Joint modeling for cognitive trajectory and risk of dementia in the presence of death.
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ABSTRACT:
SUBMITTER: Yu B
PROVIDER: S-EPMC3036971 | biostudies-literature | 2010 Mar
REPOSITORIES: biostudies-literature
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Joint modeling for cognitive trajectory and risk of dementia in the presence of death.
Biometrics 20090504 1
Dementia is characterized by accelerated cognitive decline before and after diagnosis as compared to normal aging. It has been known that cognitive impairment occurs long before the diagnosis of dementia. For individuals who develop dementia, it is important to determine the time when the rate of cognitive decline begins to accelerate and the subsequent gap time to dementia diagnosis. For normal aging individuals, it is also useful to understand the trajectory of cognitive function until their d ...[more]
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Project description:BackgroundThe relationship between risk factor or biomarker trajectories and contemporaneous short-term clinical outcomes is poorly understood. In diabetes patients, it is unknown whether hemoglobin A1c (HbA1c) trajectories are associated with clinical outcomes and can inform care in scenarios in which a single HbA1c is uninformative, for example, after a diagnosis of coronary artery disease (CAD).ObjectiveTo compare associations of HbA1c trajectories and single HbA1c values with short-term mortality in diabetes patients evaluated for CAD DESIGN: Retrospective observational cohort study PARTICIPANTS: Diabetes patients (n = 7780) with and without angiographically defined CAD MAIN MEASURES: We used joint latent class mixed models to simultaneously fit HbA1c trajectories and estimate association with 2-year mortality after cardiac catheterization, adjusting for clinical and demographic covariates.Key resultsThree HBA1c trajectory classes were identified: individuals with stable glycemia (class A; n = 6934 [89%]; mean baseline HbA1c 6.9%), with declining HbA1c (class B; n = 364 [4.7%]; mean baseline HbA1c 11.6%), and with increasing HbA1c (class C; n = 482 [6.2%]; mean baseline HbA1c 8.5%). HbA1c trajectory class was associated with adjusted 2-year mortality (3.0% [95% CI 2.8, 3.2] for class A, 3.1% [2.1, 4.2] for class B, and 4.2% [3.4, 4.9] for class C; global P = 0.047, P = 0.03 comparing classes A and C, P > 0.05 for other pairwise comparisons). Baseline HbA1c was not associated with 2-year mortality (P = 0.85; hazard ratios 1.01 [0.96, 1.06] and 1.02 [0.95, 1.10] for HbA1c 7-9% and ≥ 9%, respectively, relative to HbA1c < 7%). The association between HbA1c trajectories and mortality did not differ between those with and without CAD (interaction P = 0.1).ConclusionsIn clinical settings where single HbA1c measurements provide limited information, HbA1c trajectories may help stratify risk of complications in diabetes patients. Joint latent class modeling provides a generalizable approach to examining relationships between biomarker trajectories and clinical outcomes in the era of near-universal adoption of electronic health records.
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Project description:ObjectivesOur objective is to report prevalence of motoric cognitive risk syndrome (MCR), a newly described predementia syndrome characterized by slow gait and cognitive complaints, in multiple countries, and its association with dementia risk.MethodsPooled MCR prevalence analysis of individual data from 26,802 adults without dementia and disability aged 60 years and older from 22 cohorts from 17 countries. We also examined risk of incident cognitive impairment (Mini-Mental State Examination decline ≥4 points) and dementia associated with MCR in 4,812 individuals without dementia with baseline Mini-Mental State Examination scores ≥25 from 4 prospective cohort studies using Cox models adjusted for potential confounders.ResultsAt baseline, 2,808 of the 26,802 participants met MCR criteria. Pooled MCR prevalence was 9.7% (95% confidence interval [CI] 8.2%-11.2%). MCR prevalence was higher with older age but there were no sex differences. MCR predicted risk of developing incident cognitive impairment in the pooled sample (adjusted hazard ratio [aHR] 2.0, 95% CI 1.7-2.4); aHRs were 1.5 to 2.7 in the individual cohorts. MCR also predicted dementia in the pooled sample (aHR 1.9, 95% CI 1.5-2.3). The results persisted even after excluding participants with possible cognitive impairment, accounting for early dementia, and diagnostic overlap with other predementia syndromes.ConclusionMCR is common in older adults, and is a strong and early risk factor for cognitive decline. This clinical approach can be easily applied to identify high-risk seniors in a wide variety of settings.
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Project description:Late-life depression is associated with an increased risk for dementia but we have limited knowledge of the molecular mechanisms underlying this association. Here we investigated whether brain microRNAs, important posttranscriptional regulators of gene expression, contribute to this association. Late-life depressive symptoms were assessed annually in 300 participants of the Religious Orders Study and Rush Memory and Aging Project for a mean of 7 years. Participants underwent annual cognitive testing, clinical assessment of cognitive status, and uniform neuropathologic examination after death. microRNAs were profiled from the prefrontal cortex using NanoString platform in the discovery cohort and small RNA sequencing in the replication cohort. A global microRNA association study of late-life depressive symptoms was performed using linear mixed model adjusting for the potential confounding factors. Four brain microRNAs were associated with late-life depressive symptoms at adjusted p < 0.05: miR-484, miR-26b-5p, miR-30d-5p, and miR-197-3p. Lower expression levels of these miRNAs were associated having greater depressive symptoms. Furthermore, lower levels of miR-484 and miR-197-3p were associated with faster decline of cognition over time. Moreover, lower miR-484 level was associated with higher probability of having Alzheimer's dementia. Importantly, the associations between miR-484 and depressive symptoms and Alzheimer's dementia, respectively, were replicated in an independent cohort. Lastly, the predicted targets of miR-484 were enriched in a brain protein co-expression module involving synaptic transmission and regulation of synaptic plasticity. This study identified four brain microRNAs associated with late-life depressive symptoms assessed longitudinally. In addition, we found a molecular connection between late-life depression and dementia through miR-484.
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Project description:BackgroundCognitive health expectancy estimates the proportion of the lifespan that is lived in good cognitive health at the population level. A number of cardiovascular diseases have been identified to be risk factors for cognitive decline and dementia including diabetes, stroke, heart diseases and hypertension. The aim of this study was to examine how these cardiovascular conditions relate to cognitive health expectancy.MethodsLongitudinal data were obtained from the US Health and Retirement Study. Multistate modelling was used to estimate total life expectancy (LE), cognitive impairment free life expectancy (CIFLE) and years spent with cognitive impairment (CILE) across self-reported diabetes, hypertension, heart problems and stroke. Individual and cumulative effects of multiple cardiovascular conditions were examined.ResultsThe presence of cardiovascular disease was associated with a 5- to 9-year decrease in LE and 4- to 8-year decrease in CIFLE at age 55. The outcomes varied in a hierarchical fashion by cardiovascular condition. Relative to other conditions, individuals with stroke had the shortest LE and CIFLE. Analysis of multiple cardiovascular risk factors revealed that each additional cardiovascular condition was associated with an exponential decrease in LE and CIFLE.ConclusionsHaving a cardiovascular condition is associated with a lower CIFLE and higher proportion of life lived with cognitive impairment. However, the outcomes vary depending on the type of cardiovascular condition. Reducing incidence of stroke and minimising exposure to multiple cardiovascular risk factors may be beneficial in helping to improve population estimates of cognitive health expectancy.
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Project description:Background and objectivesCognitive reserve (CR) is meant to account for the mismatch between brain damage and cognitive decline or dementia. Generally, CR has been operationalized using proxy variables indicating exposure to enriching activities (activity-based CR). An alternative approach defines CR as residual variance in cognition, not explained by the brain status (residual-based CR). The aim of this study is to compare activity-based and residual-based CR measures in their association with cognitive trajectories and dementia. Furthermore, we seek to examine if the two measures modify the impact of brain integrity on cognitive trajectories and if they predict dementia incidence independent of brain status.MethodsWe used data on 430 older adults aged 60+ from the Swedish National Study on Aging and Care in Kungsholmen, followed for 12 years. Residual-based reserve was computed from a regression predicting episodic memory with a brain-integrity index incorporating six structural neuroimaging markers (white-matter hyperintensities volume, whole-brain gray matter volume, hippocampal volume, lateral ventricular volume, lacunes, and perivascular spaces), age, and sex. Activity-based reserve incorporated education, work complexity, social network, and leisure activities. Cognition was assessed with a composite of perceptual speed, semantic memory, letter-, and category fluency. Dementia was clinically diagnosed in accordance with DSM-IV criteria. Linear mixed models were used for cognitive change analyses. Interactions tested if reserve measures modified the association between brain-integrity and cognitive change. Cox proportional hazard models, adjusted for brain-integrity index, assessed dementia risk.ResultsBoth reserve measures were associated with cognitive trajectories [β × time (top tertile, ref.: bottom tertile) = 0.013; 95% CI: -0.126, -0.004 (residual-based) and 0.011; 95% CI: -0.001, 0.024, (activity-based)]. Residual-based, but not activity-based reserve mitigated the impact of brain integrity on cognitive decline [β (top tertile × time × brain integrity) = -0.021; 95% CI: -0.043, 0.001] and predicted 12-year dementia incidence, after accounting for the brain-integrity status [HR (top tertile) = 0.23; 95% CI: 0.09, 0.58].InterpretationThe operationalization of reserve based on residual cognitive performance may represent a more direct measure of CR than an activity-based approach. Ultimately, the two models of CR serve largely different aims. Accounting for brain integrity is essential in any model of reserve.
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