Potter, T., Horgan, G.W., Wanders, A.J., Zandstra, E.H., Zock, P.L., Fisk, H.L., Minihane, A-M., Calder, P., Mathers, J.C. and de Roos, B.
Frontiers in Nutrition 9(989716.).
precision nutrition, omega-3, fish oil, statistical modeling, secondary analysis, crossover study
||Introduction: Substantial response heterogeneity is commonly seen in dietary
intervention trials. In larger datasets, this variability can be exploited to identify
predictors, for example genetic and/or phenotypic baseline characteristics,
associated with response in an outcome of interest.
Objective: Using data from a placebo-controlled crossover study (the FINGEN
study), supplementing with two doses of long chain n-3 polyunsaturated
fatty acids (LC n-3 PUFAs), the primary goal of this analysis was to develop
models to predict change in concentrations of plasma triglycerides (TG), and
in the plasma phosphatidylcholine (PC) LC n-3 PUFAs eicosapentaenoic acid
(EPA) + docosahexaenoic acid (DHA), after fish oil (FO) supplementation.
A secondary goal was to establish if clustering of data prior to FO
supplementation would lead to identification of groups of participants who
Methods: To generate models for the outcomes of interest, variable selection
methods (forward and backward stepwise selection, LASSO and the Boruta
algorithm) were applied to identify suitable predictors. The final model
was chosen based on the lowest validation set root mean squared error
RMSE) after applying each method across multiple imputed datasets.
Unsupervised clustering of data prior to FO supplementation was
implemented using k-medoids and hierarchical clustering, with cluster
membership compared with changes in plasma TG and plasma PC EPA + DHA.
Results: Models for predicting response showed a greater TG-lowering after
1.8 g/day EPA + DHA with lower pre-intervention levels of plasma insulin,
LDL cholesterol, C20:3n-6 and saturated fat consumption, but higher pre-
intervention levels of plasma TG, and serum IL-10 and VCAM-1. Models also
showed greater increases in plasma PC EPA + DHA with age and female sex.
There were no statistically significant differences in PC EPA + DHA and TG
responses between baseline clusters.
Conclusion: Our models established new predictors of response in TG
(plasma insulin, LDL cholesterol, C20:3n-6, saturated fat consumption, TG,
IL-10 and VCAM-1) and in PC EPA + DHA (age and sex) upon intervention with
fish oil. We demonstrate how application of statistical methods can provide
new insights for precision nutrition, by predicting participants who are most
likely to respond beneficially to nutritional interventions.