Qiu Chen

Insulin resistance is a complex metabolic state involving impaired insulin sensitivity, compensatory insulin secretion, abnormal lipid metabolism, and altered fat distribution. However, the shared genetic architecture underlying these correlated phenotypes remains incompletely defined. In this study, we applied Genomic Structural Equation Modeling to genome-wide association summary statistics of fasting insulin, insulin sensitivity index, triglycerides, and waist-to-hip ratio to construct an insulin resistance-related latent genetic factor. SNP-level association analysis was then performed for the latent factor, followed by risk locus annotation, fine-mapping, transcriptome-wide association analysis, gene-level prioritization, enrichment analysis, and exploratory structural biology assessment of selected missense substitutions. The single-factor model showed acceptable but not optimal approximate fit and captured a biologically consistent genetic pattern, with positive loadings for fasting insulin, triglycerides, and waist-to-hip ratio and a negative loading for insulin sensitivity index. The latent factor showed detectable SNP heritability and revealed 3,067 genome-wide significant candidate SNPs, with Q_SNP retained as a complementary SNP-level heterogeneity diagnostic.. Functional annotation identified 100 genomic risk loci and 1,530 FDR-significant genes in MAGMA. Fine-mapping prioritized multiple candidate regions, including loci near PPARG, RSPO3, SLC38A11, SLC39A8, BCL2, GDF5, and SPATA5. TWAS and FOCUS further highlighted candidate genes involved in adipocyte biology, glucose–lipid metabolism, insulin secretion, and expression regulation, including PPARG, MLXIPL, TCF7L2, FAM13A, CCDC92, and SLC22A3. Enrichment analyses suggested that genetic signals associated with the insulin resistance-related latent factor were mainly linked to fat distribution, cardiometabolic traits, adipose tissue, mesenchymal cell lineages, and active regulatory genomic regions. In a separate exploratory analysis, AlphaFold3 modeling, ThermoMPNN prediction, and molecular dynamics simulations suggested structural perturbations for three ultra-rare missense substitutions in nominated candidate proteins: BCL2-W188G, GDF5-C400Y, and RSPO3-C129Y. These substitutions were selected using functional-impact annotations and modeling feasibility, were not GWAS lead or fine-mapped causal variants, and do not establish the mechanisms underlying the GWAS associations. These findings provide an integrated genetic and structural framework for understanding shared mechanisms underlying insulin resistance-related metabolic abnormalities, while emphasizing the need for further experimental validation.

Background/Objectives: Shift work causes recurrent circadian misalignment. This review synthesizes evidence linking shift work to acute coronary syndrome (ACS) and proposes a framework for prevention, return-to-work decisions, and future research. Methods: We conducted a narrative review using targeted searches of PubMed and major guideline repositories through July 2026. Search terms included shift work, night work, acute coronary syndrome, myocardial infarction, coronary heart disease, circadian misalignment, sleep, thrombosis, and cardiovascular risk. Revision-stage verification also examined atherosclerosis, coronary plaque, plaque vulnerability, and ischemia. Priority was given to prospective cohorts, systematic reviews, meta-analyses, controlled human studies, consensus statements, and major guidelines. Results: Night and rotating shift work are associated with a modestly increased risk of coronary heart disease (relative risk ~1.2–1.3), with duration-response gradients. Direct ACS-specific evidence is more limited but consistent with increased incidence and recurrence. Mechanistically, circadian misalignment and sleep deficiency promote autonomic dysregulation, hypertension, insulin resistance, dyslipidemia, inflammation, endothelial dysfunction, and atherosclerosis. Acute autonomic, hemodynamic, and prothrombotic changes may trigger plaque disruption or supply-demand ischemia. Risk modifiers include schedule intensity, cumulative exposure, chronotype, age, sex, comorbidities, sleep disorders, occupation, and health behaviors. Pregnancy is a specific evidence gap. Conclusions: Night and rotating shift work are probable contributory risk factors for ACS in susceptible workers. Clinical practice should integrate work schedule and sleep health into cardiovascular assessment, especially after ACS. Roster redesign offers the most defensible preventive strategy. Evidence that circadian interventions prevent ACS remains insufficient. More prospective ACS-specific studies are needed.

Metabolic syndrome (MS), characterized by central obesity, hyperglycemia, hypertension, and dyslipidemia, substantially increases the risk of cardiovascular and cerebrovascular events. Adipose tissue is an active endocrine organ, and dysregulated adipokine secretion may contribute to the link between MS and vascular injury. In this narrative review, we synthesize evidence on adipokines implicated in MS-related cardiocerebrovascular disease and discuss their therapeutic potential. Adiponectin, fibroblast growth factor 21 (FGF21), and omentin are generally associated with improved insulin sensitivity, lipid handling, and endothelial homeostasis, whereas leptin, resistin, tumor necrosis factor-α, and interleukin-6 can promote inflammation and endothelial dysfunction through pathways including NF-κB and JAK/STAT. However, these associations may be modified by obesity, insulin resistance, disease stage, and other confounding factors, and some adipokines, such as adipsin, show context-dependent effects. Current translational evidence is strongest for strategies targeting adiponectin/FGF21 signaling and IL-1/IL-6-related inflammatory pathways, while evidence for cerebrovascular outcomes remains limited. Adipokine-directed interventions therefore remain promising but require rigorous clinical validation and disease-specific stratification.

Background: Maturity-onset diabetes of the young (MODY) is the most common form of monogenic diabetes, with genetic and clinical heterogeneity emerging as significant challenges. Despite growing scholarly attention to MODY research, the overall knowledge structure, research priorities, and developmental trends in this field have not yet been systematically integrated. Methods: To ensure comprehensive coverage and compatibility with bibliometric analysis tools, relevant publications on MODY were retrieved from the Web of Science Core Collection (WoSCC) database from 2006 to 2025. Employing bibliometric methods and tools such as CiteSpace, VOSviewer, Scimago Graphica, and Microsoft Excel, we conducted visual analyses of publication trends, country and regional collaboration, institutional distribution, author contributions, co-cited journals, co-cited references, and keywords. Results: From 2006 to 2025, a total of 1,610 publications related to MODY were identified, showing an overall increasing trend over time. The United States of America (USA), the United Kingdom (UK), and China were the major contributors to this field, with close collaborative relationships, particularly between the USA and the UK. The leading contributors by country, institution, and author were the USA (391 publications), the University of Exeter (105 publications), and Sian Ellard and Andrew T. Hattersley (50 publications each). High-frequency keywords included “gene”, “monogenic diabetes”, “children”, “prevalence”, “glucokinase”, and “mutation”. In the co-citation analysis, the study by Shields BM published in 2010 showed the highest citation frequency. These findings indicate that MODY research has primarily centered on genetic mechanisms, clinical diagnosis, and precision management, with recent attention gradually moving toward functional genomics and individualized therapeutic strategies. Conclusion: MODY research has evolved into a multidisciplinary field that bridges biological mechanisms, clinical characterization, and translational application. Future studies should broaden population coverage and translate molecular findings into more precise diagnosis and individualized management.

Metabolic syndrome (MetS) is a clinical syndrome primarily characterized by insulin resistance, integrating central obesity, hyperglycemia, hypertension and dyslipidemia. It is associated with increased risks of type 2 diabetes mellitus, non‑alcoholic fatty liver disease and atherosclerosis. Due to its high prevalence, complex pathogenesis and lack of effective treatments, MetS has become a notable global health issue. Current therapeutic approaches mainly emphasize weight reduction through caloric restriction and increased physical activity, or pharmacological interventions to improve lipid profiles, blood pressure and blood glucose; however, their efficacy remains suboptimal. Growing evidence indicates that programmed cell death (PCD), as a key driver of inflammation, serves a crucial role in the development and progression of MetS. The present review provides a comprehensive overview of recent advances in the understanding of how established and emerging forms of PCD contribute to MetS pathogenesis, including apoptosis, pyroptosis, autophagy, ferroptosis, necroptosis, PANoptosis and disulfidptosis. Particular emphasis is placed on their molecular mechanisms, tissue‑specific functions, regulatory crosstalk and therapeutic potential. Additionally, the present review discusses novel regulatory approaches and potential therapeutic strategies based on PCD network intervention, offering theoretical foundations and directions for developing innovative therapies to prevent and treat MetS and its complications.

Diabetes mellitus (DM) and its complications remain major clinical challenges despite advances in prevention and treatment. Histone Deacetylase 11 (HDAC11) has been implicated in glucose metabolism, insulin sensitivity, and inflammatory cytokine (IC) regulation, suggesting potential relevance to diabetes-related phenotypes. This study investigated the associations among genetically proxied lower HDAC11 expression, ICs, and DM-related outcomes. We applied a drug-target Mendelian randomization (MR) framework to evaluate genetically proxied lower HDAC11 expression. The primary analysis used inverse-variance weighting (IVW), with MR-Egger, weighted median, simple mode, and weighted mode as complementary methods. Robustness was evaluated using heterogeneity tests, MR-Egger intercept tests, leave-one-out analysis, and MR-PRESSO. Two-step MR was used to assess IC-mediated pathways. Colocalization analysis assessed whether HDAC11 eQTL and diabetes-related outcome signals shared a causal variant. Protein-protein interaction (PPI) analysis and molecular docking explored potential molecular links. The primary instrument retained eight single-nucleotide polymorphisms (SNPs). Genetically proxied lower HDAC11 expression was associated with lower odds of diabetic ketoacidosis, diabetic nephropathy, and type 2 diabetes and with higher odds of diabetic neuropathy; associations with diabetic maculopathy and diabetic retinopathy were not statistically significant. Effect directions were generally consistent under stricter LD clumping, although statistical support weakened as fewer variants were retained. Heterogeneity tests, MR-Egger intercept analyses, and MR-PRESSO did not detect substantial heterogeneity or horizontal pleiotropy. Colocalization provided low support for shared causal variants. ICs, including IL-2, IL-10, IL-13, and IL-18, showed evidence of potential direction-consistent mediation, whereas selected ICs showed opposite-direction or suppressive pathways. PPI and molecular docking analyses identified HDAC11-related molecular relationships for further study. These analyses identify hypothesis-generating associations between genetically proxied lower HDAC11 expression and selected diabetes-related outcomes. Inflammatory pathways provide candidate mechanisms, while threshold-dependent estimates and limited colocalization support motivate tissue-relevant studies of HDAC11 biology and inhibition.

Aging is an inevitable physiological process characterized by progressive functional decline and degenerative alterations across organ systems. Metabolic disturbance, particularly the disruption of substance and energy metabolism, is increasingly recognized as a central hallmark of aging. Emerging evidence suggests that dysregulation of the urea cycle (UC) contributes to aging-related pathological processes, including cognitive impairment; however, its role in astrocyte-mediated brain aging remains unclear. In this study, we used a D-galactose-induced mouse aging model to investigate the involvement of UC activation in age-related cognitive decline. Aging mice exhibited marked cognitive impairment, accompanied by an increased proportion of reactive astrocytes in the hippocampus, a key pathological feature of brain aging. Metabolic analysis and molecular validation revealed enhanced UC activity and increased urea production in aging mice. Inhibition of UC activation reduced urea production and decreased the proportion of hippocampal reactive astrocytes. Mechanistically, the expression of key UC-related enzymes, including ornithine decarboxylase 1 (ODC1) and arginase 1 (ARG1), was significantly downregulated, accompanied by improved mitochondrial dynamics, particularly the restoration of mitochondrial fusion and fission balance. Furthermore, ODC1 knockdown confirmed its critical role in mediating UC activation in astrocytes and significantly alleviated aging-like cellular phenotypes. Notably, Ganoderma lucidum polysaccharide peptides (GLPs) effectively suppressed UC activation in aging astrocytes by downregulating ODC1 and ARG1. Collectively, this study identifies dysregulated astrocytic UC activity as a novel metabolic mechanism linking astrocyte reactivity, mitochondrial dysfunction, and age-related cognitive decline, suggesting that targeting ODC1-mediated UC activation and mitochondrial dynamics may provide a promising dual strategy for combating brain aging.

The sterility and controllable biological functionalities of implantable medical devices determine their lifespan, safety, and therapeutic efficacy. Inspired by the thermal effect of working electronic chips, which consist of various heterostructures, we have developed an electronic chip‐mimetic sonothermal platform through constructing metal‐semiconductor heterogeneous interface. Through magnetron sputtering on the surface of the sandblasted/acid‐etched pretreated titanium (pTi), we find that semiconductor coatings (TiO ₂ , Si, ZnO, and Te) endow pTi with different in situ sonothermal effects (ΔT > 18°C, 15 min) under ultrasound (US) irradiation, whereas conductor coatings do not. The sonothermal mechanism of pTi‐semiconductor is associated with US‐activated electron and phonon transport within the heterogeneous interface of implants, which is determined by the electrical and phonon characteristics of pTi‐semiconductor, including their matching degree, thermal conductivity, defected structure, and type of semiconductor. Clinical titanium screws were introduced with defected structure in oxygen layer (TiO _2‐x ) and bone‐derived whitlockite, which shows great sonothermal/sonodynamic effects for efficient elimination of biofilm infection and improved osseointegration. In addition, the prepared NiTi‐TiO _2‐x guidewire enables rapid thrombolysis (30 min) in the deep vein thrombosis of beagles after 10 min of sonothermal/urokinase treatment. The electronic chip‐mimetic sonothermal platform provides a promising and widespread clinical application prospect.

Background and Objective: Wearable sensors now make N-of-1 trials feasible, but the high-frequency streams they produce (continuous glucose monitors, actigraphy) break the assumptions of standard single-case analyses, and clear guidance is lacking. To our knowledge, this is the first fair comparison of six existing methods for such data, distilled into a decision guide that keeps single-patient conclusions valid.Methods: We identify five challenges (autocorrelation, drift, carryover, an effective-sample-size illusion, and aggregation level) and map the six methods against them. We compared them in a glucose-monitor-like simulation crossing autocorrelation, randomised treatment switches, effect size and carryover (a 96-cell factorial, 500 replications per cell).Results: Treating 24,000 dependent readings as independent gave a Type I error near 0.93, with 95% intervals covering the truth only 0.07 of the time. A single autoregressive term did not repair this (0.84 at low autocorrelation, 0.34 near glucose-monitor levels). Only period-level analyses stayed valid: the period-mean paired test held its size near 0.07, and a design-based randomisation test never falsely rejected. Power was set by the number of randomised treatment switches, not the reading count (0.10, 0.16 and 0.35 at two, three and five switches). Carryover was negligible when periods outlasted the washout.Conclusions: In high-frequency wearable trials the evidence rests on the randomised switches, not the raw reading count. Choosing a pre-specified analysis by sampling frequency and target estimand, aggregating first, and reporting under established reporting standards for N-of-1 trials keeps single-patient conclusions trustworthy.