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 RESEARCH ARTICLE

Obstructive sleep apnea and risk for late-life depression

Siddharth Bajpai, MD

Department of Psychiatry, Department of Neurology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA

Kyoung Bin Im, MD

Department of Psychiatry, Department of Neurology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA

Mark Eric Dyken, MD

Department of Neurology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA

Simrit K. Sodhi, BS

Department of Psychiatry, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA

Jess G. Fiedorowicz, MD, PhD

Department of Psychiatry, Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, Iowa, USA

BACKGROUND: Obstructive sleep apnea (OSA) is a sleep-related breathing disorder characterized by repetitive pharyngeal collapse. Because of the association between OSA, ischemia, and late-life depression, we hypothesized that older patients with OSA would have a higher prevalence of depression relative to their younger counterparts.

METHODS: We retrospectively reviewed charts of patients evaluated at the Sleep Disorders Center (SDC) at University of Iowa Hospitals and Clinics. A total of 617 patients age ≥18 seen at SDC for diagnostic and therapeutic sleep studies were identified. Patients with a chart diagnosis of depressive disorder or treatment with antidepressants were identified as having a depressive disorder. Patients with an Apnea/Hypopnea Index ≥5 were identified as having OSA.

RESULTS: No evidence of an escalating prevalence of depression with age was found in patients with OSA relative to those without the disorder. Prevalence of depression was similar in the OSA and the nonapnea groups (40.9% vs 40.3%, respectively; c2 = 0.02; df = 1; P = .89). Individuals with OSA had a significantly higher body mass index and greater number of chart diagnoses of hypertension, diabetes mellitus, and coronary artery disease compared with the nonapnea group.

CONCLUSIONS: The prevalence of depression among individuals with OSA does not appear to be moderated by age. Similarly high rates of depression were observed across the population of individuals referred for sleep studies, whether or not they were diagnosed with OSA.

KEYWORDS: obstructive sleep apnea, late-life depression, white matter lesions, sleep disorders, obesity, hypertension, diabetes mellitus, coronary artery disease

ANNALS OF CLINICAL PSYCHIATRY 2014;26(2):e1-e8

  INTRODUCTION

Obstructive sleep apnea (OSA) is a sleep-related breathing disorder1 characterized by repetitive pharyngeal collapse during sleep, often resulting in apnea, hypopnea, and snoring. OSA may contribute to the development of vascular diseases such as hypertension, cardiac ischemia, myocardial infarction, congestive heart failure, and stroke.2-4 OSA also has been linked to neurocognitive sequelae like sleepiness and affective disorders like depression.5,6

Physiological consequences of OSA include intermittent nocturnal hypoxia and frequent arousals that, along with the increased respiratory effort to counter the apnea or hypopnea, produce negative intrathoracic pressure. Other consequences of apnea/hypopnea are decreased partial arterial pressure of oxygen (Pao2), increased partial arterial pressure of carbon dioxide (Paco2), and increased sympathetic activity.7,8 These metabolic effects have been shown to cause sympathetic activation, increased oxidative stress, and endothelial dysfunction.9 Such changes contribute to the strong association of OSA with vascular diseases.3,10-15

Concerns have been raised about the heightened cardiovascular consequences of OSA with increasing age. Pecker et al reported an increased risk of cardiovascular disease in middle-aged patients with OSA, and on multivariate analysis found that current age of the study cohort was a significant variable regarding development of coronary artery disease (CAD).16 In the Wisconsin Sleep Cohort study, the risk of developing hypertension was almost 3 times greater for participants with an Apnea/Hypopnea Index (AHI) of ≥15 than for participants with an AHI of 0. Because the participants’ mean age was 46,4 the authors hypothesized that OSA may have exacerbated increases in blood pressure levels in middle-aged study participants.

OSA is characterized in a large proportion of patients by neurocognitive, affective, and autonomic nervous system disturbances, which reflect ischemic injury to cortical and subcortical neural structures.17,18 Patients with OSA appear to have significant gray matter loss and alterations in cerebral white matter involving both cortical and subcortical locations,18 including atrophy in hippocampal and parahippocampal regions.17 These changes are thought to be a consequence of ischemia and hypoxia from apnea.18,19 Depression is an important neuropsychiatric sequela of OSA. Prospectively, a dose-response relationship between severity of OSA and depression has been demonstrated, supporting a causal relationship between these 2 conditions.20

Since the mid-1990s, there has been great interest in late-life depression, variably defined based on age of onset between age 50 to 60.21-23 Differences in etiology and symptoms between early-onset depression (EOD) and late-onset depression (LOD) 21-24 have been reported. Some authors have found a greater prevalence of vascular disease by age in patients with onset of depression after age 47 and a more frequent family history of depression in patients with depression onset before age 25.25,26 Other reported differences include an increased genetic susceptibility to mood disorders in patients with EOD, whereas LOD more often involves subcortical vascular pathology.27,28 Radiologically, LOD is distinguished by the presence of white matter lesions (WMLs). These WMLs are of probable vascular etiology and occur in the deep white matter and subcortical gray matter. WMLs in the frontal deep white matter and basal ganglia lesions are associated with poorer response to antidepressant therapy.29 In the older population, WMLs are related to aging, cerebrovascular risk factors, such as hypertension and diabetes,30-33 and arteriosclerosis,34 suggesting that WMLs result from chronic cerebral hypoperfusion35 and may be an important marker of “end organ” vascular damage.34 The vascular depression hypothesis pertinent to LOD28,36 proposes that WMLs cause disruption of frontostriatal circuits, thereby causing depressive episodes in the older population. Patients with LOD have greater numbers of WMLs and poorer outcomes than those with EOD, with WML burden related to poorer outcomes.24,37

There are relatively few studies in the literature on the effects of aging on the prevalence of late-life depression in OSA. Because of the apparent association between OSA, ischemia, and LOD, we investigated the hypothesis that older patients with OSA have a higher prevalence of depression compared with younger OSA patients. We tested this hypothesis by retrospectively analyzing the charts of patients referred to the Sleep Disorders Center (SDC) at University of Iowa Hospitals and Clinics (UIHC) for sleep-related issues.

  METHODS

Sample

After obtaining the requisite approval from the University of Iowa institutional review board, we conducted a retrospective, cross-sectional database review of 617 patients referred to the SDC. SDC is a tertiary sleep disorders referral center in Iowa, conducting more than 2000 polysomnographies (PSGs), split-night studies, and continuous positive airway pressure (CPAP) titrations yearly, following referral from local/regional primary care physicians as well as other specialty/subspecialty clinics within the UIHC system. UIHC maintains electronic databases containing health care data on patients seen at the facility for the past several years, with coding of medical and psychiatric disorders based on the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM).

For the current analyses, we identified 617 consecutive patients age ≥18 seen at SDC for diagnostic polysomnography (PSG), split-night study, or CPAP titration study during the periods July 1, 2011, to December 31, 2011, and May 1, 2012, to August 30, 2012. New patients scheduled for sleep studies during those months with complete sleep study data archives were included in the study.

The outpatient records were analyzed for ICD-9-CM diagnostic codes and the following variables: age, sex, body mass index (BMI), clinical diagnosis of psychiatric illness (depressive disorder [296.2], anxiety disorder [300], attention deficit disorder [314], attention-deficit/hyperactivity disorder [314.01], bipolar disorder [296.4-296.8], schizophrenia [295], alcohol abuse/dependence [305,303], drug abuse/dependence [304]), clinical diagnosis of comorbid medical illness (hypertension [401], diabetes mellitus [250]), obesity [278, BMI >30 kg/m2], CAD [414], hyperlipidemia [272], hypothyroidism [244], gastroesophageal reflux disease [530.81], chronic obstructive pulmonary disease [491.21]), therapy with an antidepressant medication (amitriptyline, bupropion, citalopram, clomipramine, desipramine, doxepin, desvenlafaxine, duloxetine, escitalopram, fluoxetine, fluvoxamine, imipramine, mirtazapine, moclobemide, nortriptyline, paroxetine, phenelzine, selegiline, sertraline, tranylcypromine, venlafaxine), comorbid medical disorders, and therapy with other medications. Sleep disorder–related information obtained included the specific sleep-related complaints that led to the patient’s referral to the sleep clinic/SDC. The Epworth Sleepiness Scale (ESS) score and neck circumference were extrapolated from the clinical history. Sleep study parameters, which included the AHI, pres ence/absence of rapid eye movement (REM) supine sleep, snoring and its severity, and presence/absence of elevated periodic limb movement (PLM) index, were obtained from the diagnostic PSG, split-night study, or CPAP titration studies.

For study purposes, patients having a chart diagnosis of major depressive disorder or depressive disorder from either their psychiatric provider or their primary care provider or who were taking antidepressant medication were included as having depressive disorder, and patients having an AHI index ≥5 were defined as having OSA.38

Statistical analyses

All analyses were conducted using SAS version 9.3 (SAS Institute, Inc.). Descriptive statistics were compiled on sociodemographic and clinical variables. Patients with and without depressive disorders as defined above were contrasted on these variables using t tests (or the nonparametric Wilcoxon rank sum test, wherein the assumption of normality was violated) for continuous variables and chi-square tests for categorical variables. Our primary hypothesis that older patients with sleep apnea would be at greater risk for vascular disease and subsequently depression was tested in multivariate logistic regression models. Depression, defined as chart diagnosis or treatment with antidepressant medication, served as the primary dependent variable. The model was designed to test for an OSA by age interaction on the outcome of interest, depression. The general form of the logistic regression model is as follows:

logit (π) = β0 + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X1X2

where π (dichotomous dependent variable) = depression, X1 = OSA index of severity (continuous, linear effect), X2 = age (continuous, linear effect), X3 = sex, X4 = obesity (≥30 kg/m2, dichotomous), and X5 = diagnosis of diabetes mellitus. Models tested the null hypothesis: β6 = 0 for sleep apnea by age interaction and assessed for the presence of any main effects for covariates or exposures of interest. Covariates were selected for inclusion based on clinical grounds and relation to exposure and outcome in this data set to address potential confounding. The potential for nonlinear effects of age and OSA on the outcome of depression were assessed in a corresponding model in which age is dichotomized (median split) and OSA dichotomized (AHI ≥5) with interaction of these categorical variables. Nonlinear effects of age also were explored through the addition of a quadratic term to the primary model. A sensitivity analysis was conducted in which the outcome of interest was narrowed to the presence of a depressive disorder based on chart diagnosis without consideration of antidepressant status in the definition.

  RESULTS

Individuals in the study had a mean (SD) age of 49.6 years (13.4), with a range of 18 to 87 years. About 52% of the participants were men and about 48% of the participants were women. Sociodemographic and clinical characteristics are outlined in TABLE 1 and the FIGURE. The mean (SD) BMI was 36.2 (10.5) kg/m2. The mean BMI (SD) was greater for the apnea group (study participants with AHI ≥5) relative to the nonapnea group (BMI, 37.6 [10.5] vs 30.6 [8.4]; χ2 = 0.0064; df = 1; P < .0001]. In the study group, 56.7% of participants had a chart diagnosis of hypertension, with a greater prevalence in the apnea group than the nonapnea group (62.7% vs 33.1%; χ2 = 35.4; df = 1; P < .0001). Similarly, the apnea group had significantly more chart diagnoses of diabetes mellitus (27.9% vs 8.9%; χ2 = 19.8; df = 1; P < .0001) and CAD (11.4% vs 0.5%; χ2 = 13.2; df = 1; P = .0003).


TABLE 1

Clinical and sociodemographic characteristics of patients by presence or absence of obstructive sleep apnea

Demographic/clinical characteristics Total sample Apnea group (AHI ≥5) Nonapnea group (AHI <5) P
n = 617 n = 493 n = 124  
  Mean (SD)
Age, years 49.6 (13.4) 51.3 (12.8) 43.1 (14.0) <.0001
BMI, kg/m2; (N=611) 36.2 (10.5) 37.6 (10.5) 30.6 (8.4) <.0001
  n (%)
Men 319 (51.7%) 277 (56.2%) 42 (33.9%) <.0001
Women 298 (48.3%) 216 (43.8%) 82 (27.5%) <.0001
Medical comorbidities n (%)
  Obesity (BMI >30) 442 (72%) 387 (78.5%) 55 (44.4%) <.0001
  Hypertension 350 (56.7%) 309 (62.7%) 41 (33.1%) <.0001
  Diabetes 149 (24.2%) 138 (27.9%) 11 (8.9%) <.0001
  GERD 181 (29.3%) 145 (29.4%) 36 (29.0%) <.0001
  Hyperlipidemia 211 (34.2%) 189 (38.3%) 22 (17.7%) <.0001
  Hypothyroidism 90 (14.6%) 69 (14.0%) 21 (16.9%) .4071
  Coronary artery disease 73 (11.8%) 70 (14.2%) 3 (2.4%) <.0001
  COPD 46 (7.5%) 34 (6.9%) 12 (9.7%) .292
Co-occurring psychiatric diagnoses (study intake) N (%)
  Depression 252 (40.8%) 202 (40.9%) 50 (40.3%) .8951
  Anxiety disorder 84 (13.6%) 58 (11.8%) 26 (20.9%) <.0001
  Bipolar disorder 7 (1.1%) 7 (1.4%) 0 (0%) .182
  Alcohol abuse/drug abuse 19 (3.1%) 14 (2.8%) 5 (4.0%) .492
Sleep study–related parameters Mean (SD)
  ESS score, mean (SD); (N=605) 10.1 (5.1) 10.2 (5.2) 9.8 (5.1) .5642
  AHI, mean (SD) 22.0 (27.7) 27.3 (28.7) 1.3 (1.8) <.0001
Sleep study–related parameters n (%)
  REM sleep 579 (93.8%) 463 (93.9%) 116 (93.6%) .8794
  REM supine sleep 385 (62.4%) 308 (62.5%) 77 (62.1%) .9173
  Snoring 553 (89.6%) 462 (89.6%) 91 (73.4%) <.0001
  PLM index ≥15 160 (25.9%) 135 (27.4%) 25 (20.2%) .1009
AHI: Apnea/Hypopnea Index; BMI: body mass index; COPD: chronic obstructive pulmonary disease; ESS: Epworth Sleepiness Scale; GERD: gastroesophageal reflux disease; PLM: periodic limb movement; REM: rapid eye movement.

FIGURE: Prevalence rates of comorbid psychiatric and medical illnesses in the OSA and the nonapnea groups
CAD: coronary artery disease; DM: diabetes mellitus; GERD: gastroesophageal reflux disease; HTN: hypertension; OSA: obstructive sleep apnea.

A high percentage of patients had a chart diagnosis of depression (n = 252, 40.8%), which was similar in both the apnea and the nonapnea groups (40.9% vs 40.3%, respectively; χ2 = 0.02; df = 1; P = .89). The nonapnea group had a larger percentage of patients with a chart diagnosis of anxiety compared with patients in the OSA group (21.0% vs 11.8%; χ2 = 7.1; df = 1; P = .008). In the vast majority of patients in both groups, sleep studies showed REM supine sleep (93.8%). In the sleep studies, a large percentage of patients snored (90.5%), and this was significantly higher in the apnea group compared with the nonapnea group (94.9% vs 73.4%; χ2 = 53.1; df = 1; P < .0001).

In multivariate models, there was not a significant age by OSA interaction in either the continuous (χ2 = 0.16; df = 1; P = .69) or categorical models (χ2 = 0.32; df = 1; P = .57). The interaction term was subsequently removed from the final multivariate model, which is reported in TABLE 2. Female sex (odds ratio = 2.8; χ2 = 36.1; df = 1; P < .0001) and obesity (odds ratio = 1.8; χ2 = 7.9; df = 1; P = .0049) increased the risk of having a diagnosis of or receiving treatment for depression. Diagnosis of OSA, age ≥51, or diabetes mellitus were not significantly related to having a diagnosis or receiving treatment for depression.

For sensitivity analyses, in which the outcome definition was narrowed to a chart diagnosis of depressive disorder, the results did not substantively differ from those reported in the primary models and are thus not reported.


TABLE 2

Multivariate logistic regression model

Variable Odds ratio (95% CI) P
OSA 0.78 (0.49 to 1.23) .28
Greater than median age 1.16 (0.82 to 1.64) .39
Female sex 2.80 (2.00 to 3.92) <.0001
Obesity 1.79 (1.19 to 2.69) .0049
Diabetes mellitus 1.13 (0.75 to 1.69) .57
This table illustrates the relationship between variables in the final model and risk of having a diagnosis or receiving treatment for depression.
OSA: obstructive sleep apnea.

  DISCUSSION

We investigated the hypothesis that older patients with sleep apnea would be at greater risk for depression. Our study shows that the prevalence of depression is remarkably high in the study population group (patients referred to a sleep disorders clinic for sleep-related complaints) and that this was similar both in patients diagnosed as having OSA and those not diagnosed with OSA. The other main findings from this study are that the individuals with diagnosed OSA had a significantly higher BMI and greater number of chart diagnoses of hypertension, diabetes mellitus, and CAD. Female sex and chart diagnosis of obesity increased the risk or having a diagnosis of or receiving treatment for depression.

Depression is highly prevalent among individuals with OSA, with reported prevalence rates varying from 17% to 22%6,39 in community samples and from 28% to 41%40-42 in clinical samples. In our study, the prevalence of depression was at the higher end of the reported spectrum among patients with OSA as well as those without OSA. The high prevalence of depression among patients with OSA is not entirely surprising; however, the high prevalence of depression in the non–OSA group was unexpected.

These findings differ from those reported by Sharafkhaneh et al, who reported a significantly lower prevalence of depression in study participants with apnea compared with those without apnea (20% vs 8.3%) in a 4-year prevalence study.6 That study was a cross-sectional analysis of Veterans Health Administration health care recipients, whereas our study was conducted with patients referred to a sleep disorders clinic, which could explain the higher prevalence of depression in the control group of our study.

The findings from our study could be explained if patients with mood or anxiety disorders were being referred to sleep studies due to sleep abnormalities associated with the psychiatric disorder. Sleep disturbances occur in a large proportion of patients with psychiatric disorders43 and can be the primary reason for referral to primary care clinics.44 Additionally, most sleep disorders are characterized by alterations in sleep stages and sleep fragmentation,45-47 which can cause depression.48 Therefore, depression also may be more prevalent among patients with sleep disorders other than OSA. Our ability to detect a significant age by OSA interaction in either the continuous or categorical models of the multivariate analyses may be due to overrepresentation of those with depression and non- OSA–related sleep disorders referred for sleep studies.

Anxiety disorders were more prevalent among the nonapnea group patients. This finding is not consistent with several other studies that reported a higher prevalence of anxiety in OSA patients than in controls.6,49 This difference could be due to the possibility that patients are being referred to sleep clinics for comorbid psychiatric illnesses that manifest as sleep-related complaints.

Obesity (BMI >30 kg/m2) is an important independent risk factor for OSA.50,51 The BMI was significantly higher among patients with apnea than nonapnea patients, a finding consistent with other studies.50,52 The association between OSA and hypertension has been reported in several previous studies, and our findings seem to concur with those of Becker et al, who reported that 50% of the patients with OSA from their study sample had hypertension.53 The association of OSA with diabetes also has been reported, and our findings were consistent with those of Punjabi et al,54 in which the prevalence of diabetes was 15% in patients with an AHI ≥15 and 9.3% in those with an AHI <5. Shahar et al3 reported that sleep apnea was an independent predictor of CAD, also consistent with the results from our study, wherein patients with OSA had a significantly higher prevalence of CAD compared with study participants who did not have OSA.

The limitations of this study stem from the inherent constraints of retrospective analysis from a clinical sample. Our study design does not allow for demonstration of temporality between OSA and depression. The sample was derived from referrals to a tertiary care sleep disorders center and may not generalize to other settings. Selection bias related to referral for sleep study likely impacted our comparison group. Another constraint was the use of chart diagnoses of psychiatric and comorbid medical illness rather than systematized assessment, which could result in misclassification. Depression was defined by chart diagnosis of major depressive disorder or depressive disorder or from taking antidepressant medication. We considered the possibility that the study participants may have been taking an antidepressant medication for reasons other than treatment of depression, and this may have led to study participants being misclassified as having depression. However, on sensitivity analyses, in which the outcome definition was narrowed to a chart diagnosis of depressive disorder, the results did not substantively differ from those reported in the primary model. While our classification of exposure (OSA) was rigorous, as all patients underwent a sleep study, our classification of outcome (depression) was limited to the reporting of diagnoses and medications in the clinical record, and this may have led to misclassification. Although our finding of a high prevalence of depression might suggest overreporting, prior work using medical record data has found depression to be, if anything, underreported.43 The high prevalence of depression in the nonapnea subgroup of this study deserves special emphasis: it is significantly higher than has been reported in other studies,6 perhaps due to the reasons discussed (high prevalence of depression in other sleep disorders and sleep disturbances associated with depression).

Despite these constraints, the strength of this study lies in the relatively large number of participants (N = 617) and the rigorous phenotyping for sleep disorders. Very few studies in the literature have reported on the prevalence of depression in patients with sleep-related complaints in primary care or sleep disorders clinic settings.54 The possibility that depressive symptoms were missed in these clinical situations cannot be discounted. Multiple reports in the literature suggest that depression and OSA share common risk factors and clinical features and predispose to the development of similar comorbid medical illnesses55,56; this study seems to suggest that the high prevalence of depression is not the exclusive domain of OSA, but that sleep disorders other than OSA may predispose to a high prevalence of depression as well.

  Conclusions

This study demonstrates that there is a high prevalence of depression among individuals with OSA, which does not appear to be moderated by age. High rates of depression and anxiety were observed across the population of individuals referred for sleep studies, whether or not they were diagnosed with OSA. Hence, clinicians should be aware of the high prevalence of mood and anxiety issues among individuals referred for sleep studies, and systematic screening should be considered. Future study in a representative sample may be useful to fully discern the relationship between age and depression risk in individuals with OSA.

DISCLOSURES: The authors have no financial relationships with any company whose products are mentioned in this article, or with manufacturers of competing products. Dr. Fiedorowicz is supported by the National Institutes of Health (1K23MH083695-01A210).

    REFERENCES

  1. Nieto FJ, Young TB, Lind BK, et al. Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study. Sleep Heart Health Study. JAMA. 2000;283:1829–1836.
  2. Gottlieb DJ, Yenokyan G, Newman AB, et al. Prospective study of obstructive sleep apnea and incident coronary heart disease and heart failure. Circulation. 2010;122:352–360.
  3. Shahar E, Whitney CW, Redline S, et al. Sleep-disordered breathing and cardiovascular disease: cross-sectional results of the Sleep Heart Health Study. Am J Respir Crit Care Med. 2001;163:19–25.
  4. Peppard PE, Young T, Palta M, et al. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med. 2000;342:1378–1384.
  5. Teran-Santos J, Jiménez-Gómez A, Cordero-Guevara J. The association between sleep apnea and the risk of traffic accidents. N Engl J Med. 1999;340:847–851.
  6. Sharafkhaneh A, Giray N, Richardson P, et al. Association of psychiatric disorders and sleep apnea in a large cohort. Sleep. 2005;28:1405–1411.
  7. Weiss JW, Garpestad E, Parker T, et al. Changes in left-ventricular stroke volume during obstructive apneas. Sleep. 1993;16:S39–S40.
  8. Weiss JW, Remsburg S, Garpestad E, et al. Hemodynamic consequences of obstructive sleep apnea. Sleep. 1996;19:388–397.
  9. Lavie L. Obstructive sleep apnoea syndrome—an oxidative stress disorder. Sleep Med Rev. 2003;7:35–51.
  10. Kanagy NL, Walker BR, Nelin LD. Role of endothelin in intermittent hypoxia-induced hypertension. Hypertension. 2001;37:511–515.
  11. Lesske J, Fletcher EC, Bao G, et al. Hypertension caused by chronic intermittent hypoxia—influence of chemoreceptors and sympathetic nervous system. J Hypertens. 1997;15:1593–1603.
  12. Deanfield J, Donald A, Ferri C, et al; Working Group on Endothelin and Endothelial Factors of the European Society of Hypertension. Endothelial function and dysfunction. Part I: Methodological issues for assessment in the different vascular beds: a statement by the Working Group on Endothelin and Endothelial Factors of the European Society of Hypertension. J Hypertens. 2005;23:7–17.
  13. Pizza F, Biallas M, Kallweit U, et al. Cerebral hemodynamic changes in stroke during sleep-disordered breathing. Stroke. 2012;43:1951–1953.
  14. Arzt M, Young T, Finn L, et al. Association of sleep-disordered breathing and the occurrence of stroke. Am J Respir Crit Care Med. 2005;172:1447–1451.
  15. Dyken ME, Somers VK, Yamada T, et al. Investigating the relationship between stroke and obstructive sleep apnea. Stroke. 1996;27:401–407.
  16. Peker Y, Carlson J, Hedner J. Increased incidence of coronary artery disease in sleep apnoea: a long-term follow-up. Eur Respir J. 2006;28:596–602.
  17. Morrell MJ, McRobbie DW, Quest RA, et al. Changes in brain morphology associated with obstructive sleep apnea. Sleep Med. 2003;4:451–454.
  18. Macey PM, Henderson LA, Macey KE, et al. Brain morphology associated with obstructive sleep apnea. Am J Respir Crit Care Med. 2002;166:1382–1387.
  19. Macey PM, Kumar R, Yan-Go FL, et al. Sex differences in white matter alterations accompanying obstructive sleep apnea. Sleep. 2012;35:1603–1613.
  20. Peppard PE, Szklo-Coxe M, Hla KM, et al. Longitudinal association of sleep-related breathing disorder and depression. Arch Intern Med. 2006;166:1709–1715.
  21. Krishnan KR, Hays JC, Tupler LA, et al. Clinical and phenomenological comparisons of late-onset and early-onset depression. Am J Psychiatry. 1995;152:785–788.
  22. Brodaty H, Luscombe G, Parker G, et al. Early and late onset depression in old age: different aetiologies, same phenomenology. J Affect Disord. 2001;66:225–236.
  23. Lyness JM, Conwell Y, King DA, et al. Age of onset and medical illness in older depressed inpatients. Int Psychogeriatr. 1995;7:63–73.
  24. Baldwin RC, Tomenson B. Depression in later life. A comparison of symptoms and risk factors in early and late onset cases. Br J Psychiatry. 1995;167:649–652.
  25. Kendler KS, Fiske A, Gardner CO, et al. Delineation of two genetic pathways to major depression. Biol Psychiatry. 2009;65:808–811.
  26. Kendler KS, Gatz M, Gardner CO, et al. Age at onset and familial risk for major depression in a Swedish national twin sample. Psychol Med. 2005;35:1573–1579.
  27. Krishnan KRR. Depression—neuroanatomical substrates. Behav Ther. 1992;23:571–583.
  28. Alexopoulos GS, Meyers BS, Young RC, et al. ‘Vascular depression’ hypothesis. Arch Gen Psychiatry. 1997;54:915–922.
  29. Baldwin RC, Gallagley A, Gourlay M, et al. Prognosis of late life depression: a three-year cohort study of outcome and potential predictors. Int J Geriatr Psychiatry. 2006;21:57–63.
  30. Pantoni L, Garcia JH. The significance of cerebral white matter abnormalities 100 years after Binswanger’s report. A review. Stroke. 1995;26:1293–1301.
  31. Fukuda H, Kitani M. Differences between treated and untreated hypertensive subjects in the extent of periventricular hyperintensities observed on brain MRI. Stroke. 1995;26:1593–1597.
  32. Veldink JH, Scheltens P, Jonker C, et al. Progression of cerebral white matter hyperintensities on MRI is related to diastolic blood pressure. Neurology. 1998;51:319–320.
  33. Cassidy F, Ahearn E, Carroll BJ. Elevated frequency of diabetes mellitus in hospitalized manic-depressive patients. Am J Psychiatry. 1999;156:1417–1420.
  34. van Gijn  J. White matters: small vessels and slow thinking in old age. Lancet. 2000;356:612–613.
  35. Dufouil C, de Kersaint-Gilly A, Besançon V, et al. Longitudinal study of blood pressure and white matter hyperintensities: the EVA MRI Cohort. Neurology. 2001;56:921–926.
  36. Alexopoulos GS. The vascular depression hypothesis: 10 years later. Biol Psychiatry. 2006;60:1304–1305.
  37. O’Brien J, Ames D, Chiu E, et al. Severe deep white matter lesions and outcome in elderly patients with major depressive disorder: follow up study. BMJ. 1998;317:982–984.
  38. Young T, Palta M, Dempsey J, et al. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med. 1993;328:1230–1235.
  39. Ohayon MM. The effects of breathing-related sleep disorders on mood disturbances in the general population. J Clin Psychiatry. 2003;64:1195–1200.
  40. Vandeputte M, de Weerd A. Sleep disorders and depressive feelings: a global survey with the Beck depression scale. Sleep Med. 2003;4:343–345.
  41. Wahner-Roedler DL, Olson EJ, Narayanan S, et al. Gender-specific differences in a patient population with obstructive sleep apnea-hypopnea syndrome. Gend Med. 2007;4:329–338.
  42. McCall WV, Harding D, O’Donovan C. Correlates of depressive symptoms in patients with obstructive sleep apnea. J Clin Sleep Med. 2006;2:424–426.
  43. Benca RM, Okawa M, Uchiyama M, et al. Sleep and mood disorders. Sleep Med Rev. 1997;1:45–56.
  44. Gerber PD, Barrett JE, Barrett JA, et al. The relationship of presenting physical complaints to depressive symptoms in primary care patients. J Gen Intern Med. 1992;7:170–173.
  45. Bonnet MH. Cognitive effects of sleep and sleep fragmentation. Sleep. 1993;16:S65–S67.
  46. Dijk DJ. Sleep fragmentation metabolism, and sleepiness. J Sleep Res. 2013;22:1–2.
  47. Haba-Rubio J, Ibanez V, Sforza E. An alternative measure of sleep fragmentation in clinical practice: the sleep fragmentation index. Sleep Med. 2004;5:577–581.
  48. Pochat MD, Ferber C, Lemoine P. Depressive symptomatology and sleep apnea syndrome [in French]. Encephale. 1993;19:601–607.
  49. Yue W, Hao W, Liu P, et al. A case-control study on psychological symptoms in sleep apnea-hypopnea syndrome. Can J Psychiatry. 2003;48:318–323.
  50. Strobel RJ, Rosen RC. Obesity and weight loss in obstructive sleep apnea: a critical review. Sleep. 1996;19:104–115.
  51. Young T, Peppard PE, Taheri S. Excess weight and sleep-disordered breathing. J Appl Physiol (1985). 2005;99:1592–1599.
  52. Redline S, Tishler PV, Hans MG, et al. Racial differences in sleep-disordered breathing in African-Americans and Caucasians. Am J Respir Crit Care Med. 1997;155:186–192.
  53. Becker HF, Jerrentrup A, Ploch T, et al. Effect of nasal continuous positive airway pressure treatment on blood pressure in patients with obstructive sleep apnea. Circulation. 2003;107:68–73.
  54. Punjabi NM, Shahar E, Redline S. Sleep-disordered breathing glucose intolerance, and insulin resistance the sleep heart health study. Am J Epidemiol. 2004;160(6):521–530.
  55. Schröder C, O’Hara R. Depression and obstructive sleep apnea (OSA). Ann Gen Psychiatry. 2005;4:13.
  56. Gami AS, Somers VK. Obstructive sleep apnoea metabolic syndrome, and cardiovascular outcomes. Eur Heart J. 2004;25:709–711.
  57. van Diest R, Appels A. Vital exhaustion and depression: a conceptual study. J Psychosomatic Res. 1991;35:535–544.

CORRESPONDENCE: Jess G. Fiedorowicz, MD, PhD, 200 Hawkins Drive W278GH, Iowa City, IA 52242-1057 USA E-MAIL: jess-fiedorowicz@uiowa.edu