Towards an understanding of disturbed sleep phenotypes after traumatic spinal cord injury

Authors

  • Letitia Y. Graves-Dixon School of Nursing, University of Texas Medical Branch, Galveston, TX, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA https://orcid.org/0000-0002-6443-0566
  • Anna May Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA; Case Western Reserve University, Cleveland, OH, USA
  • Susan Redline Harvard University, Cambridge, MA, USA
  • Zixiang Xu George Mason University, Fairfax County, VA, USA
  • Jiayang Sun George Mason University, Fairfax County, VA, USA
  • Adam R. Ferguson University of California San Francisco, San Francisco, CA, USA; San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
  • Kath M. Bogie Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA; Case Western Reserve University, Cleveland, OH, USA

DOI:

https://doi.org/10.2340/jrm.v58.44651

Keywords:

heart disease, metabolic disease, sleep hygiene, spinal cord injuries, Veterans’ health

Abstract

Objective: Examine the Spinal Cord Injury-Pressure Injury Resource (SCI-PIR) database to assess the prevalence and identify relationships among sleep disorders and cardiometabolic risk after spinal cord injury.

Design: Retrospective observational cohort study using the Department of Veterans Affair SCI-PIR database.

Subjects/Patients: 18,894 Veterans living with spinal cord injury.

Methods: The SCI-PIR database was queried for ICD9 codes related to cardiovascular, metabolic, psychological, and sleep conditions to identify subgroups of spinal cord injury individuals with sleep disorders and associated clustering of cardiometabolic risk factors and sleep diagnoses. Multiple correspondence analysis probed the underlying associations. Cramer V statistics confirmed and quantified the associations.

Results: Sleep apnoea (6.7%) and insomnia (4.3%) were the most common sleep diagnoses. Multiple correspondence analysis demonstrated 2 phenotypic clusters: Cluster A showed robust links between sleep apnoea, hypersomnia, heart failure, and arrhythmias, and secondary associations with coronary artery disease, chronic kidney disease, obesity, diabetes, and hyperlipidaemia. Cluster B showed strong relationships between insomnia, anxiety, and post-traumatic stress disorder.

Conclusion: 2 distinct sleep clusters were identified for persons with spinal cord injury. This analysis supports previous findings that sleep disorders associate with overall health in individuals with spinal cord injury, and particularly cardiovascular health. ICD9 coding may under-report sleep diagnoses. Data-driven statistical analysis can uncover insights into the complex interplay between spinal cord injury and secondary health conditions.

Downloads

Download data is not yet available.

References

Wang T, Yi T, Chen T, Khan NU, Yuan Y. Spinal cord injury 2.0: bridging the gap between neurobiology, technology, and hope in the era of precision medicine. Stem Cell Rev Rep 2025; 21: 2597–2615.

https://doi.org/10.1007/s12015-025-10966-w DOI: https://doi.org/10.1007/s12015-025-10966-w

Noller CM, Groah SL, Nash MS. Inflammatory stress effects on health and function after spinal cord injury. Top Spinal Cord Inj Rehabil 2017; 23: 207–217.

https://doi.org/10.1310/sci2303-207 DOI: https://doi.org/10.1310/sci2303-207

Wecht JM, Harel NY, Guest J, Kirshblum SC, Forrest GF, Bloom O, et al. Cardiovascular autonomic dysfunction in spinal cord injury: epidemiology, diagnosis, and management. Semin Neurol 2020; 40: 550–559.

https://doi.org/10.1055/s-0040-1713885 DOI: https://doi.org/10.1055/s-0040-1713885

Fallah N, Hong HA, Wang D, Humphreys S, Parsons J, Walden K, et al. Network analysis of multimorbidity and health outcomes among persons with spinal cord injury in Canada. Front Neurol 2024; 14: 1286143.

https://doi.org/10.3389/fneur.2023.1286143 DOI: https://doi.org/10.3389/fneur.2023.1286143

Sankari A, Martin JL, Badr MS. Sleep-disordered breathing and spinal cord injury: challenges and opportunities. Curr Sleep Med Rep 2017; 3: 272–278.

https://doi.org/10.1007/s40675-017-0093-0 DOI: https://doi.org/10.1007/s40675-017-0093-0

Hulten VDT, Biering-Sørensen F, Jørgensen NR, Jennum PJ. A review of sleep research in patients with spinal cord injury. J Spinal Cord Med 2020; 43: 775–796.

https://doi.org/10.1080/10790268.2018.1543925 DOI: https://doi.org/10.1080/10790268.2018.1543925

Cormier RE. Sleep disturbances. In: Walker HK, Hall WD, Hurst JW, eds. Clinical Methods: The History, Physical, and Laboratory Examinations. 3rd ed. Butterworths; 1990.

Forbush TB, Gundlapalli AV, Palmer MN, Shen S, South BR, Divita G, et al. “Sitting on pins and needles”: characterization of symptom descriptions in clinical notes. AMIA Jt Summits Transl Sci Proc 2013; 2013: 67–71.

Bogie KM, Roggenkamp SK, Zeng N, Seton JM, Schwartz KR, Henzel MK, et al. Development of predictive informatics tool using electronic health records to inform personalized evidence-based pressure injury management for veterans with spinal cord injury. Mil Med 2021; 186: 651–658.

https://doi.org/10.1093/milmed/usaa469 DOI: https://doi.org/10.1093/milmed/usaa469

Bogie K, Henzel M, Richmond MA, Alvarado N. Tissue health biomarkers to predict highest risk individuals for pressure injury recurrence. Arch Phys Med Rehabil 2018; 99: e13.

https://doi.org/10.1016/j.apmr.2018.07.043 DOI: https://doi.org/10.1016/j.apmr.2018.07.043

Everitt BS, Dunn G. Applied Multivariate Data Analysis. 2nd ed. Wiley; 2010.

Korostovtseva L, Bochkarev M, Sviryaev Y. Sleep and cardiovascular risk. Sleep Med Clin 2021; 16: 485–497.

https://doi.org/10.1016/j.jsmc.2021.05.001 DOI: https://doi.org/10.1016/j.jsmc.2021.05.001

Grimaldi D, Reid KJ, Papalambros NA, Braun RI, Malkani RG, Abbott SM, et al. Autonomic dysregulation and sleep homeostasis in insomnia. Sleep 2021; 44: zsaa274.

https://doi.org/10.1093/sleep/zsaa274 DOI: https://doi.org/10.1093/sleep/zsaa274

Li X, Sotres-Alvarez D, Gallo LC, Ramos AR, Aviles-Santa L, Perreira KM, et al. Associations of sleep-disordered breathing and insomnia with incident hypertension and diabetes: The Hispanic Community Health Study/Study of Latinos. Am J Respir Crit Care Med 2021; 203: 356–365.

https://doi.org/10.1164/rccm.201912-2330OC DOI: https://doi.org/10.1164/rccm.201912-2330OC

American Diabetes Association. Standards of medical care in diabetes—2019. Diabetes Care 2019; 42: S1–S193. DOI: https://doi.org/10.2337/dc19-Sint01

https://doi.org/10.2337/dc19-S002 DOI: https://doi.org/10.2337/dc19-S002

Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, et al 2018 ESC/ESH guidelines for the management of arterial hypertension. Eur Heart J 2018; 39: 3021–3104. DOI: https://doi.org/10.1093/eurheartj/ehy439

https://doi.org/10.1093/eurheartj/ehy339 DOI: https://doi.org/10.1093/eurheartj/ehy339

Graco M, McDonald L, Green SE, Jackson ML, Berlowitz DJ. Prevalence of sleep-disordered breathing in people with tetraplegia: a systematic review and meta-analysis. Spinal Cord 2021; 59: 474–484.

https://doi.org/10.1038/s41393-020-00595-0 DOI: https://doi.org/10.1038/s41393-020-00595-0

Furlan JC. Effects of sleep apnea on cardiovascular dysfunction in individuals living with chronic spinal cord injury. Paralyzed Veterans of America Health Summit; Anaheim, CA.

Crawford MR, Chirinos DA, Iurcotta T, Edinger JD, Wyatt JK, Manber R, et al. Characterization of patients who present with insomnia: Is there room for a symptom cluster–based approach? J Clin Sleep Med 2017; 13: 911–921.

https://doi.org/10.5664/jcsm.6666 DOI: https://doi.org/10.5664/jcsm.6666

Kim HJ, Barsevick AM, Fang CY, Miaskowski C. Common biological pathways underlying the psychoneurological symptom cluster in cancer patients. Cancer Nurs 2012; 35: E1–E20.

https://doi.org/10.1097/NCC.0b013e318233a811 DOI: https://doi.org/10.1097/NCC.0b013e318233a811

Kelly MR, Zeineddine S, Mitchell MN, Sankari A, Pandya N, Carroll S, et al. Insomnia severity predicts depression, anxiety, and posttraumatic stress disorder in veterans with spinal cord injury or disease: a cross-sectional observational study. J Clin Sleep Med 2023; 19: 695–701.

https://doi.org/10.5664/jcsm.10410 DOI: https://doi.org/10.5664/jcsm.10410

Riemann D, Krone LB, Wulff K, Nissen C. Sleep, insomnia, and depression. Neuropsychopharmacology 2020; 45: 74–89.

https://doi.org/10.1038/s41386-019-0411-y DOI: https://doi.org/10.1038/s41386-019-0411-y

Miaskowski C, Aouizerat BE. Is there a biological basis for the clustering of symptoms? Semin Oncol Nurs 2007; 23: 99–105.

https://doi.org/10.1016/j.soncn.2007.01.008 DOI: https://doi.org/10.1016/j.soncn.2007.01.008

Barsevick A. Defining the symptom cluster: how far have we come? Semin Oncol Nurs 2016; 32: 334–350.

https://doi.org/10.1016/j.soncn.2016.08.001 DOI: https://doi.org/10.1016/j.soncn.2016.08.001

Lynch Kelly D, Dickinson K, Hsiao CP, Lukkahatai N, Gonzalez-Marrero V, McCabe M, et al. Biological basis for the clustering of symptoms. Semin Oncol Nurs 2016; 32: 351–360.

https://doi.org/10.1016/j.soncn.2016.08.002 DOI: https://doi.org/10.1016/j.soncn.2016.08.002

Strauss MJ, Niederkrotenthaler T, Thurner S, Kautzky-Willer A, Klimek P. Data-driven identification of complex disease phenotypes. J R Soc Interface 2021; 18: 20201040.

https://doi.org/10.1098/rsif.2020.1040 DOI: https://doi.org/10.1098/rsif.2020.1040

Pugh MJ, Kennedy E, Prager EM, Humpherys J, Dams-O’Connor K, Hack D, et al. Phenotyping the spectrum of traumatic brain injury: a review and pathway to standardization. J Neurotrauma 2021; 38: 3222–3234.

https://doi.org/10.1089/neu.2021.0059 DOI: https://doi.org/10.1089/neu.2021.0059

Khan O, Badhiwala JH, Wilson JRF, Jiang F, Martin AR, Fehlings MG. Predictive modeling of outcomes after traumatic and nontraumatic spinal cord injury using machine learning: review of current progress and future directions. Neurospine 2019; 16: 678–685.

https://doi.org/10.14245/ns.1938390.195 DOI: https://doi.org/10.14245/ns.1938390.195

Weiskopf NG, Hripcsak G, Swaminathan S, Weng C. Defining and measuring completeness of electronic health records for secondary use. J Biomed Inform 2013; 46: 830–836.

https://doi.org/10.1016/j.jbi.2013.06.010 DOI: https://doi.org/10.1016/j.jbi.2013.06.010

Koleck TA, Dreisbach C, Bourne PE, Bakken S. Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review. J Am Med Inform Assoc 2019; 26: 364–379.

https://doi.org/10.1093/jamia/ocy173 DOI: https://doi.org/10.1093/jamia/ocy173

He T, Belouali A, Patricoski J, Lehmann H, Ball R, Anagnostou V, et al. Trends and opportunities in computable clinical phenotyping: a scoping review. J Biomed Inform 2023; 140: 104335.

https://doi.org/10.1016/j.jbi.2023.104335 DOI: https://doi.org/10.1016/j.jbi.2023.104335

Published

2026-03-17

How to Cite

Graves-Dixon, L. Y., May, A., Redline, S., Xu, Z., Sun, J., Ferguson, A. R., & Bogie, K. M. (2026). Towards an understanding of disturbed sleep phenotypes after traumatic spinal cord injury. Journal of Rehabilitation Medicine, 58, jrm44651. https://doi.org/10.2340/jrm.v58.44651

Issue

Section

Original Report

Categories