Ubiquitin‐specific protease 19 (USP19) belongs to USP family and is involved in promoting skeletal muscle atrophy. Although USP19 is expressed in the heart, the role of USP19 in the heart disease remains unknown. The present study provides in vivo and in vitro data to reveal the role of USP19 in preventing pathological cardiac hypertrophy. We generated USP19‐knockout mice and isolated neonatal rat cardiomyocytes (NRCMs) that overexpressed or were deficient in USP19 to investigate the effect of USP19 on transverse aortic constriction (TAC) or phenylephrine (PE)‐mediated cardiac hypertrophy. Echocardiography, pathological and molecular analysis were used to determine the extent of cardiac hypertrophy, fibrosis, dysfunction and inflammation. USP19 expression was markedly increased in rodent hypertrophic heart or cardiomyocytes underwent TAC or PE culturing, the increase was mediated by the reduction of Seven In Absentia Homolog‐2. The extent of TAC‐induced cardiac hypertrophy, fibrosis, dysfunction and inflammation in USP19‐knockout mice was exacerbated. Consistently, gain‐of‐function and loss‐of‐function approaches that involved USP19 in cardiomyocytes suggested that the down‐regulation of USP19 promoted the hypertrophic phenotype, while the up‐regulation of USP19 improved the worsened phenotype. Mechanistically, the USP19‐elicited cardiac hypertrophy improvement was attributed to the abrogation of the transforming growth factor beta‐activated kinase 1 (TAK1)‐p38/JNK1/2 transduction. Furthermore, the inhibition of TAK1 abolished the aggravated hypertrophy induced by the loss of USP19. In conclusion, the present study revealed that USP19 and the downstream of TAK1‐p38/JNK1/2 signalling pathway might be a potential target to attenuate pathological cardiac hypertrophy.

Background: Insulin resistance (IR), a precursor to type 2 diabetes and a major risk factor for various chronic diseases, is becoming increasingly prevalent in China due to population aging and unhealthy lifestyles. Current methods like the gold-standard hyperinsulinemic-euglycemic clamp has limitations in practical application. The development of more convenient and efficient methods to predict and manage IR in nondiabetic populations will have prevention and control value. Objective: This study aimed to develop and validate a machine learning prediction model for IR in a nondiabetic population, using low-cost diagnostic indicators and questionnaire surveys. Methods: A cross-sectional study was conducted for model development, and a retrospective cohort study was used for validation. Data from 17,287 adults with normal fasting blood glucose who underwent physical exams and completed surveys at the Health Management Center of Xiangya Third Hospital, Central South University, from January 2018 to August 2022, were analyzed. IR was assessed using the Homeostasis Model Assessment (HOMA-IR) method. The dataset was split into 80% (13,128/16,411) training and 20% (32,83/16,411) testing. A total of 5 machine learning algorithms, namely random forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Machine, and CatBoost were used. Model optimization included resampling, feature selection, and hyperparameter tuning. Performance was evaluated using F ₁ -score, accuracy, sensitivity, specificity, area under the curve (AUC), and Kappa value. Shapley Additive Explanations analysis was used to assess feature importance. For clinical implication investigation, a different retrospective cohort of 20,369 nondiabetic participants (from the Xiangya Third Hospital database between January 2017 and January 2019) was used for time-to-event analysis with Kaplan-Meier survival curves. Results: Data from 16,411 nondiabetic individuals were analyzed. We randomly selected 13,128 participants for the training group, and 3283 participants for the validation group. The final model included 34 lifestyle-related questionnaire features and 17 biochemical markers. In the validation group, their AUC were all greater than 0.90. In the test group, all AUC were also greater than 0.80. The LightGBM model showed the best IR prediction performance with an accuracy of 0.7542, sensitivity of 0.6639, specificity of 0.7642, F ₁ -score of 0.6748, Kappa value of 0.3741, and AUC of 0.8456. Top 10 features included BMI, fasting blood glucose, high-density lipoprotein cholesterol, triglycerides, creatinine, alanine aminotransferase, sex, total bilirubin, age, and albumin/globulin ratio. In the validation queue, all participants were separated into the high-risk IR group and the low-risk IR group according to the LightGBM algorithm. Out of 5101 high-risk IR participants, 235 (4.6%) developed diabetes, while 137 (0.9%) of 15,268 low-risk IR participants did. This resulted in a hazard ratio of 5.1, indicating a significantly higher risk for the high-risk IR group. Conclusion: By leveraging low-cost laboratory indicators and questionnaire data, the LightGBM model effectively predicts IR status in nondiabetic individuals, aiding in large-scale IR screening and diabetes prevention, and it may potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.

Increased arterial stiffness (iAS) and microalbuminuria (MAU), which may occur simultaneously or separately in the general population and share similar risk factors, are markers of macro- and microvascular injuries. Our research investigated the comorbidity of iAS and MAU in the middle-aged population and examined the heterogeneous effects of metabolic risk factors on iAS and MAU. We selected 11,911 individuals aged 45 to 60 years who underwent a health examination at the 3rd Xiangya Hospital between 2010 and 2014. Metabolic syndrome (MetS) was determined according to IDF/NHLBI/AHA-2009 criteria. Multinomial logistic regression was applied to evaluate the influence of MetS, components of MetS and clusters of MetS on the co-occurrence (MAU(+)/iAS(+)) or non-co-occurrence (MAU(+)/iAS(−) and MAU(−)/iAS(+)) of MAU and iAS. Reference group was MAU(−)/iAS(−). A positive effect of MetS on the presence of MAU(+)/iAS(−), MAU(−)/iAS(+), or MAU(+)/iAS(+) is listed in ascending order based on odds ratios (ORs = 2.11, 2.41, 4.61, respectively; P < 0.05). Compared with MAU(+)/iAS(−), Elevated blood pressure (BP) (OR = 1.62 vs. 4.83, P < 0.05), triglycerides(TG) (OR = 1.20 vs. 1.37, P < 0.05) were more strongly associated with MAU(−)/iAS(+), whereas fasting blood glucose (FBG) was less associated (OR = 1.37 vs. 1.31, P < 0.05). Decreased high-density lipoprotein cholesterol(HDL-c) (OR = 1.84, P < 0.01) and elevated waist circumference(WC) (OR = 1.28 P < 0.01) were the most strongly associated with MAU(+)/iAS(−). Compared with the individuals without MetS, individuals with the elevated BP, FBG, TG and decreased HDL-c cluster had the greatest likelihood of presenting a MAU(−)/iAS(+) (OR = 5.98, P < 0.01) and MAU(+)/iAS(+) (OR = 13.17, P < 0.01), these likelihood was even greater than the cluster with simultaneous alteration in all five MetS components (OR = 3.89 and 10.77, respectively, P < 0.01), which showed the most strongly association with MAU(+)/iAS(+) (OR = 5.22, P < 0.01). Based on the heterogeneous influences of MetS-related risk factors on MAU and iAS, these influences could be selectively targeted to identify different types of vascular injuries.

Background The present study aims to use structural equation modelling (SEM) with multiple regression pathways to examine direct and indirect links from diet and metabolic traits to microvascular endothelial dysfunction (ED) among middle-aged Chinese males. Methods The study was conducted in middle-aged Chinese males, who underwent a health check-up between 2018 and 2019. Data on lifestyle behaviour factors (physical activity, diet pattern, sleep quality and diet data underwent factor analysis in advance) and metabolic risk factors referring to metabolic traits were introduced into the SEM to examine inter-relationship among these factors and their association with ED, as evaluated by the reactive hyperaemia index (RHI). Results Both exploratory factor analysis and confirmatory factor analysis identified two major dietary patterns: ‘prudent pattern’ and ‘western pattern’. The univariate test suggested that only triglycerides (TGs) and prudent dietary pattern were directly associated with RHI. Furthermore, prudent dietary pattern had an indirect association with RHI via TG (prudent diet→TG: β=−0.15, p<0.05; TG→RHI: β=−0.17, p<0.001). As to confirming the hypothesised association between variables apart, physical activity frequency was correlated to the decrease in TG (β=−0.29, p<0.001), but had no direct correlation to RHI. Conclusion The network of direct and indirect associations among diet pattern and cardiometabolic risk factors with RHI measured ED among middle-aged males. The most significant modifiable factors identified were TG and prudent diet pattern, which needs to be targeted as preventive strategies for early microvascular impairment.

Background: Depression affects over 40% of middle-aged and older Chinese adults living with both hypertension and diabetes, amplifying cardiovascular risk, functional decline, and mortality. Existing screening instruments—such as the 10-item Center for Epidemiologic Studies Depression Scale—focus narrowly on mood symptoms and are rarely feasible in busy primary care consultations. They also omit routine functional, cognitive, and social data that may jointly drive depressive states in cardiometabolic populations. Objective: This study aimed to develop and validate a concise, clinically actionable nomogram that quantifies individual depression risk using readily available information in Chinese adults aged ≥45 years who have diagnosed hypertension and type 2 diabetes. Methods: We analyzed anonymized wave 5 China Health and Retirement Longitudinal Study data collected between July 2020 and August 2020. Of 1504 eligible participants, 635 (42.2%) met the Center for Epidemiologic Studies Depression Scale cutoff score of >10 for probable depression. A total of 42 candidate predictors spanning demographics, laboratory values, comorbidities, functional status, and socioenvironmental factors were screened. Least absolute shrinkage and selection operator regression with 10-fold cross-validation identified the most parsimonious set. A multivariable logistic model was built on a 70% training set (n=1052) and evaluated on a 30% testing set (n=452). Performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, decision curve analysis, and Shapley additive explanations for interpretability. Multiple imputation was used to handle <20% missingness. Results: Nine nonredundant predictors entered the final nomogram: activity of daily living score, memory impairment, number of pain sites, sleep duration, life satisfaction score, self-rated health score, social activity engagement score, retirement status, and memory test score. The model achieved excellent discrimination (training AUC=0.819; testing AUC=0.825) and calibration (mean absolute error ≤0.018). Decision curves demonstrated positive net clinical benefit across clinically relevant threshold probabilities. Shapley additive explanations analysis revealed a 3-fold increase in depression odds per 1-point increase in activity of daily living score, whereas retirement conferred substantial protection (prevalence of depression: 103/635, 16.2% in the retired group vs 269/869, 31.0% in the nonretired group; P <.001), mediated by greater social participation. Conclusions: The 9-item nomogram enables <3-minute depression risk stratification in resource-limited primary care settings for adults with hypertension and diabetes. Functional decline, affective-cognitive burden, and socioeconomic disengagement constitute the dominant causal pathway. Prospective trials should examine whether interventions targeting postretirement social engagement and functional rehabilitation can reduce incident depression in this high-risk population.

Objective: The present study aims to use structural equation modeling with multiple regression pathways to examine direct and indirect association of potential behavioral (diet, physical activity and sleep) and cardio-metabolic risk factors with micro-vascular endothelial dysfunction (ED) among middle-aged Chinese males.Methods: The study was conducted in 306 middle-aged Chinese males, who underwent a health checkup between 2018 and 2019. Data on life-style behavior factors (physical activity, diet pattern, sleep quality, and diet data underwent exploratory and confirmatory factor analysis [EFA and CFA] in advance) and cardio-metabolic risk factors referring to metabolic syndrome [blood pressure, triglyceride (TG), fasting blood glucose, high-density lipoprotein cholesterol and body mass index] were introduced into the structural equation model to examine interrelationship among these factors and their association with ED, as evaluated by the reactive hyperemia index (RHI). Results: Impaired ED defined by RHI <1.67 occurred in 39.2% of the participants. Both EFA and CFA identified two major dietary patterns: "prudent pattern" and "western pattern". The univariate test suggested that only TG and prudent dietary pattern were directly associated with RHI. Furthermore, prudent dietary pattern had a direct association with RHI (β=0.16, P<0.05) and an indirect association via triglyceride (Prudent diet→TG: β=-0.15, P<0.05; TG→RHI: β=-0.17, P<0.001). As to confirming the hypothesized association between variables apart, physical activity frequency was correlated to the decrease in TG (β=-0.29, P<0.001), but had no direct correlation to RHI.Conclusion: The network of direct and indirect associations among life-style behavioral and cardio-metabolic risk factors with RHI measured ED among middle-aged males. The most significant modifiable factors identified were TG and prudent diet pattern, which needs to be targeted as preventive strategies for early micro-vascular impairment.