Advertisement

Eating Timing: Associations with Dietary Intake and Metabolic Health

Published:November 10, 2020DOI:https://doi.org/10.1016/j.jand.2020.10.001

      Abstract

      Background

      Emerging research indicates that eating timing may influence dietary intake and metabolic health. However, studies to date have not examined the association of multiple measures of eating timing with both dietary intake and metabolic health in adults with overweight and obesity.

      Objective

      To examine the association of multiple measures of eating timing with dietary intake (ie, dietary composition, diet quality, and eating frequency) and metabolic health (ie, body composition and cardiometabolic risk).

      Design

      This is a cross-sectional analysis of baseline data from a weight loss and maintenance intervention collected from May 2015 to January 2018.

      Participants/setting

      Participants were women with overweight or obesity who were dependents of active duty and retired military personnel (N = 229; mean ± standard error, BMI = 34.7 ± 0.4 kg/m2, age = 40.9 ± 0.7 years). The study was conducted at military installations in Massachusetts, Connecticut, New York, Colorado, and Kentucky.

      Main outcome measures

      Eating timing variables examined included daily eating interval (time between first and last eating occasion), time-restricted eating (≤11 hours daily eating interval), early energy eaters (eating ≥60% of energy during the first half of time awake), and bedtime eaters (eating within 2 hours of bedtime).

      Statistical analysis

      The main analysis was limited to those reporting plausible energy intake (64% of total sample [n = 146]). Linear, quantile, or logistic regression models were used to determine the association of eating timing with measures of dietary intake and metabolic health.

      Results

      In individuals reporting plausible energy intake, each additional 1 hour in daily eating interval was associated with 53 kcal higher energy intake, higher glycemic load, eating frequency, and waist circumference (P < 0.05 for all). Significant associations were observed for: time-restricted eating and a lower energy intake, glycemic load, and eating frequency; early energy eating and higher carbohydrate intake; bedtime eating and a higher energy intake, glycemic load, and eating frequency.

      Conclusions

      These findings lend support for the mechanistic targeting of eating timing in behavioral interventions aimed at improving dietary intake and body composition.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Journal of the Academy of Nutrition and Dietetics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • World Health Organization
        Obesity and overweight.
        (Published 2020. Accessed September 1, 2020)
        • Almoosawi S.
        • Winter J.
        • Prynne C.J.
        • Hardy R.
        • Stephen A.M.
        Daily profiles of energy and nutrient intakes: Are eating profiles changing over time?.
        Eur J Clin Nutr. 2012; 66: 678-686
        • Kant A.K.
        • Graubard B.I.
        40-year trends in meal and snack eating behaviors of American adults.
        J Acad Nutr Diet. 2015; 115: 50-63
        • Kant A.K.
        Eating patterns of US adults: Meals, snacks, and time of eating.
        Physiol Behav. 2018; 193: 270-278
        • Sanchez C.
        • Killgore W.
        • Gehrels J.
        • Alfonso-Miller P.
        • Grandner M.
        Nighttime dnacking: Prevalence and associations with poor sleep, health, obesity, and diabetes.
        Sleep. 2018; 41: A49-A50
        • Johnston J.D.
        • Ordovas J.M.
        • Scheer F.A.
        • Turek F.W.
        Circadian rhythms, metabolism, and chrononutrition in rodents and humans.
        Adv Nutr (Bethesda, Md). 2016; 7: 399-406
        • Mattson M.P.
        • Allison D.B.
        • Fontana L.
        • et al.
        Meal frequency and timing in health and disease.
        Proc Natl Acad Sci. 2014; 111: 16647
        • Patterson R.E.
        • Sears D.D.
        Metabolic effects of intermittent fasting.
        Annu Rev Nutr. 2017; 37: 371-393
        • Wehrens S.M.T.
        • Christou S.
        • Isherwood C.
        • et al.
        Meal timing regulates the human circadian system.
        Curr Biol. 2017; 27: 1768-1775.e1763
        • Paoli A.
        • Tinsley G.
        • Bianco A.
        • Moro T.
        The influence of meal frequency and timing on health in humans: The role of fasting.
        Nutrients. 2019; 11: 719
        • Bandin C.
        • Scheer F.A.
        • Luque A.J.
        • et al.
        Meal timing affects glucose tolerance, substrate oxidation and circadian-related variables: A randomized, crossover trial.
        Int J Obes. 2015; 39: 828-833
        • Lombardo M.
        • Bellia A.
        • Padua E.
        • et al.
        Morning meal more efficient for fat loss in a 3-month lifestyle intervention.
        J Am Coll Nutr. 2014; 33: 198-205
        • Jakubowicz D.
        • Barnea M.
        • Wainstein J.
        • Froy O.
        High caloric intake at breakfast vs. dinner differentially influences weight loss of overweight and obese women.
        Obesity. 2013; 21: 2504-2512
        • Garaulet M.
        • Gómez-Abellán P.
        • Alburquerque-Béjar J.J.
        • Lee Y.-C.
        • Ordovás J.M.
        • Scheer F.A.J.L.
        Timing of food intake predicts weight loss effectiveness.
        Int J Obes. 2013; 37: 604-611
        • Kahleova H.
        • Lloren J.I.
        • Mashchak A.
        • Hill M.
        • Fraser G.E.
        Meal frequency and timing are associated with changes in body mass index in Adventist Health Study 2.
        J Nutr. 2017; 147: 1722-1728
        • Watanabe Y.
        • Saito I.
        • Henmi I.
        • et al.
        Skipping breakfast is correlated with obesity.
        J Rural Med. 2014; 9: 51-58
        • Gluck M.E.
        • Venti C.A.
        • Salbe A.D.
        • Krakoff J.
        Nighttime eating: Commonly observed and related to weight gain in an inpatient food intake study.
        Am J Clin Nutr. 2008; 88: 900-905
        • Hutchison A.T.
        • Heilbronn L.K.
        Metabolic impacts of altering meal frequency and timing—does when we eat matter?.
        Biochimie. 2016; 124: 187-197
        • Marinac C.R.
        • Sears D.D.
        • Natarajan L.
        • Gallo L.C.
        • Breen C.I.
        • Patterson R.E.
        Frequency and circadian timing of eating may influence biomarkers of inflammation and insulin resistance associated with breast cancer risk.
        PLoS One. 2015; 10e0136240
        • Yoshida J.
        • Eguchi E.
        • Nagaoka K.
        • Ito T.
        • Ogino K.
        Association of night eating habits with metabolic syndrome and its components: A longitudinal study.
        BMC Public Health. 2018; 18 (1366-1366)
        • Longo V.D.
        • Panda S.
        Fasting, circadian rhythms, and time-restricted feeding in healthy lifespan.
        Cell Metab. 2016; 23: 1048-1059
        • Baron K.G.
        • Reid K.J.
        Circadian misalignment and health.
        Int Rev Psychiatry. 2014; 26: 139-154
        • Gill S.
        • Panda S.
        A smartphone app reveals erratic diurnal eating patterns in humans that can be modulated for health benefits.
        Cell Metab. 2015; 22: 789-798
        • Rothschild J.
        • Hoddy K.K.
        • Jambazian P.
        • Varady K.A.
        Time-restricted feeding and risk of metabolic disease: A review of human and animal studies.
        Nutr Rev. 2014; 72: 308-318
      1. Nutrition Data System for Research [database online]. Nutrition Coordinating Center, Minneapolis, MN2014
        Version: Version 14
        • Vernarelli J.A.
        • Mitchell D.C.
        • Rolls B.J.
        • Hartman T.J.
        Methods for calculating dietary energy density in a nationally representative sample.
        Procedia Food Sci. 2013; 2: 68-74
        • Reedy J.
        • Lerman J.L.
        • Krebs-Smith S.M.
        • et al.
        Evaluation of the Healthy Eating Index-2015.
        J Acad Nutr Diet. 2018; 118: 1622-1633
      2. National Cancer Institute HEI scoring algorithm.
        • Kant A.K.
        • Schatzkin A.
        • Harris T.B.
        • Ziegler R.G.
        • Block G.
        Dietary diversity and subsequent mortality in the First National Health and Nutrition Examination Survey Epidemiologic Follow-up Study.
        Am J Clin Nutr. 1993; 57: 434-440
        • Kant A.K.
        • Graubard B.I.
        A comparison of three dietary pattern indexes for predicting biomarkers of diet and disease.
        J Am Coll Nutr. 2005; 24: 294-303
        • Richardson M.T.
        • Ainsworth B.E.
        • Jacobs D.R.
        • Leon A.S.
        Validation of the Stanford 7-day recall to assess habitual physical activity.
        Ann Epidemiol. 2001; 11: 145-153
        • World Health Organization
        Waist circumference and waist–hip ratio: Report of a WHO expert consultation.
        World Health Organization, Geneva, Switzerland2008
        • Friedewald W.T.
        • Levy R.E.
        • Frederickson D.S.
        Estimation on the concentration of low density lipoprotein cholesterol in plasma without use of the ultracentrifuge.
        Clin Chem. 1972; 18: 499-502
        • Huang P.L.
        A comprehensive definition for metabolic syndrome.
        Disease Models Mechanisms. 2009; 2: 231-237
        • Grundy S.M.
        • Cleeman J.I.
        • Daniels S.R.
        • et al.
        Diagnosis and management of the metabolic syndrome: An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement.
        Circulation. 2005; 112: 2735-2752
        • Goldberg G.R.
        • Black A.E.
        • Jebb S.A.
        • et al.
        Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording.
        Eur J Clin Nutr. 1991; 45: 569-581
        • Black A.E.
        Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations.
        Int J Obes Relat Metab Disord. 2000; 24: 1119-1130
        • Mendez M.A.
        • Popkin B.M.
        • Buckland G.
        • et al.
        Alternative methods of accounting for underreporting and overreporting when measuring dietary intake-obesity relations.
        Am J Epidemiol. 2011; 173: 448-458
        • Rousseeuw P.J.
        • Leroy A.M.
        Robust Regression and Outlier Detection.
        John Wiley & Sons, Inc, New York, NY1987
        • Seber G.A.F.
        • Lee A.J.
        Linear Regression Analysis.
        2nd Edition. Wiley, New York, NY2003
      3. Stata Statistical Software [computer program]. StataCorp LLC, College Station, TX2017
        Version: Version 15
        • de Castro J.M.
        When, how much and what foods are eaten are related to total daily food intake.
        Br J Nutr. 2009; 102: 1228-1237
        • de Castro J.M.
        The time of day of food intake influences overall intake in humans.
        J Nutr. 2004; 134: 104-111
        • Reid K.J.
        • Baron K.G.
        • Zee P.C.
        Meal timing influences daily caloric intake in healthy adults.
        Nutr Res. 2014; 34: 930-935
        • Gabel K.
        • Hoddy K.K.
        • Haggerty N.
        • et al.
        Effects of 8-hour time restricted feeding on body weight and metabolic disease risk factors in obese adults: A pilot study.
        Nutr Healthy Aging. 2018; 4: 345-353
        • Nematy M.
        • Alinezhad-Namaghi M.
        • Rashed M.M.
        • et al.
        Effects of Ramadan fasting on cardiovascular risk factors: A prospective observational study.
        Nutr J. 2012; 11: 69
        • Kahleova H.
        • Belinova L.
        • Malinska H.
        • et al.
        Eating two larger meals a day (breakfast and lunch) is more effective than six smaller meals in a reduced-energy regimen for patients with type 2 diabetes: A randomised crossover study.
        Diabetologia. 2014; 57: 1552-1560
        • Sutton E.F.
        • Beyl R.
        • Early K.S.
        • Cefalu W.T.
        • Ravussin E.
        • Peterson C.M.
        Early time-restricted feeding improves insulin sensitivity, blood pressure, and oxidative stress even without weight loss in men with prediabetes.
        Cell Metab. 2018; 27: 1212-1221.e1213
        • Zare A.
        • Hajhashemi M.
        • Hassan Z.M.
        • et al.
        Effect of Ramadan fasting on serum heat shock protein 70 and serum lipid profile.
        Singapore Med J. 2011; 52: 491-495
        • Shariatpanahi Z.V.
        • Shariatpanahi M.V.
        • Shahbazi S.
        • Hossaini A.
        • Abadi A.
        Effect of Ramadan fasting on some indices of insulin resistance and components of the metabolic syndrome in healthy male adults.
        Br J Nutr. 2008; 100: 147-151
        • Hutchison A.T.
        • Regmi P.
        • Manoogian E.N.C.
        • et al.
        Time-restricted feeding improves glucose tolerance in men at risk for type 2 diabetes: A randomized crossover trial.
        Obesity (Silver Spring). 2019; 27: 724-732
        • Chowdhury E.A.
        • Richardson J.D.
        • Holman G.D.
        • Tsintzas K.
        • Thompson D.
        • Betts J.A.
        The causal role of breakfast in energy balance and health: A randomized controlled trial in obese adults.
        Am J Clin Nutr. 2016; 103: 747-756
        • Aksungar F.B.
        • Topkaya A.E.
        • Akyildiz M.
        Interleukin-6, C-reactive protein and biochemical parameters during prolonged intermittent fasting.
        Ann Nutr Metab. 2007; 51: 88-95
        • Ziaee V.
        • Razaei M.
        • Ahmadinejad Z.
        • et al.
        The changes of metabolic profile and weight during Ramadan fasting.
        Singapore Med J. 2006; 47: 409-414
        • Fakhrzadeh H.
        • Larijani B.
        • Sanjari M.
        • Baradar-Jalili R.
        • Amini M.R.
        Effect of Ramadan fasting on clinical and biochemical parameters in healthy adults.
        Ann Saudi Med. 2003; 23: 223-226
        • Chowdhury E.A.
        • Richardson J.D.
        • Tsintzas K.
        • Thompson D.
        • Betts J.A.
        Effect of extended morning fasting upon ad libitum lunch intake and associated metabolic and hormonal responses in obese adults.
        Int J Obes. 2016; 40: 305-311
        • O'Keefe Jr., J.H.
        • Cordain L.
        • Harris W.H.
        • Moe R.M.
        • Vogel R.
        Optimal low-density lipoprotein is 50 to 70 mg/dl: Lower is better and physiologically normal.
        J Am Coll Cardiol. 2004; 43: 2142-2146
        • Lewington S.
        • Clarke R.
        • Qizilbash N.
        • Peto R.
        • Collins R.
        Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies.
        Lancet. 2002; 360: 1903-1913
        • Dhingra R.
        • Vasan R.S.
        Age as a risk factor.
        Med Clin North Am. 2012; 96: 87-91
        • Larsson S.C.
        • Bäck M.
        • Rees J.M.B.
        • Mason A.M.
        • Burgess S.
        Body mass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: A Mendelian randomization study.
        Eur Heart J. 2020; 41: 221-226
        • Banna J.C.
        • McCrory M.A.
        • Fialkowski M.K.
        • Boushey C.
        Examining plausibility of self-reported energy intake data: Considerations for method selection.
        Front Nutr. 2017; 4: 45
        • Subar A.F.
        • Freedman L.S.
        • Tooze J.A.
        • et al.
        Addressing current criticism regarding the value of self-report dietary data.
        J Nutr. 2015; 145: 2639-2645
        • Kipnis V.
        • Subar A.F.
        • Midthune D.
        • et al.
        Structure of dietary measurement error: Results of the OPEN biomarker study.
        Am J Epidemiol. 2003; 158: 14-21

      Biography

      A. Taetzch is a clinical assistant professor, Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH; at the time of the study, she was a senior research dietitian, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA.

      Biography

      S. B. Roberts is lab director and a senior scientist, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA.

      Biography

      A. Bukhari is a research dietitian, US Army Research Institute of Environmental Medicine, Military Nutrition Division, Natick MA.

      Biography

      A. H. Lichtenstein is Gershoff Professor of Nutrition Science and Policy and the director, Cardiovascular Nutrition Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA.

      Biography

      C. H. Gilhooly is manager, Metabolic Research Unit and Dietary Assessment Unit, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA.

      Biography

      E. Martin is graduate fellowship manager, School of Health Sciences, Merrimack College, North Andover, MA; at the time of the study, he was a senior research coordinator, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA.

      Biography

      A. Krauss is a senior research dietitian, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA.

      Biography

      A. Hatch-McChesy is a research dietitian, US Army Research Institute of Environmental Medicine, Military Nutrition Division, Natick, MA.

      Biography

      S. K. Das is a scientist I, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA.