The Determining Risk of Vascular Events by Apnea Monitoring (DREAM) Study: Design, Rationale and Methods

Purpose The goal of the Determining Risk of Vascular Events by Apnea Monitoring (DREAM) study is to develop a prognostic model for cardiovascular outcomes, based on physiologic variables—related to breathing, sleep architecture, and oxygenation—measured during polysomnography in US veterans. Methods The DREAM study is a multi-site, retrospective observational cohort study conducted at three Veterans Affairs (VA) centers (West Haven, CT; Indianapolis, IN; Cleveland, OH). Veterans undergoing polysomnography between January 1, 2000 and December 31, 2004 were included based on referral for evaluation of sleep-disordered breathing, documented history and physical prior to sleep testing, and ≥2-h sleep monitoring. Demographic, anthropomorphic, medical, medication, and social history factors were recorded. Measures to determine sleep apnea, sleep architecture, and oxygenation were recorded from polysomnography. VA Patient Treatment File, VA–Medicare Data, Vista Computerized Patient Record System, and VA Vital Status File were reviewed on dates subsequent to polysomnography, ranging from 0.06 to 8.8 years (5.5 ± 1.3 years; mean ± SD). Results The study population includes 1840 predominantly male, middle-aged veterans. As designed, the main primary outcome is the composite endpoint of acute coronary syndrome, stroke, transient ischemic attack, or death. Secondary outcomes include incidents of neoplasm, congestive heart failure, cardiac arrhythmia, diabetes, depression, and post-traumatic stress disorder. Laboratory outcomes include measures of glycemic control, cholesterol, and kidney function. (Actual results are pending.) Conclusions This manuscript provides the rationale for the inclusion of veterans in a study to determine the association between physiologic sleep measures and cardiovascular outcomes and specifically the development of a corresponding outcome-based prognostic model

Effects of adenotonsillectomy on plasma inflammatory biomarkers in obese children with obstructive sleep apnea: A community-based study.

BackgroundObesity and obstructive sleep apnea syndrome (OSA) are highly prevalent and frequently overlapping conditions in children that lead to systemic inflammation, the latter being implicated in the various end-organ morbidities associated with these conditions.AimTo examine the effects of adenotonsillectomy (T&A) on plasma levels of inflammatory markers in obese children with polysomnographically diagnosed OSA who were prospectively recruited from the community.MethodsObese children prospectively diagnosed with OSA, underwent T&A and a second overnight polysomnogram (PSG) after surgery. Plasma fasting morning samples obtained after each of the two PSGs were assayed for multiple inflammatory and metabolic markers including interleukin (IL)-6, IL-18, plasminogen activator inhibitor-1 (PAI-1), monocyte chemoattractant protein-1 (MCP-1), matrix metalloproteinase-9 (MMP-9), adiponectin, apelin C, leptin and osteocrin.ResultsOut of 122 potential candidates, 100 obese children with OSA completed the study with only one-third exhibiting normalization of their PSG after T&A (that is, apnea-hypopnea index (AHI) ≤1/hour total sleep time). However, overall significant decreases in MCP-1, PAI-1, MMP-9, IL-18 and IL-6, and increases in adropin and osteocrin plasma concentrations occurred after T&A. Several of the T&A-responsive biomarkers exhibited excellent sensitivity and moderate specificity to predict residual OSA (that is, AHI⩾5/hTST).ConclusionsA defined subset of systemic inflammatory and metabolic biomarkers is reversibly altered in the context of OSA among community-based obese children, further reinforcing the concept on the interactive pro-inflammatory effects of sleep disorders such as OSA and obesity contributing to downstream end-organ morbidities

Sleep‐Disordered Breathing and 24‐Hour Ambulatory Blood Pressure Monitoring in Renal Transplant Patients: Longitudinal Study

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The human ECG - nonlinear deterministic versus stochastic aspects

We discuss aspects of randomness and of determinism in electrocardiographic signals. In particular, we take a critical look at attempts to apply methods of nonlinear time series analysis derived from the theory of deterministic dynamical systems. We will argue that deterministic chaos is not a likely explanation for the short time variablity of the inter-beat interval times, except for certain pathologies. Conversely, densely sampled full ECG recordings possess properties typical of deterministic signals. In the latter case, methods of deterministic nonlinear time series analysis can yield new insights.Comment: 6 pages, 9 PS figure

정상인지기능 노인에서 주관적 및 객관적 수면 지표와 인지저하의 관계

학위논문(박사)--서울대학교 대학원 :의과대학 의학과,2020. 2. 김기웅.배경 및 목적: 그 동안 집단 수준에서 주관적/객관적 수면 지표와 인지저하의 관계를 살핀 연구들이 무수히 이루어져 왔음. 그러나 다양한 주관적 수면 지표를 하나의 모델에 통합하여 분석한 연구는 수행된 바 없음. 나아가 객관적 수면지표를 기억 강화와 관련된, 비렘과 렘수면의 상호 보완적 맥락에서 분석한 과거 연구 또한 수행된 바 없음. 수면과 인지저하의 관계에 대한 여러 보고는 있었지만, 개인 수준에서 인지저하를 예측하는 데에 있어 수면지표의 타당성에는 많은 의문이 있는 실정임. 본 연구는 아래와 같은 4가지 가설을 검증하고자 함. 첫째, 정상인지기능 노년 코호트에서 다양한 기저 주관적 수면지표가 4년후 인지저하, 즉, 경도인지장애 (MCI) 또는 치매 발생과 관련이 있을 것이다 (가설 I). 둘째, 상기 코호트의 하위표본에서 수면다원검사 (PSG)를 통해 NREM/REM 수면주기 및 이와 연관된 수면 구조가 인지저하와 관련이 있을 것이다 (가설 II). 셋째, 앞선 연구에서 인지저하와 연관되었다고 밝혀진 주관적 수면 지표와, 역시 인지저하와 연관되었다고 분석된 수면다원검사지표 사이에 상관관계가 있을 것이다 (가설 III). 넷째, 개인 수준에서 주관적 수면지표가 4년후 인지저하를 만족할 만한 검증력으로 예측할 수 있을 것이다 (가설 IV). 방법: 가설 I의 분석을 위해, 자료는 한국 노인을 대표할 수 있는 전국적 인구기반의 전향적 코호트에서 기저 인지기능이 정상 (normal cognition, NC, N = 2,238)인 대상자를 모집함. 심각한 정신과적, 신경과적 질환이 있거나 수면제를 복용하는 대상자를 배제하였으며, 4년간 추적관찰 하였음. 주관적 수면 지표 (중간수면시간, 수면길이, 수면잠복기, 수면질, 수면효율, 및 주간기능장애)는 피츠버그수면질척도 (PSQI)를 통하여, 인지기능은 Consortium to Establish a Registry for Alzheimers Disease Assessment (CERAD)를 사용하여 기저와 4년 추적 시점에서 각각 이루어짐. 분석에는 로지스틱 회귀모형이 사용되었으며, 나이, 성별, 교육연수, APOE 유전형, 노인우울척도, 누적질환평가점수, 및 신체활동량으로 보정하였음. 가설 II의 분석을 위해, 앞선 분석에서 사용된 코호트의 하위표본으로부터 기저에 PSG를 시행한, 235명의 기저 NC 노인의 자료를 사용하였음. 하나의 비렘/렘 수면주기는 2분 초과의 각성시기에 의해 단절되지 않은, 연속되어 순차적으로 나타나는 비렘과 렘 수면 단위로 정의됨. 로지스틱 회귀 모형을 사용하여, 비렘/렘 수면주기 및 이와 연관된 수면구조와, 4년후 인지저하 사이의 연관성을 분석함. 가설 III의 분석을 위해, 기저에서 PSG 및 PSQI를 모두 시행하고 4년 추적을 완료한 기저 NC 노인 235명의 자료를 사용함. 켄달의 순위 상관분석을 통해 앞선 연구에서 인지저하와 관련있다고 알려진 주관적 수면 지표 및 비렘/렘 수면주기의 상관관계를 규명함. 가설 IV의 분석을 위해, NC로 구성된 전체 기저 코호트 자료 (N = 2,238)를 4:1 비율로 훈련데이터셋과 검증데이터셋으로 나눠 10겹 교차검증을 수행함. 훈련데이터셋에 이분형 로지스틱 회귀 분석을 사용하여 4년 후 인지저하 예측 모델을 수립하고, 이의 예측 타당도를 분석하기위해 검증데이터셋에서 ROC 곡선을 얻음. 추가 분석으로, 1) 기저 NC 대상자에서 4년 후 치매 발생에 대한 예측 모델을, 2) 기저 NC 또는 MCI인 대상자를 통합 (N = 2,893)하여 데이터 분할 후 위와 같은 방법으로 4년 후 치매발생에 대한 예측 모델을 수립함. 결과: 가설 I과 관련하여, 기저의 긴 수면잠복기 (30분 초과), 긴 수면길이 (7.95시간 이상), 늦은 수면중간시간 (새벽 3시이후)이 기저 NC 집단에서 4년후 인지저하와 연관되었음 (우도비, 긴 수면잠복기 1.40 [95% CI, 1.03–1.90], p = 0.03; 긴 수면길이 1.67 [95% CI, 1.18–2.35], p = 0.004; 늦은 수면중간시간 0.61 [95% CI, 0.41–0.90], p = 0.03). 이 지표들이 추적기간동안 동일한 상태를 유지하였을 때, 이 연관관계는 통계적 유의성을 유지하였으며, 동일 기간 동안 새로 발생한 긴 수면잠복기 또한 2배 높은 인지저하 위험성과 관련이 있었음 (우도비, 1.95 [95% CI, 1.36–2.81], p = 0.002). 가설 II와 관련하여, 짧은 평균 주기시간이 인지저하와 연관되어 있었음 (우도비, 0.97 [95% CI, 0.94–0.99], p = 0.02). 주기의 하위 구조와, 주기 밖의 비렘, 렘수면이 분석에 모두 포함되었을 때에는, 주기 당 렘수면 길이가 짧을수록 인지저하와 연관되어 있다는 결과가 관찰됨 (우도비, 0.87 [95% CI, 0.76–0.98], p = 0.03). 가설 III과 관련하여, 수면잠복기가 비렘/렘 수면주기 평균길이와 음의 상관관계가 있었으며 (τ = -0.11, p = 0.04), 주기 당 비렘 수면 길이와도 음의 상관관계가 관찰되었음 (τ = -0.16, p = 0.002). 가설 IV와 관련하여, 주관적 수면 지표, APOE 유전자형과 인구학적, 생활습관 인자들을 사용하여 이분형 로지스틱 모델을 구축하였음. 이를 통해 정상인지기능 노인의 4년 후 인지저하를 곡선아래면적 (AUC) 0.65, 민감도 0.60, 특이도 0.66으로 예측하였음. 이어서 정상인지기능 노인의 4년 후 치매 발생에 대해서는 AUC 0.62, 민감도 0.66, 특이도 0.73으로 예측을 하였으며, 기저인지기능이 NC인 대상자와 MCI인 대상자를 통합하여 분석했을 경우, 이 집단에서 4년후 치매 발생 예측에 대한 성능은 AUC 0.85, 민감도 0.89, 특이도 0.75로 분석됨. 결론 및 해석: 정상인지기능 노인의 긴 수면 잠복기 (30분 초과)와 긴 수면시간 (7.95시간 이상)과 같은 주관적 수면 호소가 인지저하의 높은 위험을 예측 할 가능성이 있으며, 정상인지노인의 늦은 수면 중간시간 (새벽 3시 이후)은 인지저하의 낮은 위험을 예측할 가능성이 있음. 더불어, 주관적으로 긴 수면 잠복기는, 정상인지노인의 높은 인지저하 위험과 연관된, 수면다원검사의 짧은 비렘/렘 수면주기 평균시간과 유의한 연관관계가 있었음. 주관적 수면 지표는 수면습관의 무작위적인 표현이 아니라, 인지저하와 연관된 수면구조를 반영하는 객관적 지표로 확인 가능한, 신뢰할 수 있는 지표일 가능성이 있음. 본 분석에서 정상인지기능 노인을 활용한 4년후 인지 저하 예측 모델의 성능은 만족스럽지 않았음. 그러나 비치매 노인을 대상으로 수면인자를 포함한 치매 발생 예측 모델의 개발 가능성은 확인됨. 본 연구의 일부는 아래 잡지에 기 게재된 바 있음: -Suh, Seung Wan, et al. "Sleep and Cognitive Decline: A Prospective Nondemented Elderly Cohort Study." Annals of Neurology 83.3 (2018): 472-482. -Suh, Seung Wan, et al. "Short Average Duration of NREM/REM Cycle Is Related to Cognitive Decline in an Elderly Cohort: An Exploratory Investigation." Journal of Alzheimer's Disease 70.4 (2019): 1123-1132.Background and Objectives: There have been numerous studies on the relationship between subjective/objective sleep measures and cognitive decline at the group level. However, subjective sleep characteristics have never been examined in a single, full model. Furthermore, objective sleep markers have never been examined in the aspect of the complementary roles of NREM and REM sleep in the memory consolidation process. Although the association of sleep and the risk of cognitive decline has been repeatedly reported, the validity of sleep measures for predicting cognitive decline at the individual level is still in question. This study examines four hypotheses. First, we investigated whether subjective sleep disturbances induce cognitive decline, i.e. becoming mild cognitive impairment (MCI) or dementia, over 4 years in cognitively normal elderly using a full-model fit (Hypothesis I). Second, in the subsample of this cohort, we explored whether NREM/REM sleep cycles and their associated sleep architecture are associated with the risk of cognitive decline using polysomnography in cognitively normal elderly (Hypothesis II). Third, we investigated whether the subjective sleep parameters were correlated with the polysomnographic findings, both of which were found to be associated with the risk of cognitive decline (Hypothesis III). Fourth, we examined whether the logistic regression model using subjective sleep parameters can predict cognitive decline with a satisfactory level of performance (Hypothesis IV). Methods: For the hypothesis I, data were acquired from a nationwide, population-based, prospective cohort of Korean elderly whose cognitive function was normal (NC, N = 2,238) at baseline. We excluded individuals with major psychiatric/neurological disorders or taking sleeping pills at baseline, and followed them for 4 years. Subjective sleep characteristics (midsleep time, sleep duration, sleep latency, subjective sleep quality, sleep efficiency, and daytime dysfunction) and cognitive status were measured using the Pittsburgh Sleep Quality Index (PSQI) and Consortium to Establish a Registry for Alzheimers Disease Assessment (CERAD), respectively, at baseline and 4-year follow-up assessments. We used logistic regression models adjusted for covariates including age, sex, education, apolipoprotein E genotype, Geriatric Depression Scale, Cumulative Illness Rating Scale, and physical activity. For the hypothesis II, we enrolled 235 cognitively normal subsamples from the cohort used above who underwent overnight polysomnography at baseline. A NREM/REM cycle is a sequence of NREM and REM sleep, uninterrupted by a waking period of >2 min. After 4 years, the development of MCI or dementia was related to the measures of sleep architecture, including NREM/REM cycle parameters by logistic regression analyses. For the hypothesis III, we used data from participants with NC (N = 235) who completed 4 years of follow-up and provided baseline PSQI scores and polysomnographic measures. We performed Kendalls rank correlation analyses to evaluate the correlation between subjective sleep measures and NREM/REM sleep cycle parameters that turned out to be significantly related to cognitive decline in the prior analyses. For the hypothesis IV, we randomly divided the cognitively normal baseline cohort (N = 2,238) dataset into training and testing datasets in a 4:1 ratio after which a 10-fold cross-validation analysis was conducted. We developed a predictive model for the cognitive decline after 4 years using binary logistic regression analysis in the training datasets and examined their predictive validity for the same outcome in the testing datasets using ROC analyses. Subsequently, we performed two additional analyses; 1) a prediction model for the progression to dementia after 4 years in the baseline NC group, and 2) a prediction model for the progression to dementia after 4 years in the merged dataset composed of the baseline NC or MCI (N = 2,893) group. Results: Regarding hypothesis I, long sleep latency (>30 minutes), long sleep duration (≥ 7.95 hours), and late mid-sleep time (after 3:00 AM) at baseline were associated with the risk of cognitive decline at 4-year follow-up assessment in cognitively normal participants; odds ratios (OR) was 1.40 (95% CI, 1.03–1.90; p = 0.03) for long sleep latency, 1.67 (95% CI, 1.18–2.35; p = 0.004) for long sleep duration, and 0.61 (95% CI, 0.41–0.90; p = 0.03) for late mid-sleep time. Newly developed long sleep latency during the follow-up period also doubled the risk of cognitive decline (OR, 1.95 [95% CI, 1.36–2.81]; p = 0.002). Regarding hypothesis II, a short average cycle length was significantly associated with cognitive decline (OR, 0.97 [95% CI, 0.94–0.99]; p = 0.02). When its substructure and NREM and REM sleep outside of cycles were considered simultaneously, the average REM sleep duration per cycle was significantly related to the outcome (OR, 0.87 [95% CI, 0.76–0.98]; p = 0.03). Regarding hypothesis III, Sleep latency was found to be negatively correlated with average cycle length (τ = -0.11, p = 0.04) and NREM periods in each cycle (τ = -0.16, p = 0.002). Regarding hypothesis IV, we were able to predict incident cognitive decline after 4 years in the baseline NC group with area under the curve (AUC) of 0.65 (sensitivity = 0.60; specificity = 0.66) using a binary logistic regression model made of subjective sleep characteristics, APOE ε4 allele status, and other demographic and lifestyle factors. The additional analyses revealed that we predicted incident dementia after 4 years with AUC of 0.62 (sensitivity = 0.66; specificity = 0.73) in the same baseline subjects, and also predicted incident dementia in the baseline NC or MCI group with AUC of 0.85 (sensitivity = 0.89; specificity = 0.75). Interpretation: Subjective sleep complaints such as long sleep latency (>30 minutes) and long sleep duration (≥ 7.95 hours) may predict the higher risk of cognitive decline while late mid-sleep time (after 3:00 AM) may predict the lower risk of cognitive decline in the cognitively normal elderly. Furthermore, subjective long sleep latency showed a significant association with the short average duration of NREM/REM cycles measured by polysomnography which was also associated with the future risk of cognitive decline in these populations. Subjective sleep measures may not be a random expression of a habitual sleep pattern but a reliable measure verifiable by objective markers reflecting sleep macrostructures related to cognitive decline. We observed that the predictive performance for the incident cognitive decline using only cognitively normal elderly populations was not satisfying. However, our findings indicated that it might be possible to develop a prediction model for dementia using subjective sleep measures in nondemented elderly. Part of this work was previously published on: -Suh, Seung Wan, et al. "Sleep and Cognitive Decline: A Prospective Nondemented Elderly Cohort Study." Annals of Neurology 83.3 (2018): 472-482. -Suh, Seung Wan, et al. "Short Average Duration of NREM/REM Cycle Is Related to Cognitive Decline in an Elderly Cohort: An Exploratory Investigation." Journal of Alzheimer's Disease 70.4 (2019): 1123-1132.Chapter 1. Introduction 1 1.1. Study Background 1 1.2. Purpose of Research 3 Chapter 2. Methods 5 2.1. Study population 5 2.1.1. Main cohort for subjective sleep measures 5 2.1.2. Subcohort for objective sleep measures 5 2.2. Assessment of cognitive disorders 6 2.3. Assessment of sleep parameters 7 2.3.1. Subjective sleep measures 7 2.3.2. Polysomnographic data and NREM/REM sleep cycles 8 2.4. Assessment of confounding factors 9 2.5. Statistical analyses 10 2.5.1. Subjective sleep measures from the main cohort 10 2.5.2. Objective sleep measures from the subcohort 11 2.5.3. Correlation between subjective/objective sleep measures 12 2.5.4. Predictive performance of subjective sleep measures 13 Chapter 3. Results 14 3.1. Subjective sleep measures from the main cohort 14 3.2. Objective sleep measures from the subcohort 15 3.3. Correlation between subjective and objective sleep measures 17 3.4. Predictive performance of subjective sleep measures 18 Chapter 4. Discussion 19 4.1. Subjective sleep measures from the main cohort 19 4.2. Objective sleep measures from the subcohort 23 4.3. Correlation between subjective and objective sleep measures 26 4.4. Predictive performance of subjective sleep measures 27 4.5. Comprehensive discussion and conclusion 30 Figure 32 [Figure 1] Flow chart of the subjective sleep characteristics study 32 [Figure 2] Flow chart of the NREM/REM sleep cycle study 33 [Figure 3] Receiver operating characteristics (ROC) curves of the binary logistic regression model 34 Tables 35 [Table 1] Summary of each of sleep parameters derived from the Pittsburgh Sleep Quality Index 35 [Table 2] Characteristics of the participants at the baseline evaluation 36 [Table 3] Impact of sleep on the risk of cognitive decline in the cognitively normal participants 37 [Table 4] Impact of change of sleep-parameters on the risk of cognitive decline in the cognitively normal participants 38 [Table 5] Baseline characteristics of the participants 39 [Table 6] Baseline objective sleep measures 40 [Table 7] Association between individual sleep stage parameters and the risk of developing mild cognitive impairment or dementia 41 [Table 8] Association between individual NREM/REM cycle-related parameters and the risk of developing mild cognitive impairment or dementia 42 [Table 9] Association between multiple NREM/REM cycle-related parameters and the risk of developing mild cognitive impairment or dementia 43 [Table 10] Kendalls rank correlation coefficient (τ) between subjective sleep characteristics and polysomnographic findings in cognitively normal elderly 44 [Table 11] Predictive performance of binary logistic regression models 45Docto