Background: Testosterone is an essential sex hormone that plays a vital role in the overall health and development of males. It is well known that obesity decreases testosterone levels, but it is difficult to determine the causal relationship between body composition and testosterone.Methods: To investigate potential causal associations between body composition and testosterone levels by a first time application of Mendelian randomization methods. Exposure variables in men included body composition (fat mass, fat-free mass, and body mass index). In addition to whole body fat and fat-free mass, we examined fat and fat-free mass for each body part (e.g., trunk, left arm, right arm, left leg and right leg) as exposures. Instrumental variables were defined using genome-wide association study data from the UK Biobank. Outcome variables in men included testosterone levels (total testosterone [TT], bioavailable testosterone [BT], and sex hormone-binding globulin [SHBG]). A one-sample Mendelian randomization analysis of inverse-variance weighted and weighted median was performed.Results: The number of genetic instruments for the 13 exposure traits related to body composition ranged from 156 to 540. Genetically predicted whole body fat mass was negatively associated with TT (β=-0.24, P=5.2×10-³³), BT (β=-0.18, P=5.8×10-²⁰) and SHBG (β=-0.06, P=8.0×10-⁹). Genetically predicted whole body fat-free mass was negatively associated with BT (β=-0.04, P=2.1×10-4), but not with TT and SHBG, after multiple testing corrections. When comparing the causal effect on testosterone levels, there was a consistent trend that the effect of fat mass was more potent than that of fat-free mass. There were no differences between body parts.Conclusion: These results show that reducing fat mass may increase testosterone levels.

Immune checkpoint inhibitors (ICIs) are effective in treating renal cell carcinoma (RCC) but can also cause immune-related adverse events (irAEs). The relationship between irAEs and the T-cell receptor (TCR) repertoire in RCC patients treated with ICIs remains unclear. We analyzed the relationship between the severity and diversity of irAEs and the TCR repertoire in RCC patients who received dual checkpoint inhibitors (ipilimumab + nivolumab). The TCRβ (TRB) repertoires were characterized in peripheral blood samples from six patients with RCC before the initiation of ICI therapy. The diversity and clonality of the TCR repertoire were compared between patients with grade 2 and grade 3 irAEs. The median proportion of top 10 unique reads in the TCR repertoire was significantly higher in grade 3 compared with grade 2 irAEs in RCC patients receiving immune checkpoint inhibitors (grade 2: 0.196%; grade 3: 0.346%; p = 0.0038). We provide insight into the relationship between TCR repertoire and irAEs in RCC patients treated with ICIs. TCR repertoire clonality may be associated with the development of irAEs in RCC patients.

Objectives: We attempt to assess whether prostate-specific antigen decline rate in the first month (PSADR1M = postoperative PSA in the first month/initial PSA) acts as a predictor for biochemical recurrence (BCR) and to evaluate other preoperative and postoperative variables that may predict BCR following robot-assisted laparoscopic prostatectomy (RARP). Method: Based on the D’Amico risk classification system, 777 patients who underwent RARP for localized prostate cancer were classified into a low/intermediate-risk group (n = 435) and a high-risk group (n = 342). The predictors of BCR were identified by univariate and multivariate logistic regression analyses. The area under the curve (AUC) and optimal cutoff values of PSADR1M were determined by receiver operating characteristic (ROC) analysis. Kaplan–Meier curves for biochemical recurrence-free survival (BRFS) rates were stratified by optimal cutoff values of PSADR1M. Results: Effective predictors of BCR in the entire cohort included pT3 (p < 0.001), pathological Grade Group (pGG3, pGG4+5) compared to pGG1+2 (p < 0.001, p = 0.017), positive surgical margins (PSM) (p < 0.001), seminal vesicle invasion (SVI) (p = 0.006), and PSADR1M ≥ 0.62% (p < 0.001). ROC analysis showed that PSADR1M as a predictor for BCR had an AUC of 0.762 for the whole cohort, and 0.821 for the high-risk group, respectively. The optimal cutoff values of PSADR1M were 0.62% in the whole cohort, and 0.68% in high-risk group. Conclusions: As an effective predictor of BCR, PSADR1M can assess the tumor status of prostate cancer patients intuitively and effectively after RARP, especially in the high-risk group.

UNSTRUCTURED This feasibility study of daily voice-based AI health monitoring among 14 elderly Japanese adults (mean age 71 years) within an 8-week group-based longevity intervention demonstrated 73% median daily engagement and generation of 4,876 qualitatively rich conversational messages, suggesting voice AI succeeds not by replacing human connection but by mediating it within social support structures.