Rui Li

Background: Multiple sclerosis (MS) is a neuroimmunological disorder marked by demyelination and neuronal damage, significantly impacting young individuals' health and quality of life. Recent studies suggest a pivotal role of the gut microbiota and immune system in MS development, though the precise mechanisms remain unclear. Methods: This study utilizes Mendelian randomization (MR) to explore the causal relationships between gut microbiota, immune traits, and MS. We conducted MR analysis using genetic data from 18,340 participants for gut microbiota, 3757 individuals for immune traits, and 47,429 MS cases alongside 68,374 controls. Results: Our results suggested six microbial taxa with nominally significant associations to MS, with two taxa (Roseburia and Ruminococcus2) exhibiting putative mediating trends through specific immune cell phenotypes (CD28 on CD28 ⁺ CD4 ⁺ T cells and CD45 on HLA‐DR ⁺ CD8 ⁺ T cells). Mediation analysis demonstrated that 8.97% and 12.18% of the nominal effects of these taxa on MS were potentially mediated through the aforementioned immune traits, respectively. These results only represent exploratory trends and need to be interpreted with caution. Conclusion: These nominally significant exploratory findings provide hypothesis‐generating clues that immune cell activation, particularly in CD4 ⁺ and CD8 ⁺ T cells, might be involved in the microbiota‐MS relationship. These findings require validation in independent populations and experimental studies.

Osteokines, primarily secreted by bone, have been implicated in brain function and Parkinson's disease (PD) pathogenesis, yet their circulating levels in PD and potential role in the relationship between bone mineral density (BMD) and PD remain unclear. 80 participants (40 PD patients and 40 controls) were enrolled to measure plasma levels of eight osteokines (GPNMB, OPN, SOST, DKK1, RANKL, FGF23, BMP2, and BMP4) and assess their associations with clinical scales. Mendelian randomization (MR), SMR, and colocalization analyses were performed to evaluate causal relationships between osteokines and PD. Restricted cubic spline (RCS) models were applied to explore nonlinear associations between BMD, osteokines, and PD. GPNMB levels were significantly elevated in PD patients and showed a linear association with PD risk. Higher GPNMB levels were associated with worse cognitive performance and clinical severity, while higher SOST levels correlated with milder symptoms. Genetic analyses consistently supported a causal and colocalized relationship between GPNMB and PD. Total coxa BMD and T-score were lower in PD, but not statistically significant. RCS analysis revealed an "n-shaped" association between total coxa T-score and both PD and GPNMB levels. Overall, GPNMB appears causally linked to PD risk and may mediate the bone-brain axis connecting BMD with PD susceptibility.

Alzheimer’s disease (AD) and osteoporosis (OP) are prevalent age-related degenerative diseases that often coexist. Echinacoside (ECH) has been extensively studied for its potential to mitigate AD and bone mineral density (BMD) loss. This study aimed to evaluate the simultaneous therapeutic effects of ECH on AD-OP comorbidity using the senescence-accelerated mouse-prone 8 (SAMP8) model, which exhibits both age-related memory deficits and bone metabolism abnormalities. Six-month-old male SAMP8 mice (n = 8–9) were used as the model group, while age-matched senescence-accelerated mouse resistant 1 (SAMR1) mice served as normal controls. SAMP8 mice were administered ECH intragastrically (100 mg/kg/day) for 10 weeks, while control groups received saline. Behavioral tests, including the open field test (OFT), novel object recognition test (NORT), and Morris Water Maze (MWM), assessed locomotor ability, emotionality, and cognitive functions. Bone microstructure was evaluated using micro-computed tomography (micro-CT), and pathological changes in the brain were analyzed via Western blotting and immunofluorescence. As compared to SAMR1 mice, SAMP8 mice exhibited significant locomotor activity issues, impaired memory (P < 0.05), glial activation (P < 0.01), reduced trabecular bone number (P = 0.007), and altered trabecular separation (P = 0.040). ECH treatment improved memory function and inhibited glial activation (P < 0.05). Bone-related parameters showed that ECH intervention had a trend of improvement in bone health, but this did not reach statistical significance. The SAMP8 model exhibits key features of both AD and OP, making it a valuable tool for investigating their comorbidity and underlying mechanisms. ECH improves cognitive functions and alleviates bone loss, indicating its potential as a therapeutic candidate for AD-OP comorbidity.

Previous studies have associated different beverage types with frailty and sarcopenia, it remains uncertain whether these associations are causal. This Mendelian randomization study aimed to investigate the causal effects of various beverage consumption on frailty and sarcopenia-related traits. Independent genetic variants strongly (P < 5E-8) associated with sweet and bitter beverages and their subtypes were used as instrumental variables. Summary-level data from recent genome-wide association studies on frailty index (FI), appendicular lean mass (ALM), and low hand grip strength (LHGS) were utilized, with false discovery rate (FDR) adjustment for multiple comparisons. Genetically predicted consumption of bitter non-alcoholic beverages (BNaBs) was associated with increased FI (β = 0.16, 95%CI: 0.04-0.28, P_FDR=0.020) and decreased ALM (β=-0.18, 95%CI: -0.31 to -0.06, P_FDR=0.019). Moreover, total bitter beverage and BNaBs consumption showed a suggestive association with an elevated risk of LHGS (P_raw<0.05). However, the consumption of total sweet beverages, but not sugar-sweetened beverages, was associated with decreased FI and increased ALM. No causal effects on frailty and sarcopenia-related traits were observed for coffee, tea, or bitter alcoholic beverages. These findings suggest that the consumption of bitter beverages, particularly BNaBs, may be linked to the development of muscle weakness and frailty.

Parkinson's disease (PD) is a prevalent neurodegenerative condition, the effect of obesity on PD remains controversial. We aimed to investigate the associations of obesity patterns on PD and all-cause mortality, while developing machine learning (ML)-driven predictive and prognostic models for PD. Fifty-one thousand, three hundred ninety-four adults from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 were classified into four obesity patterns via body mass index (BMI) and waist circumference (WC). Associations of obesity patterns with PD risk and all-cause mortality were evaluated via multivariable logistic and Cox proportional hazards regression across three adjusted models. Subgroup, sensitivity, and restricted cubic spline (RCS) analyses examined stability, robustness, and nonlinearity. An integrative ML-driven architecture identified key features to develop predictive and prognostic nomograms, validated by the area under the receiver operating characteristic curves (AUCROCs) and calibration curves. Survival differences were analyzed using Kaplan-Meier curves. Shapley additive explanations (SHAP) enhanced model explanation. Compound obesity significantly increased PD risk (Model 1: OR = 1.83, P < 0.001; Model 2: OR = 1.70, P = 0.002; Model 3: OR = 1.71, P = 0.006) yet correlated with reduced all-cause mortality in PD patients (Model 1: HR = 0.43, P = 0.003; Model 2: HR = 0.75, P = 0.428; Model 3: HR = 0.41, P = 0.033). Subgroup analysis revealed only HbA1c-modified association of compound obesity with PD (P_interaction = 0.031). Sensitivity analyses confirmed robustness (pooled OR = 1.83, P < 0.001; pooled HR = 0.43, P = 0.003). RCS analyses revealed BMI-dependent PD risk escalation (P_nonlinearity = 0.008, BMI < 45.0 kg/m²), inverted U-shaped WC-PD link (P_nonlinearity < 0.001), and inverse dose-response BMI-mortality relationship (P_nonlinearity = 0.003), along with multiphasic WC-mortality association (P_Threshold = 0.555 at 95 cm and P_Threshold = 0.091 at 118 cm). LASSO + RF identified eight features, achieving moderate performance in PD prediction (SMOTE set: AUCROC = 0.75, Brier = 0.20) and prognosis (train set: AUCROC = 0.72, Brier = 0.22) nomograms, with similar results in the test set (AUCROC = 0.70, Brier = 0.01 for prediction, 0.87 and 0.18 for prognosis). No 24-month survival differences were observed across four obesity patterns (train set: P_log-rank = 0.73; test set: P_log-rank = 0.32). This study preliminarily reveals that compound obesity significantly increases PD risk yet paradoxically associates with reduced all-cause mortality in PD patients. Validated predictive and prognostic nomograms for PD achieve relatively robust performances. Nonetheless, extensive longitudinal studies are required to validate these exploratory findings more comprehensively.