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Abstract Background: Life's Simple 7, the former construct of cardiovascular health (CVH) has been used to evaluate adverse non-communicable chronic diseases (NCDs). However, some flaws have been recognized in recent years and Life's Essential 8 has been established. In this study, we aimed to analyze the association between CVH defined by Life's Essential 8 and risk of 44 common NCDs and further estimate the population attributable fractions (PAFs) of low-moderate CVH scores in the 44 NCDs. Methods: In the UK Biobank, 170,726 participants free of 44 common NCDs at baseline were included. The Life's Essential 8 composite measure consists of four health behaviours (diet, physical activity, nicotine exposure, and sleep) and four health factors (body mass index, non-high density lipoprotein cholesterol, blood glucose, and blood pressure), and the maximum CVH score was 100 points. CVH score was categorized into low, moderate, and high groups. Participants were followed up for 44 NCDs diagnosis across 10 human system disorders according to the International Classification of Diseases 10th edition (ICD-10) code using linkage to national health records until 2022. Cox proportional hazard models were used in this study. The hazard ratios (HRs) and PAFs of 44 NCDs associated with CVH score were examined. Results: During the median follow-up of 10.85 years, 58,889 incident NCD cases were documented. Significant linear dose-response associations were found between higher CVH score and lower risk of 25 (56.8%) of 44 NCDs. Low-moderate CVH (<80 points) score accounted for the largest proportion of incident cases in diabetes (PAF: 80.3%), followed by gout (59.6%), sleep disorder (55.6%), chronic liver disease (45.9%), chronic kidney disease (40.9%), ischemic heart disease (40.8%), chronic obstructive pulmonary disease (40.0%), endometrium cancer (35.8%), lung cancer (34.0%), and heart failure (34.0%) as the top 10. Among the eight modifiable factors, overweight/obesity explained the largest number of cases of incident NCDs in endocrine, nutritional, and metabolic diseases (35.4%), digestive system disorders (21.4%), mental and behavioral disorders (12.6%), and cancer (10.3%); however, the PAF of ideal sleep duration ranked first in nervous system (27.5%) and neuropsychiatric disorders (9.9%). Conclusions: Improving CVH score based on Life's Essential 8 may lower risk of 25 common NCDs. Among CVH metrics, avoiding overweight/obesity may be especially important to prevent new cases of metabolic diseases, NCDs in digestive system, mental and behavioral disorders, and cancer.

Sepsis poses a significant global health burden, with millions of cases and high mortality rates annually, largely due to challenges in early diagnosis and monitoring. Traditional methods, reliant on nonspecific clinical manifestations and limited biomarkers like C-reactive protein and procalcitonin, often fail to distinguish infection from non-infectious inflammation or capture disease heterogeneity. This review synthesizes recent progress in omics technologies—genomics, transcriptomics, proteomics, and metabolomics—for advancing sepsis management. Genomics, via metagenomic next-generation sequencing, enables rapid pathogen identification and genetic variant analysis for susceptibility and prognosis. Transcriptomics reveals molecular subtypes and immune dynamics through RNA sequencing and single-cell approaches. Proteomics and metabolomics uncover protein and metabolite profiles linked to immune imbalance, organ damage, and metabolic disorders. Multi-omics integration, enhanced by artificial intelligence and machine learning, facilitates biomarker discovery, patient stratification, and predictive modeling, bridging laboratory findings to bedside applications like rapid diagnostic tools and clinical decision support systems. Despite advancements, challenges including data heterogeneity, high costs, and ethical concerns persist. Future directions emphasize single-cell and spatial omics, AI-driven personalization, and ethical frameworks to transform sepsis care from reactive to proactive, ultimately improving outcomes.

Background. Testosterone deficiency is reportedly correlated with an elevation of cholesterol in plasma, but the mechanism remains unclear. Our objective was to investigate the effects of testosterone deficiency on cholesterol metabolism and the corresponding molecular changes in vivo and in vitro. Methods. SD rats were randomized into three groups: sham-operated (SHAM), subtotal orchiectomized (SO), and orchiectomized (ORX) and fed for 8 weeks. HepG2 cells were cultured with medium containing testosterone with the final concentrations of 0, 10, 30, and 300 nM. Method of isotope tracing and fluorescence labelling was adopted to investigate cholesterol metabolism. Several key molecules of cholesterol metabolism were also analyzed. Results. SO and ORX rats displayed dysfunctional liver uptake of cholesterol. HepG2 cells incubated with testosterone of lower and excessive level exhibited reduced capacity of cholesterol uptake. Further investigation revealed that lack of testosterone induced increased proprotein convertase subtilisin/kexin type 9 (PCSK9) and decreased low-density lipoprotein receptor (LDLR) both in vivo and in vitro. Moreover, the androgen receptor (AR) antagonist flutamide mimicked the effects of testosterone deficiency on PCSK9 and LDLR indicating the role of AR as a mediator in triggering attenuating liver cholesterol uptake in which testosterone instead of dihydrotestosterone (DHT) is the major functional form of androgen. Conclusion. Testosterone deficiency attenuated cholesterol liver uptake mediated by the PCSK9-LDLR pathway, in which AR and testosterone without transforming to DHT play important roles.

Background: The blood proteome is a major source of biomarkers and therapeutic targets. We conducted a proteome-wide Mendelian randomization (MR) study to identify cardiometabolic protein markers for diabetic kidney disease (DKD). Methods: We measured all 369 proteins in the Olink Explore 384 Cardiometabolic and Cardiometabolic panel of 500 patients with type 2 diabetes from 11 communities in Shanghai. Protein quantitative trait loci (pQTLs) were derived by coupling genomic and proteomic data. Cis-pQTLs identified for proteins were used as instrumental variables in MR analyses of DKD risk, and the outcome data were obtained from 8401 Japanese individuals with type 2 diabetes (2809 cases and 5592 controls). Replication MR analysis was performed in the UK Biobank Pharma Proteomics Project (UKB-PPP). Colocalization analysis and the Heidi test were used to examine whether the identified proteins and DKD shared causal variants. Results: Among the 369 proteins, we identified 66 independent cis-pQTLs for 64 proteins. MR analysis suggested that two cardiometabolic proteins (UMOD and SIRPA) may play a causal role in increasing DKD risk, with UMOD showing replication in UKB-PPP. Bayesian colocalization further supported the causal effects of these proteins. Additional analyses indicated that UMOD is highly expressed in renal macrophages. Further downstream analyses suggested that UMOD could be a potential novel target and that SIRPA could be a potential repurposing target for DKD; however, further validation is needed. Conclusions: By integrating proteomic and genetic data from patients with type 2 diabetes, we identified two protein biomarkers potentially associated with DKD risk. These findings provide insights into DKD pathophysiology and therapeutic target development, but further replication and functional studies are needed to confirm these associations.

Objective. To investigate whether high-flow nasal cannula (HFNC) oxygen therapy is superior to conventional oxygen therapy for reducing hypoxemia and postoperative pulmonary complications (PPC) in patients with thoracoscopic lobectomy after extubation. Methods. Patients with intermediate to high risk for PPC were enrolled in this study. Subjects were randomly assigned to HFNC group (HFNCG) or conventional oxygen group (COG) following extubation. Arterial blood samples were collected after extubation at 1, 2, 6, 12, 24, 48, and 72 h. Patients with postoperative hypoxemia and PPC were recorded. Adverse events were also documented. Results. Totally 110 patients were randomly assigned to HFNCG (n=56) and COG (n=54). The occurrence rate of hypoxemia in COG was twice more than that in HFNCG (29.62% versus 12.51%, P<0.05) and PaO2, PaO2/FiO2, and SaO2/FiO2 were significantly improved in HFNCG (P<0.05) in the first 72 h following extubation. Respiratory rate and incidence of reintubation as well as needing noninvasive ventilation were also decreased in HFNCG (P<0.05), whereas the incidence of pneumonia and atelectasis were similar (P>0.05). Adverse effects as throat and nasal pain occurred more frequently in COG. Conclusions. HFNC application improves oxygenation and reduces the risk of reintubation following thoracoscopic lobectomy but cannot decrease the incidence of PPC.