Association of back pain with all-cause and cause-specific mortality among older men: a cohort study.

OBJECTIVE: We evaluated whether more severe back pain phenotypes-persistent, frequent, or disabling back pain-are associated with higher mortality rate among older men. METHODS: In this secondary analysis of a prospective cohort, the Osteoporotic Fractures in Men (MrOS) study, we evaluated mortality rates by back pain phenotype among 5215 older community-dwelling men (mean age, 73 years, SD = 5.6) from 6 sites in the United States. The primary back pain measure used baseline and Year 5 back pain questionnaire data to characterize participants as having no back pain, nonpersistent back pain, infrequent persistent back pain, or frequent persistent back pain. Secondary measures of back pain from the Year 5 questionnaire included disabling back pain phenotypes. The main outcomes measured were all-cause and cause-specific death. RESULTS: After the Year 5 exam, during up to 18 years of follow-up (mean follow-up = 10.3 years), there were 3513 deaths (1218 cardiovascular, 764 cancer, 1531 other). A higher proportion of men with frequent persistent back pain versus no back pain died (78% versus 69%; sociodemographic-adjusted HR = 1.27, 95% CI = 1.11-1.45). No association was evident after further adjustment for health-related factors, such as self-reported general health and comorbid chronic health conditions (fully adjusted HR = 1.00; 95% CI = 0.86-1.15). Results were similar for cardiovascular deaths and other deaths, but we observed no association of back pain with cancer deaths. Secondary back pain measures, including back-related disability, were associated with increased mortality risk that remained statistically significant in fully adjusted models. CONCLUSION: Although frequent persistent back pain was not independently associated with risk of death in older men, additional secondary disabling back pain phenotypes were independently associated with increased mortality rate. Future investigations should evaluate whether improvements in disabling back pain affect general health and well-being or risk of death.

From sleep patterns to heart rhythm: Predicting atrial fibrillation from overnight polysomnograms.

BACKGROUND: Atrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Given the prevalence of obstructive sleep apnea among AF patients, electrocardiogram (ECG) analysis from polysomnography (PSG) offers a unique opportunity for early AF prediction. Our aim is to identify individuals at high risk of AF development from single­lead ECGs during standard PSG. METHODS: We analyzed 18,782 single­lead ECG recordings from 13,609 subjects undergoing PSG at the Massachusetts General Hospital sleep laboratory. AF presence was identified using ICD-9/10 codes. The dataset included 15,913 recordings without AF history and 2054 recordings from patients diagnosed with AF between one month to fifteen years post-PSG. Data were partitioned into training, validation, and test cohorts ensuring that individual patients remained exclusive to each cohort. The test set was held out during the training process. We employed two different methods for feature extraction to build a final model for AF prediction: Extraction of hand-crafted ECG features and a deep learning method. For extraction of ECG-hand-crafted features, recordings were split into 30-s windows, and those with a signal quality index (SQI) below 0.95 were discarded. From each remaining window, 150 features were extracted from the time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1800 features (12 × 150). A pre-trained deep neural network from the PhysioNet Challenge 2021 was updated using transfer learning to discriminate recordings with and without AF. The model processed PSG ECGs in 16-s windows to generate AF probabilities, from which 13 statistical features were extracted. Combining 1800 features from feature extraction with 13 from the deep learning model, we performed a feature selection and subsequently trained a shallow neural network to predict future AF and evaluated its performance on the test cohort. RESULTS: On the test set, our model exhibited sensitivity, specificity, and precision of 0.67, 0.81, and 0.3, respectively, for AF prediction. Survival analysis revealed a hazard ratio of 8.36 (p-value: 1.93 × 10-52) for AF outcomes using the log-rank test. CONCLUSIONS: Our proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite modest precision, suggesting false positives. This approach could enable low-cost screening and proactive treatment for high-risk patients. Refinements, including additional physiological parameters, may reduce false positives, enhancing clinical utility and accuracy.

Sleep Duration Polygenic Risk and Phenotype: Associations with Biomarkers of Accelerated Aging in the Baltimore Longitudinal Study of Aging.

We sought to explore whether genetic risk for, and self-reported, short sleep are associated with biological aging and whether age and sex moderate these associations. Participants were a subset of individuals from the Baltimore Longitudinal Study of Aging who had complete data on self-reported sleep (n = 567) or genotype (n = 367). Outcomes included: Intrinsic Horvath age, Hannum age, PhenoAge, GrimAge, and DNAm-based estimates of plasminogen activator inhibitor-1 (PAI-1) and granulocyte count. Results demonstrated that polygenic risk for short sleep was positively associated with granulocyte count; compared to those reporting <6 hr sleep, those reporting >7 hr demonstrated faster PhenoAge and GrimAge acceleration and higher estimated PAI-1. Polygenic risk for short sleep and self-reported sleep duration interacted with age and sex in their associations with some of the outcomes. Findings highlight that polygenic risk for short sleep and self-reported long sleep is associated with variation in the epigenetic landscape and subsequently aging.

Accelerometer-assessed sleep and decline in physical function in older men.

OBJECTIVES: Assess the prospective association of actigraphically measured sleep with self-report and objective measures of physical function among community-dwelling older men. METHODS: Participants were (n = 1496) men aged ≥65 years from the Osteoporotic Fractures in Men Study and ancillary sleep study who were followed up at 4 years for physical function outcomes. Sleep predictors included baseline total sleep time (<6, 6-8 hours [reference], >8 hours), sleep efficiency (<80% or ≥80% [reference]), wake after sleep onset (<90 [reference] or ≥90 minutes), and sleep onset latency (<30 [reference] or ≥30 minutes), measured by wrist actigraphy. Outcomes included self-reported difficulties in mobility and instrumental activities of daily living and objective measures of physical performance (time to complete chair stands, gait speed, grip strength, best narrow walk pace). Multivariable regression models estimated associations between the sleep predictors and change in physical function at follow-up, adjusting for demographic and health-related variables. RESULTS: Participants with short average baseline total sleep time (<6 hours) had significantly greater slowing in their walking speed from baseline to follow-up. Participants with long baseline sleep onset latency (≥30 minutes) had significant increases in mobility difficulties and time to complete chair stands. Sleep efficiency and wake after sleep onset were not significantly associated with any outcomes. No sleep predictors were associated with change in instrumental activities of daily living. CONCLUSIONS: These findings add to the body of evidence showing links between poor sleep and subsequent declines in physical function. Further experimental research is needed to understand the mechanisms at play.

Association of Disrupted Delta Wave Activity During Sleep With Long-Term Cardiovascular Disease and Mortality.

BACKGROUND: Delta wave activity is a prominent feature of deep sleep, which is significantly associated with sleep quality. OBJECTIVES: The authors hypothesized that delta wave activity disruption during sleep could predict long-term cardiovascular disease (CVD) and CVD mortality risk. METHODS: The authors used a comprehensive power spectral entropy-based method to assess delta wave activity during sleep based on overnight polysomnograms in 4,058 participants in the SHHS (Sleep Heart Health Study) and 2,193 participants in the MrOS (Osteoporotic Fractures in Men Study) Sleep study. RESULTS: During 11.0 ± 2.8 years of follow-up in SHHS, 729 participants had incident CVD and 192 participants died due to CVD. During 15.5 ± 4.4 years of follow-up in MrOS, 547 participants had incident CVD, and 391 died due to CVD. In multivariable Cox regression models, lower delta wave entropy during sleep was associated with higher risk of coronary heart disease (SHHS: HR: 1.46; 95% CI: 1.02-2.06; P = 0.03; MrOS: HR: 1.79; 95% CI: 1.17-2.73; P < 0.01), CVD (SHHS: HR: 1.60; 95% CI: 1.21-2.11; P < 0.01; MrOS: HR: 1.43; 95% CI: 1.00-2.05; P = 0.05), and CVD mortality (SHHS: HR: 1.94; 95% CI: 1.18-3.18; P < 0.01; MrOS: HR: 1.66; 95% CI: 1.12-2.47; P = 0.01) after adjusting for covariates. The Shapley Additive Explanations method indicates that low delta wave entropy was more predictive of coronary heart disease, CVD, and CVD mortality risks than conventional sleep parameters. CONCLUSIONS: The results suggest that delta wave activity disruption during sleep may be a useful metric to identify those at increased risk for CVD and CVD mortality.

Associations between Longitudinal Changes in Sleep Stages and Risk of Cognitive Decline in Older Men.

STUDY OBJECTIVES: To investigate the relationships between longitudinal changes in sleep stages and the risk of cognitive decline in older men. METHODS: This study included 978 community-dwelling older men who participated in the first (2003-2005) and second (2009-2012) sleep ancillary study visits of the Osteoporotic Fractures in Men Study. We examined the longitudinal changes in sleep stages at the initial and follow-up visits, and the association with concurrent clinically relevant cognitive decline during the 6.5-year follow-up. RESULTS: Men with low to moderate (quartile 2, Q2) and moderate increase (Q3) in N1 sleep percentage had a reduced risk of cognitive decline on the Modified Mini-Mental State Examination compared to those with a substantial increase (Q4) in N1 sleep percentage. Additionally, men who experienced a low to moderate (Q2) increase in N1 sleep percentage had a lower risk of cognitive decline on the Trails B compared with men in the reference group (Q4). Furthermore, men with the most pronounced reduction (Q1) in N2 sleep percentage had a significantly higher risk of cognitive decline on the Trails B compared to those in the reference group (Q4). No significant association was found between changes in N3 and rapid eye movement sleep and the risk of cognitive decline. CONCLUSIONS: Our results suggested that a relatively lower increase in N1 sleep showed a reduced risk of cognitive decline. However, a pronounced decrease in N2 sleep was associated with concurrent cognitive decline. These findings may help identify older men at risk of clinically relevant cognitive decline.

From Sleep Patterns to Heart Rhythms: Predicting Atrial Fibrillation from Overnight Polysomnograms.

Background: Atrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Importantly, obstructive sleep apnea is highly prevalent among AF patients (60-90%); therefore, electrocardiogram (ECG) analysis from polysomnography (PSG), a standard diagnostic tool for subjects with suspected sleep apnea, presents a unique opportunity for the early prediction of AF. Our goal is to identify individuals at a high risk of developing AF in the future from a single-lead ECG recorded during standard PSGs. Methods: We analyzed 18,782 single-lead ECG recordings from 13,609 subjects at Massachusetts General Hospital, identifying AF presence using ICD-9/10 codes in medical records. Our dataset comprises 15,913 recordings without a medical record for AF and 2,056 recordings from patients who were first diagnosed with AF between 1 day to 15 years after the PSG recording. The PSG data were partitioned into training, validation, and test cohorts. In the first phase, a signal quality index (SQI) was calculated in 30-second windows and those with SQI < 0.95 were removed. From each remaining window, 150 hand-crafted features were extracted from time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1,800 features. We then updated a pre-trained deep neural network and data from the PhysioNet Challenge 2021 using transfer-learning to discriminate between recordings with and without AF using the same Challenge data. The model was applied to the PSG ECGs in 16-second windows to generate the probability of AF for each window. From the resultant probability sequence, 13 statistical features were extracted. Subsequently, we trained a shallow neural network to predict future AF using the extracted ECG and probability features. Results: On the test set, our model demonstrated a sensitivity of 0.67, specificity of 0.81, and precision of 0.3 for predicting AF. Further, survival analysis for AF outcomes, using the log-rank test, revealed a hazard ratio of 8.36 (p-value of 1.93 × 10 -52 ). Conclusions: Our proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite a modest precision indicating the presence of false positive cases. This approach could potentially enable low-cost screening and proactive treatment for high-risk patients. Ongoing refinement, such as integrating additional physiological parameters could significantly reduce false positives, enhancing its clinical utility and accuracy.

Psoriasis and Sleep Disturbance: A US Population-Based Study Using the NHANES Database.

INTRODUCTION: Psoriasis, a chronic inflammatory skin condition, affects approximately 3.0% of the US population, with patients often experiencing significant sleep disturbances. These disturbances include a higher prevalence of conditions such as obstructive sleep apnea, restless leg syndrome, and insomnia. Given the additional risks for cardiovascular disease, metabolic disorders, and depression linked to both poor sleep and psoriasis, addressing sleep issues in this patient group is critical. METHODS: The study utilized National Health and Nutrition Examination Survey (NHANES) data, focusing on individuals aged ≥ 20 years who provided information on psoriasis status and sleep. Multistage stratified survey methodology was applied, with multivariable logistic regression models used to examine the association between psoriasis and sleep issues, adjusting for factors such as age, gender, and health history. RESULTS: Psoriasis diagnosis was significantly associated with trouble sleeping (adjusted odds ratio [aOR] 1.88; 95% confidence interval [CI] 1.44-2.45). There was no significant association between psoriasis and sleep quantity. Older age, female gender, and a history of sleep disorders were predictors of trouble sleeping among psoriasis patients. CONCLUSIONS: Psoriasis is significantly associated with sleep disturbances, independent of sleep duration. This underscores the need for clinical screening focusing on sleep quality rather than quantity in psoriasis patients to effectively identify and treat sleep-related comorbidities. Further research using objective sleep measures is warranted to guide clinical management and improve patient quality of life.

Pioneering a Multi-Phase Framework to Harmonize Self-Reported Sleep Data Across Cohorts.

STUDY OBJECTIVES: Harmonizing and aggregating data across studies enable pooled analyses that support external validation and enhance replicability and generalizability. However, the multidimensional nature of sleep poses challenges for data harmonization and aggregation. Here we describe and implement our process for harmonizing self-reported sleep data. METHODS: We established a multi-phase framework to harmonize self-reported sleep data: (1) compile items; (2) group items into domains; (3) harmonize items; and (4) evaluate harmonizability. We applied this process to produce a pooled multi-cohort sample of five United States cohorts plus a separate yet fully harmonized sample from Rotterdam, Netherlands. Sleep and sociodemographic data are described and compared to demonstrate the utility of harmonization and aggregation. RESULTS: We collected 190 unique self-reported sleep items and grouped them into 15 conceptual domains. Using these domains as guiderails, we developed 14 harmonized items measuring aspects of Satisfaction, Alertness/Sleepiness, Timing, Efficiency, Duration, Insomnia, and Sleep Apnea. External raters determined that 13 of these 14 items had moderate-to-high harmonizability. Alertness/Sleepiness items had lower harmonizability, while continuous, quantitative items (e.g., timing, total sleep time, efficiency) had higher harmonizability. Descriptive statistics identified features that are more consistent (e.g., wake-up time, duration) and more heterogeneous (e.g., time in bed, bedtime) across samples. CONCLUSIONS: Our process can guide researchers and cohort stewards towards effective sleep harmonization and provides a foundation for further methodological development in this expanding field. Broader national and international initiatives promoting common data elements across cohorts are needed to enhance future harmonization and aggregation efforts.

Power-law properties of nocturnal arrhythmia avalanches: a novel marker for incident cardiovascular events.

BACKGROUND: Bursting non-sustained cardiac arrhythmia events, are a common observation during sleep. OBJECTIVES: We hypothesized nocturnal arrhythmia episode durations could follow a power-law, whose exponent could predict long-term clinical outcomes. METHODS: We defined 'nocturnal arrhythmia avalanche' (NAA) as any instance of a drop in electrocardiogram (ECG) template-matched R-R intervals ≥30% of R-R baseline, followed by a return to 90% of the baseline. We studied NAA in ECG recordings obtained from the Sleep Heart Health Study (SHHS), the Osteoporotic Fractures in Men Study (MrOS) Sleep and Multi-Ethnic Study of Atherosclerosis (MESA) studies. The association of the nocturnal arrhythmia durations with a power-law distribution was evaluated, and the association of derived power-law exponents (α) with major adverse cardiovascular events and mortality assessed with multivariable Cox regression. RESULTS: n=9176 participants were studied. NAA episodes distribution was with a consistent power-law versus comparator distributions in all datasets studied (Positive log likelihood ratio of power-law vs. exponential in MESA: 83%; SHHS: 69%; MrOS: 81%; power-law vs. log-normal in MESA: 95%; SHHS: 35% and MrOS: 64%). The NAA power law exponent (α) showed a significant association of with adverse CV outcomes (Association with CV mortality: SHHS (HR = 1.39[1.07-1.79], p=0.012); MrOS (HR = 1.42[1.02-1.94], p=0.039; Association with CV events: MESA (HR = 3.46[1.46-8.21], p=0.005)) in multivariable Cox regression, after adjusting for conventional CV risk factors and nocturnal ectopic rate. CONCLUSION: The NAA power-law exponent is a reproducible, predictive marker for incident cardiovascular events and mortality.

Long-term Trajectories of Low Back Pain in Older Men: A Prospective Cohort Study With 10-Year Analysis of the Osteoporotic Fractures in Men Study.

Although low back pain (LBP) may persist or recur over time, few studies have evaluated the individual course of LBP over a long-term period, particularly among older adults. Based on data from the longitudinal Osteoporotic Fractures in Men (MrOS) Study, we aimed to identify and describe different LBP trajectories in older men and characterize members in each trajectory group. A total of 5 976 community-dwelling men (mean age = 74.2) enrolled at 6 U.S. sites were analyzed. Participants self-reported LBP (yes/no) every 4 months for a maximum of 10 years. Latent class growth modeling was performed to identify unique LBP trajectory groups that explained variation in the LBP data. The association of baseline characteristics with trajectory group membership was assessed using univariable and multivariable multinominal logistic regression. A 5-class solution was chosen; no/rare LBP (n = 2 442/40.9%), low frequency-stable LBP (n = 1 040/17.4%), low frequency-increasing LBP (n = 719/12%), moderate frequency-decreasing LBP (n = 745/12.5%), and high frequency-stable LBP (n = 1 030/17.2%). History of falls (OR = 1.52), history of LBP (OR = 6.37), higher physical impairment (OR = 1.51-2.85), and worse psychological function (OR = 1.41-1.62) at baseline were all associated with worse LBP trajectory groups in this sample of older men. These findings present an opportunity for targeted interventions and/or management to older men with worse or increasing LBP trajectories and associated modifiable risk factors to reduce the impact of LBP and improve quality of life.

Sleep-wake behaviors associated with cognitive performance in middle-aged participants of the Hispanic Community Health Study/Study of Latinos.

OBJECTIVES: Many sleep-wake behaviors have been associated with cognition. We examined a panel of sleep-wake/activity characteristics to determine which are most robustly related to having low cognitive performance in midlife. Secondarily, we evaluate the predictive utility of sleep-wake measures to screen for low cognitive performance. METHODS: The outcome was low cognitive performance defined as being >1 standard deviation below average age/sex/education internally normalized composite cognitive performance levels assessed in the Hispanic Community Health Study/Study of Latinos. Analyses included 1006 individuals who had sufficient sleep-wake measurements about 2years later (mean age=54.9, standard deviation= 5.1; 68.82% female). We evaluated associations of 31 sleep-wake variables with low cognitive performance using separate logistic regressions. RESULTS: In individual models, the strongest sleep-wake correlates of low cognitive performance were measures of weaker and unstable 24-hour rhythms; greater 24-hour fragmentation; longer time-in-bed; and lower rhythm amplitude. One standard deviation worse on these sleep-wake factors was associated with ∼20%-30% greater odds of having low cognitive performance. In an internally cross-validated prediction model, the independent correlates of low cognitive performance were: lower Sleep Regularity Index scores; lower pseudo-F statistics (modellability of 24-hour rhythms); lower activity rhythm amplitude; and greater time in bed. Area under the curve was low/moderate (64%) indicating poor predictive utility. CONCLUSION: The strongest sleep-wake behavioral correlates of low cognitive performance were measures of longer time-in-bed and irregular/weak rhythms. These sleep-wake assessments were not useful to identify previous low cognitive performance. Given their potential modifiability, experimental trials could test if targeting midlife time-in-bed and/or irregular rhythms influences cognition.