The fibrosis-4 (FIB-4) index is valuable in predicting cardiovascular events in individuals with type 2 diabetes. This paper was to ascertain the predictive power of FIB-4 for mortality in patients with heart failure (HF).

Patient data were sourced from the MIMIC-IV database and allocated to quartiles based on FIB-4 values. All-cause deaths at 30 days, 90 days, and 1 year after patient admission were the endpoints. Cox models, adjusted for multiple factors, were leveraged to examine the link between FIB-4 and mortality in HF patients. Kaplan-Meier (K-M) survival curves were utilized to unveil differences in mortality among different FIB-4 subgroups. Restricted cubic spline (RCS) was adopted to testify the dose-response relationship between FIB-4 and mortality. Subgroup analyses were implemented to evaluate the consistency of results across age and sex subgroups.

A total of 6658 patients were enrolled, with 56% women and 68% whites. Cox analyses revealed considerable links of the FIB-4 index with all-cause deaths at 30 days, 90 days, and 1 year in HF patients. K-M survival analysis showed that the highest FIB-4 quartile (Q4) had the poorest survival. RCS analysis revealed a nonlinear dose-response relationship between FIB-4 and mortality. Risk-stratified analyses in multiple subgroups confirmed consistent results with overall results, with high FIB-4 levels being linked to an increased risk of death.

FIB-4 is a reliable predictor of all-cause deaths in HF patients.

High-altitude hypoxia is closely linked to dysregulated lipid metabolism, particularly elevated triglyceride (TG) levels, which increase cardiovascular and metabolic risks. This study proposes an interpretable deep learning model to predict TG levels in high-altitude migrants based on clinically accessible indicators.

Data were collected from low-altitude residents (n = 96) and high-altitude migrants (n = 388). An Uncertainty-driven Gated Feature Selection Network (UGFS-Net) was developed for TG prediction, incorporating an uncertainty-driven sample re-weighting and hard example mining strategy. The model was trained via modern optimization techniques and stratified partitioning by the TG distribution. Performance was evaluated using accuracy and calibration metrics, and interpretability was assessed via SHapley Additive exPlanations (SHAP). Five benchmark machine learning models with PCA or LASSO dimensionality reduction were used for comparison.

The UGFS-Net demonstrated a notable performance gain through uncertainty estimation, yielding an increase in R² from 0.7294 to 0.8776 for TG levels prediction under high-altitude. Predicted uncertainty showed significant correlations with errors and effectively distinguished low- from high-reliability samples, with strong calibration (bin-wise r = 0.9164). SHAP analysis highlighted that lipid metabolism, glucose metabolism, and the erythrocyte system collectively form a network that drives TG alterations in high-altitude environments and the Pearson correlation coefficient between gated the attention weights and SHAP importance scores was 0.9093. UGFS-Net consistently outperformed conventional machine learning models.

This study developed UGFS-Net, an interpretable deep learning model that accurately predicts triglyceride levels in high-altitude migrants (R² = 0.8776) and provides well-calibrated uncertainty estimates, with identified key biomarkers offering clinical insights.

Atherosclerosis and aortic aneurysms are prevalent aortic disorders. Significantly, atherosclerosis frequently impacts the common carotid artery (CA), whereas aortic aneurysms typically involve the abdominal aorta (AA), indicating possible heterogeneity between CA and AA with an ambiguous underlying mechanism. Sphingolipids, a crucial branch of lipid metabolism, has increasingly garnered attention in vascular diseases by influencing the phenotypic regulation of vascular smooth muscle cells (VSMCs). Nevertheless, whether sphingolipids play a role in the heterogeneity between CA and AA and in disease susceptibility remains uncertain.

Public transcriptomics were employed to clarify the distinctions in contractility and calcification of VSMCs in relation to the vulnerability of human AA and CA to aortic aneurysms and atherosclerosis, respectively. Bulk RNA-seq revealed transcriptomic variances in the biology of VSMCs between AA and CA from rats. Primary VSMCs from AA (AASMC) and CA (CASMC) were isolated for further validation. The specific variations in sphingolipid metabolism (comprising 8 classes of sphingolipids with 169 species) between AA and CA from rats were further characterized using UPLC-QTOF-MS-based lipidomics. Most sphingolipids, except sphingomyelin, were significantly elevated in CA compared to AA. Ceramides were the major contributor to these differences, which was further confirmed by in situ immunofluorescence of AA and CA from rats and healthy humans. The differential expression of genes involved in ceramide biosynthesis (Cers1-4), transport (Cert1), and metabolic processes (Smpd1, Sgpp1, Sphk2, and Sgms1/2), along with variations in subcellular organelles in VSMCs, contributed to the heterogeneity of sphingolipid metabolism between AA and CA. The effects of ceramides on VSMC biology were subsequently assessed using primary VSMCs. Specifically, AASMC exhibited greater contractility and lower susceptibility to calcification compared to CASMC. Exogenous ceramides heightened the susceptibility to calcification in both CASMC and AASMC, whereas the inhibition of ceramide synthases engendered an opposing consequence. Notably, in CA from patients with atherosclerosis but not AA from aortic aneurysms, the activation of sphingolipid metabolism (including ceramides) was positively correlated with calcification and negatively correlated with the regulatory processes of VSMC contraction. Furthermore, the ceramide metabolism was activated along with calcification in CA, which corresponded with the accumulation of ceramide in atherosclerotic plaques of human.

In this study, we identified ceramides, a major class of sphingolipid metabolites, as a promising determinant in unequal biology and susceptibility to calcification of CA and AA. Our results reveal the previously unappreciated role of sphingolipid metabolism, particularly ceramides, in regional vascular pathology. These findings provide novel insights that inform our understanding of disease mechanisms and highlight potential ways for future therapeutic exploration.

The aim of this umbrella review was to synthesize evidence on the effects of non-surgical periodontal therapy (NSPT) on cardiovascular biomarkers in adults with periodontitis.

Seven databases were searched up to March 2025 (PROSPERO: CRD420250656185). Included studies evaluated the impact of NSPT on cardiovascular biomarkers, including C-reactive protein (CRP), Interleukin-6, Tumor Necrosis Factor-α, lipid profiles (LDL, HDL, total cholesterol, and triglycerides), and vascular function measures (systolic and diastolic blood pressure, and flow-mediated dilation). The GROOVE tool was used to quantify review overlap, AMSTAR2 for methodological quality and GRADE for evidence certainty. Meta-analyses were recalculated using a random-effects model.

Of 1,525 records screened, 17 systematic reviews met the inclusion criteria. Pooled findings showed NSPT yielded reductions in CRP (-0.58 mg/L; 95% CI: - 0.91 to - 0.25), LDL (-0.10 mmol/L; 95% CI: - 0.17 to - 0.04), and increased HDL (0.03 mmol/L; 95% CI: 0.03 to 0.05), while other markers and vascular measures showed inconsistent and low-certainty results. Most evidence was classified as weak (Class IV) or non-significant due to heterogeneity, high risk of bias, and study overlap.

NSPT is modestly beneficial for CRP, IL-6, HDL, and LDL levels; however, the overall evidence is limited by methodological weaknesses.

NSPT may offer small yet clinically meaningful improvements in systemic cardiovascular risk markers in patients with periodontitis. Coordinated medical-dental care, targeted patient education, and timely delivery of NSPT can be pragmatically integrated to support cardiovascular risk reduction.

Kupffer cells and monocyte-derived macrophages (MoMs) are difficult to study in human primary biliary cholangitis (PBC) even though they reflect a dynamic hepatic immune population.

We aim to investigate the role of hepatic macrophage and its therapeutic potential in human PBC and murine autoimmune cholangitis.

Phenotypic analysis of hepatic macrophages in patients with PBC and dnTGFβRII mice model was performed by single-cell RNA sequencing, flow cytometry and immunohistochemistry. Depletion of hepatic macrophages and inhibition of MoMs were performed in murine autoimmune cholangitis. Lyz2-Cre-mediated Atg5 knockout mice and co-culture experiments were applied to explore the role and mechanism of macrophage autophagy in autoimmune cholangitis. Therapeutic intervention was performed using nanoparticle-capsuled small interfering RNA against Atg5.

Kupffer cells from patients with PBC and dnTGFβRII mice upregulate genes associated with inflammatory responses and exhibit increased autophagy. Further, macrophage-specific knockout of Atg5 leads to reduction of inflammation and bile duct damage. We propose that the mechanism of this modulation is secondary to decreased activation of pathogenic CD8⁺ T cells. Indeed, Kupffer cells maintain CD8⁺ T cell tolerance through expression of inducible nitric oxide synthase (iNOS) and generation of NO. Increased autophagy resulted in degradation of iNOS in Kupffer cells and abrogated their suppressive activity against CD8⁺ T cells. Finally, we report that targeted downregulation of Kupffer cell autophagy in vivo using cationic lipid-assisted nanoparticles encapsulating siRNA against Atg5 leads to reduction of liver inflammation and bile duct damage.

Macrophage autophagy promotes autoimmune cholangitis and strongly supports this pathway as a potential therapeutic target.

As a well-established inflammatory biomarker, leukocyte count is associated with higher mortality in acute pulmonary embolism (PE). We aimed to confirm the prognostic utility of leukocyte count and its integration with simplified Pulmonary Embolism Severity Index (sPESI) for predicting all-cause hospital mortality in acute PE.

Data derived from a national, multicentre and prospective registry including patients with acute PE were analysed to develop and validate an in-hospital mortality prediction model incorporating leukocyte count and sPESI. Mendelian Randomisation (MR) was performed to assess the relationship between leukocyte count and mortality.

A total of 7312 PE patients were stratified into three groups based on admission leukocyte count: < 4 × 10⁹/L (n = 301, 4.1%), (4-10) × 10⁹/L (n = 5074, 69.4%) and > 10 × 10⁹/L (n = 1937, 26.5%). Patients with leukocytosis exhibited a higher prevalence of anaemia, thrombocytopenia, hypoxemia, cardiac and renal injury, as well as haemodynamic instability. The in-hospital all-cause mortality was 3.0%, 2.3% and 6.4% (p < 0.001) across the three groups, respectively. The area under the curve (AUC) of sPESI was 0.719 (95% CI 0.681-0.756, p < 0.001), increasing to 0.738 (95% CI 0.701-0.775, p < 0.001) when combined with leukocyte count groups. The prognostic value of leukocyte count and its combination with sPESI was validated in a cohort with 7660 PE patients. MR analysis demonstrated that elevated leukocyte counts increased mortality risk (OR = 1.11, 95% CI 1.00-1.24, p = 0.047).

Admission leukocyte count enhances the prognostic accuracy of sPESI for in-hospital all-cause mortality in PE. Leukocyte count could serve as a potential biomarker for identifying PE patients at increased mortality risk.

Depression represents a significant global health burden. While cardiovascular health and depression share a well-established association, the specific relationship between "Life's Essential 8 (LE8)" and depression remains underexplored.

Utilizing data from the National Health and Nutrition Examination Survey (NHANES, 2007-2018), LE8 scores based on American Heart Association guidelines, categorizing cardiovascular health into low (<50), moderate (50-79), and high (≥80) tiers. Depression was defined as a Patient Health Questionnaire-9 (PHQ-9) score ≥ 10. Survey-weighted logistic regression, restricted cubic splines, and mediation analysis were employed to assess associations.

Among the weighted sample of 213,703,729.8 participants (mean age 45.21 years, 50.6 % male), each 1-standard deviation (SD) increase in the LE8 score, the adjusted odds ratio for depression was 0.64 (95 % CI: 0.56-0.73). Compared to the low LE8 group, high and moderate LE8 tiers exhibited substantially lower depression risks (high: OR = 0.29, 95 % CI: 0.18-0.48; moderate: OR = 0.52, 95 % CI: 0.37-0.74, both P < 0.001). Individuals with ≥5 optimal LE8 components demonstrated a 67 % risk reduction versus those with 0-1 components (OR = 0.33, 95 % CI: 0.19-0.57), with each additional component lowering risk by 23 % (OR = 0.77, 95 % CI: 0.66-0.90). Subgroup analyses revealed sex-specific variations: diet contributed most to risk reduction in males (attributable risk proportion = 0.053), while BMI was predominant in females (0.052). Mediation analyses identified cardiovascular disease (CVD; 29 % mediation) and obstructive sleep apnea (OSA; 12.2 % mediation) as partial mechanistic pathways.

LE8 scores exhibit a robust inverse association with depression risk, modulated by sex-specific component contributions and mediated through CVD and OSA. These findings underscore the value of tailored interventions.

Current metabolomic aging clocks inadequately capture individual heterogeneity in biological aging trajectories, constraining their clinical utility. Here, we developed a metabolomic age clock in the UK Biobank (n = 196,790) using a comprehensive panel of 249 plasma metabolites. This framework was trained to predict phenotypic age (PhenoAge), a validated composite biomarker that integrates clinical chemistry across multiple systems, and was evaluated for its utility to predict incident cardiovascular diseases (CVDs) and dementia. We found that this new measure accurately predicted actual PhenoAge (Pearson's r = 0.90) and was significantly associated with the incidence of seven CVDs, including major adverse cardiovascular events, atherosclerotic cardiovascular disease, myocardial infarction, stroke, aortic stenosis, heart failure, and abdominal aortic aneurysm, but not dementia. Furthermore, metabolomic aging was associated with biological, physical, and cognitive age-related phenotypes, comprising telomere length, frailty index, and reaction time. Incorporating the metabolomic age clock with PREVENT (Predicting Risk of CVD Events) risk score modestly improved the performance, as measured by C-statistic and net reclassification index. Genetic analyses revealed 91 genomic loci and 168 genes (e.g., SERPINA1, FADS cluster), with tissue-enrichment analysis highlighting the liver's significant role in metabolic aging. By bridging metabolomic profiles with multisystem aging information, this framework provides a measure of biological aging that is associated with age-related functional status and cardiovascular risk.

The Advanced Lung Cancer Inflammation Index (ALI) is a novel composite index that enables a more holistic evaluation of inflammation and nutritional status than established single or commonly used indices. However, ALI has not been extensively studied in patients with stroke. In this study, we aimed to investigate: 1) the association between ALI and stroke risk and 2) the association between ALI and all-cause mortality among patients with stroke, as well as 3) develop and interpret machine learning (ML) models to predict stroke and prognosis.

Using data from the National Health and Nutrition Examination Survey (NHANES) 1999-2018, logistic regression and Cox regression assessed associations of ALI with stroke and mortality. Non-linear relationships were analysed using restricted cubic spline and subgroup stratification. Logistic regression (LR), extreme gradient boosting (XGBoost), random forest (RF), K-Nearest Neighbor (KNN), supported vector machine (SVM), and decision tree (DT) were developed for stroke and mortality prediction and evaluated using the area under the receiver operating characteristic curve (AUCROC), and metrics such as accuracy. Shapley additive explanations (SHAP) and Gini importance enhanced the dual-interpretability of the model.

Among the 46,451 participants, higher ALI was associated with a lower stroke risk, whereas mortality decreased with increasing ALI before stabilising at an inflection point (ALI = 40.91, P_threshold < 0.001). Age stratification significantly modified the association between ALI and mortality. The RF model marginally outperformed the other models in terms of stroke identification (AUCROC: 0.9657, accuracy: 95.63 %) and mortality prediction (AUCROC: 0.7771, accuracy: 70.65 %). The SHAP and Gini importance analyses highlighted cardiovascular diseases as key factors for stroke prediction and age for mortality, with ALI being less influential.

Leveraging the nationally representative NHANES database, this exploratory analysis revealed that ALI presented a reverse dose-response association with the stroke risk and an "L-shaped" relationship with all-cause mortality among patients with stroke. Dual-interpretable RF models based on the ALI showed comparably promising potential among the six ML models for stroke identification and prognosis prediction.

The rapid aging population in Hong Kong has led to an increased reliance on family carers to support older adults age in place. This demographic shift poses significant mental health challenges for carers, who often experience elevated levels of psychological distress, including anxiety and depression. Development of effective interventions to improve the mental health of carers of older adults in Hong Kong is needed.

This study aims to evaluate the effectiveness of a low intensity cognitive behavioral therapy (Li-CBT) intervention to improve the psychological well-being among family carers of older adults in Hong Kong, specifically focusing on reducing symptoms of depression and anxiety.

A two-armed, multi-site, single-blind clustered randomized controlled trial will be conducted over 3 months. A target of 270 carers will be recruited from nine district-based carer support units and randomly assigned to either the Li-CBT intervention group or a control group. The primary outcomes will include changes in depression and anxiety levels, measured by the Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder Scale (GAD-7), assessed at baseline, immediate post-intervention (6-week) and 3-month post-intervention. Secondary outcomes will encompass a range of assessments, including carer multidimensional need assessment, social support, quality of life, caregiver burden, and resilience. It is hypothesized that participants in the Li-CBT intervention group will show a greater reduction in depression and anxiety scores compared to those in the control group. This study seeks to provide evidence for an integrated, evidence-based intervention model that can be widely disseminated to support family carers of older adults, ultimately improving their mental health and quality of life.

The findings of this study will inform future research and practice on supporting carers of older adults. If effective, the Li-CBT intervention will contribute to reduce depression and anxiety levels and improve the overall quality of life while addressing a critical literature and service gap of culturally sensitive intervention for family carers of older adults in Hong Kong.

The protocol for this study has been registered with ClinicalTrials.gov under the identifier NCT06473012. The registration was completed prospectively before the enrollment of the first participant on July 2, 2024.