Background: White matter hyperintensity (WMH) is one of the key imaging markers of cerebral small vessel disease (CSVD) and white matter microstructural injury may occur earlier than WMH. However, the associations of handgrip strength (HGS) and serum total testosterone (STT) with WMH and microstructural injury have not been thoroughly investigated. Therefore, we aimed to explore the associations of HGS and STT with WMH and microstructural injury, measured by fractional anisotropy (FA), mean diffusivity (MD), intracellular volume fraction (ICVF), and isotropic volume fraction (ISOVF).Methods: A total of 34 832 participants from the UK Biobank (aged 64.15 ± 7.75 years, 50.9% male) were included in the study. Cross‐sectional analyses were conducted using three linear regression models to assess the relationships between HGS, STT and neuroimaging outcomes. Sensitivity analyses were performed to validate the robustness of the linear regression results. Longitudinal, mediation and Mendelian randomization (MR) analyses were conducted to explore the longitudinal impact of HGS on WMH, the mediating effects of STT between HGS and neuroimaging outcomes, and probable causal relationships between HGS, STT and WMH.Results: A greater HGS was linearly associated with a smaller WMH volume (β = −0.006642, p < 0.001) and fuller microstructure (p < 0.001). These relationships persisted when stratified by sex or age decade and were supported by the results of the longitudinal analysis (β = −26.4, p = 0.0145). In females, STT was found to be linearly negatively related to WMH volume (β = −0.109, p < 0.001), MD and ISOVF, and linearly positively related to FA and ICVF (p < 0.001). In males, STT was linearly negatively linked to WMH volume (β = −0.016, p < 0.001) and MD (β = −1.6 , p = 0.007), and positively associated to ICVF (β = 0.00012, p = 0.043). STT mediated the association between HGS and WMH volume (mediation proportion: 1.06% in males and 1.57% in females), and the possible causality was suggested for males through MR analysis: the positive causality between HGS and STT (p = 0.0394) and the negative causality between STT and WMH (p = 0.0787).Conclusions: Our study revealed that greater HGS was linearly associated with reduced WMH volume and less white matter microstructural injury, mediated by STT. Improving muscle function may contribute to deferring white matter damage and preventing stroke and dementia, offering a feasible secondary prevention measure for CSVD.

Background: The risk factors of cardiovascular disease (CVD) in end-stage renal disease (ESRD) with hemodialysis remain not fully understood. In this study, we developed and validated a clinical-longitudinal model for predicting CVD in patients with hemodialysis, and employed Mendelian randomization to evaluate the causal 6study included 468 hemodialysis patients, and biochemical parameters were evaluated every three months. A generalized linear mixed (GLM) predictive model was applied to longitudinal clinical data. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the performance of the model. Kaplan-Meier curves were applied to verify the effect of selected risk factors on the probability of CVD. Genome-wide association study (GWAS) data for CVD (n = 218,792,101,866 cases), end-stage renal disease (ESRD, n = 16,405, 326 cases), diabetes (n = 202,046, 9,889 cases), creatinine (n = 7,810), and uric acid (UA, n = 109,029) were obtained from the large-open GWAS project. The inverse-variance weighted MR was used as the main analysis to estimate the causal associations, and several sensitivity analyses were performed to assess pleiotropy and exclude variants with potential pleiotropic effects.Results: The AUCs of the GLM model was 0.93 (with accuracy rates of 93.9% and 93.1% for the training set and validation set, sensitivity of 0.95 and 0.94, specificity of 0.87 and 0.86). The final clinical-longitudinal model consisted of 5 risk factors, including age, diabetes, ipth, creatinine, and UA. Furthermore, the predicted CVD response also allowed for significant (p < 0.05) discrimination between the Kaplan-Meier curves of each age, diabetes, ipth, and creatinine subclassification. MR analysis indicated that diabetes had a causal role in risk of CVD (β = 0.088, p < 0.0001) and ESRD (β = 0.26, p = 0.007). In turn, ESRD was found to have a causal role in risk of diabetes (β = 0.027, p = 0.013). Additionally, creatinine exhibited a causal role in the risk of ESRD (β = 4.42, p = 0.01).Conclusions: The results showed that old age, diabetes, and low level of ipth, creatinine, and UA were important risk factors for CVD in hemodialysis patients, and diabetes played an important bridging role in the link between ESRD and CVD.