Eating Frequency and Chrononutrition

(P09-005-21) Temporal Patterning Integrating Diet and Physical Activity Shows Stronger Links to Health Indicators Compared to Patterning of Either Diet or Physical Activity Alone

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Presenting Author(s)

Luotao Lin, Master

PhD student
Purdue University
West Lafayette, Indiana, United States
Disclosure: I do not have any relevant financial / non-financial relationships with any proprietary interests.

Co-Author(s)

JG

Jiaqi Guo, Master

Purdue University
West Lafayette, Indiana, United States
MA

Marah Aqeel, Master

Purdue University
West Lafayette, Indiana, United States
AB

Anindya Bhadra, PhD

Associate Professor
Purdue University
West Lafayette, Indiana, United States
SG

Saul Gelfand, PhD

Professor
Purdue University
West Lafayette, Indiana, United States
ED

Edward Delp, PhD

Distinguished Professor
Purdue University
West Lafayette, Indiana, United States
ER

Elizabeth A. Richards, PhD

Associate Professor
Purdue University
West Lafayette, Indiana, United States
Erin Hennessy, PhD

Assistant Professor
Tufts University
Boston, Massachusetts, United States
Heather A. Eicher-Miller, PhD

Associate Professor
Purdue University
West Lafayette, Indiana, United States

Participants should be aware of the following financial/non-financial relationships:

Luotao Lin, Master: I do not have any relevant financial / non-financial relationships with any proprietary interests.

Jiaqi Guo, Master: Disclosure information not submitted.

Marah Aqeel, Master: Disclosure information not submitted.

Anindya Bhadra, PhD: Disclosure information not submitted.

Saul Gelfand, PhD: Disclosure information not submitted.

Edward Delp, PhD: Disclosure information not submitted.

Elizabeth A. Richards, PhD: Disclosure information not submitted.

Erin Hennessy, PhD: Disclosure information not submitted.

Heather A. Eicher-Miller, PhD: Disclosure information not submitted.

Objectives: Daily temporal patterns of energy intake (temporal dietary patterns, TDPs) and physical activity (temporal PA patterns, TPAPs) have been independently and jointly (joint temporal dietary and PA patterns, TDPAPs) associated with health indicators. The strength of the association between clusters of each pattern and health indicators including body mass index (BMI), waist circumference (WC), fasting plasma glucose (FPG), hemoglobin A1c (A1c), triglyceride (TAG), high-density lipoprotein cholesterol (HDL-C), total cholesterol (Total-C), blood pressure, type 2 diabetes (T2D), metabolic syndrome (MetS), and obesity, were compared.

Methods: The reported energy throughout a day from one reliable 24-hour weekday dietary recall and activity counts from a random weekday of PA accelerometer data of 1,836 U.S. adults from the National Health and Nutrition Examination Survey (2003-2006) were used to create TDP and TPAP respectively, and jointly for TDPAP. Constrained dynamic time warping distances computed over the time series were partitioned to four clusters using kernel-k means clustering algorithm. Measured BMI, WC, FPG, A1c, TAG, HDL-C, Total-C, and classified T2DM, MetS, and obesity were outcomes in multivariate regression models to determine associations with the clusters representing each pattern, controlling for potential confounders and adjusting for multiple comparisons (p < 0.05/6). Adjusted R² and Akaike information criterion (AIC) compared the strength of the associations between clusters and continuous or categorical health indicators.

Results: All temporal patterns were significantly associated with BMI, WC, and obesity. Adjusted R² of BMI and WC models for significant predictors’ effects were higher for TDPAPs (0.129 and 0.194) than TDPs (0.117 and 0.186) or TPAPs (0.077 and 0.143), and AIC of obesity for the TDPAPs (234752082) was smaller than for TDPs (236650170) or TPAPs (239810423).

Conclusions: TDPAPs incorporating time of day with energy intake and PA had the strongest associations with BMI, WC, and obesity compared with either independent temporal dietary or PA patterns. Patterns representing the integration of multiple daily behavioral habits hold promise for early detection of obesity.

Funding Sources: NIH (R21CA224764) and Purdue University