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*

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 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.

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.

Objective:To explore the alteration of JAK2/STAT3 pathway after carbon ion ( 12C 6+) irradiation and the difference in the infiltration of CD8 + T cells in lung cancer regulated by downstream protein FOXP3. Methods:Significantly altered JAK2/STAT3 pathway and related differentially-expressed genes and proteins such as FOXP3 in lung cancer after carbon ion irradiation were screened based on RNA sequencing analysis in the Lewis tumor model of C57BL/6 mice. The correlation between FOXP3 and major immune cell infiltration in the immune microenvironment of lung cancer was analyzed using the ssGSEA immune infiltration algorithm in the R software "GSVA" and CD8 + T cell infiltration in the immune microenvironment of lung cancer was evaluated based on the carbon ion combined with STAT3 inhibition pathway (niclosamide). Results:The JAK2/STAT3 pathway was inhibited and the expression of related genes and proteins was downregulated in lung cancer after carbon ion irradiation. Immune scoring based on the ssGSEA algorithm showed that FOXP3 expression was significantly negatively correlated with CD8 + T cell infiltration in the immune microenvironment of lung cancer. The role of targeting the JAK2/STAT3 pathway in increasing CD8 + T cell infiltration in lung cancer was further clarified by carbon ion irradiation combined with STAT3 inhibition (niclosamide). Conclusion:Carbon ion irradiation ( 12C 6+) can play a synergistic role with immunotherapy by targeting the JAK2/STAT3 pathway.

Polyarteritis nodosa (PAN) is a rare vasculitis that mainly involves small and medium arteries. It often occurs at the points where the vessels bifurcate, leading to microaneurysm formation, thrombosis, aneurysm rupture and bleeding, and infarction of organs.About a third of cases are associated with hepatitis B virus (HBV) infection.All tissues and organs of the body can be affected, with skin, joints and peripheral nerves being the most common.The pathological changes were fibrinoid necrosis, inflammatory cell infiltration and luminal thrombosis in the acute stage, and fibrous hyperplasia in the chronic stage.Overall outcomes for the disease have improved in recent decades, mainly reflecting early diagnosis and more effective treatments.The main treatments for PAN are glucocorticoid and cyclophosphamide.Patients with HBV-associated PAN should receive antiviral therapy and plasma exchange.