities, which synergistically increase cardiovascular risk. The newly introduced concept of cardiovascular‑kidney‑metabolic (CKM) syndrome aims to capture the continuum of metabolic dysfunction and its direct link with cardiovascular risk. OBJECTIVES:Our aim was to assess the prevalence of MetS and CKM, compare traditional and updated MetS definitions, examine their diagnostic concordance, and explore their associations with cardiovascular risk. PATIENTS AND METHODS:We analyzed 2110 adults (mean [SD] age, 49.3 [15.3] y; 44.4% men) from the population‑based Białystok PLUS cohort. MetS was defined using the 2009 and the 2022 updated criteria. CKM staging was applied, and cardiovascular risk was estimated using the Systemic Coronary Risk Estimation 2 (SCORE2) and SCORE2‑Older Persons scales. Anthropometric, biochemical, and imaging data were assessed. Receiver operating characteristics analyses were performed to evaluate diagnostic utility of the applied criteria. RESULTS:MetS was diagnosed in 22.1% of the participants using both definitions; 5.1% met only the 2022 and 13.1% only the 2009 criteria. The individuals meeting both definitions were older, had higher body mass index (BMI), and greater adiposity. BMI (area under the curve [AUC] = 0.941; 95% CI, 0.931-0.951) and waist circumference (AUC = 0.912; 95% CI, 0.9-0.924) showed the highest diagnostic accuracy under the 2022 criteria. Cardiovascular risk and subclinical atherosclerosis were most prevalent in the patients meeting both definitions. CKM stage 2 or higher was found in 55.3% of the participants, with increasing prevalence of higher cardiovascular risk observed across stages. CONCLUSIONS:MetS and CKM are highly prevalent, yet their diagnostic overlap is limited. CKM staging captures high‑risk individuals beyond the MetS criteria, underscoring its broader utility for integrated cardiometabolic risk assessment.

Echocardiography remains a vital part of the initial assessment and monitoring of oncological patients. It allows for proper treatment selection but can also reveal life-threatening complications, including impaired left ventricular function or thromboembolism. It can rarely detect intracardiac masses that require further investigation. In the presented case, a 51-year-old female patient with left-sided breast cancer, who had undergone neoadjuvant chemotherapy, was hospitalised due to a right atrial mass identified via routine transthoracic echocardiography (TTE). Initial anticoagulation therapy showed no clinical improvement. Follow-up TTE revealed a 12 × 19 mm hyperechogenic, mobile mass in the right atrium (RA). Computed tomography angiography (CTA) ruled out pulmonary embolism and revealed that the mass was located close to the tip of the vascular access port. Transoesophageal echocardiography showed that the lesion was not connected to the vascular port. Based on location and mobility, the lesion was most consistent with a cardiac myxoma. After the Heart Team made a decision, endovascular intervention using a vacuum-assisted device was performed without complications. Histopathological examination excluded thrombosis and myxoma, revealing a fibro-inflammatory lesion. A multimodality approach is necessary to assess RA masses. However, even an extensive evaluation could be misleading, so treatment options should always be subject to the Heart Team’s decision.

INTRODUCTION:Metabolite profiling can lead to novel discoveries in cardiovascular disease (CVD) physiology. OBJECTIVES:The aim of the study was to investigate whether a metabolomic profile is associated with mortality in patients with coronary artery disease (CAD). METHODS:The study group consisted of 170 participants with CAD hospitalized for acute coronary syndrome or elective percutaneous coronary intervention 12-26 months (mean [SD], 16.3 [2.2] months) before evaluation. A total of 132 metabolites were profiled by liquid chromatography‑tandem mass spectrometry, and sums / ratios of 102 metabolite concentrations were calculated. Dates of death from all causes were obtained from a registry of the Polish Ministry of Digital Affairs. RESULTS:Median (interquartile range [IQR]) age of the group was 62 (58-66) years and 68.8% (n = 117) were men. Median (IQR) follow‑up time was 6.4 (5.5-6.5) years. After adjustment for CVD risk factors affecting survival in the analyzed population (ie, age, statin dose, current smoking, estimated glomerular filtration rate, and high‑sensitivity C‑reactive protein level) with the Bonferroni correction, tryptophan (Trp) level was negatively associated with death (hazard ratio [HR], 0.558; 95% CI, 0.38-0.82; P = 0.003), whereas indoleamine 2,3‑dioxygenase (IDO) activity was positively associated with death (HR, 2.925; 95% CI, 1.71-5.01; P <0.001). Survival analysis showed that the patients with IDO activity above the median experienced shorter survival than the patients with lower IDO activity (log‑rank test; P = 0.009). In contrast, the patients with Trp concentration below the median had worse survival than those with higher Trp levels (log‑rank test; P = 0.03). CONCLUSIONS:In patients with CAD, increased IDO activity predicts worse long‑term prognosis independently of known CVD risk factors.

Glucose metabolism disturbances and especially type 2 diabetes (T2D) are key risk factors of cardiovascular disease. Most recent epidemiological analyses suggest that they may affect more than 40% of the adult population in European countries. Biomarkers for the development of atherosclerosis in patients with T2D or prediabetes are not yet fully elucidated. In this study we focused on identifying proteomic biomarkers in patients with untreated glucose metabolism disturbances from population without diagnosed cardiovascular disease. We ran a cross-sectional study on serum samples from 508 subjects (mean age 52±10.5) without previously diagnosed diabetes, chronic inflammatory, neoplastic or cardiovascular diseases, who were stratified based to: healthy controls, prediabetes and diabetes using ESC criteria. All participants underwent detailed anthropometric measurements, body mass composition assessment using Dual-energy X-ray absorptiometry (DXA). Participants with glucose metabolism disturbances were older, had higher BMI, fat mass and HbA1C concentration than ones with prediabetes or healthy controls. Statistical analysis with correction for factors independently associated with T2D (BMI, fat mass, lean mass and age) was performed. We used a novel method that analyses over 1,000 proteins, covering a wide range of biological pathways with enrichment for immune system pathways to effectively screen the proteome and disease perturbations. Over 92% of the proteins were detected in at least 50% of the samples with intra- and inter-plate coefficients of variation of 7.6% and 7.8%, respectively. Among proteins of interest - mevalonate kinase (MVK) – an enzyme associated with cholesterol synthesis, presented levels that independently from other variables, demarcated healthy controls , prediabetes and T2D progression. Basal glycemia correlated with protein associated with cardiovascular diseases, among others: IL10, CCL20, ACE2, FGF21. Glycemia after 120 minutes of oral glucose tolerance test was associated with lipoprotein lipase, hydroxysteroid 11-beta dehydrogenase 1. Proteomic tools allow discovery of new molecules associated with cardiovascular disease. Increased MVK concentration in serum marks patients with more profound metabolic disorders and may be an insight into pathogenesis of cardiovascular disease in diabetics.Protein abundance in relation to OGTTBasal glycemia correlations

Cardiovascular disease (CVD) is a significant cause of mortality worldwide. Preventive programs are trying to reduce the burden of the disease. Recent advances in metabolomics profiling open a new avenue for developing complementary CVD evaluation strategies. The aim of the study was to investigate whether a metabolomic profile can provide an additional characterisation of individuals with coronary artery disease (CAD). The study included 167 participants with CAD aged 41–79 years. A control group was formed of 166 individuals without CAD, gender- and age-matched to the study group. A total of 188 metabolites were profiled in serum by liquid chromatography-tandem mass spectrometry. After clearing the data, associations between 132 metabolites and CAD presence were analysed using multiple linear regression models. We observed significant differences in serum metabolic profiles between analysed groups on various levels. However, a deeper analysis revealed sphingomyelin 41:1 (SM 41:1) as the main metabolite independently associated with CAD after correction for classical CV risk factors. Its concentration was lower in the CAD group (median 9.79 µmol/L, interquartile range (IQR) 7.92–12.23) compared to control one (median 13.60 µmol/L, IQR 11.30–16.15) (p < 0.001). Further analysis showed that SM 41:1 concentration was inversely correlated with CAD, current smoking, and hypertension; and positively associated with female gender and non-HDL level. CAD patients present lower plasma concentrations of SM 41:1 than healthy subjects. A better understanding of the biological function of sphingomyelin in CAD patients may help develop therapeutic approaches and risk stratification in this group.