Objective This study aimed to investigate the prevalence and determinants of overweight and obesity among students aged 6-18 years in Heilongjiang province,providing an evidence base for the development of region-specific body weight management strategies.Methods Between October 2024 and April 2025,a total of 50 734 students were recruited from six prefecture-level cities and their subordinate counties/districts via a multistage stratified random sampling design.Anthropometric measurements and structured questionnaires were administered.Overweight and obesity were classified according to the standard Screening for Overweight and Obesity among School-Age Children and Adolescents(WS/T 586-2018).Chi-square tests and multivariable logistic regression were employed for data analysis.Results The overall prevalence of overweight and obesity was 17.45%and 23.46%,respectively.Boys(20.78%and 28.41%)had higher rates than girls(14.01%and 18.35%)(χ2=1 486.520,P<0.01).Across school stages,the prevalence rates of overweight/obesity were 17.30%/30.10%in primary school students,17.22%/21.10%in junior high school students,and 18.45%/18.16%in senior high school students,with significant differences observed(χ2=640.236,P<0.01).Overweight and obesity rates varied by gender across age groups:Male overweight rate showed a"rise-decline-rise"bimodal pattern,while female overweight rate presented a"decline-rise"unimodal one.Both genders' obesity rates exhibited a steady downward trend with age.Multivariate analysis indicated that shorter mealtime duration,frequent late-night snacking,being a non-only child,lower exercise frequency,shorter exercise duration,incomplete family structure,and frequent conflicts with caregivers were associated with higher odds of overweight and obesity(all P<0.05).Stratified analysis further revealed that among primary school students,short mealtime duration and low exercise frequency increased the risk(both P<0.05),whereas among secondary school students,short mealtime duration,frequent late-night snacking,short exercise duration,conflicts with caregivers,and incomplete family structure were predictors(all P<0.05).Conclusions The prevalence of overweight and obesity among students aged 6-18 years in Heilongjiang remains high,underscoring the need for tailored interventions that integrate local dietary and lifestyle characteristics with gender-and school stage-specific management,strengthened family involvement,and school-based health education.

Objectives: We aimed to develop a practical dietary quality score reflecting the Cantonese dietary pattern and evaluate its validity against established indices. Methods: The Cantonese Dietary Index (CDI, ) was constructed based on Cantonese dietary principles. Reliability was assessed using intraclass correlation coefficients (ICC) over 5-6 years in the GNHS. Validity was evaluated using Spearman correlations with dietary indices (aMed, DASH, and DBI) and by comparing associations with metabolic syndrome (MetS) across dietary indices using regression models. The CDI was developed from the Guangzhou Nutrition and Health Study cohort (GNHS) and validated in the Tianjin Chronic Low-grade Systemic Inflammation and Health (TCLSIH) cohort and the National Health and Nutrition Examination Survey (NHANES). Results: A total of 4025 (GNHS), 29,165 (TCLSIH), and 28,890 (NHANES) participants were included. Median CDI scores were 58.5, 51.0, and 49.0, respectively. The 5-6-year ICC was 0.33 (p < 0.001). The CDI was moderately correlated with dietary indices across the three studies (GNHS: from -0.55 [DBI-LBS] to 0.61 [DASH], TCLSIH: from -0.61 [DBI-DQD] to 0.71 [DASH], NHANES: from -0.33 [DBI-DQD] to 0.68 [DASH]). The odds ratios (95% CIs) of MetS for CDI, aMed, and DASH scores were 0.80 (0.74, 0.86), 0.91 (0.84,0.99), and 0.83 (0.77, 0.90) in GNHS, 0.95 (0.92, 0.98), 0.99 (0.96, 1.02), and 0.92 (0.89, 0.95) in TCLSIH, and 0.80 (0.77, 0.84), 0.80 (0.76, 0.84), and 0.72 (0.69, 0.76) in NHANES. Conclusions: The CDI demonstrated moderate validity and reliability in Chinese populations and was inversely associated with MetS.

The protein-level functionalities of the human gut microbiota in large populations, and their associations with host factors, remain unexplored. This study reports a metaproteomic study of 1,967 fecal samples from 1,399 middle-aged and elderly Chinese individuals, identifying microbial functions linked to 44 phenotypes. We uncover aging-associated functional shifts in carbon metabolism and energy production driven by species within the Bacillota, Bacteroidota, Actinomycetota, and Pseudomonadota. Across metabolic diseases, we observe the consistent depletion of Bacillota species and their proteins involved in carbohydrate, energy, amino acid metabolism, and short-chain fatty acid production. We also identify medication-associated features across diabetes, hypertension, and dyslipidemia. Validated in an independent cohort, Megasphaera elsdenii emerged as a hub species in type 2 diabetes. Experimental validation indicates that M. elsdenii is promoted by antidiabetic drugs and may regulate glucose homeostasis through butyrate production. This study provides protein-level evidence of microbial functions in health and disease, highlighting potential therapeutic targets.

Older individuals often live with diverse combinations of chronic diseases. However, whether multimorbidity contributes to glycaemic dysregulation remains unclear. Here we show that cumulative disease trajectories shape interindividual glycaemic variability throughout the ageing process. Tracking 1,398 participants in the Guangzhou Nutrition and Health Study cohort over 12 years, we develop a systemic multimorbidity index (MMI-system) that reflects the cumulative burden of chronic disorders. We also measure individual glycaemic dynamics and responses to dietary challenges using continuous glucose monitoring at the latest follow-up visit (mean age, 69.2 years). MMI-system exhibits dose-dependent associations with glycaemic variability and sensitivity to dietary challenges, independent of diabetes status. Longitudinal proteome mapping reveals that lipid homeostasis proteins explain 12.9% of the association between MMI-system and personalized dietary responses. These findings are independently validated in the China Health and Nutrition Survey cohort. Overall, our study suggests that integrating longitudinal multimorbidity profiling with circulating proteomics may enhance precision glycaemic management, offering actionable insights for dietary interventions in the older population.

INTRODUCTION:Chronic kidney disease (CKD) is a major health burden, yet its underlying mechanisms and early predictors remain poorly understood. OBJECTIVES:This prospective study identified serum proteins associated with incident CKD and examined their upstream determinants related to inflammation and diet. METHODS:A total of 2,182 participants with baseline serum proteomic data and repeated measurements of estimated glomerular filtration rate (eGFR) over four 3-year intervals were included. Proteins associated with incident CKD were identified using multivariable-adjusted models, with internal validation from repeated measurements and external replication in the UK Biobank (UKB). Associations of CKD-related proteins with serum inflammatory markers, inflammation-related dietary indices, and serum carotenoids were also examined. RESULTS:Twenty-two proteins were associated with 9-year CKD risk (11 positively and 11 inversely; adjusted p < 0.05). A combined protein score predicted CKD with an area under the curve (AUC) of 0.75 in the discovery cohort, 0.76 in the internal validation using averaged protein data, and 0.70 in the UKB replication using 15 overlapping proteins. Standardized hazard ratios of CKD risk ranged from 1.31 to 1.66 for the top 4 risk proteins (PEDF, CFAD, RET4, APOH) and from 0.77 to 0.80 for the top 4 protective proteins (A1BG, A2AP, ENAM, GPX3) (all adjusted p < 0.01). Inflammatory markers (e.g., C-reactive protein) were positively associated with deleterious proteins and inversely associated with protective ones. Higher serum carotenoid concentrations and DASH diet scores were associated with lower inflammatory markers and more favorable CKD-related protein profiles. CONCLUSIONS:We identified 22 serum proteins associated with CKD incidence, supporting their potential for early prediction and mechanistic insight. Systemic inflammation was adversely associated with, whereas circulating carotenoids were beneficially associated with, CKD-related protein profiles.

Background and Objectives: Recent large-scale studies have consistently linked healthy dietary patterns to improved cardiometabolic health; however, the underlying biological pathways remain largely unclear, especially in non-European populations. In this study, we leverage data from four population-based cohorts (UK Biobank, NEO study, GNHS, and 10K) to investigate both common and cohort-specific biological pathways linking healthy dietary patterns to cardiometabolic disease through multi-omics profiling. Material and methods: In each cohort, we first assessed the associations between each of the five major dietary pattern scores (i.e., AMED, hPDI, DII, AHEI, and EDIH) and cardiometabolic disease risk using Cox or logistic regression models. To explore the potential mediating role, metabolomics and proteomics measurements were incorporated into the models. All models were adjusted for relevant confounders, and false discovery rate correction was applied to account for multiple testing. Results: With a total of 71,679 individuals without pre-existing cardiometabolic disease across four participating cohorts (UKB: 54,024, NEO: 4,838, GNHS: 3,201, and 10K: 9,616), we confirmed that adherence to healthy dietary patterns was associated with a 5-10% reduced risk of cardiometabolic disease. Three common biological pathways were identified: (1) mediation via large HDL particles and apolipoprotein F; (2) mediation via DNAJ/Hsp40 and triglyceride-rich lipoproteins; and (3) mediation via CRHBP-regulated HPA axis activity affecting triglyceride-rich lipoproteins. Conclusions: Our integrative multi‐omics analysis across diverse populations identifies novel biomarkers that connect healthy dietary patterns with cardiometabolic risk. These findings deepen our understanding of the biological mechanisms underlying diet‐related disease and hold promise for enhancing the development of precision nutrition interventions. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The NEO study is supported by the participating departments, the Division and the Board of Directors of the Leiden University Medical Centre, and by the Leiden University, Research Profile Area "Vascular and Regenerative Medicine". R.L-G is supported by JPI HDHL NUTRIMMUNE DIYUFOOD project. K.D is supported by China Scholarship Council (No. 202206210140). All the funders did not participant in the process of the whole study. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All studies obtained ethical approval from their respective institutional ethics committees, and all participants provided written informed consent. All the four datasets that we used in the manuscript are de-identified. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Original data are subject to access restrictions according to each cohort's data sharing policies.

Frailty is a significant health concern in the aging global population, particularly among middle-aged and older adults with gastrointestinal disease (GID). Early detection of individuals at increased risk is critical for implementing timely preventive and therapeutic interventions. This study aimed to develop and validate an interpretable machine learning (ML) model to assess frailty risk in this population. To overcome the "black box" nature of conventional ML models, we integrated Shapley Additive exPlanations (SHAP), which helps identify key predictors of frailty and improve the interpretability of the model's decision-making process. This study analyzed data from the 2013-2015 survey waves of the China Health and Retirement Longitudinal Study (CHARLS). To identify the most predictive variables for frailty, we employed a dual-method approach combining the Boruta algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) regression. We applied ten different ML algorithms to develop prediction models: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Classifier (GBC), Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbors (KNN), Decision Tree (DT), Multilayer Perceptron (MLP), Naive Bayes (NB), and Adaptive Boosting (AdaBoost). The area under the receiver operating characteristic (ROC) curve (AUC) served as the primary performance metric. Additional metrics, including sensitivity, specificity, precision, and the F1-score, were used to comprehensively evaluate model accuracy. Calibration curves were generated to assess the consistency between predicted probabilities and observed risk, and the Brier score was used as a quantitative measure of calibration accuracy. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit of each model. To understand the impact of individual predictors on the output, the SHAP method was used to provide transparent insights into each feature's contribution to the estimated frailty risk. A total of 1,404 participants met the eligibility criteria for this study, of whom 444 (31.62%) were classified as frail. Using the Boruta algorithm and LASSO regression, we identified 10 key predictors of frailty. Among all the ML models tested, the LR model showed the best overall performance, achieving an AUC of 0.759 (95% CI: 0.711-0.806). Shapley Additive exPlanations (SHAP) analysis further revealed the top five predictors of frailty in this population: depression, grip strength, education level, the total number of chronic diseases, and self-rated health. This study introduces an interpretable ML model that effectively detects frailty risk among middle-aged and older adults with GID. The model demonstrates strong predictive accuracy and transparency, supporting its potential as a clinical decision-support tool pending further external validation and real-world deployment. Such proactive measures could improve patient care and promote better long-term health outcomes in this population.

Abstract BackgroundWith the aging of the global population, preventing the onset of mobility limitations is considered a worldwide public health priority. ObjectiveThis study aimed to develop a predictive model for incident early mobility limitations (EMLs) in late middle-aged and older adults, based on a simple functional test and modifiable lifestyle factors to facilitate a home-based self-assessment of early mobility decline and promote lifestyle intervention strategies. MethodsOur study population was community dwellers aged 45 years and above who participated in the second and fourth waves of the Guangzhou Nutrition and Health Study. The included participants were healthy, nonfrail adults reporting no limitations in activities of daily living at baseline. At the 6-year follow-up, participants with poor physical performance (walking speed <1 m/s or handgrip strength <28 kg for males and <18 kg for females) or reporting some difficulty in walking and/or climbing stairs were classified as experiencing EMLs. Least absolute shrinkage and selection operator (LASSO) was used to identify predictors from various factors, and 6 machine learning models were trained and evaluated for EML prediction, using bootstrap-based techniques to address class imbalance. Predictive ability was quantified using the area under the receiver operating characteristic curve (AUC). Variable importance analysis was used to identify key predictors. ResultsA total of 1344 participants were included in the analysis, of which 206 (15.33%) developed EMLs after a median follow-up of 6.67 (IQR 5.91‐7.26) years. Those who developed EMLs were older, had a higher BMI, a lower alternate Mediterranean diet score, and poorer performance in the sit-to-stand (STS) test, as well as lower estimated muscle power from the STS at baseline. The final models included 6 out of 9 predictors: age, sex, BMI, alternate Mediterranean diet score, STS power, and dietary calcium intake. Four machine learning models (logistic regression, LASSO, support vector machine, and neural network) achieved an acceptable AUC value (≥0.70) or slightly below in the testing dataset, with the neural network performing the best (AUC 0.70, 95% CI 0.63‐0.77). Curve analysis showed positive net benefits for LASSO and logistic regression. Bootstrapping did not improve classification performance. Advanced age, lower adherence to Mediterranean diet, lower muscle power estimated from STS test, and higher BMI at baseline emerged as the most important predictors of EMLs. ConclusionsThis study shows that EMLs in Chinese adults aged 45 years and older can be predicted using easy-to-obtain physical performance measures, age, sex, BMI, and specific nutritional factors. The combination of prediction models and variable importance analysis provides valuable insights for the early identification and intervention of EMLs. More efforts are needed to validate our findings in external cohorts.

Frailty is a critical geriatric syndrome associated with modifiable lifestyle factors, yet their population-level contributions remain unclear. This study aimed to quantify the proportion of frailty incidence attributable to modifiable lifestyle factors and assess temporal trends in the US. We analyzed data from 26,247 participants in the Health and Retirement Study (2004–2020) involving adults aged ≥ 50 years. Frailty was assessed using the Paulson-Lichtenberg Frailty Index. Lifestyle exposures included smoking, drinking, physical inactivity, and sleep disturbance. Associations between lifestyle factors and frailty were examined using Cox models. Population attributable fractions (PAFs) were calculated for each factor, with temporal trends assessed using generalized estimating equations with splines. From 2004 to 2020, frailty incidence declined from 55.2 to 46.6 per 1,000 person-years. Current smoking (HR = 1.46, 95

Tea consumption may be associated with skeletal muscle health, but longitudinal evidence based on repeated assessments remains limited. We examined the associations of tea intake and serum biomarkers with repeated skeletal muscle measures and explored whether these associations might be partly explained by multi-omics features. In this prospective cohort, 3408 adults were followed for approximately 12 years. Skeletal muscle mass was measured by dual-energy X-ray absorptiometry, handgrip strength by digital dynamometry, gut microbial taxonomic and functional profiles by shotgun metagenomic sequencing, serum proteins by data-independent acquisition mass spectrometry, and fecal metabolites by targeted UPLC-MS/MS metabolomics. Linear mixed-effects models examined longitudinal associations, and mediation analyses estimated indirect effects. In longitudinal analyses, higher tea consumption frequency was associated with greater appendicular skeletal muscle mass, appendicular skeletal muscle index, and handgrip strength (β: 0.037–0.140; 95% CI: 0.002–0.205). Higher circulating flavan-3-ols showed similar associations with these muscle-related outcomes (β: 0.085–0.174; 95% CI: 0.007–0.254), whereas no significant associations were observed with walking speed. Exploratory multi-omics analyses identified tea-related differences in gut microbial species and functional pathways, fecal metabolites, and circulating proteins, including Gemmiger formicilis, amino acid biosynthesis pathways, fructose 1,6-bisphosphate, VTN, CFI, CNDP1, and ITIH4. Exploratory mediation analyses identified statistical indirect associations involving multi-omics features, with estimated proportions mediated ranging from 4.5% to 19.0%. Overall, higher tea consumption and circulating biomarkers were associated with greater skeletal muscle mass and strength, accompanied by distinct multi-omics features that may provide potential biological links between tea exposure and muscle-related outcomes.

BACKGROUND:Tea is a dietary source of flavan-3-ols, which may influence bone health, but prospective evidence linking tea intake and its circulating biomarkers with longitudinal bone mineral status remains limited. OBJECTIVES:To examine associations of tea consumption and its serum biomarkers with bone mineral status in Guangzhou Nutrition and Health Study. METHODS:In 1708 participants, whole-body, total hip, lumbar spine, and femoral neck bone mineral density (BMD) and bone mineral content (BMC) were measured using dual-energy X-ray absorptiometry at 4 repeated visits during a 13-y follow-up period. Baseline serum flavan-3-ols, including epicatechin, epigallocatechin gallate (EGCG), epicatechin gallate (ECG), epigallocatechin, and catechin, were quantified using ultra-high-performance liquid chromatography-tandem mass spectrometry. Tea consumption was categorized as non-, low-, moderate-, or high-frequency. Multivariable linear mixed-effects models, including exposure × visit interactions, were used to evaluate visit-averaged bone mineral measures and longitudinal trajectories. RESULTS:Among 1708 participants, significant tea consumption group × visit interactions were observed for whole-body, lumbar spine, and femoral neck BMD and whole-body BMC (P-interaction ranged from <0.001 to 0.022). Moderate-frequency tea drinkers generally had the highest visit-averaged adjusted marginal mean Z-scores, including 0.107 (95% CI: 0.025, 0.190) for whole-body BMD, 0.090 (0.012, 0.168) for lumbar spine BMD, and 0.108 (0.029, 0.187) for femoral neck BMD (P-diff ranged from <0.001 to 0.017). In analyses of serum tea biomarkers, higher biomarker categories were generally associated with more favorable bone mineral measures; however, the associations were not consistently linear, and several biomarkers showed nonmonotonic patterns. Visit-averaged differences were most extensive across ECG and EGCG categories. CONCLUSIONS:Moderate-frequency tea consumption and higher categories of selected circulating tea biomarkers were associated with more favorable bone mineral status.

BACKGROUND:The alternative pathway (AP) plays a crucial role in triggering complement activation and promoting chronic inflammation. This study aims to investigate the longitudinal association between AP and atherosclerosis, and explore the potential role of gut microbiota and inflammatory factors in their association. METHOD:This study was based on a 9-year prospective cohort of 3382 participants from Guangzhou, China (mean age±SD, 57.75±5.85 years; 68.8% female), with data on serum APACPs (AP-associated complement proteins) and carotid plaque (measured by ultrasound) repeatedly measured up to 3×. Baseline inflammatory markers were evaluated in 923 participants, and gut shotgun metagenome data were obtained from 1567 participants. Mendelian randomization analysis was performed using genome-wide significant genetic variants as instrumental variables to suggest potential causal associations. RESULTS:Both longitudinal and prospective analyses consistently demonstrated positive associations between carotid plaque and 3 complement components: C3 (complement C3; odds ratios [95% Cl] for the highest versus lowest quartiles, 1.36 [1.07-1.74] in longitudinal analysis and 1.29 [1.06-1.56] in prospective analysis), CFB (complement factor B; 1.36 [1.07-1.72] in longitudinal analysis and 1.39 [1.15-1.69] in prospective analysis), and CFH (complement factor H; 1.39 [1.10-1.76] in longitudinal analysis and 1.31 [1.07-1.61] in prospective analysis). Mendelian randomization analysis suggested a potential causal association between CFB and carotid plaque. Inflammatory factors (CRP [C-reactive protein] and IL-6 [interleukin-6]) and microbial species (Ruminococcus bromii, Roseburia hominis, Rothia mucilaginosa, Collinsella stercoris, Olsenella scatoligenes, and Bacteroides massiliensis) were significantly associated with both APACPs and carotid plaque (P<0.05). For example, butyrate-producing bacterium R bromii was inversely associated with CFB and carotid plaque (odds ratios [95% CI], 0.83 [0.79-0.88]) and may mediate the CFB-carotid plaque association (proportion mediated, 13.5%; P=0.005). Microbial risk score (weighted sum of selected microbial species; proportion mediated, 42.6%; P<0.001) and total immune factors (the sum of all inflammatory factors; proportion mediated, 19.0%; P=0.002) mediated the association between Total-APACPs (sum of standardized carotid plaque-related APACPs [C3, CFB, and CFH]) and carotid plaque. CONCLUSIONS:Our study showed a negative association between the AP and carotid plaque in a longitudinal cohort. Gut microbiota and inflammatory biomarkers may provide mechanistic insights into the association between the AP and atherosclerosis. Our findings pave the way for the development of new therapeutic targets for atherosclerosis.

Circulating proteomics acts as an intermediate phenotype linking genetic susceptibility to MASLD. However, current evidence rarely establishes a direct concordance between serum protein levels and hepatic gene expression. We aimed to perform a multi-cohort joint analysis of serum proteomics and transcriptomics to characterize essential molecular features for MASLD. For the serum proteomic analysis of simple steatosis (MASL), we conducted a cross-sectional investigation in an MRI-based cohort (N/cases: 1048/428) and further examined the prospective association between protein features and MASL incidence (N/cases: 2945/1947) ascertained by ultrasonography over a median 9.8-year follow-up in the Guangzhou Nutrition and Health Study (GNHS) cohort. In parallel, we characterized fibrosis and MASH-related transcriptional features using liver transcriptomics from the MASH cohort (N = 94) and validated these gene signatures for MASH in liver transcriptomes from the independent Japanese and German populations (N = 98 and 59). The serum proteomic analysis identified the C3, C9, F9, VTN, AFM, APOD, APOF, and SHBG proteins were significantly associated with MASL risk (P < 0.05). Liver transcriptomic analysis revealed a coordinated downregulation of C9, C4BPB, C1RL, APOF, and ITIH4 in the high NAS group, implicating dysregulated complement activation as a critical mechanism driving disease progression. Furthermore, SHBG, A2M, GSN, C7, LUM, IGHG3, and IGFALS were associated with liver fibrosis stages, and pathways related to extracellular exosomes and vesicles were implicated in fibrotic development. Consistently, in the Japanese and Germany cohorts, APOF, GSN, and LUM exhibited aberrant expression in both MASH patients and those with high NAS scores. The multi-cohort study identified specific serum protein signatures associated with MASL risk, which correspond to dysregulated gene expression patterns in hepatocytes. These findings bridge the gap between systemic circulatory changes and intrahepatic pathological progression, providing not only robust non-invasive biomarkers for early stratification but also potential mechanistically-driven therapeutic targets for halting the progression of MASLD.

Greater salty taste preference was consistently associated with a higher risk of metabolic dysfunction-associated steatotic liver disease across populations with varying salt intake.

The gut microbiome undergoes profound changes during aging, including shifts in the microbial antibiotic resistome: the collective repertoire of antibiotic resistance genes (ARGs). We developed a language model-based ARG explorer (LARGE), which used frozen ESM-2 and FGeneBERT encoders to embed protein sequences and three independent multilayer perceptron classifiers to predict resistance type, mechanism, and gene name. LARGE was trained on 34,008 ARG and 30,309 non-ARG sequences from the NCRD95 and UniProt databases and benchmarked against DeepARG, RGI, PLM-ARG, and ARGNet using F1-scores on multiple independent test sets. We applied LARGE to a longitudinal cohort (GNHS, n = 1078, mean baseline age 64.6 years), and validated key findings in independent cohorts (CHNS, n = 356; ZMSC, n = 1361). Linear mixed-effects models were used to assess age-related ARG trajectories, and logistic regression examined associations with 13 chronic diseases. In benchmark test, LARGE substantially outperformed all four tools, achieving superior F1-scores. In longitudinal cohorts, LARGE revealed declining ARG burdens in dominant resistance drug classes and identified 31 species with significant ARG changes. The ARGs of these 31 species are closely associated with multiple age-related chronic diseases, such as chronic kidney disease and coronary heart disease; a specific chronic kidney disease ARG score was consistently associated with disease risk in both discovery and validation cohorts. The findings highlight the pivotal role of aging in resistome evolution and potentially provide microbiome-targeted interventions to mitigate antibiotic resistance and promote healthy aging.

To elucidate the molecular characteristics of synergistic interactions across the clinical stages of coronary heart disease (CHD)-specifically stable angina pectoris (SAP), unstable angina pectoris (UAP), and acute myocardial infarction (AMI)-through integrated metabolomic and proteomic analyses. Based on a cohort including SAP, UAP, AMI, and healthy controls, metabolomic and proteomic analyses were performed to identify differentially expressed molecules, followed by KEGG pathway enrichment analysis. Pathways co-enriched across both omics platforms were selected to construct metabolite-protein interaction networks. The number of pathways co-enriched in both metabolomic and proteomic analyses increased markedly with disease stage. Only two pathways (histidine metabolism and arginine and proline metabolism) were identified in the SAP stage; this number increased to five in the UAP stage (including ferroptosis and efferocytosis) and expanded to 25 in the AMI stage, encompassing three major functional modules: immune inflammation, metabolic reprogramming, and cell signaling. The core network exhibited a stepwise increase in connectivity, shifting from a sparse structure in the SAP stage to a highly interconnected architecture in the AMI stage, with L-glutamate and KNG1 identified as the central hubs in this cross-sectional network. In addition, CNDP1 exhibited a stage-dependent functional transition, shifting from downregulation in SAP to upregulation in AMI. In this cross-sectional analysis, metabolic dysregulation and immune activation exhibited stepwise increases in interconnectivity across the SAP, UAP, and AMI groups, with the most extensive crosstalk observed in the AMI stage-a network configuration consistent with a tightly coupled "molecular storm". These findings provide novel insights into stage-associated molecular signatures of CHD and identify candidate hub molecules for stage-oriented therapeutic investigation.

Particulate matter (PM) with distinct physicochemical properties may drive differential respiratory health risks. We investigated associations of long-term exposure to size- and constituent-specific PM with chronic respiratory diseases (CRDs) and evaluated respiratory benefits linked to PM composition changes under China’s air control policies. A prospective cohort of 2100 adults (baseline mean age, 57.2 years) in Guangzhou, China, was followed between 2008 and 2021. Residential exposure to size-fractionated PM (PM1, PM2.5, PM10) and six PM2.5 chemical constituents were estimated with 1-km spatiotemporal models. Structured questionnaires collected diagnoses of incident CRDs every 3 years, including chronic bronchitis, asthma, and emphysema. Cough and wheezing were assessed during policy intervention periods (2013–2017). A time-varying Cox model examined PM-CRD associations, while a logistic regression analyzed PM reduction-associated respiratory symptoms. Variance decomposition and quantile g-computation assessed PM size-specific and constituent mixture effects. Approximately, 7.7

The genetic architecture of glycemic dynamic metrics derived from continuous-glucose monitoring (CGM) across different populations remains poorly understood. Here, we conducted a trans-ethnic genome-wide association study (GWAS) meta-analysis of 20 CGM-derived glycemic traits, building upon a previously established European-ancestry CGM dataset and extending it through the inclusion of additional cohorts, in up to 9677 individuals originating from 2051 Chinese, 901 Dutch, and 6725 Israelis. Across 20 glycemic traits, we identified 18 genome-wide significant associations, of which 9 met study-wide significance, and three variants were novel. These variants indicated a shared genetic basis for continuous glycemic regulation and exhibited consistent patterns with those of sequential fingerstick glucose tests. Our findings further demonstrated that the identified genetic variants were enriched in pathways related to the nervous system. These findings were further supported by observed associations with brain magnetic resonance imaging (MRI) metrics, high CGM-related gene expression and co-regulation of quantitative trait loci in brain tissues. Additionally, we observed a positive relationship between genetic liability for the coefficient of variation (CV) and total cholesterol and a bi-directional putative causal relationship between hyperglycemia and type 1 diabetes across trans-ethnic populations. Moreover, we established a polygenic risk score (PRS) for additional participants and reported that certain glycemic traits were significantly associated with the risk of diabetes or pre-diabetes. These variants constituting the PRS demonstrated high transferability across general populations and pregnant women. Overall, our study yields unique insights into the high trans-ethnic and generalizable genetic architecture of CGM-derived glycemic profiles, supporting improved characterization of interindividual differences in glycemic dynamics and underscoring the potential for more personalized glucose management.