Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent and associated with metabolic dysfunction, systemic inflammation, and increased cardiovascular risk. The C-reactive protein-Triglyceride-Glucose Index (CTI) is a novel composite biomarker reflecting low-grade inflammation and insulin resistance. However, its prognostic value for long-term mortality in patients with MASLD and advanced fibrosis remains unclear. Therefore, this study aimed to investigate the association between CTI and the risk of all-cause and cardiovascular mortality in individuals with MASLD using a nationally representative cohort. We used data from National Health and Nutrition Examination Survey 2001 to 2018 and included 8791 adults with MASLD. Advanced fibrosis was defined by FIB-4, NAFLD Fibrosis Score, and AST-to-Platelet Ratio Index. Survey-weighted Cox models evaluated associations between CTI (per standard deviation increase) and all-cause and cardiovascular mortality. Restricted cubic splines examined dose-response patterns. Effect modification by sex, age, and race/ethnicity was assessed in stratified analyses. Over follow-up, 1806 deaths occurred. Higher CTI was independently associated with increased all-cause (HR 1.16, 95% confidence interval [CI]: 1.12-1.20) and cardiovascular mortality (HR 1.12, 95% CI: 1.04-1.20) in MASLD. Among participants with advanced fibrosis, CTI remained associated with all-cause (HR 1.14, 95% CI: 1.07-1.23) and cardiovascular mortality (HR 1.14, 95% CI: 1.01-1.29). Splines showed a U-shaped association between CTI and mortality in MASLD (nadir CTI ~7.5-7.9) but an approximately linear positive association in advanced fibrosis. Associations were stronger in women, older adults, and racial/ethnic minorities. Our observational findings suggest that CTI is associated with long-term all-cause and cardiovascular mortality in MASLD and advanced fibrosis. As a simple, integrative biomarker, CTI may help refine early risk stratification in high-risk MASLD populations.

ST-segment elevation myocardial infarction (STEMI) requires primary percutaneous coronary intervention (PCI) and dual antiplatelet therapy with aspirin and a purinergic receptor Y12 inhibitor to reduce thrombotic risks. Ticagrelor, a potent purinergic receptor Y12 inhibitor, offers faster and stronger platelet inhibition than clopidogrel, but evidence on its efficacy and safety in STEMI patients post-PCI is conflicting. This study aims to compare the efficacy and safety of ticagrelor versus clopidogrel in STEMI patients undergoing primary PCI.

This systematic review and meta-analysis, adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, searched PubMed, Embase, ScienceDirect, and ClinicalTrials.gov up to July 2025. Included were randomized controlled trials and observational studies comparing ticagrelor to clopidogrel in adult STEMI patients post-PCI, reporting cardiovascular outcomes. Data were pooled using odds ratios (ORs) with 95% confidence intervals (CIs) via random-effects models.

Eight studies (5 randomized controlled trials, 3 observational; n = 31,729) showed ticagrelor significantly reduced all-cause mortality (OR = 0.64, 95% CI = 0.47-0.88; P = .006), cardiovascular mortality (OR = 0.68, 95% CI = 0.60-0.93; P = .01), major adverse cardiovascular events (OR = 0.71, 95% CI = 0.60-0.84; P = .01), myocardial infarction (OR = 0.71, 95% CI = 0.61-0.83; P < .00001), stent thrombosis (OR = 0.66, 95% CI = 0.55-0.79; P < .00001), and major bleeding (OR = 0.87, 95% CI = 0.78-0.97; P = .01), but increased target vessel revascularization (OR = 1.30, 95% CI = 1.05-1.61; P = .02). No significant difference was observed in stroke risk (OR = 1.16, 95% CI = 0.89-1.52; P = .28).

Ticagrelor outperforms clopidogrel in STEMI post-PCI, reducing key adverse outcomes, though higher target vessel revascularization risk warrants caution.

The recent standardization of diagnostic criteria and phenotyping for cardiovascular-kidney-metabolic (CKM) syndrome has increased attention to this multisystem disorder which arises from the interactions among metabolic dysfunction, chronic kidney disease, and cardiovascular disease. The food inflammation index (FII) quantifies diet-induced inflammation and evaluates individuals' susceptibility to inflammatory-mediated health outcomes. However, the mechanisms linking FII derived dietary inflammation to CKM syndrome progression, especially the triglyceride-glucose (TyG) index's mediating role, remain unclear. This study aimed to investigate the association between FII and advanced CKM syndrome and evaluate the mediating role of TyG index. This study used a two-phase design to determine the dose-response relationship between FII and advanced CKM syndrome (stages 3 or 4) and to assess the TyG index's contribution in this link through mediation analysis. Data obtained from the 1999 to 2018 surveys were analyzed, and 20,151 participants were included in the analysis. CKM syndrome staging was done per established criteria, with advanced CKM as stage 3 or 4. Multivariate weighted logistic regression evaluated the association between FII and advanced CKM syndrome, with the TyG index as a mediator. The prevalence of advanced CKM syndrome was 17.37%. FII was significantly associated with advanced CKM stages (odds ratio = 1.03, 95% confidence interval = 1.01-1.04, P < .001), and this association was partially mediated by the TyG index (proportion mediated = 12.52%, 95% confidence interval = 7.25%-25.48%, P < .001). Higher FII levels were associated with an increased risk of advanced CKM syndrome, suggesting that FII may serve as a useful marker for prevention and early detection. The TyG index partially mediated the relationship between FII and advanced CKM syndrome. Future longitudinal studies are warranted to confirm these findings and explore potential interventions targeting dietary inflammation.

Chronic kidney disease (CKD) patients experience a high oral disease burden and mortality. We evaluated whether loss of natural teeth predicts all-cause and cause-specific mortality in adults with CKD and whether a Frailty Index (FI) mediates this association. We analyzed 12,639 adults with CKD from the National Health and Nutrition Examination Survey 1999-2018. Tooth counts (third molars excluded) were grouped clinically. A 53-item FI was constructed. Survey-weighted Cox models with sensitivity checks, restricted cubic splines, piecewise Cox regression, and counterfactual mediation analysis were applied. Sequential models added FI and then ln(hs-CRP) to examine attenuation. Prespecified subgroup and sensitivity analyses were conducted, including cycles with prosthetic information. Greater tooth loss was independently associated with higher all-cause, cardiovascular, and cancer mortality. In fully adjusted models, compared with complete dentition, adjusted hazard ratios (HRs; 95% confidence intervals) for all-cause mortality were as follows: tooth loss, 1.53 (1.24-1.88); lacking functional, 1.93 (1.54-2.42); severe tooth loss, 1.99 (1.54-2.57); and edentulism, 2.16 (1.75-2.67). Spline analyses supported nonlinearity for all-cause, cardiovascular, and cancer mortality; segmented models were consistent with a steeper slope at lower tooth counts and a plateau thereafter. Piecewise analysis identified a threshold at 3 missing teeth: below the threshold, each additional missing tooth had an HR of 1.161 (1.108-1.216); above the threshold, HR was 1.010 (1.006-1.014) (log-rank P < .001). Associations were broadly consistent across sex and CKD risk strata, with relative effects attenuating at higher Kidney Disease: Improving Global Outcomes risk categories, where baseline hazards were greater. In cycles with prosthetic data, associations were weaker among participants receiving dental prosthetic rehabilitation. FI mediated 29.42% of the effect on all-cause mortality. Sequential adjustment indicated additional, smaller attenuation after ln(hs-CRP), while the primary associations remained significant. In US adults with CKD, tooth loss predicts higher mortality; frailty explains ~30% of this association, suggesting that the integration of routine oral health screening and dental interventions with frailty assessment and multidisciplinary care (nutritional support, rehabilitation, and psychosocial services) may identify at-risk CKD patients and offer modifiable targets to improve survival and quality of life.

Cardiometabolic index (CMI) reflects visceral fat accumulation and lipid metabolism and has been linked to metabolic and cardiovascular diseases. Its association with cognitive function in older adults remains unclear. This study aimed to assess the relationship between CMI and global cognitive performance in a nationally representative sample of United States adults aged 60 years and older. We conducted a cross-sectional analysis of National Health and Nutrition Examination Survey 2011 to 2014 data (n = 1326). CMI was analyzed both as a continuous variable and by quartiles (Q1-Q4). Survey-weighted linear regression models examined associations with global cognitive function (GCF), adjusting sequentially for demographics (ModelI) and full covariates (ModelII). Threshold effects and smoothing curves explored nonlinear links. Interaction tests and subgroup analyses evaluated effect modification by diabetes status. In the minimally adjusted model, higher continuous CMI was associated with lower GCF (β = -2.46, 95% confidence interval [CI]: 4.58--0.35; P = .0318), but this association was not significant after full adjustment (β = -0.56, 95% CI: 2.49-1.36; P = .5807). When using quartiles, a negative trend was observed in the crude (P for trend = .0392) and minimally adjusted models (P for trend = .0166) but not in the fully adjusted model (P for trend = .4825). Nonlinear analysis identified an inflection at CMI = 0.16 for digit symbol substitution test (β = 142.74, 95% CI: 70.18-215.30; P < .001) and GCF (β = 173.67, 95% CI: 77.01-270.32; P < .001). Diabetes status significantly modified the CMI-GCF relationship (P for interaction = .035). CMI is negatively associated with cognitive function in older adults. These findings suggest that higher CMI may indicate a greater risk of cognitive decline.

Acylcarnitines (ACs) are a diverse class of fatty acid esters of L-carnitine that serve as critical mediators in energy homeostasis and mitochondrial function. Beyond their classical role in newborn screening for inborn errors of metabolism, ACs have emerged as promising biomarkers for complex pathologies, including cardiovascular diseases, diabetes, and drug-induced toxicities. However, the accurate quantification and comprehensive profiling of ACs in biological matrices remain analytically challenging due to their broad polarity range, vast concentration disparities, and the presence of isomers. This review provides a comprehensive overview of the current bioanalytical strategies for ACs, covering sample preparation techniques and detection platforms, with a focus on liquid chromatography-mass spectrometry. Special attention is given to the differentiation of isomers. Finally, we discuss the clinical applications of ACs profiling and highlight future perspectives, including the integration of ion mobility spectrometry, automated high-throughput workflows, spatial and single-cell metabolomics, and AI-driven analytics, to pave the way for precision medicine.

With the rising prevalence of metabolic diseases, their comorbidity is increasingly common. Glucolipid metabolic disorders (GLMD) constitute a major health challenge associated with increased cardiovascular risk and mortality. Despite many advances in disease mechanisms and therapeutic strategies, the metabolic disease burden remains substantial. Routine clinical practice still lacks straightforward approaches for identifying individuals at elevated risk, and the challenge of effectively preventing and controlling GLMD has become an urgent clinical problem. Significant gaps remain in our understanding of metabolic disease. Advances in high-throughput sequencing and omics technologies have expanded the research perspective, shifting attention from organ-centered pathology toward microecological and metabolic networks, elucidating the interactions between the oral microbiota and host metabolism. As the second largest microbial community after the gut microbiota, the oral microbiome provides an integrative lens for examining GLMD-related microbial signatures, plausible mechanistic pathways, and translational opportunities, including inflammation, taste signaling, nitric oxide metabolism, and microbial translocation. This review aims to clarify the relationship between the oral microbiome and GLMD, outline characteristic microbial alterations in metabolic diseases, and summarize the plausible mechanisms linking oral microorganisms with metabolic homeostasis. Current evidence indicates partially recurrent microbial patterns across GLMD-related conditions, but no universal oral microbial signature has been established. Building on recent findings, we conclude by outlining the methodological requirements for evaluating the clinical utility of oral microbiome-based markers and interventions, and further discuss the value of oral microbiota in disease prediction, risk assessment, and individualized intervention. We also summarize the key limitations and challenges in the field and outline directions for future prevention and control of GLMD based on oral microecology.

Cardiac neuromodulation includes various methods, such as vagus nerve stimulation, baroreflex activation therapy, renal denervation, and stellate ganglion intervention, and targets the autonomic imbalance contributing to the pathophysiology of many cardiovascular diseases. Despite promising mechanistic evidence, several landmark trials, including INOVATE-HF, NECTAR-HF, and SYMPLICITY HTN-3, did not meet their primary clinical outcomes, with substantial numbers of non-responders observed across therapies. Variation in patient response is attributed to several unresolved issues, including insufficient stimulation dosing, off-target or non-selective fiber activation, and differences in autonomic phenotypes between patients. Both problems highlight the need for individualized approaches to patient selection, therapy delivery, and monitoring. Artificial intelligence (AI) offers tools to address these problems. In this narrative review, we describe seven families of AI techniques relevant to cardiac neuromodulation: supervised machine learning, deep learning, representation learning, reinforcement learning, multimodal fusion, digital twins with physics-informed AI, and explainable AI with federated learning. For each family, we summarize how the method works, the cardiac neuromodulation problem it addresses, and the available evidence in the field of cardiac electrophysiology. We then map these techniques to the three core problems of patient selection, real-time stimulation control, and longitudinal response monitoring. The strongest evidence to date supports representation learning for VNS responder identification, reinforcement learning for closed-loop VNS control, and digital twins for in silico testing of stimulation protocols. The opportunity for the field is to translate these methods, most of which were developed in adjacent fields, into prospective cardiac neuromodulation trials.

Beyond their conventional role as passive transdermal delivery vehicles, microneedle (MN) platforms now function as active bio-interfaces capable of modulating therapeutic responses in both localized and systemic diseases. This review summarizes recent advances in MN technology, focusing on the transition from traditional matrix-controlled delivery to bioactive microneedles. Although localized applications at barrier surfaces, such as treating cutaneous disorders and mucosal lesions, remain a fundamental focus, this review emphasizes the application of MNs in complex chronic metabolic diseases (e.g., diabetes), oncology (e.g., melanoma and glioblastoma), and deep-tissue degenerative diseases of the cardiovascular, nervous, and musculoskeletal systems. Integrating stimuli-responsive materials, including metal-organic frameworks (MOFs), aggregation-induced emission luminogens (AIEgens), and smart hydrogels, with external physical stimuli enables autonomous, closed-loop interventions, thereby advancing personalized systemic therapy. Furthermore, we summarize recent progress in applying MNs to non-traditional sites and deep-tissue repair. Finally, rather than focusing solely on phenotypic efficacy, we discuss key translational challenges, including manufacturing scalability, biosafety, and regulatory pathways, to guide future clinical translation.

Familial DCM (FDCM) is identified when two or more firstdegree relatives have idiopathic dilated cardiomyopathy (DCM) or unexplained death at a young age. This report aims to highlight the clinical manifestations of FDCM in a Nigerian family, emphasizing the importance of genetics while addressing the paucity of local data.

This report describes a 22-year-old male with DCM whose elder sibling died from DCM, and a younger one had similar echocardiographic features as the index patient, highlighting the hereditary nature of the disease within his family The patient, initially asymptomatic, reported easy fatigability, breathlessness, and cough, which worsened over three months. Clinical examinations revealed signs of advanced heart failure, including elevated jugular venous pressure and fine bibasal crepitations. Echocardiography confirmed DCM. Despite initial treatment, the patient developed an intracardiac clot and required an extensive medication regimen. Family history indicated an autosomal dominant inheritance pattern, with a younger sibling also showing features of DCM.

This case underscores the importance of genetic factors in the pathogenesis of FDCM and highlights the challenges of managing the disease, particularly in resource-limited settings. Early family screening, patient education, and adherence to treatment protocols are crucial for improving outcomes. There is a need for accessible genetic testing to facilitate early diagnosis and intervention in at-risk populations.