Background:Emerging research has associated non-conventional lipid indices with hypertension risk; however, the joint contribution of these lipid metrics and inflammatory biomarkers to hypertension susceptibility remains unclear. Using a nationally representative cohort, we aimed to examine the independent associations of non-conventional lipid indices and their inflammatory composites with incident hypertension. Methods:We included 6891 participants from the China Health and Retirement Longitudinal Study (2011-2020). We used multivariable Cox proportional hazards regression to assess the association between baseline and cumulative non-conventional lipid-inflammatory parameters and incident hypertension. We used restricted cubic spline modelling to characterise dose-response relationships. We evaluated receiver operating characteristic curves, net reclassification improvement, and integrated discrimination improvement as secondary conditional analyses. Results:Over a median follow-up of 7.79 years, 2532 incident hypertension cases occurred. Higher tertiles of non-conventional lipid indices were associated with progressively increased hypertension risk. In the primary model, elevated baseline and cumulative non-conventional lipid metrics were associated with a higher risk of hypertension across all composite indices. The lipoprotein combine index-C-reactive protein showed the largest effect size (hazard ratio = 1.59; 95% confidence interval = 1.44-1.75). Composite indices demonstrated modest incremental discrimination over individual measures (area under the curve range = 0.55-0.58). Conclusions:Non-conventional lipid indices are independently associated with incident hypertension among middle-aged and older Chinese adults. Composite lipid-inflammatory markers provide modest incremental prognostic information beyond conventional lipids, though their standalone discriminatory performance is weak.

The cardiovascular-liver-metabolic (CLM) overlap population faces an elevated risk of mortality, yet prognostic tools integrating dynamic insulin resistance (IR) remain limited. This study aimed to evaluate the prognostic impact of estimated glucose disposal rate (eGDR) on mortality within the CLM overlap population. An analysis of a prospectively linked national dataset was conducted on 1,126 eligible NHANES participants (1999–2018) with concomitant cardiovascular disease (CVD) and metabolic dysfunction-associated steatotic liver disease (MASLD). eGDR was calculated using clinical parameters. Multivariable Cox regression, restricted cubic splines, Kaplan-Meier survival analysis, and causal mediation models were employed to assess mortality associations and the mediating role of inflammation. Over a median follow-up of 85 months, 453 deaths occurred, including 155 cardiovascular deaths. Lower eGDR showed a borderline inverse association with cardiovascular mortality (HR = 0.84, 95

Background: Cardiovascular disease (CVD) is the leading cause of mortality worldwide. The C-reactive protein–triglyceride–glucose (CTI) index captures metabolic inflammation and insulin resistance, while obesity indices reflect adiposity-related cardiometabolic risk. However, the predictive value of combining CTI with obesity indices for CVD remains unclear. Objectives: To examine the prognostic utility of CTI combined with established and novel obesity indicators for CVD, and to benchmark their predictive efficacy. Design: Nationally representative prospective cohort study. Methods: Using data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative prospective cohort, 7847 adults aged 45 years and above without baseline CVD were enrolled. CTI was calculated from C-reactive protein, fasting blood glucose, and triglycerides (TG); each obesity index was combined with CTI to form composite parameters (CTI-body mass index (CTI-BMI), CTI-waist circumference (CTI-WC), CTI-waist-to-height ratio (CTI-WHtR), CTI-body roundness index (CTI-BRI), CTI-weight-adjusted waist index (CTI-WWI), CTI-Chinese visceral adiposity index (CTI-CVAI), CTI-a body shape index (CTI-ABSI)). We employed Cox proportional hazard models, restricted cubic spline analyses, Kaplan–Meier survival plots, ROC curve evaluations, and weighted quantile sum (WQS) regression to examine the links and prognostic utility of each metric with incident CVD across baseline and cumulative exposure tiers. Results: Over a 9-year follow-up period, 1938 new-onset CVD cases were documented. Following multivariable adjustment, each CTI-obesity composite metric showed a significant positive link to CVD risk ( p < 0.05). Moreover, all CTI‑obesity composite parameters showed stronger associations with CVD than CTI alone. CTI‑CVAI demonstrated the relatively best predictive accuracy among the evaluated indices, though absolute discriminatory ability remained modest (baseline area under the ROC curve (AUC): 0.596; cumulative AUC: 0.600). Trajectory clustering found that, versus the well-managed group, the poorly managed subgroup had 21%–71% higher CVD risk (max 71% for CTI-WC). WQS regression indicated that CRP and obesity indices (especially CVAI) were the major contributors in the mixed exposure; cumulative exposure WQS analysis showed that cumulative TG and cumulative obesity parameters contributed the most. Conclusion: CTI combined with obesity indices, particularly CTI-CVAI, was associated with modestly improved predictive ability for CVD risk compared with CTI alone. This parameter may offer incremental information for CVD risk stratification by integrating metabolic inflammation and visceral fat markers.

Both frailty and symptomatic knee osteoarthritis (SKOA) are prevalent among older adults, with a greater incidence noted in women. The present study aimed to classify the trajectory types pertaining to the Frailty Index (FI) in postmenopausal women, explore the longitudinal correlational link among different FI trajectory profiles as well as the likelihood of developing SKOA, and evaluate the predictive value of FI trajectories for SKOA along with the robustness of this association. A long-term prospective follow-up investigation was conducted on the basis of the CHARLS cohort of postmenopausal women. Latent Class Growth Modeling (LCGM) was applied to classify the FI trajectory patterns across five waves from 2011 to 2020. Differences in the cumulative incidence risk of SKOA among trajectory subgroups were evaluated using Kaplan-Meier survival curves paired with the log-rank statistical test. The strength of this association was assessed using Cox proportional hazards regression models. Model performance across FI trajectories, as well as baseline frailty, was assessed and contrasted using ROC curves, Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI). E-value estimation, subgroup analyses, and sensitivity analyses were performed to gauge the robustness of this association. For this investigation, data pertaining to 4636 postmenopausal women within the CHARLS study cohort were analyzed, with a systematic comparison of the predictive capacity of FI trajectories and baseline frailty status in assessing SKOA. The results showed that both FI trajectories and baseline frailty status showed statistically significant correlations with SKOA. Specifically, in contrast to the low-baseline slight-increase trajectory subgroup (Class 1), the high-baseline consistently rising trajectory subgroup (Class 3) exhibited the greatest likelihood of developing SKOA (HR: 3.86, 95% CI: 3.19-4.68), followed by the moderate-baseline gentle-increase trajectory group (Class 2) (HR: 3.35, 95% CI: 2.98-3.77). In comparison with baseline frailty, FI trajectories exhibited stronger predictive capability, as evaluated by the ROC curve, NRI, and IDI. Additional analyses, such as E-value calculations, subgroup analyses, and multiple sensitivity analyses, validated the stability of these relationships. The findings from this investigation highlight the significance of frailty and FI trajectories in evaluating SKOA among postmenopausal women.

Objective Social risk factors are key determinants of migraine occurrence and progression. This study assessed the association between the social risk profile (SRP) and the prevalence of self-reported severe headache or migraine and all-cause mortality in US adults, and developed machine learning prediction models to explore feature contributions to internal risk stratification.Methods Using data from the National Health and Nutrition Examination Survey (NHANES) 1999-2004, weighted multivariate logistic regression evaluated the SRP-migraine association, and a weighted Cox proportional hazards model assessed the influence of SRP on all-cause mortality among migraine patients. The Boruta and Lasso algorithms selected predictive features for nine machine learning classifiers to predict migraine risk and four survival models to assess mortality risk. SMOTE was applied within cross-validation folds to address class imbalance. SHAP values were utilized to identify the most critical features.Results Among 11,861 participants, 2,355 self-reported severe headache or migraine; 2,351 were included in the mortality analysis after excluding 4 individuals with missing survival status. Over a median follow-up of 206 months (IQR: 187-224), 471 deaths occurred. Higher SRP scores were associated with lower migraine prevalence (OR = 0.44, 95% CI: 0.34-0.57) and lower all-cause mortality (HR = 0.31, 95% CI: 0.19-0.50). XGBoost achieved the best performance for migraine prediction (AUC = 0.732, 95% CI: 0.712-0.753), while Random Survival Forest performed best for mortality prediction (AUC = 0.882). SHAP analysis identified age, SRP, and cotinine as key predictors. Decision curve and calibration analyses demonstrated acceptable internal performance, supported by ten-fold cross-validation.Conclusion SRP is an independent predictor of migraine risk and long-term survival. The machine learning analysis provides exploratory insights into feature importance for risk stratification within the development sample, while the regression-based association estimates support the epidemiological significance of social determinants in migraine.

Traditional Medicine(TM),particularly Traditional Chinese Medicine(TCM),is an indispensable compo-nent of the global healthcare system,offering unique insights to modern medical science.Clinical efficacy is the bedrock for the inheritance and development of TM.To meet the growing demand for high-quality healthcare,it is imperative to integrate TM with mod-ern technology to address the issue of insufficient evi-dence for the efficacy of TM.To evaluate the clinical efficacy of TM,clinical trials are necessary,especially good clinical trials,which conform to the general prin-ciples of scientific research and also take into account the characteristics of traditional therapies.To promote the development of high-quality clinical trials that are in line with the features of TM,the attending experts held an in-depth discussion and reached the Rome con-sensus on"Good Clinical Trials for TM(GCT-TM),"at the 18th Academic Annual Meeting of the Clinical Efficacy Evaluation Committee of the World Federation of Chinese Medicine Societies and the 8th International Forum on Evidence-Based Chinese Medicine,held in Rome on June 26,2025.

IntroductionTo decode the pathology of Alzheimer’s disease (AD), this study employs multi-omics approaches and bioinformatics analyses to explore AD-associated differentially expressed genes (DEGs), dissect the underlying mechanisms, and thereby facilitate the identification of core genes as well as the development of targeted therapeutic strategies.MethodsSix independent AD datasets were collected from the Gene Expression Omnibus (GEO) database, and data were processed and normalized using the R software. The evaluation of relationships between differentially expressed genes (DEGs) and AD encompassed differential expression analysis, expression quantitative trait loci (eQTL) analysis, and Mendelian randomization (MR) analysis. Additionally, gene set enrichment analysis (GSEA), immune cell correlation analysis, and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were employed to investigate the functional roles and pathways of these genes. Machine learning approaches were applied to identify potential genes from differentially expressed genes (DEGs) associated with AD. The diagnostic performance of these candidate genes was assessed using a nomogram and receiver operating characteristic curves. The expression levels of the identified genes were further validated via quantitative real-time polymerase chain reaction (qRT-PCR).ResultsDifferential gene analysis identified 294 highly expressed genes and 330 lowly expressed genes, and MR analysis identified 10 significantly co-expressed genes associated with AD, specifically METTL7A, SERPINB6, VASP, ENTPD2, CXCL1, FIBP, FUCA1, TARBP1, SORCS3, and DMXL2. Noteworthy observations naive CD4+ T cells in AD, with this distinct from CIBERSORT analysis included the presence of unique immune cell subset further underscoring the critical role of immune processes in the pathogenesis and progression of the disease. METTL7A, SERPINB6, VASP, ENTPD2, FIBP, FUCA1, TARBP1, SORCS3, and DMXL2 were selected for nomogram construction and machine learning-based assessment of diagnostic value, demonstrating considerable diagnostic potential. Furthermore, the significance of the identified key genes was corroborated using both the GEO validation set and qRT-PCR.ConclusionMETTL7A, SERPINB6, VASP, ENTPD2, FIBP, FUCA1, TARBP1, SORCS3, and DMXL2 may regulate the progression of AD. These findings not only deepen our mechanistic understanding of AD pathology but also provide potential candidate genes for the development of targeted therapeutic strategies against AD.

Identifying dependable prognostic indicators is essential for the efficient management of metabolic dysfunction-associated steatotic liver disease (MASLD). The index of hemoglobin glycation (HGI) has been demonstrated to be closely linked to the onset and advancement of MASLD. Currently, no studies have investigated the relationship between HGI and mortality rates among MASLD patients. This study analyzed data from the National Health and Nutrition Examination Surveys (NHANES) covering 1999 to 2018, involving 8,257 adult patients diagnosed with MASLD. The HGI was determined using a linear regression model that correlated hemoglobin A1c (HbA1c) with fasting plasma glucose (FPG). The study employed Kaplan-Meier survival curves and weighted Cox proportional hazards models to evaluate the independent association between HGI and mortality risk. The study utilized restricted cubic splines (RCS) to visually depict the relationship between HGI and mortality risk. Over a median follow-up duration of 97.0 months, there were 1,352 recorded deaths, among which 386 were attributed to cardiovascular disease (CVD). Participants were classified into two groups based on their HGI values: the high HGI group (≥ 0.4605) and the low HGI group (< 0.4605). The results from the weighted Cox proportional hazards model indicated that individuals in the high HGI group faced a significantly higher risk of all-cause mortality (HR 1.47, 95% CI 1.19–1.82, P < 0.001). However, no significant increase in CVD mortality risk was observed (HR 1.38, 95% CI 0.95–1.99, P = 0.090). The RCS analysis identified a U-shaped association between HGI and both all-cause mortality and CVD mortality, with critical points at -0.0564 and − 0.0573, respectively. Below the critical points, HGI was negatively correlated with all-cause mortality (HR 0.82, 95% CI: 0.72–0.92, P < 0.001) and not significantly associated with CVD mortality (HR 0.78, 95% CI: 0.57–1.07, P = 0.126). Above the critical points, HGI was significantly positively correlated with both all-cause mortality (HR 1.36, 95% CI: 1.20–1.53, P < 0.001) and CVD mortality (HR 1.44, 95% CI: 1.11–1.88, P = 0.007). Further subgroup and interaction analyses corroborated the reliability of these findings. HGI could potentially function as a useful and dependable marker for evaluating all-cause mortality and cardiovascular mortality in MASLD patients.

Traditional Chinese medicine (TCM) embodies profound theoretical and practical wisdom, yet its inheritance and development face significant challenges in the context of modern medicine's rapid advancement. Traditional inheritance models, reliant on experiential accumulation and master-apprentice transmission, lack dynamic tracking mechanisms and robust scientific validation systems. This hinders their ability to meet modern demands for precision and objectivity and constrains TCM's international competitiveness. Addressing this, the present study constructs an innovative paradigm for the "living inheritance" of TCM, using cardiovascular diseases (CVDs) as a demonstrative focus. This paradigm integrates "dynamic empirical knowledge mining, scientific evidence transformation, and humanistic evaluation iteration". The study comprises two core modules: methodological design and multidimensional evaluation. The methodological design module systematically establishes an "individual-group-intergenerational" academic evolution framework. This is achieved through: (1) longitudinal medical records analysis: employing semi-quantitative strategies to track the temporal evolution of individualized diagnosis and treatment by master TCM practitioners, capturing dynamic pattern differentiation and therapeutic adjustments over extended periods (>= 3 years). A standardized specification for recording complex cases was developed. (2) Evidence-based transformation of master practitioner experience: building a dynamic evidence chain via a stepwise approach. This involves in-depth analysis of effective longitudinal records and master interviews to extract core prescriptions and treatment logic, case-control studies to identify factors influencing treatment efficacy and optimize formulas, and prospective studies (including randomized controlled trials [RCTs] and real-world studies [RWS]) to scientifically validate clinical effectiveness and applicability boundaries in diverse settings. (3) Cross-generational integration of humanistic and academic thought: integrating classical literature and the evolving experiences of successive generations of masters (e.g., Wang Qingren, Guo Shiqiu, Weng Weiliang) to form cohesive academic lineages (e.g., the "disease-pattern-symptom combined" diagnostic system, the "treating heart disease must resolve stasis" theory, and the 12-method Blood-Activating & Stasis-Resolving system). The multidimensional evaluation module establishes a dual-track assessment system integrating pattern quantification and humanistic efficacy: (1) syndrome differentiation precision via objective four-examination techniques: developing standardized, quantifiable methods for inspection (facial/lingual imaging, infrared thermography), auscultation & olfaction (acoustic analysis, exhaled VOC detection), inquiry (structured TCM syndrome scales), and palpation (digital pulse wave analysis using sensors compliant with ISO standards). This aims to create reproducible international protocols linking TCM signs/symptoms to modern cardiovascular indicators (e. g., coronary stenosis, LVEF, BNP). (2) Humanistic efficacy evaluation via co-created medical records: centering patient narratives through semi-structured interviews, reflective diaries, and patient-reported outcomes. This captures subjective experiences, expectations, and quality-of-life impacts, integrating them with objective clinical data using natural language processing and machine learning to form a composite assessment model that reflects patient-centered values. This research provides a universal methodological framework designed to overcome key bottlenecks in TCM inheritance: fragmentation of experiential knowledge, the "black box" nature of individualized treatments, and subjective efficacy evaluation. By enabling the visualization of dynamic therapeutic trajectories, transforming individual expertise into validated group evidence, and incorporating the patient perspective, the paradigm facilitates the transition from static documentation to the "living inheritance" of TCM knowledge, particularly within cardiovascular medicine. It offers a replicable pathway for integrating TCM into modern healthcare systems, enhancing its global influence, and advancing the modernization of traditional medicine. Future directions include leveraging AI/IoT for enhanced data collection and analysis, fostering interdisciplinary cardiovascular TCM talent, expanding application in complex CVDs, and strengthening international adaptation through evidence standard alignment, data interoperability mechanisms, and cross-cultural communication strategies.

The atherogenic index of plasma (AIP), a novel composite lipid index, is closely linked to cardiovascular disease (CVD). However, lipid levels fluctuate dynamically, and it is unclear whether there are differences in the association of single-timescale, multiple-timescale, or AIP change trajectories with new-onset cardiovascular disease. Hence, the aim of this study was to investigate the correlation between different AIP parameters and the occurrence of CVD. Data were derived from the China Health and Retirement Longitudinal Study (CHARLS) conducted in 2011, 2015, 2018, and 2020, focusing on middle-aged and elderly populations aged over 45 years. Changes in AIP were classified into three groups using K-means cluster analysis: the low-level growth group (Class 1), the medium-level growth group (Class 2), and the high-level decline group (Class 3). Furthermore, participants were grouped based on tertiles (T) of cumulative AIP (Cum-AIP). Our multivariate logistic regression model integrated adjustments for potential confounders in order to investigate the association between Cum-AIP and the occurrence of CVD. Additionally, we employed restricted cubic spline (RCS) modeling to illustrate the dose-response relationship of baseline AIP, mean AIP, and Cum-AIP with CVD risk. During the 5-year follow-up period, 927 participants experienced the onset of CVD. After controlling for various potential confounding factors, it was observed that individuals in Class 2 demonstrated a notably heightened risk of CVD (OR = 1.23, 95% CI: 1.03, 1.46) and stroke (OR = 1.35, 95% CI: 1.02, 1.80) in comparison to those in Class 1. However, there was no significant difference in the risk of heart disease (OR = 1.21, 95% CI: 0.99, 1.48). In contrast, a noteworthy correlation was solely observed in the Class 3 group concerning the risk of stroke occurrence (OR = 1.60, 95% CI: 1.06, 2.42). The adjusted OR (95% CI) for CVD in the T2 and T3 groups were 1.21 (1.00, 1.46) and 1.30 (1.05, 1.62), respectively, compared to the T1 Cum-AIP group (P for trend = 0.017). Through the RCS model, we identified a positive and linear relationship between baseline AIP, mean AIP, and Cum-AIP with the incidence of CVD. However, the association between baseline AIP and CVD was weak. Sustained elevation of AIP is linked to a heightened risk of CVD in the general population. The elevated mean, and Cum-AIP levels are associated with a heightened risk of CVD. These findings indicate that AIP can serve as a valuable indicator of dyslipidemia, and continuous monitoring and early intervention targeting AIP may contribute to a further reduction in the incidence of CVD.

BackgroundEmerging evidence indicates a potential correlation between remnant cholesterol (RC) and the development of vascular damage and hypertension. Nevertheless, the precise relationship between RC and hypertension in relation to renal function remains uncertain. The objective of this investigation was to employ a cohort design to evaluate the intricate correlation between RC and renal function in relation to hypertension.MethodsThe present investigation utilized data from the China Health and Retirement Longitudinal Study (CHARLS), encompassing a total of 5,109 participants, for comprehensive data analysis and examination. Cox regression analysis was employed to examine the interplay among RC, renal function, and hypertension within the context of this research study. This study utilized restricted cubic spline (RCS) analysis to elucidate the interaction between RC, renal function, and hypertension, specifically examining the mediating role of renal function in the RC-hypertension nexus. Furthermore, we employed mediation analysis to investigate the potential mediating role of renal function in the association between RC and hypertension.ResultsAfter a 9-year follow-up period, the incidence of hypertension in the population under investigation was observed to be 19.01%. The Kaplan-Meier curves demonstrated a notable and statistically significant elevation in the prevalence of hypertension within the subgroup characterized by higher RC and impaired renal function (P <0.001). However, in Cox regression analyses, the risk of developing hypertension was significantly higher (P <0.05) in those with high RC and high estimated glomerular filtration rate (eGFR), and those with high RC and low eGFR, compared with those with low RC and high eGFR, after adjusting for confounders. The analysis of RCS demonstrated a significant positive linear correlation between baseline RC and the prevalence of hypertension. Additionally, there was a notable negative linear correlation observed between eGFR levels and the prevalence of hypertension. RC and eGFR did not interact with any of the subgroup variables. eGFR lowering mediated 6% of the associations between RC and hypertension.ConclusionThe findings of this study unveiled a substantial correlation between elevated RC, diminished eGFR levels, and the risk of developing hypertension. In addition, renal function may mediate the correlation between RC and hypertension risk.

BACKGROUND:Alzheimer's disease (AD) was a progressive neurodegenerative disorder characterised by an insidious onset and gradual cognitive decline. It remained a significant global health challenge. Methylparaben (MEP), a preservative commonly used in cosmetics and food processing, had been associated with the development and progression of AD. METHODS:First, we acquired the initial three-dimensional (3D) structure of MEP from PubChem (CID: 7456), followed by structural optimization via energy minimization using Chem3D software to complete its 3D structural characterisation. This was followed by systematic target prediction across the SwissTargetPrediction, SEA, GeneCards and OMIM databases. We then constructed protein-protein interaction (PPI) networks using STRING and visualised them in Cytoscape to identify core targets. Molecular docking simulations using CB-Dock2 elucidated the binding affinities between MEP and the key proteins. Experimental validation combined Gene Expression Omnibus (GEO) database analysis with quantitative reverse transcription polymerase chain reaction (qRT-PCR) to quantify transcriptional changes in SK-N-SH neural cells. RESULTS:A total of 153 potential targets associated with MEP and AD were identified. Ten core targets were determined through screening using the STRING platform and Cytoscape software, including HIF1A, IGF1R, PDGFRB, PTK2, VCAM1, CXCL12, ERBB2, ESR1, JAK2 and BCL2L1. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that the core MEP targets in AD primarily concentrate on the following key signalling pathways: Neuroactive ligand-receptor interactions, EGFR tyrosine kinase inhibitor resistance, HIF-1 signalling pathway and gamma-aminobutyric acid (GABA) synapse. Molecular docking simulations using CB-Dock2 confirmed a high binding affinity between MEP and these core targets. To investigate the mechanism of action of MEP, we validated the findings using clinical datasets and the human neuroblastoma cell line SK-N-SH. Upregulation of ten transcriptional expressions was observed, suggesting that MEP might influence cognitive function in patients with AD. CONCLUSION:This study elucidated the potential molecular mechanisms of MEP in the progression of Alzheimer's disease-related tau pathology, offering new insights for the prevention and intervention of degenerative diseases that might be triggered by excessive exposure to MEP environments.

Introduction: Traditional Chinese Medicine (TCM) has been found to be effective in the treatment of Helicobacter pylori (H. pylori). However, the quality of evidence is limited and there are few studies on TCM of H. pylori. This trials aimed to examine whether additional use of TCM can lead to better efficacy in the eradication rate of H. pylori. Methods: This study consisted of a multicenter randomized controlled trial and a nonrandomized cohort. Subjects from TCM hospitals were allocated to a treatment group that received triple therapy or a combination group that received triple therapy combined with 2 weeks of herbal bulk or 2 weeks of herbal soup or 4 weeks of herbal soup. Patients in western hospitals received triple therapy (non-randomized control group). The eradication rate of H. pylori, the recurrence rate, the TCM symptom score, the patient-reported outcome scale (PRO), and safety indicators were observed and compared. Results: 960 patients screened, 768 were randomly assigned to the randomized treatment group, and 192 were assigned to the non-randomized group. No difference in the H. pylori eradication rate between the combination group compared with triple therapy (P > 0.05). Significant improvements were observed in TCM symptom scores, the quality of life in the combination group (P < 0.05). Moreover, it had a lower incidence of adverse reactions and rates of H. pylori recurrence (P < 0.05). Conclusion: The additional use of TCM in triple therapy improves the quality of life, reduces adverse effects, and recurrence rates.

BACKGROUND:Prediabetes strongly increases the risk of type 2 diabetes and cardiovascular events. However, lifestyle intervention, the first-line treatment for prediabetes currently, was inconsistently beneficial for glucose metabolism, and the conventional medicines, such as metformin, is controversial for prediabetes due to the possible side effects. PURPOSE:This study was designed to evaluate the effects of Zhenyuan Capsule, a Chinese patented medicine consisting of ginseng berry saponins extracted from the mature berry of Panax Ginseng, on the glucose metabolism of prediabetic patients as a complementary therapy. STUDY DESIGN AND METHODS:In this randomized, double-Blinded, placebo-controlled, crossover trial, 195 participants with prediabetes were randomized 1:1 to receive either placebo followed by Zhenyuan Capsule, or vice versa, alongside lifestyle interventions. Each treatment period lasted 4 weeks with a 4-week washout period in between. The primary outcomes were the changes in fasting plasma glucose (FPG) and 2-h postprandial plasma glucose (2-h PG) from baseline. Secondary outcomes includes the changes in fasting and 2-h postprandial insulin and C-peptide, the homeostatic model assessment-insulin resistance (HOMA-IR) index and quantitative insulin sensitivity check index (QUICKI) from baseline. Blood lipids and adverse events were also assessed. RESULTS:Compared with placebo, Zhenyuan Capsule caused remarkable reduction in 2-h PG (-0.98 mmol/l) after adjusting treatment order. Zhenyuan Capsule also reduced the fasting and 2-h postprandial levels of insulin and C-peptide, lowered HOMA-IR index (-1.26), and raised QUICKI index (+0.012) when compared to placebo. Additionally, a significant increase in high density lipoprotein cholesterol (HDL-C; +0.25 mmol/l) was found in patients with Zhenyuan Capsule. No serious adverse event occurred during the study. CONCLUSIONS:Among prediabetic patients, Zhenyuan Capsule further reduced 2-h PG level, alleviated insulin resistance and raised HDL-C level on the background of lifestyle interventions. The study protocol is registered with the Chinese Clinical Trial Registry (ChiCTR2000034000).

Introduction: Fuqi Guben Gao (FQGBG) is a botanical drug formulation composed of FuZi (FZ; Aconitum carmichaelii Debeaux [Ranunculaceae; Aconiti radix cocta]), Wolfberry (Lycium barbarum L. [Solanaceae; Lycii fructus]), and Cinnamon (Neolitsea cassia (L.) Kosterm. [Lauraceae; Cinnamomi cortex]). It has been used to clinically treat nocturia caused by kidney-yang deficiency syndrome (KYDS) for over 30 years and warms kidney yang. However, the pharmacological mechanism and the safety of FQGBG in humans require further exploration and evaluation.Methods: We investigated the efficacy of FQGBG in reducing urination and improving immune organ damage in two kinds of KYDS model rats (hydrocortisone-induced model and natural aging model), and evaluated the safety of different oral FQGBG doses through pharmacokinetic (PK) parameters, metabonomics, and occurrence of adverse reactions in healthy Chinese participants in a randomized, double-blind, placebo-controlled, single ascending dose clinical trial. Forty-two participants were allocated to six cohorts with FQGBG doses of 12.5, 25, 50, 75, 100, and 125 g. The PKs of FQGBG in plasma were determined using a fully validated LC-MS/MS method.Results: FQGBG significantly and rapidly improved the symptoms of increased urination in both two KYDS model rats and significantly resisted the adrenal atrophy in hydrocortisone-induced KYDS model rats. No apparent increase in adverse events was observed with dose escalation. Major adverse drug reactions included toothache, thirst, heat sensation, gum pain, diarrhea, abdominal distension, T-wave changes, and elevated creatinine levels. The PK results showed a higher exposure level of benzoylhypaconine (BHA) than benzoylmesaconine (BMA) and a shorter half-life of BMA than BHA. Toxic diester alkaloids, aconitine, mesaconitine, and hypaconitine were below the lower quantitative limit. Drug-induced metabolite markers primarily included lysophosphatidylcholines, fatty acids, phenylalanine, and arginine metabolites; no safety-related metabolite changes were observed.Conclusion: Under the investigated dosing regimen, FQGBG was safe. The efficacy mechanism of FQGBG in treating nocturia caused by KYDS may be related to the improvement of the hypothalamus-pituitary-adrenal axis function and increased energy metabolism.Clinical Trial Registration:https://www.chictr.org.cn/showproj.html?proj=26934, identifier ChiCTR1800015840.

BackgroundThe association between sarcopenia and cardiovascular disease (CVD) is well known. However, the clinical diagnosis of sarcopenia is complex and not suitable for early clinical identification and prevention of CVD. Relative muscle strength (RMS) is a relatively quantitative and straightforward indicator, but its association with CVD remains unclear. Hence, the objective of this research was to investigate the correlation between RMS and CVD incidence.MethodsThis was a cross-sectional study, using data from the China Health and Retirement Longitudinal Study (CHARLS) in 2011. CVD events were assessed through self-reported physician diagnoses. The RMS was determined by dividing the maximum grip strength by the appendicular skeletal muscle mass (ASM). This study used multivariate logistic regression and restricted cubic spline (RCS) curves to explore the correlation between RMS and CVD incidence. Additionally, we conducted subgroup analyses to provide additional evidence supporting the association between the two variables.ResultsA total of 8,733 people were included in our study, with 1,152 (13.19%) CVD patients and 7,581 (86.81%) non-CVD patients. When the data were grouped according to quartiles (Q) of RMS, the inverse association between CVD and RMS remained statistically significant even after controlling for all potential confounding factors. Compared with participants in Q1 of RMS, the ORs (95% CIs) of CVD among those in Q2-Q4 were 0.99 (0.83, 1.17), 0.81 (0.67, 0.98), and 0.70 (0.57, 0.85), respectively. Moreover, the RCS results showed a negative linear correlation between the RMS and CVD incidence (P for nonlinearity = 0.555). Subgroup analysis revealed no significant interaction in any of the groups except for the sex group (P for interaction = 0.046).ConclusionOur study indicated a stable negative correlation between RMS and CVD incidence. RMS is helpful for the early identification and prevention of CVD.

Dyslipidemia plays a pivotal role in the development of diabetes mellitus (DM) and other metabolic disorders. This study aimed to investigate the trends in lipid concentrations among Chinese participants with different blood glucose statuses—ranging from DM and prediabetes mellitus (pre-DM) to normal blood glucose levels—between 2011 and 2015. Additionally, this study sought to provide a comprehensive description of the potential temporal changes in the prevalence of dyslipidemia among these populations in China during this period. The data for this study were derived from the China Health and Retirement Longitudinal Study (CHARLS), encompassing two time points in 2011 and 2015. The 2011 data sample included 11,408 participants aged 45 years and above, whereas the 2015 data sample included 12,224 participants within the same age range. In this study, a comparative analysis of data from 2011 to 2015 revealed that individuals diagnosed with DM and pre-DM experienced significant decreases in total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) and a significant increase in high-density lipoprotein cholesterol (HDL-C) (P < 0.05). For participants with pre-DM, the levels of residual cholesterol (RC) significantly increased, whereas the levels of the atherogenic index of plasma (AIP) significantly decreased (P < 0.05). Among participants with normal blood glucose, there was a significant decrease in the levels of TC and LDL-C and a significant increase in the levels of triglycerides (TGs), RCs, and the AIP (P < 0.05). Between 2011 and 2015, the concentrations of TC, TG, LDL-C, RC, and AIP, both unadjusted and adjusted, were significantly higher in individuals with DM than in those with pre-DM and normal blood glucose, with the opposite being true for HDL-C. In 2015, the prevalence of dyslipidemia among participants with DM, pre-DM, and normal blood glucose was 36.56