Association of Obstructive Sleep Apnea with Episodic Memory and Cerebral Microvascular Pathology: A Preliminary Study

Objectives: To evaluate the impact of obstructive sleep apnea (OSA) on neurocognitive function and brain morphology in older adults with depression and cognitive impairment. Methods: We prospectively screened OSA with the STOP-Bang questionnaire in the last 25 patients enrolled into the Donepezil Treatment of Cognitive Impairment and Depression (DOTCODE) trial. High and low probability of OSA were defined as a STOP-Bang score of ≥5 (h-OSA) and of <5 (l-OSA), respectively. Baseline magnetic resonance imaging (MRI) was used to evaluate brain morphology. The initial 16 weeks of antidepressant treatment were part of the DOTCODE trial. Results: After 16 weeks of antidepressant treatment, the h-OSA group performed significantly worse on the Selective Reminding Test delayed recall task than the l-OSA group, controlling for baseline performance (F = 19.1, df = 1,22, p < 0.001). In 19 of 25 participants who underwent brain MRI, the h-OSA group had significantly greater volumes of MRI hyperintensities in deep white matter, periventricular white matter, and subcortical gray matter compared with the l-OSA group. There was no significant association between OSA and hippocampal or entorhinal cortex volumes in our sample, even after controlling for intracranial volume. Conclusions: OSA is associated with impaired verbal episodic memory and microvascular damage in older adults with depression and cognitive impairment. One possibility is that by contributing to cerebral microvascular damage, OSA may exacerbate progressive memory decline

The role of measuring exhaled breath biomarkers in sarcoidosis: A systematic review

Introduction: Sarcoidosis is a chronic granulomatous disease of unknown aetiology with a variable clinical course and prognosis. There is a growing need to identify non-invasive biomarkers to differentiate between clinical phenotypes, identify those at risk of disease progression and monitor response to treatment. Objectives: We undertook a systematic review and meta-analysis, to evaluate the utility of breath-based biomarkers in discriminating sarcoidosis from healthy controls, alongside correlation with existing non-breath based biomarkers used in clinical practice, radiological stage, markers of disease activity and response to treatment. Methods: Electronic searches were undertaken during November 2017 using PubMed, Ebsco, Embase and Web of Science to capture relevant studies evaluating breath-based biomarkers in adult patients with sarcoidosis. Results: 353 papers were screened; 21 met the inclusion criteria and assessed 25 different biomarkers alongside VOCs in exhaled breath gas or condensate. Considerable heterogeneity existed amongst the studies in terms of participant characteristics, sampling and analytical methods. Elevated biomarkers in sarcoidosis included 8-isoprostane, carbon monoxide, neopterin, TGF-β1, TNFα, CysLT and several metallic elements including chromium, silicon and nickel. Three studies exploring VOCs were able to distinguish sarcoidosis from controls. Meta-analysis of four studies assessing alveolar nitric oxide showed no significant difference between sarcoidosis and healthy controls (2.22ppb; 95% CI -0.83, 5.27) however, a high degree of heterogeneity was observed with an I2 of 93.4% (p<0.001). Inconsistent or statistically insignificant results were observed for correlations between several biomarkers and radiological stage, markers of disease activity or treatment. Conclusions: The evidence for using breath biomarkers to diagnose and monitor sarcoidosis remains inconclusive with many studies limited by small sample sizes and lack of standardisation. VOCs have shown promising potential but further research is required to evaluate their prognostic role

On Wavelet-Based Methods for Scalar-on-Function Regression

This thesis consists of work done on three projects which extend and employ wavelet-based functional linear regression. In the first project, we propose a wavelet-based approach to functional mixture regression. In our approach, the functional predictor and the unknown component-specific coefficient functions are projected onto an appropriate wavelet basis and simultaneous regularization and estimation are achieved via an l1-penalized fitting procedure that is carried out using an expectation-maximization algorithm. We provide an efficient fitting algorithm, propose a technique for constructing non-parametric confidence bands, demonstrate the performance of our methods through extensive simulations, and apply them to real data in order to investigate the relationship between fractional anisotropy profiles and cognitive function in subjects with multiple sclerosis. In the second project, we propose a new wavelet-based estimator for estimating the coefficient function in a functional linear model. Our estimator attempts to take account of the structured sparsity of the wavelet coefficients used to represent the coefficient function in the fitting procedure. We propose a characterization of the neighborhood structure of wavelet coefficients and exploit this structure in our estimation procedure. We discuss the motivation for our penalized estimator, describe the fitting procedure which can be carried out with existing software, and examine properties of the estimator through simulation. The third and final project explores three novel approaches to using functional data derived from optical coherence tomography devices for diagnosing glaucoma. The first approach uses wavelet-based functional logistic regression to develop predictive models based on measures of retinal nerve fiber layer (RNFL) thickness. The estimates are obtained via an elastic net penalized fitting procedure. The second and third approaches consist of using novel measures of RNFL characteristics to discriminate between healthy and glaucomatous eyes. The three new approaches are compared with commonly used predictive models using data from a case-control study of African American subjects recruited by ophthalmologists at the Harkness Eye Center of Columbia University

Implications of the Use of Algorithmic Diagnoses or Medicare Claims to Ascertain Dementia.

INTRODUCTION: Formal dementia ascertainment with research criteria is resource-intensive, prompting growing use of alternative approaches. Our objective was to illustrate the potential bias and implications for study conclusions introduced through use of alternate dementia ascertainment approaches. METHODS: We compared dementia prevalence and risk factor associations obtained using criterion-standard dementia diagnoses to those obtained using algorithmic or Medicare-based dementia ascertainment in participants of the baseline visit of the Aging, Demographics, and Memory Study (ADAMS), a Health and Retirement Study (HRS) sub-study. RESULTS: Estimates of dementia prevalence derived using algorithmic or Medicare-based ascertainment differ substantially from those obtained using criterion-standard ascertainment. Use of algorithmic or Medicare-based dementia ascertainment can, but does not always lead to risk-factor associations that substantially differ from those obtained using criterion-standard ascertainment. DISCUSSION/CONCLUSIONS: Absolute estimates of dementia prevalence should rely on samples with formal dementia ascertainment. Use of multiple algorithms is recommended for risk-factor studies when formal dementia ascertainment is not available

Treatment Decisions Based on Scalar and Functional Baseline Covariates

The amount and complexity of patient-level data being collected in randomized-controlled trials offer both opportunities and challenges for developing personalized rules for assigning treatment for a given disease or ailment. For example, trials examining treatments for major depressive disorder are not only collecting typical baseline data such as age, gender, or scores on various tests, but also data that measure the structure and function of the brain such as images from magnetic resonance imaging (MRI), functional MRI (fMRI), or electroencephalography (EEG). These latter types of data have an inherent structure and may be considered as functional data. We propose an approach that uses baseline covariates, both scalars and functions, to aid in the selection of an optimal treatment. In addition to providing information on which treatment should be selected for a new patient, the estimated regime has the potential to provide insight into the relationship between treatment response and the set of baseline covariates. Our approach can be viewed as an extension of “advantage learning” to include both scalar and functional covariates. We describe our method and how to implement it using existing software. Empirical performance of our method is evaluated with simulated data in a variety of settings and also applied to data arising from a study of patients with major depressive disorder from whom baseline scalar covariates as well as functional data from EEG are available

Post-stroke cognitive impairment and the risk of stroke recurrence and death in patients with insulin resistance

OBJECTIVE: Post-stroke cognitive impairment (PSCI) is associated with etiology, severity, and functional outcome of stroke. The risks of recurrent stroke and death in patients with PSCI and insulin resistance (IR) is unknown. The goal of this study was to determine whether global and domain-specific cognitive impairment after stroke in patients with IR was associated with recurrent stroke and death. MATERIALS AND METHODS: We studied patients with recent stroke or transient ischemic attack (TIA) and IR with a baseline Modified Mini-Mental State Examination (3MS) cognitive exam at median of 79 days after stroke. We considered a baseline score of ≤ 88 on the 3MS to indicate global cognitive impairment, and domain-specific summary scores in the lowest quartile to indicate language, attention, orientation, memory and visuospatial impairments. The primary endpoint was fatal or non-fatal recurrent stroke, and the secondary endpoints were all-cause mortality, and fatal or non-fatal myocardial infarction (MI). RESULTS: Among studied n = 3,338 patients 13.6% had global cognitive impairment. During the median 4.96 years of follow-up, 7.4% patients experienced recurrent stroke, 3.5% MI, and 7.3% died. In the fully adjusted model, impairment in language (HR 1.35; 95% CI 1.01-1.81) and orientation (HR 1.41; 95% CI: 1.06-1.87) were associated with a higher risk of recurrent stroke, while attention impairment was associated with all-cause mortality (HR 1.34; 95% CI: 1.01-1.78). DISCUSSION/CONCLUSION: In patients with recent stroke/TIA and IR, post-stroke language and orientation impairments independently predicted recurrent stroke, while attention deficit was associated with increased risk of all-cause mortality