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In addition to considering the main effects, understanding gene‐environment (G × E) interactions is imperative for determining the etiology of diseases and the factors that affect their prognosis. In the existing statistical framework for censored survival outcomes, there are several challenges in detecting G × E interactions, such as handling high‐dimensional omics data, diverse environmental factors, and algorithmic complications in survival analysis. The effect heredity principle has widely been used in studies involving interaction identification because it incorporates the dependence of the main and interaction effects. However, Bayesian survival models that incorporate the assumption of this principle have not been developed. Therefore, we propose Bayesian heredity‐constrained accelerated failure time (BHAFT) models for identifying main and interaction (M‐I) effects with novel spike‐and‐slab or regularized horseshoe priors to incorporate the assumption of effect heredity principle. The R package rstan was used to fit the proposed models. Extensive simulations demonstrated that BHAFT models had outperformed other existing models in terms of signal identification, coefficient estimation, and prognosis prediction. Biologically plausible G × E interactions associated with the prognosis of lung adenocarcinoma were identified using our proposed model. Notably, BHAFT models incorporating the effect heredity principle could identify both main and interaction effects, which are highly useful in exploring G × E interactions in high‐dimensional survival analysis. The code and data used in our paper are available at https://github.com/SunNa‐bayesian/BHAFT. [ABSTRACT FROM AUTHOR]

Although the association between self-regulation of fatigue and health-related quality of life (HRQoL) has been confirmed, the potential mechanism remains unclear. This study aimed to explore the role of health literacy, health behavior, and exercise frequency in the relationship among middle-aged and elderly patients with recurrent stroke. A cross-sectional survey was conducted. A total of 176 patients completed the survey, in which self-regulation of fatigue, HRQoL, health literacy and health behavior were measured by questionnaires. Based on Bootstrap analyses, a moderating sequential mediation model using PROCESS software was constructed with health literacy and health behavior as mediators and exercise frequency as the moderator. Of the participants, the mean age was 65.44 ± 12.43 years. Self-regulation of fatigue was found to affect HRQoL indirectly through two significant mediation pathways: (1) health literacy (β=-0.11, 95%CI = −0.20, −0.03), which accounted for 28.79% of the total effect, and (2) health literacy and health behavior (β=-0.02, 95%CI = −0.05, −0.00), which accounted for 4.80% of the total effect. Exercise frequency moderated the relationship between self-regulating fatigue and HRQoL. Specifically, the interaction term between self-regulating fatigue and exercise frequency significantly predicted HRQoL (β = 0. 25, t = 2.55, p < 0.05). These findings highlight the role of health literacy and health behavior as sequential mediators of the relationship between self-regulating fatigue and HRQoL. Moreover, exercise frequency moderated the relationship between self-regulating fatigue and HRQoL. Encouraging patients with recurrent stroke to increase exercise frequency appropriately might improve HRQoL for patients with poor health literacy and health behavior. [ABSTRACT FROM AUTHOR]

Background Although studies have demonstrated associations between sleep quality (SQ) and grip strength (GS) in older adults, the direction and underlying mechanisms of this relationship are yet to be better delineated. We aimed to longitudinally investigate the bidirectional association between SQ and GS and the mediating role of depression in this association. Methods Based on 2 nationally representative samples with people aged ≥50 years from the China Health and Retirement Longitudinal Study (CHARLS; 4 200 participants) and English Longitudinal Study of Ageing (ELSA; 5 922 participants), cross-lagged panel models were employed to examine the potential bidirectional relationships between objectively measured GS and self-reported SQ. Results We observed a GS–SQ bidirectional association dominated by GS. After adjusting for potential confounders, a higher GS at T1 predicted better SQ at T2 (ELSA: β = 0.075; CHARLS: β = 0.104, p <.001) and vice versa (ELSA: β = 0.034; CHARLS: β = 0.030, p <.01). Moreover, depression partially mediated the impact of GS on subsequent SQ (ELSA, indirect effect: 0.0057, 95% confidence interval [CI]: 0.0035–0.0084; CHARLS, indirect effect: 0.0086, 95% CI: 0.0051, 0.0131), but not vice versa. Conclusions The results regarding data from both cohorts consistently supported a bidirectional association between GS and SQ and the mediating role of depression in the dominant pathway of this bidirectional relationship. Older adults with a low GS should be made aware of a potentially vicious cycle related to depression that can affect their sleep. Regular screening for depression may help to break this cycle. [ABSTRACT FROM AUTHOR]