Value Of Biomarkers Research Articles

Systemic immune-inflammation mediates the association between Klotho protein and metabolic syndrome: findings from a large-scale population-based study.

This study utilized large-scale population data from the National Health and Nutrition Examination Survey (NHANES) to elucidate the relationship between the Klotho protein and metabolic syndrome along with its components. We further investigated the possible mediating effect of inflammation on these relationships. Our objective was to identify biomarkers for risk stratification and potential therapeutic targets for metabolic syndrome. This study enrolled 13,119 participants aged 40-79 years, spanning five NHANES cycles from 2007 to 2016, with complete information on metabolic syndrome and the Klotho protein. The definition of metabolic syndrome followed the criteria of the National Cholesterol Education Program-Adult Treatment Panel III. Survey-weighted logistic regression and subgroup analysis were used to explore the associations between serum Klotho protein levels and metabolic syndrome, along with its components. Mediation analysis was performed to investigate the mediating effects of inflammation-related markers, including white blood cells, neutrophils, lymphocytes, monocytes, the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio (PLR), the systemic immune-inflammation index (SII) and the monocyte-to-HDL ratio (MHR), with the aim of elucidating how the Klotho protein influences the onset and progression of metabolic syndrome. The study participants had an average age of 56.06 years (95% CI: 55.76-56.37), with a Klotho protein concentration of 798.10 pg/ml (95% CI: 656.50-980.50) and a 43.77% prevalence of metabolic syndrome (n = 5742). In the crude model, Klotho was negatively correlated with metabolic syndrome and its components, including central obesity, hypertension, and hypertriglyceridemia. After adjusting for all confounding factors, Klotho was demonstrated to be negatively associated only with metabolic syndrome (OR: 0.82, 95% CI: 0.70-0.97), hypertension (OR: 0.83, 95% CI: 0.70-0.98), and hypertriglyceridemia (OR: 0.78, 95% CI: 0.67-0.91). Subgroup and interaction analyses revealed significant interactions between age, sex, race/ethnicity, body mass index, and Klotho. Additionally, mediation analysis demonstrated that leukocytes, neutrophils and monocytes accounted for 34.78%, 31.91% and 7.13%, respectively, of the associations between Klotho and metabolic syndrome. The serum concentration of Klotho protein was negatively associated with metabolic syndrome, with the relationship being partly mediated by systemic immune inflammation. The findings of this research revealed that the Klotho protein may be a valuable biomarker for risk stratification and a potential therapeutic target for metabolic syndrome.

Feasibility of plasma proteomics in patients with immune thrombocytopenia.

ITP is an acquired autoimmune bleeding disorder characterized by an isolated thrombocytopenia. The pathophysiology is highly multifactorial and involves antibody- and/or cytotoxic T cell-mediated killing of platelets and disruption of megakaryocyte function hampering platelet production. ITP remains a diagnosis of exclusion, and due to the high degree of variability between patients, it remains challenging to predict disease courses and responses to therapeutic agents. Hence, diagnostic and therapeutic laboratory biomarkers are highly warranted. To address this issue, in their paper, Jiang and colleagues have performed plasma proteomics in ITP patients (n = 40), in comparison to patients with thrombocytopenia due to other causes than ITP (non-ITP thrombocytopenia, n = 19) and healthy controls (n = 18). The data underscore that patients with ITP have a distinct plasma proteomic signature compared to non-ITP thrombocytopenia patients and healthy individuals. The findings should be further validated and investigated but suggest that the application of plasma proteomics is feasible and promising with respect to the search for potential biomarkers in patients with ITP. Commentary on: Jiang etal. Targeted proteomics profiling reveals valuable biomarkers in the diagnosis of primary immune thrombocytopenia. Br J Haematol 2024 (Online ahead of print). doi: 10.1111/bjh.19760.

Predictive value of bone metabolism markers in the progression of diabetic kidney disease: a cross-sectional study

ObjectiveThis study aimed to investigate the relationship between bone metabolism markers, including serum klotho, fibroblast growth factor 23 (FGF23), 25(OH)D3, iPTH, calcium (Ca), and PHOS and the progression of diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). Additionally, the predictive value of these markers for DKD progression was evaluated.MethodsThis study involved 126 patients with T2DM between May 2021 and March 2023. DKD staging was assessed based on urinary protein excretion rates and estimated glomerular filtration rate (eGFR). The study evaluated serum concentrations of klotho, FGF23, 25(OH)D3, iPTH, Ca and PHOS across various stages and examined their relationships with clinical parameters. Receiver operating characteristic (ROC) curve analysis was utilized to determine the predictive accuracy of these bone metabolism markers for DKD. Multivariate linear and logistic regression analyses identified risk factors linked to DKD severity.ResultsAmong the 126 participants, 30 had non-DKD with normal proteinuria, while 96 had DKD, categorized as 31 with stage III DKD (microproteinuria), 34 with stage IV DKD, and 31 with stage V DKD (massive proteinuria). With advancing DKD from stage III to V, levels of klotho, 25(OH)D3, and Ca decreased significantly, whereas FGF23, iPTH and PHOS levels increased markedly. Klotho is significantly positively correlated with eGFR (r = 0.285, P = 0.001.) and negative correlations with serum creatinine (Scr) and UACR (r = -0.255, P = 0.004; r = -0.260, P = 0.011). FGF23 was positively related to systolic blood pressure (SBP) (r = 0.224, P = 0.012), but negatively with eGFR (r = -0.294, P = 0.001). Additionally, 25(OH)D3 exhibited significant negative correlations with several adverse clinical biomarkers, and both iPTH, Ca and PHOS were strongly associated with DKD progression (P<0.05). ROC analysis showed high predictive accuracy for DKD using these bone metabolism markers, with a combined area under the curve (AUC) of 0.846. Multivariate logistic regression analysis reinforced the significance of these markers in DKD progression.ConclusionBone metabolism markers, such as klotho, FGF23, 25(OH)D3, iPTH, Ca and PHOS are intricately linked to DKD progression and may function as valuable predictive biomarkers.

A microarray-based IgE-molecular mimicry index (IgE-MMI): A biomarker for disease severity, clinical phenotypes, and therapeutic response in atopic dermatitis?

The role of autoimmune IgE responses in atopic dermatitis (AD) is highly debated. While IgE targeting self-proteins has been extensively studied, IgE responses induced by human-homologous exogenous molecular allergens (HEMAs) remains less understood. To investigate whether IgE antibody responses to HEMAs are associated with AD, its severity, and response to dupilumab. We enrolled 3325 participants with a history of allergic diseases, including 577 (17.3%) diagnosed with AD. Serum IgE antibodies against 183 exogenous allergenic molecules were measured using the IgE microarray (Allergy Explorer-ALEX-2®, MADX, Vienna). Based on international classification criteria, participants were stratified by AD severity and clinical phenotypes. For each patient, we developed an 'IgE molecular-mimicry index' (IgE-MMI), calculated from IgE reactivity to a panel of five HEMA protein families: arginine kinase, enolase (ENO), cyclophilin (CYP), lipocalin, and MnSOD. Logistic regression was employed to assess the association between IgE to HEMAs or IgE-MMI and AD, its severity, and response to dupilumab. IgE sensitization to most HEMAs (32/48, 67%), but only to a small fraction of non-HEMAs (3/135, 2.2%), was significantly more common in patients with severe AD compared to other patient groups. The IgE-MMI was positive in 295/2748 (10.7%) of allergic patients without AD, and in 58/283 (20%), 52/134 (39%), and 86/160 (54%) of patients with remitting, moderate, or severe AD, respectively. It was strongly associated with specific phenotypes, such as flexural dermatitis (OR 8.4, 95% CI: 6.3-11.2), head and neck dermatitis (OR: 16.5, 95% CI: 7.4-37.2), and generalized eczema (OR: 8.6, 95% CI: 4.9-15.6). Poor response to dupilumab was associated with IgE antibodies to ENO (OR: 22.7, 95% CI: 1.7-302.9), but inversely associated with IgE antibodies to MnSOD (OR: 0.1, 95% CI: 0.02-0.8) and NPC-2 from dust mites (OR: 0.1, 95% CI: 0.01-0.9). IgE microarrays are useful for broadly assessing IgE to HEMAs in allergic patients. IgE reactivity to HEMAs and a positive IgE-MMI may serve as valuable biomarkers for severe AD, its clinical phenotypes, and the response to dupilumab.

Preoperative hypoxic biomarkers and postoperative delirium in patients with obstructive sleep apnea.

Postoperative delirium (POD) in patients with obstructive sleep apnea (OSA) is associated with increased mortality and healthcare costs. In this study, we investigated the association of OSA risk, serum biomarkers for central nervous ischemia (S100B and NSE), and POD. After research ethics approval, patients completed the STOP BANG assessment before undergoing elective surgery. Blood was drawn for S100B and NSE measurement, and cognitive performance was tested using the Montreal Cognitive Assessment (MoCA) at study admission and postoperatively at discharge. Delirium assessment was performed using the Nursing Delirium Screening Scale (NuDESC) and the Confusion Assessment Method (CAM). One hundred twenty-four enrolled patients were separated into three OSA-risk groups based on STOP BANG score testing (low risk, n = 22; intermediate risk, n = 67; high risk, n = 35). Preoperative NSE values increased with OSA risk (NSE in ng/ml; mean [range]; low risk: 15.6 [9.2-44.3]; intermediate risk: 21.8 [7.6-114.1]; high risk: 29.2 [10.1-151]; p = 0.039). Postoperative MoCA and NuDESC assessments were not different between the OSA-risk groups. We found a decreasing incidence for POD with increasing OSA risk (positive CAM: low risk: 18.1%, intermediate risk: 12.0%; high risk: 11.5%, p = 0.043). However, this was no longer detectable in a complete case analysis. In patients with POD, postoperative ischemic biomarker values were not different between OSA-risk groups. We found a trend of decreasing POD incidence with increasing OSA risk, which was not robust in a complete case analysis. Our results possibly support the phenomenon of hypoxic preconditioning.

A Systematic Review and Meta-Analysis Assessing the Accuracy of Blood Biomarkers for the Diagnosis of Ischemic Stroke in Adult and Elderly Populations

This study aims to elucidate the methodology and compare the accuracy of different blood biomarkers for diagnosing ischemic stroke (IS). We reviewed 29 articles retrieved from PubMed, MEDLINE, Web of Science, and CINAHL Plus with Full Text. Among these, 23 articles involving 3,494 participants were suitable for meta-analysis. The pooled area under the curve (AUC) of all studies for meta-analysis was 0.89. The pooled sensitivity and specificity were 0.76 (0.74–0.78) and 0.84 (0.83–0.86), respectively. Blood biomarkers from noninpatient settings demonstrated better diagnostic performance than those in inpatient settings (AUC 0.91 vs 0.88). Smaller sample sizes (<100) showed better performance than larger ones (≥100; AUC 0.92 vs 0.86). Blood biomarkers from acute IS (AIS) patients showed higher diagnostic values than those from IS and other stroke types (AUC 0.91 vs 0.87). The diagnostic performance of multiple blood biomarkers was superior to that of a single biomarker (AUC 0.91 vs 0.88). The diagnostic value of blood biomarkers from Caucasians was higher than that from Asians and Africans (AUC 0.90 vs 0.89, 0.75). Blood biomarkers from those with comorbidities (AUC 0.92) showed a better diagnostic performance than those not reporting comorbidities (AUC 0.84). All the subgroups analyzed, including setting, sample size, target IS population, blood biomarker profiling, ethnicity, and comorbidities could lead to heterogeneity. Blood biomarkers have demonstrated sufficient diagnostic accuracy for diagnosing IS and hold promise for integration into routine clinical practice. However, further research is recommended to refine the optimal model for utilizing blood biomarkers in IS diagnosis.

Added value of inflammatory plasma biomarkers to pathologic biomarkers in predicting preclinical Alzheimer's disease.

Plasma biomarkers have recently emerged for the diagnosis, assessment, and disease monitoring of Alzheimer's disease (AD), but have yet to be fully validated in preclinical AD. In addition to AD pathologic plasma biomarkers (amyloid-β (Aβ) and phosphorylated tau (p-tau) species), a proteomic panel can discriminate between symptomatic AD and cognitively unimpaired older adults in a dementia clinic population. Examine the added value of a plasma proteomic panel, validated in symptomatic AD, over standard AD pathologic plasma biomarkers and demographic and genetic (apolipoprotein (APOE) ɛ4 status) risk factors in detecting preclinical AD. 125 cognitively unimpaired older adults (mean age = 66 years) who completed Aβ PET and plasma draw were analyzed using multiple regression with Aβ PET status (positive versus negative) as the outcome to determine the best fit for predicting preclinical AD. Model 1 included age, education, and gender. Model 2 and 3 added predictors APOE ɛ4 status (carrier versus non-carrier) and AD pathologic blood biomarkers (Aβ42/40 ratio, p-tau181), respectively. Random forest modeling established the 5 proteomic markers from the proteomic panel that best predicted Aβ PET status, and these markers were added in Model 4. The best model for predicting Aβ PET status included age, years of education, APOE ɛ4 status, Aβ42/40 ratio, and p-tau181. Adding the top 5 proteomic markers did not significantly improve the model. Proteomic markers in plasma did not add predictive value to standard AD pathologic plasma biomarkers in predicting preclinical AD in this sample.

A Gene Expression Signature that Predicts Gastric Cancer Sensitivity to PARP Inhibitor Therapy.

Biomarkers indicating sensitivity to poly ADP-ribose polymerase (PARP) inhibitors have not yet been identified in gastric cancer. PARP inhibitors target homologous recombination deficiency (HRD); however, homologous recombination (HR) induces complex changes in gene expression, which makes it difficult to identify reliable biomarkers. In this study, we identified a multi-gene expression signature as a marker of PARP inhibitor sensitivity in gastric cancer. Seven gastric cancer cell lines were evaluated for susceptibility to PARP inhibitors using a growth inhibition assay. Gene expression profiling (GEP) was used to identify differentially expressed genes between PARP inhibitor-sensitive and -resistant cell lines. The resulting gene set was subjected to cluster analysis using tumor samples from 250 patients who underwent gastrectomy for primary gastroesophageal junction and gastric adenocarcinoma. HRD was defined as a truncating mutation in one or more of 22 HR-related genes and HRD scores were calculated using whole-exome sequencing data. In the growth inhibition assays, the HGC27 and HSC39 cell lines were sensitive to the PARP inhibitors, olaparib, and rucaparib, and were significantly correlated with the GEP results. Seven (2.8%) patients harbored truncating mutations in HR-related genes. A gene expression signature based on the top 100 high and low differentially expressed genes between sensitive and resistant cell lines revealed a patient cluster with a high prevalence of HR-related gene mutations and high HRD scores. The 100-gene expression signature identified in this study may serve as a valuable predictive biomarker for PARP inhibitor sensitivity in gastric cancer.

Serum endocan (ESM-1) as diagnostic and prognostic biomarker in Multisystem inflammatory syndrome in children (MIS-C)

Endothelial-cell-specific molecule-1 (ESM-1) also called endocan is a well-known biomarker for detecting inflammation, endothelial dysfunction (ED), and cardiovascular (CV) risk in COVID-19 patients. Upon SARS-CoV-2 infection, a small percentage of children develop Multisystem Inflammatory Syndrome in children (MIS-C). Whether endocan can be used as a biomarker of MIS-C is unknown. In this study, we assessed ESM-1 levels in MIS-C (n = 19) and healthy controls (HC; n = 17). We observed a significant increase in serum ESM-1 levels in MIS-C vs HC (p = 0.0074). In addition, ROC curve analysis demonstrated that this factor has a reasonable discriminatory power between MIS-C patients and HC (AUC of 0.7585). Notably, after one week of hospitalization and care, ESM-1 levels decreased, and this reduction was observed also for other inflammatory and pro-thrombotic markers like C-reactive protein, procalcitonin, fibrinogen, D-dimer, and ferritin, suggesting a general recovery trend in MIS-C patients. In fact, we observed that serum ESM-1 levels positively correlated with procalcitonin (PCT) (r = 0.468; p = 0.043). Finally, logistic regression analysis demonstrated an association between endocan levels and cardiac complications like myocarditis. Therefore, this study suggests that ESM-1 is a valuable diagnostic and prognostic biomarker in patients with MIS-C that may help identify those MIS-C patients at higher risk for cardiovascular complications and guide treatment strategies.

Sulfur-Doped g-C3N4/Polycaprolactone Nanofibers Based Smart Sensor for 8-hydroxy-2-deoxyguanosine Biomarker Monitoring

8-Hydroxy-2′-deoxyguanosine (8-OHdG) signifies one of the primary byproducts of oxidative DNA damage, thereby being a valuable DNA biomarker. This study presents a label-free, low-cost, smart sensor that could improve evaluation, tracking, and survival rates by allowing for an early assessment of cancer. Herein, we fabricated sulfur-doped graphitic carbon nitride (S-gC₃N₄) embedded in polycaprolactone (PCL) for highly efficient monitoring of 8-OHdG, offering functional groups such as sulfur and nitrogen that facilitate strong binding against 8-OHdG. FTIR, XRD, and SEM techniques were utilized to investigate the S-gC3N4/PCL nanocomposite. Interestingly, the nanocomposite demonstrates strong electrochemical responses to the oxidation of 8-OHdG, with a detection limit of 0.24 nM across a wide dynamic concentration range (1 nM–50 μM). Additionally, it exhibits exceptional durability, selectivity, reusability, and repeatability. Additionally, the designed sensor has the potential to measure 8-OHdG levels in individuals and could be used to evaluate early cancer indicators. Furthermore, the S-gC₃N₄/PCL sensor was successfully tested to determine 8-OHdG levels in human samples.

Single-cell and machine learning approaches uncover intrinsic immune-evasion genes in the prognosis of hepatocellular carcinoma

Background and aimsHepatocellular carcinoma (HCC) is a tumor of high heterogeneity and complexity, which poses significant challenges to effective treatment and patient prognosis because of its immune evasion characteristics. To address these issues, single-cell technology and machine learning methods have emerged as a promising approach to identify genes associated with immune escape in HCC. This study aimed to develop a prognostic risk score model for HCC by identifying intrinsic immune-evasion genes (IIEGs) through single-cell technology and machine learning, providing insights into immune infiltration, enhancing predictive accuracy, and facilitating the development of more effective treatment strategies. Materials and methodsThe study utilized data from The Cancer Genome Atlas database to analyze gene expression profiles and clinical data related to intrinsic immune evasion in patients with HCC. Various tools, including the Human Protein Atlas, cBioPortal, single-cell analysis, machine learning, and Kaplan-Meier plot, were used to analyze IIEGs. Functional enrichment analysis was conducted to explore potential mechanisms. In addition, the abundance of infiltrating cells in the tumor microenvironment was investigated using single-sample gene set enrichment analysis, CIBERSORT, xCELL, and tumor immunophenotype algorithms. The expression of glycosylphosphatidylinositol anchor attachment 1 (GPAA1) was examined in the clinical sample of HCC by quantitative real-time polymerase chain reaction, Western blotting, and immunohistochemical staining. ResultsUnivariate Cox analysis identified 63 IIEGs associated with the prognosis of HCC. Using random forest, least absolute shrinkage and selection operator regression analysis, and support vector machine, a risk score model consisting of six IIEGs (carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase (CAD), phosphatidylinositol glycan anchor biosynthesis class U (PIGU), endoplasmic reticulum membrane protein complex subunit 3 (EMC3), centrosomal protein 55 (CEP55), autophagy-related 10 (ATG10), and GPAA1) developed, which was validated using 10 pairs of HCC and adjacent non-cancerous samples. Based on the calculated median risk score, HCC samples were categorized into high- and low-risk groups. The Kaplan-Meier curve analysis showed that the high-risk group had a worse prognosis compared with the low-risk group. Time-dependent receiver operating characteristic analysis demonstrated the accurate predictive capability of the risk score model for HCC prognosis. Furthermore, immune infiltration analysis showed a positive correlation between the risk score model and 40 immune checkpoint genes as well as Th2 cells. ConclusionsA prognostic risk score model was formulated by six IIEG signatures and showed promise in predicting the prognosis of patients diagnosed with HCC. The utilization of the IIEG risk score as a novel prognostic index, together with its significance as a valuable biomarker for immunotherapy in HCC, provides benefit for patients with HCC in determining therapeutic strategies for clinical application.

Functional brain changes in Mexican women with fibromyalgia

Fibromyalgia (FM) is a chronic condition marked by widespread pain, fatigue, sleep problems, cognitive decline, and other symptoms. Despite extensive research, the pathophysiology of FM remains poorly understood, complicating diagnosis and treatment, which often relies on self-report questionnaires. This study explored structural and functional brain changes in women with FM, identified potential biomarkers, and examined their relationship with FM severity. MRI data from 33 female FM patients and 33 matched healthy controls were utilized, focusing on T1-weighted MRI and resting-state fMRI scans. Functional connectivity (FC) analysis was performed using a machine learning framework to differentiate FM patients from healthy controls and predict FM symptom severity. No significant differences were found in brain structural features, such as gray matter volume, white matter volume, deformation-based morphometry, and cortical thickness. However, significant differences in FC were observed between FM patients and healthy controls, particularly in the default mode network (DMN), somatomotor network (SMN), visual network (VIS), and dorsal attention network (DAN). The FC metrics were significantly associated with FM severity. Our prediction model differentiated FM patients from healthy controls with an area under the curve of 0.65. FC measures accurately estimated FM symptom severities with a significant correlation (r = 0.45, p = 0.007). Functional connections in the DMN, VIS, and DAN were crucial in determining FM severity. These findings suggest that integrating brain FC measurements could serve as valuable biomarkers for identifying FM patients from healthy individuals and predicting FM symptom severity, improving diagnostic accuracy and facilitating the development of targeted therapeutic strategies.

Predictive Value of Inflammatory Biomarkers in Assessing Major Depression in Adults

Background: There are studies that have investigated the association of pro-inflammatory cytokines with depressive disorders, but they often present certain limitations. In this study, two substantial groups of patients were analyzed: 92 patients with major depressive disorder and 76 without depressive disorders. The strict inclusion and exclusion criteria for the analyzed groups significantly increased the value of the obtained results. The research question of this study was whether levels of inflammation, measured by the inflammatory markers IL-6, IL-1α, and TNF-α, could predict the severity of depressive symptoms. This could provide additional evidence supporting the hypothesis that inflammation plays a notable role in the pathogenesis of depression. The data analysis supports the hypothesis that the biological mechanisms of inflammation contribute to the clinical manifestations of depression. Elevated levels of inflammatory markers, especially interleukins (IL-6, IL-1α) and tumor necrosis factor-alpha (TNF α), have been identified in patients with major depressive disorder compared to the findings in healthy controls. Materials and Methods: Inflammatory markers (IL-6, IL-1α, and TNF-α) were measured in a sample of 92 patients hospitalized at the Socola Institute of Psychiatry in Iasi, Romania, and compared to a control group with no depression or inflammatory conditions (n = 76). Severity of depressive symptoms was assessed using HAM-D scores. Results: The study results indicated that values of plasma inflammatory markers were significantly higher in patients with major depressive disorder (MDD) compared to the control group (IL-1α: 1.16 ± 0.44 pg/mL vs. 0.89 ± 0.25 pg/mL, p = 0.0004; IL-6: 9.21 ± 4.82 pg/mL vs. 7.16 ± 4.32 pg/mL, p = 0.0149; and TNF-α: 2.02 ± 0.96 pg/mL vs. 1.67 ± 0.8 pg/mL, p = 0.0286). The differences remained significant after applying logarithmic transformation, which was necessary to adjust for outlier values. An analysis of demographic characteristics showed that the frequency of women (67.4% vs. 36.84%, p < 0.001), cohabiting individuals (28.26% vs. 10.53%, p = 0.0001), and alcohol consumers (67.39% vs. 47.37%, p = 0.0087) was significantly higher in patients with MDD. The level of education was significantly lower in patients with MDD (median (IQR): 12 (2.5) years vs. 14 (8) years, p = 0.0016). The evaluation of confounding variables, including patients’ gender, marital status, education level, and alcohol consumption, was performed using multiple linear regression models. The results indicated that these demographic variables did not significantly influence the correlation between the HAM-D score and the values of IL-6, IL-1α, and TNF-α. A significant correlation between the HAM-D score and the logarithmic values of inflammatory markers was observed for log IL-1α in men (r = 0.355, p = 0.0014), log IL-6 in women (r = 0.0313, p = 0.0027), and log TNF-α in women (r = 0.3922, p = 0.0001). The results of the multiple linear regression and predictive analysis indicated that IL-1α (AUC = 0.677, p = 0.0004), IL-6 (AUC = 0.724, p < 0.001), and TNF-α (AUC = 0.861, p < 0.001) demonstrate high accuracy in discriminating patients with MDD. Conclusions: The results highlighted that IL-6 (AUC = 0.724; 95% CI: 0.648–0.801) and TNF-α (AUC = 0.861; 95% CI: 0.797–0.925) are significant predictors for major depressive disorder. The study highlights the potential of cytokines (IL-1α, IL-6 and TNF-α) as diagnostic markers. These findings support the hypothesis that inflammation may play an important role in the development or exacerbation of depressive symptoms.

18F]FDG and [68Ga]Ga-FAPI-04-Directed Imaging for Outcome Prediction in Patients with High-Grade Neuroendocrine Neoplasms.

We aimed to quantitatively investigate the prognostic value of PET-based biomarkers on [18F]FDG and [68Ga]Ga-fibroblast activation protein inhibitor (FAPI)-04 PET/CT in patients with highly aggressive neuroendocrine neoplasms (NENs) and to compare the visually assessed differences in uptake on both examinations with progression-free survival (PFS). Methods: In this single-center retrospective analysis, 20 patients with high-grade NENs had undergone [18F]FDG and [68Ga]Ga-FAPI-04 PET. Both PET scans were visually compared, and the presence of [18F]FDG-positive, [68Ga]Ga-FAPI-04-negative (FDG+/FAPI-) lesions was noted. In addition, we assessed maximum, peak, and mean SUV; tumor volume (TV); and total lesion uptake (TLU = TV × SUVmean) for both radiotracers using a 40% lesion-based threshold. The results of quantitative and visual analysis were correlated with PFS using log-rank analysis or univariate Cox regression. PFS was defined radiographically using RECIST 1.1., clinically using signs of disease progression, or as death. Results: Most primary tumors were located in the gastrointestinal tract (13/20 patients, 65%) or were cancer of unknown primary (5/20 patients, 25%). FDG+/FAPI- lesions were found in 9 of 20 patients (45%). Patients with FDG+/FAPI- lesions had a significantly decreased PFS of 4 mo, compared with 9 mo for patients without FDG+/FAPI- metastases (P = 0.0063 [log-rank test]; hazard ratio [HR], 5.637; 95% CI 1.619-26.16; P = 0.0110 [univariate Cox regression]). On univariate analysis, a significant correlation was also found between PFS and TV for both radiotracers ([18F]FDG: mean TV, 258 ± 588 cm3; HR, 1.024 [per 10 cm3]; 95% CI, 1.007-1.046; P = 0.0204) ([68Ga]Ga-FAPI-04: mean TV, 130 ± 192 cm3; HR, 1.032 [per 10 cm3]; 95% CI, 1.001-1.062; P = 0.0277) and TLU on [18F]FDG PET (mean TLU, 1,931 ± 4,248 cm3; HR, 1.004 [per 10 cm3]; 95% CI, 1.001-1.007; P = 0.0135). Conclusion: The presence of discordant FDG+/FAPI- lesions is associated with a significantly shorter PFS, which might indicate more aggressive disease prone to early progression. Dual-tracer PET/CT of patients with highly aggressive NENs could help guide treatment decisions or identify high-risk lesions for additional local therapeutic approaches.

Identification of m5C-Related gene diagnostic biomarkers for sepsis: a machine learning study

BackgroundSepsis is a serious condition that occurs when the body’s response to infection becomes uncontrolled, resulting in a high risk of death. Despite improvements in healthcare, identifying sepsis early is difficult because of its diverse nature and the absence of distinct biomarkers. Recent studies suggest that 5-methylcytosine (m5C)-related genes play a significant role in immune responses, yet their diagnostic potential in sepsis remains unexplored.MethodsThis research combined and examined four sepsis-related datasets (GSE95233, GSE57065, GSE100159, and GSE65682) sourced from the Gene Expression Omnibus (GEO)database to discover m5C-related genes with differential expression. Various machine learning methods, such as decision tree, random forest, and XGBoost, were utilized in identifying crucial hub genes. Receiver Operating Characteristic (ROC) curve analysis was used to assess the diagnostic accuracy of these genetic markers. Additionally, single-gene enrichment and immune infiltration analyses were conducted to investigate the underlying mechanisms involving these hub genes in sepsis.ResultsThree hub genes, DNA Methyltransferase 1 (DNMT1), tumor protein P53 (TP53), and toll-like receptor 8 (TLR8), were identified and validated for their diagnostic efficacy, showing area under the curve (AUC) values above 0.7 in both test and validation sets. Enrichment analyses revealed that these genes are involved in key pathways such as p53 signaling and Toll-like receptor signaling. Immune infiltration analysis indicated significant correlations between hub genes and various immune cell types, suggesting their roles in modulating immune responses during sepsis.ConclusionThe study highlights the diagnostic potential of m5C-related genes in sepsis and their involvement in immune regulation. These findings offer new insights into sepsis pathogenesis and suggest that DNMT1, TP53, and TLR8 could serve as valuable biomarkers for early diagnosis. Further studies should prioritize validating these biomarkers in clinical settings and investigating their potential for therapy.

The prognostic impact of stress hyperglycemia ratio for all-cause mortality in patients with Psoriasis.

Aims The stress hyperglycemia ratio (SHR) is a valuable biomarker of acute hyperglycemia, significantly correlated with unfavorable prognosis in various conditions. However, its impact on Psoriasis has not been studied. We explored the association between SHR and long-term mortality in psoriasis patients. Methods We conducted a prospective cohort study with 288 psoriasis patients from the 2003-2006 and 2009-2014 NHANES. Participants were divided into three groups based on SHR tertiles: T1 (SHR ≤ 0.870), T2 (SHR 0.870-0.958), and T3 (SHR ≥ 0.958). Cox regression and Kaplan-Meier analyses assessed the correlation between SHR and mortality, while restricted cubic splines explored non-linear correlations. ROC analyses determined the optimal SHR cut-off value for predicting clinical outcomes. Results Out of 288 Psoriasis patients, 38 all-cause deaths occurred during an average follow-up of 112.13 ± 45.154 months. Kaplan-Meier analysis indicated that higher SHR values were linked to an increased risk of all-cause mortality (log-rank P = 0.049). A U-shaped relationship was observed between SHR and all-cause mortality (P for non-linear = 0.028). Spearman correlation revealed significant associations between SHR and WC, BMI, neutrophil, monocyte, lymphocyte counts, SCr, uric acid, DM and MetS (all P < 0.05). After adjusting for confounders, multivariate Cox regression showed that SHR was associated with a 10.937-fold risk of all-cause mortality. ROC curve analysis identified an optimal SHR cut-off value of 1.045 for predicting long-term all-cause mortality in psoriasis patients. Conclusions Elevated SHR value independently correlates with all-cause mortality in Psoriasis patients, displaying a U-shaped relationship with clinical endpoints. An optimal SHR cut-off value of 1.045 has been determined for predicting clinical outcomes.

Hyperexpression of PTAFR and PF4 as Possible Platelet Risk Biomarkers in Patients With COVID-19.

SARS-CoV-2 infection presents different severity levels that suggest the influence of genetic factors on the clinical outcome of the disease. In cases of severe COVID-19, the presence of elevated coagulation markers, increased platelet activation and aggregation and the risk of thrombotic complications are described. Given the participation of these cells in several serious viral infections and their negative role when associated with a prothrombotic response, it is important to understand the mechanistic role of SARS-CoV-2 in platelet physiology. This study evaluated the hyperexpression of platelet-activating factor receptor (PTAFR) and platelet factor 4 (PF4) in unvaccinated and hospitalized patients with COVID-19. The study included 43 COVID-19 patients stratified according to WHO guidelines. Subsequently, the expression of the PTAFR and PF4 genes were evaluated using the real-time quantitative PCR and their possible correlation with the severity of the disease and clinical variables including hospitalization, outcome, sex, age and laboratory parameters (platelet count, INR and D-dimer). The analysis demonstrated a significant (p<0.05) hyperexpression of these genes COVID-19 patients (n=43) compared to healthy controls. Expression of these genes in patients was not statistically significant (p>0.05) different between patients stratified according to clinical variables. The expression of PTAFR and PF4 suggests an important molecular pathway in the pathophysiology of the disease and may be valuable platelet biomarkers to indicate increased risk in patients with COVID-19 who require hospital care, contributing to personalized intervention strategies and improving their clinical management.

The Evidence Base for Circulating Tumor DNA-Methylation in Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis

Background: Non-Small Cell Lung Cancer (NSCLC) remains a challenging disease to manage with effectiveness. Early detection and precise monitoring are crucial for improving patient outcomes. Circulating tumor DNA (ctDNA) offers a non-invasive cancer detection and monitoring method. Emerging biomarkers, such as ctDNA methylation, have shown promise in enhancing diagnostic accuracy and prognostic assessment in NSCLC. In this review, we examined the current evidence regarding ctDNA methylation’s role in NSCLC detection through a systematic review of the existing literature and meta-analysis. Methods: We systematically searched PubMed, Medline, Embase, and Web of Science databases up to 26 June 2024 for studies on the role of ctDNA methylation analysis in NSCLC patients. We included studies from 2010 to 2024 on NSCLC patients. We excluded case reports, non-English articles, studies on cell lines or artificial samples, those without cfDNA detection, prognostic studies, and studies with non-extractable data or mixed cancer types. Funnel plots were visually examined for potential publication bias, with a p value &lt; 0.05 indicating bias. Meta-analysis was conducted using R packages (meta, forestplot, and mada). Combined sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−), positive and negative predictive values, diagnostic odds ratio (DOR), and 95% confidence intervals (95% CI) were calculated. A summary receiver operating characteristic curve (SROC) and area under the curve (AUC) with related Standard Error (SE) were used to evaluate the overall diagnostic performance. Additionally, RASSF1A, APC, SOX17, SEPT9, and RARβ2 were analyzed, since their methylation was assessed in two or more studies. Results: From 38 candidate papers, we finally identified 12 studies, including 472 NSCLC patients. The pooled sensitivity was 0.62 (0.47–0.77) and the specificity was 0.90 (0.85–0.94). The diagnostic odds ratio was 15.6 (95% CI 9.36–26.09) and the area under the curve was 0.249 (SE = 0.138). The positive and negative predictive values were 5.38 (95% CI 3.89–7.44) and 0.34 (95% CI 0.22–0.54), respectively. For single genes, the specificity reached 0.83~0.96, except for RARβ2, but the sensitivity was relatively low for each gene. Significant heterogeneity across the included studies, the potential publication bias for specificity (p = 0.0231), and the need to validate the clinical utility of ctDNA methylation for monitoring treatment response and predicting outcomes in NSCLC patients represent the main limitations of this study. Conclusions: These results provide evidence of the significant potential of ctDNA methylation as a valuable biomarker for improving the diagnosis of NSCLC, advocating for its integration into clinical practice to enhance patient management.

Mapping the evolving trend of research on leukocyte telomere length: a text-mining study

BackgroundSubstantial evidence indicates that measuring leukocyte telomere length (LTL) is a useful tool that may be considered as a valuable biomarker of individual biological age, correlating with numerous chronic disorders. However, to date, there has been a lack of in-depth understanding regarding the current landscape and forthcoming developments in the LTL field. Therefore, this study aimed to utilize bibliometric methods to summarize the knowledge structure, current focus, and emerging directions in this field.MethodScientific publications on LTL spanning the period from 2000 to 2022 were acquired from the Web of Science Core Collection database. Several bibliometric tools including CiteSpace, VOSviewer, and an online website were utilized for bibliometric analysis. The primary evaluations encompassed investigating the major contributors and their collaborative relationships among countries/regions, institutions, and authors, conducting co-citation analyses of authors, journals, as well as reference, examining reference bursts, as well as performing co-occurrence analyses of keywords.ResultsThere are 1818 papers with 66,668 citations identified. Both the annual publication and citation counts on LTL exhibited significant upward trends. The United States emerged as the most prominent contributor, as evidenced by the greatest volume of papers and the highest H-index value. University of California San Francisco and Aviv A were identified as the most productive institution and author in this domain, respectively. Reference analysis revealed that longitudinal study and mendelian randomization study are the most concerned research method in this field recently. Keywords analysis showed that the most concerned diseases in LTL fields were aging, inflammation, cardiovascular diseases, endocrine diseases, neurological and psychiatric diseases, and cancers. In addition, the following research directions such as “COPD”, “mendelian randomization”, “adiposity”, “colorectal cancer”, “National Health and Nutrition Examination Survey (NHNES)”, “telomerase reverse transcriptase”, “pregnancy” have garnered increasing attention in recent times and hold the potential to evolve into research foci in the foreseeable future.ConclusionThis is the first bibliometric study that provides comprehensive overview of LTL research. The findings of this study could become valuable references for investigators to explore and address the current and emerging challenges in LTL research.

Diagnostic value of serum thymus and activation-regulated chemokine (TARC) in fatal asthma

ObjectivesAsthma, a chronic inflammatory airway disease, is characterized by airway hyperresponsiveness and structural changes. Accurate postmortem diagnosis is crucial because of legal and insurance implications, necessitating differentiation from other causes of sudden death. Thymus and activation-regulated chemokine (TARC) is a chemokine that potentially acts as a biomarker of asthma. This study evaluated the diagnostic value of serum TARC combined with immunoglobulin E (IgE) levels as biomarkers in forensic settings. ResultsThe subjects were 100 autopsy cases, categorized into fatal asthma (n = 25), acute myocardial infarction (AMI) (n = 37), and traumatic deaths (n = 38). TARC levels were significantly elevated in asthma (525.68 ± 801.87 pg/mL) compared with AMI (180.35 ± 109.37 pg/mL) and trauma (173.26 ± 105.01 pg/mL) cases. Similarly, serum IgE levels were higher in asthma (3363.72 ± 7023.46 KU/L) than in AMI (130.92 ± 260.79 KU/L) and trauma (134.53 ± 195.41 KU/L) cases. ROC curve analysis showed that serum TARC had a sensitivity of 68.0 % and specificity of 73.6 % (AUC 0.763, cut-off value of 225 pg/mL). In comparison, serum IgE had a sensitivity of 80 % and specificity of 86.1 % (AUC 0.881, cut-off value of 307 KU/L). The combined use of TARC and IgE increased the diagnostic specificity to 95.8 %. ConclusionsSerum TARC and IgE are valuable biomarkers for diagnosing fatal asthma in forensic settings. While serum TARC levels correlate with Th2-mediated inflammation, the combined measurement of TARC and IgE enhances the diagnostic accuracy, providing significant specificity for confirming asthma diagnosis.