Gal-3 blocks the binding between PD-1 and pembrolizumab.

INTRODUCTION: Immune checkpoint inhibitors (ICI) have revolutionized the treatment of metastatic malignant melanoma (MM) and improved long-term survival. Despite the impressive results, some patients still have progressive disease, and the search for biomarkers predicting response to ICI treatment is ongoing. In this search, galectin-3 (Gal-3) has been suggested as a molecule of interest, both as a marker of treatment response and as a treatment target to potentiate ICI therapy. We have previously demonstrated the binding between programmed cell death 1 (PD-1) and Gal-3, and here, we investigated the interaction between PD-1, pembrolizumab, and Gal-3 in metastatic MM patients. METHODS: The binding between PD-1, pembrolizumab and Gal-3 was investigated by surface plasmon resonance (SPR) and cryogenic electron microscopy (cryo-EM). The function was studied in in vitro cultures and soluble levels of both PD-1 and Gal-3 were measured in metastatic MM patients, treated with pembrolizumab. RESULTS: By SPR, we demonstrated that Gal-3 can block the binding between PD-1 and pembrolizumab, and further visualized a steric inhibition using cryo-EM. T cells cultured with Gal-3 had reduced pro-inflammatory cytokine production, which could not be rescued by pembrolizumab. In patients with metastatic MM, high levels of Gal-3 in plasma were found in patients with a longer progression-free survival in the study period, whereas high Gal-3 expression in the tumor was seen in patients with disease progression. Soluble PD-1 levels in plasma increased after treatment with pembrolizumab and correlated with disease progression. CONCLUSION: We demonstrate that the interaction between PD-1 and Gal-3 interferes with the binding of pembrolizumab, supporting that an immune suppression induced by Gal-3 in the tumor microenvironment cannot be rescued by pembrolizumab.

Utilize proteomic analysis to identify potential therapeutic targets for combating sepsis and sepsis-related death.

Background: Sepsis is an inflammatory disease that leads to severe mortality, highlighting the urgent need to identify new therapeutic strategies for sepsis. Proteomic research serves as a primary source for drug target identification. We employed proteome-wide Mendelian randomization (MR), genetic correlation analysis, and colocalization analysis to identify potential targets for sepsis and sepsis-related death. Methods: Genetic data for plasma proteomics were obtained from 35,559 Icelandic individuals and an initial MR analysis was conducted using 13,531 sepsis cases from the FinnGen R10 cohort to identify associations between plasma proteins and sepsis. Subsequently, significant proteins underwent genetic correlation analysis, followed by replication in 54,306 participants from the UK Biobank Pharma Proteomics Project and validation in 11,643 sepsis cases from the UK Biobank. The identified proteins were then subjected to colocalization analysis, enrichment analysis, and protein-protein interaction network analysis. Additionally, we also investigated a MR analysis using plasma proteins on 1,896 sepsis cases with 28-day mortality from the UK Biobank. Results: After FDR correction, MR analysis results showed a significant causal relationship between 113 plasma proteins and sepsis. Genetic correlation analysis revealed that only 8 proteins had genetic correlations with sepsis. In the UKB-PPP replication analysis, only 4 proteins were found to be closely associated with sepsis, while validation in the UK Biobank sepsis cases found overlaps for 21 proteins. In total, 30 proteins were identified in the aforementioned analyses, and colocalization analysis revealed that only 2 of these proteins were closely associated with sepsis. Additionally, in the 28-day mortality MR analysis of sepsis, we also found that only 2 proteins were significant. Conclusions: The identified plasma proteins and their associated metabolic pathways have enhanced our understanding of the complex relationship between proteins and sepsis. This provides new avenues for the development of drug targets and paves the way for further research in this field.

Ultra-processed foods intake in relation to metabolic health status, serum brain-derived neurotrophic factor and adropin levels in adults.

BACKGROUND: In recent years, there has been a lot of discussion over the impact of ultra-processed foods (UPFs) intake on overall health of subjects. However, the association between UPFs intake and metabolic unhealthy (MU) status is still in a state of ambiguity. The current study assessed the relationship between UPFs intake and MU status with regard to brain-derived neurotrophic factor (BDNF) and adropin levels. METHODS: A sample of Iranian adults (aged 20-65 years) was selected to participate in this cross-sectional study using a multistage cluster random-sampling method. UPFs intake was assessed by a validated food frequency questionnaire and NOVA classification. Concentrations of metabolic parameters, BDNF and adropin were determined through fasting blood samples. MU status was assessed according to the criteria proposed by Wildman. RESULTS: The overall prevalence of MU phenotype among study participants (n = 527) was 42.5%. Higher intake of UPFs was associated with elevated odds of MU status in multivariable-adjusted model (ORT3 vs. T1=1.88; 95%CI: 1.02-3.45). Moreover, a positive association was observed between UPFs intake and hypertriglyceridemia after controlling all confounders (ORT3 vs. T1=2.07; 95%CI: 1.15-3.73). However, each tertile increase in UPFs intake was not significantly associated with serum BDNF ([Formula: see text]=0.15; 95%CI: -0.05, 0.34; P = 0.14) and adropin ([Formula: see text]=-1.37; 95%CI: -6.16, 3.42; P = 0.58) levels in multivariable-adjusted linear regression models. CONCLUSION: Our findings suggested that higher consumption of UPFs was related to increased likelihood of MU status among a sample of Iranian adults. Further longitudinal studies are needed to verify the directionality and generalizability of the results to all adult populations.

Toward Absolute Quantification of Soluble Proteins via Proton Nuclear Magnetic Resonance Spectroscopy: Total Protein Concentration in Blood Plasma.

For absolute protein quantification using nuclear magnetic resonance (NMR) spectroscopy, we considered proteins as homopolymers and effective amino acid (AA) residues (AAREff) as monomer units. For diverse classes of proteins, we determined the AAREff molecular weight as 111.5 ± 3.2 Da and the number of hydrogens per AA as 7.8 ± 0.2. Their ratio of 14.3 ± 0.3 (g/LP)/(mol/LH) remains constant across various protein classes and is equivalent to Kjeldahl's nitrogen-to-protein conversion constant of 5.78 ± 0.29 gN/gP. By analogy to the Kjeldahl method, we suggest that the total integral of a 1H NMR solution protein spectrum could be used for total protein quantification. We synthesized low-resolution protein spectra from the weighted sums of individual AA spectra and compared them with experimental spectra. In the methyl region, the ratio of the protein mass to the total number of protons in the synthetic spectra (corrected for the chemical shift mismatch) was ∼1 (mg/mL)/mM, which agrees with an earlier reported experimental ratio for urine (1.05 ± 0.06 (mg/mL)/mM). For human blood plasma, in the methyl region, we found empirical ratios of 1.115 ± 0.006 (mg/mL)/mM (using 96 patient samples) and 1.121 ± 0.011 (mg/mL)/mM for the NIST plasma standard. This numerical agreement points to universal conversion constants, i.e., protein mixtures with unknown compositions could be quantified without the need for calibration standards by measuring the millimolar proton concentration within the methyl region of the NMR spectrum using the same conversion constant.

Heparin-enriched plasma proteome is significantly altered in Alzheimer's disease.

INTRODUCTION: Heparin binding proteins (HBPs) with roles in extracellular matrix assembly are strongly correlated to ß-amyloid (Aß) and tau pathology in Alzheimer's disease (AD) brain and cerebrospinal fluid (CSF). However, it remains challenging to detect these proteins in plasma using standard mass spectrometry-based proteomic approaches. METHODS: We employed heparin-affinity chromatography, followed by off-line fractionation and tandem mass tag mass spectrometry (TMT-MS), to enrich HBPs from plasma obtained from AD (n = 62) and control (n = 47) samples. These profiles were then correlated to Aß, tau and phosphorylated tau (pTau) CSF biomarkers and plasma pTau181 from the same individuals, as well as a consensus brain proteome network to assess the overlap with AD brain pathophysiology. RESULTS: Heparin enrichment from plasma was highly reproducible, enriched well-known HBPs like APOE and thrombin, and depleted high-abundant proteins such as albumin. A total of 2865 proteins, spanning 10 orders of magnitude in abundance, were measured across 109 samples. Compared to the consensus AD brain protein co-expression network, we observed that specific plasma proteins exhibited consistent direction of change in both brain and plasma, whereas others displayed divergent changes, highlighting the complex interplay between the two compartments. Elevated proteins in AD plasma, when compared to controls, included members of the matrisome module in brain that accumulate with Aß deposits, such as SMOC1, SMOC2, SPON1, MDK, OLFML3, FRZB, GPNMB, and the APOE4 proteoform. Additionally, heparin-enriched proteins in plasma demonstrated significant correlations with conventional AD CSF biomarkers, including Aß, total tau, pTau, and plasma pTau181. A panel of five plasma proteins classified AD from control individuals with an area under the curve (AUC) of 0.85. When combined with plasma pTau181, the panel significantly improved the classification performance of pTau181 alone, increasing the AUC from 0.93 to 0.98. This suggests that the heparin-enriched plasma proteome captures additional variance in cognitive dementia beyond what is explained by pTau181. CONCLUSION: These findings support the utility of a heparin-affinity approach coupled with TMT-MS for enriching amyloid-associated proteins, as well as a wide spectrum of plasma biomarkers that reflect pathological changes in the AD brain.

Preliminary Assessment of Galectin-3 Expression in Serum and Follicular Fluid: Implications for Predicting In Vitro Fertilization Outcomes.

BACKGROUND: Galactin-3 has been found to be involved in oocyte maturation, folliculogenesis, implantation, and placentation. The expression of Galactin-3 in the endometrium of women who have successfully undergone in vitro fertilization (IVF) has been suggested as a potential biomarker for predicting successful embryo implantation. OBJECTIVES: To evaluate the expression of Galactin-3 in the sera and follicular fluid of women during IVF cycles. METHODS: This prospective research included 21 women undergoing IVF treatments. Blood samples were taken at four points: day 2 before starting stimulation, trigger day, day of oocyte retrieval, and day of the ß-human chorionic gonadotropin level test. In addition, follicular fluid samples were taken on the day of oocyte retrieval. Galactin-3 protein levels were measured in serum and follicular fluid using enzyme-linked immunosorbent assay. RESULTS: Galactin-3 levels on the stimulation day were positively correlated to estradiol levels on the day of the trigger (0.59, P = 0.02). Among women who achieved pregnancy compared to those who did not, Galectin-3 serum levels were higher on the day of the trigger (17.93 ± 4.35 ng/ml vs. 11.01 ± 3.73 ng/ml, P = 0.015). CONCLUSIONS: These findings may imply a potential role of Galectin-3 on the success of IVF treatments, underscoring the potential importance of inflammatory processes in fertility.

A proteomics perspective on 2 years of high-intensity training in horses: a pilot study.

The human plasma proteome is rather well studied, but not that of other species, including horses. The aims of this study were to (1), explore differences in plasma proteomic profile of young elite harness trotters kept under standardised conditions and subjected to two different training programmes for 2 years and (2) explore changes in proteomic profile over time during the training period. From September at age 1.5 year to March at age 2 years, 16 Standardbred horses were exposed to the same training programme. In March, high-intensity training was introduced and the horses were divided into two training groups (High and Low). Blood samples were collected at rest in December as 1.5-year-olds, July as 2-year-olds, December as 2.5-year-olds and December as 3.5-year-olds. Untargeted proteomics was performed and a hypothesis-generating approach was used in statistical analysis (t-tests). At the age of 2.5 years, the level of serotransferrin was higher in the High group (P = 0.01) and at least at one sampling occasion, proteins associated with fat metabolism, oxidant/antioxidant processes, cardiovascular responses, bone formation and inflammation were lower in High group compared to Low (P < 0.05). Analyses of changes over time revealed that levels of proteins involved in energy metabolism, red cell metabolism, circulation, oxidant/antioxidant activity, bone formation, inflammation, immune modulation and cellular and vascular damage changed (P < 0.05). The results indicate that proteomics analysis of blood plasma could be a viable tool for evaluation of exercise adaptations, performance and for health monitoring, with several potential biomarkers identified in this study.

Assessment of the effect of unfractionated heparin administered either by intravenous infusion vs. subcutaneous injection on heparin-binding protein, and plasminogen activator inhibitor-1 in critically ill septic patients: a randomized controlled trial.

OBJECTIVE: The study compared the impact of unfractionated heparin (UFH) administered via two routes (infusion and subcutaneous injection) on heparin-binding protein (HBP) and plasminogen activator inhibitor-1 (PAI-1) levels in critically ill sepsis patients. PATIENTS AND METHODS: Forty critically ill sepsis patients were randomly assigned to receive either a low-dose intravenous infusion of UFH (500 units/hour) or subcutaneous UFH (5,000 units/8 hours) for seven days. HBP and PAI-1 were measured at baseline and on days one, two, and seven. RESULTS: Intravenous administration of UFH showed a significant reduction in percentage change of HBP compared to subcutaneous administration on days one [(-35% vs. -13%, p = 0.03*) (*indicates a significant result *p < 0.05, relative to the subcutaneous group)] and seven (-62% vs. -39%, p = 0.02*). Also, the percentage change of PAI-1 was significantly reduced in the infusion group compared to the subcutaneous group on days one (-28% vs. -3%, p = 0.008*), two (-42% vs. -3%, p = 0.001*), and seven (-62% vs. 27%, p = 0.001*), respectively. Furthermore, a significant improvement in the 14-day survival was observed in the infusion group compared to the subcutaneous group (p = 0.008*). CONCLUSIONS: Intravenous infusion was the route of choice for UFH administration in critically ill septic patients, with a promising effect on HBP, PAI-1, and survival.

Potential Plasma Proteins (LGALS9, LAMP3, PRSS8 and AGRN) as Predictors of Hospitalisation Risk in COVID-19 Patients.

Background: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed unprecedented challenges to healthcare systems worldwide. Here, we have identified proteomic and genetic signatures for improved prognosis which is vital for COVID-19 research. Methods: We investigated the proteomic and genomic profile of COVID-19-positive patients (n = 400 for proteomics, n = 483 for genomics), focusing on differential regulation between hospitalised and non-hospitalised COVID-19 patients. Signatures had their predictive capabilities tested using independent machine learning models such as Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR). Results: This study has identified 224 differentially expressed proteins involved in various inflammatory and immunological pathways in hospitalised COVID-19 patients compared to non-hospitalised COVID-19 patients. LGALS9 (p-value < 0.001), LAMP3 (p-value < 0.001), PRSS8 (p-value < 0.001) and AGRN (p-value < 0.001) were identified as the most statistically significant proteins. Several hundred rsIDs were queried across the top 10 significant signatures, identifying three significant SNPs on the FSTL3 gene showing a correlation with hospitalisation status. Conclusions: Our study has not only identified key signatures of COVID-19 patients with worsened health but has also demonstrated their predictive capabilities as potential biomarkers, which suggests a staple role in the worsened health effects caused by COVID-19.

Circulating Plasma Proteins in Aortic Stenosis: Associations With Severity, Myocardial Response, and Clinical Outcomes.

BACKGROUND: Echocardiographic indexes of aortic stenosis may not comprehensively reflect disease morbidity. Plasma proteomic profiling may add prognostic value in these patients. METHODS AND RESULTS: Proximity extension assays (Olink) of 183 circulating cardiovascular and inflammatory proteins were performed in a prospective follow-up study of 122 asymptomatic/minimally symptomatic patients (mean±SD age, 69.1±10.9 years; 61% men) with moderate to severe aortic stenosis and preserved left ventricular ejection fraction. Protein signatures of higher-risk echocardiographic subgroups were determined. Associations of proteins with the primary composite outcome (heart failure hospitalization, progression to New York Heart Association class III-IV, or all-cause mortality) were evaluated using competing risk analyses, with aortic valve replacement being the competing risk. Network analysis unveiled mutually exclusive communities of proteins and echocardiographic parameters, connected only through NT-proBNP (N-terminal pro-B-type natriuretic peptide). Members of the tumor necrosis factor receptor superfamily (TNFRSF1A, TNFRSF1B, and TNFRSF14), and trefoil factor-3 were major hub proteins among the circulating biomarkers. Left ventricular global longitudinal strain >-15% was associated with higher levels of proteins, primarily of inflammation and immune regulation, whereas aortic valve area <1 cm2, E/e' >15, and left atrial reservoir strain <20% were associated with higher levels of NT-proBNP. Of 14 proteins associated with the primary end point, phospholipase-C, C-X-C motif chemokine-9, and interleukin-10 receptor subunit ß demonstrated the highest hazard ratios after adjusting for clinical factors (q<0.05). CONCLUSIONS: Plasma proteins involved in inflammation and immune regulation were differentially expressed in patients with aortic stenosis with reduced left ventricular global longitudinal strain, and associated with adverse clinical outcomes. Their incorporation into aortic stenosis risk stratification warrants further assessment.

The measurement of plasma fructosamine as a diagnostic tool to improve the interpretation of plasma glucose and proteins in alpacas (Vicugna pacos).

This study assesses the use of fructosamine as a diagnostic tool for hyperglycemia in alpacas in view of their sensitivity to stress and susceptibility to conditions like lipid mobilization syndrome. Plasma fructosamine, like in diagnosing diabetes in cats and dogs, can reveal long-term blood glucose trends, differentiating stress-induced spikes from persistent diabetic hyperglycemia. In 125 alpacas presented as patients of a veterinary clinic, plasma glucose and fructosamine concentrations were compared for correlations with findings of the general clinical examination, laboratory parameters, demographic data, and a behavioral stress assessment processed by using principal component analysis. Hyperglycemia was observed on admission of 71% (89/125) of the animals. This was significantly associated with a higher concentration of serum cortisol and a higher behavioral stress scoring. Fructosamine above the reference limit was detected in only 15% (13/89) of the hyperglycemic individuals. In addition to a positive correlation of fructosamine to glucose concentration, positive relationships with different plasma proteins were detected. A relationship to stress parameters was not observed. These findings underscore stress as a significant trigger for hyperglycemia in alpacas and suggest fructosamine as a valuable parameter for distinguishing between stress-induced and diabetic hyperglycemia. However, the dependence of fructosamine formation on total plasma protein concentration should be considered to avoid misinterpretation.

Adropin Is Expressed in Pancreatic Islet Cells and Reduces Glucagon Release in Diabetes Mellitus.

Diabetes mellitus affects 537 million adults around the world. Adropin is expressed in different cell types. Our aim was to investigate the cellular localization in the endocrine pancreas and its effect on modulating pancreatic endocrine hormone release in streptozotocin (STZ)-induced diabetic rats. Adropin expression in the pancreas was investigated in normal and diabetic rats using immunohistochemistry and immunoelectron microscopy. Serum levels of insulin, glucagon pancreatic polypeptide (PP), and somatostatin were measured using a Luminex® χMAP (Magpix®) analyzer. Pancreatic endocrine hormone levels in INS-1 832/3 rat insulinoma cells, as well as pancreatic tissue fragments of normal and diabetic rats treated with different concentrations of adropin (10-6, 10-9, and 10-12 M), were measured using ELISA. Adropin was colocalized with cells producing either insulin, glucagon, or PP. Adropin treatment reduced the number of glucagon-secreting alpha cells and suppressed glucagon release from the pancreas. The serum levels of GLP-1 and amylin were significantly increased after treatment with adropin. Our study indicates a potential role of adropin in modulating glucagon secretion in animal models of diabetes mellitus.

Enhanced Association of Novel Cardiovascular Biomarkers Fetuin-A and Catestatin with Serological and Inflammatory Markers in Rheumatoid Arthritis Patients.

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 (ß = -0.15, p = 0.038) and in women (ß = -0.25, p = 0.004). Fet-A levels were also negatively correlated with disease activity (DAS28-ESR) scores (ß = -0.29, p = 0.01) and fibrinogen concentration (ß = -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.

Measured and genetically predicted protein levels and cardiovascular diseases in UK Biobank and China Kadoorie Biobank.

Several large-scale studies have measured plasma levels of the proteome in individuals with cardiovascular diseases (CVDs)1-7. However, since the majority of such proteins are interrelated2, it is difficult for observational studies to distinguish which proteins are likely to be of etiological relevance. Here we evaluate whether plasma levels of 2,919 proteins measured in 52,164 UK Biobank participants are associated with incident myocardial infarction, ischemic stroke or heart failure. Of those proteins, 126 were associated with all three CVD outcomes and 118 were associated with at least one CVD in the China Kadoorie Biobank. Mendelian randomization and colocalization analyses indicated that genetically determined levels of 47 and 18 proteins, respectively, were associated with CVDs, including FGF5, PROCR and FURIN. While the majority of protein-CVD observational associations were noncausal, these three proteins showed evidence to support potential causality and are therefore promising targets for drug treatment for CVD outcomes.

Olink Proteomics for the Identification of Biomarkers for Early Diagnosis of Postmenopausal Osteoporosis.

This investigation aims to employ Olink proteomics in analyzing the distinct serum proteins associated with postmenopausal osteoporosis (PMOP) and identifying prognostic markers for early detection of PMOP via molecular mechanism research on postmenopausal osteoporosis. Postmenopausal women admitted to Beijing Jishuitan Hospital were randomly selected and categorized into three groups based on their dual-energy X-ray absorptiometry (DXA) T-scores: osteoporosis group (n = 24), osteopenia group (n = 20), and normal bone mass group (n = 16). Serum samples from all participants were collected for clinical and bone metabolism marker measurements. Olink proteomics was utilized to identify differentially expressed proteins (DEPs) that are highly associated with postmenopausal osteoporosis. The functional analysis of DEPs was performed using Gene Ontology and Kyto Encyclopedia Genes and Genomes (KEGG). The biological characteristics of these proteins and their correlation with PMOP were subsequently analyzed. ROC curve analysis was performed to identify potential biomarkers with the highest diagnostic accuracy for early stage PMOP. Through Olink proteomics, we identified five DEPs highly associated with PMOP, including two upregulated and three downregulated proteins. TWEAK and CDCP1 markers exhibited the highest area under the curve (0.8188 and 0.8031, respectively). TWEAK and CDCP1 have the potential to serve as biomarkers for early prediction of postmenopausal osteoporosis.

The revolution in high-throughput proteomics and AI.

The recent capability to measure thousands of plasma proteins from a tiny blood sample has provided a new dimension of expansive data that can advance our understanding of human health. For example, the company SomaLogic has developed the means to measure more than 10,000 proteins and Thermo Fisher's Olink assays over 5400 proteins from as little as 2 µl. When these rich data are integrated with other layers of information from large patient cohorts, such as the UK Biobank's genetic, health, and lifestyle information from half a million participants, we get new insights about the underpinnings of disease, the aging process, and the potential ability to forecast an individual's health trajectory.

Proteomic Mendelian randomization to identify protein biomarkers of telomere length.

Shortening of telomere length (TL) is correlated with many age-related disorders and is a hallmark of biological aging. This study used proteome-wide Mendelian randomization to identify the protein biomarkers associated with telomere length. Protein quantitative trait loci (pQTL) were derived from two studies, the deCODE Health study (4907 plasma proteins) and the UK Biobank Pharma Proteomics Project (2923 plasma proteins). Summary data from genome-wide association studies (GWAS) for TL were obtained from the UK Biobank (472,174 cases) and GWAS Catalog (418,401 cases). The association between proteins and TL was further assessed using colocalization and summary data-based Mendelian randomization (SMR) analyses. The protein-protein network, druggability assessment, and phenome-wide MR were used to further evaluate the potential biological effects, druggability, and safety of the target proteins. Proteome-wide MR analysis identified 22 plasma proteins that were causally associated with telomere length. Five of these proteins (APOE, SPRED2, MAX, RALY, and PSMB1) had the highest evidence of association with TL and should be prioritized. This study revealed telomere length-related protein biomarkers, providing new insights into the development of new treatment targets for chronic diseases and anti-aging intervention strategies.

Enhanced prediction of atrial fibrillation risk using proteomic markers: a comparative analysis with clinical and polygenic risk scores.

BACKGROUND: Proteomic biomarkers have shown promise in predicting various cardiovascular conditions, but their utility in assessing the risk of atrial fibrillation (AF) remains unclear. This study aimed to develop and validate a protein-based risk score for predicting incident AF and to compare its predictive performance with traditional clinical risk factors and polygenic risk scores in a large cohort from the UK Biobank. METHODS: We analysed data from 36 129 white British individuals without prior AF, assessing 2923 plasma proteins using the Olink Explore 3072 assay. The cohort was divided into a training set (70%) and a test set (30%) to develop and validate a protein risk score for AF. We compared the predictive performance of this score with the HARMS2-AF risk model and a polygenic risk score. RESULTS: Over an average follow-up of 11.8 years, 2450 incident AF cases were identified. A 47-protein risk score was developed, with N-terminal prohormone of brain natriuretic peptide (NT-proBNP) being the most significant predictor. In the test set, the protein risk score (per SD increment, HR 1.94; 95% CI 1.83 to 2.05) and NT-proBNP alone (HR 1.80; 95% CI 1.70 to 1.91) demonstrated superior predictive performance (C-statistic: 0.802 and 0.785, respectively) compared with HARMS2-AF and polygenic risk scores (C-statistic: 0.751 and 0.748, respectively). CONCLUSIONS: A protein-based risk score, particularly incorporating NT-proBNP, offers superior predictive value for AF risk over traditional clinical and polygenic risk scores, highlighting the potential for proteomic data in AF risk stratification.