Background: Patients with hypertension present a permanent state of low-grade inflammation, as the disease activates several pro-inflammatory cells and inflammatory pathways. Glycoproteins A, B, and F, determined by proton nuclear magnetic resonance, provide a highly sensitive method for determining a group of liver-derived pro-inflammatory proteins, and their role has not yet been explored in patients with hypertension. In this study, we evaluated the impact of plasma concentrations of these glycoproteins in patients with hypertension. Methods: This cross-sectional study involved 340 patients attending our vascular and metabolism medicine unit. Of them, 129 were normotensive and 211 were hypertensive. Standard biochemistry and carotid ultrasound measures were performed. Serum concentrations of glycoproteins A, B, and F were determined by proton nuclear magnetic resonance. Results: Hypertensive patients presented a higher prevalence of obesity, metabolic syndrome, and diabetes and higher glycoprotein A, B, and F concentrations. Glycoproteins A, B, and F were positively correlated with systolic and diastolic blood pressure. Multivariate logistic models showed that glycoproteins A, B, and F were associated with higher odds of being hypertensive. Machine learning methods corroborated the relationship between glycoproteins and high blood pressure. The higher prevalence of carotid plaques in patients with high blood pressure was partially mediated by glycoproteins A and F. Conclusions: Patients with hypertension present systemic, subclinical inflammation as assessed by liver-derived glycoprotein A, B, and F serum levels. These results support the effect of hypertension on the mechanisms of systemic inflammation. Hypertension-associated systemic inflammation plays a role in hypertension-associated vascular injury and probably in hypertension-induced damage to other organs.

Rheumatoid arthritis (RA) is a chronic autoimmune disease associated with increased cardiovascular disease (CVD) risk and mortality. This work aimed to evaluate the serum levels of the novel CV biomarkers fetuin-A (fet-A), Dickkopf-1 (DKK-1), galectin-3 (Gal-3), interleukin-32 (IL-32), and catestatin (CST) in RA patients and their associations with RA parameters and CVD markers. A cohort of 199 RA patients was assessed for traditional CVD risk factors, RA disease activity, and biomarker levels. Carotid ultrasound was used to measure carotid intima-media thickness (cIMT) and carotid plaque presence (cPP). Multivariate analyses examined correlations between biomarkers and RA parameters, serological markers, and CVD markers. Adjusted models showed that elevated CST expression levels were associated with rheumatoid factor (RF) and anti-citrullinated protein antibody (ACPA) positivity (OR = 2.45, p = 0.0001 and OR = 1.48, p = 0.04, respectively) in the overall cohort and for RF in men and women, respectively. In addition, fet-A concentration was inversely associated with the erythrocyte sedimentation rate (ESR) in the overall cohort (β = −0.15, p = 0.038) and in women (β = −0.25, p = 0.004). Fet-A levels were also negatively correlated with disease activity (DAS28-ESR) scores (β = −0.29, p = 0.01) and fibrinogen concentration (β = −0.22, p = 0.01) in women. No adjusted associations were observed for Gal-3, DKK-1 or IL32 concentration. The study revealed no significant associations between the biomarkers and cIMT or cPP. The measurement of CST and fet-A levels could enhance RA patient management and prognosis. However, the utility of biomarkers for evaluating CV risk via traditional surrogate markers is limited, highlighting the need for continued investigations into their roles in RA.

Rheumatoid arthritis (RA) is associated with problems beyond the joints such as cardiovascular (CV) disease. MicroRNA-24, -146 and -Let7a are associated with carotid plaque presence in RA patients. We evaluated whether these microRNAs were involved in the inflammatory state of RA, and we studied their gene targets to understand their role in inflammation and atherosclerosis. A total of 199 patients with RA were included. Inflammatory variables such as disease activity score 28 (DAS28) and erythrocyte sedimentation rate (ESR) were quantified. MicroRNAs were extracted from plasma and quantified with qPCR. Multivariate models and classification methods were used for analysis. The multivariate models showed that diminished expression of microRNA-146 was associated with inferior levels of DAS28-ESR, and the decreased expression of microRNA-24, -146 and -Let7a were associated with lowered ESR in the overall cohort. When microRNAs were evaluated globally, a global increase was associated with increased DAS28-ESR and ESR in the overall cohort. Sex-stratified analyses showed different associations of these microRNAs with the inflammatory variables. Finally, random forest models showed that microRNAs have a pivotal role in classifying patients with high and low inflammation. Plasmatic expressions of microRNA-24, -146 and -Let7a were associated with inflammatory markers of RA. These microRNAs are associated with both inflammation and atherosclerosis and are potential therapeutic targets for RA.

Background: The familial hypercholesterolemia (FH) diagnosis is based on clinical and genetic criteria. A relevant proportion of FH patients fulfilling the criteria for definite FH have negative genetic testing. Increasing the identification of true genetic-based FH is a clinical challenge. Deepening the analysis of lipoprotein alterations could help increase the yield of genetic testing. We evaluated whether the number, size, and composition of lipoproteins assessed by 1H-NMR could increase the identification of FH patients with pathogenic gene variants. Methods: We studied 294 clinically definite FH patients, 222 (75.5%) with positive genetic testing, as the discovery cohort. As an external validation cohort, we studied 88 children with FH, 72 (81%) with positive genetic testing. The advanced lipoprotein test based on 1H-NMR (Liposcale®) was performed at baseline after a lipid-lowering drug washout of at least 6 weeks. The association of variables with genetic variants was evaluated by random forest and logistic regression. Areas under the curve (AUCs) were calculated. A predictive formula was developed and applied to the validation cohort. Results: A formula derived from nuclear magnetic resonance (NMR) lipoprotein analyses improved the identification of genetically positive FH patients beyond low-density lipoprotein (LDL)-cholesterol levels (AUC = 0.87). The parameters contributing the most to the identification formula were LDL particle number, high-density lipoprotein size, and remnant cholesterol. The formula also increases the classification of FH children with a pathogenic genetic variation. Conclusion: NMR lipoprotein profile analysis identifies differences beyond standard lipid parameters that help identify FH with a positive pathogenic gene variant, increasing the yield of genetic testing in FH patients.