Trends in inequalities in the prevalence of dementia in the United States.

This paper presents estimates of the prevalence of dementia in the United States from 2000 to 2016 by age, sex, race and ethnicity, education, and a measure of lifetime earnings, using data on 21,442 individuals aged 65 y and older and 97,629 person-year observations from a nationally representative survey, the Health and Retirement Study (HRS). The survey includes a range of cognitive tests, and a subsample underwent clinical assessment for dementia. We developed a longitudinal, latent-variable model of cognitive status, which we estimated using the Markov Chain Monte Carlo method. This model provides more accurate estimates of dementia prevalence in population subgroups than do previously used methods on the HRS. The age-adjusted prevalence of dementia decreased from 12.2% in 2000 (95% CI, 11.7 to 12.7%) to 8.5% in 2016 (7.9 to 9.1%) in the 65+ population, a statistically significant decline of 3.7 percentage points or 30.1%. Females are more likely to live with dementia, but the sex difference has narrowed. In the male subsample, we found a reduction in inequalities across education, earnings, and racial and ethnic groups; among females, those inequalities also declined, but less strongly. We observed a substantial increase in the level of education between 2000 and 2016 in the sample. This compositional change can explain, in a statistical sense, about 40% of the reduction in dementia prevalence among men and 20% among women, whereas compositional changes in the older population by age, race and ethnicity, and cardiovascular risk factors mattered less.

Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes.

AIMS/HYPOTHESIS: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist's novel diabetes subgroups and previously analysed by Slieker et al. METHODS: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed. RESULTS: Subgroups' risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%. CONCLUSIONS/INTERPRETATION: Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators.

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.

Tracking the immune response profiles elicited by the BNT162b2 vaccine in COVID-19 unexperienced and experienced individuals.

Multiple vaccines have been approved to control COVID-19 pandemic, with Pfizer/BioNTech (BNT162b2) being widely used. We conducted a longitudinal analysis of the immune response elicited after three doses of the BNT162b2 vaccine in individuals who have previously experienced SARS-CoV-2 infection and in unexperienced ones. We conducted immunological analyses and single-cell transcriptomics of circulating T and B lymphocytes, combined to CITE-seq or LIBRA-seq, and VDJ-seq. We found that antibody levels against SARS-CoV-2 Spike, NTD and RBD from wild-type, delta and omicron VoCs show comparable dynamics in both vaccination groups, with a peak after the second dose, a decline after six months and a restoration after the booster dose. The antibody neutralization activity was maintained, with lower titers against the omicron variant. Spike-specific memory B cell response was sustained over the vaccination schedule. Clonal analysis revealed that Spike-specific B cells were polyclonal, with a partial clone conservation from natural infection to vaccination. Spike-specific T cell responses were oriented towards effector and effector memory phenotypes, with similar trends in unexperienced and experienced individuals. The CD8 T cell compartment showed a higher clonal expansion and persistence than CD4 T cells. The first two vaccinations doses tended to induce new clones rather than promoting expansion of pre-existing clones. However, we identified a fraction of Spike-specific CD8 T cell clones persisting from natural infection that were boosted by vaccination and clones specifically induced by vaccination. Collectively, our observations revealed a moderate effect of the second dose in enhancing the immune responses elicited after the first vaccination. Differently, we found that a third dose was necessary to restore comparable levels of neutralizing antibodies and Spike-specific T and B cell responses in individuals who experienced a natural SARS-CoV-2 infection.

FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data.

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

TIGER: technical variation elimination for metabolomics data using ensemble learning architecture.

Large metabolomics datasets inevitably contain unwanted technical variations which can obscure meaningful biological signals and affect how this information is applied to personalized healthcare. Many methods have been developed to handle unwanted variations. However, the underlying assumptions of many existing methods only hold for a few specific scenarios. Some tools remove technical variations with models trained on quality control (QC) samples which may not generalize well on subject samples. Additionally, almost none of the existing methods supports datasets with multiple types of QC samples, which greatly limits their performance and flexibility. To address these issues, a non-parametric method TIGER (Technical variation elImination with ensemble learninG architEctuRe) is developed in this study and released as an R package (https://CRAN.R-project.org/package=TIGERr). TIGER integrates the random forest algorithm into an adaptable ensemble learning architecture. Evaluation results show that TIGER outperforms four popular methods with respect to robustness and reliability on three human cohort datasets constructed with targeted or untargeted metabolomics data. Additionally, a case study aiming to identify age-associated metabolites is performed to illustrate how TIGER can be used for cross-kit adjustment in a longitudinal analysis with experimental data of three time-points generated by different analytical kits. A dynamic website is developed to help evaluate the performance of TIGER and examine the patterns revealed in our longitudinal analysis (https://han-siyu.github.io/TIGER_web/). Overall, TIGER is expected to be a powerful tool for metabolomics data analysis.

Breastfeeding Duration and Cardiometabolic Health during Adolescence: A Longitudinal Analysis.

OBJECTIVE: To investigate the longitudinal association between breastfeeding duration and cardiometabolic health, using repeated measures study design among children and adolescents. STUDY DESIGN: This study included 634 offsprings aged 10 to 21 years (52% female) from the Early Life Exposure in Mexico to Environmental Toxicants birth cohort followed up to four time points during adolescence. Breastfeeding duration was prospectively quantified using questionnaires during early childhood. Cardiometabolic risk factors, body composition, and weight-related biomarkers were assessed as outcomes during adolescent follow-up visits. Sex-stratified linear mixed-effects models were used to model the association between quartiles of breastfeeding duration and outcomes, adjusting for age and additional covariates. RESULTS: Median breastfeeding duration was 7 months (minimum = 0, maximum = 36). Boys in the second quartile (median breastfeeding = 5 months) had lower total fat mass % (ß (SE) -3.2 (1.5) P = .037), and higher lean mass % (3.1 (1.6) P = .049) and skeletal muscle mass % (1.8 (0.8) P = .031) compared with the reference group (median breastfeeding = 2 months). A positive linear trend between breastfeeding duration and trunk lean mass % (0.1 (0.04) P = .035) was found among girls. No association was found with other cardiometabolic indicators. CONCLUSION: Despite sex-specific associations of breastfeeding duration with body composition, there was a lack of substantial evidence for the protective effects of breastfeeding against impaired cardiometabolic health during adolescence among Mexican youth. Further longitudinal studies with a robust assessment of breastfeeding are recommended.

Digital Gait Measures Capture 1-Year Progression in Early-Stage Spinocerebellar Ataxia Type 2.

BACKGROUND: With disease-modifying drugs in reach for cerebellar ataxias, fine-grained digital health measures are highly warranted to complement clinical and patient-reported outcome measures in upcoming treatment trials and treatment monitoring. These measures need to demonstrate sensitivity to capture change, in particular in the early stages of the disease. OBJECTIVE: Our aim is to unravel gait measures sensitive to longitudinal change in the-particularly trial-relevant-early stage of spinocerebellar ataxia type 2 (SCA2). METHODS: We performed a multicenter longitudinal study with combined cross-sectional and 1-year interval longitudinal analysis in early-stage SCA2 participants (n = 23, including nine pre-ataxic expansion carriers; median, ATXN2 CAG repeat expansion 38 ± 2; median, Scale for the Assessment and Rating of Ataxia [SARA] score 4.8 ± 4.3). Gait was assessed using three wearable motion sensors during a 2-minute walk, with analyses focused on gait measures of spatio-temporal variability that have shown sensitivity to ataxia severity (eg, lateral step deviation). RESULTS: We found significant changes for gait measures between baseline and 1-year follow-up with large effect sizes (lateral step deviation P = 0.0001, effect size rprb = 0.78), whereas the SARA score showed no change (P = 0.67). Sample size estimation indicates a required cohort size of n = 43 to detect a 50% reduction in natural progression. Test-retest reliability and minimal detectable change analysis confirm the accuracy of detecting 50% of the identified 1-year change. CONCLUSIONS: Gait measures assessed by wearable sensors can capture natural progression in early-stage SCA2 within just 1 year-in contrast to a clinical ataxia outcome. Lateral step deviation represents a promising outcome measure for upcoming multicenter interventional trials, particularly in the early stages of cerebellar ataxia. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Changes in Medication Adherence Across the Posttransplant Period in Pediatric Organ Transplant Recipients.

INTRODUCTION: Limited research has explored immunosuppressant medication adherence over time in pediatric transplant patients, who often struggle with posttransplant regimen adherence, resulting in poor outcomes. METHODS: This study investigated the longitudinal growth in immunosuppressive medication levels following transplantation. Medication level variability index (MLVI) scores from tacrolimus blood levels of pediatric organ transplant recipients at a major medical center were analyzed. Linear mixed effect models (LMEM) assessed individual MLVI change trajectories and predictors of growth, exploring both linear and curvilinear growth patterns posttransplant. RESULTS: A sample of 181 patients with at least 4 years of MLVI data were analyzed. Growth curve modeling identified the cubic model as the best fit for the quarterly MLVI values, which significantly decreased within the first 2 years posttransplant before stabilizing. Gender significantly predicted MLVI change, with females showing greater MLVI decreases, while age at transplant did not significantly predict changes. Significant variation in MLVI among individual patients was found in all models. CONCLUSIONS: The study demonstrated a significant decrease in MLVI values over time, indicating improved medication adherence in pediatric transplant patients, with females exhibiting more adherent growth patterns than males. Future research should aim to identify pediatric patients at high risk of nonadherence.

Longitudinal analysis of the Alternative Healthy Eating Index-2010 and incident non-communicable diseases over 15 years in the 1973-1978 cohort of the Australian Longitudinal Study on Women's Health.

In studies that contain repeated measures of variables, longitudinal analysis accounting for time-varying covariates is one of the options. We aimed to explore longitudinal association between diet quality (DQ) and non-communicable diseases (NCDs). Participants from the 1973-1978 cohort of the Australian Longitudinal Study on Women's Health (ALSWH) were included, if they; responded to survey 3 (S3, 2003, aged 25-30 years) and at least one survey between survey 4 (S4, 2006) and survey 8 (S8, 2018), were free of NCDs at or before S3, and provided dietary data at S3 or S5. Outcomes were coronary heart disease (CHD), hypertension (HT), asthma, cancer (except skin cancer), diabetes mellitus (DM), depression and/or anxiety, and multimorbidity (MM). Longitudinal modelling using generalised estimation equation (GEE) approach with time-invariant (S4), time-varying (S4-S8) and lagged (S3-S7) covariates were performed. The mean (± standard deviation) of Alternative Healthy Eating Index-2010 (AHEI-2010) of participants (n = 8022) was 51·6 ± 11·0 (range: 19-91). Compared to women with the lowest DQ (AHEI-2010 quintile 1), those in quintile 5 had reduced odds of NCDs in time-invariant model (asthma: OR (95 % CI): 0·77 (0·62-0·96), time-varying model (HT: 0·71 (0·50-0·99); asthma: 0·62 (0·51-0·76); and MM: 0·75 (0·58-0·97) and lagged model (HT: 0·67 (0·49-0·91); and asthma: 0·70 (0·57-0·85). Temporal associations between diet and some NCDs were more prominent in lagged GEE analyses. Evidence of diet as NCD prevention in women aged 25-45 years is evolving, and more studies that consider different longitudinal analyses are needed.

Application of longitudinal multilevel zero inflated Poisson regression in modeling of infectious diseases among infants in Ethiopia.

BACKGROUND: In sub-Saharan African countries, preventable and manageable diseases such as diarrhea and acute respiratory infections still claim the lives of children. Hence, this study aims to estimate the rate of change in the log expected number of days a child suffers from Diarrhea (NOD) and flu/common cold (NOF) among children aged 6 to 11 months at the baseline of the study. METHODOLOGY: This study used secondary data which exhibit a longitudinal and multilevel structure. Based on the results of exploratory analysis, a multilevel zero-inflated Poisson regression model with a rate of change in the log expected NOD and NOF described by a quadratic trend was proposed to efficiently analyze both outcomes accounting for correlation between observations and individuals through random effects. Furthermore, residual plots were used to assess the goodness of fit of the model. RESULTS: Considering subject and cluster-specific random effects, the results revealed a quadratic trend in the rate of change of the log expected NOD. Initially, low dose iron Micronutrient Powder (MNP) users exhibited a higher rate of change compared to non-users, but this trend reversed over time. Similarly, the log expected NOF decreased for children who used MNP and exclusively breastfed for six months, in comparison to their counterparts. In addition, the odds of not having flu decreased with each two-week increment for MNP users, as compared to non-MNP users. Furthermore, an increase in NOD resulted in an increase in the log expected NOF. Region and exclusive breastfeeding also have a significant relationships with both NOD and NOF. CONCLUSION: The findings of this study underscore the importance of commencing analysis of data generated from a study with exploratory analysis. The study highlights the critical role of promoting EBF for the first six months and supporting children with additional food after six months to reduce the burden of infectious diseases.

Longitudinal Influence of Prescribed Antidepressants on Fecal and Oral Microbiomes Among Veterans With Major Depressive Disorder.

OBJECTIVE: The purpose of this study was to evaluate the influence of a new course of antidepressant monotherapy on gut and oral microbiomes and the relationship to depressive symptoms. METHODS: Longitudinal microbiome samples obtained from 10 U.S. veterans were analyzed. Baseline samples were taken before a new course of antidepressant monotherapy (either switching from a previous treatment or starting a new treatment). Targeted genomic sequencing of the microbiome samples was used to analyze changes in taxonomy and diversity across participants, medications, and medication class. Associations between these changes and Patient Health Questionnaire-9 (PHQ-9) scores were analyzed. RESULTS: Taxonomic variability was observed across participants, with the individual being the main microbial community driver. In terms of the fecal microbiome, antidepressants were associated with shifts toward Bacteroides being less abundant and Blautia, Pseudomonas, or Faecalibacterium being more abundant. Likewise, the composition of the oral microbiome was variable, with individual participants being the primary drivers of community composition. In the oral samples, the relative abundance of Haemophilus decreased after antidepressants were started. Increases in Blautia and decreases in Bacteroides were associated with lower PHQ-9 scores. CONCLUSIONS: Antidepressants were found to influence fecal and oral microbiomes such that a new course of antidepressant monotherapy was associated with taxonomic alterations toward healthier states in both fecal and oral microbiomes, which were associated with decreases in depressive symptoms. Additional longitudinal research is required to increase understanding of microbiomes and symptom-based changes, with a particular focus on potential differences between medication classes and underlying mechanisms.

A Longitudinal Multilevel Analysis of the Effects of Contraceptive Failures on Unintended Pregnancies among Women in Urban Nigeria.

Unintended pregnancy is a global public health concern. However, the effect of contraceptive failure on unintended pregnancy remains unclear in Nigeria. We undertook a longitudinal analysis to examine the effect of contraceptive failure on unintended pregnancy among urban women in Nigeria. We used panel data from the Nigerian Urban Reproductive Health Initiative. The Measurement, Learning and Evaluation program conducted the surveys among a cohort of women aged 15-49 who were first interviewed at baseline in 2010/2011 and followed up at endline in 2014/2015. Analytic sample was 4140 women aged 15-49 who ever used contraceptives. We fitted three-level multilevel binary logistic regression models estimated with GLLAMM. The study established evidence that there is a significant effect of contraceptive failure on unintended pregnancy among urban women in Nigeria. The positive effect of between-person contraceptive failure indicates that respondents who experienced more contraceptive failure than the average in the sample had 5.26 times higher odds of unintended pregnancy (OR = 5.26; p-value < 0.001). Results also established a significant effect of within-person contraceptive failures among the respondents. Findings suggest there is evidence of a significant longitudinal effect of contraceptive failure on unintended pregnancy in urban Nigeria. Efforts to reduce unintended pregnancy must include interventions to address the problem of contraceptive failure among urban women in Nigeria.

Persistent increased severity of cannabis use disorder symptoms in adolescents compared to adults: a one-year longitudinal study.

Adolescence is a developmental period characterised by increased vulnerability to cannabis use disorder (CUD). However, previous investigations of this vulnerability have relied on cross-sectional comparisons and lack a detailed assessment of cannabis quantity, a potentially important confounding factor. Here, we aimed to investigate the one-year course of CUD in adolescents compared to adults who currently use cannabis, adjusting for a comprehensive measure of cannabis quantity. Data are from a one-year observational longitudinal study (CannTeen) of adolescents and adults who currently used cannabis regularly with five waves of assessment at 3-monthly intervals, based in London, UK. Participants were n = 70 adults (26-29, 45.7% female), who did not regularly use cannabis when they were under age 18, and n = 76 adolescents (16-17, 50.0% female). The exposure was adolescent (compared to adult) frequent cannabis use. The primary outcome was CUD symptoms measured using the cannabis use disorder identification test revised (CUDIT-R) at five time points. Models were adjusted for cannabis quantity using mean weekly standard THC units (one unit = 5 mg THC). Other covariates included gender, and whether each session occurred before or during the COVID-19 pandemic. In models adjusted for pre-registered covariates, adolescents scored 3.7 points higher on the CUDIT-R compared to the adult group across the 5 assessment waves (3.66 95% CIs 1.99, 5.34). There was also evidence of a linear reduction in symptoms over time in both groups (-0.47, 95%CIs -0.67, -0.27). Adolescents had persistently increased CUD symptoms compared to adults across the 12-month period. This association was robust after adjusting for the quantity of cannabis consumed and other covariates.

Lateralization of major white matter tracts during infancy is time-varying and tract-specific.

Lateralization patterns are a major structural feature of brain white matter and have been investigated as a neural architecture that indicates and supports the specialization of cognitive processing and observed behaviors, e.g. language skills. Many neurodevelopmental disorders have been associated with atypical lateralization, reinforcing the need for careful measurement and study of this structural characteristic. Unfortunately, there is little consensus on the direction and magnitude of lateralization in major white matter tracts during the first months and years of life-the period of most rapid postnatal brain growth and cognitive maturation. In addition, no studies have examined white matter lateralization in a longitudinal pediatric sample-preventing confirmation of if and how white matter lateralization changes over time. Using a densely sampled longitudinal data set from neurotypical infants aged 0-6 months, we aim to (i) chart trajectories of white matter lateralization in 9 major tracts and (ii) link variable findings from cross-sectional studies of white matter lateralization in early infancy. We show that patterns of lateralization are time-varying and tract-specific and that differences in lateralization results during this period may reflect the dynamic nature of lateralization through development, which can be missed in cross-sectional studies.

The longitudinal structure of negative symptoms in treatment resistant schizophrenia.

BACKGROUND AND HYPOTHESIS: The negative symptoms of schizophrenia are strong prognostic factors but remain poorly understood and treated. Five negative symptom domains are frequently clustered into the motivation and pleasure (MAP) and emotional expression (EE) 'dimensions', but whether this structure remains stable and behaves as a single entity or not remains unclear. STUDY DESIGN: We examined a cohort of 153 patients taking clozapine for treatment-resistant schizophrenia in a regional mental health clinic. Patients were assessed longitudinally over a mean period of 45 months using validated scales for positive, negative and mood symptoms. Network analyses were performed to identify symptom 'communities' and their stability over time. The influence of common causes of secondary negative symptoms as well as centrality measures were also examined. STUDY RESULTS: Across patients at baseline, two distinct communities matching the clinical domains of MAP and EE were found. These communities remained highly stable and independent over time. The communities remained stabled when considering psychosis, depression, and sedation severity, and these causes of secondary negative symptoms were clustered into the MAP community. Centrality measures also remained stable over time, with similar centrality measures across symptoms. CONCLUSIONS: Our results suggest that MAP and EE are independent dimensions that remain highly stable over time in chronic schizophrenia patients treated with clozapine. Common causes of secondary negative symptoms mapped onto the MAP dimension. Our results emphasise the need for clinical trials to address either MAP or EE, and that treating causes of secondary negative symptoms may improve MAP.

Bidirectional relationships between childhood adversities and psychosocial outcomes: A cross-lagged panel study from childhood to adolescence.

Childhood adversities have been linked to psychosocial outcomes, but it remains uncertain whether subtypes of adversity exert different effects on outcomes. Research is also needed to explore the dynamic interplay between adversity and psychosocial outcomes from childhood to mid-adolescence. This study aimed to investigate these relationships and their role in shaping adolescent wellbeing. Data were extracted from three timepoints of the UK Household Longitudinal Survey when participants (n = 646) were aged 10-15. Cross-lagged panel models were used to explore the relationship between cumulative adversities, and separately non-household (i.e., bullying victimization and adverse neighborhood) and household (i.e., sibling victimization, quarrelsome relationship with parents, financial struggles, and maternal psychological distress) adversities, and psychosocial outcomes (i.e., internalizing and externalizing problems, delinquency, and life satisfaction). Our results revealed that heightened cumulative adversity predicted psychosocial outcomes from childhood to mid-adolescence. Increased levels of household adversity predicted psychosocial outcomes throughout early to mid-adolescence, while non-household adversity only predicted psychosocial outcomes in early adolescence. Furthermore, worse psychosocial outcomes predicted higher levels of adversities during adolescence, highlighting bidirectionality between adversity and psychosocial outcomes. These findings underscore the varying impacts of adversity subtypes and the mutually reinforcing effects of adversities and psychosocial functioning from childhood to mid-adolescence.

The extent and burden of high multimorbidity on older adults in the US: a descriptive analysis of Medicare beneficiaries.

BACKGROUND: The impact of multimorbidity (≥ 2 chronic diseases) on the well-being of older adults is substantial but variable. The burden of multimorbidity varies by the number and kinds of conditions, and timing of onset. The impact varies by age, race, ethnicity, socioeconomic status, and health indicators. Large scale longitudinal surveys linked to medical claims provide unique opportunities to characterize this variability. METHODS: We analyzed Medicare-linked Health and Retirement Study data for respondents 65 and older with 3 or more years of fee-for-service coverage (n = 17,199; 2000-2016). We applied standardized claims algorithms for operationalizing 21 chronic diseases. We compared multimorbidity levels, demographics, and outcomes at baseline and over time and escalation to high multimorbidity levels (≥ 5 conditions). RESULTS: At baseline, 51.2% had no multimorbidity, 36.5% had multimorbidity, and 12.4% had high multimorbidity. Loss of function, cognitive decline, and higher healthcare utilization were up to ten times more prevalent in the high multimorbidity group. Greater rates of high multimorbidity were seen among non-Hispanic Black and Hispanic groups, those with lower wealth, younger birth cohorts, and adults with obesity. Rates of transition to high multimorbidity varied greatly and was highest among Hispanic and respondents with lower education. CONCLUSIONS: The development and progression of multimorbidity in old age is influenced by many factors. Higher levels of multimorbidity are associated with sociodemographic characteristics, suggesting possible mitigation strategies.

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.

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.