Sleep health, diseases, and pain syndromes: findings from an electronic health record biobank

Dashti, Hassan S.; Cade, Brian E.; Stutaite, Gerda; Saxena, Richa; Redline, Susan; Karlson, Elizabeth W.

doi:10.1093/sleep/zsaa189

Cited by 26 publications

(31 citation statements)

References 62 publications

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“…Like other commonly used dietary assessment tools, such as 24-h dietary recalls and FFQs, recall questions rely extensively on memory and therefore are prone to random error. And like other time-based survey questions, responses are susceptible to reporting and recall biases, imprecision due to time rounding (e.g., 07:00 instead of 07:04), and other response errors resulting from military time misreporting ( 28 ). In addition, we did not collect information on the timing of the end of each eating occasion in the food records.…”

Section: Discussionmentioning

confidence: 99%

How Accurately Can We Recall the Timing of Food Intake? A Comparison of Food Times from Recall-Based Survey Questions and Daily Food Records

Gioia

Guirette

Chen

et al. 2022

Current Developments in Nutrition

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Background There currently are no standard, low-cost, and validated methods to assess the timing of food intake. Methods The concordance between recall based survey questions and food times estimated from multiple daily food records in 249 generally healthy, free-living adults from the SHIFT Study (ClinicalTrials.gov #NCT02997319) was assessed. At baseline, participants were asked: “At what time do you first start and stop eating on weekdays/workdays and weekends/non-workdays?” and “At what time do you have your main meal on weekdays/workdays and weekends/non-workdays?” Participants were then asked to complete up to 14 days of food records noting the start time of each eating occasion. The timing of the first, last, and main (largest % calories) eating occasions, and the midpoint of energy intake were determined from food records. Wilcoxon matched pairs signed rank and Kendall's coefficient of concordance were used to compare differences and determine agreements between the methods for 4 food timing parameters. Results Eating occasions on work and free days showed significant agreements between the two methods, except for the main eating occasion on free days. Significant agreements were generally modest and ranged from 0.16 (work days main eating occasion) to 0.45 (work days first eating occasion). Generally, times based on recall were later than those estimated from food records and the differences in estimated times were smaller on work days compared to free days and smaller for the first compared to the last eating occasion. Main eating occasions from food records alternated between lunch and dinner times, contributing to low concordance with recalled times. Conclusions Modest agreements were found between food times derived from simple, recall based survey questions and food times estimated from multiple daily food records. Single administration of these questions can effectively characterize the overall timing of eating occasions within a population for chrononutrition research purposes. Summary There currently are no standard and low-cost methods to assess the timing of food intake. This study validates simple, recall-based questions that can effectively characterize food timing in free-living populations. Trial Registration: Shift Work, Heredity, Insulin, and Food Timing (SHIFT) Study (ClinicalTrials.gov: # NCT02997319).

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Section: Discussionmentioning

confidence: 99%

How Accurately Can We Recall the Timing of Food Intake? A Comparison of Food Times from Recall-Based Survey Questions and Daily Food Records

Gioia

Guirette

Chen

et al. 2022

Current Developments in Nutrition

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“…The Mass General Brigham (MGB) Biobank (formerly Partners Biobank) is a hospital-based cohort study from the MGB healthcare network in Boston, MA with electronic health record (EHR), genetic, and lifestyle data [15,22,23]. The MGB Biobank includes data obtained from patients in several community-based primary care facilities and specialty tertiary care centers in Boston, MA [15,24]. The MGB network provides a wide range of healthcare services.…”

Section: Mass General Brigham Biobankmentioning

confidence: 99%

Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank

Dashti

Miranda

Cade

et al. 2022

BMC Med

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Background Genetic and lifestyle factors have considerable effects on obesity and related diseases, yet their effects in a clinical cohort are unknown. This study in a patient biobank examined associations of a BMI polygenic risk score (PRS), and its interactions with lifestyle risk factors, with clinically measured BMI and clinical phenotypes. Methods The Mass General Brigham (MGB) Biobank is a hospital-based cohort with electronic health record, genetic, and lifestyle data. A PRS for obesity was generated using 97 genetic variants for BMI. An obesity lifestyle risk index using survey responses to obesogenic lifestyle risk factors (alcohol, education, exercise, sleep, smoking, and shift work) was used to dichotomize the cohort into high and low obesogenic index based on the population median. Height and weight were measured at a clinical visit. Multivariable linear cross-sectional associations of the PRS with BMI and interactions with the obesity lifestyle risk index were conducted. In phenome-wide association analyses (PheWAS), similar logistic models were conducted for 675 disease outcomes derived from billing codes. Results Thirty-three thousand five hundred eleven patients were analyzed (53.1% female; age 60.0 years; BMI 28.3 kg/m2), of which 17,040 completed the lifestyle survey (57.5% female; age: 60.2; BMI: 28.1 (6.2) kg/m2). Each standard deviation increment in the PRS was associated with 0.83 kg/m2 unit increase in BMI (95% confidence interval (CI) =0.76, 0.90). There was an interaction between the obesity PRS and obesity lifestyle risk index on BMI. The difference in BMI between those with a high and low obesogenic index was 3.18 kg/m2 in patients in the highest decile of PRS, whereas that difference was only 1.55 kg/m2 in patients in the lowest decile of PRS. In PheWAS, the obesity PRS was associated with 40 diseases spanning endocrine/metabolic, circulatory, and 8 other disease groups. No interactions were evident between the PRS and the index on disease outcomes. Conclusions In this hospital-based clinical biobank, obesity risk conferred by common genetic variants was associated with elevated BMI and this risk was attenuated by a healthier patient lifestyle. Continued consideration of the role of lifestyle in the context of genetic predisposition in healthcare settings is necessary to quantify the extent to which modifiable lifestyle risk factors may moderate genetic predisposition and inform clinical action to achieve personalized medicine.

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“…When recent users were further categorized by frequency of use, moderate users (<20 of the past 30 days) were more likely to have long sleep (aOR 1.47, 95% CI 1. 13…”

Section: Resultsmentioning

confidence: 99%

“…Both states can predispose to traumatic injuries, cardiovascular comorbidities, depression, cognitive deficits, and pain. [11][12][13] The extremes of sleep thus exhibit a 'U-shaped' relationship with morbidity and mortality at the individual and population levels. 17 29 This is thought to be related to pathological states of generalized systemic inflammation with chronic short sleep, long sleep, or other disturbances.…”

Section: Discussionmentioning

confidence: 99%

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Recent cannabis use and nightly sleep duration in adults: a population analysis of the NHANES from 2005 to 2018

Diep

Tian

Vachhani

et al. 2021

Reg Anesth Pain Med

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BackgroundWhile popularly consumed for its perceived benefits as a sleeping aid, the impact of cannabis on sleep-wake regulation in clinical studies is inconclusive. The purpose of this study was to determine the relationship between cannabis use and nightly sleep duration in a nationally representative dataset.MethodsA cross-sectional analysis of adults was undertaken using the National Health and Nutrition Examination Survey data from 2005 to 2018. Respondents were dichotomized as recent users or non-users if they had used or not used cannabis in the past 30 days, respectively. The primary outcome was nightly sleep duration, categorized as short (<6 hours), optimal (6–9 hours), and long (>9 hours). Multinomial logistic regression was used to adjust for sociodemographic and health-related covariates, and survey sample weights were used in modeling.ResultsFrom a sample representing approximately 146 million adults in the USA, 14.5% reported recent cannabis use. In an adjusted analysis, recent users were more likely than non-users to report both short sleep (OR 1.34, 95% CI 1.12 to 1.59, p<0.001) and long sleep (OR 1.56, 95% CI 1.25 to 1.96, p<0.001). Heavy users (≥20 of the past 30 days) were even more likely to be at the extremes of nightly sleep duration.DiscussionRecent cannabis use was associated with the extremes of nightly sleep duration in a nationally representative sample of adults, with suggestions of a dose–response relationship. Our findings highlight the need to further characterize the sleep health of regular cannabis users in the population.

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Sleep health, diseases, and pain syndromes: findings from an electronic health record biobank

Cited by 26 publications

References 62 publications

How Accurately Can We Recall the Timing of Food Intake? A Comparison of Food Times from Recall-Based Survey Questions and Daily Food Records

How Accurately Can We Recall the Timing of Food Intake? A Comparison of Food Times from Recall-Based Survey Questions and Daily Food Records

Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank

Recent cannabis use and nightly sleep duration in adults: a population analysis of the NHANES from 2005 to 2018

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