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Original research
Multiple, objectively measured sleep dimensions including hypoxic burden and chronic kidney disease: findings from the Multi-Ethnic Study of Atherosclerosis
  1. Chandra L Jackson1,2,
  2. Chizoba Umesi1,3,
  3. Symielle A Gaston1,
  4. Ali Azarbarzin4,5,
  5. Joseph Lunyera6,
  6. John A McGrath7,
  7. W Braxton Jackson II7,
  8. Clarissa J Diamantidis3,6,
  9. Ebony Boulware3,6,
  10. Pamela L Lutsey8,
  11. Susan Redline4,5,9
  1. 1Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
  2. 2Intramural Program, National Institute on Minority Health and Health Disparities, Bethesda, Maryland, USA
  3. 3Duke University School of Medicine, Durham, North Carolina, USA
  4. 4Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
  5. 5Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts, USA
  6. 6Division of General Internal Medicine, Duke University School of Medicine, Durham, North Carolina, USA
  7. 7Social & Scientific Systems Inc, Durham, North Carolina, USA
  8. 8School of Public Health, Division of Epidemiology and Community Health, University of Minnesota Twin Cities, Minneapolis, Minnesota, USA
  9. 9Department of Medicine, Division of Pulmonary Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
  1. Correspondence to Dr Chandra L Jackson, Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA; Chandra.Jackson{at}


Background Poor sleep may contribute to chronic kidney disease (CKD) through several pathways, including hypoxia-induced systemic and intraglomerular pressure, inflammation, oxidative stress and endothelial dysfunction. However, few studies have investigated the association between multiple objectively measured sleep dimensions and CKD.

Methods We investigated the cross-sectional association between sleep dimensions and CKD among 1895 Multi-Ethnic Study of Atherosclerosis Sleep Ancillary Study participants who completed in-home polysomnography, wrist actigraphy and a sleep questionnaire. Using Poisson regression models with robust variance, we estimated separate prevalence ratios (PR) and 95% CIs for moderate-to-severe CKD (glomerular filtration rate <60 mL/min/1.73 m2 or albuminuria >30 mg/g) among participants according to multiple sleep dimensions, including very short (≤5 hours) sleep, Apnoea−Hypopnoea Index and sleep apnoea-specific hypoxic burden (SASHB) (total area under the respiratory event-related desaturation curve divided by total sleep duration, %min/hour)). Regression models were adjusted for sociodemographic characteristics, health behaviours and clinical characteristics.

Results Of the 1895 participants, mean age was 68.2±9.1 years, 54% were women, 37% were white, 28% black, 24% Hispanic/Latino and 11% Asian. Several sleep metrics were associated with higher adjusted PR of moderate-to-severe CKD: very short versus recommended sleep duration (PR=1.40, 95% CI 1.06 to 1.83); SASHB (Box-Cox transformed SASHB: PR=1.06, 95% CI 1.02 to 1.12); and for participants in the highest quintile of SASHB plus sleep apnoea: PR=1.28, 95% CI 1.01 to 1.63.

Conclusions Sleep apnoea associated hypoxia and very short sleep, likely representing independent biological mechanisms, were associated with a higher moderate-to-severe CKD prevalence, which highlights the potential role for novel interventions.

  • sleep apnoea
  • oxidative stress

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  • Presented at This research was presented, in part, at the World Sleep Congress in Vancouver, Canada on September 20–25, 2019.

  • Contributors Study concept: CLJ. Study design: CLJ and SR. Acquisition of data: SR. Statistical analysis: JAM, WBJ and AA. Interpretation of data: CLJ, CU, SAG, AA, JL, JAM, WBJ, EB, PLL and SR. Drafting of the manuscript: CLJ and CU. Critical revision of the manuscript for important intellectual content: CLJ, CU, SAG, AA, JL, JAM, WBJ, EB, PLL and SR. Quality assurance and control: CLJ and SR. Administrative, technical and material support: CLJ and SR. Obtaining funding: CLJ and SR. Study supervision: SR. Final approval: CLJ, CU, SAG, AA, JL, JAM, WBJ, EB, PLL and SR.

  • Funding This work was funded, in part, by the Intramural Program at the National Institutes of Health (NIH), National Institute of Environmental Health Sciences (NIEHS, Z1AES103325-01). Funding support for Chizoba Umesi was provided by NIEHS Medical Student Research Fellowship. MESA is conducted and supported by the National Heart, Lung and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001881 and DK06349. Funding support for the Sleep Polysomnography dataset was provided by grant HL56984. A full list of participating MESA investigators and institutions can be found at SR is partly funded through the National Heart, Lung and Blood Institute (1R35 HL135818-01).

  • Disclaimer The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval Institutional Review Board approval was obtained at each study site. The National Institute of Environmental Health Sciences’ Institutional Review Board waived approval for publicly available, secondary data with no identifiable information.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement All data relevant to the study are included in the article or uploaded as online supplemental information. No additional data are available.

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