Introduction: This study aimed to identify the dual trajectories of social participation (SP) and frailty index (FI) among Chinese older adults, and investigate common influential factors of both trajectories.Methods: Utilizing data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2008–2018 surveys, 1,645 individuals were analyzed. A group-based dual trajectory model and logistic regression were used to examine trajectories, their interrelations and shared influencing factors.Results: This study identified three SP, two FI trajectories and six distinct sub-groups of individuals. The study confirmed a long-term, interrelated relationship between two outcomes and identified some common factors. Compared to participants in the lower SP trajectory, those who followed the middle SP trajectory and higher SP trajectory had increased probabilities of belonging to the slow-growth FI trajectory (90.28 and 99.71%, respectively). And the participants in the slow-growth FI exhibited higher probabilities of belonging to the middle SP and the higher SP trajectory (37.64 and 25.34% higher, respectively) compared with those in the rapid-growth FI trajectory. Age, marital status, and drinking status were mutual factors associated with the dual trajectories.Discussion: The results showed significant associations between higher levels of frailty and lower levels of social participation. Related intervention policies should consider the dual trajectories and the common factors that underlie these trajectories of SP and FI.

China has experienced a serious public health burden because of the increased incidence of ischemic stroke. Evidence describing the association between short-term exposure to nitrogen dioxide (NO₂) and ischemic stroke morbidity is limited, and few studies have focused on the effects of season and temperature. This study aimed to evaluate the acute effects of NO₂ on ischemic stroke incidence in Shenzhen, a southeastern city of China, considering the modified effects of season and temperature. A time-stratified case-crossover study was conducted between 2003 and 2014 among 98,482 ischemic stroke hospitalizations. Conditional quasi-Poisson regression was used to estimate the percentage changes in ischemic stroke admissions in relation to each 10 μg/m³ increment in NO₂. NO₂ was positively associated with ischemic stroke onset over the full year, as well as in the cold season (November through April) and on cold days (ambient temperature≤median temperature), with significant single-day effects within 3 days after the exposure, and significant cumulative effects within the delayed five days. The maximum percentage changes were obtained at lag0-5, with 1.81% (95% confidence interval (CI) was 0.86-2.76%) over the full year, 2.75% (1.48-4.03%) in the cold season, and 3.04% (1.74-4.35%) on cold days. Additionally, the effects of exposure were found to be greater in males and people with higher education, and were lasting longer in subgroups of older individuals. Our findings provide evidence that reductions in NO₂ levels might decrease ischemic stroke morbidity, and enhance the understanding of ischemic stroke occurrence associated with NO₂ modified by season and temperature.

Although the X chromosome accounts for about 5% of the human genes, it is routinely excluded from genome-wide association studies probably due to its unique structure and complex biological patterns. While some statistical methods have been proposed for testing the association between X chromosomal markers and diseases, very a few of them can adjust for covariates. Unfortunately, those methods that can incorporate covariates either need to specify an X chromosome inactivation model or require the permutation procedure to compute the p value. In this article, we proposed a novel analytic approach based on logistic regression that allows for covariates and does not need to specify the underlying X chromosome inactivation pattern. Simulation studies showed that our proposed method controls the size well and has robust performance in power across various practical scenarios. We applied the proposed method to analyze Graves’ disease data to show its usefulness in practice.