Modeling the selective growth advantage of genetically variant human pluripotent stem cells to identify opportunities for manufacturing process control.

BACKGROUND AIMS: The appearance of genetically variant populations in human pluripotent stem cell (hPSC) cultures represents a concern for research and clinical applications. Genetic variations may alter hPSC differentiation potential or cause phenotype variation in differentiated cells. Further, variants may have properties such as proliferative rate, or response to the culture environment, that differ from wild-type cells. As such, understanding the behavior of these variants in culture, and any potential operational impact on manufacturing processes, will be necessary to control quality of putative hPSC-based products that include a proportion of variant threshold in their quality specification. METHODS: Here we show a computational model that mathematically describes the growth dynamics between commonly occurring genetically variant hPSCs and their counterpart wild-type cells in culture. RESULTS: We show that our model is capable of representing the growth behaviors of both wild-type and variant hPSCs in individual and co-culture systems. CONCLUSIONS: This representation allows us to identify three critical process parameters that drive critical quality attributes when genetically variant cells are present within the system: total culture density, proportion of variant cells within the culture system and variant cell overgrowth. Lastly, we used our model to predict how the variability of these parameters affects the prevalence of both populations in culture.

Use of chemotherapy in patients with oesophageal, stomach, colon, rectal, liver, pancreatic, lung, and ovarian cancer: an International Cancer Benchmarking Partnership (ICBP) population-based study.

BACKGROUND: There are few data on international variation in chemotherapy use, despite it being a key treatment type for some patients with cancer. Here, we aimed to examine the presence and size of such variation. METHODS: This population-based study used data from Norway, the four UK nations (England, Northern Ireland, Scotland, and Wales), eight Canadian provinces (Alberta, British Columbia, Manitoba, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, and Saskatchewan), and two Australian states (New South Wales and Victoria). Patients aged 15-99 years diagnosed with cancer in eight different sites (oesophageal, stomach, colon, rectal, liver, pancreatic, lung, or ovarian cancer), with no other primary cancer diagnosis occurring from within the 5 years before to 1 year after the index cancer diagnosis or during the study period were included in the study. We examined variation in chemotherapy use from 31 days before to 365 days after diagnosis and time to its initiation, alongside related variation in patient group differences. Information was obtained from cancer registry records linked to clinical or patient management system data or hospital administration data. Random-effects meta-analyses quantified interjurisdictional variation using 95% prediction intervals (95% PIs). FINDINGS: Between Jan 1, 2012, and Dec 31, 2017, of 893 461 patients with a new diagnosis of one of the studied cancers, 111 569 (12·5%) did not meet the inclusion criteria, and 781 892 were included in the analysis. There was large interjurisdictional variation in chemotherapy use for all studied cancers, with wide 95% PIs: 47·5 to 81·2 (pooled estimate 66·4%) for ovarian cancer, 34·9 to 59·8 (47·2%) for oesophageal cancer, 22·3 to 62·3 (40·8%) for rectal cancer, 25·7 to 55·5 (39·6%) for stomach cancer, 17·2 to 56·3 (34·1%) for pancreatic cancer, 17·9 to 49·0 (31·4%) for lung cancer, 18·6 to 43·8 (29·7%) for colon cancer, and 3·5 to 50·7 (16·1%) for liver cancer. For patients with stage 3 colon cancer, the interjurisdictional variation was greater than that for all patients with colon cancer (95% PI 38·5 to 78·4; 60·1%). Patients aged 85-99 years had 20-times lower odds of chemotherapy use than those aged 65-74 years, with very large interjurisdictional variation in this age difference (odds ratio 0·05; 95% PI 0·01 to 0·19). There was large variation in median time to first chemotherapy (from diagnosis date) by cancer site, with substantial interjurisdictional variation, particularly for rectal cancer (95% PI -15·5 to 193·9 days; pooled estimate 89·2 days). Patients aged 85-99 years had slightly shorter median time to first chemotherapy compared with those aged 65-74 years, consistently between jurisdictions (-3·7 days, 95% PI -7·6 to 0·1). INTERPRETATION: Large variation in use and time to chemotherapy initiation were observed between the participating jurisdictions, alongside large and variable age group differences in chemotherapy use. To guide efforts to improve patient outcomes, the underlying reasons for these patterns need to be established. FUNDING: International Cancer Benchmarking Partnership (funded by the Canadian Partnership Against Cancer, Cancer Council Victoria, Cancer Institute New South Wales, Cancer Research UK, Danish Cancer Society, National Cancer Registry Ireland, The Cancer Society of New Zealand, National Health Service England, Norwegian Cancer Society, Public Health Agency Northern Ireland on behalf of the Northern Ireland Cancer Registry, DG Health and Social Care Scottish Government, Western Australia Department of Health, and Public Health Wales NHS Trust).

Assessing Risk of Health Outcomes From Brain Activity in Sleep: A Retrospective Cohort Study.

Background and Objectives: Patterns of electrical activity in the brain (EEG) during sleep are sensitive to various health conditions even at subclinical stages. The objective of this study was to estimate sleep EEG-predicted incidence of future neurologic, cardiovascular, psychiatric, and mortality outcomes. Methods: This is a retrospective cohort study with 2 data sets. The Massachusetts General Hospital (MGH) sleep data set is a clinic-based cohort, used for model development. The Sleep Heart Health Study (SHHS) is a community-based cohort, used as the external validation cohort. Exposure is good, average, or poor sleep defined by quartiles of sleep EEG-predicted risk. The outcomes include ischemic stroke, intracranial hemorrhage, mild cognitive impairment, dementia, atrial fibrillation, myocardial infarction, type 2 diabetes, hypertension, bipolar disorder, depression, and mortality. Diagnoses were based on diagnosis codes, brain imaging reports, medications, cognitive scores, and hospital records. We used the Cox survival model with death as the competing risk. Results: There were 8673 participants from MGH and 5650 from SHHS. For all outcomes, the model-predicted 10-year risk was within the 95% confidence interval of the ground truth, indicating good prediction performance. When comparing participants with poor, average, and good sleep, except for atrial fibrillation, all other 10-year risk ratios were significant. The model-predicted 10-year risk ratio closely matched the observed event rate in the external validation cohort. Discussion: The incidence of health outcomes can be predicted by brain activity during sleep. The findings strengthen the concept of sleep as an accessible biological window into unfavorable brain and general health outcomes.

Between-year and spatial variation in body condition across the breeding cycle in a pelagic seabird, the Red-billed Tropicbird.

Body condition in pelagic seabirds impacts key fitness-related traits such as reproductive performance and breeding frequency. Regulation of body condition can be especially important for species with long incubation periods and long individual incubation shifts between foraging trips. Here, we show that body condition of adult Red-billed Tropicbirds (Phaethon aethereus) at St Helena Island, South Atlantic Ocean, exhibited considerable variation between years (2013-2017) and between different stages of the breeding cycle. Females took the first incubation shift following egg laying, after which males and females alternated incubation shifts of varying length, ranging from <1 to 12 days. Body condition declined in both sexes during an incubation shift by an average of 22 g (2.83% of starting mass) per day and over the incubation period; mass loss was significantly greater during longer incubation shifts, later within a shift and later in the total incubation period. There was also significant differences in incubation behaviour and body condition between years; in 2015, coinciding with a moderate coastal warming event along the Angolan-Namibian coastlines, adults on average undertook longer incubation shifts than in other years and had lower body condition. This suggests that substantial between-year prey fluctuations in the Angola Benguela upwelling system may influence prey availability, in turn affecting incubation behaviour and regulation of body condition. Adults rearing chicks showed a significant reduction in body condition when chicks showed the fastest rate of growth. Chick growth rates during 2017 from two localities in the Atlantic Ocean: an oceanic (St Helena) versus neritic (Cabo Verde) population were similar, but chicks from St Helena were overall heavier and larger at fledging. Results from this multi-year study highlight that flexibility and adaptability in body condition regulation will be important for populations of threatened species to optimise resources as global climate change increasingly influences prey availability.

REM Sleep Behavior Disorder and Its Possible Prodromes in General Population: Prevalence, Polysomnography Findings, and Associated Factors.

BACKGROUND AND OBJECTIVES: To evaluate the prevalence of REM sleep behavior disorder (RBD) and its possible prodromal conditions, isolated dream enactment behavior (DEB) and isolated REM without atonia (RWA), in a general population sample, and the factors associated with diagnosis and symptom frequency. METHODS: From a population-based prospective cohort in Korea, 1,075 participants (age 60.1 ± 7.0 years; range 50-80 years; men 53.7%) completed the RBD screening questionnaire (RBDSQ), a structured telephone interview for the presence and characteristics of repeated DEB, and home polysomnography (PSG). RWA was measured on submentalis EMG, including 30-second epoch-based tonic and phasic activity as well as 3-second mini-epoch-based phasic and any EMG activities. Based on the presence of repeated DEB and any EMG activity of ≥22.3%, we categorized the participants into no RBD, isolated RWA, isolated DEB, and RBD groups. RESULTS: RBD was diagnosed in 20 participants, isolated RWA in 133 participants, and isolated DEB in 48 participants. Sex and DEB frequency-adjusted prevalence of RBD was 1.4% (95% CI 1.0%-1.8%), isolated RWA was 12.5% (95% CI 11.3%-13.6%), and isolated DEB was 3.4% (95% CI 2.7%-4.1%). Total RBDSQ score was higher in the RBD and isolated DEB groups than in the isolated RWA and no RBD group (median 5 [interquartile range (IQR) 4-6] for RBD, median 4 [IQR 3-6] for isolated DEB, median 2 [IQR 1-3] for isolated RWA, and median 2 [IQR 1-4] for no RBD groups, p < 0.001). RBDSQ score of ≥5 had good specificity but poor positive predictive value (PPV) for RBD (specificity 84.1% and PPV 7.7%) and its prodromal conditions (specificity 85.2% and PPV 29.1%). Among the RWA parameters, any EMG activity showed the best association with the RBD and its possible prodromes (area under the curve, 0.917). Three-second mini-epoch-based EMG activity and phasic EMG activity were correlated with the frequency of DEB (standardized Jonckheere-Terpstra statistic [std. J-T static] for trend = 0.488, p < 0.001, and std. J-T static = 3.265, p = 0.001, respectively). DISCUSSION: This study provides prevalence estimates of RBD and its possible prodromal conditions based on a structured telephone interview and RWA measurement on PSG from the general population.

Decoding information about cognitive health from the brainwaves of sleep.

Sleep electroencephalogram (EEG) signals likely encode brain health information that may identify individuals at high risk for age-related brain diseases. Here, we evaluate the correlation of a previously proposed brain age biomarker, the "brain age index" (BAI), with cognitive test scores and use machine learning to develop and validate a series of new sleep EEG-based indices, termed "sleep cognitive indices" (SCIs), that are directly optimized to correlate with specific cognitive scores. Three overarching cognitive processes were examined: total, fluid (a measure of cognitive processes involved in reasoning-based problem solving and susceptible to aging and neuropathology), and crystallized cognition (a measure of cognitive processes involved in applying acquired knowledge toward problem-solving). We show that SCI decoded information about total cognition (Pearson's r = 0.37) and fluid cognition (Pearson's r = 0.56), while BAI correlated only with crystallized cognition (Pearson's r = - 0.25). Overall, these sleep EEG-derived biomarkers may provide accessible and clinically meaningful indicators of neurocognitive health.

Long-term blood pressure patterns in midlife and dementia in later life: Findings from the Framingham Heart Study.

INTRODUCTION: Long-term blood pressure (BP) measures, such as visit-to-visit BP variability (BPV) and cumulative BP, are strong indicators of cardiovascular risks. This study modeled up to 20 years of BP patterns representative of midlife by using BPV and cumulative BP, then examined their associations with development of dementia in later life. METHODS: For 3201 individuals from the Framingham Heart Study, multivariate logistic regression analyses were performed to examine the association between long-term BP patterns during midlife and the development of dementia (ages ≥ 65). RESULTS: After adjusting for covariates, every quartile increase in midlife cumulative BP was associated with a sequential increase in the risk of developing dementia (e.g., highest quartile of cumulative systolic blood pressure had approximately 2.5-fold increased risk of all-cause dementia). BPV was not significantly associated with dementia. DISCUSSION: Findings suggest that cumulative BP over the course of midlife predicts risk of dementia in later life. HIGHLIGHTS Long-term blood pressure (BP) patterns are strong indicators of vascular risks. Cumulative BP and BP variability (BPV) were used to reflect BP patterns across midlife. High cumulative BP in midlife is associated with increased dementia risk. Visit-to-visit BPV was not associated with the onset of dementia.

Evaluation of inactivation of bovine coronavirus by low-level radiofrequency irradiation.

Inactivation of influenza A virus by radiofrequency (RF) energy exposure at levels near Institute of Electrical and Electronics Engineers (IEEE) safety thresholds has been reported. The authors hypothesized that this inactivation was through a structure-resonant energy transfer mechanism. If this hypothesis is confirmed, such a technology could be used to prevent transmission of virus in occupied public spaces where RF irradiation of surfaces could be performed at scale. The present study aims to both replicate and expand the previous work by investigating the neutralization of bovine coronavirus (BCoV), a surrogate of SARS-CoV-2, by RF radiation in 6-12 GHz range. Results showed an appreciable reduction in BCoV infectivity (up to 77%) due to RF exposure to certain frequencies, but failed to generate enough reduction to be considered clinically significant.

Association of lifestyle with deep learning predicted electrocardiographic age.

Background: People age at different rates. Biological age is a risk factor for many chronic diseases independent of chronological age. A good lifestyle is known to improve overall health, but its association with biological age is unclear. Methods: This study included participants from the UK Biobank who had undergone 12-lead resting electrocardiography (ECG). Biological age was estimated by a deep learning model (defined as ECG-age), and the difference between ECG-age and chronological age was defined as Δage. Participants were further categorized into an ideal (score 4), intermediate (scores 2 and 3) or unfavorable lifestyle (score 0 or 1). Four lifestyle factors were investigated, including diet, alcohol consumption, physical activity, and smoking. Linear regression models were used to examine the association between lifestyle factors and Δage, and the models were adjusted for sex and chronological age. Results: This study included 44,094 individuals (mean age 64 ± 8, 51.4% females). A significant correlation was observed between predicted biological age and chronological age (correlation coefficient = 0.54, P < 0.001) and the mean Δage (absolute error of biological age and chronological age) was 9.8 ± 7.4 years. Δage was significantly associated with all of the four lifestyle factors, with the effect size ranging from 0.41 ± 0.11 for the healthy diet to 2.37 ± 0.30 for non-smoking. Compared with an ideal lifestyle, an unfavorable lifestyle was associated with an average of 2.50 ± 0.29 years of older predicted ECG-age. Conclusion: In this large contemporary population, a strong association was observed between all four studied healthy lifestyle factors and deaccelerated aging. Our study underscores the importance of a healthy lifestyle to reduce the burden of aging-related diseases.

The association between social jetlag and depression is independent of sleep debt.

OBJECTIVES: To investigate whether the association between SJLsc (sleep-corrected social jetlag) and depressive mood is significant and independent of sleep debt. METHODS: Participants from the general adult population were interviewed using structured questionnaires on sleep duration, weekday/weekend sleep schedules, and depressive mood (Patient Health Questionnaire-9). Social jetlag (SJL) was measured by SJLsc and standard SJL (SJLs). SJLs was the absolute difference between mid-sleep time on free days (MSF) and workdays (MSW). For SJLsc, both MSF and MSW were adjusted for average sleep duration across the week according to the direction of sleep debt. Sleep debt was defined by sleep extension on free days. The association of SJL with depression was investigated, and covariates included age, sex, sociodemographic factors, insomnia symptoms, sleep duration, and sleep debt. RESULTS: A total of 1982 individuals (1089 men; age 43.1 ± 14.4 years) were analyzed. SJL was present in 24.6% measured by SJLsc and 51.0% by SJLs. SJLsc and SJLs were significantly associated with depressive mood (r = 0.06, P = 0.02; r = 0.06, P = 0.01, respectively), independent of sleep debt. Sleep debt was also associated with depression (r = 0.07, P < 0.01). CONCLUSIONS: By adopting sleep-corrected formula for SJL, this study found that misaligned and insufficient sleep, at levels occurring in routine social life, can negatively affect mood. Minimizing social jetlag and sleep deprivation may promote individual psychological well-being.

Sound and light levels in intensive care units in a large urban hospital in the United States.

Intensive care units (ICUs) may disrupt sleep. Quantitative ICU studies of concurrent and continuous sound and light levels and timings remain sparse in part due to the lack of ICU equipment that monitors sound and light. Here, we describe sound and light levels across three adult ICUs in a large urban United States tertiary care hospital using a novel sensor. The novel sound and light sensor is composed of a Gravity Sound Level Meter for sound level measurements and an Adafruit TSL2561 digital luminosity sensor for light levels. Sound and light levels were continuously monitored in the room of 136 patients (mean age = 67.0 (8.7) years, 44.9% female) enrolled in the Investigation of Sleep in the Intensive Care Unit study (ICU-SLEEP; Clinicaltrials.gov: #NCT03355053), at the Massachusetts General Hospital. The hours of available sound and light data ranged from 24.0 to 72.2 hours. Average sound and light levels oscillated throughout the day and night. On average, the loudest hour was 17:00 and the quietest hour was 02:00. Average light levels were brightest at 09:00 and dimmest at 04:00. For all participants, average nightly sound levels exceeded the WHO guideline of < 35 decibels. Similarly, mean nightly light levels varied across participants (minimum: 1.00 lux, maximum: 577.05 lux). Sound and light events were more frequent between 08:00 and 20:00 than between 20:00 and 08:00 and were largely similar on weekdays and weekend days. Peaks in distinct alarm frequencies (Alarm 1) occurred at 01:00, 06:00, and at 20:00. Alarms at other frequencies (Alarm 2) were relatively consistent throughout the day and night, with a small peak at 20:00. In conclusion, we present a sound and light data collection method and results from a cohort of critically ill patients, demonstrating excess sound and light levels across multiple ICUs in a large tertiary care hospital in the United States. ClinicalTrials.gov, #NCT03355053. Registered 28 November 2017, https://clinicaltrials.gov/ct2/show/NCT03355053.

Obstructive sleep apnoea and long-term risk of incident diabetes in the middle-aged and older general population.

Background: Obstructive sleep apnoea (OSA) is associated with increased risk of type 2 diabetes. However, results from large population-based prospective cohort studies are rare. The main aim of the present study was to investigate the relative risk of 8-year incident type 2 diabetes in relation to OSA severity in a prospective cohort study of middle-aged and older adults. Methods: A total of 2918 participants (mean age 59 years) of the Korean Genome and Epidemiology Study (KoGES), who underwent home-based overnight polysomnography at baseline examination between 2011 and 2014, were followed up 4-yearly between 2015-2018 and 2019-2021. A total of 1697 participants were present in both follow-ups. After excluding participants who had diabetes at baseline (n=481), a total of 1216 participants were eligible for the analyses. Results: OSA at baseline was categorised by apnoea-hypopnoea index levels as non-OSA (0-4.9 events·h-1), mild OSA (5.0-14.9 events·h-1) and moderate-severe OSA (≥15.0 events·h-1). Incident type 2 diabetes was identified at each follow-up. Compared with non-OSA, participants with moderate-severe OSA had 1.5 times higher risk of developing type 2 diabetes at the end of the 8-year follow-up after adjusting for potential covariates (relative risk 1.50, 95% CI 1.02-2.21). In the same analytical models for 4-year relative risk of incident type 2 diabetes, none of the OSA groups were at significantly higher risk compared with the non-OSA group. Conclusion: Moderate-severe OSA, a modifiable risk factor, poses a higher risk of incident type 2 diabetes compared with non-OSA over an 8-year period in general middle-aged and older adults.

Sleep staging in the ICU with heart rate variability and breathing signals. An exploratory cross-sectional study using deep neural networks.

Introduction: To measure sleep in the intensive care unit (ICU), full polysomnography is impractical, while activity monitoring and subjective assessments are severely confounded. However, sleep is an intensely networked state, and reflected in numerous signals. Here, we explore the feasibility of estimating conventional sleep indices in the ICU with heart rate variability (HRV) and respiration signals using artificial intelligence methods Methods: We used deep learning models to stage sleep with HRV (through electrocardiogram) and respiratory effort (through a wearable belt) signals in critically ill adult patients admitted to surgical and medical ICUs, and in age and sex-matched sleep laboratory patients Results: We studied 102 adult patients in the ICU across multiple days and nights, and 220 patients in a clinical sleep laboratory. We found that sleep stages predicted by HRV- and breathing-based models showed agreement in 60% of the ICU data and in 81% of the sleep laboratory data. In the ICU, deep NREM (N2 + N3) proportion of total sleep duration was reduced (ICU 39%, sleep laboratory 57%, p < 0.01), REM proportion showed heavy-tailed distribution, and the number of wake transitions per hour of sleep (median 3.6) was comparable to sleep laboratory patients with sleep-disordered breathing (median 3.9). Sleep in the ICU was also fragmented, with 38% of sleep occurring during daytime hours. Finally, patients in the ICU showed faster and less variable breathing patterns compared to sleep laboratory patients Conclusion: The cardiovascular and respiratory networks encode sleep state information, which, together with artificial intelligence methods, can be utilized to measure sleep state in the ICU.

The sleep and wake electroencephalogram over the lifespan.

Both sleep and wake encephalograms (EEG) change over the lifespan. While prior studies have characterized age-related changes in the EEG, the datasets span a particular age group, or focused on sleep and wake macrostructure rather than the microstructure. Here, we present sex-stratified data from 3372 community-based or clinic-based otherwise neurologically and psychiatrically healthy participants ranging from 11 days to 80 years of age. We estimate age norms for key sleep and wake EEG parameters including absolute and relative powers in delta, theta, alpha, and sigma bands, as well as sleep spindle density, amplitude, duration, and frequency. To illustrate the potential use of the reference measures developed herein, we compare them to sleep EEG recordings from age-matched participants with Alzheimer's disease, severe sleep apnea, depression, osteoarthritis, and osteoporosis. Although the partially clinical nature of the datasets may bias the findings towards less normal and hence may underestimate pathology in practice, age-based EEG reference values enable objective screening of deviations from healthy aging among individuals with a variety of disorders that affect brain health.

Evaluation of Viral Inactivation on Dry Surface by High Peak Power Microwave (HPPM) Exposure.

Previous research has shown that virus infectivity can be dramatically reduced by radio frequency exposure in the gigahertz (GHz) frequency range. Given the worldwide SARS-CoV-2 pandemic, which has caused over 1 million deaths and has had a profound global economic impact, there is a need for a noninvasive technology that can reduce the transmission of virus among humans. RF is a potential wide area-of-effect viral decontamination technology that could be used in hospital rooms where patients are expelling virus, in grocery and convenience stores where local populations mix, and in first responder settings where rapid medical response spans many potentially infected locations within hours. In this study, we used bovine coronavirus (BCoV) as a surrogate of SARS-CoV-2 and exposed it to high peak power microwave (HPPM) pulses at four narrowband frequencies: 2.8, 5.6, 8.5, and 9.3 GHz. Exposures consisted of 2 µs pulses delivered at 500 Hz, with pulse counts varied by decades between 1 and 10,000. The peak field intensities (i.e. the instantaneous power density of each pulse) ranged between 0.6 and 6.5 MW/m2 , depending on the microwave frequency. The HPPM exposures were delivered to plastic coverslips containing BCoV dried on the surface. Hemagglutination (HA) and cytopathic effect analyses were performed 6 days after inoculation of host cells to assess viral infectivity. No change in viral infectivity was seen with increasing dose (pulse number) across the tested frequencies. Under all conditions tested, exposure did not reduce infectivity more than 1.0 log10. For the conditions studied, high peak power pulsed RF exposures in the 2-10 GHz range appear ineffective as a virucidal approach for hard surface decontamination. © 2023 Bioelectromagnetics Society.

Association between older subjective age and poor sleep quality: a population-based study.

OBJECTIVE: To examine the association of subjective age (SA) with sleep quality in an adult population. METHODS: In the Korean Sleep and Headache Study, 2,349 participants (49.2% men; 48.1 ± 16.4 years old) were interviewed face-to-face using structured questionnaires between September and December 2018. SA was assessed by asking participants their perceived age in years and then compared with their chronological age (CA). Participants were assigned to three groups: feeling younger, feeling their age, and feeling older. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Association between SA and sleep quality was analyzed with multiple linear regression controlling for demographics, psychosocial, and sleep characteristics. RESULTS: The group feeling older (n = 404, 17.2%; men, 58.2%; age, 46.5 ± 16.2 years) had worse sleep quality than the groups feeling younger and feeling their age (PSQI score, 4.3 ± 2.7, 3.8 ± 2.4, 3.4 ± 2.1, respectively, p <.001; prevalence of poor sleep quality, 29.0%, 18.4%, 13.5% respectively, p <.001). The association between SA and the PSQI score remained significant after adjusting for confounders (ß = 1.05, 95% confidence interval 0.26, 1.83; p <.001). Stratified analyses by sex and CA showed that the association between SA and the PSQI score was significant only in women and in middle-aged and older group (aged 50-79), suggesting that sex and CA modified the association. CONCLUSION: Age perception was associated with self-reported sleep quality, independent of CA. SA may be a useful marker that complements the conventional assessment of subjective sleep quality.