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Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine logoLink to Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine
. 2021 Mar 1;17(3):453–460. doi: 10.5664/jcsm.8924

Obstructive sleep apnea during rapid eye movement sleep in patients with diabetic kidney disease

Akihiro Nishimura 1,✉, Takatoshi Kasai 2,3, Kimio Matsumura 1, Shota Kikuno 1, Kaoru Nagasawa 1, Minoru Okubo 1, Koji Narui 2, Yasumichi Mori 1
PMCID: PMC7927344  PMID: 33108268

Abstract

Study Objectives:

Although recent studies suggest that obstructive sleep apnea during rapid eye movement (REM) is associated with different cardiometabolic and neurocognitive risks compared with non-REM (NREM) sleep, there is no information on whether obstructive sleep apnea during REM and/or NREM sleep is independently associated with diabetic kidney disease (DKD).

Methods:

In this cross-sectional study, 303 patients with type 2 diabetes who were followed up at our diabetes outpatient clinic underwent all-night polysomnography. Logistic regression analysis was performed to determine the separate effects of obstructive sleep apnea during REM and/or NREM sleep (REM and/or NREM-apnea-hypopnea index [AHI]) and several other polysomnography parameters on DKD after adjustment for several known risk factors for DKD.

Results:

The median (interquartile range) AHI, REM-AHI, and NREM-AHI of the patients (age 57.8 ± 11.8 years, male sex 86.8%, hypertension 64.3%, and DKD 35.2%) were 29.8 (18.0–45.4), 35.4 (21.1–53.3), and 29.1 (16.3–45.4) events/h, respectively. REM-AHI quartiles, but not NREM-AHI quartiles, correlated independently and significantly with DKD (P = .03 for linear trend, odds ratio (OR), and 95% confidence interval for Q2: 3.14 (1.10–8.98), Q3: 3.83 (1.26–11.60), Q4: 4.97 (1.60–15.46), compared with Q1). In addition, categorical AHI (P = .01, OR, and 95% confidence interval for ≥ 15 to < 30: 1.54 (0.64–3.71), ≥ 30: 3.08 (1.36–6.94) compared with < 15), quartiles of AHI (P = .01), quartiles of lowest arterial oxyhemoglobin saturation (P < .01), quartiles of percentage of time spent with arterial oxyhemoglobin saturation < 90 (P < .01), and quartiles of mean arterial oxyhemoglobin saturation were independently associated with DKD.

Conclusions:

Obstructive sleep apnea, especially during REM sleep, is a potential risk factor for DKD.

Citation:

Nishimura A, Kasai T, Matsumura K, et al. Obstructive sleep apnea during rapid eye movement sleep in patients with diabetic kidney disease. J Clin Sleep Med. 2021;17(3):453–460.

Keywords: sleep disorders, sleep apnea syndrome, diabetic kidney disease, rapid eye movement sleep, diabetic angiopathy, type 2 diabetes

BRIEF SUMMARY

Current Knowledge/Study Rationale: Recent studies have shown that obstructive sleep apnea is associated not only with hyperglycemia but with actual diabetes-related endpoints, including diabetic kidney disease (DKD). On the other hand, although obstructive sleep apnea during rapid eye movement (REM) and non-REM sleep leads to different cardiometabolic and neurocognitive complications, whether REM-related obstructive sleep apnea is associated with DKD is not clear because all previous studies used home sleep apnea testing, which does not distinguish between REM- and non-REM–apnea-hypopnea index.

Study Impact: In this study, we showed that overall apnea-hypopnea index and REM–apnea-hypopnea index, but not non-REM–apnea-hypopnea index, were independently correlated with DKD after adjusting several known risk factors for DKD. Obstructive sleep apnea, especially during REM sleep, is a potential risk factor for DKD.

INTRODUCTION

Sleep-disordered breathing, including obstructive sleep apnea (OSA), is characterized by repetitive upper airway collapse during sleep and causes nocturnal hypoxemia. This hypoxemia causes increased sympathetic activity, oxidative stress, thalamic-pituitary-adrenal axis activity, inflammation, and changes in adipokine, resulting in increased insulin sensitivity and hyperglycemia.1 Clinical studies have shown that OSA is associated with hyperglycemia, which can be improved by continuous positive airway pressure therapy, suggesting a causal relationship between OSA and hyperglycemia,2,3 although some studies have failed to show this relationship.4,5

Recent studies also have shown that OSA is associated not only with hyperglycemia but with actual diabetes-related endpoints (eg, diabetic microcomplications), including diabetic kidney disease (DKD).6–10 For example, it was reported that the estimated glomerular filtration rate (eGFR) decreased faster in type 2 diabetes mellitus (T2D) patients with OSA after an average of 2.5 years of follow-up compared with similar patients without OSA.11 Similarly, our previous study showed that the severity of OSA is independently associated with the presence of albuminuria, which is an early marker of DKD.10

On the other hand, growing evidence suggests that OSA during rapid eye movement (REM) and non-REM (NREM) sleep leads to different cardiometabolic and neurocognitive complications.12,13 This difference stems from the fact that OSA is more frequent and of longer duration in REM sleep and is associated with more severe hypoxemia than in NREM sleep,14 probably owing to greater pharyngeal muscle relaxation, reduction in the hypoxic and hypercapnic ventilatory response throughout REM sleep, and higher arousal threshold.15,16 For example, the apnea-hypopnea index (AHI) during REM sleep (REM-AHI) is associated with future onset of hypertension, whereas NREM-AHI is not.17 In addition, REM-AHI is associated with nondipping of nocturnal blood pressure,18 metabolic syndrome,19 and a composite cardiovascular endpoint.20

Whether REM-related OSA is associated with DKD is not clear so far, however, because all previous studies used home sleep apnea testing (HSAT), which does not distinguish between REM- and NREM-AHI. The present in-hospital full polysomnography study was conducted to determine the association between REM-related OSA and DKD.

METHODS

Patients

We screened 462 patients with diabetes who were followed up at our diabetes outpatient clinic and had undergone overnight polysomnography at our sleep center during May 2004 to June 2013. The following patients were excluded from the study: (1) age < 20 years; (2) had type 1 or other types of diabetes; (3) had other types of kidney diseases or were on hemodialysis; (4) had active pulmonary disease; (5) had a history of heart failure; (6) had a history of acute stroke (within 3 months before polysomnography); (7) had been treated for OSA at the time of polysomnography; or (8) had polysomnographically confirmed total sleep time < 180 minutes, including patients with split-night polysomnography (ie, both diagnostic polysomnography and continuous positive airway pressure titration polysomnography performed on the same night). Figure 1 shows a flowchart of the patient-recruitment process. One hundred and fifty-nine patients who met the above-mentioned exclusion criteria were excluded from a total of 462 patients with diabetes. Thus, the data of 303 patients were analyzed in the total AHI analysis. In addition, patients with REM sleep < 30 minutes (n = 121) were excluded from REM and/or NREM-AHI analysis,21 and the data of 182 patients were analyzed in the REM and/or NREM analysis. The study was approved by the Toranomon Hospital Human Ethics Review Board (no. 1263) with a waiver of the requirement to obtain informed consent from the patients based on the fact that polysomnography was part of the overall clinical management of the patients.

Figure 1. Flowchart of the patient recruitment process.

Figure 1

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DM = diabetes mellitus, OSA = obstructive sleep apnea, PSG = polysomnography.

Definitions and data collection

The diagnosis of T2D was based on the Japan Diabetes Society Committee on the Classification and Diagnostic Criteria of Diabetes Mellitus.22 Patients who took hypoglycemic agents and who had no history of type 1 or other types of diabetes were also regarded as having T2D. The glycated hemoglobin (HbA1c) value was estimated as a National Glycohemoglobin Standardization Program equivalent value calculated by the following equation: HbA1c (%) = 1.02 × HbA1c (Japan Diabetes Society) (%) + 0.25 (%).23

DKD was defined as the presence of microalbuminuria (³3.4 mg/mmol creatinine) or low eGFR (< 60 ml/min/1.73m2). eGFR was determined using following equation established by the Japanese Society of Nephrology: eGFR (ml/min/1.73 m2) = 194 × [serum creatinine]−1.094 × age − 0.287 (× 0.739 for females).24

Patients with systolic blood pressure³ 140 mm Hg, diastolic blood pressure³ 90 mm Hg,25 or taking any antihypertensive medications were regarded as hypertensive. Non–high-density lipoprotein cholesterol was calculated by total cholesterol–high-density lipoprotein cholesterol.

Based on self-administered questionnaires and medical records, we gathered information on known risk factors for DKD, including duration of diabetes and smoking status. All medications used by the participants at the time of polysomnography were listed. At the time of polysomnography, fasting venous blood was drawn, and morning urine sample was obtained for laboratory testing.

Polysomnography

All patients underwent fully attended in-hospital polysomnography at our hospital. The details of the polysomnography and the scoring system are described elsewhere.26–28 Briefly, polysomnography was performed using a digital polygraph (SomnoStar α Sleep System; SensorMedics Corp., Yorba Linda, California) manned by a sleep technician. Generally accepted definitions and scoring methods were used (apnea was defined as a reduction of airflow by > 90% for at least 10 seconds; hypopnea was defined as a reduction of airflow by > 30% for at least 10 seconds with 3% oxygen desaturation [using a baseline of 120 seconds] or an arousal). In each case, the respiratory events were manually scored, and the AHI was calculated per hour of sleep. According to the clinically used criteria, AHI values were divided into three categories: AHI < 15, ≥ 15 to < 30, and ≥ 30 events/h. In REM and/or NREM-AHI analysis, quartiles of REM and/or NREM-AHI were used based on the previous reports.17,29

Statistical analysis

All data are presented as mean ± standard deviation or median (interquartile range) according to the quartiles of REM-AHI. In all tests, a P value < .05 was considered statistically significant. All statistical analyses were performed using the SPSS Statistics Software version 22.0 (SPSS Japan Inc., Tokyo, Japan) except for the Cochran-Armitage trend test using the R Software (version 3.6.1).30

Differences among the four groups of quartiles of REM-AHI were tested by one-way analysis of variance for normally distributed continuous variables, by Kruskal-Wallis test for continuous variables with skewed distribution pattern, and by the Cochran-Armitage trend test for categorical variables.

The association of OSA with DKD was assessed using the forced logistic regression analysis. In AHI and other parameters analysis, the model was adjusted for age, sex, body mass index, T2D duration, smoking, hypertension, HbA1c, non–high-density lipoprotein cholesterol, use of insulin, angiotensin converting enzyme inhibitor/angiotensin receptor blockers, statins, and sleeping pills. In REM and/or NREM-AHI analysis, the model was adjusted for the covariates, with P < .2 in the univariable analysis because only 64 patients in the REM and/or NREM-AHI analysis had DKD. In the multivariable models, quartiles of REM-AHI and NREM-AHI were mutually adjusted for each other. In these analyses, all continuous variables with skewed distribution were treated as natural logarithm-transformed continuous variables. When the data contained zero values, natural log transformation using the formula natural logarithm-transformed X = log (X + .01) was applied.31

For AHI analysis, categorical AHI (< 15, ≥ 15 to < 30, ≥ 30) was included in the model as the independent variables because few patients had AHI < 5. In addition, quartiles of AHI were included in the model. In REM and/or NREM-AHI analysis, quartiles of REM and/or NREM-AHI were included in the model after adjustment for the aforementioned factors. Similarly, multivariable logistic regression analyses, including quartiles of lowest arterial oxyhemoglobin saturation (SaO2), percentage of time spent with SaO2 < 90% (T90SPT), or mean SaO2 were developed.

RESULTS

Patient characteristics

Table 1 lists the characteristics of all participating patients according to the quartiles of REM-AHI. The prevalence of severe OSA (AHI ≥ 30) and severe REM-related OSA (REM-AHI ≥ 30) were 57.4% and 59.9%, respectively. The four REM-AHI groups differed significantly with respect to the proportion of patients with DKD, patients with hypertension, BMI, HbA1c, AHI, REM and/or NREM-AHI, lowest SaO2, T90SPT, and REM sleep time.

Table 1.

Characteristics of participating patients.

Overall (n = 303) REM Sleep ≥ 30 Min (n = 182) REM-AHI P Value
Q1 Q2 Q3 Q4
Age, y 58.5 ± 11.9 57.8 ± 11.8 58.3 ± 2.3 60.5 ± 10.4 57.3 ± 12.5 55.3 ± 12.0 .20
Male sex, n (%) 260 (85.8) 158 (86.8) 38 (86.4) 42 (89.4) 40 (88.9) 38 (82.6) .59
Duration of T2D, y 5 (0–13) 5 (1–12) 6 (1–11) 8 (0–14) 5 (0–10) 4 (1–11) .83
Smoking, n (%) 71 (23.4) 44 (24.2) 11 (25.0) 14 (29.8) 12 (26.7) 7 (15.2) .24
DKD, n (%) 124 (40.9) 64 (35.2) 9 (20.5) 18 (38.3) 19 (42.2) 18 (39.1) .01
HT, n (%) 208 (68.6) 117 (64.3) 21 (47.7) 31 (66.0) 29 (64.4) 36 (78.3) .00
BMI, kg/m2 27 (24–29) 26 (24–29) 24 (23–27) 25 (24–29) 27 (25–29) 28 (25–32) < .01
HbA1c (NGSP), % 7.3 (6.8–8.4) 7.4 (6.8–8.5) 7.4 (6.7–8.9) 7.1 (6.7–7.7) 7.9 (7.2–9.0) 7.4 (7.0–8.5) .01
HbA1c (IFCC), mmol/mol 56 (50–68) 57 (50–69) 57 (49–73) 53 (49–60) 62 (55–75) 57 (52–69) .01
non-HDLC, mg/dl 145 (124–166) 141.3 ± 32.1 140.6 ± 33.8 140.0 ± 27.6 146.2 ± 33.9 138.7 ± 33.4 .69
Insulin use, n (%) 18 (5.9) 13 (7.1) 1 (2.3) 3 (6.4) 7 (15.6) 2 (4.3) .39
ACEI/ARB use, n (%) 87 (28.7) 48 (26.4) 10 (22.7) 15 (31.9) 12 (26.7) 11 (23.9) .94
Statin use, n (%) 71 (23.4) 45 (24.7) 9 (20.5) 14 (29.8) 9 (20.0) 13 (28.3) .88
Sleep pills use, n (%) 30 (9.9) 19 (10.4) 3 (6.8) 5 (10.6) 4 (8.9) 7 (15.2) .25
AHI, events/h 34.6 (20.4–55.1) 29.8 (18.0–45.4) 15.6 (8.0–29.8) 29.7 (19.0–39.9) 33.9 (21.8–48.8) 42.3 (27.5–64.6) < .01
REM-AHI, events/h 39.7 (21.1–55.2) 35.4 (21.1–53.3) 12.8 (7.2–17.7) 28.6 (23.7–31.3) 44.1 (41.2–48.2) 62.1 (56.1–69.8) < .01
NREM-AHI, events/h 34.5 (18.0–56.8) 29.1 (16.3–45.4) 17.7 (7.2–32.7) 30.2 (18.1–42.5) 32.6 (18.0–49.6) 38.5 (23.5–64.7) < .01
Lowest SaO2, % 78 (71–84) 80 (72–85) 84 (81–89) 82 (72–86) 76 (67–81) 76 (70–81) < .01
T90SPT, % 10.4 (2.4–35.4) 6.6 (1.6–22.3) 1.4 (0.3–6.4) 4.2 (1.3–17.2) 9.9 (4.6–30.8) 11.3 (6.1–8.0) < .01
Mean SaO2, % 94 (91–95) 94 (92–95) 95 (94–96) 94 (92–95) 93 (91.0–94.8) 93 (91–94) < .01
Total sleep time, min 352 (305–394) 379 (337–409) 385 (345–424) 376 (336–415) 363 (333–399) 377 (338–407) .52
REM sleep time, min 37 (21–54) 49.5 (40–61) 54 (43–71) 50 (43–64) 47 (37–64) 45 (39–53) .04
ESS 8 (3–11) 8 (3–12) 7(3–10) 6 (2–10) 9 (5–13) 8 (3–13) .18
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Values are mean ± standard deviation, median (interquartile range) or percentage (%). ACEI = angiotensin converting enzyme inhibitor, AHI = apnea-hypopnea index, ARB = angiotensin receptor blockers, BMI = body mass index, DKD = diabetic kidney disease, ESS = Epworth Sleepiness Scale, HbA1c = glycated hemoglobin, HDL-C = high-density lipoprotein cholesterol, HT = hypertension, IFCC = International Federation of Clinical Chemistry and Laboratory Medicine, NGSP = National Glycohemoglobin Standardization Program, NREM = non-REM, REM = rapid eye movement, SaO2 = arterial oxyhemoglobin saturation, T2D = type 2 diabetes, T90SPT = percentage of time spent with SaO2 < 90%.

Table 2 shows the results of univariable analyses using DKD as the dependent variable.

Table 2.

Results of univariable analysis for diabetic kidney disease.

Overall (n = 303) REM Sleep ≥ 30 Min (n = 182)
OR 95% CI P Value OR 95% CI P Value
Age, y 1.06 1.03–1.08 < .01 1.06 1.03–1.09 .00
Male sex, male vs female 1.73 0.86–3.49 .12 2.65 1.02–6.91 .046
Duration of T2D, y 1.07 0.99–1.16 .08 1.07 0.96–1.19 .22
Smoking, yes vs no 0.85 0.48–1.47 .55 0.92 0.44–1.91 .82
HT, yes vs no 1.78 1.05–3.01 .03 1.63 0.83–3.22 .16
BMI, kg/m2 0.80 0.19–3.44 .76 0.59 0.08–4.34 .60
HbA1c (NGSP), % 0.99 0.22–4.39 .98 0.94 0.14–6.19 .95
HbA1c (IFCC), mmol/mol 0.99 0.22–4.39 .98 0.94 0.14–6.19 .95
non-HDLC, mg/dl 0.62 0.24–1.60 .33 0.99 0.99–1.00 .26
Insulin use, yes vs no 1.24 0.48–3.24 .66 1.37 0.44–4.29 .58
ACEI/ARB use, yes vs no 1.77 1.06–2.97 .03 1.57 0.79–3.12 .197
Statin use, yes vs no 1.26 0.73–2.19 .41 1.09 0.53–2.22 .82
Sleep pills use, yes vs no 1.73 0.79–3.81 .17 1.11 0.40–3.07 .85
AHI, events/h 1.69 1.22–2.35 < .01 1.45 0.93–2.26 .10
REM-AHI, events/h 1.04 0.83–1.30 .72 1.44 0.94–2.22 .10
NREM-AHI, events/h 1.63 1.19–2.22 < .01 1.35 0.90–2.03 .15
Lowest SaO2, % 0.23 0.05–1.03 0.05 0.40 0.05–3.03 .37
T90SPT, % 1.25 1.11–1.41 < .01 1.18 1.02–1.37 .03
Mean SaO2, % 0.02 0.00–1.51 .07 0.01 0.00–10.07 .17
Total sleep time, min 0.68 0.23–2.01 .49 1.00 0.99–1.01 .86
REM sleep time, min 0.93 0.80–1.07 .29 3.06 1.09–8.66 .04
ESS 0.95 0.87–1.04 .29 0.97 0.86–1.09 .62
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ACEI = angiotensin converting enzyme inhibitor, AHI = apnea-hypopnea index, ARB = angiotensin receptor blocker, BMI = body mass index, CI = confidence interval, DKD = diabetic kidney disease, ESS = Epworth Sleepiness Scale, HbA1c = glycated hemoglobin, HDL-C = high-density lipoprotein cholesterol, HT = hypertension, IFCC = International Federation of Clinical Chemistry and Laboratory Medicine, NGSP = National Glycohemoglobin Standardization Program, NREM = non-rapid eye movement, OR = odds ratio, REM = rapid eye movement, SaO2 = arterial oxyhemoglobin saturation, T2D = type 2 diabetes, T90SPT = percentage of time spent with SaO2 < 90%.

Table S1 (20.5KB, pdf) in the supplemental material shows the prevalence of DKD in relations to quartiles of PSG variables

REM and/or NREM-AHI analysis

Figure 2 and Table 3 show the results of multivariable logistic regression analysis for DKD using both quartiles of REM-AHI and quartiles of NREM-AHI. In this analysis, the quartiles of REM-AHI (P = .04 for trend) were independently associated with DKD, whereas quartiles of NREM-AHI were not (P = .19 for trend). The OR and 95% confidence interval for each quartile of REM-AHI compared with Q1 (Reference) were the following: Q2, 3.23 (1.09–9.52), Q3, 4.03 (1.33–12.23), Q4, 4.99 (1.50–16.64).

Figure 2. Results of logistic regression analysis for diabetic kidney disease using quartiles of REM and/or NREM-AHI.

Figure 2

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The model was adjusted by age, sex, body mass index (BMI), type 2 diabetes (T2D) duration, smoking, hypertension, hemoglobin A1c (HbA1c), non-high-density lipoprotein cholesterol, insulin use, angiotensin converting enzyme (ACE) inhibitor/angiotensin receptor blockers use, statin use and sleep pill use. AHI = apnea-hypopnea index, NREM = non-rapid eye movement, REM = rapid eye movement.

Table 3.

Results of univariable and multivariable logistic regression analysis for diabetic kidney disease using both quartiles of REM-AHI and quartiles of NREM-AHI (n = 182).

Median Value Unadjusted Adjusted
B OR 95% CI P Value B OR 95% CI P Value
REM-AHI .05 .04
 Q1 12.8 1 (ref) 1 (ref)
 Q2 28.6 1.04 2.83 1.09–7.37 .03 1.17 3.23 1.09–9.52
 Q3 44.1 1.18 3.26 1.25–8.50 .02 1.39 4.03 1.33–12.23
 Q4 62.1 1.22 3.40 1.29–8.99 .01 1.61 4.99 1.50–16.64
NREM-AHI .67 .19
 Q1 9.2 1 (ref) 1 (ref)
 Q2 21.7 0.08 1.08 0.43–2.71 .87 −0.18 0.84 0.27–2.58
 Q3 36.0 0.21 1.23 0.50–3.04 .65 0.00 1.00 0.33–3.05
 Q4 63.3 0.52 1.68 0.70–4.05 .25 0.91 2.49 0.77–8.05
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The model was adjusted for age, sex, hypertension, and use of angiotensin converting enzyme inhibitor/angiotensin receptor blockers. In the multivariable models, quartiles of REM-AHI and NREM-AHI were mutually adjusted for each other. AHI = apnea-hypopnea index, B = coefficient, CI = confidence interval, DKD = diabetic kidney disease, NREM = non-REM, OR = odds ratio, ref = reference, REM = rapid eye movement.

AHI and other parameters analysis

Figure 3 and Table 4 show the results of multivariable logistic regression analysis for DKD using categorical AHI, quartiles of AHI, lowest SaO2, quartiles of T90SPT, or quartiles of mean SaO2. In this analysis, categorical AHI (P = .01 for trend) and age (odds ratio:1.07; 95% CI: 1.04–1.10, P < .01) were independently associated with DKD. The odds ratio and 95% confidence interval for each AHI categories compared with AHI < 15 (reference) were as follows: ≥ 15 to < 30: 1.541 (0.640–3.710), ≥ 30: 3.078 (1.364–6.943). Similar results were obtained when quartiles of AHI, lowest SaO2, quartiles of T90SPT, or quartiles of mean SaO2 were included in the model (Table 4).

Figure 3. Results of logistic regression analysis for diabetic kidney disease using categorical AHI.

Figure 3

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The model was adjusted by age, sex, body mass index (BMI), type 2 diabetes (T2D) duration, smoking, hypertension, hemoglobin A1c (HbA1c), non-high-density lipoprotein (HDL) cholesterol, insulin use, angiotensin converting enzyme (ACE) inhibitor/angiotensin receptor blockers use, statin use and sleep pill use. AHI = apnea-hypopnea index.

Table 4.

Results of logistic regression analysis for diabetic kidney disease using categorical AHI (< 15, ≥ 15 to < 30, ≥ 30), quartiles of AHI, quartiles of lowest SaO2 or quartiles of T90SPT (n = 303).

Median Value Unadjusted Adjusted
B OR 95% CI P Value B OR 95% CI P Value
Categorical AHI < .01 .01
 < 15 9.0 1 (ref) 1 (ref)
 ≥ 15 to < 30 22.6 0.53 1.70 0.77–3.78 .19 0.43 1.54 0.64–3.71 .33
 ≥ 30 51.9 1.63 2.89 1.42–5.90 < .01 1.12 3.08 1.36–6.94 < .01
Quartiles of AHI .02 .01
 Q1 12.8 1 (ref) 1 (ref)
 Q2 27.4 0.13 1.13 0.57–2.27 .72 0.05 1.05 0.48–2.30
 Q3 43.1 0.60 1.82 0.91–3.63 .09 0.53 1.69 0.78–3.67 .18
 Q4 70.7 0.94 2.56 1.29–5.08 < .01 1.40 4.06 1.70–9.73 < .01
Lowest SaO2 < .01 < .01
 Q1 87.0 1 (ref) 1 (ref)
 Q2 81.0 0.49 1.62 0.82–3.22 .17 0.61 1.84 0.86–3.97 .12
 Q3 74.0 1.19 3.27 1.60–6.69 < .01 1.48 4.39 1.94–9.96 < .01
 Q4 64.0 0.82 2.27 1.14–4.51 .02 1.47 4.35 1.83–10.34 < .01
T90SPT < .01 < .01
 Q1 0.8 1 (ref) 1 (ref)
 Q2 5.6 0.88 2.41 1.18–4.92 .02 0.61 1.85 0.83–4.09 .13
 Q3 18.4 0.90 2.46 1.19–5.07 .02 0.98 2.66 1.18–6.03 .02
 Q4 60.1 1.39 4.02 1.96–8.24 < .01 1.91 6.73 2.77–16.35 < .01
Mean SpO2 .07 .02
 Q1 95.0 1 (ref) 1 (ref)
 Q2 94.0 0.05 1.05 0.46–2.39 .91 −0.22 0.80 0.31–2.09 .65
 Q3 92.0 0.73 2.08 1.02–4.27 .045 0.60 1.82 0.75–4.41 .18
 Q4 88.0 0.83 2.29 1.03–5.08 .04 1.40 4.06 1.40–11.78 .01
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The model was adjusted for age, sex, BMI, T2D duration, smoking, hypertension, HbA1c, non-HDL cholesterol, and use of insulin, ACE inhibitor/angiotensin receptor blockers, statin and sleeping pills. ACE = angiotensin converting enzyme, AHI = apnea-hypopnea index, B = coefficient, BMI = body mass index, CI = confidence interval, Hb1AC = hemoglobin 1Ac, OR = odds ratio, ref = reference, SaO2 = arterial oxyhemoglobin saturation, T90SPT = percentage of time spent with SaO2 < 90.

DISCUSSION

We showed for the first time that AHI during REM sleep, but not that during NREM sleep, is independently associated with DKD. In addition, we also demonstrated the association between AHI, lowest SaO2, T90SPT, or mean SaO2 with DKD using not HSAT but polysomnography.

Analysis of the REM and/or NREM-AHI showed the independent association of REM-AHI with DKD, suggesting that REM-related OSA is a risk factor for diabetic microcomplications. Previous studies showed that REM-related OSA is associated with glucose metabolism, which is one of the main risk factors for DKD. For example, REM-AHI was reported to be strongly associated with insulin resistance,13 interstitial glucose concentration measured by continuous glucose monitoring system,32 and HbA1c,29 compared with NREM-related OSA. In addition, REM-AHI was found to be associated with hypertension, which is another risk factor for DKD.17,33 Thus, the present study confirmed that REM-related OSA is associated not only with risk factors for diabetic microcomplications (ie, hyperglycemia or hypertension) but with actual diabetes-related endpoint (ie, DKD). The association between REM-AHI and diabetic microcomplications reported here adds support to our previous findings of the independent association between REM-AHI and diabetic retinopathy.34

In the present study, REM-AHI was associated with DKD even after adjustment for HbA1c or the presence of hypertension, suggesting another pathway that links REM-related OSA to DKD. One plausible mechanism is that hypoxemia induced by REM-related OSA directly exacerbates DKD. Previous studies have suggested that OSA-induced intermittent hypoxemia can potentially cause increased oxidative stress,35 sympathetic nervous system overactivity,36 activation of the renin-angiotensin-system37 and advanced glycation end products,38 all of which are known risk factors for DKD. In addition, REM sleep is associated with greater sympathetic activity compared with NREM sleep,39 and OSA is worse during REM sleep, which includes more frequent events, longer-duration events, and greater oxygen desaturation associated with respiratory events.12 We propose that REM-related OSA strongly stimulates oxidative stress, sympathetic nervous system overactivity, and activation of the renin-angiotensin-system compared with NREM-related OSA, resulting in exacerbation of DKD.

Our all-night polysomnographic study also showed independent associations between AHI, lowest SaO2, T90SPT, or mean SaO2 with DKD. These results are similar to those reported in previous HSAT studies.9–10,40–42 For example, our previous study using HSAT showed the independent association of microalbuminuria with categorical respiratory event index (< 15, 15 to < 30, ≥ 30).10 In another cohort study, eGFR fell faster in patients with OSA than in OSA-free individuals.11 Furthermore, 3% oxygen desaturation index,40 lowest SaO241 and T90SPT42 were independently association with DKD in previous HSAT studies. Since only a few studies assessed the association between OSA and DKD and almost all studies used HSAT,9 the present study using polysomnography confirmed the robust association between OSA and DKD.

This study has several strengths and limitations. The strength points include the following: first, this study did not use HSAT, but rather all-night polysomnography, for the assessment of severity of OSA; and second, the results were adjusted for several known risk factors for DKD. The American Academy of Sleep Medicine Clinical Practice Guideline recommended that if a single HSAT is negative, polysomnography should be performed for the diagnosis of OSA.43 Furthermore, a recent study showed that HSAT seems to underestimate OSA severity44 which may result in weaker association between OSA and DKD. The limitations of this study are as follows. First, the cross-sectional nature of the study cannot confirm causality between OSA and DKD. A longitudinal study or interventional study is needed to confirm causality. Second, our study population included relatively few female patients. Because REM-related OSA occurs more commonly in women than in men,45,46 further studies that include more females are needed to generalize our findings to all T2D patients. Third, our study used single morning urine sample for the assessment of microalbuminuria. Although previous studies used a single urine measurement,11,40 obtaining a couple of urine samples for such assessment will be needed in future studies. Fourth, this study defined patients with AHI < 15 as reference because this study had few patients with AHI < 5; however, the third edition of the International Classification of Sleep Disorders defines OSA as a PSG-determined obstructive respiratory disturbance index (RDI) ≥ 5 events/h associated with the typical symptoms of OSA (eg, unrefreshing sleep, daytime sleepiness, fatigue or insomnia, awakening with a gasping or choking sensation, loud snoring, or witnessed apneas), or an obstructive RDI ≥ 15 events/h (even in the absence of symptoms).43 Fifth, although we showed independent associations between DKD and lowest SaO2 or T90SPT, we do not have REM and/or NREM-lowest SaO2 or REM and/or NREM-T90SPT data. Further studies including such parameters will be needed in the future studies. Sixth, we excluded patients with REM sleep of less than 30 minutes according to the previous report.21 This is because REM-AHI should be considered imprecise if the amount of REM sleep is less than 30 minutes. This criterion, however, has the potential to exclude patients with severe REM-related OSA who are at the greatest risk for DKD because severe REM-related OSA causes frequent arousals during REM sleep, resulting in shorter REM sleep time.

In conclusion, our study demonstrated that REM-AHI, but not NREM-AHI, is independently linearly associated with DKD. OSA, especially during REM sleep, is a potential risk factor for DKD and should be screened in patients with T2D. Further studies that assess the effect of adherent continuous positive airway pressure treatment on preventing the progression of DKD will be needed.

DISCLOSURE STATEMENT

All authors have seen and approved the manuscript. Work for this study was performed at Toranomon Hospital, Tokyo, Japan. T.K. and K.N. are affiliated with a department endowed by Philips Respironics, Fukuda Denshi, ResMed, and Teijin Home Healthcare. The other authors declare no conflict of interest. Some of the data of this study were presented as poster presentation at the 54th Annual Meeting of the European Association for the Study of Diabetes, Berlin, Germany, October 1–5, 2018.

SUPPLEMENTARY MATERIAL

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ACKNOWLEDGMENTS

The authors thank Fumie Takano for the excellent administrative work.

ABBREVIATIONS

AHI

apnea-hypopnea index

CI

confidence interval

DKD

diabetic kidney disease

eGFR

estimated glomerular filtration rate

HbA1c

glycated hemoglobin

HSAT

home sleep apnea testing

NREM

non-rapid eye movement

OSA

obstructive sleep apnea

RDI

respiratory event index

REM

rapid eye movement

SaO2

arterial oxyhemoglobin saturation

T2D

type 2 diabetes mellitus

T90SPT

percentage of time spent with SaO2 <90%

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