Temporal Dietary Patterns Derived among the Adult Participants of the National Health and Nutrition Examination Survey 1999-2004 Are Associated with Diet Quality.

BACKGROUND Temporal dietary patterns, the distribution of energy or nutrient intakes observed over a period of time, is an emerging area of dietary patterns research that incorporates time of dietary intake with frequency and amount of intake to determine population clusters that may have similar characteristics or outcomes related to diet quality. OBJECTIVE We examined whether differences in diet quality were present between clusters of individuals with similar daily temporal dietary patterns. DESIGN The first-day 24-hour dietary recall data from the cross-sectional National Health and Nutrition Examination Survey, 1999-2004, were used to determine proportional energy intake, time of intake, frequency of intake occasions, and mean diet quality. PARTICIPANTS/SETTING Data from 9,326 US adults aged 20 to 65 years were included. STATISTICAL ANALYSES PERFORMED The mean diet quality, classified by the Healthy Eating Index-2005, of participant clusters with similar temporal dietary patterns derived on the basis of individual proportional energy intake, time of intake, and frequency of intake, were inferentially compared using multiple linear regression that controlled for potential confounders and other covariates (P<0.05/6). RESULTS Diet quality differences were present between US population clusters exhibiting similar daily temporal dietary patterns (P<0.001 with one exception, which was P=0.08). Participant characteristics of race/ethnicity, age, household poverty-income ratio, and body mass index were associated with the temporal dietary patterns. The cluster representing the temporal dietary pattern with proportionally equivalent energy consumed during three evenly spaced eating occasions had a significantly greater mean total Healthy Eating Index-2005 score compared with the other temporal dietary pattern clusters. CONCLUSIONS Temporal dietary patterns are associated with differences in US adult daily diet quality, demonstrating that elements beyond food and nutrient intake, such as time, can be incorporated with dietary patterns to determine links to diet quality that enhance knowledge of the complicated interplay of time and dietary patterns.

[1]  R. Sinha,et al.  Socioeconomic status and the risk of colorectal cancer , 2012, Cancer.

[2]  Edward J. Delp,et al.  Temporal Dietary Patterns Using Kernel k-Means Clustering , 2011, 2011 IEEE International Symposium on Multimedia.

[3]  K. Dodd,et al.  Income and race/ethnicity are associated with adherence to food-based dietary guidance among US adults and children. , 2012, Journal of the Academy of Nutrition and Dietetics.

[4]  Brian Everitt,et al.  Cluster analysis , 1974 .

[5]  Katherine L Tucker,et al.  Empirically derived eating patterns using factor or cluster analysis: a review. , 2004, Nutrition reviews.

[6]  Victor Kipnis,et al.  Comparing 3 dietary pattern methods--cluster analysis, factor analysis, and index analysis--With colorectal cancer risk: The NIH-AARP Diet and Health Study. , 2010, American journal of epidemiology.

[7]  A. Lowden,et al.  Eating and shift work - effects on habits, metabolism and performance. , 2010, Scandinavian journal of work, environment & health.

[8]  Dennis Child,et al.  The essentials of factor analysis , 1970 .

[9]  R. Mattes,et al.  Beverage consumption, appetite, and energy intake: what did you expect? , 2012, The American journal of clinical nutrition.

[10]  M J Gibney,et al.  Eating patterns – temporal distribution, converging and diverging foods, meals eaten inside and outside of the home – implications for developing FBDG , 2001, Public Health Nutrition.

[11]  R. Mattes,et al.  Effects of fruit and vegetable, consumed in solid vs. beverage forms on acute and chronic appetitive responses in lean and obese adults , 2012, International Journal of Obesity.

[12]  Sparkle M. Roberts,et al.  Dietary patterns are associated with dietary recommendations but have limited relationship to BMI in the Communities Advancing the Studies of Tribal Nations Across the Lifespan (CoASTAL) cohort , 2012, Public Health Nutrition.

[13]  C. Zizza,et al.  Snacking is associated with overall diet quality among adults. , 2012, Journal of the Academy of Nutrition and Dietetics.

[14]  P. B. Eveleth,et al.  Physical Status: The Use and Interpretation of Anthropometry. Report of a WHO Expert Committee , 1996 .

[15]  E. Barnett,et al.  Local increases in coronary heart disease mortality among blacks and whites in the United States, 1985-1995. , 2001, American journal of public health.

[16]  J. Ordovás,et al.  The chronobiology, etiology and pathophysiology of obesity , 2010, International Journal of Obesity.

[17]  T. Young,et al.  Short Sleep Duration Is Associated with Reduced Leptin, Elevated Ghrelin, and Increased Body Mass Index , 2004, PLoS medicine.

[18]  Yan Liu,et al.  Do Breakfast Skipping and Breakfast Type Affect Energy Intake, Nutrient Intake, Nutrient Adequacy, and Diet Quality in Young Adults? NHANES 1999–2002 , 2010, Journal of the American College of Nutrition.

[19]  U. Berglund,et al.  Serum lipoproteins in day and shift workers: a prospective study. , 1990, British journal of industrial medicine.

[20]  M. Schulze,et al.  Specific food group combinations explaining the variation in intakes of nutrients and other important food components in the European Prospective Investigation into Cancer and Nutrition: an application of the reduced rank regression method , 2009, European Journal of Clinical Nutrition.

[21]  F Vinicor,et al.  The continuing epidemics of obesity and diabetes in the United States. , 2001, JAMA.

[22]  David R Williams,et al.  Socioeconomic disparities in health in the United States: what the patterns tell us. , 2010, American journal of public health.

[23]  Trends in Prevalence, Awareness, Treatment, and Control of Hypertension in the United States, 1988-2000 , 2003 .

[24]  D. Midthune,et al.  The food propensity questionnaire: concept, development, and validation for use as a covariate in a model to estimate usual food intake. , 2006, Journal of the American Dietetic Association.

[25]  T. Warren Liao,et al.  Clustering of time series data - a survey , 2005, Pattern Recognit..

[26]  R. Hardy,et al.  Daily profiles of energy and nutrient intakes: are eating profiles changing over time? , 2011, European Journal of Clinical Nutrition.

[27]  Seth A. Berkowitz,et al.  Food-Insecure Dietary Patterns Are Associated With Poor Longitudinal Glycemic Control in Diabetes: Results From the Boston Puerto Rican Health Study , 2014, Diabetes Care.

[28]  T Reilly,et al.  Measurement of, and Some Reasons for, Differences in Eating Habits Between Night and Day Workers , 2003, Chronobiology international.

[29]  David G. Stork,et al.  Pattern Classification , 1973 .

[30]  R. Sinha,et al.  Socioeconomic status, healthcare density, and risk of prostate cancer among African American and Caucasian men in a large prospective study , 2012, Cancer Causes & Control.

[31]  R. Pérez‐Escamilla Acculturation, nutrition, and health disparities in Latinos. , 2011, The American journal of clinical nutrition.

[32]  J. Satia Diet-related disparities: understanding the problem and accelerating solutions. , 2009, Journal of the American Dietetic Association.

[33]  R. Hardy,et al.  Time-of-day and nutrient composition of eating occasions: prospective association with the metabolic syndrome in the 1946 British birth cohort , 2012, International Journal of Obesity.

[34]  D. Jacobs,et al.  Nutrients, foods, and dietary patterns as exposures in research: a framework for food synergy. , 2003, The American journal of clinical nutrition.

[35]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[36]  Heiner Boeing,et al.  Application of a new statistical method to derive dietary patterns in nutritional epidemiology. , 2004, American journal of epidemiology.

[37]  Jill Reedy,et al.  Evaluation of the Healthy Eating Index-2005. , 2008, Journal of the American Dietetic Association.

[38]  Shuicheng Yan,et al.  Matrix-Variate Factor Analysis and Its Applications , 2008, IEEE Transactions on Neural Networks.

[39]  S. Kirkpatrick,et al.  Development of the Healthy Eating Index‐2010 , 2008, Journal of the American Dietetic Association.