Blood Biomarkers Research Articles

Blood Biomarkers for Alzheimer's Disease: Current Status and Prospects

Blood biomarkers are minimally invasive, are available at a relatively low cost, and are easily accessible; therefore, they are expected to play a pivotal role in the diagnosis of dementia. Measurement of the amyloid-β ratio and phosphorylated tau in plasma has shown high potential for accurate detection of brain pathology in patients with Alzheimer's disease. Studies have investigated blood biomarkers that reflect neurodegeneration and neuroinflammation in patients with dementia. Challenges associated with blood biomarker use include the lack of robustness of the test and the role of confounders that potentially prevent their immediate clinical application. Further real-world studies are warranted to validate the usefulness of blood biomarkers in dementia management. Appropriate recommendations for the use of blood biomarkers for dementia have been published for physicians and investigators, both in Japan and overseas. Considering the versatility of blood biomarkers, they should be cautiously introduced for clinical use.

Lemongrass extract enhances productive performance, blood biomarkers, immunity, and gut health of broilers

BackgroundLemongrass (LG) had various phytochemical components such as saponins, phenols, resins, alkaloids, tannins, flavonoids, glycosides and terpenes, minerals as well as vitamin C which had various pharmacological actions (antioxidant, anti-inflammatory, antibacterial, antibiotic, and antifungal) and growth promoter. The use of LG in broiler nutrition can be optimized the bird performance and gut health. Based on the high nutrition value of LG and absence of sufficient studies on the effect of lemongrass aqueous extract (LGX) on broiler performance and gut health (antioxidant and immune biomarkers and intestinal morphology), the aim of the present study is to investigate the impact of using LGX on productive performance, blood biomarkers, immunity and gut health of broilers. MethodsA total of two hundreds one-day- old male broiler chicks (Cobb 500) were fed on the starter basal diet for 6 days. From day 7 of age onwards, the birds were distributed, at random, into 4 groups. Each group included 5 replicates with 10 chicks per replicate. The birds in group 1 were not administered lemongrass extract (control, LGX0) while chicks in group 2 (LGX100), 3 (LGX200) and 4 (LGX300) were administered the aqueous extract of lemongrass in drinking water at levels of 100, 200 and 300 ml/l, respectively. The experimental period lasted for 35 day. Growth performance parameters, economic efficiency, hematological and biochemical biomarkers, expression of some antioxidant and immune related genes, cecal bacterial counts and intestinal morphological changes all were assessed. ResultsThe results indicated that, administration of LGX in drinking water at levels of 200, 100 ml/l, respectively significantly improved (p ≤ 0.001) body weight (BW), body weight gain (BWG) and feed conversion ratio (FCR) than the control but without any effect on economic efficiency index (EEI) and feed intake (FI). On the other hand, the addition of LGX in drinking water at levels of 300 ml/l significantly decreased (p ≤ 0.001) FI, EEI and all growth performance parameters as compared to those other groups. LGX supplemented birds groups exhibited higher Hb, PCV, MCH, platelets, and lymphocytes than the control group. However, the ratio of H/L in LGX100 and LGX200 groups was lower (p ≤ 0.001) than other groups. LGX supplemented groups showed low (p ≤ 0.001) cholesterol, creatinine, MDA and high (p ≤ 0.01) TAC. Up regulation (p ≤ 0.001) of the expressions of catalase, GPX1, and SOD1 were in LGX200 group compared to other groups. While, the proinflammatory genes expression (IL1B, IL6, IFNᵧ, and TNF) were down regulated (p ≤ 0.001) in the LGX200 compared to others. Moreover, LGX200 and LGX300 reduced (p ≤ 0.001) the intestinal pathogens counts (E.coli and Salmonella). Administration of LGX at levels of 200 and 100 ml/l, respectively enhanced (p ≤ 0.001) villi height and crypts depth. ConclusionsIt was concluded that lemongrass aqueous extract can be included at level 100 and 200 ml/l in broilersˈ drinking water since it resulted in improved weight, feed conversion ratio, blood parameters, immunity and gut health without any deleterious effect on the health and performance of the birds. LGX at a 200 ml/l supplementation level achieved the best results followed by a 100 ml/l level. Also, the tested supplements can be used as natural growth promoter instead of antibiotic and help in solving the global problem of antimicrobial resistant bacterial strains responsible for human and animal diseases.

The Physical Home Food Environment in Relation to Children’s Diet Quality and Cardiometabolic Health

BackgroundHome food availability has been identified as an important influence on dietary intake. Less is known about the relationship between the physical home food environment (HFE) and factors of cardiometabolic health in children. ObjectiveThe purpose of this study was to explore the relationship between the physical HFE and diet quality and factors of cardiometabolic health (eg, weight and blood biomarkers). DesignThis was a cross-sectional secondary analysis with 1 or more children per household. Participants/settingThis study included 44 children aged 6 to 12 years from 29 households in the Newark, DE area between August 2020 and August 2021. Main outcome measuresThe Home Food Inventory provides an obesogenic score (ie, score indicative of the presence of energy-dense foods) for the overall HFE and HFE subcategories scores; body mass index z-scores were calculated using measured height and weight; diet quality was measured using the Healthy Eating Index 2020 (HEI-2020) total scores; and cardiometabolic biomarkers were obtained from serum blood samples. Statistical analyses performedUnadjusted and adjusted linear mixed model regressions were used to test the association between the physical HFE and each of the outcome variables: body mass index z scores, HEI-2020 total scores, and cardiometabolic biomarkers. HFE subcategories (eg, fruits and vegetables) were also examined with each outcome using linear mixed model regression. ResultsMean ± SD age of the children was 9.5 ± 1.9 years, 61.4% were female, 59.1% identified as White, and 90.9% were non-Hispanic. Obesogenic score was significantly associated with body mass index z scores (β = .03, P = .029), but not HEI-2020 total scores or cardiometabolic biomarkers. As HFE fruits and vegetables subcategory increased, HEI-2020 total scores significantly increased (β = .73, P = .005) and total cholesterol (β = –1.54, P = .014) and low-density lipoprotein cholesterol levels (β = –1.31, P = .010) significantly decreased. Increased availability of sweet and salty snack food and availability of sugar-sweetened beverages was associated with increased fasting blood glucose (β = 0.65, P = .033) and insulin levels (β = 5.60, P = .035) respectively. ConclusionsThere is evidence of a relationship between the subcategories of the physical HFE and cardiometabolic factors. Future interventions are needed to understand whether altering the overall HFE or specific subcategories within the HFE can improve cardiometabolic health.

Potential of salivary biomarkers for diagnosing and prognosing rheumatoid arthritis: A systematic review and meta-analysis: Salivary biomarkers in Rheumatoid Arthritis patients – A systematic review with Meta-analysis

BackgroundRheumatoid arthritis (RA) is a chronic debilitating autoimmune disorder. Blood biomarkers, like rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPAs), lack the sensitivity and specificity for early diagnosis, delaying treatment. This review while highlighting the need for new diagnostic tools, emphasizes the promising avenue of saliva for developing RA biomarkers. ObjectiveThis systematic review and meta-analysis assess the effectiveness of salivary biomarkers in the diagnosis and prognosis of RA, examining current evidence and proposing avenues for future research. MethodologyA literature review following PRISMA 2021 guidelines was conducted using PubMed, Scopus, Web of Science, and Google Scholar to identify studies from the past five years on salivary biomarkers in RA patients compared to healthy controls. ResultThe review focused on original research articles, and meta-analysis was performed on studies reporting standard deviation values for inflammatory markers such as IL-6, IL-8, MMP-8, and TNF-alpha. The meta-analysis included eleven studies with 394 RA patients and 255 healthy controls, evaluating IL-8, IL-6, MMP-8, and TNF-α as RA biomarkers. IL-8 showed a mean difference of 7.32 (CI: -5.48 to 20.13), not statistically significant, favouring controls. IL-6 had a CI of -0.09 (CI: -2.20 to 2.02) with high heterogeneity (I² = 98%), suggesting its potential as a diagnostic and prognostic biomarker. TNF-α and MMP-8 showed no significant differences (CIs: 4.54 and 2.71, respectively). ConclusionThis systematic review and meta-analysis emphasize saliva's potential in identifying RA biomarkers, especially IL-6, which is associated with the disease's pathogenesis. However, significant evidence heterogeneity necessitates larger, multicentric studies for validation.

A prediction model of dementia conversion for mild cognitive impairment by combining plasma pTau181 and structural imaging features.

The early stages of Alzheimer's disease (AD) are no longer insurmountable. Therefore, identifying at-risk individuals is of great importance for precise treatment. We developed a model to predict cognitive deterioration in patients with mild cognitive impairment (MCI). Based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we constructed models in a derivation cohort of 761 participants with MCI (138 of whom developed dementia at the 36th month) and verified them in a validation cohort of 353 cognitively normal controls (54 developed MCI and 19 developed dementia at the 36th month). In addition, 1303 participants with available AD cerebrospinal fluid core biomarkers were selected to clarify the ability of the model to predict AD core features. We assessed 32 parameters as candidate predictors, including clinical information, blood biomarkers, and structural imaging features, and used multivariable logistic regression analysis to develop our prediction model. Six independent variables of MCI deterioration were identified: apolipoprotein E ε4 allele status, lower Mini-Mental State Examination scores, higher levels of plasma pTau181, smaller volumes of the left hippocampus and right amygdala, and a thinner right inferior temporal cortex. We established an easy-to-use risk heat map and risk score based on these risk factors. The area under the curve (AUC) for both internal and external validations was close to 0.850. Furthermore, the AUC was above 0.800 in identifying participants with high brain amyloid-β loads. Calibration plots demonstrated good agreement between the predicted probability and actual observations in the internal and external validations. We developed and validated an accurate prediction model for dementia conversion in patients with MCI. Simultaneously, the model predicts AD-specific pathological changes. We hope that this model will contribute to more precise clinical treatment and better healthcare resource allocation.

50288 Levels of galectins and other promising blood and urinary biomarkers of metabolic disorders in psoriasis.

50288 Levels of galectins and other promising blood and urinary biomarkers of metabolic disorders in psoriasis.

Neurofilaments light chains as a diagnostic and predictive biomarker for Tunisian Multiple Sclerosis patients

BackgroundMultiple Sclerosis (MS) course was shown to be more severe among North Africans compared to Caucasians. Validation of prognostic biomarkers of disease activity and severity is a priority in our practice. ObjectiveWe aimed to investigate the association between baseline cerebrospinal fluid (CSF) and serum NfL (sNFL) levels and disease activity and disability accrual in a cohort of Tunisian patients with MS. MethodsA cross-sectional study was conducted, in the department of Neurology of Razi Hospital, including patients diagnosed with MS. Patient's data were retrieved from our local MS database. Blood and CSF sampling were performed at the first visit. sNFL levels were measured using the Enzyme-Linked Immuno-Sorbent Assay (ELISA) sandwich technique. ResultsThree hundred MS patients were enrolled (sex-ratio= 3.05; mean age at MS onset=28.83 years+9.55, mean MS course = 10.21 years+8.96). MS phenotype was predominately relapsing (73%). CSF NfL levels were significantly correlated to the serum ones. NfL concentrations were significantly associated with MS activity (p = 0.012), disease progression (p = 0.001), and higher Multiple Sclerosis Severity Scores (MSSS) (p = 0.0017, r = 0.28). ConclusionsThese results support the value of NfL as a sensitive and clinically meaningful CSF and blood biomarker to evaluate MS activity and outcomes among Tunisian MS patients.

Effect of blood collection tube containing protease inhibitors on the pre-analytical stability of Alzheimer's disease plasma biomarkers.

The reliability of plasma biomarkers of Alzheimer's disease (AD) can be compromised by protease-induced degradation. This can limit the feasibility of conducting plasma biomarker studies in environments that lack the capacity for immediate processing and appropriate storage of blood samples. We hypothesized that blood collection tube supplementation with protease inhibitors can improve the stability of plasma biomarkers at room temperatures (RT). In this study, we conducted a comparative analysis of blood biomarker stability in traditional ethylenediaminetetraacetic acid (EDTA) tubes versus BD™ P100 collection tubes, the latter being coated with a protease inhibitor cocktail. The stability of six plasma AD biomarkers was evaluated over time under RT conditions. We evaluated three experimental approaches. In Approach 1, pooled plasma samples underwent storage at RT for up to 96 h. In Approach 2, plasma samples isolated upfront from whole blood collected into EDTA or P100 tubes were stored at RT for 0 h or 24 h before biomarker measurements. In Approach 3, whole blood samples were collected into paired EDTA and P100 tubes, followed by storage at RT for 0 h or 24 h before isolating the plasma for analyses. Biomarkers were measured with Single Molecule Array (Simoa) and immunoprecipitation-mass spectrometry (IP-MS) assays. Both the IP-MS and Simoa methods revealed that the use of P100 tubes significantly improves the stability of Aβ42 and Aβ40 across all approaches. However, the Aβ42/Aβ40 ratio levels were significantly stabilized only in the IP-MS assay in Approach 3. No significant differences were observed in the levels of plasma p-tau181, GFAP, and NfL for samples collected using either tube type in any of the approaches. Supplementation of blood collection tubes with protease inhibitors could reduce the protease-induced degradation of plasma Aβ42 and Aβ40, and the Aβ42/40 ratio for the IP-MS assay. These findings have crucial implications for preanalytical procedures, particularly in resource-limited settings.

Proteomic exploration of potential blood biomarkers in haemophilic arthropathy.

The pathophysiology of haemophilic arthropathy (HA) is complex and largely undefined. Proteomic analyses provide insights into the intricate mechanisms of the HA.Our study aimed to identify differentially expressed proteins in relation to the severity of HA, explore their pathophysiological roles, and evaluate their potential as HA biomarkers. Our cross-sectional observational study encompassed 30 HA patients and 15 healthy subjects. Plasma samples were pooled into three groups of 15 samples from those with severe haemophilic arthropathy (sHA), mild haemophilic arthropathy (mHA) and healthy controls. Proteomic analysis was performed using liquid chromatography-mass spectrometry. The severity of HA was assessed using the World Federation of Haemophilia Physical Examination Score and ultrasonography following the Haemophilia Early Arthropathy Detection with Ultrasound (HEAD-US) guidelines. A total of 788 proteins were identified, with 97% of the uniquely identified proteins being expressed in all analysed groups. We identified several up and downregulated proteins across the groups that were mainly related to inflammatory and immunity-modulating processes, as well as joint degeneration. We highlighted ten proteins relevant for the development of HA: cathepsin G, endoplasmic reticulum aminopeptidase 2, S100-A9, insulin-like growth factor I, apolipoprotein (a), osteopontin, pregnancy zone protein, cartilage oligomeric matrix protein, CD44, and cadherin-related family member 2. Our analysis identified several proteins that shed further light on the distinctive pathogenesis of HA and could serve for biomarker research. However, these results need to be validated on a larger patient group.

Herbal extract (Cervus elaphus Linnaeus, Angelica gigas Nakai, and Astragalus membranaceus Bunge) ameliorates chronic fatigue: A randomized, placebo-controlled, double-blind trial

BackgroundChronic fatigue syndrome (CFS) reduces the health-related quality of life in the working-age population; however, studies have rarely investigated this group. A mixture of Cervus elaphus Linnaeus, Angelica gigas Nakai, and Astragalus membranaceus Bunge (CAA) may be an effective anti-fatigue supplement. However, few clinical trials have explored the anti-fatigue effects of herbal medicines in human participants. Therefore, this study aimed to investigate the effects of the CAA herbal complex on muscle fatigue and endurance capacity in a randomized, placebo-controlled, double-blind trial. MethodsIn an 8-week trial, 80 patients with chronic fatigue symptoms were randomly assigned to the CAA (43.5 ± 1.2 years) or placebo group (41.8 ± 1.3 years). Fatigue and cardiorespiratory endurance were measured at baseline, interim, and post-intervention. Fatigue-related blood biomarkers were assessed before and at the end of the intervention. ResultsA significant improvement in overall fatigue scores was observed on the fatigue severity scale (p = 0.038), multidimensional fatigue inventory (p = 0.037), and 24-hour visual analog scale (p = 0.002) in the CAA group compared to those in the placebo group. Fatigue improvement was observed in the CAA group, as well as physiological variables, such as increased maximal exercise time to exhaustion (p = 0.003), distance until exhaustion (p = 0.003), and maximum oxygen consumption (p = 0.039). ConclusionCAA positively and significantly affected fatigue and cardiorespiratory endurance in patients with chronic fatigue, suggesting the potential use of herbal supplements for treating chronic fatigue. Trial registrationClinical Research Information Service (CRIS, https://cris.nih.go.kr/): KCT0005613.

Inferring Characteristics of the Tumor Immune Microenvironment of Patients with HNSCC from Single-Cell Transcriptomics of Peripheral Blood.

Our work offers a new and promising paradigm in liquid biopsies to unlock the power of blood single-cell transcriptomics in cancer immunotherapy.

Cognitive Dysfunction in the Addictions (CDiA): A Neuron to Neighbourhood Collaborative Research Program on Executive Dysfunction and Functional Outcomes in Outpatients Seeking Treatment for Addiction.

Substance use disorders (SUDs) are pressing global public health problems. Executive functions (EFs) are prominently featured in mechanistic models of addiction. However, there remain significant gaps in our understanding of EFs in SUDs, including the dimensional relationships of EFs to underlying neural circuits, molecular biomarkers, disorder heterogeneity, and functional ability. To improve health outcomes for people with SUDs, interdisciplinary clinical, preclinical and health services research is needed to inform policies and interventions aligned with biopsychosocial models of addiction. Here, we introduce Cognitive Dysfunction in the Addictions (CDiA), an integrative team-science and translational research program, which aims to fill these knowledge gaps and facilitate research discoveries to enhance treatments for people living with SUDs. The CDiA Program comprises seven complementary interdisciplinary projects that aim to progress understanding of EF in SUDs and investigate new biological treatment approaches. The projects draw on a diverse sample of adults aged 18-60 (target N=400) seeking treatment for addiction, who are followed prospectively over one year to identify EF domains crucial to recovery. Projects 1-3 investigate SUD symptoms, brain circuits, and blood biomarkers and their associations with both EF domains (inhibition, working memory, and set-shifting) and functional outcomes (disability, quality of life). Projects 4 and 5 evaluate interventions for addiction and their impacts on EF: a clinical trial of repetitive transcranial magnetic stimulation and a preclinical study of potential new pharmacological treatments in rodents. Project 6 links EF to healthcare utilization and is supplemented with a qualitative investigation of EF-related barriers to treatment engagement for those with substance use concerns. Project 7 uses innovative whole-person modeling to integrate the multi-modal data generated across projects, applying clustering and deep learning methods to identify patient subtypes and drive future cross-disciplinary initiatives. The CDiA program has promise to bring scientific domains together to uncover the diverse ways in which EFs are linked to SUD severity and functional recovery. These findings, supported by emerging clinical, preclinical, health service, and whole-person modeling investigations, will facilitate future discoveries about cognitive dysfunction in addiction and could enhance the future clinical care of individuals seeking treatment for SUDs.

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data

Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. Machine learning (ML) models were developed using selected features from blood-secretory proteins collected from the curated databases. The logistic regression (LR) model demonstrated the optimal performance. Transcriptome analysis across 7 LC cohorts revealed 231 common differentially expressed genes (DEGs). The encoded proteins of these DEGs were compared with the ML dataset, revealing 29 proteins overlapping with the blood-secretory dataset. The LR model also predicted 29 additional proteins as blood-secretory with the remaining protein-coding genes. As a result, 58 potential blood-secretory proteins were obtained. Among the top 20 genes, 13 common hub genes were identified. Further, area under the receiver operating characteristic curve (ROC AUC) analysis was performed to assess the genes as potential diagnostic blood biomarkers. Six genes, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6, exhibited an AUC value higher than 0.85 and were predicted as blood-secretory. This study highlights the potential of an integrative computational approach for discovering non-invasive blood-based biomarkers in LC, facilitating for further validation and clinical translation. SignificanceLiver cancer is one of the leading causes of premature death worldwide, with its prevalence and mortality rates projected to increase. Although current diagnostic methods are highly sensitive, they are invasive and unsuitable for repeated testing. Blood biomarkers offer a promising non-invasive alternative, but their wide dynamic range of protein concentration poses experimental challenges. Therefore, utilizing available omics data to develop a diagnostic model could provide a potential solution for accurate diagnosis. This study developed a computational method integrating machine learning and bioinformatics analysis to identify potential blood biomarkers. As a result, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6 biomarkers were identified, holding significant promise for improving diagnosis and understanding of liver cancer. The integrated method can be applied to other cancers, offering a possible solution for early detection and improved patient outcomes.

Effects of muscle strength training combined with aerobic training versus aerobic training alone on cardiovascular disease risk indicators in patients with coronary artery disease: a systematic review and meta-analysis of randomised clinical trials

ObjectiveTo compare the effects of aerobic training combined with muscle strength training (hereafter referred to as combined training) to aerobic training alone on cardiovascular disease risk indicators in patients with...

Association of Lipopolysaccharide-Type Endotoxins with Retinal Neurodegeneration: The Alienor Study

PurposeLipopolysaccharide (LPS) -type endotoxins are naturally found in the gut microbiota and there is emerging evidence linking gut microbiota and neuroinflammation leading to retinal neurodegeneration. Thinning of the retinal nerve fiber layer (RNFL) is a biomarker of retinal neurodegeneration, and a hallmark of glaucoma, the second leading cause of blindness worldwide. We assessed the association of a blood biomarker of LPS with peripapillary RNFL thickness (RNFLT) and its longitudinal evolution up to 11 years. DesignThe Alienor study is a single center prospective population-based cohort study. SubjectsThe studied sample of this study includes 1062 eyes of 548 participants receiving at least one gradable RNFL measurement. MethodsPlasma esterified 3-hydroxy fatty acids (3-OH FAs) were measured as a proxy of LPS burden. RNFLT was acquired using spectral domain optical coherence tomography imaging every two years from 2009 to 2020 (up to 5 visits). Main Outcome MeasureAssociations of plasma esterified 3-OH FAs with RNFLT were assessed using linear mixed models. ResultsMean age of the included 548 participants was 82.4 ± 4.3 years and 62.6 % were women. Higher plasma esterified 3-OH FAs was significantly associated with thinner RNFLT at baseline (Coefficient Beta (ß)= -1.42 microns for 1 SD-increase in 3-OH FAs, 95% confidence interval [-2.56; -0.28], p = 0.02). This association remained stable after multivariate adjustment for potential confounders. No statistically significant association was found between 3-OH FAs and longitudinal RNFLT change. ConclusionsHigher plasma esterified 3-OH FAs were associated with thinner RNFLT at baseline, indicating an involvement of LPS in the early processes of optic nerve neurodegeneration and highlighting the potential importance of the human microbiota in preserving retinal health.

Dynamics of Cognitive Impairment in MCI Patients over a Three-Year Period: The Informative Role of Blood Biomarkers, Neuroimaging, and Genetic Factors.

Given the high growth rates of cognitive decline among the elderly population and the lack of effective etiological treatments, early diagnosis of cognitive impairment progression is an imperative task for modern science and medicine. It is of particular interest to identify predictors of an unfavorable subsequent course of cognitive disorders, specifically, rapid progression. Our study assessed the informative role of various risk factors on the dynamics of cognitive impairment among mild cognitive impairment (MCI) patients. The study included patients with MCI (N = 338) who underwent neuropsychological assessment, magnetic resonance imaging (MRI) examination, blood sampling for general and biochemical analysis, APOE genotyping, and polygenic risk score (PRS) evaluation. The APOE ε4/ε4 genotype was found to be associated with a diminished overall cognitive scores initial assessment and negative cognitive dynamics. No associations were found between cognitive changes and the PRS. The progression of cognitive impairment was associated with the width of the third ventricle and hematological parameters, specifically, hematocrit and erythrocyte levels. The absence of significant associations between the dynamics of cognitive decline and PRS over three years can be attributed to the provided suitable medical care for the prevention of cognitive impairment. Adding other risk factors and their inclusion in panels assessing the risk of progression of cognitive impairment should be considered.

The Effects of Blood Flow Restriction Aerobic Exercise on Body Composition, Muscle Strength, Blood Biomarkers, and Cardiovascular Function: A Narrative Review.

Blood flow restriction exercise has emerged as a promising alternative, particularly for elderly individuals and those unable to participate in high-intensity exercise. However, existing research has predominantly focused on blood flow restriction resistance exercise. There remains a notable gap in understanding the comprehensive effects of blood flow restriction aerobic exercise (BFRAE) on body composition, lipid profiles, glycemic metabolism, and cardiovascular function. This review aims to explore the physiological effects induced by chronic BFRAE. Chronic BFRAE has been shown to decrease fat mass, increase muscle mass, and enhance muscular strength, potentially benefiting lipid profiles, glycemic metabolism, and overall function. Thus, the BFRAE offers additional benefits beyond traditional aerobic exercise effects. Notably, the BFRAE approach may be particularly suitable for individuals with low fitness levels, those prone to injury, the elderly, obese individuals, and those with metabolic disorders.

Assessing the precision of machine learning for diagnosing pulmonary arterial hypertension: a systematic review and meta-analysis of diagnostic accuracy studies.

Pulmonary arterial hypertension (PAH) is a severe cardiovascular condition characterized by pulmonary vascular remodeling, increased resistance to blood flow, and eventual right heart failure. Right heart catheterization (RHC) is the gold standard diagnostic technique, but due to its invasiveness, it poses risks such as vessel and valve injury. In recent years, machine learning (ML) technologies have offered non-invasive alternatives combined with ML for improving the diagnosis of PAH. The study aimed to evaluate the diagnostic performance of various methods, such as electrocardiography (ECG), echocardiography, blood biomarkers, microRNA, chest x-ray, clinical codes, computed tomography (CT) scan, and magnetic resonance imaging (MRI), combined with ML in diagnosing PAH. The outcomes of interest included sensitivity, specificity, area under the curve (AUC), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). This study employed the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool for quality appraisal and STATA V.12.0 for the meta-analysis. A comprehensive search across six databases resulted in 26 articles for examination. Twelve articles were categorized as low-risk, nine as moderate-risk, and five as high-risk. The overall diagnostic performance analysis demonstrated significant findings, with sensitivity at 81% (95% CI = 0.76-0.85, p < 0.001), specificity at 84% (95% CI = 0.77-0.88, p < 0.001), and an AUC of 89% (95% CI = 0.85-0.91). In the subgroup analysis, echocardiography displayed outstanding results, with a sensitivity value of 83% (95% CI = 0.72-0.91), specificity value of 93% (95% CI = 0.89-0.96), PLR value of 12.4 (95% CI = 6.8-22.9), and DOR value of 70 (95% CI = 23-231). ECG demonstrated excellent accuracy performance, with a sensitivity of 82% (95% CI = 0.80-0.84) and a specificity of 82% (95% CI = 0.78-0.84). Moreover, blood biomarkers exhibited the highest NLR value of 0.50 (95% CI = 0.42-0.59). The implementation of echocardiography and ECG with ML for diagnosing PAH presents a promising alternative to RHC. This approach shows potential, as it achieves excellent diagnostic parameters, offering hope for more accessible and less invasive diagnostic methods. PROSPERO (CRD42024496569).

Multi-omics profiling reveals peripheral blood biomarkers of multiple sclerosis: implications for diagnosis and stratification.

Multiple sclerosis (MS), a chronic autoimmune disorder marked by demyelination in the central nervous system, is exceptionally uncommon in China, and remains poorly understood in terms of its peripheral blood manifestations. We conducted a cohort study comprising 39 MS patients and 40 normal controls (NC). High-dimensional mass cytometry, protein arrays, and targeted metabolomics were utilized to profile immune subsets, proteins, and metabolites in blood. Differences in multi-omics signatures were scrutinized across varying MS subtypes. Immune profiling demonstrated an elevation in various B cell subsets and monocytes, alongside a reduction in dendritic cells among MS patients. Proteomic data revealed a downregulation in neurotrophic and tissue repair proteins. Metabolomic assessment showed a noted decrease in anti-inflammatory molecules and sphingolipids. Integrated analysis identified distinct molecular patterns distinguishing MS from controls. Additionally, multi-omics differences among different MS subtypes were uncovered. Notably, hippuric acid levels was consistently lower in MS subgroups with greater disease severity. This study represents the pioneering exploration of multi-omics in Chinese MS patients, presenting a comprehensive view of the peripheral blood changes in MS. Our study underscores the robust capability of multi-omics assessments in identifying peripheral blood biomarkers that delineate the varied clinical presentation, and facilitates future development of biomarkers and targeted therapeutic interventions in MS.

Nuclear-Magnetic-Resonance-Spectroscopy-Derived Serum Biomarkers of Metabolic Vulnerability Are Associated with Disability and Neurodegeneration in Multiple Sclerosis.

Metabolic vulnerabilities can exacerbate inflammatory injury and inhibit repair in multiple sclerosis (MS). The purpose was to evaluate whether blood biomarkers of inflammatory and metabolic vulnerability are associated with MS disability and neurodegeneration. Proton nuclear magnetic resonance spectra were obtained from serum samples from 153 healthy controls, 187 relapsing-remitting, and 91 progressive MS patients. The spectra were analyzed to obtain concentrations of lipoprotein sub-classes, glycated acute-phase proteins, and small-molecule metabolites, including leucine, valine, isoleucine, alanine, and citrate. Composite indices for inflammatory vulnerability, metabolic malnutrition, and metabolic vulnerability were computed. MS disability was measured on the Expanded Disability Status Scale. MRI measures of lesions and whole-brain and tissue-specific volumes were acquired. Valine, leucine, isoleucine, alanine, the Inflammatory Vulnerability Index, the Metabolic Malnutrition Index, and the Metabolic Vulnerability Index differed between healthy control and MS groups in regression analyses adjusted for age, sex, and body mass index. The Expanded Disability Status Scale was associated with small HDL particle levels, inflammatory vulnerability, and metabolic vulnerability. Timed ambulation was associated with inflammatory vulnerability and metabolic vulnerability. Greater metabolic vulnerability and inflammatory vulnerability were associated with lower gray matter, deep gray matter volumes, and greater lateral ventricle volume. Serum-biomarker-derived indices of inflammatory and metabolic vulnerability are associated with disability and neurodegeneration in MS.