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Volume 14 No. 03
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Scientific Investigations

Effect of Sleep-Disordered Breathing on Albuminuria in 273 Patients With Type 2 Diabetes

Akihiro Nishimura, MD, PhD1; Takatoshi Kasai, MD, PhD2,3; Shota Kikuno, MD1; Kaoru Nagasawa, MD, PhD1; Minoru Okubo, MD, PhD1; Koji Narui, MD2; Yasumichi Mori, MD, PhD1
1Department of Endocrinology and Metabolism, Toranomon Hospital, Minato-ku, Tokyo, Japan; 2Sleep Center, Toranomon Hospital, Tokyo, Japan; 3Cardiovascular Respiratory Sleep Medicine, Department of Cardiovascular Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan

ABSTRACT

Study Objectives:

Sleep-disordered breathing (SDB) can induce hyperglycemia, hypertension, and oxidative stress, conditions that are known to cause kidney damage. Therefore, SDB may exacerbate albuminuria, which is an established marker of early-stage kidney damage in patients with type 2 diabetes mellitus (T2DM). The association between SDB and albuminuria in patients with T2DM was investigated in this study.

Methods:

This cross-sectional study included 273 patients with T2DM who underwent portable sleep testing and measurement of urine albumin to creatinine ratio (UACR). The association between the severity of SDB and albuminuria was investigated. Patients were divided into three groups according to the respiratory event index (REI): the no or mild group (REI < 15 events/h), moderate (REI 15 to < 30 events/h), and severe (REI ≥ 30 events/h). Albuminuria was defined as UACR ≥ 3.4 mg/mmol creatinine. Logistic regression analysis for albuminuria included the categorical REI as the independent variable.

Results:

The median (interquartile range) REI of all patients (age 57.9 ± 11.9 years, mean ± standard deviation, male sex 81.7%, body mass index 26.7 [24.2–29.5] kg/m2, estimated glomerular filtration rate 82 [65–97] mL/min/1.73 m2) was 13.0 (7.0–24.2) events/h. The REI, as a categorical variable, was significantly associated with albuminuria after adjustment for other risk factors for albuminuria; REI 15 to < 30 events/h: odds ratio (OR) 3.35, 95% confidence interval (95% CI), 1.68–6.67, P < .001; REI ≥ 30: OR 8.52, 95% CI, 3.52–20.63, P < .001). In addition, the natural logarithm-transformed REI of all patients also correlated significantly with albuminuria.

Conclusions:

The severity of SDB is associated with albuminuria in patients with T2DM.

Citation:

Nishimura A, Kasai T, Kikuno S, Nagasawa K, Okubo M, Narui K, Mori Y. Effect of sleep-disordered breathing on albuminuria in 273 patients with type 2 diabetes. J Clin Sleep Med. 2018;14(3):401–407.


BRIEF SUMMARY

Current Knowledge/Study Rationale: The results of recent studies suggest independent associations between sleep-disordered breathing (SDB) and diabetic microangiopathies in patients with type 2 diabetes mellitus. Although some studies evaluated the association between SDB and estimated glomerular filtration rate, few studies assessed the association between SDB and albuminuria in patients with type 2 diabetes mellitus.

Study Impact: Respiratory event index (REI) had an independent and significant association with albuminuria in patients with type 2 diabetes mellitus. The odds ratio for albuminuria was approximately twofold to threefold higher in patients with moderate SDB and approximately sixfold to eightfold higher in patients with severe SDB compared to patients with mild or no SDB.

INTRODUCTION

Sleep-disordered breathing (SDB) is characterized by repetitive upper airway obstruction during sleep and is associated with oxygen desaturation. Several studies reported the high prevalence of SDB in patients with type 2 diabetes mellitus (T2DM)1 and chronic kidney disease (CKD),2 independent of obesity or other comorbidities. Because SDB can activate hyperglycemia, hypertension,3 and oxidative stress,4 factors known to play pathogenic roles in renal injury,5 SDB may exacerbate CKD and such effects might be more pronounced in patients with T2DM.

Albuminuria is an established marker of renal damage in the early stage of CKD; high levels increase the risk for mortality, progression of CKD, and end-stage renal disease, independent of glomerular filtration rate (GFR).6 In patients with diabetes mellitus who also have persistent albuminuria, the development of overt proteinuria and a decline in GFR and end-stage renal disease is more likely.7,8 Furthermore, albuminuria is a well-established marker of increased risk of coronary heart disease and death.8,9 Therefore, it is important to clarify the independent risk factors for albuminuria in patients with T2DM.

Although previous studies have shown a significant and independent association between SDB and albuminuria in patients with CKD regardless of the presence or absence of T2DM or in patients without CKD,1012 whether there is a relationship between SDB and albuminuria remains controversial. Although several studies on patients with T2DM investigated the association between SDB and GFR,1315 only a few have investigated the association between SDB and albuminuria. Furthermore, published studies with multivariate analysis have been limited, with conflicting results.16 The goal of the current study is to determine the association between the severity of SDB and albuminuria in patients with T2DM.

METHODS

Patients

Consecutive adult patients (age 20 years or older) with T2DM who were followed up at our diabetes outpatient clinic were enrolled in this study. The diagnosis of T2DM was based on the Japan Diabetes Society Committee on The Classification and Diagnostic Criteria of Diabetes Mellitus.17 Patients with type 1 or other types of diabetes mellitus, currently undergoing treatment for SDB, active lung disease, or nondiabetic renal disease including chronic glomerulonephritis, urinary tract infection, or genitourinary malignancy were excluded. Patients with heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or estimated GFR (eGFR) of < 30 mL/ min/1.73 m2 were also excluded because these conditions may affect prevalence and severity of SDB. The study was approved by the Toranomon Hospital ethics board; the requirement to obtain informed consent was waived.

Patients were divided into three groups according to the respiratory event index (REI) < 15, 15 to < 30, and ≥ 30 events/h, and compared for urinary albumin to creatinine ratio (UACR) and various risk factors for albuminuria.

Definitions and Data Collection

The glycated hemoglobin (HbA1c) value was estimated as a National Glycohemoglobin Standardization Program equivalent value calculated with the following equation18:

HbA1c (%) = 1.02 × HbA1c (Japan Diabetes Society) (%) + 0.25 (%)
The UACR was measured in each patient using a single early-morning urine sample. Albuminuria was defined as UACR ≥ 3.4 mg/mmol creatinine.19 eGFR was determined using the following equation20:
eGFR (mL/min/1.73 m2) = 194 × [serum creatinine]−1.094 × Age−0.287 (× 0.739 for females)
Renal impairment was defined as eGFR < 60 mL/min/1.73 m2 according to the Japanese Joint Committee on Diabetic Nephropathy. Low-density lipoprotein (LDL) cholesterol level was calculated using the Friedewald equation.

Based on self-administered questionnaires and medical records, we gathered information on known risk factors for albuminuria, including duration of diabetes, smoking status, and history of preexisting cardiovascular disease. The preexisting cardiovascular diseases included myocardial infarction, angina, stroke, or peripheral artery disease. Subjects who currently smoked more than 10 cigarettes per day were designated as smokers. Past smokers who had quit smoking were not considered smokers in this study. All medications used by the participants at the time of the sleep test were listed. Blood pressure (BP) was measured by an automatic device with the patient in a sitting position, and the mean BP was calculated as the diastolic pressure plus one-third of the pulse pressure. At the time of the sleep test, fasting venous blood was drawn for laboratory data.

Sleep Test

A sleep test was performed in each patient regardless of the sleep complaints. The type 4 portable sleep monitoring device (Apnomonitor Mini, Chest M.I. Inc., Tokyo, Japan) comprises an oronasal thermistor, finger pulse oximeter, microphone for tracheal sound recording, and accelerometer for body position detection. Other details about this device have been described in recent studies.21,22 In brief, the sensors were connected to the integrative unit to record the following parameters: oral/nasal airflow, snoring, arterial oxyhemoglobin saturation (SaO2), pulse rate, and body position. Apneas were defined as a 75% reduction in airflow through the nose lasting for 10 seconds, and hypopneas were defined as < 75 % and 50% reduction in airflow accompanied by ≥ 3 % reduction in SaO2. This definition of apnea was validated in previous studies using polysomnography,23,24 although many other devices define apnea as a 90% reduction in airflow. These respiratory events were automatically scored, and the REI was calculated as the frequency of apneas and hypopneas per hour of recording. Data were processed following acquisition using a personal computer. Experienced polysomnographers who were blinded to information regarding subjects manually modified automatic scoring by the sleep device. Then, sleep specialists who also were blinded to information regarding subjects double-checked the modified data. These parameters conform to the clinical guidelines for the use of unattended portable monitors in the diagnosis of obstructive sleep apnea in adult patients.25

Statistical Analysis

All data are expressed as mean ± standard deviation or median (interquartile range). Differences among the three REI categories were determined by one-way analysis of variance for normally distributed continuous variables, by Kruskal-Wallis test for continuous variables with skewed distribution pattern, and by chi-square test or Fisher exact test for categorical variables.

The association of SDB with albuminuria was analyzed using logistic regression analysis. For the main analysis, the REI value was included as a categorical independent variable for models. To determine whether the results differed at the cutoff points, we performed secondary analyses in which REI values were treated as a natural logarithm-transformed (Ln) continuous variable. Unadjusted and adjusted multivariable models were developed. For adjusted multivariate models, model 1 was adjusted for age, sex, and body mass index (BMI). Model 2 was adjusted for duration of T2DM, current smoking, mean BP, HbA1c, and LDL cholesterol, in addition to the variables included in model 1. Model 3 was adjusted for use of angiotensin-converting enzyme/angiotensin receptor blockers, insulin, statin, and sleep pills, in addition to the variables included in model 2. Model 4 was adjusted for eGFR, in addition to the variables included in model 3. Similar unadjusted and adjusted multivariate models were developed, substituting 4% oxygen desaturation index (ODI) for REI. In addition, the associations of SDB with Ln UACR or Ln eGFR were analyzed using linear regression models. In this analysis, the REI was included as a continuous Ln REI variable or qualitative variable (REI ≥ 15 or < 15). Twenty-three patients with undetectable (< 5 mg/dL) urine albumin concentrations were excluded in the linear regression with SDB and Ln UACR because it was impossible to calculate the continuous UACR values in these patients. Values for REI, 4% ODI, BMI, duration of diabetes, HbA1c, LDL cholesterol, and eGFR were calculated using natural log transformation because they showed skewed distribution patterns. For 4% ODI, because some of these data contained zero values, we applied natural log transformation using the formula26:

Ln_4% ODI = log (4% ODI + 0.01)
In all tests, a value of P < .05 was considered statistically significant. All statistical analyses were performed using the SPSS statistics software version 22.0 (SPSS Japan Inc., Tokyo, Japan).

RESULTS

Figure 1 shows a flowchart of the patient recruitment process. A total of 365 patients with diabetes mellitus were assessed for eligibility. Among them, 92 patients were excluded based on the aforementioned exclusion criteria. A total of 273 patients were included in this study.

Flowchart of the patient recruitment process.

COPD = chronic obstructive pulmonary disease, eGFR = estimated glomerular filtration rate, SDB = sleep-disordered breathing.

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Figure 1

Flowchart of the patient recruitment process.

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Table 1 lists the characteristics of the participating patients and Table 2 shows the results of the sleep studies. There were significant differences among the three REI groups with respect to the proportion of males, current smoking status, BMI, systolic BP, diastolic BP, and mean BP. Figure 2 shows the UACR values for the three REI groups. The UACR value was significantly different among the three REI groups, and the median UACR value increased with the severity of SDB.

Characteristics of participating patients.

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Table 1

Characteristics of participating patients.

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Results of the sleep tests.

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Table 2

Results of the sleep tests.

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Box plots of the UACR values in the three REI category groups.

P value for trend = P < .001. REI = respiratory event index, gCre = gram creatinine, UACR = urine albumin to creatinine ratio.

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Figure 2

Box plots of the UACR values in the three REI category groups.

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Table 3 summarizes the results of unadjusted and adjusted logistic regression analyses for albuminuria for the data from the three REI groups. REI correlated significantly and independently with UACR in all four models. Similar results were obtained when Ln REI was replaced with Ln 4% ODI (4% ODI: ≥ 15 to < 30 group: odds ratio [OR] 2.62, 95% confidence interval [95% CI] 1.31–5.22, P = .006; ≥ 30 group: OR 5.81, 95% CI 2.44–13.81, P < .001).

Results of logistic regression analysis for albuminuria using categorical REI.

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Table 3

Results of logistic regression analysis for albuminuria using categorical REI.

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Table 4 shows the results of unadjusted and adjusted logistic regression analyses for albuminuria using data from all patients. Similar to the aforementioned results, Ln REI showed a significant and independent correlation with UACR in all four models.

Results of logistic regression analysis of albuminuria using continuous Ln REI.

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Table 4

Results of logistic regression analysis of albuminuria using continuous Ln REI.

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Table 5 shows the results of linear regression for Ln UACR. Both Ln REI and qualitative REI variable showed a significant and independent association with Ln UACR in all models.

Results of linear regression analysis for Ln UACR.

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Table 5

Results of linear regression analysis for Ln UACR.

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Table 6 shows the results of linear regression for Ln eGFR. In this model, Ln REI had no significant or independent association with Ln eGFR, whereas qualitative REI variable showed a significant and independent association with Ln eGFR only in models 2 and 3.

Results of linear regression analysis for Ln eGFR.

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Table 6

Results of linear regression analysis for Ln eGFR.

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DISCUSSION

The main finding of the current study was that SDB (represented by two indexes) showed a significant and independent correlation with albuminuria, even after adjustment for other factors. Furthermore, the risk of albuminuria was approximately twofold to threefold higher in patients with moderate SDB (REI 15 to 30) and approximately sixfold to eightfold higher in patients with severe SDB (REI > 30) in comparison with patients with mild or no SDB (REI < 15). These results indicate that moderate to severe SDB is a significant correlate for albuminuria in patients with T2DM, suggesting that SDB may be an important comorbidity for progression to kidney disease in patients with T2DM.

A literature search showed that only four studies assessed the correlation between albuminuria and SDB in patients with T2DM.14,2729 Tahrani et al.14 showed an independent association between SDB and diabetic nephropathy in patients with T2DM. In their study, diabetic nephropathy was defined as the presence of albuminuria or eGFR < 60 mL/min/1.73 m2 and SDB was defined as REI ≥ 5 event/h, as examined using a portable polygraph device. Although the dependent variable in the study by Tahrani et al. included not only albuminuria but also low eGFR and SDB was included as a qualitative variable, their results are similar to those described in our study. Furukawa et al.27 reported that 3% ODI ≥ 5 events/h was independently associated with microalbuminuria only in females after adjustment for confounders. Zhang et al.29 showed that REI was not associated with microalbuminuria in the multivariate logistic regression analysis, whereas the average SPO2 and the cumulative time of SPO2 below 90% were associated with microalbuminuria. Our study had three differences compared with these studies: different sleep test parameters that were independently associated with microalbuminuria; consistent results in the analyses using REI as either continuous and categorical variable with adjustment for various known risk factors for albuminuria; and severity of SDB, probably related to changes in BMI. Accordingly, our study provides further and relevant evidence that SDB is associated with microalbuminuria. Another study by Buyukaydin et al.28 found no association between apnea-hypopnea index (AHI) and albuminuria, possibly because of small sample size.

The mechanisms underlying the relationship between SDB and albuminuria are probably multifactorial. First, SDB is known to exacerbate glycemic control and increase BP.3 Because both poor glycemic control and hypertension are the main risk factors for albuminuria in patients with T2DM,30 SDB may increase albuminuria via poor glycemic control and hypertension. However, because this study showed that SDB was an independent determinant of albuminuria after adjustment for several known risk factors for albuminuria, other direct mechanisms might be involved. We presume that nocturnal intermittent hypoxemia and apnea are potential direct mechanisms for albuminuria. Previous studies suggested that intermittent hypoxemia in association with SDB can potentially cause increased oxidative stress,4 sympathetic nervous overactivity,31 and activation of the renin-angiotensin system.32 Apnea also enhances sympathetic nervous system activity by eliminating reflex inhibition arising from pulmonary stretch receptors and by unloading carotid sinus baroreceptors caused by reduced stroke volume and blood pressure during obstructive apnea.33 Because these mechanisms are known to exacerbate kidney disease, including albuminuria, SDB could worsen albuminuria independent of poor glycemic control and high blood pressure.

Our study used a portable polygraph to assess SDB rather than the standard polysomnography. Previous studies suggested that rapid eye movement (REM) sleep is associated with greater sympathetic activity compared with non-rapid eye movement (NREM) sleep34 and that REM sleep AHI may influence long-term glycemic control.35 Because portable sleep monitors do not record sleep pattern (REM sleep and NREM sleep) and hence cannot estimate REM sleep AHI, polysomnographic recording is necessary to assess the association between REM sleep AHI and albuminuria in patients with T2DM. In addition, interventional studies to investigate the effectiveness of therapy for SDB such as continuous positive airway pressure on albuminuria in patients with T2DM are necessary because of the following reasons: (1) the current study showed that patients with moderate to severe SDB has an increased risk of albuminuria; (2) the American Academy of Sleep Medicine recommended treatment using continuous positive airway pressure for patients with moderate to severe SDB36; and (3) a previous case report had shown the reversal of proteinuria with surgical treatment of SDB.37

The current study has several limitations. First, as stated previously, assessment of SDB was based on the use of a portable polygraph; it could not be used to determine sleep stages nor was it able to distinguish obstructive from central sleep apnea. Therefore, patients with central sleep apnea may not be excluded and there might be some patients who have some central respiratory events. However, because we excluded patients with risk factors for central sleep apnea such as heart failure, atrial fibrillation, or eGFR of < 30 mL/min/1.73 m2 and because central sleep apnea is less common than obstructive sleep apnea in patients with T2DM,1 the effect of central sleep apnea in our study might be small. However, the results of the current study should be interpreted with caution in this respect. Second, this study is a cross-sectional study, and thus, a causal relationship between SDB and albuminuria cannot be determined. A longitudinal study is needed to address this issue. Third, a single albumin to creatinine ratio measurement is the limitation of this study. Although frequent measurement of the UACR value is recommended in clinical settings, previous studies used a single UACR measurement.10,14,27 Further studies to assess frequent UACR measurements are needed.

In conclusion, REI showed an independent and significant association with albuminuria, and the ORs for albuminuria were approximately twofold to threefold higher in patients with moderate SDB and approximately sixfold to eightfold higher in patients with severe SDB compared to patients with mild or no SDB.

DISCLOSURE STATEMENT

Work for this study was performed at Toranomon Hospital. The authors report no conflicts of interest.

ABBREVIATIONS

95% CI

95% confidence interval

AHI

apnea-hypopnea index

BMI

body mass index

BP

blood pressure

CKD

chronic kidney disease

eGFR

estimated GFR

GFR

glomerular filtration rate

HbA1c

glycated hemoglobin

LDL

low-density lipoprotein

Ln

natural logarithm-transformed

NREM

non-rapid eye movement

ODI

oxygen desaturation index

OR

odds ratio

REI

respiratory event index

REM

rapid eye movement

SaO2

arterial oxyhemoglobin saturation

SDB

sleep-disordered breathing

T2DM

type 2 diabetes mellitus

UACR

urine albumin to creatinine ratio

ACKNOWLEDGMENTS

The authors thank Fumie Takano for the excellent administrative work. Author contributions: research idea and trial design: A. Nishimura and T. Kasai; data acquisition, data analysis and interpretation: A. Nishimura and T Kasai; writing and editing the manuscript: A. Nishimura and T. Kasai; revising the manuscript: all authors; reading and approving final version of the manuscript: all authors.

REFERENCES

1 

Foster GD, Sanders MH, Millman R, et al. Obstructive sleep apnea among obese patients with type 2 diabetes. Diabetes Care. 2009;32(6):1017–1019. [PubMed Central][PubMed]

2 

Nicholl DD, Ahmed SB, Loewen AH, et al. Declining kidney function increases the prevalence of sleep apnea and nocturnal hypoxia. Chest. 2012;141(6):1422–1430. [PubMed]

3 

Jordan AS, McSharry DG, Malhotra A. Adult obstructive sleep apnoea. Lancet. 2014;383(9918):736–747. [PubMed]

4 

Lavie L. Oxidative stress--a unifying paradigm in obstructive sleep apnea and comorbidities. Prog Cardiovasc Dis. 2009;51(4):303–312. [PubMed]

5 

Mimura I, Nangaku M. The suffocating kidney: tubulointerstitial hypoxia in end-stage renal disease. Nat Rev Nephrol. 2010;6(11):667–678. [PubMed]

6 

Levey AS, de Jong PE, Coresh J, et al. The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report. Kidney Int. 2011;80(1):17–28. [PubMed]

7 

Gall MA, Hougaard P, Borch-Johnsen K, Parving HH. Risk factors for development of incipient and overt diabetic nephropathy in patients with non-insulin dependent diabetes mellitus: prospective, observational study. BMJ. 1997;314(7083):783–788. [PubMed Central][PubMed]

8 

Mogensen CE. Microalbuminuria predicts clinical proteinuria and early mortality in maturity-onset diabetes. N Engl J Med. 1984;310(6):356–360. [PubMed]

9 

Klausen K, Borch-Johnsen K, Feldt-Rasmussen B, et al. Very low levels of microalbuminuria are associated with increased risk of coronary heart disease and death independently of renal function, hypertension, and diabetes. Circulation. 2004;110(1):32–35. [PubMed]

10 

Chou YT, Lee PH, Yang CT, et al. Obstructive sleep apnea: a stand-alone risk factor for chronic kidney disease. Nephrol Dial Transplant. 2011;26(7):2244–2250. [PubMed]

11 

Canales MT, Paudel ML, Taylor BC, et al. Sleep-disordered breathing and urinary albumin excretion in older men. Sleep Breath. 2011;15(1):137–144. [PubMed]

12 

Chaudhary BA, Sklar AH, Chaudhary TK, Kolbeck RC, Speir WA Jr. Sleep apnea, proteinuria, and nephrotic syndrome. Sleep. 1988;11(1):69–74. [PubMed]

13 

Leong WB, Nolen M, Thomas GN, Adab P, Banerjee D, Taheri S. The impact of hypoxemia on nephropathy in extremely obese patients with type 2 diabetes mellitus. J Clin Sleep Med. 2014;10(7):773–778. [PubMed Central][PubMed]

14 

Tahrani AA, Ali A, Raymond NT, et al. Obstructive sleep apnea and diabetic nephropathy: a cohort study. Diabetes Care. 2013;36(11):3718–3725. [PubMed Central][PubMed]

15 

Banerjee D, Leong WB, Arora T, et al. The potential association between obstructive sleep apnea and diabetic retinopathy in severe obesity-the role of hypoxemia. PLoS One. 2013;8(11):e79521[PubMed Central][PubMed]

16 

Leong WB, Jadhakhan F, Taheri S, Thomas GN, Adab P. The association between obstructive sleep apnea on diabetic kidney disease: a systematic review and meta-analysis. Sleep. 2016;39(2):301–308. [PubMed Central][PubMed]

17 

Seino Y, Nanjo K, Tajima N, et al. Report of the committee on the classification and diagnostic criteria of diabetes mellitus. J Diabetes Investig. 2010;1(5):212–228. [PubMed Central][PubMed]

18 

Kashiwagi A, Kasuga M, Araki E, et al. International clinical harmonization of glycated hemoglobin in Japan: from Japan Diabetes Society to National Glycohemoglobin Standardization Program values. J Diabetes Investig. 2012;3(1):39–40. [PubMed Central][PubMed]

19 

Wada T, Haneda M, Furuichi K, et al. Clinical impact of albuminuria and glomerular filtration rate on renal and cardiovascular events, and all-cause mortality in Japanese patients with type 2 diabetes. Clinic Exp Nephrol. 2014;18(4):613–620. [PubMed]

20 

Matsuo S, Imai E, Horio M, et al. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis. 2009;53(6):982–992. [PubMed]

21 

Nishimura A, Kasai T, Tamura H, et al. Relationship between sleep disordered breathing and diabetic retinopathy: Analysis of 136 patients with diabetes. Diabetes Res Clin Pract. 2015;109(2):306–311. [PubMed]

22 

Sumida K, Hoshino J, Suwabe T, et al. Sleep-disordered breathing in patients with polycystic liver and kidney disease referred for transcatheter arterial embolization. Clin J Am Soc Nephrol. 2015;10(6):949–956. [PubMed Central][PubMed]

23 

Hida W, Shindoh C, Miki H, et al. Prevalence of sleep apnea among Japanese industrial workers determined by a portable sleep monitoring system. Respiration. 1993;60(6):332–337. [PubMed]

24 

Yagi H, Nakata S, Tsuge H, et al. Significance of a screening device (Apnomonitor 5) for sleep apnea syndrome. Auris Nasus Larynx. 2009;36(2):176–180. [PubMed]

25 

Collop NA, Anderson WM, Boehlecke B, et al. Clinical guidelines for the use of unattended portable monitors in the diagnosis of obstructive sleep apnea in adult patients. Portable Monitoring Task Force of the American Academy of Sleep Medicine. J Clin Sleep Med. 2007;3(7):737–747. [PubMed Central][PubMed]

26 

Haas DC, Foster GL, Nieto FJ, et al. Age-dependent associations between sleep-disordered breathing and hypertension: importance of discriminating between systolic/diastolic hypertension and isolated systolic hypertension in the Sleep Heart Health Study. Circulation. 2005;111(5):614–621. [PubMed]

27 

Furukawa S, Saito I, Yamamoto S, et al. Nocturnal intermittent hypoxia as an associated risk factor for microalbuminuria in Japanese patients with type 2 diabetes mellitus. Eur J Endocrinol. 2013;169(2):239–246. [PubMed]

28 

Buyukaydin B, Akkoyunlu ME, Kazancioglu R, et al. The effect of sleep apnea syndrome on the development of diabetic nephropathy in patients with type 2 diabetes. Diabetes Res Clin Pract. 2012;98(1):140–143. [PubMed]

29 

Zhang R, Zhang P, Zhao F, Han X, Ji L. Association of diabetic microvascular complications and parameters of obstructive sleep apnea in patients with type 2 diabetes. Diabetes Technol Ther. 2016;18(7):415–420. [PubMed]

30 

Parving HH, Lewis JB, Ravid M, Remuzzi G, Hunsicker LG. Prevalence and risk factors for microalbuminuria in a referred cohort of type II diabetic patients: a global perspective. Kidney Int. 2006;69(11):2057–2063. [PubMed]

31 

Turek NF, Ricardo AC, Lash JP. Sleep disturbances as nontraditional risk factors for development and progression of CKD: review of the evidence. Am J Kidney Dis. 2012;60(5):823–833. [PubMed Central][PubMed]

32 

Hanly PJ, Ahmed SB. Sleep apnea and the kidney: is sleep apnea a risk factor for chronic kidney disease? Chest. 2014;146(4):1114–1122. [PubMed]

33 

Kasai T, Bradley TD. Obstructive sleep apnea and heart failure: pathophysiologic and therapeutic implications. J Am Coll Cardiol. 2011;57(2):119–127. [PubMed]

34 

Somers VK, Dyken ME, Clary MP, Abboud FM. Sympathetic neural mechanisms in obstructive sleep apnea. J Clin Invest. 1995;96(4):1897–1904. [PubMed Central][PubMed]

35 

Grimaldi D, Beccuti G, Touma C, Van Cauter E, Mokhlesi B. Association of obstructive sleep apnea in rapid eye movement sleep with reduced glycemic control in type 2 diabetes: therapeutic implications. Diabetes Care. 2014;37(3):355–363. [PubMed Central][PubMed]

36 

Epstein LJ, Kristo D, Strollo PJ Jr, et al. Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults. J Clin Sleep Med. 2009;5(3):263–276. [PubMed Central][PubMed]

37 

Sklar AH, Chaudhary BA. Reversible proteinuria in obstructive sleep apnea syndrome. Arch Intern Med. 1988;148(1):87–89. [PubMed]