Shaoyong Xu

Background: Euthyroid sick syndrome (ESS), a common endocrine disorder in critically ill patients, lacks reliable diagnostic methods. Reverse triiodothyronine (rT3) is a promising biomarker but requires standardized diagnostic and prognostic cutoffs. This study aimed to define reference ranges of rT3 for ESS diagnosis and evaluate its independent prognostic value for mortality. Methods: This prospective multicenter cohort study was conducted between June 2022 and April 2024 in seven hospitals across three central provinces in China. A total of 782 critically ill adults were enrolled, and rT3 and other thyroid function parameters were measured at intensive care unit (ICU) admission. ESS was diagnosed based on reduced free triiodothyronine (FT3) levels in the absence of primary or secondary thyroid dysfunction. Patients were followed for in-ICU, in-hospital, and 90-day post-discharge mortality. Results: Among the 782 critically ill patients (median age 68 years; 63.68% male), ESS was diagnosed in 47.31%. The 2.5th percentile (P _2.5 ) of rT3 in ESS patients was 0.64 ng/mL, and the cohort-derived cutoff for discriminating ESS was 1.07 ng/mL, with an area under the receiver operating characteristic curve [AUC] of 0.750. In the primary prognostic analysis, restricted cubic spline modeling of baseline rT3 as a continuous variable did not show a statistically significant association with in-ICU mortality, in-hospital mortality, or 90-day post-discharge mortality. Exploratory outcome-derived cutoff analyses identified a cutoff of 1.85 ng/mL for 90-day post-discharge mortality. At the 1.85 ng/mL cutoff, lower rT3 was initially associated with reduced 90-day post-discharge mortality (adjusted hazard ratio = 0.67, 95% confidence interval: 0.48–0.94), but this finding was not robust after additional adjustment and sensitivity analyses. Similarly, rT3 showed limited predictive value for in-ICU and in-hospital mortality. Exploratory machine learning analyses suggested that rT3 may contribute to mortality prediction in selected settings, with rT3 at ICU discharge ranking among the predictors for in-hospital and 90-day mortality and baseline rT3 showing possible relevance to in-ICU mortality in patients with infectious diseases. Conclusion: In critically ill patients, rT3 was associated with an FT3-defined ESS phenotype but showed limited and unstable independent prognostic value for mortality outcomes. Data-derived rT3 cutoffs should be considered exploratory and require external validation before clinical use.

Background: Cardiovascular‐kidney‐metabolic (CKM) syndrome poses a major health risk. This study assessed the impact of a healthy lifestyle on all‐cause and cardiovascular disease (CVD) mortality in CKM individuals. Methods: We analyzed 306 831 and 9823 CKM individuals from UK Biobank and NHANES (2007–2018). A healthy lifestyle score was created based on seven factors: no current smoking, moderate drinking, healthy diet, regular physical activity, adequate sleep, low sedentary behavior and appropriate social connection. Primary outcomes were all‐cause and CVD mortality from linked health records and death registries. Results: Cox regression models showed that higher lifestyle scores were associated with reduced risks of all‐cause (hazard ratio [HR]: 0.81, 95% confidence interval [CI]: 0.80–0.82) and CVD mortality (HR: 0.82, 95% CI: 0.80–0.84) in UK Biobank. In NHANES, higher scores were associated with 15% (95% CI: 0.80–0.90) and 11% (95% CI: 0.81–0.99) lower mortality risks. Both regular physical activity (HRs: 0.85 and 0.89 for UK Biobank, 0.76 and 0.73 for NHANES) and low sedentary behavior (HRs: 0.85 and 0.91 for UK Biobank, 0.79 and 0.67 for NHANES) were related to reduced mortality risks ( p < 0.05). Participants with non‐advanced CKM syndrome and a favorable lifestyle had the lowest mortality risks compared to those with advanced CKM syndrome and an unfavorable lifestyle. Conclusion: A healthy lifestyle, particularly regular physical activity and low sedentary behavior, was significantly associated with lower all‐cause and CVD mortality in individuals with CKM syndrome. These associations were stronger in the early stages, highlighting the importance of timely and targeted lifestyle interventions.

Biological age quantifies functional decline beyond chronological aging; however, current epigenetic clocks exhibit limitations in resolving dynamic metabolic fluctuations and tissue-specific aging trajectories. Metabolomics emerges as a pivotal solution, representing the endpoint cascade of biological events shaped by multifactorial interactions that capture real-time physiological status. This review delineates aging clocks through a metabolomic lens and proposes an executable research workflow comprising data preprocessing, feature selection, model construction, and model application. Furthermore, we design a three-phase causal strategy structured as global screening, local verification, and dynamic validation. This integrated methodology aims to enhance the predictive accuracy of aging clocks while strengthening the biological plausibility and causal inference potential of metabolite-derived aging biomarkers. Additionally, we evaluate the translational prospects and applied value of metabolomic aging clocks, providing actionable guidance for extending human healthspan and advancing prevention and treatment strategies for aging-related pathologies.

Background: Psychological distress increases cancer risk, but its relationship to thyroid cancer risk remains unclear. The aims of our study were to assess whether psychological distress increases the risk of thyroid cancer, and to further explore the mediating factors between psychological distress and thyroid cancer. Methods: A total of 420,187 participants aged 39–73 years between 2006 and 2010 in the UK biobank were included in the cohort study. Psychological distress was assessed using relevant hospitalization records, the four-item Patient Health Questionnaire and self-reported psychiatric symptoms. Thyroid cancer diagnosed date was from national cancer registries and hospital inpatient records in England, Wales, and Scotland. We used Cox proportional hazard models to calculate hazard ratios and the mediation R package to assess the mediating factors. Results: With a median follow-up of 12.59 years, the incidence of thyroid cancer in the participants with psychological distress was 137.16 per 100,000, compared with 94.31 per 100,000 in the participants with non-psychological distress. Psychological distress significantly increased the risk of thyroid cancer (HR, 1.33; 95% CI, 1.09–1.62; P = 0.005). Mediation analysis indicated that BMI, waist circumference, high-density lipoprotein, triglycerides, alcohol consumption partially mediated between psychological distress and thyroid cancer, in which waist circumference had the strongest mediating effect by a proportion of 11.80% (95% CI, 4.41–36.00%; P < 0.001). Conclusions: Psychological distress is associated with an increased risk of developing thyroid cancer, which is modestly mediated by obesity, dyslipidemia, and alcohol consumption. The hypothesis that managing weight, lipids, and alcohol intake might mitigate cancer risk in individuals with psychological distress requires direct testing in future intervention trials.

Sodium p‐perfluorous nonenoxybenzene sulfonate (OBS), widely used as an alternative to per‐ and polyfluoroalkyl substances, has been implicated in toxicity across multiple organ systems. Currently, its effects on renal health remain poorly characterized. In this study, we investigated the mechanisms underlying OBS‐induced nephritis by integrating network toxicology, cellular experiments, molecular docking, and molecular dynamics simulations. A total of 28 shared targets were identified by intersecting machine learning‐predicted OBS‐related targets with nephritis‐associated targets retrieved from databases. These targets were predominantly enriched in pathways related to estrogen signaling and cellular responses to estrogenic stimuli. Subsequent cellular experiments demonstrated that OBS exposure significantly upregulated the expression of ESR1 and ESR2 in renal proximal tubular epithelial cells. Molecular docking and molecular dynamics simulation analyses further revealed that OBS forms more stable complexes with ERα than with ERβ, suggesting a preferential interaction with ERα. Collectively, these findings indicate that OBS may induce nephritis through modulation of nuclear‐initiated estrogen signaling pathways.

Cardiovascular-kidney-metabolic (CKM) syndrome represents a primary contributor to global morbidity and mortality. Despite the Life's Essential 8 framework offering a comprehensive assessment of cardiovascular health (CVH), its prognostic utility in CKM syndrome remains unclear. This study aimed to investigate the association between Life's Essential 8-defined CVH and mortality risk in CKM health stages 0-4. Data were analyzed from 302,171 UK Biobank participants in CKM health stages 0-4. CVH scores were calculated based on eight components: diet, physical activity, nicotine exposure, sleep health, body mass index, blood lipids, blood glucose, and blood pressure. All-cause and cardiovascular disease (CVD) mortality were ascertained through linked health records. Cox proportional hazards models were employed to examine associations between CVH scores and mortality risks. The predictive factors for mortality risk were explored using the gradient boosting machines method. Over a median follow-up of 12.7 years, 20,282 all-cause and 3,979 CVD deaths occurred. Compared with low CVH, high CVH was associated with 55% (95% confidence interval (CI): 0.42-0.49) and 67% (95% CI: 0.27-0.41) reductions in all-cause and CVD mortality. The inverse association of CVH scores with CVD mortality was stronger in non-advanced stages (P _{for interaction} = 0.002). Restricted cubic spline analysis revealed a significant nonlinear association for all-cause mortality (P _{for nonlinear} < 0.001), but not for CVD mortality (P _{for nonlinear} = 0.936). Machine learning analysis indicated that among the eight components, nicotine exposure and blood glucose scores had the highest relative influence on mortality prediction. Higher CVH scores were associated with lower mortality risk in CKM health stages 0-4, with stronger associations observed in non-advanced stages. Among the eight components, nicotine exposure and blood glucose scores showed stable and strong associations with mortality. These findings support the potential value of CVH assessment for risk stratification across CKM health stages.

Aims: To examine the association of discretionary screen time with the risk of type 2 diabetes among individuals with prediabetes, and to explore the mediating roles of sleep and the triglyceride‐glucose (TyG) index. Materials and Methods: This prospective cohort study included 41 978 participants with prediabetes from the UK Biobank. Discretionary screen time comprised television viewing and leisure computer use. Sleep was assessed using five dimensions: duration, chronotype, insomnia, snoring and daytime dozing. The TyG index was calculated as an indicator of insulin resistance. Incident type 2 diabetes was confirmed through linked hospital, primary care and death registry records. Results: Compared with < 2 h/day of discretionary screen time, > 5 h/day was associated with a 26% higher risk of type 2 diabetes (hazard ratio (HR): 1.26, 95% confidence interval (CI): 1.12, 1.40). Restricted cubic spline analyses revealed a non‐linear association between discretionary screen time and type 2 diabetes ( P _{for nonlinear} = 0.006). Television viewing (HR: 1.35, 95% CI: 1.21, 1.51) and leisure computer use (HR: 1.04, 95% CI: 0.88, 1.23) showed different associations with type 2 diabetes. Exploratory mediation analyses indicated that sleep and the TyG index mediated 30.2% of the association, with 10.7% mediated through sleep and 18.1% through the TyG index. Conclusions: Higher discretionary screen time was associated with an increased risk of type 2 diabetes among individuals with prediabetes, partly mediated by poor sleep and insulin resistance. Reducing discretionary screen time may be a meaningful preventive strategy against type 2 diabetes, with sleep health as an important mediator.

Background: Artificial intelligence is emerging in healthcare systems. In type 1 diabetes, AI‐enabled tools are increasingly used to support nutrition assessment and insulin decision‐making, yet their clinical utility and safety remain unclear. Methods: The study aims to identify and map the evidence on the clinical utility of AI‐based diabetes management tools in people with type 1 diabetes. We conducted a scoping review following PRISMA‐ScR guidelines, searching PubMed, CINAHL and Web of Science up to January 2026 for eligible randomised controlled trials. Results: Our findings indicate that the evidence base is small and concentrated in high‐income settings, with most trials assessing clinical utility using CGM outcomes and showing mixed improvements across interventions. No serious safety events were reported, but small sample sizes, short follow‐up and inconsistent safety reporting limit confidence. Conclusions: Future research should prioritise larger, longer‐term real‐world evaluations that use standardised safety endpoints and patient‐centred outcomes, including in low‐ and middle‐income countries to support equitable implementation.