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Nephrol Dial Transplant. 2008 December; 23(12): 3908–3914.
Published online 2008 July 20. doi:  10.1093/ndt/gfn364
PMCID: PMC2587522
NIHMSID: NIHMS55534

Renal function and sleep-disordered breathing in older men

Muna T. Canales,1,2 Li-Yung Lui,3 Brent C. Taylor,4,5,6 Areef Ishani,4,6 Reena Mehra,7 Katie L. Stone,3 Susan Redline,8 Kristine E. Ensrud,4,5,6 and for the Osteoporotic Fractures in Men (MrOS) Study Group

Abstract

Background. Sleep-disordered breathing (SDB) is common in severe chronic kidney disease (CKD) and may contribute to morbidity and mortality in this population. However, the association between mild to moderate CKD and likelihood of SDB is uncertain.

Methods. We studied 2696 men ≥65 years (mean 73.0 ± 5.5) enrolled in the Outcomes of Sleep Disorders in Older Men (MrOS Sleep) study who had serum creatinine (SCr) measured 3.4 years prior to overnight polysomnography (PSG). CKD was expressed as quartiles of estimated glomerular filtration rate (eGFR) using the four-variable Modification of Diet in Renal Disease (MDRD) formula. SDB was assessed using the respiratory disturbance index (RDI) with ≥4% oxygen desaturation.

Results. Mean SCr was 0.99 ± 0.20 mg/dl; 14.8% had eGFR <60 ml/min/1.73 m2. Median RDI was 7.4 events/hour (inter-quartile range 2.6–15.8). Lower eGFR was not associated with higher mean RDI in the unadjusted model (P for trend = 0.180). There was evidence of an interaction between eGFR and age for the prediction of RDI; an association between lower eGFR and higher RDI was evident only among men ≤72 (median) years. Among this age group, however, the association was not statistically significant after further adjustment for body mass index (BMI) (P for trend = 0.278).

Conclusions. In this cohort of older community-dwelling men, reduced renal function was not associated with greater evidence of SDB, except among younger old men. However, this association was largely explained by higher BMI at lower eGFR. Further prospective study in younger populations is needed to clarify our findings.

Keywords: chronic kidney disease, kidney dysfunction, sleep disorders

Introduction

Chronic kidney disease (CKD), as defined by an estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2, affects one in five Americans over 65 years old [1]. CKD has been linked to increased cardiovascular disease (CVD) risk and mortality as well as all-cause mortality [2,3]. While traditional cardiovascular risk factors such as diabetes, hypertension, hyperlipidemia and smoking explain part of this increased risk, other factors that are prominent in CKD may play a role [4,5]. Sleep-disordered breathing (SDB), a disorder known to negatively impact blood pressure and endothelial function, is one such factor [4].

SDB is common among individuals with severe CKD, affecting 30–80% of those on dialysis and over 50% of patients with severe CKD not-requiring dialysis [6–9]. However, most of these previous investigations are limited by small sample sizes with no comparison group and lack of applicability to patients with mild renal disease [7,10]. We recently examined the association between mild reductions in renal function and SDB in a cohort of 508 community-dwelling men over 65 years old and found that the presence of CKD as defined by eGFR <60 by the Mayo Clinic (MAYO) formula, but not the Modification of Diet in Renal Disease (MDRD) formula, was associated with a twofold greater odds of prevalent SDB despite adjustment for demographics, body mass index (BMI) and comorbidities [11]. However, conclusions from this study were limited by low power to detect associations within subgroups and across multiple measures of renal function. Thus, the association between mild CKD and SDB remains uncertain.

To determine whether mild to moderate CKD is associated with a higher likelihood of SDB in older men, we measured serum creatinine (SCr) at baseline and performed overnight polysomnography (PSG) an average of 3.4 years later in a cohort of 2696 men aged ≥65 years enrolled in the Outcomes of Sleep Disorders in Older Men (MrOS Sleep) study. We hypothesized that CKD as defined by lower eGFR would be independently associated with a higher likelihood of SDB as measured by the respiratory disturbance index (RDI).

Methods

Participants

The Osteoporotic Fractures in Men (MrOS) study, the parent cohort for the MrOS Sleep study, enrolled 5995 community-dwelling men aged 65 years and older between March 2000 and April 2002 [12]. Participants were recruited from six U.S. centers. Subjects considered for enrollment in MrOS had to be able to walk without the assistance of another person and not have a bilateral hip replacement [12,13].

A total of 3135 men from MrOS were recruited for participation in the MrOS Sleep study. The men were screened for use of mechanical devices during sleep including pressure mask for sleep apnea (CPAP or BiPAP), mouthpiece for snoring or sleep apnea or oxygen therapy. In general, those who reported nightly use of any of these devices were excluded from the MrOS sleep study. However, 17 men who reported use of one of these devices but were able to forego use during the night of the sleep study were included. The 3135 men completed an exam conducted between December 2003 and March 2005 that included a clinic visit and overnight in-home PSG. Of these men, 2911 had technically adequate PSG and, of these, 2700 also had serum creatinine (SCr) measured at baseline 3.4 years prior to PSG. We excluded the four individuals who reported being on dialysis, leaving 2696 men that we included in this analysis.

Collection of sleep data and definition of sleep parameters

In-home sleep studies were completed using unattended, portable PSG (Safiro model, Compumedics, Inc.®). The recording montage was as follows: C3/A2 and C4/A1 electroencephalograms, bilateral electrooculograms and a bipolar submental electromyogram to determine sleep status; thoracic and abdominal respiratory inductance plethysmography to determine respiratory effort; airflow (by nasal–oral thermocouple and nasal pressure cannula); finger pulse oximetry; lead I EKG; body position (mercury switch sensor) and bilateral leg movements (piezoelectric sensors). Centrally trained and certified staff performed home visits to set up the unit, verify the values of the impedances for each channel, confirm calibration of position sensors and note any problems encountered during set-up, similar to the protocol used in the Sleep Heart Health study [14]. Staff returned the next morning to collect the equipment and download the data to the Case Reading Center (Cleveland, OH, USA) to be scored by a trained technician.

SDB was defined by the RDI. Apnea was defined as complete or near complete cessation of airflow (reduction of amplitude to at least <25% of baseline) for >10 s, and hypopneas were scored if clear reductions in breathing amplitude occurred, and lasted >10 s [15]. In these analyses, only apneas and hypopneas that were each associated with a 4% or greater desaturation were included in the RDI, which was calculated by dividing the total number of apneas and hypopneas by the total time slept in hours giving units of events/hour.

Measurements to estimate renal function

Blood was collected at the MrOS baseline clinic visit (2000–2002), initially kept at room temperature for 40–90 min, and then centrifuged for 10 min. Within 30 min of centrifuging, serum was separated and frozen at −70°C. Prior to analysis, samples were thawed once for a separate analysis, and then refrozen and sent directly to the Oregon Veterans Administration Clinical Lab (Portland, OR, USA) where SCr assay was performed. SCr was measured using the Roche COBAS Integra 800 automated analyzer (Roche Diagnostics Corp., Indianapolis, IN, USA) utilizing a variation of the Jaffe enzymatic method. Inter-assay CV was 5.3%; this assay was calibrated daily (Roche Diagnostics Corp).

GFR was estimated using three creatinine-based formulae: in the primary analysis, we used the abbreviated four-variable version of the MDRD formula [16,17]. In secondary analyses, we expressed eGFR using the MAYO formula [18] because a prior study suggested that lower eGFR as defined by the MAYO formula might be more strongly related to SDB [11,18].

Because SCr measurements were made 3.4 years prior to PSG, we compared SCr at baseline and at the time of PSG in a subset of 534 men who had SCr measurements at both of these time points. In this subset of men, over 80% had an increase in SCr <0.2 mg/dl, suggesting a minimal clinically relevant change in SCr between baseline and time of PSG.

Other measurements

Candidate variables included in our analysis were collected from the baseline visit during 2000–2002 for the MrOS study and the accompanying questionnaire. Variables used in this study included demographic factors such as age and race where race was defined as Caucasian, African American or other; BMI (kg/m2) as a measure of obesity; habits such as tobacco use; self-reported health status based upon SF-12 questionnaire [19] and medical history including hypertension, self-reported diabetes and self-reported cardiovascular disease.

Statistical analysis

Baseline characteristics were examined across quartiles of MDRD eGFR, and statistical differences across quartiles were calculated using analysis of variance (ANOVA) (Kruskal–Wallis ANOVA was used for the skewed variable RDI) or chi-square tests for continuous and categorical variables, respectively.

All analyses examining the association between renal function parameters and measures of SDB included enrollment site as an independent variable. Due to the skewed distribution of RDI values, we estimated transformed (log[RDI + 1]) least-squares mean values by quartile of MDRD eGFR from multiple linear regression and back-transformed results for interpretation. Using logistic regression, we then estimated the association between quartile of MDRD eGFR and the prevalence of SDB (RDI ≥15). For each of the analyses in which the odds ratio of SDB was computed, the referent category was composed of men with highest MDRD eGFR (quartile 4). Finally, in the interest of clinical relevance, MDRD eGFR was dichotomized as ≥60 (referent group) or <60 ml/min/1.73 m2 (CKD) and logistic regression was used to estimate the association between CKD and likelihood of SDB. Since an RDI ≥15 was common (prevalence of 27%) in this cohort of older men, we also examined the association between MDRD eGFR and severe SDB as defined by an RDI ≥30. These analyses were repeated substituting MAYO eGFR for MDRD eGFR. We performed primary analyses excluding the 17 individuals who reported use of a device to treat a sleep disorder, and because the findings were not different from those for the entire cohort, we do not present these results.

For all subsequent models, age, race and BMI were selected as putative confounders. For each set of analyses, we present three models: unadjusted, age and race adjusted and, finally, age, race and BMI adjusted. In addition, since prior study (REF) has suggested that age may be an effect modifier in the association between renal function and SDB, we tested for the presence of an interaction between MDRD eGFR quartile (expressed as an ordinal variable) and age (expressed as a continuous variable) [11]. Also, because the role of BMI in the putative association between renal function and SDB may be more complex than simple confounding, we also tested for the presence of an interaction between MDRD eGFR quartile and BMI (expressed as a continuous variable) for the prediction of RDI [11]. We performed secondary analyses stratifying participants by the median value of each variable (age or BMI) if P for the interaction term was ≤0.10. The test for interaction was repeated substituting MAYO eGFR quartile for MDRD eGFR quartile. All significance levels reported were two sided with P < 0.05 and all analyses were performed using SAS software version 9.1 (SAS Institute, Inc., Cary, NC, USA).

Results

Characteristics of participants

The mean (±SD) age of the 2696 participants who met inclusion criteria was 73.0 (±5.5) years. This cohort of men was predominantly white (91.4%). Mean SCr was 0.99 ± 0.20 mg/dl; 14.8% had eGFR <60 ml/min/1.73 m2 by MDRD and 4.5% by MAYO. Median RDI was 7.4 events/hour (inter-quartile range 2.6–15.8); mean (SD) RDI was 11.9 (13.1) with the range 0.0–88.8; 27% of participants had an RDI ≥15 events/hour and 10% had an RDI ≥30 events/hour.

Baseline characteristics of the participants by quartile of MDRD eGFR are shown in Table Table1.1. Lower eGFR was associated with older age (P < 0.001), Caucasian race (P < 0.001), fair or worse self-reported health status (P = 0.03), more hypertension (P < 0.001), more self-reported cardiovascular disease (P < 0.001) and diabetes status (P = 0.02).

Table 1
Baseline characteristics of participants by quartile of eGFR via the MDRD equationa (n = 2696)

Association between eGFR and RDI

In the unadjusted model, lower quartile of MDRD eGFR was not associated with higher mean RDI (P for trend = 0.180, Table Table2a);2a); however, lower quartile of MAYO eGFR was associated with higher mean RDI (P for trend = 0.001, Table Table2b).2b). This relationship persisted after adjustment for age and race (P for trend = 0.046). After further adjustment for BMI, however, the association between lower quartile of MAYO eGFR and higher mean RDI was no longer statistically significant (P for trend = 0.192).

Table 2
Geometric mean respiratory disturbance index (95% confidence interval) by quartile of (a) MDRD eGFR and (b) MAYO eGFR

There was no evidence that BMI modified the unadjusted relationship between MDRD eGFR quartile and mean RDI (P for test of interaction = 0.96); we found similar results when MAYO eGFR was substituted for MDRD eGFR (MAYO P for test of interaction = 0.61). However, age interacted with MDRD (and MAYO) eGFR quartile to predict of mean RDI (P for test of interaction between MDRD eGFR and age = 0.02; between MAYO eGFR and age = 0.07). Among men >72 years of age (median), lower quartile of eGFR as measured by both MDRD and MAYO formulae was not associated with increasing RDI (unadjusted MDRD eGFR P for trend = 0.479, Table Table3a;3a; MAYO eGFR P for trend = 0.901, data not shown). However, among men ≤72 years of age, lower quartile of eGFR as measured by both MDRD and MAYO formulae was associated with increasing RDI (MDRD P for trend = 0.048, Table Table3b;3b; MAYO P for trend 0.002, data not shown). These associations were slightly attenuated after further adjustment for race but were no longer statistically significant after additional adjustment for BMI (MDRD P for trend = 0.278, Table Table3b;3b; MAYO P for trend = 0.076, data not shown).

Table 3
Geometric mean respiratory disturbance index (95% confidence interval) by quartile of MDRD eGFR for age (a) >72 years and (b) ≤72 years

Association between eGFR and sleep-disordered breathing

We found no evidence of an association between MDRD eGFR and prevalence odds of SDB (RDI ≥15) (Table (Table4a).4a). However, lower quartile of MAYO eGFR was associated with greater odds of SDB in the unadjusted model (P for trend = 0.034), but not after adjustment for age and race (P for trend = 0.365) (Table (Table4b).4b). Substituting RDI ≥30 for RDI ≥15 in these analyses produced similar results.

Table 4
Association between quartile of (a) MDRD eGFR and (b) MAYO eGFR moderate to severe SDBa

Association between chronic kidney disease and sleep-disordered breathing

We found no evidence that men with CKD (as defined by eGFR <60 using the MDRD or MAYO eGFR formulae) had a higher likelihood of SDB (unadjusted MDRD P = 0.402; MAYO P = 0.599, data not shown). Findings in the above analyses examining the association between CKD as defined by the MDRD or MAYO formulae and odds of SDB were essentially unchanged when SDB was defined as RDI ≥30.

Discussion

We found that reduced renal function, as defined by lower MDRD eGFR, was not associated with higher RDI, except among younger old men. Younger old men with lower eGFR had higher RDI; however, this association was largely explained by higher BMI at lower eGFR. We found no association between the presence of chronic kidney disease, as defined by eGFR <60 ml/min/1.73 m2, and odds of moderate to severe SDB.

Until recently, prior investigations examining the association between reductions in renal function (not-requiring dialysis) and SDB have been small, lacking controls and performed in highly selected populations with severe renal disease [7,10]. More recently, however, we examined a subcohort of 508 men unselected for sleep complaints or renal function at a single center of the MrOS Sleep study [11]. All men had serum cystatin C and serum creatinine measured coincident with PSG. Key findings from this study were similar to findings from the current study in that higher quartile of cystatin C (analogous to lower eGFR quartile) was associated with higher RDI but only in the youngest old men; this relationship was also due to higher BMI among men with higher cystatin C. In contrast to the current findings, however, CKD as defined by MAYO (but not MDRD) eGFR ≤60 ml/min/1.73 m2 was associated with a twofold greater odds of moderate to severe SDB despite adjustment for age, race, BMI, cardiovascular disease, hypertension and diabetes.

Findings from our previous study and the current study suggest several key issues regarding the putative association between renal function and SDB: (1) age as an effect modifier, (2) the complex role of BMI and (3) the inconsistency of findings across different measures of renal function. First, the association between reduced renal function and SDB appears to depend upon age; that is, the association was observed among the youngest old and not the oldest old. Though these associations were attenuated to borderline statistical significance after BMI was added to the age, race-adjusted model, sample size was reduced in stratified analysis, minimizing power to find small associations. There are several possible explanations for these findings. It may be that in older individuals, a lower eGFR is less indicative of true renal disease than it is in younger individuals; therefore, a reduced eGFR in the oldest old may not have the same implications for negative outcomes that a reduced eGFR has in the younger old [20]. Survival bias may also contribute to the age effect in that the sicker individuals died earlier, leaving only the healthiest with the least severe disease (sleep and/or renal) to constitute our oldest old in the cohort. Finally, it may be that older individuals have more comorbidities than their younger counterparts, many of which contribute to the pathogenesis of SDB, making it harder to tease out a small to moderate association between reduced renal function and SDB.

In both studies, higher BMI among men with reduced renal function at least partially explained the associations we observed between renal function and SDB. In our analyses, BMI was viewed as a confounder. However, the true role of BMI in the pathway between renal function and SDB is likely more complex due to the possible bidirectional nature of the association between renal function and SDB. It is plausible that SDB, through its vasculotoxic nature, may lead to development or progression of renal disease [21,22]. Obesity, for which BMI is a measure, has been linked in large epidemiological studies to development or progression of renal dysfunction [23,24]. Therefore, because SDB may be on the causal pathway between obesity and renal disease, our inclusion of BMI as a confounder may have resulted in over-adjustment, biasing our results toward the null hypothesis of no association.

Finally, findings were inconsistent across different measures of renal function in both the current and previous studies. In general, the direction of associations was similar for MDRD and MAYO formulae, though in our previous study we found a moderate association between CKD defined by MAYO eGFR ≤60 ml/min/1.73 m2 and moderate to severe SDB, despite multivariable adjustment. These disparities may be related to differences in the populations in which these formulae were developed, neither of which was developed in community-dwelling elderly men. In addition, the MAYO formula was not developed using a creatinine assay calibrated to be IDMS-traceable. We attempted to overcome the issue of calibration by using quartiles of eGFR as opposed to clinical cutpoints in an effort to preserve the order of individuals with respect to each other.

Biological pathways linking reduced renal function and SDB are unclear, and the association may be bidirectional. Nocturnal hemodialysis and renal transplantation significantly improve SDB in patients on conventional hemodialysis, suggesting that factors resulting from the reduction in renal function may explain the increased prevalence of SDB in CKD [25,26]. Such factors include fluid retention leading to airway edema and obstructive events, metabolic acidosis, high levels of circulating cytokines or other uncleared elements (particularly middle molecules) and uremic neuropathy [26–28]. Finally, enhanced chemosensitivity may lead to periodic breathing, characterized by alternating cycles of hyperventilation and hypoventilation, with apneas occurring at the nadir of this cycling [29]. Although some of these mechanisms are unlikely to be applicable to individuals with early mild reductions in renal function, elevation in inflammatory cytokines such as IL-6 that may dysregulate sleep and volume expansion may be plausible mechanisms [27,30,31].

Alternatively, SDB may play a role in development or progression of chronic kidney disease. Epidemiological data support associations between SDB and incident hypertension, prevalent cardiovascular disease and prevalent diabetes [32–34]. These adverse effects may be due in part to intermittent hypoxemia and sympathetic nervous system activation during SDB that promote oxidative stress, insulin resistance and endothelial dysfunction [35–37]. While no studies have examined the association between SDB and development or progression of reduced eGFR, recent data from a genetic epidemiological study of SDB have shown microalbuminuria to be associated with an RDI >30 despite adjusting for BMI and other covariates [22]. These results suggest that SDB may adversely affect glomerular endothelial function and, therefore, renal function.

To our knowledge, this is the largest study to date that examines the association between reduced renal function and SDB in a cohort of community-dwelling elderly men unselected for sleep complaints or renal disease. Other strengths of this study include diagnosis of SDB using state of the art in-home PSG and extensive collection of covariate data. Despite these strengths, our study has limitations. Because the majority of our cohort consists of generally healthy elderly Caucasian men, our findings are not generalizable to other populations. In addition, while SCr measurements preceded sleep measurements by 3.4 years, we have no information regarding the presence or absence of SDB at baseline; therefore, our study cannot address causality. In addition, SCr may have changed in the intervening 3.4 years; however, comparison with SCr obtained among over 500 of our cohort at the time of sleep study suggests that this change was minimal and unlikely to be clinically relevant. We cannot exclude the possibility that our largely null findings are because older men with greater evidence of renal disease and/or SDB did not survive to be part of this study. Another limitation is that this cohort of men had at most mild renal dysfunction; it is unclear if mild renal dysfunction in older people represents true renal disease, or if it is simply age-related change [20]. Moreover, there may be a threshold at which the association between renal dysfunction and SDB is seen and, because we had so few men with moderate to severe renal dysfunction, we did not have sufficient power at a more severe level of renal function to detect the association. Finally, we used surrogate measures of renal function since direct measures of GFR are not feasible in either a population-based study or the clinical practice setting.

In conclusion, reduced renal function in older community-dwelling men as measured by MDRD eGFR was not associated with greater evidence of SDB, except among the youngest old. This association was largely explained by higher BMI at lower eGFR. Future investigations should prospectively examine younger populations with a broad range of renal function to further evaluate the association between reduced renal function and SDB.

Acknowledgments

The Osteoporotic Fractures in Men (MrOS) study is supported by National Institutes of Health funding. The following institutes provide support: the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Institute on Aging (NIA), the National Cancer Institute (NCI), the National Center for Research Resources (NCRR) and NIH Roadmap for Medical Research under the following grant numbers: U01 AR45580, U01 AR45614, U01 AR45632, U01 AR45647, U01 AR45654, U01 AR45583, U01 AG18197, U01-AG027810 and UL1 RR024140. The National Heart, Lung and Blood Institute (NHLBI) provides funding for the MrOS Sleep ancillary study ‘Outcomes of Sleep Disorders in Older Men’ under the following grant numbers: R01 HL071194, R01 HL070848, R01 HL070847, R01 HL070842, R01 HL070841, R01 HL070837, R01 HL070838 and R01 HL070839. Dr Canales’ time and training supported by National Institutes of Health funding as well through the National Institute of Diabetes and Digestive and Kidney Diseases, training grant T32 DK007784. Preliminary data from this analysis were presented in abstract form at the 21st Annual Meeting of the Associated Professional Sleep Societies, LLC in Minneapolis, MN, USA, June 2007, under the title ‘Reduced Renal Function and Sleep Apnea in Community-Dwelling Elderly Men’. Finally, we would like to acknowledge Mr Kyle A. Moen for his assistance in preparation of the manuscript and formatting of the tables.

Conflict of interest statement. None declared.

References

1. Coresh J, Astor B, Greene T, et al. Prevalence of chronic kidney disease and decreased kidney function in the adult US population: third National Health and Nutrition Examination Survey. Am J Kidney Dis. 2003;41:1–12. [PubMed]
2. Go A, Chertow G, Fan D, et al. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2005;351:1296–1305. [PubMed]
3. Shlipak M, Fyr CW, Chertow G, et al. Cystatin C and mortality risk in the elderly: the health, aging, and body composition study. J Am Soc Nephrol. 2006;17:254–261. [PubMed]
4. Zoccali C, Mallamaci F, Tripepi G. Traditional and emerging cardiovascular risk factors in end-stage renal disease. Kidney Int Suppl. 2003;85:S105–10. [PubMed]
5. Keith D, Nichols G, Gullion C, et al. Longitudinal follow-up and outcomes among a population with chronic kidney disease in a large managed care organization. Arch Intern Med. 2004;164:659–663. [PubMed]
6. Kimmel P, Miller G, Mendelson W. Sleep apnea syndrome in chronic renal disease. Am J Med. 1989;86:308–314. [PubMed]
7. Markou N, Kanakaki M, Myrianthefs P, et al. Sleep-disordered breathing in nondialyzed patients with chronic renal failure. Lung. 2006;184:43–49. [PubMed]
8. Wadhwa N, Seliger M, Greenberg H, et al. Sleep related respiratory disorders in end-stage renal disease patients on peritoneal dialysis. Perit Dial Int. 1992;12:51–56. [PubMed]
9. Unruh M. Sleep apnea in patients on conventional thrice-weekly hemodialysis: comparison with matched controls from the Sleep Heart Health Study. J Am Soc Nephrol. 2006;17:3503–3509. [PubMed]
10. Parker K, Bliwise D, Bailey J, et al. Polysomnographic measures of nocturnal sleep in patients on chronic, intermittent daytime haemodialysis vs those with chronic kidney disease. Nephrol Dial Transplant. 2005;20:1422–1428. [PubMed]
11. Canales M, Taylor B, Ishani A, et al. Reduced renal function and sleep-disordered breathing in community-dwelling elderly men. Sleep Med. 2007 doi:10.1016/j.sleep.2007.08.021. [PMC free article] [PubMed]
12. Orwoll E, Blank J, Barrett-Connor E, et al. Design and baseline characteristics of the osteoporotic fractures in men (MrOS) study—a large observational study of the determinants of fracture in older men. Contemp Clin Trials. 2005;26:569–585. [PubMed]
13. Blank J, Cawthon P, Carrion-Petersen M, et al. Overview of recruitment for the osteoporotic fractures in men study (MrOS) Contemp Clin Trials. 2005;26:557–568. [PubMed]
14. Redline S, Sanders M, Lind B, et al. Methods for obtaining and analyzing unattended polysomnography data for a multicenter study. Sleep. 1998;21:759–767. [PubMed]
15. Quan S, Howard B, Iber C, et al. The Sleep Heart Health Study: design, rationale, and methods. Sleep. 1997;20:1077–1085. [PubMed]
16. Levey A, Bosch J, Lewis J, et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of diet in renal disease study group. Ann Intern Med. 1999;130:461–470. [PubMed]
17. Levey AS, Coresh J, Greene T, et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145:247–254. [PubMed]
18. Rule A, Larson T, Bergstralh E, et al. Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease. Ann Intern Med. 2004;141:929–937. [PubMed]
19. Ware J, Kosinski M, Keller S. SF-12: How to Score the SF-12 Physical and Mental Health Summary Scores. Lincoln, RI: Quality Metric Incorporated; 1998.
20. O’Hare A, Bertenthal D, Covinsky K, et al. Mortality risk stratification in chronic kidney disease: one size for all ages? J Am Soc Nephrol. 2006;17:846–853. [PubMed]
21. Shamsuzzaman A, Gersh B, Somers V. Obstructive sleep apnea: implications for cardiac and vascular disease. JAMA. 2003;290:1906–1914. [PubMed]
22. Faulx M, Storfer-Isser A, Kirchner H, et al. Obstructive sleep apnea is associated with increased urinary albumin excretion. Sleep. 2007;30:923–929. [PubMed]
23. Hsu CY, McCulloch CE, Iribarren C, et al. Body mass index and risk for end-stage renal disease. Ann Intern Med. 2006;144:21–28. [PubMed]
24. Ejerblad E, Fored CM, Lindblad P, et al. Obesity and risk for chronic renal failure. J Am Soc Nephrol. 2006;17:1695–1702. [PubMed]
25. Auckley D, Schmidt-Nowara W, Brown L. Reversal of sleep apnea hypopnea syndrome in end-stage renal disease after kidney transplantation. Am J Kidney Dis. 1999;34:739–744. [PubMed]
26. Hanly P, Pierratos A. Improvement of sleep apnea in patients with chronic renal failure who undergo nocturnal hemodialysis. N Engl J Med. 2001;344:102–107. [PubMed]
27. Shlipak MG, Fried LF, Crump C, et al. Elevations of inflammatory and procoagulant biomarkers in elderly persons with renal insufficiency. Circulation. 2003;107:87–92. [PubMed]
28. Vgontzas AN, Papanicolaou DA, Bixler EO, et al. Elevation of plasma cytokines in disorders of excessive daytime sleepiness: role of sleep disturbance and obesity. J Clin Endocrinol Metab. 1997;82:1313–1316. [PubMed]
29. Beecroft J, Duffin J, Pierratos A, et al. Enhanced chemo-responsiveness in patients with sleep apnoea and end-stage renal disease. Eur Respir J. 2006;28:151–158. [PubMed]
30. Krishnan AV, Kiernan MC. Uremic neuropathy: clinical features and new pathophysiological insights. Muscle Nerve. 2007;35:273–290. [PubMed]
31. Koomans HA, Roos JC, Boer P, et al. Salt sensitivity of blood pressure in chronic renal failure. Evidence for renal control of body fluid distribution in man. Hypertension. 1982;4:190–197. [PubMed]
32. Peppard P, Young T, Palta M, et al. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med. 2000;342:1378–1384. [PubMed]
33. Punjabi NM, Shahar E, Redline S, et al. Sleep-disordered breathing, glucose intolerance, and insulin resistance: the Sleep Heart Health Study. Am J Epidemiol. 2004;160:521–530. [PubMed]
34. Marin J, Carrizo S, Vicente E, et al. Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet. 2005;365:1046–1053. [PubMed]
35. Vgontzas AN, Papanicolaou DA, Bixler EO, et al. Sleep apnea and daytime sleepiness and fatigue: relation to visceral obesity, insulin resistance, and hypercytokinemia. J Clin Endocrinol Metab. 2000;85:1151–1158. [PubMed]
36. Lavie L. Obstructive sleep apnoea syndrome—an oxidative stress disorder. Sleep Med Rev. 2003;7:35–51. [PubMed]
37. Nieto FJ, Herrington DM, Redline S, et al. Sleep apnea and markers of vascular endothelial function in a large community sample of older adults. Am J Respir Crit Care Med. 2004;169:354–360. [PubMed]

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