Distinct Longitudinal Trajectories Research Articles

Loneliness trajectories and psychological distress in youth: Longitudinal evidence from a population-based sample.

This pre-registered secondary analysis aimed to examine distinct longitudinal loneliness trajectories in youth and whether these trajectories were associated with psychological distress at final follow-up in the UK Household Longitudinal Study. Participants (N = 827, 55.1% female, Time 1: M ± SD = 16.50 ± 0.50 years) provided data during Waves 9, 10 and 11. K-means longitudinal clustering analysis was used to identify clusters of participants with distinct loneliness trajectories across measurement waves. We identified four clusters demonstrating distinct trajectories of loneliness: stable low (40.7%), stable high (20.6%), moderate decreasing (19.6%) and low increasing (19.1%). Compared to 'stable low loneliness', 'stable high' and 'low increasing' loneliness clusters were significantly associated with psychological distress at Wave 11 following adjustment for sex, ethnicity, parent's highest educational achievement and Wave 9 psychological distress. The current study offers an important contribution to the literature on patterns of youth loneliness and mental health consequences.

Language-model-based patient embedding using electronic health records facilitates phenotyping, disease forecasting, and progression analysis.

Current studies regarding the secondary use of electronic health records (EHR) predominantly rely on domain expertise and existing medical knowledge. Though significant efforts have been devoted to investigating the application of machine learning algorithms in the EHR, efficient and powerful representation of patients is needed to unleash the potential of discovering new medical patterns underlying the EHR. Here, we present an unsupervised method for embedding high-dimensional EHR data at the patient level, aimed at characterizing patient heterogeneity in complex diseases and identifying new disease patterns associated with clinical outcome disparities. Inspired by the architecture of modern language models-specifically transformers with attention mechanisms, we use patient diagnosis and procedure codes as vocabularies and treat each patient as a sentence to perform the patient embedding. We applied this approach to 34,851 unique medical codes across 1,046,649 longitudinal patient events, including 102,739 patients from the electronic Medical Records and GEnomics (eMERGE) Network. The resulting patient vectors demonstrated excellent performance in predicting future disease events (median AUROC = 0.87 within one year) and bulk phenotyping (median AUROC = 0.84). We then illustrated the utility of these patient vectors in revealing heterogeneous comorbidity patterns, exemplified by disease subtypes in colorectal cancer and systemic lupus erythematosus, and capturing distinct longitudinal disease trajectories. External validation using EHR data from the University of Washington confirmed robust model performance, with median AUROCs of 0.83 and 0.84 for bulk phenotyping tasks and disease onset prediction, respectively. Importantly, the model reproduced the clustering results of disease subtypes identified in the eMERGE cohort and uncovered variations in overall mortality among these subtypes. Together, these results underscore the potential of representation learning in EHRs to enhance patient characterization and associated clinical outcomes, thereby advancing disease forecasting and facilitating personalized medicine.

Distinct Longitudinal Trajectories of Symptom Burden Predict Clinical Outcomes in End-Stage Liver Disease.

Little has been reported about the clinical relevance and trajectories of symptoms in end-stage liver disease (ESLD). The purpose of this prospective study was to identify trajectories of change in symptom burden over the course of 12 months in adults with ESLD. Patients were recruited from hepatology clinics at 2 healthcare systems. Validated measures were used to assess physical and psychological symptoms. Latent growth mixture modeling and survival and growth modeling were used to analyze the survey data. Data were available for 192 patients (mean age 56.5 ± 11.1 years, 64.1% male, mean Model for ESLD (MELD) 3.0 19.2 ± 5.1, ethyl alcohol as primary etiology 33.9%, ascites 88.5%, encephalopathy 70.8%); there were 38 deaths and 39 liver transplantations over 12 months. Two symptom trajectories were identified: 62 patients (32.3%) had high and unmitigated symptoms, and 130 (67.7%) had lower and improving symptoms. Patients with high and unmitigated symptoms had twice the hazard of all-cause mortality (subhazard ratio 2.53, 95% confidence interval: 1.32-4.83) and had worse physical ( P < 0.001) and mental quality of life ( P = 0.012) compared with patients with lower and improving symptoms. Symptom trajectories were not associated with MELD 3.0 scores ( P = 0.395). Female sex, social support, and level of religiosity were significant predictors of symptom trajectories ( P < 0.05 for all). There seems to be 2 distinct phenotypes of symptom experience in patients with ESLD that is independent of disease severity and associated with sex, social support, religiosity, and mortality. Identifying patients with high symptom burden can help optimize their care.

How Does Children’s Anthropomorphism of a Social Robot Develop Over Time? A Six-Wave Panel Study

Research on children’s anthropomorphism of social robots is mostly cross-sectional and based on a single measurement. However, because social robots are new type of technology with which children have little experience, children’s initial responses to social robots may be biased by a novelty effect. Accordingly, a single measurement of anthropomorphism may not accurately reflect how children anthropomorphize social robots over time. Thus, we used data from a six-wave panel study to investigate longitudinal changes in 8- to 9-year-old children’s anthropomorphism of a social robot. Latent class growth analyses revealed that anthropomorphism peaked after the first interaction with the social robot, remained stable for a brief period of time, and then decreased. Moreover, two distinct longitudinal trajectories of anthropomorphism could be identified: one with moderate to high anthropomorphism and one with low to moderate anthropomorphism. Previous media exposure to non-fictional robots increased the probability that children experienced higher levels of anthropomorphism.

Association of inflammatory trajectory with subarachnoid hemorrhage mortality.

White blood cells (WBC) play an important role in the inflammatory response of the body. Elevated WBC counts on admission in patients with subarachnoid hemorrhage (SAH) correlate with a poor prognosis. However, the role of longitudinal WBC trajectories based on repeated WBC measurements during hospitalization remains unclear. We explored the association between different WBC trajectory patterns and in-hospital mortality. We analyzed a cohort of consecutive patients with SAH between 2012 and 2020. Group-based trajectory modeling (GBTM) was used to group the patients according to their white blood cell patterns over the first 4 days. Stabilized inverse probability treatment weighting (sIPTW) was used to balance baseline demographic and clinical characteristics. We analyzed the association between the WBC trajectory groups and in-hospital mortality using a Cox proportional hazards model. In total, 506 patients with SAH were included in this retrospective cohort. The final model identified two distinct longitudinal WBC trajectories. After adjusting for confounding factors, multivariate regression analysis suggested that an elevated longitudinal WBC trajectory increased the risk of in-hospital mortality (hazard ratio [HR], 2.476; 95% confidence interval [CI] 1.081-5.227; P = 0.024) before sIPTW, and (HR, 2.472; 95%CI 1.489-4.977; P = 0.018) after sIPTW. In patients with SAH, different clinically relevant groups could be identified using WBC trajectory analysis. The WBC count trajectory-initially elevated and then decreased- may lead to an increased risk of in-hospital mortality following SAH.

Resourcefulness Among Initial Ischemic Stroke Patients: A Longitudinal Study of 12 Months.

To explore distinct longitudinal trajectories of resourcefulness among initial ischemic stroke patients from diagnosis to 12 months, and to identify whether sociodemographic factors, disease-related factors, self-efficacy, family function, and social support can predict patterns in the trajectories of resourcefulness. A prospective longitudinal study was conducted. Initial ischemic stroke patients who met inclusion and exclusion criteria were followed up when still in hospital (Preparing for discharge, Baseline, T1), at 1 month (T2), at 3 months (T3), at 6 months (T4), at 9 months (T5) and 12 months (T6) (±1 week) after discharge. General information, National Institute of Health Stroke Scale (NIHSS), Modified Rankin Scale (mRS), General Self-Efficacy Scale (GSES), General Family Functioning Subscale (FAD-GF), and Social Support Rate Scale (SSRS) were used in T1. The Resourcefulness Scale© was evaluated at 6 time points. Growth mixture modeling was used to identify trajectory patterns of resourcefulness. Logistic regression was used to identify predictors of resourcefulness trajectories. Three longitudinal trajectories of resourcefulness were identified and named as the high-stable class (38.9%, n=71), fluctuation class (41.2%, n=75), and low-stable class (19.9%, n=36), respectively. Dwelling areas (x2=6.805, P=0.009), education (x2=44.865, P=0.000), monthly income (x2=13.063, P=0.001), NIHSS scores (x2=44.730, P=0.000), mRS scores (x2=51.788, P=0.000), Hcy (x2=9.345, P=0.002), GSES (x2=56.933, P=0.000), FAD-GF (x2=41.305, P=0.000) and SSRS (x2=52.373, P=0.000) were found to be statistically significant for distinguishing between different resourcefulness trajectory patterns. Lower education (OR=0.404), higher NIHSS(OR=6.672) scores, and higher mRS(OR=21.418) scores were found to be risk factors for lower resourcefulness, whereas higher education(OR=0.404), GSES(OR=0.276), FAD-GF(OR=0.344), and SSRS(OR=0.358) scores were identified as protective factors enhancing resourcefulness. This study obtained three patterns of trajectories and identified their predictive factors in initial ischemic stroke. The findings will assist health care professionals in identifying subgroups of patients and when they may be at risk of low resourcefulness and provide timely targeted intervention to promote resourcefulness.

Trajectories of homelessness and association with mental health and substance use disorders among young people transitioning from out-of-home care in Australia

BackgroundResearchers have examined sub-groups that may exist among young people transitioning from out-of-home care (OHC) using various theoretical models. However, this population group has not been examined for trajectories of homelessness risk. ObjectivesTo examine whether different subtypes of homelessness risk exist among young people transitioning from care and whether these trajectories of homelessness are associated with mental health and substance use disorders. Participants and settingA retrospective population-based cohort study was conducted from a population of 1018 young people (aged 15–18 years) who transitioned from out-of-home in 2013 to 2014 in the state of Victoria, Australia, with follow-up to 2018. MethodsLatent Class Growth Analysis was conducted using linked data from homelessness data collections, child protection, mental health information systems, alcohol and drug use, and youth justice information systems. ResultsThree sub-groups of young people were identified. The ‘moving on’ group (88 %) had the lowest levels of homelessness, with the slope of this trajectory remaining almost stable. The ‘survivors’ (7 %) group started off with a high risk of homelessness, followed by a sharp decrease in homelessness risk over time. The ‘complex’ (5 %) group started off with a low risk of homelessness but faced sharp increases in the risk of homelessness over time. ConclusionsOur study demonstrates that subgroups of young people transitioning from care exist with distinct longitudinal trajectories of homelessness, and these classes are associated with different risk factors. Early intervention and different approaches to tackling homelessness should be considered for these three distinct groups before transitioning from care and during the first few years after leaving care.

How the dynamics of engagement explain the momentum of achievement and the inertia of disengagement: A complex systems theory approach

Engagement can be understood as a complex dynamic system that unfolds over time and interacts within the student, school, and environment. Most of the research on the dynamics of engagement comes from classroom settings and it is so far inconclusive how and why engagement and disengagement evolve over time. Using person-centered methods, sequence, transition, and covariate analysis, we examined a large dataset of 18 consecutive courses of 245 students over a full program. We identified three engagement states (active, average, and disengaged), as well as three distinct longitudinal engagement trajectories (engaged, fluctuating, and mostly disengaged). Taken together, our results showed that engagement trajectories are rather stable over time conforming to the universal dynamics of complex systems. Engaged students were driven by course materials, their achievement, and their previous engaged states (momentum). Most importantly, our results offer a novel theoretical grounding for the understanding of disengagement which has so far remained unexplained. According to our results, disengagement follows the dynamics of a complex system where stability does not require a hard-wired causal mechanism but rather, it is an attractor state that pulls the system to settle in (inertia). Thus, disengagement becomes a hard to change equilibrium state for those students t (or a stuck state).

Abstract 18509: NT-proBNP Trajectories and Heart Failure Risk Associations in Middle to Older Ages in Adulthood: The Atherosclerosis Risk in Communities (ARIC) Study

Background: NT-proBNP is a powerful marker of heart failure (HF) risk. NT-proBNP levels increase with age; however, the impact of distinct longitudinal trajectories of NT-proBNP on future HF risk with aging is largely unexplored. Hypothesis: Middle-aged adults with higher baseline concentrations and steeper increases of NT-proBNP through older ages have greater HF risk. Methods: We performed a prospective analysis of 12,124 ARIC participants with 2 or more serial NT-proBNP measurements from Visits 2 through 6, without HF at baseline or HF events between the NT-proBNP assessments. We constructed linear mixed models to evaluate NT-proBNP trajectories for males and females. We defined 4 trajectories, using sex-specific medians to define high versus low: low baseline/low slope, low baseline/high slope, high baseline/low slope, high baseline/high slope. We used Kaplan Meier survival curves and multivariable Cox regression to evaluate the HF risk associations of different NT-proBNP trajectories. Results: The mean age was 57 years with 56% women and 23% Black adults, BMI 27. The median baseline NT-proBNP concentrations were 62 and 36 pg/mL and the average NT-proBNP slopes were 4.6% and 7.2% increase/year for women and men, respectively. Black individuals were more likely to have lower baseline levels, whereas those with diabetes, obesity, or prevalent coronary heart disease had higher slopes. Over a median 22-year follow-up, 2,772 HF events occurred. There was a stepwise increase in HF risk with higher baseline levels and slopes of NT-proBNP, with individuals with the high baseline/high slope NT-proBNP trajectory having a HR for HF of 3.70 (95% CI 3.26-4.20) compared to those with low baseline-low slope ( Figure ). Conclusions: Higher baseline concentrations and steeper increases of NT-proBNP with aging are associated with greater HF risk. Identifying and addressing modifiable determinants of NT-proBNP trajectories with aging may have implications for HF prevention.

Longitudinal trajectories of glycemic control among U.S. Adults with newly diagnosed diabetes

AimsTo identify longitudinal trajectories of glycemic control among adults with newly diagnosed diabetes, overall and by diabetes type. MethodsWe analyzed claims data from OptumLabs® Data Warehouse for 119,952 adults newly diagnosed diabetes between 2005 and 2018. We applied a novel Mixed Effects Machine Learning model to identify longitudinal trajectories of hemoglobin A1c (HbA1c) over 3 years of follow-up and used multinomial regression to characterize factors associated with each trajectory. ResultsThe study population was comprised of 119,952 adults with newly diagnosed diabetes, including 696 (0.58%) with type 1 diabetes. Among patients with type 1 diabetes, 52.6% were diagnosed at very high HbA1c, partially improved, but never achieved control; 32.5% were diagnosed at low HbA1c and deteriorated over time; and 14.9% had stable low HbA1c. Among patients with type 2 diabetes, 67.7% had stable low HbA1c, 14.4% were diagnosed at very high HbA1c, partially improved, but never achieved control; 10.0% were diagnosed at moderately high HbA1c and deteriorated over time; and 4.9% were diagnosed at moderately high HbA1c and improved over time. ConclusionsClaims data identified distinct longitudinal trajectories of HbA1c after diabetes diagnosis, which can be used to anticipate challenges and individualize care plans to improve glycemic control.

Dynamic Changes in Dietary Guideline Adherence and Its Association with All-Cause Mortality among Middle-Aged Chinese: A Longitudinal Study from the China Health and Nutrition Survey.

The traditional approach to evaluating dietary quality is based on the achievement of the recommended intakes for each food group, which may overlook the achievement of correct relative proportions between food groups. We propose a "Dietary Non-Adherence Score (DNAS)" to assess the degree of similarity between subjects' diets and those recommended in the Chinese Dietary Guidelines (CDG). Furthermore, it is important to incorporate the time-dependent nature of dietary quality into mortality prediction. This study investigated the association between long-term changes in adherence to the CDG and all-cause mortality. This study included 4533 participants aged 30-60 from the China Health and Nutrition Survey study with a median follow-up of 6.9 years. Intakes from 10 food groups were collected in 5 survey rounds from 2004 to 2015. We calculated the Euclidean distance between the intake of each food and the CDG-recommended intake, and then summed all the food groups as DNAS. Mortality was assessed in 2015. Latent class trajectory modeling was used to identify three classes of participants with distinct longitudinal trajectories of DNAS during the follow-up period. The Cox proportional hazard model was used to assess the risk of all-cause mortality in the three classes of people. Risk factors for death and confounders for diets were sequentially adjusted in the models. There were 187 deaths overall. Participants in the first class identified had consistently low and decreasing DNAS levels (coefficient = -0.020) over their lifetime, compared with a hazard ratio (HR) of 4.4 (95% confidence interval [CI]: 1.5, 12.7) for participants with consistently high and increasing DNAS levels (coefficient = 0.008). Those with moderate DNAS had an HR of 3.0 (95% CI: 1.1, 8.4). In summary, we find that people with consistently high adherence to CDG-recommended dietary patterns had a significantly lower mortality risk. DNAS is a promising method to assess diet quality.

Abstract P608: Dynamic Changes in Dietary Guidelines Adherence and Its Association With All-Cause Mortality Among Middle-Aged Chinese: A Longitudinal Study From the China Health and Nutrition Survey

Background: The traditional approach to evaluating dietary quality is based on the achievement of the recommended intakes for each food group, which may overlook the achievement of correct relative proportions between food groups. We propose a “Dietary Adherence Score (DAS)” to assess the degree of similarity between subjects' diets and those recommended in the Chinese Dietary Guidelines (CDG). Furthermore, it is important to incorporate the time-dependent nature of dietary quality into the mortality prediction. Aim: To investigate the association between long-term changes in adherence to the CDG and all-cause mortality. Method: The study included 4533 participants aged 30-60 from the China Health and Nutrition Survey study with a median follow-up of 6.9 years. Intakes from 10 food groups were collected in 5 survey rounds from 2004 to 2015. We calculated the Euclidean distance between the intake of each food and the CDG-recommended intake, and then summed all the foods as DAS. Mortality was assessed in 2015. The latent class trajectory modeling was used to identify 3 classes of participants with distinct longitudinal trajectories of DAS during the follow-up period. The Cox proportional hazard model was used to assess the risk of all-cause mortality in the 3 classes of people. Risk factors for death and a traditional measure of diet quality, the Alternative Healthy Eating Index (AHEI), were adjusted in the models. Result: There were 187 deaths overall. Participants in the first class identified had consistently low and decreasing DAS levels (coefficient= -0.020) over their lifetime, compared with a hazard ratio (HR) of 4.4 (95% confidence interval [CI]: 1.5, 12.7) for participants with consistently high and increasing DAS levels (coefficient= 0.008). Those with moderate DAS had an HR of 3.0 (95% CI: 1.1, 8.4). The effect of DAS was independent of AHEI. Conclusion: People with consistently high adherence to CDG-recommended dietary patterns had a significantly lower mortality risk. DAS is a promising method to assess diet quality.

Apathy and depression in mild cognitive impairment: distinct longitudinal trajectories and clinical outcomes.

Apathy is a common symptom in mild cognitive impairment (MCI) and may predict progression to dementia. Little research, however, has investigated the longitudinal trajectory of apathy in patients with MCI or controlled for depression, which can mimic apathy, when examining its clinical correlates. The current study sought to address these issues. A prospective longitudinal study was conducted over 3 years. Nine memory clinics around Australia. One hundred and eighty-five patients with MCI at baseline. Measures of cognition, function, neuropsychiatric symptoms, caregiver burden, and medication use were completed annually with additional assessments at 3 and 6 months. Patients were also assessed for dementia by expert clinicians at these time points. Of 164 patients who completed measures of neuropsychiatric symptoms, 59 (36.0%) had apathy and 61 (37.2%) had depression. The proportion affected by apathy and overall apathy scores increased over time, in contrast to measures of depression, which remained relatively stable. Apathy was associated with incident dementia and worse cognition, function, neuropsychiatric symptoms, and caregiver burden independent of both depression and incident dementia. Depression was associated with worse function, albeit to lesser degree than apathy, and neuropsychiatric symptoms. Apathy increases in MCI and is associated with worse clinical outcomes. These findings provide further evidence for apathy as a marker of clinical decline in older people and poorer outcomes across neurocognitive disorders.

Association of poorly controlled HbA1c with increased risk of progression to end-stage kidney disease and all-cause mortality in patients with diabetes and chronic kidney disease.

Glycosylated hemoglobin (HbA1c) targets for patients with chronic kidney disease (CKD) and type 2 diabetes remain controversial. To evaluate whether baseline HbA1c and HbA1c trajectories are associated with the risk of end-stage kidney disease (ESKD) and all-cause mortality, we recruited adult patients with CKD and type 2 diabetes from a “Pre-ESKD Program” at a medical center in Taiwan from 2003 to 2017. Group-based trajectory modeling was performed to identify distinct patient groups that contained patients with similar longitudinal HbA1c patterns. Cox proportional hazard models were used to estimate hazard ratios (HRs) of ESKD and mortality associated with baseline HbA1c levels and HbA1c trajectories. In the analysis related to baseline HbA1c (n = 4543), the adjusted HRs [95% confidence interval (CI)] of all-cause mortality were 1.06 (0.95–1.18) and 1.25 (95% CI, 1.07–1.46) in patients with an HbA1c level of 7%–9% (53–75 mmol/mol) and >9% (>75 mmol/mol), respectively, as compared with those with an HbA1c level < 7% (<53 mmol/mol). In the trajectory analysis (n = 2692), three distinct longitudinal HbA1c trajectories were identified: nearly optimal (55.9%), moderate to stable (34.2%), and poor control (9.9%). Compared with the “nearly optimal” HbA1c trajectory group, the “moderate-to-stable” group did not have significantly higher mortality, but the “poorly controlled” group had 35% higher risk of mortality (adjusted HR = 1.35, 95% CI = 1.06–1.71). Neither baseline levels of HbA1c nor trajectories were associated with ESKD risk. In conclusion, in patients with CKD and type 2 diabetes, poor glycemic control was associated with an elevated risk of mortality but not associated with a risk of progression to ESKD.

Apathy and Depression in Huntington's Disease: Distinct Longitudinal Trajectories and Clinical Correlates.

Huntington's disease (HD) is an autosomal-dominant neurodegenerative disease resulting in motor disturbances, dementia, and psychiatric symptoms. Apathy is a common manifestation and rated as one of the most impactful by patients and caregivers. It can often be difficult to distinguish from depression because of shared features and frequent overlap. This study examined the longitudinal trajectories and clinical correlates of apathy and depression. Data were drawn from the Cooperative Huntington Observational Research Trial, a prospective, multicenter observational study that recruited 1,082 patients with HD. Measures of cognition, function, neuropsychiatric symptoms, motor function, and medication use were completed annually over 5 years. Overall, 423 patients (39%) showed evidence of apathy at study baseline, and both the prevalence and overall severity of apathy increased over time. Depression, by contrast, affected a similar proportion at baseline, although levels remained relatively stable over the study. Apathy was associated with worse cognition, function, neuropsychiatric symptoms, and motor symptoms. Depression was associated with worse neuropsychiatric symptoms, suicidal ideation, and independence but not other outcomes after control for other variables. Apathy in HD increased over time and was associated with worse clinical outcomes. These associations were independent of depression and other clinical variables. The findings highlight the need to distinguish between apathy and depression given their distinct implications for prognosis and management.

Temporal profiles of suicidal thoughts in daily life: Results from two mobile-based monitoring studies with high-risk adolescents

Advancements in mobile technology offer new possibilities to examine fine-grained processes underlying suicidal ideation in everyday, real-world conditions. Across two samples, this study examined temporal changes in near-term suicidal ideation in high-risk adolescents’ daily life, and whether these dynamic experiences follow distinct longitudinal trajectories. Using latent process mixed modeling for multivariate outcomes, we investigated near-term changes in two parameters of suicidal thoughts (frequency and intensity) among adolescents who completed four-daily ecological momentary assessments (EMAs) during inpatient hospitalization (Sample 1: N = 61; 843 observations) or daily surveys for four weeks after discharge (Sample 2: N = 78; 1621 observations). Proximally assessed suicidal thoughts followed three trajectories characterized by low (Sample 1: 65.6%; Sample 2: 54%), declining (Sample 1: 4.9%; Sample 2: 15%), or persistently high (Sample 1: 29.5%; Sample 2: 31%) ideation in terms of frequency and urge severity. The persistent trajectory also showed consistently high within-person variability. The persistent group was differentiated by higher hopelessness and lower coping self-efficacy compared to the declining trajectory, and by an overall more severe clinical presentation relative to the low ideation trajectory. Suicidal thoughts in everyday life, across two contexts and regardless of data resolution (EMA and daily surveys), are not homogeneous and instead follow distinct longitudinal profiles. Findings point to the importance of closely monitoring suicidal ideation to identify patterns indicative of unrelenting suicidal thinking. Addressing high hopelessness and low self-efficacy may aid in reducing persistent ideation. Improving our understanding of how suicidal ideation unfolds in real-time may be critical to optimizing timely assessment and support.

Investigating the associations of trajectories of depressive symptoms and self‐perceived health and incident dementia: An unsupervised machine learning approach

AbstractBackgroundRisk factors for dementia show inter‐individually varying trajectories over the lifespan. However, risk factors have been mainly investigated with one time‐point assessments. New research suggests that certain risk factor trajectories are associated with increased risk of adverse cognitive outcomes (Demnitz et al., 2020 [https://doi.org/10.1101/2020.05.20.20106963]; Singh‐Manoux et al., 2018 [https://doi.org/10.1016/j.jalz.2017.06.2637]). However, it remains unclear how sequential and simultaneous changes of risk factors alter the individual risk for developing dementia. Testing the joint contribution of trajectories of depressive symptoms and self‐perceived health on incidence of dementia, we hypothesized that consistently poor as well as deteriorating trajectories increase the risk for incident dementia, and explored possible interactions of the trajectories.MethodA total of 5,326 respondents to the SHARE survey, mean age 73.9 years, and 6 complete follow‐ups spanning ∼13 years, answered the EURO‐D depression scale, self‐perceived health (SPH) (t1‐t5), and self‐reported dementia diagnosis at last follow‐up (t6). To investigate the predictive ability of distinct longitudinal trajectories, we applied unsupervised statistical learning methods (K‐means cluster modelling). Clusters indicated distinct risk factor trajectories, which were used as exposures in stepwise logistic regressions to predict incident dementia, controlling for age, gender, education, and country.ResultCluster analysis revealed five distinct trajectories each for SPH and EURO‐D, with varying dementia incidence. In stepwise logistic regressions, respondents with trajectories “consistently poor health” and “consistently high depression” showed elevated risk of dementia (OR = 4.02 [1.39, 14.75] and OR = 2.26 [1.03, 4.95], respectively) compared to the combined baseline risk for “consistently low depression” and “consistently good health”. Interactions were not significant. However, respondents with the combination of “consistently high depression” and “consistently poor health” showed increased risk (N = 246; 6.1% dementia).ConclusionApplying unsupervised machine learning is helpful to incorporate longitudinal information on depressive symptoms and self‐perceived health and model these risk factors longitudinally to test their contribution to explain incidence of dementia. The predictive ability of the trajectories of depressive symptoms and self‐perceived health for dementia indicates the potential for improving the identification of people at risk for developing dementia in late life by exploiting trajectory information readily accessible through regular medical check‐ups in old age.

Multidimensional Perioperative Recovery Trajectories in a Mixed Surgical Cohort: A Longitudinal Cluster Analysis Utilizing National Institutes of Health Patient-Reported Outcome Measurement Information System Measures.

Pain trajectories have been described in numerous surgical settings where preoperative characteristics have been used to predict trajectory membership. Suboptimal pain intensity trajectories have been linked to poor longitudinal outcomes. However, numerous biopsychosocial modulators of postoperative pain may also have distinct longitudinal trajectories that may inform additional targets to improve postoperative recovery. Patients undergoing total joint arthroplasty, thoracic surgery, spine surgery, major abdominal surgery, or mastectomy completed Patient Reported Outcome Measurement Information System (PROMIS) measures and additional scales preoperatively and at 1 week, 2 weeks, 1 month, 3 months, and 6 months postoperatively. A k-means clustering for longitudinal data was utilized to explore and describe distinct pain impact (PROMIS Pain Interference and Physical Function) trajectories and associated changes in additional biopsychosocial measures. Follow-up analyses examined participant demographics and clinical characteristics associated with trajectory memberships. Three postoperative biopsychosocial symptom clusters were identified across all patients (n = 402): low (35%), average (47%), and high (18%) performance cluster trajectories. Participants undergoing total knee arthroplasty (TKA), spinal surgery, reporting presurgical opioid use, and higher pain catastrophizing scale scores were found to be associated with the low performance trajectory. Patients within the low performance trajectory, while demonstrating small improvements by 6 months, remained mild to moderately impaired in both pain impact and physical health outcomes. Alternatively, participants in the average performance trajectory demonstrated improvement in pain impact to population norms compared to baseline and demonstrated continued improvement across physical and psychological outcomes. Patients within the high performance cluster started within population norms across all measures at baseline and returned to baseline or exceeded baseline values by 6 months postoperatively. Self-reported opioid utilization was significantly higher in the low performance cluster across all time points. While a larger proportion of average performance patients reported opioid utilization during the first postoperative month compared to the high performance cluster, no differences were detected at 6 months postoperatively between these 2 clusters. These pain impact trajectories build upon previous unidimensional pain intensity trajectories and suggest that additional distinct biopsychosocial measures may have unique trajectories related to cluster assignment. Additionally, these findings highlight the importance of continued pain impact surveillance through the perioperative recovery period to detect patients at risk of experiencing a poor trajectory and subsequently poor longitudinal health outcomes.

Latent Cognitive Class at Enrollment Predicts Future Cognitive Trajectories of Decline in a Community Sample of Older Adults.

Methods that can identify subgroups with different trajectories of cognitive decline are crucial for isolating the biologic mechanisms which underlie these groupings. This study grouped older adults based on their baseline cognitive profiles using a latent variable approach and tested the hypothesis that these groups would differ in their subsequent trajectories of cognitive change. In this study we applied time-varying effects models (TVEMs) to examine the longitudinal trajectories of cognitive decline across different subgroups of older adults in the Rush Memory and Aging Project. A total of 1,662 individuals (mean age = 79.6 years, SD = 7.4, 75.4%female) participated in the study; these were categorized into five previously identified classes of older adults differing in their baseline cognitive profiles: Superior Cognition (n = 328, 19.7%), Average Cognition (n = 767, 46.1%), Mixed-Domains Impairment (n = 71, 4.3%), Memory-Specific Impairment (n = 274, 16.5%), and Frontal Impairment (n = 222, 13.4%). Differences in the trajectories of cognition for these five classes persisted during 8 years of follow-up. Compared with the Average Cognition class, The Mixed-Domains and Memory-Specific Impairment classes showed steeper rates of decline, while other classes showed moderate declines. Baseline cognitive classes of older adults derived through the use of latent variable methods were associated with distinct longitudinal trajectories of cognitive decline that did not converge during an average of 8 years of follow-up.

Developmental heatmaps of brain functional connectivity from newborns to 6-year-olds

Different functional networks exhibit distinct longitudinal trajectories throughout development, but the timeline of the dynamics of functional connectivity across the whole brain remains to be elucidated. Here we used resting-state fMRI to investigate the development of voxel-level changes in functional connectivity across the first six years of life. Globally, we found that developmental changes in functional connectivity are nonlinear with more changes during the first postnatal year than the second, followed by most significant changes from ages 2–4 and from ages 4–6. However, the overall global difference observed between the first and second year appears to have been driven by girls. Limbic and subcortical areas consistently demonstrated the most substantial changes, whereas primary sensory areas were the most stable. These patterns were consistent in full-term and preterm subgroups. Validation on randomly divided subsamples as well as in an independent cross-sectional sample revealed global patterns consistent with the main results. Overall, the derived developmental heatmaps reveal novel dynamics underlying functional circuit development during the first 6 years of life.