Self-reported head injury and risk of late-life impairment and AD pathology in an AD center cohort.

AIMS: To evaluate the relationship between self-reported head injury and cognitive impairment, dementia, mortality, and Alzheimer's disease (AD)-type pathological changes. METHODS: Clinical and neuropathological data from participants enrolled in a longitudinal study of aging and cognition (n = 649) were analyzed to assess the chronic effects of self-reported head injury. RESULTS: The effect of self-reported head injury on the clinical state depended on the age at assessment: for a 1-year increase in age, the OR for the transition to clinical mild cognitive impairment (MCI) at the next visit for participants with a history of head injury was 1.21 and 1.34 for the transition from MCI to dementia. Without respect to age, head injury increased the odds of mortality (OR = 1.54). Moreover, it increased the odds of a pathological diagnosis of AD for men (OR = 1.47) but not women (OR = 1.18). Men with a head injury had higher mean amyloid plaque counts in the neocortex and entorhinal cortex than men without. CONCLUSIONS: Self-reported head injury is associated with earlier onset, increased risk of cognitive impairment and dementia, increased risk of mortality, and AD-type pathological changes.

Estimation of Multi-state Models with Missing Covariate Values Based on Observed Data Likelihood.

Continuous-time multi-state models are commonly used to study diseases with multiple stages. Potential risk factors associated with the disease are added to the transition intensities of the model as covariates, but missing covariate measurements arise frequently in practice. We propose a likelihood-based method that deals efficiently with a missing covariate in these models. Our simulation study showed that the method performs well for both 'missing completely at random' and 'missing at random' mechanisms. We also applied our method to a real dataset, the Einstein Aging Study.

Multi-state models and missing covariate data: Expectation-Maximization algorithm for likelihood estimation.

Multi-state models have been widely used to analyze longitudinal event history data obtained in medical and epidemiological studies. The tools and methods developed recently in this area require completely observed data. However, missing data within variables of interest is very common in practice, and it has been an issue in applications. We propose a type of EM algorithm, which handles missingness within multiple binary covariates efficiently, for multi-state model applications. Simulation studies show that the EM algorithm performs well for both missing completely at random (MCAR) and missing at random (MAR) covariate data. We apply the method to a longitudinal aging and cognition study dataset, the Klamath Exceptional Aging Project (KEAP), whose data were collected at Oregon Health & Science University and integrated into the Statistical Models of Aging and Risk of Transition (SMART) database at the University of Kentucky.

A comparison of time-homogeneous Markov chain and Markov process multi-state models.

Time-homogeneous Markov models are widely used tools for analyzing longitudinal data about the progression of a chronic disease over time. There are advantages to modeling the true disease progression as a discrete time stationary Markov chain. However, one limitation of this method is its inability to handle uneven follow-up assessments or skipped visits. A continuous time version of a homogeneous Markov process multi-state model could be an alternative approach. In this article, we conduct comparisons of these two methods for unevenly spaced observations. Simulations compare the performance of the two methods and two applications illustrate the results.

Self-reported memory complaints: implications from a longitudinal cohort with autopsies.

OBJECTIVE: We assessed salience of subjective memory complaints (SMCs) by older individuals as a predictor of subsequent cognitive impairment while accounting for risk factors and eventual neuropathologies. METHODS: Subjects (n = 531) enrolled while cognitively intact at the University of Kentucky were asked annually if they perceived changes in memory since their last visit. A multistate model estimated when transition to impairment occurred while adjusting for intervening death. Risk factors affecting the timing and probability of an impairment were identified. The association between SMCs and Alzheimer-type neuropathology was assessed from autopsies (n = 243). RESULTS: SMCs were reported by more than half (55.7%) of the cohort, and were associated with increased risk of impairment (unadjusted odds ratio = 2.8, p < 0.0001). Mild cognitive impairment (dementia) occurred 9.2 (12.1) years after SMC. Multistate modeling showed that SMC reporters with an APOE ε4 allele had double the odds of impairment (adjusted odds ratio = 2.2, p = 0.036). SMC smokers took less time to transition to mild cognitive impairment, while SMC hormone-replaced women took longer to transition directly to dementia. Among participants (n = 176) who died without a diagnosed clinical impairment, SMCs were associated with elevated neuritic amyloid plaques in the neocortex and medial temporal lobe. CONCLUSION: SMC reporters are at a higher risk of future cognitive impairment and have higher levels of Alzheimer-type brain pathology even when impairment does not occur. As potential harbingers of future cognitive decline, physicians should query and monitor SMCs from their older patients.

Adjusting for mortality when identifying risk factors for transitions to mild cognitive impairment and dementia.

Risk factors for mild cognitive impairment (MCI) and dementia are often investigated without accounting for the competing risk of mortality, which can bias results and lead to spurious conclusions, particularly regarding protective factors. Here, we apply a semi-Markov modeling approach to 531 participants in the University of Kentucky Biologically Resilient Adults in Neurological Studies (BRAiNS) longitudinal cohort, over one-third of whom died without transitioning to a cognitively impaired clinical state. A semi-Markov approach enables a statistical study of clinical state transitions while accounting for the competing risk of death and facilitates insights into both the odds that a risk factor will affect clinical transitions as well as the age at which the transition to MCI or dementia will occur. Risk factors assessed in the current study were identified by matching those reported in the literature with the data elements collected on participants. The presence of Type II diabetes at baseline shortens the time it takes cognitively intact individuals to transition to MCI by seven years on average while use of estrogen replacement therapy at enrollment (baseline) decreases the time required to convert from MCI to dementia by 1.5 years. Finally, smoking and being overweight do not promote transitions to impaired states but instead hasten death without a dementia. In contrast, conventional statistical analyses based on Cox proportional hazards models fail to recognize diabetes as a risk, show that being overweight increases the risk of clinical MCI, and that high blood pressure at baseline increases the risk of a dementia.

Mild cognitive impairment: statistical models of transition using longitudinal clinical data.

Mild cognitive impairment (MCI) refers to the clinical state between normal cognition and probable Alzheimer's disease (AD), but persons diagnosed with MCI may progress to non-AD forms of dementia, remain MCI until death, or recover to normal cognition. Risk factors for these various clinical changes, which we term "transitions," may provide targets for therapeutic interventions. Therefore, it is useful to develop new approaches to assess risk factors for these transitions. Markov models have been used to investigate the transient nature of MCI represented by amnestic single-domain and mixed MCI states, where mixed MCI comprised all other MCI subtypes based on cognitive assessments. The purpose of this study is to expand this risk model by including a clinically determined MCI state as an outcome. Analyses show that several common risk factors play different roles in affecting transitions to MCI and dementia. Notably, APOE-4 increases the risk of transition to clinical MCI but does not affect the risk for a final transition to dementia, and baseline hypertension decreases the risk of transition to dementia from clinical MCI.