Identifying longitudinal cognitive resilience from cross-sectional amyloid, tau, and neurodegeneration.

BACKGROUND: Leveraging Alzheimer's disease (AD) imaging biomarkers and longitudinal cognitive data may allow us to establish evidence of cognitive resilience (CR) to AD pathology in-vivo. Here, we applied latent class mixture modeling, adjusting for sex, baseline age, and neuroimaging biomarkers of amyloid, tau and neurodegeneration, to a sample of cognitively unimpaired older adults to identify longitudinal trajectories of CR. METHODS: We identified 200 Harvard Aging Brain Study (HABS) participants (mean age = 71.89 years, SD = 9.41 years, 59% women) who were cognitively unimpaired at baseline with 2 or more timepoints of cognitive assessment following a single amyloid-PET, tau-PET and structural MRI. We examined latent class mixture models with longitudinal cognition as the dependent variable and time from baseline, baseline age, sex, neocortical Aß, entorhinal tau, and adjusted hippocampal volume as independent variables. We then examined group differences in CR-related factors across the identified subgroups from a favored model. Finally, we applied our favored model to a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI; n = 160, mean age = 73.9 years, SD = 7.6 years, 60% women). RESULTS: The favored model identified 3 latent subgroups, which we labelled as Normal (71% of HABS sample), Resilient (22.5%) and Declining (6.5%) subgroups. The Resilient subgroup exhibited higher baseline cognitive performance and a stable cognitive slope. They were differentiated from other groups by higher levels of verbal intelligence and past cognitive activity. In ADNI, this model identified a larger Normal subgroup (88.1%), a smaller Resilient subgroup (6.3%) and a Declining group (5.6%) with a lower cognitive baseline. CONCLUSION: These findings demonstrate the value of data-driven approaches to identify longitudinal CR groups in preclinical AD. With such an approach, we identified a CR subgroup who reflected expected characteristics based on previous literature, higher levels of verbal intelligence and past cognitive activity.

Longitudinal Exposure to Neighborhood Concentrated Poverty Contributes to Differences in Adiposity in Midlife Women.

Background: Neighborhood poverty is associated with adiposity in women, though longitudinal designs, annually collected residential histories, objectively collected anthropometric measures, and geographically diverse samples of midlife women remain limited. Objective: To investigate whether longitudinal exposure to neighborhood concentrated poverty is associated with differences in body mass index (BMI) and waist circumference (WC) among 2,328 midlife women (age 42-52 years at baseline) from 6 U.S. cities enrolled in the Study of Women's Health Across the Nation (SWAN) from 1996 to 2007. Methods: Residential addresses and adiposity measures were collected at approximately annual intervals from the baseline visit through a 10-year follow-up. We used census poverty data and local spatial statistics to identify hot-spots of high concentrated poverty areas and cold-spots of low concentrated poverty located within each SWAN site region, and used linear mixed-effect models to estimate percentage differences (95% confidence interval [CI]) in average BMI and WC levels between neighborhood concentrated poverty categories. Results: After adjusting for individual-level sociodemographics, health-related factors, and residential mobility, compared to residents of moderate concentrated poverty communities, women living in site-specific hot-spots of high concentrated poverty had 1.5% higher (95% CI: 0.6, 2.3) BMI and 1.3% higher (95% CI: 0.5, 2.0) WC levels, whereas women living in cold-spots of low concentrated poverty had 0.7% lower (95% CI: -1.2, -0.1) BMI and 0.3% lower (95% CI: -0.8, 0.2) WC. Site-stratified results remained in largely similar directions to overall estimates, despite wide CIs and small sample sizes. Conclusions: Longitudinal exposure to neighborhood concentrated poverty is associated with slightly higher BMI and WC among women across midlife.

Trajectories of BMI before and after diagnosis of type 2 diabetes in a real-world population.

AIMS/HYPOTHESIS: Few studies have examined the clinical characteristics associated with changes in weight before and after diagnosis of type 2 diabetes. Using a large real-world cohort, we derived trajectories of BMI before and after diabetes diagnosis, and examined the clinical characteristics associated with these trajectories, including assessing the impact of pre-diagnosis weight change on post-diagnosis weight change. METHODS: We performed an observational cohort study using electronic medical records from individuals in the Scottish Care Information Diabetes Collaboration database. Two trajectories were calculated, based on observed BMI measurements between 3 years and 6 months before diagnosis and between 1 and 5 years after diagnosis. In the post-diagnosis trajectory, each BMI measurement was time-dependently adjusted for the effects of diabetes medications and HbA1c change. RESULTS: A total of 2736 individuals were included in the study. There was a pattern of pre-diagnosis weight gain, with 1944 individuals (71%) gaining weight overall, and 875 (32%) gaining more than 0.5 kg/m2 per year. This was followed by a pattern of weight loss after diagnosis, with 1722 individuals (63%) losing weight. Younger age and greater social deprivation were associated with increased weight gain before diagnosis. Pre-diagnosis weight change was unrelated to post-diagnosis weight change, but post-diagnosis weight loss was associated with older age, female sex, higher BMI, higher HbA1c and weight gain during the peri-diagnosis period. When considering the peri-diagnostic period (defined as from 6 months before to 12 months after diagnosis), we identified 986 (36%) individuals who had a high HbA1c at diagnosis but who lost weight rapidly and were most aggressively treated at 1 year; this subgroup had the best glycaemic control at 5 years. CONCLUSIONS/INTERPRETATION: Average weight increases before diagnosis and decreases after diagnosis; however, there were significant differences across the population in terms of weight changes. Younger individuals gained weight pre-diagnosis, but, in older individuals, type 2 diabetes is less associated with weight gain, consistent with other drivers for diabetes aetiology in older adults. We have identified a substantial group of individuals who have a rapid deterioration in glycaemic control, together with weight loss, around the time of diagnosis, and who subsequently stabilise, suggesting that a high HbA1c at diagnosis is not inevitably associated with a poor outcome and may be driven by reversible glucose toxicity.

The transition from physical sibling abuse to bullying perpetration through trait anger: A longitudinal study.

OBJECTIVES: Experiencing physical sibling abuse is a form of family violence that is common but understudied. While it is often perceived as a normative aspect of sibling relationships, there are apparent behavioral consequences. The current study aims to advance the literature by utilizing the displaced aggression model and I3 theory to longitudinally examine trait anger as a pathway linking physical sibling abuse to bullying perpetration. METHODS: Using data from the Bullying, Sexual, and Dating Violence Trajectories from Early to Late Adolescence in the Midwestern United States, 2008-2013, adolescents (n = 851, M = 14.8 years) completed questionnaires at baseline and were reassessed 6 months later. RESULTS: Results suggested that when adolescents experience physical sibling abuse, they are more likely to engage in bullying perpetration. Mediation analyses indicated that as adolescents were physically abused by a sibling at home, they were more likely to report higher levels of trait anger, which subsequently increased their risk of engaging in bullying perpetration. CONCLUSION: These results suggest that experiencing physical sibling abuse has long-term detrimental consequences, including elicitation of trait anger, subsequently predicting bullying perpetration.

Perceived but not objective measures of neighborhood safety and food environments are associated with longitudinal changes in processing speed among urban older adults.

BACKGROUND: Although a growing body of literature documents the importance of neighborhood effects on late-life cognition, little is known about the relative strength of objective and subjective neighborhood measures on late-life cognitive changes. This study examined effects of objective and subjective neighborhood measures in three neighborhood domains (neighborhood safety, physical disorder, food environments) on longitudinal changes in processing speed, an early marker of cognitive aging and impairment. METHODS: The analysis sample included 306 community-dwelling older adults enrolled in the Einstein Aging Study (mean age = 77, age range = 70 to 91; female = 67.7%; non-Hispanic White: 45.1%, non-Hispanic Black: 40.9%). Objective and subjective measures of neighborhood included three neighborhood domains (i.e., neighborhood safety, physical disorder, food environments). Processing speed was assessed using a brief Symbol Match task (unit: second), administered on a smartphone device six times a day for 16 days and repeated annually for up to five years. Years from baseline was used as the within-person time index. RESULTS: Results from mixed effects models showed that subjective neighborhood safety (ß= -0.028) and subjective availability of healthy foods (ß= -0.028) were significantly associated with less cognitive slowing over time. When objective and subjective neighborhood measures were simultaneously examined, subjective availability of healthy foods remained significant (ß= -0.028) after controlling for objective availability of healthy foods. Associations of objective neighborhood crime and physical disorder with processing speed seemed to be confounded by individual-level race and socioeconomic status; after controlling for these confounders, none of objective neighborhood measures showed significant associations with processing speed. CONCLUSION: Subjective neighborhood safety and subjective availability of healthy foods, rather than objective measures, were associated with less cognitive slowing over time over a five-year period. Perception of one's neighborhood may be a more proximal predictor of cognitive health outcomes as it may reflect one's experiences in the environment. It would be important to improve our understanding of both objective and subjective neighborhood factors to improve cognitive health among older adults.

Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning.

Temporally consistent and accurate registration and parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains. However, most existing methods are developed for registration or parcellation of a single cortical surface. When applying to longitudinal studies, these methods independently register/parcellate each surface from longitudinal scans, thus often generating longitudinally inconsistent and inaccurate results, especially in small or ambiguous cortical regions. Essentially, longitudinal cortical surface registration and parcellation are highly correlated tasks with inherently shared constraints on both spatial and temporal feature representations, which are unfortunately ignored in existing methods. To this end, we unprecedentedly propose a novel semi-supervised learning framework to exploit these inherent relationships from limited labeled data and extensive unlabeled data for more robust and consistent registration and parcellation of longitudinal cortical surfaces. Our method utilizes the spherical topology characteristic of cortical surfaces. It employs a spherical network to function as an encoder, which extracts high-level cortical features. Subsequently, we build two specialized decoders dedicated to the tasks of registration and parcellation, respectively. To extract more meaningful spatial features, we design a novel parcellation map similarity loss to utilize the relationship between registration and parcellation tasks, i.e., the parcellation map warped by the deformation field in registration should match the atlas parcellation map, thereby providing extra supervision for the registration task and augmented data for parcellation task by warping the atlas parcellation map to unlabeled surfaces. To enable temporally more consistent feature representation, we additionally enforce longitudinal consistency among longitudinal surfaces after registering them together using their concatenated features. Experiments on two longitudinal datasets of infants and adults have shown that our method achieves significant improvements on both registration/parcellation accuracy and longitudinal consistency compared to existing methods, especially in small and challenging cortical regions.

Elucidating the Link Between Anxiety/Depression and Alzheimer's Dementia in the Australian Imaging Biomarkers and Lifestyle (AIBL) Study.

BACKGROUND: The associations between mood disorders (anxiety and depression) and mild cognitive impairment (MCI) or Alzheimer's dementia (AD) remain unclear. METHODS: Data from the Australian Imaging, Biomarker & Lifestyle (AIBL) study were subjected to logistic regression to determine both cross-sectional and longitudinal associations between anxiety/depression and MCI/AD. Effect modification by selected covariates was analysed using the likelihood ratio test. RESULTS: Cross-sectional analysis was performed to explore the association between anxiety/depression and MCI/AD among 2,209 participants with a mean [SD] age of 72.3 [7.4] years, of whom 55.4% were female. After adjusting for confounding variables, we found a significant increase in the odds of AD among participants with two mood disorders (anxiety: OR 1.65 [95% CI 1.04-2.60]; depression: OR 1.73 [1.12-2.69]). Longitudinal analysis was conducted to explore the target associations among 1,379 participants with a mean age of 71.2 [6.6] years, of whom 56.3% were female. During a mean follow-up of 5.0 [4.2] years, 163 participants who developed MCI/AD (refer to as PRO) were identified. Only anxiety was associated with higher odds of PRO after adjusting for covariates (OR 1.56 [1.03-2.39]). However, after additional adjustment for depression, the association became insignificant. Additionally, age, sex, and marital status were identified as effect modifiers for the target associations. CONCLUSION: Our study provides supportive evidence that anxiety and depression impact on the evolution of MCI/AD, which provides valuable epidemiological insights that can inform clinical practice, guiding clinicians in offering targeted dementia prevention and surveillance programs to the at-risk populations.

Definition and analysis of gray matter atrophy subtypes in mild cognitive impairment based on data-driven methods.

Introduction: Mild cognitive impairment (MCI) is an important stage in Alzheimer's disease (AD) research, focusing on early pathogenic factors and mechanisms. Examining MCI patient subtypes and identifying their cognitive and neuropathological patterns as the disease progresses can enhance our understanding of the heterogeneous disease progression in the early stages of AD. However, few studies have thoroughly analyzed the subtypes of MCI, such as the cortical atrophy, and disease development characteristics of each subtype. Methods: In this study, 396 individuals with MCI, 228 cognitive normal (CN) participants, and 192 AD patients were selected from ADNI database, and a semi-supervised mixture expert algorithm (MOE) with multiple classification boundaries was constructed to define AD subtypes. Moreover, the subtypes of MCI were obtained by using the multivariate linear boundary mapping of support vector machine (SVM). Then, the gray matter atrophy regions and severity of each MCI subtype were analyzed and the features of each subtype in demography, pathology, cognition, and disease progression were explored combining the longitudinal data collected for 2 years and analyzed important factors that cause conversion of MCI were analyzed. Results: Three MCI subtypes were defined by MOE algorithm, and the three subtypes exhibited their own features in cortical atrophy. Nearly one-third of patients diagnosed with MCI have almost no significant difference in cerebral cortex from the normal aging population, and their conversion rate to AD are the lowest. The subtype characterized by severe atrophy in temporal lobe and frontal lobe have a faster decline rate in many cognitive manifestations than the subtype featured with diffuse atrophy in the whole cortex. APOE ε4 is an important factor that cause the conversion of MCI to AD. Conclusion: It was proved through the data-driven method that MCI collected by ADNI baseline presented different subtype features. The characteristics and disease development trajectories among subtypes can help to improve the prediction of clinical progress in the future and also provide necessary clues to solve the classification accuracy of MCI.

Longitudinal Analysis of Predictive Factors for Stroke and Bleeding Events in Atrial Fibrillation Patients: Insights From a Tertiary Care Center Cohort.

BACKGROUND: Atrial fibrillation (AF) represents a prevalent cardiac arrhythmia associated with increased risks of stroke and bleeding events, necessitating comprehensive risk assessment and management strategies. OBJECTIVE: This retrospective cohort research aimed to longitudinally analyze risk factors associated with stroke and bleeding incidents in patients diagnosed with AF, focusing on identifying predictive factors and their impact on patient outcomes. METHODS: The research enrolled 480 AF patients from a tertiary care center over an 18-month period (2021-2022). Baseline demographic, clinical, and medication data were collected from electronic health records. Patients were monitored for occurrences of stroke and bleeding events during follow-up. Cox proportional hazards models and Kaplan-Meier estimates were utilized to assess risk factor associations and cumulative event incidences, respectively. RESULTS: A cohort of 480 AF patients, with a mean age of 65.4 years, was observed over 18 months. Stroke patients tended to be older (72.1 years), and bleeders slightly younger (68.8 years). Cox models revealed higher stroke risk in >70-year-olds (hazard ratio (HR): 1.85, 95% confidence interval (95% CI): 1.21-2.78, p < 0.001) and with prior stroke history (HR: 2.13, 95% CI: 1.45-3.12, p < 0.001). Prior stroke linked to bleeding risk (HR: 1.88, 95% CI: 1.26-2.81, p = 0.003). At six months, stroke incidence was 5.2%, bleeding 3.8%; at 18 months, 12.5% experienced strokes, 9.3% bleeding. These findings underscore age and prior stroke as vital predictors of adverse outcomes in AF patients. CONCLUSION: This research reaffirms age and prior stroke as pivotal risk factors for adverse outcomes in AF patients. The findings emphasize the necessity for tailored risk stratification and interventions to mitigate stroke and bleeding risks, thereby enhancing patient care and prognosis in AF management.

Are We Adding Pain-Free Years to Life? A Test of Compression Versus Expansion of Morbidity.

BACKGROUND: There has been debate regarding whether increases in longevity result in longer and healthier lives or more disease and suffering. To address the issue, this paper uses health expectancy methods and tests an expansion versus compression of morbidity with respect to pain. METHODS: Data are from 1993 to 2018 Health and Retirement Study. Pain is categorized as no pain, non-limiting and limiting pain. Multistate life tables examine 77,996 wave-to-wave transitions across pain states or death using the Stochastic Population Analysis for Complex Events program. Results are presented as expected absolute and relative years of life for 70-, 80- and 90-year-old males and females. Confidence intervals assess significance of differences over time. Population- and status-based results are presented. RESULTS: For those 70 and 80 years old, relative and absolute life with non-limiting and limiting pain increased substantially for males and females, and despite variability on a wave-to-wave basis, results generally confirm an expanding pain morbidity trend. Results do not vary by baseline status, indicating those already in pain are just as likely to experience expansion of morbidity as those pain-free at baseline. Results are different for 90-year-olds who have not experienced expanding pain morbidity and do not show an increase in life expectancy. CONCLUSIONS: Findings are consistent with extant literature indicating increasing pain prevalence among older Americans and portend a need for attention on pain-coping resources, therapies, and prevention strategies.

Longitudinal changes in sarcopenia was associated with survival among cirrhotic patients.

Background: Sarcopenia is common in patients with liver cirrhosis and is an independent predictor of multiple clinical outcomes. Most studies to date have used a static assessment of sarcopenia. However, there is very limited data evaluating the temporal course of muscle area in cirrhosis. To bridge this gap in clinical studies, we performed a longitudinal analysis to evaluate the impact of changes in sarcopenia for cirrhotic patients. Methods: Adult patients with clinically diagnosed liver cirrhosis who underwent at least 2 abdominal computed tomography (CT) scans in the hospital were enrolled. The interval between the two abdominal scans was 6 ± 1 months. Patients were categorized into persistent non-sarcopenia, new-onset sarcopenia, sarcopenia to non-sarcopenia, and persistent sarcopenia based on changes in sarcopenia. Kaplan-Meier method and Log-rank tests were used to separately compare unadjusted survival curves by different statuses of sarcopenia. Cox regression analysis was performed to assess the associations between different states of sarcopenia and overall mortality. The association between persistent non-sarcopenia and new-onset sarcopenia was analyzed by multivariate logistic regression analysis. Results: A total of 307 patients were included for analysis. At the second assessment, 10.10% (31/307) patients were new-onset sarcopenia, 27.69% (85/307) with persistent sarcopenia status, while 13.03% (40/307) patients with sarcopenia developed non-sarcopenia and 49.19% (151/307) with persistent non-sarcopenia status. The overall survival rate was significantly lower in the persistent sarcopenia and new-onset sarcopenia than in the non-sarcopenia group and sarcopenia to non-sarcopenia group (p < 0.001). Persistent sarcopenia (HR 5.799, 95%CI 1.563-21.521, p = 0.009) and new onset sarcopenia (HR 5.205, 95%CI 1.482-18.282, p = 0.010) were identified as poor prognostic factors for cirrhotic patients. The etiology of cirrhosis and the initial skeletal muscle mass were independent risk factors for new-onset sarcopenia. Conclusion: Sarcopenia is a dynamically changing process in patients with cirrhosis. Persistent and new-onset sarcopenia were independently and robustly associated with overall survival.

Loneliness and posttraumatic stress in U.S. adolescents: A longitudinal study.

BACKGROUND: Loneliness and posttraumatic stress (PTS) are common in adolescence. However, there has been little longitudinal research on their association. To address this deficit, this study examined the longitudinal association between these phenomena in a sample of U.S. school students while also exploring if gender was important in this context. METHODS: Data were analysed from 2807 adolescents (52.1 % female; age at baseline 11-16 years (M = 12.79)) who were followed over a one-year period. Information was obtained on loneliness in year 1 using a single-item question, while PTS was assessed with the self-report Child Post-Traumatic Stress - Reaction Index (CPTS-RI). A full path analysis was performed to assess the across time associations. RESULTS: Almost one-third of the students reported some degree of loneliness while most students had 'mild' PTS. In the path analysis, when controlling for baseline PTS and other covariates, loneliness in year 1 was significantly associated with PTS in year 2 (ß = 0.06, 95%CI: 0.02, 0.09). Similarly, PTS in year 1 was significantly associated with loneliness in year 2 (ß = 0.19, 95%CI: 0.15, 0.23). An interaction analysis further showed that loneliness was higher in girls with PTS than in their male counterparts. LIMITATIONS: The use of a single-item measure to assess loneliness that used the word 'lonely' may have resulted in underreporting. CONCLUSION: Loneliness and PTS are bidirectionally associated in adolescence. Efforts to reduce loneliness in adolescence may help in combatting PTS, while clinicians should intervene to address loneliness if detected in adolescents with PTS.

Bayesian Learning of Personalized Longitudinal Biomarker Trajectory.

This work concerns the effective personalized prediction of longitudinal biomarker trajectory, motivated by a study of cancer targeted therapy for patients with chronic myeloid leukemia (CML). Continuous monitoring with a confirmed biomarker of residual disease is a key component of CML management for early prediction of disease relapse. However, the longitudinal biomarker measurements have highly heterogeneous trajectories between subjects (patients) with various shapes and patterns. It is believed that the trajectory is clinically related to the development of treatment resistance, but there was limited knowledge about the underlying mechanism. To address the challenge, we propose a novel Bayesian approach to modeling the distribution of subject-specific longitudinal trajectories. It exploits flexible Bayesian learning to accommodate complex changing patterns over time and non-linear covariate effects, and allows for real-time prediction of both in-sample and out-of-sample subjects. The generated information can help make clinical decisions, and consequently enhance the personalized treatment management of precision medicine.

Paradigm shift in Parkinson's disease: using continuous telemonitoring to improve symptoms control. Results from a 2-years journey.

Introduction: Conventional care in Parkinson's disease (PD) faces limitations due to the significant time and location commitments needed for regular assessments, lacking quantitative measurements. Telemonitoring offers clinicians an opportunity to evaluate patient symptomatology throughout the day during activities of daily living. Methods: The progression of PD symptoms over a two-year period was investigated in patients undergoing traditional evaluation, supplemented by insights from ambulatory measurements. Physicians integrated a telemonitoring device, the PDMonitor®, into daily practice, using it for informed medication adjustments. Results: Statistical analyses examining intra-subject changes for 17 subjects revealed a significant relative decrease of -43.9% in the device-reported percentage of time spent in "OFF" state (from 36.2 to 20.3%). Following the 24-month period, the majority of the subjects improved or exhibited stable symptom manifestation. In addition to positively impacting motor symptom control, telemonitoring was found to enhance patient satisfaction about their condition, medication effectiveness, and communication with physicians. Discussion: Considering that motor function is significantly worsened over time in patients with PD, these findings suggest a positive impact of objective telemonitoring on symptoms control. Patient satisfaction regarding disease management through telemonitoring can potentially improve adherence to treatment plans. In conclusion, remote continuous monitoring paves the way for a paradigm shift in PD, focusing on actively managing and potentially improve symptoms control.

Age of type 2 diabetes onset as a risk factor for dementia: A 13-year retrospective cohort study.

AIMS: To examine whether age at type 2 diabetes onset is an independent predictor of dementia risk. METHODS: Retrospective cohort drawn from healthcare administrative records of all inhabitants within Romagna's catchment area, Italy, with an estimated onset of type 2 diabetes in 2008-2017 and aged ≥ 55, with follow-up until 2020. Time to dementia or censoring was estimated with the Kaplan-Meier method, using diabetes onset as the time origin. Age groups were compared with the log-rank test. Multivariable competing-risks analysis was used to assess predictors of dementia. RESULTS: In patients aged ≥ 75 years, dementia-free survival (DFS) declined to below 90 % within five years and linearly decreased to 68.8 % until the end of follow-up. In contrast, DFS for those aged 55-64 years showed a marginal decrease, reaching 97.4 % after 13 years. Competing-risks regression showed that individuals aged ≥ 75 and 65-74 had a significantly higher risk of dementia compared to those aged 55-64 years. Having more comorbidities at diabetes onset and initial treatment with ≥ 2 antidiabetics were clinical predictors. CONCLUSIONS: Later age at onset of diabetes is strongly associated with dementia. A better understanding of the diabetes-dementia relationship is needed to inform strategies for promoting specific healthcare pathways.

Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.

We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to the number of sequential MRI scans. We included four sequential MRI scans for 194 patients with BM and 369 target lesions for the Developmental dataset. The data were randomly split (8:2 ratio) for training and testing. For external validation, 172 MRI scans from 43 patients with BM and 62 target lesions were additionally enrolled. The maximum axial diameter (Dmax), radiomics, and deep learning (DL) models were generated for comparison. We evaluated the simple convolutional neural network (CNN) model and a gated recurrent unit (Conv-GRU)-based CNN model in the DL arm. The Conv-GRU model performed superior to the simple CNN models. For both datasets, the area under the curve (AUC) was significantly higher for the two-dimensional (2D) Conv-GRU model than for the 3D Conv-GRU, Dmax, and radiomics models. The accuracy of the 2D Conv-GRU model increased with the number of follow-up studies. In conclusion, using longitudinal MRI data, the 2D Conv-GRU model outperformed all other models in predicting the treatment response after SRS of BM.

Explore athletes' sports attitude and its influence on mental health.

This study is devoted to exploring how athletes' sports attitude affects their mental health, and explores this complex relationship through descriptive statistics, longitudinal analysis, correlation analysis and regression analysis. The research sample includes athlete data at multiple time points, covering mental health indicators such as positive attitude, negative attitude, anxiety, depression and self-esteem. Descriptive statistical results reveal the overall trend of athletes in positive attitude, anxiety, depression and self-esteem. On average, athletes show a positive attitude towards sports, but there are some variability in mental health indicators. The results of longitudinal analysis show that with the progress of the season, the positive attitude shows an upward trend, while the level of anxiety and depression shows a downward trend in some cases, which provides a detailed observation for the long-term evolution of athletes' psychological state. Correlation analysis reveals the positive correlation between positive attitude and self-esteem, positive correlation between negative attitude and anxiety, and negative correlation between teamwork attitude and depression. Regression analysis further verified the influence of positive attitude and negative attitude on anxiety. The results emphasize that the improvement of positive attitude may help to slow down the increase of anxiety level, while the increase of negative attitude may be related to the increase of anxiety. Generally speaking, the findings of this study highlight the complex relationship between athletes' mental health and their attitude towards sports. This study provides profound insights for formulating targeted psychological support strategies and emphasizes the importance of comprehensively considering multi-dimensional factors in athletes' mental health management.

TSH Trajectories During Levothyroxine Treatment in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) Cohort.

CONTEXT: Thyroid-stimulating hormone (TSH) trajectory classification represents a novel approach to defining the adequacy of levothyroxine (LT4) treatment for hypothyroidism over time. OBJECTIVE: This is a proof of principle study that uses longitudinal clinical data, including thyroid hormone levels from a large prospective study to define classes of TSH trajectories and examine changes in cardiovascular (CV) health markers over the study period. METHODS: Growth mixture modeling (GMM), including latent class growth analysis (LCGA), was used to classify LT4-treated individuals participating in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) based on serial TSH levels. Repeated measure analyses were then utilized to assess within-class changes in blood pressure, lipid levels, hemoglobin A1c, and CV-related medication utilization. RESULTS: From the 621 LT4-treated study participants, the best-fit GMM approach identified 4 TSH trajectory classes, as defined by their relationship to the normal TSH range: (1) high-high normal TSH, (2) normal TSH, (3) normal to low TSH, and (4) low to normal TSH. Notably, the average baseline LT4 dose was lowest in the high-high normal TSH group (77.7 µg, P < .001). There were no significant differences in CV health markers between the classes at baseline. At least 1 significant difference in CV markers occurred in all classes, highlighted by the low to normal class, in which total and high-density lipoprotein cholesterol, triglycerides, and A1c all increased significantly (P = .049, P < .001, P < .001, and P = .001, respectively). Utilization of antihypertensive, antihyperlipidemic, and antidiabetes medications increased in all classes. CONCLUSION: GMM/LCGA represents a viable approach to define and examine LT4 treatment by TSH trajectory. More comprehensive datasets should allow for more complex trajectory modeling and analysis of clinical outcome differences between trajectory classes.

Effects of Future Subjective Expectations on Cognitive Decline and Dementia among Middle-Aged and Older Adults.

This study investigated the impact of subjective expectations of the future (e.g., income, life expectancy, and national policies) on the onset of dementia and mild cognitive impairment by sex and age in middle-aged and older adults. The Korean Longitudinal Study of Aging (KLoSA) data from 2008 to 2020, comprising 4116 people above 45 years, were used. A time-series analysis and multiple panel logistic regression were conducted to highlight subjective expectation trends and their effect on dementia and mild cognitive impairment, respectively. Low subjective expectations of the future negatively affected cognitive impairment (total: odds ratio [OR] = 1.02, 95% confidence interval [CI] = 1.01-1.03) and dementia (total: OR = 1.05, 95% CI = 1.03-1.06), and those of national policies were the biggest risk factors for cognitive impairment (total: OR = 1.17, 95% CI = 1.12-1.22) and dementia (total: OR = 1.10, 95% CI = 1.07-1.13). Individuals about to retire and with low expectations of workability were more likely to develop cognitive impairment (total: OR = 1.03, 95% CI = 1.02-1.06). Subjective expectations of economic downturn also caused cognitive impairment, especially in women (OR = 1.04, 95% CI = 1.01-1.07) and early stage older adults (OR = 1.06, 95% CI = 1.02-1.10). Policymakers must consider the impact of changes in national policies and living environments on cognitive impairment and dementia in older adults.

Association of Life's Essential 8 with all-cause mortality and risk of cancer: a prospective cohort study.

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