Introduction: Obesity is a heterogeneous metabolic disease in which individuals with similar body weight may exhibit markedly different physiological risk profiles. Conventional monitoring using weight alone may fail to capture underlying metabolic and cellular changes during lifestyle intervention. Integrating body composition and biochemical markers may improve risk stratification in clinical practice. Methodology: A prospective observational analysis was conducted among participants enrolled in a structured lifestyle programme. Baseline and follow-up assessments included biochemical markers (fasting glucose, renal function, lipid profile, and liver enzymes) alongside body composition parameters derived from bioelectrical impedance analysis, including percent body fat, visceral fat area, skeletal muscle index (SMI), and phase angle (PhA). Changes over time and associations between metabolic and body composition variables were analyzed. Results: Metabolic responses varied substantially despite similar anthropometric profiles. Renal function improved significantly, with increased estimated glomerular filtration rate (eGFR) observed over time (p = 0.01), and was inversely associated with fasting glucose (r = −0.41, p <0.01). Lipid abnormalities persisted, although reductions in lowdensity lipoprotein cholesterol were noted (p = 0.04). Total cholesterol remained elevated and correlated positively with fasting glucose (r = 0.36, p = 0.02). Improvements in aspartate aminotransferase were observed (p = 0.03), while other liver markers remained stable. Notably, body composition parameters, including PhA and SMI, demonstrated variability independent of weight change, reflecting heterogeneous physiological adaptation. Conclusion Metabolic and physiological responses to lifestyle intervention are heterogeneous and not fully captured by changes in body weight alone. The integration of bioimpedance-derived parameters with biochemical markers provides a more comprehensive approach to metabolic risk stratification and may support more personalized clinical management in obesity care.
Biomarkers ; Risk Assessment ; Body Composition

Heart failure is a complex clinical syndrome and pediatric heart failure (PHF) has a high mortality rate. Early diagnosis is crucial for treatment and management of PHF. In clinical practice, various tests and examinations play a key role in the diagnosis of PHF, including continuously updated biomarkers, echocardiography, and cardiac magnetic resonance imaging. This article focuses on summarizing relevant research on biomarkers, examinations, combined testing, clinical models, and the grading and staging of PHF diagnosis, aiming to provide insights and directions for the diagnosis of PHF.
Humans ; Heart Failure/diagnosis* ; Child ; Biomarkers/blood* ; Echocardiography ; Magnetic Resonance Imaging

OBJECTIVES: To explore the lipidomic characteristics of children with bronchial asthma (hereafter referred to as asthma) and identify potential biomarkers for asthma. METHODS: A total of 26 asthmatic children were prospectively enrolled as the asthma group, and 20 healthy children served as the healthy control group. The asthma group was further divided into atopic (n=13) and non-atopic (n=13) subgroups based on IgE levels. Serum lipid metabolites were analyzed using liquid chromatography-mass spectrometry, followed by statistical analysis and data visualization. RESULTS: A total of 1 435 lipids were detected in the 46 children, primarily glycerophospholipids (625/1 435, 43.55%). Significant differences were observed in serum lipid profiles between the asthma and control groups. Twelve significantly differential lipids were identified, with receiver operating characteristic curve analysis showing that phosphatidylserine (PS)(18:0/20:4) and ceramide (Cer)(c16:0) exhibited the highest diagnostic value for asthma. The relative abundances of PS(18:0/20:4) and PS(18:0/22:6) were higher in the atopic subgroup than in the non-atopic subgroup (P<0.05) and positively correlated with total IgE levels in asthmatic children (r=0.675 and 0.740, respectively; P<0.05). CONCLUSIONS Asthmatic children exhibit significant lipid metabolic disturbances, primarily characterized by abnormal glycerophospholipid metabolism. Among these, PS(18:0/20:4) and Cer(c16:0) demonstrate specific alterations and may serve as potential diagnostic biomarkers for asthma. Furthermore, the positive correlation between PS(18:0/20:4) and PS(18:0/22:6) levels and serum total IgE suggests their possible involvement in immune regulation in asthma.
Humans ; Asthma/metabolism* ; Male ; Child ; Female ; Prospective Studies ; Mass Spectrometry/methods* ; Lipids/blood* ; Chromatography, Liquid/methods* ; Child, Preschool ; Immunoglobulin E/blood* ; Biomarkers/blood* ; Adolescent ; Liquid Chromatography-Mass Spectrometry

OBJECTIVES: To investigate the clinical and immunological features of children with primary immune thrombocytopenia (pITP) or connective tissue disease (CTD) with thrombocytopenia as the initial manifestation at initial diagnosis, and to provide a basis for early differentiation. METHODS: A retrospective study was performed on 236 children with pITP (pITP group) or CTD with thrombocytopenia as the initial manifestation (CTD-TP group) who were admitted from January 2019 to August 2024. Clinical and immunological indicators were compared between the two groups to identify potential influencing factors for early differentiation and their discriminative validity. RESULTS: Compared with the pITP group, the CTD-TP group had a significantly older age of onset and significantly lower leukocyte count, eosinophil count, lymphocyte count, and complement C4 level (P<0.05), as well as significantly higher levels of C-reactive protein, IgE, and IgM (P<0.05). The logistic regression analysis showed that age, IgE, IgM, total B cells, and complement C4 were predictive factors for early differentiation between pITP and CTD-TP (P<0.05). The receiver operating characteristic curve analysis showed that a combination of these five factors had a good discriminative validity, with an area under the curve of 0.944. The correlation analysis showed a negative correlation between IgG and platelet count in the pITP group (r_s=-0.363, P<0.05) and a positive correlation between NK cells and platelet count in the CTD-TP group (r_s=0.713, P<0.05). CONCLUSIONS There is heterogeneity in the clinical and immunological indicators between children with pITP and CTD-TP at initial diagnosis, and these research findings can help with the early differentiation between the two diseases.
Purpura, Thrombocytopenic, Idiopathic/immunology* ; Diagnosis, Differential ; Connective Tissue Diseases/immunology* ; Retrospective Studies ; Early Diagnosis ; Age of Onset ; Leukocyte Count ; Complement C4/immunology* ; C-Reactive Protein/immunology* ; Immunoglobulin E/immunology* ; Immunoglobulin M/immunology* ; Humans ; Male ; Female ; Infant ; Child, Preschool ; Child ; Adolescent ; Biomarkers/blood*

OBJECTIVES: To investigate the correlation between bone mineral density (BMD) and bone metabolic markers in preschool children and the influencing factors for BMD, and to provide a clinical basis for promoting bone health in children. METHODS: A retrospective analysis was performed for the data of 127 preschool children who underwent physical examination in the Department of Child Health Care of the First Affiliated Hospital of Xinjiang Medical University, from June to December 2024. BMD and bone metabolic markers were measured, and physical examination was performed. A multiple linear regression analysis was used to investigate the effect of general information on BMD Z-score in preschool children. Spearman's rank correlation test was used to investigate the correlation of BMD Z-score with 25-hydroxyvitamin D (25-OHD), serum bone Gla protein (BGP), and parathyroid hormone (PTH). RESULTS: BMD Z-score significantly differed by ethnicity, weight category, and height category (all P<0.05). The multiple linear regression analysis indicated that weight and height significantly influenced BMD Z-score (P<0.05), whereas sex, age, ethnicity, and parental education level did not (P>0.05). In children, BMD Z-score was positively correlated with 25-OHD level (r_s=0.260, P<0.001) and BGP level (r_s=0.075, P=0.025) and was negatively correlated with PTH level (r_s=-0.043, P=0.032). CONCLUSIONS Weight, height, 25-OHD, BGP, and PTH are influencing factors for BMD in preschool children. In clinical practice, combined measurement of bone metabolic markers may provide a scientific basis for early identification of children with abnormal BMD and prevention of osteoporosis and osteomalacia.
Humans ; Bone Density ; Child, Preschool ; Female ; Male ; Retrospective Studies ; Vitamin D/blood* ; Parathyroid Hormone/blood* ; Biomarkers/blood* ; Osteocalcin/blood* ; Bone and Bones/metabolism* ; Calcium-Binding Proteins/blood* ; Linear Models ; Matrix Gla Protein ; Extracellular Matrix Proteins/blood* ; Body Weight ; Infant

OBJECTIVES: To identify potential plasma lipidomic biomarkers that distinguish non-metastatic medulloblastoma (nmMB) from metastatic medulloblastoma (mMB) in children. METHODS: In this prospective study, 17 children with mMB and 20 matched children with nmMB were enrolled. Plasma samples were analyzed using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Lipid metabolites were evaluated for their associations and diagnostic performance. RESULTS: Orthogonal partial least squares discriminant analysis based on lipid profiles clearly separated nmMB from mMB, and 14 differential lipids were identified, including DG(18:2/20:4/0:0) and SM(d18:1/20:0). Receiver operating characteristic analysis showed nine metabolites with area under the curve greater than 0.7. Differential lipids were enriched in sphingolipid, glycerophospholipid, and arachidonic acid metabolism, suggesting an association with the metastatic phenotype. CONCLUSIONS Plasma lipidomics provides a new approach to identify mMB, and the identified lipid metabolites may support early diagnosis and treatment, prognostic assessment, and selection of therapeutic targets for metastatic medulloblastoma.
Humans ; Medulloblastoma/diagnosis* ; Lipidomics ; Child ; Male ; Female ; Child, Preschool ; Cerebellar Neoplasms/blood* ; Biomarkers, Tumor/blood* ; Neoplasm Metastasis ; Prospective Studies ; Adolescent ; Lipids/blood*

OBJECTIVES: To study the predictive value of serum macrophage M1/M2 markers for the risk of cardiac lesions in obese children. METHODS: A total of 60 obese children (mild-to-moderate obesity, n=32; severe obesity, n=28) and 50 healthy controls who visited the Second Affiliated Hospital of Nanchang University from June 2024 to December 2024 were included. The baseline characteristics and the levels of laboratory indicators, echocardiographic parameters, and macrophage markers (MCP-1, Arg-1, CD206, and CD86) were compared among the three groups. The correlation between macrophage marker levels and echocardiographic parameters and the influencing factors of cardiac lesions in obese children were analyzed. The receiver operating characteristic curve analysis was used to evaluate the predictive performance of each influencing factor for cardiac lesions in obese children. RESULTS: Multiple echocardiographic parameters differed significantly among the mild-to-moderate obesity, severe obesity, and control groups (P<0.01). Significant differences were also observed in MCP-1 and Arg-1 levels, CD206 positivity rate, and the CD86/CD206 ratio among the three groups (P<0.05). In obese children, MCP-1 and Arg-1 levels, as well as CD86 and CD206 positivity rates, were correlated with echocardiographic parameters (P<0.05). Univariate logistic regression identified MCP-1, Arg-1, the CD86/CD206 ratio, and the CD206 positivity rate as factors associated with cardiac lesions (P<0.05). The combined prediction model based on these markers yielded an area under the receiver operating characteristic curve of 0.887 (P<0.01). CONCLUSIONS Combined measurement of macrophage markers can predict the risk of early cardiac lesions in obese children.
Humans ; Male ; Female ; Child ; Biomarkers/blood* ; Macrophages ; Obesity/blood* ; Chemokine CCL2/blood* ; ROC Curve ; Adolescent ; Child, Preschool ; Heart Diseases/diagnosis* ; Echocardiography

OBJECTIVES: To analyze the association between biological aging markers (phenotypic age and phenotypic age acceleration) and valvular heart diseases. METHODS: Research subjects who met the inclusion and exclusion criteria were selected from the UK Biobank from 2006 to 2010. The phenotypic age and phenotypic age acceleration were calculated. Cox multivariate analysis was used to examine the relationship between the aging markers and valvular heart diseases. Sensitivity analysis was conducted by removing missing values and subgroup analysis. The predictive accuracy of phenotypic age and phenotypic age acceleration for valvular heart diseases was analyzed using receiver operating characteristic (ROC) curves, and a clinical decision curve was generated based on logistic regression. RESULTS: A total of 411 687 subjects were included in the study, among whom there were 14 258 patients with valvular heart diseases. The overall median follow-up time was 12.80 years, the median follow-up time for patients with non-rheumatic aortic valve diseases (n=5238), non-rheumatic mitral valve diseases (n=4558), and non-rheumatic tricuspid valve diseases (n=411) were 12.82 years, 12.83 years and 12.84 years, respectively. After adjusting for demographic factors (gender, race, education, Townsend deprivation index), anthropometric factors (body mass index), lifestyle factors (smoking, alcohol consumption, Dietary Approaches to Stop Hypertension score), hypertension and hyperlipidemia, Cox multivariate analysis showed phenotypic age and phenotypic age acceleration were independent risk factors for valvular heart diseases, including non-rheumatic aortic valve diseases, non-rheumatic mitral valve diseases, and non-rheumatic tricuspid valve diseases (phenotypic age: corrected HR=1.04, P<0.01; phenotypic age acceleration: corrected HR=1.03, P<0.01), which was also confirmed by sensitivity analysis. ROC curves and clinical decision curves demonstrated that compared with the phenotypic age acceleration, phenotypic age had higher accuracy (the areas and the curves were 0.721 and 0.599) and higher net benefit in predicting valvular heart diseases. Moreover, compared with a single indicator, the combination of the two indicators had higher accuracy (the area under the curve was 0.725) and higher net benefit. CONCLUSIONS Phenotypic age and phenotypic age acceleration,as markers of biological aging, are independent risk factors for valvular heart diseases. Compared with phenotypic age acceleration, phenotypic age has a greater advantage in predicting valvular heart diseases. Overall, the combination of the two indicators offers a more effective approach for predicting valvular heart diseases.
Humans ; Male ; Female ; Heart Valve Diseases/epidemiology* ; Middle Aged ; Aged ; Aging ; Adult ; Biomarkers ; Phenotype ; Risk Factors ; Aged, 80 and over

In recent years, as a new omics field, metabolomics has been proved to be of great value in the study of the mechanism of occurrence and progression, the screening of new biomarkers and the development of novel therapeutic strategies in many diseases including tumors. In this review, we briefly summarized the research methods and techniques of metabolomics, and focused on the latest research progress of metabolomics in the pathogenesis of hematological malignancies represented by leukemia, lymphoma and multiple myeloma, screening of biomarkers for diagnosis and prognosis, and development of new therapeutic strategies. This article proposes the limitations of metabolomics and future research strategies, and provides a new exploration direction for accurate diagnosis and treatment as well as prognosis evaluation of hematological malignancies.
Humans ; Metabolomics/methods* ; Hematologic Neoplasms/diagnosis* ; Biomarkers, Tumor

BACKGROUND: Epidermal growth factor receptor (EGFR) sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung cancer (NSCLC). However, due to the difficulty of obtaining some primary tissues and the economic factors in some underdeveloped areas, some patients cannot undergo traditional genetic testing. The aim of this study is to establish a machine learning (ML) model using non-invasive peripheral blood markers to explore the biomarkers closely related to EGFR mutation status in NSCLC and evaluate their potential prognostic value. METHODS: 2642 lung cancer patients who visited Jiangsu Cancer Hospital from November 2016 to May 2023 were retrospectively enrolled and finally 175 NSCLC patients with complete follow-up data were included in the study. The ML model was constructed based on peripheral blood indicators and divided into training set and test set according to the ratio of 8:2. Unsupervised learning algorithms were used for clustering blood features and mutual information method for feature selection, and an ensemble learning algorithm based on Shapley value was designed to calculate the contribution of each feature to the model prediction result. The receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the model. RESULTS: Through the feature extraction and contribution analysis of the predictive results of the interpretable ML model based on the Shapley value, the top ten indicators with the highest contribution were: pathological type, phosphorus, eosinophils, monocyte count, activated partial thromboplastin time, potassium, total bilirubin, sodium, eosinophil percentage, and total cholesterol. The area under the curve (AUC) of the model was 0.80. In addition, patients with hyponatremia and squamous cell carcinoma group had a poor prognosis (P<0.05). CONCLUSIONS The interpretable model constructed in this study provides a new approach for the prediction of EGFR mutation status in NSCLC patients, which provides a scientific basis for the diagnosis and treatment of patients who cannot undergo genetic testing.
Humans ; Carcinoma, Non-Small-Cell Lung/diagnosis* ; Machine Learning ; Lung Neoplasms/diagnosis* ; Male ; Female ; Mutation ; Middle Aged ; ErbB Receptors/genetics* ; Prognosis ; Aged ; Retrospective Studies ; Adult ; Biomarkers, Tumor/genetics*