Triglyceride levels and risk of cardiovascular disease and all-cause mortality in Chinese adults younger than 40 years old: a prospective cohort study.

Background: Data on the associations of triglyceride (TG) levels with cardiovascular disease (CVD) and all-cause mortality mainly focused on the middle-aged or elderly population, with limited information available for younger adults. This study aimed to identify such associations among Chinese young adults. Methods: This study included Chinese adults younger than 40 years free of CVD, cancer, and lipid-lowering agents at baseline in the Kailuan study who were enrolled during 2006 through 2016. All participants were biennially followed up till December 2020. The enzymatic colorimetric method was used to measure baseline fasting TG. Participants were categorized into four groups by quartiles of TG, with the lowest quartile (Q1) as the reference group. The primary outcomes were CVD [composite of myocardial infarction (MI) and ischemic stroke] and all-cause mortality. CVD and mortality risks were estimated with Cox regression models. Results: A total of 43,882 participants were included. Their mean age was 30.6±5.56 years, and 80.2% were males. During a median follow-up of 11.2 years, 298 CVD events and 345 deaths occurred. The incidences of CVD and all-cause mortality were 0.67 and 0.76 per 1,000 person-years, respectively. Compared with individuals in the lowest quartile (Q1), participants in the highest quartile (Q4) showed a 126% higher risk of developing CVD [adjusted hazard ratio (HR) 2.26; 95% confidence interval (CI): 1.56 to 3.29; P=0.001] and a 61% higher risk of all-cause mortality (adjusted HR 1.61; 95% CI: 1.14 to 2.28; P=0.007). In addition, analyses of CVD subtypes showed that adjusted HRs (Q4 vs. Q1) were 3.25 (95% CI: 1.33 to 7.97; P=0.01) for MI, and 1.88 (95% CI: 1.16 to 3.04; P=0.01) for ischemic stroke. Conclusions: Among Chinese young adults, elevated fasting TG levels were associated with increased CVD and all-cause mortality risks.

Construction and evaluation of immune-related diagnostic model in patients with heart failure caused by idiopathic dilated cardiomyopathy.

OBJECTIVE: The purpose of the study was to construct the potential diagnostic model of immune-related genes during the development of heart failure caused by idiopathic dilated cardiomyopathy. METHOD: GSE5406 and GSE57338 were downloaded from the GEO website ( https://www.ncbi.nlm.nih.gov/geo/ ). CIBERSORT was used for the evaluation of immune infiltration in idiopathic dilated cardiomyopathy (DCM) of GSE5406. Differently expressed genes were calculated by the limma R package and visualized by the volcano plot. The immune-related genes were downloaded from Immport, TISIDB, and InnateDB. Then the immune-related differential genes (IRDGs) were acquired from the intersection. Protein-protein interaction network (PPI) and Cytoscape were used to visualize the hub genes. Three machine learning methods such as random forest, logical regression, and elastic network regression model were adopted to construct the prediction model. The diagnostic value was also validated in GSE57338. RESULTS: Our study demonstrated the obvious different ratio of T cell CD4 memory activated, T cell regulatory Tregs, and neutrophils between DCM and control donors. As many as 2139 differential genes and 274 immune-related different genes were identified. These genes were mainly enriched in lipid and atherosclerosis, human cytomegalovirus infection, and cytokine-cytokine receptor interaction. At the same time, as many as fifteen hub genes were identified as the IRDGs (IFITM3, IFITM2, IFITM1, IFIT3, IFIT1, HLA-A, HLA-B, HLA-C, ADAR, STAT1, SAMHD1, RSAD2, MX1, ISG20, IRF2). Moreover, we also discovered that the elastic network and logistic regression models had a higher diagnostic value than that of random forest models based on these hub genes. CONCLUSION: Our study demonstrated the pivotal role of immune function during the development of heart failure caused by DCM. This study may offer new opportunities for the detection and intervention of immune-related DCM.

Risk and effect modifiers for poor glycemic control among the chinese diabetic adults on statin therapy: the kailuan study.

BACKGROUND: Limited studies have investigated the association between statin therapy and poor glycemic control, especially in the Chinese diabetic population. METHODS: Two prospective diabetes cohorts were drawn from the Kailuan Cohort. In Cohort 1, linear regression models were used to evaluate the association between statin therapy and glycated hemoglobin (HbA1c) level change. In Cohort 2, new user design and conditional logistic models were used to assess associations between statin initiation and poor glycemic control which was a composite outcome comprised of hypoglycemic agent escalation and new-onset hyperglycemia. RESULTS: Among 11,755 diabetic patients with medication information, 1400 statin users and 1767 statin nonusers with repeated HbA1c measurements were included in Cohort 1 (mean age: 64.6 ± 10.0 years). After a median follow-up of 3.02 (1.44, 5.00) years, statin therapy was associated with higher HbA1c levels (ß: 0.20%; 95%CI: 0.05% to 0.34%). In Cohort 2, 1319 pairs of matched cases/controls were included (mean age: 61.6 ± 9.75 years). After a median follow-up of 4.87 (2.51, 8.42) years, poor glycemic control occurred in 43.0% of statin new users and 31.8% of statin nonusers (OR: 1.69; 95% CI: 1.32 to 2.17; P < 0.001). The statin-associated poor glycemic control risk was significantly higher among patients with lower body mass index (Pint = 0.089). Furthermore, a nonlinear association was observed between statin therapy duration and poor glycemic control (P = 0.003). CONCLUSIONS: Among Chinese diabetic adults, statin therapy was associated with a higher level of HbA1c, and a higher risk of hypoglycemic agent escalation and new-onset hyperglycemia, especially among those who had lower body mass index levels and longer statin therapy duration.

Better Life's Essential 8 Is Associated With Lower Risk of Diabetic Kidney Disease: A Community-Based Study.

Background Diabetic kidney disease (DKD) is a common diabetic complication and increases the complexity of diabetes management. No prospective study has focused on the association between DKD and Life's Essential 8 (LE8). Our study aims to examine the association between LE8 and DKD risk. Methods and Results A total of 7605 participants, aged 54.32±9.77 years, and 4688 participants, aged 56.11±10.38 years, were included in the longitudinal and trajectory analyses, respectively, from 2006 to 2020. The DKD was confirmed using data collected during each follow-up. LE8 was based on 4 health behaviors and 4 health factors. The range of each metric was 0 to 100, and the overall LE8 score was calculated as the unweighted average of all 8 component metric scores. The trajectories of LE8 during 2006 to 2010 were classified using latent mixture models. Cox models and restricted cubic splines were applied. After a median follow-up of 12.41 and 6.71 years in longitudinal and trajectory analyses, respectively, the DKD incidence decreased, with the LE8 level increasing (P-trend<0.05), and the linearity assumption for this relationship (P-nonlinear=0.685) had been satisfied. Adjusted hazard ratios (HRs) for the highest tertile were 0.77 (95% CI, 0.69-0.87) and 0.70 (95% CI, 0.62-0.78) in baseline and time-updated LE8 scores, respectively, compared with the lowest tertile. Adjusted HR was 0.53 (95% CI, 0.41-0.69) for the stable-high pattern compared with the stable-low pattern. Conclusions Although LE8 is an indicator of cardiovascular health, the beneficial impact of a high LE8 score is also evident in the protection of renal health among patients with diabetes.

Patterns of Circulating Microbiota during the Acute Phase Following ST-Segment Elevation Myocardial Infarction Predict Long-Term Cardiovascular Events.

Limited information exists regarding whether circulating microbiota could predict long-term clinical outcomes following ST-segment elevation myocardial infarction (STEMI). A total of 244 consecutive patients with STEMI were followed for 2.8 years, and 64 first major adverse cardiovascular events (MACEs) were recorded. Both microbiota abundance [Corynebacterium tuberculostearicum (HR, 1.28; 95% CI, 1.03-1.58) and Staphylococcus aureus (S. aureus) (HR, 1.16; 95% CI, 1.02-1.33) ] and microbiota clusters (Cluster 2 versus Cluster 1: HR, 1.84; 95% CI, 1.04-3.27) could independently predict MACE. Furthermore, a model based on established independent predictors alone was significantly improved by the addition of different microbiota patterns. In addition, CD14++CD16+ monocytes (Mon2) had a significant mediation effect on the microbiota patterns → MACE association. The present study demonstrated that the abundance and clusters of circulating microbiota are associated with future adverse cardiovascular events independent of traditional risk factors, which were partially mediated by an increase in Mon2.

Combination of peripheral blood mononuclear cell miR-19b-5p, miR- 221, miR-25-5p, and hypertension correlates with an increased heart failure risk in coronary heart disease patients.

OBJECTIVE: The aim of this study was to explore the differences in microRNA (miRNA) profiles in peripheral blood mononuclear cells (PBMCs) between coronary heart disease (CHD) patients with and without heart failure (HF) and to assess the values of differentially expressed miRNAs (DEMs) regarding HF risk in CHD patients. METHODS: Six CHD patients with HF and six age- and gender-matched CHD patients without HF were enrolled in the exploration stage, and 44 CHD patients with HF and 42 age- and gender-matched CHD patients without HF were recruited in the validation stage. Peripheral blood samples were collected from all the participants, and PBMCs were separated for miRNA detection. miRNA microarray and quantitative polymerase chain reaction were performed to assess the miRNA expression. RESULTS: In the exploration stage, heat map analysis showed that CHD patients with HF could be distinguished from those without HF using PMBC miRNA expressions; 63 downregulated DEMs and 84 upregulated DEMs in PBMCs were identified in CHD patients with HF using volcano map, and top 8 DEMs were selected based on their p values. In the validation stage, PBMC miR-221, miR-19b-5p, and miR-25-5p were found to be markedly dysregulated in CHD patients with HF. Multiple logistic regression analysis showed PBMC miR-221, miR-19b-5p, miR-25-5p, and hypertension to be the independent predictive factors for HF in CHD patients. A receiver operating characteristic curve demonstrated that area under curve of the combination of miR-221, miR-19b-5p, miR-25-5p, and hypertension was 0.871 (95% CI: 0.794-0.944). CONCLUSION: CHD patients with and without HF could be differentiated according to PBMC miRNA profiles, and the combination of PBMC miR-19b-5p, miR-221, miR-25-5p, and hypertension correlates with an increased HF risk in CHD patients.