(PO08-30-22) The Discovery of Data-Driven Temporal Dietary Patterns and a Validation of Their Description Using Energy and Time Cut-Offs

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Presenting Author(s)

Luotao Lin, Master
– PhD student, Purdue University, West Lafayette, Indiana, United States

Co-Author(s)

JG

Jiaqi Guo, Master
– Purdue University, West Lafayette, Indiana, United States
YL

Yitao Li
– Purdue University
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
AB

Anindya Bhadra, PhD
– Associate 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

Disclosure(s):

Luotao Lin, Master: No relevant financial relationship(s) with ineligible companies to disclose.

Objectives: Data-driven methods were recently applied to create temporal dietary patterns (TDPs) incorporating timing and amount of energy intake over 24-hours; their relationships to obesity were determined. However, description of the data-driven TDPs using energy and time cut-offs were not validated against obesity. Aims were to (1) create data-driven TDPs, describe pattern characteristics using energy and time cut-offs, and determine relationships to BMI and waist circumference (WC); (2) assess the concurrent validity of TDPs derived using the cut-offs by determining relationships with BMI and WC.

Methods: Amount and timing of energy intake from the first day 24-hour dietary recall of 17,916 U.S. adults in NHANES 2007-2016 was used to pattern 4 TDPs. Clusters were created using data-driven methods: dynamic time warping coupled with kernel-k means clustering algorithm. Relationships with BMI and WC were assessed using multivariate regression. Heat maps plotting the cluster proportion by energy amount throughout the day were used to visualize the data and find energy and time cut-offs for mutually exclusive clusters. Next, the cut-off-based descriptions were used to create new clusters and multivariate regression determined their associations with BMI and WC. Strength to predict obesity was evaluated by comparing both inferential model results. Percent of participant overlap between data-driven and cut-off derived clusters was also calculated.

Results: Both cut-off and data-driven methods showed a cluster, representing a TDP with proportionally equivalent average energy consumed during three eating events throughout a day, was associated with significantly lower BMI (R²=0.12 for both methods) and WC (R²=0.17 for both methods) compared to the other 3 clusters that had one energy peak throughout a day (all P < 0.0001). Participant membership of ≥82% overlapped between the cut-off and data-driven TDP clusters.

Conclusions: Four cut-off derived clusters highly overlapped with data-driven clusters and showed no differences in strength or pattern relationships with obesity. TDP discovery using a data-driven method was validated through practically interpretable descriptions of energy intake and timing cut-offs. TDPs hold promise for the prediction of obesity and translation to dietary guidance.

Funding Sources: Clifford B. Kinley Trust, Purdue University