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Sleep Biomarkers, Health Comorbidities, and Neurocognition in Obstructive Sleep Apnea

Published online by Cambridge University Press:  07 September 2018

Ciaran M. Considine*
Affiliation:
Vanderbilt University School of Medicine, Neurology Department, Nashville, Tennessee Clement J. Zablocki VA Medical Center, Mental Health Department, Milwaukee, Wisconsin
Hillary A. Parker
Affiliation:
Clement J. Zablocki VA Medical Center, Mental Health Department, Milwaukee, Wisconsin
Jeralee Briggs
Affiliation:
Clement J. Zablocki VA Medical Center, Mental Health Department, Milwaukee, Wisconsin
Erin E. Quasney
Affiliation:
Medical College of Wisconsin, Neurology Department or Psychiatry & Behavioral Medicine, Milwaukee, Wisconsin
Eric R. Larson
Affiliation:
Clement J. Zablocki VA Medical Center, Mental Health Department, Milwaukee, Wisconsin Medical College of Wisconsin, Neurology Department or Psychiatry & Behavioral Medicine, Milwaukee, Wisconsin
Heather Smith
Affiliation:
Clement J. Zablocki VA Medical Center, Mental Health Department, Milwaukee, Wisconsin Medical College of Wisconsin, Neurology Department or Psychiatry & Behavioral Medicine, Milwaukee, Wisconsin
Skyler G. Shollenbarger
Affiliation:
Clement J. Zablocki VA Medical Center, Mental Health Department, Milwaukee, Wisconsin Henry Ford Health System, Behavioral Health Department, Detroit, Michigan
Christopher A. Abeare
Affiliation:
University of Windsor, Psychology Department, Windsor, Ontario
*
Correspondence and reprint requests to: Ciaran M. Considine, Department Neurology, Vanderbilt University, 1500 21st Avenue South, Suite 3000, Nashville, TN, 37212. E-mail: [email protected]

Abstract

Objectives: Obstructive sleep apnea (OSA) is associated with cognitive impairment but the relationships between specific biomarkers and neurocognitive domains remain unclear. The present study examined the influence of common health comorbidities on these relationships. Adults with suspected OSA (N=60; 53% male; M age=52 years; SD=14) underwent neuropsychological evaluation before baseline polysomnography (PSG). Apneic syndrome severity, hypoxic strain, and sleep architecture disturbance were assessed through PSG. Methods: Depression (Center for Epidemiological Studies Depression Scale, CESD), pain, and medical comorbidity (Charlson Comorbidity Index) were measured via questionnaires. Processing speed, attention, vigilance, memory, executive functioning, and motor dexterity were evaluated with cognitive testing. A winnowing approach identified 9 potential moderation models comprised of a correlated PSG variable, comorbid health factor, and cognitive performance. Results: Regression analyses identified one significant moderation model: average blood oxygen saturation (AVO2) and depression predicting recall memory, accounting for 31% of the performance variance, p<.001. Depression was a significant predictor of recall memory, p<.001, but AVO2 was not a significant predictor. The interaction between depression and AVO2 was significant, accounting for an additional 10% of the variance, p<.001. The relationship between low AVO2 and low recall memory performance emerged when depression severity ratings approached a previously established clinical cutoff score (CESD=16). Conclusions: This study examined sleep biomarkers with specific neurocognitive functions among individuals with suspected OSA. Findings revealed that depression burden uniquely influence this pathophysiological relationship, which may aid clinical management. (JINS, 2018, 28, 864–875)

Type
Regular Research
Copyright
Copyright © The International Neuropsychological Society 2018 

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References

REFERENCES

Aickin, M., & Gensler, H. (1996). Adjusting for multiple testing when reporting research results: The Bonferroni vs Holm methods. American Journal of Public Health, 86(5), 726728.Google Scholar
Aloia, M.S., Arnedt, J.T., Davis, J.D., Riggs, R.L., & Byrd, D. (2004). Neuropsychological sequelae of obstructive sleep apnea-hypopnea syndrome: A critical review. Journal of the International Neuropsychological Society, 10, 772785.Google Scholar
American Academy of Sleep Medicine. (2005). The International Classification of Sleep Disorders (2nd ed.). Diagnostic and coding manual. Westchester, IL: American Academy of Sleep Medicine.Google Scholar
Arli, B., Bilen, S., Titiz, A.P., Ulusoy, E.K., Mungan, S., Gurkas, E., & Ak, F. (2015). Comparison of cognitive functions between obstructive sleep apnea syndrome and simple snoring patients: OSAS may be a modifiable risk factor for cognitive decline. Applied Neuropsychology: Adult, 22(4), 282286.Google Scholar
Ayalon, L., & Peterson, S. (2007). Functional central nervous system imaging in the investigation of obstructive sleep apnea. Current Opinion in Pulmonary Medicine, 13(6), 479483.Google Scholar
Beebe, D.W., & Gozal, D. (2002). Obstructive sleep apnea and the prefrontal cortex: Towards a comprehensive model linking nocturnal upper airway obstruction to daytime cognitive and behavioral deficits. Journal of Sleep Research, 11(1), 116.Google Scholar
Bucks, R., Olaithe, M., & Marshall, M. (2011). Self-reported memory function in a community sample: More sleep difficulties lead to lower memory ability (self-appraisal of one’s memory capabilities). Journal of Sleep Research, 20, 50.Google Scholar
Bucks, R.S., Olaithe, M., & Eastwood, P. (2013). Neurocognitive function in obstructive sleep apnea: A meta-review. Respirology, 18, 6170.Google Scholar
Cha, J., Zea-Hernandez, J.A., Sin, S., Graw-Panzer, K., Shifteh, K., Isasi, C.R., & Arens, R. (2017). The effects of obstructive sleep apnea syndrome on the dentate gyrus and learning and memory in children. Journal of Neuroscience, 37(16), 42804288.Google Scholar
Charlson, M.E., Pompei, P., Ales, K.L., & MacKenzie, C.R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases, 40(5), 373383.Google Scholar
Charlson, M., Szatrowski, T.P., Peterson, J., & Gold, J. (1994). Validation of a combined comorbidity index. Journal of Clinical Epidemiology, 47(11), 12451251.Google Scholar
Crawford, M.R., Espie, C.A., Bartlett, D.J., & Grunstein, R.R. (2014). Integrating psychology and medicine in CPAP adherence - New concepts? Sleep Medicine Reviews, 18(2), 123139.Google Scholar
Delis, D.C., Kramer, J.H., Kaplan, E., & Ober, B.A. (2000). CVLT-II. New York: The Psychological Corporation.Google Scholar
Dorrian, J., Rogers, N.L., & Dinges, D.F. (2005). Psychomotor vigilance performance: Neurocognitive assay sensitive to sleep loss. Sleep deprivation: Clinical Issues, Pharmacology and Sleep Loss Effects (pp. 3970). New York: Marcel Dekker, Inc.Google Scholar
Engleman, H.M., & Douglas, N.J. (2004). Sleep· 4: Sleepiness, cognitive function, and quality of life in obstructive sleep apnoea/hypopnoea syndrome. Thorax, 59(7), 618622.Google Scholar
Epstein, L.J., Kristo, D., Strollo, P.J., Friedman, N., Malhotra, A., Patil, S.P., & Weinstein, M.D. (2009). Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults. Journal of Clinical Sleep Medicine, 5(3), 263276.Google Scholar
Finan, P.H., Goodin, B.R., & Smith, M.T. (2013). The association of sleep and pain: An update and a path forward. The Journal of Pain, 14(12), 15391552.Google Scholar
Fulda, S., & Schulz, H. (2003). Cognitive dysfunction in sleep-related breathing disorders: A meta-analysis. Sleep Research Online, 5(1), 1951.Google Scholar
Gibson, G.J. (2005). Obstructive sleep apnoea syndrome: Underestimated and undertreated. British Medical Bulletin, 72, 4965.Google Scholar
Golden, C.J. (1978). Stroop Color and Word Test: Cat. No. 30150M; A Manual for Clinical and Experimental Uses. Chicago, IL: Stoelting.Google Scholar
Gupta, M.A., & Simpson, F.C. (2015). Obstructive sleep apnea and psychiatric disorders: A systematic review. Journal of Clinical Sleep Medicine, 11(2), 165175.Google Scholar
Hayes, A.F. (2017). Introduction to mediation, moderation, and conditional process analysis, second edition: A regression-based approach. New York: Guilford Publications.Google Scholar
Hesselbacher, S., Subramanian, S., Allen, J., Surani, S., & Surani, S. (2012). Body mass index, gender, and ethnic variations alter the clinical implications of the Epworth Sleepiness Scale in patients with suspected obstructive sleep apnea. The Open Respiratory Medicine Journal, 6, 2027.Google Scholar
Hoth, K.F., Zimmerman, M.E., Meschede, K.A., Arnedt, J.T., & Aloia, M.S. (2013). Obstructive sleep apnea: Impact of hypoxemia on memory. Sleep and Breathing, 17, 811817.Google Scholar
Iber, C. (2007). The AASM manual for the scoring of sleep and associated events: Rules, terminology and technical specifications. Darien, IL: American Academy of Sleep Medicine.Google Scholar
Insel, T.R., & Cuthbert, B.N. (2009). Endophenotypes: Bridging genomic complexity and disorder heterogeneity. Biological Psychiatry, 66(11), 988989.Google Scholar
Kerner, N.A., & Roose, S.P. (2016). Obstructive sleep apnea is linked to depression and cognitive impairment: Evidence and potential mechanisms. The American Journal of Geriatric Psychiatry, 24(6), 496508.Google Scholar
Kilpinen, R., Saunamaki, T., & Jehkonen, M. (2014). Information processing speed in obstructive sleep apnea syndrome: A review. Acta Neurologica Scandinavica, 129, 209218.Google Scholar
Lee, W., Nagubadi, S., Kryger, M.H., & Mokhlesi, B. (2008). Epidemiology of obstructive sleep apnea: A population-based perspective. Expert Review of Respiratory Medicine, 2(3), 349364.Google Scholar
Levine, C.G., & Weaver, E.M. (2014). Functional comorbidity index in sleep apnea. Otolaryngology–Head and Neck Surgery, 150(3), 494500.Google Scholar
Lewis, R.F. (1995). Digit vigilance test. Lutz, FL: Psychological Assessment Resources.Google Scholar
Lewinsohn, P.M., Seeley, J.R., Roberts, R.E., & Allen, N.B. (1997). Center for Epidemiological Studies-Depression Scale (CES-D) as a screening instrument for depression among community-residing older adults. Psychology and Aging, 12, 277287.Google Scholar
Matthews, C.G., & Klove, H. (1964). Instruction manual for the adult neuropsychology test battery. Madison, WI: University of Wisconsin Medical School.Google Scholar
Monteleone, P., & Maj, M. (2008). The circadian basis of mood disorders: Recent developments and treatment implications. European Neuropsychopharmacology, 18(10), 701711.Google Scholar
Naismith, S., Winter, V., Gotsopoulos, H., Hickie, I., & Cistulli, P. (2004). Neurobehavioral functioning in obstructive sleep apnea: Differential effects of sleep quality, hypoxemia, and subjective sleepiness. Journal of Clinical and Experimental Neuropsychology, 26(1), 4354.Google Scholar
Peppard, P.E., Szklo-Coxe, M., Hla, K.M., & Young, T. (2006). Longitudinal association of sleep-related breathing disorder and depression. Archives of Internal Medicine, 166(16), 17091715.Google Scholar
Punjabi, N.M. (2008). The epidemiology of adult obstructive sleep apnea. Proceedings of the American Thoracic Society, 5(2), 136143.Google Scholar
Quan, S.F., Chan, C.S., Dement, W.C., Gevins, A., Goodwin, J.L., Gootlieb, D.J., & Kushida, C.A. (2011). The association between obstructive sleep apnea and neurocognitive performance-the apnea positive pressure long-term efficacy study (APPLES). Sleep, 34(3), 303B314B.Google Scholar
Quan, S.F., Wright, R., Baldwin, C.M., Kaemingk, K.L., Goodwin, J.L., Kuo, T.F., & Bootzin, R.R. (2006). Obstructive sleep apnea-hypopnea and neurocognitive functioning in the Sleep Heart Health Study. Sleep Medicine, 7, 498507.Google Scholar
Radloff, L.S. (1977). The CES-D scale A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385401.Google Scholar
Ralls, F.M., & Grigg-Damberger, M. (2012). Roles of gender, age, race/ethnicity, and residential socioeconomics in obstructive sleep apnea syndromes. Current Opinion in Pulmonary Medicine, 18(6), 568573.Google Scholar
Reid, M.C., Fiellin, D.A., & O’Connor, P.G. (1999). Hazardous and harmful alcohol consumption in primary care. Archives of Internal Medicine, 159(15), 16811689.Google Scholar
Reiten, R.M. (1955). The relation of trail making test to organic brain damage. Journal of Consulting Psychology, 10, 7688.Google Scholar
Rosen, W. G. (1980). Verbal fluency in aging and dementia. Journal of Clinical and Experimental Neuropsychology, 2(2), 135–146. Google Scholar
Santarnecchi, E., Sicilia, I., Richiardi, J., Vatti, G., Polizzotto, N.R., Marino, D., & Rossi, A. (2013). Altered cortical and subcortical local coherence in obstructive sleep apnea: A functional magnetic resonance imaging study. Journal of Sleep Research, 22(3), 337347.Google Scholar
Smith, A. (1982). Symbol Digit Modality Test Manual. Los Angeles: Western Psychological Services.Google Scholar
Spreen, O., & Benton, A.L. (1977). Neurosensory Center Comprehensive Examination for Aphasia (NCCEA), 1977 Revision: Manual of Instructions. Victoria, BC: Neuropsychology Laboratory, University of Victoria.Google Scholar
Sugarman, M.A., & Axelrod, B.N. (2015). Embedded measures of performance validity using verbal fluency tests in a clinical sample. Applied Neuropsychology: Adult, 22(2), 141146.Google Scholar
Swartz, R.H., Cayley, M.L., Lanctôt, K.L., Murray, B.J., Cohen, A., Thorpe, K.E., & Herrmann, N. (2017). The “DOC” screen: Feasible and valid screening for depression, Obstructive Sleep Apnea (OSA) and cognitive impairment in stroke prevention clinics. PLoS One, 12(4), e0174451.Google Scholar
Torelli, F., Moscufo, N., Garreffa, G., Placidi, F., Romigi, A., Zannino, S., & Malhotra, A. (2011). Cognitive profile and brain morphological changes in obstructive sleep apnea. Neuroimage, 54(2), 787793.Google Scholar
Waters, F., & Bucks, R.S. (2011). Neuropsychological effects of sleep loss: Implication for neuropsychologists. Journal of the International Neuropsychological Society, 17(04), 571586.Google Scholar
Wechsler, D. (2008). Wechsler adult intelligence scale: Fourth edition (WAIS–IV) [Assessment instrument] . San Antonio, TX: Pearson.Google Scholar
Wolfe, P.L., Millis, S.R., Hanks, R., Fichtenberg, N., Larrabee, G.J., & Sweet, J.J. (2010). Effort indicators within the California verbal learning test-II (CVLT-II). The Clinical Neuropsychologist, 24(1), 153168.Google Scholar
Yaffe, K., Laffan, A.M., Harrison, S.L., Redline, S., Spira, A.P., Ensrud, K.E., & Stone, K.L. (2011). Sleep-disordered breather, hypoxia, and risk of mild cognitive impairment and dementia in older women. Journal of the American Medical Association, 306(6), 613619. doi:10.1001/jama.2011.1115 Google Scholar
Yao, Z., Wang, L., Lu, Q., Liu, H., & Teng, G. (2009). Regional homogeneity in depression and its relationship with separate depressive symptom clusters: A resting-state fMRI study. Journal of Affective Disorders, 115(3), 430438.Google Scholar
Young, T., Palta, M., Dempsey, J., Skatrud, J., Weber, S., & Badr., S. (1993). The occurrence of sleep-disordered breathing among middle-aged adults. New England Journal of Medicine, 328, 12301235.Google Scholar