Monitoring Gait and Physical Activity of Elderly Frail Individuals in Free-Living Environment: A Feasibility Study.

INTRODUCTION: Frailty is conventionally diagnosed using clinical tests and self-reported assessments. However, digital health technologies (DHTs), such as wearable accelerometers, can capture physical activity and gait during daily life, enabling more objective assessments. In this study, we assess the feasibility of deploying DHTs in community-dwelling older individuals, and investigate the relationship between digital measurements of physical activity and gait in naturalistic environments and participants' frailty status, as measured by conventional assessments. METHODS: Fried Frailty Score (FFS) was used to classify fifty healthy individuals as non-frail (FFS = 0, n/female = 21/11, mean ± SD age: 71.10 ± 3.59 years), pre-frail (FFS = 1-2, n/female = 23/9, age: 73.74 ± 5.52 years), or frail (FFS = 3+, n/female = 6/6, age: 70.70 ± 6.53 years). Participants wore wrist-worn and lumbar-worn GENEActiv accelerometers (Activinsights Ltd., Kimbolton, UK) during three in-laboratory visits, and at-home for 2 weeks, to measure physical activity and gait. After this period, they completed a comfort and usability questionnaire. Compliant days at-home were defined as follows: those with ≥18 h of wear time, for the wrist-worn accelerometer, and those with ≥1 detected walking bout, for the lumbar-worn accelerometer. For each at-home measurement, a group analysis was performed using a linear regression model followed by ANOVA, to investigate the effect of frailty on physical activity and gait. Correlation between at-home digital measurements and conventional in-laboratory assessments was also investigated. RESULTS: Participants were highly compliant in wearing the accelerometers, as 94% indicated willingness to wear the wrist device, and 66% the lumbar device, for at least 1 week. Time spent in sedentary activity and time spent in moderate activity as measured from the wrist device, as well as average gait speed and its 95th percentile from the lumbar device were significantly different between frailty groups. Moderate correlations between digital measurements and self-reported physical activity were found. CONCLUSIONS: This work highlights the feasibility of deploying DHTs in studies involving older individuals. The potential of digital measurements in distinguishing frailty phenotypes, while unobtrusively collecting unbiased data, thus minimizing participants' travels to sites, will be further assessed in a follow-up study.

Maximizing utility of neuropsychological measures in sex-specific predictive models of incident Alzheimer's disease in the Framingham Heart Study.

INTRODUCTION: Sex differences in neuropsychological (NP) test performance might have important implications for the diagnosis of Alzheimer's disease (AD). This study investigates sex differences in neuropsychological performance among individuals without dementia at baseline. METHODS: Neuropsychological assessment data, both standard test scores and process coded responses, from Framingham Heart Study participants were analyzed for sex differences using regression model and Cox proportional hazards model. Optimal NP profiles were identified by machine learning methods for men and women. RESULTS: Sex differences were observed in both summary scores and composite process scores of NP tests in terms of adjusted means and their associations with AD incidence. The optimal NP profiles for men and women have 10 and 8 measures, respectively, and achieve 0.76 mean area under the curve for AD prediction. DISCUSSION: These results suggest that NP tests can be leveraged for developing more sensitive, sex-specific indices for the diagnosis of AD.

Examination of potentially modifiable dementia risk factors across the adult life course: The Framingham Heart Study.

INTRODUCTION: We examined for associations between potentially modifiable risk factors across the adult life course and incident dementia. METHODS: Participants from the Framingham Heart Study were included (n = 4015). Potential modifiable risk factors included education, alcohol intake, smoking, body mass index (BMI), physical activity, social network, diabetes, and hypertension. Cox models were used to examine associations between each factor and incident dementia, stratified by early adult life (33-44 years), midlife (45-65 years), and late life (66-80 years). RESULTS: Increased dementia risk was associated with diabetes (hazard ratio [HR] = 1.62; 95% confidence interval [CI] = 1.07-2.46) and physical inactivity (HR = 1.57; 95% CI = 1.12-2.20) in midlife, and with obesity (HR = 1.76; 95% CI = 1.08-2.87) in late life. Having multiple potential modifiable risk factors in midlife and late life was associated with greater risk. DISCUSSION: Potentially modifiable risk factors individually have limited impact on dementia risk in this population across the adult life course, although in combination they may have a synergistic effect. HIGHLIGHTS: Diabetes and physical inactivity in midlife is associated with increased dementia risk. Obesity in late life is associated with increased dementia risk. Having more potentially modifiable risk factors in midlife and late life is associated with greater dementia risk.

A machine learning eye movement detection algorithm using electrooculography.

STUDY OBJECTIVES: Eye movement quantification in polysomnograms (PSG) is difficult and resource intensive. Automated eye movement detection would enable further study of eye movement patterns in normal and abnormal sleep, which could be clinically diagnostic of neurologic disorders, or used to monitor potential treatments. We trained a long short-term memory (LSTM) algorithm that can identify eye movement occurrence with high sensitivity and specificity. METHODS: We conducted a retrospective, single-center study using one-hour PSG samples from 47 patients 18-90 years of age. Team members manually identified and trained an LSTM algorithm to detect eye movement presence, direction, and speed. We performed a 5-fold cross validation and implemented a "fuzzy" evaluation method to account for misclassification in the preceding and subsequent 1-second of gold standard manually labeled eye movements. We assessed G-means, discrimination, sensitivity, and specificity. RESULTS: Overall, eye movements occurred in 9.4% of the analyzed EOG recording time from 47 patients. Eye movements were present 3.2% of N2 (lighter stages of sleep) time, 2.9% of N3 (deep sleep), and 19.8% of REM sleep. Our LSTM model had average sensitivity of 0.88 and specificity of 0.89 in 5-fold cross validation, which improved to 0.93 and 0.92 respectively using the fuzzy evaluation scheme. CONCLUSION: An automated algorithm can detect eye movements from EOG with excellent sensitivity and specificity. Noninvasive, automated eye movement detection has several potential clinical implications in improving sleep study stage classification and establishing normal eye movement distributions in healthy and unhealthy sleep, and in patients with and without brain injury.

Midlife lipid and glucose levels are associated with Alzheimer's disease.

INTRODUCTION: It is unknown whether vascular and metabolic diseases assessed in early adulthood are associated with Alzheimer's disease (AD) later in life. METHODS: Association of AD with lipid fractions, glucose, blood pressure, body mass index (BMI), and smoking obtained prospectively from 4932 Framingham Heart Study (FHS) participants across nine quadrennial examinations was evaluated using Cox proportional hazard and Kaplan-Meier models. Age-, sex-, and education-adjusted models were tested for each factor measured at each exam and within three adult age groups (early = 35-50, middle = 51-60, and late = 61-70). RESULTS: A 15 mg/dL increase in high density lipoprotein (HDL) cholesterol was associated with decreased AD risk during early (15.4%, P = 0.041) and middle (17.9%, P = 0.014) adulthood. A 15 mg/dL increase in glucose measured during middle adulthood was associated with 14.5% increased AD risk (P = 0.00029). These findings remained significant after adjusting for treatment. DISCUSSION: Our findings suggest that careful management of cholesterol and glucose beginning in early adulthood can lower AD risk.

Associations Between the Digital Clock Drawing Test and Brain Volume: Large Community-Based Prospective Cohort (Framingham Heart Study).

BACKGROUND: The digital Clock Drawing Test (dCDT) has been recently used as a more objective tool to assess cognition. However, the association between digitally obtained clock drawing features and structural neuroimaging measures has not been assessed in large population-based studies. OBJECTIVE: We aimed to investigate the association between dCDT features and brain volume. METHODS: This study included participants from the Framingham Heart Study who had both a dCDT and magnetic resonance imaging (MRI) scan, and were free of dementia or stroke. Linear regression models were used to assess the association between 18 dCDT composite scores (derived from 105 dCDT raw features) and brain MRI measures, including total cerebral brain volume (TCBV), cerebral white matter volume, cerebral gray matter volume, hippocampal volume, and white matter hyperintensity (WMH) volume. Classification models were also built from clinical risk factors, dCDT composite scores, and MRI measures to distinguish people with mild cognitive impairment (MCI) from those whose cognition was intact. RESULTS: A total of 1656 participants were included in this study (mean age 61 years, SD 13 years; 50.9% women), with 23 participants diagnosed with MCI. All dCDT composite scores were associated with TCBV after adjusting for multiple testing (P value <.05/18). Eleven dCDT composite scores were associated with cerebral white matter volume, but only 1 dCDT composite score was associated with cerebral gray matter volume. None of the dCDT composite scores was associated with hippocampal volume or WMH volume. The classification model for differentiating MCI and normal cognition participants, which incorporated age, sex, education, MRI measures, and dCDT composite scores, showed an area under the curve of 0.897. CONCLUSIONS: dCDT composite scores were significantly associated with multiple brain MRI measures in a large community-based cohort. The dCDT has the potential to be used as a cognitive assessment tool in the clinical diagnosis of MCI.

Is dietary choline intake related to dementia and Alzheimer's disease risks? Results from the Framingham Heart Study.

BACKGROUND: The positive association of choline for cognition has been reported in both animal and human studies, yet the associations of choline with the risks of incident dementia or Alzheimer's disease (AD) in humans is unclear. OBJECTIVES: Our objective was to test the hypothesis that lower or higher dietary choline intake is associated with increased or decreased, respectively, risks of incident dementia and AD. METHODS: Data from the Framingham Heart Study Offspring Cohort exam 5 to exam 9 were used. Participants were free of dementia and stroke, with a valid self-reported 126-item Harvard FFQ at exam 5. The intakes of total choline, its contributing compounds, and betaine were estimated based on a published nutrient database. The intakes were updated at each exam to represent the cumulative average intake across the 5 exams. The associations between dietary choline intakes and incident dementia and AD were examined in mixed-effect Cox proportional hazard models, adjusting for covariates. RESULTS: A total of 3224 participants (53.8% female; mean ± SD age, 54.5 ± 9.7 y) were followed up for a mean ± SD of 16.1 ± 5.1 y (1991-2011). There were 247 incident dementia cases, of which 177 were AD. Dietary choline intake showed nonlinear relationships with incident dementia and AD. After adjusting for covariates, low choline intake (defined as ≤ 219 and ≤ 215 mg/d for dementia and AD, respectively) was significantly associated with incident dementia and incident AD. CONCLUSIONS: Low choline intake was associated with increased risks of incident dementia and AD.

Questionnaire and Portable Sleep Test Screening of Sleep Disordered Breathing in Acute Stroke and TIA.

Sleep disordered breathing (SDB) is highly prevalent, but frequently unrecognized among stroke patients. Polysomnography (PSG) is difficult to perform soon after a stroke. We evaluated the use of screening questionnaires and portable sleep testing (PST) for patients with acute stroke, subarachnoid hemorrhage, or transient ischemic attack to expedite SDB diagnosis and management. We performed a single-center retrospective analysis of a quality improvement study on SDB screening of consecutive daytime, weekday, adult admissions to a stroke unit. We excluded patients who were unable to communicate and lacked available family members. Patients were screened with the Epworth Sleepiness Scale, Berlin Questionnaire, and STOP-BANG Questionnaire and underwent overnight PST and/or outpatient PSG. The 4-item STOP Questionnaire was derived from STOP-BANG for a secondary analysis. We compared the sensitivity and specificity of the questionnaires for the diagnosis of at least mild SDB (apnea hypopnea index (AHI) ≥5) on PST and correlated AHI measurements between PST and PSG using the Spearman correlation. Out of sixty-eight patients included in the study, 54 (80%) were diagnosed with SDB. Only one (1.5%) had a previous SDB diagnosis. Thirty-three patients completed all questionnaires and a PST. The STOP-BANG questionnaire had the highest sensitivity for at least mild SDB (0.81, 95% CI (confidence interval): 0.65-0.92) but a low specificity (0.33, 95% CI 0.10, 0.65). The discrimination of all questionnaires was overall poor (C statistic range 0.502-0.640). There was a strong correlation (r = 0.71) between the AHI results estimated using PST and outpatient PSG among 28 patients. The 4-item STOP Questionnaire was the easiest to administer and had a comparable or better sensitivity than the other questionnaires. Inpatient PSTs were useful for screening in the acute setting to facilitate an early diagnosis of SDB and to establish further outpatient evaluations with sleep medicine.

The impact of chronotype on circadian rest-activity rhythm and sleep characteristics across the week.

Circadian rhythms are maintained by a complex "system of systems" that continuously coordinates biological processes with each other and the environment. Although humans predominantly entrain to solar time, individual persons vary in their precise behavioral timing due to endogenous and exogenous factors. Endogenous differences in the timing of individual circadian rhythms relative to a common environmental cue are known as chronotypes, ranging from earlier than average (Morningness) to later than average (Eveningness). Furthermore, individual behavior is often constrained by social constructs such as the 7-day week, and the "sociogenic" impact our social calendar has on our behavioral rhythms is likely modified by chronotype. Our aim in this study was to identify and characterize differences in sleep and rest-activity rhythms (RAR) between weekends and weekdays and between-chronotypes. Male volunteers (n = 24, mean age = 23.46 y) were actigraphically monitored for 4 weeks to derive objective behavioral measures of sleep and RARs. Chronotype was assessed through self-report on the Morningness-Eveningness Questionnaire. Sleep characteristics were derived using Actiware; daily rest-activity rhythms were modeled using a basic 3-parameter cosinor function. We observed that both Eveningness and Morningness Chronotypes were more active and slept later on the weekends than on weekdays. Significant between-chronotype differences in sleep timing and duration were observed within individual days of the week, especially during transitions between weekends and the workweek. Moreover, chronotypes significantly varied in their weekly rhythms: e.g. Morningness Chronotypes generally shifted their sleep duration, timing and quality across work/rest transitions quicker than Eveningness Chronotypes. Although our results should be interpreted with caution due to the limitations of our cosinor model and a homogenous cohort, they reinforce a growing body of evidence that day of the week, chronotype and their interactions must be accounted for in observational studies of human behavior, especially when circadian rhythms are of interest.

Association Between the Digital Clock Drawing Test and Neuropsychological Test Performance: Large Community-Based Prospective Cohort (Framingham Heart Study).

BACKGROUND: The Clock Drawing Test (CDT) has been widely used in clinic for cognitive assessment. Recently, a digital Clock Drawing Text (dCDT) that is able to capture the entire sequence of clock drawing behaviors was introduced. While a variety of domain-specific features can be derived from the dCDT, it has not yet been evaluated in a large community-based population whether the features derived from the dCDT correlate with cognitive function. OBJECTIVE: We aimed to investigate the association between dCDT features and cognitive performance across multiple domains. METHODS: Participants from the Framingham Heart Study, a large community-based cohort with longitudinal cognitive surveillance, who did not have dementia were included. Participants were administered both the dCDT and a standard protocol of neuropsychological tests that measured a wide range of cognitive functions. A total of 105 features were derived from the dCDT, and their associations with 18 neuropsychological tests were assessed with linear regression models adjusted for age and sex. Associations between a composite score from dCDT features were also assessed for associations with each neuropsychological test and cognitive status (clinically diagnosed mild cognitive impairment compared to normal cognition). RESULTS: The study included 2062 participants (age: mean 62, SD 13 years, 51.6% women), among whom 36 were diagnosed with mild cognitive impairment. Each neuropsychological test was associated with an average of 50 dCDT features. The composite scores derived from dCDT features were significantly associated with both neuropsychological tests and mild cognitive impairment. CONCLUSIONS: The dCDT can potentially be used as a tool for cognitive assessment in large community-based populations.

Mid- to Late-Life Body Mass Index and Dementia Risk: 38 Years of Follow-up of the Framingham Study.

Growing evidence relates body mass index (BMI) to poorer health outcomes; however, results across studies associating BMI and dementia are conflicting. A total of 3,632 Framingham Offspring participants aged 20 to 60 years at their second health examination (1979-1983) were included in this study, with 190 cases of incident dementia identified by 2017. Cox proportional hazards regression models were fitted to investigate the association of BMI at each of their 8 exams as a baseline for dementia risk and the associations between obesity and dementia across age groups. Spline models were fitted to investigate nonlinear associations between BMI and dementia. Each 1-unit increase in BMI at ages 40-49 years was associated with higher risk of dementia, but with lower risk after age 70 years. Obesity at ages 40-49 years was associated with higher risk of dementia. Overall, the relationship between BMI and dementia risk was heterogeneous across the adult age range. Monitoring BMI at different ages might mediate risk for dementia across an individual's lifetime.

Disparities in Objective Sleep Quality as Assessed Through Wrist Actigraphy in Minority Patients With Inflammatory Bowel Disease.

BACKGROUND: Inflammatory bowel disease (IBD) is associated with a reduced quality of life. Minority patients with IBD specifically report more impairing symptoms compared with nonminority patients. Sleep quality, a key component of quality of life, is significantly compromised in minority patients compared with nonminority patients. Nevertheless, subjective and objective sleep assessments in minority patients with IBD have not explicitly been assessed. The purpose of this prospective cohort study is to assess and compare objective sleep parameters utilizing wrist actigraphy between minority and nonminority IBD patients. METHODS: In this institutional review board approved study, 74 patients with IBD were recruited and stratified into 2 cohorts by self-identification: white nonminority patients and minority patients. Patients in the minority cohort included black and Hispanic individuals (black and nonblack). Exclusion criteria included significant comorbidity, a history of an underlying sleep disorder, or patients who did not self-identify into categorized cohorts. Sleep was measured not only through wrist-based actigraphy but also with sleep surveys. Sleep parameters were compared between minority and nonminority cohorts. Regression analyses were performed to assess for factors independently associated with parameters of poor sleep quality. RESULTS: Sixty-four patients (86.4%) were included in the final analysis. Thirty-one individuals (48.4%) were categorized into the nonminority cohort, and 33 (51.6%) patients were in the minority cohort. A significantly higher number of minority patients had poorer sleep efficiency and fragmented sleep compared with nonminority patients (90.9% vs 67.7%; P = 0.03 and 87.8% vs 61.3%; P = 0.02). In the adjusted analysis, minority status was independently associated with poor sleep efficiency (odds ratio = 6.41; 95% confidence interval, 1.48-28.17; P = 0.0139) and fragmented sleep (odds ratio = 4.98; 95% confidence interval, 1.09-22.89; P = 0.0389). CONCLUSIONS: Minority patients with IBD were shown to have poorer objective measures of sleep as assessed through wrist actigraphy compared to nonminority patients. Cultural competency in the care of minority patients with IBD, specifically focusing on the management of psychosocial issues, is needed to address these disparities in sleep. The inclusion of minority patients with IBD in studies investigating sleep and other psychosocial issues are warranted not only to assess potential disparities in disease course but also to determine the etiologies of poor sleep in minority patients with IBD.

Development and validation of an interpretable deep learning framework for Alzheimer's disease classification.

Alzheimer's disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer's disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer's disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer's disease and cognitively normal subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer's Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer's disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

Exploring the Hierarchical Influence of Cognitive Functions for Alzheimer Disease: The Framingham Heart Study.

BACKGROUND: Although some neuropsychological (NP) tests are considered more central for the diagnosis of Alzheimer disease (AD), there is a lack of understanding about the interaction between different cognitive tests. OBJECTIVE: This study aimed to demonstrate a global view of hierarchical probabilistic dependencies between NP tests and the likelihood of cognitive impairment to assist physicians in recognizing AD precursors. METHODS: Our study included 2091 participants from the Framingham Heart Study. These participants had undergone a variety of NP tests, including Wechsler Memory Scale, Wechsler Adult Intelligence Scale, and Boston Naming Test. Heterogeneous cognitive Bayesian networks were developed to understand the relationship between NP tests and the cognitive status. The performance of probabilistic inference was evaluated by the 10-fold cross validation. RESULTS: A total of 4512 NP tests were used to build the Bayesian network for the dementia diagnosis. The network demonstrated conditional dependency between different cognitive functions that precede the development of dementia. The prediction model reached an accuracy of 82.24%, with sensitivity of 63.98% and specificity of 92.74%. This probabilistic diagnostic system can also be applied to participants that exhibit more heterogeneous profiles or with missing responses for some NP tests. CONCLUSIONS: We developed a probabilistic dependency network for AD diagnosis from 11 NP tests. Our study revealed important psychological functional segregations and precursor evidence of AD development and heterogeneity.

Variations in rest-activity rhythm are associated with clinically measured disease severity in Parkinson's disease.

The continuous, longitudinal nature of accelerometry monitoring is well-suited to capturing the regular 24-hour oscillations in human activity across the day, the cumulative effect of our circadian rhythm and behavior. Disruption of the circadian rhythm in turn disrupts rest-activity rhythms. Although circadian disruption is a major feature of Parkinson's disease (PD), rest-activity rhythms and their relationship with disease severity have not been well characterized in PD. 13 PD participants (Hoehn & Yahr Stage [H&Y] 1-3) wore a Philips Actiwatch Spectrum PRO continuously for two separate weeks. Rest-activity rhythms were quantified by fitting an oscillating 24-hour cosinor model to each participant-day of activity data. One-way ANOVAs adjusted for demographics revealed significant variation in the amount (MESOR, F = 12.76, p < .01), range (Amplitude, F = 9.62, p < .01), and timing (Acrophase, F = 2.7, p = .05) of activity across H&Y Stages. Those with higher H&Y Stages were significantly more likely to be active later in the day, where-as those who shifted between H&Y Stages during the study were significantly more active than those who did not change H&Y Stage. Being active later in the day was also significantly associated with higher scores on the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Section III (motor symptom severity, p = .02), Section II (self-reported impact of motor symptoms on daily living, p = .01), and Total Score (p = .01) in an adjusted linear regression model; significant associations between MDS-UPDRS scores and activity levels were observed only in the unadjusted model. These findings demonstrate that continuous actigraphy is capable of detecting rest-activity disruption in PD, and provides preliminary evidence that rest-activity rhythms are associated with motor symptom severity and H&Y Stage.

Association of Plasma Amylin Concentration With Alzheimer Disease and Brain Structure in Older Adults.

Importance: Preclinical studies suggest that amylin has a U-shaped dose-response association with risk of Alzheimer disease (AD). The association of plasma amylin with AD in humans is unknown. Objectives: To measure amylin concentration in plasma by using enzyme-linked immunosorbent assay and to study the association between plasma amylin, incidence of AD, and brain structure in humans. Design, Setting, and Participants: This cohort study used data from the Framingham Heart Study offspring cohort from 1998 to 2015. Using a Monte Carlo approach, participants were divided into 3 plasma amylin concentration groups: (1) low (<75 pmol/L), (2) high (75-2800 pmol/L), and (3) extremely high (≥2800 pmol/L). Data analyses were conducted October 5, 2017, to December 18, 2018. Exposures: Baseline plasma amylin concentrations at examination 7. Main Outcomes and Measures: Incidence of dementia or AD and brain volumetric measures from structural magnetic resonance imaging data. Results: From the Framingham Heart Study offspring cohort, 3061 participants (mean [SD] age at baseline, 61.0 [9.5] years; 1653 [54.0%] women) who had plasma amylin measurements, dementia incidence, and brain volume measurements on record were included in this study. The distribution of plasma amylin concentrations was highly skewed (median [interquartile range], 7.5 [4.6-18.9] pmol/L; mean [SD], 302.3 [1941.0] pmol/L; range, 0.03-44 623.7 pmol/L). Compared with the low plasma amylin concentration group, the high plasma amylin concentration group had a lower rate of AD incidence (2.3% vs 5.6%; P = .04), but the extremely high plasma amylin concentration group had a higher rate of AD incidence (14.3%; P < .001). After adjusting for age, sex, education, body mass index, diabetes, cardiovascular disease, high-density lipoprotein level, and APOE4, high plasma amylin was not associated with decreased AD risk (hazard ratio, 0.42 [95% CI, 0.16-1.14]; P = .09) but was positively associated with volume of gray matter in the temporal lobe (ß = 0.17 [SE, 0.05]; P < .001). In contrast, extremely high plasma amylin concentration was associated with a higher AD risk (hazard ratio, 2.51 [95% CI, 1.38-4.57]; P = .003) but not associated with temporal lobe volume (ß = 0.02 [SE, 0.07]; P = .82). Conclusions and Relevance: This study found that plasma amylin concentration was associated with AD incidence and brain structure with a U-shaped pattern. These findings are consistent with preclinical findings that suggest amylin is a neuropeptide that is physiological; however, at extremely high concentrations, it may lead to amylin aggregation and therefore may be a risk factor for AD.

Using data science to diagnose and characterize heterogeneity of Alzheimer's disease.

INTRODUCTION: Despite the availability of age- and education-adjusted standardized scores for most neuropsychological tests, there is a lack of objective rules in how to interpret multiple concurrent neuropsychological test scores that characterize the heterogeneity of Alzheimer's disease. METHODS: Using neuropsychological test scores of 2091 participants from the Framingham Heart Study, we devised an automated algorithm that follows general diagnostic criteria and explores the heterogeneity of Alzheimer's disease. RESULTS: We developed a series of stepwise diagnosis rules that evaluate information from multiple neuropsychological tests to produce an intuitive and objective Alzheimer's disease dementia diagnosis with more than 80% accuracy. DISCUSSION: A data-driven stepwise diagnosis system is useful for diagnosis of Alzheimer's disease from neuropsychological tests. It demonstrated better performance than the traditional dichotomization of individuals' performance into satisfactory and unsatisfactory outcomes, making it more reflective of dementia as a spectrum disorder. This algorithm can be applied to both within clinic and outside-of-clinic settings.

Oximetry as an Accurate Tool for Identifying Moderate to Severe Sleep Apnea in Patients With Acute Stroke.

STUDY OBJECTIVES: Sleep-disordered breathing (SDB) is highly prevalent in patients with acute stroke. SDB is often underdiagnosed and associated with neurological deterioration and stroke recurrence. Polysomnography or home sleep apnea testing (HSAT) is typically used as the diagnostic modality; however, it may not be feasible to use regularly in patients with acute stroke. We investigated the predictive performance of pulse oximetry, a simpler alternative, to identify SDB. METHODS: The records of 254 patients, who were admitted to Boston Medical Center for acute stroke and underwent HSAT, were retrospectively reviewed. Oxygen desaturation index (ODI) from pulse oximetry channel were compared to respiratory event index (REI) obtained from HSAT devices. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ODI were calculated, and different ODI cutoff values to predict SDB were proposed. RESULTS: ODI had a strong correlation (r = .902) and agreement with REI. ODI was accurate in predicting SDB at different REI thresholds (REI ≥ 5, REI ≥ 15, and REI ≥ 30 events/h) with the area under the curve (AUC) of .965, .974, and .951, respectively. An ODI ≥ 5 events/h rules in the presence of SDB (specificity 91.7%, PPV 96.3%). An ODI ≥ 15 events/h rules in moderate to severe SDB (specificity 96.4%, PPV 95%) and an ODI < 5 events/h rules out moderate to severe SDB (sensitivity 100%, NPV 100%). CONCLUSIONS: Nocturnal pulse oximetry has a high diagnostic accuracy in predicting moderate to severe SDB in patients with acute stroke. Oximetry can be a simple modality to rapidly recognize patients with more severe SDB and facilitate the referral to the confirmation sleep study.

Lewy Body Pathology and Chronic Traumatic Encephalopathy Associated With Contact Sports.

Traumatic brain injury has been associated with increased risk of Parkinson disease and parkinsonism, and parkinsonism and Lewy body disease (LBD) can occur with chronic traumatic encephalopathy (CTE). To test whether contact sports and CTE are associated with LBD, we compared deceased contact sports athletes (n = 269) to cohorts from the community (n = 164) and the Boston University Alzheimer disease (AD) Center (n = 261). Participants with CTE and LBD were more likely to have ß-amyloid deposition, dementia, and parkinsonism than CTE alone (p < 0.05). Traditional and hierarchical clustering showed a similar pattern of LBD distribution in CTE compared to LBD alone that was most frequently neocortical, limbic, or brainstem. In the community-based cohort, years of contact sports play were associated with neocortical LBD (OR = 1.30 per year, p = 0.012), and in a pooled analysis a threshold of >8 years of play best predicted neocortical LBD (ROC analysis, OR = 6.24, 95% CI = 1.5-25, p = 0.011), adjusting for age, sex, and APOE ɛ4 allele status. Clinically, dementia was significantly associated with neocortical LBD, CTE stage, and AD; parkinsonism was associated with LBD pathology but not CTE stage. Contact sports participation may increase risk of developing neocortical LBD, and increased LBD frequency may partially explain extrapyramidal motor symptoms sometimes observed in CTE.

Position Paper for the Treatment of Nightmare Disorder in Adults: An American Academy of Sleep Medicine Position Paper.

INTRODUCTION: Nightmare disorder affects approximately 4% of adults, occurring in isolation or as part of other disorders such as posttraumatic stress disorder (PTSD), and can significantly impair quality of life. This paper provides the American Academy of Sleep Medicine (AASM) position regarding various treatments of nightmare disorder in adults. METHODS: A literature search was performed based upon the keywords and MeSH terms from the Best Practice Guide for the Treatment of Nightmare Disorder in Adults that was published in 2010 by the AASM. The search used the date range March 2009 to August of 2017, and sought to find available evidence pertaining to the use of behavioral, psychological, and pharmacologic therapies for the treatment of nightmares. A task force developed position statements based on a thorough review of these studies and their clinical expertise. The AASM Board of Directors approved the final position statements. DETERMINATION OF POSITION: Positions of "recommended" and "not recommended" indicate that a treatment option is determined to be clearly useful or ineffective/harmful for most patients, respectively, based on a qualitative assessment of the available evidence and clinical judgement of the task force. Positions of "may be used" indicate that the evidence or expert consensus is less clear, either in favor or against the use of a treatment option. The interventions listed below are in alphabetical order within the position statements rather than clinical preference: this is not meant to be instructive of the order in which interventions should be used. POSITION STATEMENTS: The following therapy is recommended for the treatment of PTSD-associated nightmares and nightmare disorder: image rehearsal therapy. The following therapies may be used for the treatment of PTSD-associated nightmares: cognitive behavioral therapy; cognitive behavioral therapy for insomnia; eye movement desensitization and reprocessing; exposure, relaxation, and rescripting therapy; the atypical antipsychotics olanzapine, risperidone and aripiprazole; clonidine; cyproheptadine; fluvoxamine; gabapentin; nabilone; phenelzine; prazosin; topiramate; trazodone; and tricyclic antidepressants. The following therapies may be used for the treatment of nightmare disorder: cognitive behavioral therapy; exposure, relaxation, and rescripting therapy; hypnosis; lucid dreaming therapy; progressive deep muscle relaxation; sleep dynamic therapy; self-exposure therapy; systematic desensitization; testimony method; nitrazepam; prazosin; and triazolam. The following are not recommended for the treatment of nightmare disorder: clonazepam and venlafaxine. The ultimate judgment regarding propriety of any specific care must be made by the clinician, in light of the individual circumstances presented by the patient, accessible treatment options, and resources.