To combat the increasing prevalence of obesity that has been rising for the last decades, the best diet for optimal weight loss has been investigated. However, studies consistently show large interindividual variation in response to the same dietary treatment, thus a more personalized diet might minimize this variation. Focusing on each and every individual of the population is unrealistic in terms of cost-benefit but grouping individuals into smaller, relatively homogeneous subgroups based on their metabolic phenotype holds great potential for precision nutrition and has been coined as metabotyping. Accordingly, the objective of this thesis was to explore the potential of metabotyping for optimizing weight loss success in response to diet treatment among individuals with obesity.

In Paper I, the existing literature on the already utilized metabotypes in relation to weight loss was reviewed, and it was established that only a very limited number of studies had investigated metabotypes; and those that did were very heterogeneous and applied different approaches making comparison very difficult.

In Paper II and Paper III a randomized controlled trial was designed and conducted to test the efficacy of individualized diet treatments driven by literature-defined metabotypes, compared to generic dietary recommendations, during a 10-week nutrition intervention. Here, the study demonstrated no additional benefit of personalizing dietary plans over a generic approach on the change in fat mass and body weight in individuals with overweight or obesity and elevated waist circumference. Accordingly, personalization of the diet did not significantly improve health parameters beyond the changes induced by the control diet.

In Paper IV a prediction model was developed to predict weight loss success for subjects with overweight or obesity following a New Nordic Diet. By utilizing baseline metabolomics data, a model with two metabolites, urinary levels of adipic acid and argininic acid, was identified that in combination provided a predictive signature for discriminating weight loss responders and non-responders at baseline. These metabolites could potentially be a point of direction for new metabotypes that respond differently on a fiber-rich diet.

In Paper V factors other than biological or metabolic variables, such as behavioral and psychological factors, which potentially could influence the outcome of a weight loss intervention were reviewed. Here, it became evident that the variation in weight loss responses may be partly explained by differences between individuals in a variety of behavioral and psychological factors, which emerged as important parameters for successful weight loss.

Collectively, this work suggests that there is still much work to be done for optimizing personalized approaches to dietary recommendations for successful weight loss. Future studies should be designed with much more careful consideration of the definition of the metabotypes, and target dietary recommendations on the diet level rather than at the level of specific foods and food ingredients. Last but not least, future studies should also track a variety of behavioral and psychological factors that could affect weight loss outcomes.