BACKGROUND: This study aims to examine the association of depressive trajectories with disability-free-survival (DFS).
METHODS: This prospective cohort study used data from the China Health and Retirement Longitudinal Study, 2011-2015. Depressive symptoms were assessed using the Centre for Epidemiology Studies Depression Scale-10. Disability was assessed using activities of daily living (ADLs) and instrumental ADLs. Trajectories of depressive symptoms were identified and classified by latent mixture modelling. Logistic regression models were used to examine the association between depressive trajectories and DFS.
RESULTS: A total of 8373 participants aged 45 years and older were included. We identified four distinct trajectories of depressive symptoms: \'no depressive symptoms\', \'decreasing depressive symptoms\', \'increasing depressive symptoms\', and \'persistent depressive symptoms\'. Compared to participants in the no depressive symptom trajectory, those in the decreasing depressive symptoms, increasing depressive symptoms and persistent depressive symptoms trajectories had an increased risk of disability or mortality, with multiple-adjusted hazard ratios (95% confidence intervals) of 1.75 (1.45-2.12), 2.05 (1.77-2.38) and 3.50 (2.77-4.42).
CONCLUSIONS: Our study shows that among middle-aged and older Chinese adults, individuals with a trajectory of depressive symptoms are at increased risk of disability or mortality. Our findings underscore the importance of early prevention, identification and intervention of depression in clinical care to promote healthy ageing.

Aims/Background Gestational diabetes mellitus is a common pregnancy complication that affects approximately 14% of pregnancies worldwide and can lead to adverse maternal and neonatal outcomes. This study aimed to investigate the trajectories of gestational weight gain among gestational diabetes mellitus patients and to inform the development of effective weight management strategies. Methods Demographic and antenatal examination data from 1421 pregnant women diagnosed with gestational diabetes mellitus were retrospectively analysed. Quantitative data comparisons were performed using Chi-square tests, Fisher\'s exact test, t-tests, and one-way analysis of variance. Group-based trajectory modelling was employed to identify the trajectories of gestational weight gain among patients with gestational diabetes mellitus. Results This study revealed that pre-pregnancy body mass index and types of gestational diabetes mellitus significantly influence gestational weight gain (p < 0.05). Group-based trajectory modelling identified three distinct gestational weight gain trajectories. Patients with gestational diabetes mellitus demonstrated a continuous weight gain throughout pregnancy, while women who were overweight or obese before pregnancy were more likely to follow a low-speed growth trajectory. Women in the rapid growth trajectory group were more inclined to deliver by caesarean section and were more likely to give birth to macrosomic infants. Conclusion Our research underscores the importance of identifying and distinguishing between different gestational weight gain trajectories in pregnant women, thereby identifying high-risk groups, which is crucial for improving the health conditions of both mothers and newborns.

The present study aims to explore the potential changing trajectory patterns of body mass index (BMI) for Chinese young adults and identify the relationship of BMI trajectory patterns with kidney stone disease (KSD) incidence. Latent class growth analysis was used to identify distinct trajectories of BMI during young adulthood. Cox proportion hazard models were conducted to explore the association between the BMI trajectory group memberships and incident KSD. Subgroup and sensitivity analyses were undertaken to test the robustness of the findings. In total, 2,966 young adults who attended at least three annual check-ups from 2014 to 2021 without KSD at baseline were enrolled in the cohort analysis. Three district BMI trajectories were identified for young adults, labeled as low-stable in normal BMI (28.5%), medium-rising to high BMI (67.4%), and rapid-rising to high BMI (4.1%). Compared with the low-stable in normal BMI group, Hazard ratios (HRs) of the rapid-rising and medium-rising to high BMI groups were 3.19 (95% CI: 1.54-6.63) and 1.78 (95% CI: 1.08-2.92) after adjusting the covariates. The cumulative incidence curves likewise illustrated that young adults in the rapid-rising to high BMI group had the highest risk of developing KSD compared to the other two groups. The rapid BMI growth trajectories during young adulthood were identified to be independently associated with a higher risk of KSD. The findings supplied novel insights that monitoring the BMI changing pattern may be favorable to early intervention of KSD during young adulthood.

BACKGROUND: A high triglyceride-glucose index (TyG) is associated with a higher risk of incident heart failure. However, the effects of longitudinal patterns of TyG index on the risk of heart failure remain to be characterized. Therefore, in the present study, we aimed to characterize the relationship between the trajectory of TyG index and the risk of heart failure.
METHODS: We performed a prospective study of 56,149 participants in the Kailuan study who attended three consecutive surveys in 2006-2007, 2008-2009, and 2010-2011 and had no history of heart failure or cancer before the third wave survey (2010-2011). The TyG index was calculated as ln [fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2], and we used latent mixture modeling to characterize the trajectory of the TyG index over the period 2006-2010. Additionally, Cox proportional risk models were used to calculate the hazard ratio (HR) and 95% confidence interval (CI) for incident heart failure for the various TyG index trajectory groups.
RESULTS: From 2006 to 2010, four different TyG trajectories were identified: low-stable (n = 13,554; range, 7.98-8.07), moderate low-stable (n = 29,435; range, 8.60-8.65), moderate high-stable (n = 11,262; range, 9.31-9.30), and elevated-stable (n = 1,898; range, 10.04-10.25). A total of 1,312 new heart failure events occurred during a median follow-up period of 10.04 years. After adjustment for potential confounders, the hazard ratios (HRs) and 95% confidence intervals (CIs) for incident heart failure for the elevated-stable, moderate high-stable, and moderate low-stable groups were 1.55 (1.15, 2.08), 1.32 (1.08, 1.60), and 1.17 (0.99, 1.37), respectively, compared to the low-stable group.
CONCLUSIONS: Higher TyG index trajectories were associated with a higher risk of heart failure. This suggests that monitoring TyG index trajectory may help identify individuals at high risk for heart failure and highlights the importance of early control of blood glucose and lipids for the prevention of heart failure.

BACKGROUND: The atherogenic index of plasma (AIP) has been shown to be positively correlated with cardiovascular events. However, it remains unclear whether hypertensive patients with long-term high AIP levels are at greater risk of developing heart failure (HF). Therefore, the aim of this study was to investigate the association between AIP trajectory and the incidence of HF in hypertensive patients.
METHODS: This prospective study included 22,201 hypertensive patients from the Kailuan Study who underwent three waves of surveys between 2006 and 2010. Participants were free of HF or cancer before or during 2010. The AIP was calculated as the logarithmic conversion ratio of triglycerides to high-density lipoprotein cholesterol. Latent mixed modeling was employed to identify different trajectory patterns for AIP during the exposure period (2006-2010). Cox proportional hazard models were then used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for incident HF among different trajectory groups.
RESULTS: Four distinct trajectory patterns were identified through latent mixture modeling analysis: low-stable group (n = 3,373; range, -0.82 to -0.70), moderate-low stable group (n = 12,700; range, -0.12 to -0.09), moderate-high stable group (n = 5,313; range, 0.53 to 0.58), and elevated-increasing group (n = 815; range, 1.22 to 1.56). During a median follow-up period of 9.98 years, a total of 822 hypertensive participants experienced HF. After adjusting for potential confounding factors, compared with those in the low-stable group, the HR and corresponding CI for incident HF in the elevated-increasing group, moderate-high stable group, and moderate-low stable group were estimated to be 1.79 (1.21,2.66), 1.49 (1.17,1.91), and 1.27 (1.02,1.58), respectively. These findings remained consistent across subgroup analyses and sensitivity analyses.
CONCLUSIONS: Prolonged elevation of AIP in hypertensive patients is significantly associated with an increased risk of HF. This finding suggests that regular monitoring of AIP could aid in identifying individuals at a heightened risk of HF within the hypertensive population.

UNASSIGNED: Whether and how coronary artery calcium (CAC) progress contributes to cardiovascular outcomes has not been fully elucidated. The aim of this study was to identify different patterns of CAC change and evaluate the associations with different cardiovascular outcomes.
UNASSIGNED: Data from the Multi-Ethnic Study of Atherosclerosis study were analyzed. Participants with at least three CT measurements were included. The main study outcome is hard cardiovascular disease (CVD). CAC scores were determined as phantom-adjusted Agatston scores. A group-based trajectory model was used to identify latent groups and estimated the hazard ratios (HR) and 95% confidence intervals (CI) using Cox proportional regression models.
UNASSIGNED: A total of 3,616 participants were finally enrolled [mean age 60.55 (SD 9.54) years, 47.76% men and 39.30% Caucasian]. Four distinct trajectories in CAC were identified: class 1, low-stable (24.17%); class 2, low-increasing (27.60%); class 3, moderate-increasing (30.56%); and class 4, elevated-increasing (17.67%). During 13.58 (SD 2.25) years of follow-up, 291 cases of hard CVD occurred. The event rates of hard CVD per 1,000 person-years were 2.23 (95% CI 1.53-3.25), 4.60 (95% CI 3.60-5.89), 7.67 (95% CI 6.38-9.21) and 10.37 (95% CI 8.41-12.80) for classes 1-4, respectively. Compared to participants assigned to class 1, the full-adjusted HRs of hard CVD for classes 2-4 were 2.10 (95% CI 1.33-3.01), 3.17 (95% CI 2.07-4.87), and 4.30 (95% CI 2.73-6.78), respectively. The graded positive associations with hard CVD were consistently observed in subgroups of age, sex, and race, with the presence or absence of hypertension or diabetes. By analyzing potential risk factors for distinctive CAC trajectories, risk factors for the onset and progression of CAC could possibly differ: age, male sex, history of hypertension, and diabetes are consistently associated with the low-, moderate-, and elevated-increasing trajectories. However, Caucasian race, cigarette smoking, and a higher body mass index was related only to risk of progression but not to incident CAC.
UNASSIGNED: In this multi-ethnic population-based cohort, four unique trajectories in CAC change over a 10-year span were identified. These findings signal an underlying high-risk population and may inspire future studies on risk management.

UNASSIGNED: The causal relationships of late-life body mass index (BMI) with Alzheimer\'s disease (AD) remains debated.
UNASSIGNED: We aimed to assess the associations of dynamic BMI features (ΔBMIs) with cognitive trajectories, AD biomarkers, and incident AD risk.
UNASSIGNED: We analyzed an 8-year cohort of 542 non-demented individuals who were aged ≥65 years at baseline and had BMI measurements over the first 4 years. ΔBMIs were defined as changing extent (change ≤ or >5%), variability (standard deviation), and trajectories over the first 4 years measured using latent class trajectory modeling. Linear mixed-effect models were utilized to examine the influence of ΔBMIs on changing rates of AD pathology biomarkers, hippocampus volume, and cognitive functions. Cox proportional hazards models were used to test the associations with AD risk. Stratified analyzes were conducted by the baseline BMI group and age.
UNASSIGNED: Over the 4-year period, compared to those with stable BMI, individuals who experienced BMI decreases demonstrated accelerated declined memory function (p = 0.006) and amyloid-β deposition (p = 0.034) while BMI increases were associated with accelerated hippocampal atrophy (p = 0.036). Three BMI dynamic features, including stable BMI, low BMI variability, and persistently high BMI, were associated with lower risk of incident AD (p < 0.005). The associations were validated over the 8-year period after excluding incident AD over the first 4 years. No stratified effects were revealed by the BMI group and age.
UNASSIGNED: High and stable BMI in late life could predict better cognitive trajectory and lower risk of AD.

BACKGROUND: People with chronic pain experience variability in their trajectories of pain severity. Previous studies have explored pain trajectories by clustering sparse data; however, to understand daily pain variability, there is a need to identify clusters of weekly trajectories using daily pain data. Between-week variability can be explored by quantifying the week-to-week movement between these clusters. We propose that future work can use clusters of pain severity in a forecasting model for short-term (eg, daily fluctuations) and longer-term (eg, weekly patterns) variability. Specifically, future work can use clusters of weekly trajectories to predict between-cluster movement and within-cluster variability in pain severity.
OBJECTIVE: This study aims to understand clusters of common weekly patterns as a first stage in developing a pain-forecasting model.
METHODS: Data from a population-based mobile health study were used to compile weekly pain trajectories (n=21,919) that were then clustered using a k-medoids algorithm. Sensitivity analyses tested the impact of assumptions related to the ordinal and longitudinal structure of the data. The characteristics of people within clusters were examined, and a transition analysis was conducted to understand the movement of people between consecutive weekly clusters.
RESULTS: Four clusters were identified representing trajectories of no or low pain (1714/21,919, 7.82%), mild pain (8246/21,919, 37.62%), moderate pain (8376/21,919, 38.21%), and severe pain (3583/21,919, 16.35%). Sensitivity analyses confirmed the 4-cluster solution, and the resulting clusters were similar to those in the main analysis, with at least 85% of the trajectories belonging to the same cluster as in the main analysis. Male participants spent longer (participant mean 7.9, 95% bootstrap CI 6%-9.9%) in the no or low pain cluster than female participants (participant mean 6.5, 95% bootstrap CI 5.7%-7.3%). Younger people (aged 17-24 y) spent longer (participant mean 28.3, 95% bootstrap CI 19.3%-38.5%) in the severe pain cluster than older people (aged 65-86 y; participant mean 9.8, 95% bootstrap CI 7.7%-12.3%). People with fibromyalgia (participant mean 31.5, 95% bootstrap CI 28.5%-34.4%) and neuropathic pain (participant mean 31.1, 95% bootstrap CI 27.3%-34.9%) spent longer in the severe pain cluster than those with other conditions, and people with rheumatoid arthritis spent longer (participant mean 7.8, 95% bootstrap CI 6.1%-9.6%) in the no or low pain cluster than those with other conditions. There were 12,267 pairs of consecutive weeks that contributed to the transition analysis. The empirical percentage remaining in the same cluster across consecutive weeks was 65.96% (8091/12,267). When movement between clusters occurred, the highest percentage of movement was to an adjacent cluster.
CONCLUSIONS: The clusters of pain severity identified in this study provide a parsimonious description of the weekly experiences of people with chronic pain. These clusters could be used for future study of between-cluster movement and within-cluster variability to develop accurate and stakeholder-informed pain-forecasting tools.

OBJECTIVE: This study aimed to enrich the research on frailty trajectories by using FRAIL scale and frailty index (FI), and analyze the determinants of the different trajectories in older Chinese.
METHODS: 2268 older adults from the Chinese Longitudinal Healthy Longevity Survey were included. The FRAIL scale was constructed from 5 items and FI was constructed from 39 deficits. Latent Class Trajectory Model was used to depict frailty trajectories. Lasso - logistic model was applied to exploration of influencing factors.
RESULTS: Four FRAIL trajectories and three FI trajectories were identified. Women, smoking, illiteracy, more than two chronic diseases, and poor instrumental activities of daily living (all p < 0.05) were associated with frailty trajectories, regardless of the frailty instrument employed.
CONCLUSIONS: Frailty trajectories of older Chinese adults are diverse and they are influenced by different frailty measurement tools. Long-term assessment and management of frailty are recommended as routine care in community healthcare centers.

OBJECTIVE: Post-traumatic growth can improve the quality of life of cancer survivors. The objective of this study was to investigate post-traumatic growth heterogeneity trajectory in perioperative gastric cancer survivors, and to identify characteristics that predict membership for each trajectory.
METHODS: Gastric cancer survivors (n = 403) were recruited before surgery, their baseline assessment (including post-traumatic growth and related characteristics) was completed, and post-traumatic growth levels were followed up on the day they left the intensive care unit, at discharge, and 1 month after discharge. Latent growth mixture mode was used to identify the heterogeneous trajectory of post-traumatic growth, and the core predictors of trajectory subtypes were explored using a decision tree model.
RESULTS: Three post-traumatic growth development trajectories were identified among gastric cancer survivors: stable high of PTG group (20.6%), fluctuation of PTG group (44.4%), persistent low of PTG group (35.0%). The decision tree model showed anxiety, coping style, and psychological resilience-which was the primary predictor-might be used to predict the PTG trajectory subtypes of gastric cancer survivors.
CONCLUSIONS: There was considerable variability in the experience of post-traumatic growth among gastric cancer survivors. Recognition of high-risk gastric cancer survivors who fall into the fluctuation or persistent low of PTG group and provision of psychological resilience-centered support might allow medical professionals to improve patients\' post-traumatic growth and mitigate the impact of negative outcomes.