BACKGROUND: The binary diagnosis of Metabolic Syndrome(MetS) fails to accurately evaluate its severity, and the association between MetS severity and frailty progression remains inadequately elucidated. This study aims to clarify the relationship between the severity of MetS and the progression of frailty among the middle-aged and elderly population in China.
METHODS: Participants from the 2011-2018 China Health and Retirement Longitudinal Study(CHARLS) were included for a longitudinal analysis. The study employs a frailty index(FI) based on 32 health deficits to diagnose frailty and to assess FI trajectories. An age-sex-ethnicity-specific MetS scoring model (MetS score) was used to assess metabolic syndrome severity in Chinese adults. The Cumulative MetS score from 2012 to 2015 was calculated using the formula: (MetS score in wave 1 + MetS score in wave 3) / 2 × time(2015 - 2012). The association between MetS score, Cumulative MetS score, and the risk and trajectory of frailty were evaluated using Cox regression/logistic regression, and linear mixed models. Restricted Cubic Splines(RCS) models were utilized to detect potential non-linear associations.
RESULTS: A higher MetS score was significantly associated with an increased risk of frailty(HR per 1 SD increase = 1.205; 95%CI: 1.14 to 1.273) and an accelerated FI trajectory(β per 1 SD increase = 0.113 per year; 95%CI: 0.075 to 0.15 per year). Evaluating changes in MetS score using a Cumulative MetS score indicated that each 1 SD increase in the Cumulative MetS score increased the risk of frailty by 22.2%(OR = 1.222; 95%CI: 1.133 to 1.319) and accelerated the rate of increase in FI(β = 0.098 per year; 95%CI: 0.058 to 0.138 per year). RCS model results demonstrated a dose-response curve relationship between MetS score and Cumulative MetS score with frailty risk. Stratified analysis showed consistency across subgroups. The interaction results indicate that in males and individuals under aged 60, MetS score may accelerate the increase in FI, a finding consistent across both models.
CONCLUSIONS: Our findings underscore the positive correlation between the severity of MetS and frailty progression in the middle-aged and elderly, highlighting the urgent need for early identification of MetS and targeted interventions to reduce the risk of frailty.

Life satisfaction is vital for older adults\' well-being, impacting various life aspects. It is dynamic, necessitating nuanced approaches to capture its trajectories accurately. This study aimed to explore the diverse trajectories and predictors of life satisfaction among older adults in China using longitudinal data from the China Health and Retirement Longitudinal Study. Latent class growth modeling and growth mixture modeling were employed to identify distinct trajectories of life satisfaction. Machine learning (ML) models were developed to predict different trajectories and identify important predictors of different trajectories. Four distinct trajectories of life satisfaction were identified, showcasing nuanced patterns of life satisfaction that changed over time. ML models, especially random forest, effectively predicted these trajectories. Emotional experiences (particularly the frequency of happiness and loneliness), body mass index, and self-report health emerged as significant predictors of different life satisfaction trajectories. Our finding revealed the importance of focusing on individuals or groups with consistently low life satisfaction and paying more attention to mental and physical health predictors. Our models might guide future targeted preventative treatments.

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD), and its pathogenesis has not been clarified. Current research suggests that DKD involves multiple cell types and extra-renal factors, and it is particularly important to clarify the pathogenesis and identify new therapeutic targets. Single-cell RNA sequencing (scRNA-seq) technology is high-throughput sequencing of the transcriptomes of individual cells at the single-cell level, which is an effective technology for exploring the development of diseases by comparing genetic information, reflecting the differences in genetic information between cells, and identifying different cell subpopulations. Accumulating evidence supports the role of scRNA-seq in revealing the pathogenesis of diabetes and strengthening our understanding of the molecular mechanisms of DKD. We reviewed the scRNA-seq data this time. Then, we analyzed and discussed the applications of scRNA-seq technology in DKD research, including annotation of cell types, identification of novel cell types (or subtypes), identification of intercellular communication, analysis of cell differentiation trajectories, gene expression detection, and analysis of gene regulatory networks, and lastly, we explored the future perspectives of scRNA-seq technology in DKD research.

Molecular dynamics simulation is a crucial research domain within the life sciences, focusing on comprehending the mechanisms of biomolecular interactions at atomic scales. Protein simulation, as a critical subfield, often utilizes MD for implementation, with trajectory data play a pivotal role in drug discovery. The advancement of high-performance computing and deep learning technology becomes popular and critical to predict protein properties from vast trajectory data, posing challenges regarding data features extraction from the complicated simulation data and dimensionality reduction. Simultaneously, it is essential to provide a meaningful explanation of the biological mechanism behind dimensionality. To tackle this challenge, we propose a new unsupervised model named RevGraphVAMP to intelligently analyze the simulation trajectory. This model is based on the variational approach for Markov processes (VAMP) and integrates graph convolutional neural networks and physical constraint optimization to enhance the learning performance. Additionally, we introduce attention mechanism to assess the importance of key interaction region, facilitating the interpretation of molecular mechanism. In comparison to other VAMPNets models, our model showcases competitive performance, improved accuracy in state transition prediction, as demonstrated through its application to two public datasets and the Shank3-Rap1 complex, which is associated with autism spectrum disorder. Moreover, it enhanced dimensionality reduction discrimination across different substates and provides interpretable results for protein structural characterization.

Cellular movement is essential for many vital biological functions where it plays a pivotal role both at the single cell level, such as during division or differentiation, and at the macroscopic level within tissues, where coordinated migration is crucial for proper morphogenesis. It also has an impact on various pathological processes, one for all, cancer spreading. Cell migration is a complex phenomenon and diverse experimental methods have been developed aimed at dissecting and analysing its distinct facets independently. In parallel, corresponding analytical procedures and tools have been devised to gain deep insight and interpret experimental results. Here we review established experimental techniques designed to investigate specific aspects of cell migration and present a broad collection of historical as well as cutting-edge computational tools used in quantitative analysis of cell motion.

BACKGROUND: The association between exposure to air pollutants and cardiovascular disease (CVD) trajectory in individuals with circadian syndrome remains inconclusive.
METHODS: The individual exposure levels of air pollutants, including particulate matter (PM) with aerodynamic diameter ≤ 2.5 μm (PM2.5), PM with aerodynamic diameter ≤ 10 μm (PM10), PM2.5 absorbance, PM with aerodynamic diameter between 2.5 μm and 10 μm, nitrogen dioxide (NO2), nitrogen oxides (NOx), and air pollution score (overall air pollutants exposure), were estimated for 48,850 participants with circadian syndrome from the UK Biobank. Multistate regression models were employed to estimate associations between exposure to air pollutants and trajectories from circadian syndrome to CVD/CVD subtypes (including coronary heart disease [CHD], atrial fibrillation [AF], heart failure [HF], and stroke) and death. Mediation roles of CVD/CVD subtypes in the associations between air pollutants and death were evaluated.
RESULTS: After a mean follow-up time over 12 years, 12,570 cases of CVD occurred, including 8192 CHD, 1693 AF, 1085 HF, and 1600 stroke cases. In multistate model, per-interquartile range increment in PM2.5 (hazard ratio: 1.08; 95 % confidence interval: 1.06, 1.10), PM10 (1.04; 1.01, 1.06), PM2.5 absorbance (1.04; 1.02, 1.06), NO2 (1.07; 1.03, 1.11), NOx (1.08; 1.04, 1.12), or air pollution score (1.06; 1.03, 1.08) was associated with trajectory from circadian syndrome to CVD. Significant associations between the above-mentioned air pollutants and trajectories from circadian syndrome and CVD to death were observed. CVD, particularly CHD, significantly mediated the associations of PM2.5, NO2, NOx, and air pollution score with death.
CONCLUSIONS: Long-term exposure to air pollutants during circadian syndrome was associated with subsequent CVD and death. CHD emerged as the most prominent CVD subtype in CVD progression driven by exposure to air pollutants during circadian syndrome. Our study highlights the importance of controlling air pollutants exposure and preventing CHD in people with circadian syndrome.

OBJECTIVE: To evaluate the association between psychosocial stress (PS) trajectories and pubertal outcomes of girls and boys in a Chinese cohort (2015-2022).
METHODS: Pubertal outcomes of 732 girls and 688 boys were physically examined every 6 months. Stressful life events were repeatedly assessed 7 times. Group-Based Trajectory Model was fitted for the optimum trajectories of total PS and PS from 5 sources. Cox model adjusted for age, BMI and socioeconomic factors was used to evaluate the association.
RESULTS: Compared to the \"low, gradual decline\" trajectory, the \"moderate, gradual decline\" trajectory of total PS was associated with late menarche (HR: 0.816, 95% CI: 0.677-0.983), late pubic hair development (HR: 0.729, 95% CI: 0.609-0.872) and late axillary hair development (HR: 0.803, 95% CI: 0.661 - 0.975) in girls. Girls following the \"high, rise then decline\" trajectory of PS from family life demonstrated delayed axillary hair development (HR: 0.752, 95% CI: (0.571-0.990). As for boys, the \"high, rise then decline\" trajectory of PS from academic adaptation (HR: 0.670, 95% CI: 0.476 - 0.945) and life adaptation (HR: 0.642, 95% CI: 0.445 - 0.925) was associated with late axillary hair development. Boys in the \"moderate, gradual decline\" trajectory of PS from peer relationship was at risk of early testicular development (HR: 1.353, 95% CI: 1.108 - 1.653).
CONCLUSIONS: Chronic PS may be associated with delayed onset of several pubertal signs in both girls and boys. It may also accelerate testicular development of boys, indicating its varying impact on pubertal timing during early and later stages.

OBJECTIVE: White blood cells (WBC) play an important role in the inflammatory response of the body. Elevated WBC counts on admission in patients with subarachnoid hemorrhage (SAH) correlate with a poor prognosis. However, the role of longitudinal WBC trajectories based on repeated WBC measurements during hospitalization remains unclear. We explored the association between different WBC trajectory patterns and in-hospital mortality.
METHODS: We analyzed a cohort of consecutive patients with SAH between 2012 and 2020. Group-based trajectory modeling (GBTM) was used to group the patients according to their white blood cell patterns over the first 4 days. Stabilized inverse probability treatment weighting (sIPTW) was used to balance baseline demographic and clinical characteristics. We analyzed the association between the WBC trajectory groups and in-hospital mortality using a Cox proportional hazards model.
RESULTS: In total, 506 patients with SAH were included in this retrospective cohort. The final model identified two distinct longitudinal WBC trajectories. After adjusting for confounding factors, multivariate regression analysis suggested that an elevated longitudinal WBC trajectory increased the risk of in-hospital mortality (hazard ratio [HR], 2.476; 95% confidence interval [CI] 1.081-5.227; P = 0.024) before sIPTW, and (HR, 2.472; 95%CI 1.489-4.977; P = 0.018) after sIPTW.
CONCLUSIONS: In patients with SAH, different clinically relevant groups could be identified using WBC trajectory analysis. The WBC count trajectory-initially elevated and then decreased- may lead to an increased risk of in-hospital mortality following SAH.

BACKGROUND: No study has concentrated on the association of LE8 with cancer risk and death. We aim to examine the association of LE8 with death and cancer.
METHODS: A total of 94733 adults aged 51.42 ± 12.46 years and 77551 participants aged 54.09±12.06 years were enrolled in longitudinal and trajectory analysis respectively. Baseline LE8 was divided into three groups based on the American Heart Association criteria and three trajectory patterns by latent mixture models. We reviewed medical records and clinical examinations to confirm incident cancer during the period from 2006 to 2020. Death information was collected from provincial vital statistics offices. Cox models were used.
RESULTS: 12807 all-cause deaths and 5060 cancers were documented during a 14-year follow-up. Relative to participants with high LE8 at baseline, participants with lower levels of LE8 have a significantly increased risk of mortality and incident cancer. All these risks have an increasing trend with LE8 level decreasing. Meanwhile, the trajectory analysis recorded 7483 all-cause deaths and 3037 incident cancers after approximately 10 years. The associations of LE8 with death and cancer were identical to the longitudinal study. In the subtype cancer analysis, LE8 has a strong effect on colorectal cancer risk. Moreover, the cut point is 56.67 in the association between LE8 and death, while the cut point altered to 64.79 in the association between LE8 and incident cancers. These associations were enhanced among younger adults.
CONCLUSIONS: There was a significant association of LE8 with death and cancer risk, especially for the young population.

BACKGROUND: The beet webworm, Loxostege sticticalis, a worldwide pest of many crops, performs a seasonal migration, causing periodic outbreaks in Asia, Europe and North America. Although long-distance migration is well documented in China, patterns of transboundary migration among China, Russia and Mongolia are largely unknown. We performed a phase analysis of L. sticticalis periodic outbreaks among three countries based on 30 years of historical population data, analyzed the wind systems during migration over boundary regions, and traced the migratory routes in a case study of outbreaks in 2008 by trajectory simulation.
RESULTS: Highly synchronized outbreak years of L. sticticalis were observed between China and Mongolia, China and eastern Siberia, China and western Siberia, Mongolia and eastern Siberia, eastern Siberia and western Siberia from 1978 to 2008, indicating possible transboundary migration between these regions. Winds at 300-600 m altitude, where adult migration usually occurs, also showed a high probability of northwestern winds in Haila\'er (China), Chita (Russia) and Choybalsan (Mongolia), favoring successful adult migration from these areas to northern and northeastern China. Back trajectory analysis further showed that the first-generation adults that caused the severe outbreak of second-generation larvae in 2008 originated from eastern Siberia, eastern Mongolia, and the boundary regions of China-Russia and China-Mongolia.
CONCLUSIONS: Our findings demonstrated that the source of L. sticticalis outbreaks in northern China was closely related to the outbreaks in Siberia and Mongolia via long-distance transboundary windborne migration. This information will help guide international monitoring and management strategies against this notorious pest. © 2024 Society of Chemical Industry.