J C E M
O N L I N E
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R e p o r t — E n d o c r i n e
C a r e
Hypoxemia and Glycemic Control in Type 2 Diabetes Mellitus With Extreme Obesity Wen Bun Leong, Dev Banerjee, Melissa Nolen, Peymané Adab, G. Neil Thomas, and Shahrad Taheri Specialist Weight Management Services (W.B.L., S.T.) and Academic Department of Sleep and Ventilation (D.B., M.N.), Heart of England National Health Service Foundation Trust, Birmingham, United Kingdom; Theme 8 (Diabetes) (W.B.L., S.T.), Birmingham and Black Country National Institute for Health Research Collaborations for Leadership in Applied Health Research and Care, University of Birmingham, Birmingham, B15, 2TT, United Kingdom; Thoracic and Sleep Medicine Department (D.B.), St Vincent’s Hospital, Darlinghurst, Sydney, NSW 2010 Australia; National Health and Medical Research Council Centre for Integrated Research and Understanding Sleep (D.B,), Woolcock Institute of Medical Research, Glebe, Sydney, NSW 2037 Australia; Public Health, Epidemiology, and Biostatistics (P.A., G.N.T.), University of Birmingham, United Kingdom; Institute of Public Health (G.N.T.), Social and Preventive Medicine, Mannheim Medical Faculty, Heidelberg University, Mannheim, D-68167, Germany; Department of Medicine (S.T.), Weill Cornell Medical College New York, New York 10021, and Doha, 24144, Qatar; and Department of Medicine (S.T.), King’s College London, London, SE5 9RJ, United Kingdom
Context: Obstructive sleep apnea (OSA) has been shown to be associated with type 2 diabetes mellitus (DM). Studies on healthy individuals found that OSA is associated with lower insulin sensitivity. We hypothesized that nocturnal hypoxemia from OSA is associated with poorer glycemia in severely obese DM individuals. Design and Setting: This was a retrospective observational study of 122 non-DM, 126 non–insulintreated DM, and 35 insulin-treated DM patients. Data were collected on demographic characteristics, body mass index, and comorbidities. An overnight sleep study was performed in all patients, and OSA was defined as an apnea-hypopnea index of ⱖ5 events/h. Results: There were more males (P ⫽ .003) and a lower proportion of white Europeans (P ⫽ .010) among DM patients. The prevalence of OSA was 80.1% in DM and 63.1% in non-DM individuals (P ⫽ .001). DM individuals also had lower oxygen saturation (O2) (P ⫽ .0106), greater percentage of time spent under 90% oxygen saturation (%TST⬍90%) (P ⫽ .0067), and higher apnea-hypopnea index (P ⫽ .0085). Regression analysis showed that %TST⬍90% and minimum O2 saturations were associated with worse hemoglobin A1c results among DM individuals. Every 10% reduction in minimum O2 was associated with a 0.3% increase in HbA1c, whereas a 10% increase in %TST⬍90% was associated with a 0.2% increase in hemoglobin A1c after adjusting for a range of potential confounders. Conclusion: The high OSA prevalence in DM individuals and a positive relationship between nocturnal hypoxemia and glycemia supports the need to assess correction of hypoxemia as a management strategy for glycemic control. (J Clin Endocrinol Metab 99: E1650 –E1654, 2014)
B
oth obesity and type 2 diabetes mellitus (DM) are associated with multiple comorbidities that contribute to the perpetuation of obesity and deterioration of glyce-
mic control. A common comorbidity accompanying obesity and DM is obstructive sleep apnea (OSA) (1). OSA is a chronic condition resulting in intermittent hypoxemia
ISSN Print 0021-972X ISSN Online 1945-7197 Printed in U.S.A. Copyright © 2014 by the Endocrine Society Received January 28, 2014. Accepted June 9, 2014. First Published Online July 17, 2014
Abbreviations: AHI, apnea-hypopnea index; BMI, body mass index; BP, blood pressure; CPAP, continuous positive airway pressure; DM, diabetes mellitus; HbA1c, hemoglobin A1c; OSA, obstructive sleep apnea; %TST⬍90%, percentage of time spent below 90% oxygen saturation.
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doi: 10.1210/jc.2014-1260
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doi: 10.1210/jc.2014-1260
and activation of the sympathetic nervous system. Several studies have shown that OSA is associated with DM (1), and some have suggested a mechanistic relationship between OSA and glycemia. A recent meta-analysis of 6 cohort studies reported that moderate to severe OSA has greater risk for incidence of DM (1). Currently, the management of glycemia consists mainly of lifestyle advice and pharmacotherapy, and in those with extreme obesity, bariatric surgery is increasingly advocated. Because DM is associated with OSA, the purpose of the current evaluation was to explore whether OSA is an important factor for the management of diabetes in extreme obesity. The impact of OSA-associated nocturnal hypoxemia on glycosylated hemoglobin A1c (HbA1c) levels were investigated in this high-risk patient group. The study also compared the prevalence of OSA in DM and non-DM individuals with extreme obesity.
Patients and Methods The study is a retrospective cross-sectional analysis of anonymized data from patients attending a regional specialist weight management clinic as part of a larger service evaluation aiming to improve care of patients with extreme obesity. Data on consecutive patients who attended the Heart of England National Health Service Foundation Trust weight management service between January 2009 and January 2012 were used. All patients were referred based on the following criteria: body mass index (BMI) ⱖ35 kg/m2 with at least 1 comorbidity or BMI ⱖ40 kg/m2 without comorbidity. All patients were offered and referred for an overnight sleep study as part of the comprehensive assessment and evaluation. Available data included demographics (age, gender, and selfreported ethnicity), physical examination (weight, height, BMI, and BP) and presence of comorbidities (DM, hypertension, and coronary artery disease). The presence of DM was either based on general practitioner referral or self-reported at initial assessment or HbA1c of ⱖ6.5%. HbA1c results were collected on the first clinic visit. For those with DM, use of antihyperglycemia medications was recorded. The sleep laboratory provided home portable Embletta (Embla Systems) to patients for overnight sleep studies as part of the clinical service protocol. Waiting times between the first clinic visit and sleep test were from 2 to 4 weeks. A trained sleep physiologist demonstrated and provided instructions for patients on the day of sleep examination. After demonstration, trained staff subsequently observed patients applying the device. Patients were instructed to return the device on the following day. The Embletta device measures airflow using nasal cannula, thoracic and abdominal movements using inductance plethysmography, and oxygen saturations and heart rate via finger pulse oximetry. Analysis and scoring of the respiratory records were performed for patients with at least 4 hours of quality signals. Scoring of respiratory data was performed by sleep physiologists and later confirmed and if necessary rescored by a trained sleep physician (D.B.). Data were scored manually, and all artifacts were excluded before analysis. Both sleep physiologists and
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DB were blinded to patients’ health status during the scoring process. All scoring was performed based on recommendations by the American Academy of Sleep Medicine sleep scoring guidelines (2). The following respiratory data were scored and collected: apnea-hypopnea index (AHI), mean and minimum oxygen (O2) saturations during sleep, and the percentage of time spent below 90% oxygen saturation (%TST⬍90%). OSA was defined as an AHI of ⱖ5 events/h. Mild, moderate, and severe OSA are based on AHI cutoff points of 5 to 15, 15 to 30, and ⱖ30, respectively. Definitions of apnea and hypopnea were no or ⱖ30% reduction in airflow for ⱖ10 seconds, respectively. Hypopnea was further characterized by a reduction of ⱖ4% oxygen saturation with thoracic and abdominal movements.
Statistical analysis This study was part of a comprehensive specialist weight management service evaluation; therefore, no formal ethical approval was required as per recommendation by the United Kingdom National Research Ethics Service. All data were anonymized before any statistical analysis. Multivariate linear regression analysis was carried out for DM patients only. Univariate analyses were performed to reconfirm the significant positive associations. Model 1 was adjusted for age, gender, BMI, and ethnicity, whereas further adjustments for number of DM medications were performed for model 2. Results are reported as -coefficient with 95% confidence interval. A P value ⬍ .05 was considered significant. All statistical analyses were performed using Stata version 13 (StataCorp LP).
Results A total of 433 patients attended the specialist weight management service. However, 145 patients had missing HbA1c results, 4 patients did not have analyzable results from the overnight sleep study, and 1 patient had missing data. Therefore, data from 283 eligible patients were examined, including 161 with DM (56.9%), of whom 35 (21.7%) were receiving insulin therapy. Table 1 shows the characteristics of non-DM and DM individuals in this study. DM patients were, on average, 10 years older (52 ⫾ 13 vs 51 ⫾ 10 years, P ⬍ .0001) compared with non-DM patients. There were also more males (P ⫽ .003) and a lower proportion of white Europeans (P ⫽ .010) among the DM patients. However, there were no significant differences in BMI or systolic and diastolic BP between the groups. As expected, there were greater proportions of DM patients with hypertension (P ⬍ .001) and coronary artery disease (P ⫽ .008) when compared with non-DM individuals. There were also significant differences in respiratory parameters (P ⬍ .05). The overall OSA prevalence (AHI ⱖ5 events/h) was 72.8%. It was significantly higher in DM (80.1%) compared with non-DM individuals (P ⫽ .001). There were also greater proportions of DM individuals with moderate and severe AHI (P ⫽ .015). DM individuals
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Table 1.
Hypoxemia and Glycemic Control
Characteristics of Obese Insulin- and Non–Insulin-Treated DM and Non-DM Patientsa
Age, y Gender Females Males Ethnicity White European South Asian Afro-Caribbean BMI, kg/m2 Systolic BP, mm Hg Diastolic BP, mm Hg Comorbidities Hypertension Coronary artery disease Respiratory parameters AHI, events/h Mean O2 sat, % Minimum O2 sat, % %TST⬍90% OSA (AHI ⬎ 5/h) Classification of OSA Mild Moderate Severe a
J Clin Endocrinol Metab, September 2014, 99(9):E1650 –E1654
Overall (n ⴝ 283)
Non-DM (n ⴝ 122)
DM (n ⴝ 126)
P Value
47.1 ⫾ 12.1
41.8 ⫾ 12.5
51.1 ⫾ 10.2
⬍.0001
189 (66.8%) 93 (33.2%)
99 (81.1%) 29 (23.8%)
96 (59.6%) 65 (40.4%)
.003
230 (81.3%) 39 (13.8%) 14 (4.9) 49.2 ⫾ 8.8 143.5 ⫾ 19.3 86.8 ⫾ 11.7
113 (92.6%) 10 (8.2%) 4 (3.2%) 49.59 ⫾ 8.38 141.1 ⫾ 18.0 86.6 ⫾ 12.6
121 (75.2%) 30 (18.6%) 10 (6.2%) 48.8 ⫾ 9.1 145.4 ⫾ 20.1 87.0 ⫾ 11.1
.010 .4666 .1232 .8384
122 (43.1%) 20 (7.1%)
29 (23.8%) 3 (2.5%)
93 (57.8%) 17 (10.6%)
⬍.001 .008
11.8 (4.0, 31.8) 93.4 (91.6, 95.0) 83.0 (76.0, 87.0) 2.3 (0.2, 15.0) 206 (72.8%)
8.0 (3.5, 24.5) 94.0 (92.0, 95.0) 83.0 (78.0, 88.0) 1.0 (0.1, 13.2) 77 (63.1%)
14.0 (6.0, 36.0) 93.0 (91.2, 95.0) 81.5 (74.0, 85.0) 4.0 (0.7, 15.8) 129 (80.1%)
.0085 .0260 .0106 .0067 .001
87 (30.7%) 45 (15.9%) 74 (26.1%)
34 (26.6%) 17 (13.3%) 26 (21.1%)
53 (32.9%) 28 (17.4%) 48 (29.8%)
.015
Data presented as mean ⫾ SD, median (interquartile range), or number (percent).
had higher median AHI, lower median minimum O2 saturations, and significantly longer median time spent under 90% O2 saturations compared with non-DM patients (P ⬍ .05). Unsurprisingly, DM individuals had higher HbA1c levels (P ⫽ .0001). Linear regression analysis showed that %TST⬍90% and lowest O2 saturation level were associated with worse HbA1c (Table 2). A 0.3% increase in HbA1c was associated with a 10% reduction in minimum O2 saturation, whereas a 0.2% increase in HbA1c was associated with a 10% decrease in %TST⬍90%. Nonsignificant associations with HbA1c were found for AHI and mean O2 saturations.
Discussion Limited studies have examined the relationship between OSA and glycemic control among DM individuals. A Table 2.
study in Oxford (United Kingdom), including 240 DM male individuals recruited from community and hospital settings, used the oxygen desaturation index to diagnose OSA and found a low correlation between OSA and HbA1c (r ⫽ 0.2, P ⫽ .0006) in a subgroup analysis of hospital-recruited DM individuals (3). However, this positive correlation was lost when both community- and hospital-recruited DM individuals were included (3). Another study by Aronsohn and colleagues (4) examined 60 DM individuals with mean recording for 6 hours and suggested that mild OSA increases HbA1c by 1.49%, moderate OSA by 1.93%, and severe OSA by 3.69%. Our results did not show any significant association between AHI and glycemia in DM individuals. This could be due to differences in the hours of respiratory data recorded. We included all individuals with at least 4 hours of recording as per American Academy of Sleep Medicine guidelines and Aronsohn and colleagues (4) found that when they used only the first
Independent Predictors of HbA1c in DM Patients (n ⫽ 161)a -Coefficients (95% Confidence Intervals)
AHI Mean O2 Min O2 %TST⬍90
Univariate Analyses
Model 1
Model 2
0.0018 (⫺0.0079 to 0.0115) ⫺0.0577 (⫺0.1441 to 0.0287) ⫺0.0267 (⫺0.0542 to 0.0008) 0.0155 (0.0026 to 0.0284)
0.0003 (⫺0.0099 to 0.0106) ⫺0.0690 (⫺0.1637 to 0.0256) ⫺ 0.0320 ( ⫺ 0.0622 to ⫺ 0.0018) 0.0189 (0.0050 to 0.0327)
0.0011 (⫺0.0088 to 0.0109) ⫺0.0688 (⫺0.1598 to 0.0221) ⫺ 0.0304 ( ⫺ 0.0595 to ⫺ 0.0013) 0.0177 (0.0043 to 0.0310)
a Analysis was performed using linear regression. Model 1 was adjusted for age, gender, BMI, and ethnicity, and model 2 was adjusted further for number of diabetes medications.
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4 hours of respiratory data, the association between OSA and HbA1c was no longer significant. One hypothesis is that during rapid eye movement (REM) sleep, there are higher frequencies of apnea/hypopnea episodes compared with other sleep stages. From our experience, some patients did not achieve 6 hours of quality respiratory recording mainly due to device discomfort and, in other cases, the loss of connection with part of the device during sleep. Although our results showed no significant association between AHI and levels of glycemia, we found a significant relationship between minimum O2 saturation as well as %TST⬎90% and HbA1c levels. To our knowledge, no study has yet shown significant associations between the duration of hypoxemia and glycemic control in the obese DM population. In the non-DM population, a study of 116 hypertensive individuals, found significant correlation between minimum O2 level and HbA1c (r ⫽ ⫺0.25, P ⬍ .001) (5), and another study on non-DM individuals concurred with the results ( ⫽ ⫺0.29, P ⬍ .001) (6). This indicates that in DM individuals with limited respiratory recording, nocturnal hypoxemia and not AHI may be a useful indicator of greater insulin resistance and might be a significant contributor to poor glycemic control. Moreover, studies have shown that severity and duration of nocturnal hypoxemia may be an important factor for DM microvascular complications, such as progression of diabetic retinal complication (7) and diabetic nephropathy (8). This also suggests that apart from using usual glucoselowering medications, correcting nocturnal hypoxemia may be beneficial in reducing HbA1c notably among insulin-resistant obese patients. The effects of continuous positive airway pressure (CPAP) on glycemia have been inconclusive, but in a recent observational study, CPAPtreated individuals maintained better diabetes control (9). Well-designed randomized prospective controlled trials of longer-duration CPAP treatment on extremely obese hypoxemic individuals are needed. The prevalence of OSA was significantly higher in DM individuals with extreme obesity, being particularly prevalent among insulin-treated individuals. Our observations are consistent with findings from several studies. The Sleep Ahead study reported an OSA prevalence of 86% among DM individuals (10). In the MOBIL (morbid obesity treatment, bariatric surgery vs intensive lifestyle intervention) study, the prevalence of OSA was 78% among DM individuals, significantly higher than in glucose-tolerant patients (33%) (11). Aronsohn and colleagues (4) reported an OSA rate of 77% for 60 DM individuals, whereas the Sleep Heart Health Study found 58% of DM had OSA compared with 43% in non-DM individuals (P ⬍ .001) (12). The lower prevalence rate reported by the Sleep
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Heart Health Study compared with our study is likely due to the lower adiposity levels in their population (mean BMI 31.3 ⫾ 6.0 kg/m2) (12). There are several limitations to our study. First, we examined only extremely obese individuals, an especially high-risk group for health complications and mortality. However, when only limited data are available in this area, one may cautiously consider the significance of our results. Second, the limitations of observational studies apply to our study, such as difficulty excluding reverse causality. Third, there were no data on smoking and alcohol status. We also did not have data on antihypertensive medications as well as other drugs such as antipsychotics. However, this study has a large number of patients and is representative of the local population with extreme obesity. The analyses also adjusted for multiple potential confounders including antidiabetes medications. In summary, we observed an inverse relationship between nocturnal hypoxemia and glycemia control among extremely obese DM individuals. We also observed that the prevalence of OSA among DM individuals was very high. Every 10% reduction in lowest O2 was associated with a 0.3% HbA1c increase and every 10% increase in %TST⬍90% was associated with a 0.2% HbA1c increase, after adjusting for a range of potential confounders. This supports the notion that OSA, in particular hypoxemia, may have an effect on glucose metabolism. Given that a significant number of patients with DM do not achieve glycemic targets, further studies on the impact of correcting nocturnal hypoxemia on glycemia are needed.
Acknowledgments Address all correspondence and requests for reprints to: Dr Shahrad Taheri, Department of Medicine, Weill Cornell Medical College, PO Box 24144, Doha, Qatar. E-mail: [email protected]; or Dr Dev Banerjee, Woolcock Institute of Medical Research, University of Sydney, Glebe, Sydney, NSW 2037 Australia. E-mail: [email protected]. This work was funded by the National Institute for Health Research (NIHR) through the Collaborations for Leadership in Applied Health Research and Care for Birmingham and Black Country (CLAHRC-BBC) program. W.B.L. is funded through an unrestricted educational grant from Allergan. S.T. was funded by the National Institute for Health Research (NIHR) through the CLAHRC-BBC program. The views expressed in this publication are not necessarily those of the NIHR, the Department of Health, National Health Service South Birmingham, University of Birmingham, or the CLAHRC-BBC Theme 8 Management/Steering Group. Disclosure Summary: S.T. has received educational funding support from Lilly UK and research support from Novo Nordisk, Allergan, Philips Respironics, and ResMed. D.B. has received research support from Philips Respironics.
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Hypoxemia and Glycemic Control
References 1. Wang X, Bi Y, Zhang Q, Pan F. Obstructive sleep apnoea and the risk of type 2 diabetes: a meta-analysis of prospective cohort studies. Respirology. 2013;18:140 –146. 2. Iber C, Ancoli-Israel S, Chesson A Jr, Quan S. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. Westchester, IL: American Academy of Sleep Medicine; 2007. 3. West SD, Nicoll DJ, Stradling JR. Prevalence of obstructive sleep apnoea in men with type 2 diabetes. Thorax. 2006;61:945–950. 4. Aronsohn RS, Whitmore H, Van Cauter E, Tasali E. Impact of untreated obstructive sleep apnea on glucose control in type 2 diabetes. Am J Respir Crit Care Med. 2010;181:507–513. 5. Elmasry A, Lindberg E, Berne C, et al. Sleep-disordered breathing and glucose metabolism in hypertensive men: a population-based study. J Intern Med. 2001;249:153–161. 6. Tamura A, Kawano Y, Watanabe T, Kadota J. Obstructive sleep apnea increases hemoglobin A1c levels regardless of glucose tolerance status. Sleep Med. 2012;13:1050 –1055.
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7. 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:e79521. 8. 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; 169:239 –246. 9. Guest JF, Panca M, Sladkevicius E, Taheri S, Stradling J. Clinical outcomes and cost-effectiveness of continuous positive airway pressure to manage obstructive sleep apnea in patients with type 2 diabetes in the U.K. Diabetes Care. 2014;37:1263–1271. 10. Foster GD, Sanders MH, Millman R, et al. Obstructive sleep apnea among obese patients with type 2 diabetes. Diabetes Care. 2009; 32:1017–1019. 11. Fredheim JM, Rollheim J, Omland T, et al. Type 2 diabetes and pre-diabetes are associated with obstructive sleep apnea in extremely obese subjects: a cross-sectional study. Cardiovasc Diabetol. 2011;10:84. 12. Resnick HE, Redline S, Shahar E, et al. Diabetes and sleep disturbances: findings from the Sleep Heart Health Study. Diabetes Care. 2003;26:702–709.
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O N L I N E
B r i e f
R e p o r t — E n d o c r i n e
C a r e
Hypoxemia and Glycemic Control in Type 2 Diabetes Mellitus With Extreme Obesity Wen Bun Leong, Dev Banerjee, Melissa Nolen, Peymané Adab, G. Neil Thomas, and Shahrad Taheri Specialist Weight Management Services (W.B.L., S.T.) and Academic Department of Sleep and Ventilation (D.B., M.N.), Heart of England National Health Service Foundation Trust, Birmingham, United Kingdom; Theme 8 (Diabetes) (W.B.L., S.T.), Birmingham and Black Country National Institute for Health Research Collaborations for Leadership in Applied Health Research and Care, University of Birmingham, Birmingham, B15, 2TT, United Kingdom; Thoracic and Sleep Medicine Department (D.B.), St Vincent’s Hospital, Darlinghurst, Sydney, NSW 2010 Australia; National Health and Medical Research Council Centre for Integrated Research and Understanding Sleep (D.B,), Woolcock Institute of Medical Research, Glebe, Sydney, NSW 2037 Australia; Public Health, Epidemiology, and Biostatistics (P.A., G.N.T.), University of Birmingham, United Kingdom; Institute of Public Health (G.N.T.), Social and Preventive Medicine, Mannheim Medical Faculty, Heidelberg University, Mannheim, D-68167, Germany; Department of Medicine (S.T.), Weill Cornell Medical College New York, New York 10021, and Doha, 24144, Qatar; and Department of Medicine (S.T.), King’s College London, London, SE5 9RJ, United Kingdom
Context: Obstructive sleep apnea (OSA) has been shown to be associated with type 2 diabetes mellitus (DM). Studies on healthy individuals found that OSA is associated with lower insulin sensitivity. We hypothesized that nocturnal hypoxemia from OSA is associated with poorer glycemia in severely obese DM individuals. Design and Setting: This was a retrospective observational study of 122 non-DM, 126 non–insulintreated DM, and 35 insulin-treated DM patients. Data were collected on demographic characteristics, body mass index, and comorbidities. An overnight sleep study was performed in all patients, and OSA was defined as an apnea-hypopnea index of ⱖ5 events/h. Results: There were more males (P ⫽ .003) and a lower proportion of white Europeans (P ⫽ .010) among DM patients. The prevalence of OSA was 80.1% in DM and 63.1% in non-DM individuals (P ⫽ .001). DM individuals also had lower oxygen saturation (O2) (P ⫽ .0106), greater percentage of time spent under 90% oxygen saturation (%TST⬍90%) (P ⫽ .0067), and higher apnea-hypopnea index (P ⫽ .0085). Regression analysis showed that %TST⬍90% and minimum O2 saturations were associated with worse hemoglobin A1c results among DM individuals. Every 10% reduction in minimum O2 was associated with a 0.3% increase in HbA1c, whereas a 10% increase in %TST⬍90% was associated with a 0.2% increase in hemoglobin A1c after adjusting for a range of potential confounders. Conclusion: The high OSA prevalence in DM individuals and a positive relationship between nocturnal hypoxemia and glycemia supports the need to assess correction of hypoxemia as a management strategy for glycemic control. (J Clin Endocrinol Metab 99: E1650 –E1654, 2014)
B
oth obesity and type 2 diabetes mellitus (DM) are associated with multiple comorbidities that contribute to the perpetuation of obesity and deterioration of glyce-
mic control. A common comorbidity accompanying obesity and DM is obstructive sleep apnea (OSA) (1). OSA is a chronic condition resulting in intermittent hypoxemia
ISSN Print 0021-972X ISSN Online 1945-7197 Printed in U.S.A. Copyright © 2014 by the Endocrine Society Received January 28, 2014. Accepted June 9, 2014. First Published Online July 17, 2014
Abbreviations: AHI, apnea-hypopnea index; BMI, body mass index; BP, blood pressure; CPAP, continuous positive airway pressure; DM, diabetes mellitus; HbA1c, hemoglobin A1c; OSA, obstructive sleep apnea; %TST⬍90%, percentage of time spent below 90% oxygen saturation.
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doi: 10.1210/jc.2014-1260
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and activation of the sympathetic nervous system. Several studies have shown that OSA is associated with DM (1), and some have suggested a mechanistic relationship between OSA and glycemia. A recent meta-analysis of 6 cohort studies reported that moderate to severe OSA has greater risk for incidence of DM (1). Currently, the management of glycemia consists mainly of lifestyle advice and pharmacotherapy, and in those with extreme obesity, bariatric surgery is increasingly advocated. Because DM is associated with OSA, the purpose of the current evaluation was to explore whether OSA is an important factor for the management of diabetes in extreme obesity. The impact of OSA-associated nocturnal hypoxemia on glycosylated hemoglobin A1c (HbA1c) levels were investigated in this high-risk patient group. The study also compared the prevalence of OSA in DM and non-DM individuals with extreme obesity.
Patients and Methods The study is a retrospective cross-sectional analysis of anonymized data from patients attending a regional specialist weight management clinic as part of a larger service evaluation aiming to improve care of patients with extreme obesity. Data on consecutive patients who attended the Heart of England National Health Service Foundation Trust weight management service between January 2009 and January 2012 were used. All patients were referred based on the following criteria: body mass index (BMI) ⱖ35 kg/m2 with at least 1 comorbidity or BMI ⱖ40 kg/m2 without comorbidity. All patients were offered and referred for an overnight sleep study as part of the comprehensive assessment and evaluation. Available data included demographics (age, gender, and selfreported ethnicity), physical examination (weight, height, BMI, and BP) and presence of comorbidities (DM, hypertension, and coronary artery disease). The presence of DM was either based on general practitioner referral or self-reported at initial assessment or HbA1c of ⱖ6.5%. HbA1c results were collected on the first clinic visit. For those with DM, use of antihyperglycemia medications was recorded. The sleep laboratory provided home portable Embletta (Embla Systems) to patients for overnight sleep studies as part of the clinical service protocol. Waiting times between the first clinic visit and sleep test were from 2 to 4 weeks. A trained sleep physiologist demonstrated and provided instructions for patients on the day of sleep examination. After demonstration, trained staff subsequently observed patients applying the device. Patients were instructed to return the device on the following day. The Embletta device measures airflow using nasal cannula, thoracic and abdominal movements using inductance plethysmography, and oxygen saturations and heart rate via finger pulse oximetry. Analysis and scoring of the respiratory records were performed for patients with at least 4 hours of quality signals. Scoring of respiratory data was performed by sleep physiologists and later confirmed and if necessary rescored by a trained sleep physician (D.B.). Data were scored manually, and all artifacts were excluded before analysis. Both sleep physiologists and
jcem.endojournals.org
E1651
DB were blinded to patients’ health status during the scoring process. All scoring was performed based on recommendations by the American Academy of Sleep Medicine sleep scoring guidelines (2). The following respiratory data were scored and collected: apnea-hypopnea index (AHI), mean and minimum oxygen (O2) saturations during sleep, and the percentage of time spent below 90% oxygen saturation (%TST⬍90%). OSA was defined as an AHI of ⱖ5 events/h. Mild, moderate, and severe OSA are based on AHI cutoff points of 5 to 15, 15 to 30, and ⱖ30, respectively. Definitions of apnea and hypopnea were no or ⱖ30% reduction in airflow for ⱖ10 seconds, respectively. Hypopnea was further characterized by a reduction of ⱖ4% oxygen saturation with thoracic and abdominal movements.
Statistical analysis This study was part of a comprehensive specialist weight management service evaluation; therefore, no formal ethical approval was required as per recommendation by the United Kingdom National Research Ethics Service. All data were anonymized before any statistical analysis. Multivariate linear regression analysis was carried out for DM patients only. Univariate analyses were performed to reconfirm the significant positive associations. Model 1 was adjusted for age, gender, BMI, and ethnicity, whereas further adjustments for number of DM medications were performed for model 2. Results are reported as -coefficient with 95% confidence interval. A P value ⬍ .05 was considered significant. All statistical analyses were performed using Stata version 13 (StataCorp LP).
Results A total of 433 patients attended the specialist weight management service. However, 145 patients had missing HbA1c results, 4 patients did not have analyzable results from the overnight sleep study, and 1 patient had missing data. Therefore, data from 283 eligible patients were examined, including 161 with DM (56.9%), of whom 35 (21.7%) were receiving insulin therapy. Table 1 shows the characteristics of non-DM and DM individuals in this study. DM patients were, on average, 10 years older (52 ⫾ 13 vs 51 ⫾ 10 years, P ⬍ .0001) compared with non-DM patients. There were also more males (P ⫽ .003) and a lower proportion of white Europeans (P ⫽ .010) among the DM patients. However, there were no significant differences in BMI or systolic and diastolic BP between the groups. As expected, there were greater proportions of DM patients with hypertension (P ⬍ .001) and coronary artery disease (P ⫽ .008) when compared with non-DM individuals. There were also significant differences in respiratory parameters (P ⬍ .05). The overall OSA prevalence (AHI ⱖ5 events/h) was 72.8%. It was significantly higher in DM (80.1%) compared with non-DM individuals (P ⫽ .001). There were also greater proportions of DM individuals with moderate and severe AHI (P ⫽ .015). DM individuals
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Table 1.
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Characteristics of Obese Insulin- and Non–Insulin-Treated DM and Non-DM Patientsa
Age, y Gender Females Males Ethnicity White European South Asian Afro-Caribbean BMI, kg/m2 Systolic BP, mm Hg Diastolic BP, mm Hg Comorbidities Hypertension Coronary artery disease Respiratory parameters AHI, events/h Mean O2 sat, % Minimum O2 sat, % %TST⬍90% OSA (AHI ⬎ 5/h) Classification of OSA Mild Moderate Severe a
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Overall (n ⴝ 283)
Non-DM (n ⴝ 122)
DM (n ⴝ 126)
P Value
47.1 ⫾ 12.1
41.8 ⫾ 12.5
51.1 ⫾ 10.2
⬍.0001
189 (66.8%) 93 (33.2%)
99 (81.1%) 29 (23.8%)
96 (59.6%) 65 (40.4%)
.003
230 (81.3%) 39 (13.8%) 14 (4.9) 49.2 ⫾ 8.8 143.5 ⫾ 19.3 86.8 ⫾ 11.7
113 (92.6%) 10 (8.2%) 4 (3.2%) 49.59 ⫾ 8.38 141.1 ⫾ 18.0 86.6 ⫾ 12.6
121 (75.2%) 30 (18.6%) 10 (6.2%) 48.8 ⫾ 9.1 145.4 ⫾ 20.1 87.0 ⫾ 11.1
.010 .4666 .1232 .8384
122 (43.1%) 20 (7.1%)
29 (23.8%) 3 (2.5%)
93 (57.8%) 17 (10.6%)
⬍.001 .008
11.8 (4.0, 31.8) 93.4 (91.6, 95.0) 83.0 (76.0, 87.0) 2.3 (0.2, 15.0) 206 (72.8%)
8.0 (3.5, 24.5) 94.0 (92.0, 95.0) 83.0 (78.0, 88.0) 1.0 (0.1, 13.2) 77 (63.1%)
14.0 (6.0, 36.0) 93.0 (91.2, 95.0) 81.5 (74.0, 85.0) 4.0 (0.7, 15.8) 129 (80.1%)
.0085 .0260 .0106 .0067 .001
87 (30.7%) 45 (15.9%) 74 (26.1%)
34 (26.6%) 17 (13.3%) 26 (21.1%)
53 (32.9%) 28 (17.4%) 48 (29.8%)
.015
Data presented as mean ⫾ SD, median (interquartile range), or number (percent).
had higher median AHI, lower median minimum O2 saturations, and significantly longer median time spent under 90% O2 saturations compared with non-DM patients (P ⬍ .05). Unsurprisingly, DM individuals had higher HbA1c levels (P ⫽ .0001). Linear regression analysis showed that %TST⬍90% and lowest O2 saturation level were associated with worse HbA1c (Table 2). A 0.3% increase in HbA1c was associated with a 10% reduction in minimum O2 saturation, whereas a 0.2% increase in HbA1c was associated with a 10% decrease in %TST⬍90%. Nonsignificant associations with HbA1c were found for AHI and mean O2 saturations.
Discussion Limited studies have examined the relationship between OSA and glycemic control among DM individuals. A Table 2.
study in Oxford (United Kingdom), including 240 DM male individuals recruited from community and hospital settings, used the oxygen desaturation index to diagnose OSA and found a low correlation between OSA and HbA1c (r ⫽ 0.2, P ⫽ .0006) in a subgroup analysis of hospital-recruited DM individuals (3). However, this positive correlation was lost when both community- and hospital-recruited DM individuals were included (3). Another study by Aronsohn and colleagues (4) examined 60 DM individuals with mean recording for 6 hours and suggested that mild OSA increases HbA1c by 1.49%, moderate OSA by 1.93%, and severe OSA by 3.69%. Our results did not show any significant association between AHI and glycemia in DM individuals. This could be due to differences in the hours of respiratory data recorded. We included all individuals with at least 4 hours of recording as per American Academy of Sleep Medicine guidelines and Aronsohn and colleagues (4) found that when they used only the first
Independent Predictors of HbA1c in DM Patients (n ⫽ 161)a -Coefficients (95% Confidence Intervals)
AHI Mean O2 Min O2 %TST⬍90
Univariate Analyses
Model 1
Model 2
0.0018 (⫺0.0079 to 0.0115) ⫺0.0577 (⫺0.1441 to 0.0287) ⫺0.0267 (⫺0.0542 to 0.0008) 0.0155 (0.0026 to 0.0284)
0.0003 (⫺0.0099 to 0.0106) ⫺0.0690 (⫺0.1637 to 0.0256) ⫺ 0.0320 ( ⫺ 0.0622 to ⫺ 0.0018) 0.0189 (0.0050 to 0.0327)
0.0011 (⫺0.0088 to 0.0109) ⫺0.0688 (⫺0.1598 to 0.0221) ⫺ 0.0304 ( ⫺ 0.0595 to ⫺ 0.0013) 0.0177 (0.0043 to 0.0310)
a Analysis was performed using linear regression. Model 1 was adjusted for age, gender, BMI, and ethnicity, and model 2 was adjusted further for number of diabetes medications.
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doi: 10.1210/jc.2014-1260
4 hours of respiratory data, the association between OSA and HbA1c was no longer significant. One hypothesis is that during rapid eye movement (REM) sleep, there are higher frequencies of apnea/hypopnea episodes compared with other sleep stages. From our experience, some patients did not achieve 6 hours of quality respiratory recording mainly due to device discomfort and, in other cases, the loss of connection with part of the device during sleep. Although our results showed no significant association between AHI and levels of glycemia, we found a significant relationship between minimum O2 saturation as well as %TST⬎90% and HbA1c levels. To our knowledge, no study has yet shown significant associations between the duration of hypoxemia and glycemic control in the obese DM population. In the non-DM population, a study of 116 hypertensive individuals, found significant correlation between minimum O2 level and HbA1c (r ⫽ ⫺0.25, P ⬍ .001) (5), and another study on non-DM individuals concurred with the results ( ⫽ ⫺0.29, P ⬍ .001) (6). This indicates that in DM individuals with limited respiratory recording, nocturnal hypoxemia and not AHI may be a useful indicator of greater insulin resistance and might be a significant contributor to poor glycemic control. Moreover, studies have shown that severity and duration of nocturnal hypoxemia may be an important factor for DM microvascular complications, such as progression of diabetic retinal complication (7) and diabetic nephropathy (8). This also suggests that apart from using usual glucoselowering medications, correcting nocturnal hypoxemia may be beneficial in reducing HbA1c notably among insulin-resistant obese patients. The effects of continuous positive airway pressure (CPAP) on glycemia have been inconclusive, but in a recent observational study, CPAPtreated individuals maintained better diabetes control (9). Well-designed randomized prospective controlled trials of longer-duration CPAP treatment on extremely obese hypoxemic individuals are needed. The prevalence of OSA was significantly higher in DM individuals with extreme obesity, being particularly prevalent among insulin-treated individuals. Our observations are consistent with findings from several studies. The Sleep Ahead study reported an OSA prevalence of 86% among DM individuals (10). In the MOBIL (morbid obesity treatment, bariatric surgery vs intensive lifestyle intervention) study, the prevalence of OSA was 78% among DM individuals, significantly higher than in glucose-tolerant patients (33%) (11). Aronsohn and colleagues (4) reported an OSA rate of 77% for 60 DM individuals, whereas the Sleep Heart Health Study found 58% of DM had OSA compared with 43% in non-DM individuals (P ⬍ .001) (12). The lower prevalence rate reported by the Sleep
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Heart Health Study compared with our study is likely due to the lower adiposity levels in their population (mean BMI 31.3 ⫾ 6.0 kg/m2) (12). There are several limitations to our study. First, we examined only extremely obese individuals, an especially high-risk group for health complications and mortality. However, when only limited data are available in this area, one may cautiously consider the significance of our results. Second, the limitations of observational studies apply to our study, such as difficulty excluding reverse causality. Third, there were no data on smoking and alcohol status. We also did not have data on antihypertensive medications as well as other drugs such as antipsychotics. However, this study has a large number of patients and is representative of the local population with extreme obesity. The analyses also adjusted for multiple potential confounders including antidiabetes medications. In summary, we observed an inverse relationship between nocturnal hypoxemia and glycemia control among extremely obese DM individuals. We also observed that the prevalence of OSA among DM individuals was very high. Every 10% reduction in lowest O2 was associated with a 0.3% HbA1c increase and every 10% increase in %TST⬍90% was associated with a 0.2% HbA1c increase, after adjusting for a range of potential confounders. This supports the notion that OSA, in particular hypoxemia, may have an effect on glucose metabolism. Given that a significant number of patients with DM do not achieve glycemic targets, further studies on the impact of correcting nocturnal hypoxemia on glycemia are needed.
Acknowledgments Address all correspondence and requests for reprints to: Dr Shahrad Taheri, Department of Medicine, Weill Cornell Medical College, PO Box 24144, Doha, Qatar. E-mail: [email protected]; or Dr Dev Banerjee, Woolcock Institute of Medical Research, University of Sydney, Glebe, Sydney, NSW 2037 Australia. E-mail: [email protected]. This work was funded by the National Institute for Health Research (NIHR) through the Collaborations for Leadership in Applied Health Research and Care for Birmingham and Black Country (CLAHRC-BBC) program. W.B.L. is funded through an unrestricted educational grant from Allergan. S.T. was funded by the National Institute for Health Research (NIHR) through the CLAHRC-BBC program. The views expressed in this publication are not necessarily those of the NIHR, the Department of Health, National Health Service South Birmingham, University of Birmingham, or the CLAHRC-BBC Theme 8 Management/Steering Group. Disclosure Summary: S.T. has received educational funding support from Lilly UK and research support from Novo Nordisk, Allergan, Philips Respironics, and ResMed. D.B. has received research support from Philips Respironics.
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Hypoxemia and Glycemic Control
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7. 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:e79521. 8. 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; 169:239 –246. 9. Guest JF, Panca M, Sladkevicius E, Taheri S, Stradling J. Clinical outcomes and cost-effectiveness of continuous positive airway pressure to manage obstructive sleep apnea in patients with type 2 diabetes in the U.K. Diabetes Care. 2014;37:1263–1271. 10. Foster GD, Sanders MH, Millman R, et al. Obstructive sleep apnea among obese patients with type 2 diabetes. Diabetes Care. 2009; 32:1017–1019. 11. Fredheim JM, Rollheim J, Omland T, et al. Type 2 diabetes and pre-diabetes are associated with obstructive sleep apnea in extremely obese subjects: a cross-sectional study. Cardiovasc Diabetol. 2011;10:84. 12. Resnick HE, Redline S, Shahar E, et al. Diabetes and sleep disturbances: findings from the Sleep Heart Health Study. Diabetes Care. 2003;26:702–709.
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