Trajectories Of Risk Factors Research Articles

Using Joint Longitudinal and Time-to-Event Models to Improve the Parameterization of Chronic Disease Microsimulation Models: an Application to Cardiovascular Disease.

Chronic disease microsimulation models often simulate disease incidence as a function of risk factors that evolve over time (e.g., blood pressure increasing with age) in order to facilitate decision analyses of different disease screening and prevention strategies. Existing models typically rely on incidence rates estimated with standard survival analysis techniques (e.g., proportional hazards from baseline data) that are not designed to be continually updated each model cycle. We introduce the use of joint longitudinal and time-to-event to parameterize microsimulations to avoid potential issues from using these existing methods. These joint models include random effects regressions to estimate the risk factor trajectories and a survival model to predict disease risk based on those estimated trajectories. In a case study on cardiovascular disease (CVD), we compare the validity of microsimulation models parameterized with this joint model approach to those parameterized with the standard approaches. A CVD microsimulation model was constructed that modeled the trajectory of seven CVD risk factors/predictors as a function of age (smoking, diabetes, systolic blood pressure, antihypertensive medication use, total cholesterol, HDL, and statin use) and predicted yearly CVD incidence as a function of these predictors, plus age, sex, and race. We parameterized the model using data from the Atherosclerosis Risk in Communities study (ARIC). The risk of CVD in the microsimulation was parameterized with three approaches: (1) joint longitudinal and time-to-event model, (2) proportional hazards model estimated using baseline data, and (3) proportional hazards model estimated using time-varying data. We accounted for non-CVD mortality across all the parameterization approaches. We simulated risk factor trajectories and CVD incidence from age 70y to 85y for an external test set comprised of individuals from the Multi-Ethnic Study of Atherosclerosis (MESA). We compared the simulated to observed incidence using both average survival curves and the E50 and E90 calibration metrics (the median and 90th percentile absolute difference between observed and predicted incidence) to measure the validity of each parameterization approach. The average CVD survival curve estimated by the microsimulation model parameterized with the joint model approach matched the observed curve from the test set relatively closely. The other parameterization methods generally performed worse, especially the proportional hazards model estimated using baseline data. Similar results were observed for the calibration metrics, with the joint model performing particularly well on the E90 metric compared to the other models. Using a joint longitudinal and time-to-event model to parameterize a CVD simulation model produced incidence predictions that more accurately reflected observed data than a model parameterized with standard approaches. This parameterization approach could lead to more reliable microsimulation models, especially for models that evaluate policies which depend on tracking dynamic risk factors over time. Beyond this single case study, more work is needed to identify the specific circumstances where the joint model approach will outperform existing methods.

Forecasting cognitive functioning: an artificial intelligence approach with Dynamic Bayesian Networks

Abstract Background Several potentially modifiable risk factors are associated with subjective cognitive decline (SCD). However, developmental patterns of these risk factors have not been used before to forecast later SCD. Practical tools for the prevention of cognitive decline are needed. We examined multifactorial trajectories of risk factors, and their associations with SCD using an artificial intelligence approach to build a score that forecasts later SCD. Methods Five repeated surveys (2000-2022) of the Helsinki Health Study (n = 8960, 79% women, aged 40-60 at Phase 1) were used to build dynamic Bayesian networks (DBN) for estimating the odds of SCD. A score-based approach was implemented for learning DBN using the quotient normalized maximum likelihood criterion. The model was used to predict SCD based on the history of consumption of fruit and vegetables, smoking, alcohol consumption, leisure-time physical activity, body mass index, and insomnia symptoms, adjusting for sociodemographic covariates. Results Of the participants, 31-48% reported decline in memory, learning, and concentration in 2022. Physical activity was the strongest predictor of SCD in a 5-year interval, with an odds ratio of 0.76 (95% Bayesian credible interval 0.59-0.99) for physically active compared to inactive participants. Alcohol consumption showed a U-shaped relationship with SCD. Other risk factors had minor effects. Conclusions A new online risk score tool was developed that enables individuals to inspect their own risk profiles, as well as explore potential targets for interventions and their estimated contributions to later SCD. Dynamic decision heatmap was presented as a communication tool to be used at healthcare consultations. Key messages • Potentially modifiable health-related behaviours such as physical activity can contribute to subjective cognitive decline. • Artificial intelligence can be applied to discover the complex interactions between risk factors and subjective cognitive decline, to support preventive healthcare at individual and population levels.

Trajectories and management of vascular risk following the diagnosis of multiple sclerosis: A population-based matched cohort study between 1987 and 2018 in England

Background: People with multiple sclerosis (PwMS) have an increased cardiovascular and cerebrovascular disease burden, but this could be mitigated by vascular risk factor management. Objectives: We compared the trajectories of vascular risk factors, vascular comorbidities and clinical management in PwMS against the general population post-MS diagnosis while controlling for frailty. Methods: Retrospective longitudinal analysis using English data from the Clinical Practice Research Datalink between 1987 and 2018 comprising PwMS matched with up to six controls without MS by age, sex and general practice. Results: We compared 12,251 PwMS with 72,572 matched controls; 3.8% of PwMS had mild–moderate frailty, 1.2% more than matched controls. Compared to controls, PwMS had an elevated incidence of Type 2 diabetes (HR 1.18, 95% CI (1.04, 1.34)), and starting antihypertensive medications (HR 1.40, 95% CI (1.33, 1.47)). Among those with hypertension at baseline, blood pressure trajectories did not differ between PwMS and controls. PwMS had increased rates of meeting targets for hypertension management (HR 1.25, 95% CI (1.12, 1.41)). Conclusion: The observation that PwMS with hypertension are more likely to meet treatment targets than matched controls is encouraging, but the elevated rates of vascular comorbidities suggest that tighter vascular management may be needed in this population.

A Causal Model for Primary Prevention of Cardiovascular Disease: The Health Economic Model for the Primary Prevention of Cardiovascular Disease

ObjectivesOur objective was to design and develop an open-source model capable of simulating interventions for primary prevention of cardiovascular disease (CVD) that incorporated the cumulative effects of risk factors (eg, cholesterol years or blood-pressure years) to enhance health economic modeling in settings which clinical trials are not possible. MethodsWe reviewed the literature to design the model structure by selecting the most important causal risk factors for CVD—low-density lipoprotein-cholesterol (LDL-C), systolic blood pressure (SBP), smoking, diabetes, and lipoprotein (a) (Lp[a])—and most common CVDs—myocardial infarction and stroke. The epidemiological basis of the model involves the simulation of risk factor trajectories, which are used to modify CVD risk via causal effect estimates derived from Mendelian randomization. LDL-C, SBP, Lp(a), and smoking all have cumulative impacts on CVD risk, which were incorporated into the health economic model. The data for the model were primarily sourced from the UK Biobank study. We calibrated the model using clinical trial data and validated the model against the observed UK Biobank data. Finally, we performed an example health economic analysis to demonstrate the utility of the model. The model is open source. ResultsThe model performed well in all validation tests. It was able to produce interpretable and plausible (consistent with expectations of the existing literature) results from an example health economic analysis. ConclusionsWe have constructed an open-source health economic model capable of incorporating the cumulative effect of LDL-C (ie, cholesterol years), SBP (SBP-years), Lp(a), and smoking on lifetime CVD risk.

Estimating Risk Factor Time Paths Among People with Type 2 Diabetes and QALY Gains from Risk Factor Management.

Most type 2 diabetes simulation models utilise equations mapping out lifetime trajectories of risk factors [e.g. glycated haemoglobin (HbA1c)]. Existing equations, using historic data or assuming constant risk factors, frequently underestimate or overestimate complication rates. Updated risk factor time path equations are needed for simulation models to more accurately predict complication rates. (1) Update United Kingdom Prospective Diabetes Study Outcomes Model (UKPDS-OM2) risk factor time path equations; (2) compare quality-adjusted life-years (QALYs) using original and updated equations; and (3) compare QALY gains for reference case simulations using different risk factor equations. Using pooled contemporary data from two randomised trials EXSCEL and TECOS (n = 28,608), we estimated: dynamic panel models of seven continuous risk factors (high-density lipoprotein cholesterol, low density lipoprotein cholesterol, HbA1c, haemoglobin, heart rate, blood pressure and body mass index); two-step models of estimated glomerular filtration rate; and survival analyses of peripheral arterial disease, atrial fibrillation and albuminuria. UKPDS-OM2-derived lifetime QALYs were extrapolated over 70 years using historical and the new risk factor equations. All new risk factor equation predictions were within 95% confidence intervals of observed values, displaying good agreement between observed and estimated values. Historical risk factor time path equations predicted trial participants would accrue 9.84 QALYs, increasing to 10.98 QALYs using contemporary equations. Incorporating updated risk factor time path equations into diabetes simulation models could give more accurate predictions of long-term health, costs, QALYs and cost-effectiveness estimates, as well as a more precise understanding of the impact of diabetes on patients' health, expenditure and quality of life. ClinicalTrials.gov NCT01144338 and NCT00790205.

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

Aims/hypothesisThis 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.MethodsWe 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.ResultsSubgroups’ 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/interpretationData-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.Graphical

Clusters of longitudinal risk profile trajectories are associated with cardiometabolic diseases: Results from the population-based KORA cohort.

Multiple risk factors contribute jointly to the development and progression of cardiometabolic diseases. Therefore, joint longitudinal trajectories of multiple risk factors might represent different degrees of cardiometabolic risk. We analyzed population-based data comprising three examinations (Exam 1: 1999-2001, Exam 2: 2006-2008, Exam 3: 2013-2014) of 976 male and 1004 female participants of the KORA cohort (Southern Germany). Participants were followed up for cardiometabolic diseases, including cardiovascular mortality, myocardial infarction and stroke, or a diagnosis of type 2 diabetes, until 2016. Longitudinal multivariate k-means clustering identified sex-specific trajectory clusters based on nine cardiometabolic risk factors (age, systolic and diastolic blood pressure, body-mass-index, waist circumference, Hemoglobin-A1c, total cholesterol, high- and low-density lipoprotein cholesterol). Associations between clusters and cardiometabolic events were assessed by logistic regression models. We identified three trajectory clusters for men and women, respectively. Trajectory clusters reflected a distinct distribution of cardiometabolic risk burden and were associated with prevalent cardiometabolic disease at Exam 3 (men: odds ratio (OR)ClusterII = 2.0, 95% confidence interval: (0.9-4.5); ORClusterIII = 10.5 (4.8-22.9); women: ORClusterII = 1.7 (0.6-4.7); ORClusterIII = 5.8 (2.6-12.9)). Trajectory clusters were furthermore associated with incident cardiometabolic cases after Exam 3 (men: ORClusterII = 3.5 (1.1-15.6); ORClusterIII = 7.5 (2.4-32.7); women: ORClusterII = 5.0 (1.1-34.1); ORClusterIII = 8.0 (2.2-51.7)). Associations remained significant after adjusting for a single time point cardiovascular risk score (Framingham). On a population-based level, distinct longitudinal risk profiles over a 14-year time period are differentially associated with cardiometabolic events. Our results suggest that longitudinal data may provide additional information beyond single time-point measures. Their inclusion in cardiometabolic risk assessment might improve early identification of individuals at risk.

Abstract P135: Effect of Cardiovascular Health Improvement at Midlife on Cardiovascular Event and Mortality Risk in Diabetes

Objective: This prospective cohort study examined the relationships between changes in CVH during midlife, as assessed by the Life's Simple 7, and risks of CVD events and all-cause mortality among individuals with diabetes and compared to individuals without diabetes. Methods: The study utilized data from the Atherosclerosis Risk in Communities Study, collected during visits 1 (1987-1989) and visit 3 (1993-1995) with a median follow-up of 23 years. Results: Among the final sample (N=8,741), 806 had diabetes (9.2%). Among participants with diabetes at baseline, 62.3% had consistently low CVH at both visits, 12.0% maintained moderate CVH, and 0.4% maintained favorable CVH. Meanwhile, 15.0% showed improvement, and 10.3% experienced a decline in CVH. Those who improved their CVH status had lower CVD event risks (HR=0.69; 95% CI: 0.50 to 0.93), as did those who maintained moderate CVH (HR=0.68; 95% CI: 0.50 to 0.94) or shifted from moderate to low CVH (HR=0.60; 95% CI: 0.41 to 0.88). These results were largely influenced by positive changes in blood pressure, cholesterol levels, physical activity, and smoking. Similar patterns were observed for all-cause mortality. In comparison to participants without diabetes who maintained a favorable CVH trajectory at midlife, those with diabetes consistently displayed higher risks of CVD events and mortality, regardless of their CVH trajectory (Figure 1). However, individuals with diabetes who maintained a favorable CVH trajectory at midlife had CVD event and mortality risks similar to those without diabetes who had consistently low CVH. Conclusions: For individuals with diabetes at midlife, achieving or maintaining ideal CVH levels through lifestyle factors may help reduce excess CVD events and mortality risk. Nonetheless, CVD risk in diabetes is not completely mitigated by favorable cardiovascular risk factor trajectories.

Postpartum weight retention and the early evolution of cardiovascular risk over the first 5 years after pregnancy

BackgroundThe cumulative effect of postpartum weight retention from each pregnancy in a woman’s life may contribute to her risk of ultimately developing type 2 diabetes and cardiovascular disease. However, there is limited direct evidence supporting this hypothesis. Thus, we sought to characterize the impact of postpartum weight retention on the trajectories of cardiovascular risk factors over the first 5-years after pregnancy.MethodsIn this prospective observational cohort study, 330 women (mean age 35.7 ± 4.3 years, mean pre-pregnancy body mass index 25.2 ± 4.8 kg/m2, 50.9% primiparous) underwent serial cardiometabolic characterization (anthropometry, blood pressure, lipids, oral glucose tolerance test, insulin sensitivity/resistance (Matsuda index, HOMA-IR), C-reactive protein (CRP), adiponectin) at 1-year, 3-years, and 5-years postpartum. Based on the magnitude of weight change between pre-pregnancy and 5-years postpartum, they were stratified into the following 3 groups: weight loss (n = 100), weight gain 0–6% (n = 110), and weight gain ≥ 6% (n = 120).ResultsAt 1-year postpartum, cardiovascular risk factors did not differ between the groups. However, an adverse risk factor profile progressively emerged in the weight retention groups at 3- and 5-years. Indeed, after covariate adjustment, there was stepwise worsening (from the weight loss group to weight gain 0–6% to weight gain ≥ 6% group) of the following cardiovascular risk factors at 5-years: triglycerides (p = 0.001), HDL (p = 0.02), LDL (p = 0.01), apolipoprotein-B (p = 0.003), Matsuda index (p < 0.0001), HOMA-IR (p < 0.0001), fasting glucose (p = 0.07), and CRP (p = 0.01). Moreover, on logistic regression analyses, weight gain ≥ 6% emerged as an independent predictor of pre-diabetes/diabetes at 5-years (adjusted OR = 3.40, 95%CI: 1.63–7.09).ConclusionPostpartum weight retention predicts trajectories of worsening cardiovascular risk factors and glucose intolerance over the first 5-years after delivery, consistent with its postulated contribution to future vascular disease in women.

Cross-model validation of public health microsimulation models; comparing two models on estimated effects of a weight management intervention

BackgroundHealth economic modelling indicates that referral to a behavioural weight management programme is cost saving and generates QALY gains compared with a brief intervention. The aim of this study was to conduct a cross-model validation comparing outcomes from this cost-effectiveness analysis to those of a comparator model, to understand how differences in model structure contribute to outcomes.MethodsThe outcomes produced by two models, the School for Public Health Research diabetes prevention (SPHR) and Health Checks (HC) models, were compared for three weight-management programme strategies; Weight Watchers (WW) for 12 weeks, WW for 52 weeks, and a brief intervention, and a simulated no intervention scenario. Model inputs were standardised, and iterative adjustments were made to each model to identify drivers of differences in key outcomes.ResultsThe total QALYs estimated by the HC model were higher in all treatment groups than those estimated by the SPHR model, and there was a large difference in incremental QALYs between the models. SPHR simulated greater QALY gains for 12-week WW and 52-week WW relative to the Brief Intervention. Comparisons across socioeconomic groups found a stronger socioeconomic gradient in the SPHR model. Removing the impact of treatment on HbA1c from the SPHR model, running both models only with the conditions that the models have in common and, to a lesser extent, changing the data used to estimate risk factor trajectories, resulted in more consistent model outcomes.ConclusionsThe key driver of difference between the models was the inclusion of extra evidence-based detail in SPHR on the impacts of treatments on HbA1c. The conclusions were less sensitive to the dataset used to inform the risk factor trajectories. These findings strengthen the original cost-effectiveness analyses of the weight management interventions and provide an increased understanding of what is structurally important in the models.

Variation in symptoms of common mental disorders in the general population during the COVID-19 pandemic: longitudinal cohort study.

A significant rise in mental health disorders was expected during the COVID-19 pandemic. However, referrals to mental health services dropped for several months before rising to pre-pandemic levels. To identify trajectories of incidence and risk factors for common mental disorders among the general population during 14 months of the COVID-19 pandemic, to inform potential mental health service needs. A cohort of 33 703 adults in England in the University College London COVID-19 Social Study provided data from March 2020 to May 2021. Growth mixture modelling was used to identify trajectories based on the probability of participants reporting symptoms of depression (Patient Health Questionnaire-9) or anxiety (Generalised Anxiety Disorder-7) in the clinical range, for each month. Sociodemographic and personality-related characteristics associated with each trajectory class were explored. Five trajectory classes were identified for depression and anxiety. Participants in the largest class (62%) were very unlikely to report clinically significant symptom levels. Other trajectories represented participants with a high likelihood of clinically significant symptoms throughout, early clinically significant symptoms that reduced over time, clinically significant symptoms that emerged as the pandemic unfolded and a moderate likelihood of clinically significant symptoms throughout. Females, younger adults, carers, those with existing mental health diagnoses, those that socialised frequently pre-pandemic and those with higher neuroticism scores were more likely to experience depression or anxiety. Nearly 40% of participants followed trajectories indicating risk of clinically significant symptoms of depression or anxiety. The identified risk factors could inform public health interventions to target individuals at risk in future health emergencies.

Topic evolution before fall incidents in new fallers through natural language processing of general practitioners' clinical notes.

Falls involve dynamic risk factors that change over time, but most studies on fall-risk factors are cross-sectional and do not capture this temporal aspect. The longitudinal clinical notes within electronic health records (EHR) provide an opportunity to analyse fall risk factor trajectories through Natural Language Processing techniques, specifically dynamic topic modelling (DTM). This study aims to uncover fall-related topics for new fallers and track their evolving trends leading up to falls. This case-cohort study utilised primary care EHR data covering information on older adults between 2016 and 2019. Cases were individuals who fell in 2019 but had no falls in the preceding three years (2016-18). The control group was randomly sampled individuals, with similar size to the cases group, who did not endure falls during the whole study follow-up period. We applied DTM on the clinical notes collected between 2016 and 2018. We compared the trend lines of the case and control groups using the slopes, which indicate direction and steepness of the change over time. A total of 2,384 fallers (cases) and an equal number of controls were included. We identified 25 topics that showed significant differences in trends between the case and control groups. Topics such as medications, renal care, family caregivers, hospital admission/discharge and referral/streamlining diagnostic pathways exhibited a consistent increase in steepness over time within the cases group before the occurrence of falls. Early recognition of health conditions demanding care is crucial for applying proactive and comprehensive multifactorial assessments that address underlying causes, ultimately reducing falls and fall-related injuries.

Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning

It is increasingly clear that longitudinal risk factor levels and trajectories are related to risk for atherosclerotic cardiovascular disease (ASCVD) above and beyond single measures. Currently used in clinical care, the Pooled Cohort Equations (PCE) are based on regression methods that predict ASCVD risk based on cross-sectional risk factor levels. Deep learning (DL) models have been developed to incorporate longitudinal data for risk prediction but its benefit for ASCVD risk prediction relative to the traditional Pooled Cohort Equations (PCE) remain unknown. Our study included 15,565 participants from four cardiovascular disease cohorts free of baseline ASCVD who were followed for adjudicated ASCVD. Ten-year ASCVD risk was calculated in the training set using our benchmark, the PCE, and a longitudinal DL model, Dynamic-DeepHit. Predictors included those incorporated in the PCE: sex, race, age, total cholesterol, high density lipid cholesterol, systolic and diastolic blood pressure, diabetes, hypertension treatment and smoking. The discrimination and calibration performance of the two models were evaluated in an overall hold-out testing dataset. Of the 15,565 participants in our dataset, 2170 (13.9%) developed ASCVD. The performance of the longitudinal DL model that incorporated 8 years of longitudinal risk factor data improved upon that of the PCE [AUROC: 0.815 (CI 0.782–0.844) vs 0.792 (CI 0.760–0.825)] and the net reclassification index was 0.385. The brier score for the DL model was 0.0514 compared with 0.0542 in the PCE. Incorporating longitudinal risk factors in ASCVD risk prediction using DL can improve model discrimination and calibration.

The long-term collateral consequences of juvenile justice involvement for females.

Females are the fastest growing justice involved population in the United States, yet there is relatively little empirical research on the collateral consequences of juvenile justice involvement specifically for females. A growing body of empirical research underscores linkages between juvenile justice involvement and negative health and psychosocial outcomes, both in the short and long term. The current study describes the long-term collateral consequences of juvenile justice involvement for females previously involved in the juvenile justice system, drawing from a longitudinal dataset of 166 women who were initially recruited in adolescence due to chronic and severe justice system involvement. Participants were 15 years-old on average at study enrollment and 35 years-old on average at the current assessment. This paper describes the adolescent and adult experiences of the sample, therefore depicting the developmental trajectories of risk and protective factors for females involved with juvenile justice. As adults, 73% of the sample experienced arrest and 36% experienced incarceration. High rates of mental and physical health problems were reported, including that 50% of the sample met diagnostic criteria for posttraumatic stress disorder. Over 400 children were born to the sample, with high rates of documented intergenerational child welfare involvement. Study findings are discussed in the context of best practices for supporting adolescent girls involved with the juvenile justice system.

Trajectories of insomnia following bereavement

BackgroundInsomnia symptoms are common following bereavement and may exacerbate severe and protracted grief reactions, such as prolonged grief disorder (PGD). However, typical trajectories of insomnia symptoms and risk factors for having a more chronic insomnia trajectory following bereavement are yet unknown. MethodIn the current investigation, 220 recently bereaved (≤6 months post-loss) participants, completed questionnaires assessing sociodemographic and loss-related characteristics, rumination, experiential avoidance and symptoms of (prolonged) grief and depression, on three time-points (6 months apart). We applied growth mixture models to investigate the typical trajectories of insomnia symptoms following bereavement. ResultsThree insomnia trajectory classes emerged, characterized by a resilient (47 %), recovering (43 %), and a chronic trajectory (10 %). Baseline depression symptoms best predicted the type of insomnia trajectory. At one-year follow-up, 9 %, 27 %, and 60 % of participants met the criteria for probable PGD within the resilient, recovering and chronic trajectory, respectively. A parallel process model showed that temporal changes in insomnia symptoms were strongly related to changes in prolonged grief symptoms. ConclusionThe results suggest, that targeting insomnia symptoms in the treatment of PGD, particularly with comorbid depression, may be a viable option.

Population trajectories and age-dependent associations of obesity risk factors with body mass index from childhood to adolescence across European regions: A two-cohort study.

To investigate population trajectories of behavioural risk factors of obesity from childhood to adolescence and their associations with body mass index (BMI) in children across European regions. Data were harmonised between the European multi-centre IDEFICS/I.Family and the Amsterdam Born Children and their Development Cohort. Participants were aged 2.0-9.9 and 5.0-7.5 years at baseline, respectively, and were followed until age 18 years. Behavioural risk factors of interest included diet, physical activity, media use and sleep. Mixed effects models were used for statistical analyses to account for repeated measurements taken from the same child. The study included a total of 14 328 individuals: 4114, 4582, 3220 and 2412 participants from Northern, Southern, Eastern Europe and Amsterdam, respectively. Risk factor means and prevalences changed with age, but the trajectories were mostly similar across regions. Almost no associations between behavioural factors and BMI were found at the age of 6 years. At 11 years, daily sugar-sweetened foods consumption, use of active transport, sports club membership and longer nocturnal sleep duration were negatively associated with BMI in most regions; positive associations were found with media use. Most associations at 11 years of age persisted to 15 years. Whilst population trajectories of media use and nocturnal sleep duration are similar across European regions, those of other behavioural risk factors like active transport and daily vegetable consumption differ. Also, associations between behavioural risk factors and BMI become stronger with age and show similar patterns across regions.

Associations between Distinct Trajectories of Cardiometabolic Risk Factors (Hypertension, Hypercholesterolemia, and Diabetes) over 15 Years Preceding Dementia and Incidence of Dementia

AbstractBackgroundCardiometabolic Risk Factors (Hypertension, Hypercholesterolemia, and Diabetes) have been reported inconsistently to associate with dementia, with both increased and decreased risks. The purpose of this study was to examine longitudinal distinct trajectories of cardiometabolic risk factors over 15 years preceding dementia with incident dementia using the large national dataset from National Alzheimer’s Coordinating Center (NACC).MethodWe used NACC dataset from 2005 to December 2020 and performed a nested case‐control study (up to 1 to 5 match). The sample included 720 cases of all‐causes dementia with at least 5–years follow‐up time preceding dementia diagnosis, and 2526 non‐dementia controls matched on age, sex, race, education and baseline cognitive status. Group‐based trajectory models were applied to identify longitudinal distinct trajectory groups of binary status measures (absent/inactive vs. recent/active) of hypertension, hypercholesterolemia and diabetes among this sample. Conditional logistic regression models were used to evaluate the effect of distinct trajectories of each cardiometabolic risk factor, APOE genotype and the interaction between them with incident dementia.ResultFor the case group (n = 720), mean age at enrollment was 75.5 years and average education in years was 15.6, participants were more female (58.7%), non‐Hispanic white (87.1%) and healthy cognitive status at initial visit (61.9%). All three binary outcome trajectory models identified similar three trajectory groups over 15 years preceding dementia: “low‐flat,” “low‐to‐high,” and “high‐flat”. Conditional logistic regression model revealed no significant main effects of hypercholesterolemia and hypertension as well as no interaction between them and APOE genotype on the incidence of dementia. However, there is borderline significant effect of diabetes trajectory group on the incidence of dementia. Comparing to “low‐flat” diabetes trajectory group, “high‐flat” group were more likely to develop dementia [OR = 1.32, 95% CI (0.99‐1.76), P = 0.056] after controlling APOE genotype.ConclusionOur findings suggest the existence of distinct trajectories of cardiometabolic risk factors preceding incident dementia. Active diabetes status may play an important role for developing dementia especially those who are with long‐term diabetes are with greater risk for the incidence of dementia.

Tracing the temporal trends of modifiable risk factors in dementia: insights from the English Longitudinal Study of Ageing (2004–2019)

BackgroundDementia's growing impact, especially in ageing societies such as the UK, emphasises the importance of modifiable risk factors as primary prevention targets. Despite this, the temporal progression and the population attributable fraction (PAF) of dementia attributable to these factors remain unclear. This investigation aims to examine the temporal trajectories of the modifiable risk factors for dementia in England from 2004 to 2019. MethodsWe used data from the English Longitudinal Study of Ageing collected between June, 2004, and July, 2019, covering 76 904 participants. We calculated the PAFs for 12 modifiable risk factors, as recommended by the Lancet Commission on dementia prevention, intervention, and care, and the individual weighted PAFs (IW-PAFs) for each risk factor. We analysed temporal trends to understand the changes in the overall PAF and IW-PAF over the study period. FindingsThe overall PAF for dementia showed a decrease from 46·31% in 2004–05 to 43·95% in 2018–19, but this trend was not significant (p=0·226). Hypertension, with an average IW-PAF of 8·67%, has been the primary modifiable determinant of dementia, trailed by obesity (6·42%), social isolation (5·84%), hearing loss (5·02%), depression (4·89%), low education (4·80%), physical inactivity (3·40%), diabetes (2·61%), smoking (2·08%), excessive alcohol consumption (1·22%), air pollution (0·44%), and traumatic brain injury (0·28%). During 2004–19, only IW-PAFs of low education (p=0·001), social isolation (p=0·034), and smoking (p=0·007) showed significant decreasing trends, whereas IW-PAFs of other factors had either stagnated with insignificant changes or, worryingly, climbed upwards. InterpretationThis investigation provides valuable insights into the temporal trends of modifiable risk factors for dementia in England. The observed trends underscore the continuing relevance of these risk factors and the need for targeted public health strategies to address them. Notable, PAF was based on a theoretical scenario in which dementia risk can be wholly eliminated by removing risk factors, which should be explained with caution in practice. FundingUK Foreign, Commonwealth and Development Office; National Institute for Health and Care Research (NIHR).

Age-Specific Quantification of Overweight/Obesity Risk Factors From Infancy to Adolescence and Differences by Educational Level of Parents.

Objectives: To explore the age-dependent associations between 26 risk factors and BMI in early life, and differences by parental educational level. Methods: Data of 10,310 children (24,155 measurements) aged 2-16years participating in a multi-centre European cohort from 2007 to 2014 were utilized. Trajectories of overweight/obesity risk factors and their age-specific associations with BMI were estimated using polynomial mixed-effects models. Results: Exposure to most unfavourable factors was higher in the low/medium compared to the high education group, e.g., for PC/TV time (12.6 vs. 10.6h/week). Trajectories of various risk factors markedly changed at an age of 9-11years. Having a family history of obesity, maternal BMI, pregnancy weight gain and birth weight were positively associated with BMI trajectories throughout childhood/adolescence in both education groups; associations of behavioural factors with BMI were small. Parental unemployment and migrant background were positively associated with BMI in the low/medium education group. Conclusion: Associations of risk factors with BMI trajectories did not essentially differ by parental education except for social vulnerabilities. The age period of 9-11years may be a sensitive period for adopting unfavourable behaviours.

Early life cardiovascular risk factor trajectories and vascular aging in midlife: a 30-year prospective cohort study

Abstract Background Vascular aging, as assessed by structural and functional arterial properties, is an independent predictor of cardiovascular outcomes. We aimed to explore the associations of individual cardiovascular risk factors from childhood to midlife and their accumulation over a 30-year span with vascular aging in midlife. Methods Using data from the ongoing cohort of Hanzhong Adolescent Hypertension study, 2180 participants aged 6 to 18 years at baseline were followed for over 30 years. Distinct trajectories of systolic blood pressure (SBP), body mass index (BMI), and heart rate (HR) from childhood to midlife were identified by group-based trajectory modeling. Vascular aging was assessed by carotid intima–media thickness (cIMT) or brachial-ankle pulse wave velocity (baPWV). Results We identified 4 distinct SBP trajectories, 3 distinct BMI trajectories and 2 distinct HR trajectories from childhood to midlife. Persistently-increasing SBP, high-increasing BMI, and high-stable HR were all shown to have a positive association with baPWV in midlife. For cIMT, similar associations were observed for persistently-increasing SBP and high-increasing BMI. After further adjustment for SBP, BMI and HR at the time of vascular assessment in 2017, associations were also observed for cardiovascular risk factor trajectories accumulation with baPWV (β=0.656 (95%CI, 0.265-1.047)] and with cIMT [β=0.045 (95%CI, 0.011-0.079)] in adulthood. Conclusions Longitudinal exposure to individual cardiovascular risk factors from childhood to midlife and cardiovascular risk factor accumulation were associated with an increased risk of vascular aging in midlife. Our study lends support for early targeting of risk factors in order to prevent cardiovascular disease later in life.Long-term SBP, BMI, HR trajectories