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      Association between Macronutrient Intake and Excessive Daytime Sleepiness: An Iso-Caloric Substitution Analysis from the North West Adelaide Health Study

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          Abstract

          Epidemiological evidence on the association between macronutrient intake and excessive daytime sleepiness (EDS) is scarce. Using data from the North West Adelaide Health Study, we aimed to determine the association between iso-caloric substitution of macronutrients and EDS. Data from 1997 adults aged ≥ 24 years were analyzed. Daytime sleepiness was measured using the Epworth Sleepiness Scale, a score ≥ 11 was considered EDS. Dietary intake data were collected using a food frequency questionnaire. We determined absolute and relative energy intake based on consumption of saturated and unsaturated fats, protein, and carbohydrate. Odds ratios (ORs) were used to determine the associations using log-binomial logistic regression with and without iso-caloric substitution methods, and models were adjusted for confounders. The prevalence of EDS in the sample was 10.6%. After adjusting for potential confounders, substituting 5% energy intake from protein with an equal amount of saturated fat (OR = 1.57; 95% CI: 1.00–2.45) and carbohydrate (OR = 1.23; 95% CI: 0.92–1.65) increased the odds of EDS. When carbohydrate was substituted with saturated fat (OR = 1.27; 95% CI: 0.93–1.59), the odds of EDS were increased. The odds of EDS were lower when saturated fat was substituted with unsaturated fat (OR = 0.74; 95% CI: 0.51–1.06), protein (OR = 0.63; 95% CI: 0.41–0.99) or carbohydrate (OR = 0.79; 95% CI: 0.57–1.08). While these results were consistent over different iso-caloric substitution methods, inconsistent results were found with standard regression. While substitution of fat and carbohydrate with protein was inversely associated with EDS, substitution of protein with fat and carbohydrate was positively associated with EDS. Randomized trials are needed to confirm if dietary interventions can be used to improve daytime alertness in those with EDS.

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          Reliability and factor analysis of the Epworth Sleepiness Scale.

          The Epworth Sleepiness Scale (ESS) is a self-administered eight-item questionnaire that has been proposed as a simple method for measuring daytime sleepiness in adults. This investigation was concerned with the reliability and internal consistency of the ESS. When 87 healthy medical students were tested and retested 5 months later, their paired ESS scores did not change significantly and were highly correlated (r = 0.82). By contrast, ESS scores that were initially high in 54 patients suffering from obstructive sleep apnea syndrome returned to more normal levels, as expected, after 3-9 months' treatment with nasal continuous positive airway pressure. The questionnaire had a high level of internal consistency as measured by Cronbach's alpha (0.88). Factor analysis of item scores showed that the ESS had only one factor for 104 medical students and for 150 patients with various sleep disorders. The ESS is a simple and reliable method for measuring persistent daytime sleepiness in adults.
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            Dietary fat and coronary heart disease: a comparison of approaches for adjusting for total energy intake and modeling repeated dietary measurements.

            Previous cohort studies of fat intake and risk of coronary heart disease (CHD) have been inconsistent, probably due in part to methodological differences and various limitations, including inadequate dietary assessment and incomplete adjustment for total energy intake. The authors analyzed repeated assessment of diet from the Nurses' Health Study to examine the associations between intakes of four major types of fat (saturated, monounsaturated, polyunsaturated, and trans fats) and risk of CHD during 14 years of follow-up (1980-1994) by using alternative methods for energy adjustment. In particular, the authors compared four risk models for energy adjustment: the standard multivariate model, the energy-partition model, the nutrient residual model, and the multivariate nutrient density model. Within each model, the authors compared four different approaches for analyzing repeated dietary measurements: baseline diet only, the most recent diet, and two different algorithms for calculating cumulative average diets. The substantive results were consistent across all models; that is, higher intakes of saturated and trans fats were associated with increased risk of CHD, while higher intakes of monounsaturated and polyunsaturated fats were associated with reduced risk. When nutrients were considered as continuous variables, the four energy-adjustment methods yielded similar associationS. However, the interpretation of the relative risks differed across models. In addition, within each model, the methods using the cumulative averages in general yielded stronger associations than did those using either only baseline diet or the most recent diet. When the nutrients were categorized according to quintiles, the residual and the nutrient density models, which gave similar results, yielded statistically more significant tests for linear trend than did the standard and the partition models.
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              The Anti Cancer Council of Victoria FFQ: relative validity of nutrient intakes compared with weighed food records in young to middle-aged women in a study of iron supplementation.

              To assess the validity of the Anti Cancer Council of Victoria food frequency questionnaire (ACCVFFQ) relative to seven-day weighed food records (WFRs) in 63 women of child-bearing age. 63 women completed WFRs to assess iron intake as part of a study on iron deficiency. These women also completed the ACCVFFQ. Nutrient intakes were computed independently for the WFRs and FFQs. Intakes were compared as group means, by correlation and by quintile classification, adjusting for day-to-day variation in intakes, and for energy intake. Individual differences in results were also examined. The strongest associations between WFR and FFQ results were energy-adjusted, log-transformed and adjusted for day-to-day variability in intake. Correlation coefficients ranged from 0.28 for vitamin A to 0.78 for carbohydrate. Mean intakes from the WFRs and FFQs were within +/- 20% for 21 of 27 nutrients. Poor agreement between FFQs and WFRs for retinol intake was due to the inclusion of liver in two WFRs, an item which is not included in the FFQ. The ACCVFFQ performs as well as other FFQs for which validation data are available. The relatively poor measurement of retinol is consistent with other data, and with the limited number of foods in which this nutrient is abundant. The availability of an optically scannable valid instrument for assessing dietary intake will facilitate epidemiological studies of diet and disease, an area of current research priority.
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                Author and article information

                Journal
                Nutrients
                Nutrients
                nutrients
                Nutrients
                MDPI
                2072-6643
                05 October 2019
                October 2019
                : 11
                : 10
                : 2374
                Affiliations
                [1 ]Adelaide Institute for Sleep Health, College of Medicine and Public Health, Flinders University, Bedford Park 5042, SA, Australia; sarah.appleton@ 123456flinders.edu.au (S.A.); robert.adams@ 123456flinders.edu.au (R.A.)
                [2 ]The Appleton Institute, CQ University Australia, Adelaide 5034, SA, Australia; a.reynolds@ 123456cqu.edu.au
                [3 ]School of Health, Medical and Applied Sciences, CQ University Australia Adelaide Campus, Adelaide 5034, SA, Australia
                [4 ]Adelaide Medical School, University of Adelaide, Adelaide 5005, SA, Australia; tiffany.gill@ 123456adelaide.edu.au
                [5 ]The Health Observatory, Discipline of Medicine, The Queen Elizabeth Hospital Campus, University of Adelaide, Woodville, SA 5011, Australia
                [6 ]Freemason’s Centre for Men’s Health, Discipline of Medicine, The University of Adelaide, Adelaide 5005, SA, Australia
                Author notes
                [* ]Correspondence: yohannes.melaku@ 123456flinders.edu.au ; Tel.: +61-4-5150-6630; Fax: +61-8-7421-9746
                Author information
                https://orcid.org/0000-0002-3051-7313
                https://orcid.org/0000-0001-9534-8699
                https://orcid.org/0000-0002-2822-2436
                https://orcid.org/0000-0001-7292-9714
                Article
                nutrients-11-02374
                10.3390/nu11102374
                6835535
                31590356
                d3935ffb-38e6-4f7d-bdf8-6af114dc8fd6
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 08 August 2019
                : 26 September 2019
                Categories
                Article

                Nutrition & Dietetics
                fat,saturated fat,unsaturated fat,carbohydrate,protein,substitution analysis,sleepiness,excessive daytime sleepiness

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