Longitudinal Trajectories Research Articles

Changes in thalamic functional connectivity in post-Covid patients with and without fatigue

BackgroundFunctional brain alterations in post-Covid-19 condition have been minimally explored to date. Here, we investigate differences in resting-state thalamic functional connectivity among post-Covid patients with and without fatigue, alongside structural brain changes and cognition. MethodsThirty-nine post-Covid patients (n=15 fatigued, n=24 non-fatigued) participated in our study, undergoing comprehensive cognitive assessments, as well as functional and structural neuroimaging. We conducted a seed-based functional connectivity analysis using the thalamus as a seed region, exploring its connectivity with the entire brain. To further elucidate our findings, correlation analyses were performed using the functional coupling between the thalamus and regions showing different connectivity between the two patient groups. ResultsOur results reveal that patients experiencing fatigue exhibit anti-correlated functional coupling between the thalamus and motor-associated regions, including the motor cortex (M1), supplementary motor area (SMA), and anterior cingulate cortex (ACC), compared to non-fatigued patients, who are showing positive functional coupling. Furthermore, this observed coupling was found to correlate with both the fatigue scores obtained from a fatigue questionnaire and performance on the Trail Making Test, Part A, which represents a measure of processing speed. ConclusionsOur study highlights significant differences in resting-state functional connectivity between post-Covid patients with and without fatigue, particularly within motor-associated brain regions. These findings suggest a potential neural mechanism underlying post-Covid fatigue and underscore the importance of considering both functional and structural brain changes in understanding the symptomatic sequelae of post-Covid-19 condition. Further research is warranted to provide insight into the longitudinal trajectories of these neural alterations.

LVEF trajectory in HFimpEF and its association with prognosis

Abstract Background The 2021 revised Universal Definition of Heart Failure (HF) introduced the subgroup known as HF with improved ejection fraction (HFimpEF), aiming to establish a standardized definition for left ventricular ejection fraction (LVEF) improvement and emphasize the importance of longitudinally assessment of LVEF. However, there remains an insufficient characterization of the association between serial LVEF trajectory before and after impEF, and outcomes in HFimpEF. Purpose We aim to delineate the LVEF trajectory in patients with impEF and to discern specific LVEF trajectory pattern that portends an unfavorable prognosis. Methods We conducted a retrospective analysis by including hospitalized HF patients with baseline LVEF ≤ 40% and satisfactory serial echocardiographic follow-ups. ImpEF was defined as a follow-up LVEF measurement of > 40% with a ≥10-point increase from baseline. Longitudinal LVEF trajectories for patients with impEF (before and after LVEF improvement) were plotted according to the clinical outcome of all-cause death using Loess curves. LVEF changes over time were analyzed by unconditional non-linear mixed-effects modeling, and compared between the groups of patients who died and those who survived using conditional non-linear mixed-effects modeling. Results Among the initial 923 patients with reduced LVEF, 517 had impEF. According to Loess curves and non-linear mixed-effects analysis, LVEF trajectory of impEF differed according to the clinical outcome of death. For patients who suffered all-cause death, there was a comparatively limited improvement in LVEF followed by a more rapid plateauing and decline, exhibiting an inverted U-shaped pattern with lower LVEF values towards both ends of the distribution. Conclusions In HFimpEF, an inverted U-shaped trajectory of LVEF, characterized by limited initial improvement followed by subsequent decline, is associated with poorer prognosis. Serial LVEF monitoring in patients with HFimpEF could help with early identification of at-risk patients.Graphical Abstract

Green and blue spaces residential exposure over childhood and multimorbidity in adolescence

Abstract Background Previous studies have explored associations of exposure to natural spaces with specific adverse health outcomes over childhood. However, knowledge is lacking on its effects on multimorbidity, defined as the co-occurrence of two or more diseases in the same individual. We aimed to assess the associations between exposure to green and blue spaces, at multiple time points and through trajectories over childhood, with multimorbidity in early adolescence. Methods In 2388 children from the Generation XXI birth cohort, we assessed, at birth, 4, 7 and 10 years, the residential greenness through the Normalized Difference Vegetation Index (NDVI), the distance from the child’s residence to the closest green and blue space, and the presence of green and blue space (>5,000m²) within 300m distance from the child’s residence. We identified, from birth to 10 years, longitudinal trajectories of exposure to green and blue spaces, using latent class mixed models. At 13 years, participants were classified as having 0, 1 or ≥ 2 cardiometabolic, respiratory and/or neurodevelopmental adverse health outcomes. Adjusted multinomial logistic regression models were used. Results The prevalence of multimorbidity at 13 years was 43%, with overweight/obesity (35%), allergies and asthma (30%), and dyslipidemia (26%) as the most common adverse health outcomes. We observed that the presence of green spaces > 5,000m² within a distance of 300m at birth, 4, 7, and 10 years was associated with lower odds of developing multimorbidity (Odds Ratio (OR) [95% CI] 0.55 [0.32-0.95]; 0.52 [0.32-0.84]; 0.51 [0.32-0.82]; 0.51 [0.32-0.80], respectively). No other significant associations were observed. Conclusions Green spaces around the child’s residence seem to have a protective effect on the development of multimorbidity at 13 years. These findings reinforce the importance of urban design, and the creation of green spaces close to residential areas to optimize health across childhood and adolescence. Key messages • The prevalence of multimorbidity at 13 years is high and worrying. • Increasing accessibility to green spaces might decrease the risk of multimorbidity in adolescence.

The substantial role of sleep, stress and physical activity in persistent high levels of fatigue in patients with inflammatory bowel disease: A longitudinal trajectory study.

Fatigue is a common and burdensome problem for patients with inflammatory bowel disease (IBD). Previous studies lack insight into the individual variability in fatigue severity and course over time, and the characteristics of patients at risk of severe and persistent fatigue. This study aimed to identify distinct groups of IBD patients based on their level and course of fatigue over five years. Subsequently, we examined the relationship between these trajectories, patient characteristics and trajectories of perceived stress, sleep and physical activity. This longitudinal cohort study used prospectively collected data from the myIBDcoach telemedicine monitoring tool, including 320 IBD patients who completed three or more online consultations between 2016 and 2021. Latent class growth analyses were performed to identify distinct trajectories. We found five subgroups with distinct trajectories of fatigue, differing in level and course over time, with 33% of patients experiencing chronic stable and high levels of fatigue. Few differences in patient characteristics were found between trajectories, yet the chronic high fatigue subgroup was more likely to report persistent stable sleep problems, perceived stress, and little physical activity over time compared to the other groups. Distinct subgroups of IBD patients can be identified based on longitudinal fatigue trajectories. The relatively stable levels of fatigue, stress, sleep, and physical activity suggest that a one-time screening of patients on these topics may be sufficient to identify those at risk. Interventions aiming to reduce fatigue should target persistent stress, sleep problems, and low levels of physical activity.

Dynamic changes in hs-CRP and risk of all-cause mortality among middle-aged and elderly adults: findings from a nationwide prospective cohort and mendelian randomization

IntroductionThe general population experiences mortality rates that are related to high levels of high-sensitivity C-reactive protein (hs-CRP). We aim to assess the linkage of longitudinal trajectories in hs-CRP levels with all-cause mortality in Chinese participants.MethodsWe utilized data from the China Health and Retirement Longitudinal Study (CHARLS). The exposures were dynamic changes in the hs-CRP and cumulative hs-CRP from 2012 to 2015, and the outcome was all-cause mortality. All participants were categorized into four trajectories according to hs-CRP levels. Multivariable logistic regression analysis, adjusted for potential confounders, was employed to evaluate the relationship of different trajectories of hs-CRP with mortality risk. A two-sample Mendelian randomization (TSMR) method and SHapley Additive exPlanations (SHAP) for identifying determinants of mortality risk were also employed.ResultsThe study included 5,445 participants with 233 deaths observed, yielding a mortality proportion of 4.28%. Compared to individuals maintaining low, stable levels of hs-CRP (Class 1), individuals with sustained elevated levels of hs-CRP (Class 4), those experiencing a progressive rise in hs-CRP levels (Class 2), or those transitioning from elevated to reduced hs-CRP levels (Class 3) all faced a significantly heighted death risk, with adjusted Odds Ratios (ORs) ranging from 2.34 to 2.47 across models. Moreover, a non-linear relationship was found between them. Further TSMR analysis also supported these findings. SHAP showed that hs-CRP was the fifth most important determinant of mortality risk.ConclusionsOur study shows all-cause mortality increases with dynamic changes in hs-CRP levels among middle-aged and elderly adults in China, and cumulative hs-CRP shows an L-shaped relationship with all-cause mortality.

Pathway enrichment in genome-wide analysis of longitudinal Alzheimer's disease biomarker endophenotypes.

The genetic pathways that influence longitudinal heterogeneous changes in Alzheimer's disease (AD) may provide insight into disease mechanisms and potential therapeutic targets. Longitudinal endophenotypes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) representing amyloid, tau, neurodegeneration (A/T/N), and cognition were selected. Genome-wide association analysis was performed using a linear mixed model (LMM) approach, followed by gene and pathway enrichment with significant and functionally relevant SNPs. A total of 33 and 19 statistically significant pathways were identified associating with the intercept and longitudinal trajectory, respectively. The longitudinal intercept pathways represent eight groups: immune, metabolic, cell growth and survival, DNA maintenance, neuronal signaling, RAS/MAPK/ERKsignalingpathways, vesicle and lysosomal transport, and transcription modification. Longitudinal trajectory pathways represented six groups: Immune, metabolic, cell signaling, cytoskeleton, and glycosylation. Longitudinal enrichment identified pathways that uniquely associate with trajectories of key AD biomarkers and cognition, providing new insight into AD course-related mechanisms and potential new therapeutic targets. A systematic genome-wide analysis with longitudinal AD biomarker endophenotypes was performed. Enriched pathways were identified with functionally derived SNP to gene analysis. Fifty-two pathways were associated with longitudinal trajectory and intercept. Many of the identified pathways are specific steps in larger pathways implicated in AD. The identified pathways may provide therapeutic targets and areas for further study.

ReMiND: Recovery of missing neuroimaging using diffusion models with application to Alzheimer’s disease

Abstract Missing data is a significant challenge in medical research. In longitudinal studies of Alzheimer’s disease (AD) where structural magnetic resonance imaging (MRI) is collected from individuals at multiple time points, participants may miss a study visit or drop out. Additionally, technical issues such as participant motion in the scanner may result in unusable imaging data at designated visits. Such missing data may hinder the development of high-quality imaging-based biomarkers. To address the problem of missing MRI data in studies of AD, we introduced a novel 3D diffusion model specifically designed for imputing missing structural MRI (Recovery of Missing Neuroimaging using Diffusion models (ReMiND)). The model generates a whole-brain image conditional on a single structural MRI observed at a past visit or conditional on one past and one future observed structural MRI relative to the missing observation. The performance of models was compared with two alternative imputation approaches: forward filling and image generation using variational autoencoders. Experimental results show that our method can generate 3D structural MRI with high similarity to ground-truth images at designated visits. Furthermore, images generated using ReMiND show relatively lower differences in volume estimation between the imputed and observed images compared to images generated by forward filling or autoencoders. Additionally, ReMiND provides more accurate estimated rates of atrophy over time in important anatomical brain regions than the two comparator methods. Our 3D diffusion model can impute missing structural MRI data at a single designated visit and outperforms alternative methods for imputing whole-brain images that are missing from longitudinal trajectories.

Symptom- and function-based trajectories of patients with dementia in hospital and community palliative care settings in the last two weeks of life: a retrospective cohort study

BackgroundThe prevalence of dementia is increasing worldwide and many people with the condition require some level of palliative care. However, the trajectories of function and symptom burden in palliative care services at the end of life remain unclear. This study aimed to describe and compare the longitudinal trajectories of function and symptom burden among patients with dementia between hospital versus palliative community care services in the last two weeks of life.MethodsA retrospective cohort study used data from the Australian Palliative Care Outcomes Collaboration. Patients with dementia who died between 1 January 2013 and 31 December 2020 from the Australian Palliative Care Outcomes Collaboration. Four validated clinical instruments were used to collect outcomes on each individual’s function and symptom distress and severity. Multilevel models were used to estimate the differences in clinical trajectories between hospital and community-based palliative care in the last two weeks of life.ResultsPatients with dementia tended to have low levels of distress for most symptoms but increasing levels of functional impairment. There were no or only marginally significant differences in the symptom trajectories between the community and hospital groups (OR ranged from 0.57 to 1.97). Although clinical trajectories of function were relatively similar between two groups, statistically higher functional indicators were observed for people when admitted to community palliative care services (OR = 0.42 and 2.27, respectively).ConclusionsOur findings suggest that community-based palliative care services can be as effective as hospital-based care for many patients with dementia nearing the end of life. With appropriate support for families, community-based care could serve as a viable alternative to hospital-based care for some patients in the final stages of dementia.

Association between blood glucose level trajectories and 30-day mortality risk in patients with acute ischemic stroke: analysis of the MIMIC database 2001–2019

BackgroundHyperglycemia is one of the most common comorbidities in patients with acute ischemic stroke (AIS). This study aimed to assess the impact of short-term longitudinal blood glucose level change trajectories on the 30-day mortality risk in patients with AIS.MethodsData for AIS patients were obtained from the 2001–2019 Medical Information Mart for Intensive Care (MIMIC) database. The latent growth mixture modeling (LGMM) was utilized to classify a patient’s blood glucose level trajectory within 24 h of admission. Cox regression analyses were applied to examine the relationship between blood glucose levels at admission and blood glucose level trajectories and the risk of 30-day mortality in patients with AIS.ResultsA total of 2,432 patients with AIS were included in this retrospective cohort study, with 30-day mortality occurring in 574 (23.60%) patients. The median glucose levels of all patients were 136.00 (110.00, 178.00) mg/dL. Four blood glucose level trajectories were identified: low level-stable trend (type 1), moderate level-stable trend (type 2), high level-decreasing-increasing trend (type 3), and moderate level-increasing-decreasing trend (type 4). Type 2 blood glucose level trajectory was associated with an increased risk of 30-day mortality compared with type 1 blood glucose level trajectory [hazard ratio (HR) = 1.28, 95% confidence interval (CI): 1.03–1.59), but there were no significant associations between type 3 (HR = 1.16, 95%CI: 0.77–1.74) and type 4 (HR = 1.44, 95%CI: 0.84–2.45) trajectories and 30-day mortality risk. Subgroup analysis demonstrated that the association between type 2 trajectory and 30-day mortality risk was observed in patients aged ≥ 65 years (HR = 1.37, 95%CI: 1.05–1.79), female (HR = 1.42, 95%CI: 1.05–1.94), with (HR = 1.44, 95%CI: 1.02–2.02) or without (HR = 1.42, 95%CI: 1.01–1.99) diabetes, and not using insulin (HR = 2.80, 95%CI: 1.43–5.49).ConclusionAIS patients with consistently high blood glucose levels within 24 h of admission increased the risk of 30-day mortality.

Population clustering of structural brain aging and its association with brain development

Structural brain aging has demonstrated strong inter-individual heterogeneity and mirroring patterns with brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, most of the existing research focused on the cross-sectional changes of brain aging. In this investigation, we present a data-driven approach that incorporate both cross-sectional changes and longitudinal trajectories of structural brain aging and identified two brain aging patterns among 37,013 healthy participants from UK Biobank. Participants with accelerated brain aging also demonstrated accelerated biological aging, cognitive decline and increased genetic susceptibilities to major neuropsychiatric disorders. Further, by integrating longitudinal neuroimaging studies from a multi-center adolescent cohort, we validated the ‘last in, first out’ mirroring hypothesis and identified brain regions with manifested mirroring patterns between brain aging and brain development. Genomic analyses revealed risk loci and genes contributing to accelerated brain aging and delayed brain development, providing molecular basis for elucidating the biological mechanisms underlying brain aging and related disorders.

Population clustering of structural brain aging and its association with brain development.

Structural brain aging has demonstrated strong inter-individual heterogeneity and mirroring patterns with brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, most of the existing research focused on the cross-sectional changes of brain aging. In this investigation, we present a data-driven approach that incorporate both cross-sectional changes and longitudinal trajectories of structural brain aging and identified two brain aging patterns among 37,013 healthy participants from UK Biobank. Participants with accelerated brain aging also demonstrated accelerated biological aging, cognitive decline and increased genetic susceptibilities to major neuropsychiatric disorders. Further, by integrating longitudinal neuroimaging studies from a multi-center adolescent cohort, we validated the 'last in, first out' mirroring hypothesis and identified brain regions with manifested mirroring patterns between brain aging and brain development. Genomic analyses revealed risk loci and genes contributing to accelerated brain aging and delayed brain development, providing molecular basis for elucidating the biological mechanisms underlying brain aging and related disorders.

Organizing planning knowledge for automated vehicles and intelligent transportation systems

AbstractIntelligent Transportation Systems (ITS) are crucial for developing fully automated vehicles. While significant progress has been made with advanced driver assistance systems and automation technology, challenges remain, such as improving traffic information, enhancing planning and control systems, and developing better decision‐making capabilities. Despite these hurdles, the potential benefits of ITS are so many that its challenges have attracted substantial industrial investment and research groups interested in the automated driving field. In this work, a methodology based on state space search for planning knowledge integration is proposed. The main goal of the proposal is to provide a planning system with the necessary information to perform properly any task related to lateral and longitudinal control, path following, trajectory generation, arbitration and behavior execution by localizing the vehicle with respect to a high‐level road plan. To this end, this research compares cutting‐edge methods for rapidly finding the K nearest neighbor in relatively high dimensional road plans constructed from the traffic information stored in a high definition map. During the experimentation phase, promising real‐time results have been obtained for fast KNN algorithms, leading to a robust tree index‐based methodology for decision making in self‐driving vehicles combining path planning, trajectory tracking, trajectory creation, knowledge aggregation and precise vehicle control.

Altered Gut Microbiota Patterns in Young Children with Recent Maltreatment Exposure

Background: The brain and the intestinal microbiota are highly interconnected and especially vulnerable to disruptions in early life. Emerging evidence indicates that psychosocial adversity detrimentally impacts the intestinal microbiota, affecting both physical and mental health. This study aims to investigate the gut microbiome in young children in the immediate aftermath of maltreatment exposure. Methods: Maltreatment exposure was assessed in 88 children (ages 3–7) using the Maternal Interview for the Classification of Maltreatment [MICM]. Children were allocated to three groups according to the number of experienced maltreatment categories: no maltreatment, low maltreatment, and high maltreatment exposures. Stool samples were collected and analyzed by 16S rRNA sequencing. Results: Children subjected to high maltreatment exposure exhibited lower alpha diversity in comparison to those with both no and low maltreatment exposure (Simpson Index, Tukey post hoc, p = 0.059 and p = 0.007, respectively). No significant distinctions in beta diversity were identified. High maltreatment exposure was associated with the enrichment of several genera from the class Clostridia (Clostridium, Intestinibacter, Howardella and Butyrivibrio) and the depletion of the genus Phocaeicola (class Bacteriodia). Conclusions: Severe maltreatment exposure is associated with alterations in the gut microbiota of young children. Longitudinal trajectories of intestinal microbiota composition in the context of maltreatment may reveal important insights related to psychiatric and somatic health outcomes.

Trajectories of triglyceride-glucose index changes and their association with all-cause and cardiovascular mortality: a competing risk analysis

BackgroundThe association between changes in insulin resistance, reflected by the triglyceride-glucose (TyG) index, and mortality remains unclear. This study investigated whether longitudinal trajectories of TyG index changes are associated with all-cause and cardiovascular disease (CVD) mortality.MethodsThis retrospective cohort study analyzed data from 233,546 adults aged ≥ 19 years from the Korea National Health Insurance Service-National Sample Cohort. Participants were categorized as having increasing, stable, or decreasing TyG index changes during a 4-year exposure period (2009–2014). Mortality outcomes were assessed during an 8.13-year follow-up period (2015–2021). Cox proportional hazards regression and competing risk analysis were used to evaluate all-cause and CVD mortality.ResultsA total of 7918 mortality events, including 651 CVD deaths, were recorded. Compared with the stable group, adjusted hazard ratios for all-cause mortality were 1.09 (95% CI 1.03–1.15) in the increasing group and 1.23 (95% CI 1.01–1.50) for CVD mortality. An increased TyG index was significantly associated with all-cause mortality in individuals aged < 50 years; men; and individuals with obesity, hypertension, diabetes, and/or dyslipidemia. For CVD mortality, significant associations were found in individuals aged 50–69 years, with obesity, with diabetes, or without dyslipidemia.ConclusionAn increasing TyG index from baseline during follow-up was independently associated with higher risks of all-cause and CVD mortality. Serial monitoring of TyG index changes could enhance risk stratification and inform targeted interventions to reduce insulin resistance, and ultimately lower mortality risk.

A unified longitudinal trajectory dataset for automated vehicle

Automated Vehicles (AVs) promise significant advances in transportation. Critical to these improvements is understanding AVs’ longitudinal behavior, relying heavily on real-world trajectory data. Existing open-source trajectory datasets of AV, however, often fall short in refinement, reliability, and completeness, hindering effective performance metrics analysis and model development. This study addresses these challenges by creating a Unified longitudinal trajectory dataset for AVs (Ultra-AV) to analyze their microscopic longitudinal driving behaviors. This dataset compiles data from 14 distinct sources, encompassing various AV types, test sites, and experiment scenarios. We established a three-step data processing: 1. extraction of longitudinal trajectory data, 2. general data cleaning, and 3. data-specific cleaning to obtain the longitudinal trajectory data and car-following trajectory data. The validity of the processed data is affirmed through performance evaluations across safety, mobility, stability, and sustainability, along with an analysis of the relationships between variables in car-following models. Our work not only furnishes researchers with standardized data and metrics for longitudinal AV behavior studies but also sets guidelines for data collection and model development.

Longitudinal trajectories of general cognitive and daily functions in data-driven subtypes of MCI: A longitudinal cohort analysis of older adults

ObjectivesTo derive data-driven subtypes of mild cognitive impairment (MCI) and characterize the complicated changes of general cognitive and daily functions over time in MCI subtypes. MethodsA total of 813 subjects diagnosed as MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were included. Data-driven MCI subtypes were derived from group-based multi-trajectory modeling (GBMTM) analyses using longitudinal measurement scores in the cognitive domains of visuospatial function, language, and executive function. General cognitive and daily functions were measured by the Mini-Mental State Examination (MMSE) and the Functional Assessment Questionnaire (FAQ), respectively, whose longitudinal trajectory changes were depicted by Linear mixed models. ResultsThree MCI subtypes were derived, which were defined as “Cognitive decline group”, “Mild cognitive decline group” and “No cognitive decline group”. The “Mild cognitive decline group” had the highest percentage in the sample (46.2 %), followed by the “No cognitive decline group” (35.2 %). Patients in the “Cognitive decline group” had the highest mean age (74.69 years) at baseline, the highest APOE ε4 carriers (63.2 %), and the greatest dementia conversion rate (77.0 %). The changes in MMSE and FAQ score trajectories were fastest in the “Cognitive decline group” in the first 36 months and most slowly in the “No cognitive decline group”. ConclusionMCI individuals could be subdivided into more fine-grained cognitive subtypes, and identifying these distinct MCI subtypes and their different trajectories of cognitive decline may have important prognostic value for improving clinical course prediction.

Disease trajectories before dementia: evidence from a large-scale community-based prospective study.

Systemic changes in multiple diseases may influence the onset of dementia. However, the specific temporality between exposure diseases and dementia remains uncertain. By characterising the full spectrum of temporal disease trajectories before dementia, this study aims to yield a global picture of precursor diseases to dementia and to provide detailed instructions for risk management and primary prevention of dementia. Using the multicentre, community-based prospective UK Biobank, we constructed disease trajectories before dementia utilising the phenome-wide association analysis, paired directional test and association quantification. Stratified disease trajectories were constructed by dementia subtypes, gender, age of diagnosis and Apolipoprotein E (ApoE) status, respectively. Our study population comprised 434 266 participants without baseline dementia and 4638 individuals with all-cause dementia. In total, 1253 diseases were extracted as potential components of the disease trajectory before dementia. We identified three clusters of disease trajectories preceding all-cause dementia, initiated by circulatory, metabolic and respiratory diseases occurring approximately 5-15 years before dementia. Cerebral infarction or chronic renal failure following chronic ischaemic heart disease was the specific trajectory before vascular dementia. Apolipoprotein E (ApoE) ε4 non-carriers exhibited more complex trajectories compared with carriers. Lipid metabolism disorders remained in the trajectories regardless of dementia subtypes, gender, age of diagnosis and ApoE status. This study provides a comprehensive view of the longitudinal disease trajectories before dementia and highlights the potential targets of midlife cardiometabolic dysfunction for dementia screening and prevention.

Longitudinal multi-omics reveals pathogenic TSC2 variants disrupt developmental trajectories of human cortical organoids derived from Tuberous Sclerosis Complex.

Tuberous Sclerosis Complex (TSC), an autosomal dominant condition, is caused by heterozygous mutations in either the TSC1 or TSC2 genes, manifesting in systemic growth of benign tumors. In addition to brain lesions, neurologic sequelae represent the greatest morbidity in TSC patients. Investigations utilizing TSC1/2 -knockout animal or human stem cell models suggest that TSC deficiency-causing hyper-activation of mTOR signaling might precipitate anomalous neurodevelopmental processes. However, how the pathogenic variants of TSC1/2 genes affect the longitudinal trajectory of human brain development remains largely unexplored. Here, we employed 3-dimensional cortical organoids derived from induced pluripotent stem cells (iPSCs) from TSC patients harboring TSC2 variants, alongside organoids from age- and sex-matched healthy individuals as controls. Through comprehensively longitudinal molecular and cellular analyses of TSC organoids, we found that TSC2 pathogenic variants dysregulate neurogenesis, synaptogenesis, and gliogenesis, particularly for reactive astrogliosis. The altered developmental trajectory of TSC organoids significantly resembles the molecular signatures of neuropsychiatric disorders, including autism spectrum disorders, epilepsy, and intellectual disability. Intriguingly, single cell transcriptomic analyses on TSC organoids revealed that TSC2 pathogenic variants disrupt the neuron/reactive astrocyte crosstalk within the NLGN-NRXN signaling network. Furthermore, cellular and electrophysiological assessments of TSC cortical organoids, along with proteomic analyses of synaptosomes, demonstrated that the TSC2 variants precipitate perturbations in synaptic transmission, neuronal network activity, mitochondrial translational integrity, and neurofilament formation. Notably, similar perturbations were observed in surgically resected cortical specimens from TSC patients. Collectively, our study illustrates that disease-associated TSC2 variants disrupt the neurodevelopmental trajectories through perturbations of gene regulatory networks during early cortical development, leading to mitochondrial dysfunction, aberrant neurofilament formation, impaired synaptic formation and neuronal network activity.

Longitudinal trajectory classes and correlates of the Multidimensional Personality Questionnaire-triarchic psychopathy scales from adolescence to young adulthood.

The present study examined longitudinal trajectory classes and correlates of triarchic psychopathy domains (boldness, meanness, and disinhibition) from age 16 to 22, leveraging Multidimensional Personality Questionnaire (MPQ)-based triarchic scales data gathered on a large community sample (ns ranging between 483 and 775 across waves) oversampled for parental substance use disorder (SUD). Growth mixture models were conducted to examine longitudinal trajectory classes for each domain, and their associations with environmental covariates (e.g., neighborhood disadvantage and parental SUD) and outcomes at age 22 (e.g., violent behavior, antisocial personality disorder, and an overall problem index capturing internalizing symptoms and social problems). For boldness, all participants fell in the same class showing relative stability over time. Comparable solutions were recovered for meanness and disinhibition (high-stable/increasing, mid-range decreasing, and low-decreasing). Links with external correlates supported well-known differences between boldness and both meanness and disinhibition and additionally revealed interesting differences between meanness and disinhibition, suggesting that environmental covariates better discriminated meanness trajectory classes. These results demonstrate considerable developmental heterogeneity in these traits across adolescence into young adulthood, which relates to outcomes associated with antisociality and general life struggles. Further, these findings support the adequacy of the MPQ as an operationalization tool for longitudinal investigations on psychopathy.

Analysis of Longitudinal Trajectory and Influencing Factors of Supportive Care Needs in Colorectal Cancer Patients With Enterostomy.

The aim of this study was to understand the dynamic changes in the needs of patients with enterostomy during the 3 months after discharge and its possible influencing factors. A prospective observational design. This study investigated the supportive care needs of patients who underwent colorectal cancer surgery with colostomy in three Chinese hospitals from May 2023 to October 2023 during the 3 months following discharge from the hospital. The growth mix model was used to describe the needs trajectory and analyse the heterogeneity of the trajectory. Univariate analysis was used to find the factors that might affect the heterogeneity of needs trajectory of patients with enterostomy, and then logistic regression analysis was used to determine the influencing factors of the heterogeneity of needs trajectory of patients with enterostomy. The reporting of this study adhered to the STROBE checklist. A total of 232 patients with enterostomy completed follow-up. There was heterogeneity in the developmental trajectories of supportive care needs of enterostomy patients and the trajectories of the five dimensions of supportive care needs. The heterogeneity factors affecting the trajectory of supportive care needs included the enterostomy patient's psychosocial adjustment score, type of enterostomy, and educational background and the heterogeneity factors affecting the five dimensions trajectory of supportive care needs include psychosocial adjustment score, tumour staging, type of enterostomy, smoking, chemotherapy and enterostomy self-care knowledge score. The needs of patients with enterostomy within 3 months after discharge were dynamic. Identifying and meeting the unmet needs of patients with enterostomy was crucial to improving the health-related quality of life of patients with enterostomy. None. The needs of patients with enterostomy were dynamic, with the needs of most patients with enterostomy decreasing within 3 months of discharge, but some patients with enterostomy continued to have high needs at 3 months after discharge, and clinical nurses were expected to pay special attention to these patients.