BACKGROUND: The American Heart Association (AHA) introduced the concept of cardiovascular-kidney-metabolic (CKM) health and stage, reflecting the interaction among metabolism, chronic kidney disease (CKD), and the cardiovascular system. However, the association between CKM stage and the long-term risk of cardiovascular disease (CVD) has not been validated. This study aimed to evaluate the long-term CVD risk associated with CKM health metrics and CKM stage using data from a population-based cohort study. METHODS: In total, 5293 CVD-free participants were followed up to around 13 years in the Chinese Multi-provincial Cohort Study (CMCS). Considering the pathophysiologic progression of CKM health metrics abnormalities (comprising obesity, central adiposity, prediabetes, diabetes, hypertriglyceridemia, CKD, and metabolic syndrome), participants were divided into CKM stages 0, 1, and 2. The time-dependent Cox regression models were used to estimate the cardiovascular risk associated with CKM health metrics and stage. Additionally, broader CVD outcomes were examined, with a specific assessment of the impact of stage 3 in 2581 participants from the CMCS-Beijing subcohort. RESULTS: Among participants, 91.2% (4825/5293) had at least one abnormal CKM health metric, 8.8% (468/5293), 13.3% (704/5293), and 77.9% (4121/5293) were in CKM stages 0, 1, and 2, respectively; and 710 incident CVD cases occurred during a median follow-up time of 13.3 years (interquartile range: 12.1 to 13.6 years). Participants with each poor CKM health metric exhibited significantly higher CVD risk. Compared with stage 0, the hazard ratio (HR) (95% confidence interval [CI]) for CVD incidence was 1.31 (0.84-2.04) in stage 1 and 2.27 (1.57-3.28) in stage 2. Significant interactive impacts existed between CKM stage and age or sex, with higher CVD risk related to increased CKM stages in participants aged <60 years or females. CONCLUSION These findings highlight the contribution of CKM health metrics and CKM stage to the long-term risk of CVD, suggesting the importance of multi-component recognition and management of poor CKM health in CVD prevention.
Humans ; Female ; Male ; Cardiovascular Diseases/etiology* ; Middle Aged ; Adult ; Cohort Studies ; Renal Insufficiency, Chronic/metabolism* ; Aged ; Risk Factors ; Metabolic Syndrome/metabolism* ; China ; East Asian People

Psychiatric comorbidity is common in symptom-based diagnoses like autism spectrum disorder (ASD), attention/deficit hyper-activity disorder (ADHD), and obsessive-compulsive disorder (OCD). However, these co-occurring symptoms mediated by shared and/or distinct neural mechanisms are difficult to profile at the individual level. Capitalizing on unsupervised machine learning with a hierarchical Bayesian framework, we derived latent disease factors from resting-state functional connectivity data in a hybrid cohort of ASD and ADHD and delineated individual associations with dimensional symptoms based on canonical correlation analysis. Models based on the same factors generalized to previously unseen individuals in a subclinical cohort and one local OCD database with a subset of patients undergoing neurosurgical intervention. Four factors, identified as variably co-expressed in each patient, were significantly correlated with distinct symptom domains (r = -0.26-0.53, P < 0.05): behavioral regulation (Factor-1), communication (Factor-2), anxiety (Factor-3), adaptive behaviors (Factor-4). Moreover, we demonstrated Factor-1 expressed in patients with OCD and Factor-3 expressed in participants with anxiety, at the degree to which factor expression was significantly predictive of individual symptom scores (r = 0.18-0.5, P < 0.01). Importantly, peri-intervention changes in Factor-1 of OCD were associated with variable treatment outcomes (r = 0.39, P < 0.05). Our results indicate that these data-derived latent disease factors quantify individual factor expression to inform dimensional symptom and treatment outcomes across cohorts, which may promote quantitative psychiatric diagnosis and personalized intervention.
Humans ; Male ; Female ; Attention Deficit Disorder with Hyperactivity ; Obsessive-Compulsive Disorder ; Adult ; Autism Spectrum Disorder ; Bayes Theorem ; Adolescent ; Young Adult ; Magnetic Resonance Imaging ; Middle Aged ; Child ; Brain/metabolism* ; Cohort Studies ; Comorbidity

Humans ; Asthma/drug therapy* ; Biological Therapy

Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.
Humans ; Obsessive-Compulsive Disorder/epidemiology* ; Brain/pathology* ; Neuroimaging/methods* ; Machine Learning ; Comorbidity ; Magnetic Resonance Imaging/methods*

Objective To investigate the correlation between the expression of coagulation markers thrombomodulin(TM) and tissue plasminogen activator-plasminogen activator inhibitor-1 complex(t-PAIC) and white matter lesions(WMLs). Methods A total of 69 patients with WMLs who were hospitalized in the Department of Neurology of Shanxi Provincial People's Hospital from July 2020 to October 2022 were selected. After admission,novel coagulation markers were tested,and cranial magnetic resonance examination was completed. WMLs were graded according to the Fazekas visual rating scale,and the correlation between novel coagulation markers and WML severity in patients with WMLs was analyzed by the Kendall stau-b method. Multivariate logistic regression was used to analyze the influencing factors for WML severity. Results There were significant differences in serum TM and t-PAIC levels between the mild,moderate,and severe WML groups,and the levels of serum TM and t-PAIC in the moderate and severe WML groups were significantly increased compared with the mild WML group(P<0.05). There was a correlation between serum TM and t-PAIC levels and the severity of WMLs in the three groups(P<0.05). The high level of t-PAIC and age were risk factors for the aggravation of WMLs,with odds ratios(95% confidence interval) of 1.274(1.052-1.544) and 1.063(1.015-1.114),respectively(P<0.05). Conclusion The expression of serum TM and t-PAIC in patients with WMLs is positively correlated with the degree of white matter lesions,and t-PAIC is a risk factor for the exacerbation of white matter lesions, which may be used as a serum marker to indicate the development of WML patients.

Background: Serum C-peptide results from various routine methods used in China are highly variable, warranting well-performing methods to serve as an accuracy base to improve the harmonization of C-peptide measurements in China. We developed an accurate isotope dilution liquid chromatography-tandem mass spectrometry (ID-LC–MS/MS) method for serum C-peptide measurement and explored its use in harmonization. Methods: After protein precipitation with ZnSO4 solution, C-peptide was extracted from serum samples by anion-exchange solid-phase extraction and quantified by ID-LC–MS/MS in positive ion mode. The precision and analytical recovery of the ID-LC–MS/MS method were assessed. Seventy-six serum samples were analyzed using the ID-LC–MS/MS method and six routine immunoassays. Ordinary linear regression (OLR) and Bland-Altman (BA) analyses were conducted to evaluate the relationship between the ID-LC–MS/MS method and routine immunoassays. Five serum pool samples assigned using the ID-LC–MS/MS method were used to recalibrate the routine assays. OLR and BA analyses were re-conducted after recalibration. Results: The within-run, between-run, and total precision for the ID-LC–MS/MS method at four concentrations were 1.0%–2.1%, 0.6%–1.2%, and 1.3%–2.2%, respectively. The analytical recoveries for the ID-LC–MS/MS method at three concentrations were 100.3%–100.7%, 100.4%–101.0%, and 99.6%–100.7%. The developed method and the immunoassays were strongly correlated, with all R2 >0.98. The comparability among the immunoassays was substantially improved after recalibration. Conclusions The performance of the ID-LC–MS/MS method was carefully validated, and this method can be used to improve the harmonization of serum C-peptide measurements in China.

Objective:The investigation and research on the application status of Hepatic Venous Pressure Gradient (HVPG) is very important to understand the real situation and future development of this technology in China.Methods:This study comprehensively investigated the basic situation of HVPG technology in China, including hospital distribution, hospital level, annual number of cases, catheters used, average cost, indications and existing problems.Results:According to the survey, there were 70 hospitals in China carrying out HVPG technology in 2021, distributed in 28 provinces (autonomous regions and municipalities directly under the central Government). A total of 4 398 cases of HVPG were performed in all the surveyed hospitals in 2021, of which 2 291 cases (52.1%) were tested by HVPG alone. The average cost of HVPG detection was (5 617.2±2 079.4) yuan. 96.3% of the teams completed HVPG detection with balloon method, and most of the teams used thrombectomy balloon catheter (80.3%).Conclusion:Through this investigation, the status of domestic clinical application of HVPG has been clarified, and it has been confirmed that many domestic medical institutions have mastered this technology, but it still needs to continue to promote and popularize HVPG technology in the future.

Metabolic-associated fatty liver disease (MAFLD), which is previously known as non-alcoholic fatty liver disease (NAFLD), represents a major health concern worldwide with limited therapy. Here, we provide evidence that ferroptosis, a novel form of regulated cell death characterized by iron-driven lipid peroxidation, was comprehensively activated in liver tissues from MAFLD patients. The canonical-GPX4 (cGPX4), which is the most important negative controller of ferroptosis, is downregulated at protein but not mRNA level. Interestingly, a non-canonical GPX4 transcript-variant is induced (inducible-GPX4, iGPX4) in MAFLD condition. The high fat-fructose/sucrose diet (HFFD) and methionine/choline-deficient diet (MCD)-induced MAFLD pathologies, including hepatocellular ballooning, steatohepatitis and fibrosis, were attenuated and aggravated, respectively, in cGPX4-and iGPX4-knockin mice. cGPX4 and iGPX4 isoforms also displayed opposing effects on oxidative stress and ferroptosis in hepatocytes. Knockdown of iGPX4 by siRNA alleviated lipid stress, ferroptosis and cell injury. Mechanistically, the triggered iGPX4 interacts with cGPX4 to facilitate the transformation of cGPX4 from enzymatic-active monomer to enzymatic-inactive oligomers upon lipid stress, and thus promotes ferroptosis. Co-immunoprecipitation and nano LC-MS/MS analyses confirmed the interaction between iGPX4 and cGPX4. Our results reveal a detrimental role of non-canonical GPX4 isoform in ferroptosis, and indicate selectively targeting iGPX4 may be a promising therapeutic strategy for MAFLD.

Objective:To evaluate the myocardial work of patients with different degrees of coronary artery stenosis with normal left ventricular ejection fraction and no segmental ventricular wall motion abnormality by left ventricular pressure-strain ring (PSL), and to explore the clinical value of myocardial work parameters in predicting severe coronary artery stenosis.Methods:The data of 238 patients undergoing coronary angiography (CAG) in the Affiliated Hospital of Jining Medical University from December 2020 to August 2021 was prospectively collected. According to the results of CAG, the patients were divided into control group, moderate stenosis group, severe stenosis (1-2 branches) group, severe stenosis (complex multiple branches) group. Global longitudinal strain (GLS), global work index (GWI), global constructive work (GCW), global work waste (GWW) and global work efficiency (GWE) were measured by PSL. Univariate and multivariate logistics regression were used to analyze the influencing factors of severe coronary artery stenosis. The receiver operating characteristic (ROC) curve was constructed to analyze the predictive value of GLS, GWI, GCW, GWW and GWE for severe coronary artery stenosis.Results:The GLS, GWI, GCW and GWE in severe stenosis group were lower than those in control group and moderate stenosis group (all P<0.05), while GWW was higher than those in control group and moderate stenosis group (all P<0.05); the GWI, GCW and GWE in severe stenosis (complex multiple branches) group were lower than those in severe stenosis (1-2 branches) group (all P<0.05), while GWW was higher than those in severe stenosis (1-2 branches) group (all P<0.05). Multivariate logistic regression analysis showed that GWE was an independent influencing factor for severe coronary stenosis ( OR=0.266, P<0.05). Compared with GLS, GWI, GCW and GWW, GWE had the largest area under the curve (0.920) to predict severe coronary stenosis, with sensitivity of 92.24% and specificity of 73.77%. The intra observer and inter observer correlation coefficients of GWI, GCW, GWW and GWE analyzed by two ultrasound physicians were 0.916 and 0.907, 0.989 and 0.981, 0.932 and 0.955, 0.931 and 0.937, respectively, which showed good repeatability. Conclusions:PSL provides a new method for quantitative evaluation of left ventricular systolic function in patients with coronary artery stenosis. GWE can be used as a sensitive indicator to predict patients with severe coronary artery stenosis, and is worth to be popularized and applied in the clinical.