Fuyan Shi

Background: Although specific nutrients have been linked to rheumatoid arthritis (RA), the impact of overall nutrient intake patterns remains unclear. Here, we aimed to explore the association between the nutrient risk score (NRS) and incident RA, as well as the modifying effect of genetic susceptibility and the potential mediating role of obesity. Methods: This prospective study included 161,725 participants from the UK Biobank. Nutrients were selected using a LASSO-Cox model. We used Cox proportional hazards models to investigate the association between NRS and the risk of RA. Generalized propensity score (GPS) and inverse probability weighting (IPW) methods were employed to control for potential confounding factors. Stratified analyses were conducted to assess effect heterogeneity across subgroups. Mediation analysis was used to estimate the mediating effect of obesity. Results: During a median follow-up of 13.4 years, 1,756 participants developed RA. Each unit increase in NRS was associated with a 96% higher risk of RA (HR: 1.96, 95% CI: 1.64-2.33) after adjusting for covariates. The association remained robust in analyses using GPS and IPW weighting. Stratified analyses showed a more pronounced association among individuals reporting short sleep duration (<7 hours) and low levels of physical activity. Furthermore, a gradient association between NRS and RA risk was observed across genetic risk strata. Specifically, compared with participants at low genetic risk with the lowest NRS (Q1), those at high genetic risk with the highest NRS (Q4) had nearly a threefold higher risk of incident RA (HR: 3.28, 95% CI: 2.49-4.32). In addition, several obesity indicators showed potential mediating effects, with the proportion mediated ranging from 3.81% to 13.57%. Conclusion: Higher NRS was associated with an increased risk of incident RA, especially among individuals with high genetic susceptibility. Obesity partially mediated the association, highlighting NRS as a potential tool for RA risk stratification and intervention.

Our study aimed to systematically identify T1-weighted MRI-derived brain structural features associated with progression from mild cognitive impairment (MC

Background: Childhood adversity (CA) is associated with increased cardiovascular and cardiometabolic risk, but the molecular mechanisms remain unclear. We aimed to identify CA‐related metabolomic and proteomic signatures and evaluate their roles in linking CA to incident diseases. Methods: This prospective cohort study included 153 225 participants aged 48 to 64 years. CA was assessed using the Childhood Trauma Screener‐5, capturing cumulative (0–5 domains) and individual adversity exposures. Plasma metabolomics and proteomics data were integrated to derive CA‐related molecular signatures. Cox proportional hazards model was used to evaluate associations between CA, molecular signatures, and 58 incident diseases and mortality. Mediation analyses quantified the role of multiomics signatures. Results: Each additional CA domain was associated with higher risk of 49 of 58 incident diseases (hazard ratios [HRs], 1.024–1.338) and a 7.3% higher all‐cause mortality risk. Focusing on cardiometabolic health, the CA‐related metabolic and proteomic signatures were independently associated with incident disease. Per 1‐SD increase in the cumulative CA metabolic signature, the highest observed HR was 1.313 (95% CI, 1.278–1.349) for incident diabetes, and the risk for hypertension was also increased (HR, 1.136 95% CI, 1.114–1.159). Similarly, the proteomic signature was strongly associated with incident diabetes (HR, 1.645 95% CI, 1.489–1.818) and hypertension (HR, 1.223 95% CI, 1.155–1.295). The cumulative CA metabolic and proteomic signatures mediated up to 25.5% and 46.8% of the association with hypertension, respectively. Conclusions: CA is associated with a broad spectrum of diseases and mortality, with particularly strong links to cardiometabolic health. Multiomics signatures partially mediated these associations.

Background: Chronic obstructive pulmonary disease (COPD) continues to pose a significant global burden, which may be exacerbated by pro-inflammatory diets. This study aimed to investigate the association between the Dietary Inflammatory Index (DII) and the Energy-adjusted Dietary Inflammatory Index (E-DII) and the risk of COPD. Methods: A total of 167,440 participants were recruited for a prospective analysis from the UK Biobank. Cox proportional hazards models were employed to analyze the associations of DII and E-DII with COPD. Restricted cubic spline (RCS) and subgroup analyses were also performed. Additionally, mediation analyses were conducted to explore the potential mediating role of inflammatory biomarkers between DII, E-DII and COPD risk. Results: During a median follow-up of 13.4 years, 4,041 COPD cases occurred. After adjusting for covariates, each unit increase in DII and E-DII was associated with a 5% [HR: 1.05 (95% CI: 1.04–1.07)] and 20% [HR: 1.20 (95% CI: 1.16–1.25)] higher risk of COPD. Participants in the highest quartile of DII and E-DII scores had a higher risk of COPD compared to those in the lowest quartile, with HR = 1.27 (95% CI: 1.17–1.39) and HR = 1.42 (95% CI: 1.30–1.55). In mediation analyses, C-reactive protein (CRP) showed the highest proportion mediated for both DII and E-DII, followed by systemic immune-inflammation index (SII). Subgroup analyses indicated population heterogeneity, and sensitivity analyses confirmed the robustness of the findings. Conclusion: Both DII and E-DII are associated with an increased risk of COPD, and this association may be partially explained by systemic inflammation, particularly CRP and SII.

Accurate prediction of progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is critical for early intervention. Many existing models lack the ability to capture the nonlinear nature of cognitive decline and individual heterogeneity. This study employed a semi‑parametric joint model to analyze longitudinal cognitive trajectories and identify robust predictors of conversion. Data from 596 participants (184 AD converters, 412 stable MCI) were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Longitudinal assessments included ADAS‑Cog13, ADAS‑Cog11, CDR‑SB, FAQ, RAVLT‑IR, RAVLT‑L, and MMSE. A semi‑parametric joint model combining B‑splines for the longitudinal process with a Cox survival submodel was fitted for each cognitive measure. Model performance was evaluated using AIC, BIC, intraclass correlation coefficient (ICC), time‑dependent C‑index, dynamic AUC, and calibration curves. Subgroup analyses were conducted by APOE‑ε4 carrier status. In multivariable joint models, APOE‑ε4 carriage was a consistent risk factor (HR range: 1.38–1.77). Higher scores on ADAS‑Cog13 (HR = 3.71 per SD), ADAS‑Cog11 (HR = 2.71), CDR‑SB (HR = 3.79), and FAQ (HR = 2.85) increased the hazard of conversion, whereas higher scores on RAVLT‑IR (HR = 0.23), RAVLT‑L (HR = 0.14), and MMSE (HR = 0.53) were protective. All models showed high ICCs (0.94–0.98) and moderate‑to‑good predictive accuracy over 2, 5, and 8 year horizons (C‑index: 0.585–0.668). CDR‑SB and FAQ exhibited the strongest effect sizes and clearest dose‑dependent trajectories across APOE‑ε4 subgroups. Calibration curves demonstrated good agreement between predicted and observed survival. The semi‑parametric joint model effectively captures nonlinear cognitive‑functional decline and provides validated predictions of AD risk. APOE‑ε4 genotype combined with longitudinal monitoring of CDR‑SB and FAQ offers a robust framework for stratifying progression risk in clinical MCI management.

Objectives: Multimorbidity in individuals with coronary heart disease (CHD) is associated with an increased incidence of falls. We aimed to identify multimorbidity patterns and investigate the impact of different comorbidity patterns on fall and consequent fractures in older adults with CHD. Methods: We identified 189,558 older adults (aged ≥ 60 years) with CHD between January 2015 and December 2021 from the Shandong Multicenter Healthcare Big Data Platform. Multimorbidity patterns were identified through latent class analysis (LCA). Fine and Gray competing risk regression models were used to assess the associations between multimorbidity pattern and falls and fall-related fractures. Results: Four distinct multimorbidity clusters were observed: a gastrointestinal-dominant pattern (Class 1, 49.34%), a core cardiovascular disease pattern (Class 2, 15.50%), a metabolic-cardiovascular disease pattern (Class 3, 19.00%), and a cerebrovascular-osteoarticular complex pattern (Class 4, 13.60%). Older adults in Class 3 (HR = 1.19, 95% CI: 1.02–1.39), and Class 4 (HR = 1.68, 95% CI: 1.46–1.95) had an increased fall risk after confounder adjustment compared with those in Class 1, with mild comorbidities. The highest risk of falls with fractures (especially for sternum fractures) was found in older adults in Class 4. The risks of falls and falls with fracture were greater for females and patients aged ≥ 80 years. Conclusion: In this study, we analysis multimorbidity clusters among older patients with CHD and their association with fall and falls with fracture risk. We found that CHD patients with a cerebrovascular-osteoarticular complex pattern face the highest unintentional falls and fractures risk. Our findings can help stratify the risk of falls in older CHD patients and support precision public health initiatives.

Low birth weight (LBW), defined as a newborn weighing less than 2500 g, is an increasingly significant public health concern. Exploring the risk and protective factors for LBW is getting more and more important. This study aimed to utilize predictive models to identify the critical factors associated with LBW in singleton pregnancies. A retrospective cohort study was conducted at the Binzhou Medical University Hospital, China, from 2022 to 2023. Singleton pregnancies with gestational age exceeding 27 weeks were included, while multiple pregnancies and fetal anomalies were excluded. Logistic regression (LR) model and four machine learning (ML) algorithms were tested(random forest, light gradient boosting machine, support vector machine, and extreme gradient boosting). The LR model was interpreted through odds ratio analysis and clinical nomogram visualization. Shapley Additive Explanations (SHAP) analysis was used to interpret the importance and impact of individual features on ML model. In this cohort of 10,227 deliveries, 237 cases were classified as LBW. The XGBoost model exhibited superior performance in predicting LBW, with an AUC of 0.786 (training set) and 0.741 (test set, ) AUPRC of 0.350 and 0.186, respectively. Both LR and XGBoost model identified maternal age, gestational age, BMI, hypertensive disorders of pregnancy (HDP), fetal distress as the critical factor. Additionally, a follow-up study on LBW found that LBW infants prone to significant health challenges, such as a high rate of hospitalization and the complex conditions including congenital anomalies, neonatal respiratory distress syndrome (NRDS) and neonatal hyperbilirubinemia. This study demonstrated that the LR and XGBoost model exhibited clinically meaningful predictive performance in identifying factors associated with LBW in singleton pregnancies. Pregnant women with a gestational age of less than 37 weeks, a gestational BMI below 18 kg/m², maternal age under than 25 years, and maternal comorbidities such as HDP or fetal distress are at higher risk of delivering LBW infants.

Purpose This study aimed to characterise the epidemiology, clinical presentation, and temporal trends of falls among adults aged ≥60 years in Shandong Province, China. Methods We evaluated incidence rates, temporal trends and demographic patterns among 631 001 older adults between 1 January 2015 and 31 December 2021. Annual percentage changes (APCs) were estimated using logarithmic linear regression, while Poisson regression models calculated incidence rate ratios to assess risk factors. Results During the study period, 5314 fall cases were recorded, yielding an overall incidence of 196.64 per 100 000 person-years with a significant upward trend. Females demonstrated 1.78 times higher risk than males, while adults ≥85 years faced twice the risk of those aged 60–64 years. Geographically, Weihai recorded the highest incidence (446.80/100 000 person-years), while Dezhou exhibited the most rapid increase (APC=42.13%). Seasonal analysis revealed peak incidence in summer (1394, 26.23%), with significant differences in seasonal distribution by sex and age groups. Gender-specific injury patterns emerged: males experienced more head (488, 50.36%) and neck trauma (77, 53.85%) and females showed higher fracture rates (2892, 68.11%). Conclusions Our findings reveal a growing incidence of falls among older adults, exacerbated by demographic ageing and geographic disparities. Urgent interventions are needed, particularly for high-risk groups including women, the oldest old (≥85 years) and residents of coastal areas.