Nocturia, Other Lower Urinary Tract Symptoms and Sleep Dysfunction in a Community-Dwelling Cohort of Men

Accepted Manuscript Title: Nocturia, Other Lower Urinary Tract Symptoms (LUTS) and Sleep Dysfunction in a Community-Dwelling Cohort of Men. Author: Sean A. Martin, Sarah L. Appleton, Robert J. Adams, Anne W. Taylor, Peter G. Catcheside, Andrew Vakulin, R. Douglas McEvoy, Nick A. Antic, Gary A. Wittert PII: DOI: Reference:

S0090-4295(16)30313-2 http://dx.doi.org/doi: 10.1016/j.urology.2016.06.022 URL 19867

To appear in:

Urology

Received date: Accepted date:

4-3-2016 14-6-2016

Please cite this article as: Sean A. Martin, Sarah L. Appleton, Robert J. Adams, Anne W. Taylor, Peter G. Catcheside, Andrew Vakulin, R. Douglas McEvoy, Nick A. Antic, Gary A. Wittert, Nocturia, Other Lower Urinary Tract Symptoms (LUTS) and Sleep Dysfunction in a Community-Dwelling Cohort of Men., Urology (2016), http://dx.doi.org/doi: 10.1016/j.urology.2016.06.022. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Nocturia, other lower urinary tract symptoms (LUTS) and sleep dysfunction in a community-dwelling cohort of men. (Running title: Nocturia, LUTS, and Sleep in men)

Sean A. Martin, PhD 1*, Sarah L. Appleton, PhD 2, Robert J. Adams, MBBS, MD 2, Anne W. Taylor, PhD 3, Peter G. Catcheside, PhD 4 , Andrew Vakulin, PhD 4,5, R. Douglas McEvoy MBBS, MD 4 , Nick A. Antic MBBS, PhD 4 , Gary A. Wittert, MB Bch, MD 1

1

Freemasons Foundation Centre for Men’s Health, University of Adelaide, Adelaide, South Australia

2

The Health Observatory, Discipline of Medicine, University of Adelaide, The Queen Elizabeth Hospital

Campus, Woodville, South Australia, Australia. 3

Population Research and Outcome Studies, University of Adelaide, Adelaide, South Australia.

4

Adelaide Institute for Sleep Health: A Flinders Centre of Research Excellence, School of Medicine, Faculty

of Medicine, Nursing and Health Sciences, Flinders University, Bedford Park, South Australia, Australia. 5

The NHMRC Centres of Research Excellence, CIRUS and NEUROSLEEP, Woolcock Institute of Medical

Research, Central Clinical School, University of Sydney, New South Wales, Australia. * Corresponding author: Dr. Sean A. Martin Freemasons Foundation Centre for Men's Health (University of Adelaide) c/o South Australian Health & Medical Research Institute (SAHMRI) Lv 7 (Men's Health) North Terrace, Adelaide SA 5005, Australia Phone: + 61 8 8313 4723 Fax: + 61883130355 E-mail: [email protected];[email protected]

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Keywords: Nocturia; Lower Urinary Tract Symptoms; Sleep Apnea, Obstructive ; Polysomnography; Sleep stages; Men's Health (MeSH terms) Word Count: Abstract: 231; Text: 2915

ACKNOWLEDGEMENTS

The authors would like to acknowledge the clinic and sleep staff for their invaluable efforts. Particular thanks are extended to Leanne Owen, Janet Grant, Sandy Pickering, Tina Stavropoulos, and other staff of the Florey Adelaide Male Ageing Study and North West Adelaide Health Study for all their assistance. Thanks to Chris Seaborn and Erika Bowden at the Department of Nuclear Medicine, The Queen Elizabeth Hospital for providing expertise and assistance with DXA procedures. The authors also acknowledge Siemens Healthcare for providing the radioimmunoassay kits for all Immulite assays. Finally, thanks are extended to our participants and their families for their invaluable contributions.

Objectives: To examine the influence of obstructive sleep apnea (OSA) on nocturia, controlling for their shared co-morbidities, in a population of community-based middle aged to elderly men. Methods: Participants were drawn from a randomly-selected, community-dwelling cohort of men in Adelaide, Australia. 708 men (mean:60.7 [41.6-88.2] years) who had PSG recordings, complete LUTS measures (IPSS), without prostate or bladder cancer and/or surgery, and no prior OSA diagnosis were selected. Nocturia was defined as ≥2 voids per main sleep. Unadjusted and multi-adjusted regression models of nocturia were combined with OSA, wake after sleep onset (WASO), total sleep period, excessive daytime

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sleepiness (EDS), and sleep quality (SQ) data, together with socio-demographic, and health-related factors. Results: Men with nocturia were found to have higher levels of OSA (32.2%(n=65)), WASO time (97.2±52.9 mins), sleep period (467.3±58.4mins), EDS (18.2%(n=37)), and poorer SQ (54.3%(n=108)). Multiple-adjusted models showed nocturia was positively associated with OSA (OR:1.64, 95%CI [1.03,2.55]), EDS (1.72[1.01,2.93]) and poorer SQ (1.65[1.10,2.48]). Including other storage and voiding LUTS attenuated the effect of OSA and strengthened the association with EDS (2.44[1.45,4.10)]& 2.24[1.19,4.22]), while voiding LUTS also strengthened the association with poorer SQ (2.61[1.63,4.17]). Men with increasing nocturic frequency spent less time in N2 and REM stage sleep. Conclusions: Nocturia is strongly associated with OSA in community-based men. Nocturia also reduces SE/SQ, N2 and REM sleep time, while increasing EDS. Other LUTS increase EDS through non-OSA means.

The prevalence of total LUTS in middle-aged to elderly men varies between 18-67% depending on location and instrument used, however storage symptoms are generally more common than voiding symptoms [1]. Nocturia alone, when defined as two or more episodes per night, is present in 19-39% of men aged over 45 years [2] and commonly reported as the most bothersome LUTS [3]. Nocturia has been associated with an increased risk of cardiovascular [4] and all-cause mortality [5]. Sleep-disorders such as obstructive sleep apnea (OSA) are also highly prevalent among older adults, with prevalence estimates of 20-50%, depending on the specific definition employed [6-8]. Nocturia and OSA share multiple comorbidities, including cardiovascular disease, type 2 diabetes, kidney function, hypercholesterolemia, alcohol and smoking behaviour, physical activity, inflammation, low testosterone and medication usage (see [9] for review). Both conditions are associated with excessive daytime sleepiness [10, 11]. However it is unclear 3 Page 3 of 23

whether this is a direct effect of nocturia or OSA, or comorbidities associated with both these conditions. Most studies of OSA and nocturia have occurred in convenience samples of patients, thus have limited applicability to the general population [9]. In a large-scale study of community-based participants using polysomnography (PSG) testing, nocturia was found to be associated with an increased risk of sleep-disordered breathing [4]. This study however used a non-standardised definition of nocturia (night-time voiding occurring at least 5 times per month), over-sampled snorers, and did not adjust for kidney function. Recently published data from community-based, elderly Japanese participants confirmed an association with nocturic frequency and sleep dysfunction [12]. However, given the use of actigraphy rather than PSG to measure sleep quality, no specific information was available for OSA. We therefore examined the association between nocturia (with and without other LUTS) and sleep disorders as assessed by overnight PSG testing in a large cohort of communitydwelling middle-aged and elderly men.

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METHODS

Study design and sampling The Men Androgen Inflammation Lifestyle Environment and Stress (MAILES) Study is comprised of randomly-selected, urban, community-dwelling men aged at least 40 years and has been described previously [13]. In 2010, 1445 MAILES participants without a previous diagnosis of OSA by overnight sleep study were invited to participate in an inhome PSG testing session. Of these, n=1087 agreed (participation rate: 75.2%). By the conclusion of the study period 857 had undergone PSG testing, with 837 successful PSG recordings. When compared with men from the target populations (Northern and Western Statistical Divisions), participants from the present study were older, more likely to be married, and less likely to be in full-time work [13].

The study was approved by the North West Adelaide Health Service and the Royal Adelaide Hospital institutional ethics committee, and all subjects gave written informed consent (NHMRC Project Grant #627227).

Outcome variables

Lower urinary tract symptoms (LUTS) Nocturia was assessed using item 7 of the American Urology Association – Symptom Index (AUA-SI [14]). Men who typically voided two or more times were classified as having nocturia. The AUA-SI was also used to evaluate the presence of all other LUTS (including: urgency, increased frequency, incomplete emptying, straining, intermittent and weak stream). Subjects were classified as having storage symptoms if the sum of their

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score on AUA-SI items 2, 4 and 7 was  4 and having voiding symptoms if the sum of their score on AUA-SI items 1, 3, 5 and 6 was  5. Glomerular filtration rate (GFR) was calculated using the CKD-EPI Creatinine Equation [15].

Sleep measures Two weeks prior to overnight PSG testing, participants were sent a brief questionnaire including the Epworth Sleepiness Scale (ESS) [16] and STOP questionnaire [17]. Total sleep time (TST) was defined as the time spent in N1, N2, N3 and REM sleep stages. Awakenings were defined as the return to an EEG-defined awakened state from any REM or non-REM sleep stage. Wake after sleep onset (WASO) was defined as the time spent in wake after first sleep but before the final awakening. Total sleep period (SP) was defined as WASO plus TST. OSA was defined using the 2007 AASM alternate criteria [18] as an apnea hypopnea index (AHI) ≥10/hour of sleep, with further categorisation; mild: AHI of 10-19/hour, moderate: 20-29/hour, and severe: ≥30/hour. The number of oxygen desaturation events of 3% or more per h (3% oxygen desaturation index [ODI 3%]) was used as the indicator of nocturnal intermittent hypoxia.

Covariate data Demographic factors, health status & medication usage Information on age, education, marital, occupational, smoking, lifestyle factors (alcohol consumption, smoking, and physical activity) and disease status (cardiovascular disease, anxiety, asthma, depression, diabetes, erectile dysfunction, benign prostatic hyperplasia, insomnia, osteoarthritis, and cancer) was obtained by self-report questionnaire [13]. Medication use was determined by self-report and data linkage with the national medication registry.

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Serum assays Morning fasting venous blood samples were obtained by venepuncture at clinic and stored at -80°C until assay. Serum total testosterone (TT) and estradiol (E2) were measured by a validated stable-isotope dilution LC–MS/MS. HDL cholesterol and triglycerides (TG) were measured enzymatically using a Hitachi 911, with LDL cholesterol calculated using the Friedwald equation (LDL = Total cholesterol (TC) - HDL - TG/5.0). Plasma glucose was determined using an automated chemistry analyser system, with glycated haemoglobin (HbA1c) measured by high-pressure liquid chromatography using a spherical cation exchange gel. C-reactive protein (CRP) was quantitated using a Cobas Integra automated clinical chemistry analyzer. Data on precision and instrumentation have been previously published [13].

Statistical analyses Initial descriptive analyses of relationships between selected independents and outcome measures were conducted using χ2-tests (categorical) and t-tests (continuous). Unadjusted regression models assessed the association between selected covariates and outcome measures. Binary logistic regression models were used for moderate and severe OSA (AHI 20+), excessive daytime sleepiness (EDS; Eppworth Sleepiness Scale total >10), and poor sleep quality (SQ; Pittsburgh Sleep Quality Index total >5). Linear regression models were used for (naturally log-transformed) sleep period, WASO, and awakenings. For multipleadjusted regression models, selected independents with an unadjusted association with the outcome variable of p ≤0.15 were included in the final model to account for multiple testing. Statistical analyses were performed using SPSS version 20.0 for Windows (IBM SPSS Inc., IL, USA). A two-tailed p-value of <0.05 was considered to be statistically significant.

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RESULTS

Descriptive Characteristics of Men with Nocturia and Obstructive Sleep Apnea Overall, 28.4% of those men examined were found to have nocturia (2 or more voids per night). In men with nocturia, 34.8% and 21.4% also had increased frequency and urgency (compared with 20.5% & 9.8%, respectively, for men without nocturia). For voiding symptoms, men with nocturia had higher levels of intermittent stream (21.3%, compared with 7.5% of men without nocturia), weak stream (25.2%, cf. 10.0%), straining (12.6%, cf. 2.1%) and incomplete emptying (24.3%, cf. 6.9%) (Table 1). Men with nocturia Moderate to severe OSA was present in 32.2% and 21.5% of men with and without nocturia, respectively. Total sleep period was also higher in men with nocturia (467.3 mins, cf. 444.5 mins). EDS was also present in 18.2% of men with nocturia, and 11.5% of those without. Finally, poorer SQ was present in 54.3% of men with nocturia, compared with 43.6% of men without (Table 1). Men with nocturia were also older, had lower handgrip strength, less likely to be in fulltime work, had lower household income, had higher serum triglycerides, lower HDL cholesterol, and had higher levels of CVD, ED, BPH, hypercholesterolemia, hypertension, insomnia and arthritis, and α –adrenoblockers and diuretic usage (Table 1).

Sleep disturbance in men with nocturia In unadjusted models, men with moderate to severe OSA (OR: 1.72, 95%CI [1.20, 2.49]), EDS (1.72 [1.10, 2.68]), poorer SQ (1.54 [1.10, 2.14]), and longer WASO and sleep period (0.90 [0.82, 0.97]) were all associated with nocturia. These effects were maintained in the multi-adjusted models of nocturia, with the exception of longer WASO and sleep period. Other associations with nocturia were higher age (OR: 1.32 [95%CI: 1.13-1.62]), lower

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handgrip strength (0.87 [0.79–0.98]), being unemployed (1.67 [1.01-2.76]), higher serum CRP (1.07 [1.02–1.16]), lower total testosterone (0.89 [0.81–0.97]), cardiovascular disease (2.92 [1.36–6.26]), and depression (1.31 [1.11–1.72]) (Table 2). Models of nocturia were then adjusted to include other LUTS to examine whether the presence of these symptoms strengthened the association between nocturia and sleep disorders. When the other storage symptoms (urgency, frequency) were included, there was an increased likelihood of men having higher daytime sleepiness, both in the uni- and multi-adjusted models (2.22 [1.45-3.45] & 2.41 [1.43-2.93], respectively). The effect on sleep quality remained unchanged (1.51 [1.11-2.03]) &1.66 [1.15-2.40], respectively). Including the voiding symptoms increased the association between daytime sleepiness (1.99 [1.23-3.07] & 2.24 [1.19-4.22], respectively), and poorer sleep quality (1.96 [1.442.68] & 2.61 [1.64-4.17], respectively), and showed an association between nocturia and longer WASO and sleep period (0.229 [0.162-0.330] & 0.123[0.018-0.229] for WASO, and 0.187 [0.165-0.376] & 0.104[0.020-0.205] for sleep period) (Table 2).

Effect of nocturia on sleep architecture and awakenings Figure 1 shows the percentage of the recorded sleep period spent in N1, N2, N3 and REM sleep stages for men who reported no voids (23.6%; n=167), 1 void (47.9%; n=339), 2 voids (18.4%; n=130 ) or 3-5 voids (10.1%; n=72) during their main sleep period. Overall, participants spent 11.9 ± 4.9%, 45.1 ± 8.5%, 13.0 ± 7.8%, and 12.2 ± 4.6% of their sleep period in N1, N2, N3, and REM stage sleep. When examined by nocturic frequency, men who had 3-5 voids per main sleep spent less time in N2 sleep stage (43.2 ± 9.4%) than those with no voids (46.8 ± 8.5%, p=.032), while those who had 2 and 3-5 voids spent less time in REM sleep (11.5 ± 4.5% & 10.9 ± 5.7%) than men with no voids (12.8 ± 4.1%, p=.049 & .026, respectively).

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Overall, men with nocturia (2+ voids) were founds to have 45 ± 19 awakenings per main sleep, whereas men with moderate to severe OSA had on average 49 ± 21 awakenings. Those men who had both nocturia and OSA had on average 45 ± 19 awakenings per main sleep. In unadjusted models of (ln-transformed) awakenings, there was a positive association with OSA (β=0.174, 95%CI [0.070-0.276]) and nocturia (0.080 [0.006-0.152]) (as well as their combined term (0.160 [0.087-0.204]). However, after adjustment for qualifying covariates, only OSA remained associated with awakenings (0.174 [0.0700.276]) (Figure 2).

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DISCUSSION

In this sample of community-based, middle-aged and elderly men who underwent overnight PSG testing, we observed a strong effect of OSA on the presence of nocturia, independent of a variety of confounders for both conditions. Men with nocturia were also found to have excessive daytime sleepiness (EDS) and poorer sleep quality. Including other storage and voiding symptoms to the models of nocturia, attenuated the effect of OSA and strengthened the association with EDS, while voiding symptoms also decreased sleep period and quality. Direct sleep measures of men with nocturia demonstrated a compensatory extended total sleep period combined with an increased wake time and decreased period spent in REM sleep. Finally, night-time awakenings and subsequent voiding appeared largely influenced by apnea-hypopnea events.

Although previous studies have examined OSA as a driver of nocturia, most occurred in smaller sample sizes [19, 20] or had studied a clinic-based population [21, 22]. Our findings are more generalizable as we studied a large community-based sample of middleaged to older men. Moreover, our cross-sectional study revealed a robust relationship between nocturia and OSA after adjustment for many potential confounders, including kidney function, morning voided volume, muscle strength, steroid level, inflammation and medication use. To our knowledge, only three previous studies have used polysomnography (PSG) recordings to examine nocturia in community-based participants. An earlier study of 58 elderly men and women demonstrated a higher number of nocturia episodes (based on averaged data from a 3-day voiding diary) for severe OSA (defined as AHI ≥ 25) only [23]. However, the smaller sample size (only n=14 men) and age of the participants (mean: 78 ± 8 years) limits the applicability of these findings to this study and

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the broader population. In a large study of men and women from the Sleep Heart Health Study (SHHS), nocturia was shown to be independently associated with an AHI as low as 5 (OR: 1.16 [1.03-1.30]), up to an AHI ≥15 (OR: 1.31 [1.13–1.51]) [4]. The effect observed in our study (1.64 [1.03-2.55]) among participants of an identical age range is comparable, even with the larger sample size and lower AHI cut-off of the SHHS. Importantly, our study confirms these findings using a definition of nocturia consistent with the ICS definition [24]. The inclusion of kidney function data in the present study is also important given nocturnal polyuria, the primary determinant of most cases of nocturia, is largely a kidney-driven urine production disorder, as opposed to a bladder-driven urine storage disease [9]. Finally, a recent study of Japanese general elderly participants also confirmed a strong association between nocturic frequency and both objective and subjective measures [12]. Consistent with the present study, this study demonstrated a strong association with increasing episodes of nocturia and daytime sleepiness and overall sleep quality. However, the authors used actigraphy to measures sleep disturbances only, thus had no data available on OSA from PSG devices, known to be a causal factor in the link between sleep disruption and nocturia [9].

To our knowledge this is the first study to directly examine the effect of including other LUTS on the association between nocturia & OSA in home-based, otherwise healthy participants. Previous studies have demonstrated that patients with overactive bladder (OAB) syndrome report poorer sleep quality and increased daytime sleepiness (see [25]). (OAB is defined by the ICS as a symptom syndrome of urinary urgency, with or without urgency incontinence [24], usually with urinary frequency and nocturia, thus is broader than the focus of the present study). The key symptom of OAB is however urgency and we were able to demonstrate that the inclusion of other storage LUTS (frequency, urgency) in

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adjusted models of nocturia produced a strong association with EDS, independent of any effect of OSA. This concurs with the only other known study of LUTS and sleep dysfunction that used PSG testing, which also found urgency to have an independent effect on subjective, but not objective measures of sleep. This may explain why CPAP therapy is only partially effective in treating patients reporting poor sleep quality [26].

This study also adds to the paucity of available data on the effect of nocturia on sleep architecture. Our study is again very similar to data published from the SHHS showing men with nocturia have a longer sleep period for an equivalent sleep time, with higher WASO [4]. Likewise, recent data from a Japanese cohort of elderly, community-based men and women showed a longer sleep period and WASO for increasing nocturic frequency [12]. However, this compensatory sleep period extension seen in men with nocturia seems dependent on opportunity, with our data suggesting men still engaged in the workforce end up with ever lower sleep periods for an equivalent total sleep time. Our finding that men with nocturia spend less time in REM (and N2) stage sleep is also broadly consistent with the SHHS [4]. This has important implications given the multiple comorbidities recently identified with chronic deprivation of REM sleep (cardiovascular [27], diabetes [28], cognitive and mood disorders [29]), and also suggests another mechanism whereby nocturia may lead to increased cardiovascular risk.

While our data strongly suggest a role of OSA in the development of nocturnal polyuria, it is feasible that night-time voids occur secondary to sleep disturbances resulting in participants becoming conscious of the need to void. Recently published data from the Boston Area Community Health (BACH) survey demonstrated that incident storage symptoms (including nocturia) were predicted by poorer quality sleep and sleep restriction

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[30]. However, this study did not include any direct measures of sleep function or questions related to OSA. One of the earliest studies of nocturia and OSA, demonstrated that 76% of nocturnal voiding episodes were immediately preceded by an episode of obstructive apnea [20]. Our PSG data demonstrated that awakenings (i.e. arousals of 15 sec or greater) were associated with moderate and severe OSA, independent of other confounders. Both nocturia and the interaction term of OSA and nocturia showed no association with awakenings after adjustment. While tempting to suggest this is supportive of the role OSA may play in producing conscious arousals that lead to an awareness of voiding sensation, our data contains no information of timing of voids or subjective bladder sensations.

The strengths of this study include the use of both objective and subjective measures of sleep disturbance and a range of LUTS in a representative sample of community-dwelling men in assessing the association between OSA and nocturia. The ability to control for a wide variety of bio-psychosocial confounders is a further strength. Limitations include the cross-sectional nature of the study limiting our ability to ascribe cause-and-effect, a lack of data on melatonin or bedroom lighting conditions (although previous research on nocturia has found no differences for these variables [12]), the absence of detailed fluid intake and voiding data (e.g. voiding diaries), and the reliance on self-report measures for some measures of chronic disease and demographic data.

To conclude, in the present study we demonstrated that obstructive sleep apnea, as measured by polysomnography, does appear to be independently related to the development of nocturia in community-based men. Other LUTS, such as increased urgency, appear to impair sleep function and quality through non-OSA means. Nocturia

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was also found to reduce the average time spent in REM sleep, the implications of which require further long-term study.

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FIGURE LEGENDS Figure 1. Percentage of sleep period recorded in each stage of sleep for increasing frequencies of nocturic episodes in a population of middle-aged to elderly Australian men. Data show percentage (mean, upper 95% CI) of sleep period spent in N1, N2, N3, and REM Stage sleep for increasing nocturic frequency. * p<.001 vs none; # p<.05 vs none.

Figure 2. Regression estimates for awakenings in men with nocturia, obstructive sleep apnea (OSA) & both conditions in a population of middle-aged to elderly Australian men. Data show standardised coefficients estimates (β; 95%CI) for unadjusted and multiple adjusted models of nocturia (≥ 2 voids / main sleep); OSA (AHI ≥ 20); and men with both conditions. Final model of awakenings and: 1) Nocturia, also adjusted for: age, wake after sleep onset; oxygen desaturation index (>3%/hr); glomerular filtration rate; BMI; systolic BP; work status; smoking; alcohol 18 Page 18 of 23

consumption; HDL cholesterol; erectile function; total testosterone; diabetes; depression; asthma; diuretic usage. 2) OSA: As per nocturia, also: handgrip strength; diastolic BP; LDL cholesterol; cardiovascular disease; minus: BMI, systolic BP, total testosterone; HDL cholesterol. 3) Nocturia * OSA: As per nocturia, also: handgrip strength; marital status; triglycerides; household income; minus: BMI, total testosterone; asthma.

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Table 1. Descriptive characteristics of participants with and without nocturia in a cohort of Australian men attending an overnight polysomnographic testing (2010-11). Nocturia 0-1 Times (71.6%; 2 + times (28.4%; n=522) n=207) Mean / % SD / N Mean / % SD / N Age Group 35-44 years 45-54 years 55-64 years 65-74 years 75+ years Storage LUTS Frequency Urgency Voiding LUTS Intermittent Stream Weak Stream Straining Incomplete Emptying LUTS QoL (AUI-SI) Glomerular filtration rate (est.) (mL/min/1.73 m2) Voided volume morning (mL) Sleep Parameters OSA (AHI 20+) Total Sleep Time (mins) Total Sleep Period (mins) Wake After Sleep Onset (mins) Sleep Efficiency (%) Desaturation Index >3%(/hr) Excessive Daytime Sleepiness (ESS>11) Poor Sleep quality (PSQI>5) Covariates BMI (kg/m2) Waist circumference (cm) Hand grip strength *(Dominant; Nm) Blood pressure (>140mmHg/100mmHg)

p 0.001

8.4% 36.4% 33.7% 15.5% 5.9%

44 190 176 81 31

3.9% 22.2% 31.4% 27.1% 15.5%

8 46 65 56 32

20.50% 9.80%

107 51

34.80% 21.40%

72 44

0.001 0.001

7.5% 10.0% 2.1% 6.9% 1.28

39 52 11 36 1.21

21.3% 25.2% 12.6% 24.3% 1.94

44 52 26 50 1.33

0.001 0.001 0.001 0.001 0.021

91.70

14.47

87.45

14.45

0.001

229.7

167.7

225.4

153.7

0.812

21.5% 376.7 445.5 77.9

109 58.6 65.4 49.5

32.2% 370.1 467.3 97.2

65 56.0 58.4 52.9

0.003 0.175 0.016 0.001 0.001 0.042

80.2 11.4 11.5%

9.8 12.1 59

76.6 13.6 18.2%

10.3 13.8 37

43.6%

218

54.3%

108

0.017 0.032

28.8 102.2

4.2 11.9

28.6 103.0

4.5 12.4

0.541 0.611

44.3

0.32

41.1

0.34

0.013

20.3

102

27.1

121

0.058

Health-related QoL (SF-36) 20 Page 20 of 23

Physical Component Summary Mental Component Summary Marital status Married / Partner Separated / Divorced / Widowed Work status Full time /Part time / Casual Unemployed Shift-worker (current) Educational status Bachelor or higher Trade/Apprenticeship Certificate/Diploma Household Income Low Middle High Leisure time physical activity  150 mins or more 1-149 mins None Smoking status (current) Yes No Alcohol consumption (std. drinks/day) Triglycerides (>2.0 mmol/L) LDL chol. (>3.7 mmol/L) HDL chol. (>2.0mmol/L) Total T (nmol/L) SHBG (nmol/L) E2 (pmol/L) C-reactive protein (mg/L) Health conditions  CVD Anxiety Asthma Depression Diabetes Erectile dysfunction ∞ BPH Insomnia # Osteoarthritis Cancer

50.45

8.53

48.70

9.42

0.017

51.07

8.89

49.53

9.69

0.042

82.0%

428

77.3%

160

11.9%

62

16.8%

35

72.8% 21.9% 11.3%

379 114 61

56.5% 40.0% 13.6%

113 80 60

19.8% 42.6% 36.7%

98 203 199

19.8% 39.2% 41.6%

39 82 91

25.5% 34.9% 37.6%

125 182 196

35.1% 36.6% 21.5%

70 75 44

0.146

0.001 0.075 0.858

0.019

0.821

41.0% 22.3% 36.7%

320 174 286

41.0% 41.0% 17.9%

83 83 38

17.0% 82.8%

89 432

12.6% 87.5%

26 181

1.83

4.23

1.40

2.26

0.174

16.3% 11.0% 14.2% 16.93 33.53 94.22 2.19

89 60 78 5.78 14.68 34.92 2.28

27.1% 13.1% 27.1% 15.79 34.3 92.83 2.77

121 28 121 5.87 14.53 31.29 2.92

0.027 0.311 0.011 0.211 0.412 0.610 0.106

4.8% 8.2% 11.8% 8.5% 20.6% 16.5% 4.4% 9.1% 7.3% 7.1%

25 64 61 44 105 82 21 34 34 33

12.2% 9.5% 13.7% 9.3% 28.9% 33.7% 10.7% 13.1% 10.1% 8.7%

25 74 28 19 56 65 24 14 22 19

0.049 0.569 0.891 0.359 0.001 0.001 0.001 0.001 0.012 0.323

0.085

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Medications α -adrenoblockers Anti-cholinergics Diuretics 5α-reductase inhibitors

7.4 2.3 11.4 5.5

61 27 91 43

9.1 4.4 14.9 4.2

75 37 88 32

0.412 0.124 0.100 0.311

Data presented are mean & standard deviation (continuous) or percentage & number (categorical). *NonΔ normally distributed data are presented as median & SEM. Percent abdominal fat mass as measured by  DEXA; LTPA as measured by the National Physical Activity Survey;  OSA probability as measured by the Maislin Survey Screen;  Health conditions (excl. erectile dysfunction; ED) refer to previous physician ∞ diagnosis; Erectile function assessed by responses to Global Impotence Rating (GIR; Feldman et al., J. Clin. Epidemiol., 1994. 47(5): 457-467). ED defined as the presence of moderate & severe symptoms. Insomnia data only available for FAMAS-men only (46.1% of the analytic sample)  Medication usage assessed through Pharmaceutical Benefits Scheme linkage and / or self-report.

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Table 2. Univariable and multivariable regression estimates for nocturia (+ other LUTS) on sleep dysfunction in a cohort of Australian men attending an overnight polysomnographic testing (2010-11). Nocturia (2+ times) Unadjusted Multi-adjusted * Sleep Parameters OR / β 95%CI OR / β 95%CI OSA (AHI 20+) Wake After Sleep Onset (mins) * Total Sleep Period (mins) *

1.72 0.187

Daytime Sleepiness (ESS 11+) Sleep quality (PSQI>5) OSA (AHI 20+) Wake After Sleep Onset (mins) * Total Sleep Period (mins) *

1.23 0.122

Daytime Sleepiness (ESS 11+) Sleep quality (PSQI>5)

1.64 0.077

(1.03, 2.55) (-0.026, 0.178)

0.071

(-0.097, 0.151)

1.72

(1.20, 2.49) (0.115, 0.261) (0.141, 0.390) (1.10, 2.68)

1.72

(1.01, 2.93)

1.54

(1.10, 2.14)

1.65

(1.10, 2.48)

0.159

Nocturia + other storage LUTS (0.88, 1.74) 1.13 (0.74, 1.73) (0.052, 0.024 (-0.083, 0.131) 0.212) 0.111 (0.052, 0.055 (-0.142, 0.260) 0.256) 2.22 (1.45, 3.45) 2.44 (1.45, 4.10) 1.51

OSA (AHI 20+) Wake After Sleep Onset (mins) * Total Sleep Period (mins) *

1.33 0.229

Daytime Sleepiness (ESS 11+) Sleep quality (PSQI>5)

1.99

0.187

1.96

(1.11, 2.03)

1.65

(1.15, 2.39)

Nocturia + other voiding LUTS (0.94, 1.81) 1.48 (0.86, 2.56) (0.162, 0.123 (0.018, 0.229) 0.330) (0.165, 0.104 (0.020, 0.205) 0.376) (1.23, 3.07) 2.24 (1.19, 4.22) (1.44, 2.68)

2.61

(1.63, 4.17)

Data presented are univariable and multivariable regression estimates for the presence of nocturia against sleep parameters (referent category: 0-1 visits per night). Nocturia was defined using the following question: “Over the past month, how many times did you most typically get up to urinate from the time you went to bed at night until the time you got up in the morning?” (Item 7; AUA-SI). Apnea hypopnea index (AHI), sleep duration, Wake After Sleep Onset (WASO), and total sleep period were measured by in-home 8-channel polysomnography recording session (Embletta X100). Obstructive sleep apnea (OSA) was defined as an AHI ≥20. Daytime sleepiness was measured with the Eppworth Sleepiness Scale (ESS; daytime sleepiness=total score≥11) and sleep quality with the Pittsburgh Sleep Quality Index (PSQI; poor sleep quality=total score >5). *Multivariable models of nocturia & sleep parameters were also adjusted for age, BMI, handgrip strength, systolic BP, work status, household income, smoking status, glomerular filtration rate, serum CRP, LDL and HDL cholesterol, total testosterone, CVD, anxiety, depression, diabetes, BPH, insomnia, anticholinergics, diuretics. Other significant predictors included: Age (OR: 1.32 (95%CI: 1.13 - 1.62); handgrip strength (1.12; 1.03 – 1.24); being unemployed (1.67; 1.01-2.76); serum CRP (1.07; 1.02 – 1.16); total testosterone (0.89 (0.81 – 0.97)); cardiovascular disease (2.92 (1.36 – 6.26), depression (1.31 (1.11 – 2 1.72). Model fit was assessed by Nagelkerke R = 0.355.

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