Trajectories of depression and predictors in lung cancer patients undergoing chemotherapy: growth mixture model
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ABSTRACT:
SUBMITTER: Luo Y
PROVIDER: S-EPMC11344456 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundGiven the rapidly rising proportion of the older population in China and the relatively high prevalence of depressive symptoms among this population, this study aimed to identify the trajectories of depressive symptoms and the factors associated with the trajectory class to gain a better understanding of the long-term course of depressive symptoms in this population.MethodsData were obtained from four wave's survey of the China Health and Retirement Longitudinal Study (CHARLS). A total of 3646 participants who aged 60 years or older during baseline survey, and completed all follow-ups were retained in this study. Depressive symptoms were measured using the 10-item version of the Center for Epidemiologic Studies Depression Scale (CES-D-10). Growth mixture modelling (GMM) was adopted to identify the trajectory classes of depressive symptoms, and both linear and quadratic functions were considered. A multivariate logistic regression model was used to calculate the adjusted odds ratios (ORs) of the associated factors to predict the trajectory class of participants.ResultsA four-class quadratic function model was the best-fitting model for the trajectories of depressive symptoms in the older Chinese population. The four trajectories were labelled as increasing (16.70%), decreasing (12.31%), high and stable (7.30%), and low and stable (63.69%), according to their trends. Except for the low and stable trajectory, the other trajectories were almost above the threshold for depressive symptoms. The multivariate logistic regression model suggested that the trajectories of chronic depressive symptoms could be predicted by being female, living in a village (rural area), having a lower educational level, and having chronic diseases.ConclusionsThis study identified four depressive symptom trajectories in the older Chinese population and analysed the factors associated with the trajectory class. These findings can provide references for prevention and intervention to reduce the chronic course of depressive symptoms in the older Chinese population.
| S-EPMC10276362 | biostudies-literature
Project description:ObjectiveThe identification of modifiable cognitive antecedents of trajectories of grief is of clinical and theoretical interest.MethodThe study gathered 3-wave data on 275 bereaved adults in the first 12-18 months postloss (T1 = 0-6 months, T2 = 6-12 months, T3 = 12-18 months). Participants completed measures of grief severity, cognitive factors (loss-related memory characteristics, negative appraisals, unhelpful coping strategies, and grief resilience), as well as measures of interpersonal individual differences (attachment and dependency). Latent growth mixture modeling was used to identify classes of grief trajectories. Predictors of class membership were identified using multinomial logistic regression and multigroup structural equation modeling.ResultsFour latent classes were identified: 3 high grief classes (Stable, Low Adaptation, and High Adaptation) and a low grief class (Low Grief). When considered separately, variance in all four cognitive factors predicted membership of the high grief classes. When considered together, membership of the high grief classes was predicted by higher mean scores on memory characteristics. More negative appraisals predicted low or no adaptation from high grief severity. Losing a child also predicted membership to the stable class. Fast adaptation of high grief was predicted by a pattern of high memory characteristics but low engagement with unhelpful coping strategies.ConclusionsThe findings have implications for clinical practice and point to early cognitive predictors of adaptation patterns in grief. Findings are consistent with cognitive models highlighting the importance of characteristics of memory, negative appraisals, and unhelpful coping strategies in the adaptation to highly negative life events. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
| S-EPMC6939605 | biostudies-literature
Project description:BackgroundCurrent research on perinatal depression rarely pays attention to the continuity and volatility of depression symptoms over time, which is very important for the early prediction and prognostic evaluation of perinatal depression. This study investigated the trajectories of perinatal depression symptoms and aimed to explore the factors related to these trajectories.MethodsThe study recruited 550 women during late pregnancy (32 ± 4 weeks of gestation) and followed them up 1 and 6 weeks postpartum. Depressive symptoms were measured using the Edinburgh Postnatal Depression Scale (EPDS). Latent growth mixture modelling (LGMM) was used to identify trajectories of depressive symptoms during pregnancy.ResultsTwo trajectories of perinatal depressive symptoms were identified: "decreasing" (n = 524, 95.3%) and "increasing" (n = 26, 4.7%). History of smoking, alcohol use and gestational hypertension increased the chance of belonging to the increasing trajectories, and a high level of social support was a protective factor for maintaining a decreasing trajectory.ConclusionsThis study identified two trajectories of perinatal depression and the factors associated with each trajectory. Paying attention to these factors and providing necessary psychological support services during pregnancy would effectively reduce the incidence of perinatal depression and improve patient prognosis.
| S-EPMC8751241 | biostudies-literature
Project description:We aimed to identify distinct longitudinal trends of LDL-cholesterol (LDL-C) levels and investigate these trajectories' association with statin treatment. This retrospective cohort study used electronic health records from 8592 type 2 diabetes patients in North Karelia, Finland, comprising all primary and specialised care visits 2011‒2017. We compared LDL-C trajectory groups assessing LDL-C treatment target achievement and changes in statin treatment intensity. Using a growth mixture model, we identified four LDL-C trajectory groups. The majority (85.9%) had "moderate-stable" LDL-C levels around 2.3 mmol/L. The second-largest group (7.7%) consisted of predominantly untreated patients with alarmingly "high-stable" LDL-C levels around 3.9 mmol/L. The "decreasing" group (3.8%) was characterised by large improvements in initially very high LDL-C levels, along with the highest statin treatment intensification rates, while among patients with "increasing" LDL-C (2.5%), statin treatment declined drastically. In all the trajectory groups, women had significantly higher average LDL-C levels and received less frequent any statin treatment and high-intensity treatment than men. Overall, 41.9% of patients had no statin prescribed at the end of follow-up. Efforts to control LDL-C should be increased-especially in patients with continuously elevated levels-by initiating and intensifying statin treatment earlier and re-initiating the treatment after discontinuation if possible.
| S-EPMC8604948 | biostudies-literature
Project description:Chemotherapy and the inflammatory response were associated with persistent depressive symptoms in breast cancer patients undergoing radiation. Treatments targeting inflammation before radiation may reduce depression post radiation therapy, especially in patients who have been treated previously with chemotherapy.
2013-01-01 | GSE41112 | GEO
Project description:Chemotherapy and the inflammatory response were associated with persistent depressive symptoms in breast cancer patients undergoing radiation. Treatments targeting inflammation before radiation may reduce depression post radiation therapy, especially in patients who have been treated previously with chemotherapy. Total RNA was isolated from the peripheral blood mononuclear cells (PBMC) obtained from the women (n=61) with Stage 0-III breast cancer treated with or without chemotherapy.
2013-01-01 | E-GEOD-41112 | biostudies-arrayexpress
Project description:BackgroundSocial engagement is closely related to well-being among older adults. However, studies on the changing trajectory and influencing factors (especially time-varying factors) of social engagement are limited. This study aimed to examine the social engagement trajectory of older Chinese adults and explore its time-fixed and time-varying factors, thus providing evidence for the development of strategies to promote a rational implementation for healthy aging.MethodsThis study included 2,195 participants from a subset of four surveys from the Chinese Longitudinal Healthy Longevity Survey conducted from 2008 to 2018 (with the latest survey completed in 2018), with follow-ups conducted approximately every three years. Growth mixture modeling was used to explore the social engagement trajectory of older adults and the effects of time-varying variables. In addition, multinomial logistic regression was employed to analyze the association between time-fixed variables and latent classes.ResultsThree distinct trajectories of social engagement among older adults in China were identified: slow declining (n = 204; 9.3%), which meant social engagement score decreased continuously, but social engagement level improved; slow rising (n = 1,039; 47.3%), marked by an increased score of social engagement, but with an depressed engagement level; and middle stabilizing (n = 952; 43.4%), which meant social engagement score and engagement level remained quite stable. A time-fixed analysis indicated that age, marital status, educational level, and annual family income had a significant impact on social engagement (P < 0.05). In contrast, the time-varying analysis showed that a decline in functional ability, insufficient exercise (means no exercise at present), deteriorating self-reported health and quality of life, negative mood, monotonous diet, and reduced community services were closely related to the reduction in social engagement levels (P < 0.05).ConclusionThree trends were observed at the social engagement level. Older adults with initially high levels of social engagement exhibited a continuous upward trend, whereas those with initially low levels experienced a decline in their social engagement, and those with initially intermediate levels remained quite stable. Considering the primary heterogeneous factors, it is imperative for governments to enhance basic services and prioritize the well-being of older adults. Additionally, families should diligently monitor the emotional well-being of older adults and make appropriate arrangements for meals.
| S-EPMC11488232 | biostudies-literature
Project description:The influence of Positive Affect (PA) on people's well-being and happiness and the related positive consequences on everyday life have been extensively described by positive psychology in the past decades. This study shows an application of Latent Growth Mixture Modeling (LGMM) to explore the existence of different trajectories of variation of PA over time, corresponding to different groups of people, and to observe the effect of emotion regulation strategies on these trajectories. We involved 108 undergraduates in a 1-week daily on-line survey, assessing their PA. We also measured their emotion regulation strategies before the survey. We identified three trajectories of PA over time: a constantly high PA profile, an increasing PA profile, and a decreasing PA profile. Considering emotion regulation strategies as covariates, reappraisal showed an effect on trajectories and class membership, whereas suppression regulation strategy did not.
| S-EPMC7396512 | biostudies-literature
Project description:Background: Individuals with severe mental illnesses are at greater risk of offenses and violence, though the relationship remains unclear due to the interplay of static and dynamic risk factors. Static factors have generally been emphasized, leaving little room for temporal changes in risk. Hence, this longitudinal study aims to identify subgroups of psychiatric populations at risk of violence and criminality by taking into account the dynamic changes of symptomatology and substance use. Method: A total of 825 patients from the MacArthur Violence Risk Assessment Study having completed five postdischarge follow-ups were analyzed. Individuals were classified into outcome trajectories (violence and criminality). Trajectories were computed for each substance (cannabis, alcohol, and cocaine, alone or combined) and for symptomatology and inputted as dynamic factors, along with other demographic and psychiatric static factors, into binary logistic regressions for predicting violence and criminality. Best predictors were then identified using backward elimination, and receiver operator characteristic (ROC) curves were calculated for both models. Results: Two trajectories were found for violence (low versus high violence). Best predictors for belonging in the high-violence group were low verbal intelligence (baseline), higher psychopathy (baseline) and anger (mean) scores, persistent cannabis use (alone), and persistent moderate affective symptoms. The model's area under the curve (AUC) was 0.773. Two trajectories were also chosen as being optimal for criminality. The final model to predict high criminality yielded an AUC of 0.788, retaining as predictors male sex, lower educational level, higher score of psychopathy (baseline), persistent polysubstance use (cannabis, cocaine, and alcohol), and persistent cannabis use (alone). Both models were moderately predictive of outcomes. Conclusion: Static factors identified as predictors are consistent with previously published literature. Concerning dynamic factors, unexpectedly, cannabis alone was an independent co-occurring variable, as well as affective symptoms, in the violence model. For criminality, our results are novel, as there are very few studies on criminal behaviors in nonforensic psychiatric populations. In conclusion, these results emphasize the need to further study the predictors of crime, separately from violence and the impact of longitudinal patterns of specific substance use and high affective symptoms.
| S-EPMC6520437 | biostudies-literature
Project description:BackgroundPatients with cooccurring mental health and substance use disorders often find it difficult to sustain long-term recovery. One predictor of recovery may be how depression symptoms and Alcoholics Anonymous (AA) involvement influence alcohol consumption during and after inpatient psychiatric treatment. This study utilized a parallel growth mixture model to characterize the course of alcohol use, depression, and AA involvement in patients with cooccurring diagnoses.MethodsParticipants were adults with cooccurring disorders (n = 406) receiving inpatient psychiatric care as part of a telephone monitoring clinical trial. Participants were assessed at intake, 3-, 9-, and 15-month follow-up.ResultsA 3-class solution was the most parsimonious based upon fit indices and clinical relevance of the classes. The classes identified were high AA involvement with normative depression (27%), high stable depression with uneven AA involvement (11%), and low AA involvement with normative depression (62%). Both the low and high AA classes reduced their drinking across time and were drinking at less than half their baseline levels at all follow-ups. The high stable depression class reported an uneven pattern of AA involvement and drank at higher daily frequencies across the study timeline. Depression symptoms and alcohol use decreased substantially from intake to 3 months and then stabilized for 90% of patients with cooccurring disorders following inpatient psychiatric treatment.ConclusionsThese findings can inform future clinical interventions among patients with cooccurring mental health and substance use disorders. Specifically, patients with more severe symptoms of depression may benefit from increased AA involvement, whereas patients with less severe symptoms of depression may not.
| S-EPMC7863278 | biostudies-literature