Latent Class Research Articles

Substance use patterns and mental health comorbidities in youth with a history of depression or suicidality: Findings from TX-YDSRN

BackgroundThere is a robust relationship between depression and substance use in youth, with higher levels of substance use associated with greater depressive symptomatology. However, previous research has examined individual substances, without consideration of psychiatric comorbidities. Here, we investigate patterns of substance use among depressed and/or suicidal youth within the context of psychiatric comorbidities. Methods945 youth with depression and/or suicidality from the Texas Youth Depression and Suicide Research Network (TX-YDSRN) were assessed for current use of alcohol, nicotine, cannabis, and other drugs and comorbid psychiatric diagnoses. We used latent class analysis to identify patterns of past-year substance use, then examined if demographics or psychiatric disorders predicted class membership. ResultsWe identified three patterns of substance use: non-use (63.4 %), moderate likelihood of using alcohol, nicotine and cannabis (23.8 %), and high likelihood of using all substances (12.7 %). Compared to non-users, individuals in the moderate and high likelihood classes were more likely to be older. Individuals in the high likelihood class were more likely to have a substance use disorder, ADHD, and higher suicidality scores. LimitationsWe cannot ascertain the causal or temporal ordering of substance use and psychiatric diagnoses due to the cross-sectional nature of the study. ConclusionsUsing a brief, self-report measure of substance use, we identified three classes of substance users differing in probability of past-year use, which were predicted by older age and some psychiatric comorbidities. While research on universal screening of substance use in youth remains limited, we discuss who may benefit from such screening among depressed youth.

Modeling pedestrians' activity time-use choices in a virtual urban public space: The influences of the environment and affective experience

Understanding pedestrians' time-use choices for multiple activities in urban public spaces is crucial for developing diverse and inclusive public spaces. Few studies have explored how pedestrians' time-use choices are influenced by the physical environment when engaging in various micro-level activities in public spaces, nor have they captured the influence of experience-based factors (e.g., emotion and perceptions) triggered by varying environments, as well as heterogeneity between individuals. This study employs the multiple discrete-continuous extreme value (MDCEV) model to estimate the effects of public space environmental attributes, visit purpose, emotion, and perceptions on individuals' activity time-use choices using data collected in varying virtual hypothetical public spaces. A latent class MDCEV model incorporating personal variables is used to further examine individual heterogeneity. The results indicate that outdoor dining spaces and benches significantly promote outdoor activities. Emotional pleasure and arousal, as well as perceived legibility and liveness, significantly influence various activities in varying ways and to varying degrees. Differences in activity time-use choice patterns between two groups are revealed - one more emotionally driven and the other more cognitively driven. The results complement existing knowledge of pedestrians' activities and inform the planning and design decisions for positive human-environment interactions in urban public spaces.

Identifying electric vehicle charging styles among consumers: a latent class cluster analysis

The market share of electric vehicles (EVs) is growing rapidly, making it crucial to understand the charging behaviour of current and prospective users. Such understanding is essential for designing policies that positively influence consumers’ charging behaviour and facilitate EV adoption. In this study, we examined the heterogeneity in charging preferences of 994 respondents across Australia using a latent class cluster model that considers indicators of charging behaviour as outcomes of interest. We used sociodemographic characteristics, travel needs, and EV adoption status as covariates to predict class membership. Our findings indicate five segments of consumers with distinct charging preferences: routine-focused frugals, cost-oriented deliberators, range seekers, flexibility seekers, and indifferent late adopters. These segments differ in the importance they attach to charging attributes, their coping strategies with limited battery resources, and their risk attitude. Our results suggest that a uniform approach to EV-related policies is not appropriate, as each consumer segment has unique charging preferences and requirements. Furthermore, the study emphasizes the significance of accounting for charging behaviour heterogeneity in demand modelling, as assumptions in current models may not accurately represent the decision-making of most segments.

Trajectories of depression and predictors in lung cancer patients undergoing chemotherapy: growth mixture model

BackgroundDepression is prevalent among lung cancer patients undergoing chemotherapy, and the symptom cluster of fatigue-pain-insomnia may influence their depression. Identifying characteristics of patients with different depression trajectories can aid in developing more targeted interventions. This study aimed to identify the trajectories of depression and the fatigue-pain-insomnia symptom cluster, and to explore the predictive factors associated with the categories of depression trajectories.MethodsIn this longitudinal study, 187 lung cancer patients who were undergoing chemotherapy were recruited and assessed at the first (T1), second(T2), and fourth(T3) months using the Patient Health Questionnaire-9 (PHQ-9), the Brief Pain Inventory (BPI), the Brief Fatigue Inventory (BFI), and the Athens Insomnia Scale (AIS). Growth Mixture Model (GMM) and Latent Class Analysis (LCA) were used to identify the different trajectories of the fatigue-pain-insomnia symptom cluster and depression. Binary logistic regression was utilized to analyze the predictive factors of different depressive trajectories.ResultsGMM identified two depressive trajectories: a high decreasing depression trajectory (40.64%) and a low increasing depression trajectory (59.36%). LCA showed that 48.66% of patients were likely members of the high symptom cluster trajectory. Binary logistic regression analysis indicated that having a history of alcohol consumption, a higher symptom cluster burden, unemployed, and a lower monthly income predicted a high decreasing depression trajectory.ConclusionsDepression and fatigue-pain-insomnia symptom cluster in lung cancer chemotherapy patients exhibited two distinct trajectories. When managing depression in these patients, it is recommended to strengthen symptom management and pay particular attention to individuals with a history of alcohol consumption, unemployed, and a lower monthly income.

Socially desirable resources and activities – evaluation of a new scale for the separate measurement of economic and cultural capital in educational research

ABSTRACT For the measurement of social status, many studies rely on a one-dimensional operationalization, even though the theoretical background that is typically referred to – Bourdieu’s theory of capital (1986) – suggests doing otherwise. A multi-dimensional empirical representation of social status is challenging because the indicators that are typically used in educational research cannot easily be attributed to only one form of capital. In this paper, we test a new scale for the separate measurement of economic and cultural capital. It consists of a list of socially desirable resources and activities for which respondents are asked to state whether they have/do them. In TIMSS 2015 Germany, the scale was implemented for the first time in a large-scale assessment study. Using multi-dimensional scaling, we identify two dimensions: economic and cultural capital. Conducting latent class analyses, we are able to group answers to the scale into four classes, which differ by the amount of economic (low/high) and cultural capital (low/high) students’ parents possess. In multivariate analyses with student achievement, the scale shows additional explanatory power. Overall, our analyses show that economic and cultural capital can be measured separately and that social status is a multi-layered construct that requires a multi-dimensional operationalization.

A Study of Marketing Strategies for Electricity Plans Considering Renewable Energy Using Latent Class Analysis

A Study of Marketing Strategies for Electricity Plans Considering Renewable Energy Using Latent Class Analysis

Unraveling Perceived Ostracism: The Role of Antagonistic Traits and Attachment Orientation

Perceived ostracism (e.g., feeling ignored and excluded) can lead to psychological distress. There has been little empirical research into the types (profiles) of people more likely to perceive ostracism. The present study (N = 604) used latent class analysis (LCA) to (a) explore classes based on antagonistic traits (narcissism, machiavellianism, psychopathy, and sadism)while controlling for attachment orientation (attachment anxiety and attachment avoidance) and (b) examine whether such classes could reliably differentiate levels of self-reported perceived ostracism. We extracted five classes: (a) Average Low, (b) the Non-Antagonisers, (c) Average High, (d) Spiteful Manipulators, and (e) the High Antagonisers. Those in the High Antagonisers class reported significantly higher levels of perceived ostracism compared to all other classes. No other differences between classes were observed. There were also significant positive relationships for avoidant and anxious attachment on perceived ostracism, respectively. This study provides new insight into the profiles of individuals who may be more likely to perceive ostracism. However, further research is needed to explore the association between personality and perceived ostracism. Researchers may consider measuring the potential outcomes following perceived ostracism for such groups and/or design potential interventions for those at risk of such experiences.

Health lifestyles of six Zhiguo ethnic groups in China: a latent class analysis

BackgroundZhiguo ethnic groups, commonly known as “the directly-entering-socialism ethnic groups”, represent Chinese ethnic minorities who have undergone a unique social development trajectory by transforming directly from primitive societies to the socialist stage. In recent decades, significant lifestyle transformations have occurred among Zhiguo ethnic groups. Understanding their health lifestyles can play a strategic role in China’s pursuit of universal health coverage. This study aims to examine patterns of health-related lifestyle among Zhiguo ethnic groups and explore whether sociodemographic features and specific indicators related to health status are associated with particular classes.MethodsA cross-sectional study was conducted in Yunnan Province, China, from July to December 2022. Stratified random sampling method was employed to recruit residents belonging to six Zhiguo ethnic groups aged between 15 and 64. Latent class analysis was performed to identify clusters of health-related behaviors within each ethnic group. Logistic regression was utilized to determine the predictors of health lifestyles.ResultsA total of 1,588 individuals from the Zhiguo ethnic groups participated in this study. Three latent classes representing prevalent health lifestyles among the Zhiguo ethnic groups were identified: “unhealthy lifestyle” (31.80%), “mixed lifestyle” (57.37%), and “healthy lifestyle” (10.83%). In the overall population, individuals belonging to the “healthy lifestyle” group exhibited a higher likelihood of being non-farmers (OR: 2.300, 95% CI: 1.347–3.927), women (OR: 21.459, 95% CI: 13.678–33.667), married individuals (OR: 1.897, 95% CI: 1.146–3.138), and those residing within a walking distance of less than 15 min from the nearest health facility (OR: 2.133, 95% CI: 1.415–3.215). Conversely, individuals in the age cohorts of 30–39 years (OR: 0.277, 95% CI: 0.137–0.558) and 40–49 years (OR: 0.471, 95% CI: 0.232–0.958) showed a decreased likelihood of adopting a healthy lifestyle.ConclusionsA considerable proportion of the Zhiguo ethnic groups have not adopted healthy lifestyles. Targeted interventions aimed at improving health outcomes within these communities should prioritize addressing the clustering of unfavorable health behaviors, with particular emphasis on single male farmers aged 30–49, and expanding healthcare coverage for individuals residing more than 15 min away from accessible facilities.

The more, the merrier! A multilevel latent class analysis of college students’ drinking game behaviors during the COVID-19 pandemic.

The more, the merrier! A multilevel latent class analysis of college students’ drinking game behaviors during the COVID-19 pandemic.

Cascade of care for substance use and mental health disorders for justice-involved populations

IntroductionJustice-involved populations have dramatically higher rates of substance use disorders (SUD) and mental health disorders (MHD) compared to the general population. Despite high rates of SUD and MHD, treatment for this population is often limited and not evidence-based. The cascade of care model estimates drop-offs in the continuum of care from screening to identification of need, referral, care initiation, care engagement, and care completion. Recently, healthcare providers have utilized the cascade of care to improve the continuity of care for people with SUD and MHD in justice settings. The purpose of the current study is to 1) identify typologies that explain the proportion of new intakes that pass through each level of the cascade of care for SUD and MHD, and 2) describe agency-level factors that predict typology assignments and agency ability to assess client flow through the levels of the care cascade. MethodUsing Latent Class Analysis, we classify 791 agencies serving justice-involved individuals into typologies according to utilization of each stage in the mental health and substance cascades of care. Then, we examined county and agency characteristics that affect three stages of the cascade process: identification of need for behavioral health services, referrals to appropriate services, and treatment initiation. We build on previous work by exploring these patterns for both SUD and MHD treatment. ResultsThe study identified four SUD/MHD treatment patterns: Low Access, SUD-Focused, High Need-High Access, and Lower Need-High Access classes. Factors influencing typology alignment include location, specialized staff availability, warm hand-off coordination, Medicaid reimbursement, and performance measure tracking. Thirty-nine percent (39 %) of agencies could not be classified because they were unable to report their rate of care along the cascade measures. ConclusionFocusing on factors influencing typology assignment can help counties in assessing service delivery, identifying barriers, and targeting areas for improvements in policies and practices, potentially facilitating long-term changes and overall improvement in the care of individuals with mental health and substance use disorders. Identification of these factors and typologies can improve mental health treatment and access in counties and agencies with large resource barriers or limited attention to mental health treatment.

Identifying phenotypes in persons with temporomandibular disorders, using latent class analyses: Temporomandibular disorders and phenotypes.

The heterogeneity of persons with temporomandibular disorders (TMD) and the lack of effective treatments have called for a biopsychosocial model and the development of a more personalised treatment approach. Emphasis on phenotypes might be a beneficial approach. Identifying phenotypes among persons with TMD using potential prognostic factors, including personal characteristics and responses to clinical tests. Additionally, examining the distribution of TMD diagnoses within the identified phenotypes. A cross-sectional study including 208 persons (85% females) seeking physiotherapy for problems in the temporomandibular area. All participants were examined clinically and answered questionnaires electronically. The phenotypes were identified using latent class analysis based on seven potential prognostic factors selected within pain, function and psychological domains. Table analysis was used to explore the distribution of TMD diagnoses within the identified phenotypes. Most participants fit into one of three identified phenotypes. Phenotype 1 (32%) was characterised by functional disability, low psychosocial scores and low risk for developing chronicity and future work disability; Phenotype 2 (29%) by parafunctional habits, low psychosocial score and seeking treatment to reduce pain; and Phenotype 3 (39%) by high levels of mental distress, fear avoidance and a large risk of future work disability. Intra-articular disorders dominated Phenotype 1, myalgia and TMD-related headache Phenotype 2, while Phenotype 3 included all the different TMD diagnoses. The knowledge about the three identified phenotypes might be useful for clinicians treating persons with TMD and for the development of preventive strategies and more personalised treatment.

An empirical study on train choice preferences of high-speed railway passengers: the case of Nanning-Guangzhou railway and Nanning-Beihai railway

ABSTRACT This paper presents a systematic investigation of the train choice preference heterogeneity of High-Speed Railway (HSR) passengers under market segmentation to understand their train choice comprehensively. A stated preference survey was conducted for the Nanning-Guangzhou Railway and Nanning-Beihai Railway. Latent Class Analysis (LCA) was employed to identify homogeneous subgroups and segment the passenger market of each line into three segments: private travelers with a long total duration (PTLTD), business travelers (BT), and private travelers with a short total duration (PTSTD). Mixed Logit (ML) models were constructed for each subgroup sample to assess passengers' preferences in train choice. The results show that each class exhibited unique characteristics and preferences, and train fare and running time, departure date and time, and train frequency were statistically significant factors affecting train choice. This study can furnish theoretical and decisional support for HSR operators to design train operating schemes and flexible fare systems.

Segmenting the potential users of MaaS by combining latent class cluster analysis and structural equation modeling

To reduce problems caused by the excessive use of private vehicles, transportation planners have been actively looking for sustainable transport related solutions. Mobility as a Service (MaaS) is gaining popularity due to its capability to integrate transport opportunities into one platform. Studies on travelers’ acceptance of MaaS have been conducted, but there remains a gap in understanding heterogeneity regarding intentions of using MaaS particularly concerning attitudinal factors. Based upon the extension of the unified theory of acceptance and use of technology, we plan to reveal travelers’ segmentation by using latent class cluster analysis and to examine path heterogeneities in the identified traveler clusters by applying a structural equation modeling. The results confirm that the traveler clusters consider the importance of the relatives and media's viewpoints toward MaaS, while the car user cluster is more interested in adopting MaaS if the service could promise enough flexibility of traveling in terms of time and destination. The results related to the heterogeneity in transport mode choice are useful for constructing MaaS packages. This study could be advantageous for MaaS operators as it enables them to understand some attitudinal factors influencing willingness to take up MaaS thus realizing suitable, effective promotions.

Trajectory modeling and response prediction in transcranial magnetic stimulation for depression

BackgroundRepetitive transcranial magnetic stimulation (rTMS) therapy could be improved by more accurate and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modeling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models are useful in predicting clinically meaningful change in symptom severity, i.e. categorical (non)response as opposed to continuous scores. MethodsWe compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression, across multiple coils and protocols. We then compared the predictive power of those models. ResultsLCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC=0.70, 95 % CI=[0.52–0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC=0.76, 95 % CI=[0.58–0.94], but likewise, not before. ConclusionsIn showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.

Relationship Between Treatment Hours of Selected Disciplines And Change in Pain Impact During the Year Following Referral to an Interdisciplinary Pain Management Center: A Latent Class Analysis.

Introduction: Pain management clinics differ in treatments offered, and little evidence exists regarding which combinations of therapies result in best outcomes. This study analyzed clinical encounters and pain outcomes data for associations between treatment composition and outcomes. Methods: Retrospective observational study of 2,142 predominantly active-duty US service members referred to an interdisciplinary pain management center between 2014 and 2021. Latent class analysis was used to identify treatment groups with distinct outcome patterns during the year following initial assessment. The primary outcome measure was the National Institutes of Health Task Force on Research Standards for Chronic Low-Back Pain impact score. Results: Four distinct treatment groups were identified: 1 group engaged in conventional medical therapies alone (n = 726, median 3.5 treatment hours), and 3 groups used a combination of conventional, psychological, rehabilitative, and complementary therapies, with different levels of treatment hours: low (n = 814, median 15.7 h), medium (n = 177, median 40.1 h), and high (n = 425, median 72.5 h). All groups showed significant improvement in pain impact score from baseline up to 9 months but not at 12 months following initial assessment. At the 6-month time point, the group with high multimodal treatment hours had the most improvement in pain impact (-3.1 [95% CI -3.8, -2.4]) compared with the group using only conventional therapies (-1.0 points [95% CI -1.8, -0.1]) or with low multimodal treatment hours (-1.3 points [95% CI -1.9, -0.7]). There were no between-group differences at the 9- or 12-month time points. Conclusion: These results suggest that a combination of pain therapy approaches results in greater reduction in pain impact than the use of conventional medical treatment alone for up to 6 months after initiating therapy and that there may be a threshold of treatment hours that must be exceeded to achieve this benefit. Clinical trial registration: As this study is not a clinical trial, no registration was required.

Recall of Flavor at First E-cigarette Use and Its Association with E-cigarette Progression: The Mediating Effects of First Sensory Experience

To test whether the association between flavor at first vape and continued use is mediated through subjective experience at first vape. In a 2020 cross-sectional survey, 955 young adult ever-vapers recalled their first flavor vaped, subjective experiences at first use, current vaping behavior, nicotine dependence, and quit attempts. A latent class model grouped first-use subjective experiences into classes. Two-part negative binomial hurdle models for each vaping behavior evaluated whether the association of first flavor used with vaping outcomes was mediated by positive experience. Four latent classes (positive, positive and negative, negative, and minimal experience) were further reduced to "any positive experience" (only positive, positive and negative) vs "no positive experience" (negative or minimal). Class membership mediated the association of first flavor used (mint/menthol/ice [ie, "cooling"] or sweet vs other) with each vaping outcome. For example, cooling flavor (vs. other) was associated with positive class membership (OR=3.5; 95% CI: 1.5, 8.1), which was then associated with any past 30-day vaping (OR=3.9; 95% CI: 2.7, 5.8) and greater number of vaping days among current vapers (RR=1.9; 95% CI: 1.3, 2.7) in the two-part hurdle model. Similar results were observed for nicotine dependence and quit attempts, and for sweet (vs other) flavor for any dependence or quit attempts, but not number of dependence symptoms or quit attempts. Use of a cooling or sweet flavor at first use was associated with having a positive first vaping sensory experience, and then greater likelihood as a young adult of reporting past 30-day vaping, more vaping days, and greater risk for nicotine dependence, suggesting a key mediating role of first use experience.

Trajectories of Inflammation in Youth and Risk of Mental and Cardiometabolic Disorders in Adulthood

Research suggests that low-grade, nonresolving inflammation may predate adult mental and physical illness. However, evidence to date is largely cross-sectional or focuses on single disorder outcomes. To examine trajectories of inflammation as measured by C-reactive protein (CRP) levels in a large sample of children and adolescents, and to explore associations between different identified trajectories and mental and related cardiometabolic health outcomes in early adulthood. In a longitudinal cohort study using data from the large UK-based Avon Longitudinal Study of Parents and Children (ALSPAC), latent class growth analysis (LCGA) was used to explore different trajectories of inflammation, with logistic regression exploring association with mental and physical health outcomes. Participants with measurable CRP data and associated mental and cardiometabolic health outcomes recorded were included in the analysis. Data analysis was performed from May 1, 2023, to March 30, 2024. Inflammation was assessed via CRP levels at ages 9, 15, and 17 years. LCGA was used to identify different trajectories of inflammation. Outcomes assessed at age 24 years included psychotic disorders, depressive disorders, anxiety disorders, hypomania, and, as a measure of insulin resistance, Homeostasis Model Assessment (HOMA2) score. A total of 6556 participants (3303 [50.4%] female) were included. Three classes of inflammation were identified: persistently low CRP levels (reference class, n = 6109); persistently raised CRP levels, peaking at age 9 years (early peak, n = 197); and persistently raised CRP levels, peaking at age 17 years (late peak, n = 250). Participants in the early peak group were associated with a higher risk of psychotic disorder (odds ratio [OR], 4.60; 95% CI, 1.81-11.70; P = .008), a higher risk of severe depression (OR, 4.37; 95% CI, 1.64-11.63; P = .02), and higher HOMA2 scores (β = 0.05; 95% CI, 0.01-0.62, P = .04) compared with participants with persistently low CRP. The late peak group was not associated with any outcomes at age 24 years. Low-grade systemic inflammation peaking in midchildhood was associated with specific mental and cardiometabolic disorders in young adulthood. These findings suggest that low-grade persistent inflammation in early life may be an important shared common factor for mental-physical comorbidity and so could be relevant to future efforts of patient stratification and risk profiling.

Exploring social activity patterns among community-dwelling older adults in South Korea: a latent class analysis

BackgroundWith the trend of digitalization, social activities among the older population are becoming more diverse as they increasingly adopt technology-based alternatives. To gain a comprehensive understanding of social activities, this study aimed to identify the patterns of digital and in-person social activities among community-dwelling older adults in South Korea, examine the associated factors, and explore the difference in depressive symptoms by the identified latent social activity patterns.MethodsData were extracted from a nationwide survey conducted with 1,016 community-dwelling older adults (mean age 68.0 ± 6.5 years, 47.8% male). The main variables assessed were digital social activities (eight items), in-person social activities (six items), and depressive symptoms (20 items). Data were analyzed using latent class analysis, multinomial logistic regression, and multiple linear regression.ResultsWe identified four distinct social activity patterns: “minimal in both digital and in-person” (22.0%), “moderate in both digital and in-person” (46.7%), “moderate in digital & very high in in-person” (14.5%), and “high in both digital and in-person” (16.8%). Younger age, living in multi-generational households, and higher digital literacy were associated with a higher likelihood of being in the “moderate in both digital and in-person” than the “minimal in both digital and in-person” group. Younger age, male, living in multi-generational households, residing in metropolitan areas, no dependency on IADL items, doing daily physical exercise, and higher digital literacy were associated with a higher likelihood of being in the “moderate in digital & very high in in-person” than the “minimal in both digital and in-person” group. Younger age, living in multi-generational households, no dependency on IADL items, doing daily physical exercise, and higher digital literacy were associated with a higher likelihood of being in the “high in both digital and in-person” than the “minimal in both digital and in-person” group. Depressive symptoms were significantly higher in the group with minimal engagement in both digital and in-person activities, compared to other three groups.ConclusionsThis study highlights distinct patterns of social activities among Korean community-dwelling older adults. Since older adults with minimal social activity engagement can be more vulnerable to depressive symptoms, interventions that address modifiable attributes, such as supporting digital literacy and facilitating physical activity of older adults, could serve as potential strategies to enhance their social activity engagement and, consequently, their mental well-being.

Trajectories of body mass index and incident kidney stone disease: a prospective cohort study in Chinese young adults.

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.

Trajectories of perioperative nutritional status in patients with pancreatic tumor after surgery in six months

PurposeTo characterize subgroups with similar nutritional status trajectories during the 6-month period after pancreatectomy and to identify demographic and clinical characteristics influencing changes in nutritional status in each subgroup. MethodsThis longitudinal prospective study recruited 112 patients with newly diagnosed pancreatic tumor from an outpatient pancreatic surgical department of a medical center in northern Taiwan between September 2016 and April 2019. Patients completed a demographic and clinical characteristics form, the Mini Nutritional Assessment scale, and the Symptom Severity Scale prior to surgery (T0), 3 months after surgery (T1), and 6 months after surgery (T2). Latent class growth analysis was used to investigate the trajectories of nutritional status. Generalized estimating equations were used to identify significant factors influencing each trajectory. ResultsTwo latent groups of nutritional status trajectories were identified. Among 112 patients, 74.11% and 25.89% were classified as having high and low nutritional status trajectories, respectively. High nutritional status was significantly negatively correlated with changes in symptom severity. Low nutritional status was significantly negatively correlated with older age, surgical complications, and changes in symptom severity. ConclusionsSymptom severity has the most significant negative effect on perioperative nutritional status. Older age and surgical complications exert negative effects on perioperative nutritional status among patients with low nutritional status. These findings emphasize the need for nurses to identify at-risk individuals and provide individualized nutritional care to improve nutritional status in this population. Clinical trials registrationThis study was registered on ClinicalTrials.gov (trial registration number: NCT02900677; approved date: September 14th, 2016). Link: https://clinicaltrials.gov/ct2/show/NCT02900677.