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Yarosh RA, Jackson CL, Anderson C, Nichols HB, Sandler DP. Sleep disturbances among cancer survivors. Cancer Epidemiol 2023; 87:102471. [PMID: 37837808 PMCID: PMC10873004 DOI: 10.1016/j.canep.2023.102471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 10/07/2023] [Accepted: 10/10/2023] [Indexed: 10/16/2023]
Abstract
PURPOSE We investigated sleep disturbances among cancer survivors compared to similarly aged women without cancer history. METHODS We identified 2067 women with a history of cancer other than breast or non-melanoma skin cancer at enrollment in the Sister Study, a US-wide cohort of women with a family history of breast cancer. Cancer survivors were matched with up to 5 cancer-free women (N = 9717) on age at enrollment. An index age (for covariate classification) was defined as the age at cancer diagnosis for survivors and the same age for their matched comparators. Sleep disturbances included duration, sleep medication usage, insomnia symptoms, long sleep-latency onset (≥30 min to fall asleep), frequent night awakenings (waking ≥3/night, ≥ 3 times/week), frequent napping (≥ 3 times/week), and a composite outcome of ≥ 1sleep disturbance. Multivariable linear regression (effect estimate, 95% confidence interval (CI)) and logistic regression (odds ratio, OR, 95% CI) were used for continuous and dichotomous outcomes, respectively. RESULTS At enrollment, cancer survivors were on average 13.8 years (range=0, 62) from diagnosis. After adjustment for age at enrollment and depression, diabetes, hypertension, and menopausal status prior to the index age, sleep disturbances were generally not more common among cancer survivors compared to those without cancer. However, among cancer survivors, those > 2 years from diagnosis were more likely to report ≥ 1 sleep disturbance (OR=1.44; 1.07, 1.93) compared to survivors 0-2 years from diagnosis. CONCLUSION Addressing sleep disturbances may improve well-being for cancer survivors.
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Affiliation(s)
- Rina A Yarosh
- Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC, USA.
| | - Chandra L Jackson
- Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA; Intramural Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.
| | - Chelsea Anderson
- Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC, USA.
| | - Hazel B Nichols
- Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC, USA.
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA.
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Avery CL, Howard AG, Lee HH, Downie CG, Lee MP, Koenigsberg SH, Ballou AF, Preuss MH, Raffield LM, Yarosh RA, North KE, Gordon-Larsen P, Graff M. Branched chain amino acids harbor distinct and often opposing effects on health and disease. Commun Med (Lond) 2023; 3:172. [PMID: 38017291 PMCID: PMC10684599 DOI: 10.1038/s43856-023-00382-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 10/10/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND The branched chain amino acids (BCAA) leucine, isoleucine, and valine are essential nutrients that have been associated with diabetes, cancers, and cardiovascular diseases. Observational studies suggest that BCAAs exert homogeneous phenotypic effects, but these findings are inconsistent with results from experimental human and animal studies. METHODS Hypothesizing that inconsistencies between observational and experimental BCAA studies reflect bias from shared lifestyle and genetic factors in observational studies, we used data from the UK Biobank and applied multivariable Mendelian randomization causal inference methods designed to address these biases. RESULTS In n = 97,469 participants of European ancestry (mean age = 56.7 years; 54.1% female), we estimate distinct and often opposing total causal effects for each BCAA. For example, of the 117 phenotypes with evidence of a statistically significant total causal effect for at least one BCAA, almost half (44%, n = 52) are associated with only one BCAA. These 52 associations include total causal effects of valine on diabetic eye disease [odds ratio = 1.51, 95% confidence interval (CI) = 1.31, 1.76], valine on albuminuria (odds ratio = 1.14, 95% CI = 1.08, 1.20), and isoleucine on angina (odds ratio = 1.17, 95% CI = 1.31, 1.76). CONCLUSIONS Our results suggest that the observational literature provides a flawed picture of BCAA phenotypic effects that is inconsistent with experimental studies and could mislead efforts developing novel therapeutics. More broadly, these findings motivate the development and application of causal inference approaches that enable 'omics studies conducted in observational settings to account for the biasing effects of shared genetic and lifestyle factors.
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Affiliation(s)
- Christy L Avery
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA.
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA.
| | - Annie Green Howard
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Harold H Lee
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Carolina G Downie
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Moa P Lee
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Sarah H Koenigsberg
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Anna F Ballou
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Michael H Preuss
- The Charles Bronfman Institute for Personalized Medicine, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Rina A Yarosh
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Penny Gordon-Larsen
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
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