Actigraphy-derived measures of sleep and risk of prostate cancer in the UK Biobank

Joshua R Freeman; Pedro F Saint-Maurice; Eleanor L Watts; Steven C Moore; Marissa M Shams-White; Dana L Wolff-Hughes; Daniel E Russ; Jonas S Almeida; Neil E Caporaso; Hyokyoung G Hong; Erikka Loftfield; Charles E Matthews

doi:10.1093/jnci/djad210

. 2023 Nov 28;116(3):434–444. doi: 10.1093/jnci/djad210

Actigraphy-derived measures of sleep and risk of prostate cancer in the UK Biobank

Joshua R Freeman ^1,^✉, Pedro F Saint-Maurice ², Eleanor L Watts ³, Steven C Moore ⁴, Marissa M Shams-White ⁵, Dana L Wolff-Hughes ⁶, Daniel E Russ ⁷, Jonas S Almeida ⁸, Neil E Caporaso ⁹, Hyokyoung G Hong ¹⁰, Erikka Loftfield ¹¹, Charles E Matthews ¹²

¹ Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

² Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

³ Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

⁴ Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

⁵ Risk Factor Assessment Branch, Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

⁶ Risk Factor Assessment Branch, Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

⁷ Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

⁸ Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

⁹ Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

¹⁰ Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

¹¹ Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

¹² Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

^✉

Correspondence to: Joshua R. Freeman, PhD, MPH, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Dr, MSC 9776, Bethesda, MD, USA 20892 (e-mail: josh.freeman@nih.gov).

Roles

Joshua R Freeman: PhD, MPH, Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing - original draft, Writing - review & editing

Pedro F Saint-Maurice: PhD, Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Writing - review & editing

Eleanor L Watts: DPhil, MPH, Methodology, Writing - review & editing

Steven C Moore: PhD, MPH, Methodology, Writing - review & editing

Marissa M Shams-White: PhD, MSTOM, MS, MPH, Writing - review & editing

Dana L Wolff-Hughes: PhD, Writing - review & editing

Daniel E Russ: PhD, Data curation, Writing - review & editing

Jonas S Almeida: PhD, Data curation, Writing - review & editing

Neil E Caporaso: MD, Methodology, Writing - review & editing

Hyokyoung G Hong: PhD, Methodology, Supervision, Writing - review & editing

Erikka Loftfield: PhD, MPH, Methodology, Writing - review & editing

Charles E Matthews: PhD, Conceptualization, Methodology, Resources, Supervision, Writing - original draft, Writing - review & editing

PMCID: PMC10919343 PMID: 38013591

Abstract

Background

Studies of sleep and prostate cancer are almost entirely based on self-report, with limited research using actigraphy. Our goal was to evaluate actigraphy-measured sleep and prostate cancer and to expand on findings from prior studies of self-reported sleep.

Methods

We prospectively examined 34 260 men without a history of prostate cancer in the UK Biobank. Sleep characteristics were measured over 7 days using actigraphy. We calculated sleep duration, onset, midpoint, wake-up time, social jetlag (difference in weekend-weekday sleep midpoints), sleep efficiency (percentage of time spent asleep between onset and wake-up time), and wakefulness after sleep onset. Cox proportional hazards models were used to estimate covariate-adjusted hazards ratios (HRs) and 95% confidence intervals (CIs).

Results

Over 7.6 years, 1152 men were diagnosed with prostate cancer. Sleep duration was not associated with prostate cancer risk. Sleep midpoint earlier than 4:00 am was not associated with prostate cancer risk, though sleep midpoint of 5:00 am or later was suggestively associated with lower prostate cancer risk but had limited precision (earlier than 4:00 am vs 4:00-4:59 am HR = 1.00, 95% CI = 0.87 to 1.16; 5:00 am or later vs 4:00-4:59 am HR = 0.79, 95% CI = 0.57 to 1.10). Social jetlag was not associated with greater prostate cancer risk (1 to <2 hours vs <1 hour HR = 1.06, 95% CI = 0.89 to 1.25; ≥2 hours vs <1 hour HR = 0.90, 95% CI = 0.65 to 1.26). Compared with men who averaged less than 30 minutes of wakefulness after sleep onset per day, men with 60 minutes or more had a higher risk of prostate cancer (HR = 1.20, 95% CI = 1.00 to 1.43).

Conclusions

Of the sleep characteristics studied, higher wakefulness after sleep onset—a measure of poor sleep quality—was associated with greater prostate cancer risk. Replication of our findings between wakefulness after sleep onset and prostate cancer are warranted.

Prostate cancer is the most common male-specific cancer and is estimated to account for 14.7% of US cases in 2023 and made up 27% of UK cases between 2016 and 2018 (1,2). Circadian disruption—that is, sleep timing misaligned with natural tendencies—through shift work has been declared a probable carcinogen by the International Agency for Research on Cancer (3,4). Sleep patterns that may lead to circadian disruption are common in the general population. The US Centers for Disease Control and Prevention estimates that 35% of US adults in 2020 did not get enough sleep (ie, ≥7 hours) (5). Moreover, chronic social jetlag, the difference in sleep timing between workdays and nonworkdays, may also lead to circadian disruption and is common, with 46.5% of US adults reporting 1 hour or more of social jetlag (3,6,7). Thus, understanding the role of sleep and its many dimensions in prostate cancer risk is important (8).

Current evidence on sleep and prostate cancer has come from studies using self-reported sleep duration, timing, and quality. A few studies found an association with either short sleep duration and increased prostate cancer mortality (9) or long sleep duration and lower prostate cancer risk (10). Most studies of sleep duration, however, found no consistent association with prostate cancer risk or mortality (11-18). Sleep timing studies remain rare (15,18-21), though evidence from a single study suggested that even 1 hour of social jetlag was associated with 45% greater prostate cancer risk (21). Findings from studies of sleep disorders and sleep quality have been mixed, with some finding associations between poor sleep quality or insomnia and greater overall and high-grade prostate cancer risk (14,22-25), while others found no consistent association for prostate cancer risk or mortality (9,11,16,18,26). Self-reported sleep measures may be limited in capturing nightly sleep variation and may be prone to measurement error (27,28). Studies evaluating actigraphy-measured sleep may help provide novel insight into the role of sleep and prostate cancer etiology.

Our objective was to evaluate actigraphy-measured sleep characteristics and prostate cancer risk among men in the UK Biobank. In addition, we explored whether sleep characteristics were similarly associated with nonfatal prostate cancer incidence and prostate cancer–specific mortality as a potential indicator of tumor aggressiveness. We hypothesized that short sleep duration, later sleep timing, and poorer sleep quality would be associated with higher prostate cancer risk.

Methods

The UK Biobank is a prospective cohort study (29-31). Study details have previously been published, and data are globally accessible to approved researchers. In 2006-2010, 9.2 million UK National Health Service members 40 to 69 years of age who lived within approximately 40 km of assessment centers were invited to participate (29-31). The UK Biobank study was approved by the National Information Governance Board for Health and Social Care and the North West Multi-centre Research Ethics Committee; participants completed an electronic informed consent form, and 502 366 continue to participate. In 2013-2015, 236 519 participants were invited to complete a 7-day actigraphy protocol (32). In Figure 1, we detail our analytic sample derivation and exclusion criteria starting from 103 696, with actigraphy data available at the initial data request, and ending with 34 260 men without a history of prostate cancer. Small differences between those participating in the baseline UK Biobank cohort (n = 502 366) and those in the actigraphy subcohort that are still participating (n = 103 662) are shown in Supplementary Table 1 (available online).

Figure 1. — **Flowchart of analytic sample derivation**. ^aExcludes files that were damaged or of low size (field 90002) or not calibrated (field 90016). ^bExcludes first and last days of actigraphy and days with less than 19 hours of recorded actigraphy data. ^cExcludes days in which the average acceleration value was ±3 SDs from the sample mean, days in which the total duration of the day was ±3 SDs from the sample mean, and days in which sedentary time was on average 0%. ^dExcludes days in which sleep onset occurred at 12:00 pm, where wake time occurred at 6:00 pm, and days in which sleep duration was less than 3 hours/night or more than 12 hours/night.

Sleep and covariate assessment

Sleep duration, sleep onset, sleep midpoint, wake-up time, social jetlag, sleep efficiency, wakefulness after sleep onset, and the frequency of days with wakefulness after sleep onset of 30 minutes or longer standardized to the 7-day protocol were ascertained from actigraphy data using the R package GGIR, version 2.6 (R Foundation for Statistical Computing, Vienna, Austria) (33-40). Details on data collection, processing, and sleep characteristics are provided in the Supplementary Methods (available online). Sleep characteristic calculation and operationalization, summary statistics, and frequencies are provided in Supplementary Tables 2 through 4 (available online), respectively.

Potential confounders included baseline covariates such as age; body mass index (BMI); health status; smoking status; alcohol intake; income; education level; employment; shift work; race and ethnicity; Townsend Deprivation Index; tea intake; coffee intake; diabetes status; family history of prostate cancer; and history of prostate-specific antigen (PSA) testing, prostatic hyperplasia, prostate disorders, and prostate inflammation (41). Moderate to vigorous physical activity was ascertained from processed actigraphy data using GGIR, version 2.6 (33,34,36,38-40,42). Additional details are available in the Supplementary Methods (available online). In Supplementary Table 5 (available online), we list study variables and their corresponding UK Biobank data fields (41).

Outcome assessment

Men were followed from the end of monitor wear until their cancer diagnosis date, death, loss to follow-up, or end of follow-up (for England and Wales, February 29, 2020; for Scotland, January 31, 2021), whichever came first. End of follow-up for death registrations was September 30, 2021, for England and Wales, and October 31, 2021, for Scotland. The primary outcome was incident prostate cancer (International Statistical Classification of Diseases, Tenth Revision [ICD-10] code C61) derived from record linkage with National Health Service Digital (England and Wales) and the National Health Service Central Register (Scotland), which provides cancer registrations and deaths (43,44). As exploratory endpoints, we evaluated prostate cancer–specific mortality among those diagnosed with incident prostate cancer over follow-up and incident, nonfatal prostate cancers. We calculated person-time from the actigraphy end date to cancer registration (first incident cancer, except for nonmelanoma skin cancers, ICD-10 code C44), death, loss to follow-up, or censoring date for incidence analyses and from actigraphy end date to death, loss to follow-up, or censoring date for mortality.

Statistical analysis

We used Cox proportional hazards models to estimate hazards ratios (HRs) and 95% confidence intervals (CIs) for associations of sleep characteristics with prostate cancer risk and prostate cancer–specific mortality, with time on study as the time scale. We restricted social jetlag analyses to those with weekend sleep data (n = 25 826). Missing covariate data was less than 10% and addressed using multiple imputation (45). We found no violation of the proportional hazards assumption. We evaluated tests for linear trend for sleep onset, wake-up time, social jetlag, sleep efficiency, wakefulness after sleep onset, and wakefulness after sleep onset frequency associations. Tests for homogeneity in sleep duration and midpoint associations were evaluated using log-rank tests. Tests were 2-sided, and P-values less than .05 were considered statistically significant.

We conducted several sensitivity analyses for primary associations. First, we evaluated effect modification by age (<65 years vs ≥65 years), BMI (<30 kg/m² vs ≥30 kg/m²), and PSA testing history. Second, we examined light, moderate, and vigorous activity events together during wakefulness after sleep onset periods as a potential indicator of nocturia; we examined it as a confounder (continuous) and an effect modifier by examining participants below and above the median threshold of active wakefulness after sleep onset (0-2.7 minutes vs >2.7 minutes) (46,47). We evaluated interaction tests for age, BMI, PSA testing history, and active wakefulness after sleep onset. Third, we excluded participants with preexisting prostatic hyperplasia, prostate disorders, and prostate inflammation; shift workers; and those with preexisting diabetes separately to address potential residual confounding from these factors. Fourth, we restricted analyses to participants who had more than 2, more than 3, and more than 4 years of follow-up to evaluate the potential for reverse-causality. Finally, we explored associations with nonfatal prostate cancer risk and prostate cancer–specific mortality separately, given that prostate cancer–specific mortality may serve as an indicator of more aggressive tumors compared with nonfatal prostate cancers (48). Due to the limited number of prostate cancer deaths, models adjusting for age and BMI are presented in addition to fully adjusted models; modifications were also made to covariates in fully adjusted prostate cancer mortality models due to constraints with case counts. Analyses were conducted using SAS, version 9.4, software (SAS Institute Inc, Cary, NC).

Results

Participant characteristics

Among 34 260 men, 1152 were diagnosed with prostate cancer over 7.6 years of follow-up (Table 1). Based on an average (SD) of 4.8 (0.4) nights, average (SD) sleep duration was 6.6 (0.9) hours. Few men slept 5 hours or less (5.3%) or more than 8 hours (6.0%) (Table 1). Sleep duration was similar across average age. Those with shorter sleep duration (≤5 hours) tended to have higher BMI and poorer health. Those with shorter sleep tended to have later sleep onset, lower sleep efficiency, higher social jetlag, and higher wakefulness after sleep onset (Table 1).

Table 1.

Participant characteristics, by sleep duration, in the UK Biobank (N = 34 260)

Variable^a	≤5 h n = 1817	>5 to 6 h n = 7047	>6 to 7 h n = 13 760	>7 to 8 h n = 9565	>8 h n = 2071
Age, mean (SD), y	64.0 (7.4)	62.4 (8.0)	62.2 (8.0)	63.4 (7.7)	64.4 (7.4)
Moderate to vigorous physical activity, mean (SD), h/d	1.2 (0.6)	1.3 (0.6)	1.3 (0.6)	1.2 (0.6)	1.1 (0.6)
Sleep onset, mean (SD)	12:48 am (1:28)	12:10 am (1:08)	11:44 pm (0:58)	11:22 pm (0:54)	10:52 pm (0:58)
Sleep midpoint, mean (SD)	3:35 am (1:26)	3:25 am (1:07)	3:24 am (0:56)	3:26 am (0:53)	3:28 am (0:56)
Wake-up time, mean (SD)	6:22 am (1:28)	6:40 am (1:08)	7:04 am (0:58)	7:31 am (0:55)	8:03 am (0:58)
Sleep efficiency, mean (SD), %	84 (7.1)	87 (6.0)	89 (4.0)	91 (3.5)	92 (3.2)
Social jetlag, mean (SD), h	1.13 (1.0)	0.93 (0.8)	0.79 (0.7)	0.68 (0.6)	0.62 (0.6)
Wakefulness after sleep onset, mean (SD), h	0.97 (0.5)	0.91 (0.4)	0.82 (0.4)	0.75 (0.3)	0.77 (0.3)
Frequency of wakefulness after sleep onset ≥30 min, mean (SD), d	5.1 (2.1)	5.2 (2.0)	5.0 (2.0)	4.7 (2.1)	4.8 (2.1)
Follow-up time, mean (SD), d	1860 (409)	1883 (383)	1891 (381)	1878 (394)	1865 (396)
Sleep midpoint, No. (%), clock time
<4:00 am	1175 (64.7)	5205 (73.9)	10 667 (77.5)	7438 (77.8)	1604 (77.5)
4:00-4:59 am	398 (21.9)	1381 (19.6)	2475 (18.0)	1765 (18.5)	369 (17.8)
≥5:00 am	244 (13.4)	461 (6.5)	618 (4.5)	362 (3.8)	98 (4.7)
Body mass index (kg/m²), No. (%)
<25.0	350 (19.4)	1791 (25.5)	4084 (29.7)	3173 (33.2)	692 (33.5)
25.0 to <30.0	854 (47.2)	3454 (49.2)	6972 (50.8)	4760 (49.9)	1028 (49.8)
30.0 to <35.0	442 (24.5)	1392 (19.8)	2189 (15.9)	1390 (14.6)	286 (13.8)
35.0 to <40.0	114 (6.3)	293 (4.2)	397 (2.9)	187 (2.0)	53 (2.6)
≥40.0	48 (2.7)	95 (1.4)	95 (0.7)	39 (0.4)	7 (0.3)
Overall health rating, No. (%)
Excellent	312 (17.2)	1409 (20.0)	3023 (22.0)	2118 (22.2)	423 (20.5)
Good	985 (54.4)	4121 (58.6)	8189 (59.7)	5814 (60.9)	1193 (57.7)
Fair	420 (23.2)	1311 (18.6)	2205 (16.1)	1422 (14.9)	377 (18.3)
Poor	95 (5.2)	193 (2.7)	310 (2.3)	197 (2.1)	73 (3.5)
Alcohol intake, No. (%)
Daily or almost daily	525 (28.9)	1987 (28.2)	3822 (27.8)	2694 (28.2)	540 (26.1)
Weekly	888 (49.0)	3613 (51.3)	7423 (54.0)	5283 (55.3)	1126 (54.4)
1-3 times/mo	163 (9.0)	630 (9.0)	1170 (8.5)	733 (7.7)	179 (8.7)
Special occasions	127 (7.0)	452 (6.4)	740 (5.4)	498 (5.2)	140 (6.8)
Never	111 (6.1)	360 (5.1)	595 (4.3)	351 (3.7)	85 (4.1)
Smoking status, No. (%)
Never	879 (48.6)	3538 (50.3)	7322 (53.4)	5207 (54.6)	1125 (54.5)
Former	745 (41.2)	2823 (40.1)	5313 (38.7)	3698 (38.8)	795 (38.5)
Current	185 (10.2)	672 (9.6)	1078 (7.9)	636 (6.7)	144 (7.0)
Education level, No. (%)
College or university degree; National Vocational Qualification, Higher National Diploma, or Higher National Certificate, or equivalent; other professional qualifications	1278 (71.3)	5151 (73.8)	10 289 (75.5)	7077 (74.8)	1424 (69.4)
Advanced or Advanced Subsidiary levels or equivalent; Ordinary levels or General Certificate of Secondary Education or equivalent; Certificate of Secondary Education or equivalent	312 (17.4)	1221 (17.5)	2304 (16.9)	1567 (16.6)	373 (18.2)
None of the above qualifications	202 (11.3)	605 (8.7)	1038 (7.6)	819 (8.7)	255 (12.4)
Income, No. (%)
<£18 000	285 (16.9)	833 (12.7)	1465 (11.4)	1015 (11.5)	299 (15.8)
£18 000-£30 999	421 (25.0)	1451 (22.1)	2718 (21.2)	2066 (23.3)	509 (26.9)
£31 000-£51 999	452 (26.8)	1963 (29.9)	3770 (29.4)	2678 (30.2)	545 (28.7)
£52 000-£100 000	415 (24.6)	1787 (27.2)	3736 (29.1)	2431 (27.4)	430 (22.7)
>£100 000	112 (6.7)	527 (8.0)	1132 (8.8)	673 (7.6)	113 (6.0)
Employment and shift work,^b No. (%)
Employed—mostly daytime work	858 (47.4)	3952 (56.2)	7888 (57.4)	5083 (53.2)	977 (47.2)
Employed—job involves shift work	98 (5.4)	343 (4.9)	580 (4.2)	358 (3.8)	99 (4.8)
Employed—job involves night shift work	114 (6.3)	393 (5.6)	719 (5.2)	419 (4.4)	78 (3.8)
Retired	608 (33.6)	2003 (28.5)	3999 (29.1)	3297 (34.5)	793 (38.4)
Not employed	131 (7.2)	341 (4.9)	546 (4.0)	394 (4.1)	121 (5.9)
Coffee intake, No. (%)
None or <1 cup/d	464 (25.6)	1731 (24.6)	3323 (24.2)	2317 (24.2)	577 (27.9)
1-2 cups/d	669 (36.9)	2649 (37.7)	5427 (39.5)	3897 (40.8)	837 (40.5)
3-4 cups/d	431 (23.8)	1729 (24.6)	3307 (24.1)	2271 (23.8)	444 (21.5)
5-6 cups/d	176 (9.7)	665 (9.5)	1278 (9.3)	847 (8.9)	157 (7.6)
>6 cups/d	72 (4.0)	261 (3.7)	411 (3.0)	225 (2.4)	53 (2.6)
Tea intake, No. (%)
None or <1 cup/d	380 (21.0)	1255 (17.8)	2368 (17.2)	1543 (16.1)	331 (16.0)
1-2 cups/d	428 (23.7)	1750 (24.9)	3407 (24.8)	2356 (24.7)	500 (24.1)
3-4 cups/d	497 (27.5)	2038 (29.0)	4032 (29.3)	2818 (29.5)	622 (30.0)
5-6 cups/d	313 (17.3)	1336 (19.0)	2646 (19.3)	1983 (20.7)	424 (20.5)
>6 cups/d	192 (10.6)	658 (9.4)	1289 (9.4)	859 (9.0)	194 (9.4)
Townsend Deprivation Index,^c No. (%)
Quartile 1: ‒6.26 to ‒3.86	398 (21.9)	1603 (22.8)	3491 (25.4)	2547 (26.7)	514 (24.8)
Quartile 2: ‒3.86 to ‒2.52	438 (24.1)	1628 (23.1)	3375 (24.6)	2559 (26.8)	555 (26.8)
Quartile 3: ‒2.52 to ‒0.32	412 (22.7)	1817 (25.8)	3450 (25.1)	2333 (24.4)	542 (26.2)
Quartile 4: ‒0.32 to 9.89	569 (31.3)	1989 (28.3)	3422 (24.9)	2115 (22.1)	459 (22.2)
Race,^d No. (%)
Asian participants	42 (2.3)	137 (2.0)	199 (1.5)	68 (0.7)	—^e
Black participants	35 (1.9)	105 (1.5)	75 (0.6)	21 (0.2)	—^e
Mixed-race participants	6 (0.3)	39 (0.6)	58 (0.4)	24 (0.3)	—^e
Other participants	18 (1.0)	37 (0.5)	61 (0.4)	26 (0.3)	—^e
White participants	1708 (94.4)	6694 (95.5)	13 323 (97.1)	9391 (98.5)	2040 (98.7)
Prostate-specific antigen test history, No. (%)	583 (33.7)	2048 (30.7)	4152 (31.7)	3043 (33.4)	713 (36.0)
Family history of prostate cancer, No. (%)	140 (7.7)	554 (7.9)	1127 (8.2)	771 (8.1)	149 (7.2)
Prostate disorders, No. (%)	16 (0.9)	53 (0.8)	134 (1.0)	104 (1.1)	23 (1.1)
Prostate hyperplasia, No. (%)	192 (10.6)	594 (8.4)	1258 (9.1)	914 (9.6)	234 (11.3)
Prostate inflammation, No. (%)	28 (1.5)	80 (1.1)	203 (1.5)	155 (1.6)	46 (2.2)
Diabetes status, No. (%)	159 (8.8)	390 (5.6)	601 (4.4)	368 (3.9)	87 (4.2)

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Variables in this table reflect complete case data among 34 260 men.

Employment and shift work data are derived from employment (data field: 6142), shift work (data field: 826), and night shift work (data field: 3426). All participants who endorsed being in paid employment or self-employment were able to endorse how often their job involves shift work and night shift work. Employed participants were recoded as mostly daytime work, job involves shift work, and job involves night shift work, based on their combined responses. The category “not employed” reflects participants who endorsed “unemployed,” “unable to work because of sickness or disability,” “looking after home and/or family,” “doing unpaid or voluntary work,” “full- or part-time student,” or “none of the above.”

Due to rounding, categories appear to overlap; however, each quartile is distinct.

Race and ethnicity were evaluated on the UK Biobank baseline questionnaire through 5 questions querying self-reported ethnic group (see UK Biobank Data Showcase data field 21000; https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=21000). Categorization was based on “Top” categories denoted in the UK Biobank Data Showcase. Following are the subcategories that were concatenated into the categories presented: Asian participants (participants reporting ethnicity as Asian or British Asian, Chinese, Indian, Pakistani, Bangladeshi, or any other Asian background); Black participants (participants reporting ethnicity as Black or Black British, Caribbean, African, or any other Black background); Mixed-race participants (participants reporting ethnicity as mixed, White and Black Caribbean, White and Black African, White and Asian, or any other mixed background); Other participants (participants reporting ethnicity as other ethnic group); White participants (participants reporting ethnicity as White, British, Irish, or any other White background). Due to data sparsity, analyses use a binary variable comparing White participants with Asian participants, Black participants, Mixed-race participants, and Other participants.

Cell sizes were omitted due to small numbers.

Sleep duration, sleep timing, and prostate cancer risk

Contrary to our hypotheses, neither short (≤5 hours vs >7-8 hours; HR = 0.99, 95% CI = 0.75 to 1.30) nor long (>8 hours vs >7-8 hours; HR = 1.09, 95% CI = 0.86 to 1.39) sleep duration was associated with greater prostate cancer risk (Table 2). Later sleep onset was also not associated with greater prostate cancer risk (quartile 4 vs quartile 1; HR = 0.99, 95% CI = 0.83 to 1.17). Although sleep midpoints earlier than 4:00 am were not associated with greater prostate cancer risk (<4:00 am vs 4:00-4:59 am; HR = 1.00, 95% CI = 0.87 to 1.16), sleep midpoints of 5:00 am or later were suggestively associated with lower prostate cancer risk but had limited precision (≥5:00 am vs 4:00-4:59 am; HR = 0.79, 95% CI = 0.57 to 1.10). Later wake-up time (quartile 4 vs quartile 1; HR = 1.05, 95% CI = 0.89 to 1.24) and social jetlag (1 to <2 hours vs <1 hour; HR = 1.06, 95% CI = 0.89 to 1.25; ≥2 hours vs <1 hour; HR = 0.90, 95% CI = 0.65 to 1.26) were not associated with greater prostate cancer risk (Table 2).

Table 2.

Association between sleep duration, sleep timing, and risk of prostate cancer from the UK Biobank cohort (N = 34 260)

Sleep characteristic	Total, No.	Person-years	Prostate cancer cases n = 1152	Unadjusted hazard ratio (95% confidence interval)	Multivariate adjusted hazard ratio (95% confidence interval)^a	Null-hypothesis test, P
Sleep duration, h						.25^b
≤5	1817	9251	62	0.98 (0.75 to 1.29)	0.99 (0.75 to 1.30)
>5 to 6	7047	36 329	216	0.87 (0.73 to 1.03)	0.94 (0.79 to 1.11)
>6 to 7	13 760	71 245	456	0.94 (0.81 to 1.08)	1.01 (0.88 to 1.17)
>7 to 8	9565	49 173	336	(Referent)	(Referent)
>8	2071	10 574	82	1.14 (0.89 to 1.45)	1.09 (0.86 to 1.39)
Sleep onset ^d						.74^c
Quartile 1: 1:17 pm-11:03 pm	8565	44 063	272	(Referent)	(Referent)
Quartile 2: 11:03 pm-11:38 pm	8565	44 262	293	1.07 (0.91 to 1.26)	1.02 (0.86 to 1.20)
Quartile 3: 11:38 pm-12:19 am	8565	44 180	307	1.13 (0.96 to 1.32)	1.05 (0.89 to 1.24)
Quartile 4: ≥12:19 am	8565	44 068	280	1.03 (0.87 to 1.22)	0.99 (0.83 to 1.17)
Continuous (per clock hour increase)	—	—	—	1.00 (0.94 to 1.05)	0.99 (0.94 to 1.05)
Sleep midpoint						.07^b
<4:00 am	26 089	134 491	884	0.96 (0.83 to 1.11)	1.00 (0.87 to 1.16)
4:00-4:59 am	6388	32 930	225	(Referent)	(Referent)
≥5:00 am	1783	9152	43	0.69 (0.50 to 0.95)	0.79 (0.57 to 1.10)
Wake-up time ^d						.37^c
Quartile 1: 6:26 pm-6:29 am	8565	44 143	257	(Referent)	(Referent)
Quartile 2: 6:29 am-7:08 am	8565	44 212	273	1.06 (0.89 to 1.26)	1.01 (0.85 to 1.20)
Quartile 3: 7:08 am-7:47 am	8566	44 164	323	1.26 (1.07 to 1.48)	1.11 (0.95 to 1.31)
Quartile 4: ≥7:47 am	8564	44 054	299	1.17 (0.99 to 1.38)	1.05 (0.89 to 1.24)
Continuous (per clock hour increase)	—	—	—	1.05 (0.99 to 1.10)	1.03 (0.97 to 1.08)
Social jetlag (873/25 826), h						.77^c
<1	18 485	94 671	666	(Referent)	(Referent)
1 to <2	5638	28 945	170	0.84 (0.71 to 0.99)	1.06 (0.89 to 1.25)
≥2	1703	8844	37	0.59 (0.43 to 0.83)	0.90 (0.65 to 1.26)
Continuous (per hour increase)	—	—	—	0.81 (0.73 to 0.89)	0.98 (0.89 to 1.09)

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Adjusted for age (continuous; years), body mass index (kg/m²; categorical: <25.0, 25 to <30, 30 to <35, 35 to <40, ≥40), overall health rating (categorical: excellent, good, fair, poor), smoking status (categorical: never, former, current), alcohol intake (categorical: never, special occasions, 1-3 times/month, weekly, daily or almost daily), education (categorical: college or university degree; National Vocational Qualification or Higher National Diploma or Higher National Certificate equivalent; other professional qualifications, Advanced or Advanced Subsidiary levels or equivalent; Ordinary levels; General Certificate of Secondary Education equivalent; Certificate of Secondary Education or equivalent, none of the above qualifications), income (categorical: <£18 000, £18 000-£30 999, £31 000-£51 999, £52 000-£100 000, >£100 000), moderate to vigorous physical activity (quartiles: 0.001-0.79 hours, 0.79-1.16 hours, 1.16-1.59 hours, 1.59-5.4 hours), employment and shift work (categorical: employed—mostly daytime work, employed—job involves shift work, employed—job involves night shift work, retired, not employed), race and ethnicity (binary: White participants, Asian participants; Black participants; Mixed-race participants; Other participants), Townsend Deprivation Index (quartiles: ‒6.26 to ‒3.86, ‒3.86 to ‒2.52, ‒2.52 to ‒0.32, ‒0.32 to 9.89), history of prostate-specific antigen testing (binary: yes, no), family history of prostate cancer (binary: yes, no), diabetes status (binary: yes, no), coffee intake (categorical: none or <1, 1-2, 3-4, 5-6, >6 cups/day), tea intake (categorical: none or <1, 1-2, 3-4, 5-6, >6 cups/day).

Test for homogeneity.

Test for linear trend.

Due to rounding, categories appear to overlap; however, each quartile is distinct.

Sleep quality and prostate cancer risk

Higher sleep efficiency was not associated with lower prostate cancer risk (quartile 4 vs quartile 1; HR = 0.92, 95% CI = 0.78 to 1.08) (Table 3). Higher wakefulness after sleep onset was associated with greater prostate cancer risk; compared with men with no to less than 30 minutes of wakefulness after sleep onset, men with 60 minutes or more of wakefulness after sleep onset had 20% greater prostate cancer risk (HR = 1.20, 95% CI = 1.00 to 1.43) (Table 3). Given the consistently elevated risk for wakefulness after sleep onset of 30 minutes or more, we also evaluated wakefulness after sleep onset of 30 minutes or more vs no to less than 30 minutes and observed a 16% greater risk overall (HR = 1.16, 95% CI = 1.00 to 1.36). For every 1 day per week increase in wakefulness after sleep onset of 30 minutes or more, there was a 4% greater prostate cancer risk (HR = 1.04, 95% CI = 1.01 to 1.07) (Table 3).

Table 3.

Association between sleep quality and risk of prostate cancer from the UK Biobank cohort (N = 34 260)

Sleep characteristic	Total, No.	Person-years	Prostate cancer cases n = 1152	Unadjusted hazard ratio (95% confidence interval)	Multivariate adjusted hazard ratio (95% confidence interval)^a	Null-hypothesis test, P
Sleep efficiency ^b						.28^c
Quartile 1 (48.7%-86.3%)	8565	44 160	291	(Referent)	(Referent)
Quartile 2 (86.3%-89.8%)	8565	44 176	295	1.01 (0.86 to 1.19)	0.99 (0.84 to 1.16)
Quartile 3 (89.8%-92.6%)	8565	44 106	291	1.00 (0.85 to 1.18)	0.98 (0.83 to 1.15)
Quartile 4 (92.6%-99.2%)	8565	44 130	275	0.95 (0.80 to 1.12)	0.92 (0.78 to 1.08)
Wakefulness after sleep onset, min						.18^c
0 to <30	6609	34 083	196	(Referent)	(Referent)
30 to <45	10 144	52 295	347	1.15 (0.97 to 1.37)	1.15 (0.96 to 1.36)
45 to <60	8280	42 637	285	1.16 (0.97 to 1.39)	1.15 (0.96 to 1.39)
≥60	9227	47 557	324	1.18 (0.99 to 1.41)	1.20 (1.00 to 1.43)
Wakefulness after sleep onset, min
0 to <30	6609	34 083	196	(Referent)	(Referent)
≥30	27 651	142 490	956	1.17 (1.00 to 1.36)	1.16 (1.00 to 1.36)
Continuous (per hour)	—	—	—	1.08 (0.93 to 1.26)	1.11 (0.95 to 1.29)
Frequency of wakefulness after sleep onset ≥30 min, d						.02^c
0-1	3850	19 851	114	(Referent)	(Referent)
2	541	2808	19	1.18 (0.72 to 1.91)	1.37 (0.84 to 2.24)
3	3602	18 585	117	1.10 (0.85 to 1.42)	1.09 (0.85 to 1.42)
4	5786	29 826	178	1.04 (0.82 to 1.32)	1.06 (0.84 to 1.34)
5-7	20 481	105 502	724	1.20 (0.98 to 1.46)	1.21 (1.00 to 1.48)
Continuous (per day)	—	—	—	1.03 (1.01 to 1.06)	1.04 (1.01 to 1.07)

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Adjusted for age (continuous: years), body mass index (kg/m²; categorical: <25.0, 25 to <30, 30 to <35, 35 to <40, ≥40), overall health rating (categorical: excellent, good, fair, poor), smoking status (categorical: never, former, current), alcohol intake (categorical: never, special occasions, 1-3 times/month, weekly, daily or almost daily), education (categorical: college or university degree; National Vocational Qualification or Higher National Diploma or Higher National Certificate equivalent; other professional qualifications, Advanced or Advanced Subsidiary levels or equivalent; Ordinary levels; General Certificate of Secondary Education equivalent; Certificate of Secondary Education or equivalent, none of the above qualifications), income (categorical: <£18 000, £18 000-£30 999, £31 000-£51 999, £52 000-£100 000, >£100 000), moderate to vigorous physical activity (quartiles: 0.001-0.79 hours, 0.79-1.16 hours, 1.16-1.59 hours, 1.59-5.4 hours), employment and shift work (categorical: employed—mostly daytime work, employed—job involves shift work, employed—job involves night shift work, retired, not employed), race and ethnicity (binary: White participants, Asian participants; Black participants; Mixed-race participants; Other participants), Townsend Deprivation Index (quartiles: ‒6.26 to ‒3.86, ‒3.86 to ‒2.52, ‒2.52 to ‒0.32, ‒0.32 to 9.89), history of prostate-specific antigen testing (binary: yes, no), family history of prostate cancer (binary: yes, no), diabetes status (binary: yes, no), coffee intake (categorical: none or <1, 1-2, 3-4, 5-6, >6 cups/day), tea intake (categorical: none or <1, 1-2, 3-4, 5-6, >6 cups/day).

Due to rounding, categories appear to overlap; however, each quartile is distinct.

Test for linear trend.

Sensitivity analyses

In Figure 2, we present sensitivity analyses for wakefulness after sleep onset (≥60 minutes vs 0 to <30 minutes) and prostate cancer to evaluate effect modification, residual confounding, and reverse-causality. There were no significant interactions between age (P = .34), BMI (P = .60), or active wakefulness after sleep onset (P = .26) and the wakefulness after sleep onset and prostate cancer association (Figure 2). In stratified analyses, the association between wakefulness after sleep onset and prostate cancer was observed among men 65 years of age or older, but not men younger than 65 years of age. The association retained similar direction and magnitude but was attenuated when restricting to active wakefulness after sleep onset beyond 2.7 minutes. Adjusting for active wakefulness after sleep onset did not appreciably influence the association. The association remained similar to the main findings when evaluating residual confounding from shift work, diabetes status, and prostate issues. Excluding the first 2 to 4 years of follow-up did not alter the magnitude or direction of the wakefulness after sleep onset and prostate cancer association but was lower in power.

There was no significant interaction between history of PSA testing and the wakefulness after sleep onset and prostate cancer association (P = .38). In Supplementary Table 6 (available online), we stratified the wakefulness after sleep onset and prostate cancer associations by PSA testing history and observed associations similar to the main analysis. Findings were comparable to the results for men with no history of PSA testing. Men with any history of testing had slightly stronger associations with wakefulness after sleep onset.

Nonfatal prostate cancer and prostate cancer–specific mortality

In Supplementary Table 7 (available online), we present exploratory analyses comparing wakefulness after sleep onset with nonfatal prostate cancer (n = 1092) and prostate cancer–specific mortality (n = 60) in models adjusted for age and BMI (model 1) and models adjusted for additional covariates (model 2). Wakefulness after sleep onset and nonfatal prostate cancer associations were similar to associations with all incident prostate cancers, retaining a similar magnitude and direction. Overall, there was no consistent evidence that our main results were different for nonfatal prostate cancers. Associations between wakefulness after sleep onset and prostate cancer–specific mortality were stronger but based on limited numbers of cases. Associations remained similar between models 1 and 2.

Discussion

In a large sample of men without a history of prostate cancer, actigraphy-measured wakefulness after sleep onset of 60 minutes or more was associated with greater prostate cancer risk. Sleep duration, timing, and efficiency were not associated with prostate cancer risk, and our findings for sleep duration are consistent with most prior literature, including a recent study among UK Biobank men (11-18). Our work using actigraphy-derived sleep complements prior research on self-reported sleep and prostate cancer risk. We also used these objective measures to evaluate active wakefulness after sleep onset periods as a proxy of ambulation for nocturia. Furthermore, we conducted exploratory analyses evaluating nonfatal prostate cancer incidence and prostate cancer–specific mortality and observed similar results comparing the main analysis to nonfatal prostate cancer incidence. Stronger associations were observed for prostate cancer–specific mortality, although confidence intervals were wide.

Higher wakefulness after sleep onset was associated with elevated prostate cancer risk. Previous studies evaluating self-reported sleep quality (eg, problems falling or staying asleep, perceived sleep quality) and sleep disorders have been mixed, with some studies finding no consistent association with prostate cancer risk or mortality (9,11,16,18,26). Importantly, our results were consistent with and add onto a previous analysis among men at baseline in the UK Biobank (n = 213 999), which found that self-reported trouble falling or staying asleep was associated with elevated prostate cancer risk (HR = 1.11, 95% CI = 1.04 to 1.19) (14). Others have found associations between sleep disorders, sleep quality, and general and high-grade prostate cancer (22-25). We were unable to evaluate high-grade prostate cancer as cancer stage and grade were not available. These findings suggest that sleep disturbances may be related to higher prostate cancer risk.

Nocturia disrupts sleep and may be a sign of prostate cancer (24). We hypothesized that more active wakefulness after sleep onset may suggest ambulation to the bathroom consistent with nocturia. Thus, we adjusted for active wakefulness after sleep onset as a confounder and stratified associations by active wakefulness after sleep onset. Adjustment for active wakefulness after sleep onset did not alter our primary findings. Evaluating effect modification by the median of active wakefulness after sleep onset showed no difference with the primary association among lower levels of active wakefulness after sleep onset. The association was attenuated above the median threshold of active wakefulness after sleep onset but retained similar magnitude and direction. Together, this finding suggests that nocturia may not have influenced the primary findings; however, without data regarding participants’ frequency of nocturia, we cannot fully discount this possibility (24).

We hypothesized that earlier and later sleep timing (sleep onset, midpoint, and wake-up time) as well as social jetlag would be associated with prostate cancer risk. Although sleep onset timing is related to metabolic syndrome, a risk factor for prostate cancer, we found no association with prostate cancer risk (49,50). Our study is the first to evaluate the associations between wake-up time, using either self-report or actigraphy, and prostate cancer risk and found no association. More studies have examined self-reported chronotype, the tendency to be a morning or evening person, or sleep midpoint as a chronotype proxy (51). These studies have been mixed, with most finding no association (14,15,19,20,52) or that evening chronotypes may have greater prostate cancer risk (11). Mendelian randomization studies of chronotype have supplemented this work by reporting that genetic instruments of “morningness” were associated with lower prostate cancer risk (53,54). Conversely, however, we found a suggestive association between sleep midpoints of 5:00 am or later (vs 4:00-4:59 am) and lower prostate cancer risk, though this finding was based on limited statistical power. We also evaluated social jetlag and found no association with prostate cancer risk. The Alberta Tomorrow Project is the only other study, to our knowledge, to prospectively evaluate social jetlag and prostate cancer; that study found that self-reported social jetlag of 1 to less than 2 hours (HR = 1.45, 95% CI = 1.05 to 2.01) and 2 hours or more (HR = 1.54, 95% CI = 1.04 to 2.27) were associated with prostate cancer risk (21). Fewer participants in the UK Biobank had valid weekend-specific data, which may have limited social jetlag analyses.

Work on mechanisms linking sleep and prostate cancer is scarce, though studies in mice suggest links between disrupted sleep patterns, circadian rhythms, and cancer. Mice in experimental groups in which the light-dark cycle was altered or who were in permanently lit environments had higher cancer incidence than controls in normal light-dark cycles; this finding suggests that strong circadian disruption may promote cancer (55,56). Altered sleep timing from circadian disruption may also lead to shorter sleep duration and poorer sleep quality in humans (57-60). Furthermore, sleep and circadian disruption may impair immunity and alter endogenous melatonin, which evidence suggests suppresses tumor growth and metastasis (61-66). Lower melatonin has been associated with advanced prostate cancer and thus may link poor sleep quality to prostate cancer risk (67). Poor sleep quality is also associated with increased insulin resistance and metabolic dysregulation, which may increase prostate cancer risk, as well (68-70).

Strengths of this prospective study include its size, design, and use of actigraphy-measured sleep. Device-based sleep measures help mitigate measurement error associated with self-report of typical sleep (27,28,71,72). We attempted to address detection bias of prostate cancer incidence by including PSA testing history as a covariate and as a potential effect modifier (48). We also explored analyses evaluating wakefulness after sleep onset with nonfatal prostate cancers as a potential indicator of cancer aggressiveness and found similar associations with wakefulness after sleep onset and overall prostate cancer incidence. We used actigraphy data to identify potential ambulatory episodes that could proxy nocturia. There are some potential limitations, as well. There were only 7 days of accelerometer measurements, and longer assessment protocols may be needed (73). Accelerometers may be limited in detecting motionless wakeful periods, which may underestimate wakefulness after sleep onset (74). Covariate data, excluding physical activity, were assessed at baseline and may have changed over time. We cannot rule out potential residual and unmeasured confounding from health-seeking behaviors and PSA testing (48). Nonfatal prostate cancer incidence and prostate cancer–specific mortality were based on prostate cancer deaths accrued over a short follow-up and thus may miss aggressive cancers. Due to the few prostate cancer deaths, covariates included in the fully adjusted prostate cancer mortality models were modified compared with fully adjusted prostate cancer incidence models. We additionally presented models adjusting only for age and BMI in this exploratory analysis and noted that associations were similar across adjustment sets. We may have been underpowered to detect interactions. In the actigraphy subcohort of the UK Biobank, most men reported generally good health status, and most self-identified as White participants, which may limit study generalizability.

In summary, our findings support an association between poor sleep quality (as measured by wakefulness after sleep onset) and greater prostate cancer risk. We uniquely expand on prior studies that focused on self-reported sleep characteristics. These results suggest that frequent sleep disturbances may be a prostate cancer risk factor. Future research should replicate our findings and use self-report and actigraphy-based sleep assessments to comprehensively measure sleep patterns, especially wakefulness after sleep onset, in the context of prostate cancer risk.

Supplementary Material

djad210_Supplementary_Data

djad210_supplementary_data.pdf^{(283.4KB, pdf)}

Acknowledgements

This work used the computational resources of the National Institutes of Health High-Performance Computing Biowulf cluster (http://hpc.nih.gov).

The study sponsor had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; or the decision to submit the manuscript for publication.

The content in this publication does not necessarily reflect the views or policies of the US Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government.

Contributor Information

Joshua R Freeman, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Pedro F Saint-Maurice, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Eleanor L Watts, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Steven C Moore, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Marissa M Shams-White, Risk Factor Assessment Branch, Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Dana L Wolff-Hughes, Risk Factor Assessment Branch, Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Daniel E Russ, Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Jonas S Almeida, Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Neil E Caporaso, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Hyokyoung G Hong, Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Erikka Loftfield, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Charles E Matthews, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Data availability

The data underlying this article have been provided by the UK Biobank Resources under application No. 43456. We do not have permission to share it directly. UK Biobank data are globally available to approved researchers through the UK Biobank research portal (https://www.ukbiobank.ac.uk/). Sleep exposures generated from the original data were derived using open source software (GGIR, version 2.6; https://cran.r-project.org/web/packages/GGIR/index.html) and will be returned with analytical code to the UK Biobank for future distribution (33). Analytical code used in the analyses presented in this manuscript are available at: https://github.com/NCI-DCEG/Sleep-Prostate-Cancer-Analyses.

Author contributions

Joshua R. Freeman, PhD, MPH (Conceptualization; Data curation; Formal analysis; Methodology; Visualization; Writing—original draft; Writing—review & editing), Pedro F. Saint-Maurice, PhD (Conceptualization; Data curation; Formal analysis; Methodology; Supervision; Writing—review & editing), Eleanor L. Watts, DPhil, MPH (Methodology; Writing—review & editing), Steven C. Moore, PhD, MPH (Methodology; Writing—review & editing), Marissa M. Shams-White, PhD, MSTOM, MS, MPH (Writing—review & editing), Dana L. Wolff-Hughes, PhD (Writing—review & editing), Daniel E. Russ, PhD (Data curation; Writing—review & editing), Jonas S. Almeida, PhD (Data curation; Writing—review & editing), Neil E. Caporaso, MD (Methodology; Writing—review & editing), Hyokyoung G. Hong, PhD (Methodology; Supervision; Writing—review & editing), Erikka Loftfield, PhD, MPH (Methodology; Writing—review & editing), Charles E. Matthews, PhD (Conceptualization; Methodology; Resources; Supervision; Writing—original draft; Writing—review & editing).

Funding

This work was supported by the Intramural Research Program Cancer Research Training Award, National Cancer Institute, US National Institutes of Health (Z99 CA999999).

Conflicts of interest

The authors report no disclosures or conflicts of interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

djad210_Supplementary_Data

djad210_supplementary_data.pdf^{(283.4KB, pdf)}

Data Availability Statement

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[djad210-B57] 57. Casjens S, Brenscheidt F, Tisch A, et al. Social jetlag and sleep debts are altered in different rosters of night shift work. PLoS One. 2022;17(1):e0262049. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[djad210-B62] 62. Reiter RJ, Mayo JC, Tan DX, Sainz RM, Alatorre-Jimenez M, Qin L.. Melatonin as an antioxidant: under promises but over delivers. J Pineal Res. 2016;61(3):253-278. [DOI] [PubMed] [Google Scholar]

[djad210-B63] 63. Jung-Hynes B, Schmit TL, Reagan-Shaw SR, Siddiqui IA, Mukhtar H, Ahmad N.. Melatonin, a novel Sirt1 inhibitor, imparts antiproliferative effects against prostate cancer in vitro in culture and in vivo in TRAMP model. J Pineal Res. 2011;50(2):140-149. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Actigraphy-derived measures of sleep and risk of prostate cancer in the UK Biobank

Joshua R Freeman, PhD, MPH

Pedro F Saint-Maurice, PhD

Eleanor L Watts, DPhil, MPH

Steven C Moore, PhD, MPH

Marissa M Shams-White, PhD, MSTOM, MS, MPH

Dana L Wolff-Hughes, PhD

Daniel E Russ, PhD

Jonas S Almeida, PhD

Neil E Caporaso, MD

Hyokyoung G Hong, PhD

Erikka Loftfield, PhD, MPH

Charles E Matthews, PhD

Roles

Abstract

Background

Methods

Results

Conclusions

Methods

Figure 1.

Sleep and covariate assessment

Outcome assessment

Statistical analysis

Results

Participant characteristics

Table 1.

Sleep duration, sleep timing, and prostate cancer risk

Table 2.

Sleep quality and prostate cancer risk

Table 3.

Sensitivity analyses

Figure 2.

Nonfatal prostate cancer and prostate cancer–specific mortality

Discussion

Supplementary Material

Acknowledgements

Contributor Information

Data availability

Author contributions

Funding

Conflicts of interest

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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