Individual Biomarkers Research Articles

Allostatic Load as a Short-Term Prognostic and Predictive Marker.

It would be highly valuable to possess a tool for evaluating disease progression and identifying patients at risk of experiencing a more severe clinical course and potentially worse outcomes. The concept of allostatic load, which represents the overall strain on the body from repeated stress responses, has been recognized as a precursor to the development of chronic illnesses. It functions as a cumulative measure of the body's capacity to adapt to stress. Numerous studies have demonstrated that elevated allostatic load levels are associated with various negative health outcomes, both physical and mental, and are more predictive of mortality than individual biomarkers. Leveraging the unique circumstances presented by the COVID-19 pandemic, we evaluated different clinical and laboratory parameters in hospitalised COVID-19 patients to assess allostatic load. Our results indicated that allostatic load acts as a strong predictor of prolonged hospitalisation, increased ICU days, and mortality. This highlights its efficacy as a precise gauge of biological dysregulation linked to the response to COVID-19 during disease progression. Allostatic load is easily obtainable and provides an early, cost-effective indication of disease prognosis. Additionally, it has the potential to forecast the necessity for ICU admission. As a result, this parameter, indicative of the comprehensive physiological disruption in response to stress, emerges as a promising prognostic marker for hospitalised patients, extending beyond COVID-19.

Cut-Off Points for Low Relative 30-s Sit-to-Stand Power and Their Associations With Adverse Health Conditions.

Despite muscle power derived from the 5-rep sit-to-stand (STS) test having been demonstrated to be a valuable biomarker in older individuals, there is limited information regarding muscle power derived from the 30-s STS test, a widely used test in the clinical setting. This study aimed (i) to compare relative 30-s STS power values between older men and women, (ii) to identify cut-off points for low relative 30-s STS power, (iii) to compare the prevalence of low relative STS power between sexes and (iv) to evaluate the association of low relative 30-s STS power with adverse conditions in older people. A total of 1475 community-dwelling older adults (65-98 years; 45% men) from the Toledo Study for Healthy Aging were included. Relative STS power was assessed using the 30-s STS test and the Alcazar's equation. Adverse health conditions considered encompassed frailty, depression, disability in basic (BADL) and instrumental activities of daily living (IADL), cognitive impairment and low habitual gait speed (HGS). Relative STS power decreased linearly at an average rate of 1.0% year-1 in men and 1.5% year-1 in women. The cut-off points for low relative STS power were 2.53 and 2.01 W·kg-1 for men and women, respectively. The prevalence of low relative STS power was significantly lower in older men compared with older women (43.5% vs. 50.0%, respectively; p = 0.005). In men, low relative STS power was associated with frailty (OR [95% CI] = 4.4 [2.4-8.0]), cognitive impairment (OR [95% CI] = 1.7 [1.0-2.7]), disability in BADL (OR [95% CI] = 4.5 [1.5-13.8]) and low HGS (OR [95% CI] = 3.4 [1.9-5.9]). In women, low relative STS power was associated with frailty (OR [95% CI] = 5.2 [3.5-7.7]), disability in BADL (OR [95% CI] = 4.3 [1.8-9.9]) and IADL (OR [95% CI] = 3.1 [2.2-4.3]) and low HGS (OR [95% CI] = 6.1 [2.8-13.1]). No associations were found between low relative STS power and disability in IADL or depression in men, nor between low relative STS power and cognitive impairment or depression in women. Relative STS power decreased with increasing age in both men and women. The provided sex-specific cut-off points for low relative STS power using the 30-s STS test adequately identified older people with frailty and were associated with an increased risk of experiencing adverse conditions.

OP19 Pre- and Post-diagnostic Metabolomic Biomarker Profiling of Over 700,000 Individuals in Three National Biobanks Enables Prediction of Inflammatory Bowel Disease Onset and Complications

Abstract Background We currently lack sensitive and specific prognostic biomarkers of inflammatory bowel disease (IBD) onset and severity. In three large, population-scale cohorts, we investigated metabolomic biomarkers for predicting disease onset and course. Methods We measured 250 metabolomic biomarkers using nuclear magnetic resonance (NMR) in ~500,000 individuals from the UK Biobank [1]. 2036 individuals had prevalent Crohn’s disease (CD) and 4029 had ulcerative colitis (UC) at recruitment. 1242 participants developed incident CD and 2283 incident UC during 10-year follow-up, allowing us to study the pre-diagnostic metabolic profile of IBD. We investigated associations between individual metabolomic biomarkers with prevalent and incident IBD using logistic and proportional hazards regressions. Among the incident IBD cohort we used LASSO with five-fold cross-validation on 50% of the cohort to build predictive models of future IBD onset. The resulting predictive model was validated in the Estonian Biobank and the Finnish THL Biobank (N ~218,000), confirming its robustness in multiple cohorts. Finally, in the UK Biobank we identified individuals with prevalent IBD who had IBD-related emergency admission, surgery or death and used these to develop a disease course score. Results GlycA, a biomarker of systemic inflammation, was the strongest association for both incident and prevalent UC and CD. We also identified associations between non-inflammatory metabolomic markers and IBD, such as albumin, branched-chain amino acids, cardiovascular lipid factors, fatty acids and creatinine. Notably, the perturbation in metabolites was markedly greater in CD versus UC, showing that CD is a more systemic illness (Fig. 1). Metabolomics scores were predictive of CD (AUC=0.69) and UC (0.62) onset up to 10 years prior to diagnosis, with performance for CD improving closer to diagnosis (2y follow-up AUC: CD 0.76, UC 0.62). Patients in the highest decile of incident metabolomic score were substantially more likely to develop the disease, with this observation being consistent in other two biobanks (Fig. 2). Metabolite profiles were predictive of IBD complications in prevalent patients (AUC 0.68 for CD; 0.62 for UC), outperforming both genetic and blood biomarker scores. Conclusion Leveraging cohort-wide profiling of population-scale biobanks, we present the largest metabolomics study and the largest pre-diagnostic study of IBD to date. Pre-diagnostic metabolomic scores are predictive of disease onset. These results warrant further investigation of multi-omic prediction of the onset, comorbidities and disease course of IBD. References * A.S, K.S. contributed equally. # T.J., J.C.B., C.L. contributed equally. [1] Nightingale Health Biobank Collaborative Group, Barrett, J. C., Esko, T., Fischer, K., Jostins-Dean, L., Jousilahti, P., ... & Estonian Biobank Research Team. (2023). Metabolomic and genomic prediction of common diseases in 477,706 participants in three national biobanks. medRxiv, 2023-06. Figure 1: Associations between clinically validated biomarkers and prevalent IBD Figure 2: UK Biobank-derived score consistency across the three biobanks

MAPK pathway activating alteration and immunotherapy efficacy in squamous cell lung carcinoma: results from the randomized, prospective SQUINT trial.

The role of activating alterations in the MAPK pathway in predicting immunotherapy efficacy in lung squamous cell carcinoma (LSCC) patients is largely unknown. The aims of the randomized, phase II SQUINT trial were to assess the efficacy of nivolumab plus ipilimumab (NI) versus platinum-based chemotherapy plus nivolumab (N-CT) and to identify clinically available biomarkers of response to immunotherapy in patients with advanced or metastatic LSCC. SQUINT was an open-label, randomized, parallel, non-comparative, phase II trial of NI versus N-CT in chemo-naïve, metastatic or recurrent LSCC adult patients. The study was conducted across 15 Italian centers from September 2017 to February 2022 (ClinicalTrials.gov ID: NCT03823625). 45 patients were included in the NI arm and 46 in the N-CT arm. At 12 months, the overall survival (OS) rate was 62% in the NI arm and 50% in the N-CT arm. 74 patients were included in the analyses for individual biomarkers. In patients with mutations or copy number variations of genes involved in the MAPK pathway, we observed higher response to immunotherapy (43% vs 15%), longer progression-free survival (PFS) (p=0.03) and OS (p<0.001). A higher density of CD8+PD1+ T cells (p=0.04) among MAPK-altered tumors versus wild-type, together with an increased CD8+PD1+/FOXP3 ratio (p=0.047) were observed. In the validation cohort of patients not exposed to immunotherapy, OS was similar between MAPK13 mutant and wild-type LSCC. We showed for the first time that MAPK pathway activating alteration influences the outcome of LSCC treated with immunotherapy, highlighting the relevance of gene profiling.

Combined replacement of lnc-MEG3 and miR-155 elicit tumor suppression in multiple myeloma.

To investigate the biological impact of simultaneous overexpression of lncRNA MEG3 and miR-155, termed a "double hit," on multiple myeloma (MM) cells compared to individual biomarker substitution. Human MM cells were transfected with MEG3-overexpressed plasmids and miR-155 mimics. Cell cytotoxicity, apoptosis, and gene expression were evaluated in transfected cells and clinical samples. MEG3 and miR-155 were significantly downregulated in MM patients, with lower expression levels correlating with advanced disease stages and poorer survival. Dual overexpression induced potent cytotoxic effects in MM cells. MEG3 and miR-155 are potential tumor suppressors in MM. Simultaneous overexpression of both biomarkers could represent a novel therapeutic strategy, and their levels could serve as diagnostic and prognostic markers.

Association between controlling nutritional status score and the prognosis of patients with acute myocardial infarction: a systematic review and meta-analysis

BackgroundRecent studies have reported growing evidence supporting applying the controlling nutritional status (CONUT) score in acute myocardial infarction (AMI) patients. This investigation intended to ascertain the link between CONUT scores and the prognosis in the AMI population.MethodsMultiple electronic databases, encompassing PubMed, Web of Science, Embase, and the Cochrane Library, were retrieved from the inception of the databases until July 20, 2024, to explore the link between CONUT scores and adverse clinical outcomes in individuals with AMI. Primary outcomes consisted of major adverse cardiovascular events (MACE) and mortality, while secondary outcomes encompassed stroke, cardiac death, myocardial reinfarction, revascularization, ventricular arrhythmias, and atrioventricular block. A random-effects meta-analysis was executed, with CONUT scores treated as either categorical or continuous variables. Sensitivity analyses and Egger’s test were conducted to appraise the robustness of results and publication bias, respectively. Subgroup analyses were executed to account for various confounding factors. Moreover, the GRADE system was leveraged to appraise the quality of evidence for all outcomes.ResultsFifteen studies were included in our analysis. The statistical analyses on both categorical and continuous variables unraveled that a high CONUT score was markedly linked to an elevated risk of MACE [categorical variable: odds ratio (OR) = 1.75, 95% confidence interval (CI) = 1.42–2.15; continuous variable: standardized mean difference (SMD) = 1.02, 95% CI = 0.78–1.26], mortality (categorical variable: OR = 2.08, 95% CI = 1.70–2.55; continuous variable: SMD = 1.16, 95% CI = 0.57–1.74), cardiac death (categorical variable: OR = 2.81, 95% CI = 1.67–4.73), myocardial reinfarction (categorical variable: OR = 2.21, 95% CI = 1.28–3.83), and atrioventricular block (categorical variable: OR = 5.21, 95% CI = 1.83–14.89) in AMI patients. However, no significant association was found between a high CONUT score and stroke (categorical variable: OR = 1.52, 95% CI = 0.98–2.35), revascularization (categorical variable: OR = 2.92, 95% CI = 0.58–14.79), and ventricular arrhythmias (categorical variable: OR = 2.57, 95% CI = 0.06–107.21).ConclusionThe CONUT score may serve as a promising and cost-effective prognostic biomarker for individuals with AMI.Systematic review registrationPROSPERO: CRD42024574048.

Diagnostic Accuracy of Microbiome-Derived Biomarkers in Periodontitis: Systematic Review and Meta-Analysis.

To evaluate the diagnostic accuracy of microbiome-derived biomarkers for periodontitis in oral fluids (saliva and subgingival samples). This systematic review followed PRISMA guidelines. Electronic searches were performed across multiple databases from December 2022 to November 2024. Subgroup analyses, divided into saliva and subgingival samples, were performed using the Random Effects Model (REM), while individual biomarker sensitivity and specificity were evaluated through the Bivariate Random-Effects Model (BREM). Ten studies were included, stratified by sample type. In the saliva group, Porphyromonas gingivalis, Tannerella forsythia and Prevotella intermedia demonstrated the highest diagnostic accuracy, with sensitivities reaching 89.2%, 89.2% and 86.5%, and specificities of 94.6%, 86.5% and 83.8%, respectively, achieving AUC values above 0.80. Porphyromonas gingivalis was further analysed using BREM, with the Summary Receiver Operating Characteristic (SROC) curve indicating a combined sensitivity and specificity of 84.2% and 85.4%, with an AUC of 0.864. In the subgingival group, biomarkers such as endotoxin activity and combined bacterial biomarkers (5 bacterial species) displayed the highest diagnostic performance, with sensitivities of 90.6% and 85.1% and specificities of 87.9% and 100%, respectively, and AUC values of 0.93 and 0.88. Microbiome-derived biomarkers show good clinical utility for improving diagnoses of periodontitis, offering high specificity and sensitivity. Future research should focus on standardising methodologies, increasing sample sizes, and including diverse populations to validate these findings, thereby improving diagnostic precision and facilitating the screening methods for the onset of periodontitis and dysbiotic activity.

The ratio of plasma pTau217 to Aβ42 outperforms individual measurements in detecting brain amyloidosis.

Early detection of brain amyloidosis (Aβ+) is pivotal for diagnosing Alzheimer's disease (AD) and optimizing patient management, especially in light of emerging treatments. While plasma biomarkers are promising, their combined diagnostic value through specific ratios remains underexplored. To evaluate the diagnostic accuracy of plasma pTau isoform (pTau181 and pTau217) to Aβ42 ratios in detecting Aβ+ status. This study included 423 participants from the multicenter prospective ALZAN cohort, recruited for cognitive complaints. Aβ+ status was determined using cerebrospinal fluid (CSF) biomarkers. Validation of the key findings was performed in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, where Aβ+ status was determined using PET imaging. Plasma biomarkers (pTau181, pTau217, Aβ40, Aβ42) were measured using immunoassays and mass spectrometry, with specific ratios calculated. In the ALZAN cohort, the impact of confounding factors such as age, renal function, ApoE4 status, body mass index, and the delay between blood collection and processing was also evaluated to assess their influence on biomarker concentrations and diagnostic performance. The primary outcome was the diagnostic performance of plasma biomarkers and their ratios for detecting Aβ+ status. Secondary outcomes included the proportion of patients classified as low, intermediate, or high risk for Aβ+ using a two-cutoff approach. The pTau181/Aβ42 ratio matched the diagnostic performance of pTau217 with AUC of 0.911 (0.880-0.936). The pTau217/Aβ42 ratio demonstrated the highest diagnostic accuracy in the ALZAN cohort, with an AUC of 0.927 (0.898-0.950), outperforming individual biomarkers. Both ratios effectively mitigated confounding factors, such as variations in renal function, and were particularly excellent in identifying Aβ+ status in individuals with early cognitive decline. Validation in the ADNI cohort confirmed these findings, with consistent performance across different measurement methods. The two-cutoff workflow using pTau217/Aβ42 reduced the intermediate-risk zone from 16% to 8%, enhancing stratification for clinical decision-making. The pTau217/Aβ42 ratio offers superior diagnostic accuracy for detecting Aβ+ compared to individual biomarkers and reduces diagnostic uncertainty. These findings highlight the clinical utility of plasma biomarker ratios for early AD detection, paving the way for broader implementation in clinical and research settings.

Differences of longitudinal plasma biomarkers between single memory domain and multidomain subject cognitive decline: Evidence from SILCODE.

Plasma biomarkers demonstrated potential in identifying amyloid pathology in early Alzheimer's disease. Different subtypes of subjective cognitive decline (SCD) may lead to different cognitive impairment conversion risks. To investigate the differences of plasma biomarkers in SCD subtypes individuals, which were unclear. The 347 individuals were involved, including 93 normal controls (NC), 76 single memory domain SCD (sd-SCD), 79 multidomain SCD (md-SCD), 55 mild cognitive impairment and 44 dementia. We investigated plasma biomarkers (Aβ42/40, p-tau181, p-tau217, NfL, and GFAP) and neuropsychological scales in the baseline and follow-up. The Kaplan-Meier survival analysis and Cox proportional hazards model were performed to investigate the risk of cognitive decline conversion. The t-test, Mann-Whitney U and multiple linear regression analysis were employed to evaluate the rate of change and correlation between PET-SUVR and plasma biomarker change. In cognitively normal subjects, md-SCD exhibited lower Aβ42/40 and higher p-tau181 and p-tau217 levels. Kaplan-Meier survival analysis revealed that md-SCD group exhibited a higher risk of cognitive decline conversion compared to NC and sd-SCD. Within SCD subgroups, those with positive GFAP status showed higher conversion risk than negative. In the Cox model, the risk of conversion in the md-SCD group was 2.77 times higher than sd-SCD. The md-SCD group demonstrated a faster rate of Aβ42/40 decline than sd-SCD. The study utilized plasma biomarkers to highlight the significance of staging in SCD. In cognitively normal subjects, md-SCD presents a higher risk of cognitive decline than sd-SCD, providing a valuable reference and convenient tool for early identification of individuals at risk for AD.

The influence of prolonged aerobic exercise on cardiac, muscular, and renal biomarkers in trained individuals with obesity.

The aim of this study was to investigate the influence of prolonged aerobic exercise on cardiac, muscular and renal inflammatory markers in a group of trained obese men. Seventeen men (aged 40 ± 6years; body mass index [BMI] 31.3 ± 2.8kgm-2, maximal oxygen uptake [V'O2max] 41.5 ± 5.6mlkg-1min-1) ran a half, 30km, or full marathon. Troponin I (cTnI), the n-terminal creatine kinase-myocardial band (CK-MB), pro b-type natriuretic peptide (NT-proBNP), lactate dehydrogenase (LDH), myoglobin, creatinine (CREA) and the estimated glomerular filtration rate (eGFR)were measured before (T0), immediately after (T1) and 3days after the race (T2). The concentrations of cTnI, myoglobin, LDH, CK-MB and CREA significantly increased (P < 0.05), whereas eGRF decreased at T1 (P < 0.05). All the above parameters returned to baseline at T2, except for eGFR, which remained lower than that at T0 (P < 0.05). A positive association was observed between ΔCK-MB (%) and the time spent in Zone 3 during the race (R = 0.686, P = 0.014). The Δmyoglobin (%) was positively correlated with race time, race mean speed and time in Zone 3 (R = 0.574-0.862, P < 0.05). The ∆CREA values were moderately correlated with the race mean HRMAX (%) and time spent in Zone 3 (%) (R = 0.514-0.610; P = 0.05). The ∆eGRF values were moderately inversely correlated with the time spent in Zone 3 (%) (R = - 0.627; P < 0.05). Changes in cardiac, muscular and renal inflammatory markers in trained men with obesity are consistent with those described in the literature in normal-weight individuals. Finally, running parameters, such as running time, average running intensity and time in Zone 3 appear to be responsible for the changes in cardiac, muscular and renal function markers after long-distance running.

Identification and validation of serum MUC17 as a non-invasive early warning biomarker for screening of gastric intraepithelial neoplasia.

The early diagnosis and treatment of Gastric Intraepithelial Neoplasia (GIN) are pivotal for improving the survival rates of patients with gastric cancer (GC). Regrettably, reliable noninvasive biomarkers for GIN screening are currently lacking. mRNA data from the GEO database, pan-cancer data from the TCGA database, and a gene list of exocrine proteins were subjected to integrated analysis to identify a noninvasive biomarker for GIN. The scRNA-seq data analysis, IHC and Elisa were employed to validate the expression of the biomarker in the serum and tissues of clinical patients across different pathological stages. MUC17 has been identified as a non-invasive diagnostic marker for GIN. It is upregulated in GIN prior to the onset of gastric carcinogenesis and downregulated in other tumors, with high GC specificity. The area under the curve values of serum MUC17 for differentiating chronic gastritis (CG) from low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and early gastric cancer (EGC) were 0.8788, 0.8544, and 0.9513, respectively. Additionally, low plasma MUC17 levels were found to be significantly lower in gastric ulcer (GU), gastric neuroendocrine tumor (GNET), and gastrointestinal stromal tumor (GIST) compared to GIN. The AUC for differentiating between GIN and GU, GNET, or GIST was 0.7803, 0.9244 and 0.9796, respectively. These findings suggest that plasma MUC17 levels hold substantial promise as a screening biomarker for individuals with GIN and EGC, effectively identifying high-risk groups that necessitate further gastroscopy.

Development and Validation of a Diagnostic Model for Stanford Type B Aortic Dissection Based on Proteomic Profiling.

Stanford Type B Aortic Dissection (TBAD), a critical aortic disease, has exhibited stable mortality rates over the past decade. However, diagnostic approaches for TBAD during routine health check-ups are currently lacking. This study focused on developing a model to improve the diagnosis in a population. Serum biomarkers were investigated in 88 participants using proteomic profiling combined with machine learning. The findings were validated using ELISA in other 80 participants. Subsequently, a diagnostic model for TBAD integrating biomarkers with clinical indicators was developed and assessed using machine learning. Six differentially expressed proteins (DEPs) were identified through proteomic profiling and machine learning in discovery and derivation cohorts. Five of these (GDF-15, IL6, CD58, LY9, and Siglec-7) were further verified through ELISA validation within the validation cohort. In addition, ten blood-related indicators were selected as clinical indicators. Combining biomarkers and clinical indicators, the machine learning-based models performed well (AUC of the biomarker model = 0.865, AUC of the clinical model = 0.904, and AUC of the combined model = 0.909) using relative quantitation. The performance of the three models was verified (AUC of biomarker model = 0.866, AUC of clinical model = 0.868, and AUC of combined model = 0.886) using absolute quantitation. Crucially, the combined models outperformed individual biomarkers and clinical models, demonstrating superior efficacy. Using proteomic profiling, we identified serum IL-6, GDF-15, CD58, LY9, and Siglec-7 as TBAD biomarkers. The machine-learning-based diagnostic model exhibited significant potential for TBAD diagnosis using only blood samples within the population.

Association of Albumin-To-Creatinine Ratio With Diabetic Retinopathy Among US Adults (NHANES 2009-2016).

This study investigates the relationship between the albumin-to-creatinine ratio and diabetic retinopathy (DR) in US adults using NHANES data from 2009 to 2016. This study assesses the predictive efficacy of the urinary serum albumin-to-creatinine ratio (UACR/SACR Ratio) against traditional biomarkers such as the serum albumin-to-creatinine ratio (SACR) and urinary albumin-to-creatinine ratio (UACR) for evaluating DR risk. Additionally, the study explores the potential of these biomarkers, both individually and in combination with HbA1c, for early detection and risk stratification of DR. This cross-sectional study analysed data from 2594 diabetic adults in the National Health and Nutrition Examination Survey (NHANES 2009-2016). Multivariate logistic regression models, adjusted for demographic (sex, age, race and education) and clinical factors (WBC, PLT, RDW, HbA1c, HBP and CHD), examined the associations between biomarkers and DR. Biomarkers were analysed both continuously and in quartiles to assess dose-response relationships. Receiver operating characteristic (ROC) curve analysis evaluated the predictive accuracy of individual biomarkers and combined models. Elevated SACR levels were inversely related to DR risk, while UACR showed a positive correlation. The UACR/SACR ratio demonstrated superior predictive capability for DR compared to SACR and UACR alone. The most accurate predictive model combined SACR, UACR, UACR/SACR ratio and HbA1c (AUC = 0.614), highlighting DR development complexity. Subgroup analyses revealed stronger associations in participants aged < 60 years and those with hypertension (both p < 0.05), with more pronounced effects observed in males and Mexican Americans, while lifestyle factors showed no significant modifying effect. The UACR/SACR Ratio emerged as a potentially superior DR predictor, particularly in younger patients and those with hypertension, suggesting its utility in enhancing early detection and risk stratification. Comprehensive evaluation of renal function and glycaemic control biomarkers, considering age- and comorbidity-specific patterns, could improve DR risk prediction and management. Future longitudinal studies should validate these findings, particularly in identified high-risk subgroups, and investigate underlying mechanisms, potentially advancing personalised DR prediction and management strategies.

Serum O-glycosylated HBsAg levels correlate with HBV RNA in HBeAg positive CHB patients during antiviral therapy.

Recent evidence has indicated that the O-glycosylated PreS2 domain of the middle HBsAg is a distinguishing characteristic that allows the identification of HBsAg of HBV Dane particles and SVPs. This study's objective was to assess the changes in serum O-glycosylated HBsAg levels in CHB patients undergoing ETV or Peg-IFNα treatment. Our retrospective study enrolled 86 patients with genotype C CHB. We determined the O-glycosylated HBsAg, HBsAg, HBeAg, HBV DNA, and HBV RNA at baseline and during ETV or Peg-IFNα treatment. The correlations between O-glycosylated HBsAg and conventional HBV marker levels were also examined. Furthermore, we performed a ROC analysis to evaluate the predictive value of individual biomarkers for virological response. At baseline, the serum O-glycosylated HBsAg levels were significantly correlated with the HBsAg (r=0.754), HBV DNA (r=0.498), HBeAg (r=0.404), and HBV RNA (r=0.399) in HBeAg positive patients. O-glycosylated HBsAg decreased after antiviral therapy. Both O-glycosylated HBsAg and HBsAg were significantly correlated with serum HBV DNA as well as HBV RNA at baseline, while only O-glycosylated HBsAg still correlated with HBV RNA (r=0.397) in DNA-undetectable patients after ETV therapy. O-glycosylated HBsAg was significantly correlated with HBV RNA (r=0.846) in DNA-undetectable patients after Peg-IFNα therapy compared to that of HBsAg (r=0.800). Serum O-glycosylated HBsAg level decreased during anti-viral therapy and correlated well with conventional HBV markers in HBeAg positive genotype C patients, suggesting that it could be a potential monitoring biomarker in HBV DNA-suppressed patients.

Modular DNA origami–based electrochemical detection of DNA and proteins

The diversity and heterogeneity of biomarkers has made the development of general methods for single-step quantification of analytes difficult. For individual biomarkers, electrochemical methods that detect a conformational change in an affinity binder upon analyte binding have shown promise. However, because the conformational change must operate within a nanometer-scale working distance, an entirely new sensor, with a unique conformational change, must be developed for each analyte. Here, we demonstrate a modular electrochemical biosensor, built from DNA origami, which is easily adapted to diverse molecules by merely replacing its analyte binding domains. Instead of relying on a unique nanometer-scale movement of a single redox reporter, all sensor variants rely on the same 100-nm scale conformational change, which brings dozens of reporters close enough to a gold electrode surface that a signal can be measured via square-wave voltammetry, a standard electrochemical technique. To validate our sensor's mechanism, we used single-stranded DNA as an analyte, and optimized the number of redox reporters and various linker lengths. Adaptation of the sensor to streptavidin and Platelet-Derived Growth Factor-BB (PDGF-BB) analytes was achieved by simply adding biotin or anti-PDGF aptamers to appropriate DNA linkers. Geometrically optimized streptavidin sensors exhibited signal gain and limit of detection markedly better than comparable reagentless electrochemical sensors. After use, the same sensors could be regenerated under mild conditions: Performance was largely maintained over four cycles of DNA strand displacement and rehybridization. By leveraging the modularity of DNA nanostructures, our work provides a straightforward route to the single-step quantification of arbitrary nucleic acids and proteins.

Inflammatory Markers and Measures of Cardiovascular Autonomic Neuropathy in Type 1 Diabetes.

Cardiovascular autonomic neuropathy (CAN) and inflammation predict more severe outcomes in type 1 diabetes (T1D). However, the link between CAN and inflammation in T1D remains unclear. We examined associations between CAN measures and inflammatory biomarkers in individuals with T1D. In a cross-sectional study, we measured cardiovascular autonomic reflex tests and heart rate variability (established CAN measures) and a panel of 39 inflammatory biomarkers, including soluble urokinase plasminogen activator receptor (suPAR), in T1D participants of the TINSAL-T1DN (Targeting Inflammation with Salsalate in Individuals with T1D Neuropathy) trial (n=57, discovery), and the PERL (Preventing Early Renal Loss in Diabetes) trial (n=468, validation). Amongst 39 inflammatory biomarkers measured in TINSAL-T1DN, suPAR levels had the strongest negative correlations with CAN measures: expiration/inspiration (r=-0.48), Valsalva (r=-0.28), 30:15 (r=-0.37), SD of the normal RR interval (r=-0.37), and root mean square of differences of successive RR intervals (r=-0.31) (all P<0.05). Findings were validated in PERL. In unadjusted analyses, median suPAR levels significantly differed between the lowest and highest SD of the normal RR interval tertiles (3.79 versus 3.12 ng/mL, P<0.001) and root mean square of differences of successive RR intervals (3.76 versus 3.17 ng/mL, P<0.001). After adjusting for covariates (age, sex, hemoglobin A1c, and estimated glomerular filtration rate), median suPAR values remained significantly elevated in the lowest tertiles of SD of the normal RR interval (P=0.004) and root mean square of differences of successive RR intervals (P=0.006). Amongst several inflammatory biomarkers, suPAR, an immune-mediated glycoprotein, has a singular association with CAN measures. The potential of targeting suPAR as a disease-modifying approach for CAN in T1D warrants further exploration. URL: https://www.clinicaltrials.gov; Unique identifiers: NCT02936843, NCT02017171.

Plasma biomarkers in patients with age-related sarcopenia: a proteomic exploration and experimental validation

BackgroundVarious biomarkers associated with sarcopenia have been identified. However, there is a scarcity of studies exploring and validating biomarkers in individuals with age-related sarcopenia.AimsThis study aimed to investigate the proteome and identify potential biomarkers for age-related sarcopenia.MethodsProteomic analysis and experimental validation were conducted using plasma from hospitalized older adults. Sarcopenia diagnosis was based on the Asian Working Group for Sarcopenia 2019 criteria. Data-independent acquisition-based proteomics was performed on plasma from 60 participants, with 30 diagnosed with sarcopenia and 30 without sarcopenia. Differentially expressed proteins (DEPs) were selected and evaluated by Receiver Operating Characteristic (ROC) analysis. Biomarker candidates were further quantitatively validated by enzyme-linked immunosorbent assay (ELISA) utilizing plasma from 6 participants with sarcopenia and 6 without sarcopenia.ResultsA total of 39 DEPs were identified and 12 DEPs were selected for ROC analysis. 8 DEPs were included for ELISA validation based on their predictive performance. Paraoxonase-3 (PON3) consistently showed down-regulation in the sarcopenic group across both methodologies. Insulin-like growth factor-binding protein-2 (IGFBP2) showed inconsistency in the sarcopenic group, with up-regulation observed in proteomic analysis but down-regulation in ELISA.DiscussionDecline in PON3 may result in an overload of oxidative stress in skeletal muscles and contribute to sarcopenia. Protein modifications of IGFBP2 might exhibit during sarcopenia pathogenesis.ConclusionsPlasma proteins are implicated in sarcopenia pathogenesis. PON3 is highlighted as a potential biomarker for patients with age-related sarcopenia. Further studies are imperative to gain an in-depth understanding of PON3 and IGFBP2.

Evolution in electrophysiology 100 years after Einthoven: translational and computational innovations in rhythm control of atrial fibrillation.

In 1924, the Dutch physiologist Willem Einthoven received the Nobel Prize in Physiology or Medicine for his discovery of the mechanism of the electrocardiogram (ECG). Anno 2024, the ECG is commonly used as a diagnostic tool in cardiology. In the paper 'Le Télécardiogramme', Einthoven described the first recording of the now most common cardiac arrhythmia: atrial fibrillation (AF). Treatment of AF includes rhythm control, aiming to alleviate symptoms and improve quality of life. Recent studies found that early rhythm control might additionally improve clinical outcomes. However, current therapeutic options have suboptimal efficacy and safety, highlighting a need for better rhythm control strategies. In this review, we address the challenges related to antiarrhythmic drugs (AADs) and catheter ablation for rhythm control of AF, including significant recurrence rates and adverse side effects such as proarrhythmia. Furthermore, we discuss potential solutions to these challenges including novel tools such as atrial-specific AADs and-digital twin-guided AF ablation. In particular, digital twins are a promising method to integrate a wide range of clinical data to address the heterogeneity in AF mechanisms. This may enable a more mechanism-based tailored approach that may overcome the limitations of previous precision medicine approaches based on individual biomarkers. However, several translational challenges need to be addressed before digital twins can be routinely applied in clinical practice, which we discuss at the end of this narrative review. Ultimately, the significant advances in the detection, understanding, and treatment of AF since its first ECG documentation are expected to help reduce the burden of this troublesome condition.

Biomarker Analysis in Upper Respiratory Tract Infections: Associations with Demographics and Clinical Outcomes.

Upper respiratory tract infections (URTIs) are a significant global health burden, and understanding the immune response is crucial for developing effective diagnostic tools and treatment strategies. This study investigated the levels of specific biomarkers in 188 patients with URTIs and their association with demographic factors, comorbidities, and clinical outcomes. Immunoglobulin A (IgA), immunoglobulin E (IgE), neutrophils, serum iron, C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR) were measured. The median age of the patients was 5 years, with 46% being female and 63% from urban areas. Adenoiditis (37%), otitis (25%), and rhinitis (20%) were the most common diagnoses. While most biomarkers did not vary significantly by gender, neutrophil levels were significantly higher in females (p = 0.020). IgE levels were significantly elevated in rural patients compared to urban counterparts (p = 0.034). ESR was significantly associated with rhinitis diagnosis, and IgE and ESR were predictive of otitis in multivariate models. However, many biomarkers did not significantly correlate with other diagnoses, contradicting previous research focusing on individual biomarkers. This study highlights the complexity of immune responses in URTIs and the need for more effective diagnostic tools. The findings can inform the development of tailored treatment strategies based on gender, area of origin, and infection type.

Multimodal neuroimaging biomarkers and subtle cognitive decline in a population-based cohort without dementia.

The relationship between subtle cognitive decline and Alzheimer's disease (AD) pathology as measured by biomarkers in settings outside of specialty memory clinics is not well characterized. To investigate how subtle longitudinal cognitive decline relates to neuroimaging biomarkers in individuals drawn from a population-based study in an economically depressed, small-town area in southwestern Pennsylvania, USA. A subset of participants without dementia (N = 115, age 76.53 years ± 6.25) from the Monongahela Youghiogheny Healthy Aging Team (MYHAT) study completed neuroimaging including magnetic resonance imaging (MRI) measures of AD-signature region cortical thickness and white matter hyperintensities (WMH), Pittsburgh compound B (PiB)-positron emission tomography (PET) for amyloid-β (Aβ) deposition, and [18F]AV-1451-PET for tau deposition. Neuropsychological evaluations were completed at multiple timepoints up to 11 years prior to neuroimaging. Aβ positivity was determined using a regional approach. We used linear mixed models to examine neuroimaging biomarker associations with retrospective cognitive slopes in five domains and a global cognitive composite. Among Aβ(+) participants (38%), there were associations between (i) tau Braak III/IV and language decline (p < 0.05), (ii) cortical thickness and both memory decline (p < 0.001) and global cognitive decline (p < 0.01), and (iii) WMH and decline in executive function (p < 0.05) and global cognition (p < 0.05). Among Aβ(-) participants, there was an association between tau Braak III/IV and decline on tests of attention/psychomotor speed (p < 0.05). These findings confirm an Aβ-dependent early AD biomarker pathway, and suggest a possible Aβ-independent, non-AD process underlying subtle cognitive decline in a population-based sample of older adults without dementia.